diff --git a/docling/backend/xml/jats_backend.py b/docling/backend/xml/jats_backend.py index d1807501f1..43dcaee9ba 100755 --- a/docling/backend/xml/jats_backend.py +++ b/docling/backend/xml/jats_backend.py @@ -21,6 +21,7 @@ import logging import traceback +from dataclasses import dataclass, replace from io import BytesIO from pathlib import Path from typing import Final, cast @@ -36,6 +37,8 @@ TableData, TextItem, ) +from docling_core.types.doc.document import Formatting, Script +from lxml import etree from typing_extensions import TypedDict, override from docling.backend.abstract_backend import DeclarativeDocumentBackend @@ -68,6 +71,34 @@ DEFAULT_HEADER_REFERENCES: Final[str] = "References" DEFAULT_TEXT_ETAL: Final[str] = "et al." +# Maps JATS formatting tags to docling-core formatting attributes. +_JATS_FORMAT_TAG_MAP: Final[dict[str, dict[str, bool | Script]]] = { + "bold": {"bold": True}, + "italic": {"italic": True}, + "underline": {"underline": True}, + "strike": {"strikethrough": True}, + "sub": {"script": Script.SUB}, + "sup": {"script": Script.SUPER}, +} + + +@dataclass(slots=True) +class InlineSegment: + """An ordered inline run of styled text or an inline formula. + + Attributes: + label: Docling item label that classifies this inline run when it is + emitted as a text item. + text: Literal text carried by the run, or the LaTeX body when the run + is a formula. + formatting: Emphasis accumulated from the enclosing tags (bold, italic, + underline, strike, sub, sup), or ``None`` when the run is unstyled. + """ + + label: DocItemLabel + text: str + formatting: Formatting | None = None + class Abstract(TypedDict): label: str @@ -507,21 +538,6 @@ def _add_citation(self, doc: DoclingDocument, parent: NodeItem, text: str) -> No return - def _handle_inline_formula( - self, - child: etree._Element, - node: etree._Element, - node_text: str, - inline_segments: list[tuple[DocItemLabel, str]], - ) -> str: - formula = self._get_inline_equation(child) if node.tag == "p" else None - if formula is not None: - if node_text.strip(): - inline_segments.append((DocItemLabel.TEXT, node_text.strip())) - node_text = "" - inline_segments.append((DocItemLabel.FORMULA, formula)) - return node_text - def _parse_element_citation(self, node: etree._Element) -> str: citation: Citation = { "author_names": "", @@ -651,22 +667,130 @@ def _parse_element_citation(self, node: etree._Element) -> str: def _add_equation( self, doc: DoclingDocument, parent: NodeItem, node: etree._Element ) -> None: - math_text = node.text - if not math_text: - return - math_parts = math_text.split("$$") - if len(math_parts) == 3: - math_formula = math_parts[1] - doc.add_text(label=DocItemLabel.FORMULA, text=math_formula, parent=parent) + formula = JatsDocumentBackend._extract_tex_math(node) + if formula: + doc.add_text(label=DocItemLabel.FORMULA, text=formula, parent=parent) return - def _get_inline_equation(self, node: etree._Element) -> str | None: - tex_math = node.find("tex-math") - if tex_math is None or not tex_math.text: + @staticmethod + def _extract_tex_math(node: etree._Element) -> str | None: + if not node.text: return None - math_parts = tex_math.text.split("$$") - return math_parts[1] if len(math_parts) == 3 else tex_math.text.strip() + text = node.text.strip() + for delimiter in ("$$", "$"): + if ( + len(text) > 2 * len(delimiter) + and text.startswith(delimiter) + and text.endswith(delimiter) + ): + text = text[len(delimiter) : -len(delimiter)].strip() + break + return text or None + + @staticmethod + def _merge_formatting(formatting: Formatting | None, tag: str) -> Formatting | None: + if tag not in _JATS_FORMAT_TAG_MAP: + return formatting + base = formatting if formatting else Formatting() + return base.model_copy(update=_JATS_FORMAT_TAG_MAP[tag]) + + @staticmethod + def _strip_segments(segments: list[InlineSegment]) -> list[InlineSegment]: + stripped: list[InlineSegment] = [] + for segment in segments: + text = segment.text.strip() + if text: + stripped.append(replace(segment, text=text)) + return stripped + + @staticmethod + def _walk_inline_formula( + node: etree._Element, formatting: Formatting | None = None + ) -> list[InlineSegment]: + current = JatsDocumentBackend._merge_formatting(formatting, node.tag) + segments: list[InlineSegment] = [] + if node.text: + text = node.text.replace("\n", " ") + if text: + segments.append( + InlineSegment( + label=DocItemLabel.TEXT, text=text, formatting=current + ) + ) + for child in node: + tag = child.tag + if not isinstance(tag, str) or tag.endswith("}math"): + # Skip comments, processing instructions, and MathML. + pass + elif tag == "tex-math": + formula = JatsDocumentBackend._extract_tex_math(child) + if formula is not None: + segments.append( + InlineSegment(label=DocItemLabel.FORMULA, text=formula) + ) + else: + segments.extend( + JatsDocumentBackend._walk_inline_formula(child, current) + ) + if child.tail: + tail = child.tail.replace("\n", " ") + if tail: + segments.append( + InlineSegment( + label=DocItemLabel.TEXT, text=tail, formatting=current + ) + ) + return segments + + @staticmethod + def _append_run( + segments: list[InlineSegment], text: str, formatting: Formatting | None + ) -> None: + """Append a text run, coalescing into the previous run when formatting matches.""" + text = text.replace("\n", " ") + if not text: + return + if ( + segments + and segments[-1].label == DocItemLabel.TEXT + and segments[-1].formatting == formatting + ): + segments[-1] = replace(segments[-1], text=segments[-1].text + text) + else: + segments.append( + InlineSegment(label=DocItemLabel.TEXT, text=text, formatting=formatting) + ) + + @staticmethod + def _extend_segments( + segments: list[InlineSegment], more: list[InlineSegment] + ) -> None: + """Extend ``segments`` with ``more``, coalescing adjacent equal-format text.""" + for segment in more: + if segment.label == DocItemLabel.TEXT: + JatsDocumentBackend._append_run( + segments, segment.text, segment.formatting + ) + else: + segments.append(segment) + + @staticmethod + def _emit_inline( + doc: DoclingDocument, parent: NodeItem, segments: list[InlineSegment] + ) -> None: + """Emit inline segments under ``parent``, wrapping many in an inline group.""" + segments = JatsDocumentBackend._strip_segments(segments) + if not segments: + return + container = doc.add_inline_group(parent=parent) if len(segments) > 1 else parent + for segment in segments: + doc.add_text( + label=segment.label, + text=segment.text, + formatting=segment.formatting, + parent=container, + ) def _add_figure_captions( self, doc: DoclingDocument, parent: NodeItem, node: etree._Element @@ -920,27 +1044,27 @@ def _add_footnote_group( ) def _walk_linear( - self, doc: DoclingDocument, parent: NodeItem, node: etree._Element - ) -> str: + self, + doc: DoclingDocument, + parent: NodeItem, + node: etree._Element, + formatting: Formatting | None = None, + ) -> list[InlineSegment]: skip_tags = ["term"] flush_tags = ["ack", "sec", "list", "boxed-text", "disp-formula", "fig"] new_parent: NodeItem = parent - inline_segments: list[tuple[DocItemLabel, str]] = [] - node_text: str = ( - node.text.replace("\n", " ") - if (node.tag not in skip_tags and node.text) - else "" - ) + current = JatsDocumentBackend._merge_formatting(formatting, node.tag) + inline_segments: list[InlineSegment] = [] + if node.tag not in skip_tags and node.text: + JatsDocumentBackend._append_run(inline_segments, node.text, current) for child in list(node): stop_walk: bool = False - # flush text into TextItem for some tags in paragraph nodes - if node.tag == "p" and node_text.strip() and child.tag in flush_tags: - doc.add_text( - label=DocItemLabel.TEXT, text=node_text.strip(), parent=parent - ) - node_text = "" + # flush pending inline content before a block child in a paragraph + if node.tag == "p" and child.tag in flush_tags: + JatsDocumentBackend._emit_inline(doc, parent, inline_segments) + inline_segments = [] # add elements and decide whether to stop walking if child.tag in ("sec", "ack"): @@ -1022,40 +1146,29 @@ def _walk_linear( self._add_equation(doc, parent, child) stop_walk = True elif child.tag == "inline-formula": - node_text = self._handle_inline_formula( - child, - node, - node_text, + # Inline formula: tex-math stays inline, unlike block . + JatsDocumentBackend._extend_segments( inline_segments, + JatsDocumentBackend._walk_inline_formula(child, current), ) stop_walk = True # step into child if not stop_walk: - new_text = self._walk_linear(doc, new_parent, child) + child_segments = self._walk_linear(doc, new_parent, child, current) if not (node.getparent().tag == "p" and node.tag in flush_tags): - node_text += new_text + JatsDocumentBackend._extend_segments( + inline_segments, child_segments + ) if child.tag in ("sec", "ack") and text: self.hlevel -= 1 # pick up the tail text - node_text += child.tail.replace("\n", " ") if child.tail else "" + if child.tail: + JatsDocumentBackend._append_run(inline_segments, child.tail, current) - # create paragraph + # emit the paragraph, or backpropagate inline content to the parent if node.tag == "p": - if node_text.strip(): - inline_segments.append((DocItemLabel.TEXT, node_text.strip())) - if inline_segments: - # Wrap in an inline group only when several segments flow together - # (e.g. text + formula); a single segment is added directly. - container = ( - doc.add_inline_group(parent=parent) - if len(inline_segments) > 1 - else parent - ) - for label, text in inline_segments: - doc.add_text(label=label, text=text, parent=container) - return "" - else: - # backpropagate the text - return node_text + JatsDocumentBackend._emit_inline(doc, parent, inline_segments) + return [] + return inline_segments diff --git a/tests/data/jats/groundtruth/elife-56337.nxml.itxt b/tests/data/jats/groundtruth/elife-56337.nxml.itxt index 12c4f6c546..c428ce22c6 100644 --- a/tests/data/jats/groundtruth/elife-56337.nxml.itxt +++ b/tests/data/jats/groundtruth/elife-56337.nxml.itxt @@ -6,186 +6,234 @@ item-0 at level 0: unspecified: group _root_ item-5 at level 3: text: The Krüppel-associated box zinc ... edundant role restricting TE activity. item-6 at level 2: section_header: Introduction item-7 at level 3: text: Nearly half of the human and mou ... s are active beyond early development. - item-8 at level 3: text: TEs, especially long terminal re ... f evolutionarily young KRAB-ZFP genes. - item-9 at level 2: section_header: Results - item-10 at level 3: section_header: Mouse KRAB-ZFPs target retrotransposons - item-11 at level 4: text: We analyzed the RNA expression p ... duplications (Kauzlaric et al., 2017). - item-12 at level 4: text: To determine the binding sites o ... ctive in the early embryo (Figure 1A). - item-13 at level 4: picture - item-13 at level 5: caption: Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white. - item-14 at level 4: table with [9x5] - item-14 at level 5: caption: Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study. - item-15 at level 4: text: We generally observed that KRAB- ... responsible for this silencing effect. - item-16 at level 4: text: To further test the hypothesis t ... t easily evade repression by mutation. - item-17 at level 4: text: Our KRAB-ZFP ChIP-seq dataset al ... ntirely shift the mode of DNA binding. - item-18 at level 3: section_header: Genetic deletion of KRAB-ZFP gen ... leads to retrotransposon reactivation - item-19 at level 4: text: The majority of KRAB-ZFP genes a ... ung et al., 2014; Deniz et al., 2018). - item-20 at level 4: picture - item-20 at level 5: caption: Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test. - item-21 at level 3: section_header: KRAB-ZFP cluster deletions license TE-borne enhancers - item-22 at level 4: text: We next used our RNA-seq dataset ... vating effects of TEs on nearby genes. - item-23 at level 4: picture - item-23 at level 5: caption: Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines. - item-24 at level 4: text: While we generally observed that ... he internal region and not on the LTR. - item-25 at level 3: section_header: ETn retrotransposition in Chr4-cl KO and WT mice - item-26 at level 4: text: IAP, ETn/ETnERV and MuLV/RLTR4 r ... s may contribute to reduced viability. - item-27 at level 4: text: We reasoned that retrotransposon ... Tn insertions at a high recovery rate. - item-28 at level 4: text: Using this dataset, we first con ... nsertions in our pedigree (Figure 4A). - item-29 at level 4: picture - item-29 at level 5: caption: Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis. - item-30 at level 4: text: To validate some of the novel ET ... ess might have truncated this element. - item-31 at level 4: text: Besides novel ETn insertions tha ... tions (Figure 4—figure supplement 3D). - item-32 at level 4: text: Finally, we asked whether there ... s clearly also play an important role. - item-33 at level 2: section_header: Discussion - item-34 at level 3: text: C2H2 zinc finger proteins, about ... ) depending upon their insertion site. - item-35 at level 3: text: Despite a lack of widespread ETn ... ion of the majority of KRAB-ZFP genes. - item-36 at level 2: section_header: Materials and methods - item-37 at level 3: table with [31x5] - item-37 at level 4: caption: Key resources table - item-38 at level 3: section_header: Cell lines and transgenic mice - item-39 at level 4: text: Mouse ES cells and F9 EC cells w ... KO/KO and KO/WT (B6/129 F2) offspring. - item-40 at level 3: section_header: Generation of KRAB-ZFP expressing cell lines - item-41 at level 4: text: KRAB-ZFP ORFs were PCR-amplified ... led and further expanded for ChIP-seq. - item-42 at level 3: section_header: CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion - item-43 at level 4: text: All gRNAs were expressed from th ... PCR genotyping (Supplementary file 3). - item-44 at level 3: section_header: ChIP-seq analysis - item-45 at level 4: text: For ChIP-seq analysis of KRAB-ZF ... 010 or Khil et al., 2012 respectively. - item-46 at level 4: text: ChIP-seq libraries were construc ... were re-mapped using Bowtie (--best). - item-47 at level 3: section_header: Luciferase reporter assays - item-48 at level 4: text: For KRAB-ZFP repression assays, ... after transfection as described above. - item-49 at level 3: section_header: RNA-seq analysis - item-50 at level 4: text: Whole RNA was purified using RNe ... lemented in the R function p.adjust(). - item-51 at level 3: section_header: Reduced representation bisulfite sequencing (RRBS-seq) - item-52 at level 4: text: For RRBS-seq analysis, Chr4-cl W ... h sample were considered for analysis. - item-53 at level 3: section_header: Retrotransposition assay - item-54 at level 4: text: The retrotransposition vectors p ... were stained with Amido Black (Sigma). - item-55 at level 3: section_header: Capture-seq screen - item-56 at level 4: text: To identify novel retrotransposo ... assembly using the Unicycler software. - item-57 at level 2: section_header: Funding Information - item-58 at level 3: text: This paper was supported by the following grants: - item-59 at level 3: list: group list - item-60 at level 4: list_item: http://dx.doi.org/10.13039/10000 ... ment 1ZIAHD008933 to Todd S Macfarlan. - item-61 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... ndation 310030_152879 to Didier Trono. - item-62 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... dation 310030B_173337 to Didier Trono. - item-63 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... ch Council No. 268721 to Didier Trono. - item-64 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... rch Council No 694658 to Didier Trono. - item-65 at level 2: section_header: Acknowledgements - item-66 at level 3: text: We thank Alex Grinberg, Jeanne Y ... 268721; Transpos-X, No. 694658) (DT). - item-67 at level 2: section_header: Additional information - item-68 at level 3: section_header: Competing interests - item-69 at level 4: list: group footnotes - item-70 at level 5: list_item: - item-71 at level 6: inline: group group - item-72 at level 7: footnote: No competing interests declared. - item-73 at level 3: section_header: Author contributions - item-74 at level 4: list: group footnotes - item-75 at level 5: list_item: - item-76 at level 6: inline: group group - item-77 at level 7: footnote: Conceptualization, Data curation ... Methodology, Writing - original draft. - item-78 at level 5: list_item: - item-79 at level 6: inline: group group - item-80 at level 7: footnote: Conceptualization, Data curation ... l draft, Writing - review and editing. - item-81 at level 5: list_item: - item-82 at level 6: inline: group group - item-83 at level 7: footnote: Conceptualization, Data curation ... odology, Writing - review and editing. - item-84 at level 5: list_item: - item-85 at level 6: inline: group group - item-86 at level 7: footnote: Conceptualization, Formal analys ... igation, Writing - review and editing. - item-87 at level 5: list_item: - item-88 at level 6: inline: group group - item-89 at level 7: footnote: Investigation. - item-90 at level 5: list_item: - item-91 at level 6: inline: group group - item-92 at level 7: footnote: Investigation. - item-93 at level 5: list_item: - item-94 at level 6: inline: group group - item-95 at level 7: footnote: Data curation, Software, Formal analysis, Visualization. - item-96 at level 5: list_item: - item-97 at level 6: inline: group group - item-98 at level 7: footnote: Investigation. - item-99 at level 5: list_item: - item-100 at level 6: inline: group group - item-101 at level 7: footnote: Conceptualization, Resources, Su ... igation, Writing - review and editing. - item-102 at level 5: list_item: - item-103 at level 6: inline: group group - item-104 at level 7: footnote: Conceptualization, Resources, Su ... tration, Writing - review and editing. - item-105 at level 3: section_header: Ethics - item-106 at level 4: list: group footnotes - item-107 at level 5: list_item: - item-108 at level 6: inline: group group - item-109 at level 7: footnote: Animal experimentation: All stud ... er IACUC animal protocol (ASP )18-026. - item-110 at level 2: section_header: Additional files - item-111 at level 2: section_header: Data availability - item-112 at level 3: text: All NGS data has been deposited ... GenBank database (MH449667- MH449669). - item-113 at level 3: text: The following datasets were generated: - item-114 at level 3: text: Wolf G. Retrotransposon reactiva ... ession Omnibus (2019). NCBI: GSE115291 - item-115 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449667 - item-116 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449668 - item-117 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449669 - item-118 at level 3: text: The following previously published datasets were used: - item-119 at level 3: text: Castro-Diaz N, Ecco G, Coluccio ... ssion Omnibus (2014). NCBI: GSM1406445 - item-120 at level 3: text: Andrew ZX. H3K9me3_ChIPSeq (Ctrl ... ssion Omnibus (2014). NCBI: GSM1327148 - item-121 at level 2: section_header: References - item-122 at level 3: list: group list - item-123 at level 4: list_item: Bailey TL, Boden M, Buske FA, Fr ... 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JVI.69.2.1142-1149.1995, PMID: 7529329 - item-178 at level 4: list_item: Zhang Y, Liu T, Meyer CA, Eeckho ... .1186/gb-2008-9-9-r137, PMID: 18798982 - item-179 at level 1: caption: Figure 1. Genome-wide binding pa ... onsensus fingers highlighted in white. - item-180 at level 1: caption: Table 1. KRAB-ZFP genes clusters ... ChIP-seq was performed in this study. - item-181 at level 1: caption: Figure 2. Retrotransposon reacti ... s were calculated using paired t-test. - item-182 at level 1: caption: Figure 3. TE-dependent gene acti ... Gm13051 are indicated by dashed lines. - item-183 at level 1: caption: Figure 4. ETn retrotransposition ... combined for the statistical analysis. - item-184 at level 1: caption: Key resources table \ No newline at end of file + item-8 at level 3: inline: group group + item-9 at level 4: text: TEs, especially long terminal re ... ion by recruiting KAP1 to TEs. Indeed, + item-10 at level 4: text: Zfp809 + item-11 at level 4: text: knock-out (KO) in mice resulted ... f evolutionarily young KRAB-ZFP genes. + item-12 at level 2: section_header: Results + item-13 at level 3: section_header: Mouse KRAB-ZFPs target retrotransposons + item-14 at level 4: text: We analyzed the RNA expression p ... duplications (Kauzlaric et al., 2017). + item-15 at level 4: inline: group group + item-16 at level 5: text: To determine the binding sites o ... overlapped at least one TE subfamily ( + item-17 at level 5: text: adjusted p-value + item-18 at level 5: text: <1e-5). Altogether, 81 LTR retro ... ctive in the early embryo (Figure 1A). + item-19 at level 4: picture + item-19 at level 5: caption: Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white. + item-20 at level 4: table with [9x5] + item-20 at level 5: caption: Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study. + item-21 at level 4: inline: group group + item-22 at level 5: text: We generally observed that KRAB- ... ZFP961 motif closely resembles the PBS + item-23 at level 5: text: Lys1,2 + item-24 at level 5: text: (Figure 1—figure supplement 3A), ... f et al., 2008). Repression of the PBS + item-25 at level 5: text: Lys1,2 + item-26 at level 5: text: by ZFP961 was also confirmed in ... responsible for this silencing effect. + item-27 at level 4: text: To further test the hypothesis t ... t easily evade repression by mutation. + item-28 at level 4: text: Our KRAB-ZFP ChIP-seq dataset al ... ntirely shift the mode of DNA binding. + item-29 at level 3: section_header: Genetic deletion of KRAB-ZFP gen ... leads to retrotransposon reactivation + item-30 at level 4: text: The majority of KRAB-ZFP genes a ... ung et al., 2014; Deniz et al., 2018). + item-31 at level 4: picture + item-31 at level 5: caption: Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test. + item-32 at level 3: section_header: KRAB-ZFP cluster deletions license TE-borne enhancers + item-33 at level 4: text: We next used our RNA-seq dataset ... vating effects of TEs on nearby genes. + item-34 at level 4: picture + item-34 at level 5: caption: Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines. + item-35 at level 4: inline: group group + item-36 at level 5: text: While we generally observed that ... ETn insertion to the first exon of the + item-37 at level 5: text: Cd59a + item-38 at level 5: text: gene, which is strongly activate ... is located about 60 kb from the TSS of + item-39 at level 5: text: Chst1 + item-40 at level 5: text: , one of the top-upregulated gen ... C). RT-qPCR analysis revealed that the + item-41 at level 5: text: Chst1 + item-42 at level 5: text: upregulation phenotype in Chr4-c ... ontrolled ETn-borne enhancer regulates + item-43 at level 5: text: Chst1 + item-44 at level 5: text: expression (Figure 3D). Furtherm ... he internal region and not on the LTR. + item-45 at level 3: section_header: ETn retrotransposition in Chr4-cl KO and WT mice + item-46 at level 4: text: IAP, ETn/ETnERV and MuLV/RLTR4 r ... s may contribute to reduced viability. + item-47 at level 4: text: We reasoned that retrotransposon ... Tn insertions at a high recovery rate. + item-48 at level 4: text: Using this dataset, we first con ... nsertions in our pedigree (Figure 4A). + item-49 at level 4: picture + item-49 at level 5: caption: Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis. + item-50 at level 4: text: To validate some of the novel ET ... ess might have truncated this element. + item-51 at level 4: text: Besides novel ETn insertions tha ... tions (Figure 4—figure supplement 3D). + item-52 at level 4: text: Finally, we asked whether there ... s clearly also play an important role. + item-53 at level 2: section_header: Discussion + item-54 at level 3: text: C2H2 zinc finger proteins, about ... ) depending upon their insertion site. + item-55 at level 3: text: Despite a lack of widespread ETn ... ion of the majority of KRAB-ZFP genes. + item-56 at level 2: section_header: Materials and methods + item-57 at level 3: table with [31x5] + item-57 at level 4: caption: Key resources table + item-58 at level 3: section_header: Cell lines and transgenic mice + item-59 at level 4: inline: group group + item-60 at level 5: text: Mouse ES cells and F9 EC cells w ... nes originate from JM8A3.N1 C57BL/6N-A + item-61 at level 5: text: tm1Brd + item-62 at level 5: text: ES cells (KOMP Repository). Chr2 ... KO/KO and KO/WT (B6/129 F2) offspring. + item-63 at level 3: section_header: Generation of KRAB-ZFP expressing cell lines + item-64 at level 4: inline: group group + item-65 at level 5: text: KRAB-ZFP ORFs were PCR-amplified ... r 3XHA tags in F9 EC or ES cells using + item-66 at level 5: text: Sleeping beauty + item-67 at level 5: text: transposon-based (Wolf et al., 2 ... led and further expanded for ChIP-seq. + item-68 at level 3: section_header: CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion + item-69 at level 4: inline: group group + item-70 at level 5: text: All gRNAs were expressed from th ... ddgene_42230) and nucleofected into 10 + item-71 at level 5: text: 6 + item-72 at level 5: text: ES cells using Amaxa nucleofecti ... PCR genotyping (Supplementary file 3). + item-73 at level 3: section_header: ChIP-seq analysis + item-74 at level 4: inline: group group + item-75 at level 5: text: For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 10 + item-76 at level 5: text: 7 + item-77 at level 5: text: cells were crosslinked and immun ... 010 or Khil et al., 2012 respectively. + item-78 at level 4: text: ChIP-seq libraries were construc ... were re-mapped using Bowtie (--best). + item-79 at level 3: section_header: Luciferase reporter assays + item-80 at level 4: inline: group group + item-81 at level 5: text: For KRAB-ZFP repression assays, ... y of the MMETn element upstream of the + item-82 at level 5: text: Cd59a + item-83 at level 5: text: gene, fragments of varying sizes ... after transfection as described above. + item-84 at level 3: section_header: RNA-seq analysis + item-85 at level 4: inline: group group + item-86 at level 5: text: Whole RNA was purified using RNe ... function of BED tools. The probability + item-87 at level 5: text: p + item-88 at level 5: text: in the binomial distribution was ... apped with KRAB-ZFP peaks. Then, given + item-89 at level 5: text: n + item-90 at level 5: text: which is the number of specific groups of genes, and + item-91 at level 5: text: x + item-92 at level 5: text: which is the number of this grou ... lemented in the R function p.adjust(). + item-93 at level 3: section_header: Reduced representation bisulfite sequencing (RRBS-seq) + item-94 at level 4: text: For RRBS-seq analysis, Chr4-cl W ... h sample were considered for analysis. + item-95 at level 3: section_header: Retrotransposition assay + item-96 at level 4: inline: group group + item-97 at level 5: text: The retrotransposition vectors p ... MusD retrotransposition assays, 5 × 10 + item-98 at level 5: text: 4 + item-99 at level 5: text: HeLa cells (ATCC CCL-2) were tra ... were stained with Amido Black (Sigma). + item-100 at level 3: section_header: Capture-seq screen + item-101 at level 4: inline: group group + item-102 at level 5: text: To identify novel retrotransposo ... target enrichment using the SureSelect + item-103 at level 5: text: QXT + item-104 at level 5: text: Target Enrichment kit (Agilent). ... assembly using the Unicycler software. + item-105 at level 2: section_header: Funding Information + item-106 at level 3: text: This paper was supported by the following grants: + item-107 at level 3: list: group list + item-108 at level 4: list_item: http://dx.doi.org/10.13039/10000 ... ment 1ZIAHD008933 to Todd S Macfarlan. + item-109 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... ndation 310030_152879 to Didier Trono. + item-110 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... dation 310030B_173337 to Didier Trono. + item-111 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... ch Council No. 268721 to Didier Trono. + item-112 at level 4: list_item: http://dx.doi.org/10.13039/50110 ... rch Council No 694658 to Didier Trono. + item-113 at level 2: section_header: Acknowledgements + item-114 at level 3: text: We thank Alex Grinberg, Jeanne Y ... 268721; Transpos-X, No. 694658) (DT). + item-115 at level 2: section_header: Additional information + item-116 at level 3: section_header: Competing interests + item-117 at level 4: list: group footnotes + item-118 at level 5: list_item: + item-119 at level 6: inline: group group + item-120 at level 7: footnote: No competing interests declared. + item-121 at level 3: section_header: Author contributions + item-122 at level 4: list: group footnotes + item-123 at level 5: list_item: + item-124 at level 6: inline: group group + item-125 at level 7: footnote: Conceptualization, Data curation ... Methodology, Writing - original draft. + item-126 at level 5: list_item: + item-127 at level 6: inline: group group + item-128 at level 7: footnote: Conceptualization, Data curation ... l draft, Writing - review and editing. + item-129 at level 5: list_item: + item-130 at level 6: inline: group group + item-131 at level 7: footnote: Conceptualization, Data curation ... odology, Writing - review and editing. + item-132 at level 5: list_item: + item-133 at level 6: inline: group group + item-134 at level 7: footnote: Conceptualization, Formal analys ... igation, Writing - review and editing. + item-135 at level 5: list_item: + item-136 at level 6: inline: group group + item-137 at level 7: footnote: Investigation. + item-138 at level 5: list_item: + item-139 at level 6: inline: group group + item-140 at level 7: footnote: Investigation. + item-141 at level 5: list_item: + item-142 at level 6: inline: group group + item-143 at level 7: footnote: Data curation, Software, Formal analysis, Visualization. + item-144 at level 5: list_item: + item-145 at level 6: inline: group group + item-146 at level 7: footnote: Investigation. + item-147 at level 5: list_item: + item-148 at level 6: inline: group group + item-149 at level 7: footnote: Conceptualization, Resources, Su ... igation, Writing - review and editing. + item-150 at level 5: list_item: + item-151 at level 6: inline: group group + item-152 at level 7: footnote: Conceptualization, Resources, Su ... tration, Writing - review and editing. + item-153 at level 3: section_header: Ethics + item-154 at level 4: list: group footnotes + item-155 at level 5: list_item: + item-156 at level 6: inline: group group + item-157 at level 7: footnote: Animal experimentation: All stud ... er IACUC animal protocol (ASP )18-026. + item-158 at level 2: section_header: Additional files + item-159 at level 2: section_header: Data availability + item-160 at level 3: text: All NGS data has been deposited ... GenBank database (MH449667- MH449669). + item-161 at level 3: text: The following datasets were generated: + item-162 at level 3: text: Wolf G. Retrotransposon reactiva ... ession Omnibus (2019). NCBI: GSE115291 + item-163 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449667 + item-164 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449668 + item-165 at level 3: text: Wolf G. Mus musculus musculus st ... e. NCBI GenBank (2019). NCBI: MH449669 + item-166 at level 3: text: The following previously published datasets were used: + item-167 at level 3: text: Castro-Diaz N, Ecco G, Coluccio ... ssion Omnibus (2014). NCBI: GSM1406445 + item-168 at level 3: text: Andrew ZX. H3K9me3_ChIPSeq (Ctrl ... ssion Omnibus (2014). NCBI: GSM1327148 + item-169 at level 2: section_header: References + item-170 at level 3: list: group list + item-171 at level 4: list_item: Bailey TL, Boden M, Buske FA, Fr ... 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OI: 10.1038/nmeth.1923, PMID: 22388286 + item-195 at level 4: list_item: Legiewicz M, Zolotukhin AS, Pilk ... 0.1074/jbc.M110.182840, PMID: 20978285 + item-196 at level 4: list_item: Lehoczky JA, Thomas PE, Patrie K ... 1/journal.pgen.1003967, PMID: 24339789 + item-197 at level 4: list_item: Leung D, Du T, Wagner U, Xie W, ... 0.1073/pnas.1322273111, PMID: 24757056 + item-198 at level 4: list_item: Lilue J, Doran AG, Fiddes IT, Ab ... 1038/s41588-018-0223-8, PMID: 30275530 + item-199 at level 4: list_item: Liu S, Brind'Amour J, Karimi MM, ... 10.1101/gad.244848.114, PMID: 25228647 + item-200 at level 4: list_item: Love MI, Huber W, Anders S. Mode ... 1186/s13059-014-0550-8, PMID: 25516281 + item-201 at level 4: list_item: Lugani F, Arora R, Papeta N, Pat ... 1/journal.pgen.1003206, PMID: 23437001 + item-202 at level 4: list_item: Macfarlan TS, Gifford WD, Drisco ... I: 10.1038/nature11244, PMID: 22722858 + item-203 at level 4: list_item: Maksakova IA, Romanish MT, Gagni ... 1/journal.pgen.0020002, PMID: 16440055 + item-204 at level 4: list_item: Matsui T, Leung D, Miyashita H, ... I: 10.1038/nature08858, PMID: 20164836 + item-205 at level 4: list_item: Najafabadi HS, Mnaimneh S, Schmi ... DOI: 10.1038/nbt.3128, PMID: 25690854 + item-206 at level 4: list_item: Nellåker C, Keane TM, Yalcin B, ... .1186/gb-2012-13-6-r45, PMID: 22703977 + item-207 at level 4: list_item: O'Geen H, Frietze S, Farnham PJ. ... 7/978-1-60761-753-2_27, PMID: 20680851 + item-208 at level 4: list_item: Patel A, Yang P, Tinkham M, Prad ... 016/j.cell.2018.02.058, PMID: 29551271 + item-209 at level 4: list_item: Ribet D, Dewannieux M, Heidmann ... OI: 10.1101/gr.2924904, PMID: 15479948 + item-210 at level 4: list_item: Richardson SR, Gerdes P, Gerhard ... 10.1101/gr.219022.116, PMID: 28483779 + item-211 at level 4: list_item: Rowe HM, Jakobsson J, Mesnard D, ... I: 10.1038/nature08674, PMID: 20075919 + item-212 at level 4: list_item: Rowe HM, Kapopoulou A, Corsinott ... 10.1101/gr.147678.112, PMID: 23233547 + item-213 at level 4: list_item: Schauer SN, Carreira PE, Shukla ... 10.1101/gr.226993.117, PMID: 29643204 + item-214 at level 4: list_item: Schultz DC, Ayyanathan K, Negore ... OI: 10.1101/gad.973302, PMID: 11959841 + item-215 at level 4: list_item: Semba K, Araki K, Matsumoto K, S ... 1/journal.pgen.1003204, PMID: 23436999 + item-216 at level 4: list_item: Sripathy SP, Stevens J, Schultz ... : 10.1128/MCB.00487-06, PMID: 16954381 + item-217 at level 4: list_item: Thomas JH, Schneider S. Coevolut ... 10.1101/gr.121749.111, PMID: 21784874 + item-218 at level 4: list_item: Thompson PJ, Macfarlan TS, Lorin ... 6/j.molcel.2016.03.029, PMID: 27259207 + item-219 at level 4: list_item: Treger RS, Pope SD, Kong Y, Toku ... 6/j.immuni.2018.12.022, PMID: 30709743 + item-220 at level 4: list_item: Vlangos CN, Siuniak AN, Robinson ... 1/journal.pgen.1003205, PMID: 23437000 + item-221 at level 4: list_item: Wang J, Xie G, Singh M, Ghanbari ... I: 10.1038/nature13804, PMID: 25317556 + item-222 at level 4: list_item: Wolf D, Hug K, Goff SP. TRIM28 m ... 0.1073/pnas.0805540105, PMID: 18713861 + item-223 at level 4: list_item: Wolf G, Greenberg D, Macfarlan T ... 1186/s13100-015-0050-8, PMID: 26435754 + item-224 at level 4: list_item: Wolf G, Yang P, Füchtbauer AC, F ... 10.1101/gad.252767.114, PMID: 25737282 + item-225 at level 4: list_item: Yamauchi M, Freitag B, Khan C, B ... JVI.69.2.1142-1149.1995, PMID: 7529329 + item-226 at level 4: list_item: Zhang Y, Liu T, Meyer CA, Eeckho ... .1186/gb-2008-9-9-r137, PMID: 18798982 + item-227 at level 1: caption: Figure 1. Genome-wide binding pa ... onsensus fingers highlighted in white. + item-228 at level 1: caption: Table 1. KRAB-ZFP genes clusters ... ChIP-seq was performed in this study. + item-229 at level 1: caption: Figure 2. Retrotransposon reacti ... s were calculated using paired t-test. + item-230 at level 1: caption: Figure 3. TE-dependent gene acti ... Gm13051 are indicated by dashed lines. + item-231 at level 1: caption: Figure 4. ETn retrotransposition ... combined for the statistical analysis. + item-232 at level 1: caption: Key resources table \ No newline at end of file diff --git a/tests/data/jats/groundtruth/elife-56337.nxml.json b/tests/data/jats/groundtruth/elife-56337.nxml.json index 6f56b9b005..f9a1eeea63 100644 --- a/tests/data/jats/groundtruth/elife-56337.nxml.json +++ b/tests/data/jats/groundtruth/elife-56337.nxml.json @@ -21,22 +21,22 @@ "$ref": "#/texts/0" }, { - "$ref": "#/texts/12" + "$ref": "#/texts/16" }, { - "$ref": "#/texts/13" + "$ref": "#/texts/17" }, { - "$ref": "#/texts/19" + "$ref": "#/texts/27" }, { - "$ref": "#/texts/22" + "$ref": "#/texts/30" }, { - "$ref": "#/texts/28" + "$ref": "#/texts/44" }, { - "$ref": "#/texts/36" + "$ref": "#/texts/52" } ], "content_layer": "body", @@ -47,51 +47,63 @@ { "self_ref": "#/groups/0", "parent": { - "$ref": "#/texts/56" + "$ref": "#/texts/5" }, "children": [ { - "$ref": "#/texts/58" - }, - { - "$ref": "#/texts/59" - }, - { - "$ref": "#/texts/60" + "$ref": "#/texts/7" }, { - "$ref": "#/texts/61" + "$ref": "#/texts/8" }, { - "$ref": "#/texts/62" + "$ref": "#/texts/9" } ], "content_layer": "body", - "name": "list", - "label": "list" + "name": "group", + "label": "inline" }, { "self_ref": "#/groups/1", "parent": { - "$ref": "#/texts/66" + "$ref": "#/texts/11" }, "children": [ { - "$ref": "#/texts/67" + "$ref": "#/texts/13" + }, + { + "$ref": "#/texts/14" + }, + { + "$ref": "#/texts/15" } ], "content_layer": "body", - "name": "footnotes", - "label": "list" + "name": "group", + "label": "inline" }, { "self_ref": "#/groups/2", "parent": { - "$ref": "#/texts/67" + "$ref": "#/texts/11" }, "children": [ { - "$ref": "#/texts/68" + "$ref": "#/texts/18" + }, + { + "$ref": "#/texts/19" + }, + { + "$ref": "#/texts/20" + }, + { + "$ref": "#/texts/21" + }, + { + "$ref": "#/texts/22" } ], "content_layer": "body", @@ -101,52 +113,55 @@ { "self_ref": "#/groups/3", "parent": { - "$ref": "#/texts/69" + "$ref": "#/texts/28" }, "children": [ { - "$ref": "#/texts/70" - }, - { - "$ref": "#/texts/72" + "$ref": "#/texts/31" }, { - "$ref": "#/texts/74" + "$ref": "#/texts/32" }, { - "$ref": "#/texts/76" + "$ref": "#/texts/33" }, { - "$ref": "#/texts/78" + "$ref": "#/texts/34" }, { - "$ref": "#/texts/80" + "$ref": "#/texts/35" }, { - "$ref": "#/texts/82" + "$ref": "#/texts/36" }, { - "$ref": "#/texts/84" + "$ref": "#/texts/37" }, { - "$ref": "#/texts/86" + "$ref": "#/texts/38" }, { - "$ref": "#/texts/88" + "$ref": "#/texts/39" } ], "content_layer": "body", - "name": "footnotes", - "label": "list" + "name": "group", + "label": "inline" }, { "self_ref": "#/groups/4", "parent": { - "$ref": "#/texts/70" + "$ref": "#/texts/53" }, "children": [ { - "$ref": "#/texts/71" + "$ref": "#/texts/54" + }, + { + "$ref": "#/texts/55" + }, + { + "$ref": "#/texts/56" } ], "content_layer": "body", @@ -156,11 +171,17 @@ { "self_ref": "#/groups/5", "parent": { - "$ref": "#/texts/72" + "$ref": "#/texts/57" }, "children": [ { - "$ref": "#/texts/73" + "$ref": "#/texts/58" + }, + { + "$ref": "#/texts/59" + }, + { + "$ref": "#/texts/60" } ], "content_layer": "body", @@ -170,11 +191,17 @@ { "self_ref": "#/groups/6", "parent": { - "$ref": "#/texts/74" + "$ref": "#/texts/61" }, "children": [ { - "$ref": "#/texts/75" + "$ref": "#/texts/62" + }, + { + "$ref": "#/texts/63" + }, + { + "$ref": "#/texts/64" } ], "content_layer": "body", @@ -184,11 +211,17 @@ { "self_ref": "#/groups/7", "parent": { - "$ref": "#/texts/76" + "$ref": "#/texts/65" }, "children": [ { - "$ref": "#/texts/77" + "$ref": "#/texts/66" + }, + { + "$ref": "#/texts/67" + }, + { + "$ref": "#/texts/68" } ], "content_layer": "body", @@ -198,11 +231,17 @@ { "self_ref": "#/groups/8", "parent": { - "$ref": "#/texts/78" + "$ref": "#/texts/70" }, "children": [ { - "$ref": "#/texts/79" + "$ref": "#/texts/71" + }, + { + "$ref": "#/texts/72" + }, + { + "$ref": "#/texts/73" } ], "content_layer": "body", @@ -212,9 +251,27 @@ { "self_ref": "#/groups/9", "parent": { - "$ref": "#/texts/80" + "$ref": "#/texts/74" }, "children": [ + { + "$ref": "#/texts/75" + }, + { + "$ref": "#/texts/76" + }, + { + "$ref": "#/texts/77" + }, + { + "$ref": "#/texts/78" + }, + { + "$ref": "#/texts/79" + }, + { + "$ref": "#/texts/80" + }, { "$ref": "#/texts/81" } @@ -226,11 +283,17 @@ { "self_ref": "#/groups/10", "parent": { - "$ref": "#/texts/82" + "$ref": "#/texts/84" }, "children": [ { - "$ref": "#/texts/83" + "$ref": "#/texts/85" + }, + { + "$ref": "#/texts/86" + }, + { + "$ref": "#/texts/87" } ], "content_layer": "body", @@ -240,11 +303,17 @@ { "self_ref": "#/groups/11", "parent": { - "$ref": "#/texts/84" + "$ref": "#/texts/88" }, "children": [ { - "$ref": "#/texts/85" + "$ref": "#/texts/89" + }, + { + "$ref": "#/texts/90" + }, + { + "$ref": "#/texts/91" } ], "content_layer": "body", @@ -254,39 +323,37 @@ { "self_ref": "#/groups/12", "parent": { - "$ref": "#/texts/86" + "$ref": "#/texts/92" }, "children": [ { - "$ref": "#/texts/87" - } - ], - "content_layer": "body", - "name": "group", - "label": "inline" - }, - { - "self_ref": "#/groups/13", - "parent": { - "$ref": "#/texts/88" - }, - "children": [ + "$ref": "#/texts/94" + }, { - "$ref": "#/texts/89" + "$ref": "#/texts/95" + }, + { + "$ref": "#/texts/96" + }, + { + "$ref": "#/texts/97" + }, + { + "$ref": "#/texts/98" } ], "content_layer": "body", - "name": "group", - "label": "inline" + "name": "list", + "label": "list" }, { - "self_ref": "#/groups/14", + "self_ref": "#/groups/13", "parent": { - "$ref": "#/texts/90" + "$ref": "#/texts/102" }, "children": [ { - "$ref": "#/texts/91" + "$ref": "#/texts/103" } ], "content_layer": "body", @@ -294,13 +361,13 @@ "label": "list" }, { - "self_ref": "#/groups/15", + "self_ref": "#/groups/14", "parent": { - "$ref": "#/texts/91" + "$ref": "#/texts/103" }, "children": [ { - "$ref": "#/texts/92" + "$ref": "#/texts/104" } ], "content_layer": "body", @@ -308,119 +375,220 @@ "label": "inline" }, { - "self_ref": "#/groups/16", + "self_ref": "#/groups/15", "parent": { - "$ref": "#/texts/104" + "$ref": "#/texts/105" }, "children": [ - { - "$ref": "#/texts/105" - }, { "$ref": "#/texts/106" }, - { - "$ref": "#/texts/107" - }, { "$ref": "#/texts/108" }, - { - "$ref": "#/texts/109" - }, { "$ref": "#/texts/110" }, - { - "$ref": "#/texts/111" - }, { "$ref": "#/texts/112" }, - { - "$ref": "#/texts/113" - }, { "$ref": "#/texts/114" }, - { - "$ref": "#/texts/115" - }, { "$ref": "#/texts/116" }, - { - "$ref": "#/texts/117" - }, { "$ref": "#/texts/118" }, - { - "$ref": "#/texts/119" - }, { "$ref": "#/texts/120" }, - { - "$ref": "#/texts/121" - }, { "$ref": "#/texts/122" }, - { - "$ref": "#/texts/123" - }, { "$ref": "#/texts/124" - }, - { - "$ref": "#/texts/125" - }, - { - "$ref": "#/texts/126" - }, + } + ], + "content_layer": "body", + "name": "footnotes", + "label": "list" + }, + { + "self_ref": "#/groups/16", + "parent": { + "$ref": "#/texts/106" + }, + "children": [ { - "$ref": "#/texts/127" - }, + "$ref": "#/texts/107" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/17", + "parent": { + "$ref": "#/texts/108" + }, + "children": [ { - "$ref": "#/texts/128" - }, - { - "$ref": "#/texts/129" - }, - { - "$ref": "#/texts/130" - }, + "$ref": "#/texts/109" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/18", + "parent": { + "$ref": "#/texts/110" + }, + "children": [ { - "$ref": "#/texts/131" - }, + "$ref": "#/texts/111" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/19", + "parent": { + "$ref": "#/texts/112" + }, + "children": [ { - "$ref": "#/texts/132" - }, + "$ref": "#/texts/113" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/20", + "parent": { + "$ref": "#/texts/114" + }, + "children": [ { - "$ref": "#/texts/133" - }, + "$ref": "#/texts/115" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/21", + "parent": { + "$ref": "#/texts/116" + }, + "children": [ { - "$ref": "#/texts/134" - }, + "$ref": "#/texts/117" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/22", + "parent": { + "$ref": "#/texts/118" + }, + "children": [ { - "$ref": "#/texts/135" - }, + "$ref": "#/texts/119" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/23", + "parent": { + "$ref": "#/texts/120" + }, + "children": [ { - "$ref": "#/texts/136" - }, + "$ref": "#/texts/121" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/24", + "parent": { + "$ref": "#/texts/122" + }, + "children": [ { - "$ref": "#/texts/137" - }, + "$ref": "#/texts/123" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/25", + "parent": { + "$ref": "#/texts/124" + }, + "children": [ { - "$ref": "#/texts/138" - }, + "$ref": "#/texts/125" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/26", + "parent": { + "$ref": "#/texts/126" + }, + "children": [ { - "$ref": "#/texts/139" - }, + "$ref": "#/texts/127" + } + ], + "content_layer": "body", + "name": "footnotes", + "label": "list" + }, + { + "self_ref": "#/groups/27", + "parent": { + "$ref": "#/texts/127" + }, + "children": [ { - "$ref": "#/texts/140" - }, + "$ref": "#/texts/128" + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/28", + "parent": { + "$ref": "#/texts/140" + }, + "children": [ { "$ref": "#/texts/141" }, @@ -480,6 +648,114 @@ }, { "$ref": "#/texts/160" + }, + { + "$ref": "#/texts/161" + }, + { + "$ref": "#/texts/162" + }, + { + "$ref": "#/texts/163" + }, + { + "$ref": "#/texts/164" + }, + { + "$ref": "#/texts/165" + }, + { + "$ref": "#/texts/166" + }, + { + "$ref": "#/texts/167" + }, + { + "$ref": "#/texts/168" + }, + { + "$ref": "#/texts/169" + }, + { + "$ref": "#/texts/170" + }, + { + "$ref": "#/texts/171" + }, + { + "$ref": "#/texts/172" + }, + { + "$ref": "#/texts/173" + }, + { + "$ref": "#/texts/174" + }, + { + "$ref": "#/texts/175" + }, + { + "$ref": "#/texts/176" + }, + { + "$ref": "#/texts/177" + }, + { + "$ref": "#/texts/178" + }, + { + "$ref": "#/texts/179" + }, + { + "$ref": "#/texts/180" + }, + { + "$ref": "#/texts/181" + }, + { + "$ref": "#/texts/182" + }, + { + "$ref": "#/texts/183" + }, + { + "$ref": "#/texts/184" + }, + { + "$ref": "#/texts/185" + }, + { + "$ref": "#/texts/186" + }, + { + "$ref": "#/texts/187" + }, + { + "$ref": "#/texts/188" + }, + { + "$ref": "#/texts/189" + }, + { + "$ref": "#/texts/190" + }, + { + "$ref": "#/texts/191" + }, + { + "$ref": "#/texts/192" + }, + { + "$ref": "#/texts/193" + }, + { + "$ref": "#/texts/194" + }, + { + "$ref": "#/texts/195" + }, + { + "$ref": "#/texts/196" } ], "content_layer": "body", @@ -507,31 +783,31 @@ "$ref": "#/texts/5" }, { - "$ref": "#/texts/8" + "$ref": "#/texts/10" }, { - "$ref": "#/texts/32" + "$ref": "#/texts/48" }, { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, { - "$ref": "#/texts/56" + "$ref": "#/texts/92" }, { - "$ref": "#/texts/63" + "$ref": "#/texts/99" }, { - "$ref": "#/texts/65" + "$ref": "#/texts/101" }, { - "$ref": "#/texts/93" + "$ref": "#/texts/129" }, { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, { - "$ref": "#/texts/104" + "$ref": "#/texts/140" } ], "content_layer": "body", @@ -603,7 +879,7 @@ "$ref": "#/texts/6" }, { - "$ref": "#/texts/7" + "$ref": "#/groups/0" } ], "content_layer": "body", @@ -628,32 +904,63 @@ { "self_ref": "#/texts/7", "parent": { - "$ref": "#/texts/5" + "$ref": "#/groups/0" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes.", - "text": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes." + "orig": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed,", + "text": "TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed," }, { "self_ref": "#/texts/8", "parent": { - "$ref": "#/texts/0" + "$ref": "#/groups/0" }, - "children": [ - { - "$ref": "#/texts/9" - }, - { - "$ref": "#/texts/17" - }, - { - "$ref": "#/texts/20" - }, - { - "$ref": "#/texts/24" + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Zfp809", + "text": "Zfp809", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/9", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes.", + "text": "knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes." + }, + { + "self_ref": "#/texts/10", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/11" + }, + { + "$ref": "#/texts/25" + }, + { + "$ref": "#/texts/28" + }, + { + "$ref": "#/texts/40" } ], "content_layer": "body", @@ -664,16 +971,16 @@ "level": 1 }, { - "self_ref": "#/texts/9", + "self_ref": "#/texts/11", "parent": { - "$ref": "#/texts/8" + "$ref": "#/texts/10" }, "children": [ { - "$ref": "#/texts/10" + "$ref": "#/texts/12" }, { - "$ref": "#/texts/11" + "$ref": "#/groups/1" }, { "$ref": "#/pictures/0" @@ -682,13 +989,13 @@ "$ref": "#/tables/0" }, { - "$ref": "#/texts/14" + "$ref": "#/groups/2" }, { - "$ref": "#/texts/15" + "$ref": "#/texts/23" }, { - "$ref": "#/texts/16" + "$ref": "#/texts/24" } ], "content_layer": "body", @@ -699,9 +1006,9 @@ "level": 2 }, { - "self_ref": "#/texts/10", + "self_ref": "#/texts/12", "parent": { - "$ref": "#/texts/9" + "$ref": "#/texts/11" }, "children": [], "content_layer": "body", @@ -711,19 +1018,50 @@ "text": "We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1—figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1—figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1—figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017)." }, { - "self_ref": "#/texts/11", + "self_ref": "#/texts/13", "parent": { - "$ref": "#/texts/9" + "$ref": "#/groups/1" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).", - "text": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A)." + "orig": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (", + "text": "To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (" }, { - "self_ref": "#/texts/12", + "self_ref": "#/texts/14", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "adjusted p-value", + "text": "adjusted p-value", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/15", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).", + "text": "<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A)." + }, + { + "self_ref": "#/texts/16", "parent": { "$ref": "#/body" }, @@ -735,7 +1073,7 @@ "text": "Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white." }, { - "self_ref": "#/texts/13", + "self_ref": "#/texts/17", "parent": { "$ref": "#/body" }, @@ -747,21 +1085,83 @@ "text": "Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in this study. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study." }, { - "self_ref": "#/texts/14", + "self_ref": "#/texts/18", "parent": { - "$ref": "#/texts/9" + "$ref": "#/groups/2" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.", - "text": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect." + "orig": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBS", + "text": "We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBS" }, { - "self_ref": "#/texts/15", + "self_ref": "#/texts/19", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Lys1,2", + "text": "Lys1,2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/20", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "(Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBS", + "text": "(Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBS" + }, + { + "self_ref": "#/texts/21", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Lys1,2", + "text": "Lys1,2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/22", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.", + "text": "by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect." + }, + { + "self_ref": "#/texts/23", "parent": { - "$ref": "#/texts/9" + "$ref": "#/texts/11" }, "children": [], "content_layer": "body", @@ -771,9 +1171,9 @@ "text": "To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1—figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3’ end of the internal region of ETn (Figure 1—figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation." }, { - "self_ref": "#/texts/16", + "self_ref": "#/texts/24", "parent": { - "$ref": "#/texts/9" + "$ref": "#/texts/11" }, "children": [], "content_layer": "body", @@ -783,13 +1183,13 @@ "text": "Our KRAB-ZFP ChIP-seq dataset also provided unique insights into the emergence of new KRAB-ZFPs and binding patterns. The Chr4-cl KRAB-ZFPs REX2 and ZFP600 bind to the same target within ETn but with varying affinity (Figure 1C). Comparison of the amino acids responsible for DNA contact revealed a high similarity between REX2 and ZFP600, with the main differences at the most C-terminal zinc fingers. Additionally, we found that GM30910, another KRAB-ZFP encoded in the Chr4-cl, also shows a strong similarity to both KRAB-ZFPs yet targets entirely different groups of TEs (Figure 1C and Supplementary file 1). Together with previously shown data (Ecco et al., 2016), this example highlights how addition of a few new zinc fingers to an existing array can entirely shift the mode of DNA binding." }, { - "self_ref": "#/texts/17", + "self_ref": "#/texts/25", "parent": { - "$ref": "#/texts/8" + "$ref": "#/texts/10" }, "children": [ { - "$ref": "#/texts/18" + "$ref": "#/texts/26" }, { "$ref": "#/pictures/1" @@ -803,9 +1203,9 @@ "level": 2 }, { - "self_ref": "#/texts/18", + "self_ref": "#/texts/26", "parent": { - "$ref": "#/texts/17" + "$ref": "#/texts/25" }, "children": [], "content_layer": "body", @@ -815,7 +1215,7 @@ "text": "The majority of KRAB-ZFP genes are harbored in large, highly repetitive clusters that have formed by successive complex segmental duplications (Kauzlaric et al., 2017), rendering them inaccessible to conventional gene targeting. We therefore developed a strategy to delete entire KRAB-ZFP gene clusters in ES cells (including the Chr2-cl and Chr4-cl as well as two clusters on chromosome 13 and a cluster on chromosome 10) using two CRISPR/Cas9 gRNAs targeting unique regions flanking each cluster, and short single-stranded repair oligos with homologies to both sides of the projected cut sites. Using this approach, we generated five cluster KO ES cell lines in at least two biological replicates and performed RNA sequencing (RNA-seq) to determine TE expression levels. Strikingly, four of the five cluster KO ES cells exhibited distinct TE reactivation phenotypes (Figure 2A). Chr2-cl KO resulted in reactivation of several L1 subfamilies as well as RLTR10 (up to more than 100-fold as compared to WT) and IAPEz ERVs. In contrast, the most strongly upregulated TEs in Chr4-cl KO cells were ETn/ETnERV (up to 10-fold as compared to WT), with several other ERV groups modestly reactivated. ETn/ETnERV elements were also upregulated in Chr13.2-cl KO ES cells while the only upregulated ERVs in Chr13.1-cl KO ES cells were MMERVK10C elements (Figure 2A). Most reactivated retrotransposons were targeted by at least one KRAB-ZFP that was encoded in the deleted cluster (Figure 2A and Supplementary file 1), indicating a direct effect of these KRAB-ZFPs on TE expression levels. Furthermore, we observed a loss of KAP1 binding and H3K9me3 at several TE subfamilies that are targeted by at least one KRAB-ZFP within the deleted Chr2-cl and Chr4-cl (Figure 2B, Figure 2—figure supplement 1A), including L1, ETn and IAPEz elements. Using reduced representation bisulfite sequencing (RRBS-seq), we found that a subset of KRAB-ZFP bound TEs were partially hypomethylated in Chr4-cl KO ES cells, but only when grown in genome-wide hypomethylation-inducing conditions (Blaschke et al., 2013; Figure 2C and Supplementary file 2). These data are consistent with the hypothesis that KRAB-ZFPs/KAP1 are not required to establish DNA methylation, but under certain conditions they protect specific TEs and imprint control regions from genome-wide demethylation (Leung et al., 2014; Deniz et al., 2018)." }, { - "self_ref": "#/texts/19", + "self_ref": "#/texts/27", "parent": { "$ref": "#/body" }, @@ -827,19 +1227,19 @@ "text": "Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test." }, { - "self_ref": "#/texts/20", + "self_ref": "#/texts/28", "parent": { - "$ref": "#/texts/8" + "$ref": "#/texts/10" }, "children": [ { - "$ref": "#/texts/21" + "$ref": "#/texts/29" }, { "$ref": "#/pictures/2" }, { - "$ref": "#/texts/23" + "$ref": "#/groups/3" } ], "content_layer": "body", @@ -850,9 +1250,9 @@ "level": 2 }, { - "self_ref": "#/texts/21", + "self_ref": "#/texts/29", "parent": { - "$ref": "#/texts/20" + "$ref": "#/texts/28" }, "children": [], "content_layer": "body", @@ -862,7 +1262,7 @@ "text": "We next used our RNA-seq datasets to determine the effect of KRAB-ZFP cluster deletions on gene expression. We identified 195 significantly upregulated and 130 downregulated genes in Chr4-cl KO ES cells, and 108 upregulated and 59 downregulated genes in Chr2-cl KO ES cells (excluding genes on the deleted cluster) (Figure 3A). To address whether gene deregulation in Chr2-cl and Chr4-cl KO ES cells is caused by nearby TE reactivation, we determined whether genes near certain TE subfamilies are more frequently deregulated than random genes. We found a strong correlation of gene upregulation and TE proximity for several TE subfamilies, of which many became transcriptionally activated themselves (Figure 3B). For example, nearly 10% of genes that are located within 100 kb (up- or downstream of the TSS) of an ETn element are upregulated in Chr4-cl KO ES cells, as compared to 0.8% of all genes. In Chr2-cl KO ES cells, upregulated genes were significantly enriched near various LINE groups but also IAPEz-int and RLTR10-int elements, indicating that TE-binding KRAB-ZFPs in these clusters limit the potential activating effects of TEs on nearby genes." }, { - "self_ref": "#/texts/22", + "self_ref": "#/texts/30", "parent": { "$ref": "#/body" }, @@ -874,43 +1274,167 @@ "text": "Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines." }, { - "self_ref": "#/texts/23", + "self_ref": "#/texts/31", "parent": { - "$ref": "#/texts/20" + "$ref": "#/groups/3" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR.", - "text": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR." + "orig": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the", + "text": "While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the" }, { - "self_ref": "#/texts/24", + "self_ref": "#/texts/32", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Cd59a", + "text": "Cd59a", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/33", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of", + "text": "gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of" + }, + { + "self_ref": "#/texts/34", "parent": { - "$ref": "#/texts/8" + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Chst1", + "text": "Chst1", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/35", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the", + "text": ", one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the" + }, + { + "self_ref": "#/texts/36", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Chst1", + "text": "Chst1", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/37", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates", + "text": "upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates" + }, + { + "self_ref": "#/texts/38", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Chst1", + "text": "Chst1", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/39", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR.", + "text": "expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR." + }, + { + "self_ref": "#/texts/40", + "parent": { + "$ref": "#/texts/10" }, "children": [ { - "$ref": "#/texts/25" + "$ref": "#/texts/41" }, { - "$ref": "#/texts/26" + "$ref": "#/texts/42" }, { - "$ref": "#/texts/27" + "$ref": "#/texts/43" }, { "$ref": "#/pictures/3" }, { - "$ref": "#/texts/29" + "$ref": "#/texts/45" }, { - "$ref": "#/texts/30" + "$ref": "#/texts/46" }, { - "$ref": "#/texts/31" + "$ref": "#/texts/47" } ], "content_layer": "body", @@ -921,9 +1445,9 @@ "level": 2 }, { - "self_ref": "#/texts/25", + "self_ref": "#/texts/41", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -933,9 +1457,9 @@ "text": "IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nellåker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4—figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value<0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4—figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability." }, { - "self_ref": "#/texts/26", + "self_ref": "#/texts/42", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -945,9 +1469,9 @@ "text": "We reasoned that retrotransposon activation could account for the reduced viability of Chr2-cl and Chr4-cl KO mice in some matings. However, since only rare matings produced non-viable KO embryos, we instead turned to the viable KO mice to assay for increased transposon activity. RNA-seq in blood, brain and testis revealed that, with a few exceptions, retrotransposons upregulated in Chr2 and Chr4 KRAB-ZFP cluster KO ES cells are not expressed at higher levels in adult tissues (Figure 4—figure supplement 1B). Likewise, no strong transcriptional TE reactivation phenotype was observed in liver and kidney of Chr4-cl KO mice (data not shown) and ChIP-seq with antibodies against H3K4me1, H3K4me3 and H3K27ac in testis of Chr4-cl WT and KO mice revealed no increase of active histone marks at ETn elements or other TEs (data not shown). This indicates that Chr2-cl and Chr4-cl KRAB-ZFPs are primarily required for TE repression during early development. This is consistent with the high expression of these KRAB-ZFPs uniquely in ES cells (Figure 1—figure supplement 1A). To determine whether retrotransposition occurs at a higher frequency in Chr4-cl KO mice during development, we screened for novel ETn (ETn/ETnERV) and MuLV (MuLV/RLTR4_MM) insertions in viable Chr4-cl KO mice. For this purpose, we developed a capture-sequencing approach to enrich for ETn/MuLV DNA and flanking sequences from genomic DNA using probes that hybridize with the 5’ and 3’ ends of ETn and MuLV LTRs prior to deep sequencing. We screened genomic DNA samples from a total of 76 mice, including 54 mice from ancestry-controlled Chr4-cl KO matings in various strain backgrounds, the two ES cell lines the Chr4-cl KO mice were generated from, and eight mice from a Chr2-cl KO mating which served as a control (since ETn and MuLVs are not activated in Chr2-cl KO ES cells) (Supplementary file 4). Using this approach, we were able to enrich reads mapping to ETn/MuLV LTRs about 2,000-fold compared to genome sequencing without capture. ETn/MuLV insertions were determined by counting uniquely mapped reads that were paired with reads mapping to ETn/MuLV elements (see materials and methods for details). To assess the efficiency of the capture approach, we determined what proportion of a set of 309 largely intact (two LTRs flanking an internal sequence) reference ETn elements could be identified using our sequencing data. 95% of these insertions were called with high confidence in the majority of our samples (data not shown), indicating that we are able to identify ETn insertions at a high recovery rate." }, { - "self_ref": "#/texts/27", + "self_ref": "#/texts/43", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -957,7 +1481,7 @@ "text": "Using this dataset, we first confirmed the polymorphic nature of both ETn and MuLV retrotransposons in laboratory mouse strains (Figure 4—figure supplement 2A), highlighting the potential of these elements to retrotranspose. To identify novel insertions, we filtered out insertions that were supported by ETn/MuLV-paired reads in more than one animal. While none of the 54 ancestry-controlled mice showed a single novel MuLV insertion, we observed greatly varying numbers of up to 80 novel ETn insertions in our pedigree (Figure 4A)." }, { - "self_ref": "#/texts/28", + "self_ref": "#/texts/44", "parent": { "$ref": "#/body" }, @@ -969,9 +1493,9 @@ "text": "Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis." }, { - "self_ref": "#/texts/29", + "self_ref": "#/texts/45", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -981,9 +1505,9 @@ "text": "To validate some of the novel ETn insertions, we designed specific PCR primers for five of the insertions and screened genomic DNA of the mice in which they were identified as well as their parents. For all tested insertions, we were able to amplify their flanking sequence and show that these insertions are absent in their parents (Figure 4—figure supplement 3A). To confirm their identity, we amplified and sequenced three of the novel full-length ETn insertions. Two of these elements (Genbank accession: MH449667-68) resembled typical ETnII elements with identical 5’ and 3’ LTRs and target site duplications (TSD) of 4 or 6 bp, respectively. The third sequenced element (MH449669) represented a hybrid element that contains both ETnI and MusD (ETnERV) sequences. Similar insertions can be found in the B6 reference genome; however, the identified novel insertion has a 2.5 kb deletion of the 5’ end of the internal region. Additionally, the 5’ and 3’ LTR of this element differ in one nucleotide near the start site and contain an unusually large 248 bp TSD (containing a SINE repeat) indicating that an improper integration process might have truncated this element." }, { - "self_ref": "#/texts/30", + "self_ref": "#/texts/46", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -993,9 +1517,9 @@ "text": "Besides novel ETn insertions that were only identified in one specific animal, we also observed three ETn insertions that could be detected in several siblings but not in their parents or any of the other screened mice. This strongly indicates that these retrotransposition events occurred in the germ line of the parents from which they were passed on to some of their offspring. One of these germ line insertions was evidently passed on from the offspring to the next generation (Figure 4A). As expected, the read numbers supporting these novel germ line insertions were comparable to the read numbers that were found in the flanking regions of annotated B6 ETn insertions (Figure 4—figure supplement 3B). In contrast, virtually all novel insertions that were only found in one animal were supported by significantly fewer reads (Figure 4—figure supplement 3B). This indicates that these elements resulted from retrotransposition events in the developing embryo and not in the zygote or parental germ cells. Indeed, we detected different sets of insertions in various tissues from the same animal (Figure 4—figure supplement 3C). Even between tail samples that were collected from the same animal at different ages, only a fraction of the new insertions were present in both samples, while technical replicates from the same genomic DNA samples showed a nearly complete overlap in insertions (Figure 4—figure supplement 3D)." }, { - "self_ref": "#/texts/31", + "self_ref": "#/texts/47", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -1005,16 +1529,16 @@ "text": "Finally, we asked whether there were more novel ETn insertions in mice lacking the Chr4-cl relative to their wild type and heterozygous littermates in our pedigree. Interestingly, only one out of the eight Chr4-cl KO mice in a pure C57BL/6 strain background and none of the eight offspring from a Chr2-cl mating carried a single novel ETn insertion (Figure 4A). When crossing into a 129Sv background for a single generation before intercrossing heterozygous mice (F1), we observed 4 out of 8 Chr4-cl KO mice that contained at least one new ETn insertion, whereas none of 3 heterozygous mice contained any insertions. After crossing to the 129Sv background for a second generation (F2), we determined the number of novel ETn insertions in the offspring of one KO/WT x KO and two KO/WT x KO/WT matings, excluding all samples that were not derived from juvenile tail tissue. Only in the offspring of the KO/WT x KO mating, we observed a statistically significant higher average number of ETn insertions in KO vs. KO/WT animals (7.3 vs. 29.6, p=0.045, Figure 4B). Other than that, only a non-significant trend towards greater average numbers of ETn insertions in KO (11 vs. 27.8, p=0.192, Figure 4B) was apparent in one of the WT/KO x KO/WT matings whereas no difference in ETn insertion numbers between WT and KO mice could be observed in the second mating WT/KO x KO/WT (26 vs. 31, p=0.668, Figure 4B). When comparing all KO with all WT and WT/KO mice from these three matings, a trend towards more ETn insertions in KO remained but was not supported by strong significance (26 vs. 13, p=0.057, Figure 4B). Altogether, we observed a high variability in the number of new ETn insertions in both KO and WT but our data suggest that the Chr4-cl KRAB-ZFPs may have a modest effect on ETn retrotransposition rates in some mouse strains but other genetic and epigenetic effects clearly also play an important role." }, { - "self_ref": "#/texts/32", + "self_ref": "#/texts/48", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/33" + "$ref": "#/texts/49" }, { - "$ref": "#/texts/34" + "$ref": "#/texts/50" } ], "content_layer": "body", @@ -1025,9 +1549,9 @@ "level": 1 }, { - "self_ref": "#/texts/33", + "self_ref": "#/texts/49", "parent": { - "$ref": "#/texts/32" + "$ref": "#/texts/48" }, "children": [], "content_layer": "body", @@ -1037,9 +1561,9 @@ "text": "C2H2 zinc finger proteins, about half of which contain a KRAB repressor domain, represent the largest DNA-binding protein family in mammals. Nevertheless, most of these factors have not been investigated using loss-of-function studies. The most comprehensive characterization of human KRAB-ZFPs revealed a strong preference to bind TEs (Imbeault et al., 2017; Najafabadi et al., 2015) yet their function remains unknown. In humans, very few TEs are capable of retrotransposition yet many of them, often tens of million years old, are bound by KRAB-ZFPs. While this suggests that human KRAB-ZFPs mainly serve to control TE-borne enhancers and may have potentially transcription-independent functions, we were interested in the biological significance of KRAB-ZFPs in restricting potentially active TEs. The mouse is an ideal model for such studies since the mouse genome contains several active TE families, including IAP, ETn and L1 elements. We found that many of the young KRAB-ZFPs present in the genomic clusters of KRAB-ZFPs on chromosomes 2 and 4, which are highly expressed in a restricted pattern in ES cells, bound redundantly to these three active TE families. In several cases, KRAB-ZFPs bound to functionally constrained sequence elements we and others have demonstrated to be necessary for retrotransposition, including PBS and viral packaging signals. Targeting such sequences may help the host defense system keep pace with rapidly evolving mouse transposons. This provides strong evidence that many young KRAB-ZFPs are indeed expanding in response to TE activity. But do these young KRAB-ZFP genes limit the mobilization of TEs? Despite the large number of polymorphic ETn elements in mouse strains (Nellåker et al., 2012) and several reports of phenotype-causing novel ETn germ line insertions, no new ETn insertions were reported in recent screens of C57BL/6 mouse genomes (Richardson et al., 2017; Gagnier et al., 2019), indicating that the overall rate of ETn germ line mobilization in inbred mice is rather low. We have demonstrated that Chr4-cl KRAB-ZFPs control ETn/ETnERV expression in ES cells, but this does not lead to widespread ETn mobility in viable C57BL/6 mice. In contrast, we found numerous novel, including several germ line, ETn insertions in both WT and Chr4-cl KO mice in a C57BL/6 129Sv mixed genetic background, with generally more insertions in KO mice and in mice with more 129Sv DNA. This is consistent with a report detecting ETn insertions in FVB.129 mice (Schauer et al., 2018). Notably, there was a large variation in the number of new insertions in these mice, possibly caused by hyperactive polymorphic ETn insertions that varied from individual to individual, epigenetic variation at ETn insertions between individuals and/or the general stochastic nature of ETn mobilization. Furthermore, recent reports have suggested that KRAB-ZFP gene content is distinct in different strains of laboratory mice (Lilue et al., 2018; Treger et al., 2019), and reduced KRAB-ZFP gene content could contribute to increased activity in individual mice. Although we have yet to find obvious phenotypes in the mice carrying new insertions, novel ETn germ line insertions have been shown to cause phenotypes from short tails (Lugani et al., 2013; Semba et al., 2013; Vlangos et al., 2013) to limb malformation (Kano et al., 2007) and severe morphogenetic defects including polypodia (Lehoczky et al., 2013) depending upon their insertion site." }, { - "self_ref": "#/texts/34", + "self_ref": "#/texts/50", "parent": { - "$ref": "#/texts/32" + "$ref": "#/texts/48" }, "children": [], "content_layer": "body", @@ -1049,7 +1573,7 @@ "text": "Despite a lack of widespread ETn activation in Chr4-cl KO mice, it still remains to be determined whether other TEs, like L1, IAP or other LTR retrotransposons are activated in any of the KRAB-ZFP cluster KO mice, which will require the development of additional capture-seq based assays. Notably, two of the heterozygous matings from Chr2-cl KO mice failed to produce viable knockout offspring, which could indicate a TE-reactivation phenotype. It may also be necessary to generate compound homozygous mutants of distinct KRAB-ZFP clusters to eliminate redundancy before TEs become unleashed. The KRAB-ZFP cluster knockouts produced here will be useful reagents to test such hypotheses. In sum, our data supports that a major driver of KRAB-ZFP gene expansion in mice is recent retrotransposon insertions, and that redundancy within the KRAB-ZFP gene family and with other TE restriction pathways provides protection against widespread TE mobility, explaining the non-essential function of the majority of KRAB-ZFP genes." }, { - "self_ref": "#/texts/35", + "self_ref": "#/texts/51", "parent": { "$ref": "#/texts/0" }, @@ -1058,31 +1582,31 @@ "$ref": "#/tables/1" }, { - "$ref": "#/texts/37" + "$ref": "#/texts/53" }, { - "$ref": "#/texts/39" + "$ref": "#/texts/57" }, { - "$ref": "#/texts/41" + "$ref": "#/texts/61" }, { - "$ref": "#/texts/43" + "$ref": "#/texts/65" }, { - "$ref": "#/texts/46" + "$ref": "#/texts/70" }, { - "$ref": "#/texts/48" + "$ref": "#/texts/74" }, { - "$ref": "#/texts/50" + "$ref": "#/texts/82" }, { - "$ref": "#/texts/52" + "$ref": "#/texts/84" }, { - "$ref": "#/texts/54" + "$ref": "#/texts/88" } ], "content_layer": "body", @@ -1093,7 +1617,7 @@ "level": 1 }, { - "self_ref": "#/texts/36", + "self_ref": "#/texts/52", "parent": { "$ref": "#/body" }, @@ -1105,202 +1629,450 @@ "text": "Key resources table" }, { - "self_ref": "#/texts/37", + "self_ref": "#/texts/53", "parent": { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, "children": [ { - "$ref": "#/texts/38" + "$ref": "#/groups/4" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Cell lines and transgenic mice", + "text": "Cell lines and transgenic mice", + "level": 2 + }, + { + "self_ref": "#/texts/54", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-A", + "text": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-A" + }, + { + "self_ref": "#/texts/55", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "tm1Brd", + "text": "tm1Brd", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/56", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring.", + "text": "ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring." + }, + { + "self_ref": "#/texts/57", + "parent": { + "$ref": "#/texts/51" + }, + "children": [ + { + "$ref": "#/groups/5" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Generation of KRAB-ZFP expressing cell lines", + "text": "Generation of KRAB-ZFP expressing cell lines", + "level": 2 + }, + { + "self_ref": "#/texts/58", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using", + "text": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using" + }, + { + "self_ref": "#/texts/59", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Sleeping beauty", + "text": "Sleeping beauty", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/60", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq.", + "text": "transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq." + }, + { + "self_ref": "#/texts/61", + "parent": { + "$ref": "#/texts/51" + }, + "children": [ + { + "$ref": "#/groups/6" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Cell lines and transgenic mice", - "text": "Cell lines and transgenic mice", - "level": 2 + "orig": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion", + "text": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion", + "level": 2 + }, + { + "self_ref": "#/texts/62", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 10", + "text": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 10" + }, + { + "self_ref": "#/texts/63", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "6", + "text": "6", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/64", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3).", + "text": "ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3)." + }, + { + "self_ref": "#/texts/65", + "parent": { + "$ref": "#/texts/51" + }, + "children": [ + { + "$ref": "#/groups/7" + }, + { + "$ref": "#/texts/69" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "ChIP-seq analysis", + "text": "ChIP-seq analysis", + "level": 2 + }, + { + "self_ref": "#/texts/66", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 10", + "text": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 10" + }, + { + "self_ref": "#/texts/67", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "7", + "text": "7", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/68", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively.", + "text": "cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively." }, { - "self_ref": "#/texts/38", + "self_ref": "#/texts/69", "parent": { - "$ref": "#/texts/37" + "$ref": "#/texts/65" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring.", - "text": "Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring." + "orig": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).", + "text": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best)." }, { - "self_ref": "#/texts/39", + "self_ref": "#/texts/70", "parent": { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, "children": [ { - "$ref": "#/texts/40" + "$ref": "#/groups/8" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Generation of KRAB-ZFP expressing cell lines", - "text": "Generation of KRAB-ZFP expressing cell lines", + "orig": "Luciferase reporter assays", + "text": "Luciferase reporter assays", "level": 2 }, { - "self_ref": "#/texts/40", + "self_ref": "#/texts/71", "parent": { - "$ref": "#/texts/39" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq.", - "text": "KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq." + "orig": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the", + "text": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the" }, { - "self_ref": "#/texts/41", + "self_ref": "#/texts/72", "parent": { - "$ref": "#/texts/35" + "$ref": "#/groups/8" }, - "children": [ - { - "$ref": "#/texts/42" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion", - "text": "CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion", - "level": 2 + "orig": "Cd59a", + "text": "Cd59a", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/42", + "self_ref": "#/texts/73", "parent": { - "$ref": "#/texts/41" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3).", - "text": "All gRNAs were expressed from the pX330-U6-Chimeric_BB-CBh-hSpCas9 vector (RRID:Addgene_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3)." + "orig": "gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above.", + "text": "gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above." }, { - "self_ref": "#/texts/43", + "self_ref": "#/texts/74", "parent": { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, "children": [ { - "$ref": "#/texts/44" - }, - { - "$ref": "#/texts/45" + "$ref": "#/groups/9" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "ChIP-seq analysis", - "text": "ChIP-seq analysis", + "orig": "RNA-seq analysis", + "text": "RNA-seq analysis", "level": 2 }, { - "self_ref": "#/texts/44", + "self_ref": "#/texts/75", "parent": { - "$ref": "#/texts/43" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively.", - "text": "For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB_307019 or Covance Cat# MMS-101P-200, RRID:AB_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively." + "orig": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability", + "text": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability" }, { - "self_ref": "#/texts/45", + "self_ref": "#/texts/76", "parent": { - "$ref": "#/texts/43" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).", - "text": "ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best)." + "orig": "p", + "text": "p", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/46", + "self_ref": "#/texts/77", "parent": { - "$ref": "#/texts/35" + "$ref": "#/groups/9" }, - "children": [ - { - "$ref": "#/texts/47" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Luciferase reporter assays", - "text": "Luciferase reporter assays", - "level": 2 + "orig": "in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given", + "text": "in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given" }, { - "self_ref": "#/texts/47", + "self_ref": "#/texts/78", "parent": { - "$ref": "#/texts/46" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above.", - "text": "For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above." + "orig": "n", + "text": "n", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/48", + "self_ref": "#/texts/79", "parent": { - "$ref": "#/texts/35" + "$ref": "#/groups/9" }, - "children": [ - { - "$ref": "#/texts/49" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "RNA-seq analysis", - "text": "RNA-seq analysis", - "level": 2 + "orig": "which is the number of specific groups of genes, and", + "text": "which is the number of specific groups of genes, and" }, { - "self_ref": "#/texts/49", + "self_ref": "#/texts/80", "parent": { - "$ref": "#/texts/48" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust().", - "text": "Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR_016383) or HiSeq3000 (RRID:SCR_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust()." + "orig": "x", + "text": "x", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/50", + "self_ref": "#/texts/81", "parent": { - "$ref": "#/texts/35" + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust().", + "text": "which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust()." + }, + { + "self_ref": "#/texts/82", + "parent": { + "$ref": "#/texts/51" }, "children": [ { - "$ref": "#/texts/51" + "$ref": "#/texts/83" } ], "content_layer": "body", @@ -1311,9 +2083,9 @@ "level": 2 }, { - "self_ref": "#/texts/51", + "self_ref": "#/texts/83", "parent": { - "$ref": "#/texts/50" + "$ref": "#/texts/82" }, "children": [], "content_layer": "body", @@ -1323,13 +2095,13 @@ "text": "For RRBS-seq analysis, Chr4-cl WT and KO ES cells were grown in either standard ES cell media containing FCS or for one week in 2i media containing vitamin C as described previously (Blaschke et al., 2013). Genomic DNA was purified from WT and Chr4-cl KO ES cells using the Quick-gDNA purification kit (Zymo Research) and bisulfite-converted with the NEXTflex Bisulfite-Seq Kit (Bio Scientific) using Msp1 digestion to fragment DNA. Libraries were sequenced as 50 bp paired-end reads on an Illumina HiSeq. The reads were processed using Trim Galore (--illumina --paired –rrbs) to trim poor quality bases and adaptors. Additionally, the first 5 nt of R2 and the last 3 nt of R1 and R2 were trimmed. Reads were then mapped to the reference genome (mm9) using Bismark (Krueger and Andrews, 2011) to extract methylation calling results. The CpG methylation pattern for each covered CpG dyads (two complementary CG dinucleotides) was calculated using a custom script (Source code 1: get_CpG_ML.pl). For comparison of CpG methylation between WT and Chr4-cl KO ES cells (in serum or 2i + Vitamin C conditions) only CpG sites with at least 10-fold coverage in each sample were considered for analysis." }, { - "self_ref": "#/texts/52", + "self_ref": "#/texts/84", "parent": { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, "children": [ { - "$ref": "#/texts/53" + "$ref": "#/groups/10" } ], "content_layer": "body", @@ -1340,25 +2112,56 @@ "level": 2 }, { - "self_ref": "#/texts/53", + "self_ref": "#/texts/85", "parent": { - "$ref": "#/texts/52" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma).", - "text": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma)." + "orig": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 10", + "text": "The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 10" }, { - "self_ref": "#/texts/54", + "self_ref": "#/texts/86", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/87", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma).", + "text": "HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma)." + }, + { + "self_ref": "#/texts/88", "parent": { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, "children": [ { - "$ref": "#/texts/55" + "$ref": "#/groups/11" } ], "content_layer": "body", @@ -1369,28 +2172,59 @@ "level": 2 }, { - "self_ref": "#/texts/55", + "self_ref": "#/texts/89", "parent": { - "$ref": "#/texts/54" + "$ref": "#/groups/11" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software.", - "text": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software." + "orig": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelect", + "text": "To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelect" }, { - "self_ref": "#/texts/56", + "self_ref": "#/texts/90", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "QXT", + "text": "QXT", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/91", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software.", + "text": "Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software." + }, + { + "self_ref": "#/texts/92", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/57" + "$ref": "#/texts/93" }, { - "$ref": "#/groups/0" + "$ref": "#/groups/12" } ], "content_layer": "body", @@ -1401,9 +2235,9 @@ "level": 1 }, { - "self_ref": "#/texts/57", + "self_ref": "#/texts/93", "parent": { - "$ref": "#/texts/56" + "$ref": "#/texts/92" }, "children": [], "content_layer": "body", @@ -1413,9 +2247,9 @@ "text": "This paper was supported by the following grants:" }, { - "self_ref": "#/texts/58", + "self_ref": "#/texts/94", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/12" }, "children": [], "content_layer": "body", @@ -1427,9 +2261,9 @@ "marker": "" }, { - "self_ref": "#/texts/59", + "self_ref": "#/texts/95", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/12" }, "children": [], "content_layer": "body", @@ -1441,9 +2275,9 @@ "marker": "" }, { - "self_ref": "#/texts/60", + "self_ref": "#/texts/96", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/12" }, "children": [], "content_layer": "body", @@ -1455,9 +2289,9 @@ "marker": "" }, { - "self_ref": "#/texts/61", + "self_ref": "#/texts/97", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/12" }, "children": [], "content_layer": "body", @@ -1469,9 +2303,9 @@ "marker": "" }, { - "self_ref": "#/texts/62", + "self_ref": "#/texts/98", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/12" }, "children": [], "content_layer": "body", @@ -1483,13 +2317,13 @@ "marker": "" }, { - "self_ref": "#/texts/63", + "self_ref": "#/texts/99", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/64" + "$ref": "#/texts/100" } ], "content_layer": "body", @@ -1500,9 +2334,9 @@ "level": 1 }, { - "self_ref": "#/texts/64", + "self_ref": "#/texts/100", "parent": { - "$ref": "#/texts/63" + "$ref": "#/texts/99" }, "children": [], "content_layer": "body", @@ -1512,19 +2346,19 @@ "text": "We thank Alex Grinberg, Jeanne Yimdjo and Victoria Carter for generating and maintaining transgenic mice. We also thank members of the Macfarlan and Trono labs for useful discussion, Steven Coon, James Iben, Tianwei Li and Anna Malawska for NGS and computational support. This work was supported by NIH grant 1ZIAHD008933 and the NIH DDIR Innovation Award program (TSM), and by subsidies from the Swiss National Science Foundation (310030_152879 and 310030B_173337) and the European Research Council (KRABnKAP, No. 268721; Transpos-X, No. 694658) (DT)." }, { - "self_ref": "#/texts/65", + "self_ref": "#/texts/101", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/66" + "$ref": "#/texts/102" }, { - "$ref": "#/texts/69" + "$ref": "#/texts/105" }, { - "$ref": "#/texts/90" + "$ref": "#/texts/126" } ], "content_layer": "body", @@ -1535,13 +2369,13 @@ "level": 1 }, { - "self_ref": "#/texts/66", + "self_ref": "#/texts/102", "parent": { - "$ref": "#/texts/65" + "$ref": "#/texts/101" }, "children": [ { - "$ref": "#/groups/1" + "$ref": "#/groups/13" } ], "content_layer": "body", @@ -1552,13 +2386,13 @@ "level": 2 }, { - "self_ref": "#/texts/67", + "self_ref": "#/texts/103", "parent": { - "$ref": "#/groups/1" + "$ref": "#/groups/13" }, "children": [ { - "$ref": "#/groups/2" + "$ref": "#/groups/14" } ], "content_layer": "body", @@ -1570,9 +2404,9 @@ "marker": "" }, { - "self_ref": "#/texts/68", + "self_ref": "#/texts/104", "parent": { - "$ref": "#/groups/2" + "$ref": "#/groups/14" }, "children": [], "content_layer": "body", @@ -1582,13 +2416,13 @@ "text": "No competing interests declared." }, { - "self_ref": "#/texts/69", + "self_ref": "#/texts/105", "parent": { - "$ref": "#/texts/65" + "$ref": "#/texts/101" }, "children": [ { - "$ref": "#/groups/3" + "$ref": "#/groups/15" } ], "content_layer": "body", @@ -1599,13 +2433,13 @@ "level": 2 }, { - "self_ref": "#/texts/70", + "self_ref": "#/texts/106", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/4" + "$ref": "#/groups/16" } ], "content_layer": "body", @@ -1617,9 +2451,9 @@ "marker": "" }, { - "self_ref": "#/texts/71", + "self_ref": "#/texts/107", "parent": { - "$ref": "#/groups/4" + "$ref": "#/groups/16" }, "children": [], "content_layer": "body", @@ -1629,13 +2463,13 @@ "text": "Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft." }, { - "self_ref": "#/texts/72", + "self_ref": "#/texts/108", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/5" + "$ref": "#/groups/17" } ], "content_layer": "body", @@ -1647,9 +2481,9 @@ "marker": "" }, { - "self_ref": "#/texts/73", + "self_ref": "#/texts/109", "parent": { - "$ref": "#/groups/5" + "$ref": "#/groups/17" }, "children": [], "content_layer": "body", @@ -1659,13 +2493,13 @@ "text": "Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing." }, { - "self_ref": "#/texts/74", + "self_ref": "#/texts/110", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/6" + "$ref": "#/groups/18" } ], "content_layer": "body", @@ -1677,9 +2511,9 @@ "marker": "" }, { - "self_ref": "#/texts/75", + "self_ref": "#/texts/111", "parent": { - "$ref": "#/groups/6" + "$ref": "#/groups/18" }, "children": [], "content_layer": "body", @@ -1689,13 +2523,13 @@ "text": "Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing." }, { - "self_ref": "#/texts/76", + "self_ref": "#/texts/112", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/7" + "$ref": "#/groups/19" } ], "content_layer": "body", @@ -1707,9 +2541,9 @@ "marker": "" }, { - "self_ref": "#/texts/77", + "self_ref": "#/texts/113", "parent": { - "$ref": "#/groups/7" + "$ref": "#/groups/19" }, "children": [], "content_layer": "body", @@ -1719,13 +2553,13 @@ "text": "Conceptualization, Formal analysis, Investigation, Writing - review and editing." }, { - "self_ref": "#/texts/78", + "self_ref": "#/texts/114", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/8" + "$ref": "#/groups/20" } ], "content_layer": "body", @@ -1737,9 +2571,9 @@ "marker": "" }, { - "self_ref": "#/texts/79", + "self_ref": "#/texts/115", "parent": { - "$ref": "#/groups/8" + "$ref": "#/groups/20" }, "children": [], "content_layer": "body", @@ -1749,13 +2583,13 @@ "text": "Investigation." }, { - "self_ref": "#/texts/80", + "self_ref": "#/texts/116", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/9" + "$ref": "#/groups/21" } ], "content_layer": "body", @@ -1767,9 +2601,9 @@ "marker": "" }, { - "self_ref": "#/texts/81", + "self_ref": "#/texts/117", "parent": { - "$ref": "#/groups/9" + "$ref": "#/groups/21" }, "children": [], "content_layer": "body", @@ -1779,13 +2613,13 @@ "text": "Investigation." }, { - "self_ref": "#/texts/82", + "self_ref": "#/texts/118", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/10" + "$ref": "#/groups/22" } ], "content_layer": "body", @@ -1797,9 +2631,9 @@ "marker": "" }, { - "self_ref": "#/texts/83", + "self_ref": "#/texts/119", "parent": { - "$ref": "#/groups/10" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", @@ -1809,13 +2643,13 @@ "text": "Data curation, Software, Formal analysis, Visualization." }, { - "self_ref": "#/texts/84", + "self_ref": "#/texts/120", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/11" + "$ref": "#/groups/23" } ], "content_layer": "body", @@ -1827,9 +2661,9 @@ "marker": "" }, { - "self_ref": "#/texts/85", + "self_ref": "#/texts/121", "parent": { - "$ref": "#/groups/11" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", @@ -1839,13 +2673,13 @@ "text": "Investigation." }, { - "self_ref": "#/texts/86", + "self_ref": "#/texts/122", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/12" + "$ref": "#/groups/24" } ], "content_layer": "body", @@ -1857,9 +2691,9 @@ "marker": "" }, { - "self_ref": "#/texts/87", + "self_ref": "#/texts/123", "parent": { - "$ref": "#/groups/12" + "$ref": "#/groups/24" }, "children": [], "content_layer": "body", @@ -1869,13 +2703,13 @@ "text": "Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing - review and editing." }, { - "self_ref": "#/texts/88", + "self_ref": "#/texts/124", "parent": { - "$ref": "#/groups/3" + "$ref": "#/groups/15" }, "children": [ { - "$ref": "#/groups/13" + "$ref": "#/groups/25" } ], "content_layer": "body", @@ -1887,9 +2721,9 @@ "marker": "" }, { - "self_ref": "#/texts/89", + "self_ref": "#/texts/125", "parent": { - "$ref": "#/groups/13" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", @@ -1899,13 +2733,13 @@ "text": "Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing." }, { - "self_ref": "#/texts/90", + "self_ref": "#/texts/126", "parent": { - "$ref": "#/texts/65" + "$ref": "#/texts/101" }, "children": [ { - "$ref": "#/groups/14" + "$ref": "#/groups/26" } ], "content_layer": "body", @@ -1916,13 +2750,13 @@ "level": 2 }, { - "self_ref": "#/texts/91", + "self_ref": "#/texts/127", "parent": { - "$ref": "#/groups/14" + "$ref": "#/groups/26" }, "children": [ { - "$ref": "#/groups/15" + "$ref": "#/groups/27" } ], "content_layer": "body", @@ -1934,9 +2768,9 @@ "marker": "" }, { - "self_ref": "#/texts/92", + "self_ref": "#/texts/128", "parent": { - "$ref": "#/groups/15" + "$ref": "#/groups/27" }, "children": [], "content_layer": "body", @@ -1946,7 +2780,7 @@ "text": "Animal experimentation: All studies using mice were performed in accordance to the Guide for the Care and Use of Laboratory Animals of the NIH, under IACUC animal protocol (ASP )18-026." }, { - "self_ref": "#/texts/93", + "self_ref": "#/texts/129", "parent": { "$ref": "#/texts/0" }, @@ -1959,37 +2793,37 @@ "level": 1 }, { - "self_ref": "#/texts/94", + "self_ref": "#/texts/130", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/95" + "$ref": "#/texts/131" }, { - "$ref": "#/texts/96" + "$ref": "#/texts/132" }, { - "$ref": "#/texts/97" + "$ref": "#/texts/133" }, { - "$ref": "#/texts/98" + "$ref": "#/texts/134" }, { - "$ref": "#/texts/99" + "$ref": "#/texts/135" }, { - "$ref": "#/texts/100" + "$ref": "#/texts/136" }, { - "$ref": "#/texts/101" + "$ref": "#/texts/137" }, { - "$ref": "#/texts/102" + "$ref": "#/texts/138" }, { - "$ref": "#/texts/103" + "$ref": "#/texts/139" } ], "content_layer": "body", @@ -2000,9 +2834,9 @@ "level": 1 }, { - "self_ref": "#/texts/95", + "self_ref": "#/texts/131", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2012,9 +2846,9 @@ "text": "All NGS data has been deposited in GEO (GSE115291). Sequences of full-length de novo ETn insertions have been deposited in the GenBank database (MH449667- MH449669)." }, { - "self_ref": "#/texts/96", + "self_ref": "#/texts/132", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2024,9 +2858,9 @@ "text": "The following datasets were generated:" }, { - "self_ref": "#/texts/97", + "self_ref": "#/texts/133", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2036,9 +2870,9 @@ "text": "Wolf G. Retrotransposon reactivation and mobilization upon deletions of megabase scale KRAB zinc finger gene clusters in mice. NCBI Gene Expression Omnibus (2019). NCBI: GSE115291" }, { - "self_ref": "#/texts/98", + "self_ref": "#/texts/134", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2048,9 +2882,9 @@ "text": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449667" }, { - "self_ref": "#/texts/99", + "self_ref": "#/texts/135", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2060,9 +2894,9 @@ "text": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449668" }, { - "self_ref": "#/texts/100", + "self_ref": "#/texts/136", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2072,9 +2906,9 @@ "text": "Wolf G. Mus musculus musculus strain C57BL/6x129X1/SvJ retrotransposon MMETn-int, complete sequence. NCBI GenBank (2019). NCBI: MH449669" }, { - "self_ref": "#/texts/101", + "self_ref": "#/texts/137", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2084,9 +2918,9 @@ "text": "The following previously published datasets were used:" }, { - "self_ref": "#/texts/102", + "self_ref": "#/texts/138", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2096,9 +2930,9 @@ "text": "Castro-Diaz N, Ecco G, Coluccio A, Kapopoulou A, Duc J, Trono D. Evollutionally dynamic L1 regulation in embryonic stem cells. NCBI Gene Expression Omnibus (2014). NCBI: GSM1406445" }, { - "self_ref": "#/texts/103", + "self_ref": "#/texts/139", "parent": { - "$ref": "#/texts/94" + "$ref": "#/texts/130" }, "children": [], "content_layer": "body", @@ -2108,13 +2942,13 @@ "text": "Andrew ZX. H3K9me3_ChIPSeq (Ctrl). NCBI Gene Expression Omnibus (2014). NCBI: GSM1327148" }, { - "self_ref": "#/texts/104", + "self_ref": "#/texts/140", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/groups/16" + "$ref": "#/groups/28" } ], "content_layer": "body", @@ -2125,9 +2959,9 @@ "level": 1 }, { - "self_ref": "#/texts/105", + "self_ref": "#/texts/141", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2139,9 +2973,9 @@ "marker": "" }, { - "self_ref": "#/texts/106", + "self_ref": "#/texts/142", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2153,9 +2987,9 @@ "marker": "" }, { - "self_ref": "#/texts/107", + "self_ref": "#/texts/143", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2167,9 +3001,9 @@ "marker": "" }, { - "self_ref": "#/texts/108", + "self_ref": "#/texts/144", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2181,9 +3015,9 @@ "marker": "" }, { - "self_ref": "#/texts/109", + "self_ref": "#/texts/145", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2195,9 +3029,9 @@ "marker": "" }, { - "self_ref": "#/texts/110", + "self_ref": "#/texts/146", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2209,9 +3043,9 @@ "marker": "" }, { - "self_ref": "#/texts/111", + "self_ref": "#/texts/147", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2223,9 +3057,9 @@ "marker": "" }, { - "self_ref": "#/texts/112", + "self_ref": "#/texts/148", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2237,9 +3071,9 @@ "marker": "" }, { - "self_ref": "#/texts/113", + "self_ref": "#/texts/149", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2251,9 +3085,9 @@ "marker": "" }, { - "self_ref": "#/texts/114", + "self_ref": "#/texts/150", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2265,9 +3099,9 @@ "marker": "" }, { - "self_ref": "#/texts/115", + "self_ref": "#/texts/151", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2279,9 +3113,9 @@ "marker": "" }, { - "self_ref": "#/texts/116", + "self_ref": "#/texts/152", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2293,9 +3127,9 @@ "marker": "" }, { - "self_ref": "#/texts/117", + "self_ref": "#/texts/153", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2307,9 +3141,9 @@ "marker": "" }, { - "self_ref": "#/texts/118", + "self_ref": "#/texts/154", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2321,9 +3155,9 @@ "marker": "" }, { - "self_ref": "#/texts/119", + "self_ref": "#/texts/155", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2335,9 +3169,9 @@ "marker": "" }, { - "self_ref": "#/texts/120", + "self_ref": "#/texts/156", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2349,9 +3183,9 @@ "marker": "" }, { - "self_ref": "#/texts/121", + "self_ref": "#/texts/157", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2363,9 +3197,9 @@ "marker": "" }, { - "self_ref": "#/texts/122", + "self_ref": "#/texts/158", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2377,9 +3211,9 @@ "marker": "" }, { - "self_ref": "#/texts/123", + "self_ref": "#/texts/159", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2391,9 +3225,9 @@ "marker": "" }, { - "self_ref": "#/texts/124", + "self_ref": "#/texts/160", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2405,9 +3239,9 @@ "marker": "" }, { - "self_ref": "#/texts/125", + "self_ref": "#/texts/161", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2419,9 +3253,9 @@ "marker": "" }, { - "self_ref": "#/texts/126", + "self_ref": "#/texts/162", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2433,9 +3267,9 @@ "marker": "" }, { - "self_ref": "#/texts/127", + "self_ref": "#/texts/163", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2447,9 +3281,9 @@ "marker": "" }, { - "self_ref": "#/texts/128", + "self_ref": "#/texts/164", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2461,9 +3295,9 @@ "marker": "" }, { - "self_ref": "#/texts/129", + "self_ref": "#/texts/165", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2475,9 +3309,9 @@ "marker": "" }, { - "self_ref": "#/texts/130", + "self_ref": "#/texts/166", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2489,9 +3323,9 @@ "marker": "" }, { - "self_ref": "#/texts/131", + "self_ref": "#/texts/167", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2503,9 +3337,9 @@ "marker": "" }, { - "self_ref": "#/texts/132", + "self_ref": "#/texts/168", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2517,9 +3351,9 @@ "marker": "" }, { - "self_ref": "#/texts/133", + "self_ref": "#/texts/169", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2531,9 +3365,9 @@ "marker": "" }, { - "self_ref": "#/texts/134", + "self_ref": "#/texts/170", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2545,9 +3379,9 @@ "marker": "" }, { - "self_ref": "#/texts/135", + "self_ref": "#/texts/171", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2559,9 +3393,9 @@ "marker": "" }, { - "self_ref": "#/texts/136", + "self_ref": "#/texts/172", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2573,9 +3407,9 @@ "marker": "" }, { - "self_ref": "#/texts/137", + "self_ref": "#/texts/173", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2587,9 +3421,9 @@ "marker": "" }, { - "self_ref": "#/texts/138", + "self_ref": "#/texts/174", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2601,9 +3435,9 @@ "marker": "" }, { - "self_ref": "#/texts/139", + "self_ref": "#/texts/175", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2615,9 +3449,9 @@ "marker": "" }, { - "self_ref": "#/texts/140", + "self_ref": "#/texts/176", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2629,9 +3463,9 @@ "marker": "" }, { - "self_ref": "#/texts/141", + "self_ref": "#/texts/177", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2643,9 +3477,9 @@ "marker": "" }, { - "self_ref": "#/texts/142", + "self_ref": "#/texts/178", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2657,9 +3491,9 @@ "marker": "" }, { - "self_ref": "#/texts/143", + "self_ref": "#/texts/179", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2671,9 +3505,9 @@ "marker": "" }, { - "self_ref": "#/texts/144", + "self_ref": "#/texts/180", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2685,9 +3519,9 @@ "marker": "" }, { - "self_ref": "#/texts/145", + "self_ref": "#/texts/181", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2699,9 +3533,9 @@ "marker": "" }, { - "self_ref": "#/texts/146", + "self_ref": "#/texts/182", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2713,9 +3547,9 @@ "marker": "" }, { - "self_ref": "#/texts/147", + "self_ref": "#/texts/183", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2727,9 +3561,9 @@ "marker": "" }, { - "self_ref": "#/texts/148", + "self_ref": "#/texts/184", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2741,9 +3575,9 @@ "marker": "" }, { - "self_ref": "#/texts/149", + "self_ref": "#/texts/185", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2755,9 +3589,9 @@ "marker": "" }, { - "self_ref": "#/texts/150", + "self_ref": "#/texts/186", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2769,9 +3603,9 @@ "marker": "" }, { - "self_ref": "#/texts/151", + "self_ref": "#/texts/187", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2783,9 +3617,9 @@ "marker": "" }, { - "self_ref": "#/texts/152", + "self_ref": "#/texts/188", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2797,9 +3631,9 @@ "marker": "" }, { - "self_ref": "#/texts/153", + "self_ref": "#/texts/189", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2811,9 +3645,9 @@ "marker": "" }, { - "self_ref": "#/texts/154", + "self_ref": "#/texts/190", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2825,9 +3659,9 @@ "marker": "" }, { - "self_ref": "#/texts/155", + "self_ref": "#/texts/191", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2839,9 +3673,9 @@ "marker": "" }, { - "self_ref": "#/texts/156", + "self_ref": "#/texts/192", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2853,9 +3687,9 @@ "marker": "" }, { - "self_ref": "#/texts/157", + "self_ref": "#/texts/193", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2867,9 +3701,9 @@ "marker": "" }, { - "self_ref": "#/texts/158", + "self_ref": "#/texts/194", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2881,9 +3715,9 @@ "marker": "" }, { - "self_ref": "#/texts/159", + "self_ref": "#/texts/195", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2895,9 +3729,9 @@ "marker": "" }, { - "self_ref": "#/texts/160", + "self_ref": "#/texts/196", "parent": { - "$ref": "#/groups/16" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2913,7 +3747,7 @@ { "self_ref": "#/pictures/0", "parent": { - "$ref": "#/texts/9" + "$ref": "#/texts/11" }, "children": [], "content_layer": "body", @@ -2921,7 +3755,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/12" + "$ref": "#/texts/16" } ], "references": [], @@ -2931,7 +3765,7 @@ { "self_ref": "#/pictures/1", "parent": { - "$ref": "#/texts/17" + "$ref": "#/texts/25" }, "children": [], "content_layer": "body", @@ -2939,7 +3773,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/19" + "$ref": "#/texts/27" } ], "references": [], @@ -2949,7 +3783,7 @@ { "self_ref": "#/pictures/2", "parent": { - "$ref": "#/texts/20" + "$ref": "#/texts/28" }, "children": [], "content_layer": "body", @@ -2957,7 +3791,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/22" + "$ref": "#/texts/30" } ], "references": [], @@ -2967,7 +3801,7 @@ { "self_ref": "#/pictures/3", "parent": { - "$ref": "#/texts/24" + "$ref": "#/texts/40" }, "children": [], "content_layer": "body", @@ -2975,7 +3809,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/28" + "$ref": "#/texts/44" } ], "references": [], @@ -2987,7 +3821,7 @@ { "self_ref": "#/tables/0", "parent": { - "$ref": "#/texts/9" + "$ref": "#/texts/11" }, "children": [], "content_layer": "body", @@ -2995,7 +3829,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/13" + "$ref": "#/texts/17" } ], "references": [], @@ -4202,7 +5036,7 @@ { "self_ref": "#/tables/1", "parent": { - "$ref": "#/texts/35" + "$ref": "#/texts/51" }, "children": [], "content_layer": "body", @@ -4210,7 +5044,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/36" + "$ref": "#/texts/52" } ], "references": [], diff --git a/tests/data/jats/groundtruth/elife-56337.nxml.md b/tests/data/jats/groundtruth/elife-56337.nxml.md index a7551e8bba..4816c0fda2 100644 --- a/tests/data/jats/groundtruth/elife-56337.nxml.md +++ b/tests/data/jats/groundtruth/elife-56337.nxml.md @@ -12,7 +12,7 @@ The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in Nearly half of the human and mouse genomes consist of transposable elements (TEs). TEs shape the evolution of species, serving as a source for genetic innovation (Chuong et al., 2016; Frank and Feschotte, 2017). However, TEs also potentially harm their hosts by insertional mutagenesis, gene deregulation and activation of innate immunity (Maksakova et al., 2006; Kano et al., 2007; Brodziak et al., 2012; Hancks and Kazazian, 2016). To protect themselves from TE activity, host organisms have developed a wide range of defense mechanisms targeting virtually all steps of the TE life cycle (Dewannieux and Heidmann, 2013). In tetrapods, KRAB zinc finger protein (KRAB-ZFP) genes have amplified and diversified, likely in response to TE colonization (Thomas and Schneider, 2011; Najafabadi et al., 2015; Wolf et al., 2015a; Wolf et al., 2015b; Imbeault et al., 2017). Conventional ZFPs bind DNA using tandem arrays of C2H2 zinc finger domains, each capable of specifically interacting with three nucleotides, whereas some zinc fingers can bind two or four nucleotides and include DNA backbone interactions depending on target DNA structure (Patel et al., 2018). This allows KRAB-ZFPs to flexibly bind to large stretches of DNA with high affinity. The KRAB domain binds the corepressor KAP1, which in turn recruits histone modifying enzymes including the NuRD histone deacetylase complex and the H3K9-specific methylase SETDB1 (Schultz et al., 2002; Sripathy et al., 2006), which induces persistent and heritable gene silencing (Groner et al., 2010). Deletion of KAP1 (Rowe et al., 2010) or SETDB1 (Matsui et al., 2010) in mouse embryonic stem (ES) cells induces TE reactivation and cell death, but only minor phenotypes in differentiated cells, suggesting KRAB-ZFPs are most important during early embryogenesis where they mark TEs for stable epigenetic silencing that persists through development. However, SETDB1-containing complexes are also required to repress TEs in primordial germ cells (Liu et al., 2014) and adult tissues (Ecco et al., 2016), indicating KRAB-ZFPs are active beyond early development. -TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes. +TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, *Zfp809* knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes. ## Results @@ -20,7 +20,7 @@ TEs, especially long terminal repeat (LTR) retrotransposons, also known as endog We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1—figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1—figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1—figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017). -To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A). +To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily ( *adjusted p-value* <1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A). Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white. @@ -39,7 +39,7 @@ Table 1. KRAB-ZFP genes clusters in the mouse genome that were investigated in t | Chr9 | Chr9 qA3 | 0.1 | 4 | 2 | | Other | - | - | 248 | 4 | -We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect. +We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBS Lys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBS Lys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect. To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1—figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3’ end of the internal region of ETn (Figure 1—figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation. @@ -61,7 +61,7 @@ Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Diff -While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR. +While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the *Cd59a* gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of *Chst1* , one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the *Chst1* upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates *Chst1* expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR. ### ETn retrotransposition in Chr4-cl KO and WT mice @@ -126,29 +126,29 @@ Key resources table ### Cell lines and transgenic mice -Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring. +Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-A tm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring. ### Generation of KRAB-ZFP expressing cell lines -KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq. +KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using *Sleeping beauty* transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq. ### CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion -All gRNAs were expressed from the pX330-U6-Chimeric\_BB-CBh-hSpCas9 vector (RRID:Addgene\_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3). +All gRNAs were expressed from the pX330-U6-Chimeric\_BB-CBh-hSpCas9 vector (RRID:Addgene\_42230) and nucleofected into 10 6 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3). ### ChIP-seq analysis -For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB\_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB\_307019 or Covance Cat# MMS-101P-200, RRID:AB\_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB\_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB\_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB\_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB\_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB\_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively. +For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 10 7 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB\_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB\_307019 or Covance Cat# MMS-101P-200, RRID:AB\_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB\_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB\_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB\_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB\_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB\_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively. ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR\_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR\_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR\_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR\_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best). ### Luciferase reporter assays -For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above. +For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the *Cd59a* gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above. ### RNA-seq analysis -Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR\_016383) or HiSeq3000 (RRID:SCR\_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR\_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR\_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR\_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust(). +Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR\_016383) or HiSeq3000 (RRID:SCR\_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR\_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR\_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR\_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability *p* in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given *n* which is the number of specific groups of genes, and *x* which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust(). ### Reduced representation bisulfite sequencing (RRBS-seq) @@ -156,11 +156,11 @@ For RRBS-seq analysis, Chr4-cl WT and KO ES cells were grown in either standard ### Retrotransposition assay -The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma). +The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 10 4 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma). ### Capture-seq screen -To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4\_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4\_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software. +To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelect QXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4\_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4\_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software. ## Funding Information diff --git a/tests/data/jats/groundtruth/pntd.0008301.nxml.itxt b/tests/data/jats/groundtruth/pntd.0008301.nxml.itxt index 3629cf1d35..0787b59170 100644 --- a/tests/data/jats/groundtruth/pntd.0008301.nxml.itxt +++ b/tests/data/jats/groundtruth/pntd.0008301.nxml.itxt @@ -21,115 +21,191 @@ item-0 at level 0: unspecified: group _root_ item-19 at level 4: text: Information on baseline prevalen ... publicly available sources (Table 1). item-20 at level 3: section_header: Outcome and covariate variables item-21 at level 4: text: The outcome of interest for this ... r than or equal to 1% Mf or 2% Ag [4]. - item-22 at level 4: text: Potential covariates were derive ... is and the final categorizations used. - item-23 at level 4: section_header: Baseline prevalence - item-24 at level 5: text: Baseline prevalence can be assum ... (2) using the cut-off of <10% or ≥10%. - item-25 at level 4: section_header: Agent - item-26 at level 5: text: In terms of differences in trans ... dazole (DEC-ALB)] from the MDA domain. - item-27 at level 4: section_header: Environment - item-28 at level 5: text: LF transmission intensity is inf ... dicates a higher level of “greenness.” - item-29 at level 5: text: We included the socio-economic v ... proxy for socio-economic status [33]. - item-30 at level 5: text: Finally, all or parts of distric ... s were co-endemic with onchocerciasis. - item-31 at level 4: section_header: MDA - item-32 at level 5: text: Treatment effectiveness depends ... esent a threat to elimination [41,42]. - item-33 at level 5: text: We considered three approaches w ... unds ever documented in that district. - item-34 at level 4: section_header: Pre-TAS implementation - item-35 at level 5: text: Pre-TAS results can be influence ... d throughout the time period of study. - item-36 at level 3: section_header: Data inclusion criteria - item-37 at level 4: text: The dataset, summarized at the d ... al analysis dataset had 554 districts. - item-38 at level 3: section_header: Statistical analysis and modeling - item-39 at level 4: text: Statistical analysis and modelin ... d the number of variables accordingly. - item-40 at level 4: text: Sensitivity analysis was perform ... ot have been truly LF-endemic [43,44]. - item-41 at level 2: section_header: Results - item-42 at level 3: text: The overall pre-TAS pass rate fo ... ts had baseline prevalences below 20%. - item-43 at level 3: picture - item-43 at level 4: caption: Fig 1 Number of pre-TAS by country. - item-44 at level 3: picture - item-44 at level 4: caption: Fig 2 District-level baseline prevalence by country. - item-45 at level 3: text: Fig 3 shows the unadjusted analy ... overage, and sufficient rounds of MDA. - item-46 at level 3: picture - item-46 at level 4: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted). - item-47 at level 3: text: The final log-binomial model inc ... igh baseline and diagnostic test used. - item-48 at level 3: text: Fig 4 shows the risk ratio resul ... of failing pre-TAS (95% CI 1.95–4.83). - item-49 at level 3: picture - item-49 at level 4: caption: Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model. - item-50 at level 3: text: Sensitivity analyses were conduc ... gnified by large confidence intervals. - item-51 at level 3: table with [11x6] - item-51 at level 4: caption: Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis. - item-52 at level 3: text: Overall 74 districts in the data ... or 51% of all the failures (38 of 74). - item-53 at level 3: picture - item-53 at level 4: caption: Fig 5 Analysis of failures by model combinations. - item-54 at level 2: section_header: Discussion - item-55 at level 3: text: This paper reports for the first ... ctors associated with TAS failure [7]. - item-56 at level 3: text: Though diagnostic test used was ... FTS was more sensitive than ICT [45]. - item-57 at level 3: text: Elevation was the only environme ... ich impact vector chances of survival. - item-58 at level 3: text: The small number of failures ove ... search has shown the opposite [15,16]. - item-59 at level 3: text: All other variables included in ... are not necessary to lower prevalence. - item-60 at level 3: text: Limitations to this study includ ... reducing LF prevalence [41,48,51–53]. - item-61 at level 3: text: Fourteen districts were excluded ... ta to extreme outliners in a district. - item-62 at level 3: text: As this analysis used data acros ... of individuals included in the survey. - item-63 at level 3: text: This paper provides evidence fro ... th high baseline and/or low elevation. - item-64 at level 2: section_header: Acknowledgments - item-65 at level 3: text: The authors would like to thank ... e surveys financially and technically. - item-66 at level 2: section_header: References - item-67 at level 3: list: group list - item-68 at level 4: list_item: World Health Organization. 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Vegetation indices 16 ... /lpdaac.usgs.gov/products/myd13a1v006/ - item-78 at level 4: list_item: FunkC, PetersonP, LandsfeldM, Pe ... 2015;2 10.1038/sdata.2015.66 26646728 - item-79 at level 4: list_item: LloydCT, SorichettaA, TatemAJ. H ... : 170001 10.1038/sdata.2017.1 28140386 - item-80 at level 4: list_item: ElvidgeCD, BaughKE, ZhizhinM, Hs ... Network; 2013;35: 62 10.7125/apan.35.7 - item-81 at level 4: list_item: JambulingamP, SubramanianS, De V ... 18. 10.1186/s13071-015-1291-6 26728523 - item-82 at level 4: list_item: MichaelE, Malecela-LazaroMN, Sim ... 10.1016/S1473-3099(04)00973-9 15050941 - item-83 at level 4: list_item: StolkWA, SwaminathanS, van Oortm ... ;188: 1371–81. 10.1086/378354 14593597 - item-84 at level 4: list_item: GradyCA, De RocharsMB, DirenyAN, ... 8–610. 10.3201/eid1304.061063 17553278 - item-85 at level 4: list_item: EvansD, McFarlandD, AdamaniW, Ei ... 0.1179/2047773211Y.0000000010 22325813 - item-86 at level 4: list_item: RichardsFO, EigegeA, MiriES, Kal ... 10.1371/journal.pntd.0001346 22022627 - item-87 at level 4: list_item: BiritwumNK, YikpoteyP, MarfoBK, ... 90–695. 10.1093/trstmh/trx007 28938053 - item-88 at level 4: list_item: MoragaP, CanoJ, BaggaleyRF, Gyap ... 16. 10.1186/s13071-014-0608-1 25561160 - item-89 at level 4: list_item: IrvineMA, NjengaSM, GunawardenaS ... 18–124. 10.1093/trstmh/trv096 26822604 - item-90 at level 4: list_item: OttesenEA. Efficacy of diethylca ... iae in humans. Rev Infect Dis. 1985;7. - item-91 at level 4: list_item: GambhirM, BockarieM, TischD, Kaz ... 2010;8 10.1186/1741-7007-8-22 20236528 - item-92 at level 4: list_item: World Health Organization. Globa ... cal entomology handbook. Geneva; 2013. - item-93 at level 4: list_item: SlaterH, MichaelE. Predicting th ... 10.1371/journal.pone.0032202 22359670 - item-94 at level 4: list_item: SlaterH, MichaelE. Mapping, Baye ... 10.1371/journal.pone.0071574 23951194 - item-95 at level 4: list_item: SabesanS, RajuKHK, SubramanianS, ... 57–665. 10.1089/vbz.2012.1238 23808973 - item-96 at level 4: list_item: StantonMC, MolyneuxDH, KyelemD, ... : 159–173. 10.4081/gh.2013.63 24258892 - item-97 at level 4: list_item: ManhenjeI, Teresa Galán-Puchades ... : 391–398. 10.4081/gh.2013.96 23733300 - item-98 at level 4: list_item: NgwiraBM, TambalaP, Perez aM, Bo ... ;6: 12 10.1186/1475-2883-6-12 18047646 - item-99 at level 4: list_item: SimonsenPE, MwakitaluME. Urban l ... 44. 10.1007/s00436-012-3226-x 23239094 - item-100 at level 4: list_item: ProvilleJ, Zavala-AraizaD, Wagne ... 10.1371/journal.pone.0174610 28346500 - item-101 at level 4: list_item: EndeshawT, TayeA, TadesseZ, Kata ... 10.1080/20477724.2015.1103501 26878935 - item-102 at level 4: list_item: RichardsFO, EigegeA, PamD, KalA, ... 4: 3–5. 10.1186/1475-2883-4-3 15916708 - item-103 at level 4: list_item: KyelemD, SanouS, BoatinB a., Med ... 8. 10.1179/000349803225002462 14754495 - item-104 at level 4: list_item: WeilGJ, LammiePJ, RichardsFO, Eb ... –816. 10.1093/infdis/164.4.814 1894943 - item-105 at level 4: list_item: KumarA, SachanP. Measuring impac ... rop Biomed. 2014;31: 225–229. 25134891 - item-106 at level 4: list_item: NjengaSM, MwandawiroCS, WamaeCN, ... 4: 1–9. 10.1186/1756-3305-4-1 21205315 - item-107 at level 4: list_item: BoydA, WonKY, McClintockSK, Dono ... 10.1371/journal.pntd.0000640 20351776 - item-108 at level 4: list_item: IrvineMA, ReimerLJ, NjengaSM, Gu ... 19. 10.1186/s13071-014-0608-1 25561160 - item-109 at level 4: list_item: IrvineMA, StolkWA, SmithME, Subr ... 10.1016/S1473-3099(16)30467-4 28012943 - item-110 at level 4: list_item: PionSD, MontavonC, ChesnaisCB, K ... 7–1423. 10.4269/ajtmh.16-0547 27729568 - item-111 at level 4: list_item: WanjiS, EsumME, NjouendouAJ, Mbe ... 10.1371/journal.pntd.0007192 30849120 - item-112 at level 4: list_item: ChesnaisCB, Awaca-UvonNP, BolayF ... 10.1371/journal.pntd.0005703 28892473 - item-113 at level 4: list_item: SilumbweA, ZuluJM, HalwindiH, Ja ... 15. 10.1186/s12889-017-4414-5 28532397 - item-114 at level 4: list_item: AdamsAM, VuckovicM, BirchE, Bran ... ;3 10.3390/tropicalmed3040122 30469342 - item-115 at level 4: list_item: RaoRU, SamarasekeraSD, Nagodavit ... 10.1371/journal.pntd.0006066 29084213 - item-116 at level 4: list_item: XuZ, GravesPM, LauCL, ClementsA, ... 10.1016/j.epidem.2018.12.003 30611745 - item-117 at level 4: list_item: IdCM, TetteviEJ, MechanF, IdunB, ... Ghana. PLoS Negl Trop Dis. 2019; 1–17. - item-118 at level 4: list_item: EigegeA, KalA, MiriE, SallauA, U ... 10.1371/journal.pntd.0002508 24205421 - item-119 at level 4: list_item: Van den BergH, Kelly-HopeLA, Lin ... 10.1016/S1473-3099(12)70148-2 23084831 - item-120 at level 4: list_item: WebberR. Eradication of Wucherer ... ol. Trans R Soc Trop Med Hyg. 1979;73. - item-121 at level 1: caption: Table 1 Categorization of potential factors influencing pre-TAS results. - item-122 at level 1: caption: Fig 1 Number of pre-TAS by country. - item-123 at level 1: caption: Fig 2 District-level baseline prevalence by country. - item-124 at level 1: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted). - item-125 at level 1: caption: Fig 4 Adjusted risk ratios for p ... ence Interval from log-binomial model. - item-126 at level 1: caption: Table 2 Adjusted risk ratios for ... g-binomial model sensitivity analysis. - item-127 at level 1: caption: Fig 5 Analysis of failures by model combinations. \ No newline at end of file + item-22 at level 4: inline: group group + item-23 at level 5: text: Potential covariates were derive ... ous variables were derived from either + item-24 at level 5: text: a priori + item-25 at level 5: text: knowledge or through exploratory ... is and the final categorizations used. + item-26 at level 4: section_header: Baseline prevalence + item-27 at level 5: text: Baseline prevalence can be assum ... (2) using the cut-off of <10% or ≥10%. + item-28 at level 4: section_header: Agent + item-29 at level 5: inline: group group + item-30 at level 6: text: In terms of differences in trans ... mics by agent, research has shown that + item-31 at level 6: text: Brugia + item-32 at level 6: text: spp. are more susceptible to the anti-filarial drug regimens than + item-33 at level 6: text: Wuchereria bancrofti + item-34 at level 6: text: parasites [23]. Thus, we combined districts reporting + item-35 at level 6: text: B + item-36 at level 6: text: . + item-37 at level 6: text: malayi + item-38 at level 6: text: and + item-39 at level 6: text: B + item-40 at level 6: text: . + item-41 at level 6: text: timori + item-42 at level 6: text: and compared them to areas with + item-43 at level 6: text: W + item-44 at level 6: text: . + item-45 at level 6: text: bancrofti + item-46 at level 6: text: or mixed parasites. Two variable ... e were variables delineating vectors ( + item-47 at level 6: text: Anopheles + item-48 at level 6: text: or + item-49 at level 6: text: Mansonia + item-50 at level 6: text: compared to + item-51 at level 6: text: Culex + item-52 at level 6: text: ) from the environmental domain ... dazole (DEC-ALB)] from the MDA domain. + item-53 at level 4: section_header: Environment + item-54 at level 5: text: LF transmission intensity is inf ... dicates a higher level of “greenness.” + item-55 at level 5: inline: group group + item-56 at level 6: text: We included the socio-economic v ... countries LF is mostly transmitted by + item-57 at level 6: text: Culex + item-58 at level 6: text: in urban areas and by + item-59 at level 6: text: Anopheles + item-60 at level 6: text: in rural areas [32]. Additionall ... proxy for socio-economic status [33]. + item-61 at level 5: text: Finally, all or parts of distric ... s were co-endemic with onchocerciasis. + item-62 at level 4: section_header: MDA + item-63 at level 5: text: Treatment effectiveness depends ... esent a threat to elimination [41,42]. + item-64 at level 5: text: We considered three approaches w ... unds ever documented in that district. + item-65 at level 4: section_header: Pre-TAS implementation + item-66 at level 5: text: Pre-TAS results can be influence ... d throughout the time period of study. + item-67 at level 3: section_header: Data inclusion criteria + item-68 at level 4: text: The dataset, summarized at the d ... al analysis dataset had 554 districts. + item-69 at level 3: section_header: Statistical analysis and modeling + item-70 at level 4: text: Statistical analysis and modelin ... d the number of variables accordingly. + item-71 at level 4: inline: group group + item-72 at level 5: text: Sensitivity analysis was perform ... ica, (3) including only districts with + item-73 at level 5: text: W + item-74 at level 5: text: . + item-75 at level 5: text: bancrofti + item-76 at level 5: text: parasite, and (4) including only districts with + item-77 at level 5: text: Anopheles + item-78 at level 5: text: as the primary vector. The sensi ... ot have been truly LF-endemic [43,44]. + item-79 at level 2: section_header: Results + item-80 at level 3: text: The overall pre-TAS pass rate fo ... ts had baseline prevalences below 20%. + item-81 at level 3: picture + item-81 at level 4: caption: Fig 1 Number of pre-TAS by country. + item-82 at level 3: picture + item-82 at level 4: caption: Fig 2 District-level baseline prevalence by country. + item-83 at level 3: inline: group group + item-84 at level 4: text: Fig 3 shows the unadjusted analy ... FTS diagnostic test, primary vector of + item-85 at level 4: text: Culex + item-86 at level 4: text: , treatment with DEC-ALB, higher ... overage, and sufficient rounds of MDA. + item-87 at level 3: picture + item-87 at level 4: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted). + item-88 at level 3: text: The final log-binomial model inc ... igh baseline and diagnostic test used. + item-89 at level 3: text: Fig 4 shows the risk ratio resul ... of failing pre-TAS (95% CI 1.95–4.83). + item-90 at level 3: picture + item-90 at level 4: caption: Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model. + item-91 at level 3: inline: group group + item-92 at level 4: text: Sensitivity analyses were conduc ... cts in Africa, (3) only districts with + item-93 at level 4: text: W + item-94 at level 4: text: . + item-95 at level 4: text: bancrofti + item-96 at level 4: text: , and (4) only districts with + item-97 at level 4: text: Anopheles + item-98 at level 4: text: as primary vector. The results o ... hough the estimate was unstable in the + item-99 at level 4: text: W + item-100 at level 4: text: . + item-101 at level 4: text: bancrofti + item-102 at level 4: text: -only and + item-103 at level 4: text: Anopheles + item-104 at level 4: text: -only models (models 3 and 4 res ... gnified by large confidence intervals. + item-105 at level 3: table with [11x6] + item-105 at level 4: caption: Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis. + item-106 at level 3: text: Overall 74 districts in the data ... or 51% of all the failures (38 of 74). + item-107 at level 3: picture + item-107 at level 4: caption: Fig 5 Analysis of failures by model combinations. + item-108 at level 2: section_header: Discussion + item-109 at level 3: inline: group group + item-110 at level 4: text: This paper reports for the first ... similar to what was found in Goldberg + item-111 at level 4: text: et al + item-112 at level 4: text: ., which explored ecological and ... ctors associated with TAS failure [7]. + item-113 at level 3: text: Though diagnostic test used was ... FTS was more sensitive than ICT [45]. + item-114 at level 3: inline: group group + item-115 at level 4: text: Elevation was the only environme ... ous studies [8,31], including Goldberg + item-116 at level 4: text: et al + item-117 at level 4: text: . [7], who used a cutoff of 200 ... ich impact vector chances of survival. + item-118 at level 3: inline: group group + item-119 at level 4: text: The small number of failures ove ... ied in the bivariate analyses, such as + item-120 at level 4: text: Culex + item-121 at level 4: text: vector, higher population densit ... ecisions. Other models have shown that + item-122 at level 4: text: Culex + item-123 at level 4: text: as the predominant vector in a district, compared to + item-124 at level 4: text: Anopheles + item-125 at level 4: text: , results in more intense interv ... search has shown the opposite [15,16]. + item-126 at level 3: inline: group group + item-127 at level 4: text: All other variables included in ... -TAS failure in our analysis. Goldberg + item-128 at level 4: text: et al + item-129 at level 4: text: . found + item-130 at level 4: text: Brugia + item-131 at level 4: text: spp. to be significantly associa ... to the small number of districts with + item-132 at level 4: text: Brugia + item-133 at level 4: text: spp. in our dataset (6%) compared to 46% in the Goldberg + item-134 at level 4: text: et al + item-135 at level 4: text: . article [7]. MDA coverage leve ... are not necessary to lower prevalence. + item-136 at level 3: text: Limitations to this study includ ... reducing LF prevalence [41,48,51–53]. + item-137 at level 3: text: Fourteen districts were excluded ... ta to extreme outliners in a district. + item-138 at level 3: text: As this analysis used data acros ... of individuals included in the survey. + item-139 at level 3: text: This paper provides evidence fro ... th high baseline and/or low elevation. + item-140 at level 2: section_header: Acknowledgments + item-141 at level 3: text: The authors would like to thank ... e surveys financially and technically. + item-142 at level 2: section_header: References + item-143 at level 3: list: group list + item-144 at level 4: list_item: World Health Organization. Lymph ... trategic plan 2010–2020. Geneva; 2010. + item-145 at level 4: list_item: World Health Organization. Valid ... a public health problem. Geneva; 2017. + item-146 at level 4: list_item: World Health Organization. Globa ... Wkly Epidemiol Rec. 2019;94: 457–472. + item-147 at level 4: list_item: World Health Organization. Globa ... ass drug administration. Geneva; 2011. + item-148 at level 4: list_item: World Health Organization. Stren ... Disease-specific Indicators. 2016; 42. + item-149 at level 4: list_item: KyelemD, BiswasG, BockarieMJ, Br ... Trop Med Hyg. 2008;79: 480–4. 18840733 + item-150 at level 4: list_item: GoldbergEM, KingJD, MupfasoniD, ... . 2019; 10.4269/ajtmh.18-0721 31115301 + item-151 at level 4: list_item: CanoJ, RebolloMP, GoldingN, Pull ... : 1–19. 10.1186/1756-3305-7-1 24411014 + item-152 at level 4: list_item: CGIAR-CSI. CGIAR-CSI SRTM 90m DE ... Available: http://srtm.csi.cgiar.org/ + item-153 at level 4: list_item: USGS NASA. Vegetation indices 16 ... /lpdaac.usgs.gov/products/myd13a1v006/ + item-154 at level 4: list_item: FunkC, PetersonP, LandsfeldM, Pe ... 2015;2 10.1038/sdata.2015.66 26646728 + item-155 at level 4: list_item: LloydCT, SorichettaA, TatemAJ. H ... : 170001 10.1038/sdata.2017.1 28140386 + item-156 at level 4: list_item: ElvidgeCD, BaughKE, ZhizhinM, Hs ... Network; 2013;35: 62 10.7125/apan.35.7 + item-157 at level 4: list_item: JambulingamP, SubramanianS, De V ... 18. 10.1186/s13071-015-1291-6 26728523 + item-158 at level 4: list_item: MichaelE, Malecela-LazaroMN, Sim ... 10.1016/S1473-3099(04)00973-9 15050941 + item-159 at level 4: list_item: StolkWA, SwaminathanS, van Oortm ... ;188: 1371–81. 10.1086/378354 14593597 + item-160 at level 4: list_item: GradyCA, De RocharsMB, DirenyAN, ... 8–610. 10.3201/eid1304.061063 17553278 + item-161 at level 4: list_item: EvansD, McFarlandD, AdamaniW, Ei ... 0.1179/2047773211Y.0000000010 22325813 + item-162 at level 4: list_item: RichardsFO, EigegeA, MiriES, Kal ... 10.1371/journal.pntd.0001346 22022627 + item-163 at level 4: list_item: BiritwumNK, YikpoteyP, MarfoBK, ... 90–695. 10.1093/trstmh/trx007 28938053 + item-164 at level 4: list_item: MoragaP, CanoJ, BaggaleyRF, Gyap ... 16. 10.1186/s13071-014-0608-1 25561160 + item-165 at level 4: list_item: IrvineMA, NjengaSM, GunawardenaS ... 18–124. 10.1093/trstmh/trv096 26822604 + item-166 at level 4: list_item: OttesenEA. Efficacy of diethylca ... iae in humans. Rev Infect Dis. 1985;7. + item-167 at level 4: list_item: GambhirM, BockarieM, TischD, Kaz ... 2010;8 10.1186/1741-7007-8-22 20236528 + item-168 at level 4: list_item: World Health Organization. Globa ... cal entomology handbook. Geneva; 2013. + item-169 at level 4: list_item: SlaterH, MichaelE. Predicting th ... 10.1371/journal.pone.0032202 22359670 + item-170 at level 4: list_item: SlaterH, MichaelE. Mapping, Baye ... 10.1371/journal.pone.0071574 23951194 + item-171 at level 4: list_item: SabesanS, RajuKHK, SubramanianS, ... 57–665. 10.1089/vbz.2012.1238 23808973 + item-172 at level 4: list_item: StantonMC, MolyneuxDH, KyelemD, ... : 159–173. 10.4081/gh.2013.63 24258892 + item-173 at level 4: list_item: ManhenjeI, Teresa Galán-Puchades ... : 391–398. 10.4081/gh.2013.96 23733300 + item-174 at level 4: list_item: NgwiraBM, TambalaP, Perez aM, Bo ... ;6: 12 10.1186/1475-2883-6-12 18047646 + item-175 at level 4: list_item: SimonsenPE, MwakitaluME. Urban l ... 44. 10.1007/s00436-012-3226-x 23239094 + item-176 at level 4: list_item: ProvilleJ, Zavala-AraizaD, Wagne ... 10.1371/journal.pone.0174610 28346500 + item-177 at level 4: list_item: EndeshawT, TayeA, TadesseZ, Kata ... 10.1080/20477724.2015.1103501 26878935 + item-178 at level 4: list_item: RichardsFO, EigegeA, PamD, KalA, ... 4: 3–5. 10.1186/1475-2883-4-3 15916708 + item-179 at level 4: list_item: KyelemD, SanouS, BoatinB a., Med ... 8. 10.1179/000349803225002462 14754495 + item-180 at level 4: list_item: WeilGJ, LammiePJ, RichardsFO, Eb ... –816. 10.1093/infdis/164.4.814 1894943 + item-181 at level 4: list_item: KumarA, SachanP. Measuring impac ... rop Biomed. 2014;31: 225–229. 25134891 + item-182 at level 4: list_item: NjengaSM, MwandawiroCS, WamaeCN, ... 4: 1–9. 10.1186/1756-3305-4-1 21205315 + item-183 at level 4: list_item: BoydA, WonKY, McClintockSK, Dono ... 10.1371/journal.pntd.0000640 20351776 + item-184 at level 4: list_item: IrvineMA, ReimerLJ, NjengaSM, Gu ... 19. 10.1186/s13071-014-0608-1 25561160 + item-185 at level 4: list_item: IrvineMA, StolkWA, SmithME, Subr ... 10.1016/S1473-3099(16)30467-4 28012943 + item-186 at level 4: list_item: PionSD, MontavonC, ChesnaisCB, K ... 7–1423. 10.4269/ajtmh.16-0547 27729568 + item-187 at level 4: list_item: WanjiS, EsumME, NjouendouAJ, Mbe ... 10.1371/journal.pntd.0007192 30849120 + item-188 at level 4: list_item: ChesnaisCB, Awaca-UvonNP, BolayF ... 10.1371/journal.pntd.0005703 28892473 + item-189 at level 4: list_item: SilumbweA, ZuluJM, HalwindiH, Ja ... 15. 10.1186/s12889-017-4414-5 28532397 + item-190 at level 4: list_item: AdamsAM, VuckovicM, BirchE, Bran ... ;3 10.3390/tropicalmed3040122 30469342 + item-191 at level 4: list_item: RaoRU, SamarasekeraSD, Nagodavit ... 10.1371/journal.pntd.0006066 29084213 + item-192 at level 4: list_item: XuZ, GravesPM, LauCL, ClementsA, ... 10.1016/j.epidem.2018.12.003 30611745 + item-193 at level 4: list_item: IdCM, TetteviEJ, MechanF, IdunB, ... Ghana. PLoS Negl Trop Dis. 2019; 1–17. + item-194 at level 4: list_item: EigegeA, KalA, MiriE, SallauA, U ... 10.1371/journal.pntd.0002508 24205421 + item-195 at level 4: list_item: Van den BergH, Kelly-HopeLA, Lin ... 10.1016/S1473-3099(12)70148-2 23084831 + item-196 at level 4: list_item: WebberR. Eradication of Wucherer ... ol. Trans R Soc Trop Med Hyg. 1979;73. + item-197 at level 1: caption: Table 1 Categorization of potential factors influencing pre-TAS results. + item-198 at level 1: caption: Fig 1 Number of pre-TAS by country. + item-199 at level 1: caption: Fig 2 District-level baseline prevalence by country. + item-200 at level 1: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted). + item-201 at level 1: caption: Fig 4 Adjusted risk ratios for p ... ence Interval from log-binomial model. + item-202 at level 1: caption: Table 2 Adjusted risk ratios for ... g-binomial model sensitivity analysis. + item-203 at level 1: caption: Fig 5 Analysis of failures by model combinations. \ No newline at end of file diff --git a/tests/data/jats/groundtruth/pntd.0008301.nxml.json b/tests/data/jats/groundtruth/pntd.0008301.nxml.json index b9562b7ffb..36c7c0587c 100644 --- a/tests/data/jats/groundtruth/pntd.0008301.nxml.json +++ b/tests/data/jats/groundtruth/pntd.0008301.nxml.json @@ -24,22 +24,22 @@ "$ref": "#/texts/16" }, { - 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}, - { - "$ref": "#/texts/98" - }, - { - "$ref": "#/texts/99" - }, - { - "$ref": "#/texts/100" - }, - { - "$ref": "#/texts/101" - }, + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/6", + "parent": { + "$ref": "#/texts/101" + }, + "children": [ { "$ref": "#/texts/102" }, @@ -169,10 +289,18 @@ }, { "$ref": "#/texts/104" - }, - { - "$ref": "#/texts/105" - }, + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/7", + "parent": { + "$ref": "#/texts/101" + }, + "children": [ { "$ref": "#/texts/106" }, @@ -181,7 +309,18 @@ }, { "$ref": "#/texts/108" - }, + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/8", + "parent": { + "$ref": "#/texts/101" + }, + "children": [ { "$ref": "#/texts/109" }, @@ -202,7 +341,18 @@ }, { "$ref": "#/texts/115" - }, + } + ], + "content_layer": "body", + "name": "group", + "label": "inline" + }, + { + "self_ref": "#/groups/9", + "parent": { + "$ref": "#/texts/101" + }, + "children": [ { "$ref": "#/texts/116" }, @@ -211,289 +361,1441 @@ }, { "$ref": "#/texts/118" + }, + { + "$ref": "#/texts/119" + }, + { + "$ref": "#/texts/120" + }, + { + "$ref": "#/texts/121" + }, + { + "$ref": "#/texts/122" + }, + { + "$ref": "#/texts/123" + }, + { + "$ref": "#/texts/124" } ], "content_layer": "body", - 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"$ref": "#/texts/63" + "$ref": "#/texts/156" + }, + { + "$ref": "#/texts/157" + }, + { + "$ref": "#/texts/158" + }, + { + "$ref": "#/texts/159" + }, + { + "$ref": "#/texts/160" + }, + { + "$ref": "#/texts/161" + }, + { + "$ref": "#/texts/162" + }, + { + "$ref": "#/texts/163" + }, + { + "$ref": "#/texts/164" + }, + { + "$ref": "#/texts/165" + }, + { + "$ref": "#/texts/166" + }, + { + "$ref": "#/texts/167" + }, + { + "$ref": "#/texts/168" + }, + { + "$ref": "#/texts/169" + }, + { + "$ref": "#/texts/170" + }, + { + "$ref": "#/texts/171" + }, + { + "$ref": "#/texts/172" + }, + { + "$ref": "#/texts/173" + }, + { + "$ref": "#/texts/174" + }, + { + "$ref": "#/texts/175" + }, + { + "$ref": "#/texts/176" + }, + { + "$ref": "#/texts/177" + }, + { + "$ref": "#/texts/178" + }, + { + "$ref": "#/texts/179" + }, + { + "$ref": "#/texts/180" + }, + { + "$ref": "#/texts/181" + }, + { + "$ref": "#/texts/182" + }, + { + "$ref": "#/texts/183" + }, + { + "$ref": "#/texts/184" + } + ], + "content_layer": "body", + "name": "list", + "label": "list" + } + ], + "texts": [ + { + "self_ref": "#/texts/0", + "parent": { + "$ref": "#/body" + }, + "children": [ + { + "$ref": "#/texts/1" + }, + { + "$ref": "#/texts/2" + }, + { + "$ref": "#/texts/3" + }, + { + "$ref": "#/texts/5" + }, + { + "$ref": "#/texts/7" + }, + { + "$ref": "#/texts/13" + }, + { + "$ref": "#/texts/74" + }, + { + "$ref": "#/texts/101" + }, + { + "$ref": "#/texts/129" + }, + { + "$ref": "#/texts/131" + } + ], + "content_layer": "body", + "label": "title", + "prov": [], + "orig": "Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis", + "text": "Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis" + }, + { + "self_ref": "#/texts/1", + "parent": { + "$ref": "#/texts/0" + }, + "children": [], + "content_layer": "body", + "label": "paragraph", + "prov": [], + "orig": "Clara R. Burgert-Brucker, Kathryn L. Zoerhoff, Maureen Headland, Erica A. Shoemaker, Rachel Stelmach, Mohammad Jahirul Karim, Wilfrid Batcho, Clarisse Bougouma, Roland Bougma, Biholong Benjamin Didier, Nko'Ayissi Georges, Benjamin Marfo, Jean Frantz Lemoine, Helena Ullyartha Pangaribuan, Eksi Wijayanti, Yaya Ibrahim Coulibaly, Salif Seriba Doumbia, Pradip Rimal, Adamou Bacthiri Salissou, Yukaba Bah, Upendo Mwingira, Andreas Nshala, Edridah Muheki, Joseph Shott, Violetta Yevstigneyeva, Egide Ndayishimye, Margaret Baker, John Kraemer, Molly Brady", + "text": "Clara R. Burgert-Brucker, Kathryn L. Zoerhoff, Maureen Headland, Erica A. Shoemaker, Rachel Stelmach, Mohammad Jahirul Karim, Wilfrid Batcho, Clarisse Bougouma, Roland Bougma, Biholong Benjamin Didier, Nko'Ayissi Georges, Benjamin Marfo, Jean Frantz Lemoine, Helena Ullyartha Pangaribuan, Eksi Wijayanti, Yaya Ibrahim Coulibaly, Salif Seriba Doumbia, Pradip Rimal, Adamou Bacthiri Salissou, Yukaba Bah, Upendo Mwingira, Andreas Nshala, Edridah Muheki, Joseph Shott, Violetta Yevstigneyeva, Egide Ndayishimye, Margaret Baker, John Kraemer, Molly Brady" + }, + { + "self_ref": "#/texts/2", + "parent": { + "$ref": "#/texts/0" + }, + "children": [], + "content_layer": "body", + "label": "paragraph", + "prov": [], + "orig": "Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Ministry of Health, Port-au-Prince, Haiti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America", + "text": "Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Ministry of Health, Port-au-Prince, Haiti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America" + }, + { + "self_ref": "#/texts/3", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/4" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Abstract", + "text": "Abstract", + "level": 1 + }, + { + "self_ref": "#/texts/4", + "parent": { + "$ref": "#/texts/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.", + "text": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS." + }, + { + "self_ref": "#/texts/5", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/6" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Author summary", + "text": "Author summary", + "level": 1 + }, + { + "self_ref": "#/texts/6", + "parent": { + "$ref": "#/texts/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.", + "text": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation." + }, + { + "self_ref": "#/texts/7", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/8" + }, + { + "$ref": "#/texts/9" + }, + { + "$ref": "#/texts/10" + }, + { + "$ref": "#/texts/11" + }, + { + "$ref": "#/texts/12" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Introduction", + "text": "Introduction", + "level": 1 + }, + { + "self_ref": "#/texts/8", + "parent": { + "$ref": "#/texts/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3].", + "text": "Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3]." + }, + { + "self_ref": "#/texts/9", + "parent": { + "$ref": "#/texts/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated.", + "text": "The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated." + }, + { + "self_ref": "#/texts/10", + "parent": { + "$ref": "#/texts/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5].", + "text": "Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5]." + }, + { + "self_ref": "#/texts/11", + "parent": { + "$ref": "#/texts/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed.", + "text": "When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed." + }, + { + "self_ref": "#/texts/12", + "parent": { + "$ref": "#/texts/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources.", + "text": "This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources." + }, + { + "self_ref": "#/texts/13", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/14" + }, + { + "$ref": "#/texts/15" + }, + { + "$ref": "#/tables/0" + }, + { + "$ref": "#/texts/17" + }, + { + "$ref": "#/texts/19" + }, + { + "$ref": "#/texts/63" + }, + { + "$ref": "#/texts/65" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Methods", + "text": "Methods", + "level": 1 + }, + { + "self_ref": "#/texts/14", + "parent": { + "$ref": "#/texts/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required.", + "text": "This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required." + }, + { + "self_ref": "#/texts/15", + "parent": { + "$ref": "#/texts/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.", + "text": "Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets." + }, + { + "self_ref": "#/texts/16", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Table 1 Categorization of potential factors influencing pre-TAS results.", + "text": "Table 1 Categorization of potential factors influencing pre-TAS results." + }, + { + "self_ref": "#/texts/17", + "parent": { + "$ref": "#/texts/13" + }, + "children": [ + { + "$ref": "#/texts/18" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Data sources", + "text": "Data sources", + "level": 2 + }, + { + "self_ref": "#/texts/18", + "parent": { + "$ref": "#/texts/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).", + "text": "Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1)." + }, + { + "self_ref": "#/texts/19", + "parent": { + "$ref": "#/texts/13" + }, + "children": [ + { + "$ref": "#/texts/20" + }, + { + "$ref": "#/groups/0" + }, + { + "$ref": "#/texts/24" + }, + { + "$ref": "#/texts/26" + }, + { + "$ref": "#/texts/50" + }, + { + "$ref": "#/texts/58" + }, + { + "$ref": "#/texts/61" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Outcome and covariate variables", + "text": "Outcome and covariate variables", + "level": 2 + }, + { + "self_ref": "#/texts/20", + "parent": { + "$ref": "#/texts/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4].", + "text": "The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4]." + }, + { + "self_ref": "#/texts/21", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either", + "text": "Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either" + }, + { + "self_ref": "#/texts/22", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "a priori", + "text": "a priori", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/23", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used.", + "text": "knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used." + }, + { + "self_ref": "#/texts/24", + "parent": { + "$ref": "#/texts/19" + }, + "children": [ + { + "$ref": "#/texts/25" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Baseline prevalence", + "text": "Baseline prevalence", + "level": 3 + }, + { + "self_ref": "#/texts/25", + "parent": { + "$ref": "#/texts/24" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.", + "text": "Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%." + }, + { + "self_ref": "#/texts/26", + "parent": { + "$ref": "#/texts/19" + }, + "children": [ + { + "$ref": "#/groups/1" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Agent", + "text": "Agent", + "level": 3 + }, + { + "self_ref": "#/texts/27", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In terms of differences in transmission dynamics by agent, research has shown that", + "text": "In terms of differences in transmission dynamics by agent, research has shown that" + }, + { + "self_ref": "#/texts/28", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Brugia", + "text": "Brugia", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/29", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "spp. are more susceptible to the anti-filarial drug regimens than", + "text": "spp. are more susceptible to the anti-filarial drug regimens than" + }, + { + "self_ref": "#/texts/30", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Wuchereria bancrofti", + "text": "Wuchereria bancrofti", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/31", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "parasites [23]. Thus, we combined districts reporting", + "text": "parasites [23]. Thus, we combined districts reporting" + }, + { + "self_ref": "#/texts/32", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "B", + "text": "B", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/33", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ".", + "text": "." + }, + { + "self_ref": "#/texts/34", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "malayi", + "text": "malayi", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/35", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/36", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "B", + "text": "B", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/37", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ".", + "text": "." + }, + { + "self_ref": "#/texts/38", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "timori", + "text": "timori", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/39", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and compared them to areas with", + "text": "and compared them to areas with" + }, + { + "self_ref": "#/texts/40", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "W", + "text": "W", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/41", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ".", + "text": "." + }, + { + "self_ref": "#/texts/42", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "bancrofti", + "text": "bancrofti", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/43", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (", + "text": "or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (" + }, + { + "self_ref": "#/texts/44", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Anopheles", + "text": "Anopheles", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/45", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "or", + "text": "or" + }, + { + "self_ref": "#/texts/46", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Mansonia", + "text": "Mansonia", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/47", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "compared to", + "text": "compared to" + }, + { + "self_ref": "#/texts/48", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Culex", + "text": "Culex", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/49", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain.", + "text": ") from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain." + }, + { + "self_ref": "#/texts/50", + "parent": { + "$ref": "#/texts/19" + }, + "children": [ + { + "$ref": "#/texts/51" + }, + { + "$ref": "#/groups/2" + }, + { + "$ref": "#/texts/57" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Environment", + "text": "Environment", + "level": 3 + }, + { + "self_ref": "#/texts/51", + "parent": { + "$ref": "#/texts/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”", + "text": "LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”" + }, + { + "self_ref": "#/texts/52", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by", + "text": "We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by" + }, + { + "self_ref": "#/texts/53", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Culex", + "text": "Culex", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/54", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in urban areas and by", + "text": "in urban areas and by" + }, + { + "self_ref": "#/texts/55", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Anopheles", + "text": "Anopheles", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/56", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33].", + "text": "in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33]." + }, + { + "self_ref": "#/texts/57", + "parent": { + "$ref": "#/texts/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis.", + "text": "Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis." + }, + { + "self_ref": "#/texts/58", + "parent": { + "$ref": "#/texts/19" + }, + "children": [ + { + "$ref": "#/texts/59" }, { - "$ref": "#/texts/65" + "$ref": "#/texts/60" } ], "content_layer": "body", - "label": "title", + "label": "section_header", "prov": [], - "orig": "Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis", - "text": "Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis" + "orig": "MDA", + "text": "MDA", + "level": 3 }, { - "self_ref": "#/texts/1", + "self_ref": "#/texts/59", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/58" }, "children": [], "content_layer": "body", - "label": "paragraph", + "label": "text", "prov": [], - "orig": "Clara R. Burgert-Brucker, Kathryn L. Zoerhoff, Maureen Headland, Erica A. Shoemaker, Rachel Stelmach, Mohammad Jahirul Karim, Wilfrid Batcho, Clarisse Bougouma, Roland Bougma, Biholong Benjamin Didier, Nko'Ayissi Georges, Benjamin Marfo, Jean Frantz Lemoine, Helena Ullyartha Pangaribuan, Eksi Wijayanti, Yaya Ibrahim Coulibaly, Salif Seriba Doumbia, Pradip Rimal, Adamou Bacthiri Salissou, Yukaba Bah, Upendo Mwingira, Andreas Nshala, Edridah Muheki, Joseph Shott, Violetta Yevstigneyeva, Egide Ndayishimye, Margaret Baker, John Kraemer, Molly Brady", - "text": "Clara R. Burgert-Brucker, Kathryn L. Zoerhoff, Maureen Headland, Erica A. Shoemaker, Rachel Stelmach, Mohammad Jahirul Karim, Wilfrid Batcho, Clarisse Bougouma, Roland Bougma, Biholong Benjamin Didier, Nko'Ayissi Georges, Benjamin Marfo, Jean Frantz Lemoine, Helena Ullyartha Pangaribuan, Eksi Wijayanti, Yaya Ibrahim Coulibaly, Salif Seriba Doumbia, Pradip Rimal, Adamou Bacthiri Salissou, Yukaba Bah, Upendo Mwingira, Andreas Nshala, Edridah Muheki, Joseph Shott, Violetta Yevstigneyeva, Egide Ndayishimye, Margaret Baker, John Kraemer, Molly Brady" + "orig": "Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42].", + "text": "Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42]." }, { - "self_ref": "#/texts/2", + "self_ref": "#/texts/60", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/58" }, "children": [], "content_layer": "body", - "label": "paragraph", + "label": "text", "prov": [], - "orig": "Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Ministry of Health, Port-au-Prince, Haiti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America", - "text": "Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Ministry of Health, Port-au-Prince, Haiti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America" + "orig": "We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district.", + "text": "We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district." }, { - "self_ref": "#/texts/3", + "self_ref": "#/texts/61", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/19" }, "children": [ { - "$ref": "#/texts/4" + "$ref": "#/texts/62" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Abstract", - "text": "Abstract", - "level": 1 + "orig": "Pre-TAS implementation", + "text": "Pre-TAS implementation", + "level": 3 }, { - "self_ref": "#/texts/4", + "self_ref": "#/texts/62", "parent": { - "$ref": "#/texts/3" + "$ref": "#/texts/61" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS.", - "text": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS." + "orig": "Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study.", + "text": "Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study." }, { - "self_ref": "#/texts/5", + "self_ref": "#/texts/63", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/13" }, "children": [ { - "$ref": "#/texts/6" + "$ref": "#/texts/64" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Author summary", - "text": "Author summary", - "level": 1 + "orig": "Data inclusion criteria", + "text": "Data inclusion criteria", + "level": 2 }, { - "self_ref": "#/texts/6", + "self_ref": "#/texts/64", "parent": { - "$ref": "#/texts/5" + "$ref": "#/texts/63" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.", - "text": "Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation." + "orig": "The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts.", + "text": "The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts." }, { - "self_ref": "#/texts/7", + "self_ref": "#/texts/65", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/13" }, "children": [ { - "$ref": "#/texts/8" - }, - { - "$ref": "#/texts/9" - }, - { - "$ref": "#/texts/10" - }, - { - "$ref": "#/texts/11" + "$ref": "#/texts/66" }, { - "$ref": "#/texts/12" + "$ref": "#/groups/3" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Introduction", - "text": "Introduction", - "level": 1 + "orig": "Statistical analysis and modeling", + "text": "Statistical analysis and modeling", + "level": 2 }, { - "self_ref": "#/texts/8", + "self_ref": "#/texts/66", "parent": { - "$ref": "#/texts/7" + "$ref": "#/texts/65" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3].", - "text": "Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3]." + "orig": "Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly.", + "text": "Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly." }, { - "self_ref": "#/texts/9", + "self_ref": "#/texts/67", "parent": { - "$ref": "#/texts/7" + "$ref": "#/groups/3" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated.", - "text": "The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated." + "orig": "Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with", + "text": "Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with" }, { - "self_ref": "#/texts/10", + "self_ref": "#/texts/68", "parent": { - "$ref": "#/texts/7" + "$ref": "#/groups/3" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5].", - "text": "Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5]." + "orig": "W", + "text": "W", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/11", + "self_ref": "#/texts/69", "parent": { - "$ref": "#/texts/7" + "$ref": "#/groups/3" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed.", - "text": "When a country fails to meet the established thresholds in a pre-TAS, they must implement at least two more rounds of MDA. National programs need to forecast areas that might fail pre-TAS and need repeated MDA, so that they can inform the community and district decision-makers of the implications of pre-TAS failure, including the need for continued MDA to lower prevalence effectively. In addition, financial and human resources must be made available for ordering drugs, distributing drugs, supervision and monitoring to implement the further MDA rounds. Ordering drugs and providing MDA budgets often need to be completed before the pre-TAS are implemented, so contingency planning and funding are important to ensure rounds of MDA are not missed." + "orig": ".", + "text": "." }, { - "self_ref": "#/texts/12", + "self_ref": "#/texts/70", "parent": { - "$ref": "#/texts/7" + "$ref": "#/groups/3" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources.", - "text": "This study aims to understand which factors are associated with the need for additional rounds of MDA as identified by pre-TAS results using programmatic data from 13 countries. The factors associated with failing pre-TAS are not well understood and have not previously been examined at a multi-country scale in the literature. We examine the association between pre-TAS failure and baseline prevalence, parasites, environmental factors, MDA implementation, and pre-TAS implementation. Understanding determinants of pre-TAS failure will help countries identify where elimination may be most difficult and prioritize the use of limited LF elimination resources." + "orig": "bancrofti", + "text": "bancrofti", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/13", + "self_ref": "#/texts/71", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "parasite, and (4) including only districts with", + "text": "parasite, and (4) including only districts with" + }, + { + "self_ref": "#/texts/72", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Anopheles", + "text": "Anopheles", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/73", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44].", + "text": "as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44]." + }, + { + "self_ref": "#/texts/74", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/14" + "$ref": "#/texts/75" }, { - "$ref": "#/texts/15" + "$ref": "#/pictures/0" }, { - "$ref": "#/tables/0" + "$ref": "#/pictures/1" }, { - "$ref": "#/texts/17" + "$ref": "#/groups/4" }, { - "$ref": "#/texts/19" + "$ref": "#/pictures/2" }, { - "$ref": "#/texts/35" + "$ref": "#/texts/82" }, { - "$ref": "#/texts/37" + "$ref": "#/texts/83" + }, + { + "$ref": "#/pictures/3" + }, + { + "$ref": "#/groups/5" + }, + { + "$ref": "#/tables/1" + }, + { + "$ref": "#/texts/99" + }, + { + "$ref": "#/pictures/4" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Methods", - "text": "Methods", + "orig": "Results", + "text": "Results", "level": 1 }, { - "self_ref": "#/texts/14", + "self_ref": "#/texts/75", "parent": { - "$ref": "#/texts/13" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required.", - "text": "This is a secondary data analysis using existing data, collected for programmatic purposes. Data for this analysis come from 568 districts in 13 countries whose LF elimination programs were supported by the United States Agency for International Development (USAID) through the ENVISION project, led by RTI International, and the END in Africa and END in Asia projects, led by FHI 360. These countries are Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. The data represent all pre-TAS funded by USAID from 2012 to 2017 and, in some cases, surveys funded by host government or other non-United States government funders. Because pre-TAS data were collected as part of routine program activities in most countries, in general, ethical clearance was not sought for these surveys. Our secondary analysis only included the aggregated survey results and therefore did not constitute human subjects research; no ethical approval was required." + "orig": "The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.", + "text": "The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%." }, { - "self_ref": "#/texts/15", + "self_ref": "#/texts/76", "parent": { - "$ref": "#/texts/13" + "$ref": "#/body" }, "children": [], "content_layer": "body", - "label": "text", + "label": "caption", "prov": [], - "orig": "Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.", - "text": "Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets." + "orig": "Fig 1 Number of pre-TAS by country.", + "text": "Fig 1 Number of pre-TAS by country." }, { - "self_ref": "#/texts/16", + "self_ref": "#/texts/77", "parent": { "$ref": "#/body" }, @@ -501,659 +1803,718 @@ "content_layer": "body", "label": "caption", "prov": [], - "orig": "Table 1 Categorization of potential factors influencing pre-TAS results.", - "text": "Table 1 Categorization of potential factors influencing pre-TAS results." + "orig": "Fig 2 District-level baseline prevalence by country.", + "text": "Fig 2 District-level baseline prevalence by country." }, { - "self_ref": "#/texts/17", + "self_ref": "#/texts/78", "parent": { - "$ref": "#/texts/13" + "$ref": "#/groups/4" }, - "children": [ - { - "$ref": "#/texts/18" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Data sources", - "text": "Data sources", - "level": 2 + "orig": "Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of", + "text": "Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of" }, { - "self_ref": "#/texts/18", + "self_ref": "#/texts/79", "parent": { - "$ref": "#/texts/17" + "$ref": "#/groups/4" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).", - "text": "Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1)." + "orig": "Culex", + "text": "Culex", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/19", + "self_ref": "#/texts/80", "parent": { - "$ref": "#/texts/13" + "$ref": "#/groups/4" }, - "children": [ - { - "$ref": "#/texts/20" - }, - { - "$ref": "#/texts/21" - }, - { - "$ref": "#/texts/22" - }, - { - "$ref": "#/texts/24" - }, - { - "$ref": "#/texts/26" - }, - { - "$ref": "#/texts/30" - }, - { - "$ref": "#/texts/33" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Outcome and covariate variables", - "text": "Outcome and covariate variables", - "level": 2 + "orig": ", treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.", + "text": ", treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA." }, { - "self_ref": "#/texts/20", + "self_ref": "#/texts/81", "parent": { - "$ref": "#/texts/19" + "$ref": "#/body" }, "children": [], "content_layer": "body", - "label": "text", + "label": "caption", "prov": [], - "orig": "The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4].", - "text": "The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4]." + "orig": "Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).", + "text": "Fig 3 Percent pre-TAS failure by each characteristic (unadjusted)." }, { - "self_ref": "#/texts/21", + "self_ref": "#/texts/82", "parent": { - "$ref": "#/texts/19" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used.", - "text": "Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used." + "orig": "The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used.", + "text": "The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used." }, { - "self_ref": "#/texts/22", + "self_ref": "#/texts/83", "parent": { - "$ref": "#/texts/19" + "$ref": "#/texts/74" }, - "children": [ - { - "$ref": "#/texts/23" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Baseline prevalence", - "text": "Baseline prevalence", - "level": 3 + "orig": "Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).", + "text": "Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83)." }, { - "self_ref": "#/texts/23", + "self_ref": "#/texts/84", "parent": { - "$ref": "#/texts/22" + "$ref": "#/body" }, "children": [], "content_layer": "body", - "label": "text", + "label": "caption", "prov": [], - "orig": "Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.", - "text": "Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%." + "orig": "Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.", + "text": "Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model." }, { - "self_ref": "#/texts/24", + "self_ref": "#/texts/85", "parent": { - "$ref": "#/texts/19" + "$ref": "#/groups/5" }, - "children": [ - { - "$ref": "#/texts/25" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Agent", - "text": "Agent", - "level": 3 + "orig": "Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with", + "text": "Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with" }, { - "self_ref": "#/texts/25", + "self_ref": "#/texts/86", "parent": { - "$ref": "#/texts/24" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain.", - "text": "In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain." + "orig": "W", + "text": "W", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/26", + "self_ref": "#/texts/87", "parent": { - "$ref": "#/texts/19" + "$ref": "#/groups/5" }, - "children": [ - { - "$ref": "#/texts/27" - }, - { - "$ref": "#/texts/28" - }, - { - "$ref": "#/texts/29" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Environment", - "text": "Environment", - "level": 3 + "orig": ".", + "text": "." }, { - "self_ref": "#/texts/27", + "self_ref": "#/texts/88", "parent": { - "$ref": "#/texts/26" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”", - "text": "LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”" + "orig": "bancrofti", + "text": "bancrofti", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/28", + "self_ref": "#/texts/89", "parent": { - "$ref": "#/texts/26" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33].", - "text": "We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33]." + "orig": ", and (4) only districts with", + "text": ", and (4) only districts with" }, { - "self_ref": "#/texts/29", + "self_ref": "#/texts/90", "parent": { - "$ref": "#/texts/26" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis.", - "text": "Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis." + "orig": "Anopheles", + "text": "Anopheles", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/30", + "self_ref": "#/texts/91", "parent": { - "$ref": "#/texts/19" + "$ref": "#/groups/5" }, - "children": [ - { - "$ref": "#/texts/31" - }, - { - "$ref": "#/texts/32" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "MDA", - "text": "MDA", - "level": 3 + "orig": "as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the", + "text": "as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the" }, { - "self_ref": "#/texts/31", + "self_ref": "#/texts/92", "parent": { - "$ref": "#/texts/30" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42].", - "text": "Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42]." + "orig": "W", + "text": "W", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/32", + "self_ref": "#/texts/93", "parent": { - "$ref": "#/texts/30" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district.", - "text": "We considered three approaches when analyzing the MDA data: median MDA coverage in the most recent 5 rounds, number of rounds with sufficient coverage in the most recent 5 rounds, and count of the total number of rounds. MDA coverage is considered sufficient at or above 65% of the total population who were reported to have ingested the drugs; this was used as the cut point for MDA median coverage for the most recent 5 rounds. The ‘rounds of sufficient coverage’ variable was categorized as having 2 or fewer rounds compared to 3 or more sufficient rounds. The ‘total number of MDA rounds’ variable was categorized at 5 or fewer rounds compared to 6 or more rounds ever documented in that district." + "orig": ".", + "text": "." }, { - "self_ref": "#/texts/33", + "self_ref": "#/texts/94", "parent": { - "$ref": "#/texts/19" + "$ref": "#/groups/5" }, - "children": [ - { - "$ref": "#/texts/34" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Pre-TAS implementation", - "text": "Pre-TAS implementation", - "level": 3 + "orig": "bancrofti", + "text": "bancrofti", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/34", + "self_ref": "#/texts/95", "parent": { - "$ref": "#/texts/33" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study.", - "text": "Pre-TAS results can be influenced by the implementation of the survey itself, including the use of a particular diagnostic test, the selection of sites, the timing of survey, and the appropriate application of methods for population recruitment and diagnostic test adminstration. We included two variables in the pre-TAS implementation domain: `type of diagnostic method used’ and `diagnostic test used.’ The ‘type of diagnostic method used’ variable categorized districts by either using Mf or Ag. The ‘diagnostic test used’ variable examined Mf (reference category) compared to ICT and compared to FTS (categorical variable with 3 values). This approach was used to compare each test to each other. Countries switched from ICT to FTS during 2016, while Mf testing continued to be used throughout the time period of study." + "orig": "-only and", + "text": "-only and" }, { - "self_ref": "#/texts/35", + "self_ref": "#/texts/96", "parent": { - "$ref": "#/texts/13" + "$ref": "#/groups/5" }, - "children": [ - { - "$ref": "#/texts/36" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Data inclusion criteria", - "text": "Data inclusion criteria", - "level": 2 + "orig": "Anopheles", + "text": "Anopheles", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/36", + "self_ref": "#/texts/97", "parent": { - "$ref": "#/texts/35" + "$ref": "#/groups/5" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts.", - "text": "The dataset, summarized at the district level, included information from 568 districts where a pre-TAS was being implemented for the first time. A total of 14 districts were removed from the final analysis due to missing data related to the following points: geospatial boundaries (4), baseline prevalence (4), and MDA coverage (6). The final analysis dataset had 554 districts." + "orig": "-only models (models 3 and 4 respectively), as signified by large confidence intervals.", + "text": "-only models (models 3 and 4 respectively), as signified by large confidence intervals." }, { - "self_ref": "#/texts/37", + "self_ref": "#/texts/98", "parent": { - "$ref": "#/texts/13" + "$ref": "#/body" }, - "children": [ - { - "$ref": "#/texts/38" - }, - { - "$ref": "#/texts/39" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "caption", "prov": [], - "orig": "Statistical analysis and modeling", - "text": "Statistical analysis and modeling", - "level": 2 + "orig": "Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.", + "text": "Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis." }, { - "self_ref": "#/texts/38", + "self_ref": "#/texts/99", "parent": { - "$ref": "#/texts/37" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly.", - "text": "Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly." + "orig": "Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).", + "text": "Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74)." }, { - "self_ref": "#/texts/39", + "self_ref": "#/texts/100", "parent": { - "$ref": "#/texts/37" + "$ref": "#/body" }, "children": [], "content_layer": "body", - "label": "text", + "label": "caption", "prov": [], - "orig": "Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44].", - "text": "Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44]." + "orig": "Fig 5 Analysis of failures by model combinations.", + "text": "Fig 5 Analysis of failures by model combinations." }, { - "self_ref": "#/texts/40", + "self_ref": "#/texts/101", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/41" - }, - { - "$ref": "#/pictures/0" - }, - { - "$ref": "#/pictures/1" - }, - { - "$ref": "#/texts/44" + "$ref": "#/groups/6" }, { - "$ref": "#/pictures/2" + "$ref": "#/texts/105" }, { - "$ref": "#/texts/46" + "$ref": "#/groups/7" }, { - "$ref": "#/texts/47" + "$ref": "#/groups/8" }, { - "$ref": "#/pictures/3" + "$ref": "#/groups/9" }, { - "$ref": "#/texts/49" + "$ref": "#/texts/125" }, { - "$ref": "#/tables/1" + "$ref": "#/texts/126" }, { - "$ref": "#/texts/51" + "$ref": "#/texts/127" }, { - "$ref": "#/pictures/4" + "$ref": "#/texts/128" } ], "content_layer": "body", - "label": "section_header", + "label": "section_header", + "prov": [], + "orig": "Discussion", + "text": "Discussion", + "level": 1 + }, + { + "self_ref": "#/texts/102", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg", + "text": "This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg" + }, + { + "self_ref": "#/texts/103", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "et al", + "text": "et al", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/104", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "., which explored ecological and socioeconomic factors associated with TAS failure [7].", + "text": "., which explored ecological and socioeconomic factors associated with TAS failure [7]." + }, + { + "self_ref": "#/texts/105", + "parent": { + "$ref": "#/texts/101" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].", + "text": "Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45]." + }, + { + "self_ref": "#/texts/106", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", "prov": [], - "orig": "Results", - "text": "Results", - "level": 1 + "orig": "Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg", + "text": "Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg" }, { - "self_ref": "#/texts/41", + "self_ref": "#/texts/107", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/7" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.", - "text": "The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%." + "orig": "et al", + "text": "et al", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/42", + "self_ref": "#/texts/108", "parent": { - "$ref": "#/body" + "$ref": "#/groups/7" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 1 Number of pre-TAS by country.", - "text": "Fig 1 Number of pre-TAS by country." + "orig": ". [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.", + "text": ". [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival." }, { - "self_ref": "#/texts/43", + "self_ref": "#/texts/109", "parent": { - "$ref": "#/body" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 2 District-level baseline prevalence by country.", - "text": "Fig 2 District-level baseline prevalence by country." + "orig": "The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as", + "text": "The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as" }, { - "self_ref": "#/texts/44", + "self_ref": "#/texts/110", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.", - "text": "Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA." + "orig": "Culex", + "text": "Culex", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/45", + "self_ref": "#/texts/111", "parent": { - "$ref": "#/body" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).", - "text": "Fig 3 Percent pre-TAS failure by each characteristic (unadjusted)." + "orig": "vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that", + "text": "vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that" }, { - "self_ref": "#/texts/46", + "self_ref": "#/texts/112", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used.", - "text": "The final log-binomial model included the variables of baseline prevalence ≥10%, the diagnostic test used (FTS and ICT), and elevation. The final model also included a significant interaction term between high baseline and diagnostic test used." + "orig": "Culex", + "text": "Culex", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/47", + "self_ref": "#/texts/113", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).", - "text": "Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83)." + "orig": "as the predominant vector in a district, compared to", + "text": "as the predominant vector in a district, compared to" }, { - "self_ref": "#/texts/48", + "self_ref": "#/texts/114", "parent": { - "$ref": "#/body" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.", - "text": "Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model." + "orig": "Anopheles", + "text": "Anopheles", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/49", + "self_ref": "#/texts/115", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/8" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals.", - "text": "Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals." + "orig": ", results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].", + "text": ", results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16]." }, { - "self_ref": "#/texts/50", + "self_ref": "#/texts/116", "parent": { - "$ref": "#/body" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.", - "text": "Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis." + "orig": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg", + "text": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg" }, { - "self_ref": "#/texts/51", + "self_ref": "#/texts/117", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).", - "text": "Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74)." + "orig": "et al", + "text": "et al", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/52", + "self_ref": "#/texts/118", "parent": { - "$ref": "#/body" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 5 Analysis of failures by model combinations.", - "text": "Fig 5 Analysis of failures by model combinations." + "orig": ". found", + "text": ". found" }, { - "self_ref": "#/texts/53", + "self_ref": "#/texts/119", "parent": { - "$ref": "#/texts/0" + "$ref": "#/groups/9" }, - "children": [ - { - "$ref": "#/texts/54" - }, - { - "$ref": "#/texts/55" - }, - { - "$ref": "#/texts/56" - }, - { - "$ref": "#/texts/57" - }, - { - "$ref": "#/texts/58" - }, - { - "$ref": "#/texts/59" - }, - { - "$ref": "#/texts/60" - }, - { - "$ref": "#/texts/61" - }, - { - "$ref": "#/texts/62" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Discussion", - "text": "Discussion", - "level": 1 + "orig": "Brugia", + "text": "Brugia", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/54", + "self_ref": "#/texts/120", "parent": { - "$ref": "#/texts/53" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7].", - "text": "This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7]." + "orig": "spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with", + "text": "spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with" }, { - "self_ref": "#/texts/55", + "self_ref": "#/texts/121", "parent": { - "$ref": "#/texts/53" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].", - "text": "Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45]." + "orig": "Brugia", + "text": "Brugia", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/56", + "self_ref": "#/texts/122", "parent": { - "$ref": "#/texts/53" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.", - "text": "Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival." + "orig": "spp. in our dataset (6%) compared to 46% in the Goldberg", + "text": "spp. in our dataset (6%) compared to 46% in the Goldberg" }, { - "self_ref": "#/texts/57", + "self_ref": "#/texts/123", "parent": { - "$ref": "#/texts/53" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].", - "text": "The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16]." + "orig": "et al", + "text": "et al", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } }, { - "self_ref": "#/texts/58", + "self_ref": "#/texts/124", "parent": { - "$ref": "#/texts/53" + "$ref": "#/groups/9" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.", - "text": "All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence." + "orig": ". article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.", + "text": ". article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence." }, { - "self_ref": "#/texts/59", + "self_ref": "#/texts/125", "parent": { - "$ref": "#/texts/53" + "$ref": "#/texts/101" }, "children": [], "content_layer": "body", @@ -1163,9 +2524,9 @@ "text": "Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53]." }, { - "self_ref": "#/texts/60", + "self_ref": "#/texts/126", "parent": { - "$ref": "#/texts/53" + "$ref": "#/texts/101" }, "children": [], "content_layer": "body", @@ -1175,9 +2536,9 @@ "text": "Fourteen districts were excluded from the analysis because we were not able to obtain complete data for baseline prevalence, MDA coverage, or geographic boundaries. One of these districts had failed pre-TAS. It is likely these exclusions had minimal impact on the conclusions, as they represented a small number of districts and were similar to other included districts in terms of key variables. Unreported data could have occurred if a country conducted a pre-TAS that failed and then chose not to report it or reported it as a mid-term survey instead. Anecdotally, we know this has occurred occasionally, but we do not believe the practice to be widespread. Another limitation in the analysis is a potential misassignment of key variable values to a district due to changes in the district over time. Redistricting, changes in district size or composition, was pervasive in many countries during the study period; however, we expect the impact on the study outcome to be minimal, as the historical prevalence and MDA data from the “mother” districts are usually flowed down to these new “daughter” districts. However, it is possible that the split created an area of higher prevalence or lower MDA coverage than would have been found on average in the overall larger original “mother” district. Finally, the aggregation or averaging of results to the district level may mask heterogeneity within districts. Though this impact could be substantial in districts with considerable heterogeneity, the use of median values and binomial variables mitigated the likelihood of skewing the data to extreme outliners in a district." }, { - "self_ref": "#/texts/61", + "self_ref": "#/texts/127", "parent": { - "$ref": "#/texts/53" + "$ref": "#/texts/101" }, "children": [], "content_layer": "body", @@ -1187,9 +2548,9 @@ "text": "As this analysis used data across a variety of countries and epidemiological situations, the results are likely relevant for other districts in the countries examined and in countries with similar epidemiological backgrounds. In general, as more data become available at site level through the increased use of electronic data collection tools, further analysis of geospatial variables and associations will be possible. For example, with the availability of GPS coordinates, it may become possible to analyze outcomes by site and to link the geospatial environmental domain variables at a smaller scale. Future analyses also might seek to include information from coverage surveys or qualitative research studies on vector control interventions such as bed net usage, MDA compliance, population movement, and sub-populations that might be missed during MDA. Future pre-TAS using electronic data collection could include sex and age of individuals included in the survey." }, { - "self_ref": "#/texts/62", + "self_ref": "#/texts/128", "parent": { - "$ref": "#/texts/53" + "$ref": "#/texts/101" }, "children": [], "content_layer": "body", @@ -1199,13 +2560,13 @@ "text": "This paper provides evidence from analysis of 554 districts and 13 countries on the factors associated with pre-TAS results. Baseline prevalence, elevation, vector, population density, EVI, rainfall, and number of MDA rounds were all significant in either bivariate or multivariate analyses. This information along with knowledge of local context can help countries more effectively plan pre-TAS and forecast program activities, such as the potential need for more than five rounds of MDA in areas with high baseline and/or low elevation." }, { - "self_ref": "#/texts/63", + "self_ref": "#/texts/129", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/64" + "$ref": "#/texts/130" } ], "content_layer": "body", @@ -1216,9 +2577,9 @@ "level": 1 }, { - "self_ref": "#/texts/64", + "self_ref": "#/texts/130", "parent": { - "$ref": "#/texts/63" + "$ref": "#/texts/129" }, "children": [], "content_layer": "body", @@ -1228,13 +2589,13 @@ "text": "The authors would like to thank all those involved from the Ministries of Health, volunteers and community members in the sentinel and spot-check site surveys for their tireless commitment to ridding the world of LF. In addition, gratitude is given to Joseph Koroma and all the partners, including USAID, RTI International, FHI 360, IMA World Health, and Helen Keller International, who supported the surveys financially and technically." }, { - "self_ref": "#/texts/65", + "self_ref": "#/texts/131", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/groups/0" + "$ref": "#/groups/10" } ], "content_layer": "body", @@ -1245,9 +2606,9 @@ "level": 1 }, { - "self_ref": "#/texts/66", + "self_ref": "#/texts/132", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1259,9 +2620,9 @@ "marker": "" }, { - "self_ref": "#/texts/67", + "self_ref": "#/texts/133", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1273,9 +2634,9 @@ "marker": "" }, { - "self_ref": "#/texts/68", + "self_ref": "#/texts/134", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1287,9 +2648,9 @@ "marker": "" }, { - "self_ref": "#/texts/69", + "self_ref": "#/texts/135", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1301,9 +2662,9 @@ "marker": "" }, { - "self_ref": "#/texts/70", + "self_ref": "#/texts/136", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1315,9 +2676,9 @@ "marker": "" }, { - "self_ref": "#/texts/71", + "self_ref": "#/texts/137", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1329,9 +2690,9 @@ "marker": "" }, { - "self_ref": "#/texts/72", + "self_ref": "#/texts/138", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1343,9 +2704,9 @@ "marker": "" }, { - "self_ref": "#/texts/73", + "self_ref": "#/texts/139", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1357,9 +2718,9 @@ "marker": "" }, { - "self_ref": "#/texts/74", + "self_ref": "#/texts/140", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1371,9 +2732,9 @@ "marker": "" }, { - "self_ref": "#/texts/75", + "self_ref": "#/texts/141", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1385,9 +2746,9 @@ "marker": "" }, { - "self_ref": "#/texts/76", + "self_ref": "#/texts/142", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1399,9 +2760,9 @@ "marker": "" }, { - "self_ref": "#/texts/77", + "self_ref": "#/texts/143", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1413,9 +2774,9 @@ "marker": "" }, { - "self_ref": "#/texts/78", + "self_ref": "#/texts/144", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1427,9 +2788,9 @@ "marker": "" }, { - "self_ref": "#/texts/79", + "self_ref": "#/texts/145", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1441,9 +2802,9 @@ "marker": "" }, { - "self_ref": "#/texts/80", + "self_ref": "#/texts/146", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1455,9 +2816,9 @@ "marker": "" }, { - "self_ref": "#/texts/81", + "self_ref": "#/texts/147", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1469,9 +2830,9 @@ "marker": "" }, { - "self_ref": "#/texts/82", + "self_ref": "#/texts/148", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1483,9 +2844,9 @@ "marker": "" }, { - "self_ref": "#/texts/83", + "self_ref": "#/texts/149", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1497,9 +2858,9 @@ "marker": "" }, { - "self_ref": "#/texts/84", + "self_ref": "#/texts/150", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1511,9 +2872,9 @@ "marker": "" }, { - "self_ref": "#/texts/85", + "self_ref": "#/texts/151", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1525,9 +2886,9 @@ "marker": "" }, { - "self_ref": "#/texts/86", + "self_ref": "#/texts/152", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1539,9 +2900,9 @@ "marker": "" }, { - "self_ref": "#/texts/87", + "self_ref": "#/texts/153", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1553,9 +2914,9 @@ "marker": "" }, { - "self_ref": "#/texts/88", + "self_ref": "#/texts/154", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1567,9 +2928,9 @@ "marker": "" }, { - "self_ref": "#/texts/89", + "self_ref": "#/texts/155", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1581,9 +2942,9 @@ "marker": "" }, { - "self_ref": "#/texts/90", + "self_ref": "#/texts/156", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1595,9 +2956,9 @@ "marker": "" }, { - "self_ref": "#/texts/91", + "self_ref": "#/texts/157", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1609,9 +2970,9 @@ "marker": "" }, { - "self_ref": "#/texts/92", + "self_ref": "#/texts/158", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1623,9 +2984,9 @@ "marker": "" }, { - "self_ref": "#/texts/93", + "self_ref": "#/texts/159", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1637,9 +2998,9 @@ "marker": "" }, { - "self_ref": "#/texts/94", + "self_ref": "#/texts/160", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1651,9 +3012,9 @@ "marker": "" }, { - "self_ref": "#/texts/95", + "self_ref": "#/texts/161", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1665,9 +3026,9 @@ "marker": "" }, { - "self_ref": "#/texts/96", + "self_ref": "#/texts/162", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1679,9 +3040,9 @@ "marker": "" }, { - "self_ref": "#/texts/97", + "self_ref": "#/texts/163", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1693,9 +3054,9 @@ "marker": "" }, { - "self_ref": "#/texts/98", + "self_ref": "#/texts/164", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1707,9 +3068,9 @@ "marker": "" }, { - "self_ref": "#/texts/99", + "self_ref": "#/texts/165", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1721,9 +3082,9 @@ "marker": "" }, { - "self_ref": "#/texts/100", + "self_ref": "#/texts/166", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1735,9 +3096,9 @@ "marker": "" }, { - "self_ref": "#/texts/101", + "self_ref": "#/texts/167", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1749,9 +3110,9 @@ "marker": "" }, { - "self_ref": "#/texts/102", + "self_ref": "#/texts/168", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1763,9 +3124,9 @@ "marker": "" }, { - "self_ref": "#/texts/103", + "self_ref": "#/texts/169", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1777,9 +3138,9 @@ "marker": "" }, { - "self_ref": "#/texts/104", + "self_ref": "#/texts/170", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1791,9 +3152,9 @@ "marker": "" }, { - "self_ref": "#/texts/105", + "self_ref": "#/texts/171", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1805,9 +3166,9 @@ "marker": "" }, { - "self_ref": "#/texts/106", + "self_ref": "#/texts/172", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1819,9 +3180,9 @@ "marker": "" }, { - "self_ref": "#/texts/107", + "self_ref": "#/texts/173", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1833,9 +3194,9 @@ "marker": "" }, { - "self_ref": "#/texts/108", + "self_ref": "#/texts/174", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1847,9 +3208,9 @@ "marker": "" }, { - "self_ref": "#/texts/109", + "self_ref": "#/texts/175", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1861,9 +3222,9 @@ "marker": "" }, { - "self_ref": "#/texts/110", + "self_ref": "#/texts/176", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1875,9 +3236,9 @@ "marker": "" }, { - "self_ref": "#/texts/111", + "self_ref": "#/texts/177", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1889,9 +3250,9 @@ "marker": "" }, { - "self_ref": "#/texts/112", + "self_ref": "#/texts/178", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1903,9 +3264,9 @@ "marker": "" }, { - "self_ref": "#/texts/113", + "self_ref": "#/texts/179", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1917,9 +3278,9 @@ "marker": "" }, { - "self_ref": "#/texts/114", + "self_ref": "#/texts/180", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1931,9 +3292,9 @@ "marker": "" }, { - "self_ref": "#/texts/115", + "self_ref": "#/texts/181", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1945,9 +3306,9 @@ "marker": "" }, { - "self_ref": "#/texts/116", + "self_ref": "#/texts/182", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1959,9 +3320,9 @@ "marker": "" }, { - "self_ref": "#/texts/117", + "self_ref": "#/texts/183", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1973,9 +3334,9 @@ "marker": "" }, { - "self_ref": "#/texts/118", + "self_ref": "#/texts/184", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/10" }, "children": [], "content_layer": "body", @@ -1991,7 +3352,7 @@ { "self_ref": "#/pictures/0", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", @@ -1999,7 +3360,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/42" + "$ref": "#/texts/76" } ], "references": [], @@ -2009,7 +3370,7 @@ { "self_ref": "#/pictures/1", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", @@ -2017,7 +3378,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/43" + "$ref": "#/texts/77" } ], "references": [], @@ -2027,7 +3388,7 @@ { "self_ref": "#/pictures/2", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", @@ -2035,7 +3396,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/45" + "$ref": "#/texts/81" } ], "references": [], @@ -2045,7 +3406,7 @@ { "self_ref": "#/pictures/3", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", @@ -2053,7 +3414,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/48" + "$ref": "#/texts/84" } ], "references": [], @@ -2063,7 +3424,7 @@ { "self_ref": "#/pictures/4", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", @@ -2071,7 +3432,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/52" + "$ref": "#/texts/100" } ], "references": [], @@ -5890,7 +7251,7 @@ { "self_ref": "#/tables/1", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/74" }, "children": [], "content_layer": "body", @@ -5898,7 +7259,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/50" + "$ref": "#/texts/98" } ], "references": [], diff --git a/tests/data/jats/groundtruth/pntd.0008301.nxml.md b/tests/data/jats/groundtruth/pntd.0008301.nxml.md index eec53de325..6522294806 100644 --- a/tests/data/jats/groundtruth/pntd.0008301.nxml.md +++ b/tests/data/jats/groundtruth/pntd.0008301.nxml.md @@ -60,7 +60,7 @@ Information on baseline prevalence, MDA coverage, the number of MDA rounds, and The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4]. -Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used. +Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either *a priori* knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used. #### Baseline prevalence @@ -68,13 +68,13 @@ Baseline prevalence can be assumed as a proxy for local transmission conditions #### Agent -In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain. +In terms of differences in transmission dynamics by agent, research has shown that *Brugia* spp. are more susceptible to the anti-filarial drug regimens than *Wuchereria bancrofti* parasites [23]. Thus, we combined districts reporting *B* . *malayi* and *B* . *timori* and compared them to areas with *W* . *bancrofti* or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors ( *Anopheles* or *Mansonia* compared to *Culex* ) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain. #### Environment LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.” -We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33]. +We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by *Culex* in urban areas and by *Anopheles* in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33]. Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis. @@ -96,7 +96,7 @@ The dataset, summarized at the district level, included information from 568 dis Statistical analysis and modeling were done with Stata MP 15.1 (College Station, TX). Descriptive statistics comparing various variables to the principle outcome were performed. Significant differences were identified using a chi-square test. A generalized linear model (GLM) with a log link and binomial error distribution—which estimates relative risks—was developed using forward stepwise modeling methods (called log-binomial model). Models with higher pseudo-r-squared and lower Akaike information criterion (AIC) were retained at each step. Pseudo-r-squared is a value between 0 and 1 with the higher the value, the better the model is at predicting the outcome of interest. AIC values are used to compare the relative quality of models compared to each other; in general, a lower value indicates a better model. Variables were tested by factor group. Once a variable was selected from the group, no other variable in that same group was eligible to be included in the final model due to issues of collinearity and small sample sizes. Interaction between terms in the model was tested after model selection, and interaction terms that modified the original terms’ significance were included in the final model. Overall, the number of potential variables able to be included in the model remained low due to the relatively small number of failure results (13%) in the dataset. Furthermore, the models with more than 3 variables and one interaction term either were unstable (indicated by very large confidence interval widths) or did not improve the model by being significant predictors or by modifying other parameters already in the model. These models were at heightened risk of non-convergence; we limited the number of variables accordingly. -Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44]. +Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with *W* . *bancrofti* parasite, and (4) including only districts with *Anopheles* as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44]. ## Results @@ -110,7 +110,7 @@ Fig 2 District-level baseline prevalence by country. -Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA. +Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of *Culex* , treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA. Fig 3 Percent pre-TAS failure by each characteristic (unadjusted). @@ -124,7 +124,7 @@ Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from -Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals. +Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with *W* . *bancrofti* , and (4) only districts with *Anopheles* as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the *W* . *bancrofti* -only and *Anopheles* -only models (models 3 and 4 respectively), as signified by large confidence intervals. Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis. @@ -149,15 +149,15 @@ Fig 5 Analysis of failures by model combinations. ## Discussion -This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7]. +This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg *et al* ., which explored ecological and socioeconomic factors associated with TAS failure [7]. Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45]. -Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival. +Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg *et al* . [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival. -The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16]. +The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as *Culex* vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that *Culex* as the predominant vector in a district, compared to *Anopheles* , results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16]. -All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence. +All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg *et al* . found *Brugia* spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with *Brugia* spp. in our dataset (6%) compared to 46% in the Goldberg *et al* . article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence. Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53]. diff --git a/tests/data/jats/groundtruth/pone.0234687.nxml.itxt b/tests/data/jats/groundtruth/pone.0234687.nxml.itxt index 0d855d7312..f96ccd7396 100644 --- a/tests/data/jats/groundtruth/pone.0234687.nxml.itxt +++ b/tests/data/jats/groundtruth/pone.0234687.nxml.itxt @@ -5,172 +5,566 @@ item-0 at level 0: unspecified: group _root_ item-4 at level 2: section_header: Abstract item-5 at level 3: text: Carbon (C) footprint of dairy pr ... uce the C footprint to a small extent. item-6 at level 2: section_header: Introduction - item-7 at level 3: text: Greenhouse gas (GHG) emissions f ... suitable for food crop production [4]. - item-8 at level 3: text: Considering the key role of live ... anagement to mitigate the C footprint. - item-9 at level 3: text: In subtropical climate zones, co ... t in tropical pastures (e.g. [17–19]). - item-10 at level 3: text: It has been shown that dairy cow ... sions from crop and reduced DM intake. - item-11 at level 3: text: The aim of this work was to quan ... uring lactation periods was evaluated. - item-12 at level 2: section_header: Materials and methods - item-13 at level 3: text: An LCA was developed according t ... 90816 - https://www.udesc.br/cav/ceua. - item-14 at level 3: section_header: System boundary - item-15 at level 4: text: The goal of the study was to ass ... n were outside of the system boundary. - item-16 at level 4: picture - item-16 at level 5: caption: Fig 1 Overview of the milk production system boundary considered in the study. - item-17 at level 3: section_header: Functional unit - item-18 at level 4: text: The functional unit was one kilo ... tein according to NRC [20] as follows: - item-19 at level 4: text: ECM = Milk production × (0.0929 ... characteristics described in Table 1. - item-20 at level 4: table with [13x3] - item-20 at level 5: caption: Table 1 Descriptive characteristics of the herd. - item-21 at level 3: section_header: Data sources and livestock system description - item-22 at level 4: text: The individual feed requirements ... ed to the ad libitum TMR intake group. - item-23 at level 4: text: Using experimental data, three s ... med during an entire lactation period. - item-24 at level 3: section_header: Impact assessment - item-25 at level 4: text: The CO2e emissions were calculat ... 65 for CO2, CH4 and N2O, respectively. - item-26 at level 3: section_header: Feed production - item-27 at level 4: section_header: Diets composition - item-28 at level 5: text: The DM intake of each ingredient ... collected throughout the experiments. - item-29 at level 5: table with [21x11] - item-29 at level 6: caption: Table 2 Dairy cows’ diets in different scenariosa. - item-30 at level 4: section_header: GHG emissions from crop and pasture production - item-31 at level 5: text: GHG emission factors used for of ... onsume 70% of pastures during grazing. - item-32 at level 5: table with [9x5] - item-32 at level 6: caption: Table 3 GHG emission factors for Off- and On-farm feed production. - item-33 at level 5: text: Emissions from on-farm feed prod ... factors described by Rotz et al. [42]. - item-34 at level 5: table with [28x5] - item-34 at level 6: caption: Table 4 GHG emissions from On-farm feed production. - item-35 at level 3: section_header: Animal husbandry - item-36 at level 4: text: The CH4 emissions from enteric f ... 1) = 13.8 + 0.185 × NDF (% DM intake). - item-37 at level 3: section_header: Manure from confined cows and urine and dung from grazing animals - item-38 at level 4: text: The CH4 emission from manure (kg ... for dietary GE per kg of DM (MJ kg-1). - item-39 at level 4: text: The OM digestibility was estimat ... h were 31%, 26% and 46%, respectively. - item-40 at level 4: text: The N2O-N emissions from urine a ... using the IPCC [38] emission factors. - item-41 at level 3: section_header: Farm management - item-42 at level 4: text: Emissions due to farm management ... crop and pasture production’ section. - item-43 at level 4: table with [12x4] - item-43 at level 5: caption: Table 5 Factors for major resource inputs in farm management. - item-44 at level 4: text: The amount of fuel use for manur ... me that animals stayed on confinement. - item-45 at level 4: text: The emissions from fuel were est ... × kg CO2e (kg machinery mass)-1 [42]. - item-46 at level 4: text: Emissions from electricity for m ... ws in naturally ventilated barns [47]. - item-47 at level 3: section_header: Co-product allocation - item-48 at level 4: text: The C footprint for milk produce ... directly assigned to milk production. - item-49 at level 3: section_header: Sensitivity analysis - item-50 at level 4: text: A sensitivity index was calculat ... ses a similar change in the footprint. - item-51 at level 2: section_header: Results and discussion - item-52 at level 3: text: The study has assessed the impac ... , feed production and electricity use. - item-53 at level 3: section_header: Greenhouse gas emissions - item-54 at level 4: text: Depending on emission factors us ... more than 5% of overall GHG emissions. - item-55 at level 4: picture - item-55 at level 5: caption: Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. - item-56 at level 4: text: Considering IPCC emission factor ... the C footprint of the dairy systems. - item-57 at level 4: text: The similarity of C footprint be ... of TMR was replaced by pasture access. - item-58 at level 4: text: The lower C footprint in scenari ... r, averaging 0.004 kg N2O-N kg-1 [37]. - item-59 at level 3: section_header: Methane emissions - item-60 at level 4: text: The enteric CH4 intensity was si ... ], which did not happen in this study. - item-61 at level 4: picture - item-61 at level 5: caption: Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. - item-62 at level 4: text: The lack of difference in enteri ... same scenarios as in this study [26]. - item-63 at level 3: section_header: Emissions from excreta and feed production - item-64 at level 4: text: Using IPCC emission factors for ... may not be captured by microbes [65]. - item-65 at level 4: picture - item-65 at level 5: caption: Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture. - item-66 at level 4: text: Using local emission factors for ... be revised for the subtropical region. - item-67 at level 4: text: Emissions for feed production de ... act, particularly in confinements [9]. - item-68 at level 3: section_header: Farm management - item-69 at level 4: text: The lower impact of emissions fr ... greater than 5% of total C footprint. - item-70 at level 4: text: Emissions from farm management d ... gas and hard coal, respectively [46]. - item-71 at level 3: section_header: Assumptions and limitations - item-72 at level 4: text: The milk production and composit ... ions as a function of soil management. - item-73 at level 3: section_header: Further considerations - item-74 at level 4: text: The potential for using pasture ... g ECM)-1 in case of foot lesions [72]. - item-75 at level 4: text: Grazing lands may also improve b ... hange of CO2 would be negligible [76]. - item-76 at level 2: section_header: Conclusions - item-77 at level 3: text: This study assessed the C footpr ... on with or without access to pastures. - item-78 at level 2: section_header: Acknowledgments - item-79 at level 3: text: Thanks to Anna Naranjo for helpf ... of the herd considered in this study. - item-80 at level 2: section_header: References - item-81 at level 3: list: group list - item-82 at level 4: list_item: IPCC. 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Soil Carbon Sequestration ... 1627. 10.1126/science.1097396 15192216 - item-156 at level 4: list_item: BoddeyRM, JantaliaCP, ConceiçaoP ... –795. 10.1111/j.1365-2486.2009.02020.x - item-157 at level 4: list_item: McConkeyB, AngersD, BenthamM, Bo ... the LULUCF sector for NIR 2014. 2014. - item-158 at level 1: caption: Fig 1 Overview of the milk produ ... stem boundary considered in the study. - item-159 at level 1: caption: Table 1 Descriptive characteristics of the herd. - item-160 at level 1: caption: Table 2 Dairy cows’ diets in different scenariosa. - item-161 at level 1: caption: Table 3 GHG emission factors for Off- and On-farm feed production. - item-162 at level 1: caption: Table 4 GHG emissions from On-farm feed production. - item-163 at level 1: caption: Table 5 Factors for major resource inputs in farm management. - item-164 at level 1: caption: Fig 2 Overall greenhouse gas emi ... lectricity = 0.205 kg CO2e kWh-1 [46]. - item-165 at level 1: caption: Fig 3 Sensitivity of the C footp ... lectricity = 0.205 kg CO2e kWh-1 [46]. - item-166 at level 1: caption: Fig 4 Greenhouse gas emissions ( ... uestered CO2-C from perennial pasture. \ No newline at end of file + item-7 at level 3: inline: group group + item-8 at level 4: text: Greenhouse gas (GHG) emissions f ... ssions [1], ranging from 5.5–7.5 Gt CO + item-9 at level 4: text: 2 + item-10 at level 4: text: equivalents (CO + item-11 at level 4: text: 2 + item-12 at level 4: text: e) yr + item-13 at level 4: text: -1 + item-14 at level 4: text: , with almost 30% coming from da ... suitable for food crop production [4]. + item-15 at level 3: inline: group group + item-16 at level 4: text: Considering the key role of live ... n investigated to mitigate methane (CH + item-17 at level 4: text: 4 + item-18 at level 4: text: ) emissions from enteric ferment ... ment [6] and manure management [7]. CH + item-19 at level 4: text: 4 + item-20 at level 4: text: emissions from enteric fermentat ... anagement to mitigate the C footprint. + item-21 at level 3: text: In subtropical climate zones, co ... t in tropical pastures (e.g. [17–19]). + item-22 at level 3: text: It has been shown that dairy cow ... sions from crop and reduced DM intake. + item-23 at level 3: text: The aim of this work was to quan ... uring lactation periods was evaluated. + item-24 at level 2: section_header: Materials and methods + item-25 at level 3: text: An LCA was developed according t ... 90816 - https://www.udesc.br/cav/ceua. + item-26 at level 3: section_header: System boundary + item-27 at level 4: text: The goal of the study was to ass ... n were outside of the system boundary. + item-28 at level 4: picture + item-28 at level 5: caption: Fig 1 Overview of the milk production system boundary considered in the study. + item-29 at level 3: section_header: Functional unit + item-30 at level 4: text: The functional unit was one kilo ... tein according to NRC [20] as follows: + item-31 at level 4: text: ECM = Milk production × (0.0929 ... characteristics described in Table 1. + item-32 at level 4: table with [13x3] + item-32 at level 5: caption: Table 1 Descriptive characteristics of the herd. + item-33 at level 3: section_header: Data sources and livestock system description + item-34 at level 4: inline: group group + item-35 at level 5: text: The individual feed requirements ... cows that received both 75 and 50% of + item-36 at level 5: text: ad libitum + item-37 at level 5: text: TMR intake with access to grazing a tropical pasture (pearl-millet, + item-38 at level 5: text: Pennisetum glaucum + item-39 at level 5: text: ‘Campeiro’) compared to cows receiving + item-40 at level 5: text: ad libitum + item-41 at level 5: text: TMR intake. Cows grazing on a temperate pasture (ryegrass, + item-42 at level 5: text: Lolium multiflorum + item-43 at level 5: text: ‘Maximus’) did not need changes to ECM production compared to the + item-44 at level 5: text: ad libitum + item-45 at level 5: text: TMR intake group. + item-46 at level 4: inline: group group + item-47 at level 5: text: Using experimental data, three s ... evaluated during the lactation period: + item-48 at level 5: text: ad libitum + item-49 at level 5: text: TMR intake, and 75, and 50% of + item-50 at level 5: text: ad libitum + item-51 at level 5: text: TMR intake with access to grazin ... nction of month ([26], Civiero et al., + item-52 at level 5: text: in press + item-53 at level 5: text: ). From April to October (210 da ... med during an entire lactation period. + item-54 at level 3: section_header: Impact assessment + item-55 at level 4: inline: group group + item-56 at level 5: text: The CO + item-57 at level 5: text: 2 + item-58 at level 5: text: e emissions were calculated by multiplying the emissions of CO + item-59 at level 5: text: 2 + item-60 at level 5: text: , CH + item-61 at level 5: text: 4 + item-62 at level 5: text: and N + item-63 at level 5: text: 2 + item-64 at level 5: text: O by their 100-year global warming potential (GWP + item-65 at level 5: text: 100 + item-66 at level 5: text: ), based on IPCC assessment report 5 (AR5; [27]). The values of GWP + item-67 at level 5: text: 100 + item-68 at level 5: text: are 1, 28 and 265 for CO + item-69 at level 5: text: 2 + item-70 at level 5: text: , CH + item-71 at level 5: text: 4 + item-72 at level 5: text: and N + item-73 at level 5: text: 2 + item-74 at level 5: text: O, respectively. + item-75 at level 3: section_header: Feed production + item-76 at level 4: section_header: Diets composition + item-77 at level 5: inline: group group + item-78 at level 6: text: The DM intake of each ingredient ... ennial tropical pasture (kikuyu grass, + item-79 at level 6: text: Pennisetum clandestinum + item-80 at level 6: text: ). The DM intake of calves, heif ... meet the net energy for lactation (NE + item-81 at level 6: text: L + item-82 at level 6: text: ) and metabolizable protein (MP) ... collected throughout the experiments. + item-83 at level 5: table with [21x11] + item-83 at level 6: caption: Table 2 Dairy cows’ diets in different scenariosa. + item-84 at level 4: section_header: GHG emissions from crop and pasture production + item-85 at level 5: inline: group group + item-86 at level 6: text: GHG emission factors used for of ... s productions were 3300 and 1500 kg CO + item-87 at level 6: text: 2 + item-88 at level 6: text: e ha + item-89 at level 6: text: -1 + item-90 at level 6: text: , respectively [32]. The DM production (kg ha + item-91 at level 6: text: -1 + item-92 at level 6: text: ) of corn silage and pastures we ... onsume 70% of pastures during grazing. + item-93 at level 5: table with [9x5] + item-93 at level 6: caption: Table 3 GHG emission factors for Off- and On-farm feed production. + item-94 at level 5: inline: group group + item-95 at level 6: text: Emissions from on-farm feed prod ... ary sources were direct and indirect N + item-96 at level 6: text: 2 + item-97 at level 6: text: O-N emissions from organic and s ... tilizers and crop/pasture residues, CO + item-98 at level 6: text: 2 + item-99 at level 6: text: -C emissions from lime and urea ... well as fuel combustion. The direct N + item-100 at level 6: text: 2 + item-101 at level 6: text: O-N emission factor (kg (kg N input) + item-102 at level 6: text: -1 + item-103 at level 6: text: ) is based on a local study performed previously [37]. For indirect N + item-104 at level 6: text: 2 + item-105 at level 6: text: O-N emissions (kg N + item-106 at level 6: text: 2 + item-107 at level 6: text: O-N (kg NH + item-108 at level 6: text: 3 + item-109 at level 6: text: -N + NO + item-110 at level 6: text: x + item-111 at level 6: text: ) + item-112 at level 6: text: -1 + item-113 at level 6: text: ), as well as CO + item-114 at level 6: text: 2 + item-115 at level 6: text: -C emissions from lime + urea, d ... stures, a C sequestration of 0.57 t ha + item-116 at level 6: text: -1 + item-117 at level 6: text: was used based on a 9-year study ... red was 8.9 (no-tillage) and 14.3 L ha + item-118 at level 6: text: -1 + item-119 at level 6: text: (disking) for annual tropical an ... te pastures, respectively [40]. The CO + item-120 at level 6: text: 2 + item-121 at level 6: text: from fuel combustion was 2.7 kg CO + item-122 at level 6: text: 2 + item-123 at level 6: text: L + item-124 at level 6: text: -1 + item-125 at level 6: text: [41]. Secondary sources of emiss ... factors described by Rotz et al. [42]. + item-126 at level 5: table with [28x5] + item-126 at level 6: caption: Table 4 GHG emissions from On-farm feed production. + item-127 at level 3: section_header: Animal husbandry + item-128 at level 4: inline: group group + item-129 at level 5: text: The CH + item-130 at level 5: text: 4 + item-131 at level 5: text: emissions from enteric fermentation intensity (g (kg ECM) + item-132 at level 5: text: -1 + item-133 at level 5: text: ) was a function of estimated CH + item-134 at level 5: text: 4 + item-135 at level 5: text: yield (g (kg DM intake) + item-136 at level 5: text: -1 + item-137 at level 5: text: ), actual DM intake and ECM. The enteric CH + item-138 at level 5: text: 4 + item-139 at level 5: text: yield was estimated as a functio ... proposed by Niu et al. [43], where: CH + item-140 at level 5: text: 4 + item-141 at level 5: text: yield (g (kg DM intake) + item-142 at level 5: text: -1 + item-143 at level 5: text: ) = 13.8 + 0.185 × NDF (% DM intake). + item-144 at level 3: section_header: Manure from confined cows and urine and dung from grazing animals + item-145 at level 4: inline: group group + item-146 at level 5: text: The CH + item-147 at level 5: text: 4 + item-148 at level 5: text: emission from manure (kg (kg ECM) + item-149 at level 5: text: -1 + item-150 at level 5: text: ) was a function of daily CH + item-151 at level 5: text: 4 + item-152 at level 5: text: emission from manure (kg cow + item-153 at level 5: text: -1 + item-154 at level 5: text: ) and daily ECM (kg cow + item-155 at level 5: text: -1 + item-156 at level 5: text: ). The daily CH + item-157 at level 5: text: 4 + item-158 at level 5: text: emission from manure was estimat ... olatile solid (VS) excreted (kg DM cow + item-159 at level 5: text: -1 + item-160 at level 5: text: ) in manure. The daily VS was es ... n an organic matter (OM) basis (kg day + item-161 at level 5: text: -1 + item-162 at level 5: text: ), NDOMI = non-digestible OM intake (kg day + item-163 at level 5: text: -1 + item-164 at level 5: text: ): (1- OM digestibility) × OM in ... .04), GE = gross energy intake (MJ day + item-165 at level 5: text: -1 + item-166 at level 5: text: ), OM = organic matter (g), 18.4 ... tor for dietary GE per kg of DM (MJ kg + item-167 at level 5: text: -1 + item-168 at level 5: text: ). + item-169 at level 4: text: The OM digestibility was estimat ... h were 31%, 26% and 46%, respectively. + item-170 at level 4: inline: group group + item-171 at level 5: text: The N + item-172 at level 5: text: 2 + item-173 at level 5: text: O-N emissions from urine and fec ... ations 10 and 12, respectively). The N + item-174 at level 5: text: 2 + item-175 at level 5: text: O emissions from stored manure a ... alculated based on the conversion of N + item-176 at level 5: text: 2 + item-177 at level 5: text: O-N emissions to N + item-178 at level 5: text: 2 + item-179 at level 5: text: O emissions, where N + item-180 at level 5: text: 2 + item-181 at level 5: text: O emissions = N + item-182 at level 5: text: 2 + item-183 at level 5: text: O-N emissions × 44/28. The emission factors were 0.002 kg N + item-184 at level 5: text: 2 + item-185 at level 5: text: O-N (kg N) + item-186 at level 5: text: -1 + item-187 at level 5: text: stored in a pit below animal confinements, and 0.02 kg N + item-188 at level 5: text: 2 + item-189 at level 5: text: O-N (kg of urine and dung) + item-190 at level 5: text: -1 + item-191 at level 5: text: deposited on pasture [38]. The indirect N + item-192 at level 5: text: 2 + item-193 at level 5: text: O emissions from storage manure ... using the IPCC [38] emission factors. + item-194 at level 3: section_header: Farm management + item-195 at level 4: text: Emissions due to farm management ... crop and pasture production’ section. + item-196 at level 4: table with [12x4] + item-196 at level 5: caption: Table 5 Factors for major resource inputs in farm management. + item-197 at level 4: inline: group group + item-198 at level 5: text: The amount of fuel use for manur ... quired for manure handling (L diesel t + item-199 at level 5: text: -1 + item-200 at level 5: text: ) [42]. The amount of manure was estimated from OM excretions (kg cow + item-201 at level 5: text: -1 + item-202 at level 5: text: ), assuming that the manure has ... me that animals stayed on confinement. + item-203 at level 4: inline: group group + item-204 at level 5: text: The emissions from fuel were est ... uired for manure handling (L) × (kg CO + item-205 at level 5: text: 2 + item-206 at level 5: text: e L + item-207 at level 5: text: -1 + item-208 at level 5: text: ) [41]. The secondary emissions ... roduction and transport of fuel (kg CO + item-209 at level 5: text: 2 + item-210 at level 5: text: e L + item-211 at level 5: text: -1 + item-212 at level 5: text: ) [41]. Emissions from manufactu ... w (t) × (kg machinery mass (kg manure) + item-213 at level 5: text: -1 + item-214 at level 5: text: × 10 + item-215 at level 5: text: −3 + item-216 at level 5: text: ) [42] × kg CO + item-217 at level 5: text: 2 + item-218 at level 5: text: e (kg machinery mass) + item-219 at level 5: text: -1 + item-220 at level 5: text: [42]. + item-221 at level 4: inline: group group + item-222 at level 5: text: Emissions from electricity for m ... ated using two emission factors (kg CO + item-223 at level 5: text: 2 + item-224 at level 5: text: kWh + item-225 at level 5: text: -1 + item-226 at level 5: text: ). The first one is based on Uni ... ivities is 0.06 kWh (kg milk produced) + item-227 at level 5: text: -1 + item-228 at level 5: text: [47]. The annual electricity use for lighting was 75 kWh cow + item-229 at level 5: text: -1 + item-230 at level 5: text: , which is the value considered ... ws in naturally ventilated barns [47]. + item-231 at level 3: section_header: Co-product allocation + item-232 at level 4: inline: group group + item-233 at level 5: text: The C footprint for milk produce ... ibed by Thoma et al. [48]. Briefly, AR + item-234 at level 5: text: milk + item-235 at level 5: text: = 1–6.04 × BMR, where + item-236 at level 5: text: : + item-237 at level 5: text: AR + item-238 at level 5: text: milk + item-239 at level 5: text: is the allocation ratio for milk ... during cow`s entire life (kg). The AR + item-240 at level 5: text: milk + item-241 at level 5: text: were 0.854 and 0.849 for TMR and ... asture scenarios, respectively. The AR + item-242 at level 5: text: milk + item-243 at level 5: text: was applied to the whole emissio ... directly assigned to milk production. + item-244 at level 3: section_header: Sensitivity analysis + item-245 at level 4: text: A sensitivity index was calculat ... ses a similar change in the footprint. + item-246 at level 2: section_header: Results and discussion + item-247 at level 3: text: The study has assessed the impac ... , feed production and electricity use. + item-248 at level 3: section_header: Greenhouse gas emissions + item-249 at level 4: inline: group group + item-250 at level 5: text: Depending on emission factors us ... 2A and 2B) or decreased by 0.04 kg CO + item-251 at level 5: text: 2 + item-252 at level 5: text: e (kg ECM) + item-253 at level 5: text: -1 + item-254 at level 5: text: (Fig 2C and 2D) in scenarios that included pastures compared to + item-255 at level 5: text: ad libitum + item-256 at level 5: text: TMR intake. Due to differences i ... values ranged from 0.92 to 1.04 kg CO + item-257 at level 5: text: 2 + item-258 at level 5: text: e (kg ECM) + item-259 at level 5: text: -1 + item-260 at level 5: text: for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO + item-261 at level 5: text: 2 + item-262 at level 5: text: e (kg ECM) + item-263 at level 5: text: -1 + item-264 at level 5: text: for cows with access to pasture. ... ocal emission factors for estimating N + item-265 at level 5: text: 2 + item-266 at level 5: text: O-N emissions [37], manure emiss ... more than 5% of overall GHG emissions. + item-267 at level 4: picture + item-267 at level 5: caption: Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. + item-268 at level 4: inline: group group + item-269 at level 5: text: Considering IPCC emission factors for N + item-270 at level 5: text: 2 + item-271 at level 5: text: O emissions from urine and dung ... otprint ranged from 0.99 to 1.04 kg CO + item-272 at level 5: text: 2 + item-273 at level 5: text: e (kg ECM) + item-274 at level 5: text: -1 + item-275 at level 5: text: , and was close to those reporte ... , which ranged from 0.98 to 1.16 kg CO + item-276 at level 5: text: 2 + item-277 at level 5: text: e (kg ECM) + item-278 at level 5: text: -1 + item-279 at level 5: text: . When local emission factors for N + item-280 at level 5: text: 2 + item-281 at level 5: text: O emissions from urine and dung ... without accounting for sequestered CO + item-282 at level 5: text: 2 + item-283 at level 5: text: -C from perennial pasture—0.91 kg CO + item-284 at level 5: text: 2 + item-285 at level 5: text: e (kg ECM) + item-286 at level 5: text: -1 + item-287 at level 5: text: —was lower than the range of val ... , which ranged from 0.52 to 0.89 kg CO + item-288 at level 5: text: 2 + item-289 at level 5: text: e (kg ECM) + item-290 at level 5: text: -1 + item-291 at level 5: text: . Regardless of which emission f ... nt, which ranged from 1.4 to 2.3 kg CO + item-292 at level 5: text: 2 + item-293 at level 5: text: e (kg ECM) + item-294 at level 5: text: -1 + item-295 at level 5: text: [8,55]. Thus, even though differ ... e emissions estimation, sequestered CO + item-296 at level 5: text: 2 + item-297 at level 5: text: -C, etc.), herd productivity and ... the C footprint of the dairy systems. + item-298 at level 4: inline: group group + item-299 at level 5: text: The similarity of C footprint be ... served that an increase of 1000 kg cow + item-300 at level 5: text: -1 + item-301 at level 5: text: (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO + item-302 at level 5: text: 2 + item-303 at level 5: text: e (kg ECM) + item-304 at level 5: text: -1 + item-305 at level 5: text: , which may explain an apparent ... milk production of around 7000 kg cow + item-306 at level 5: text: -1 + item-307 at level 5: text: . Experimental data showed a red ... of TMR was replaced by pasture access. + item-308 at level 4: inline: group group + item-309 at level 5: text: The lower C footprint in scenari ... emission factors [37] were used for N + item-310 at level 5: text: 2 + item-311 at level 5: text: O emissions from urine and dung ... enarios including pastures compared to + item-312 at level 5: text: ad libitum + item-313 at level 5: text: TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N + item-314 at level 5: text: 2 + item-315 at level 5: text: O-N (kg N) + item-316 at level 5: text: -1 + item-317 at level 5: text: for urine and dung from grazing ... five times lower, averaging 0.004 kg N + item-318 at level 5: text: 2 + item-319 at level 5: text: O-N kg + item-320 at level 5: text: -1 + item-321 at level 5: text: [37]. + item-322 at level 3: section_header: Methane emissions + item-323 at level 4: inline: group group + item-324 at level 5: text: The enteric CH + item-325 at level 5: text: 4 + item-326 at level 5: text: intensity was similar between di ... Fig 3). The large effect of enteric CH + item-327 at level 5: text: 4 + item-328 at level 5: text: emissions on the whole C footpri ... cted, because the impact of enteric CH + item-329 at level 5: text: 4 + item-330 at level 5: text: on GHG emissions of milk product ... o range from 44 to 60% of the total CO + item-331 at level 5: text: 2 + item-332 at level 5: text: e [50,52,57,58]. However, emissi ... trate feeds are greater than 0.7 kg CO + item-333 at level 5: text: 2 + item-334 at level 5: text: e kg + item-335 at level 5: text: -1 + item-336 at level 5: text: [59], which did not happen in this study. + item-337 at level 4: picture + item-337 at level 5: caption: Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. + item-338 at level 4: inline: group group + item-339 at level 5: text: The lack of difference in enteric CH + item-340 at level 5: text: 4 + item-341 at level 5: text: emissions in different systems c ... nual temperate pastures (495 g (kg DM) + item-342 at level 5: text: -1 + item-343 at level 5: text: ) compared to corn silage (550 g (kg DM) + item-344 at level 5: text: -1 + item-345 at level 5: text: ). Hence, an expected, increase ... have shown that the actual enteric CH + item-346 at level 5: text: 4 + item-347 at level 5: text: emissions may decrease with incl ... ybean meal [60] or increase enteric CH + item-348 at level 5: text: 4 + item-349 at level 5: text: emissions when dairy cows grazin ... silage [61]. Additionally, enteric CH + item-350 at level 5: text: 4 + item-351 at level 5: text: emissions did not differ between ... same scenarios as in this study [26]. + item-352 at level 3: section_header: Emissions from excreta and feed production + item-353 at level 4: inline: group group + item-354 at level 5: text: Using IPCC emission factors for N + item-355 at level 5: text: 2 + item-356 at level 5: text: O emissions from urine and dung [38] and those from Table 3, CH + item-357 at level 5: text: 4 + item-358 at level 5: text: emissions from manure decreased 0.07 kg CO + item-359 at level 5: text: 2 + item-360 at level 5: text: e (kg ECM) + item-361 at level 5: text: -1 + item-362 at level 5: text: , but N + item-363 at level 5: text: 2 + item-364 at level 5: text: O emissions from manure increased 0.09 kg CO + item-365 at level 5: text: 2 + item-366 at level 5: text: e (kg ECM) + item-367 at level 5: text: -1 + item-368 at level 5: text: , as TMR intake was restricted to 50% + item-369 at level 5: text: ad libitum + item-370 at level 5: text: (Fig 4A). Emissions for pastures increased by 0.06 kg CO + item-371 at level 5: text: 2 + item-372 at level 5: text: e (kg ECM) + item-373 at level 5: text: -1 + item-374 at level 5: text: , whereas emissions for producin ... nd corn silage decreased by 0.09 kg CO + item-375 at level 5: text: 2 + item-376 at level 5: text: e (kg ECM) + item-377 at level 5: text: -1 + item-378 at level 5: text: , as TMR intake decreased (Fig 4 ... ctating dairy cow diets. The greater N + item-379 at level 5: text: 2 + item-380 at level 5: text: O-N emissions from manure with pasture was a consequence of higher N + item-381 at level 5: text: 2 + item-382 at level 5: text: O-N emissions due to greater CP ... g CP content from 185 to 152 g (kg DM) + item-383 at level 5: text: -1 + item-384 at level 5: text: , N intake decreased by 20% and ... g dairy cows ranged from 150 g (kg DM) + item-385 at level 5: text: -1 + item-386 at level 5: text: on TMR system to 198 g (kg DM) + item-387 at level 5: text: -1 + item-388 at level 5: text: on 50% TMR with pasture. Additio ... may not be captured by microbes [65]. + item-389 at level 4: picture + item-389 at level 5: caption: Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture. + item-390 at level 4: inline: group group + item-391 at level 5: text: Using local emission factors for N + item-392 at level 5: text: 2 + item-393 at level 5: text: O emissions from urine and dung ... d those from Table 4, reductions in CH + item-394 at level 5: text: 4 + item-395 at level 5: text: emissions from stocked manure, w ... iets, did not offset by increases in N + item-396 at level 5: text: 2 + item-397 at level 5: text: O emissions from excreta (Fig 4C ... much lower emission factors used for N + item-398 at level 5: text: 2 + item-399 at level 5: text: O emissions from urine and dung. ... be revised for the subtropical region. + item-400 at level 4: text: Emissions for feed production de ... act, particularly in confinements [9]. + item-401 at level 3: section_header: Farm management + item-402 at level 4: text: The lower impact of emissions fr ... greater than 5% of total C footprint. + item-403 at level 4: inline: group group + item-404 at level 5: text: Emissions from farm management d ... or electricity generation (0.205 kg CO + item-405 at level 5: text: 2 + item-406 at level 5: text: e kWh + item-407 at level 5: text: -1 + item-408 at level 5: text: [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO + item-409 at level 5: text: 2 + item-410 at level 5: text: e kWh + item-411 at level 5: text: -1 + item-412 at level 5: text: [42]). This apparent discrepancy ... has an emission factor of 0.074 kg CO + item-413 at level 5: text: 2 + item-414 at level 5: text: e kWh + item-415 at level 5: text: -1 + item-416 at level 5: text: against 0.382 and 0.926 kg CO + item-417 at level 5: text: 2 + item-418 at level 5: text: e kWh + item-419 at level 5: text: -1 + item-420 at level 5: text: produced by natural gas and hard coal, respectively [46]. + item-421 at level 3: section_header: Assumptions and limitations + item-422 at level 4: inline: group group + item-423 at level 5: text: The milk production and composit ... sion factors for direct and indirect N + item-424 at level 5: text: 2 + item-425 at level 5: text: O emissions from urine and dung ... sary to reduce the uncertainty. The CO + item-426 at level 5: text: 2 + item-427 at level 5: text: emitted from lime and urea appli ... ions as a function of soil management. + item-428 at level 3: section_header: Further considerations + item-429 at level 4: inline: group group + item-430 at level 5: text: The potential for using pasture ... reduced the C footprint by 0.12 kg CO + item-431 at level 5: text: 2 + item-432 at level 5: text: e (kg ECM) + item-433 at level 5: text: -1 + item-434 at level 5: text: , whereas an increase from 10,00 ... ced the C footprint by only 0.06 kg CO + item-435 at level 5: text: 2 + item-436 at level 5: text: e (kg ECM) + item-437 at level 5: text: -1 + item-438 at level 5: text: . Hence, the impact of increasin ... C footprint may increase by 0.03 kg CO + item-439 at level 5: text: 2 + item-440 at level 5: text: e (kg ECM) + item-441 at level 5: text: -1 + item-442 at level 5: text: in subclinical ketosis [71] and by 0.02 kg CO + item-443 at level 5: text: 2 + item-444 at level 5: text: e (kg ECM) + item-445 at level 5: text: -1 + item-446 at level 5: text: in case of foot lesions [72]. + item-447 at level 4: inline: group group + item-448 at level 5: text: Grazing lands may also improve b ... n subtropical areas by 0.5 to 1.5 t ha + item-449 at level 5: text: -1 + item-450 at level 5: text: [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha + item-451 at level 5: text: -1 + item-452 at level 5: text: would be sequestered, further re ... e C footprint by 0.015 and 0.025 kg CO + item-453 at level 5: text: 2 + item-454 at level 5: text: e (kg ECM) + item-455 at level 5: text: -1 + item-456 at level 5: text: for the scenarios using 75 and 5 ... emissions would be 0.03 and 0.05 kg CO + item-457 at level 5: text: 2 + item-458 at level 5: text: e (kg ECM) + item-459 at level 5: text: -1 + item-460 at level 5: text: for 75 and 50% TMR, respectively ... C storage, so that net exchange of CO + item-461 at level 5: text: 2 + item-462 at level 5: text: would be negligible [76]. + item-463 at level 2: section_header: Conclusions + item-464 at level 3: inline: group group + item-465 at level 4: text: This study assessed the C footpr ... attributable to the evidence of low N + item-466 at level 4: text: 2 + item-467 at level 4: text: O emissions from urine and dung ... ical areas. Even though the enteric CH + item-468 at level 4: text: 4 + item-469 at level 4: text: intensity was the largest source of CO + item-470 at level 4: text: 2 + item-471 at level 4: text: e emissions, it did not change b ... on with or without access to pastures. + item-472 at level 2: section_header: Acknowledgments + item-473 at level 3: text: Thanks to Anna Naranjo for helpf ... of the herd considered in this study. + item-474 at level 2: section_header: References + item-475 at level 3: list: group list + item-476 at level 4: list_item: IPCC. 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"label": "list" - } - ], - "texts": [ + "name": "group", + "label": "inline" + }, { - "self_ref": "#/texts/0", + "self_ref": "#/groups/10", "parent": { - "$ref": "#/body" + "$ref": "#/texts/134" }, "children": [ { - "$ref": "#/texts/1" + "$ref": "#/texts/159" }, { - "$ref": "#/texts/2" + "$ref": "#/texts/160" }, { - "$ref": "#/texts/3" + "$ref": "#/texts/161" }, { - "$ref": "#/texts/5" + "$ref": "#/texts/162" }, { - "$ref": "#/texts/11" + "$ref": "#/texts/163" }, { - "$ref": "#/texts/50" + "$ref": "#/texts/164" }, { - "$ref": "#/texts/75" + "$ref": "#/texts/165" }, { - "$ref": "#/texts/77" + "$ref": "#/texts/166" }, { - "$ref": "#/texts/79" + "$ref": "#/texts/167" + }, + { + "$ref": "#/texts/168" + }, + { + "$ref": "#/texts/169" + }, + { + "$ref": "#/texts/170" + }, + { + "$ref": "#/texts/171" + }, + { + "$ref": "#/texts/172" + }, + { + "$ref": "#/texts/173" + }, + { + "$ref": "#/texts/174" + }, + { + "$ref": "#/texts/175" + }, + { + "$ref": "#/texts/176" + }, + { + "$ref": "#/texts/177" + }, + { + "$ref": "#/texts/178" + }, + { + "$ref": "#/texts/179" + }, + { + "$ref": "#/texts/180" + }, + { + "$ref": "#/texts/181" } ], "content_layer": "body", - "label": "title", - "prov": [], - "orig": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions", - "text": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions" + "name": "group", + "label": "inline" }, { - "self_ref": "#/texts/1", + "self_ref": "#/groups/11", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/182" }, - "children": [], + "children": [ + { + "$ref": "#/texts/185" + }, + { + "$ref": "#/texts/186" + }, + { + "$ref": "#/texts/187" + }, + { + "$ref": "#/texts/188" + }, + { + "$ref": "#/texts/189" + } + ], "content_layer": "body", - "label": "paragraph", - "prov": [], - "orig": "Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab", - "text": "Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab" + "name": "group", + "label": "inline" }, { - "self_ref": "#/texts/2", + "self_ref": "#/groups/12", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/182" }, - "children": [], + "children": [ + { + "$ref": "#/texts/190" + }, + { + "$ref": "#/texts/191" + }, + { + "$ref": "#/texts/192" + }, + { + "$ref": "#/texts/193" + }, + { + "$ref": "#/texts/194" + }, + { + "$ref": "#/texts/195" + }, + { + "$ref": "#/texts/196" + }, + { + "$ref": "#/texts/197" + }, + { + "$ref": "#/texts/198" + }, + { + "$ref": "#/texts/199" + }, + { + "$ref": "#/texts/200" + }, + { + "$ref": "#/texts/201" + }, + { + "$ref": "#/texts/202" + }, + { + "$ref": "#/texts/203" + }, + { + "$ref": "#/texts/204" + }, + { + "$ref": "#/texts/205" + }, + { + "$ref": "#/texts/206" + } + ], "content_layer": "body", - "label": "paragraph", - "prov": [], - "orig": "Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil", - "text": "Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil" + "name": "group", + "label": "inline" }, { - "self_ref": "#/texts/3", + "self_ref": "#/groups/13", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/182" }, "children": [ { - "$ref": "#/texts/4" + "$ref": "#/texts/207" + }, + { + "$ref": "#/texts/208" + }, + { + "$ref": "#/texts/209" + }, + { + "$ref": "#/texts/210" + }, + { + "$ref": "#/texts/211" + }, + { + "$ref": "#/texts/212" + }, + { + "$ref": "#/texts/213" + }, + { + "$ref": "#/texts/214" + }, + { + "$ref": "#/texts/215" } ], "content_layer": "body", - "label": "section_header", - "prov": [], - "orig": "Abstract", - "text": "Abstract", - "level": 1 + "name": "group", + "label": "inline" }, { - "self_ref": "#/texts/4", + "self_ref": "#/groups/14", "parent": { - "$ref": "#/texts/3" + "$ref": "#/texts/216" }, - "children": [], + "children": [ + { + "$ref": "#/texts/217" + }, + { + "$ref": "#/texts/218" + }, + { + "$ref": "#/texts/219" + }, + { + "$ref": "#/texts/220" + }, + { + "$ref": "#/texts/221" + }, + { + "$ref": "#/texts/222" + }, + { + "$ref": "#/texts/223" + }, + { + "$ref": "#/texts/224" + }, + { + "$ref": "#/texts/225" + }, + { + "$ref": "#/texts/226" + }, + { + "$ref": "#/texts/227" + } + ], "content_layer": "body", - "label": "text", - "prov": [], - "orig": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.", - "text": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent." + "name": "group", + "label": "inline" }, { - "self_ref": "#/texts/5", + "self_ref": "#/groups/15", "parent": { - "$ref": "#/texts/0" + "$ref": "#/texts/232" }, "children": [ { - "$ref": "#/texts/6" + "$ref": "#/texts/233" }, { - "$ref": "#/texts/7" + "$ref": "#/texts/234" }, { - "$ref": "#/texts/8" + "$ref": "#/texts/235" }, { - "$ref": "#/texts/9" + "$ref": "#/texts/236" }, { - "$ref": "#/texts/10" + "$ref": "#/texts/237" + }, + { + "$ref": "#/texts/238" + }, + { + "$ref": "#/texts/239" + }, + { + "$ref": "#/texts/240" + }, + { + "$ref": "#/texts/241" + }, + { + "$ref": "#/texts/242" + }, + { + "$ref": "#/texts/243" + }, + { + "$ref": "#/texts/244" + }, + { + "$ref": "#/texts/245" + }, + { + "$ref": "#/texts/246" + }, + { + "$ref": "#/texts/247" + }, + { + "$ref": "#/texts/248" + }, + { + "$ref": 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"#/texts/2" + }, + { + "$ref": "#/texts/3" + }, + { + "$ref": "#/texts/5" + }, + { + "$ref": "#/texts/21" + }, + { + "$ref": "#/texts/230" + }, + { + "$ref": "#/texts/435" + }, + { + "$ref": "#/texts/443" + }, + { + "$ref": "#/texts/445" + } + ], + "content_layer": "body", + "label": "title", + "prov": [], + "orig": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions", + "text": "Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions" + }, + { + "self_ref": "#/texts/1", + "parent": { + "$ref": "#/texts/0" + }, + "children": [], + "content_layer": "body", + "label": "paragraph", + "prov": [], + "orig": "Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab", + "text": "Henrique M. N. Ribeiro-Filho, Maurício Civiero, Ermias Kebreab" + }, + { + "self_ref": "#/texts/2", + "parent": { + "$ref": "#/texts/0" + }, + "children": [], + "content_layer": "body", + "label": "paragraph", + "prov": [], + "orig": "Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil", + "text": "Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil" + }, + { + "self_ref": "#/texts/3", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/4" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Abstract", + "text": "Abstract", + "level": 1 + }, + { + "self_ref": "#/texts/4", + "parent": { + "$ref": "#/texts/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.", + "text": "Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent." + }, + { + "self_ref": "#/texts/5", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/groups/0" + }, + { + "$ref": "#/groups/1" + }, + { + "$ref": "#/texts/18" + }, + { + "$ref": "#/texts/19" + }, + { + "$ref": "#/texts/20" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Introduction", + "text": "Introduction", + "level": 1 + }, + { + "self_ref": "#/texts/6", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO", + "text": "Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO" + }, + { + "self_ref": "#/texts/7", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/8", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "equivalents (CO", + "text": "equivalents (CO" + }, + { + "self_ref": "#/texts/9", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/10", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e) yr", + "text": "e) yr" + }, + { + "self_ref": "#/texts/11", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/12", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].", + "text": ", with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4]." + }, + { + "self_ref": "#/texts/13", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH", + "text": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH" + }, + { + "self_ref": "#/texts/14", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/15", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH", + "text": ") emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH" + }, + { + "self_ref": "#/texts/16", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/17", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.", + "text": "emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint." + }, + { + "self_ref": "#/texts/18", + "parent": { + "$ref": "#/texts/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]).", + "text": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19])." + }, + { + "self_ref": "#/texts/19", + "parent": { + "$ref": "#/texts/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.", + "text": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake." + }, + { + "self_ref": "#/texts/20", + "parent": { + "$ref": "#/texts/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated.", + "text": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated." + }, + { + "self_ref": "#/texts/21", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/22" + }, + { + "$ref": "#/texts/23" + }, + { + "$ref": "#/texts/26" + }, + { + "$ref": "#/texts/30" + }, + { + "$ref": "#/texts/49" + }, + { + "$ref": "#/texts/69" + }, + { + "$ref": "#/texts/118" + }, + { + "$ref": "#/texts/134" + }, + { + "$ref": "#/texts/182" + }, + { + "$ref": "#/texts/216" + }, + { + "$ref": "#/texts/228" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Materials and methods", + "text": "Materials and methods", + "level": 1 + }, + { + "self_ref": "#/texts/22", + "parent": { + "$ref": "#/texts/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua.", + "text": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua." + }, + { + "self_ref": "#/texts/23", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/texts/24" + }, + { + "$ref": "#/pictures/0" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "System boundary", + "text": "System boundary", + "level": 2 + }, + { + "self_ref": "#/texts/24", + "parent": { + "$ref": "#/texts/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.", + "text": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary." + }, + { + "self_ref": "#/texts/25", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Fig 1 Overview of the milk production system boundary considered in the study.", + "text": "Fig 1 Overview of the milk production system boundary considered in the study." + }, + { + "self_ref": "#/texts/26", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/texts/27" + }, + { + "$ref": "#/texts/28" + }, + { + "$ref": "#/tables/0" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Functional unit", + "text": "Functional unit", + "level": 2 + }, + { + "self_ref": "#/texts/27", + "parent": { + "$ref": "#/texts/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:", + "text": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:" + }, + { + "self_ref": "#/texts/28", + "parent": { + "$ref": "#/texts/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.", + "text": "ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1." + }, + { + "self_ref": "#/texts/29", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Table 1 Descriptive characteristics of the herd.", + "text": "Table 1 Descriptive characteristics of the herd." + }, + { + "self_ref": "#/texts/30", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/groups/2" + }, + { + "$ref": "#/groups/3" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Data sources and livestock system description", + "text": "Data sources and livestock system description", + "level": 2 + }, + { + "self_ref": "#/texts/31", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of", + "text": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of" + }, + { + "self_ref": "#/texts/32", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/33", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake with access to grazing a tropical pasture (pearl-millet,", + "text": "TMR intake with access to grazing a tropical pasture (pearl-millet," + }, + { + "self_ref": "#/texts/34", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Pennisetum glaucum", + "text": "Pennisetum glaucum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/35", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "‘Campeiro’) compared to cows receiving", + "text": "‘Campeiro’) compared to cows receiving" + }, + { + "self_ref": "#/texts/36", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/37", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake. Cows grazing on a temperate pasture (ryegrass,", + "text": "TMR intake. Cows grazing on a temperate pasture (ryegrass," + }, + { + "self_ref": "#/texts/38", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Lolium multiflorum", + "text": "Lolium multiflorum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/39", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "‘Maximus’) did not need changes to ECM production compared to the", + "text": "‘Maximus’) did not need changes to ECM production compared to the" + }, + { + "self_ref": "#/texts/40", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/41", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake group.", + "text": "TMR intake group." + }, + { + "self_ref": "#/texts/42", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Using experimental data, three scenarios were evaluated during the lactation period:", + "text": "Using experimental data, three scenarios were evaluated during the lactation period:" + }, + { + "self_ref": "#/texts/43", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/44", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake, and 75, and 50% of", + "text": "TMR intake, and 75, and 50% of" + }, + { + "self_ref": "#/texts/45", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/46", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al.,", + "text": "TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al.," + }, + { + "self_ref": "#/texts/47", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in press", + "text": "in press", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/48", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.", + "text": "). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period." + }, + { + "self_ref": "#/texts/49", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/groups/4" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Impact assessment", + "text": "Impact assessment", + "level": 2 + }, + { + "self_ref": "#/texts/50", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The CO", + "text": "The CO" + }, + { + "self_ref": "#/texts/51", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/52", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e emissions were calculated by multiplying the emissions of CO", + "text": "e emissions were calculated by multiplying the emissions of CO" + }, + { + "self_ref": "#/texts/53", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/54", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", CH", + "text": ", CH" + }, + { + "self_ref": "#/texts/55", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/56", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and N", + "text": "and N" + }, + { + "self_ref": "#/texts/57", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/58", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O by their 100-year global warming potential (GWP", + "text": "O by their 100-year global warming potential (GWP" + }, + { + "self_ref": "#/texts/59", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "100", + "text": "100", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/60", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), based on IPCC assessment report 5 (AR5; [27]). The values of GWP", + "text": "), based on IPCC assessment report 5 (AR5; [27]). The values of GWP" + }, + { + "self_ref": "#/texts/61", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "100", + "text": "100", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/62", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are 1, 28 and 265 for CO", + "text": "are 1, 28 and 265 for CO" + }, + { + "self_ref": "#/texts/63", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/64", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", CH", + "text": ", CH" + }, + { + "self_ref": "#/texts/65", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/66", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and N", + "text": "and N" + }, + { + "self_ref": "#/texts/67", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/68", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O, respectively.", + "text": "O, respectively." + }, + { + "self_ref": "#/texts/69", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/texts/70" + }, + { + "$ref": "#/texts/77" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Feed production", + "text": "Feed production", + "level": 2 + }, + { + "self_ref": "#/texts/70", + "parent": { + "$ref": "#/texts/69" + }, + "children": [ + { + "$ref": "#/groups/5" + }, + { + "$ref": "#/tables/1" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Diets composition", + "text": "Diets composition", + "level": 3 + }, + { + "self_ref": "#/texts/71", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass,", + "text": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass," + }, + { + "self_ref": "#/texts/72", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Pennisetum clandestinum", + "text": "Pennisetum clandestinum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/73", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NE", + "text": "). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NE" + }, + { + "self_ref": "#/texts/74", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "L", + "text": "L", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/75", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.", + "text": ") and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments." + }, + { + "self_ref": "#/texts/76", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Table 2 Dairy cows’ diets in different scenariosa.", + "text": "Table 2 Dairy cows’ diets in different scenariosa." + }, + { + "self_ref": "#/texts/77", + "parent": { + "$ref": "#/texts/69" + }, + "children": [ + { + "$ref": "#/groups/6" + }, + { + "$ref": "#/tables/2" + }, + { + "$ref": "#/groups/7" + }, + { + "$ref": "#/tables/3" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "GHG emissions from crop and pasture production", + "text": "GHG emissions from crop and pasture production", + "level": 3 + }, + { + "self_ref": "#/texts/78", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO", + "text": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO" + }, + { + "self_ref": "#/texts/79", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/80", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e ha", + "text": "e ha" + }, + { + "self_ref": "#/texts/81", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/82", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", respectively [32]. The DM production (kg ha", + "text": ", respectively [32]. The DM production (kg ha" + }, + { + "self_ref": "#/texts/83", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/84", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.", + "text": ") of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing." + }, + { + "self_ref": "#/texts/85", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Table 3 GHG emission factors for Off- and On-farm feed production.", + "text": "Table 3 GHG emission factors for Off- and On-farm feed production." + }, + { + "self_ref": "#/texts/86", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N", + "text": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N" + }, + { + "self_ref": "#/texts/87", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/88", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO", + "text": "O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO" + }, + { + "self_ref": "#/texts/89", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/90", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-C emissions from lime and urea applications, as well as fuel combustion. The direct N", + "text": "-C emissions from lime and urea applications, as well as fuel combustion. The direct N" + }, + { + "self_ref": "#/texts/91", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/92", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emission factor (kg (kg N input)", + "text": "O-N emission factor (kg (kg N input)" + }, + { + "self_ref": "#/texts/93", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/94", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") is based on a local study performed previously [37]. For indirect N", + "text": ") is based on a local study performed previously [37]. For indirect N" + }, + { + "self_ref": "#/texts/95", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/96", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions (kg N", + "text": "O-N emissions (kg N" + }, + { + "self_ref": "#/texts/97", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/98", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N (kg NH", + "text": "O-N (kg NH" + }, + { + "self_ref": "#/texts/99", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "3", + "text": "3", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/100", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-N + NO", + "text": "-N + NO" + }, + { + "self_ref": "#/texts/101", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "x", + "text": "x", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/102", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ")", + "text": ")" + }, + { + "self_ref": "#/texts/103", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/104", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), as well as CO", + "text": "), as well as CO" + }, + { + "self_ref": "#/texts/105", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/106", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha", + "text": "-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha" + }, + { + "self_ref": "#/texts/107", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/108", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha", + "text": "was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha" + }, + { + "self_ref": "#/texts/109", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/110", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "(disking) for annual tropical and temperate pastures, respectively [40]. The CO", + "text": "(disking) for annual tropical and temperate pastures, respectively [40]. The CO" + }, + { + "self_ref": "#/texts/111", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/112", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "from fuel combustion was 2.7 kg CO", + "text": "from fuel combustion was 2.7 kg CO" + }, + { + "self_ref": "#/texts/113", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/114", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "L", + "text": "L" + }, + { + "self_ref": "#/texts/115", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/116", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].", + "text": "[41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42]." + }, + { + "self_ref": "#/texts/117", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Table 4 GHG emissions from On-farm feed production.", + "text": "Table 4 GHG emissions from On-farm feed production." + }, + { + "self_ref": "#/texts/118", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/groups/8" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Animal husbandry", + "text": "Animal husbandry", + "level": 2 + }, + { + "self_ref": "#/texts/119", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The CH", + "text": "The CH" + }, + { + "self_ref": "#/texts/120", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/121", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions from enteric fermentation intensity (g (kg ECM)", + "text": "emissions from enteric fermentation intensity (g (kg ECM)" + }, + { + "self_ref": "#/texts/122", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/123", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") was a function of estimated CH", + "text": ") was a function of estimated CH" + }, + { + "self_ref": "#/texts/124", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/125", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "yield (g (kg DM intake)", + "text": "yield (g (kg DM intake)" + }, + { + "self_ref": "#/texts/126", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/127", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), actual DM intake and ECM. The enteric CH", + "text": "), actual DM intake and ECM. The enteric CH" + }, + { + "self_ref": "#/texts/128", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/129", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH", + "text": "yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH" + }, + { + "self_ref": "#/texts/130", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/131", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "yield (g (kg DM intake)", + "text": "yield (g (kg DM intake)" + }, + { + "self_ref": "#/texts/132", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/133", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") = 13.8 + 0.185 × NDF (% DM intake).", + "text": ") = 13.8 + 0.185 × NDF (% DM intake)." + }, + { + "self_ref": "#/texts/134", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/groups/9" + }, + { + "$ref": "#/texts/158" + }, + { + "$ref": "#/groups/10" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Manure from confined cows and urine and dung from grazing animals", + "text": "Manure from confined cows and urine and dung from grazing animals", + "level": 2 + }, + { + "self_ref": "#/texts/135", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The CH", + "text": "The CH" + }, + { + "self_ref": "#/texts/136", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/137", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emission from manure (kg (kg ECM)", + "text": "emission from manure (kg (kg ECM)" + }, + { + "self_ref": "#/texts/138", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/139", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") was a function of daily CH", + "text": ") was a function of daily CH" + }, + { + "self_ref": "#/texts/140", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/141", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emission from manure (kg cow", + "text": "emission from manure (kg cow" + }, + { + "self_ref": "#/texts/142", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/143", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") and daily ECM (kg cow", + "text": ") and daily ECM (kg cow" + }, + { + "self_ref": "#/texts/144", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/145", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "). The daily CH", + "text": "). The daily CH" + }, + { + "self_ref": "#/texts/146", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/147", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow", + "text": "emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow" + }, + { + "self_ref": "#/texts/148", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/149", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day", + "text": ") in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day" + }, + { + "self_ref": "#/texts/150", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/151", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), NDOMI = non-digestible OM intake (kg day", + "text": "), NDOMI = non-digestible OM intake (kg day" + }, + { + "self_ref": "#/texts/152", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/153", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day", + "text": "): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day" + }, + { + "self_ref": "#/texts/154", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/155", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg", + "text": "), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg" + }, + { + "self_ref": "#/texts/156", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/157", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ").", + "text": ")." + }, + { + "self_ref": "#/texts/158", + "parent": { + "$ref": "#/texts/134" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.", + "text": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively." + }, + { + "self_ref": "#/texts/159", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The N", + "text": "The N" + }, + { + "self_ref": "#/texts/160", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/161", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N", + "text": "O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N" + }, + { + "self_ref": "#/texts/162", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/163", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N", + "text": "O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N" + }, + { + "self_ref": "#/texts/164", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/165", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions to N", + "text": "O-N emissions to N" + }, + { + "self_ref": "#/texts/166", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/167", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions, where N", + "text": "O emissions, where N" + }, + { + "self_ref": "#/texts/168", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/169", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions = N", + "text": "O emissions = N" + }, + { + "self_ref": "#/texts/170", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/171", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions × 44/28. The emission factors were 0.002 kg N", + "text": "O-N emissions × 44/28. The emission factors were 0.002 kg N" + }, + { + "self_ref": "#/texts/172", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/173", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N (kg N)", + "text": "O-N (kg N)" + }, + { + "self_ref": "#/texts/174", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/175", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "stored in a pit below animal confinements, and 0.02 kg N", + "text": "stored in a pit below animal confinements, and 0.02 kg N" + }, + { + "self_ref": "#/texts/176", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/177", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N (kg of urine and dung)", + "text": "O-N (kg of urine and dung)" + }, + { + "self_ref": "#/texts/178", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/179", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "deposited on pasture [38]. The indirect N", + "text": "deposited on pasture [38]. The indirect N" + }, + { + "self_ref": "#/texts/180", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/181", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.", + "text": "O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors." + }, + { + "self_ref": "#/texts/182", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/texts/183" + }, + { + "$ref": "#/tables/4" + }, + { + "$ref": "#/groups/11" + }, + { + "$ref": "#/groups/12" + }, + { + "$ref": "#/groups/13" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Farm management", + "text": "Farm management", + "level": 2 + }, + { + "self_ref": "#/texts/183", + "parent": { + "$ref": "#/texts/182" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.", + "text": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section." + }, + { + "self_ref": "#/texts/184", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Table 5 Factors for major resource inputs in farm management.", + "text": "Table 5 Factors for major resource inputs in farm management." + }, + { + "self_ref": "#/texts/185", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t", + "text": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t" + }, + { + "self_ref": "#/texts/186", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/187", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") [42]. The amount of manure was estimated from OM excretions (kg cow", + "text": ") [42]. The amount of manure was estimated from OM excretions (kg cow" + }, + { + "self_ref": "#/texts/188", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/189", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.", + "text": "), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement." + }, + { + "self_ref": "#/texts/190", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO", + "text": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO" + }, + { + "self_ref": "#/texts/191", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/192", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e L", + "text": "e L" + }, + { + "self_ref": "#/texts/193", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/194", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO", + "text": ") [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO" + }, + { + "self_ref": "#/texts/195", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/196", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e L", + "text": "e L" + }, + { + "self_ref": "#/texts/197", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/198", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)", + "text": ") [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)" + }, + { + "self_ref": "#/texts/199", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/200", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "× 10", + "text": "× 10" + }, + { + "self_ref": "#/texts/201", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "−3", + "text": "−3", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/202", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") [42] × kg CO", + "text": ") [42] × kg CO" + }, + { + "self_ref": "#/texts/203", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/204", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg machinery mass)", + "text": "e (kg machinery mass)" + }, + { + "self_ref": "#/texts/205", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/206", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[42].", + "text": "[42]." + }, + { + "self_ref": "#/texts/207", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO", + "text": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO" + }, + { + "self_ref": "#/texts/208", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/209", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "kWh", + "text": "kWh" + }, + { + "self_ref": "#/texts/210", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/211", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)", + "text": "). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)" + }, + { + "self_ref": "#/texts/212", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/213", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[47]. The annual electricity use for lighting was 75 kWh cow", + "text": "[47]. The annual electricity use for lighting was 75 kWh cow" + }, + { + "self_ref": "#/texts/214", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/215", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", which is the value considered for lactating cows in naturally ventilated barns [47].", + "text": ", which is the value considered for lactating cows in naturally ventilated barns [47]." + }, + { + "self_ref": "#/texts/216", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/groups/14" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Co-product allocation", + "text": "Co-product allocation", + "level": 2 + }, + { + "self_ref": "#/texts/217", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, AR", + "text": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, AR" + }, + { + "self_ref": "#/texts/218", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "milk", + "text": "milk", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/219", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "= 1–6.04 × BMR, where", + "text": "= 1–6.04 × BMR, where" + }, + { + "self_ref": "#/texts/220", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ":", + "text": ":", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/221", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "AR", + "text": "AR" + }, + { + "self_ref": "#/texts/222", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "milk", + "text": "milk", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/223", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The AR", + "text": "is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The AR" + }, + { + "self_ref": "#/texts/224", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "milk", + "text": "milk", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/225", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The AR", + "text": "were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The AR" + }, + { + "self_ref": "#/texts/226", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "milk", + "text": "milk", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/227", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.", + "text": "was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production." + }, + { + "self_ref": "#/texts/228", + "parent": { + "$ref": "#/texts/21" + }, + "children": [ + { + "$ref": "#/texts/229" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Sensitivity analysis", + "text": "Sensitivity analysis", + "level": 2 + }, + { + "self_ref": "#/texts/229", + "parent": { + "$ref": "#/texts/228" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.", + "text": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint." + }, + { + "self_ref": "#/texts/230", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/231" + }, + { + "$ref": "#/texts/232" + }, + { + "$ref": "#/texts/302" + }, + { + "$ref": "#/texts/330" + }, + { + "$ref": "#/texts/377" + }, + { + "$ref": "#/texts/396" + }, + { + "$ref": "#/texts/402" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Results and discussion", + "text": "Results and discussion", + "level": 1 + }, + { + "self_ref": "#/texts/231", + "parent": { + "$ref": "#/texts/230" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.", + "text": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use." + }, + { + "self_ref": "#/texts/232", + "parent": { + "$ref": "#/texts/230" + }, + "children": [ + { + "$ref": "#/groups/15" + }, + { + "$ref": "#/pictures/1" + }, + { + "$ref": "#/groups/16" + }, + { + "$ref": "#/groups/17" + }, + { + "$ref": "#/groups/18" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Greenhouse gas emissions", + "text": "Greenhouse gas emissions", + "level": 2 + }, + { + "self_ref": "#/texts/233", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO", + "text": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO" + }, + { + "self_ref": "#/texts/234", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/235", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/236", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/237", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "(Fig 2C and 2D) in scenarios that included pastures compared to", + "text": "(Fig 2C and 2D) in scenarios that included pastures compared to" + }, + { + "self_ref": "#/texts/238", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/239", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO", + "text": "TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO" + }, + { + "self_ref": "#/texts/240", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/241", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/242", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/243", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO", + "text": "for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO" + }, + { + "self_ref": "#/texts/244", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/245", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/246", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/247", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N", + "text": "for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N" + }, + { + "self_ref": "#/texts/248", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/249", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.", + "text": "O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions." + }, + { + "self_ref": "#/texts/250", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].", + "text": "Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]." + }, + { + "self_ref": "#/texts/251", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Considering IPCC emission factors for N", + "text": "Considering IPCC emission factors for N" + }, + { + "self_ref": "#/texts/252", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/253", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO", + "text": "O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO" + }, + { + "self_ref": "#/texts/254", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/255", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/256", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/257", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO", + "text": ", and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO" + }, + { + "self_ref": "#/texts/258", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/259", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/260", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/261", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". When local emission factors for N", + "text": ". When local emission factors for N" + }, + { + "self_ref": "#/texts/262", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/263", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO", + "text": "O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO" + }, + { + "self_ref": "#/texts/264", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/265", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-C from perennial pasture—0.91 kg CO", + "text": "-C from perennial pasture—0.91 kg CO" + }, + { + "self_ref": "#/texts/266", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/267", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/268", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/269", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO", + "text": "—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO" + }, + { + "self_ref": "#/texts/270", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/271", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/272", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/273", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO", + "text": ". Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO" + }, + { + "self_ref": "#/texts/274", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/275", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/276", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/277", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO", + "text": "[8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO" + }, + { + "self_ref": "#/texts/278", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/279", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.", + "text": "-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems." + }, + { + "self_ref": "#/texts/280", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow", + "text": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow" + }, + { + "self_ref": "#/texts/281", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/282", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "(5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO", + "text": "(5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO" + }, + { + "self_ref": "#/texts/283", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/284", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/285", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/286", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow", + "text": ", which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow" + }, + { + "self_ref": "#/texts/287", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/288", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.", + "text": ". Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access." + }, + { + "self_ref": "#/texts/289", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N", + "text": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N" + }, + { + "self_ref": "#/texts/290", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/291", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to", + "text": "O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to" + }, + { + "self_ref": "#/texts/292", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/293", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N", + "text": "TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N" + }, + { + "self_ref": "#/texts/294", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/295", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N (kg N)", + "text": "O-N (kg N)" + }, + { + "self_ref": "#/texts/296", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/297", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N", + "text": "for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N" + }, + { + "self_ref": "#/texts/298", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/299", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N kg", + "text": "O-N kg" + }, + { + "self_ref": "#/texts/300", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/301", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[37].", + "text": "[37]." + }, + { + "self_ref": "#/texts/302", + "parent": { + "$ref": "#/texts/230" + }, + "children": [ + { + "$ref": "#/groups/19" + }, + { + "$ref": "#/pictures/2" + }, + { + "$ref": "#/groups/20" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Methane emissions", + "text": "Methane emissions", + "level": 2 + }, + { + "self_ref": "#/texts/303", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The enteric CH", + "text": "The enteric CH" + }, + { + "self_ref": "#/texts/304", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/305", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH", + "text": "intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH" + }, + { + "self_ref": "#/texts/306", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/307", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions on the whole C footprint was expected, because the impact of enteric CH", + "text": "emissions on the whole C footprint was expected, because the impact of enteric CH" + }, + { + "self_ref": "#/texts/308", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/309", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO", + "text": "on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO" + }, + { + "self_ref": "#/texts/310", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/311", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO", + "text": "e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO" + }, + { + "self_ref": "#/texts/312", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/313", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e kg", + "text": "e kg" + }, + { + "self_ref": "#/texts/314", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/315", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[59], which did not happen in this study.", + "text": "[59], which did not happen in this study." + }, + { + "self_ref": "#/texts/316", + "parent": { + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].", + "text": "Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]." + }, + { + "self_ref": "#/texts/317", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The lack of difference in enteric CH", + "text": "The lack of difference in enteric CH" + }, + { + "self_ref": "#/texts/318", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/319", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)", + "text": "emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)" + }, + { + "self_ref": "#/texts/320", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/321", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") compared to corn silage (550 g (kg DM)", + "text": ") compared to corn silage (550 g (kg DM)" + }, + { + "self_ref": "#/texts/322", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/323", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH", + "text": "). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH" + }, + { + "self_ref": "#/texts/324", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/325", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH", + "text": "emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH" + }, + { + "self_ref": "#/texts/326", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/327", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH", + "text": "emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH" + }, + { + "self_ref": "#/texts/328", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/329", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].", + "text": "emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26]." + }, + { + "self_ref": "#/texts/330", + "parent": { + "$ref": "#/texts/230" + }, + "children": [ + { + "$ref": "#/groups/21" + }, + { + "$ref": "#/pictures/3" + }, + { + "$ref": "#/groups/22" + }, + { + "$ref": "#/texts/376" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Emissions from excreta and feed production", + "text": "Emissions from excreta and feed production", + "level": 2 + }, + { + "self_ref": "#/texts/331", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Using IPCC emission factors for N", + "text": "Using IPCC emission factors for N" + }, + { + "self_ref": "#/texts/332", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/333", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from urine and dung [38] and those from Table 3, CH", + "text": "O emissions from urine and dung [38] and those from Table 3, CH" + }, + { + "self_ref": "#/texts/334", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/335", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "emissions from manure decreased 0.07 kg CO", + "text": "emissions from manure decreased 0.07 kg CO" + }, + { + "self_ref": "#/texts/336", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/337", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/338", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/339", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", but N", + "text": ", but N" + }, + { + "self_ref": "#/texts/340", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/341", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O emissions from manure increased 0.09 kg CO", + "text": "O emissions from manure increased 0.09 kg CO" + }, + { + "self_ref": "#/texts/342", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/343", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/344", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/345", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", as TMR intake was restricted to 50%", + "text": ", as TMR intake was restricted to 50%" + }, + { + "self_ref": "#/texts/346", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "ad libitum", + "text": "ad libitum", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/347", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "(Fig 4A). Emissions for pastures increased by 0.06 kg CO", + "text": "(Fig 4A). Emissions for pastures increased by 0.06 kg CO" + }, + { + "self_ref": "#/texts/348", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/349", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/350", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/351", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO", + "text": ", whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO" + }, + { + "self_ref": "#/texts/352", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/353", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/354", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/355", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N", + "text": ", as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N" + }, + { + "self_ref": "#/texts/356", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/357", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions from manure with pasture was a consequence of higher N", + "text": "O-N emissions from manure with pasture was a consequence of higher N" + }, + { + "self_ref": "#/texts/358", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/359", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)", + "text": "O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)" + }, + { + "self_ref": "#/texts/360", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/361", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)", + "text": ", N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)" + }, + { + "self_ref": "#/texts/362", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/363", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "on TMR system to 198 g (kg DM)", + "text": "on TMR system to 198 g (kg DM)" + }, + { + "self_ref": "#/texts/364", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/365", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", "prov": [], - "orig": "Introduction", - "text": "Introduction", - "level": 1 + "orig": "on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].", + "text": "on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65]." }, { - "self_ref": "#/texts/6", + "self_ref": "#/texts/366", "parent": { - "$ref": "#/texts/5" + "$ref": "#/body" + }, + "children": [], + "content_layer": "body", + "label": "caption", + "prov": [], + "orig": "Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.", + "text": "Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture." + }, + { + "self_ref": "#/texts/367", + "parent": { + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].", - "text": "Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4]." + "orig": "Using local emission factors for N", + "text": "Using local emission factors for N" }, { - "self_ref": "#/texts/7", + "self_ref": "#/texts/368", "parent": { - "$ref": "#/texts/5" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.", - "text": "Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/8", + "self_ref": "#/texts/369", "parent": { - "$ref": "#/texts/5" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]).", - "text": "In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19])." + "orig": "O emissions from urine and dung [37] and those from Table 4, reductions in CH", + "text": "O emissions from urine and dung [37] and those from Table 4, reductions in CH" }, { - "self_ref": "#/texts/9", + "self_ref": "#/texts/370", "parent": { - "$ref": "#/texts/5" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.", - "text": "It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake." + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/10", + "self_ref": "#/texts/371", "parent": { - "$ref": "#/texts/5" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated.", - "text": "The aim of this work was to quantify the C footprint and land use of dairy systems using cows with a medium milk production potential in a subtropical region. The effect of replacing total mixed ration (TMR) with pastures during lactation periods was evaluated." + "orig": "emissions from stocked manure, when pastures were included on diets, did not offset by increases in N", + "text": "emissions from stocked manure, when pastures were included on diets, did not offset by increases in N" }, { - "self_ref": "#/texts/11", + "self_ref": "#/texts/372", "parent": { - "$ref": "#/texts/0" + "$ref": "#/groups/22" }, - "children": [ - { - "$ref": "#/texts/12" - }, - { - "$ref": "#/texts/13" - }, - { - "$ref": "#/texts/16" - }, - { - "$ref": "#/texts/20" - }, - { - "$ref": "#/texts/23" - }, - { - "$ref": "#/texts/25" - }, - { - "$ref": "#/texts/34" - }, - { - "$ref": "#/texts/36" - }, - { - "$ref": "#/texts/40" - }, - { - "$ref": "#/texts/46" - }, - { - "$ref": "#/texts/48" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Materials and methods", - "text": "Materials and methods", - "level": 1 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/12", + "self_ref": "#/texts/373", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua.", - "text": "An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua." + "orig": "O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N", + "text": "O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N" }, { - "self_ref": "#/texts/13", + "self_ref": "#/texts/374", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/22" }, - "children": [ - { - "$ref": "#/texts/14" - }, - { - "$ref": "#/pictures/0" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "System boundary", - "text": "System boundary", - "level": 2 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/14", + "self_ref": "#/texts/375", "parent": { - "$ref": "#/texts/13" + "$ref": "#/groups/22" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.", - "text": "The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary." + "orig": "O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.", + "text": "O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region." }, { - "self_ref": "#/texts/15", + "self_ref": "#/texts/376", "parent": { - "$ref": "#/body" + "$ref": "#/texts/330" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 1 Overview of the milk production system boundary considered in the study.", - "text": "Fig 1 Overview of the milk production system boundary considered in the study." + "orig": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].", + "text": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9]." }, { - "self_ref": "#/texts/16", + "self_ref": "#/texts/377", "parent": { - "$ref": "#/texts/11" + "$ref": "#/texts/230" }, "children": [ { - "$ref": "#/texts/17" - }, - { - "$ref": "#/texts/18" + "$ref": "#/texts/378" }, { - "$ref": "#/tables/0" + "$ref": "#/groups/23" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Functional unit", - "text": "Functional unit", + "orig": "Farm management", + "text": "Farm management", "level": 2 }, { - "self_ref": "#/texts/17", + "self_ref": "#/texts/378", "parent": { - "$ref": "#/texts/16" + "$ref": "#/texts/377" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:", - "text": "The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:" + "orig": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.", + "text": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint." }, { - "self_ref": "#/texts/18", + "self_ref": "#/texts/379", "parent": { - "$ref": "#/texts/16" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.", - "text": "ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1." + "orig": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO", + "text": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO" }, { - "self_ref": "#/texts/19", + "self_ref": "#/texts/380", "parent": { - "$ref": "#/body" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Table 1 Descriptive characteristics of the herd.", - "text": "Table 1 Descriptive characteristics of the herd." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/20", + "self_ref": "#/texts/381", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/23" }, - "children": [ - { - "$ref": "#/texts/21" - }, - { - "$ref": "#/texts/22" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Data sources and livestock system description", - "text": "Data sources and livestock system description", - "level": 2 + "orig": "e kWh", + "text": "e kWh" }, { - "self_ref": "#/texts/21", + "self_ref": "#/texts/382", "parent": { - "$ref": "#/texts/20" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group.", - "text": "The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/22", + "self_ref": "#/texts/383", "parent": { - "$ref": "#/texts/20" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.", - "text": "Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period." + "orig": "[46]) is much lower than that in a LCA study conducted in US (0.73 kg CO", + "text": "[46]) is much lower than that in a LCA study conducted in US (0.73 kg CO" }, { - "self_ref": "#/texts/23", + "self_ref": "#/texts/384", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/23" }, - "children": [ - { - "$ref": "#/texts/24" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Impact assessment", - "text": "Impact assessment", - "level": 2 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/24", + "self_ref": "#/texts/385", "parent": { - "$ref": "#/texts/23" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively.", - "text": "The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively." + "orig": "e kWh", + "text": "e kWh" }, { - "self_ref": "#/texts/25", + "self_ref": "#/texts/386", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/23" }, - "children": [ - { - "$ref": "#/texts/26" - }, - { - "$ref": "#/texts/29" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Feed production", - "text": "Feed production", - "level": 2 + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/26", + "self_ref": "#/texts/387", "parent": { - "$ref": "#/texts/25" + "$ref": "#/groups/23" }, - "children": [ - { - "$ref": "#/texts/27" - }, - { - "$ref": "#/tables/1" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Diets composition", - "text": "Diets composition", - "level": 3 + "orig": "[42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO", + "text": "[42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO" }, { - "self_ref": "#/texts/27", + "self_ref": "#/texts/388", "parent": { - "$ref": "#/texts/26" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.", - "text": "The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/28", + "self_ref": "#/texts/389", "parent": { - "$ref": "#/body" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Table 2 Dairy cows’ diets in different scenariosa.", - "text": "Table 2 Dairy cows’ diets in different scenariosa." + "orig": "e kWh", + "text": "e kWh" }, { - "self_ref": "#/texts/29", + "self_ref": "#/texts/390", "parent": { - "$ref": "#/texts/25" + "$ref": "#/groups/23" }, - "children": [ - { - "$ref": "#/texts/30" - }, - { - "$ref": "#/tables/2" - }, - { - "$ref": "#/texts/32" - }, - { - "$ref": "#/tables/3" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "GHG emissions from crop and pasture production", - "text": "GHG emissions from crop and pasture production", - "level": 3 + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/30", + "self_ref": "#/texts/391", "parent": { - "$ref": "#/texts/29" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.", - "text": "GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing." + "orig": "against 0.382 and 0.926 kg CO", + "text": "against 0.382 and 0.926 kg CO" }, { - "self_ref": "#/texts/31", + "self_ref": "#/texts/392", "parent": { - "$ref": "#/body" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Table 3 GHG emission factors for Off- and On-farm feed production.", - "text": "Table 3 GHG emission factors for Off- and On-farm feed production." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/32", + "self_ref": "#/texts/393", "parent": { - "$ref": "#/texts/29" + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e kWh", + "text": "e kWh" + }, + { + "self_ref": "#/texts/394", + "parent": { + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].", - "text": "Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42]." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/33", + "self_ref": "#/texts/395", "parent": { - "$ref": "#/body" + "$ref": "#/groups/23" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Table 4 GHG emissions from On-farm feed production.", - "text": "Table 4 GHG emissions from On-farm feed production." + "orig": "produced by natural gas and hard coal, respectively [46].", + "text": "produced by natural gas and hard coal, respectively [46]." }, { - "self_ref": "#/texts/34", + "self_ref": "#/texts/396", "parent": { - "$ref": "#/texts/11" + "$ref": "#/texts/230" }, "children": [ { - "$ref": "#/texts/35" + "$ref": "#/groups/24" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Animal husbandry", - "text": "Animal husbandry", + "orig": "Assumptions and limitations", + "text": "Assumptions and limitations", "level": 2 }, { - "self_ref": "#/texts/35", + "self_ref": "#/texts/397", "parent": { - "$ref": "#/texts/34" + "$ref": "#/groups/24" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).", - "text": "The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake)." + "orig": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N", + "text": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N" }, { - "self_ref": "#/texts/36", + "self_ref": "#/texts/398", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/24" }, - "children": [ - { - "$ref": "#/texts/37" - }, - { - "$ref": "#/texts/38" - }, - { - "$ref": "#/texts/39" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Manure from confined cows and urine and dung from grazing animals", - "text": "Manure from confined cows and urine and dung from grazing animals", - "level": 2 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/37", + "self_ref": "#/texts/399", "parent": { - "$ref": "#/texts/36" + "$ref": "#/groups/24" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).", - "text": "The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1)." + "orig": "O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO", + "text": "O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO" }, { - "self_ref": "#/texts/38", + "self_ref": "#/texts/400", "parent": { - "$ref": "#/texts/36" + "$ref": "#/groups/24" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.", - "text": "The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/39", + "self_ref": "#/texts/401", "parent": { - "$ref": "#/texts/36" + "$ref": "#/groups/24" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.", - "text": "The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors." + "orig": "emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.", + "text": "emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management." }, { - "self_ref": "#/texts/40", + "self_ref": "#/texts/402", "parent": { - "$ref": "#/texts/11" + "$ref": "#/texts/230" }, "children": [ { - "$ref": "#/texts/41" - }, - { - "$ref": "#/tables/4" - }, - { - "$ref": "#/texts/43" - }, - { - "$ref": "#/texts/44" + "$ref": "#/groups/25" }, { - "$ref": "#/texts/45" + "$ref": "#/groups/26" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Farm management", - "text": "Farm management", + "orig": "Further considerations", + "text": "Further considerations", "level": 2 }, { - "self_ref": "#/texts/41", + "self_ref": "#/texts/403", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.", - "text": "Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section." + "orig": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO", + "text": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO" }, { - "self_ref": "#/texts/42", + "self_ref": "#/texts/404", "parent": { - "$ref": "#/body" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Table 5 Factors for major resource inputs in farm management.", - "text": "Table 5 Factors for major resource inputs in farm management." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/43", + "self_ref": "#/texts/405", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.", - "text": "The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement." + "orig": "e (kg ECM)", + "text": "e (kg ECM)" }, { - "self_ref": "#/texts/44", + "self_ref": "#/texts/406", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].", - "text": "The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42]." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/45", + "self_ref": "#/texts/407", "parent": { - "$ref": "#/texts/40" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].", - "text": "Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47]." + "orig": ", whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO", + "text": ", whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO" }, { - "self_ref": "#/texts/46", + "self_ref": "#/texts/408", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/25" }, - "children": [ - { - "$ref": "#/texts/47" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Co-product allocation", - "text": "Co-product allocation", - "level": 2 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/47", + "self_ref": "#/texts/409", "parent": { - "$ref": "#/texts/46" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.", - "text": "The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production." + "orig": "e (kg ECM)", + "text": "e (kg ECM)" }, { - "self_ref": "#/texts/48", + "self_ref": "#/texts/410", "parent": { - "$ref": "#/texts/11" + "$ref": "#/groups/25" }, - "children": [ - { - "$ref": "#/texts/49" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Sensitivity analysis", - "text": "Sensitivity analysis", - "level": 2 + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/49", + "self_ref": "#/texts/411", "parent": { - "$ref": "#/texts/48" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.", - "text": "A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint." + "orig": ". Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO", + "text": ". Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO" }, { - "self_ref": "#/texts/50", + "self_ref": "#/texts/412", "parent": { - "$ref": "#/texts/0" + "$ref": "#/groups/25" }, - "children": [ - { - "$ref": "#/texts/51" - }, - { - "$ref": "#/texts/52" - }, - { - "$ref": "#/texts/58" - }, - { - "$ref": "#/texts/62" - }, - { - "$ref": "#/texts/67" - }, - { - "$ref": "#/texts/70" - }, - { - "$ref": "#/texts/72" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Results and discussion", - "text": "Results and discussion", - "level": 1 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/51", + "self_ref": "#/texts/413", "parent": { - "$ref": "#/texts/50" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use.", - "text": "The study has assessed the impact of tropical and temperate pastures in dairy cows fed TMR on the C footprint of dairy production in subtropics. Different factors were taken in to consideration to estimate emissions from manure (or urine and dung) of grazing animals, feed production and electricity use." + "orig": "e (kg ECM)", + "text": "e (kg ECM)" }, { - "self_ref": "#/texts/52", + "self_ref": "#/texts/414", "parent": { - "$ref": "#/texts/50" + "$ref": "#/groups/25" }, - "children": [ - { - "$ref": "#/texts/53" - }, - { - "$ref": "#/pictures/1" - }, - { - "$ref": "#/texts/55" - }, - { - "$ref": "#/texts/56" - }, - { - "$ref": "#/texts/57" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Greenhouse gas emissions", - "text": "Greenhouse gas emissions", - "level": 2 + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/53", + "self_ref": "#/texts/415", "parent": { - "$ref": "#/texts/52" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.", - "text": "Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions." + "orig": "in subclinical ketosis [71] and by 0.02 kg CO", + "text": "in subclinical ketosis [71] and by 0.02 kg CO" }, { - "self_ref": "#/texts/54", + "self_ref": "#/texts/416", "parent": { - "$ref": "#/body" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].", - "text": "Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/55", + "self_ref": "#/texts/417", "parent": { - "$ref": "#/texts/52" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.", - "text": "Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems." + "orig": "e (kg ECM)", + "text": "e (kg ECM)" }, { - "self_ref": "#/texts/56", + "self_ref": "#/texts/418", "parent": { - "$ref": "#/texts/52" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.", - "text": "The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/57", + "self_ref": "#/texts/419", "parent": { - "$ref": "#/texts/52" + "$ref": "#/groups/25" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].", - "text": "The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37]." + "orig": "in case of foot lesions [72].", + "text": "in case of foot lesions [72]." }, { - "self_ref": "#/texts/58", + "self_ref": "#/texts/420", "parent": { - "$ref": "#/texts/50" + "$ref": "#/groups/26" }, - "children": [ - { - "$ref": "#/texts/59" - }, - { - "$ref": "#/pictures/2" - }, - { - "$ref": "#/texts/61" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Methane emissions", - "text": "Methane emissions", - "level": 2 + "orig": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha", + "text": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha" }, { - "self_ref": "#/texts/59", + "self_ref": "#/texts/421", "parent": { - "$ref": "#/texts/58" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.", - "text": "The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/60", + "self_ref": "#/texts/422", "parent": { - "$ref": "#/body" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].", - "text": "Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]." + "orig": "[75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha", + "text": "[75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha" }, { - "self_ref": "#/texts/61", + "self_ref": "#/texts/423", "parent": { - "$ref": "#/texts/58" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].", - "text": "The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26]." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/62", + "self_ref": "#/texts/424", "parent": { - "$ref": "#/texts/50" + "$ref": "#/groups/26" }, - "children": [ - { - "$ref": "#/texts/63" - }, - { - "$ref": "#/pictures/3" - }, - { - "$ref": "#/texts/65" - }, - { - "$ref": "#/texts/66" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Emissions from excreta and feed production", - "text": "Emissions from excreta and feed production", - "level": 2 + "orig": "would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO", + "text": "would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO" }, { - "self_ref": "#/texts/63", + "self_ref": "#/texts/425", "parent": { - "$ref": "#/texts/62" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].", - "text": "Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65]." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/64", + "self_ref": "#/texts/426", "parent": { - "$ref": "#/body" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", - "label": "caption", + "label": "text", "prov": [], - "orig": "Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.", - "text": "Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture." + "orig": "e (kg ECM)", + "text": "e (kg ECM)" }, { - "self_ref": "#/texts/65", + "self_ref": "#/texts/427", "parent": { - "$ref": "#/texts/62" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.", - "text": "Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/66", + "self_ref": "#/texts/428", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO", + "text": "for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO" + }, + { + "self_ref": "#/texts/429", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } + }, + { + "self_ref": "#/texts/430", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e (kg ECM)", + "text": "e (kg ECM)" + }, + { + "self_ref": "#/texts/431", "parent": { - "$ref": "#/texts/62" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].", - "text": "Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9]." + "orig": "-1", + "text": "-1", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } }, { - "self_ref": "#/texts/67", + "self_ref": "#/texts/432", "parent": { - "$ref": "#/texts/50" + "$ref": "#/groups/26" }, - "children": [ - { - "$ref": "#/texts/68" - }, - { - "$ref": "#/texts/69" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Farm management", - "text": "Farm management", - "level": 2 + "orig": "for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO", + "text": "for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO" }, { - "self_ref": "#/texts/68", + "self_ref": "#/texts/433", "parent": { - "$ref": "#/texts/67" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.", - "text": "The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/69", + "self_ref": "#/texts/434", "parent": { - "$ref": "#/texts/67" + "$ref": "#/groups/26" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].", - "text": "Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46]." + "orig": "would be negligible [76].", + "text": "would be negligible [76]." }, { - "self_ref": "#/texts/70", + "self_ref": "#/texts/435", "parent": { - "$ref": "#/texts/50" + "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/71" + "$ref": "#/groups/27" } ], "content_layer": "body", "label": "section_header", "prov": [], - "orig": "Assumptions and limitations", - "text": "Assumptions and limitations", - "level": 2 + "orig": "Conclusions", + "text": "Conclusions", + "level": 1 }, { - "self_ref": "#/texts/71", + "self_ref": "#/texts/436", "parent": { - "$ref": "#/texts/70" + "$ref": "#/groups/27" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management.", - "text": "The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management." + "orig": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N", + "text": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N" }, { - "self_ref": "#/texts/72", + "self_ref": "#/texts/437", "parent": { - "$ref": "#/texts/50" + "$ref": "#/groups/27" }, - "children": [ - { - "$ref": "#/texts/73" - }, - { - "$ref": "#/texts/74" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Further considerations", - "text": "Further considerations", - "level": 2 + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/73", + "self_ref": "#/texts/438", "parent": { - "$ref": "#/texts/72" + "$ref": "#/groups/27" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].", - "text": "The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72]." + "orig": "O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH", + "text": "O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH" }, { - "self_ref": "#/texts/74", + "self_ref": "#/texts/439", "parent": { - "$ref": "#/texts/72" + "$ref": "#/groups/27" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].", - "text": "Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76]." + "orig": "4", + "text": "4", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/75", + "self_ref": "#/texts/440", "parent": { - "$ref": "#/texts/0" + "$ref": "#/groups/27" }, - "children": [ - { - "$ref": "#/texts/76" - } - ], + "children": [], "content_layer": "body", - "label": "section_header", + "label": "text", "prov": [], - "orig": "Conclusions", - "text": "Conclusions", - "level": 1 + "orig": "intensity was the largest source of CO", + "text": "intensity was the largest source of CO" }, { - "self_ref": "#/texts/76", + "self_ref": "#/texts/441", "parent": { - "$ref": "#/texts/75" + "$ref": "#/groups/27" }, "children": [], "content_layer": "body", "label": "text", "prov": [], - "orig": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.", - "text": "This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures." + "orig": "2", + "text": "2", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "sub" + } }, { - "self_ref": "#/texts/77", + "self_ref": "#/texts/442", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures.", + "text": "e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures." + }, + { + "self_ref": "#/texts/443", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/texts/78" + "$ref": "#/texts/444" } ], "content_layer": "body", @@ -1517,9 +8680,9 @@ "level": 1 }, { - "self_ref": "#/texts/78", + "self_ref": "#/texts/444", "parent": { - "$ref": "#/texts/77" + "$ref": "#/texts/443" }, "children": [], "content_layer": "body", @@ -1529,13 +8692,13 @@ "text": "Thanks to Anna Naranjo for helpful comments throughout the elaboration of this manuscript, and to André Thaler Neto and Roberto Kappes for providing the key characteristics of the herd considered in this study." }, { - "self_ref": "#/texts/79", + "self_ref": "#/texts/445", "parent": { "$ref": "#/texts/0" }, "children": [ { - "$ref": "#/groups/0" + "$ref": "#/groups/28" } ], "content_layer": "body", @@ -1546,9 +8709,9 @@ "level": 1 }, { - "self_ref": "#/texts/80", + "self_ref": "#/texts/446", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1560,9 +8723,9 @@ "marker": "" }, { - "self_ref": "#/texts/81", + "self_ref": "#/texts/447", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1574,9 +8737,9 @@ "marker": "" }, { - "self_ref": "#/texts/82", + "self_ref": "#/texts/448", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1588,9 +8751,9 @@ "marker": "" }, { - "self_ref": "#/texts/83", + "self_ref": "#/texts/449", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1602,9 +8765,9 @@ "marker": "" }, { - "self_ref": "#/texts/84", + "self_ref": "#/texts/450", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1616,9 +8779,9 @@ "marker": "" }, { - "self_ref": "#/texts/85", + "self_ref": "#/texts/451", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1630,9 +8793,9 @@ "marker": "" }, { - "self_ref": "#/texts/86", + "self_ref": "#/texts/452", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1644,9 +8807,9 @@ "marker": "" }, { - "self_ref": "#/texts/87", + "self_ref": "#/texts/453", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1658,9 +8821,9 @@ "marker": "" }, { - "self_ref": "#/texts/88", + "self_ref": "#/texts/454", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1672,9 +8835,9 @@ "marker": "" }, { - "self_ref": "#/texts/89", + "self_ref": "#/texts/455", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1686,9 +8849,9 @@ "marker": "" }, { - "self_ref": "#/texts/90", + "self_ref": "#/texts/456", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1700,9 +8863,9 @@ "marker": "" }, { - "self_ref": "#/texts/91", + "self_ref": "#/texts/457", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1714,9 +8877,9 @@ "marker": "" }, { - "self_ref": "#/texts/92", + "self_ref": "#/texts/458", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1728,9 +8891,9 @@ "marker": "" }, { - "self_ref": "#/texts/93", + "self_ref": "#/texts/459", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1742,9 +8905,9 @@ "marker": "" }, { - "self_ref": "#/texts/94", + "self_ref": "#/texts/460", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1756,9 +8919,9 @@ "marker": "" }, { - "self_ref": "#/texts/95", + "self_ref": "#/texts/461", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1770,9 +8933,9 @@ "marker": "" }, { - "self_ref": "#/texts/96", + "self_ref": "#/texts/462", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1784,9 +8947,9 @@ "marker": "" }, { - "self_ref": "#/texts/97", + "self_ref": "#/texts/463", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1798,9 +8961,9 @@ "marker": "" }, { - "self_ref": "#/texts/98", + "self_ref": "#/texts/464", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1812,9 +8975,9 @@ "marker": "" }, { - "self_ref": "#/texts/99", + "self_ref": "#/texts/465", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1826,9 +8989,9 @@ "marker": "" }, { - "self_ref": "#/texts/100", + "self_ref": "#/texts/466", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1840,9 +9003,9 @@ "marker": "" }, { - "self_ref": "#/texts/101", + "self_ref": "#/texts/467", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1854,9 +9017,9 @@ "marker": "" }, { - "self_ref": "#/texts/102", + "self_ref": "#/texts/468", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1868,9 +9031,9 @@ "marker": "" }, { - "self_ref": "#/texts/103", + "self_ref": "#/texts/469", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1882,9 +9045,9 @@ "marker": "" }, { - "self_ref": "#/texts/104", + "self_ref": "#/texts/470", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1896,9 +9059,9 @@ "marker": "" }, { - "self_ref": "#/texts/105", + "self_ref": "#/texts/471", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1910,9 +9073,9 @@ "marker": "" }, { - "self_ref": "#/texts/106", + "self_ref": "#/texts/472", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1924,9 +9087,9 @@ "marker": "" }, { - "self_ref": "#/texts/107", + "self_ref": "#/texts/473", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1938,9 +9101,9 @@ "marker": "" }, { - "self_ref": "#/texts/108", + "self_ref": "#/texts/474", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1952,9 +9115,9 @@ "marker": "" }, { - "self_ref": "#/texts/109", + "self_ref": "#/texts/475", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1966,9 +9129,9 @@ "marker": "" }, { - "self_ref": "#/texts/110", + "self_ref": "#/texts/476", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1980,9 +9143,9 @@ "marker": "" }, { - "self_ref": "#/texts/111", + "self_ref": "#/texts/477", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -1994,9 +9157,9 @@ "marker": "" }, { - "self_ref": "#/texts/112", + "self_ref": "#/texts/478", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2008,9 +9171,9 @@ "marker": "" }, { - "self_ref": "#/texts/113", + "self_ref": "#/texts/479", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2022,9 +9185,9 @@ "marker": "" }, { - "self_ref": "#/texts/114", + "self_ref": "#/texts/480", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2036,9 +9199,9 @@ "marker": "" }, { - "self_ref": "#/texts/115", + "self_ref": "#/texts/481", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2050,9 +9213,9 @@ "marker": "" }, { - "self_ref": "#/texts/116", + "self_ref": "#/texts/482", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2064,9 +9227,9 @@ "marker": "" }, { - "self_ref": "#/texts/117", + "self_ref": "#/texts/483", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2078,9 +9241,9 @@ "marker": "" }, { - "self_ref": "#/texts/118", + "self_ref": "#/texts/484", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2092,9 +9255,9 @@ "marker": "" }, { - "self_ref": "#/texts/119", + "self_ref": "#/texts/485", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2106,9 +9269,9 @@ "marker": "" }, { - "self_ref": "#/texts/120", + "self_ref": "#/texts/486", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2120,9 +9283,9 @@ "marker": "" }, { - "self_ref": "#/texts/121", + "self_ref": "#/texts/487", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2134,9 +9297,9 @@ "marker": "" }, { - "self_ref": "#/texts/122", + "self_ref": "#/texts/488", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2148,9 +9311,9 @@ "marker": "" }, { - "self_ref": "#/texts/123", + "self_ref": "#/texts/489", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2162,9 +9325,9 @@ "marker": "" }, { - "self_ref": "#/texts/124", + "self_ref": "#/texts/490", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2176,9 +9339,9 @@ "marker": "" }, { - "self_ref": "#/texts/125", + "self_ref": "#/texts/491", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2190,9 +9353,9 @@ "marker": "" }, { - "self_ref": "#/texts/126", + "self_ref": "#/texts/492", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2204,9 +9367,9 @@ "marker": "" }, { - "self_ref": "#/texts/127", + "self_ref": "#/texts/493", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2218,9 +9381,9 @@ "marker": "" }, { - "self_ref": "#/texts/128", + "self_ref": "#/texts/494", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2232,9 +9395,9 @@ "marker": "" }, { - "self_ref": "#/texts/129", + "self_ref": "#/texts/495", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2246,9 +9409,9 @@ "marker": "" }, { - "self_ref": "#/texts/130", + "self_ref": "#/texts/496", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2260,9 +9423,9 @@ "marker": "" }, { - "self_ref": "#/texts/131", + "self_ref": "#/texts/497", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2274,9 +9437,9 @@ "marker": "" }, { - "self_ref": "#/texts/132", + "self_ref": "#/texts/498", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2288,9 +9451,9 @@ "marker": "" }, { - "self_ref": "#/texts/133", + "self_ref": "#/texts/499", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2302,9 +9465,9 @@ "marker": "" }, { - "self_ref": "#/texts/134", + "self_ref": "#/texts/500", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2316,9 +9479,9 @@ "marker": "" }, { - "self_ref": "#/texts/135", + "self_ref": "#/texts/501", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2330,9 +9493,9 @@ "marker": "" }, { - "self_ref": "#/texts/136", + "self_ref": "#/texts/502", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2344,9 +9507,9 @@ "marker": "" }, { - "self_ref": "#/texts/137", + "self_ref": "#/texts/503", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2358,9 +9521,9 @@ "marker": "" }, { - "self_ref": "#/texts/138", + "self_ref": "#/texts/504", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2372,9 +9535,9 @@ "marker": "" }, { - "self_ref": "#/texts/139", + "self_ref": "#/texts/505", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2386,9 +9549,9 @@ "marker": "" }, { - "self_ref": "#/texts/140", + "self_ref": "#/texts/506", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2400,9 +9563,9 @@ "marker": "" }, { - "self_ref": "#/texts/141", + "self_ref": "#/texts/507", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2414,9 +9577,9 @@ "marker": "" }, { - "self_ref": "#/texts/142", + "self_ref": "#/texts/508", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2428,9 +9591,9 @@ "marker": "" }, { - "self_ref": "#/texts/143", + "self_ref": "#/texts/509", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2442,9 +9605,9 @@ "marker": "" }, { - "self_ref": "#/texts/144", + "self_ref": "#/texts/510", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2456,9 +9619,9 @@ "marker": "" }, { - "self_ref": "#/texts/145", + "self_ref": "#/texts/511", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2470,9 +9633,9 @@ "marker": "" }, { - "self_ref": "#/texts/146", + "self_ref": "#/texts/512", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2484,9 +9647,9 @@ "marker": "" }, { - "self_ref": "#/texts/147", + "self_ref": "#/texts/513", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2498,9 +9661,9 @@ "marker": "" }, { - "self_ref": "#/texts/148", + "self_ref": "#/texts/514", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2512,9 +9675,9 @@ "marker": "" }, { - "self_ref": "#/texts/149", + "self_ref": "#/texts/515", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2526,9 +9689,9 @@ "marker": "" }, { - "self_ref": "#/texts/150", + "self_ref": "#/texts/516", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2540,9 +9703,9 @@ "marker": "" }, { - "self_ref": "#/texts/151", + "self_ref": "#/texts/517", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2554,9 +9717,9 @@ "marker": "" }, { - "self_ref": "#/texts/152", + "self_ref": "#/texts/518", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2568,9 +9731,9 @@ "marker": "" }, { - "self_ref": "#/texts/153", + "self_ref": "#/texts/519", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2582,9 +9745,9 @@ "marker": "" }, { - "self_ref": "#/texts/154", + "self_ref": "#/texts/520", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2596,9 +9759,9 @@ "marker": "" }, { - "self_ref": "#/texts/155", + "self_ref": "#/texts/521", "parent": { - "$ref": "#/groups/0" + "$ref": "#/groups/28" }, "children": [], "content_layer": "body", @@ -2614,7 +9777,7 @@ { "self_ref": "#/pictures/0", "parent": { - "$ref": "#/texts/13" + "$ref": "#/texts/23" }, "children": [], "content_layer": "body", @@ -2622,7 +9785,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/15" + "$ref": "#/texts/25" } ], "references": [], @@ -2632,7 +9795,7 @@ { "self_ref": "#/pictures/1", "parent": { - "$ref": "#/texts/52" + "$ref": "#/texts/232" }, "children": [], "content_layer": "body", @@ -2640,7 +9803,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/54" + "$ref": "#/texts/250" } ], "references": [], @@ -2650,7 +9813,7 @@ { "self_ref": "#/pictures/2", "parent": { - "$ref": "#/texts/58" + "$ref": "#/texts/302" }, "children": [], "content_layer": "body", @@ -2658,7 +9821,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/60" + "$ref": "#/texts/316" } ], "references": [], @@ -2668,7 +9831,7 @@ { "self_ref": "#/pictures/3", "parent": { - "$ref": "#/texts/62" + "$ref": "#/texts/330" }, "children": [], "content_layer": "body", @@ -2676,7 +9839,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/64" + "$ref": "#/texts/366" } ], "references": [], @@ -2688,7 +9851,7 @@ { "self_ref": "#/tables/0", "parent": { - "$ref": "#/texts/16" + "$ref": "#/texts/26" }, "children": [], "content_layer": "body", @@ -2696,7 +9859,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/19" + "$ref": "#/texts/29" } ], "references": [], @@ -3755,7 +10918,7 @@ { "self_ref": "#/tables/1", "parent": { - "$ref": "#/texts/26" + "$ref": "#/texts/70" }, "children": [], "content_layer": "body", @@ -3763,7 +10926,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/28" + "$ref": "#/texts/76" } ], "references": [], @@ -9362,7 +16525,7 @@ { "self_ref": "#/tables/2", "parent": { - "$ref": "#/texts/29" + "$ref": "#/texts/77" }, "children": [], "content_layer": "body", @@ -9370,7 +16533,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/31" + "$ref": "#/texts/85" } ], "references": [], @@ -10577,7 +17740,7 @@ { "self_ref": "#/tables/3", "parent": { - "$ref": "#/texts/29" + "$ref": "#/texts/77" }, "children": [], "content_layer": "body", @@ -10585,7 +17748,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/33" + "$ref": "#/texts/117" } ], "references": [], @@ -14300,7 +21463,7 @@ { "self_ref": "#/tables/4", "parent": { - "$ref": "#/texts/40" + "$ref": "#/texts/182" }, "children": [], "content_layer": "body", @@ -14308,7 +21471,7 @@ "prov": [], "captions": [ { - "$ref": "#/texts/42" + "$ref": "#/texts/184" } ], "references": [], diff --git a/tests/data/jats/groundtruth/pone.0234687.nxml.md b/tests/data/jats/groundtruth/pone.0234687.nxml.md index 80314e319e..7d9f965810 100644 --- a/tests/data/jats/groundtruth/pone.0234687.nxml.md +++ b/tests/data/jats/groundtruth/pone.0234687.nxml.md @@ -10,9 +10,9 @@ Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equiva ## Introduction -Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4]. +Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO 2 equivalents (CO 2 e) yr -1 , with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4]. -Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint. +Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH 4 ) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH 4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint. In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]). @@ -57,19 +57,19 @@ Table 1 Descriptive characteristics of the herd. ### Data sources and livestock system description -The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of ad libitum TMR intake with access to grazing a tropical pasture (pearl-millet, Pennisetum glaucum ‘Campeiro’) compared to cows receiving ad libitum TMR intake. Cows grazing on a temperate pasture (ryegrass, Lolium multiflorum ‘Maximus’) did not need changes to ECM production compared to the ad libitum TMR intake group. +The individual feed requirements, as well as the milk production responses based on feed strategies were based on data recorded from the herd described above and two experiments performed using lactating cows from the same herd. Due to the variation on herbage production throughout the year, feed requirements were estimated taking into consideration that livestock systems have a calving period in April, which represents the beginning of fall season in the southern Hemisphere. The experiments have shown a 10% reduction in ECM production in dairy cows that received both 75 and 50% of *ad libitum* TMR intake with access to grazing a tropical pasture (pearl-millet, *Pennisetum glaucum* ‘Campeiro’) compared to cows receiving *ad libitum* TMR intake. Cows grazing on a temperate pasture (ryegrass, *Lolium multiflorum* ‘Maximus’) did not need changes to ECM production compared to the *ad libitum* TMR intake group. -Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period. +Using experimental data, three scenarios were evaluated during the lactation period: *ad libitum* TMR intake, and 75, and 50% of *ad libitum* TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., *in press* ). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period. ### Impact assessment -The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively. +The CO 2 e emissions were calculated by multiplying the emissions of CO 2 , CH 4 and N 2 O by their 100-year global warming potential (GWP 100 ), based on IPCC assessment report 5 (AR5; [27]). The values of GWP 100 are 1, 28 and 265 for CO 2 , CH 4 and N 2 O, respectively. ### Feed production #### Diets composition -The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments. +The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, *Pennisetum clandestinum* ). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NE L ) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments. Table 2 Dairy cows’ diets in different scenariosa. @@ -98,7 +98,7 @@ Table 2 Dairy cows’ diets in different scenariosa. #### GHG emissions from crop and pasture production -GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing. +GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO 2 e ha -1 , respectively [32]. The DM production (kg ha -1 ) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing. Table 3 GHG emission factors for Off- and On-farm feed production. @@ -113,7 +113,7 @@ Table 3 GHG emission factors for Off- and On-farm feed production. | Pearl milletd | 11,000 | 0.195 | kg CO2e (kg DM)-1 | [32,35] | | Kikuyu grasse | 9,500 | 0.226 | kg CO2e (kg DM)-1 | [32,36] | -Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42]. +Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N 2 O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO 2 -C emissions from lime and urea applications, as well as fuel combustion. The direct N 2 O-N emission factor (kg (kg N input) -1 ) is based on a local study performed previously [37]. For indirect N 2 O-N emissions (kg N 2 O-N (kg NH 3 -N + NO x ) -1 ), as well as CO 2 -C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha -1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha -1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO 2 from fuel combustion was 2.7 kg CO 2 L -1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42]. Table 4 GHG emissions from On-farm feed production. @@ -149,15 +149,15 @@ Table 4 GHG emissions from On-farm feed production. ### Animal husbandry -The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake). +The CH 4 emissions from enteric fermentation intensity (g (kg ECM) -1 ) was a function of estimated CH 4 yield (g (kg DM intake) -1 ), actual DM intake and ECM. The enteric CH 4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH 4 yield (g (kg DM intake) -1 ) = 13.8 + 0.185 × NDF (% DM intake). ### Manure from confined cows and urine and dung from grazing animals -The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1). +The CH 4 emission from manure (kg (kg ECM) -1 ) was a function of daily CH 4 emission from manure (kg cow -1 ) and daily ECM (kg cow -1 ). The daily CH 4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow -1 ) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day -1 ), NDOMI = non-digestible OM intake (kg day -1 ): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day -1 ), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg -1 ). The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively. -The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors. +The N 2 O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N 2 O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N 2 O-N emissions to N 2 O emissions, where N 2 O emissions = N 2 O-N emissions × 44/28. The emission factors were 0.002 kg N 2 O-N (kg N) -1 stored in a pit below animal confinements, and 0.02 kg N 2 O-N (kg of urine and dung) -1 deposited on pasture [38]. The indirect N 2 O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors. ### Farm management @@ -179,15 +179,15 @@ Table 5 Factors for major resource inputs in farm management. | Electricity for milking | 0.06 | kWh (kg milk)-1 | [47] | | Electricity for lightingd | 75 | kWh cow-1 | [47] | -The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement. +The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t -1 ) [42]. The amount of manure was estimated from OM excretions (kg cow -1 ), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement. -The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42]. +The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO 2 e L -1 ) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO 2 e L -1 ) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure) -1 × 10 −3 ) [42] × kg CO 2 e (kg machinery mass) -1 [42]. -Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47]. +Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO 2 kWh -1 ). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced) -1 [47]. The annual electricity use for lighting was 75 kWh cow -1 , which is the value considered for lactating cows in naturally ventilated barns [47]. ### Co-product allocation -The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production. +The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, AR milk = 1–6.04 × BMR, where : AR milk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The AR milk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The AR milk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production. ### Sensitivity analysis @@ -199,37 +199,37 @@ The study has assessed the impact of tropical and temperate pastures in dairy co ### Greenhouse gas emissions -Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions. +Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO 2 e (kg ECM) -1 (Fig 2C and 2D) in scenarios that included pastures compared to *ad libitum* TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO 2 e (kg ECM) -1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO 2 e (kg ECM) -1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N 2 O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions. Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. -Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems. +Considering IPCC emission factors for N 2 O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO 2 e (kg ECM) -1 , and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO 2 e (kg ECM) -1 . When local emission factors for N 2 O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO 2 -C from perennial pasture—0.91 kg CO 2 e (kg ECM) -1 —was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO 2 e (kg ECM) -1 . Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO 2 e (kg ECM) -1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO 2 -C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems. -The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access. +The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow -1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO 2 e (kg ECM) -1 , which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow -1 . Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access. -The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37]. +The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N 2 O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to *ad libitum* TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N 2 O-N (kg N) -1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N 2 O-N kg -1 [37]. ### Methane emissions -The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study. +The enteric CH 4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH 4 emissions on the whole C footprint was expected, because the impact of enteric CH 4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO 2 e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO 2 e kg -1 [59], which did not happen in this study. Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. -The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26]. +The lack of difference in enteric CH 4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM) -1 ) compared to corn silage (550 g (kg DM) -1 ). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH 4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH 4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH 4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26]. ### Emissions from excreta and feed production -Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65]. +Using IPCC emission factors for N 2 O emissions from urine and dung [38] and those from Table 3, CH 4 emissions from manure decreased 0.07 kg CO 2 e (kg ECM) -1 , but N 2 O emissions from manure increased 0.09 kg CO 2 e (kg ECM) -1 , as TMR intake was restricted to 50% *ad libitum* (Fig 4A). Emissions for pastures increased by 0.06 kg CO 2 e (kg ECM) -1 , whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO 2 e (kg ECM) -1 , as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N 2 O-N emissions from manure with pasture was a consequence of higher N 2 O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM) -1 , N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM) -1 on TMR system to 198 g (kg DM) -1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65]. Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture. -Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region. +Using local emission factors for N 2 O emissions from urine and dung [37] and those from Table 4, reductions in CH 4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N 2 O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N 2 O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region. Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9]. @@ -237,21 +237,21 @@ Emissions for feed production decreased when pasture was included due to the gre The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint. -Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46]. +Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO 2 e kWh -1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO 2 e kWh -1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO 2 e kWh -1 against 0.382 and 0.926 kg CO 2 e kWh -1 produced by natural gas and hard coal, respectively [46]. ### Assumptions and limitations -The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N2O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management. +The milk production and composition data are the average for a typical herd, which might have great animal-to-animal variability. Likewise, DM yield of crops and pastures were collected from experimental observations, and may change as a function of inter-annual variation, climatic conditions, soil type, fertilization level etc. The emission factors for direct and indirect N 2 O emissions from urine and dung were alternatively estimated using local data, but more experiments are necessary to reduce the uncertainty. The CO 2 emitted from lime and urea application was estimated from IPCC default values, which may not represent emissions in subtropical conditions. This LCA may be improved by reducing the uncertainty of factors for estimating emissions from excreta and feed production, including the C sequestration or emissions as a function of soil management. ### Further considerations -The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72]. +The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO 2 e (kg ECM) -1 , whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO 2 e (kg ECM) -1 . Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO 2 e (kg ECM) -1 in subclinical ketosis [71] and by 0.02 kg CO 2 e (kg ECM) -1 in case of foot lesions [72]. -Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76]. +Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha -1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha -1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO 2 e (kg ECM) -1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO 2 e (kg ECM) -1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO 2 would be negligible [76]. ## Conclusions -This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N2O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH4 intensity was the largest source of CO2e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures. +This study assessed the C footprint of dairy cattle systems with or without access to pastures. Including pastures showed potential to maintain or decrease to a small extent the C footprint, which may be attributable to the evidence of low N 2 O emissions from urine and dung in dairy systems in subtropical areas. Even though the enteric CH 4 intensity was the largest source of CO 2 e emissions, it did not change between different scenarios due to the narrow range of NDF content in diets and maintaining the same milk production with or without access to pastures. ## Acknowledgments diff --git a/tests/data/jats/groundtruth/ptag100.xml.itxt b/tests/data/jats/groundtruth/ptag100.xml.itxt new file mode 100644 index 0000000000..1ba0dbcbe2 --- /dev/null +++ b/tests/data/jats/groundtruth/ptag100.xml.itxt @@ -0,0 +1,688 @@ +item-0 at level 0: unspecified: group _root_ + item-1 at level 1: title: Time-Dependent 3D Oscillator wit ... for Analyzing Quark–Antiquark Systems + item-2 at level 2: paragraph: Jeong Ryeol Choi, Salim Medjber, Salah Menouar, Ramazan Sever + item-3 at level 2: section_header: Abstract + item-4 at level 3: text: In this work, the dynamics of qu ... its connection to the Higgs mechanism. + item-5 at level 2: section_header: 1. + item-6 at level 3: text: Quark–antiquark pairs are a key ... arches and dark matter theories [4–7]. + item-7 at level 3: text: In light of this, it is essentia ... functions in their own right [15–19]. + item-8 at level 3: text: To overcome this limitation, the ... the help of the Schrödinger equation. + item-9 at level 3: text: Regarding coupling of potentials ... effects and nonequilibrium processes. + item-10 at level 3: text: The main contributions of the pr ... verned by time-dependent Hamiltonians. + item-11 at level 2: section_header: 2. + item-12 at level 3: section_header: 2.1. + item-13 at level 4: section_header: 2.1.1. + item-14 at level 5: inline: group group + item-15 at level 6: text: We consider a quark–antiquark pa ... time-dependent effective reduced mass + item-16 at level 6: formula: \mu (t) + item-17 at level 6: text: in spherical coordinates, governed by a central potential + item-18 at level 6: formula: g(t)r^{2}-\frac{Z(t)}{r} + item-19 at level 6: text: where + item-20 at level 6: formula: g(t) + item-21 at level 6: text: and + item-22 at level 6: formula: Z(t) + item-23 at level 6: text: are coefficients that depend on time. While + item-24 at level 6: formula: g(t)r^{2} + item-25 at level 6: text: represents a confining trap potential with adjustable strength, + item-26 at level 6: formula: -\frac{Z(t)}{r} + item-27 at level 6: text: is a Coulomb perturbation with the constraint + item-28 at level 6: formula: Z(t)\gt 0 + item-29 at level 6: text: . As the distance between quarks ... force between the quark and antiquark. + item-30 at level 5: text: Time-dependent Hamiltonians natu ... s within a quantum-mechanical setting. + item-31 at level 5: text: Based on the above description, ... of the pair of quarks is expressed as: + item-32 at level 5: formula: \begin{eqnarray} +H(t)=\frac{p^{2 ... )r^{2}-\frac{Z(t)}{r} . +\end{eqnarray} + item-33 at level 5: inline: group group + item-34 at level 6: text: This Hamiltonian enables the cal ... pherical coordinates is represented as + item-35 at level 6: formula: p^{2}=p_{r}^{2}+\frac{L^{2}}{r^{2}} + item-36 at level 6: text: , where + item-37 at level 6: formula: p_{r}=-i\hbar (\frac{\partial }{\partial r}+\frac{1}{r}) + item-38 at level 6: text: and + item-39 at level 6: text: L + item-40 at level 6: text: is the total angular momentum, the formula for which is given by + item-41 at level 5: formula: \begin{eqnarray} +L^{2}=-\hbar ^{ ... \theta }\bigg )\bigg ]. +\end{eqnarray} + item-42 at level 5: inline: group group + item-43 at level 6: text: To investigate the dynamical fea ... we denote the system’s state vector as + item-44 at level 6: formula: |\Psi (t)\rangle + item-45 at level 6: text: , the corresponding Schrödinger equation is of the form + item-46 at level 5: formula: \begin{eqnarray} +i\hbar \frac{\p ... =H(t)|\Psi (t)\rangle . +\end{eqnarray} + item-47 at level 5: text: The time dependence of the Hamil ... on, as will be presented subsequently. + item-48 at level 4: section_header: 2.1.2. + item-49 at level 5: inline: group group + item-50 at level 6: text: Because + item-51 at level 6: formula: H(t) + item-52 at level 6: text: in Eq. (1) is represented in ter ... rms of a nontrivial invariant operator + item-53 at level 6: formula: I(t) + item-54 at level 6: text: rather than + item-55 at level 6: formula: H(t) + item-56 at level 6: text: in Eq. (1), where + item-57 at level 6: formula: I(t) + item-58 at level 6: text: does not involve time-derivative operators. Moreover, + item-59 at level 6: formula: I(t) + item-60 at level 6: text: is closely related to the concep ... the quantum–classical correspondence. + item-61 at level 5: inline: group group + item-62 at level 6: text: By the definition of the invariant operator, + item-63 at level 6: formula: I(t) + item-64 at level 6: text: satisfies the equation: + item-65 at level 5: formula: \begin{eqnarray} +\frac{dI}{dt}=\ ... rac{1}{i\hbar }[I,H]=0. +\end{eqnarray} + item-66 at level 5: inline: group group + item-67 at level 6: text: If such an invariant exists, it ... tion (3) in terms of its eigenfunction + item-68 at level 6: formula: \Phi (\vec{r},t) + item-69 at level 6: text: , such that + item-70 at level 5: formula: \begin{eqnarray} +\Psi (\vec{r},t ... a (t)}\Phi (\vec{r},t), +\end{eqnarray} + item-71 at level 5: inline: group group + item-72 at level 6: text: where + item-73 at level 6: formula: \alpha (t) + item-74 at level 6: text: is a phase function that can be obtained from + item-75 at level 5: formula: \begin{eqnarray} +\hbar \frac{d\a ... ht|\Phi \right\rangle . +\end{eqnarray} + item-76 at level 5: inline: group group + item-77 at level 6: text: To find the expression of the invariant + item-78 at level 6: formula: I(t) + item-79 at level 6: text: utilizing Eq. (4), we set + item-80 at level 5: formula: \begin{eqnarray} +I(t)=A(t)p^{2}+ ... t)r^{2}-\frac{D(t)}{r}, +\end{eqnarray} + item-81 at level 5: inline: group group + item-82 at level 6: text: where + item-83 at level 6: formula: A(t) + item-84 at level 6: text: , + item-85 at level 6: formula: B(t) + item-86 at level 6: text: , + item-87 at level 6: formula: C(t) + item-88 at level 6: text: , and + item-89 at level 6: formula: D(t) + item-90 at level 6: text: are time-dependent functions to ... procedure with this assumed formula of + item-91 at level 6: formula: I(t) + item-92 at level 6: text: after substituting Eqs. (1) and (7) into Eq. (4) yields + item-93 at level 5: formula: \begin{eqnarray} +A(t) =A_{0}\rho ^{2}(t), +\end{eqnarray} + item-94 at level 5: formula: \begin{eqnarray} +B(t) =-A_{0}\mu (t)\rho (t)\dot{\rho }(t), +\end{eqnarray} + item-95 at level 5: formula: \begin{eqnarray} +C(t) =A_{0}\big ... {4\rho ^{2}(t)}\bigg ), +\end{eqnarray} + item-96 at level 5: formula: \begin{eqnarray} +D(t) =2A_{0}\mu (t)Z(t)\rho ^{2}(t), +\end{eqnarray} + item-97 at level 5: inline: group group + item-98 at level 6: text: where + item-99 at level 6: formula: A_{0} + item-100 at level 6: text: is an arbitrary real constant and + item-101 at level 6: formula: \rho (t) + item-102 at level 6: text: is a solution of the following equation + item-103 at level 5: formula: \begin{eqnarray} +\ddot{\rho }(t) ... u ^{2}(t)\rho ^{3}(t)}, +\end{eqnarray} + item-104 at level 5: inline: group group + item-105 at level 6: text: with + item-106 at level 6: formula: \Omega + item-107 at level 6: text: being a real constant. The above ... iant is valid under the condition that + item-108 at level 6: formula: Z(t) + item-109 at level 6: text: follows the relation + item-110 at level 5: formula: \begin{eqnarray} +\dot{Z}(t)+\big ... \rho (t)}\bigg )Z(t)=0. +\end{eqnarray} + item-111 at level 5: text: Equation (7) with Eqs. (8)–(11) ... l methodology for obtaining solutions. + item-112 at level 4: section_header: 2.1.3. + item-113 at level 5: inline: group group + item-114 at level 6: text: Because the quantum wave functio ... nted in terms of the eigenfunctions of + item-115 at level 6: formula: I(t) + item-116 at level 6: text: , it is now necessary to evaluat ... e eigenvalue equation of the invariant + item-117 at level 6: formula: I(t) + item-118 at level 6: text: as + item-119 at level 5: formula: \begin{eqnarray} +I(t)\Phi _{n}(\ ... n}\Phi _{n}(\vec{r},t), +\end{eqnarray} + item-120 at level 5: inline: group group + item-121 at level 6: text: where + item-122 at level 6: formula: \Lambda _{n} + item-123 at level 6: text: are the eigenvalues and + item-124 at level 6: formula: \Phi _{n}(\vec{r},t) + item-125 at level 6: text: are time-dependent eigenfunctions. Given that + item-126 at level 6: formula: I(t) + item-127 at level 6: text: , as defined in Eq. (7) along wi ... r a unitary transformation of the form + item-128 at level 5: formula: \begin{eqnarray} +\Phi _{n}^{\pri ... t)\Phi _{n}(\vec{r},t), +\end{eqnarray} + item-129 at level 5: inline: group group + item-130 at level 6: text: where + item-131 at level 6: formula: U(t) + item-132 at level 6: text: is a time-dependent unitary operator given by + item-133 at level 5: formula: \begin{eqnarray} +U(t)=\exp \left ... \rho (t)}r^{2}\right] . +\end{eqnarray} + item-134 at level 5: inline: group group + item-135 at level 6: text: Then, if we write the transformed invariant operator as + item-136 at level 6: formula: I_0 + item-137 at level 6: text: , the eigenvalue equation in the ... ormed frame is represented in terms of + item-138 at level 6: formula: \Phi _{n}^{\prime }(\vec{r}) + item-139 at level 6: text: , such that + item-140 at level 5: formula: \begin{eqnarray} +UIU^{-1}\Phi _{ ... {n}^{\prime }(\vec{r}). +\end{eqnarray} + item-141 at level 5: text: Now, from a mathematical procedure using Eq. (16), we obtain that + item-142 at level 5: formula: \begin{eqnarray} +\left[ A_{0}p^{ ... }(r,\theta ,\varphi ), +\end{eqnarray} + item-143 at level 5: inline: group group + item-144 at level 6: text: where + item-145 at level 6: formula: D_{0} = 2A_{0}\mu (t)Z(t)\rho (t) + item-146 at level 6: text: while + item-147 at level 6: formula: n=0,1,2,\cdots + item-148 at level 6: text: . An infinite number of bound st ... the way, the direct differentiation of + item-149 at level 6: formula: D_0 + item-150 at level 6: text: with respect to + item-151 at level 6: text: t + item-152 at level 6: text: , followed by the application of Eq. (13), yields + item-153 at level 6: formula: d D_0/dt=0 + item-154 at level 6: text: . This means that + item-155 at level 6: formula: D_0 + item-156 at level 6: text: is in fact a constant of motion, ... rmed version of the original invariant + item-157 at level 6: formula: I(t) + item-158 at level 6: text: . It is worth noting that Eq. (1 ... alue equation represented in Eq. (14). + item-159 at level 5: text: The existence of a perturbation ... alue equation (18) can be rewritten as + item-160 at level 5: formula: \begin{eqnarray} +\begin{bmatrix} ... }(r,\theta ,\varphi ). +\end{eqnarray} + item-161 at level 5: inline: group group + item-162 at level 6: text: According to the invariant opera ... med system are represented in terms of + item-163 at level 6: formula: \Phi _{nlm}^{\prime }(r,\theta ,\varphi ) + item-164 at level 6: text: . Because this equation is indep ... to solve it. Considering this, we take + item-165 at level 5: formula: \begin{eqnarray} +\Phi _{nlm}^{\p ... (\theta ,\varphi )}{r}, +\end{eqnarray} + item-166 at level 5: inline: group group + item-167 at level 6: text: where we have introduced + item-168 at level 6: formula: u_{nl}(r) = r R_{nl}(r) + item-169 at level 6: text: , whereas + item-170 at level 6: formula: Y_{lm}(\theta ,\varphi ) + item-171 at level 6: text: are the spherical harmonics. The ... t of the eigenfunctions takes the form + item-172 at level 5: formula: \begin{eqnarray} +Y_{lm}(\theta , ... _{l}^{m}(\cos \theta ), +\end{eqnarray} + item-173 at level 5: inline: group group + item-174 at level 6: text: where + item-175 at level 6: formula: P_{l}^{m}(x) + item-176 at level 6: text: are the associated Legendre poly ... spherical normalization constants are + item-177 at level 6: formula: N_{lm}=(-1)^m \lbrace [(2l+1)/(4\pi )](l-m)!/(l+m)! \rbrace ^{1/2} + item-178 at level 6: text: . The allowed orbital and magnetic quantum numbers are + item-179 at level 6: formula: l=0,1,2,\cdots + item-180 at level 6: text: and + item-181 at level 6: formula: m=-l,-l+1,\cdots ,l + item-182 at level 6: text: , respectively. + item-183 at level 5: text: Some algebraic manipulation, aft ... eads to the following radial equation: + item-184 at level 5: formula: \begin{eqnarray} +\frac{d^{2}u_{n ... 2}}\right) u_{nl}(r)=0. +\end{eqnarray} + item-185 at level 5: inline: group group + item-186 at level 6: text: The equation is analytically intractable unless + item-187 at level 6: formula: \Omega = 0 + item-188 at level 6: text: owing to the coexistence of + item-189 at level 6: formula: r^{2} + item-190 at level 6: text: and + item-191 at level 6: formula: -\frac{1}{r^{2}} + item-192 at level 6: text: terms. To resolve this difficult ... proximation scheme of the form [36–39] + item-193 at level 5: formula: \begin{eqnarray} +\frac{1}{r^{2}} ... lta }{1-e^{-\delta r}}, +\end{eqnarray} + item-194 at level 5: inline: group group + item-195 at level 6: text: together with a coordinate transformation + item-196 at level 6: formula: y=e^{-\delta r} + item-197 at level 6: text: . This type of approximation is ... creened potential, where the parameter + item-198 at level 6: formula: \delta + item-199 at level 6: text: is typically chosen to be equal ... n our system is not screened, choosing + item-200 at level 6: formula: \delta + item-201 at level 6: text: to be similar to the typical scr ... istances comparable to or smaller than + item-202 at level 6: formula: 1/\delta + item-203 at level 6: text: . For example, in quarkonium models, + item-204 at level 6: formula: \delta + item-205 at level 6: text: is usually taken to be in the ra ... 00 to 900 MeV [40,41]. Thus, selecting + item-206 at level 6: formula: \delta + item-207 at level 6: text: within this range is a reasonable choice. + item-208 at level 5: inline: group group + item-209 at level 6: text: Then, by rewriting the notation as + item-210 at level 6: formula: u_{nl}(r)\rightarrow U_{nl}(y) + item-211 at level 6: text: , the differential equation (22) becomes + item-212 at level 5: formula: \begin{eqnarray} +\frac{d^{2}U_{n ... d{bmatrix} U_{nl}(y)=0, +\end{eqnarray} + item-213 at level 5: text: where + item-214 at level 5: formula: \begin{eqnarray} +\varepsilon _{n ... ta },~~~\gamma =l(l+1). +\end{eqnarray} + item-215 at level 5: inline: group group + item-216 at level 6: text: In the representation of Eq. (24 ... ed higher-order terms corresponding to + item-217 at level 6: formula: y^{3} + item-218 at level 6: text: and + item-219 at level 6: formula: y^{4} + item-220 at level 6: text: . To solve this equation, we use ... [29] outlined in the next subsection. + item-221 at level 3: section_header: 2.2. + item-222 at level 4: text: A second-order differential equa ... consider the following equation [31]: + item-223 at level 4: formula: \begin{eqnarray} +\left[ \frac{d^ ... {2}}\right] \phi (s)=0, +\end{eqnarray} + item-224 at level 4: inline: group group + item-225 at level 5: text: where + item-226 at level 5: formula: \alpha _{i} + item-227 at level 5: formula: (i=1,2,3) + item-228 at level 5: text: and + item-229 at level 5: formula: \zeta _{i} + item-230 at level 5: text: are parameters appropriately cho ... this equation has two singularities at + item-231 at level 5: formula: s\rightarrow 0 + item-232 at level 5: text: and + item-233 at level 5: formula: s\rightarrow 1/\alpha _{3} + item-234 at level 5: text: , it is possible to take the solution in the form + item-235 at level 4: formula: \begin{eqnarray} +\phi (s)=s^{\la ... _{3}s)^{\upsilon }f(s). +\end{eqnarray} + item-236 at level 4: text: By substituting this formula int ...  (26) along with the following choice, + item-237 at level 4: formula: \begin{eqnarray} +\lambda =\frac{ ... ta _{3}]^{1/2}\rbrace , +\end{eqnarray} + item-238 at level 4: formula: \begin{eqnarray} +\upsilon &=&\fr ... a _{3})]^{1/2}\rbrace , +\end{eqnarray} + item-239 at level 4: text: we have [29] + item-240 at level 4: formula: \begin{eqnarray} +s(1-\alpha _{3} ... pa _+ \kappa _- f(s)=0, +\end{eqnarray} + item-241 at level 4: text: where + item-242 at level 4: formula: \begin{eqnarray} +\kappa = \alpha ... pha _{3}+\alpha _{2})s, +\end{eqnarray} + item-243 at level 4: formula: \begin{eqnarray} +\kappa _\pm = \ ... {3}^{2}} \bigg ]^{1/2}. +\end{eqnarray} + item-244 at level 4: text: Thus, the equation is reduced to a tractable form. + item-245 at level 4: inline: group group + item-246 at level 5: text: We now examine a simple case where + item-247 at level 5: formula: \alpha _3=1 + item-248 at level 5: text: , which meets the equation assoc ... rincipal quantum number takes the form + item-249 at level 4: formula: \begin{eqnarray} +n=- \kappa _- , +\end{eqnarray} + item-250 at level 4: inline: group group + item-251 at level 5: text: the function + item-252 at level 5: formula: f(s) + item-253 at level 5: text: is represented by the Gauss hypergeometric function as [29] + item-254 at level 4: formula: \begin{eqnarray} +f (s) = {_{2}F_{1}}(a,b;c;s), +\end{eqnarray} + item-255 at level 4: inline: group group + item-256 at level 5: text: where + item-257 at level 5: formula: a=\kappa _- + item-258 at level 5: text: , + item-259 at level 5: formula: b=\kappa _+ + item-260 at level 5: text: , and + item-261 at level 5: formula: c=\alpha _{1}+2\lambda + item-262 at level 5: text: . The mathematical formula of this hypergeometric function is as follows: + item-263 at level 5: formula: _{2}F_{1}(a,b;c;s)=\sum _{k=0}^{ ... ht) _{k}}{\left( c\right) _{k}k!}s^{k} + item-264 at level 5: text: , where + item-265 at level 5: formula: \left( a\right) _{k}=a(a+1)(a+2)\cdots (a+k-1) + item-266 at level 5: text: for + item-267 at level 5: formula: k\ge 1 + item-268 at level 5: text: and + item-269 at level 5: formula: \left( a\right) _{0}=1 + item-270 at level 5: text: . + item-271 at level 4: inline: group group + item-272 at level 5: text: The Gauss hypergeometric function + item-273 at level 5: formula: _{2}F_{1}(a,b;c;s) + item-274 at level 5: text: serves as a fundamental and unif ... n Eq. (34) with Eq. (33), the function + item-275 at level 5: formula: _{2}F_{1}(a,b;c;s) + item-276 at level 5: text: terminates and reduces to a polynomial when + item-277 at level 5: text: a + item-278 at level 5: text: is zero or a negative integer, a ... h Laguerre polynomials. In particular, + item-279 at level 5: formula: _{2}F_{1}(a,b;c;s) + item-280 at level 5: text: is a generalization of Kummer’s confluent hypergeometric function + item-281 at level 5: formula: _{1}F_{1}(a;c;s) + item-282 at level 5: text: . Hence, in the limiting case where + item-283 at level 5: formula: b \rightarrow \infty + item-284 at level 5: text: , + item-285 at level 5: formula: _{2}F_{1}(a,b;c;s) + item-286 at level 5: text: converges to + item-287 at level 5: formula: _{1}F_{1}(a;c;s) + item-288 at level 5: text: . This convergence is rooted in the fact that, while + item-289 at level 5: formula: {}_2F_1 + item-290 at level 5: text: has two regular singular points, ... rges the singularities, giving rise to + item-291 at level 5: formula: {}_1F_1 + item-292 at level 5: text: [44]. Additionally, the Jacobi p ... see formula 15.9.1 of Ref. [45]). The + item-293 at level 5: formula: {}_2F_1 + item-294 at level 5: text: function thus plays a major role ... turally leads to the appearance of the + item-295 at level 5: formula: {}_2F_1 + item-296 at level 5: text: function as a solution, is appli ... rse, well suited to handling Eq. (24). + item-297 at level 3: section_header: 2.3. + item-298 at level 4: section_header: 2.3.1. + item-299 at level 5: inline: group group + item-300 at level 6: text: We now proceed to derive the wav ... are described by the eigenfunctions of + item-301 at level 6: formula: I(t) + item-302 at level 6: text: (instead of + item-303 at level 6: formula: H(t) + item-304 at level 6: text: ), we need to determine the eigenfunctions + item-305 at level 6: formula: \Phi _n + item-306 at level 6: text: of Eq. (14). However, directly solving for + item-307 at level 6: formula: \Phi _n + item-308 at level 6: text: is likely to be a formidable tas ... mvent this difficulty, we first derive + item-309 at level 6: formula: \Phi _n^{\prime } + item-310 at level 6: text: from Eq. (17), and then reconstruct the desired eigenfunctions + item-311 at level 6: formula: \Phi _n + item-312 at level 6: text: by inversely transforming the obtained + item-313 at level 6: formula: \Phi _n^{\prime } + item-314 at level 6: text: , utilizing the inverse relation ... e Eq. (46), which appears later). Once + item-315 at level 6: formula: \Phi _n + item-316 at level 6: text: are obtained, the wave functions + item-317 at level 6: formula: \Psi _n + item-318 at level 6: text: can be expressed in terms of them accordingly. + item-319 at level 5: inline: group group + item-320 at level 6: text: To obtain + item-321 at level 6: formula: \Phi _n^{\prime } + item-322 at level 6: text: , we need to solve Eq. (24) by s ... tions, in accordance with Eq. (27), as + item-323 at level 5: formula: \begin{eqnarray} +U_{nl}(y)=N_{nl ... )^{\upsilon }f_{nl}(y), +\end{eqnarray} + item-324 at level 5: inline: group group + item-325 at level 6: text: where + item-326 at level 6: formula: N_{nl} + item-327 at level 6: text: are normalization constants for the final wave functions. We applied + item-328 at level 6: formula: \alpha _{3}=1 + item-329 at level 6: text: in this equation. In addition, f ... ire the following parameter relations: + item-330 at level 5: formula: \begin{eqnarray} +&&\alpha _{1}=\ ... {nl}-P+Q+\gamma .~~~~~~ +\end{eqnarray} + item-331 at level 5: text: Furthermore, the singularity exponents reduce to + item-332 at level 5: formula: \begin{eqnarray} +\lambda =\sqrt{ ... \right) ^{2}-3P}.~~~~~~ +\end{eqnarray} + item-333 at level 5: inline: group group + item-334 at level 6: text: Although the original NUFA representations of + item-335 at level 6: formula: \lambda + item-336 at level 6: text: and + item-337 at level 6: formula: \upsilon + item-338 at level 6: text: , given in Eqs. (28) and (29), include both positive and negative signs ( + item-339 at level 6: formula: \pm + item-340 at level 6: text: ) from a purely mathematical per ... finite throughout the entire range of + item-341 at level 6: text: r + item-342 at level 6: text: and must vanish as + item-343 at level 6: formula: r \rightarrow 0 + item-344 at level 6: text: and + item-345 at level 6: formula: r \rightarrow \infty + item-346 at level 6: text: . + item-347 at level 5: text: The expansion of Eq. (33) under the first condition in Eq. (36) yields + item-348 at level 5: formula: \begin{eqnarray} +n+\lambda +\upsilon = \sqrt{\zeta _1}. +\end{eqnarray} + item-349 at level 5: inline: group group + item-350 at level 6: text: By squaring both sides of this e ... Eq. (36) together with the formula of + item-351 at level 6: formula: \lambda + item-352 at level 6: text: given in Eq. (37), the equation  ... ith the eigenvalues can be obtained as + item-353 at level 5: formula: \begin{eqnarray} +2[\varepsilon _ ... n )^{2}+5P+Q+\gamma =0. +\end{eqnarray} + item-354 at level 5: inline: group group + item-355 at level 6: text: It is possible to rearrange this equation into a formula for + item-356 at level 6: formula: \varepsilon _{nl} + item-357 at level 6: text: by first isolating the square ro ... sides. Substituting the expression for + item-358 at level 6: formula: \upsilon + item-359 at level 6: text: from Eq. (37) into the resulting ... 5), we finally obtain the eigenvalues: + item-360 at level 5: formula: \begin{eqnarray} +\Lambda _{nl} & ... \upsilon }\right] ^{2}. +\end{eqnarray} + item-361 at level 5: text: The eigenvalues obtained in Eq.  ... parameter-dependent quantum mechanics. + item-362 at level 5: inline: group group + item-363 at level 6: text: In addition, by considering Eq.  ... e functions can be derived in terms of + item-364 at level 6: formula: \Lambda _{nl} + item-365 at level 6: text: to be + item-366 at level 5: formula: \begin{eqnarray} +U_{nl}(y) &=& N ... {a},\bar{b};\bar{c};y), +\end{eqnarray} + item-367 at level 5: text: where the parameters are defined as + item-368 at level 5: formula: \begin{eqnarray} +\bar{a}=\lambda ... {\varepsilon _{nl}-6P}, +\end{eqnarray} + item-369 at level 5: formula: \begin{eqnarray} +\bar{b}=\lambda ... {\varepsilon _{nl}-6P}, +\end{eqnarray} + item-370 at level 5: formula: \begin{eqnarray} +\bar{c}=1+2\lambda . +\end{eqnarray} + item-371 at level 5: inline: group group + item-372 at level 6: text: From a mathematical standpoint, it is possible to define the parameter + item-373 at level 6: formula: \bar{a} + item-374 at level 6: text: interchangeably with + item-375 at level 6: formula: \bar{b} + item-376 at level 6: text: . However, by choosing + item-377 at level 6: formula: \bar{a} + item-378 at level 6: text: as in Eq. (42), we allow the possibility for + item-379 at level 6: formula: \bar{a} + item-380 at level 6: text: to take on negative values. When + item-381 at level 6: formula: \bar{a} + item-382 at level 6: text: is a nonpositive integer (i.e. a ... nction in Eq. (41) terminates at the ( + item-383 at level 6: formula: n+1 + item-384 at level 6: text: )th term and reduces to a polyno ... genfunctions of the invariant operator + item-385 at level 6: formula: I_{0} + item-386 at level 6: text: are represented in the form + item-387 at level 5: formula: \begin{eqnarray} +\Phi _{nlm}^{\p ... {lm}(\theta ,\varphi ). +\end{eqnarray} + item-388 at level 5: inline: group group + item-389 at level 6: text: The complete eigenfunctions of the invariant + item-390 at level 6: text: I + item-391 at level 6: text: that belongs to the original sys ... rmed system, are given by the relation + item-392 at level 5: formula: \begin{eqnarray} +\Phi _{nlm}(\ve ... lm}^{\prime }(\vec{r}). +\end{eqnarray} + item-393 at level 5: text: The evaluation of this using Eqs. (16) and (45) results in + item-394 at level 5: formula: \begin{eqnarray} +\Phi _{nlm}(r,\ ... {lm}(\theta ,\varphi ). +\end{eqnarray} + item-395 at level 5: inline: group group + item-396 at level 6: text: Meanwhile, + item-397 at level 6: formula: N_{nl} + item-398 at level 6: text: are derived by normalizing these ... Appendix A for a detailed evaluation) + item-399 at level 5: formula: \begin{eqnarray} +N_{nl}=\sqrt{\d ... rime }) \Bigg ]^{-1/2}, +\end{eqnarray} + item-400 at level 5: text: where + item-401 at level 5: formula: \begin{eqnarray} +\mathcal {N}_{n ... \lambda ,2\upsilon +1), +\end{eqnarray} + item-402 at level 5: inline: group group + item-403 at level 6: text: whereas + item-404 at level 6: formula: {\mathrm{B}}(a_1,a_2) + item-405 at level 6: text: denotes the Beta function [48]. ... functions in the following subsection. + item-406 at level 4: section_header: 2.3.2. + item-407 at level 5: inline: group group + item-408 at level 6: text: According to Lewis–Riesenfeld th ... esented in terms of the eigenfunctions + item-409 at level 6: formula: \Phi _{nlm}(\vec{r},t) + item-410 at level 6: text: of the invariant operator, which ... . Another required factor is the phase + item-411 at level 6: formula: \alpha (t) + item-412 at level 6: text: associated with each quantum sta ... in terms of the transformed eigenstate + item-413 at level 6: formula: \vert \Phi ^\prime \rangle + item-414 at level 6: text: instead of + item-415 at level 6: formula: \vert \Phi \rangle + item-416 at level 6: text: , such that + item-417 at level 5: formula: \begin{eqnarray} +\hbar \frac{d\a ... \prime }\right\rangle . +\end{eqnarray} + item-418 at level 5: text: Then, a minor evaluation using Eq. (18) yields the phases in the form + item-419 at level 5: formula: \begin{eqnarray} +\alpha _{nl}(t) ... rime }+\alpha _{nl}(0), +\end{eqnarray} + item-420 at level 5: inline: group group + item-421 at level 6: text: where + item-422 at level 6: formula: \Lambda _{nl} + item-423 at level 6: text: are given by Eq. (40). These pha ... riptions to experimental observations. + item-424 at level 5: text: The solutions of the original Sc ... w be written in configuration space as + item-425 at level 5: formula: \begin{eqnarray} +\Psi _{nlm}(r,\ ... (r,\theta ,\varphi ,t), +\end{eqnarray} + item-426 at level 5: inline: group group + item-427 at level 6: text: where the explicit forms of + item-428 at level 6: formula: \alpha _{nl}(t) + item-429 at level 6: text: and + item-430 at level 6: formula: \Phi _{nlm}(r,\theta ,\varphi ,t) + item-431 at level 6: text: are given in Eqs. (51) and (47), ... ing wave functions (52), together with + item-432 at level 6: formula: \Lambda _{nl} + item-433 at level 6: text: in Eq. (40), serve as fundamenta ... roperties of the quark–antiquark pair. + item-434 at level 2: section_header: 3. + item-435 at level 3: text: In this study, we investigated t ... tood special functions in mathematics. + item-436 at level 3: inline: group group + item-437 at level 4: text: Our solutions are essential for ... strong CP problem and the role of the + item-438 at level 4: formula: \theta + item-439 at level 4: text: -term, as well as the difficulty ... nd decay modes of heavy mesons such as + item-440 at level 4: formula: J/\psi + item-441 at level 4: text: [5,55]. The analytical solutions ... addressing these longstanding issues. + item-442 at level 2: section_header: Funding + item-443 at level 3: inline: group group + item-444 at level 4: text: Open Access funding: SCOAP + item-445 at level 4: text: 3 + item-446 at level 4: text: . + item-447 at level 3: text: Although normalization is formal ... n associated with Eq. (47) is given by + item-448 at level 3: formula: \begin{eqnarray} +\int _0^{2\pi } ... dr d\theta d\varphi =1. +\end{eqnarray} + item-449 at level 3: inline: group group + item-450 at level 4: text: Because the angular part + item-451 at level 4: formula: Y_{lm} (\theta ,\varphi ) + item-452 at level 4: text: , defined in Eq. (21), is alread ... q. (47), Eq. (A.1) can be rewritten as + item-453 at level 3: formula: \begin{eqnarray} +\frac{\left|N_{ ... t)}})\right] ^{2}dr =1. +\end{eqnarray} + item-454 at level 3: text: To simplify the integral, we introduce the substitution + item-455 at level 3: formula: \begin{eqnarray} +z=e^{-\delta \frac{r}{\rho (t)}}, +\end{eqnarray} + item-456 at level 3: inline: group group + item-457 at level 4: text: and consider the fact that + item-458 at level 4: formula: \bar{a} + item-459 at level 4: text: and + item-460 at level 4: formula: \bar{b} + item-461 at level 4: text: in this case can be expressed in terms of + item-462 at level 4: text: n + item-463 at level 4: text: as + item-464 at level 3: formula: \begin{eqnarray} +\bar{a}=-n,~~~~ ... (\upsilon +\lambda )+n, +\end{eqnarray} + item-465 at level 3: inline: group group + item-466 at level 4: text: along with Eq. (44) as the formula of + item-467 at level 4: formula: \bar{c} + item-468 at level 4: text: . Then, Eq. (A.2) becomes + item-469 at level 3: formula: \begin{eqnarray} +\frac{\left|N_{ ... {\delta }g_{\rm int}=1, +\end{eqnarray} + item-470 at level 3: inline: group group + item-471 at level 4: text: where + item-472 at level 4: formula: g_{\rm int} + item-473 at level 4: text: is the integral part: + item-474 at level 3: formula: \begin{eqnarray} +g_{\rm int}=\in ... a +1;z)\right] ^{2}dz . +\end{eqnarray} + item-475 at level 3: inline: group group + item-476 at level 4: text: To evaluate the integration + item-477 at level 4: formula: g_{\rm int} + item-478 at level 4: text: , we use the expansion formula f ... ion (see formula 15.2.4 of Ref. [45]): + item-479 at level 3: formula: \begin{eqnarray} +{_{2}F_{1}}(-n, ... \frac{(b)_k}{(c)_k}s^k, +\end{eqnarray} + item-480 at level 3: inline: group group + item-481 at level 4: text: which holds when + item-482 at level 4: formula: n=0,1,2,\cdots + item-483 at level 4: text: and + item-484 at level 4: formula: c \ne 0,-1,-2,\cdots + item-485 at level 4: text: . This relation also holds when + item-486 at level 4: formula: c=-n-j + item-487 at level 4: text: , where + item-488 at level 4: formula: j=0,1,2,\cdots + item-489 at level 4: text: . Substituting this series expansion into Eq. (A.6), we obtain + item-490 at level 3: formula: \begin{eqnarray} +g_{\rm int}&=&\ ... ight) ^{2\upsilon }dz . +\end{eqnarray} + item-491 at level 3: text: The integral in the above equati ... eta function, which is defined as [48] + item-492 at level 3: formula: \begin{eqnarray} +\mathrm{B}(a_1, ... _1-1} (1-s)^{a_2-1} ds, +\end{eqnarray} + item-493 at level 3: inline: group group + item-494 at level 4: text: under the conditions + item-495 at level 4: formula: {\rm Re}a_1\gt 0 + item-496 at level 4: text: and + item-497 at level 4: formula: {\rm Re}a_2\gt 0 + item-498 at level 4: text: . The two shape parameters of this function, in our case, are + item-499 at level 4: formula: a_1=k+k^{\prime }+2\lambda + item-500 at level 4: text: and + item-501 at level 4: formula: a_2=2\upsilon +1 + item-502 at level 4: text: . Thus, considering that the Pochhammer number is given by + item-503 at level 4: formula: (a)_k=\Gamma (a+k)/\Gamma (a) + item-504 at level 4: text: , where + item-505 at level 4: formula: a \ne 0,-1,-2,\cdots + item-506 at level 4: text: , a rearrangement of Eq. (A.5) w ... ession for the normalization constants + item-507 at level 4: formula: N_{nl} + item-508 at level 4: text: , as presented in Eq. (48) of the text. + item-509 at level 2: section_header: References + item-510 at level 3: list: group list + item-511 at level 4: list_item: H.-T. Ding, O. Kaczmarek, A.-L. ... (2019).10.1016/j.nuclphysa.2018.09.075 + item-512 at level 4: list_item: É. Chapon et al., Prog. Part. Nu ... 3906 (2022).10.1016/j.ppnp.2021.103906 + item-513 at level 4: list_item: H. Bahl, S. Koren, L.-T. Wang, E ... (2024).10.1140/epjc/s10052-024-13411-3 + item-514 at level 4: list_item: M. Doser, G. Farrar, G. Kornakov ... (2023).10.1140/epjc/s10052-023-12319-8 + item-515 at level 4: list_item: T. Li, X.-D. 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"#/texts/2" + }, + { + "$ref": "#/texts/4" + }, + { + "$ref": "#/texts/10" + }, + { + "$ref": "#/texts/391" + }, + { + "$ref": "#/texts/398" + }, + { + "$ref": "#/texts/457" + } + ], + "content_layer": "body", + "label": "title", + "prov": [], + "orig": "Time-Dependent 3D Oscillator with Coulomb Interaction: An Alternative Approach for Analyzing Quark–Antiquark Systems", + "text": "Time-Dependent 3D Oscillator with Coulomb Interaction: An Alternative Approach for Analyzing Quark–Antiquark Systems" + }, + { + "self_ref": "#/texts/1", + "parent": { + "$ref": "#/texts/0" + }, + "children": [], + "content_layer": "body", + "label": "paragraph", + "prov": [], + "orig": "Jeong Ryeol Choi, Salim Medjber, Salah Menouar, Ramazan Sever", + "text": "Jeong Ryeol Choi, Salim Medjber, Salah Menouar, Ramazan Sever" + }, + { + "self_ref": "#/texts/2", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/3" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Abstract", + "text": "Abstract", + "level": 1 + }, + { + "self_ref": "#/texts/3", + "parent": { + "$ref": "#/texts/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In this work, the dynamics of quark–antiquark pair systems is investigated by modeling them as general time-dependent 3D oscillators perturbed by a Coulomb potential. Solving this model enables the prediction of key mesonic properties such as the probability density, energy spectra, and quadrature uncertainties, offering theoretical insights into the confinement of quarks via gluon-mediated strong interactions. To tackle the mathematical difficulty raised by the time dependence of parameters in the system, special mathematical techniques, such as the invariant operator method, unitary transformation method, and the Nikiforov–Uvarov functional analysis are used. The wave functions of the system, derived using these mathematical techniques, are expressed analytically in terms of the Gauss hypergeometric function whose mathematical properties are well characterized. Our results provide the quantum-mechanical framework of quark–antiquark systems that are essential for exploring the nonperturbative aspects of quantum chromodynamics. In addition, the underlying mathematical structure may serve as a foundation for addressing broader challenges in particle physics, including the origin of mass and its connection to the Higgs mechanism.", + "text": "In this work, the dynamics of quark–antiquark pair systems is investigated by modeling them as general time-dependent 3D oscillators perturbed by a Coulomb potential. Solving this model enables the prediction of key mesonic properties such as the probability density, energy spectra, and quadrature uncertainties, offering theoretical insights into the confinement of quarks via gluon-mediated strong interactions. To tackle the mathematical difficulty raised by the time dependence of parameters in the system, special mathematical techniques, such as the invariant operator method, unitary transformation method, and the Nikiforov–Uvarov functional analysis are used. The wave functions of the system, derived using these mathematical techniques, are expressed analytically in terms of the Gauss hypergeometric function whose mathematical properties are well characterized. Our results provide the quantum-mechanical framework of quark–antiquark systems that are essential for exploring the nonperturbative aspects of quantum chromodynamics. In addition, the underlying mathematical structure may serve as a foundation for addressing broader challenges in particle physics, including the origin of mass and its connection to the Higgs mechanism." + }, + { + "self_ref": "#/texts/4", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/5" + }, + { + "$ref": "#/texts/6" + }, + { + "$ref": "#/texts/7" + }, + { + "$ref": "#/texts/8" + }, + { + "$ref": "#/texts/9" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "1.", + "text": "1.", + "level": 1 + }, + { + "self_ref": "#/texts/5", + "parent": { + "$ref": "#/texts/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Quark–antiquark pairs are a key concept in particle physics, essential for understanding the strong interaction and the structure of hadrons such as mesons and baryons. Typical examples of quark–antiquark pairs are bound states of a heavy quark and its antiquark, such as charmonium and bottomonium, collectively referred to as quarkonia [1]. Quarkonia can be readily produced in experiments such as the LHC, and their properties can be measured with high precision. Quarkonia’s distinct mass spectra, unique decay patterns, and relatively long lifetimes are of significant interest in the search for physics that extends past the Standard Model. As such, they may offer a promising avenue for probing potential new phenomena not accounted for by the Higgs sector [2,3]. This makes quarkonia an important topic requiring collaborative research between experimental and theoretical physicists. Indeed, they are actively studied in connection with new particle searches and dark matter theories [4–7].", + "text": "Quark–antiquark pairs are a key concept in particle physics, essential for understanding the strong interaction and the structure of hadrons such as mesons and baryons. Typical examples of quark–antiquark pairs are bound states of a heavy quark and its antiquark, such as charmonium and bottomonium, collectively referred to as quarkonia [1]. Quarkonia can be readily produced in experiments such as the LHC, and their properties can be measured with high precision. Quarkonia’s distinct mass spectra, unique decay patterns, and relatively long lifetimes are of significant interest in the search for physics that extends past the Standard Model. As such, they may offer a promising avenue for probing potential new phenomena not accounted for by the Higgs sector [2,3]. This makes quarkonia an important topic requiring collaborative research between experimental and theoretical physicists. Indeed, they are actively studied in connection with new particle searches and dark matter theories [4–7]." + }, + { + "self_ref": "#/texts/6", + "parent": { + "$ref": "#/texts/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In light of this, it is essential to establish a theoretical framework that enables systematic analysis and interpretation of quarkonium systems. In particle physics, quark–antiquark systems are commonly modeled using a potential that combines harmonic and Coulomb terms [8–10], where the harmonic component captures the long-range confinement effect and the Coulomb term accounts for the short-range interactions based on quantum chromodynamics (QCD). Motivated by this, we focus on the analysis of time-dependent 3D oscillators perturbed by a Coulomb potential throughout this work. The quantum-mechanical solutions of such systems are known to be expressible in terms of biconfluent Heun functions [11,12]. However, limited knowledge of the properties of such functions continues to make these systems difficult to analyze. At present, the characterization of these functions remains confined to their behavior in certain special cases and their connections to specific recursion relations, particularly their representations in terms of these relations [13,14]. This lack of comprehensive understanding is the reason why many researchers refer to them primarily through their defining equation, the biconfluent Heun equation, rather than treating them as fully characterized functions in their own right [15–19].", + "text": "In light of this, it is essential to establish a theoretical framework that enables systematic analysis and interpretation of quarkonium systems. In particle physics, quark–antiquark systems are commonly modeled using a potential that combines harmonic and Coulomb terms [8–10], where the harmonic component captures the long-range confinement effect and the Coulomb term accounts for the short-range interactions based on quantum chromodynamics (QCD). Motivated by this, we focus on the analysis of time-dependent 3D oscillators perturbed by a Coulomb potential throughout this work. The quantum-mechanical solutions of such systems are known to be expressible in terms of biconfluent Heun functions [11,12]. However, limited knowledge of the properties of such functions continues to make these systems difficult to analyze. At present, the characterization of these functions remains confined to their behavior in certain special cases and their connections to specific recursion relations, particularly their representations in terms of these relations [13,14]. This lack of comprehensive understanding is the reason why many researchers refer to them primarily through their defining equation, the biconfluent Heun equation, rather than treating them as fully characterized functions in their own right [15–19]." + }, + { + "self_ref": "#/texts/7", + "parent": { + "$ref": "#/texts/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "To overcome this limitation, the present work explores an alternative analytical treatment by expressing the solutions in terms of well-established mathematical functions, thereby providing a more accessible and rigorous theoretical understanding of the system. Deriving such solutions to fundamental equations for our general physical systems governed by time-dependent Hamiltonians [20–22] may require special mathematical techniques beyond conventional methods such as the separation of variables. One promising approach is the invariant operator method [23–26], which has proven effective in the study of time-dependent Hamiltonian systems (TDHSs). This method allows for the construction of exact quantum solutions in systems with time-dependent parameters. Specifically, if an invariant operator can be identified for a given system, the quantum wave functions, apart from phase parts, can be expressed by eigenfunctions of this operator. Consequently, solving the quantum system reduces to finding the eigenfunctions of the invariant operator, while the required phase factors can be determined with the help of the Schrödinger equation.", + "text": "To overcome this limitation, the present work explores an alternative analytical treatment by expressing the solutions in terms of well-established mathematical functions, thereby providing a more accessible and rigorous theoretical understanding of the system. Deriving such solutions to fundamental equations for our general physical systems governed by time-dependent Hamiltonians [20–22] may require special mathematical techniques beyond conventional methods such as the separation of variables. One promising approach is the invariant operator method [23–26], which has proven effective in the study of time-dependent Hamiltonian systems (TDHSs). This method allows for the construction of exact quantum solutions in systems with time-dependent parameters. Specifically, if an invariant operator can be identified for a given system, the quantum wave functions, apart from phase parts, can be expressed by eigenfunctions of this operator. Consequently, solving the quantum system reduces to finding the eigenfunctions of the invariant operator, while the required phase factors can be determined with the help of the Schrödinger equation." + }, + { + "self_ref": "#/texts/8", + "parent": { + "$ref": "#/texts/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Regarding coupling of potentials, as well as the system’s time dependence, the general form of the invariant operator is typically complicated. Therefore, to solve its eigenvalue equation, more advanced mathematical techniques, such as the unitary transformation method [27,28] and the Nikiforov–Uvarov functional analysis (NUFA) [29], are necessary. The NUFA method builds upon and integrates several approaches, including the original Nikiforov–Uvarov (NU) method [30], the parametric NU method [31–34], and elements of functional analysis [35]. This enhanced method enables one to obtain the solution of related equations, including those of hypergeometric type, in a systematic and elegant manner, allowing the determination of the eigenvalue spectrum and the corresponding wave functions. We demonstrate that the wave functions derived through this approach can be expressed in terms of the well-known Gauss hypergeometric function. This may provide a significantly more tractable framework for analyzing the system compared to existing solutions that are based on biconfluent Heun functions. Taking advantage of these strengths, we show that the newly obtained solutions enable a thorough investigation of the system, particularly in dynamically evolving hadronic environments where effective quark interactions may vary due to medium-induced effects and nonequilibrium processes.", + "text": "Regarding coupling of potentials, as well as the system’s time dependence, the general form of the invariant operator is typically complicated. Therefore, to solve its eigenvalue equation, more advanced mathematical techniques, such as the unitary transformation method [27,28] and the Nikiforov–Uvarov functional analysis (NUFA) [29], are necessary. The NUFA method builds upon and integrates several approaches, including the original Nikiforov–Uvarov (NU) method [30], the parametric NU method [31–34], and elements of functional analysis [35]. This enhanced method enables one to obtain the solution of related equations, including those of hypergeometric type, in a systematic and elegant manner, allowing the determination of the eigenvalue spectrum and the corresponding wave functions. We demonstrate that the wave functions derived through this approach can be expressed in terms of the well-known Gauss hypergeometric function. This may provide a significantly more tractable framework for analyzing the system compared to existing solutions that are based on biconfluent Heun functions. Taking advantage of these strengths, we show that the newly obtained solutions enable a thorough investigation of the system, particularly in dynamically evolving hadronic environments where effective quark interactions may vary due to medium-induced effects and nonequilibrium processes." + }, + { + "self_ref": "#/texts/9", + "parent": { + "$ref": "#/texts/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The main contributions of the present work can be summarized as follows. First, we develop an analytical treatment for a time-dependent quark–antiquark model based on a 3D oscillator perturbed by a Coulomb interaction within the framework of invariant operator theory. Second, by combining the invariant operator method, unitary transformation techniques, and the NUFA formalism, we derive analytical expressions for the eigenspectrum, quantum phases, and wave functions of the system in closed form. In contrast to several previous approaches in which the solutions are represented in terms of biconfluent Heun functions, the present treatment expresses the wave functions in terms of the Gauss hypergeometric function, whose mathematical properties are more thoroughly established. This provides a more tractable analytical framework for investigating the dynamical properties of quark–antiquark systems governed by time-dependent Hamiltonians.", + "text": "The main contributions of the present work can be summarized as follows. First, we develop an analytical treatment for a time-dependent quark–antiquark model based on a 3D oscillator perturbed by a Coulomb interaction within the framework of invariant operator theory. Second, by combining the invariant operator method, unitary transformation techniques, and the NUFA formalism, we derive analytical expressions for the eigenspectrum, quantum phases, and wave functions of the system in closed form. In contrast to several previous approaches in which the solutions are represented in terms of biconfluent Heun functions, the present treatment expresses the wave functions in terms of the Gauss hypergeometric function, whose mathematical properties are more thoroughly established. This provides a more tractable analytical framework for investigating the dynamical properties of quark–antiquark systems governed by time-dependent Hamiltonians." + }, + { + "self_ref": "#/texts/10", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/11" + }, + { + "$ref": "#/texts/197" + }, + { + "$ref": "#/texts/268" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.", + "text": "2.", + "level": 1 + }, + { + "self_ref": "#/texts/11", + "parent": { + "$ref": "#/texts/10" + }, + "children": [ + { + "$ref": "#/texts/12" + }, + { + "$ref": "#/texts/44" + }, + { + "$ref": "#/texts/100" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.1.", + "text": "2.1.", + "level": 2 + }, + { + "self_ref": "#/texts/12", + "parent": { + "$ref": "#/texts/11" + }, + "children": [ + { + "$ref": "#/groups/0" + }, + { + "$ref": "#/texts/28" + }, + { + "$ref": "#/texts/29" + }, + { + "$ref": "#/texts/30" + }, + { + "$ref": "#/groups/1" + }, + { + "$ref": "#/texts/38" + }, + { + "$ref": "#/groups/2" + }, + { + "$ref": "#/texts/42" + }, + { + "$ref": "#/texts/43" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.1.1.", + "text": "2.1.1.", + "level": 3 + }, + { + "self_ref": "#/texts/13", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "We consider a quark–antiquark pair with a time-dependent effective reduced mass", + "text": "We consider a quark–antiquark pair with a time-dependent effective reduced mass" + }, + { + "self_ref": "#/texts/14", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\mu (t)", + "text": "\\mu (t)" + }, + { + "self_ref": "#/texts/15", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in spherical coordinates, governed by a central potential", + "text": "in spherical coordinates, governed by a central potential" + }, + { + "self_ref": "#/texts/16", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "g(t)r^{2}-\\frac{Z(t)}{r}", + "text": "g(t)r^{2}-\\frac{Z(t)}{r}" + }, + { + "self_ref": "#/texts/17", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/18", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "g(t)", + "text": "g(t)" + }, + { + "self_ref": "#/texts/19", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/20", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "Z(t)", + "text": "Z(t)" + }, + { + "self_ref": "#/texts/21", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are coefficients that depend on time. While", + "text": "are coefficients that depend on time. While" + }, + { + "self_ref": "#/texts/22", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "g(t)r^{2}", + "text": "g(t)r^{2}" + }, + { + "self_ref": "#/texts/23", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "represents a confining trap potential with adjustable strength,", + "text": "represents a confining trap potential with adjustable strength," + }, + { + "self_ref": "#/texts/24", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "-\\frac{Z(t)}{r}", + "text": "-\\frac{Z(t)}{r}" + }, + { + "self_ref": "#/texts/25", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is a Coulomb perturbation with the constraint", + "text": "is a Coulomb perturbation with the constraint" + }, + { + "self_ref": "#/texts/26", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "Z(t)\\gt 0", + "text": "Z(t)\\gt 0" + }, + { + "self_ref": "#/texts/27", + "parent": { + "$ref": "#/groups/0" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". As the distance between quarks increases, the harmonic potential rises sharply, effectively prohibiting their free separation within a bound state. This potential acts as a simplified representation of the QCD confinement mechanism and dominates at large separations. Meanwhile, the Coulomb term represents the electromagnetic or color interaction mediated by the color charge of one quark acting on the other, effectively serving as a short-range attractive force between the quark and antiquark.", + "text": ". As the distance between quarks increases, the harmonic potential rises sharply, effectively prohibiting their free separation within a bound state. This potential acts as a simplified representation of the QCD confinement mechanism and dominates at large separations. Meanwhile, the Coulomb term represents the electromagnetic or color interaction mediated by the color charge of one quark acting on the other, effectively serving as a short-range attractive force between the quark and antiquark." + }, + { + "self_ref": "#/texts/28", + "parent": { + "$ref": "#/texts/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Time-dependent Hamiltonians naturally arise in hadron physics when quark–antiquark systems are embedded in dynamical environments, such as quark–gluon plasma or nonequilibrium hadronic media produced in high-energy collisions [20–22]. In such situations, the effective interaction between quarks may vary with time due to medium evolution, color-screening effects, confinement modifications, or changes in the effective quark mass. Consequently, the parameters appearing in phenomenological quarkonium potentials, including the confining strength and Coulomb-like coupling terms, can acquire explicit time dependence. The present model therefore provides an effective framework for investigating dynamical quark–antiquark systems within a quantum-mechanical setting.", + "text": "Time-dependent Hamiltonians naturally arise in hadron physics when quark–antiquark systems are embedded in dynamical environments, such as quark–gluon plasma or nonequilibrium hadronic media produced in high-energy collisions [20–22]. In such situations, the effective interaction between quarks may vary with time due to medium evolution, color-screening effects, confinement modifications, or changes in the effective quark mass. Consequently, the parameters appearing in phenomenological quarkonium potentials, including the confining strength and Coulomb-like coupling terms, can acquire explicit time dependence. The present model therefore provides an effective framework for investigating dynamical quark–antiquark systems within a quantum-mechanical setting." + }, + { + "self_ref": "#/texts/29", + "parent": { + "$ref": "#/texts/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Based on the above description, the Hamiltonian that characterizes the motion of the pair of quarks is expressed as:", + "text": "Based on the above description, the Hamiltonian that characterizes the motion of the pair of quarks is expressed as:" + }, + { + "self_ref": "#/texts/30", + "parent": { + "$ref": "#/texts/12" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nH(t)=\\frac{p^{2}}{2\\mu (t)}+g(t)r^{2}-\\frac{Z(t)}{r} .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nH(t)=\\frac{p^{2}}{2\\mu (t)}+g(t)r^{2}-\\frac{Z(t)}{r} .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/31", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "This Hamiltonian enables the calculation of the quantum-mechanical spectrum including wave functions and excitation properties, which is then used to theoretically explain the mass and structure of mesons. The momentum operator in spherical coordinates is represented as", + "text": "This Hamiltonian enables the calculation of the quantum-mechanical spectrum including wave functions and excitation properties, which is then used to theoretically explain the mass and structure of mesons. The momentum operator in spherical coordinates is represented as" + }, + { + "self_ref": "#/texts/32", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "p^{2}=p_{r}^{2}+\\frac{L^{2}}{r^{2}}", + "text": "p^{2}=p_{r}^{2}+\\frac{L^{2}}{r^{2}}" + }, + { + "self_ref": "#/texts/33", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", where", + "text": ", where" + }, + { + "self_ref": "#/texts/34", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "p_{r}=-i\\hbar (\\frac{\\partial }{\\partial r}+\\frac{1}{r})", + "text": "p_{r}=-i\\hbar (\\frac{\\partial }{\\partial r}+\\frac{1}{r})" + }, + { + "self_ref": "#/texts/35", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/36", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "L", + "text": "L", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/37", + "parent": { + "$ref": "#/groups/1" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is the total angular momentum, the formula for which is given by", + "text": "is the total angular momentum, the formula for which is given by" + }, + { + "self_ref": "#/texts/38", + "parent": { + "$ref": "#/texts/12" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nL^{2}=-\\hbar ^{2}\\bigg [\\frac{1}{\\sin ^{2}\\theta }\\frac{\\partial ^{2}}{\\partial \\varphi ^{2}}+\\frac{1}{\\sin \\theta }\\frac{\\partial }{\\partial \\theta }\\bigg (\\sin \\theta \\frac{\\partial }{\\partial \\theta }\\bigg )\\bigg ].\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nL^{2}=-\\hbar ^{2}\\bigg [\\frac{1}{\\sin ^{2}\\theta }\\frac{\\partial ^{2}}{\\partial \\varphi ^{2}}+\\frac{1}{\\sin \\theta }\\frac{\\partial }{\\partial \\theta }\\bigg (\\sin \\theta \\frac{\\partial }{\\partial \\theta }\\bigg )\\bigg ].\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/39", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "To investigate the dynamical features of the system, we must solve the Schrödinger equation for the Hamiltonian in Eq. (1). If we denote the system’s state vector as", + "text": "To investigate the dynamical features of the system, we must solve the Schrödinger equation for the Hamiltonian in Eq. (1). If we denote the system’s state vector as" + }, + { + "self_ref": "#/texts/40", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "|\\Psi (t)\\rangle", + "text": "|\\Psi (t)\\rangle" + }, + { + "self_ref": "#/texts/41", + "parent": { + "$ref": "#/groups/2" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", the corresponding Schrödinger equation is of the form", + "text": ", the corresponding Schrödinger equation is of the form" + }, + { + "self_ref": "#/texts/42", + "parent": { + "$ref": "#/texts/12" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\ni\\hbar \\frac{\\partial }{\\partial t}|\\Psi (t)\\rangle =H(t)|\\Psi (t)\\rangle .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\ni\\hbar \\frac{\\partial }{\\partial t}|\\Psi (t)\\rangle =H(t)|\\Psi (t)\\rangle .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/43", + "parent": { + "$ref": "#/texts/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The time dependence of the Hamiltonian requires a specialized approach to solve this equation, as will be presented subsequently.", + "text": "The time dependence of the Hamiltonian requires a specialized approach to solve this equation, as will be presented subsequently." + }, + { + "self_ref": "#/texts/44", + "parent": { + "$ref": "#/texts/11" + }, + "children": [ + { + "$ref": "#/groups/3" + }, + { + "$ref": "#/groups/4" + }, + { + "$ref": "#/texts/59" + }, + { + "$ref": "#/groups/5" + }, + { + "$ref": "#/texts/63" + }, + { + "$ref": "#/groups/6" + }, + { + "$ref": "#/texts/67" + }, + { + "$ref": "#/groups/7" + }, + { + "$ref": "#/texts/71" + }, + { + "$ref": "#/groups/8" + }, + { + "$ref": "#/texts/83" + }, + { + "$ref": "#/texts/84" + }, + { + "$ref": "#/texts/85" + }, + { + "$ref": "#/texts/86" + }, + { + "$ref": "#/groups/9" + }, + { + "$ref": "#/texts/92" + }, + { + "$ref": "#/groups/10" + }, + { + "$ref": "#/texts/98" + }, + { + "$ref": "#/texts/99" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.1.2.", + "text": "2.1.2.", + "level": 3 + }, + { + "self_ref": "#/texts/45", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Because", + "text": "Because" + }, + { + "self_ref": "#/texts/46", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "H(t)", + "text": "H(t)" + }, + { + "self_ref": "#/texts/47", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in Eq. (1) is represented in terms of time functions, directly solving Eq. (3) can be very challenging. To overcome this difficulty, we adopt the dynamical invariant method [24]. The core idea of this method is to treat the system in terms of a nontrivial invariant operator", + "text": "in Eq. (1) is represented in terms of time functions, directly solving Eq. (3) can be very challenging. To overcome this difficulty, we adopt the dynamical invariant method [24]. The core idea of this method is to treat the system in terms of a nontrivial invariant operator" + }, + { + "self_ref": "#/texts/48", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/49", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "rather than", + "text": "rather than" + }, + { + "self_ref": "#/texts/50", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "H(t)", + "text": "H(t)" + }, + { + "self_ref": "#/texts/51", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in Eq. (1), where", + "text": "in Eq. (1), where" + }, + { + "self_ref": "#/texts/52", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/53", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "does not involve time-derivative operators. Moreover,", + "text": "does not involve time-derivative operators. Moreover," + }, + { + "self_ref": "#/texts/54", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/55", + "parent": { + "$ref": "#/groups/3" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is closely related to the concept of invariants in classical mechanics (e.g. constants of motion), thereby facilitating interpretation from the perspective of the quantum–classical correspondence.", + "text": "is closely related to the concept of invariants in classical mechanics (e.g. constants of motion), thereby facilitating interpretation from the perspective of the quantum–classical correspondence." + }, + { + "self_ref": "#/texts/56", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "By the definition of the invariant operator,", + "text": "By the definition of the invariant operator," + }, + { + "self_ref": "#/texts/57", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/58", + "parent": { + "$ref": "#/groups/4" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "satisfies the equation:", + "text": "satisfies the equation:" + }, + { + "self_ref": "#/texts/59", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\frac{dI}{dt}=\\frac{\\partial I}{\\partial t}+\\frac{1}{i\\hbar }[I,H]=0.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\frac{dI}{dt}=\\frac{\\partial I}{\\partial t}+\\frac{1}{i\\hbar }[I,H]=0.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/60", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "If such an invariant exists, it is possible to put the solution of the Schrödinger equation (3) in terms of its eigenfunction", + "text": "If such an invariant exists, it is possible to put the solution of the Schrödinger equation (3) in terms of its eigenfunction" + }, + { + "self_ref": "#/texts/61", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi (\\vec{r},t)", + "text": "\\Phi (\\vec{r},t)" + }, + { + "self_ref": "#/texts/62", + "parent": { + "$ref": "#/groups/5" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", such that", + "text": ", such that" + }, + { + "self_ref": "#/texts/63", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Psi (\\vec{r},t)=e^{i\\alpha (t)}\\Phi (\\vec{r},t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Psi (\\vec{r},t)=e^{i\\alpha (t)}\\Phi (\\vec{r},t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/64", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/65", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\alpha (t)", + "text": "\\alpha (t)" + }, + { + "self_ref": "#/texts/66", + "parent": { + "$ref": "#/groups/6" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is a phase function that can be obtained from", + "text": "is a phase function that can be obtained from" + }, + { + "self_ref": "#/texts/67", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\hbar \\frac{d\\alpha (t)}{dt}=\\left\\langle \\Phi \\left|\\bigg (i\\hbar \\frac{\\partial }{\\partial t}-H\\bigg )\\right|\\Phi \\right\\rangle .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\hbar \\frac{d\\alpha (t)}{dt}=\\left\\langle \\Phi \\left|\\bigg (i\\hbar \\frac{\\partial }{\\partial t}-H\\bigg )\\right|\\Phi \\right\\rangle .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/68", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "To find the expression of the invariant", + "text": "To find the expression of the invariant" + }, + { + "self_ref": "#/texts/69", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/70", + "parent": { + "$ref": "#/groups/7" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "utilizing Eq. (4), we set", + "text": "utilizing Eq. (4), we set" + }, + { + "self_ref": "#/texts/71", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nI(t)=A(t)p^{2}+B(t)\\left( rp_{r}+p_{r}r\\right) +C(t)r^{2}-\\frac{D(t)}{r},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nI(t)=A(t)p^{2}+B(t)\\left( rp_{r}+p_{r}r\\right) +C(t)r^{2}-\\frac{D(t)}{r},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/72", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/73", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "A(t)", + "text": "A(t)" + }, + { + "self_ref": "#/texts/74", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ",", + "text": "," + }, + { + "self_ref": "#/texts/75", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "B(t)", + "text": "B(t)" + }, + { + "self_ref": "#/texts/76", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ",", + "text": "," + }, + { + "self_ref": "#/texts/77", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "C(t)", + "text": "C(t)" + }, + { + "self_ref": "#/texts/78", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", and", + "text": ", and" + }, + { + "self_ref": "#/texts/79", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "D(t)", + "text": "D(t)" + }, + { + "self_ref": "#/texts/80", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are time-dependent functions to be determined. Then, a mathematical procedure with this assumed formula of", + "text": "are time-dependent functions to be determined. Then, a mathematical procedure with this assumed formula of" + }, + { + "self_ref": "#/texts/81", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/82", + "parent": { + "$ref": "#/groups/8" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "after substituting Eqs. (1) and (7) into Eq. (4) yields", + "text": "after substituting Eqs. (1) and (7) into Eq. (4) yields" + }, + { + "self_ref": "#/texts/83", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nA(t) =A_{0}\\rho ^{2}(t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nA(t) =A_{0}\\rho ^{2}(t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/84", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nB(t) =-A_{0}\\mu (t)\\rho (t)\\dot{\\rho }(t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nB(t) =-A_{0}\\mu (t)\\rho (t)\\dot{\\rho }(t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/85", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nC(t) =A_{0}\\bigg (\\mu ^{2}(t)[\\dot{\\rho }(t)]^{2}+\\frac{\\Omega ^{2}}{4\\rho ^{2}(t)}\\bigg ),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nC(t) =A_{0}\\bigg (\\mu ^{2}(t)[\\dot{\\rho }(t)]^{2}+\\frac{\\Omega ^{2}}{4\\rho ^{2}(t)}\\bigg ),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/86", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nD(t) =2A_{0}\\mu (t)Z(t)\\rho ^{2}(t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nD(t) =2A_{0}\\mu (t)Z(t)\\rho ^{2}(t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/87", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/88", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "A_{0}", + "text": "A_{0}" + }, + { + "self_ref": "#/texts/89", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is an arbitrary real constant and", + "text": "is an arbitrary real constant and" + }, + { + "self_ref": "#/texts/90", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\rho (t)", + "text": "\\rho (t)" + }, + { + "self_ref": "#/texts/91", + "parent": { + "$ref": "#/groups/9" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is a solution of the following equation", + "text": "is a solution of the following equation" + }, + { + "self_ref": "#/texts/92", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\ddot{\\rho }(t)+\\frac{\\dot{\\mu }(t)}{\\mu (t) }\\dot{\\rho }(t)+2\\frac{g(t)}{\\mu (t)}\\rho (t) =\\frac{\\Omega ^{2}}{4\\mu ^{2}(t)\\rho ^{3}(t)},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\ddot{\\rho }(t)+\\frac{\\dot{\\mu }(t)}{\\mu (t) }\\dot{\\rho }(t)+2\\frac{g(t)}{\\mu (t)}\\rho (t) =\\frac{\\Omega ^{2}}{4\\mu ^{2}(t)\\rho ^{3}(t)},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/93", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "with", + "text": "with" + }, + { + "self_ref": "#/texts/94", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Omega", + "text": "\\Omega" + }, + { + "self_ref": "#/texts/95", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "being a real constant. The above invariant is valid under the condition that", + "text": "being a real constant. The above invariant is valid under the condition that" + }, + { + "self_ref": "#/texts/96", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "Z(t)", + "text": "Z(t)" + }, + { + "self_ref": "#/texts/97", + "parent": { + "$ref": "#/groups/10" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "follows the relation", + "text": "follows the relation" + }, + { + "self_ref": "#/texts/98", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\dot{Z}(t)+\\bigg (\\frac{\\dot{\\mu }(t)}{\\mu (t)}+\\frac{\\dot{\\rho }(t)}{\\rho (t)}\\bigg )Z(t)=0.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\dot{Z}(t)+\\bigg (\\frac{\\dot{\\mu }(t)}{\\mu (t)}+\\frac{\\dot{\\rho }(t)}{\\rho (t)}\\bigg )Z(t)=0.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/99", + "parent": { + "$ref": "#/texts/44" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Equation (7) with Eqs. (8)–(11) constitutes the complete quadratic invariant operator of the system. This operator provides a robust analytical framework for the system, in which direct solutions are difficult to obtain due to the complicated time evolution of the Hamiltonian. It also enables a deeper understanding of dynamical properties of the system, including phase transitions and topological changes, beyond merely providing a powerful methodology for obtaining solutions.", + "text": "Equation (7) with Eqs. (8)–(11) constitutes the complete quadratic invariant operator of the system. This operator provides a robust analytical framework for the system, in which direct solutions are difficult to obtain due to the complicated time evolution of the Hamiltonian. It also enables a deeper understanding of dynamical properties of the system, including phase transitions and topological changes, beyond merely providing a powerful methodology for obtaining solutions." + }, + { + "self_ref": "#/texts/100", + "parent": { + "$ref": "#/texts/11" + }, + "children": [ + { + "$ref": "#/groups/11" + }, + { + "$ref": "#/texts/106" + }, + { + "$ref": "#/groups/12" + }, + { + "$ref": "#/texts/114" + }, + { + "$ref": "#/groups/13" + }, + { + "$ref": "#/texts/118" + }, + { + "$ref": "#/groups/14" + }, + { + "$ref": "#/texts/124" + }, + { + "$ref": "#/texts/125" + }, + { + "$ref": "#/texts/126" + }, + { + "$ref": "#/groups/15" + }, + { + "$ref": "#/texts/142" + }, + { + "$ref": "#/texts/143" + }, + { + "$ref": "#/groups/16" + }, + { + "$ref": "#/texts/147" + }, + { + "$ref": "#/groups/17" + }, + { + "$ref": "#/texts/153" + }, + { + "$ref": "#/groups/18" + }, + { + "$ref": "#/texts/163" + }, + { + "$ref": "#/texts/164" + }, + { + "$ref": "#/groups/19" + }, + { + "$ref": "#/texts/172" + }, + { + "$ref": "#/groups/20" + }, + { + "$ref": "#/groups/21" + }, + { + "$ref": "#/texts/189" + }, + { + "$ref": "#/texts/190" + }, + { + "$ref": "#/texts/191" + }, + { + "$ref": "#/groups/22" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.1.3.", + "text": "2.1.3.", + "level": 3 + }, + { + "self_ref": "#/texts/101", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Because the quantum wave functions of the system are represented in terms of the eigenfunctions of", + "text": "Because the quantum wave functions of the system are represented in terms of the eigenfunctions of" + }, + { + "self_ref": "#/texts/102", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/103", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", it is now necessary to evaluate its eigenvalue equation. We begin by writing the eigenvalue equation of the invariant", + "text": ", it is now necessary to evaluate its eigenvalue equation. We begin by writing the eigenvalue equation of the invariant" + }, + { + "self_ref": "#/texts/104", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/105", + "parent": { + "$ref": "#/groups/11" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "as", + "text": "as" + }, + { + "self_ref": "#/texts/106", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nI(t)\\Phi _{n}(\\vec{r},t)=\\Lambda _{n}\\Phi _{n}(\\vec{r},t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nI(t)\\Phi _{n}(\\vec{r},t)=\\Lambda _{n}\\Phi _{n}(\\vec{r},t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/107", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/108", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Lambda _{n}", + "text": "\\Lambda _{n}" + }, + { + "self_ref": "#/texts/109", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are the eigenvalues and", + "text": "are the eigenvalues and" + }, + { + "self_ref": "#/texts/110", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _{n}(\\vec{r},t)", + "text": "\\Phi _{n}(\\vec{r},t)" + }, + { + "self_ref": "#/texts/111", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are time-dependent eigenfunctions. Given that", + "text": "are time-dependent eigenfunctions. Given that" + }, + { + "self_ref": "#/texts/112", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/113", + "parent": { + "$ref": "#/groups/12" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", as defined in Eq. (7) along with Eqs. (8)–(11), has a complicated form, it is favorable to solve Eq. (14) after we transform it mathematically into a simple form. To do this, we consider a unitary transformation of the form", + "text": ", as defined in Eq. (7) along with Eqs. (8)–(11), has a complicated form, it is favorable to solve Eq. (14) after we transform it mathematically into a simple form. To do this, we consider a unitary transformation of the form" + }, + { + "self_ref": "#/texts/114", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Phi _{n}^{\\prime }(\\vec{r})=U(t)\\Phi _{n}(\\vec{r},t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Phi _{n}^{\\prime }(\\vec{r})=U(t)\\Phi _{n}(\\vec{r},t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/115", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/116", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "U(t)", + "text": "U(t)" + }, + { + "self_ref": "#/texts/117", + "parent": { + "$ref": "#/groups/13" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is a time-dependent unitary operator given by", + "text": "is a time-dependent unitary operator given by" + }, + { + "self_ref": "#/texts/118", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nU(t)=\\exp \\left[ \\frac{i\\ln \\rho (t)}{2\\hbar }\\left( rp_{r}+p_{r}r\\right) \\right] \\exp \\left[ -i\\frac{\\mu (t)\\dot{\\rho }(t)}{2\\hbar \\rho (t)}r^{2}\\right] .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nU(t)=\\exp \\left[ \\frac{i\\ln \\rho (t)}{2\\hbar }\\left( rp_{r}+p_{r}r\\right) \\right] \\exp \\left[ -i\\frac{\\mu (t)\\dot{\\rho }(t)}{2\\hbar \\rho (t)}r^{2}\\right] .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/119", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Then, if we write the transformed invariant operator as", + "text": "Then, if we write the transformed invariant operator as" + }, + { + "self_ref": "#/texts/120", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I_0", + "text": "I_0" + }, + { + "self_ref": "#/texts/121", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", the eigenvalue equation in the transformed frame is represented in terms of", + "text": ", the eigenvalue equation in the transformed frame is represented in terms of" + }, + { + "self_ref": "#/texts/122", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _{n}^{\\prime }(\\vec{r})", + "text": "\\Phi _{n}^{\\prime }(\\vec{r})" + }, + { + "self_ref": "#/texts/123", + "parent": { + "$ref": "#/groups/14" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", such that", + "text": ", such that" + }, + { + "self_ref": "#/texts/124", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nUIU^{-1}\\Phi _{n}^{\\prime }(\\vec{r})=I_{0}\\Phi _{n}^{\\prime }(\\vec{r})=\\Lambda _{n}\\Phi _{n}^{\\prime }(\\vec{r}).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nUIU^{-1}\\Phi _{n}^{\\prime }(\\vec{r})=I_{0}\\Phi _{n}^{\\prime }(\\vec{r})=\\Lambda _{n}\\Phi _{n}^{\\prime }(\\vec{r}).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/125", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Now, from a mathematical procedure using Eq. (16), we obtain that", + "text": "Now, from a mathematical procedure using Eq. (16), we obtain that" + }, + { + "self_ref": "#/texts/126", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\left[ A_{0}p^{2}+A_{0}\\frac{\\Omega ^{2}}{4}r^{2}-D_{0}\\frac{1}{r}\\right] \\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )=\\Lambda _{nl}\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi ),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\left[ A_{0}p^{2}+A_{0}\\frac{\\Omega ^{2}}{4}r^{2}-D_{0}\\frac{1}{r}\\right] \\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )=\\Lambda _{nl}\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi ),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/127", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/128", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "D_{0} = 2A_{0}\\mu (t)Z(t)\\rho (t)", + "text": "D_{0} = 2A_{0}\\mu (t)Z(t)\\rho (t)" + }, + { + "self_ref": "#/texts/129", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "while", + "text": "while" + }, + { + "self_ref": "#/texts/130", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "n=0,1,2,\\cdots", + "text": "n=0,1,2,\\cdots" + }, + { + "self_ref": "#/texts/131", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". An infinite number of bound states may arise from the strong confinement of quarkonium, which is primarily attributed to the harmonic term dominating at large distances. By the way, the direct differentiation of", + "text": ". An infinite number of bound states may arise from the strong confinement of quarkonium, which is primarily attributed to the harmonic term dominating at large distances. By the way, the direct differentiation of" + }, + { + "self_ref": "#/texts/132", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "D_0", + "text": "D_0" + }, + { + "self_ref": "#/texts/133", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "with respect to", + "text": "with respect to" + }, + { + "self_ref": "#/texts/134", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "t", + "text": "t", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/135", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", followed by the application of Eq. (13), yields", + "text": ", followed by the application of Eq. (13), yields" + }, + { + "self_ref": "#/texts/136", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "d D_0/dt=0", + "text": "d D_0/dt=0" + }, + { + "self_ref": "#/texts/137", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". This means that", + "text": ". This means that" + }, + { + "self_ref": "#/texts/138", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "D_0", + "text": "D_0" + }, + { + "self_ref": "#/texts/139", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is in fact a constant of motion, as expected—since the operator on the left-hand side of Eq. (18) is the transformed version of the original invariant", + "text": "is in fact a constant of motion, as expected—since the operator on the left-hand side of Eq. (18) is the transformed version of the original invariant" + }, + { + "self_ref": "#/texts/140", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/141", + "parent": { + "$ref": "#/groups/15" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". It is worth noting that Eq. (18) is very simple compared to the original eigenvalue equation represented in Eq. (14).", + "text": ". It is worth noting that Eq. (18) is very simple compared to the original eigenvalue equation represented in Eq. (14)." + }, + { + "self_ref": "#/texts/142", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The existence of a perturbation of Coulomb potential may make the problem much more difficult. In what follows, the eigenvalue equation (18) can be rewritten as", + "text": "The existence of a perturbation of Coulomb potential may make the problem much more difficult. In what follows, the eigenvalue equation (18) can be rewritten as" + }, + { + "self_ref": "#/texts/143", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\begin{bmatrix}-\\hbar ^{2}A_{0}\\Big [\\frac{1}{r^{2}}\\frac{\\partial }{\\partial r}\\left( r^{2}\\frac{\\partial }{\\partial r}\\right) +\\frac{1}{r^{2}\\sin ^{2}\\theta }\\frac{\\partial ^{2}}{\\partial \\varphi ^{2}} \\\\+\\frac{1}{r^{2}\\sin \\theta }\\frac{\\partial }{\\partial \\theta }(\\sin \\theta \\frac{\\partial }{\\partial \\theta })\\Big ]+A_{0}\\frac{\\Omega ^{2}}{4}r^{2}-\\frac{D_{0}}{r} \\end{bmatrix} \\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )=\\Lambda _{nl}\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi ).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\begin{bmatrix}-\\hbar ^{2}A_{0}\\Big [\\frac{1}{r^{2}}\\frac{\\partial }{\\partial r}\\left( r^{2}\\frac{\\partial }{\\partial r}\\right) +\\frac{1}{r^{2}\\sin ^{2}\\theta }\\frac{\\partial ^{2}}{\\partial \\varphi ^{2}} \\\\+\\frac{1}{r^{2}\\sin \\theta }\\frac{\\partial }{\\partial \\theta }(\\sin \\theta \\frac{\\partial }{\\partial \\theta })\\Big ]+A_{0}\\frac{\\Omega ^{2}}{4}r^{2}-\\frac{D_{0}}{r} \\end{bmatrix} \\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )=\\Lambda _{nl}\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi ).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/144", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "According to the invariant operator theory, the wave functions of the transformed system are represented in terms of", + "text": "According to the invariant operator theory, the wave functions of the transformed system are represented in terms of" + }, + { + "self_ref": "#/texts/145", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )", + "text": "\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )" + }, + { + "self_ref": "#/texts/146", + "parent": { + "$ref": "#/groups/16" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Because this equation is independent of time, we can apply the method of separation of variables in order to solve it. Considering this, we take", + "text": ". Because this equation is independent of time, we can apply the method of separation of variables in order to solve it. Considering this, we take" + }, + { + "self_ref": "#/texts/147", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )=R_{nl}(r)Y_{lm}(\\theta ,\\varphi )=\\frac{u_{nl}(r)Y_{lm}(\\theta ,\\varphi )}{r},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi )=R_{nl}(r)Y_{lm}(\\theta ,\\varphi )=\\frac{u_{nl}(r)Y_{lm}(\\theta ,\\varphi )}{r},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/148", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where we have introduced", + "text": "where we have introduced" + }, + { + "self_ref": "#/texts/149", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "u_{nl}(r) = r R_{nl}(r)", + "text": "u_{nl}(r) = r R_{nl}(r)" + }, + { + "self_ref": "#/texts/150", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", whereas", + "text": ", whereas" + }, + { + "self_ref": "#/texts/151", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "Y_{lm}(\\theta ,\\varphi )", + "text": "Y_{lm}(\\theta ,\\varphi )" + }, + { + "self_ref": "#/texts/152", + "parent": { + "$ref": "#/groups/17" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are the spherical harmonics. The angular part of the eigenfunctions takes the form", + "text": "are the spherical harmonics. The angular part of the eigenfunctions takes the form" + }, + { + "self_ref": "#/texts/153", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nY_{lm}(\\theta ,\\varphi )= N_{lm}e^{im\\varphi }P_{l}^{m}(\\cos \\theta ),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nY_{lm}(\\theta ,\\varphi )= N_{lm}e^{im\\varphi }P_{l}^{m}(\\cos \\theta ),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/154", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/155", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "P_{l}^{m}(x)", + "text": "P_{l}^{m}(x)" + }, + { + "self_ref": "#/texts/156", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are the associated Legendre polynomials, while the spherical normalization constants are", + "text": "are the associated Legendre polynomials, while the spherical normalization constants are" + }, + { + "self_ref": "#/texts/157", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "N_{lm}=(-1)^m \\lbrace [(2l+1)/(4\\pi )](l-m)!/(l+m)! \\rbrace ^{1/2}", + "text": "N_{lm}=(-1)^m \\lbrace [(2l+1)/(4\\pi )](l-m)!/(l+m)! \\rbrace ^{1/2}" + }, + { + "self_ref": "#/texts/158", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". The allowed orbital and magnetic quantum numbers are", + "text": ". The allowed orbital and magnetic quantum numbers are" + }, + { + "self_ref": "#/texts/159", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "l=0,1,2,\\cdots", + "text": "l=0,1,2,\\cdots" + }, + { + "self_ref": "#/texts/160", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/161", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "m=-l,-l+1,\\cdots ,l", + "text": "m=-l,-l+1,\\cdots ,l" + }, + { + "self_ref": "#/texts/162", + "parent": { + "$ref": "#/groups/18" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", respectively.", + "text": ", respectively." + }, + { + "self_ref": "#/texts/163", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Some algebraic manipulation, after substituting Eq. (20) into Eq. (19), leads to the following radial equation:", + "text": "Some algebraic manipulation, after substituting Eq. (20) into Eq. (19), leads to the following radial equation:" + }, + { + "self_ref": "#/texts/164", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\frac{d^{2}u_{nl}(r)}{dr^{2}}+\\left( \\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}}-\\frac{\\Omega ^{2}}{4\\hbar ^{2}}r^{2}+\\frac{D_{0}}{\\hbar ^{2}A_{0}}\\frac{1}{r}-\\frac{l(l+1)}{r^{2}}\\right) u_{nl}(r)=0.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\frac{d^{2}u_{nl}(r)}{dr^{2}}+\\left( \\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}}-\\frac{\\Omega ^{2}}{4\\hbar ^{2}}r^{2}+\\frac{D_{0}}{\\hbar ^{2}A_{0}}\\frac{1}{r}-\\frac{l(l+1)}{r^{2}}\\right) u_{nl}(r)=0.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/165", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The equation is analytically intractable unless", + "text": "The equation is analytically intractable unless" + }, + { + "self_ref": "#/texts/166", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Omega = 0", + "text": "\\Omega = 0" + }, + { + "self_ref": "#/texts/167", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "owing to the coexistence of", + "text": "owing to the coexistence of" + }, + { + "self_ref": "#/texts/168", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "r^{2}", + "text": "r^{2}" + }, + { + "self_ref": "#/texts/169", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/170", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "-\\frac{1}{r^{2}}", + "text": "-\\frac{1}{r^{2}}" + }, + { + "self_ref": "#/texts/171", + "parent": { + "$ref": "#/groups/19" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "terms. To resolve this difficulty, we employ the Greene–Aldrich approximation scheme of the form [36–39]", + "text": "terms. To resolve this difficulty, we employ the Greene–Aldrich approximation scheme of the form [36–39]" + }, + { + "self_ref": "#/texts/172", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\frac{1}{r^{2}} \\simeq \\frac{\\delta ^{2}}{\\left( 1-e^{-\\delta r}\\right) ^{2}},~~~~~\\frac{1}{r} \\simeq \\frac{\\delta }{1-e^{-\\delta r}},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\frac{1}{r^{2}} \\simeq \\frac{\\delta ^{2}}{\\left( 1-e^{-\\delta r}\\right) ^{2}},~~~~~\\frac{1}{r} \\simeq \\frac{\\delta }{1-e^{-\\delta r}},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/173", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "together with a coordinate transformation", + "text": "together with a coordinate transformation" + }, + { + "self_ref": "#/texts/174", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "y=e^{-\\delta r}", + "text": "y=e^{-\\delta r}" + }, + { + "self_ref": "#/texts/175", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". This type of approximation is commonly used to describe particles governed by a screened potential, where the parameter", + "text": ". This type of approximation is commonly used to describe particles governed by a screened potential, where the parameter" + }, + { + "self_ref": "#/texts/176", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\delta", + "text": "\\delta" + }, + { + "self_ref": "#/texts/177", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is typically chosen to be equal to the screening mass [29,36]. Though the potential in our system is not screened, choosing", + "text": "is typically chosen to be equal to the screening mass [29,36]. Though the potential in our system is not screened, choosing" + }, + { + "self_ref": "#/texts/178", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\delta", + "text": "\\delta" + }, + { + "self_ref": "#/texts/179", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "to be similar to the typical screening mass still provides a good approximation, particularly accurate for distances comparable to or smaller than", + "text": "to be similar to the typical screening mass still provides a good approximation, particularly accurate for distances comparable to or smaller than" + }, + { + "self_ref": "#/texts/180", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "1/\\delta", + "text": "1/\\delta" + }, + { + "self_ref": "#/texts/181", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". For example, in quarkonium models,", + "text": ". For example, in quarkonium models," + }, + { + "self_ref": "#/texts/182", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\delta", + "text": "\\delta" + }, + { + "self_ref": "#/texts/183", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is usually taken to be in the range of approximately 200 to 900 MeV [40,41]. Thus, selecting", + "text": "is usually taken to be in the range of approximately 200 to 900 MeV [40,41]. Thus, selecting" + }, + { + "self_ref": "#/texts/184", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\delta", + "text": "\\delta" + }, + { + "self_ref": "#/texts/185", + "parent": { + "$ref": "#/groups/20" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "within this range is a reasonable choice.", + "text": "within this range is a reasonable choice." + }, + { + "self_ref": "#/texts/186", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Then, by rewriting the notation as", + "text": "Then, by rewriting the notation as" + }, + { + "self_ref": "#/texts/187", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "u_{nl}(r)\\rightarrow U_{nl}(y)", + "text": "u_{nl}(r)\\rightarrow U_{nl}(y)" + }, + { + "self_ref": "#/texts/188", + "parent": { + "$ref": "#/groups/21" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", the differential equation (22) becomes", + "text": ", the differential equation (22) becomes" + }, + { + "self_ref": "#/texts/189", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\frac{d^{2}U_{nl}(y)}{dy^{2}}+\\frac{\\left( 1-y\\right) }{y(1-y)}\\frac{dU_{nl}(y)}{dy}+\\frac{1}{y^{2}(1-y)^{2}}\\begin{bmatrix}-\\left( \\varepsilon _{nl}-6P\\right) y^{2} \\\\+\\left( 2\\varepsilon _{nl}-4P+Q\\right) y \\\\-\\left( \\varepsilon _{nl}-P+Q+\\gamma \\right) \\end{bmatrix} U_{nl}(y)=0,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\frac{d^{2}U_{nl}(y)}{dy^{2}}+\\frac{\\left( 1-y\\right) }{y(1-y)}\\frac{dU_{nl}(y)}{dy}+\\frac{1}{y^{2}(1-y)^{2}}\\begin{bmatrix}-\\left( \\varepsilon _{nl}-6P\\right) y^{2} \\\\+\\left( 2\\varepsilon _{nl}-4P+Q\\right) y \\\\-\\left( \\varepsilon _{nl}-P+Q+\\gamma \\right) \\end{bmatrix} U_{nl}(y)=0,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/190", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/191", + "parent": { + "$ref": "#/texts/100" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\varepsilon _{nl}=\\frac{-\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}},~~~P=-\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}},~~~Q=-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta },~~~\\gamma =l(l+1).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\varepsilon _{nl}=\\frac{-\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}},~~~P=-\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}},~~~Q=-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta },~~~\\gamma =l(l+1).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/192", + "parent": { + "$ref": "#/groups/22" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In the representation of Eq. (24), we neglected higher-order terms corresponding to", + "text": "In the representation of Eq. (24), we neglected higher-order terms corresponding to" + }, + { + "self_ref": "#/texts/193", + "parent": { + "$ref": "#/groups/22" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "y^{3}", + "text": "y^{3}" + }, + { + "self_ref": "#/texts/194", + "parent": { + "$ref": "#/groups/22" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/195", + "parent": { + "$ref": "#/groups/22" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "y^{4}", + "text": "y^{4}" + }, + { + "self_ref": "#/texts/196", + "parent": { + "$ref": "#/groups/22" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". To solve this equation, we use the NUFA method [29] outlined in the next subsection.", + "text": ". To solve this equation, we use the NUFA method [29] outlined in the next subsection." + }, + { + "self_ref": "#/texts/197", + "parent": { + "$ref": "#/texts/10" + }, + "children": [ + { + "$ref": "#/texts/198" + }, + { + "$ref": "#/texts/199" + }, + { + "$ref": "#/groups/23" + }, + { + "$ref": "#/texts/210" + }, + { + "$ref": "#/texts/211" + }, + { + "$ref": "#/texts/212" + }, + { + "$ref": "#/texts/213" + }, + { + "$ref": "#/texts/214" + }, + { + "$ref": "#/texts/215" + }, + { + "$ref": "#/texts/216" + }, + { + "$ref": "#/texts/217" + }, + { + "$ref": "#/texts/218" + }, + { + "$ref": "#/texts/219" + }, + { + "$ref": "#/groups/24" + }, + { + "$ref": "#/texts/223" + }, + { + "$ref": "#/groups/25" + }, + { + "$ref": "#/texts/227" + }, + { + "$ref": "#/groups/26" + }, + { + "$ref": "#/groups/27" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.2.", + "text": "2.2.", + "level": 2 + }, + { + "self_ref": "#/texts/198", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "A second-order differential equation of hypergeometric type can be solved in a simple and elegant way by using the so-called NUFA method. This is an improvement over the parametric NU method, which is relatively cumbersome due to the need to find the square of the polynomials and other conditions required. With the NUFA method, many diffierential equations, including our wave equation, can be properly tackled, allowing for the identification of singularities and the determination of the eigenvalue spectrum. As a general type of second-order differential equation, which encompasses Eq. (24), we consider the following equation [31]:", + "text": "A second-order differential equation of hypergeometric type can be solved in a simple and elegant way by using the so-called NUFA method. This is an improvement over the parametric NU method, which is relatively cumbersome due to the need to find the square of the polynomials and other conditions required. With the NUFA method, many diffierential equations, including our wave equation, can be properly tackled, allowing for the identification of singularities and the determination of the eigenvalue spectrum. As a general type of second-order differential equation, which encompasses Eq. (24), we consider the following equation [31]:" + }, + { + "self_ref": "#/texts/199", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\left[ \\frac{d^{2}}{ds^{2}}+\\frac{\\alpha _{1}-\\alpha _{2}s}{s(1-\\alpha _{3}s)}\\frac{d}{ds}+\\frac{-\\zeta _{1}s^{2}+\\zeta _{2}s-\\zeta _{3}}{s^{2}(1-\\alpha _{3}s)^{2}}\\right] \\phi (s)=0,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\left[ \\frac{d^{2}}{ds^{2}}+\\frac{\\alpha _{1}-\\alpha _{2}s}{s(1-\\alpha _{3}s)}\\frac{d}{ds}+\\frac{-\\zeta _{1}s^{2}+\\zeta _{2}s-\\zeta _{3}}{s^{2}(1-\\alpha _{3}s)^{2}}\\right] \\phi (s)=0,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/200", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/201", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\alpha _{i}", + "text": "\\alpha _{i}" + }, + { + "self_ref": "#/texts/202", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "(i=1,2,3)", + "text": "(i=1,2,3)" + }, + { + "self_ref": "#/texts/203", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/204", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\zeta _{i}", + "text": "\\zeta _{i}" + }, + { + "self_ref": "#/texts/205", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are parameters appropriately chosen depending on a given system. Because this equation has two singularities at", + "text": "are parameters appropriately chosen depending on a given system. Because this equation has two singularities at" + }, + { + "self_ref": "#/texts/206", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "s\\rightarrow 0", + "text": "s\\rightarrow 0" + }, + { + "self_ref": "#/texts/207", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/208", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "s\\rightarrow 1/\\alpha _{3}", + "text": "s\\rightarrow 1/\\alpha _{3}" + }, + { + "self_ref": "#/texts/209", + "parent": { + "$ref": "#/groups/23" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", it is possible to take the solution in the form", + "text": ", it is possible to take the solution in the form" + }, + { + "self_ref": "#/texts/210", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\phi (s)=s^{\\lambda }(1-\\alpha _{3}s)^{\\upsilon }f(s).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\phi (s)=s^{\\lambda }(1-\\alpha _{3}s)^{\\upsilon }f(s).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/211", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "By substituting this formula into Eq. (26) along with the following choice,", + "text": "By substituting this formula into Eq. (26) along with the following choice," + }, + { + "self_ref": "#/texts/212", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\lambda =\\frac{1}{2}\\lbrace 1-\\alpha _{1}\\pm [\\left( 1-\\alpha _{1}\\right) ^{2}+4\\zeta _{3}]^{1/2}\\rbrace ,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\lambda =\\frac{1}{2}\\lbrace 1-\\alpha _{1}\\pm [\\left( 1-\\alpha _{1}\\right) ^{2}+4\\zeta _{3}]^{1/2}\\rbrace ,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/213", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\upsilon &=&\\frac{1}{2\\alpha _{3}} \\lbrace \\alpha _{3}+\\alpha _{1}\\alpha _{3}-\\alpha _{2}\\pm [\\left( \\alpha _{3}+\\alpha _{1}\\alpha _{3}-\\alpha _{2}\\right) ^{2} \\\\&&+4\\alpha _3 (\\zeta _{3}\\alpha _{3}-\\zeta _{2}+\\zeta _{1}/\\alpha _{3})]^{1/2}\\rbrace ,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\upsilon &=&\\frac{1}{2\\alpha _{3}} \\lbrace \\alpha _{3}+\\alpha _{1}\\alpha _{3}-\\alpha _{2}\\pm [\\left( \\alpha _{3}+\\alpha _{1}\\alpha _{3}-\\alpha _{2}\\right) ^{2} \\\\&&+4\\alpha _3 (\\zeta _{3}\\alpha _{3}-\\zeta _{2}+\\zeta _{1}/\\alpha _{3})]^{1/2}\\rbrace ,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/214", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "we have [29]", + "text": "we have [29]" + }, + { + "self_ref": "#/texts/215", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\ns(1-\\alpha _{3}s)\\frac{d^{2}f(s)}{ds^{2}}+\\kappa \\frac{df(s)}{ds} -\\alpha _{3} \\kappa _+ \\kappa _- f(s)=0,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\ns(1-\\alpha _{3}s)\\frac{d^{2}f(s)}{ds^{2}}+\\kappa \\frac{df(s)}{ds} -\\alpha _{3} \\kappa _+ \\kappa _- f(s)=0,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/216", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/217", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\kappa = \\alpha _{1}+2\\lambda -(2\\lambda \\alpha _{3}+2\\upsilon \\alpha _{3}+\\alpha _{2})s,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\kappa = \\alpha _{1}+2\\lambda -(2\\lambda \\alpha _{3}+2\\upsilon \\alpha _{3}+\\alpha _{2})s,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/218", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\kappa _\\pm = \\lambda +\\upsilon +\\frac{1}{2}\\left( \\frac{\\alpha _{2}}{\\alpha _{3}}-1\\right) \\pm \\bigg [ \\frac{1}{4}\\left( \\frac{\\alpha _{2}}{\\alpha _{3}}-1\\right) ^{2}+\\frac{\\zeta _{1}}{\\alpha _{3}^{2}} \\bigg ]^{1/2}.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\kappa _\\pm = \\lambda +\\upsilon +\\frac{1}{2}\\left( \\frac{\\alpha _{2}}{\\alpha _{3}}-1\\right) \\pm \\bigg [ \\frac{1}{4}\\left( \\frac{\\alpha _{2}}{\\alpha _{3}}-1\\right) ^{2}+\\frac{\\zeta _{1}}{\\alpha _{3}^{2}} \\bigg ]^{1/2}.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/219", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Thus, the equation is reduced to a tractable form.", + "text": "Thus, the equation is reduced to a tractable form." + }, + { + "self_ref": "#/texts/220", + "parent": { + "$ref": "#/groups/24" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "We now examine a simple case where", + "text": "We now examine a simple case where" + }, + { + "self_ref": "#/texts/221", + "parent": { + "$ref": "#/groups/24" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\alpha _3=1", + "text": "\\alpha _3=1" + }, + { + "self_ref": "#/texts/222", + "parent": { + "$ref": "#/groups/24" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", which meets the equation associated with our considered system. Under the condition that the principal quantum number takes the form", + "text": ", which meets the equation associated with our considered system. Under the condition that the principal quantum number takes the form" + }, + { + "self_ref": "#/texts/223", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nn=- \\kappa _- ,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nn=- \\kappa _- ,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/224", + "parent": { + "$ref": "#/groups/25" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "the function", + "text": "the function" + }, + { + "self_ref": "#/texts/225", + "parent": { + "$ref": "#/groups/25" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "f(s)", + "text": "f(s)" + }, + { + "self_ref": "#/texts/226", + "parent": { + "$ref": "#/groups/25" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is represented by the Gauss hypergeometric function as [29]", + "text": "is represented by the Gauss hypergeometric function as [29]" + }, + { + "self_ref": "#/texts/227", + "parent": { + "$ref": "#/texts/197" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nf (s) = {_{2}F_{1}}(a,b;c;s),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nf (s) = {_{2}F_{1}}(a,b;c;s),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/228", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/229", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "a=\\kappa _-", + "text": "a=\\kappa _-" + }, + { + "self_ref": "#/texts/230", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ",", + "text": "," + }, + { + "self_ref": "#/texts/231", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "b=\\kappa _+", + "text": "b=\\kappa _+" + }, + { + "self_ref": "#/texts/232", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", and", + "text": ", and" + }, + { + "self_ref": "#/texts/233", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "c=\\alpha _{1}+2\\lambda", + "text": "c=\\alpha _{1}+2\\lambda" + }, + { + "self_ref": "#/texts/234", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". The mathematical formula of this hypergeometric function is as follows:", + "text": ". The mathematical formula of this hypergeometric function is as follows:" + }, + { + "self_ref": "#/texts/235", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{2}F_{1}(a,b;c;s)=\\sum _{k=0}^{\\infty }\\frac{\\left( a\\right) _{k}\\left( b\\right) _{k}}{\\left( c\\right) _{k}k!}s^{k}", + "text": "_{2}F_{1}(a,b;c;s)=\\sum _{k=0}^{\\infty }\\frac{\\left( a\\right) _{k}\\left( b\\right) _{k}}{\\left( c\\right) _{k}k!}s^{k}" + }, + { + "self_ref": "#/texts/236", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", where", + "text": ", where" + }, + { + "self_ref": "#/texts/237", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\left( a\\right) _{k}=a(a+1)(a+2)\\cdots (a+k-1)", + "text": "\\left( a\\right) _{k}=a(a+1)(a+2)\\cdots (a+k-1)" + }, + { + "self_ref": "#/texts/238", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "for", + "text": "for" + }, + { + "self_ref": "#/texts/239", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "k\\ge 1", + "text": "k\\ge 1" + }, + { + "self_ref": "#/texts/240", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/241", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\left( a\\right) _{0}=1", + "text": "\\left( a\\right) _{0}=1" + }, + { + "self_ref": "#/texts/242", + "parent": { + "$ref": "#/groups/26" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ".", + "text": "." + }, + { + "self_ref": "#/texts/243", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The Gauss hypergeometric function", + "text": "The Gauss hypergeometric function" + }, + { + "self_ref": "#/texts/244", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{2}F_{1}(a,b;c;s)", + "text": "_{2}F_{1}(a,b;c;s)" + }, + { + "self_ref": "#/texts/245", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "serves as a fundamental and unifying special function that encompasses many other functions as special or limiting cases [42–45]. As defined in Eq. (34) with Eq. (33), the function", + "text": "serves as a fundamental and unifying special function that encompasses many other functions as special or limiting cases [42–45]. As defined in Eq. (34) with Eq. (33), the function" + }, + { + "self_ref": "#/texts/246", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{2}F_{1}(a,b;c;s)", + "text": "_{2}F_{1}(a,b;c;s)" + }, + { + "self_ref": "#/texts/247", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "terminates and reduces to a polynomial when", + "text": "terminates and reduces to a polynomial when" + }, + { + "self_ref": "#/texts/248", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "a", + "text": "a", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/249", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is zero or a negative integer, a case that is often associated with Laguerre polynomials. In particular,", + "text": "is zero or a negative integer, a case that is often associated with Laguerre polynomials. In particular," + }, + { + "self_ref": "#/texts/250", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{2}F_{1}(a,b;c;s)", + "text": "_{2}F_{1}(a,b;c;s)" + }, + { + "self_ref": "#/texts/251", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is a generalization of Kummer’s confluent hypergeometric function", + "text": "is a generalization of Kummer’s confluent hypergeometric function" + }, + { + "self_ref": "#/texts/252", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{1}F_{1}(a;c;s)", + "text": "_{1}F_{1}(a;c;s)" + }, + { + "self_ref": "#/texts/253", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Hence, in the limiting case where", + "text": ". Hence, in the limiting case where" + }, + { + "self_ref": "#/texts/254", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "b \\rightarrow \\infty", + "text": "b \\rightarrow \\infty" + }, + { + "self_ref": "#/texts/255", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ",", + "text": "," + }, + { + "self_ref": "#/texts/256", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{2}F_{1}(a,b;c;s)", + "text": "_{2}F_{1}(a,b;c;s)" + }, + { + "self_ref": "#/texts/257", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "converges to", + "text": "converges to" + }, + { + "self_ref": "#/texts/258", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "_{1}F_{1}(a;c;s)", + "text": "_{1}F_{1}(a;c;s)" + }, + { + "self_ref": "#/texts/259", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". This convergence is rooted in the fact that, while", + "text": ". This convergence is rooted in the fact that, while" + }, + { + "self_ref": "#/texts/260", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{}_2F_1", + "text": "{}_2F_1" + }, + { + "self_ref": "#/texts/261", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "has two regular singular points, sending one of them to infinity (a process known as confluence) effectively merges the singularities, giving rise to", + "text": "has two regular singular points, sending one of them to infinity (a process known as confluence) effectively merges the singularities, giving rise to" + }, + { + "self_ref": "#/texts/262", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{}_1F_1", + "text": "{}_1F_1" + }, + { + "self_ref": "#/texts/263", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[44]. Additionally, the Jacobi polynomials can also be expressed in terms of the Gauss hypergeometric function (for a typical representation, see formula 15.9.1 of Ref. [45]). The", + "text": "[44]. Additionally, the Jacobi polynomials can also be expressed in terms of the Gauss hypergeometric function (for a typical representation, see formula 15.9.1 of Ref. [45]). The" + }, + { + "self_ref": "#/texts/264", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{}_2F_1", + "text": "{}_2F_1" + }, + { + "self_ref": "#/texts/265", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "function thus plays a major role in constructing analytical solutions and enables rigorous, systematic analysis across a broad spectrum of scientific disciplines beyond mathematics and physics. The method presented in this subsection, which naturally leads to the appearance of the", + "text": "function thus plays a major role in constructing analytical solutions and enables rigorous, systematic analysis across a broad spectrum of scientific disciplines beyond mathematics and physics. The method presented in this subsection, which naturally leads to the appearance of the" + }, + { + "self_ref": "#/texts/266", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{}_2F_1", + "text": "{}_2F_1" + }, + { + "self_ref": "#/texts/267", + "parent": { + "$ref": "#/groups/27" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "function as a solution, is applicable to the analysis of a wide class of physical systems governed by one or more interaction potentials. It is, of course, well suited to handling Eq. (24).", + "text": "function as a solution, is applicable to the analysis of a wide class of physical systems governed by one or more interaction potentials. It is, of course, well suited to handling Eq. (24)." + }, + { + "self_ref": "#/texts/268", + "parent": { + "$ref": "#/texts/10" + }, + "children": [ + { + "$ref": "#/texts/269" + }, + { + "$ref": "#/texts/366" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.3.", + "text": "2.3.", + "level": 2 + }, + { + "self_ref": "#/texts/269", + "parent": { + "$ref": "#/texts/268" + }, + "children": [ + { + "$ref": "#/groups/28" + }, + { + "$ref": "#/groups/29" + }, + { + "$ref": "#/texts/292" + }, + { + "$ref": "#/groups/30" + }, + { + "$ref": "#/texts/298" + }, + { + "$ref": "#/texts/299" + }, + { + "$ref": "#/texts/300" + }, + { + "$ref": "#/groups/31" + }, + { + "$ref": "#/texts/314" + }, + { + "$ref": "#/texts/315" + }, + { + "$ref": "#/groups/32" + }, + { + "$ref": "#/texts/319" + }, + { + "$ref": "#/groups/33" + }, + { + "$ref": "#/texts/325" + }, + { + "$ref": "#/texts/326" + }, + { + "$ref": "#/groups/34" + }, + { + "$ref": "#/texts/330" + }, + { + "$ref": "#/texts/331" + }, + { + "$ref": "#/texts/332" + }, + { + "$ref": "#/texts/333" + }, + { + "$ref": "#/texts/334" + }, + { + "$ref": "#/groups/35" + }, + { + "$ref": "#/texts/350" + }, + { + "$ref": "#/groups/36" + }, + { + "$ref": "#/texts/354" + }, + { + "$ref": "#/texts/355" + }, + { + "$ref": "#/texts/356" + }, + { + "$ref": "#/groups/37" + }, + { + "$ref": "#/texts/360" + }, + { + "$ref": "#/texts/361" + }, + { + "$ref": "#/texts/362" + }, + { + "$ref": "#/groups/38" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.3.1.", + "text": "2.3.1.", + "level": 3 + }, + { + "self_ref": "#/texts/270", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "We now proceed to derive the wave functions of the system. For clarity and better reader understanding, we briefly summarize our strategy at this point. Because, in our time-dependent case, the wave functions are described by the eigenfunctions of", + "text": "We now proceed to derive the wave functions of the system. For clarity and better reader understanding, we briefly summarize our strategy at this point. Because, in our time-dependent case, the wave functions are described by the eigenfunctions of" + }, + { + "self_ref": "#/texts/271", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I(t)", + "text": "I(t)" + }, + { + "self_ref": "#/texts/272", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "(instead of", + "text": "(instead of" + }, + { + "self_ref": "#/texts/273", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "H(t)", + "text": "H(t)" + }, + { + "self_ref": "#/texts/274", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "), we need to determine the eigenfunctions", + "text": "), we need to determine the eigenfunctions" + }, + { + "self_ref": "#/texts/275", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n", + "text": "\\Phi _n" + }, + { + "self_ref": "#/texts/276", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "of Eq. (14). However, directly solving for", + "text": "of Eq. (14). However, directly solving for" + }, + { + "self_ref": "#/texts/277", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n", + "text": "\\Phi _n" + }, + { + "self_ref": "#/texts/278", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is likely to be a formidable task. To circumvent this difficulty, we first derive", + "text": "is likely to be a formidable task. To circumvent this difficulty, we first derive" + }, + { + "self_ref": "#/texts/279", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n^{\\prime }", + "text": "\\Phi _n^{\\prime }" + }, + { + "self_ref": "#/texts/280", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "from Eq. (17), and then reconstruct the desired eigenfunctions", + "text": "from Eq. (17), and then reconstruct the desired eigenfunctions" + }, + { + "self_ref": "#/texts/281", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n", + "text": "\\Phi _n" + }, + { + "self_ref": "#/texts/282", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "by inversely transforming the obtained", + "text": "by inversely transforming the obtained" + }, + { + "self_ref": "#/texts/283", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n^{\\prime }", + "text": "\\Phi _n^{\\prime }" + }, + { + "self_ref": "#/texts/284", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", utilizing the inverse relation of Eq. (15) (for the inverse relation, see Eq. (46), which appears later). Once", + "text": ", utilizing the inverse relation of Eq. (15) (for the inverse relation, see Eq. (46), which appears later). Once" + }, + { + "self_ref": "#/texts/285", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n", + "text": "\\Phi _n" + }, + { + "self_ref": "#/texts/286", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are obtained, the wave functions", + "text": "are obtained, the wave functions" + }, + { + "self_ref": "#/texts/287", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Psi _n", + "text": "\\Psi _n" + }, + { + "self_ref": "#/texts/288", + "parent": { + "$ref": "#/groups/28" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "can be expressed in terms of them accordingly.", + "text": "can be expressed in terms of them accordingly." + }, + { + "self_ref": "#/texts/289", + "parent": { + "$ref": "#/groups/29" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "To obtain", + "text": "To obtain" + }, + { + "self_ref": "#/texts/290", + "parent": { + "$ref": "#/groups/29" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _n^{\\prime }", + "text": "\\Phi _n^{\\prime }" + }, + { + "self_ref": "#/texts/291", + "parent": { + "$ref": "#/groups/29" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", we need to solve Eq. (24) by setting the associated functions, in accordance with Eq. (27), as", + "text": ", we need to solve Eq. (24) by setting the associated functions, in accordance with Eq. (27), as" + }, + { + "self_ref": "#/texts/292", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nU_{nl}(y)=N_{nl}y^{\\lambda }(1-y)^{\\upsilon }f_{nl}(y),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nU_{nl}(y)=N_{nl}y^{\\lambda }(1-y)^{\\upsilon }f_{nl}(y),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/293", + "parent": { + "$ref": "#/groups/30" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/294", + "parent": { + "$ref": "#/groups/30" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "N_{nl}", + "text": "N_{nl}" + }, + { + "self_ref": "#/texts/295", + "parent": { + "$ref": "#/groups/30" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are normalization constants for the final wave functions. We applied", + "text": "are normalization constants for the final wave functions. We applied" + }, + { + "self_ref": "#/texts/296", + "parent": { + "$ref": "#/groups/30" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\alpha _{3}=1", + "text": "\\alpha _{3}=1" + }, + { + "self_ref": "#/texts/297", + "parent": { + "$ref": "#/groups/30" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in this equation. In addition, from the comparison of Eq. (24) with the NUFA equation (26), we acquire the following parameter relations:", + "text": "in this equation. In addition, from the comparison of Eq. (24) with the NUFA equation (26), we acquire the following parameter relations:" + }, + { + "self_ref": "#/texts/298", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n&&\\alpha _{1}=\\alpha _{2}=1,\\quad\\quad\\quad\\zeta _{1}=\\varepsilon _{nl}-6P,~~~~~~ \\\\&&\\zeta _{2}=2\\varepsilon _{nl}-4P+Q,\\quad\\quad\\zeta _{3}=\\varepsilon _{nl}-P+Q+\\gamma .~~~~~~\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n&&\\alpha _{1}=\\alpha _{2}=1,\\quad\\quad\\quad\\zeta _{1}=\\varepsilon _{nl}-6P,~~~~~~ \\\\&&\\zeta _{2}=2\\varepsilon _{nl}-4P+Q,\\quad\\quad\\zeta _{3}=\\varepsilon _{nl}-P+Q+\\gamma .~~~~~~\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/299", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Furthermore, the singularity exponents reduce to", + "text": "Furthermore, the singularity exponents reduce to" + }, + { + "self_ref": "#/texts/300", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\lambda =\\sqrt{\\varepsilon _{nl}-P+Q+\\gamma },\\quad\\quad\\upsilon =\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}-3P}.~~~~~~\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\lambda =\\sqrt{\\varepsilon _{nl}-P+Q+\\gamma },\\quad\\quad\\upsilon =\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}-3P}.~~~~~~\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/301", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Although the original NUFA representations of", + "text": "Although the original NUFA representations of" + }, + { + "self_ref": "#/texts/302", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\lambda", + "text": "\\lambda" + }, + { + "self_ref": "#/texts/303", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/304", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\upsilon", + "text": "\\upsilon" + }, + { + "self_ref": "#/texts/305", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", given in Eqs. (28) and (29), include both positive and negative signs (", + "text": ", given in Eqs. (28) and (29), include both positive and negative signs (" + }, + { + "self_ref": "#/texts/306", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\pm", + "text": "\\pm" + }, + { + "self_ref": "#/texts/307", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ") from a purely mathematical perspective, we choose to retain only the positive sign in this case as can be seen from Eq. (37). This selection is due to the fundamental physical constraint, which is that the wave functions must remain finite throughout the entire range of", + "text": ") from a purely mathematical perspective, we choose to retain only the positive sign in this case as can be seen from Eq. (37). This selection is due to the fundamental physical constraint, which is that the wave functions must remain finite throughout the entire range of" + }, + { + "self_ref": "#/texts/308", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "r", + "text": "r", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/309", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and must vanish as", + "text": "and must vanish as" + }, + { + "self_ref": "#/texts/310", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "r \\rightarrow 0", + "text": "r \\rightarrow 0" + }, + { + "self_ref": "#/texts/311", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/312", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "r \\rightarrow \\infty", + "text": "r \\rightarrow \\infty" + }, + { + "self_ref": "#/texts/313", + "parent": { + "$ref": "#/groups/31" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ".", + "text": "." + }, + { + "self_ref": "#/texts/314", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The expansion of Eq. (33) under the first condition in Eq. (36) yields", + "text": "The expansion of Eq. (33) under the first condition in Eq. (36) yields" + }, + { + "self_ref": "#/texts/315", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nn+\\lambda +\\upsilon = \\sqrt{\\zeta _1}.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nn+\\lambda +\\upsilon = \\sqrt{\\zeta _1}.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/316", + "parent": { + "$ref": "#/groups/32" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "By squaring both sides of this equation and using the second relation from Eq. (36) together with the formula of", + "text": "By squaring both sides of this equation and using the second relation from Eq. (36) together with the formula of" + }, + { + "self_ref": "#/texts/317", + "parent": { + "$ref": "#/groups/32" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\lambda", + "text": "\\lambda" + }, + { + "self_ref": "#/texts/318", + "parent": { + "$ref": "#/groups/32" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "given in Eq. (37), the equation associated with the eigenvalues can be obtained as", + "text": "given in Eq. (37), the equation associated with the eigenvalues can be obtained as" + }, + { + "self_ref": "#/texts/319", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n2[\\varepsilon _{nl}-P+Q+\\gamma ]^{1/2}(n+\\upsilon )+(n+\\upsilon )^{2}+5P+Q+\\gamma =0.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n2[\\varepsilon _{nl}-P+Q+\\gamma ]^{1/2}(n+\\upsilon )+(n+\\upsilon )^{2}+5P+Q+\\gamma =0.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/320", + "parent": { + "$ref": "#/groups/33" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "It is possible to rearrange this equation into a formula for", + "text": "It is possible to rearrange this equation into a formula for" + }, + { + "self_ref": "#/texts/321", + "parent": { + "$ref": "#/groups/33" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\varepsilon _{nl}", + "text": "\\varepsilon _{nl}" + }, + { + "self_ref": "#/texts/322", + "parent": { + "$ref": "#/groups/33" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "by first isolating the square root term on the left-hand side, shifting all other terms to the right-hand side, and then squaring both sides. Substituting the expression for", + "text": "by first isolating the square root term on the left-hand side, shifting all other terms to the right-hand side, and then squaring both sides. Substituting the expression for" + }, + { + "self_ref": "#/texts/323", + "parent": { + "$ref": "#/groups/33" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\upsilon", + "text": "\\upsilon" + }, + { + "self_ref": "#/texts/324", + "parent": { + "$ref": "#/groups/33" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "from Eq. (37) into the resulting equation, and using Eq. (25), we finally obtain the eigenvalues:", + "text": "from Eq. (37) into the resulting equation, and using Eq. (25), we finally obtain the eigenvalues:" + }, + { + "self_ref": "#/texts/325", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Lambda _{nl} &=& l(l+1)\\hbar ^{2}A_{0}\\delta ^{2}+\\frac{A_{0}\\Omega ^{2}}{4\\delta ^{2}}-D_{0}\\delta \\\\&&-\\frac{\\hbar ^{2}A_{0}\\delta ^{2}}{4}\\left[ \\frac{l(l+1)-\\frac{5\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+\\left( n+\\upsilon \\right) ^{2}}{n+\\upsilon }\\right] ^{2}.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Lambda _{nl} &=& l(l+1)\\hbar ^{2}A_{0}\\delta ^{2}+\\frac{A_{0}\\Omega ^{2}}{4\\delta ^{2}}-D_{0}\\delta \\\\&&-\\frac{\\hbar ^{2}A_{0}\\delta ^{2}}{4}\\left[ \\frac{l(l+1)-\\frac{5\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+\\left( n+\\upsilon \\right) ^{2}}{n+\\upsilon }\\right] ^{2}.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/326", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The eigenvalues obtained in Eq. (40) depend explicitly on several parameters of the system, including the effective oscillator frequency and coupling constants, which may exhibit time dependence through the Hamiltonian. This feature reflects the fact that the present model belongs to the class of parameter-dependent quantum systems. In such systems, variations of the energy spectrum with respect to the relevant parameters are conceptually related to the Hellmann–Feynman theorem [46,47], which expresses derivatives of eigenvalues in terms of expectation values of derivatives of the Hamiltonian. Although the present work does not explicitly employ the Hellmann–Feynman theorem in deriving Eq. (40), the obtained spectrum is consistent with the general framework of parameter-dependent quantum mechanics.", + "text": "The eigenvalues obtained in Eq. (40) depend explicitly on several parameters of the system, including the effective oscillator frequency and coupling constants, which may exhibit time dependence through the Hamiltonian. This feature reflects the fact that the present model belongs to the class of parameter-dependent quantum systems. In such systems, variations of the energy spectrum with respect to the relevant parameters are conceptually related to the Hellmann–Feynman theorem [46,47], which expresses derivatives of eigenvalues in terms of expectation values of derivatives of the Hamiltonian. Although the present work does not explicitly employ the Hellmann–Feynman theorem in deriving Eq. (40), the obtained spectrum is consistent with the general framework of parameter-dependent quantum mechanics." + }, + { + "self_ref": "#/texts/327", + "parent": { + "$ref": "#/groups/34" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In addition, by considering Eq. (27) with Eqs. (28), (29), and (34), the corresponding radial wave functions can be derived in terms of", + "text": "In addition, by considering Eq. (27) with Eqs. (28), (29), and (34), the corresponding radial wave functions can be derived in terms of" + }, + { + "self_ref": "#/texts/328", + "parent": { + "$ref": "#/groups/34" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Lambda _{nl}", + "text": "\\Lambda _{nl}" + }, + { + "self_ref": "#/texts/329", + "parent": { + "$ref": "#/groups/34" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "to be", + "text": "to be" + }, + { + "self_ref": "#/texts/330", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nU_{nl}(y) &=& N_{nl}y^{\\sqrt{-\\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}}+\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+l(l+1)}} \\\\&&\\times {(1-y)^{\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}+\\frac{3\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}}}}{_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};y),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nU_{nl}(y) &=& N_{nl}y^{\\sqrt{-\\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}}+\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+l(l+1)}} \\\\&&\\times {(1-y)^{\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}+\\frac{3\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}}}}{_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};y),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/331", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where the parameters are defined as", + "text": "where the parameters are defined as" + }, + { + "self_ref": "#/texts/332", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\bar{a}=\\lambda +\\upsilon -\\sqrt{\\varepsilon _{nl}-6P},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\bar{a}=\\lambda +\\upsilon -\\sqrt{\\varepsilon _{nl}-6P},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/333", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\bar{b}=\\lambda +\\upsilon +\\sqrt{\\varepsilon _{nl}-6P},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\bar{b}=\\lambda +\\upsilon +\\sqrt{\\varepsilon _{nl}-6P},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/334", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\bar{c}=1+2\\lambda .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\bar{c}=1+2\\lambda .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/335", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "From a mathematical standpoint, it is possible to define the parameter", + "text": "From a mathematical standpoint, it is possible to define the parameter" + }, + { + "self_ref": "#/texts/336", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{a}", + "text": "\\bar{a}" + }, + { + "self_ref": "#/texts/337", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "interchangeably with", + "text": "interchangeably with" + }, + { + "self_ref": "#/texts/338", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{b}", + "text": "\\bar{b}" + }, + { + "self_ref": "#/texts/339", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". However, by choosing", + "text": ". However, by choosing" + }, + { + "self_ref": "#/texts/340", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{a}", + "text": "\\bar{a}" + }, + { + "self_ref": "#/texts/341", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "as in Eq. (42), we allow the possibility for", + "text": "as in Eq. (42), we allow the possibility for" + }, + { + "self_ref": "#/texts/342", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{a}", + "text": "\\bar{a}" + }, + { + "self_ref": "#/texts/343", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "to take on negative values. When", + "text": "to take on negative values. When" + }, + { + "self_ref": "#/texts/344", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{a}", + "text": "\\bar{a}" + }, + { + "self_ref": "#/texts/345", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is a nonpositive integer (i.e. a negative integer or zero), the Gauss hypergeometric function in Eq. (41) terminates at the (", + "text": "is a nonpositive integer (i.e. a negative integer or zero), the Gauss hypergeometric function in Eq. (41) terminates at the (" + }, + { + "self_ref": "#/texts/346", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "n+1", + "text": "n+1" + }, + { + "self_ref": "#/texts/347", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ")th term and reduces to a polynomial. Through the use of Eq. (20) with Eq. (41), the full eigenfunctions of the invariant operator", + "text": ")th term and reduces to a polynomial. Through the use of Eq. (20) with Eq. (41), the full eigenfunctions of the invariant operator" + }, + { + "self_ref": "#/texts/348", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "I_{0}", + "text": "I_{0}" + }, + { + "self_ref": "#/texts/349", + "parent": { + "$ref": "#/groups/35" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are represented in the form", + "text": "are represented in the form" + }, + { + "self_ref": "#/texts/350", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi ) &=& N_{nl}\\frac{1}{r}e^{-\\left( \\sqrt{-\\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}}+\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+l(l+1)}\\right) \\delta r} \\\\&&\\times \\left( 1-e^{-\\delta r}\\right) ^{\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}+\\frac{3\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}}}{_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};e^{-\\delta r})Y_{lm}(\\theta ,\\varphi ).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Phi _{nlm}^{\\prime }(r,\\theta ,\\varphi ) &=& N_{nl}\\frac{1}{r}e^{-\\left( \\sqrt{-\\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}}+\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+l(l+1)}\\right) \\delta r} \\\\&&\\times \\left( 1-e^{-\\delta r}\\right) ^{\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}+\\frac{3\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}}}{_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};e^{-\\delta r})Y_{lm}(\\theta ,\\varphi ).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/351", + "parent": { + "$ref": "#/groups/36" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The complete eigenfunctions of the invariant", + "text": "The complete eigenfunctions of the invariant" + }, + { + "self_ref": "#/texts/352", + "parent": { + "$ref": "#/groups/36" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "I", + "text": "I", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/353", + "parent": { + "$ref": "#/groups/36" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "that belongs to the original system, i.e., the untransformed system, are given by the relation", + "text": "that belongs to the original system, i.e., the untransformed system, are given by the relation" + }, + { + "self_ref": "#/texts/354", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Phi _{nlm}(\\vec{r},t)=U^{-1}(t)\\Phi _{nlm}^{\\prime }(\\vec{r}).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Phi _{nlm}(\\vec{r},t)=U^{-1}(t)\\Phi _{nlm}^{\\prime }(\\vec{r}).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/355", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The evaluation of this using Eqs. (16) and (45) results in", + "text": "The evaluation of this using Eqs. (16) and (45) results in" + }, + { + "self_ref": "#/texts/356", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Phi _{nlm}(r,\\theta ,\\varphi ,t) &=& N_{nl} \\frac{1 }{\\sqrt{\\rho (t)}r} \\exp \\left( \\frac{i\\mu (t)\\dot{\\rho }(t)}{2\\hbar \\rho (t)}r^{2}\\right) \\\\&&\\times e^{-\\left( \\sqrt{-\\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}}+\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+l(l+1)}\\right) \\delta \\frac{r}{\\rho (t) }} \\\\&&\\times \\left( 1-e^{-\\delta \\frac{r}{\\rho (t) }}\\right) ^{\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}+\\frac{3\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}}}{_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};e^{-\\delta \\frac{r}{\\rho (t) }})Y_{lm}(\\theta ,\\varphi ).\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Phi _{nlm}(r,\\theta ,\\varphi ,t) &=& N_{nl} \\frac{1 }{\\sqrt{\\rho (t)}r} \\exp \\left( \\frac{i\\mu (t)\\dot{\\rho }(t)}{2\\hbar \\rho (t)}r^{2}\\right) \\\\&&\\times e^{-\\left( \\sqrt{-\\frac{\\Lambda _{nl}}{\\hbar ^{2}A_{0}\\delta ^{2}}+\\frac{\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}-\\frac{D_{0}}{\\hbar ^{2}A_{0}\\delta }+l(l+1)}\\right) \\delta \\frac{r}{\\rho (t) }} \\\\&&\\times \\left( 1-e^{-\\delta \\frac{r}{\\rho (t) }}\\right) ^{\\frac{1}{2}+\\sqrt{\\left( l+\\frac{1}{2}\\right) ^{2}+\\frac{3\\Omega ^{2}}{4\\hbar ^{2}\\delta ^{4}}}}{_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};e^{-\\delta \\frac{r}{\\rho (t) }})Y_{lm}(\\theta ,\\varphi ).\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/357", + "parent": { + "$ref": "#/groups/37" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Meanwhile,", + "text": "Meanwhile," + }, + { + "self_ref": "#/texts/358", + "parent": { + "$ref": "#/groups/37" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "N_{nl}", + "text": "N_{nl}" + }, + { + "self_ref": "#/texts/359", + "parent": { + "$ref": "#/groups/37" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are derived by normalizing these resultant eigenfunctions. The normalization procedure yields (see Appendix A for a detailed evaluation)", + "text": "are derived by normalizing these resultant eigenfunctions. The normalization procedure yields (see Appendix A for a detailed evaluation)" + }, + { + "self_ref": "#/texts/360", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nN_{nl}=\\sqrt{\\delta } \\frac{\\Gamma (2(\\upsilon +\\lambda )+n)}{n!\\Gamma (2\\lambda +1)} \\Bigg [ \\sum _{k=0}^n\\sum _{k^{\\prime }=0}^n (-1)^{k+k^{\\prime }} \\mathcal {N}_{nl}(k,k^{\\prime }) \\Bigg ]^{-1/2},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nN_{nl}=\\sqrt{\\delta } \\frac{\\Gamma (2(\\upsilon +\\lambda )+n)}{n!\\Gamma (2\\lambda +1)} \\Bigg [ \\sum _{k=0}^n\\sum _{k^{\\prime }=0}^n (-1)^{k+k^{\\prime }} \\mathcal {N}_{nl}(k,k^{\\prime }) \\Bigg ]^{-1/2},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/361", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/362", + "parent": { + "$ref": "#/texts/269" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\mathcal {N}_{nl}(k,k^{\\prime }) &=& \\frac{\\Gamma (2(\\upsilon +\\lambda )+n+k) \\Gamma (2(\\upsilon +\\lambda )+n+k^{\\prime })}{k!k^{\\prime }!(n-k)!(n-k^{\\prime })!\\Gamma (2\\lambda +k+1)\\Gamma (2\\lambda +k^{\\prime }+1)} \\\\&& \\times {\\mathrm{B}}(k+k^{\\prime }+2\\lambda ,2\\upsilon +1),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\mathcal {N}_{nl}(k,k^{\\prime }) &=& \\frac{\\Gamma (2(\\upsilon +\\lambda )+n+k) \\Gamma (2(\\upsilon +\\lambda )+n+k^{\\prime })}{k!k^{\\prime }!(n-k)!(n-k^{\\prime })!\\Gamma (2\\lambda +k+1)\\Gamma (2\\lambda +k^{\\prime }+1)} \\\\&& \\times {\\mathrm{B}}(k+k^{\\prime }+2\\lambda ,2\\upsilon +1),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/363", + "parent": { + "$ref": "#/groups/38" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "whereas", + "text": "whereas" + }, + { + "self_ref": "#/texts/364", + "parent": { + "$ref": "#/groups/38" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{\\mathrm{B}}(a_1,a_2)", + "text": "{\\mathrm{B}}(a_1,a_2)" + }, + { + "self_ref": "#/texts/365", + "parent": { + "$ref": "#/groups/38" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "denotes the Beta function [48]. The eigenfunctions in Eq. (47) are essentially related to the Schrödinger solutions of the system. We demonstrate how to construct the full quantum solutions in terms of these eigenfunctions in the following subsection.", + "text": "denotes the Beta function [48]. The eigenfunctions in Eq. (47) are essentially related to the Schrödinger solutions of the system. We demonstrate how to construct the full quantum solutions in terms of these eigenfunctions in the following subsection." + }, + { + "self_ref": "#/texts/366", + "parent": { + "$ref": "#/texts/268" + }, + "children": [ + { + "$ref": "#/groups/39" + }, + { + "$ref": "#/texts/376" + }, + { + "$ref": "#/texts/377" + }, + { + "$ref": "#/texts/378" + }, + { + "$ref": "#/groups/40" + }, + { + "$ref": "#/texts/382" + }, + { + "$ref": "#/texts/383" + }, + { + "$ref": "#/groups/41" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "2.3.2.", + "text": "2.3.2.", + "level": 3 + }, + { + "self_ref": "#/texts/367", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "According to Lewis–Riesenfeld theory [24], the wave functions of our time-dependent system are represented in terms of the eigenfunctions", + "text": "According to Lewis–Riesenfeld theory [24], the wave functions of our time-dependent system are represented in terms of the eigenfunctions" + }, + { + "self_ref": "#/texts/368", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _{nlm}(\\vec{r},t)", + "text": "\\Phi _{nlm}(\\vec{r},t)" + }, + { + "self_ref": "#/texts/369", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "of the invariant operator, which are given in Eq. (47). However, these eigenfunctions alone do not fully define the time-dependent wave functions. Another required factor is the phase", + "text": "of the invariant operator, which are given in Eq. (47). However, these eigenfunctions alone do not fully define the time-dependent wave functions. Another required factor is the phase" + }, + { + "self_ref": "#/texts/370", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\alpha (t)", + "text": "\\alpha (t)" + }, + { + "self_ref": "#/texts/371", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "associated with each quantum state. We begin from Eq. (6) to derive this factor. Based on the unitary relation given in Eq. (46), Eq. (6) can be expressed in terms of the transformed eigenstate", + "text": "associated with each quantum state. We begin from Eq. (6) to derive this factor. Based on the unitary relation given in Eq. (46), Eq. (6) can be expressed in terms of the transformed eigenstate" + }, + { + "self_ref": "#/texts/372", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\vert \\Phi ^\\prime \\rangle", + "text": "\\vert \\Phi ^\\prime \\rangle" + }, + { + "self_ref": "#/texts/373", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "instead of", + "text": "instead of" + }, + { + "self_ref": "#/texts/374", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\vert \\Phi \\rangle", + "text": "\\vert \\Phi \\rangle" + }, + { + "self_ref": "#/texts/375", + "parent": { + "$ref": "#/groups/39" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", such that", + "text": ", such that" + }, + { + "self_ref": "#/texts/376", + "parent": { + "$ref": "#/texts/366" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\hbar \\frac{d\\alpha (t)}{dt}=\\left\\langle \\Phi ^{\\prime }\\left|\\Bigg ( i \\hbar \\frac{\\partial }{\\partial t}-\\frac{I_{0}}{2A_{0}\\mu (t)\\rho ^{2}(t)} \\Bigg ) \\right|\\Phi ^{\\prime }\\right\\rangle .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\hbar \\frac{d\\alpha (t)}{dt}=\\left\\langle \\Phi ^{\\prime }\\left|\\Bigg ( i \\hbar \\frac{\\partial }{\\partial t}-\\frac{I_{0}}{2A_{0}\\mu (t)\\rho ^{2}(t)} \\Bigg ) \\right|\\Phi ^{\\prime }\\right\\rangle .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/377", + "parent": { + "$ref": "#/texts/366" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Then, a minor evaluation using Eq. (18) yields the phases in the form", + "text": "Then, a minor evaluation using Eq. (18) yields the phases in the form" + }, + { + "self_ref": "#/texts/378", + "parent": { + "$ref": "#/texts/366" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\alpha _{nl}(t)=-\\frac{\\Lambda _{nl}}{2A_{0}\\hbar }\\int _{0}^{t}\\frac{1}{\\mu (t^{\\prime })\\rho ^{2}(t^{\\prime })}dt^{\\prime }+\\alpha _{nl}(0),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\alpha _{nl}(t)=-\\frac{\\Lambda _{nl}}{2A_{0}\\hbar }\\int _{0}^{t}\\frac{1}{\\mu (t^{\\prime })\\rho ^{2}(t^{\\prime })}dt^{\\prime }+\\alpha _{nl}(0),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/379", + "parent": { + "$ref": "#/groups/40" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/380", + "parent": { + "$ref": "#/groups/40" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Lambda _{nl}", + "text": "\\Lambda _{nl}" + }, + { + "self_ref": "#/texts/381", + "parent": { + "$ref": "#/groups/40" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are given by Eq. (40). These phases emerge as a result of the time evolution of the wave functions and can produce observable consequences, such as interference patterns, through relative phase differences between quantum states. Both the geometrical phase and the conventional dynamical phase contribute to the total phase. While the dynamical phase results from the cumulation of energy over time, the geometric phase originates from the intrinsic geometry of the path traced by the state vector during evolution, often under adiabatic conditions. The significance of the geometrical phase lies in the fact that it is not a mathematical artifact but a physically meaningful, gauge-invariant quantity that can influence the actual quantum state of the system. It not only takes place in this system, but appears ubiquitously across various physical systems as well, and can be measured via interference experiments. Unlike the dynamical phase, which depends on the eigenvalues of the Hamiltonian, the geometrical phase depends on the trajectory of the eigenvectors themselves. As such, it plays a critical role in associated phenomena such as energy level splitting and the orbital behavior of quasiparticles, making it a subject of continued theoretical and experimental interest. In particular, in systems with irregular and nonlinear interactions, such as quark–antiquark systems, it reflects the underlying symmetries, binding mechanisms, and topological features of the system [49,50]. Accordingly, the geometrical phase constitutes a fundamental component in the evolution of the system, linking theoretical descriptions to experimental observations.", + "text": "are given by Eq. (40). These phases emerge as a result of the time evolution of the wave functions and can produce observable consequences, such as interference patterns, through relative phase differences between quantum states. Both the geometrical phase and the conventional dynamical phase contribute to the total phase. While the dynamical phase results from the cumulation of energy over time, the geometric phase originates from the intrinsic geometry of the path traced by the state vector during evolution, often under adiabatic conditions. The significance of the geometrical phase lies in the fact that it is not a mathematical artifact but a physically meaningful, gauge-invariant quantity that can influence the actual quantum state of the system. It not only takes place in this system, but appears ubiquitously across various physical systems as well, and can be measured via interference experiments. Unlike the dynamical phase, which depends on the eigenvalues of the Hamiltonian, the geometrical phase depends on the trajectory of the eigenvectors themselves. As such, it plays a critical role in associated phenomena such as energy level splitting and the orbital behavior of quasiparticles, making it a subject of continued theoretical and experimental interest. In particular, in systems with irregular and nonlinear interactions, such as quark–antiquark systems, it reflects the underlying symmetries, binding mechanisms, and topological features of the system [49,50]. Accordingly, the geometrical phase constitutes a fundamental component in the evolution of the system, linking theoretical descriptions to experimental observations." + }, + { + "self_ref": "#/texts/382", + "parent": { + "$ref": "#/texts/366" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The solutions of the original Schrödinger equation (3) corresponding to the Hamiltonian in Eq. (1) can now be written in configuration space as", + "text": "The solutions of the original Schrödinger equation (3) corresponding to the Hamiltonian in Eq. (1) can now be written in configuration space as" + }, + { + "self_ref": "#/texts/383", + "parent": { + "$ref": "#/texts/366" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\Psi _{nlm}(r,\\theta ,\\varphi ,t)=e^{i\\alpha _{nl}(t)}\\Phi _{nlm}(r,\\theta ,\\varphi ,t),\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\Psi _{nlm}(r,\\theta ,\\varphi ,t)=e^{i\\alpha _{nl}(t)}\\Phi _{nlm}(r,\\theta ,\\varphi ,t),\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/384", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where the explicit forms of", + "text": "where the explicit forms of" + }, + { + "self_ref": "#/texts/385", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\alpha _{nl}(t)", + "text": "\\alpha _{nl}(t)" + }, + { + "self_ref": "#/texts/386", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/387", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Phi _{nlm}(r,\\theta ,\\varphi ,t)", + "text": "\\Phi _{nlm}(r,\\theta ,\\varphi ,t)" + }, + { + "self_ref": "#/texts/388", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "are given in Eqs. (51) and (47), respectively. The resulting wave functions (52), together with", + "text": "are given in Eqs. (51) and (47), respectively. The resulting wave functions (52), together with" + }, + { + "self_ref": "#/texts/389", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\Lambda _{nl}", + "text": "\\Lambda _{nl}" + }, + { + "self_ref": "#/texts/390", + "parent": { + "$ref": "#/groups/41" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in Eq. (40), serve as fundamental solutions for the quark–antiquark systems under consideration. The obtained wave functions enable us to compute spatial distributions (densities), transition probabilities, correlations, etc. Consequently, they provide a foundational framework for analyzing these systems, advancing our perspective on the interaction mechanisms between quarks and antiquarks, particularly in the context of quantum effects. The spatial structure of the wave functions enables estimation of key physical properties, such as the size of the particle (e.g. root-mean-square radius) and the binding strength of the bound state, which are critical for characterizing the complexity of strong interaction systems [51,52]. These wave functions thus serve as important tools for bridging theoretical predictions and experimental results, helping reveal the internal structure, confinement dynamics, and symmetry properties of the quark–antiquark pair.", + "text": "in Eq. (40), serve as fundamental solutions for the quark–antiquark systems under consideration. The obtained wave functions enable us to compute spatial distributions (densities), transition probabilities, correlations, etc. Consequently, they provide a foundational framework for analyzing these systems, advancing our perspective on the interaction mechanisms between quarks and antiquarks, particularly in the context of quantum effects. The spatial structure of the wave functions enables estimation of key physical properties, such as the size of the particle (e.g. root-mean-square radius) and the binding strength of the bound state, which are critical for characterizing the complexity of strong interaction systems [51,52]. These wave functions thus serve as important tools for bridging theoretical predictions and experimental results, helping reveal the internal structure, confinement dynamics, and symmetry properties of the quark–antiquark pair." + }, + { + "self_ref": "#/texts/391", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/texts/392" + }, + { + "$ref": "#/groups/42" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "3.", + "text": "3.", + "level": 1 + }, + { + "self_ref": "#/texts/392", + "parent": { + "$ref": "#/texts/391" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "In this study, we investigated the interaction between a quark and an antiquark by modeling its mechanical behavior using a nonstationary 3D harmonic oscillator coupled with a Coulomb potential. To solve the associated Schrödinger equation, we employed the recently refined NUFA method, together with the invariant operator method and the unitary transformation approach. The NUFA method, based on a novel conceptual framework, allowed us to bypass the complex mathematical manipulations commonly encountered in other techniques. As a result, we obtained the wave functions of the system in closed form, expressed in terms of the Gauss hypergeometric function. These solutions offer advantages over previous ones that employed biconfluent Heun functions, as Gauss hypergeometric functions are among the most thoroughly characterized and best understood special functions in mathematics.", + "text": "In this study, we investigated the interaction between a quark and an antiquark by modeling its mechanical behavior using a nonstationary 3D harmonic oscillator coupled with a Coulomb potential. To solve the associated Schrödinger equation, we employed the recently refined NUFA method, together with the invariant operator method and the unitary transformation approach. The NUFA method, based on a novel conceptual framework, allowed us to bypass the complex mathematical manipulations commonly encountered in other techniques. As a result, we obtained the wave functions of the system in closed form, expressed in terms of the Gauss hypergeometric function. These solutions offer advantages over previous ones that employed biconfluent Heun functions, as Gauss hypergeometric functions are among the most thoroughly characterized and best understood special functions in mathematics." + }, + { + "self_ref": "#/texts/393", + "parent": { + "$ref": "#/groups/42" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Our solutions are essential for analyzing quark–antiquark pairs, which form the core of mesons and thus play a central role in the strong interaction. Although their dynamics are governed by QCD, the theory exhibits intricate behavior that significantly deviates from classical, nonrelativistic mechanics, encompassing numerous unresolved challenges. These include the incomplete theoretical understanding of quark confinement and hadronization mechanisms, the precise determination of quark masses and meson spectra, and the possible existence of exotic hadrons featuring internal structure beyond conventional quark–antiquark configurations [53,54]. Additional open questions involve the quantitative description of the quark condensate mechanism, the strong CP problem and the role of the", + "text": "Our solutions are essential for analyzing quark–antiquark pairs, which form the core of mesons and thus play a central role in the strong interaction. Although their dynamics are governed by QCD, the theory exhibits intricate behavior that significantly deviates from classical, nonrelativistic mechanics, encompassing numerous unresolved challenges. These include the incomplete theoretical understanding of quark confinement and hadronization mechanisms, the precise determination of quark masses and meson spectra, and the possible existence of exotic hadrons featuring internal structure beyond conventional quark–antiquark configurations [53,54]. Additional open questions involve the quantitative description of the quark condensate mechanism, the strong CP problem and the role of the" + }, + { + "self_ref": "#/texts/394", + "parent": { + "$ref": "#/groups/42" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\theta", + "text": "\\theta" + }, + { + "self_ref": "#/texts/395", + "parent": { + "$ref": "#/groups/42" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "-term, as well as the difficulty in accurately predicting the spectral properties and decay modes of heavy mesons such as", + "text": "-term, as well as the difficulty in accurately predicting the spectral properties and decay modes of heavy mesons such as" + }, + { + "self_ref": "#/texts/396", + "parent": { + "$ref": "#/groups/42" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "J/\\psi", + "text": "J/\\psi" + }, + { + "self_ref": "#/texts/397", + "parent": { + "$ref": "#/groups/42" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "[5,55]. The analytical solutions developed in this study may provide insights toward addressing these longstanding issues.", + "text": "[5,55]. The analytical solutions developed in this study may provide insights toward addressing these longstanding issues." + }, + { + "self_ref": "#/texts/398", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/groups/43" + }, + { + "$ref": "#/texts/402" + }, + { + "$ref": "#/texts/403" + }, + { + "$ref": "#/groups/44" + }, + { + "$ref": "#/texts/407" + }, + { + "$ref": "#/texts/408" + }, + { + "$ref": "#/texts/409" + }, + { + "$ref": "#/groups/45" + }, + { + "$ref": "#/texts/417" + }, + { + "$ref": "#/groups/46" + }, + { + "$ref": "#/texts/421" + }, + { + "$ref": "#/groups/47" + }, + { + "$ref": "#/texts/425" + }, + { + "$ref": "#/groups/48" + }, + { + "$ref": "#/texts/429" + }, + { + "$ref": "#/groups/49" + }, + { + "$ref": "#/texts/439" + }, + { + "$ref": "#/texts/440" + }, + { + "$ref": "#/texts/441" + }, + { + "$ref": "#/groups/50" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "Funding", + "text": "Funding", + "level": 1 + }, + { + "self_ref": "#/texts/399", + "parent": { + "$ref": "#/groups/43" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Open Access funding: SCOAP", + "text": "Open Access funding: SCOAP" + }, + { + "self_ref": "#/texts/400", + "parent": { + "$ref": "#/groups/43" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "3", + "text": "3", + "formatting": { + "bold": false, + "italic": false, + "underline": false, + "strikethrough": false, + "script": "super" + } + }, + { + "self_ref": "#/texts/401", + "parent": { + "$ref": "#/groups/43" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ".", + "text": "." + }, + { + "self_ref": "#/texts/402", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Although normalization is formally defined for the full wave functions, the phase factors do not affect the outcome. Therefore, in the normalization process, it suffices to consider only Eq. (47), which corresponds to the eigenfunctions. The normalization condition associated with Eq. (47) is given by", + "text": "Although normalization is formally defined for the full wave functions, the phase factors do not affect the outcome. Therefore, in the normalization process, it suffices to consider only Eq. (47), which corresponds to the eigenfunctions. The normalization condition associated with Eq. (47) is given by" + }, + { + "self_ref": "#/texts/403", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\int _0^{2\\pi } \\int _0^\\pi \\int _0^\\infty \\left|\\Phi _{nlm} (r,\\theta ,\\varphi ,t) \\right|^{2}r^{2} \\sin \\theta dr d\\theta d\\varphi =1.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\int _0^{2\\pi } \\int _0^\\pi \\int _0^\\infty \\left|\\Phi _{nlm} (r,\\theta ,\\varphi ,t) \\right|^{2}r^{2} \\sin \\theta dr d\\theta d\\varphi =1.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/404", + "parent": { + "$ref": "#/groups/44" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "Because the angular part", + "text": "Because the angular part" + }, + { + "self_ref": "#/texts/405", + "parent": { + "$ref": "#/groups/44" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "Y_{lm} (\\theta ,\\varphi )", + "text": "Y_{lm} (\\theta ,\\varphi )" + }, + { + "self_ref": "#/texts/406", + "parent": { + "$ref": "#/groups/44" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", defined in Eq. (21), is already normalized, we only need to consider the radial part in this evaluation. Thus, based on the radial part of Eq. (47), Eq. (A.1) can be rewritten as", + "text": ", defined in Eq. (21), is already normalized, we only need to consider the radial part in this evaluation. Thus, based on the radial part of Eq. (47), Eq. (A.1) can be rewritten as" + }, + { + "self_ref": "#/texts/407", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\frac{\\left|N_{nl}\\right|^{2}}{\\rho (t) }\\int _{0}^{\\infty }e^{-2\\lambda \\delta \\frac{r}{\\rho (t)}}\\left( 1-e^{-\\delta \\frac{r}{\\rho (t)}}\\right) ^{2\\upsilon }\\left[ {_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};e^{-\\delta \\frac{r}{\\rho (t)}})\\right] ^{2}dr =1.\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\frac{\\left|N_{nl}\\right|^{2}}{\\rho (t) }\\int _{0}^{\\infty }e^{-2\\lambda \\delta \\frac{r}{\\rho (t)}}\\left( 1-e^{-\\delta \\frac{r}{\\rho (t)}}\\right) ^{2\\upsilon }\\left[ {_{2}F_{1}}(\\bar{a},\\bar{b};\\bar{c};e^{-\\delta \\frac{r}{\\rho (t)}})\\right] ^{2}dr =1.\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/408", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "To simplify the integral, we introduce the substitution", + "text": "To simplify the integral, we introduce the substitution" + }, + { + "self_ref": "#/texts/409", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\nz=e^{-\\delta \\frac{r}{\\rho (t)}},\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\nz=e^{-\\delta \\frac{r}{\\rho (t)}},\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/410", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and consider the fact that", + "text": "and consider the fact that" + }, + { + "self_ref": "#/texts/411", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{a}", + "text": "\\bar{a}" + }, + { + "self_ref": "#/texts/412", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/413", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{b}", + "text": "\\bar{b}" + }, + { + "self_ref": "#/texts/414", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "in this case can be expressed in terms of", + "text": "in this case can be expressed in terms of" + }, + { + "self_ref": "#/texts/415", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "n", + "text": "n", + "formatting": { + "bold": false, + "italic": true, + "underline": false, + "strikethrough": false, + "script": "baseline" + } + }, + { + "self_ref": "#/texts/416", + "parent": { + "$ref": "#/groups/45" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "as", + "text": "as" + }, + { + "self_ref": "#/texts/417", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\bar{a}=-n,~~~~~~~ \\bar{b}=2(\\upsilon +\\lambda )+n,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\bar{a}=-n,~~~~~~~ \\bar{b}=2(\\upsilon +\\lambda )+n,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/418", + "parent": { + "$ref": "#/groups/46" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "along with Eq. (44) as the formula of", + "text": "along with Eq. (44) as the formula of" + }, + { + "self_ref": "#/texts/419", + "parent": { + "$ref": "#/groups/46" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\bar{c}", + "text": "\\bar{c}" + }, + { + "self_ref": "#/texts/420", + "parent": { + "$ref": "#/groups/46" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Then, Eq. (A.2) becomes", + "text": ". Then, Eq. (A.2) becomes" + }, + { + "self_ref": "#/texts/421", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\frac{\\left|N_{nl}\\right|^{2}}{\\delta }g_{\\rm int}=1,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\frac{\\left|N_{nl}\\right|^{2}}{\\delta }g_{\\rm int}=1,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/422", + "parent": { + "$ref": "#/groups/47" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "where", + "text": "where" + }, + { + "self_ref": "#/texts/423", + "parent": { + "$ref": "#/groups/47" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "g_{\\rm int}", + "text": "g_{\\rm int}" + }, + { + "self_ref": "#/texts/424", + "parent": { + "$ref": "#/groups/47" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "is the integral part:", + "text": "is the integral part:" + }, + { + "self_ref": "#/texts/425", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\ng_{\\rm int}=\\int _{0}^{1}z^{2\\lambda -1} \\left( 1-z\\right) ^{2\\upsilon }\\left[ {_{2}F_{1}}(-n,2(\\upsilon +\\lambda )+n;2\\lambda +1;z)\\right] ^{2}dz .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\ng_{\\rm int}=\\int _{0}^{1}z^{2\\lambda -1} \\left( 1-z\\right) ^{2\\upsilon }\\left[ {_{2}F_{1}}(-n,2(\\upsilon +\\lambda )+n;2\\lambda +1;z)\\right] ^{2}dz .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/426", + "parent": { + "$ref": "#/groups/48" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "To evaluate the integration", + "text": "To evaluate the integration" + }, + { + "self_ref": "#/texts/427", + "parent": { + "$ref": "#/groups/48" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "g_{\\rm int}", + "text": "g_{\\rm int}" + }, + { + "self_ref": "#/texts/428", + "parent": { + "$ref": "#/groups/48" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", we use the expansion formula for the hypergeometric function (see formula 15.2.4 of Ref. [45]):", + "text": ", we use the expansion formula for the hypergeometric function (see formula 15.2.4 of Ref. [45]):" + }, + { + "self_ref": "#/texts/429", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n{_{2}F_{1}}(-n,b;c;s)=\\sum _{k=0}^n (-1)^k \\binom{n}{k} \\frac{(b)_k}{(c)_k}s^k,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n{_{2}F_{1}}(-n,b;c;s)=\\sum _{k=0}^n (-1)^k \\binom{n}{k} \\frac{(b)_k}{(c)_k}s^k,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/430", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "which holds when", + "text": "which holds when" + }, + { + "self_ref": "#/texts/431", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "n=0,1,2,\\cdots", + "text": "n=0,1,2,\\cdots" + }, + { + "self_ref": "#/texts/432", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/433", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "c \\ne 0,-1,-2,\\cdots", + "text": "c \\ne 0,-1,-2,\\cdots" + }, + { + "self_ref": "#/texts/434", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". This relation also holds when", + "text": ". This relation also holds when" + }, + { + "self_ref": "#/texts/435", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "c=-n-j", + "text": "c=-n-j" + }, + { + "self_ref": "#/texts/436", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", where", + "text": ", where" + }, + { + "self_ref": "#/texts/437", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "j=0,1,2,\\cdots", + "text": "j=0,1,2,\\cdots" + }, + { + "self_ref": "#/texts/438", + "parent": { + "$ref": "#/groups/49" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Substituting this series expansion into Eq. (A.6), we obtain", + "text": ". Substituting this series expansion into Eq. (A.6), we obtain" + }, + { + "self_ref": "#/texts/439", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\ng_{\\rm int}&=&\\sum _{k=0}^n\\sum _{k^{\\prime }=0}^n(-1)^{k+k^{\\prime }}\\binom{n}{k} \\binom{n}{k^{\\prime }} \\frac{(2(\\upsilon +\\lambda )+n)_k (2(\\upsilon +\\lambda )+n)_{k^{\\prime }}}{(2\\lambda +1)_k(2\\lambda +1)_{k^{\\prime }}} \\\\&&\\times \\int _{0}^{1}z^{k+k^{\\prime }+2\\lambda -1} \\left( 1-z\\right) ^{2\\upsilon }dz .\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\ng_{\\rm int}&=&\\sum _{k=0}^n\\sum _{k^{\\prime }=0}^n(-1)^{k+k^{\\prime }}\\binom{n}{k} \\binom{n}{k^{\\prime }} \\frac{(2(\\upsilon +\\lambda )+n)_k (2(\\upsilon +\\lambda )+n)_{k^{\\prime }}}{(2\\lambda +1)_k(2\\lambda +1)_{k^{\\prime }}} \\\\&&\\times \\int _{0}^{1}z^{k+k^{\\prime }+2\\lambda -1} \\left( 1-z\\right) ^{2\\upsilon }dz .\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/440", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "The integral in the above equation is the Beta function, which is defined as [48]", + "text": "The integral in the above equation is the Beta function, which is defined as [48]" + }, + { + "self_ref": "#/texts/441", + "parent": { + "$ref": "#/texts/398" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "\\begin{eqnarray}\n\\mathrm{B}(a_1,a_2)= \\int _0^1 s^{a_1-1} (1-s)^{a_2-1} ds,\n\\end{eqnarray}", + "text": "\\begin{eqnarray}\n\\mathrm{B}(a_1,a_2)= \\int _0^1 s^{a_1-1} (1-s)^{a_2-1} ds,\n\\end{eqnarray}" + }, + { + "self_ref": "#/texts/442", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "under the conditions", + "text": "under the conditions" + }, + { + "self_ref": "#/texts/443", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{\\rm Re}a_1\\gt 0", + "text": "{\\rm Re}a_1\\gt 0" + }, + { + "self_ref": "#/texts/444", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/445", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "{\\rm Re}a_2\\gt 0", + "text": "{\\rm Re}a_2\\gt 0" + }, + { + "self_ref": "#/texts/446", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". The two shape parameters of this function, in our case, are", + "text": ". The two shape parameters of this function, in our case, are" + }, + { + "self_ref": "#/texts/447", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "a_1=k+k^{\\prime }+2\\lambda", + "text": "a_1=k+k^{\\prime }+2\\lambda" + }, + { + "self_ref": "#/texts/448", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": "and", + "text": "and" + }, + { + "self_ref": "#/texts/449", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "a_2=2\\upsilon +1", + "text": "a_2=2\\upsilon +1" + }, + { + "self_ref": "#/texts/450", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ". Thus, considering that the Pochhammer number is given by", + "text": ". Thus, considering that the Pochhammer number is given by" + }, + { + "self_ref": "#/texts/451", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "(a)_k=\\Gamma (a+k)/\\Gamma (a)", + "text": "(a)_k=\\Gamma (a+k)/\\Gamma (a)" + }, + { + "self_ref": "#/texts/452", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", where", + "text": ", where" + }, + { + "self_ref": "#/texts/453", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "a \\ne 0,-1,-2,\\cdots", + "text": "a \\ne 0,-1,-2,\\cdots" + }, + { + "self_ref": "#/texts/454", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", a rearrangement of Eq. (A.5) with Eq. (A.8) leads directly to the expression for the normalization constants", + "text": ", a rearrangement of Eq. (A.5) with Eq. (A.8) leads directly to the expression for the normalization constants" + }, + { + "self_ref": "#/texts/455", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "formula", + "prov": [], + "orig": "N_{nl}", + "text": "N_{nl}" + }, + { + "self_ref": "#/texts/456", + "parent": { + "$ref": "#/groups/50" + }, + "children": [], + "content_layer": "body", + "label": "text", + "prov": [], + "orig": ", as presented in Eq. (48) of the text.", + "text": ", as presented in Eq. (48) of the text." + }, + { + "self_ref": "#/texts/457", + "parent": { + "$ref": "#/texts/0" + }, + "children": [ + { + "$ref": "#/groups/51" + } + ], + "content_layer": "body", + "label": "section_header", + "prov": [], + "orig": "References", + "text": "References", + "level": 1 + }, + { + "self_ref": "#/texts/458", + "parent": { + "$ref": "#/groups/51" + }, + "children": [], + "content_layer": "body", + "label": "list_item", + "prov": [], + "orig": "H.-T. 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C. 84, 813 (2024).", + "enumerated": false, + "marker": "" + } + ], + "pictures": [], + "tables": [], + "key_value_items": [], + "form_items": [], + "pages": {} +} \ No newline at end of file diff --git a/tests/data/jats/groundtruth/ptag100.xml.md b/tests/data/jats/groundtruth/ptag100.xml.md new file mode 100644 index 0000000000..20fb1092b7 --- /dev/null +++ b/tests/data/jats/groundtruth/ptag100.xml.md @@ -0,0 +1,495 @@ +# Time-Dependent 3D Oscillator with Coulomb Interaction: An Alternative Approach for Analyzing Quark–Antiquark Systems + +Jeong Ryeol Choi, Salim Medjber, Salah Menouar, Ramazan Sever + +## Abstract + +In this work, the dynamics of quark–antiquark pair systems is investigated by modeling them as general time-dependent 3D oscillators perturbed by a Coulomb potential. Solving this model enables the prediction of key mesonic properties such as the probability density, energy spectra, and quadrature uncertainties, offering theoretical insights into the confinement of quarks via gluon-mediated strong interactions. To tackle the mathematical difficulty raised by the time dependence of parameters in the system, special mathematical techniques, such as the invariant operator method, unitary transformation method, and the Nikiforov–Uvarov functional analysis are used. The wave functions of the system, derived using these mathematical techniques, are expressed analytically in terms of the Gauss hypergeometric function whose mathematical properties are well characterized. Our results provide the quantum-mechanical framework of quark–antiquark systems that are essential for exploring the nonperturbative aspects of quantum chromodynamics. In addition, the underlying mathematical structure may serve as a foundation for addressing broader challenges in particle physics, including the origin of mass and its connection to the Higgs mechanism. + +## 1. + +Quark–antiquark pairs are a key concept in particle physics, essential for understanding the strong interaction and the structure of hadrons such as mesons and baryons. Typical examples of quark–antiquark pairs are bound states of a heavy quark and its antiquark, such as charmonium and bottomonium, collectively referred to as quarkonia [1]. Quarkonia can be readily produced in experiments such as the LHC, and their properties can be measured with high precision. Quarkonia’s distinct mass spectra, unique decay patterns, and relatively long lifetimes are of significant interest in the search for physics that extends past the Standard Model. As such, they may offer a promising avenue for probing potential new phenomena not accounted for by the Higgs sector [2,3]. This makes quarkonia an important topic requiring collaborative research between experimental and theoretical physicists. Indeed, they are actively studied in connection with new particle searches and dark matter theories [4–7]. + +In light of this, it is essential to establish a theoretical framework that enables systematic analysis and interpretation of quarkonium systems. In particle physics, quark–antiquark systems are commonly modeled using a potential that combines harmonic and Coulomb terms [8–10], where the harmonic component captures the long-range confinement effect and the Coulomb term accounts for the short-range interactions based on quantum chromodynamics (QCD). Motivated by this, we focus on the analysis of time-dependent 3D oscillators perturbed by a Coulomb potential throughout this work. The quantum-mechanical solutions of such systems are known to be expressible in terms of biconfluent Heun functions [11,12]. However, limited knowledge of the properties of such functions continues to make these systems difficult to analyze. At present, the characterization of these functions remains confined to their behavior in certain special cases and their connections to specific recursion relations, particularly their representations in terms of these relations [13,14]. This lack of comprehensive understanding is the reason why many researchers refer to them primarily through their defining equation, the biconfluent Heun equation, rather than treating them as fully characterized functions in their own right [15–19]. + +To overcome this limitation, the present work explores an alternative analytical treatment by expressing the solutions in terms of well-established mathematical functions, thereby providing a more accessible and rigorous theoretical understanding of the system. Deriving such solutions to fundamental equations for our general physical systems governed by time-dependent Hamiltonians [20–22] may require special mathematical techniques beyond conventional methods such as the separation of variables. One promising approach is the invariant operator method [23–26], which has proven effective in the study of time-dependent Hamiltonian systems (TDHSs). This method allows for the construction of exact quantum solutions in systems with time-dependent parameters. Specifically, if an invariant operator can be identified for a given system, the quantum wave functions, apart from phase parts, can be expressed by eigenfunctions of this operator. Consequently, solving the quantum system reduces to finding the eigenfunctions of the invariant operator, while the required phase factors can be determined with the help of the Schrödinger equation. + +Regarding coupling of potentials, as well as the system’s time dependence, the general form of the invariant operator is typically complicated. Therefore, to solve its eigenvalue equation, more advanced mathematical techniques, such as the unitary transformation method [27,28] and the Nikiforov–Uvarov functional analysis (NUFA) [29], are necessary. The NUFA method builds upon and integrates several approaches, including the original Nikiforov–Uvarov (NU) method [30], the parametric NU method [31–34], and elements of functional analysis [35]. This enhanced method enables one to obtain the solution of related equations, including those of hypergeometric type, in a systematic and elegant manner, allowing the determination of the eigenvalue spectrum and the corresponding wave functions. We demonstrate that the wave functions derived through this approach can be expressed in terms of the well-known Gauss hypergeometric function. This may provide a significantly more tractable framework for analyzing the system compared to existing solutions that are based on biconfluent Heun functions. Taking advantage of these strengths, we show that the newly obtained solutions enable a thorough investigation of the system, particularly in dynamically evolving hadronic environments where effective quark interactions may vary due to medium-induced effects and nonequilibrium processes. + +The main contributions of the present work can be summarized as follows. First, we develop an analytical treatment for a time-dependent quark–antiquark model based on a 3D oscillator perturbed by a Coulomb interaction within the framework of invariant operator theory. Second, by combining the invariant operator method, unitary transformation techniques, and the NUFA formalism, we derive analytical expressions for the eigenspectrum, quantum phases, and wave functions of the system in closed form. In contrast to several previous approaches in which the solutions are represented in terms of biconfluent Heun functions, the present treatment expresses the wave functions in terms of the Gauss hypergeometric function, whose mathematical properties are more thoroughly established. This provides a more tractable analytical framework for investigating the dynamical properties of quark–antiquark systems governed by time-dependent Hamiltonians. + +## 2. + +### 2.1. + +#### 2.1.1. + +We consider a quark–antiquark pair with a time-dependent effective reduced mass $\mu (t)$ in spherical coordinates, governed by a central potential $g(t)r^{2}-\frac{Z(t)}{r}$ where $g(t)$ and $Z(t)$ are coefficients that depend on time. While $g(t)r^{2}$ represents a confining trap potential with adjustable strength, $-\frac{Z(t)}{r}$ is a Coulomb perturbation with the constraint $Z(t)\gt 0$ . As the distance between quarks increases, the harmonic potential rises sharply, effectively prohibiting their free separation within a bound state. This potential acts as a simplified representation of the QCD confinement mechanism and dominates at large separations. Meanwhile, the Coulomb term represents the electromagnetic or color interaction mediated by the color charge of one quark acting on the other, effectively serving as a short-range attractive force between the quark and antiquark. + +Time-dependent Hamiltonians naturally arise in hadron physics when quark–antiquark systems are embedded in dynamical environments, such as quark–gluon plasma or nonequilibrium hadronic media produced in high-energy collisions [20–22]. In such situations, the effective interaction between quarks may vary with time due to medium evolution, color-screening effects, confinement modifications, or changes in the effective quark mass. Consequently, the parameters appearing in phenomenological quarkonium potentials, including the confining strength and Coulomb-like coupling terms, can acquire explicit time dependence. The present model therefore provides an effective framework for investigating dynamical quark–antiquark systems within a quantum-mechanical setting. + +Based on the above description, the Hamiltonian that characterizes the motion of the pair of quarks is expressed as: + +$$\begin{eqnarray} +H(t)=\frac{p^{2}}{2\mu (t)}+g(t)r^{2}-\frac{Z(t)}{r} . +\end{eqnarray}$$ + +This Hamiltonian enables the calculation of the quantum-mechanical spectrum including wave functions and excitation properties, which is then used to theoretically explain the mass and structure of mesons. The momentum operator in spherical coordinates is represented as $p^{2}=p_{r}^{2}+\frac{L^{2}}{r^{2}}$ , where $p_{r}=-i\hbar (\frac{\partial }{\partial r}+\frac{1}{r})$ and *L* is the total angular momentum, the formula for which is given by + +$$\begin{eqnarray} +L^{2}=-\hbar ^{2}\bigg [\frac{1}{\sin ^{2}\theta }\frac{\partial ^{2}}{\partial \varphi ^{2}}+\frac{1}{\sin \theta }\frac{\partial }{\partial \theta }\bigg (\sin \theta \frac{\partial }{\partial \theta }\bigg )\bigg ]. +\end{eqnarray}$$ + +To investigate the dynamical features of the system, we must solve the Schrödinger equation for the Hamiltonian in Eq. (1). If we denote the system’s state vector as $|\Psi (t)\rangle$ , the corresponding Schrödinger equation is of the form + +$$\begin{eqnarray} +i\hbar \frac{\partial }{\partial t}|\Psi (t)\rangle =H(t)|\Psi (t)\rangle . +\end{eqnarray}$$ + +The time dependence of the Hamiltonian requires a specialized approach to solve this equation, as will be presented subsequently. + +#### 2.1.2. + +Because $H(t)$ in Eq. (1) is represented in terms of time functions, directly solving Eq. (3) can be very challenging. To overcome this difficulty, we adopt the dynamical invariant method [24]. The core idea of this method is to treat the system in terms of a nontrivial invariant operator $I(t)$ rather than $H(t)$ in Eq. (1), where $I(t)$ does not involve time-derivative operators. Moreover, $I(t)$ is closely related to the concept of invariants in classical mechanics (e.g. constants of motion), thereby facilitating interpretation from the perspective of the quantum–classical correspondence. + +By the definition of the invariant operator, $I(t)$ satisfies the equation: + +$$\begin{eqnarray} +\frac{dI}{dt}=\frac{\partial I}{\partial t}+\frac{1}{i\hbar }[I,H]=0. +\end{eqnarray}$$ + +If such an invariant exists, it is possible to put the solution of the Schrödinger equation (3) in terms of its eigenfunction $\Phi (\vec{r},t)$ , such that + +$$\begin{eqnarray} +\Psi (\vec{r},t)=e^{i\alpha (t)}\Phi (\vec{r},t), +\end{eqnarray}$$ + +where $\alpha (t)$ is a phase function that can be obtained from + +$$\begin{eqnarray} +\hbar \frac{d\alpha (t)}{dt}=\left\langle \Phi \left|\bigg (i\hbar \frac{\partial }{\partial t}-H\bigg )\right|\Phi \right\rangle . +\end{eqnarray}$$ + +To find the expression of the invariant $I(t)$ utilizing Eq. (4), we set + +$$\begin{eqnarray} +I(t)=A(t)p^{2}+B(t)\left( rp_{r}+p_{r}r\right) +C(t)r^{2}-\frac{D(t)}{r}, +\end{eqnarray}$$ + +where $A(t)$ , $B(t)$ , $C(t)$ , and $D(t)$ are time-dependent functions to be determined. Then, a mathematical procedure with this assumed formula of $I(t)$ after substituting Eqs. (1) and (7) into Eq. (4) yields + +$$\begin{eqnarray} +A(t) =A_{0}\rho ^{2}(t), +\end{eqnarray}$$ + +$$\begin{eqnarray} +B(t) =-A_{0}\mu (t)\rho (t)\dot{\rho }(t), +\end{eqnarray}$$ + +$$\begin{eqnarray} +C(t) =A_{0}\bigg (\mu ^{2}(t)[\dot{\rho }(t)]^{2}+\frac{\Omega ^{2}}{4\rho ^{2}(t)}\bigg ), +\end{eqnarray}$$ + +$$\begin{eqnarray} +D(t) =2A_{0}\mu (t)Z(t)\rho ^{2}(t), +\end{eqnarray}$$ + +where $A_{0}$ is an arbitrary real constant and $\rho (t)$ is a solution of the following equation + +$$\begin{eqnarray} +\ddot{\rho }(t)+\frac{\dot{\mu }(t)}{\mu (t) }\dot{\rho }(t)+2\frac{g(t)}{\mu (t)}\rho (t) =\frac{\Omega ^{2}}{4\mu ^{2}(t)\rho ^{3}(t)}, +\end{eqnarray}$$ + +with $\Omega$ being a real constant. The above invariant is valid under the condition that $Z(t)$ follows the relation + +$$\begin{eqnarray} +\dot{Z}(t)+\bigg (\frac{\dot{\mu }(t)}{\mu (t)}+\frac{\dot{\rho }(t)}{\rho (t)}\bigg )Z(t)=0. +\end{eqnarray}$$ + +Equation (7) with Eqs. (8)–(11) constitutes the complete quadratic invariant operator of the system. This operator provides a robust analytical framework for the system, in which direct solutions are difficult to obtain due to the complicated time evolution of the Hamiltonian. It also enables a deeper understanding of dynamical properties of the system, including phase transitions and topological changes, beyond merely providing a powerful methodology for obtaining solutions. + +#### 2.1.3. + +Because the quantum wave functions of the system are represented in terms of the eigenfunctions of $I(t)$ , it is now necessary to evaluate its eigenvalue equation. We begin by writing the eigenvalue equation of the invariant $I(t)$ as + +$$\begin{eqnarray} +I(t)\Phi _{n}(\vec{r},t)=\Lambda _{n}\Phi _{n}(\vec{r},t), +\end{eqnarray}$$ + +where $\Lambda _{n}$ are the eigenvalues and $\Phi _{n}(\vec{r},t)$ are time-dependent eigenfunctions. Given that $I(t)$ , as defined in Eq. (7) along with Eqs. (8)–(11), has a complicated form, it is favorable to solve Eq. (14) after we transform it mathematically into a simple form. To do this, we consider a unitary transformation of the form + +$$\begin{eqnarray} +\Phi _{n}^{\prime }(\vec{r})=U(t)\Phi _{n}(\vec{r},t), +\end{eqnarray}$$ + +where $U(t)$ is a time-dependent unitary operator given by + +$$\begin{eqnarray} +U(t)=\exp \left[ \frac{i\ln \rho (t)}{2\hbar }\left( rp_{r}+p_{r}r\right) \right] \exp \left[ -i\frac{\mu (t)\dot{\rho }(t)}{2\hbar \rho (t)}r^{2}\right] . +\end{eqnarray}$$ + +Then, if we write the transformed invariant operator as $I_0$ , the eigenvalue equation in the transformed frame is represented in terms of $\Phi _{n}^{\prime }(\vec{r})$ , such that + +$$\begin{eqnarray} +UIU^{-1}\Phi _{n}^{\prime }(\vec{r})=I_{0}\Phi _{n}^{\prime }(\vec{r})=\Lambda _{n}\Phi _{n}^{\prime }(\vec{r}). +\end{eqnarray}$$ + +Now, from a mathematical procedure using Eq. (16), we obtain that + +$$\begin{eqnarray} +\left[ A_{0}p^{2}+A_{0}\frac{\Omega ^{2}}{4}r^{2}-D_{0}\frac{1}{r}\right] \Phi _{nlm}^{\prime }(r,\theta ,\varphi )=\Lambda _{nl}\Phi _{nlm}^{\prime }(r,\theta ,\varphi ), +\end{eqnarray}$$ + +where $D_{0} = 2A_{0}\mu (t)Z(t)\rho (t)$ while $n=0,1,2,\cdots$ . An infinite number of bound states may arise from the strong confinement of quarkonium, which is primarily attributed to the harmonic term dominating at large distances. By the way, the direct differentiation of $D_0$ with respect to *t* , followed by the application of Eq. (13), yields $d D_0/dt=0$ . This means that $D_0$ is in fact a constant of motion, as expected—since the operator on the left-hand side of Eq. (18) is the transformed version of the original invariant $I(t)$ . It is worth noting that Eq. (18) is very simple compared to the original eigenvalue equation represented in Eq. (14). + +The existence of a perturbation of Coulomb potential may make the problem much more difficult. In what follows, the eigenvalue equation (18) can be rewritten as + +$$\begin{eqnarray} +\begin{bmatrix}-\hbar ^{2}A_{0}\Big [\frac{1}{r^{2}}\frac{\partial }{\partial r}\left( r^{2}\frac{\partial }{\partial r}\right) +\frac{1}{r^{2}\sin ^{2}\theta }\frac{\partial ^{2}}{\partial \varphi ^{2}} \\+\frac{1}{r^{2}\sin \theta }\frac{\partial }{\partial \theta }(\sin \theta \frac{\partial }{\partial \theta })\Big ]+A_{0}\frac{\Omega ^{2}}{4}r^{2}-\frac{D_{0}}{r} \end{bmatrix} \Phi _{nlm}^{\prime }(r,\theta ,\varphi )=\Lambda _{nl}\Phi _{nlm}^{\prime }(r,\theta ,\varphi ). +\end{eqnarray}$$ + +According to the invariant operator theory, the wave functions of the transformed system are represented in terms of $\Phi _{nlm}^{\prime }(r,\theta ,\varphi )$ . Because this equation is independent of time, we can apply the method of separation of variables in order to solve it. Considering this, we take + +$$\begin{eqnarray} +\Phi _{nlm}^{\prime }(r,\theta ,\varphi )=R_{nl}(r)Y_{lm}(\theta ,\varphi )=\frac{u_{nl}(r)Y_{lm}(\theta ,\varphi )}{r}, +\end{eqnarray}$$ + +where we have introduced $u_{nl}(r) = r R_{nl}(r)$ , whereas $Y_{lm}(\theta ,\varphi )$ are the spherical harmonics. The angular part of the eigenfunctions takes the form + +$$\begin{eqnarray} +Y_{lm}(\theta ,\varphi )= N_{lm}e^{im\varphi }P_{l}^{m}(\cos \theta ), +\end{eqnarray}$$ + +where $P_{l}^{m}(x)$ are the associated Legendre polynomials, while the spherical normalization constants are $N_{lm}=(-1)^m \lbrace [(2l+1)/(4\pi )](l-m)!/(l+m)! \rbrace ^{1/2}$ . The allowed orbital and magnetic quantum numbers are $l=0,1,2,\cdots$ and $m=-l,-l+1,\cdots ,l$ , respectively. + +Some algebraic manipulation, after substituting Eq. (20) into Eq. (19), leads to the following radial equation: + +$$\begin{eqnarray} +\frac{d^{2}u_{nl}(r)}{dr^{2}}+\left( \frac{\Lambda _{nl}}{\hbar ^{2}A_{0}}-\frac{\Omega ^{2}}{4\hbar ^{2}}r^{2}+\frac{D_{0}}{\hbar ^{2}A_{0}}\frac{1}{r}-\frac{l(l+1)}{r^{2}}\right) u_{nl}(r)=0. +\end{eqnarray}$$ + +The equation is analytically intractable unless $\Omega = 0$ owing to the coexistence of $r^{2}$ and $-\frac{1}{r^{2}}$ terms. To resolve this difficulty, we employ the Greene–Aldrich approximation scheme of the form [36–39] + +$$\begin{eqnarray} +\frac{1}{r^{2}} \simeq \frac{\delta ^{2}}{\left( 1-e^{-\delta r}\right) ^{2}},~~~~~\frac{1}{r} \simeq \frac{\delta }{1-e^{-\delta r}}, +\end{eqnarray}$$ + +together with a coordinate transformation $y=e^{-\delta r}$ . This type of approximation is commonly used to describe particles governed by a screened potential, where the parameter $\delta$ is typically chosen to be equal to the screening mass [29,36]. Though the potential in our system is not screened, choosing $\delta$ to be similar to the typical screening mass still provides a good approximation, particularly accurate for distances comparable to or smaller than $1/\delta$ . For example, in quarkonium models, $\delta$ is usually taken to be in the range of approximately 200 to 900 MeV [40,41]. Thus, selecting $\delta$ within this range is a reasonable choice. + +Then, by rewriting the notation as $u_{nl}(r)\rightarrow U_{nl}(y)$ , the differential equation (22) becomes + +$$\begin{eqnarray} +\frac{d^{2}U_{nl}(y)}{dy^{2}}+\frac{\left( 1-y\right) }{y(1-y)}\frac{dU_{nl}(y)}{dy}+\frac{1}{y^{2}(1-y)^{2}}\begin{bmatrix}-\left( \varepsilon _{nl}-6P\right) y^{2} \\+\left( 2\varepsilon _{nl}-4P+Q\right) y \\-\left( \varepsilon _{nl}-P+Q+\gamma \right) \end{bmatrix} U_{nl}(y)=0, +\end{eqnarray}$$ + +where + +$$\begin{eqnarray} +\varepsilon _{nl}=\frac{-\Lambda _{nl}}{\hbar ^{2}A_{0}\delta ^{2}},~~~P=-\frac{\Omega ^{2}}{4\hbar ^{2}\delta ^{4}},~~~Q=-\frac{D_{0}}{\hbar ^{2}A_{0}\delta },~~~\gamma =l(l+1). +\end{eqnarray}$$ + +In the representation of Eq. (24), we neglected higher-order terms corresponding to $y^{3}$ and $y^{4}$ . To solve this equation, we use the NUFA method [29] outlined in the next subsection. + +### 2.2. + +A second-order differential equation of hypergeometric type can be solved in a simple and elegant way by using the so-called NUFA method. This is an improvement over the parametric NU method, which is relatively cumbersome due to the need to find the square of the polynomials and other conditions required. With the NUFA method, many diffierential equations, including our wave equation, can be properly tackled, allowing for the identification of singularities and the determination of the eigenvalue spectrum. As a general type of second-order differential equation, which encompasses Eq. (24), we consider the following equation [31]: + +$$\begin{eqnarray} +\left[ \frac{d^{2}}{ds^{2}}+\frac{\alpha _{1}-\alpha _{2}s}{s(1-\alpha _{3}s)}\frac{d}{ds}+\frac{-\zeta _{1}s^{2}+\zeta _{2}s-\zeta _{3}}{s^{2}(1-\alpha _{3}s)^{2}}\right] \phi (s)=0, +\end{eqnarray}$$ + +where $\alpha _{i}$ $(i=1,2,3)$ and $\zeta _{i}$ are parameters appropriately chosen depending on a given system. Because this equation has two singularities at $s\rightarrow 0$ and $s\rightarrow 1/\alpha _{3}$ , it is possible to take the solution in the form + +$$\begin{eqnarray} +\phi (s)=s^{\lambda }(1-\alpha _{3}s)^{\upsilon }f(s). +\end{eqnarray}$$ + +By substituting this formula into Eq. (26) along with the following choice, + +$$\begin{eqnarray} +\lambda =\frac{1}{2}\lbrace 1-\alpha _{1}\pm [\left( 1-\alpha _{1}\right) ^{2}+4\zeta _{3}]^{1/2}\rbrace , +\end{eqnarray}$$ + +$$\begin{eqnarray} +\upsilon &=&\frac{1}{2\alpha _{3}} \lbrace \alpha _{3}+\alpha _{1}\alpha _{3}-\alpha _{2}\pm [\left( \alpha _{3}+\alpha _{1}\alpha _{3}-\alpha _{2}\right) ^{2} \\&&+4\alpha _3 (\zeta _{3}\alpha _{3}-\zeta _{2}+\zeta _{1}/\alpha _{3})]^{1/2}\rbrace , +\end{eqnarray}$$ + +we have [29] + +$$\begin{eqnarray} +s(1-\alpha _{3}s)\frac{d^{2}f(s)}{ds^{2}}+\kappa \frac{df(s)}{ds} -\alpha _{3} \kappa _+ \kappa _- f(s)=0, +\end{eqnarray}$$ + +where + +$$\begin{eqnarray} +\kappa = \alpha _{1}+2\lambda -(2\lambda \alpha _{3}+2\upsilon \alpha _{3}+\alpha _{2})s, +\end{eqnarray}$$ + +$$\begin{eqnarray} +\kappa _\pm = \lambda +\upsilon +\frac{1}{2}\left( \frac{\alpha _{2}}{\alpha _{3}}-1\right) \pm \bigg [ \frac{1}{4}\left( \frac{\alpha _{2}}{\alpha _{3}}-1\right) ^{2}+\frac{\zeta _{1}}{\alpha _{3}^{2}} \bigg ]^{1/2}. +\end{eqnarray}$$ + +Thus, the equation is reduced to a tractable form. + +We now examine a simple case where $\alpha _3=1$ , which meets the equation associated with our considered system. Under the condition that the principal quantum number takes the form + +$$\begin{eqnarray} +n=- \kappa _- , +\end{eqnarray}$$ + +the function $f(s)$ is represented by the Gauss hypergeometric function as [29] + +$$\begin{eqnarray} +f (s) = {_{2}F_{1}}(a,b;c;s), +\end{eqnarray}$$ + +where $a=\kappa _-$ , $b=\kappa _+$ , and $c=\alpha _{1}+2\lambda$ . The mathematical formula of this hypergeometric function is as follows: $_{2}F_{1}(a,b;c;s)=\sum _{k=0}^{\infty }\frac{\left( a\right) _{k}\left( b\right) _{k}}{\left( c\right) _{k}k!}s^{k}$ , where $\left( a\right) _{k}=a(a+1)(a+2)\cdots (a+k-1)$ for $k\ge 1$ and $\left( a\right) _{0}=1$ . + +The Gauss hypergeometric function $_{2}F_{1}(a,b;c;s)$ serves as a fundamental and unifying special function that encompasses many other functions as special or limiting cases [42–45]. As defined in Eq. (34) with Eq. (33), the function $_{2}F_{1}(a,b;c;s)$ terminates and reduces to a polynomial when *a* is zero or a negative integer, a case that is often associated with Laguerre polynomials. In particular, $_{2}F_{1}(a,b;c;s)$ is a generalization of Kummer’s confluent hypergeometric function $_{1}F_{1}(a;c;s)$ . Hence, in the limiting case where $b \rightarrow \infty$ , $_{2}F_{1}(a,b;c;s)$ converges to $_{1}F_{1}(a;c;s)$ . This convergence is rooted in the fact that, while ${}_2F_1$ has two regular singular points, sending one of them to infinity (a process known as confluence) effectively merges the singularities, giving rise to ${}_1F_1$ [44]. Additionally, the Jacobi polynomials can also be expressed in terms of the Gauss hypergeometric function (for a typical representation, see formula 15.9.1 of Ref. [45]). The ${}_2F_1$ function thus plays a major role in constructing analytical solutions and enables rigorous, systematic analysis across a broad spectrum of scientific disciplines beyond mathematics and physics. The method presented in this subsection, which naturally leads to the appearance of the ${}_2F_1$ function as a solution, is applicable to the analysis of a wide class of physical systems governed by one or more interaction potentials. It is, of course, well suited to handling Eq. (24). + +### 2.3. + +#### 2.3.1. + +We now proceed to derive the wave functions of the system. For clarity and better reader understanding, we briefly summarize our strategy at this point. Because, in our time-dependent case, the wave functions are described by the eigenfunctions of $I(t)$ (instead of $H(t)$ ), we need to determine the eigenfunctions $\Phi _n$ of Eq. (14). However, directly solving for $\Phi _n$ is likely to be a formidable task. To circumvent this difficulty, we first derive $\Phi _n^{\prime }$ from Eq. (17), and then reconstruct the desired eigenfunctions $\Phi _n$ by inversely transforming the obtained $\Phi _n^{\prime }$ , utilizing the inverse relation of Eq. (15) (for the inverse relation, see Eq. (46), which appears later). Once $\Phi _n$ are obtained, the wave functions $\Psi _n$ can be expressed in terms of them accordingly. + +To obtain $\Phi _n^{\prime }$ , we need to solve Eq. (24) by setting the associated functions, in accordance with Eq. (27), as + +$$\begin{eqnarray} +U_{nl}(y)=N_{nl}y^{\lambda }(1-y)^{\upsilon }f_{nl}(y), +\end{eqnarray}$$ + +where $N_{nl}$ are normalization constants for the final wave functions. We applied $\alpha _{3}=1$ in this equation. In addition, from the comparison of Eq. (24) with the NUFA equation (26), we acquire the following parameter relations: + +$$\begin{eqnarray} +&&\alpha _{1}=\alpha _{2}=1,\quad\quad\quad\zeta _{1}=\varepsilon _{nl}-6P,~~~~~~ \\&&\zeta _{2}=2\varepsilon _{nl}-4P+Q,\quad\quad\zeta _{3}=\varepsilon _{nl}-P+Q+\gamma .~~~~~~ +\end{eqnarray}$$ + +Furthermore, the singularity exponents reduce to + +$$\begin{eqnarray} +\lambda =\sqrt{\varepsilon _{nl}-P+Q+\gamma },\quad\quad\upsilon =\frac{1}{2}+\sqrt{\left( l+\frac{1}{2}\right) ^{2}-3P}.~~~~~~ +\end{eqnarray}$$ + +Although the original NUFA representations of $\lambda$ and $\upsilon$ , given in Eqs. (28) and (29), include both positive and negative signs ( $\pm$ ) from a purely mathematical perspective, we choose to retain only the positive sign in this case as can be seen from Eq. (37). This selection is due to the fundamental physical constraint, which is that the wave functions must remain finite throughout the entire range of *r* and must vanish as $r \rightarrow 0$ and $r \rightarrow \infty$ . + +The expansion of Eq. (33) under the first condition in Eq. (36) yields + +$$\begin{eqnarray} +n+\lambda +\upsilon = \sqrt{\zeta _1}. +\end{eqnarray}$$ + +By squaring both sides of this equation and using the second relation from Eq. (36) together with the formula of $\lambda$ given in Eq. (37), the equation associated with the eigenvalues can be obtained as + +$$\begin{eqnarray} +2[\varepsilon _{nl}-P+Q+\gamma ]^{1/2}(n+\upsilon )+(n+\upsilon )^{2}+5P+Q+\gamma =0. +\end{eqnarray}$$ + +It is possible to rearrange this equation into a formula for $\varepsilon _{nl}$ by first isolating the square root term on the left-hand side, shifting all other terms to the right-hand side, and then squaring both sides. Substituting the expression for $\upsilon$ from Eq. (37) into the resulting equation, and using Eq. (25), we finally obtain the eigenvalues: + +$$\begin{eqnarray} +\Lambda _{nl} &=& l(l+1)\hbar ^{2}A_{0}\delta ^{2}+\frac{A_{0}\Omega ^{2}}{4\delta ^{2}}-D_{0}\delta \\&&-\frac{\hbar ^{2}A_{0}\delta ^{2}}{4}\left[ \frac{l(l+1)-\frac{5\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}-\frac{D_{0}}{\hbar ^{2}A_{0}\delta }+\left( n+\upsilon \right) ^{2}}{n+\upsilon }\right] ^{2}. +\end{eqnarray}$$ + +The eigenvalues obtained in Eq. (40) depend explicitly on several parameters of the system, including the effective oscillator frequency and coupling constants, which may exhibit time dependence through the Hamiltonian. This feature reflects the fact that the present model belongs to the class of parameter-dependent quantum systems. In such systems, variations of the energy spectrum with respect to the relevant parameters are conceptually related to the Hellmann–Feynman theorem [46,47], which expresses derivatives of eigenvalues in terms of expectation values of derivatives of the Hamiltonian. Although the present work does not explicitly employ the Hellmann–Feynman theorem in deriving Eq. (40), the obtained spectrum is consistent with the general framework of parameter-dependent quantum mechanics. + +In addition, by considering Eq. (27) with Eqs. (28), (29), and (34), the corresponding radial wave functions can be derived in terms of $\Lambda _{nl}$ to be + +$$\begin{eqnarray} +U_{nl}(y) &=& N_{nl}y^{\sqrt{-\frac{\Lambda _{nl}}{\hbar ^{2}A_{0}\delta ^{2}}+\frac{\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}-\frac{D_{0}}{\hbar ^{2}A_{0}\delta }+l(l+1)}} \\&&\times {(1-y)^{\frac{1}{2}+\sqrt{\left( l+\frac{1}{2}\right) ^{2}+\frac{3\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}}}}{_{2}F_{1}}(\bar{a},\bar{b};\bar{c};y), +\end{eqnarray}$$ + +where the parameters are defined as + +$$\begin{eqnarray} +\bar{a}=\lambda +\upsilon -\sqrt{\varepsilon _{nl}-6P}, +\end{eqnarray}$$ + +$$\begin{eqnarray} +\bar{b}=\lambda +\upsilon +\sqrt{\varepsilon _{nl}-6P}, +\end{eqnarray}$$ + +$$\begin{eqnarray} +\bar{c}=1+2\lambda . +\end{eqnarray}$$ + +From a mathematical standpoint, it is possible to define the parameter $\bar{a}$ interchangeably with $\bar{b}$ . However, by choosing $\bar{a}$ as in Eq. (42), we allow the possibility for $\bar{a}$ to take on negative values. When $\bar{a}$ is a nonpositive integer (i.e. a negative integer or zero), the Gauss hypergeometric function in Eq. (41) terminates at the ( $n+1$ )th term and reduces to a polynomial. Through the use of Eq. (20) with Eq. (41), the full eigenfunctions of the invariant operator $I_{0}$ are represented in the form + +$$\begin{eqnarray} +\Phi _{nlm}^{\prime }(r,\theta ,\varphi ) &=& N_{nl}\frac{1}{r}e^{-\left( \sqrt{-\frac{\Lambda _{nl}}{\hbar ^{2}A_{0}\delta ^{2}}+\frac{\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}-\frac{D_{0}}{\hbar ^{2}A_{0}\delta }+l(l+1)}\right) \delta r} \\&&\times \left( 1-e^{-\delta r}\right) ^{\frac{1}{2}+\sqrt{\left( l+\frac{1}{2}\right) ^{2}+\frac{3\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}}}{_{2}F_{1}}(\bar{a},\bar{b};\bar{c};e^{-\delta r})Y_{lm}(\theta ,\varphi ). +\end{eqnarray}$$ + +The complete eigenfunctions of the invariant *I* that belongs to the original system, i.e., the untransformed system, are given by the relation + +$$\begin{eqnarray} +\Phi _{nlm}(\vec{r},t)=U^{-1}(t)\Phi _{nlm}^{\prime }(\vec{r}). +\end{eqnarray}$$ + +The evaluation of this using Eqs. (16) and (45) results in + +$$\begin{eqnarray} +\Phi _{nlm}(r,\theta ,\varphi ,t) &=& N_{nl} \frac{1 }{\sqrt{\rho (t)}r} \exp \left( \frac{i\mu (t)\dot{\rho }(t)}{2\hbar \rho (t)}r^{2}\right) \\&&\times e^{-\left( \sqrt{-\frac{\Lambda _{nl}}{\hbar ^{2}A_{0}\delta ^{2}}+\frac{\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}-\frac{D_{0}}{\hbar ^{2}A_{0}\delta }+l(l+1)}\right) \delta \frac{r}{\rho (t) }} \\&&\times \left( 1-e^{-\delta \frac{r}{\rho (t) }}\right) ^{\frac{1}{2}+\sqrt{\left( l+\frac{1}{2}\right) ^{2}+\frac{3\Omega ^{2}}{4\hbar ^{2}\delta ^{4}}}}{_{2}F_{1}}(\bar{a},\bar{b};\bar{c};e^{-\delta \frac{r}{\rho (t) }})Y_{lm}(\theta ,\varphi ). +\end{eqnarray}$$ + +Meanwhile, $N_{nl}$ are derived by normalizing these resultant eigenfunctions. The normalization procedure yields (see Appendix A for a detailed evaluation) + +$$\begin{eqnarray} +N_{nl}=\sqrt{\delta } \frac{\Gamma (2(\upsilon +\lambda )+n)}{n!\Gamma (2\lambda +1)} \Bigg [ \sum _{k=0}^n\sum _{k^{\prime }=0}^n (-1)^{k+k^{\prime }} \mathcal {N}_{nl}(k,k^{\prime }) \Bigg ]^{-1/2}, +\end{eqnarray}$$ + +where + +$$\begin{eqnarray} +\mathcal {N}_{nl}(k,k^{\prime }) &=& \frac{\Gamma (2(\upsilon +\lambda )+n+k) \Gamma (2(\upsilon +\lambda )+n+k^{\prime })}{k!k^{\prime }!(n-k)!(n-k^{\prime })!\Gamma (2\lambda +k+1)\Gamma (2\lambda +k^{\prime }+1)} \\&& \times {\mathrm{B}}(k+k^{\prime }+2\lambda ,2\upsilon +1), +\end{eqnarray}$$ + +whereas ${\mathrm{B}}(a_1,a_2)$ denotes the Beta function [48]. The eigenfunctions in Eq. (47) are essentially related to the Schrödinger solutions of the system. We demonstrate how to construct the full quantum solutions in terms of these eigenfunctions in the following subsection. + +#### 2.3.2. + +According to Lewis–Riesenfeld theory [24], the wave functions of our time-dependent system are represented in terms of the eigenfunctions $\Phi _{nlm}(\vec{r},t)$ of the invariant operator, which are given in Eq. (47). However, these eigenfunctions alone do not fully define the time-dependent wave functions. Another required factor is the phase $\alpha (t)$ associated with each quantum state. We begin from Eq. (6) to derive this factor. Based on the unitary relation given in Eq. (46), Eq. (6) can be expressed in terms of the transformed eigenstate $\vert \Phi ^\prime \rangle$ instead of $\vert \Phi \rangle$ , such that + +$$\begin{eqnarray} +\hbar \frac{d\alpha (t)}{dt}=\left\langle \Phi ^{\prime }\left|\Bigg ( i \hbar \frac{\partial }{\partial t}-\frac{I_{0}}{2A_{0}\mu (t)\rho ^{2}(t)} \Bigg ) \right|\Phi ^{\prime }\right\rangle . +\end{eqnarray}$$ + +Then, a minor evaluation using Eq. (18) yields the phases in the form + +$$\begin{eqnarray} +\alpha _{nl}(t)=-\frac{\Lambda _{nl}}{2A_{0}\hbar }\int _{0}^{t}\frac{1}{\mu (t^{\prime })\rho ^{2}(t^{\prime })}dt^{\prime }+\alpha _{nl}(0), +\end{eqnarray}$$ + +where $\Lambda _{nl}$ are given by Eq. (40). These phases emerge as a result of the time evolution of the wave functions and can produce observable consequences, such as interference patterns, through relative phase differences between quantum states. Both the geometrical phase and the conventional dynamical phase contribute to the total phase. While the dynamical phase results from the cumulation of energy over time, the geometric phase originates from the intrinsic geometry of the path traced by the state vector during evolution, often under adiabatic conditions. The significance of the geometrical phase lies in the fact that it is not a mathematical artifact but a physically meaningful, gauge-invariant quantity that can influence the actual quantum state of the system. It not only takes place in this system, but appears ubiquitously across various physical systems as well, and can be measured via interference experiments. Unlike the dynamical phase, which depends on the eigenvalues of the Hamiltonian, the geometrical phase depends on the trajectory of the eigenvectors themselves. As such, it plays a critical role in associated phenomena such as energy level splitting and the orbital behavior of quasiparticles, making it a subject of continued theoretical and experimental interest. In particular, in systems with irregular and nonlinear interactions, such as quark–antiquark systems, it reflects the underlying symmetries, binding mechanisms, and topological features of the system [49,50]. Accordingly, the geometrical phase constitutes a fundamental component in the evolution of the system, linking theoretical descriptions to experimental observations. + +The solutions of the original Schrödinger equation (3) corresponding to the Hamiltonian in Eq. (1) can now be written in configuration space as + +$$\begin{eqnarray} +\Psi _{nlm}(r,\theta ,\varphi ,t)=e^{i\alpha _{nl}(t)}\Phi _{nlm}(r,\theta ,\varphi ,t), +\end{eqnarray}$$ + +where the explicit forms of $\alpha _{nl}(t)$ and $\Phi _{nlm}(r,\theta ,\varphi ,t)$ are given in Eqs. (51) and (47), respectively. The resulting wave functions (52), together with $\Lambda _{nl}$ in Eq. (40), serve as fundamental solutions for the quark–antiquark systems under consideration. The obtained wave functions enable us to compute spatial distributions (densities), transition probabilities, correlations, etc. Consequently, they provide a foundational framework for analyzing these systems, advancing our perspective on the interaction mechanisms between quarks and antiquarks, particularly in the context of quantum effects. The spatial structure of the wave functions enables estimation of key physical properties, such as the size of the particle (e.g. root-mean-square radius) and the binding strength of the bound state, which are critical for characterizing the complexity of strong interaction systems [51,52]. These wave functions thus serve as important tools for bridging theoretical predictions and experimental results, helping reveal the internal structure, confinement dynamics, and symmetry properties of the quark–antiquark pair. + +## 3. + +In this study, we investigated the interaction between a quark and an antiquark by modeling its mechanical behavior using a nonstationary 3D harmonic oscillator coupled with a Coulomb potential. To solve the associated Schrödinger equation, we employed the recently refined NUFA method, together with the invariant operator method and the unitary transformation approach. The NUFA method, based on a novel conceptual framework, allowed us to bypass the complex mathematical manipulations commonly encountered in other techniques. As a result, we obtained the wave functions of the system in closed form, expressed in terms of the Gauss hypergeometric function. These solutions offer advantages over previous ones that employed biconfluent Heun functions, as Gauss hypergeometric functions are among the most thoroughly characterized and best understood special functions in mathematics. + +Our solutions are essential for analyzing quark–antiquark pairs, which form the core of mesons and thus play a central role in the strong interaction. Although their dynamics are governed by QCD, the theory exhibits intricate behavior that significantly deviates from classical, nonrelativistic mechanics, encompassing numerous unresolved challenges. These include the incomplete theoretical understanding of quark confinement and hadronization mechanisms, the precise determination of quark masses and meson spectra, and the possible existence of exotic hadrons featuring internal structure beyond conventional quark–antiquark configurations [53,54]. Additional open questions involve the quantitative description of the quark condensate mechanism, the strong CP problem and the role of the $\theta$ -term, as well as the difficulty in accurately predicting the spectral properties and decay modes of heavy mesons such as $J/\psi$ [5,55]. The analytical solutions developed in this study may provide insights toward addressing these longstanding issues. + +## Funding + +Open Access funding: SCOAP 3 . + +Although normalization is formally defined for the full wave functions, the phase factors do not affect the outcome. Therefore, in the normalization process, it suffices to consider only Eq. (47), which corresponds to the eigenfunctions. The normalization condition associated with Eq. (47) is given by + +$$\begin{eqnarray} +\int _0^{2\pi } \int _0^\pi \int _0^\infty \left|\Phi _{nlm} (r,\theta ,\varphi ,t) \right|^{2}r^{2} \sin \theta dr d\theta d\varphi =1. +\end{eqnarray}$$ + +Because the angular part $Y_{lm} (\theta ,\varphi )$ , defined in Eq. (21), is already normalized, we only need to consider the radial part in this evaluation. Thus, based on the radial part of Eq. (47), Eq. (A.1) can be rewritten as + +$$\begin{eqnarray} +\frac{\left|N_{nl}\right|^{2}}{\rho (t) }\int _{0}^{\infty }e^{-2\lambda \delta \frac{r}{\rho (t)}}\left( 1-e^{-\delta \frac{r}{\rho (t)}}\right) ^{2\upsilon }\left[ {_{2}F_{1}}(\bar{a},\bar{b};\bar{c};e^{-\delta \frac{r}{\rho (t)}})\right] ^{2}dr =1. +\end{eqnarray}$$ + +To simplify the integral, we introduce the substitution + +$$\begin{eqnarray} +z=e^{-\delta \frac{r}{\rho (t)}}, +\end{eqnarray}$$ + +and consider the fact that $\bar{a}$ and $\bar{b}$ in this case can be expressed in terms of *n* as + +$$\begin{eqnarray} +\bar{a}=-n,~~~~~~~ \bar{b}=2(\upsilon +\lambda )+n, +\end{eqnarray}$$ + +along with Eq. (44) as the formula of $\bar{c}$ . Then, Eq. (A.2) becomes + +$$\begin{eqnarray} +\frac{\left|N_{nl}\right|^{2}}{\delta }g_{\rm int}=1, +\end{eqnarray}$$ + +where $g_{\rm int}$ is the integral part: + +$$\begin{eqnarray} +g_{\rm int}=\int _{0}^{1}z^{2\lambda -1} \left( 1-z\right) ^{2\upsilon }\left[ {_{2}F_{1}}(-n,2(\upsilon +\lambda )+n;2\lambda +1;z)\right] ^{2}dz . +\end{eqnarray}$$ + +To evaluate the integration $g_{\rm int}$ , we use the expansion formula for the hypergeometric function (see formula 15.2.4 of Ref. [45]): + +$$\begin{eqnarray} +{_{2}F_{1}}(-n,b;c;s)=\sum _{k=0}^n (-1)^k \binom{n}{k} \frac{(b)_k}{(c)_k}s^k, +\end{eqnarray}$$ + +which holds when $n=0,1,2,\cdots$ and $c \ne 0,-1,-2,\cdots$ . This relation also holds when $c=-n-j$ , where $j=0,1,2,\cdots$ . Substituting this series expansion into Eq. (A.6), we obtain + +$$\begin{eqnarray} +g_{\rm int}&=&\sum _{k=0}^n\sum _{k^{\prime }=0}^n(-1)^{k+k^{\prime }}\binom{n}{k} \binom{n}{k^{\prime }} \frac{(2(\upsilon +\lambda )+n)_k (2(\upsilon +\lambda )+n)_{k^{\prime }}}{(2\lambda +1)_k(2\lambda +1)_{k^{\prime }}} \\&&\times \int _{0}^{1}z^{k+k^{\prime }+2\lambda -1} \left( 1-z\right) ^{2\upsilon }dz . +\end{eqnarray}$$ + +The integral in the above equation is the Beta function, which is defined as [48] + +$$\begin{eqnarray} +\mathrm{B}(a_1,a_2)= \int _0^1 s^{a_1-1} (1-s)^{a_2-1} ds, +\end{eqnarray}$$ + +under the conditions ${\rm Re}a_1\gt 0$ and ${\rm Re}a_2\gt 0$ . The two shape parameters of this function, in our case, are $a_1=k+k^{\prime }+2\lambda$ and $a_2=2\upsilon +1$ . 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Hellmann, Einführung in die Quantenchemie (F. Deuticke, Leipzig, 1937). +- R. P. Feynman, Phys. Rev. 56, 340 (1939).10.1103/PhysRev.56.340 +- I. S. Gradshteyn, I. M. Ryzhik, Table of Integrals, Series, and Products (Elsevier, Amsterdam, 2007), 7th ed., p. 908 (formula 3.380(1)) or p. 315 (formula 3.191(3)). +- S. Banerjee, M. Dorband, J. Erdmenger, A.-L. Weigel, J. High Energy Phys. 2023, 026 (2023).10.1007/JHEP10(2023)026 +- T. Asselmeyer-Maluga, M. Lulli, A. Marcianò, R. Pasechnik, E. Zappala, arXiv:2408.15986v3 [hep-th] [Search inSPIRE]. +- M. Alberg, G. A. Miller, Phys. Rev. C. 110, L042201 (2024).10.1103/PhysRevC.110.L042201 +- G. Ganbold, EPJ Web Conf. 3, 03014 (2010).10.1051/epjconf/20100303014 +- G. F. de Téramond, S. J. Brodsky, Int. J. Mod. Phys. A. 39, 2441007 (2024). +- N. Hüsken, E. S. Norella, I. Polyakov, Mod. Phys. Lett. A. 40, 2530002 (2025). +- ALICE Collaboration, Eur. Phys. J. C. 84, 813 (2024). \ No newline at end of file diff --git a/tests/data/jats/sources/ptag100.xml b/tests/data/jats/sources/ptag100.xml new file mode 100644 index 0000000000..bc8c4d4044 --- /dev/null +++ b/tests/data/jats/sources/ptag100.xml @@ -0,0 +1,662 @@ + + +
+ + +ptep + +Progress of Theoretical and Experimental Physics +Prog. Theor. Exp. Phys. +PTEPHY + +2050-3911 + +Oxford University Press + + + +10.1093/ptep/ptag100 +ptag100 + + +Paper + +General and Mathematical Physics + + + +PTEP/A12 +PTEP/A60 +PTEP/B87 + + +AcademicSubjects/SCI01970 + + + +Time-Dependent 3D Oscillator with Coulomb Interaction: An Alternative Approach for Analyzing Quark–Antiquark Systems + + + +https://orcid.org/0000-0002-4416-4408 +Choi Jeong Ryeol +https://ror.org/032xf8h46School of Electronic Engineering, Kyonggi University, Yeongtong-gu, Suwon, Gyeonggi-do 16227, Republic of Korea + + +https://orcid.org/0000-0003-4896-9470 +Medjber Salim +https://ror.org/055rz8d64Laboratory of Physical and Chemistry Materials, Faculty of Science, University of M’sila, University Campus, Road Bourdj Bou Arreiridj, M’sila 28000, Algeria + + +https://orcid.org/0000-0003-1298-9226 +Menouar Salah +menouar_salah@yahoo.fr +https://ror.org/02rzqza52Laboratory of Optoelectronics and Compounds (LOC), Department of Physics, Faculty of Science, Setif 1 University-Ferhat Abbas, Setif 19000, Algeria + + + +https://orcid.org/0000-0001-6207-8098 +Sever Ramazan +Department of Physics, https://ror.org/014weej12Middle East Technical University, Ankara 06531, Turkey + + + +menouar_salah@yahoo.fr + +July2026 +04072026 +30052026 +2026 +7 +073A01 + +10102025 +14052026 +19052026 +06072026 + + +© The Author(s) 2026. Published by Oxford University Press on behalf of the Physical Society of Japan. +2026 + +This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. +Funded by SCOAP3 + + + +Abstract +

In this work, the dynamics of quark–antiquark pair systems is investigated by modeling them as general time-dependent 3D oscillators perturbed by a Coulomb potential. Solving this model enables the prediction of key mesonic properties such as the probability density, energy spectra, and quadrature uncertainties, offering theoretical insights into the confinement of quarks via gluon-mediated strong interactions. To tackle the mathematical difficulty raised by the time dependence of parameters in the system, special mathematical techniques, such as the invariant operator method, unitary transformation method, and the Nikiforov–Uvarov functional analysis are used. The wave functions of the system, derived using these mathematical techniques, are expressed analytically in terms of the Gauss hypergeometric function whose mathematical properties are well characterized. Our results provide the quantum-mechanical framework of quark–antiquark systems that are essential for exploring the nonperturbative aspects of quantum chromodynamics. In addition, the underlying mathematical structure may serve as a foundation for addressing broader challenges in particle physics, including the origin of mass and its connection to the Higgs mechanism.

+ + + + + +SCOAP + + + + + + + + + + + + +Introduction +

Quark–antiquark pairs are a key concept in particle physics, essential for understanding the strong interaction and the structure of hadrons such as mesons and baryons. Typical examples of quark–antiquark pairs are bound states of a heavy quark and its antiquark, such as charmonium and bottomonium, collectively referred to as quarkonia [1]. Quarkonia can be readily produced in experiments such as the LHC, and their properties can be measured with high precision. Quarkonia’s distinct mass spectra, unique decay patterns, and relatively long lifetimes are of significant interest in the search for physics that extends past the Standard Model. As such, they may offer a promising avenue for probing potential new phenomena not accounted for by the Higgs sector [2,3]. This makes quarkonia an important topic requiring collaborative research between experimental and theoretical physicists. Indeed, they are actively studied in connection with new particle searches and dark matter theories [4–7].

+

In light of this, it is essential to establish a theoretical framework that enables systematic analysis and interpretation of quarkonium systems. In particle physics, quark–antiquark systems are commonly modeled using a potential that combines harmonic and Coulomb terms [8–10], where the harmonic component captures the long-range confinement effect and the Coulomb term accounts for the short-range interactions based on quantum chromodynamics (QCD). Motivated by this, we focus on the analysis of time-dependent 3D oscillators perturbed by a Coulomb potential throughout this work. The quantum-mechanical solutions of such systems are known to be expressible in terms of biconfluent Heun functions [11,12]. However, limited knowledge of the properties of such functions continues to make these systems difficult to analyze. At present, the characterization of these functions remains confined to their behavior in certain special cases and their connections to specific recursion relations, particularly their representations in terms of these relations [13,14]. This lack of comprehensive understanding is the reason why many researchers refer to them primarily through their defining equation, the biconfluent Heun equation, rather than treating them as fully characterized functions in their own right [15–19].

+

To overcome this limitation, the present work explores an alternative analytical treatment by expressing the solutions in terms of well-established mathematical functions, thereby providing a more accessible and rigorous theoretical understanding of the system. Deriving such solutions to fundamental equations for our general physical systems governed by time-dependent Hamiltonians [20–22] may require special mathematical techniques beyond conventional methods such as the separation of variables. One promising approach is the invariant operator method [23–26], which has proven effective in the study of time-dependent Hamiltonian systems (TDHSs). This method allows for the construction of exact quantum solutions in systems with time-dependent parameters. Specifically, if an invariant operator can be identified for a given system, the quantum wave functions, apart from phase parts, can be expressed by eigenfunctions of this operator. Consequently, solving the quantum system reduces to finding the eigenfunctions of the invariant operator, while the required phase factors can be determined with the help of the Schrödinger equation.

+

Regarding coupling of potentials, as well as the system’s time dependence, the general form of the invariant operator is typically complicated. Therefore, to solve its eigenvalue equation, more advanced mathematical techniques, such as the unitary transformation method [27,28] and the Nikiforov–Uvarov functional analysis (NUFA) [29], are necessary. The NUFA method builds upon and integrates several approaches, including the original Nikiforov–Uvarov (NU) method [30], the parametric NU method [31–34], and elements of functional analysis [35]. This enhanced method enables one to obtain the solution of related equations, including those of hypergeometric type, in a systematic and elegant manner, allowing the determination of the eigenvalue spectrum and the corresponding wave functions. We demonstrate that the wave functions derived through this approach can be expressed in terms of the well-known Gauss hypergeometric function. This may provide a significantly more tractable framework for analyzing the system compared to existing solutions that are based on biconfluent Heun functions. Taking advantage of these strengths, we show that the newly obtained solutions enable a thorough investigation of the system, particularly in dynamically evolving hadronic environments where effective quark interactions may vary due to medium-induced effects and nonequilibrium processes.

+

The main contributions of the present work can be summarized as follows. First, we develop an analytical treatment for a time-dependent quark–antiquark model based on a 3D oscillator perturbed by a Coulomb interaction within the framework of invariant operator theory. Second, by combining the invariant operator method, unitary transformation techniques, and the NUFA formalism, we derive analytical expressions for the eigenspectrum, quantum phases, and wave functions of the system in closed form. In contrast to several previous approaches in which the solutions are represented in terms of biconfluent Heun functions, the present treatment expresses the wave functions in terms of the Gauss hypergeometric function, whose mathematical properties are more thoroughly established. This provides a more tractable analytical framework for investigating the dynamical properties of quark–antiquark systems governed by time-dependent Hamiltonians.

+ + + +Analysis of the system + + +Basic formulations: Hamiltonian and invariant + + +Setup of the Hamiltonian +

We consider a quark–antiquark pair with a time-dependent effective reduced mass in spherical coordinates, governed by a central potential  where and are coefficients that depend on time. While  represents a confining trap potential with adjustable strength, is a Coulomb perturbation with the constraint . As the distance between quarks increases, the harmonic potential rises sharply, effectively prohibiting their free separation within a bound state. This potential acts as a simplified representation of the QCD confinement mechanism and dominates at large separations. Meanwhile, the Coulomb term represents the electromagnetic or color interaction mediated by the color charge of one quark acting on the other, effectively serving as a short-range attractive force between the quark and antiquark.

+

Time-dependent Hamiltonians naturally arise in hadron physics when quark–antiquark systems are embedded in dynamical environments, such as quark–gluon plasma or nonequilibrium hadronic media produced in high-energy collisions [20–22]. In such situations, the effective interaction between quarks may vary with time due to medium evolution, color-screening effects, confinement modifications, or changes in the effective quark mass. Consequently, the parameters appearing in phenomenological quarkonium potentials, including the confining strength and Coulomb-like coupling terms, can acquire explicit time dependence. The present model therefore provides an effective framework for investigating dynamical quark–antiquark systems within a quantum-mechanical setting.

+

Based on the above description, the Hamiltonian that characterizes the motion of the pair of quarks is expressed as:

+ + + + +

This Hamiltonian enables the calculation of the quantum-mechanical spectrum including wave functions and excitation properties, which is then used to theoretically explain the mass and structure of mesons. The momentum operator in spherical coordinates is represented as , where and L is the total angular momentum, the formula for which is given by

+ + + + +

To investigate the dynamical features of the system, we must solve the Schrödinger equation for the Hamiltonian in Eq. (1). If we denote the system’s state vector as , the corresponding Schrödinger equation is of the form

+ + + + +

The time dependence of the Hamiltonian requires a specialized approach to solve this equation, as will be presented subsequently.

+ + + +Formulation of the invariant operator +

Because in Eq. (1) is represented in terms of time functions, directly solving Eq. (3) can be very challenging. To overcome this difficulty, we adopt the dynamical invariant method [24]. The core idea of this method is to treat the system in terms of a nontrivial invariant operator rather than in Eq. (1), where does not involve time-derivative operators. Moreover, is closely related to the concept of invariants in classical mechanics (e.g. constants of motion), thereby facilitating interpretation from the perspective of the quantum–classical correspondence.

+

By the definition of the invariant operator, satisfies the equation:

+ + + + +

If such an invariant exists, it is possible to put the solution of the Schrödinger equation (3) in terms of its eigenfunction , such that

+ + + + +

where is a phase function that can be obtained from

+ + + + +

To find the expression of the invariant utilizing Eq. (4), we set

+ + + + +

where , , , and are time-dependent functions to be determined. Then, a mathematical procedure with this assumed formula of after substituting Eqs. (1) and (7) into Eq. (4) yields

+ + + + + + + + + + + + + + + + +

where is an arbitrary real constant and is a solution of the following equation

+ + + + +

with being a real constant. The above invariant is valid under the condition that follows the relation

+ + + + +

Equation (7) with Eqs. (8)–(11) constitutes the complete quadratic invariant operator of the system. This operator provides a robust analytical framework for the system, in which direct solutions are difficult to obtain due to the complicated time evolution of the Hamiltonian. It also enables a deeper understanding of dynamical properties of the system, including phase transitions and topological changes, beyond merely providing a powerful methodology for obtaining solutions.

+ + + +Solving the eigenvalue equation +

Because the quantum wave functions of the system are represented in terms of the eigenfunctions of , it is now necessary to evaluate its eigenvalue equation. We begin by writing the eigenvalue equation of the invariant as

+ + + + +

where are the eigenvalues and are time-dependent eigenfunctions. Given that , as defined in Eq. (7) along with Eqs. (8)–(11), has a complicated form, it is favorable to solve Eq. (14) after we transform it mathematically into a simple form. To do this, we consider a unitary transformation of the form

+ + + + +

where is a time-dependent unitary operator given by

+ + + + +

Then, if we write the transformed invariant operator as , the eigenvalue equation in the transformed frame is represented in terms of , such that

+ + + + +

Now, from a mathematical procedure using Eq. (16), we obtain that

+ + + + +

where while . An infinite number of bound states may arise from the strong confinement of quarkonium, which is primarily attributed to the harmonic term dominating at large distances. By the way, the direct differentiation of with respect to t, followed by the application of Eq. (13), yields . This means that is in fact a constant of motion, as expected—since the operator on the left-hand side of Eq. (18) is the transformed version of the original invariant . It is worth noting that Eq. (18) is very simple compared to the original eigenvalue equation represented in Eq. (14).

+

The existence of a perturbation of Coulomb potential may make the problem much more difficult. In what follows, the eigenvalue equation (18) can be rewritten as

+ + + + +

According to the invariant operator theory, the wave functions of the transformed system are represented in terms of . Because this equation is independent of time, we can apply the method of separation of variables in order to solve it. Considering this, we take

+ + + + +

where we have introduced , whereas are the spherical harmonics. The angular part of the eigenfunctions takes the form

+ + + + +

where are the associated Legendre polynomials, while the spherical normalization constants are . The allowed orbital and magnetic quantum numbers are and , respectively.

+

Some algebraic manipulation, after substituting Eq. (20) into Eq. (19), leads to the following radial equation:

+ + + + +

The equation is analytically intractable unless owing to the coexistence of and terms. To resolve this difficulty, we employ the Greene–Aldrich approximation scheme of the form [36–39]

+ + + + +

together with a coordinate transformation . This type of approximation is commonly used to describe particles governed by a screened potential, where the parameter  is typically chosen to be equal to the screening mass [29,36]. Though the potential in our system is not screened, choosing to be similar to the typical screening mass still provides a good approximation, particularly accurate for distances comparable to or smaller than . For example, in quarkonium models, is usually taken to be in the range of approximately 200 to 900 MeV [40,41]. Thus, selecting within this range is a reasonable choice.

+

Then, by rewriting the notation as , the differential equation (22) becomes

+ + + + +

where

+ + + + +

In the representation of Eq. (24), we neglected higher-order terms corresponding to and . To solve this equation, we use the NUFA method [29] outlined in the next subsection.

+ + + + +The NUFA method +

A second-order differential equation of hypergeometric type can be solved in a simple and elegant way by using the so-called NUFA method. This is an improvement over the parametric NU method, which is relatively cumbersome due to the need to find the square of the polynomials and other conditions required. With the NUFA method, many diffierential equations, including our wave equation, can be properly tackled, allowing for the identification of singularities and the determination of the eigenvalue spectrum. As a general type of second-order differential equation, which encompasses Eq. (24), we consider the following equation [31]:

+ + + + +

where and are parameters appropriately chosen depending on a given system. Because this equation has two singularities at and , it is possible to take the solution in the form

+ + + + +

By substituting this formula into Eq. (26) along with the following choice,

+ + + + + + + + +

we have [29]

+ + + + +

where

+ + + + + + + + +

Thus, the equation is reduced to a tractable form.

+

We now examine a simple case where , which meets the equation associated with our considered system. Under the condition that the principal quantum number takes the form

+ + + + +

the function is represented by the Gauss hypergeometric function as [29]

+ + + + +

where , , and . The mathematical formula of this hypergeometric function is as follows: , where for and .

+

The Gauss hypergeometric function serves as a fundamental and unifying special function that encompasses many other functions as special or limiting cases [42–45]. As defined in Eq. (34) with Eq. (33), the function terminates and reduces to a polynomial when a is zero or a negative integer, a case that is often associated with Laguerre polynomials. In particular, is a generalization of Kummer’s confluent hypergeometric function . Hence, in the limiting case where , converges to . This convergence is rooted in the fact that, while has two regular singular points, sending one of them to infinity (a process known as confluence) effectively merges the singularities, giving rise to [44]. Additionally, the Jacobi polynomials can also be expressed in terms of the Gauss hypergeometric function (for a typical representation, see formula 15.9.1 of Ref. [45]). The function thus plays a major role in constructing analytical solutions and enables rigorous, systematic analysis across a broad spectrum of scientific disciplines beyond mathematics and physics. The method presented in this subsection, which naturally leads to the appearance of the function as a solution, is applicable to the analysis of a wide class of physical systems governed by one or more interaction potentials. It is, of course, well suited to handling Eq. (24).

+ + + +Wave functions + + +Eigenfunctions of the invariant operator +

We now proceed to derive the wave functions of the system. For clarity and better reader understanding, we briefly summarize our strategy at this point. Because, in our time-dependent case, the wave functions are described by the eigenfunctions of (instead of ), we need to determine the eigenfunctions of Eq. (14). However, directly solving for is likely to be a formidable task. To circumvent this difficulty, we first derive from Eq. (17), and then reconstruct the desired eigenfunctions by inversely transforming the obtained , utilizing the inverse relation of Eq. (15) (for the inverse relation, see Eq. (46), which appears later). Once are obtained, the wave functions can be expressed in terms of them accordingly.

+

To obtain , we need to solve Eq. (24) by setting the associated functions, in accordance with Eq. (27), as

+ + + + +

where are normalization constants for the final wave functions. We applied in this equation. In addition, from the comparison of Eq. (24) with the NUFA equation (26), we acquire the following parameter relations:

+ + + + +

Furthermore, the singularity exponents reduce to

+ + + + +

Although the original NUFA representations of and , given in Eqs. (28) and (29), include both positive and negative signs () from a purely mathematical perspective, we choose to retain only the positive sign in this case as can be seen from Eq. (37). This selection is due to the fundamental physical constraint, which is that the wave functions must remain finite throughout the entire range of r and must vanish as and .

+

The expansion of Eq. (33) under the first condition in Eq. (36) yields

+ + + + +

By squaring both sides of this equation and using the second relation from Eq. (36) together with the formula of given in Eq. (37), the equation associated with the eigenvalues can be obtained as

+ + + + +

It is possible to rearrange this equation into a formula for by first isolating the square root term on the left-hand side, shifting all other terms to the right-hand side, and then squaring both sides. Substituting the expression for from Eq. (37) into the resulting equation, and using Eq. (25), we finally obtain the eigenvalues:

+ + + + +

The eigenvalues obtained in Eq. (40) depend explicitly on several parameters of the system, including the effective oscillator frequency and coupling constants, which may exhibit time dependence through the Hamiltonian. This feature reflects the fact that the present model belongs to the class of parameter-dependent quantum systems. In such systems, variations of the energy spectrum with respect to the relevant parameters are conceptually related to the Hellmann–Feynman theorem [46,47], which expresses derivatives of eigenvalues in terms of expectation values of derivatives of the Hamiltonian. Although the present work does not explicitly employ the Hellmann–Feynman theorem in deriving Eq. (40), the obtained spectrum is consistent with the general framework of parameter-dependent quantum mechanics.

+

In addition, by considering Eq. (27) with Eqs. (28), (29), and (34), the corresponding radial wave functions can be derived in terms of to be

+ + + + +

where the parameters are defined as

+ + + + + + + + + + + + +

From a mathematical standpoint, it is possible to define the parameter interchangeably with . However, by choosing as in Eq. (42), we allow the possibility for to take on negative values. When is a nonpositive integer (i.e. a negative integer or zero), the Gauss hypergeometric function in Eq. (41) terminates at the ()th term and reduces to a polynomial. Through the use of Eq. (20) with Eq. (41), the full eigenfunctions of the invariant operator are represented in the form

+ + + + +

The complete eigenfunctions of the invariant I that belongs to the original system, i.e., the untransformed system, are given by the relation

+ + + + +

The evaluation of this using Eqs. (16) and (45) results in

+ + + + +

Meanwhile, are derived by normalizing these resultant eigenfunctions. The normalization procedure yields (see Appendix A for a detailed evaluation)

+ + + + +

where

+ + + + +

whereas denotes the Beta function [48]. The eigenfunctions in Eq. (47) are essentially related to the Schrödinger solutions of the system. We demonstrate how to construct the full quantum solutions in terms of these eigenfunctions in the following subsection.

+ + + +Quantum phases and the Schrödinger solutions +

According to Lewis–Riesenfeld theory [24], the wave functions of our time-dependent system are represented in terms of the eigenfunctions of the invariant operator, which are given in Eq. (47). However, these eigenfunctions alone do not fully define the time-dependent wave functions. Another required factor is the phase associated with each quantum state. We begin from Eq. (6) to derive this factor. Based on the unitary relation given in Eq. (46), Eq. (6) can be expressed in terms of the transformed eigenstate instead of , such that

+ + + + +

Then, a minor evaluation using Eq. (18) yields the phases in the form

+ + + + +

where are given by Eq. (40). These phases emerge as a result of the time evolution of the wave functions and can produce observable consequences, such as interference patterns, through relative phase differences between quantum states. Both the geometrical phase and the conventional dynamical phase contribute to the total phase. While the dynamical phase results from the cumulation of energy over time, the geometric phase originates from the intrinsic geometry of the path traced by the state vector during evolution, often under adiabatic conditions. The significance of the geometrical phase lies in the fact that it is not a mathematical artifact but a physically meaningful, gauge-invariant quantity that can influence the actual quantum state of the system. It not only takes place in this system, but appears ubiquitously across various physical systems as well, and can be measured via interference experiments. Unlike the dynamical phase, which depends on the eigenvalues of the Hamiltonian, the geometrical phase depends on the trajectory of the eigenvectors themselves. As such, it plays a critical role in associated phenomena such as energy level splitting and the orbital behavior of quasiparticles, making it a subject of continued theoretical and experimental interest. In particular, in systems with irregular and nonlinear interactions, such as quark–antiquark systems, it reflects the underlying symmetries, binding mechanisms, and topological features of the system [49,50]. Accordingly, the geometrical phase constitutes a fundamental component in the evolution of the system, linking theoretical descriptions to experimental observations.

+

The solutions of the original Schrödinger equation (3) corresponding to the Hamiltonian in Eq. (1) can now be written in configuration space as

+ + + + +

where the explicit forms of and are given in Eqs. (51) and (47), respectively. The resulting wave functions (52), together with in Eq. (40), serve as fundamental solutions for the quark–antiquark systems under consideration. The obtained wave functions enable us to compute spatial distributions (densities), transition probabilities, correlations, etc. Consequently, they provide a foundational framework for analyzing these systems, advancing our perspective on the interaction mechanisms between quarks and antiquarks, particularly in the context of quantum effects. The spatial structure of the wave functions enables estimation of key physical properties, such as the size of the particle (e.g. root-mean-square radius) and the binding strength of the bound state, which are critical for characterizing the complexity of strong interaction systems [51,52]. These wave functions thus serve as important tools for bridging theoretical predictions and experimental results, helping reveal the internal structure, confinement dynamics, and symmetry properties of the quark–antiquark pair.

+ + + + + +Conclusion +

In this study, we investigated the interaction between a quark and an antiquark by modeling its mechanical behavior using a nonstationary 3D harmonic oscillator coupled with a Coulomb potential. To solve the associated Schrödinger equation, we employed the recently refined NUFA method, together with the invariant operator method and the unitary transformation approach. The NUFA method, based on a novel conceptual framework, allowed us to bypass the complex mathematical manipulations commonly encountered in other techniques. As a result, we obtained the wave functions of the system in closed form, expressed in terms of the Gauss hypergeometric function. These solutions offer advantages over previous ones that employed biconfluent Heun functions, as Gauss hypergeometric functions are among the most thoroughly characterized and best understood special functions in mathematics.

+

Our solutions are essential for analyzing quark–antiquark pairs, which form the core of mesons and thus play a central role in the strong interaction. Although their dynamics are governed by QCD, the theory exhibits intricate behavior that significantly deviates from classical, nonrelativistic mechanics, encompassing numerous unresolved challenges. These include the incomplete theoretical understanding of quark confinement and hadronization mechanisms, the precise determination of quark masses and meson spectra, and the possible existence of exotic hadrons featuring internal structure beyond conventional quark–antiquark configurations [53,54]. Additional open questions involve the quantitative description of the quark condensate mechanism, the strong CP problem and the role of the -term, as well as the difficulty in accurately predicting the spectral properties and decay modes of heavy mesons such as [5,55]. The analytical solutions developed in this study may provide insights toward addressing these longstanding issues.

+ + + + +Funding +

Open Access funding: SCOAP3.

+ + +Derivation of the normalization constants +

Although normalization is formally defined for the full wave functions, the phase factors do not affect the outcome. Therefore, in the normalization process, it suffices to consider only Eq. (47), which corresponds to the eigenfunctions. The normalization condition associated with Eq. (47) is given by

+ + + + +

Because the angular part , defined in Eq. (21), is already normalized, we only need to consider the radial part in this evaluation. Thus, based on the radial part of Eq. (47), Eq. (A.1) can be rewritten as

+ + + + +

To simplify the integral, we introduce the substitution

+ + + + +

and consider the fact that and in this case can be expressed in terms of n as

+ + + + +

along with Eq. (44) as the formula of . Then, Eq. (A.2) becomes

+ + + + +

where is the integral part:

+ + + + +

To evaluate the integration , we use the expansion formula for the hypergeometric function (see formula 15.2.4 of Ref. [45]):

+ + + + +

which holds when and . This relation also holds when , where . Substituting this series expansion into Eq. (A.6), we obtain

+ + + + +

The integral in the above equation is the Beta function, which is defined as [48]

+ + + + +

under the conditions and . The two shape parameters of this function, in our case, are and . Thus, considering that the Pochhammer number is given by , where , a rearrangement of Eq. (A.5) with Eq. (A.8) leads directly to the expression for the normalization constants , as presented in Eq. (48) of the text.

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\ No newline at end of file diff --git a/tests/test_backend_jats.py b/tests/test_backend_jats.py index e2d926a41f..6f610bf4e3 100644 --- a/tests/test_backend_jats.py +++ b/tests/test_backend_jats.py @@ -1,9 +1,60 @@ +"""Test module for the JATS backend parser. + +Third-party test data notice +----------------------------------------------------------------------- +The JATS source files in tests/data/jats/sources/ and their derived +groundtruth files (*.itxt, *.json, *.md) are based on the following +open-access articles: + +1. ptag100.xml — CC BY 4.0 + Choi JR, Medjber S, Menouar S, Sever R (2026). "Time-Dependent 3D + Oscillator with Coulomb Interaction: An Alternative Approach for + Analyzing Quark-Antiquark Systems." Progress of Theoretical and + Experimental Physics, 2026(7), 073A01. + https://doi.org/10.1093/ptep/ptag100 + Copyright © 2026 The Author(s). Published by Oxford University Press + on behalf of the Physical Society of Japan. + Originally obtained from SCOAP3 (https://scoap3.org), the Sponsoring + Consortium for Open Access Publishing in Particle Physics: + https://scoap3-prod-backend.s3.cern.ch/media/harvested_files/10.1093/ptep/ptag100/ptag100.xml + License: https://creativecommons.org/licenses/by/4.0/ + The derived groundtruth files are adaptations of the original work. + +2. elife-56337.nxml — CC0 1.0 (public domain) + Wolf G, de Iaco A, Sun M-A, Bruno M, Tinkham M, Hoang D, et al. + (2020). "KRAB-zinc finger protein gene expansion in response to + active retrotransposons in the murine lineage." eLife, 9, e56337. + https://doi.org/10.7554/eLife.56337 + Originally obtained from PubMed Central (PMC7289599). + License: https://creativecommons.org/publicdomain/zero/1.0/ + +3. pone.0234687.nxml — CC0 1.0 (public domain) + Ribeiro-Filho HMN, Civiero M, Kebreab E, Sainju UM, et al. (2020). + "Potential to reduce greenhouse gas emissions through different dairy + cattle systems in subtropical regions." PLoS ONE, 15(6), e0234687. + https://doi.org/10.1371/journal.pone.0234687 + Originally obtained from PubMed Central (PMC7302504). + License: https://creativecommons.org/publicdomain/zero/1.0/ + +4. pntd.0008301.nxml — CC0 1.0 (public domain) + Burgert-Brucker CR, Zoerhoff KL, Headland M, Shoemaker EA, + Stelmach R, Karim MJ, et al. (2020). "Risk factors associated with + failing pre-transmission assessment surveys (pre-TAS) in lymphatic + filariasis elimination programs: Results of a multi-country + analysis." PLoS Neglected Tropical Diseases, 14(6), e0008301. + https://doi.org/10.1371/journal.pntd.0008301 + Originally obtained from PubMed Central (PMC7289444). + License: https://creativecommons.org/publicdomain/zero/1.0/ +----------------------------------------------------------------------- +""" + import os from io import BytesIO from pathlib import Path import pytest from docling_core.types.doc import DocItemLabel, DoclingDocument, GroupLabel, TextItem +from docling_core.types.doc.document import Script from docling.datamodel.base_models import DocumentStream, InputFormat from docling.datamodel.document import ConversionResult @@ -17,8 +68,11 @@ def get_jats_paths(): directory = Path(os.path.dirname(__file__) + "/data/jats/") - xml_files = sorted(directory.rglob("*.nxml")) - return xml_files + nxml_files = list(directory.rglob("*.nxml")) + nxml_stems = {p.stem for p in nxml_files} + # Avoid running duplicate conversions of the same content. + xml_files = [p for p in directory.rglob("*.xml") if p.stem not in nxml_stems] + return sorted(nxml_files + xml_files) def get_converter(): @@ -26,6 +80,36 @@ def get_converter(): return converter +def _formatting_tuple(item) -> tuple: + """Compact, comparable view of a text item's formatting.""" + f = item.formatting + if f is None: + return (item.label, item.text, None) + return ( + item.label, + item.text, + (f.bold, f.italic, f.underline, f.strikethrough, f.script), + ) + + +def convert_jats_body(body: str) -> DoclingDocument: + xml = f""" +
+ + {body} + +
+""" + stream = DocumentStream( + name="body-test.nxml", + stream=BytesIO(xml.encode()), + ) + + conv_result: ConversionResult = get_converter().convert(stream) + return conv_result.document + + def convert_jats_article_meta(article_meta: str) -> DoclingDocument: xml = f""" @@ -151,33 +235,103 @@ def _inline_group_items(doc: DoclingDocument) -> list[list]: pytest.param( "The mass energy relation $$E=mc^2$$ is famous.", [ - (DocItemLabel.TEXT, "The mass energy relation"), - (DocItemLabel.FORMULA, "E=mc^2"), - (DocItemLabel.TEXT, "is famous."), + (DocItemLabel.TEXT, "The mass energy relation", None), + (DocItemLabel.FORMULA, "E=mc^2", None), + (DocItemLabel.TEXT, "is famous.", None), ], id="text-formula-text", ), pytest.param( "Given $$a^2$$ and $$b^2$$ we sum.", [ - (DocItemLabel.TEXT, "Given"), - (DocItemLabel.FORMULA, "a^2"), - (DocItemLabel.TEXT, "and"), - (DocItemLabel.FORMULA, "b^2"), - (DocItemLabel.TEXT, "we sum."), + (DocItemLabel.TEXT, "Given", None), + (DocItemLabel.FORMULA, "a^2", None), + (DocItemLabel.TEXT, "and", None), + (DocItemLabel.FORMULA, "b^2", None), + (DocItemLabel.TEXT, "we sum.", None), ], id="multiple-formulas", ), + pytest.param( + # loose text inside around the is preserved + # (adjacent unstyled runs are coalesced into one segment) + "The relation foo $$E=mc^2$$ bar holds.", + [ + (DocItemLabel.TEXT, "The relation foo", None), + (DocItemLabel.FORMULA, "E=mc^2", None), + (DocItemLabel.TEXT, "bar holds.", None), + ], + id="text-inside-inline-formula", + ), pytest.param( # tex-math is not always wrapped in $$...$$ in real JATS files "The relation E=mc^2 holds.", [ - (DocItemLabel.TEXT, "The relation"), - (DocItemLabel.FORMULA, "E=mc^2"), - (DocItemLabel.TEXT, "holds."), + (DocItemLabel.TEXT, "The relation", None), + (DocItemLabel.FORMULA, "E=mc^2", None), + (DocItemLabel.TEXT, "holds.", None), ], id="bare-tex-math", ), + pytest.param( + "We use x $$a^2$$ here.", + [ + (DocItemLabel.TEXT, "We use", None), + (DocItemLabel.TEXT, "x", (False, True, False, False, Script.BASELINE)), + (DocItemLabel.FORMULA, "a^2", None), + (DocItemLabel.TEXT, "here.", None), + ], + id="italic-inside-formula", + ), + pytest.param( + "Index xi $$x_i$$ shown.", + [ + (DocItemLabel.TEXT, "Index x", None), + (DocItemLabel.TEXT, "i", (False, False, False, False, Script.SUB)), + (DocItemLabel.FORMULA, "x_i", None), + (DocItemLabel.TEXT, "shown.", None), + ], + id="subscript-inside-formula", + ), + pytest.param( + "Take v $$v$$ next.", + [ + (DocItemLabel.TEXT, "Take", None), + (DocItemLabel.TEXT, "v", (True, True, False, False, Script.BASELINE)), + (DocItemLabel.FORMULA, "v", None), + (DocItemLabel.TEXT, "next.", None), + ], + id="nested-emphasis-inside-formula", + ), + pytest.param( + # a tex-math nested inside an emphasis tag is still parsed as a + # formula (not leaked as raw ``$$...$$`` text) + "Val $$x^2$$ shown.", + [ + (DocItemLabel.TEXT, "Val", None), + (DocItemLabel.FORMULA, "x^2", None), + (DocItemLabel.TEXT, "shown.", None), + ], + id="tex-math-inside-emphasis", + ), + pytest.param( + # emphasis outside the formula is now preserved (general text styling), + # and adjacent unstyled runs are coalesced + "Compare lhs $$a$$ rhs and $$b$$ now.", + [ + (DocItemLabel.TEXT, "Compare", None), + ( + DocItemLabel.TEXT, + "lhs", + (False, True, False, False, Script.BASELINE), + ), + (DocItemLabel.FORMULA, "a", None), + (DocItemLabel.TEXT, "rhs and", None), + (DocItemLabel.FORMULA, "b", None), + (DocItemLabel.TEXT, "now.", None), + ], + id="formula-with-surrounding-elements", + ), ], ) def test_jats_inline_formula_is_grouped(paragraph, expected): @@ -185,7 +339,7 @@ def test_jats_inline_formula_is_grouped(paragraph, expected): groups = _inline_group_items(doc) assert len(groups) == 1 - assert [(item.label, item.text) for item in groups[0]] == expected + assert [_formatting_tuple(item) for item in groups[0]] == expected @pytest.mark.parametrize( @@ -197,6 +351,12 @@ def test_jats_inline_formula_is_grouped(paragraph, expected): ["E=mc^2"], id="standalone-formula", ), + pytest.param( + # per the JATS spec tex-math is bare; a stray single-$ pair is stripped + "$x^2$", + ["x^2"], + id="single-dollar-delimiters", + ), pytest.param( # an inline-formula with no usable tex-math is dropped "Energy equation.", @@ -213,6 +373,64 @@ def test_jats_inline_formula_is_not_grouped(paragraph, expected_formulas): assert formulas == expected_formulas +def test_jats_paragraph_emphasis_is_preserved(): + doc = convert_jats_body( + "T" + "

The species Homo sapiens is common.

" + "" + ) + + groups = _inline_group_items(doc) + assert len(groups) == 1 + assert [_formatting_tuple(item) for item in groups[0]] == [ + (DocItemLabel.TEXT, "The species", None), + ( + DocItemLabel.TEXT, + "Homo sapiens", + (False, True, False, False, Script.BASELINE), + ), + (DocItemLabel.TEXT, "is", None), + (DocItemLabel.TEXT, "common", (True, False, False, False, Script.BASELINE)), + (DocItemLabel.TEXT, ".", None), + ] + + +def test_jats_plain_paragraph_stays_a_single_text_item(): + doc = convert_jats_body( + "T

Plain text with a 1 citation.

" + ) + + # no emphasis (and coalesced xref) → a single TEXT item, no inline group + assert _inline_group_items(doc) == [] + texts = [t.text for t in doc.texts if t.label == DocItemLabel.TEXT] + assert texts == ["Plain text with a 1 citation."] + + +@pytest.mark.parametrize( + ("body", "expected"), + [ + pytest.param( + "$$E=mc^2$$", + "E=mc^2", + id="direct-tex-math", + ), + pytest.param( + "$$a+b$$" + "", + "a+b", + id="tex-math-under-alternatives", + ), + ], +) +def test_jats_disp_formula_is_block_formula(body, expected): + doc = convert_jats_body(f"T{body}") + + formulas = [t.text for t in doc.texts if t.label == DocItemLabel.FORMULA] + assert formulas == [expected] + # a block formula is emitted standalone, not inside an inline group + assert _inline_group_items(doc) == [] + + def test_jats_empty_display_formula_does_not_drop_following_content(): # A display equation whose is empty must be skipped, not crash the # walk. _add_equation used to call node.text.split("$$") unconditionally, so an