diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 000000000..5b5661291
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,3 @@
+.venv
+__pycache__/
+*.pyc
\ No newline at end of file
diff --git a/.python-version b/.python-version
new file mode 100644
index 000000000..e4fba2183
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/00_index.md b/00_index.md
index c900f36fe..266899a97 100644
--- a/00_index.md
+++ b/00_index.md
@@ -1,18 +1,25 @@
 ---
-title: 'Unbiased representative images using embedded microscopy maps'
-short_title: Re-Imaging the Wheel
+title: 'Reimag(in)ing Representative Images'
+short_title: Virtual Microscopy
 numbering:
   heading_2: false
 ---
 
 +++ {"part": "abstract"}
 
-Add your abstract here.
-Avoid complicated equations / citations here, for crossref compatibility.
+Static figures are the standard interface for scientific discourse and peer review, so microscopists typically communicate experimental results through representative images and plots.
+This article focuses on virtual microscopy, a novel visualization strategy for researchers to share microscopy data in publications.
+Web-based microscopy maps improve the transparency, accessibility, and archival of bioimages as an unbiased alternative to representative images.
+These features make virtual microscopy useful for the critical appraisal of observational studies.
+Using this approach, an original claim of reovirus-induced endoplasmic reticulum remodeling was reexamined.
+Virtual microscopy subjects the results of qualitative experiments to unbiased peer review by giving readers direct access to the data.
+While data visualization can be refined to minimize subjective biases, the interpretation of data inevitably reflects the author's perspective.
+Virtual microscopy allows readers to interpret the same data as the author, which is a meaningful step forward, yet how authors determine the outcome of qualitative microscopy experiments remains unclear.
 
 +++
 
-+++{"part":"epigraph"}
++++ {"part":"epigraph"}
+
 :::{warning} Pre-print
 This article has not yet been peer-reviewed.  
 _Updated 2025 September 27_
@@ -20,20 +27,22 @@ _Updated 2025 September 27_
 
 +++
 
-+++ {"part": "acknowledgements"}
++++ {"part":"acknowledgements"}
+
+We thank the [Baker Institute for Animal Health](https://www.vet.cornell.edu/departments-centers-and-institutes/baker-institute-animal-health) for supporting this unfunded project.
 
-Add your acknowledgments, if any, here.
+This work used the Open Storage Network through allocation BIO250450 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296 [@doi:10.1145/3569951.3597559].
 
 +++
 
-+++ {"part": "competing interests"}
++++ {"part":"competing interests"}
 
 ## Competing Interests
 
-Add your competing interests, if any, here.
+All authors declared no conflicts of interest.
+
 +++
 
-## Data availability
+## Data Availability
 
-NucleusNet was [deposited](#huggingface-upload) to Hugging Face to facilitate data sharing and the repository was streamed to present images in interactive figures.
-The full collection of 250,000 microscopy fields, 1,600 stitched images and corresponding mask files, as well as the cropped images were zipped and archived at Zenodo.
\ No newline at end of file
+OME-Zarr data is hosted on the Open Storage Network (https://doi.org/10.6075/J0RR1ZCR), see [Metadata Availability](#metadata_page) for URLs.
\ No newline at end of file
diff --git a/01_introduction.md b/01_introduction.md
index 678053635..87ff1c680 100644
--- a/01_introduction.md
+++ b/01_introduction.md
@@ -1,71 +1,30 @@
 ---
 title: Introduction
 numbering:
-  enumerator: 1.%s
-label : introduction_page
+  enumerator: 0.%s
 ---
 
-Representative microscopy images are visual communication tools used by microscopists to present the results of research to other scientists.
-The earliest representative microscopy images were hand drawings in [Micrographia](<wiki:Micrographia>) in 1665 by Robert Hooke.
-Nowadays, roughly three-quarters of publications in biomedical journals report at least one microscopy image [@doi:10.7554/eLife.55133].
-In scientific journals, microscopists present stereotypical images of described phenomena in static figures.
-An un-spoken rule is that scientists trust that representative images are honest and accurate.
+Representative microscopy images are visual communication tools used by microscopists to present research results to other scientists.
+The earliest representative microscopy images were hand-drawn in Robert Hooke's [Micrographia](<wiki:Micrographia>) in 1665.
+Today, roughly three-quarters of publications in biomedical journals report at least one microscopy image [@doi:10.7554/eLife.55133].
+There is an unspoken expectation that scientists rely on these images to be honest and accurate.
 However, microscopy image selection and quantitative analyses thereof are vulnerable to bias and deception [@doi:10.1242/jcs.261567].
-Aside from issues with integrity, scientists sincerely misinterpret observations.
-The problem is that it is difficult for readers to critically evaluate representative images in static figures.
-Furthermore, archival and review of primary microscopy data is disorganized and time-consuming if at all available.
-
-Virtual nanoscopy was an innovative visualization strategy for unbiased presentation of transmission electron microscopy data [@doi:10.1083/jcb.201201140].
-It used digital map software developed for satellite images to present high-magnification tiled microscopy data in an interactive viewer, like Google Earth for cell biologists.
-In practice, it is easy to implement [@doi:10.1242/jcs.262198] but difficult to publish because scientific journals use static figures.
-This project introduces embedded microscopy maps as a proof of principle to share immunofluorescence bioimaging data without external links or software.
-
-Microscopists contribute to the [reproducibility crisis](<wiki:Replication_crisis>) in science, which is being adressed through protocol standardization [@doi:10.1111/jmi.13041].
-From the perspective of most biomedical researchers, the perceived cause is the pressure to publish [@doi:10.1371/journal.pbio.3002870].
-Large-scale replicability studies performed by scientists at Amgen and Bayer assessed that 11% [@doi:10.1038/483531a] and 25% [@doi:10.1038/nrd3439-c1] of key findings from preclinical cancer research papers were robust and replicable.
-Another reproducibility project evaluated replication success with several criteria, though the 'lowest bar' determined a replication rate of 79% for representative images [@doi:10.7554/eLife.71601]. 
-Notably, 26% of claimed effects were evidenced by a representative image without quantitative analysis.
-Consider that representative images cannot describe population variation and the uncertainty of an observation.
-
-A study critical of representative microscopy images focused on methods of objective image selection to limit bias (https://doi.org/10.1016/s0006-3495(99)77379-0).
-The authors implemented a web server that chose typical images from uploaded data, but it is now unsupported and there is no modern equivalent.
-This article started by revisiting the idea of objective image selection, as Barkley and Parker thought it was an interesting use case for artificial intelligence.
-Barkley automated the process of sample collection and image selection from a collection of one million single-cell images of the [cell nucleus](<wiki:Cell_nucleus>), called _NucleusNet_.
-Cropped images from NucleusNet were presented in interactive figures, including embedded microscopy maps using free and open-source technology [@doi:10.1242/jcs.262198].
-Representative images were selected from NucleusNet by an [autoencoder](<wiki:Autoencoder>) model.
-Autoencoders are unsupervised deep learning models that compress and reconstruct images through a vector bottleneck referred to as _latent space_.
-The structure of latent space is a [black box](<wiki:Black_box>), though it can be shaped to be more useful with the art of representation learning [@doi:10.1109/TPAMI.2013.50].
-Barkley determined that it would be irresponsible to use an uninterpretable embedding for the task of image selection. 
-To define what is representative, both the subjective determinations made by humans and the objective determinations made by autoencoders are opaque.
-Therefore, this project shifted towards demonstrations of virtual nanoscopy as an alternative to image selection.
-Barkley saw the value of using virtual nanoscopy to replicate microscopy experiments and challenged the claim of reovirus-induced endoplasmic reticulum (ER) organelle remodelling.
-[OrganelleBox](https://organellebox.sf.czbiohub.org/), was used for cross-validation and to expand the investigation to study sixteen organelles during reovirus infection to create an atlas of bioimaging data.
-
----
-
-## Contributions
-
-1. **A single-cell confocal microscopy image dataset of 1,061,277 unique cell nuclei.**
-
-2. **Demonstration of a novel use case for autoencoders: typical image selection.**
-
-3. **Embedded microscopy maps for unbiased data sharing with virtual nanoscopy**
-
-4. **An image atlas of cellular organelles during mammalian orthoreovirus infection.**
-
----
-
-## Definitions
-
-_Virtual nanoscopy_ was a term used to describe the process of automated image acquisition, followed by unbiased presentation in an interactive tile-based viewer. [@doi:10.1083/jcb.201201140]
-
-_Microscopy map_ is the interactive figure used to view virtual nanoscopy data. [@doi:10.1083/jcb.201201140]
-The original term '_electron_ microscopy map' was redefined to fit a broader use case because our study used confocal microscopy data.
-
-A _representative image_ is defined as a visual communication tool used by microscopists to communicate research findings to other scientists.
-This definition is agnostic to the research context and it emphasizes the use of pictures as scientific evidence.
-Parker suggested that _stereotypical image_ would be an apt name for the current use of representative images.
-
-A _grayscale image_ is defined as a two-dimensional matrix where each pixel is a single intensity value ranging from $0-1$ that represents the amount of light or intensity information at a specific point [@doi:10.1109/BIP60195.2023.10379342].
-
-A _latent space_ is a collection of vectors that form a reduced-dimensionality embedding of the data, fit by a machine learning model [@doi:10.1111/cgf.13672].
+Besides cherry-picking, scientists can sincerely misinterpret observations.
+The problem is that readers find it difficult to critically evaluate representative images.
+Furthermore, archival and review of primary microscopy data is disorganized and time-consuming, if at all available.
+These problems can be addressed with novel visualization strategies like [virtual microscopy](<wiki:Virtual_microscopy>) that transmit entire interactive datasets over the internet.
+An example was the unbiased presentation of electron microscopy data to readers as interactive digital maps — or microscopy maps [@doi:10.1083/jcb.201201140].
+In practice, microscopy maps are easy to share but are accessed via external links [@doi:10.1242/jcs.262198].
+
+[Reproducibility](<wiki:Reproducibility>) is an essential principle of the [scientific method](<wiki:Scientific_method>) for acquiring knowledge, yet there is a contentious [reproducibility crisis](<wiki:Replication_crisis>) in science.
+From the perspective of biomedical researchers, the perceived cause is the pressure to publish [@doi:10.1371/journal.pbio.3002870].
+Repeatability within the same study by the same researchers is distinguished from replicability by independent researchers using the same methodology.
+Large-scale replicability studies found that only 11% [@doi:10.1038/483531a] and 25% [@doi:10.1038/nrd3439-c1] of key findings from preclinical cancer research papers were replicable.
+The replication crisis is multifaceted, but the irreproducibility of representative images deserves scrutiny in its own right.
+According to one study, replication rates for representative images ranged from 27% to 90% [@doi:10.7554/eLife.71601].
+This wide range reflects how difficult it is to assess the replicability of representative images without dedicated tools.
+A feature of virtual microscopy is the unbiased sampling and transmission of multi-scale image data, making it useful as a method to replicate representative images. 
+If results are reproducible, original claims should be readily observable in microscopy maps, which capture a broader range of variation.
+Here, microscopy maps were used to reassess the claim that mammalian orthoreovirus remodels the endoplasmic reticulum (ER) of cells during infection [@doi:10.1128/mBio.01253-18].
+Using the protocols described in the original paper, hundreds of overlapping tiles were sampled at high-magnification from replicate immunofluorescence experiments on a fully motorized confocal microscope.
+Microscopy maps were embedded in the article using the next generation file format OME-Zarr [@doi:10.1007/s00418-023-02209-1] and custom viewers based on Viv [@doi:10.1038/s41592-022-01482-7].
diff --git a/02_approach.md b/02_approach.md
new file mode 100644
index 000000000..d245784bb
--- /dev/null
+++ b/02_approach.md
@@ -0,0 +1,97 @@
+---
+title: Approach
+numbering:
+  enumerator: 1.%s
+---
+
+Already adopted by some clinicians and educators, virtual microscopy could also benefit researchers as an interface for communicating and interpreting image data.
+Indeed, interactive microscopy maps were first proposed for precisely this purpose, as an unbiased way to present electron microscopy data [@doi:10.1083/jcb.201201140].
+Free and open-source solutions [@doi:10.1242/jcs.262198] and recent advances in file formats [@doi:10.1007/s00418-023-02209-1] now make this feasible, yet these tools have not been adopted in scientific publishing.
+Our approach combined automated image acquisition, multi-scale [pyramid](<wiki:Pyramid_(image_processing)>) formats and embedded interactive viewers ([](#abstract)).
+
+```{figure} ./figures/Virtual_microscopy.png
+:label: abstract
+Virtual microscopy. 1. Automated dataset collection with any suitable microscope. ZDC: Z-drift compensation. 2. Stitch tiles then convert the image to DZI or OME-Zarr, host online. 3. Image tiles or data chunks are retrieved from cloud storage as readers interact with microscopy maps.
+```
+
+Elemental Microscopy supports two interactive viewers; [Vizarr](https://github.com/hms-dbmi/vizarr) [@doi:10.1038/s41592-022-01482-7] and [OpenSeadragon](https://openseadragon.github.io/), enabling two methods of virtual microscopy based on OME-Zarr or Deep Zoom. 
+Authors can also build custom viewers. 
+Brodrick developed [microATLAS](https://github.com/LadInTheLab/microATLAS-widget), which was used to view OME-Zarr data in this article. 
+All three viewers are readily embeddable in Elemental Microscopy. 
+For Vizarr and OpenSeadragon, authors simply copy the markdown and replace the source URL with a link to their own data. 
+For microATLAS, authors are encouraged to use the [widget builder](https://ladinthelab.github.io/microATLAS-widget/builder.html) to generate code.
+
+## OME-Zarr
+
+OME-Zarr is a standardized and flexible image format developed for use cases like virtual microscopy [@doi:10.1007/s00418-023-02209-1].
+Most native microscopy file formats can be converted to OME-Zarr with preserved metadata using [Bio-Formats](https://www.openmicroscopy.org/bio-formats/).
+It is a chunked file type that can support multiple resolution levels.
+As an advanced implementation of microscopy maps [@doi:10.1242/jcs.262198], pyramidal OME-Zarr data can be viewed online [@doi:10.1038/s41592-022-01482-7].
+
+### Vizarr
+
+```{code}
+:label: vizarr
+:caption: Minimal markdown to embed Vizarr in Elemental Microscopy. Replace the source with your data.
+:::{any:bundle} https://curvenote.github.io/widgets/widgets/vizarr-viewer.js
+{
+  "source": "https://your-bucket.s3.amazonaws.com/sample.zarr/",
+  "height": "600px"
+}
+:::
+```
+
+### microATLAS
+
+```{code}
+:label: microatlas
+:caption: Example markdown to embed microATLAS in Elemental Microscopy. Create your own with the [builder](https://ladinthelab.github.io/microATLAS-widget/builder.html).
+:::{any:bundle} https://ladinthelab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://your-bucket.s3.amazonaws.com/sample.zarr/",
+  "views": [
+    {
+      "name": "Overview",
+      "zoom": -4,
+      "target": [4886, 4886, 0]
+    }
+  ],
+  "annotations": [
+    { "name": "Region A", "target": [2400, 2400], "color": [100, 200, 255] }
+  ],
+  "scaleBar": { "maxWidth": 120, "position": "bottom-right" }
+}
+:::
+```
+
+```{figure} ./thumbnail.gif
+:label: demo
+Interacting with microATLAS: 
+Use the menu (☰, top-left corner) to change the displayed channels, colours, or contrast.
+Scroll (↕) to zoom into cells of interest.
+```
+
+## Deep Zoom
+
+[Deep Zoom](<wiki:Deep_Zoom>) is a free and open-source solution, described elsewhere [@doi:10.1242/jcs.262198], now embeddable in Elemental Microscopy.
+Pyramidal Deep Zoom image (DZI) data hosted on Github Pages is streamed to the [OpenSeadragon](https://openseadragon.github.io/) viewer.
+By default, readers cannot control dimensions like channels or time, so it is best suited for single-channel or composite images.
+
+### OpenSeadragon
+
+```{code}
+:label: openseadragon
+:caption: Minimal markdown to embed OpenSeadragon in Elemental Microscopy. Replace the source with your DZI.
+:::{any:bundle} https://curvenote.github.io/widgets/widgets/openseadragon.mjs
+{
+    "tileSources": "https://your-github.github.io/your-repository/sample.dzi"
+}
+:::
+```
+
+:::{dropdown} Imaging
+Data collection was fully-motorized for unbiased sampling of large panoramas from replicate immunofluorescence experiments.
+Grids of overlapping tiles were acquired with a robotic XY stage (IX3-SSU) and Z-drift compensation (IX3-ZDC).
+Coverslips were imaged with an Olympus Fluoview FV3000 confocal microscope using a 100X objective lens (Olympus model UPLAPO100X, NA 1.50) in immersion oil. 
+Pinholes in each channel were maximized to capture the widest depth of field.
+:::
\ No newline at end of file
diff --git a/02_nucleusnet.md b/02_nucleusnet.md
deleted file mode 100644
index e15dded28..000000000
--- a/02_nucleusnet.md
+++ /dev/null
@@ -1,53 +0,0 @@
----
-title: NucleusNet
-numbering:
-  enumerator: 1.%s
-label : nucleusnet_page
----
-
-# NucleusNet: one million single-cell microscopy images
-
-## 1600 tiled panoramas
-
-Artificial intelligence models need to train on lots of data, motivating Barkley to create a large collection cropped images of the [cell nucleus](<wiki:Cell_nucleus>), called NucleusNet.
-It is the sum of 100 automated imaging experiments that sampled ~37.25cm² of cultured cells, yielding 1,061,277 cropped single-cell images extracted from 1600 stitched panoramas.
-Images of cell monolayers were collected over twenty-one passages of the same cell line.
-[CV-1 cells](https://www.atcc.org/products/ccl-70) were seeded at varying densities then were fixed in paraformaldehyde (PFA) after at least one day of incubation, so the populations had asynchronous cell cycles.
-Fixed cells were stained with DAPI which binds to DNA, making the cell nucleus fluoresce upon excitation with ultraviolet light.
-As expected, we observed variation in cell confluence and signal intensity between experiments, and even between regions of the same coverslip ([](#sfig1a)).
-Ten stitched images closest to the centroid were selected to represent those with an average number and brightness of ROIs ([](#table1)).
-
-## 1,061,277 single-cell images
-
-Nuclei were masked in the stitched images with a custom [cellpose](https://github.com/MouseLand/cellpose) segmentation model [@doi:10.1038/s41592-020-01018-x].
-Cellpose was suitable for segmentation because cell division created instances where two nuclear bodies were separated in space but should count as one ROI.
-Specifically, mitotic figures in [anaphase](#anaphase) or [telophase](#telophase) were considered one ROI, and [micronuclei](<wiki:Micronucleus>) and nuclei were masked together.
-There were also instances where two or more distinct nuclei made contact but should be separate masks.
-Barkley re-trained and tuned the cellpose model until it handled these challenges sufficiently.
-Browse the ten representative stitched images ([](#table3)) from [](#sfig1a) to evaluate the cellpose segmentation model using an overlay.
-Unique masks were assigned random colours to help differentiate ROIs.
-False-positive detections by cellpose were rare but expected as a consequence of high-throughput automation.
-
-**Figure 1A: Embed OpenSeadragon viewer with this [image](https://russellbarkley.github.io/cellpose_masks/)**
-
-## NucleusNet-10K
-
-A small version (n=10,000) of the single-cell dataset was created with one-hundred random images from each imaging session.
-An interactive cell browser was made that shows random examples from NucleusNet-10K [](#fig1b), so every reader will see a different subset of the collection.
-Generally, nuclei were properly centered, well segemented, and pre-aligned in the cropped images.
-Pixels were set to zero outside of the mask, leaving background signal around the nucleus, which was notable because this property seemed to emerge in the reconstructions from the trained autoencoder.
-Otherwise, cropped images in this version of NucleusNet were not processed further.
-
-:::{figure} #nucleusnet10k_cellbrowser
-:label: fig1b
-:placeholder: ./figures/cellbrowser.png
-Figure legend.
-:::
-
-With collections of images in the literature, it is common to see several random examples presented in a static figure.
-Barkley fused NucleusNet-10K into a 100x100 tile grid and embedded the stitched image (Figure 1C).
-Experiments were reported in chronological order by row from top to bottom and clearly there was variation in signal intensity between experiments.
-This observation would warrant image pre-processing techniques that normalize or standardize pixel intensities in NucleusNet.
-Interestingly, there is another [example](https://www.allencell.org/deep-cell-zoom.html) of this visualization strategy used to present 200,000 single-cell images of human-induced pluripotent stem cells.
-
-**Figure 1C: Embed OpenSeadragon viewer with NucleusNet-10K**
\ No newline at end of file
diff --git a/03_autoencoders.md b/03_autoencoders.md
deleted file mode 100644
index 222f47e54..000000000
--- a/03_autoencoders.md
+++ /dev/null
@@ -1,177 +0,0 @@
----
-title: Autoencoders
-numbering:
-  enumerator: 2.%s
-label : autoencoder_page
----
-
-# Preliminaries
-
-## Autoencoders
-
-From the Deep Learning book by {cite}`Goodfellow-et-al-2016`;
-an autoencoder is a neural network that is trained to copy its input to its output.
-Autoencoders originated in the eighties and its primary application was dimensionality reduction for information storage and retrieval.
-Autoencoders consist of two parts: an encoder and a decoder with hidden layers that describe the code used to represent the data.
-The autoencoder is restricted in some way that it is forced to prioritize which aspects of the input to copy, so it often learns useful properties of the data.
-
-## Model architecture
-
-During training, the input to the model was a fixed-size $256*256$ grayscale image.
-The architecture of the networks in this study followed a common configuration in autoencoder-based anomaly detection methods [@doi:10.48550/arXiv.2501.13864].
-Rectified linear unit (ReLU) activation functions were used throughout the network for nonlinearity {cite}`NIPS2012_c399862d`, except with a sigmoid activation function at the final layer.
-Linear activation functions were used in the fully-connected dense layer connections to and from the latent space bottleneck.
-Stacked $3*3$ kernels were used throughout the network, so each layer has two convolutions without spatial pooling in between, giving an effective receptive field of $5*5$ with fewer parameters and greater nonlinearity [@doi:10.48550/arXiv.1409.1556].
-Spatial pooling is carried out by average pooling layers, which is performed over a $2*2$ window with a stride of 2.
-A stack of four convolutional layers is followed by a fully-connected dense layer into latent space $z$. The decoder mirrors the encoder.
-Latent vectors are reshaped then upsampled with Conv2DTranspose (stride 2) and Conv2D through a stack of convolutional layers to reconstruct a $256*256$ grayscale image.
-The network was trained with Adam optimizer [@doi:10.48550/arXiv.1412.6980] to minimize the mean squared error (MSE) between original and reconstructed images.
-These configurations were used for all autoencoders in this study.
-Models varied in the number of layers, number of filters, latent dimensions, learning rate and batch size which were parameters that were manually tuned to the dataset.
-In deciding on a model, Barkley considered the importance of limiting the number of parameters in the model and decided on the vanilla model.
-There are variations of autoencoders that have been compared to show the efficiencies and trade-offs of different models at image reconstruction, latent representation and accuracy at anomaly detection [@doi:10.1016/j.mlwa.2024.100572].
-
-# Representative images from the MNIST dataset
-
-## Literature reproduction
-
-A method of objective representative image selection [@doi:10.1109/BIP60195.2023.10379342] was tested on real-world data like the [MNIST database](<wiki:MNIST_database>), which is a collection of 70,000 images of handwritten numbers that were manually annotated into ten classes corresponding to the digits 0-9.
-Briefly, the proposed two-step approach to objective representative image selection calculated theoretical average images then measured the distance between real and theoretical images in a vector space.
-Measures of central tendency to compute theoretical representative images were the arithmetic mean, median and geometric median, chosen due to their ease of computation.
-Averages were calculated as a 784-dimensional vector then reshaped to reconstruct 28x28 pixel theoretical images.
-One of the examples shown in the literature used a sub-set of MNIST images (n=720) labelled "four".
-Though the reproduction used all MNIST "four" images (N=6824), the outcome was consistent with the primary literature ([](#fig2a)) [@doi:10.1109/BIP60195.2023.10379342].
-
-:::{figure} #fig2a_data
-:label: fig2a
-:placeholder: ./figures/fig2a.png
-Computation of theoretical representative images of the MNIST digit '4'.
-N=6824 grayscale images with the label '4' were flattened to 784-dimensional vectors to compute then reshape reconstructed images of the arithmetic mean (left), median (middle) and geometric median (right).
-:::
-
-Chosen exemplars were not the same as the literature ([](#fig2b)) [@doi:10.1109/BIP60195.2023.10379342], though this was unsurprising because our subset of MNIST was not the same.
-
-:::{figure} #fig2b_data
-:label: fig2b
-:placeholder: ./figures/fig2b.png
-Computation of practical representative images of the MNIST digit '4' using the arithmetic mean (left), median (middle) and geometric median (right).
-:::
-
-## Using autoencoders to select representative MNIST digits
-
-Barkley adopted this approach to representative image selection using the latent space of an autoencoder model.
-First, theoretical average latent vectors were calculated using the same measures of central tendency.
-Then, practical examples in the embedding were ranked by Euclidean distance to the calculated centroids.
-
-### 1. Compute and decode centroid latent vectors
-
-Barkley trained a convolutional autoencoder model on the MNIST dataset and saved the encoder and decoder weights ([](#sfig2a)). 
-n=6824 latent vectors labelled "four" were averaged and reconstructed using the decoder weights to synthesize theoretical images of the digit "four" ([](#fig2c)).
-Interestingly, the decoded centroid latent vectors appeared like the mean and median reconstructions calculated using independent pixel values ([](#fig2a)).
-
-:::{figure} #fig2c_data
-:name: fig2c
-:placeholder: ./figures/fig2c.png
-Decoded latent vectors: arithmetic mean (left), median (middle) and geometric median (right).
-:::
-
-### 2. Define practical images in Euclidean space
-
-The behaviour of the theoretical image generally does not correspond to a distinct image, therefore it is not considered the final representative image.
-However, it can be used to select representative examples from the dataset [@doi:10.1109/BIP60195.2023.10379342].
-The closest latent vector to each centroid was found based on the lowest Euclidean distance in the vector embedding ([](#fig2d)).
-The resulting images were remarkably similar, if not identical to the practical representative images in [](#fig2b), which suggests that these methods were comparable.
-
-:::{figure} #fig2d_data
-:name: fig2d
-:placeholder: ./figures/fig2d.png
-Closest examples to the arithmetic mean (left), median (middle) and geometric median (right).
-:::
-
-## Limitation to current methods of objective image selection
-
-These examples of image selection, while clearly effective, relied on class information from an annotated dataset.
-As a counterexample, both methods of image selection fell apart when reconstructing the theoretical average using the global centroid.
-Again, the reconstructed images from the latent space of the autoencoder had characteristics like that of the pixel-wise average reconstructions that are difficult to describe, which sheds light on the black box.
-
-:::{figure} #fig2e_data
-:name: fig2e
-:placeholder: ./figures/fig2e.png
-Figure legend.
-:::
-
-:::{figure} #fig2f_data
-:name: fig2f
-:placeholder: ./figures/fig2f.png
-Figure legend.
-:::
-
-Reconstructing the global average did not produce meaningful images given our knowledge of the digits 0-9.
-This suggests that determinations on computing the global or class average will make or break this two step approach to image selection, therefore these methods are not generalizable.
-Conceptually it made sense to find an average image of the digit 4, but not to find the average of all digits.
-Perhaps this approach to image selection could be viable if averaging by label, but this would require an upstream classifier model or manual annotation.
-
-# Representative images from NucleusNet
-
-## Model training
-
-The entire single-cell image collection was sharded into [TFRecords](https://www.tensorflow.org/tutorials/load_data/tfrecord) to better load the dataset into memory and shuffle it during training.
-Barkley trained an autoencoder model for fifty epochs on the full dataset and saved the encoder and decoder weights after training, then encoded the grayscale images into latent vectors.
-The purpose of this analysis was not to interpret the latent space for scientific inquiry or biological discovery, because artificial models are black boxes.
-Rather, the goal was to show that the autoencoder formed a latent space, then images were chosen near the centroid for presentation as representative images.
-
-### Loss plot
-
-:::{figure} #ae1m-loss-plot
-:label: fig2g
-:placeholder: ./figures/fig2g.png
-Mean squared error (MSE) reconstruction loss (batch size: 32) plot for the training and validation datasets recorded during training.
-:::
-
-## Latent space interpolation
-
-A method to evaluate the quality of latent space is interpolation, whereby mixing codes in latent space and decoding the result creates a semantically meaningful combination of the datapoints [@doi:10.48550/arXiv.1807.07543].
-Interpolating with an autoencoder describes the process of using the decoder to decode a convex combination of two latent vectors [@doi:10.48550/arXiv.1807.07543].
-A high-quality interpolation should have two characteristics: intermediate points along the interpolation should resemble real data and they should provide a semantically meaningful transition between the endpoints [@doi:10.48550/arXiv.1807.07543].
-Interpolating between any two latent vectors of embedded NucleusNet images produced reasonable intermediate reconstructions from the decoder with a smooth transition between endpoints ([](#fig2h)).
-This result is consistent with the literature describing smooth interpolations with base model autoencoders [@doi:10.48550/arXiv.1807.07543].
-The authors noted that intermediate points did not always resemble real data, which was true with some of the NucleusNet-10K interpolations.
-
-:::{figure} #nucleusnet10k-interpolation
-:label: fig2h
-:placeholder: ./figures/fig2h.png
-
-Decoded latent vectors along intermediate points of interpolations between random pairs of images from NucleusNet-10K.
-Executing the code will randomly draw fifty pairs for interpolation.
-Images that you see are decoded latent vectors, with t=0.00 and t=1.00 representing latent vectors corresponding to real images.
-Four intermediate interpolations at t=0.20, t=0.40, t=0.60 and t=0.80 are mixed codes.
-:::
-
-## Autoencoder-based determination of representative images
-
-1. Computation of a theoretical image
-
-Barkley calculated theoretical latent vectors using measures of central tendency, including the arithmetic mean, median and geometric median in latent space.
-Averaged latent vectors were reconstructed with the decoder to synthesize theoretical representative images of the nucleus ([](#fig2i)).
-Theoretical representative images do not necessarily look like real data [@doi:10.1109/BIP60195.2023.10379342], so these reconstructions do not represent real data.
-Otherwise, theoretical average nuclei appeared to blend all image features from the dataset.
-As noted earlier, there is what appears to be background signal around the theoretical nucleus.
-
-:::{figure} #ae1m-theoretical
-:label: fig2i
-:placeholder: ./figures/fig2i.png
-Figure legend.
-:::
-
-2. Determination of a prototypical image
-
-Real images were selected based on distance to theoretical centroids in latent space.
-Specifically, representative images were defined by the lowest euclidean distance or highest cosine similarity to theoretical averages.
-One hundred representative images from NucleusNet were ranked based on each distance metric and measure of central tendency ([](#fig2j)).
-
-
-:::{figure} #ae1m-practical
-:label: fig2j
-:placeholder: ./figures/fig2j.png
-Figure legend.
-:::
\ No newline at end of file
diff --git a/03_model.md b/03_model.md
new file mode 100644
index 000000000..96eaeeb6f
--- /dev/null
+++ b/03_model.md
@@ -0,0 +1,25 @@
+---
+title: Model
+numbering:
+  enumerator: 2.%s
+---
+
+## Immunofluorescence assay of transfected-infected cells
+
+The objective of this experiment was to visualize the co-occurrence of plasmid transfection and reovirus infection in cells.
+In the original study, cells were transfected with mCherry-KDEL using TransIT-2020 before reovirus infection at MOI 1.
+Transfected-infected cells are desirable for comparing ER morphology during infection, but a heterogeneous population was expected with four outcomes ([](#model)).
+Viral and ER antigens were co-stained with reovirus μNS and calreticulin antibodies.
+
+```{figure} ./figures/transfectedinfectedmodel.png
+:label: model
+Overview of the replication experiment. mCherry-KDEL transfected cells (TransIT-2020) were infected with reovirus (MOI 100) then ER and viral antigens were detected by immunofluorescence.
+```
+
+Over the following pages, individual components of this model were tested before combining them in the final replication experiment. 
+In the results, Barkley annotated transfected cells by infection status to compare ER morphology across hundreds of cells in a navigable dataset.
+
+:::{dropdown} Cells
+HeLa CCL-2 cells were cultured in Dulbecco’s modification of Eagle’s Medium (DMEM) (50-003-PB, Corning) supplemented with 10% fetal bovine serum (FS-0500-AD, Atlas).
+Antibiotics were not used in the replication study, although the original study used 100 U/ml penicillin G, 100μg/ml streptomycin and 0.25μg/ml amphotericin B.
+:::
\ No newline at end of file
diff --git a/04_plasmids.md b/04_plasmids.md
new file mode 100644
index 000000000..90659bb62
--- /dev/null
+++ b/04_plasmids.md
@@ -0,0 +1,782 @@
+---
+title: Plasmids
+numbering:
+  enumerator: 3.%s
+---
+
+## mCherry-ER reporters
+
+mCherry-KDEL was the same construct used in the original study; mCherry-ER-3 plasmid (55041, Addgene).
+mCherry-CytERM-N-17 (55024, Addgene) was included as a positive control for ER remodelling because organized smooth endoplasmic reticulum (OSER) whorls form through fluorescent protein dimerization [@doi:10.1083/jcb.200306020].
+An [OSER assay](#oser_page) (Figure 8.1) was performed to compare whorl formation across different fluorescent proteins relative to mCherry-CytERM.
+Two additional constructs were included with ER proteins fused to mCherry; mCh-Climp63 (136293, Addgene) and mCh-Sec61β (49155, Addgene).
+An earlier experiment combining moxGFP::μNS with these constructs was inconclusive (Figure 17.1).
+
+Cells transiently expressing mCherry-ER fusions were fixed 24 hours after transfection with TransIT-2020 and Lipofectamine 3000.
+Parker identified saturated signal in the original experiment and advised Barkley to repeat it, adjusting illumination settings individually.  
+The corrected maps are presented below (Figure 3.1) and the [saturated maps](#saturated_page) (Figure 15.1) and [discarded maps](#discarded_page) (Figures 16.1 to 16.5) are presented in the Supplemental.
+
+:::::{figure}
+:label: figure3
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_kdel_Cycle_02.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-KDEL (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "ER remodeling",
+      "zoom": -0.798,
+      "target": [
+        11053,
+        10028,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2208
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_KDEL_Cycle_02.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-KDEL (Trans-IT 2020)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "ER remodeling",
+      "zoom": -1.066,
+      "target": [
+        9952,
+        424,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_cytER_Cycle.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-CytERM (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjusted contrast",
+      "description": "Calreticulin channel",
+      "zoom": -5.512,
+      "target": [
+        7323,
+        7323,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            3500
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    },
+    {
+      "name": "ER remodeling",
+      "zoom": -0.51,
+      "target": [
+        4018,
+        9351,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_cyter_Cycle_03.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-CytERM (Trans-IT 2020)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjusted contrast",
+      "description": "Calreticulin channel",
+      "zoom": -5.512,
+      "target": [
+        7323,
+        7323,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            3500
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    },
+    {
+      "name": "ER remodeling",
+      "zoom": -0.51,
+      "target": [
+        10587,
+        9413,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_sec61_Cycle_02.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Sec61β (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjusted contrast",
+      "description": "Calreticulin channel",
+      "zoom": -5.512,
+      "target": [
+        7323,
+        7323,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            3000
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    },
+    {
+      "name": "ER remodeling",
+      "zoom": -0.51,
+      "target": [
+        11070,
+        3818,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_sec_Cycle_01.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Sec61β (Trans-IT 2020)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "ER remodeling",
+      "zoom": -0.51,
+      "target": [
+        9912,
+        1666,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_climp_Cycle.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Climp63 (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "ER remodeling",
+      "zoom": -1.621,
+      "target": [
+        11242,
+        13229,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_climp_Cycle_01.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Climp63 (TransIT-2020)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 16,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "ER remodeling",
+      "zoom": -0.332,
+      "target": [
+        6544,
+        6534,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 3.1:** Cells expressing mCherry-ER fusion proteins. Select the 'ER remodeling' view from the menu to see representative fields of punctate structures suspected to be mCherry artifacts.
+:::::
+
+:::{important} Alternative hypothesis
+For each of the four mCherry-ER reporters, at least one cell exhibits what Barkley interprets as ER remodeling (see the preset views). 
+These artifacts were also observed in live cells ([](#figure9)), indicating that they are not a consequence of fixation. 
+Since these cells were not infected, it is possible that previous investigators misinterpreted mCherry-ER reporter artifacts as an outcome of reovirus infection.
+:::
+
+:::{dropdown} Transfection protocol
+3 × 10⁵ HeLa cells were seeded on 35 mm glass-bottom dishes (Mattek, P35G-1.5-20-C). 
+Lipofectamine 3000 (L3000015, Invitrogen) volumes were based on those for 6-well plates: 125 µl Opti-MEM (31985-062, Gibco) was mixed with 5 µl P3000 reagent and 2.5 µg DNA, and a separate tube of 125 µl Opti-MEM was mixed with 3.75 µl Lipofectamine 3000 reagent. 
+The tubes were combined, incubated for 20 minutes, then added dropwise to the samples. 
+TransIT-2020 reagent (MIR 5400, Mirus) volumes were based on those for 6-well plates: 250 µl Opti-MEM was mixed with 2.5 µg DNA and mixed gently, then 7.5 µl TransIT-2020 was added and mixed, followed by 20 minutes of incubation and dropwise addition to the samples.
+:::
\ No newline at end of file
diff --git a/04_replication_study.md b/04_replication_study.md
deleted file mode 100644
index ca8c12b8b..000000000
--- a/04_replication_study.md
+++ /dev/null
@@ -1,35 +0,0 @@
----
-title: Replication Study
-numbering:
-  enumerator: 4.%s
-label : replication_page
----
-
-# Background
-
-[Reproducibility](<wiki:Reproducibility>) is an essential principle of the [scientific method](<wiki:Scientific_method>) for acquiring knowledge.
-Repeatability in the same study by the same researchers is distinguished from replicability by independent researchers using the same methodology.
-Barkley noticed a lack of methods for reproducing representative images and proposed virtual nanoscopy as a method to assess the replicability of observations evidenced by representative images.
-Results should be observed again with a high degree of reliability, therefore the original claim should be evident within a sample of hundreds of cells.
-To test this, Barkley challenged the claim that reovirus remodels the ER [@doi:10.1128/mBio.01253-18].
-Most evidence, including key findings, were representative images without quantitation ([](#table4)), which is problematic given the inability to communicate population variation and uncertainty.
-The study concluded that reovirus induced fragmented, collapsed, and aggregated ER elements, shown in images of infected cells expressing mCherry-KDEL.
-This fluorescent ER reporter was expressed using plasmid transfection or with engineered cell lines.
-Barkley designed the replication study using the transfected-infected cell model, and stable cells were used for cross-validation.
-In the transfected-infected model, plasmid transfection was followed by reovirus infection, then the cells were fixed and immunostained for ER (calreticulin) and viral (µNS) antigens.
-Therefore, the calreticulin channel shows the ER of all cells, reovirus µNS shows the viral factories of infected cells, and mCherry-KDEL shows the ER of transfected cells.
-From a sample of hundreds of cells, putative virus-induced ER remodelling should be detectable in the calreticulin and mCherry-KDEL channels between infected and uninfected cells.
-Heterogeneity was expected in the population due to viral permissivity and incomplete plasmid transfection efficiency, and coincidence of µNS and mCherry was considered desirable in this context.
-Another confounding variable was interferon induction from liposomal transfection reagents [@doi:10.1089/jir.1998.18.947] and plasmid DNA.
-
-# Results
-
-:::{any:bundle} https://curvenote.github.io/widgets/widgets/vizarr-viewer.js
-:class: w-full
-
-{
-  "source": "https://barkley-replication.s3.us-east-2.amazonaws.com/experiment1/sample1.zarr/",
-  "height": "600px"
-}
-
-:::
\ No newline at end of file
diff --git a/05_organellebox.md b/05_organellebox.md
deleted file mode 100644
index 7c1710622..000000000
--- a/05_organellebox.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: OrganelleBox
-numbering:
-  enumerator: 5.%s
-label : organellebox_page
----
\ No newline at end of file
diff --git a/05_virus.md b/05_virus.md
new file mode 100644
index 000000000..8f6c9cd89
--- /dev/null
+++ b/05_virus.md
@@ -0,0 +1,746 @@
+---
+title: Virus
+numbering:
+  enumerator: 4.%s
+---
+
+## Mammalian orthoreovirus strains
+
+Parker described the morphology of reovirus strain Type 1 Lang (T1L) factories as filamentous and T3D factories as globular, and identified the M1(T1L)-P208S mutation that disrupted an interaction between μ2 and microtubules determining the filamentous phenotype [@doi:10.1128/JVI.76.9.4483-4496.2002]. 
+The mBio study did not provide a rationale for using this M1(T1L)-P208S mutant, so wildtype T1L was used instead in the replication study. 
+Wildtype T1L tends to form filamentous viral factories, whereas the M1(T1L)-P208S mutant would have formed globular factories. 
+A stock of unpurified reovirus strain Type 3 Dearing (T3D), which forms globular viral factories, was included to compare these morphologies. 
+Others have reported using MOI 100 due to the low permissivity of HeLa cells to reovirus [@doi:10.1128/jvi.00199-19], so ten-fold dilutions from MOI 0 to 100 were tested (Figure 4.1). 
+Infected cells were visualized with α-μNS chicken serum and AlexaFluor647-conjugated secondary antibodies. 
+Most HeLa cells were infected with reovirus T1L at MOI 100, so this concentration was used for replication experiments, an increase from MOI 1 used in the original study. 
+At MOI 1, only a few infected cells were positively identified and pinned on the map.
+
+:::::{figure}
+:label: reovirus-moi-figure
+
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi0.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T1L MOI 0",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi0.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T3D MOI 0",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi1.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T1L MOI 1",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Infected cell",
+      "target": [
+        7471,
+        983
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected cell",
+      "target": [
+        7362,
+        850
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi1.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T3D MOI 1",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Infected cell",
+      "target": [
+        6460,
+        2626
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected cell",
+      "target": [
+        5199,
+        7702
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected cell",
+      "target": [
+        8381,
+        6188
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected cell",
+      "target": [
+        9605,
+        5645
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected cell",
+      "target": [
+        2486,
+        8493
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected cell",
+      "target": [
+        7598,
+        9699
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi10.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T1L MOI 10",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi10.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T3D MOI 10",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi100.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T1L MOI 100",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 2000",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi100.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Reovirus T3D MOI 100",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Adjust contrast",
+      "description": "Reovirus channel min 0 max 1250",
+      "zoom": -4.928,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            1250
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+
+**Figure 4.1:** Reovirus strains T1L (left) and T3D (right) 24 hours post infection, with increasing MOI from top to bottom (0, 1, 10, 100).
+:::::
+
+
+:::{tip}
+Evaluate the calreticulin channel for reported reovirus-induced ER remodeling.
+The uninfected control is MOI 0 and ER morphology can be compared within populations.
+:::
+
+:::{dropdown} Infection protocol
+Stocks of purified reovirus T1L were titrated in L-929 (L) cells to estimate the concentration of plaque-forming units (PFU). 
+L cells were also used for virus titration in the original study, so the calculation of multiplicity of infection (MOI, PFU/cell) was consistent between studies. 
+For infection, purified reovirus was diluted in 100 µl of serum-free DMEM and adsorbed to cells for one hour at room temperature with agitation every ten minutes. 
+Thereafter, pre-warmed serum-containing DMEM was added and samples were incubated for one day before fixation.
+:::
diff --git a/06_antibody_ER.md b/06_antibody_ER.md
new file mode 100644
index 000000000..e8cd57eff
--- /dev/null
+++ b/06_antibody_ER.md
@@ -0,0 +1,125 @@
+---
+title: Calreticulin
+numbering:
+  enumerator: 5.%s
+---
+
+The selectivity and specificity of the calreticulin monoclonal antibody was validated as an immunofluorescence marker of the ER. 
+The staining pattern was punctate but sufficient to observe ER elements (Figure 5.1).
+
+::::{figure}
+:label: figure5a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/primary_secondary.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "+ Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Immunofluorescence staining of calreticulin.
+::::
+
+:::::{figure}
+:label: figure5b
+::::{grid} 3 3 3 3
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/primary.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "+ Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/secondary.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/mock.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 5.2:** Immunofluorescence antibody controls.
+:::::
+
+:::{dropdown} Materials and immunofluorescence protocol
+The original study used an undisclosed polyclonal antiserum for calreticulin, so it was substituted with a monoclonal antibody for the replication. 
+Mouse calreticulin monoclonal antibody (MA5-15382, Thermo) was stored at -20°C in 5 µl aliquots for use at 1:1000 dilution. 
+Donkey α-mouse antibodies conjugated to AlexaFluor488 (715-545-150) were used at 1:800 dilution.
+HeLa cells were seeded on 12 mm diameter glass coverslips (1.0 glass) (1254580, Fisher) in 12-well plates (665180, Cellstar). 
+To fix the cells, media was aspirated and 4% paraformaldehyde (PFA) was applied without washing. 
+PFA was incubated for twenty minutes at room temperature, followed by three PBS washes. 
+After fixation, cells were permeabilized and blocked for twenty minutes in PBS with 0.1% Triton X-100 and 1% bovine serum albumin. 
+Antibodies were diluted in this buffer and incubated for one hour at room temperature in the dark, each followed by three PBS washes. 
+Nuclei were labelled for 15 minutes in Nucblue Fixed Cell Stain ReadyProbes reagent (containing DAPI) (R37606, Fisher) diluted in PBS. 
+Coverslips were mounted (P36980, Fisher) on glass slides (1255015, Fisher) and cured overnight in the dark.
+:::
diff --git a/06_discussion.md b/06_discussion.md
deleted file mode 100644
index b152de773..000000000
--- a/06_discussion.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: Discussion
-numbering:
-  enumerator: 7.%s
-label : discussion_page
----
diff --git a/07_antibody_reovirus.md b/07_antibody_reovirus.md
new file mode 100644
index 000000000..d15a28d19
--- /dev/null
+++ b/07_antibody_reovirus.md
@@ -0,0 +1,665 @@
+---
+title: Reovirus μNS
+numbering:
+  enumerator: 6.%s
+---
+
+Mammalian orthoreoviruses are thought to replicate and assemble viral particles in cytoplasmic compartments called viral factories. 
+μNS is a viral protein that self-assembles the scaffold of viral factories and recruits other viral proteins like σNS [@doi:10.1128/JVI.79.10.6194-6206.2005]. 
+The original study labelled viral factories with anti-μNS and anti-σNS antibodies, including an α-μNS chicken polyclonal serum from our laboratory. 
+The chicken antiserum was corroborated with an α-μNS antiserum from guinea pigs. 
+All samples were infected with reovirus T1L at MOI 100 and co-stained with calreticulin antibodies to observe ER elements. 
+Both the chicken ([Figure 6.1](#figure6a)) and guinea pig ([Figure 6.3](#figure6c)) α-μNS antisera were sufficient to observe virus-infected cells and intracellular viral factories at high magnification.
+Since the chicken antiserum paired with AlexaFluor647-conjugated secondary antibodies was the combination used in the replication study, a dedicated [repeat](#antibody_page) ([Figure 14.1](#figure14a)) is presented in the Supplemental.
+
+## Chicken polyclonal antiserum
+
+::::{figure}
+:label: figure6a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_primary_secondary_Cycle_02.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "+ Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -4.296,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Testing the chicken α-μNS antiserum in cells infected with reovirus T1L.
+::::
+
+:::::{figure}
+:label: figure6b
+::::{grid} 3 3 3 3
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_primary_Cycle_02.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "+ Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -5.555,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_secondary_Cycle.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -5.555,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_noantibody_Cycle.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -5.555,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 6.2:** Immunofluorescence antibody controls.
+:::::
+
+## Guinea pig polyclonal antiserum
+
+::::{figure}
+:label: figure6c
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_primary_secondary_Cycle_01.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "+ Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -4.296,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Testing the guinea pig α-μNS antiserum in cells infected with reovirus T1L.
+::::
+
+:::::{figure}
+:label: figure6d
+::::{grid} 3 3 3 3
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_primary_Cycle_02.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "+ Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -5.555,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_secondary_Cycle.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -5.555,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_noantibody_Cycle.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER and adjust contrast",
+      "description": "DAPI channel min 0 max 2000",
+      "zoom": -5.555,
+      "target": [
+        4891,
+        4891,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 6.4:** Immunofluorescence antibody controls.
+:::::
+
+:::{tip}
+Hidden by default, evaluate the calreticulin channel for reported reovirus-induced ER remodeling.
+ER morphology can be compared within populations of infected cells.
+:::
+
+:::{dropdown} Materials and immunofluorescence protocol
+Chicken anti-μNS serum from the Parker lab (1:1000 dilution) was the same used in the original study. 
+Guinea pig anti-μNS serum was gifted by the Stanifer lab (1:5000 dilution). 
+Donkey anti-chicken antibodies conjugated to AlexaFluor647 (703-605-155), donkey anti-chicken conjugated to AlexaFluor594 (703-585-155), and donkey anti-guinea pig conjugated to AlexaFluor594 (706-585-148) were stored in 1:1 glycerol and used at 1:800 dilution.
+HeLa cells were seeded on 35 mm diameter glass-bottom dishes with 20 mm diameter 1.5 coverslips (Mattek, P35G-1.5-20-C) and infected with reovirus T1L at MOI 100 for 24 hours before fixation with 4% PFA. 
+Calreticulin antibodies were used in all samples. 
+Combinations of primary and secondary antibodies to reovirus μNS were tested, with AlexaFluor594-conjugated secondary antibodies used for both antisera to maintain consistent illumination settings. 
+Antibodies were multiplexed and incubated overnight, primaries and secondaries separately. 
+Nuclei were labelled for 15 minutes in Nucblue Fixed Cell Stain ReadyProbes reagent (containing DAPI) (R37606, Fisher) diluted in PBS, and cells were imaged in this solution.
+:::
diff --git a/07_methods.md b/07_methods.md
deleted file mode 100644
index 52b80272d..000000000
--- a/07_methods.md
+++ /dev/null
@@ -1,200 +0,0 @@
----
-title: Materials and Methods
-numbering:
-  enumerator: 8.%s
-label : methods_page
----
-
-# NucleusNet
-
-## Cell culture
-
-The CV-1 cell line is fibroblast-like and derived from the kidney of an African green monkey.
-CV-1 cells were cultured in Eagle's minimal essential medium supplemented with fetal bovine serum (10% volume/volume) at 37 degrees celsius with 5 percent environmental carbon dioxide to buffer pH.
-At each passage, six glass-bottom cell culture dishes with a 20mm diameter coverslip (Mattek Corporation, P35G-1.5-20-C) were seeded with varied densities of CV-1 cells (~1e4-5e4 cells) to promote heterogeneous cell confluence.
-
-## Sample preparation
-
-The samples were fixed with 4% paraformaldehyde for twenty minutes after one to three days of incubation when the cells approached confluence.
-After fixation, the samples were washed with phosphate buffered saline (PBS) then were refrigerated at 4 degrees celsius for storage.
-Nuclei were labelled with Nucblue Fixed Cell ReadyProbes Reagent (DAPI) (ThermoFisher Scientific, R37606) in 1 milliliter of PBS for at least 30 minutes then were imaged in the staining solution at room temperature.
-
-## Image acquisition
-
-Large regions of no. 1.5 glass coverslips were sampled using a motorized stage (IX3-SSU) on a laser scanning confocal microscope (Olympus Fluoview FV3000) with an oil immersion objective lens at 100X magnification (Olympus, model UPLAPO100X, NA 1.50).
-The pinhole diameter was opened to 800μm to maximize the depth of field.
-N=2500 fields were imaged in a 50x50 tile grid (snake pattern by rows: right down left down) with 5% overlap at each edge.
-Z-drift compensation was active to maintain autofocus.
-The confocal microscope was on a pressurized CleanBench isolation laboratory table (TMC Vibration Control).
-
-```{list-table} Illumination and image processing controls on the Olympus Fluoview FV3000 microscope.
-:label: table1
-
-* - Excitation wavelength
-  - 405nm
-* - Emission wavelength
-  - 418nm-496nm
-* - Laser intensity
-  - 0.12%-0.15%
-* - Detector sensitivity
-  - 500V
-* - Gain
-  - 1.0X
-* - Offset
-  - 3%
-* - Field resolution
-  - 1024x1024 pixels
-* - Pixel size
-  - 8.0453 pixels/micrometer
-* - Pixel type
-  - uint16
-* - Bits per pixel
-  - 12
-```
-
-## Data pre-processing
-
-```{figure} ./figures/fig4.png
-:label: fig4
-:align: center
-:width: 100%
-
-An overview of data collection and pre-processing. 
-A) Tiles were imaged in a 50x50 grid. 
-B) Then stitched into 25x25 tile grids (quarters). 
-C) Quarters were cropped into four stitched images.
-```
-
-1. Conversion to 8-bit .TIF format
-
-Raw 12-bit files are in the proprietary Olympus .oir file format with metadata.
-The raw were converted to 8-bit TIF format using FIJI [@doi:10.1038/nmeth.2019] and were renamed 0-2500.tif corresponding to the order that the tiles were imaged.
-
-2. Tiles stitched with Microscopy Image Stitching Tool (MIST)
-
-The size of a full stitched image approaches the maximum value that a 32-bit integer can hold, therefore the N=2500 .tif files were divided into sub-folders containing n=625 images corresponding to four quarters ([](#fig4)).
-The images in these sub-folders were stitched into 25x25 tile panoramas using the MIST plugin [@doi:10.1038/s41598-017-04567-y] in FIJI.
-MIST was useful to correct for inaccurate step sizes that sometimes occurred with the motorized stage.
-
-```{list-table} MIST plugin settings.
-:label: table2
-
-* - Filename pattern type
-  - Sequential
-* - Starting point
-  - Upper left
-* - Direction
-  - Horizontal continuous
-* - Grid width
-  - 25
-* - Grid height
-  - 25
-* - Grid start tile
-  - 0
-* - Timeslices
-  - 0
-* - Filename pattern
-  - {pppp}.tif
-* - Blending mode
-  - Linear
-* - Compression mode 
-  - Uncompressed
-* - Pixel size metadata
-  - Mirometer X 8.0453 Y 8.0453
-```
-
-The stitched quarter was displayed and saved, named by run and position.
-For example, Run53TL was from the top left quarter of the 53rd imaging experiment.
-The stitched quarters were then cropped into four quarters, yielding n=16 stitched images per coverslip ([](#fig4)) and N=1600 stitched images from all one-hundred experiments.
-
-(cellpose-segmentation)=
-3. Segmentation with cellpose
-
-Nuclei were masked in the stitched images using a custom [cellpose](https://github.com/MouseLand/cellpose) model [@doi:10.1038/s41592-020-01018-x].
-The pre-trained cellpose nucleus model was re-trained on N=125000 fields from NucleusNet, including n=1000 fields with manually-segmented nuclei.
-Weights were saved as CP_20250418_Nuclei_1Kmasks.
-Advanced parameters in the graphical user interface were adjusted to flow_threshold: 0.5, cellprob_threshold: -2.0, diameter (pixels): 152.91.
-The cellpose model segmented the nuclei in all stitched images and the mask files were saved as PNG files where each region of interest (ROI) is defined by a unique pixel value.
-
-```{admonition} Bias in NucleusNet from data processing methods
-:class: warning
-The detection of nuclei and the accuracy of the masks relied on the cellpose model. There are undesirable artifacts like blank images and inaccurate masks in the dataset, and sampling was likely inconsistent between stitched images due to variations in signal intensity. The cellpose model was re-trained and hand-tuned by a human. Training was also semi-supervised with manual masks. Thus, there are human and model biases.
-```
-
-4. Cropped single cell masked dataset
-
-The orientation of a cell is known to confound the vector embedding of autoencoder models trained on single-cell microscopy data, motivating the development of orientation-invariant autoencoder models [@doi:10.1038/s41467-024-45362-4].
-Similarly, a multi-encoder variational autoencoder model controlled for several transformational features like orientation that were 'uninformative' in single-cell analyses [@doi:10.1038/s42003-022-03218-x].
-Barkley [pre-aligned](https://github.com/jmhb0/o2vae/tree/master/prealignment) and by fitting and rotating a minimal area rectangle to the cellpose mask.
-Nuclei were center-cropped and all values outside of the mask were set to zero in the cropped images.
-
-## Deep zoom microscopy maps
-
-Microscopy maps are a novel visualization strategy to present large high-magnification microscopy images as zoomable digital maps.
-There are free and open-source options to create microscopy maps [@doi:10.1242/jcs.262198].
-[VIPS](https://www.libvips.org/) was used to generate the Deep Zoom image (.dzi) image tile pyramid and [OpenSeadragon](https://openseadragon.github.io/) v4.1.0 viewed the tiles.
-OpenSeadragon v5.0.0+ had unstable performance on mobile devices.
-Code and data was hosted on Github Pages.
-
-# Replication Study
-
-## Cell culture
-
-HeLa CCL-2 cells were cultured in Dulbecco’s modification of Eagle’s Medium (DMEM) (50-003-PB, Corning) which was supplemented with 10% fetal bovine serum (FS-0500-AD, Atlas).
-Antibiotics were not added to the medium in the replication study, although in the original study the authors used 100 U/ml penicillin G, 100ug/ml streptomycin and 0.25ug/ml amphotericin B.
-
-## Immunofluorescence
-
-Cells were seeded on 12mm diameter glass coverslips (1.0 glass) (1254580, Fisher) in 12-well plates (665180, Cellstar).
-To fix the cells, media was aspirated and 4% PFA solution was applied without washing.
-4% PFA was incubated for twenty minutes at room temperature, followed by three PBS washes.
-After fixation the cells were permeabilized and blocked for twenty minutes in PBS with Triton X-100 and bovine serum albumin.
-Antibodies were diluted in this buffer and were incubated for one hour at room temperature in the dark, both followed by three PBS washes.
-Nuclei were labelled for 15 minutes in Nucblue Fixed Cell Stain ReadyProbes reagent (containing DAPI) (R37606, Fisher) diluted in PBS.
-Coverslips were mounted (P36980, Fisher) on glass slides (1255015, Fisher) and cured overnight at room temperature in the dark.
-Slides were gently cleaned then stored at four degrees celsius.
-
-## Antibodies
-
-The original study used a polyclonal serum for calreticulin but did not include the product number, so it was substituted for a monoclonal antibody.
-Mouse calreticulin monoclonal antibody (MA5-15382, Thermo) was stored at -20C in 5ul aliquots to be used at 1:1000 dilution.
-Chicken anti-muNS serum was previously made in the Parker lab (1:1000 dilution).
-Guinea pig anti-muNS serum was made by the Stanifer lab (1:5000 dilution).
-Donkey anti-mouse antibodies conjugated to AlexaFluor488 (715-545-150), donkey anti-chicken antibodies conjugated to AlexaFluor647 (703-605-155) and donkey anti-guinea pig conjugated to AlexaFluor594 (706-585-148) were stored diluted in 1:1 glycerol and were used at 1:800 dilutions.
-
-## Transfection
-
-Lipofectamine 3000 (L3000015, Invitrogen) was used with 12-well plates; 62.5ul Opti-MEM (31985-062, Gibco) was mixed with 2.5ul P3000 reagent and 1.25ug DNA, and another tube of 62.5ul Opti-MEM was mixed with 1.875ul lipofectamine 3000 reagent. The tubes were mixed, incubated for 20 minutes, then added dropwise to the samples.
-TransIT-2020 Reagent (MIR 5400, Mirus) was used with 12-well plates; 125ul Opti-MEM was mixed with 1.25ug DNA, mixed gently, then 3.75ul TransIT-2020 was added and mixed, followed by 20 minutes of incubation and dropwise addition to the samples.
-
-## Plasmids
-
-The mCherry-KDEL construct was the same as the original study; mCherry-ER-3 plasmid (55041, Addgene), which retained mCherry in the lumen of the ER.
-Barkley included mCherry-CytERM-N-17 (55024, Addgene) to retain mCherry on the cytoplasmic side of the ER.
-mCherry-CytERM was considered a positive control for ER remodelling because of the propensity of fluorescent proteins to cause organized smooth endoplasmic reticulum (OSER) whorls [@doi:10.1083/jcb.200306020].
-Two additional constructs were included with ER proteins fused to mCherry; mCh-Climp63 (136293, Addgene) and mCh-Sec61 beta (49155, Addgene).
-mCherry-Climp63 was a control for ER remodelling, given its role in determining ER morphology [@doi:10.1016/j.cell.2010.11.007], while mCherry-Sec61β was considered a general ER marker [@doi:10.1126/science.aay7108].
-
-## Virus infection
-
-The study used a mutant mammalian orthoreovirus strain Type 1 Lang (T1L) M1-P208S, which makes globular viral factories through the loss of interaction with microtubules [@doi:10.1128/JVI.76.9.4483-4496.2002].
-No rationale was provided for using the P208S mutant, nor did our lab have it, so wildtype mammalian orthoreovirus strains T1L and Type 3 Dearing (T3D) were used in the replication study.
-The important distinction is that the morphology of viral factories between strains T1L and T3D differ.
-T1L tends to form filamentous viral factories, and mutant reovirus M1-P208S used the T1L genetic backbone but formed globular viral factories.
-A stock of unpurified reovirus T3D was used in one experiment to compare strains.
-Stocks of purified reovirus T1L were titrated in L-929 cells to estimate the concentration of plaque-forming units (PFU).
-L-929, or L cells are permissive to reovirus infection and were used for virus titration in the original study, so there was consistency in calculating the multiplicity of infection (MOI).
-To clarify, MOI was PFU/cell, and PFU was estimated on L-929 cells in both the original study and the replication study.
-
-For infection of cells on coverslips in 12-well plates, purified reovirus was diluted in a volume of 100ul of serum-free DMEM.
-Media was aspirated from the cells, replaced with 100ul serum-free DMEM to wet the coverslip, then the virus-containing medium was added.
-Reovirus was adsorbed to the cells for one hour at room temperature and the plate was rocked every ten minutes to redistribute the inoculum.
-Thereafter, one milliliter of pre-warmed serum-containing DMEM was added to the cells, and the plate was incubated at 37C for 24h before PFA fixation.
-
-## Imaging
-
-Samples were imaged on an Olympus Fluoview FV3000 laser-scanning confocal microscope with a 100X objective lens using immersion oil. 
-The pinhole was opened for all channels for the widest depth of field. 
-Z-draft compensation was used to autofocus the samples during acquisition. 
-Each channel was excited separately and care was taken to avoid spectral overlap. 
-The coordinates for a 10x10 grid with 5% overlap was set and the one hundred fields were captured using the multi-area time lapse protocol.
\ No newline at end of file
diff --git a/08_results.md b/08_results.md
new file mode 100644
index 000000000..37ce24112
--- /dev/null
+++ b/08_results.md
@@ -0,0 +1,5043 @@
+---
+title: Results
+numbering:
+  enumerator: 7.%s
+---
+
+This study is not an exact replication because some reagents and protocols were modified due to availability. 
+Specifically, the reovirus strain, MOI, and calreticulin antibody were substituted, while the cell line, plasmid, transfection reagent, and reovirus antibody were consistent. 
+Rather than relying on a single representative image, the data is presented as an interactive figure ([Figure 7.1](#figure7)), allowing readers to observe hundreds of annotated cells.
+
+::::{figure}
+:label: figure7
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_4/goodZDC.zarr",
+  "width": "500px",
+  "height": "500px",
+  "views": [
+    {
+      "name": "Compare mCherry-ER",
+      "zoom": -1.474,
+      "target": [
+        16291,
+        1765,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          false,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            1768
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "Compare mCherry-ER",
+      "zoom": -1.186,
+      "target": [
+        16127,
+        12089,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          false,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
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+          [
+            0,
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+          ],
+          [
+            0,
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+          ],
+          [
+            0,
+            2490
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "Compare mCherry-ER",
+      "zoom": -1.186,
+      "target": [
+        22766,
+        7555,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          false,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
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+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2624
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "mCherry-KDEL artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -0.362,
+      "target": [
+        17738,
+        18782,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
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+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
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+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
+        2747,
+        347
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
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+      "target": [
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+      ],
+      "color": [
+        235,
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+        87
+      ]
+    },
+    {
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+      ],
+      "color": [
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+      ]
+    },
+    {
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+      ],
+      "color": [
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+      ]
+    },
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+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
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+        1553,
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+      ],
+      "color": [
+        235,
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+        87
+      ]
+    },
+    {
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+      ],
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
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+      ],
+      "color": [
+        235,
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1510,
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+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
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+      ],
+      "color": [
+        235,
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+        87
+      ]
+    },
+    {
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+      ],
+      "color": [
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+        87
+      ]
+    },
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+      ],
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+        87
+      ]
+    },
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+      ],
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+        87
+      ]
+    },
+    {
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+      ],
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+      ]
+    },
+    {
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+      ],
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+        87
+      ]
+    },
+    {
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+      ],
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+      ]
+    },
+    {
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+      ],
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+      ]
+    },
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+      ],
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+      ]
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+      ],
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+      ]
+    },
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+      ],
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+      ]
+    },
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+      ],
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+    },
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+      ]
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+      ],
+      "color": [
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+        87
+      ]
+    },
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+      ],
+      "color": [
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+      ]
+    },
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+      ],
+      "color": [
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+      ]
+    },
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+      ],
+      "color": [
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+      ]
+    },
+    {
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
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+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
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+      ]
+    },
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+      "color": [
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+      ]
+    },
+    {
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+      "color": [
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+      ]
+    },
+    {
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+      ],
+      "color": [
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+      ]
+    },
+    {
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
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+      ],
+      "color": [
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+      ]
+    },
+    {
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+        12288
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        17941,
+        12579
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        16237,
+        12409
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        15953,
+        12125
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21773,
+        8223
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22622,
+        7728
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21810,
+        6189
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22428,
+        5795
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        24014,
+        529
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        24178,
+        893
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        23060,
+        3561
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22278,
+        2995
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        19692,
+        13684
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21348,
+        14477
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20943,
+        14774
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22821,
+        20494
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22949,
+        20593
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        22838,
+        20839
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21472,
+        20425
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        20672,
+        23355
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        17275,
+        23508
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        17717,
+        18784
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        14665,
+        17750
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        15787,
+        22923
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12018,
+        21544
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11925,
+        21913
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7913,
+        16541
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6231,
+        12016
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4816,
+        18286
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8993,
+        18028
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10254,
+        19581
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10240,
+        19784
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3092,
+        13295
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        18364,
+        19834
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Immunofluorescence imaging of reovirus-infected HeLa cells expressing mCherry-KDEL. Select preset views from the menu: "Compare mCherry-ER" navigates to fields containing both infected (red pin) and uninfected (blue pin) transfected cells for morphological comparison, while "mCherry-KDEL artifacts" shows an example of punctate ER structures potentially caused by the fluorescent protein.
+::::
+
+Barkley annotated transfected cells using the [microATLAS](https://github.com/LadInTheLab/microATLAS-widget) [builder](https://ladinthelab.github.io/microATLAS-widget/builder.html) to facilitate the comparison of ER morphology between infected and uninfected cells expressing mCherry-KDEL.
+This protocol was repeated five times over four experiments ([](#table5) and supplementary data).
+Across the five repetitions, the proportion of transfected cells co-infected with reovirus varied from 24% ([](#figure10a)) to 76% ([](#figure7)).
+This variability demonstrates that co-infection of transfected cells cannot be reliably assumed, as was done in the original study.
+
+```{list-table} microATLAS annotation of transfected cell populations expressing mCherry-KDEL.
+:label: table5
+:header-rows: 1
+
+* - Sample
+  - Infected
+  - Uninfected
+  - Infection rate
+  - Grid size (fields)
+* - [Figure 10.1](#figure10a)
+  - 39
+  - 126
+  - 24%
+  - 10x10 (n=100)
+* - [Figure 11.1](#figure11a)
+  - 64
+  - 125
+  - 34%
+  - 15x15 (n=225)
+* - [Figure 12.1](#figure12a)
+  - 55
+  - 40
+  - 58%
+  - 15x15 (n=225)
+* - [Figure 7.1](#figure7)
+  - 299
+  - 94
+  - 76%
+  - 25x25 (n=625)
+* - [Figure 13.1](#figure13)
+  - 227
+  - 75
+  - 75%
+  - 25x25 (n=625)
+```
+
+## Conclusion
+
+Barkley found no robust evidence of reovirus-induced ER remodeling in transfected cells expressing mCherry-KDEL. 
+Irregular ER morphologies were observed in transfected cells expressing mCherry-KDEL, with or without reovirus infection, suggesting that fluorescent protein artifacts could have been misinterpreted as reovirus-induced ER remodeling. 
+As illustrated in the preset views titled _Compare mCherry-ER_, there was no obvious difference in ER morphology between infected (red pin) and uninfected (blue pin) transfected cells.
+
+:::{dropdown} Methods
+3 × 10⁵ HeLa cells were seeded on 35 mm diameter glass-bottom dishes with 20 mm diameter no. 1.5 coverslips (Mattek, P35G-1.5-20-C). 
+mCherry-KDEL was transfected with TransIT-2020 for 24 hours, then cells were infected with reovirus T1L (MOI 100) for 24 hours before fixation (4% PFA, 20 minutes). 
+Calreticulin and reovirus μNS antigens were detected by immunofluorescence. 
+Chicken antiserum was paired with AlexaFluor647 secondary antibodies. 
+Nuclei were labelled for 15 minutes in Nucblue Fixed Cell Stain ReadyProbes reagent (containing DAPI) (R37606, Fisher) diluted in PBS, then a 25×25 grid of 625 fields was imaged. 
+Barkley annotated transfected cells expressing mCherry-KDEL based on the presence or absence of reovirus infection using the microATLAS builder. 
+Transfected-infected cells were labelled "Coincidence" because the co-occurrence of transfection and infection in the same cell is incidental, while transfected-uninfected cells were labelled "Transfection".
+Ambiguous cells were left unlabelled, and it is likely that some transfected cells were overlooked.
+:::
diff --git a/08_supplementary_data.md b/08_supplementary_data.md
deleted file mode 100644
index e68ab8c6b..000000000
--- a/08_supplementary_data.md
+++ /dev/null
@@ -1,294 +0,0 @@
----
-title: Supplementary Data
-numbering:
-  enumerator: 9.%s
-label : supplementary_page
----
-
-# NucleusNet
-
-:::{figure} #scatter-plot
-:label: sfig1a
-:placeholder: ./figures/sfig1a.png
-The number and brightness of ROIs in the stitched images was measured using the associated cellpose mask files. A pre-computed centroid 694.5 ROIs and 79.0 mean intensity was marked.
-:::
-
-```{list-table} Representative stitched images ranked by distance to centroid: 694.5 ROIs, 79.0 mean intensity
-:label: table3
-:header-rows: 1
-
-* - Filename
-  - Number of ROIs
-  - Mean intensity
-  - Distance*
-* - Run72TR_bottom_left
-  - 685
-  - 78.6
-  - 0.046
-* - Run63TR_top_right
-  - 709
-  - 78.6
-  - 0.066
-* - Run72BR_top_right
-  - 687
-  - 80.3
-  - 0.074
-* - Run68TR_top_right
-  - 711
-  - 79.6
-  - 0.076
-* - Run25BR_top_right
-  - 698
-  - 77.5
-  - 0.079
-* - Run25BR_bottom_left
-  - 681
-  - 78.0
-  - 0.080
-* - Run72BR_top_left
-  - 712
-  - 79.5
-  - 0.080
-* - Run102TR_top_left
-  - 716
-  - 78.3
-  - 0.099
-* - Run50TL_top_right
-  - 714
-  - 77.8
-  - 0.104
-* - Run72BL_top_right
-  - 720
-  - 79.1
-  - 0.109
-```
-Z-score-normalized euclidean distance was used for equal weighting because the features had different scales.
-
-# MNIST autoencoder
-
-## Loss plot
-
-:::{figure} #sfig2a_data
-:label: sfig2a
-:placeholder: ./figures/sfig2a.png
-Autoencoder reconstruction loss over one-hundred epochs of training on the MNIST dataset.
-:::
-
-# Replication study
-
-Barkley noted the type of data in each figure and supplementary figure, excluding videos, in order of presentation.
-For data type, there were four possible options; light microscopy image, electron microscopy image, correlative light-electron microscopy image, quantitation.
-Quantitation was considered any analysis with numbers, while the other three data types were representative images.
-Some figure panels had multiple images.
-If the images were of different samples, then they were counted as unique images (>1 images).
-Others were multi-channel or time-lapse images, which was considered one image with multiple frames.
-
-```{list-table} Assessment of the types of data in the original study [@doi:10.1128/mBio.01253-18].
-:label: table4
-:header-rows: 1
-* - Figure
-  - Data type
-  - Images
-  - Frames
-  - Frame type
-* - 1A
-  - Light microscopy image
-  - 1
-  - 1
-  -
-* - 1B
-  - Light microscopy image
-  - 1
-  - 1
-  -
-* - 1C
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 1D
-  - Correlative light-electron microscopy image
-  - 1
-  - 1
-  -
-* - 1E
-  - Correlative light-electron microscopy image
-  - 1
-  - 1
-  -
-* - 1F
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - S1A
-  - Light microscopy image
-  - 2
-  - 1
-  -
-* - S1B
-  - Electron microscopy image
-  - 4
-  - 1
-  -
-* - 2A
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2B
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2C
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2D
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2E
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2F
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2G
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 2H
-  - Electron microscopy image
-  - 4
-  - 1
-  -
-* - 3
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 4A
-  - Light microscopy image
-  - 1
-  - 4
-  - Time
-* - 4B
-  - Light microscopy image
-  - 1
-  - 2
-  - Channel
-* - 4C
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S2A
-  - Light microscopy image
-  - 1
-  - 3
-  - Time
-* - S2B
-  - Light microscopy image
-  - 1
-  - 3
-  - Time
-* - 5A
-  - Light microscopy image
-  - 4
-  - 1
-  -
-* - 5B
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - 5C
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - 5D
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - 5E
-  - Electron microscopy image
-  - 1
-  - 1
-  -
-* - S3A
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3B
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3C
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3D
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3E
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3F
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3G
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3H
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - S3I
-  - Light microscopy image
-  - 1
-  - 3
-  - Channel
-* - 6A
-  - Quantitation
-  - 0
-  - 0
-  -
-* - 6C
-  - Quantitation
-  - 0
-  - 0
-  -
-```
-
-# Stereotypical images from NucleusNet-10K
-
-```{image} ./figures/anaphase.png
-:label: anaphase
-```
-```{image} ./figures/telophase.png
-:label: telophase
-```
-
diff --git a/09_discussion.md b/09_discussion.md
new file mode 100644
index 000000000..84d68d5dd
--- /dev/null
+++ b/09_discussion.md
@@ -0,0 +1,44 @@
+---
+title: Discussion
+numbering:
+  enumerator: 0.%s
+---
+
+Every academic discipline has its own ways of deciding what counts as valid knowledge (see [epistemology](<wiki:Epistemology>)).
+If biomedical researchers endorse empiricism and objectivity, then why are representative images sufficient to make claims?
+A representative image shows that an observation is possible, but a single image cannot describe frequency or variance.
+The scientific method demands reproducibility, yet representative images offer no way to communicate repeatability without quantitative analysis.
+In practice, representative images can show rare, idealized, or exaggerated examples to embellish results.
+The literature is full of exciting outcomes, but at the bench, experiments are nuanced and prone to failure.
+
+Representative images are figures, which should not be confused with data.
+Data is the unprocessed file from the microscope, while figures are visual representations of data, shaped by the microscopist.
+Figures are the interface for peer review between the experimentalist and coauthors, principal investigators, reviewers and readers.
+Indeed, peer review seldom involves data and scientific discourse is limited by the information provided in figures.
+This bottleneck is alleviated by virtual microscopy, which is practical to the broader bioimaging community.
+For example, OME-Zarr [@doi:10.1007/s00418-023-02209-1] supports several modalities including multiplexed imaging, spatial transcriptomics, light-sheet, infrared, scanning EM and digital pathology.
+To maximize accessibility for readers, embedded interactive viewers retrieve data from cloud storage, which is supported by this journal.
+Virtual microscopy requires an internet connection, but this limitation is relatively unimportant for contemporary and future publishing.
+Web-based microscopy maps are trivial to make and share on Github [@doi:10.1242/jcs.262198], so they can be readily requested by reviewers of all journals.
+Data transparency could incentivize publishers to adopt this technology as an alternative to static figures.
+Because it nurtures open and rigorous peer review, microscopists and principal investigators might find virtual microscopy advantageous to make more robust claims with their bioimaging data.
+
+Barkley advocated for the use of virtual microscopy as an "unbiased" method to replicate highly magnified representative images.
+This process involves automated sampling of reasonable populations from which the original claim should be reliably observed.
+Virtual microscopy is "unbiased" to the extent that the data was sampled with robotics and is hosted interactively on the web.
+To test this approach, an immunofluorescence imaging experiment in reovirus-infected cells [@doi:10.1128/mBio.01253-18] was reexamined with virtual microscopy.
+Barkley hypothesized that fluorescent proteins could have restructured the ER [@doi:10.1083/jcb.200306020], which was not addressed by the previous investigators.
+In replication attempts, ER remodeling was observed in transfected cells expressing mCherry-KDEL in the absence of reovirus.
+This suggests that artifacts from mCherry-ER reporters could have been misinterpreted as an outcome of infection.
+Otherwise, it was not clear that reovirus affected ER morphology, certainly not to the extent of fragmentation, collapse, and aggregation as described, though we recognize that this interpretation was influenced by confirmation bias.
+
+When Barkley sought to uncover ground truths through virtual microscopy, Parker offered the following perspective: 
+There is no ground truth. 
+The term is misleading, as all "established facts" are simply our current interpretation of available findings, data, or measurements. 
+Science does not generate truth; it generates findings or data that are analyzed and then interpreted by humans, and those interpretations can differ, sometimes widely. 
+Through the accrual of many findings, a consensus picture of current "reality" emerges that is sometimes called "ground truth," but this is only a snapshot, not the final word.
+Truth is not a straightforward concept. 
+All truths can be challenged, and while some become more certain with the passage of time, they are never without caveats. 
+Science is the process of observing, imagining explanations for the observations, turning those explanations into hypotheses, and then testing them through experimentation. 
+The truths we derive from this process are "interpreted truths" that may become irrelevant as new information is gathered. 
+Our job as scientists is not to identify ground truths, but to test hypotheses and garner evidence that supports or contradicts them, and to challenge the prevailing consensus.
diff --git a/10_oser.md b/10_oser.md
new file mode 100644
index 000000000..c10e3fd5b
--- /dev/null
+++ b/10_oser.md
@@ -0,0 +1,165 @@
+---
+title: OSER Assay
+numbering:
+  enumerator: 8.%s
+label : oser_page
+---
+
+Fluorescent proteins tend to oligomerize when confined to the ER, which can restructure the organelle into organized smooth ER (OSER) whorls [@doi:10.1083/jcb.200306020]. 
+This phenomenon is used to assess the propensity of fluorescent proteins to oligomerize [@doi:10.1111/j.1600-0854.2012.01336.x]. 
+Six fluorescent proteins were tested in an OSER assay using interactive microscopy maps (Figure 8.1).
+Four of these can be cross-referenced with published estimates of the percentage of cells lacking visible OSER whorls: mEGFP (98.8±1.2), mCherry (95.0±0.8), FusionRed (91.5±3.0), and mNeonGreen (90.4±2.1) [@doi:10.1038/nmeth.3891].
+Among the samples tested here, Dendra2 formed the most prominent whorls, visible in the majority of cells.
+
+:::::{figure}
+:label: figure8
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/fusionred.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "FusionRed-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/mcherry.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/dendra2.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "Dendra2-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/megfp.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mEGFP-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/mneongreen.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mNeonGreen-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/moxgfp.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "moxGFP-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 8.1:** Qualitative OSER assay presented as interactive microscopy maps.
+:::::
+
+:::{dropdown} Methods
+3 × 10⁵ HeLa cells were seeded on 35 mm glass-bottom dishes. 
+An OSER assay was performed with six fluorescent proteins cloned into the CytERM plasmid. 
+2.5 µg of each plasmid was transfected with Lipofectamine 3000 for 24 hours before PFA fixation and automated imaging of 15×15 (225 field) grids with a 100X objective lens. 
+Illumination settings, including excitation and emission wavelengths, were adjusted individually. 
+mEGFP, moxGFP, Dendra2, and mNeonGreen shared the same wavelengths.
+:::
\ No newline at end of file
diff --git a/11_video.md b/11_video.md
new file mode 100644
index 000000000..c0264b3ff
--- /dev/null
+++ b/11_video.md
@@ -0,0 +1,72 @@
+---
+title: Video
+numbering:
+  enumerator: 9.%s
+label : video_page
+---
+
+::::{figure}
+:label: figure9
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/video.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mNeonGreen::μNS + mCherry-KDEL",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Play by default",
+      "zoom": -3.784,
+      "target": [
+        3431,
+        3431,
+        0
+      ],
+      "playback": {
+        "playing": true,
+        "fps": 24,
+        "startFrame": 0
+      },
+      "default": true
+    },
+    {
+      "name": "mCherry-KDEL artifacts",
+      "zoom": 0.106,
+      "target": [
+        3718,
+        5505,
+        0
+      ],
+      "playback": {
+        "playing": true,
+        "fps": 24,
+        "startFrame": 0
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Cotransfection of mNeonGreen::μNS and mCherry-KDEL in live cells.
+::::
+
+:::{dropdown} Methods
+HeLa cells were seeded on 35 mm glass-bottom dishes (20 mm coverslips, no. 1.5 glass). 
+pCI-neo mNeonGreen::μNS(1-721) and mCherry-KDEL plasmids were co-transfected (1:1 ratio, 1.25 µg each) for 24 hours with Lipofectamine 3000. 
+The stage was heated to 37°C but lacked CO₂, so the media was supplemented with 25 mM HEPES buffer before imaging. 
+A 7×7 grid of 49 fields was imaged in a serpentine pattern at 4-minute intervals for thirty frames (~2 hours total).
+:::
\ No newline at end of file
diff --git a/12_replicate_one.md b/12_replicate_one.md
new file mode 100644
index 000000000..592f565a8
--- /dev/null
+++ b/12_replicate_one.md
@@ -0,0 +1,3504 @@
+---
+title: Experiment 1
+numbering:
+  enumerator: 10.%s
+---
+
+## Testing transfection reagents
+
+Plasmid transfection was a potential confound for reovirus infection in this model. 
+Two transfection reagents were compared, and TransIT-2020 was deemed more suitable, consistent with the original study.
+
+::::{figure}
+:label: figure10a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_1/Transit.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "TransIT-2020",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Contrast adjusted",
+      "description": "Calreticulin DAPI min 0 max 2000",
+      "zoom": -4.294,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    },
+    {
+      "name": "ER remodeling without reovirus",
+      "zoom": -0.672,
+      "target": [
+        6945,
+        1675,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          false,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "ER remodeling without reovirus",
+      "zoom": -0.672,
+      "target": [
+        8224,
+        1300,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          false,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
+        226,
+        677
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2116,
+        98
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2139,
+        23
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1277,
+        2539
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1538,
+        2527
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        899,
+        3635
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        907,
+        5407
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        801,
+        5741
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        197,
+        9656
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        295,
+        9748
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4608,
+        8776
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4596,
+        8587
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4708,
+        8347
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5593,
+        7819
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5993,
+        7922
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3949,
+        6820
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4038,
+        6985
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3381,
+        7622
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3585,
+        7672
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2752,
+        7199
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3189,
+        6309
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3927,
+        5162
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3404,
+        3561
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3690,
+        2719
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3905,
+        962
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5302,
+        1996
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7114,
+        835
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7894,
+        733
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7438,
+        7642
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7273,
+        8345
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7962,
+        8522
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8584,
+        8713
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8762,
+        5642
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8013,
+        5240
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8258,
+        5341
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2265,
+        3006
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        578,
+        909
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1812,
+        871
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1681,
+        1166
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1576,
+        1353
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1892,
+        1487
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1297,
+        1971
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        414,
+        1992
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        853,
+        2491
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        179,
+        3784
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        94,
+        4098
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1662,
+        1794
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1034,
+        2162
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1189,
+        3857
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        467,
+        4351
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        405,
+        4664
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        601,
+        4963
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        622,
+        5083
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        111,
+        5760
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        972,
+        5771
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1131,
+        5263
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1192,
+        5016
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1398,
+        5478
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1861,
+        5341
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2298,
+        6147
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2343,
+        6519
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+    {
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+    {
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+    {
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+      ],
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+      ]
+    },
+    {
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+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ],
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ],
+      "color": [
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8101,
+        8446
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7925,
+        8617
+      ],
+      "color": [
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+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Transfected-infected cells. TransIT-2020 transfection before reovirus T1L infection (MOI 100).
+::::
+
+::::{figure}
+:label: figure10b
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_1/LF3000.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "Lipofectamine 3000",
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+    },
+    {
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+    },
+    {
+      "name": "Uninfected",
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+      "color": [
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+        237
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+  ],
+  "scaleBar": {
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+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Transfected-infected cells. Uninfected with Lipofectamine 3000 before reovirus infection.
+::::
+
+::::{figure}
+:label: figure10c
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_1/Untransfected.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "Untransfected control",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
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+      "description": "Calreticulin DAPI min 0 max 2000",
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+        4886,
+        0
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+      "appearance": {
+        "channelsVisible": [
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+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
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+            0
+          ],
+          [
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+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
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+          ],
+          [
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+          ],
+          [
+            0,
+            2000
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Untransfected control. Mock transfection (no plasmid or reagent) before reovirus infection.
+::::
+
+:::{dropdown} Methods
+Specimens were glass coverslips (1.0 glass) mounted on slides. 
+Reovirus μNS was detected with chicken α-μNS serum and AlexaFluor647 secondary antibodies, each incubated for one hour. 
+A 10×10 grid of 100 fields was recorded.
+:::
diff --git a/13_replicate_two.md b/13_replicate_two.md
new file mode 100644
index 000000000..5e1402775
--- /dev/null
+++ b/13_replicate_two.md
@@ -0,0 +1,4838 @@
+---
+title: Experiment 2
+numbering:
+  enumerator: 11.%s
+---
+
+## Testing other mCherry-ER fusion proteins
+
+Other mCherry-ER reporters were tested, though few cells were found expressing mCherry-Climp63. 
+Signal intensity was low, particularly in the calreticulin channel, making it difficult to interpret ER morphology with the antibody.
+
+::::{figure}
+:label: figure11a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-KDEL_T1L.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "+ mCherry-KDEL + reovirus T1L",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
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+      "name": "Adjusted contrast",
+      "zoom": -4.877,
+      "target": [
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+        7316,
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+      "appearance": {
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+          true,
+          true
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+        "channelColors": [
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+        ],
+        "blendMode": "single",
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+    {
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+        "blendMode": "single",
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+      }
+    },
+    {
+      "name": "ER remodeling without reovirus",
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+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2645,
+        4787
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3325,
+        4379
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3126,
+        3588
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3857,
+        3455
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        4259,
+        3742
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9720,
+        11415
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9703,
+        11788
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Transfected-infected cells transiently expressing mCherry-KDEL with TransIT-2020.
+::::
+
+:::::{figure}
+:label: figure11b
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-KDEL.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "+ mCherry-KDEL - reovirus T1L",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Contrast adjusted",
+      "zoom": -5.51,
+      "target": [
+        7316,
+        7316,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    },
+    {
+      "name": "mCherry-KDEL artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -1.064,
+      "target": [
+        12890,
+        1231,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "mCherry-KDEL artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -0.509,
+      "target": [
+        12090,
+        3861,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "mCherry-KDEL artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -1.065,
+      "target": [
+        6020,
+        11222,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-Climp63_T1L.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "+ mCherry-Climp63 + reovirus T1L",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Contrast adjusted",
+      "zoom": -5.51,
+      "target": [
+        7316,
+        7316,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
+        9881,
+        7352
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2115,
+        6584
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3260,
+        1602
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7022,
+        5940
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8256,
+        14363
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2198,
+        7793
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-CytERM_T1L.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "+ mCherry-CytERM + reovirus T1L",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Contrast adjusted",
+      "zoom": -5.51,
+      "target": [
+        7316,
+        7316,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            3500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    },
+    {
+      "name": "mCherry-CytERM artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -1.064,
+      "target": [
+        1409,
+        11175,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            3500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "mCherry-CytERM artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -1.064,
+      "target": [
+        5239,
+        11306,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            3500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    },
+    {
+      "name": "mCherry-CytERM artifacts",
+      "description": "ER remodeling without reovirus",
+      "zoom": -0.509,
+      "target": [
+        14118,
+        10449,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            3500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      }
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Uninfected",
+      "target": [
+        2738,
+        640
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6230,
+        1406
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8265,
+        896
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6366,
+        3056
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7309,
+        3193
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        12187,
+        3043
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        12457,
+        3114
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        12289,
+        4700
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11898,
+        5925
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11892,
+        6974
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11311,
+        6303
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10760,
+        6727
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8389,
+        5918
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8404,
+        6387
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5160,
+        6037
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5072,
+        5716
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        4606,
+        5775
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5018,
+        5338
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5170,
+        3207
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1409,
+        1077
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        63,
+        150
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        796,
+        3568
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        104,
+        5254
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        556,
+        6277
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        173,
+        7385
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1423,
+        11113
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        788,
+        11620
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        565,
+        11482
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1338,
+        13002
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1875,
+        13462
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2080,
+        13318
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5004,
+        12700
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6165,
+        12838
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9202,
+        13606
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        10661,
+        13233
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8905,
+        12377
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9324,
+        12131
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11323,
+        11191
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8315,
+        9167
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8558,
+        8592
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9092,
+        8623
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5174,
+        11329
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6261,
+        11434
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7557,
+        12108
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13251,
+        14108
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13921,
+        14581
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        14321,
+        13203
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13031,
+        11823
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14111,
+        2116
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10319,
+        2970
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8763,
+        870
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6889,
+        97
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5252,
+        569
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7196,
+        2693
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-Sec61b_T1L.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "+ mCherry-Sec61β + reovirus T1L",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Contrast adjusted",
+      "zoom": -5.51,
+      "target": [
+        7316,
+        7316,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          true,
+          true,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            255,
+            0,
+            255
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            0
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            1000
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ],
+          [
+            0,
+            2500
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
+        1594,
+        1084
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1629,
+        3398
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        594,
+        4335
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        677,
+        5416
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        451,
+        7189
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        924,
+        7666
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2600,
+        8712
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3598,
+        8718
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1936,
+        11131
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1277,
+        12128
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7573,
+        12645
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14547,
+        13731
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14296,
+        12365
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13415,
+        12017
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14475,
+        10511
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13581,
+        8741
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13284,
+        7291
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6203,
+        6854
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        9678,
+        3561
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        9018,
+        3886
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11016,
+        2089
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11375,
+        386
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5670,
+        1921
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6525,
+        2807
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        10571,
+        8940
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11205,
+        9416
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6993,
+        7969
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7850,
+        6195
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2200,
+        4802
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2703,
+        9138
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4250,
+        7955
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        856,
+        810
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2165,
+        372
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3095,
+        2167
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        82,
+        4500
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        250,
+        6610
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2270,
+        8665
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2660,
+        8324
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2935,
+        10007
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6223,
+        13507
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5812,
+        13001
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6164,
+        12811
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7159,
+        13725
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8677,
+        13301
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11948,
+        12145
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12368,
+        12421
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13905,
+        13260
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13517,
+        11692
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        14530,
+        9122
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13459,
+        7994
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13557,
+        7985
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13772,
+        3785
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13769,
+        2871
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13096,
+        126
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10262,
+        6664
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10557,
+        6525
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9350,
+        7170
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9587,
+        7468
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6588,
+        6651
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6601,
+        6943
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7005,
+        4604
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6521,
+        193
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5589,
+        1299
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5373,
+        847
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5223,
+        1534
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3144,
+        384
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3910,
+        9229
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8170,
+        480
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9779,
+        1036
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5588,
+        10264
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5685,
+        9125
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 11.2:** Additional mCherry-ER reporters and an uninfected control.
+:::::
+
+:::{dropdown} Methods
+Specimens were 35 mm glass-bottom dishes with 20 mm coverslips (no. 1.5 glass). 
+Reovirus μNS was detected with chicken α-μNS serum and AlexaFluor647 secondary antibodies, each incubated for one hour. 
+A 15×15 grid of 225 fields was recorded.
+:::
\ No newline at end of file
diff --git a/14_replicate_three.md b/14_replicate_three.md
new file mode 100644
index 000000000..c67f7b8f6
--- /dev/null
+++ b/14_replicate_three.md
@@ -0,0 +1,3640 @@
+---
+title: Experiment 3
+numbering:
+  enumerator: 12.%s
+---
+
+::::{figure}
+:label: figure12a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mCh-KDEL_T1L.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mCherry-KDEL",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Compare mCherry-ER",
+      "zoom": -0.72,
+      "target": [
+        9129,
+        3538,
+        0
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+      "appearance": {
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
+        99,
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6141,
+        3516
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Transfected-infected cells expressing mCherry-KDEL.
+::::
+
+:::::{figure}
+:label: figure12b
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mCh-Sec61b_T1L.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Sec1β",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
+      "name": "Infected",
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ],
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ],
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+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ]
+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
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+      ]
+    },
+    {
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+      ],
+      "color": [
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+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
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+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
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+      ]
+    },
+    {
+      "name": "Infected",
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+      "color": [
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+      ]
+    },
+    {
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+    },
+    {
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+      ]
+    },
+    {
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Uninfected",
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+      ],
+      "color": [
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+      ]
+    },
+    {
+      "name": "Infected",
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+      ],
+      "color": [
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+      ]
+    },
+    {
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+      ]
+    },
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+    },
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+    },
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+      ]
+    },
+    {
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+      ]
+    },
+    {
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+      ]
+    },
+    {
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+      ]
+    },
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+    },
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+      ]
+    },
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+      "color": [
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+      ]
+    },
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+      "name": "Infected",
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+    },
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+    },
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+      ]
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+    },
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+    },
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+      ]
+    },
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+    },
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+    },
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+      ]
+    },
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+    },
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+      ]
+    },
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+    },
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+      ]
+    },
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+      ]
+    },
+    {
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+      "color": [
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+      ]
+    },
+    {
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+      ]
+    },
+    {
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+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13735,
+        1233
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13898,
+        1576
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13794,
+        2155
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        14476,
+        3915
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13393,
+        4340
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14264,
+        4699
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14250,
+        4872
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        4823,
+        7316
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        4321,
+        6438
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3683,
+        6040
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6284,
+        14142
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6351,
+        14627
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        5842,
+        14088
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7236,
+        8480
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8762,
+        5569
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8342,
+        4460
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8478,
+        5207
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9808,
+        234
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9600,
+        39
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9993,
+        8181
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        10169,
+        8750
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9965,
+        8845
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8611,
+        10107
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6604,
+        2533
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mCh-CytERM_T1L.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 20,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
+        1350,
+        1928
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1145,
+        7357
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        250,
+        7669
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3709,
+        14083
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4149,
+        11877
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3080,
+        7303
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5511,
+        4412
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5471,
+        3945
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7073,
+        3042
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7513,
+        2262
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8357,
+        1659
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8126,
+        1253
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4681,
+        7683
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4779,
+        9267
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7387,
+        13068
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        7289,
+        11965
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8152,
+        11511
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6992,
+        7440
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        6026,
+        6912
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        9752,
+        1380
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        8963,
+        3590
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        9120,
+        6192
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        9475,
+        6200
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        13788,
+        8513
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        14111,
+        8201
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7553,
+        7194
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        934,
+        1224
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1840,
+        2275
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2211,
+        2452
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1695,
+        8175
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2816,
+        8396
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2635,
+        11165
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2491,
+        10668
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2819,
+        13318
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6949,
+        13077
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6994,
+        12354
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9734,
+        4554
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9767,
+        3929
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11130,
+        4602
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11236,
+        3661
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11970,
+        1020
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12180,
+        1396
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11997,
+        336
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11810,
+        32
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12964,
+        1561
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13687,
+        1009
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13975,
+        6365
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12975,
+        7105
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11802,
+        12147
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9010,
+        12613
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6491,
+        3327
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7842,
+        2315
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7902,
+        2015
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2209,
+        8272
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6376,
+        2190
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        8928,
+        4231
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10154,
+        4385
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10882,
+        7621
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13910,
+        6783
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13935,
+        268
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        5374,
+        7265
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6992,
+        14435
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 12.2:** Additional mCherry-ER reporters.
+:::::
+
+:::{dropdown} Methods
+Specimens were 35 mm glass-bottom dishes with 20 mm coverslips (no. 1.5 glass). 
+Reovirus μNS was detected with chicken α-μNS serum and AlexaFluor647 secondary antibodies. 
+Primary and secondary antibodies were each multiplexed and incubated overnight at 4°C in the dark. 
+A 15×15 grid of 225 fields was recorded.
+:::
\ No newline at end of file
diff --git a/15_replicate_four.md b/15_replicate_four.md
new file mode 100644
index 000000000..ac99a0d99
--- /dev/null
+++ b/15_replicate_four.md
@@ -0,0 +1,3660 @@
+---
+title: Experiment 4
+numbering:
+  enumerator: 13.%s
+---
+
+::::{figure}
+:label: figure13
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/replicate_4/badZDC.zarr",
+  "width": "500px",
+  "height": "500px",
+  "annotations": [
+    {
+      "name": "Infected",
+      "target": [
+        1396,
+        1594
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2048,
+        4496
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2358,
+        4955
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2737,
+        5506
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2253,
+        5783
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        1238,
+        6127
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        795,
+        5239
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2200,
+        8343
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        2406,
+        8601
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        405,
+        11538
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
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+    {
+      "name": "Infected",
+      "target": [
+        9735,
+        16620
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        10644,
+        16254
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9378,
+        17939
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        9926,
+        18400
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        10238,
+        20305
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        10222,
+        20678
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        10675,
+        23865
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11294,
+        23770
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12602,
+        18562
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12791,
+        19415
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        13168,
+        17886
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        12031,
+        4953
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        15737,
+        2041
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        16138,
+        1918
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        17264,
+        1567
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        19022,
+        4091
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        19808,
+        4904
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        20756,
+        3612
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        20472,
+        3882
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        20429,
+        4144
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20701,
+        6429
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        19913,
+        7496
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20159,
+        7869
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21231,
+        7440
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21795,
+        8119
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        20742,
+        8515
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        19555,
+        9972
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        18866,
+        10582
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        16088,
+        11703
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        15822,
+        13729
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        16064,
+        13701
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        15361,
+        14042
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        16059,
+        17861
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        17291,
+        19642
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20296,
+        23693
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20320,
+        24170
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20510,
+        22212
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        19813,
+        22344
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21213,
+        18268
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21304,
+        17831
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        18902,
+        17251
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        19029,
+        17035
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        18183,
+        14278
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        18297,
+        12409
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        20873,
+        10491
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21211,
+        10724
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        17935,
+        8824
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21028,
+        5514
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        21508,
+        6070
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        20261,
+        5459
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        23352,
+        4580
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        23525,
+        2214
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        24211,
+        716
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22716,
+        6778
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22982,
+        7373
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        22931,
+        10415
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        23171,
+        10663
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22612,
+        16664
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22782,
+        17542
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        22713,
+        18401
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        23084,
+        22115
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        24110,
+        23610
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        11483,
+        12520
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11436,
+        11865
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11730,
+        17554
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        11801,
+        17091
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        6760,
+        22829
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        7286,
+        22758
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        1040,
+        21957
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        2717,
+        11138
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        3561,
+        2170
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        3688,
+        2858
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        4878,
+        719
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        15768,
+        5951
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        16067,
+        5974
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        19362,
+        11020
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Infected",
+      "target": [
+        19492,
+        11556
+      ],
+      "color": [
+        235,
+        87,
+        87
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        19272,
+        9094
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    },
+    {
+      "name": "Uninfected",
+      "target": [
+        12851,
+        8471
+      ],
+      "color": [
+        99,
+        179,
+        237
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Duplicate sample of the transfected-infected cell model expressing mCherry-KDEL.
+::::
+
+:::{dropdown} Methods
+HeLa cells were seeded on 35 mm glass-bottom dishes with 20 mm coverslips (no. 1.5 glass). 
+Reovirus μNS was detected with chicken α-μNS serum and AlexaFluor647 secondary antibodies. 
+Primary antibodies were multiplexed and incubated overnight at 4°C in the dark. 
+Secondary antibodies were multiplexed and incubated over two consecutive nights. 
+A 25×25 grid of 625 fields was recorded. 
+Z-drift compensation malfunctioned in Figure 14.1 but functioned correctly in Figure 7.1. 
+Overlapping edges were blended to reduce the appearance of stitching artifacts.
+:::
\ No newline at end of file
diff --git a/16_antibody_reovirus.md b/16_antibody_reovirus.md
new file mode 100644
index 000000000..d8e94c446
--- /dev/null
+++ b/16_antibody_reovirus.md
@@ -0,0 +1,658 @@
+---
+title: μNS Antibody
+numbering:
+  enumerator: 14.%s
+label : antibody_page
+---
+
+This experiment used AlexaFluor647 secondary antibodies paired with the chicken α-μNS antiserum, which was used in all replication attempts. 
+The other used AlexaFluor594 secondary antibodies with both the chicken and guinea pig α-μNS antisera, so that illumination settings were consistent between the two antisera.
+
+## Chicken polyclonal antiserum
+
+::::{figure}
+:label: figure14a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_primary_secondary.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "+ Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -4.294,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Immunofluorescence imaging of infected cells with chicken α-μNS antisera.
+::::
+
+:::::{figure}
+:label: figure14b
+::::{grid} 3 3 3 3
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_primary.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "+ Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -5.554,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_secondary.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -5.554,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_mock.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -5.554,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 14.2:** Immunofluorescence antibody controls.
+:::::
+
+## Guinea pig polyclonal antiserum
+
+::::{figure}
+:label: figure14c
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_primary_secondary.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "+ Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -4.294,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Immunofluorescence imaging of infected cells with guinea pig α-μNS antisera.
+::::
+
+:::::{figure}
+:label: figure14d
+::::{grid} 3 3 3 3
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_primary.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "+ Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -5.554,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_secondary.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary + Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -5.554,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_mock.zarr",
+  "width": "210px",
+  "height": "210px",
+  "title": {
+    "text": "- Primary - Secondary",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 12,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Hide ER",
+      "description": "Calreticulin channel off",
+      "zoom": -5.554,
+      "target": [
+        4886,
+        4886,
+        0
+      ],
+      "appearance": {
+        "channelsVisible": [
+          false,
+          true,
+          true
+        ],
+        "channelColors": [
+          [
+            0,
+            255,
+            0
+          ],
+          [
+            0,
+            0,
+            255
+          ],
+          [
+            255,
+            0,
+            255
+          ]
+        ],
+        "contrastLimits": [
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ],
+          [
+            0,
+            4095
+          ]
+        ],
+        "blendMode": "single",
+        "colormap": "viridis"
+      },
+      "default": true
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 50,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 14.4:** Immunofluorescence antibody controls.
+:::::
+
+:::{tip}
+Hidden by default, evaluate the calreticulin channel for reported reovirus-induced ER remodeling.
+ER morphology can be compared within populations of infected cells.
+:::
+
+:::{dropdown} Methods
+HeLa cells were seeded on 12 mm diameter glass coverslips (1.0 glass) (1254580, Fisher) in 12-well plates (665180, Cellstar). 
+All samples were infected with reovirus T1L (MOI 100) for 24 hours. 
+AlexaFluor594 was used with the guinea pig α-μNS antiserum, while AlexaFluor647 was used with the chicken α-μNS antiserum. 
+Z-drift compensation malfunctioned, possibly due to the specimen, resulting in visible stitching artifacts. 
+Overlapping tiles were cropped rather than blended to preserve these artifacts as evidence of the autofocus failure.
+:::
diff --git a/17_saturated_maps.md b/17_saturated_maps.md
new file mode 100644
index 000000000..7a0aa5d9a
--- /dev/null
+++ b/17_saturated_maps.md
@@ -0,0 +1,298 @@
+---
+title: Saturated Maps
+numbering:
+  enumerator: 15.%s
+label : saturated_page
+---
+
+Illumination settings were set based on the mCherry-KDEL sample with TransIT-2020, resulting in pixel saturation in some samples. 
+Parker identified the saturated signal and advised repeating the experiment. 
+The original maps are preserved here (Figure 15.1).
+
+## Lipofectamine 3000 (left) and TransIT-2020 (right)
+
+:::::{figure}
+:label: figure15
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-KDEL.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-KDEL",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        7507,
+        3372,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-KDEL.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-KDEL",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        5703,
+        1706,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-CytERM.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        6561,
+        5919,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-CytERM.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-CytERM",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        5346,
+        5514,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-Sec61b.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Sec61β",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        5296,
+        5118,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-Sec61b.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Sec61β",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        7222,
+        3355,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-Climp63.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Climp63",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        4553,
+        6248,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-Climp63.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "mCherry-Climp63",
+    "position": "bottom-left",
+    "margin": 12,
+    "fontSize": 22,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "views": [
+    {
+      "name": "Zoom to ER remodeling",
+      "zoom": 0.074,
+      "target": [
+        6622,
+        1307,
+        0
+      ]
+    }
+  ],
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 15.1:** Original transfection experiment showing the distributions of mCherry-ER reporters.
+:::::
+
+:::{dropdown} Methods
+HeLa cells were seeded on 12 mm diameter glass coverslips (1.0 glass) (1254580, Fisher) in 12-well plates (665180, Cellstar). 
+Lipofectamine 3000 (L3000015, Invitrogen) volumes were based on 12-well plates: 62.5 µl Opti-MEM (31985-062, Gibco) was mixed with 2.5 µl P3000 reagent and 1.25 µg DNA, and a separate tube of 62.5 µl Opti-MEM was mixed with 1.875 µl Lipofectamine 3000 reagent. 
+The tubes were combined, incubated for 20 minutes, then added dropwise to the samples. 
+TransIT-2020 reagent (MIR 5400, Mirus) volumes were based on 12-well plates: 125 µl Opti-MEM was mixed with 1.25 µg DNA and mixed gently, then 3.75 µl TransIT-2020 was added and mixed, followed by 20 minutes of incubation and dropwise addition to the samples.
+:::
\ No newline at end of file
diff --git a/18_discarded_maps.md b/18_discarded_maps.md
new file mode 100644
index 000000000..76989cde4
--- /dev/null
+++ b/18_discarded_maps.md
@@ -0,0 +1,150 @@
+---
+title: Discarded Maps
+numbering:
+  enumerator: 16.%s
+label : discarded_page
+---
+
+In this experiment, fixed specimens were refrigerated and select maps were reimaged to address technical issues such as air bubbles in the immersion oil or pixel saturation in the mCherry channel. 
+Some maps resampled the same location with different illumination settings, while others were new locations on the specimen. 
+Maps not used in the main figure are shown here.
+
+::::{figure}
+:label: figure16a
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_kdel_Cycle_01.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mCherry-KDEL (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Pixel saturation in the mCherry channel.
+::::
+
+::::{figure}
+:label: figure16b
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_kdel_Cycle.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mCherry-KDEL (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Pixel saturation in the mCherry channel and an air bubble.
+::::
+
+::::{figure}
+:label: figure16c
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_sec61_Cycle_01.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mCherry-Sec61β (Lipofectamine)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Pixel saturation in the mCherry channel.
+::::
+
+::::{figure}
+:label: figure16d
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_cytER_Cycle_01.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mCherry-CytERM (Trans-IT 2020)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Air bubbles.
+::::
+
+::::{figure}
+:label: figure16e
+:::{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_sec61b_Cycle_04.zarr",
+  "width": "500px",
+  "height": "500px",
+  "title": {
+    "text": "mCherry-Sec61β (Trans-IT 2020)",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 24,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+:::
+Air bubble.
+::::
diff --git a/19_archived_maps.md b/19_archived_maps.md
new file mode 100644
index 000000000..e0160bad7
--- /dev/null
+++ b/19_archived_maps.md
@@ -0,0 +1,109 @@
+---
+title: Archived Maps
+numbering:
+  enumerator: 17.%s
+label : archival_page
+---
+
+We were interested in the finding that reovirus μNS colocalized with the ER [@doi:10.1128/mBio.01253-18], so we performed experiments with moxGFP::μNS and mCherry-ER reporters.
+The results were unclear, so this research direction was abandoned.
+Fortunately, overlapping tiles were recorded in an experiment dated _September 14 2022_.
+This data was stitched and converted to OME-Zarr for reinterpretation as interactive microscopy maps (Figure 17.1).
+
+:::::{figure}
+:label: figure17
+::::{grid} 2 2 2 2
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/archival/FLclimp.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "moxGFP::μNS(1-721) + mCherry-Climp63",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/archival/FLsec61b.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "moxGFP::μNS(1-721) + mCherry-Sec61β",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/archival/MRclimp.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "moxGFP::μNS(472-721) + mCherry-Climp63",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+```{any:bundle} https://LadInTheLab.github.io/microATLAS-widget/widget.js
+{
+  "source": "https://nyu1.osn.mghpcc.org/barkley-replication/archival/MRsec61b.zarr",
+  "width": "323px",
+  "height": "323px",
+  "title": {
+    "text": "moxGFP::μNS(472-721) + mCherry-Sec61β",
+    "position": "top-center",
+    "margin": 12,
+    "fontSize": 14,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.95)",
+    "style": "text"
+  },
+  "scaleBar": {
+    "maxWidth": 100,
+    "position": "bottom-right",
+    "fontSize": 10,
+    "font": "Arial",
+    "color": "rgba(255,255,255,0.9)"
+  }
+}
+```
+::::
+**Figure 17.1:** Reinterpretation of archived μNS–ER colocalization data as interactive microscopy maps.
\ No newline at end of file
diff --git a/20_metadata.md b/20_metadata.md
new file mode 100644
index 000000000..8fbad4ebf
--- /dev/null
+++ b/20_metadata.md
@@ -0,0 +1,119 @@
+---
+title: Metadata Availability
+numbering:
+  enumerator: 18.%s
+label : metadata_page
+---
+
+## Instructions
+
+Each dataset is stored as an OME-Zarr on the Open Storage Network (https://doi.org/10.6075/J0RR1ZCR).
+Acquisition metadata extracted from the Olympus .oir files using Bio-Formats is available as a JSON file within each Zarr store.
+Click any link to view the acquisition parameters directly in your browser, or right-click and copy the link address to use the URL directly.
+
+### Plasmids
+- Figure 3.1: [mCherry-KDEL, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_kdel_Cycle_02.zarr/metadata.json)
+- Figure 3.1: [mCherry-KDEL, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_KDEL_Cycle_02.zarr/metadata.json)
+- Figure 3.1: [mCherry-CytERM, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_cytER_Cycle.zarr/metadata.json)
+- Figure 3.1: [mCherry-CytERM, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_cyter_Cycle_03.zarr/metadata.json)
+- Figure 3.1: [mCherry-Sec61β, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_sec61_Cycle_02.zarr/metadata.json)
+- Figure 3.1: [mCherry-Sec61β, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_sec_Cycle_01.zarr/metadata.json)
+- Figure 3.1: [mCherry-Climp63, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_climp_Cycle.zarr/metadata.json)
+- Figure 3.1: [mCherry-Climp63, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_climp_Cycle_01.zarr/metadata.json)
+
+### Virus
+- Figure 4.1: [Reovirus T1L MOI 0](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi0.zarr/metadata.json)
+- Figure 4.1: [Reovirus T1L MOI 1](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi1.zarr/metadata.json)
+- Figure 4.1: [Reovirus T1L MOI 10](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi10.zarr/metadata.json)
+- Figure 4.1: [Reovirus T1L MOI 100](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t1lmoi100.zarr/metadata.json)
+- Figure 4.1: [Reovirus T3D MOI 0](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi0.zarr/metadata.json)
+- Figure 4.1: [Reovirus T3D MOI 1](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi1.zarr/metadata.json)
+- Figure 4.1: [Reovirus T3D MOI 10](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi10.zarr/metadata.json)
+- Figure 4.1: [Reovirus T3D MOI 100](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_moi/t3dmoi100.zarr/metadata.json)
+
+### Calreticulin Antibody
+- Figure 5.1: [+ Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/primary_secondary.zarr/metadata.json)
+- Figure 5.2: [+ Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/primary.zarr/metadata.json)
+- Figure 5.2: [- Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/secondary.zarr/metadata.json)
+- Figure 5.2: [- Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/calreticulin_antibody/mock.zarr/metadata.json)
+
+### Reovirus μNS Antibody
+- Figure 6.1: [Chicken Antiserum: + Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_primary_secondary_Cycle_02.zarr/metadata.json)
+- Figure 6.2: [Chicken Antiserum: + Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_primary_Cycle_02.zarr/metadata.json)
+- Figure 6.2: [Chicken Antiserum: - Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_secondary_Cycle.zarr/metadata.json)
+- Figure 6.2: [Chicken Antiserum: - Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/chicken_noantibody_Cycle.zarr/metadata.json)
+- Figure 6.3: [Guinea Pig Antiserum: + Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_primary_secondary_Cycle_01.zarr/metadata.json)
+- Figure 6.4: [Guinea Pig Antiserum: + Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_primary_Cycle_02.zarr/metadata.json)
+- Figure 6.4: [Guinea Pig Antiserum: - Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_secondary_Cycle.zarr/metadata.json)
+- Figure 6.4: [Guinea Pig Antiserum: - Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/antibody_repeat/guineapig_noantibody_Cycle.zarr/metadata.json)
+
+### Results
+- Figure 7.1: [Replication Experiment](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_4/goodZDC.zarr/metadata.json)
+
+### OSER Assay
+- Figure 8.1: [Dendra2-CytERM](https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/dendra2.zarr/metadata.json)
+- Figure 8.1: [FusionRed-CytERM](https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/fusionred.zarr/metadata.json)
+- Figure 8.1: [mCherry-CytERM](https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/mcherry.zarr/metadata.json)
+- Figure 8.1: [mEGFP-CytERM](https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/megfp.zarr/metadata.json)
+- Figure 8.1: [mNeonGreen-CytERM](https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/mneongreen.zarr/metadata.json)
+- Figure 8.1: [moxGFP-CytERM](https://nyu1.osn.mghpcc.org/barkley-replication/oser_assay/moxgfp.zarr/metadata.json)
+
+### Video
+- Figure 9.1: [mNeonGreen::μNS + mCherry-KDEL](https://nyu1.osn.mghpcc.org/barkley-replication/video.zarr/metadata.json)
+
+### Replication Attempt 1
+- Figure 10.1: [Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_1/Transit.zarr/metadata.json)
+- Figure 10.2: [Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_1/LF3000.zarr/metadata.json)
+- Figure 10.3: [Untransfected](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_1/Untransfected.zarr/metadata.json)
+
+### Replication Attempt 2
+- Figure 11.1: [+ mCherry-KDEL + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-KDEL_T1L.zarr/metadata.json)
+- Figure 11.2: [+ mCherry-KDEL - Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-KDEL.zarr/metadata.json)
+- Data not shown: [- mCherry-KDEL + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/T1L.zarr/metadata.json)
+- Data not shown: [- mCherry-KDEL - Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mock.zarr/metadata.json)
+- Figure 11.2: [+ mCherry-Sec61β + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-Sec61b_T1L.zarr/metadata.json)
+- Figure 11.2: [+ mCherry-CytERM + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-CytERM_T1L.zarr/metadata.json)
+- Figure 11.2: [+ mCherry-Climp63 + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_2/mCh-Climp63_T1L.zarr/metadata.json)
+
+### Replication Attempt 3
+- Figure 12.1: [+ mCherry-KDEL + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mCh-KDEL_T1L.zarr/metadata.json)
+- Figure 12.2: [+ mCherry-CytERM + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mCh-CytERM_T1L.zarr/metadata.json)
+- Figure 12.2: [+ mCherry-Sec61β + Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mCh-Sec61b_T1L.zarr/metadata.json)
+- Data not shown: [Reovirus](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/T1L.zarr/metadata.json)
+- Data not shown: [Mock](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_3/mock.zarr/metadata.json)
+
+### Replication Attempt 4
+- Figure 13.1: [Replication Experiment](https://nyu1.osn.mghpcc.org/barkley-replication/replicate_4/badZDC.zarr/metadata.json)
+
+### μNS Antibody Repetition
+- Figure 14.1: [Chicken Antiserum: + Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_primary_secondary.zarr/metadata.json)
+- Figure 14.2: [Chicken Antiserum: + Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_primary.zarr/metadata.json)
+- Figure 14.2: [Chicken Antiserum: - Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_secondary.zarr/metadata.json)
+- Figure 14.2: [Chicken Antiserum: - Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/chix_mock.zarr/metadata.json)
+- Figure 14.3: [Guinea Pig Antiserum: + Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_primary_secondary.zarr/metadata.json)
+- Figure 14.4: [Guinea Pig Antiserum: + Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_primary.zarr/metadata.json)
+- Figure 14.4: [Guinea Pig Antiserum: - Primary + Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_secondary.zarr/metadata.json)
+- Figure 14.4: [Guinea Pig Antiserum: - Primary - Secondary](https://nyu1.osn.mghpcc.org/barkley-replication/reovirus_antibody/gp_mock.zarr/metadata.json)
+
+### Saturated Maps
+- Figure 15.1: [mCherry-KDEL, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-KDEL.zarr/metadata.json)
+- Figure 15.1: [mCherry-KDEL, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-KDEL.zarr/metadata.json)
+- Figure 15.1: [mCherry-CytERM, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-CytERM.zarr/metadata.json)
+- Figure 15.1: [mCherry-CytERM, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-CytERM.zarr/metadata.json)
+- Figure 15.1: [mCherry-Sec61β, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-Sec61b.zarr/metadata.json)
+- Figure 15.1: [mCherry-Sec61β, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-Sec61b.zarr/metadata.json)
+- Figure 15.1: [mCherry-Climp63, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/LF3000_mCh-Climp63.zarr/metadata.json)
+- Figure 15.1: [mCherry-Climp63, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/mCherry-ER_plasmids/Transit_mCh-Climp63.zarr/metadata.json)
+
+### Discarded Maps
+- Figure 16.1: [mCherry-KDEL, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_kdel_Cycle_01.zarr/metadata.json)
+- Figure 16.2: [mCherry-KDEL, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_kdel_Cycle.zarr/metadata.json)
+- Figure 16.3: [mCherry-Sec61β, Lipofectamine 3000](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/lf3000_sec61_Cycle_01.zarr/metadata.json)
+- Figure 16.4: [mCherry-CytERM, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_cytER_Cycle_01.zarr/metadata.json)
+- Figure 16.5: [mCherry-Sec61β, Trans-IT 2020](https://nyu1.osn.mghpcc.org/barkley-replication/plasmid_repeat/transit_sec61b_Cycle_04.zarr/metadata.json)
+
+### Archived Maps
+- Figure 17.1: [mCherry-Climp63 + moxGFP::μNS(1-721)](https://nyu1.osn.mghpcc.org/barkley-replication/archival/FLclimp.zarr/metadata.json)
+- Figure 17.1: [mCherry-Climp63 + moxGFP::μNS(472-721)](https://nyu1.osn.mghpcc.org/barkley-replication/archival/MRclimp.zarr/metadata.json)
+- Figure 17.1: [mCherry-Sec61β + moxGFP::μNS(1-721)](https://nyu1.osn.mghpcc.org/barkley-replication/archival/FLsec61b.zarr/metadata.json)
+- Figure 17.1: [mCherry-Sec61β + moxGFP::μNS(472-721)](https://nyu1.osn.mghpcc.org/barkley-replication/archival/MRsec61b.zarr/metadata.json)
diff --git a/Dockerfile b/Dockerfile
index 848221d8d..cb8408a53 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,16 +4,28 @@ FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim
 # uv optimization env variables
 ENV UV_COMPILE_BYTECODE=1
 ENV UV_SYSTEM_PYTHON=1
+ENV UV_LINK_MODE=copy
 
-# install deps (system is safe, already isolated)
-COPY ./requirements.in .
-RUN uv pip install -r requirements.in
+# some additional compilation libs
+USER root
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential cmake zlib1g-dev liblz4-dev libblosc-dev pkg-config \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy only dependency metadata
+COPY requirements.txt .
+
+# Sync dependencies into system Python (no project install, no editable)
+RUN uv pip install --no-cache-dir -r requirements.txt
+
+# Remove build-only packages
+RUN apt-get purge -y --auto-remove build-essential cmake pkg-config
 
 # create user with a home directory for binder
 ARG NB_USER
 ARG NB_UID
-ENV USER ${NB_USER}
-ENV HOME /home/${NB_USER}
+ENV USER=${NB_USER}
+ENV HOME=/home/${NB_USER}
 
 RUN adduser --disabled-password \
     --gecos "Default user" \
diff --git a/README.md b/README.md
index e62addb9f..732e2a202 100644
--- a/README.md
+++ b/README.md
@@ -1,58 +1,7 @@
-# Elemental Microscopy Quickstart
+# Reimag(in)ing representative images
 
-This repository contains minimal configuration files to get started for an `Elemental Microscopy` article.
+Web-based microscopy maps as an unbiased method to replicate highly magnified representative microscopy images.
 
-## People
+![Visual abstract depicting virtual microscopy as a novel interface in scientific publications.](figures/summary.png)
 
-This quickstart guide was developed by [Colin Ophus](mailto:cophus@gmail.com) and [Georgios Varnavides](mailto:gvarnavides@berkeley.edu). 
-
-## Installation Instructions
-
-### Edit locally and push changes
-
-> [!WARNING]
-> First, ask one of the organization members to give you `write` access to the forked repo.
-
-> [!WARNING]
-> In what follows and the configuration files, you will see references to `change-to-repo-name`, which you should replace with the repo name. E.g. this article would be `em-quickstart`.
-
-Steps to edit locally:
-- git clone repo (`git clone git@github.com:msa-em/change-to-repo-name.git`)
-- switch to `dev` branch (`git checkout dev`)
-- edit `environment.yml` to
-  - change the environment `name` to the repo name
-  - add your package dependencies, pinning versions as necessary
-- create virtual environment (`conda env create -f environment.yml`)
-  - you might need to remove the environment if it already exists (`conda remove -n change-to-repo-name --all`)
-- activate virtual environment in two terminal windows (`conda activate change-to-repo-name`)
-- edit `myst.yml` file by:
-  - commenting these lines out
-  ```yml
-    jupyter: true
-  ```
-  - uncommenting these lines
-  ```yml
-  #  jupyter:
-    #    server:
-    #      url: 'http://localhost:8888'
-    #      token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c'
-  ```
-- start the jupyter server in one of the terminal windows (`jupyter lab --IdentityProvider.token=512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c --ServerApp.allow_origin='http://localhost:3000' --port=8888`)
-- start MyST in the other terminal window (`myst start`)
-- edit, commit, and push to `dev` as per usual
-  - make sure **NOT** to commit your `must.yml` changes!
-- open a draft pull request into `main` (if one doesn't already exist) and keep pushing your changes to `dev`
-  - this will enable live previews and checks (see below)
-  - when you're ready, merge pull request into `main`. Note this need to be "final" -- it's best practice to merge thematic content changes together
-
-> [!NOTE]
-> If you don't plan on editing the notebooks, you can skip the `myst.yml` and `jupyter lab` steps above
-
-## Preview draft deployed site
-The repo has two github actions to automatically deploy computational sites, for the following two case:
-- Commits to `main` directly
-- Pull requests into `main`
-
-If you followed the instructions above (i.e. working off of `dev` and have an open pull request into `main`), then you should see a `github-actions` bot at the top of your pull request which will keep getting edited.
-Simply click on the `Inspect` link to see the curvenote staging site, and press `preview` to see the deployed site based on your latest commit.
 
diff --git a/data/mnist-ae/decoder_weights.h5 b/data/mnist-ae/decoder_weights.h5
deleted file mode 100644
index db80cd570..000000000
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diff --git a/data/mnist-ae/encoder_weights.h5 b/data/mnist-ae/encoder_weights.h5
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index 2729d4a5b..000000000
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diff --git a/data/mnist-ae/training_log.csv b/data/mnist-ae/training_log.csv
deleted file mode 100644
index fa007b55a..000000000
--- a/data/mnist-ae/training_log.csv
+++ /dev/null
@@ -1,101 +0,0 @@
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diff --git a/data/nucleus-ae/results.csv b/data/nucleus-ae/results.csv
deleted file mode 100644
index f299f123c..000000000
--- a/data/nucleus-ae/results.csv
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diff --git a/data/nucleus-ae/training_log.csv b/data/nucleus-ae/training_log.csv
deleted file mode 100644
index 54f989e91..000000000
--- a/data/nucleus-ae/training_log.csv
+++ /dev/null
@@ -1,51 +0,0 @@
-epoch,loss,mse,val_loss,val_mse
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diff --git a/environment.yml b/environment.yml
index e8292f8a0..dbce09d02 100644
--- a/environment.yml
+++ b/environment.yml
@@ -16,7 +16,4 @@ dependencies:
   - jupyterlab-myst
   - 'nodejs>=20,<21'
   - mystmd
-  - tifffile
-  - tensorflow
-  - pillow
   - vizarr 
\ No newline at end of file
diff --git a/figures/Virtual_microscopy.png b/figures/Virtual_microscopy.png
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diff --git a/figures/summary.png b/figures/summary.png
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diff --git a/myst.doi.bib b/myst.doi.bib
new file mode 100644
index 000000000..9e1e43e20
--- /dev/null
+++ b/myst.doi.bib
@@ -0,0 +1,292 @@
+% AUTOGENERATED FILE - EDITS MAY BE LOST
+% To regenerate, run `myst build --doi-bib`
+
+@article{Broering_2005,
+	author = {Broering, Teresa J. and Arnold, Michelle M. and Miller, Cathy L. and Hurt, Jessica A. and Joyce, Patricia L. and Nibert, Max L.},
+	journal = {Journal of Virology},
+	doi = {10.1128/jvi.79.10.6194-6206.2005},
+	issn = {1098-5514},
+	number = {10},
+	year = {2005},
+	month = {5},
+	pages = {6194--6206},
+	publisher = {American Society for Microbiology},
+	title = {Carboxyl-{Proximal} {Regions} of {Reovirus} {Nonstructural} {Protein} $\mu${NS} {Necessary} and {Sufficient} for {Forming} {Factory}-{Like} {Inclusions}},
+	url = {http://dx.doi.org/10.1128/JVI.79.10.6194-6206.2005},
+	volume = {79},
+}
+
+
+@article{Moore_2023,
+	author = {Moore, Josh and Basurto-Lozada, Daniela and Besson, S{\' e}bastien and Bogovic, John and Bragantini, Jord{\~ a}o and Brown, Eva M. and Burel, Jean-Marie and Casas Moreno, Xavier and de Medeiros, Gustavo and Diel, Erin E. and Gault, David and Ghosh, Satrajit S. and Gold, Ilan and Halchenko, Yaroslav O. and Hartley, Matthew and Horsfall, Dave and Keller, Mark S. and Kittisopikul, Mark and Kovacs, Gabor and K{\" u}pc{\" u} Yolda{\c s}, Ayb{\" u}ke and Kyoda, Koji and le Tournoulx de la Villegeorges, Albane and Li, Tong and Liberali, Prisca and Lindner, Dominik and Linkert, Melissa and L{\" u}thi, Joel and Maitin-Shepard, Jeremy and Manz, Trevor and Marconato, Luca and McCormick, Matthew and Lange, Merlin and Mohamed, Khaled and Moore, William and Norlin, Nils and Ouyang, Wei and {\" O}zdemir, Bugra and Palla, Giovanni and Pape, Constantin and Pelkmans, Lucas and Pietzsch, Tobias and Preibisch, Stephan and Prete, Martin and Rzepka, Norman and Samee, Sameeul and Schaub, Nicholas and Sidky, Hythem and Solak, Ahmet Can and Stirling, David R. and Striebel, Jonathan and Tischer, Christian and Toloudis, Daniel and Virshup, Isaac and Walczysko, Petr and Watson, Alan M. and Weisbart, Erin and Wong, Frances and Yamauchi, Kevin A. and Bayraktar, Omer and Cimini, Beth A. and Gehlenborg, Nils and Haniffa, Muzlifah and Hotaling, Nathan and Onami, Shuichi and Royer, Loic A. and Saalfeld, Stephan and Stegle, Oliver and Theis, Fabian J. and Swedlow, Jason R.},
+	journal = {Histochemistry and Cell Biology},
+	doi = {10.1007/s00418-023-02209-1},
+	issn = {1432-119X},
+	number = {3},
+	year = {2023},
+	month = {7},
+	pages = {223--251},
+	publisher = {Springer Science},
+	title = {OME-{Zarr}: a cloud-optimized bioimaging file format with international community support},
+	url = {http://dx.doi.org/10.1007/s00418-023-02209-1},
+	volume = {160},
+}
+
+
+@article{Wohlmann_2024,
+	author = {Wohlmann, Jens},
+	journal = {Journal of Cell Science},
+	doi = {10.1242/jcs.262198},
+	issn = {1477-9137},
+	number = {20},
+	year = {2024},
+	month = {10},
+	publisher = {The Company of Biologists},
+	title = {Expanding the field of view -- a simple approach for interactive visualisation of electron microscopy data},
+	url = {http://dx.doi.org/10.1242/jcs.262198},
+	volume = {137},
+}
+
+
+@article{Tenorio2018Reovirus,
+	author = {Tenorio, Raquel and Fern{\' a}ndez de Castro, Isabel and Knowlton, Jonathan J. and Zamora, Paula F. and Lee, Christopher H. and Mainou, Bernardo A. and Dermody, Terence S. and Risco, Cristina},
+	journal = {mBio},
+	doi = {10.1128/mbio.01253-18},
+	editor = {Griffin, Diane E.},
+	issn = {2161-2129},
+	number = {4},
+	year = {2018},
+	month = {sep 5},
+	publisher = {American Society for Microbiology},
+	title = {Reovirus $\sigma${NS} and $\mu${NS} {Proteins} {Remodel} the {Endoplasmic} {Reticulum} to {Build} {Replication} {Neo}-{Organelles}},
+	url = {http://dx.doi.org/10.1128/mBio.01253-18},
+	volume = {9},
+}
+
+
+@article{Snapp_2003,
+	author = {Snapp, Erik L. and Hegde, Ramanujan S. and Francolini, Maura and Lombardo, Francesca and Colombo, Sara and Pedrazzini, Emanuela and Borgese, Nica and Lippincott-Schwartz, Jennifer},
+	journal = {The Journal of Cell Biology},
+	doi = {10.1083/jcb.200306020},
+	issn = {0021-9525},
+	number = {2},
+	year = {2003},
+	month = {10},
+	pages = {257--269},
+	publisher = {Rockefeller University Press},
+	title = {Formation of stacked {ER} cisternae by low affinity protein interactions},
+	url = {http://dx.doi.org/10.1083/jcb.200306020},
+	volume = {163},
+}
+
+
+@article{Cranfill_2016,
+	author = {Cranfill, Paula J and Sell, Brittney R and Baird, Michelle A and Allen, John R and Lavagnino, Zeno and de Gruiter, H Martijn and Kremers, Gert-Jan and Davidson, Michael W and Ustione, Alessandro and Piston, David W},
+	journal = {Nature Methods},
+	doi = {10.1038/nmeth.3891},
+	issn = {1548-7105},
+	number = {7},
+	year = {2016},
+	month = {5},
+	pages = {557--562},
+	publisher = {Springer Science},
+	title = {Quantitative assessment of fluorescent proteins},
+	url = {http://dx.doi.org/10.1038/nmeth.3891},
+	volume = {13},
+}
+
+
+@inproceedings{Boerner_2023,
+	author = {Boerner, Timothy J. and Deems, Stephen and Furlani, Thomas R. and Knuth, Shelley L. and Towns, John},
+	series = {PEARC \textquoteright{}23},
+	booktitle = {Practice and {Experience} in {Advanced} {Research} {Computing}},
+	doi = {10.1145/3569951.3597559},
+	year = {2023},
+	pages = {173--176},
+	organization = {ACM},
+	title = {ACCESS: Advancing {Innovation}: NSF\textquoteright{}s {Advanced} {Cyberinfrastructure} {Coordination} {Ecosystem}: Services \&amp; {Support}},
+	url = {http://dx.doi.org/10.1145/3569951.3597559},
+}
+
+
+@article{Marqu_s_2020,
+	author = {Marqu{\' e}s, Guillermo and Pengo, Thomas and Sanders, Mark A},
+	journal = {eLife},
+	doi = {10.7554/elife.55133},
+	issn = {2050-084X},
+	year = {2020},
+	month = {8},
+	publisher = {eLife Sciences Publications, Ltd},
+	title = {Imaging methods are vastly underreported in biomedical research},
+	url = {http://dx.doi.org/10.7554/eLife.55133},
+	volume = {9},
+}
+
+
+@article{Lee_2024,
+	author = {Lee, Rachel M. and Eisenman, Leanna R. and Khuon, Satya and Aaron, Jesse S. and Chew, Teng-Leong},
+	journal = {Journal of Cell Science},
+	doi = {10.1242/jcs.261567},
+	issn = {1477-9137},
+	number = {1},
+	year = {2024},
+	month = {1},
+	publisher = {The Company of Biologists},
+	title = {Believing is seeing -- the deceptive influence of bias in quantitative microscopy},
+	url = {http://dx.doi.org/10.1242/jcs.261567},
+	volume = {137},
+}
+
+
+@article{Faas_2012,
+	author = {Faas, Frank G.A. and Avramut, M. Cristina and M. van den Berg, Bernard and Mommaas, A. Mieke and Koster, Abraham J. and Ravelli, Raimond B.G.},
+	journal = {Journal of Cell Biology},
+	doi = {10.1083/jcb.201201140},
+	issn = {0021-9525},
+	number = {3},
+	year = {2012},
+	month = {8},
+	pages = {457--469},
+	publisher = {Rockefeller University Press},
+	title = {Virtual nanoscopy: Generation of ultra-large high resolution electron microscopy maps},
+	url = {http://dx.doi.org/10.1083/jcb.201201140},
+	volume = {198},
+}
+
+
+@article{Cobey_2024,
+	author = {Cobey, Kelly D. and Ebrahimzadeh, Sanam and Page, Matthew J. and Thibault, Robert T. and Nguyen, Phi-Yen and Abu-Dalfa, Farah and Moher, David},
+	journal = {PLOS Biology},
+	doi = {10.1371/journal.pbio.3002870},
+	editor = {Lino de Oliveira, Cilene},
+	issn = {1545-7885},
+	number = {11},
+	year = {2024},
+	month = {11},
+	pages = {e3002870},
+	publisher = {Public Library of Science (PLoS)},
+	title = {Biomedical researchers\textquoteright{} perspectives on the reproducibility of research},
+	url = {http://dx.doi.org/10.1371/journal.pbio.3002870},
+	volume = {22},
+}
+
+
+@article{Begley2012Raise,
+	author = {Begley, C. Glenn and Ellis, Lee M.},
+	journal = {Nature},
+	doi = {10.1038/483531a},
+	issn = {0028-0836},
+	number = {7391},
+	year = {2012},
+	month = {mar 28},
+	pages = {531--533},
+	publisher = {{Springer Science and Business Media LLC}},
+	title = {Raise standards for preclinical cancer research},
+	url = {http://dx.doi.org/10.1038/483531a},
+	volume = {483},
+}
+
+
+@article{Prinz_2011,
+	author = {Prinz, Florian and Schlange, Thomas and Asadullah, Khusru},
+	journal = {Nature Reviews Drug Discovery},
+	doi = {10.1038/nrd3439-c1},
+	issn = {1474-1784},
+	number = {9},
+	year = {2011},
+	month = {8},
+	pages = {712--712},
+	publisher = {Springer Science},
+	title = {Believe it or not: how much can we rely on published data on potential drug targets?},
+	url = {http://dx.doi.org/10.1038/nrd3439-c1},
+	volume = {10},
+}
+
+
+@article{Errington_2021,
+	author = {Errington, Timothy M and Mathur, Maya and Soderberg, Courtney K and Denis, Alexandria and Perfito, Nicole and Iorns, Elizabeth and Nosek, Brian A},
+	journal = {eLife},
+	doi = {10.7554/elife.71601},
+	issn = {2050-084X},
+	year = {2021},
+	month = {12},
+	publisher = {eLife Sciences Publications, Ltd},
+	title = {Investigating the replicability of preclinical cancer biology},
+	url = {http://dx.doi.org/10.7554/eLife.71601},
+	volume = {10},
+}
+
+
+@article{Manz_2022,
+	author = {Manz, Trevor and Gold, Ilan and Patterson, Nathan Heath and McCallum, Chuck and Keller, Mark S. and Herr, Bruce W. and B{\" o}rner, Katy and Spraggins, Jeffrey M. and Gehlenborg, Nils},
+	journal = {Nature Methods},
+	doi = {10.1038/s41592-022-01482-7},
+	issn = {1548-7105},
+	number = {5},
+	year = {2022},
+	month = {5},
+	pages = {515--516},
+	publisher = {Springer Science},
+	title = {Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web},
+	url = {http://dx.doi.org/10.1038/s41592-022-01482-7},
+	volume = {19},
+}
+
+
+@article{Parker_2002,
+	author = {Parker, John S. L. and Broering, Teresa J. and Kim, Jonghwa and Higgins, Darren E. and Nibert, Max L.},
+	journal = {Journal of Virology},
+	doi = {10.1128/jvi.76.9.4483-4496.2002},
+	issn = {1098-5514},
+	number = {9},
+	year = {2002},
+	month = {5},
+	pages = {4483--4496},
+	publisher = {American Society for Microbiology},
+	title = {Reovirus {Core} {Protein} $\mu$2 {Determines} the {Filamentous} {Morphology} of {Viral} {Inclusion} {Bodies} by {Interacting} with and {Stabilizing} {Microtubules}},
+	url = {http://dx.doi.org/10.1128/JVI.76.9.4483-4496.2002},
+	volume = {76},
+}
+
+
+@article{Roebke_2019,
+	author = {Roebke, Katherine E. and Danthi, Pranav},
+	journal = {Journal of Virology},
+	doi = {10.1128/jvi.00199-19},
+	editor = {Dermody, Terence S.},
+	issn = {1098-5514},
+	number = {11},
+	year = {2019},
+	month = {6},
+	publisher = {American Society for Microbiology},
+	title = {Cell {Entry}-{Independent} {Role} for the {Reovirus} $\mu$1 {Protein} in {Regulating} {Necroptosis} and the {Accumulation} of {Viral} {Gene} {Products}},
+	url = {http://dx.doi.org/10.1128/jvi.00199-19},
+	volume = {93},
+}
+
+
+@article{Costantini_2012,
+	author = {Costantini, Lindsey M. and Fossati, Matteo and Francolini, Maura and Snapp, Erik Lee},
+	journal = {Traffic},
+	doi = {10.1111/j.1600-0854.2012.01336.x},
+	issn = {1600-0854},
+	number = {5},
+	year = {2012},
+	month = {2},
+	pages = {643--649},
+	publisher = {Wiley},
+	title = {Assessing the {Tendency} of {Fluorescent} {Proteins} to {Oligomerize} {Under} {Physiologic} {Conditions}},
+	url = {http://dx.doi.org/10.1111/j.1600-0854.2012.01336.x},
+	volume = {13},
+}
+
+
+@misc{https://doi.org/10.6075/j0rr1zcr,
+	author = {Kirkpatrick, Christine R. and Coakley, Kevin and Cragin, Melissa H. and Cramer, Catherine and McHenry, Kenton and Marini, Luigi and Kooper, Rob and Glasgow, James and Foster, Ian T. and Szalay, Alex S. and Simmel, Derek},
+	doi = {10.6075/J0RR1ZCR},
+	year = {2021},
+	publisher = {UC San Diego Library Digital Collections},
+	title = {Open {Storage} {Network} {Concept} {Paper}: Open {Storage} {Network} {Retrospective} \&amp; the {Future} of {Distributed} {Storage} for {eInfrastructure}. {In} {San} {Diego} {Supercomputer} {Center} ({SDSC}) {Research} {Data} {Services} {Materials} {Collection}},
+	url = {https://library.ucsd.edu/dc/object/bb56058395},
+}
+
diff --git a/myst.yml b/myst.yml
index 723c7d8d9..b6b05bb2f 100644
--- a/myst.yml
+++ b/myst.yml
@@ -2,26 +2,27 @@
 version: 1
 project:
   id: msa-em-cell-browser-1
-  title: Unbiased representative images using embedded microscopy maps
-  description: Using automated imaging and next generation file formats for honest presentation and archival of light microscopy data.
-  short_title: Re-Imaging the Wheel
+  title: Reimag(in)ing representative images
+  description: Web-based microscopy maps as an unbiased method to replicate highly magnified representative microscopy images.
+  short_title: Virtual Microscopy
   keywords:
     - NucleusNet
+    - Virtual
     - Microscopy
-    - Maps
-    - Virtual nanoscopy
-    - Reproducibility
+    - Map
+    - Replication
     - OME-Zarr
+    - Deep Zoom
   github: https://github.com/msa-em/cell-browser 
- jupyter: true
- # jupyter:
- #   server:
- #     url: 'http://localhost:8888'
- #     token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c'
+  jupyter: true
+  # jupyter:
+  #   server:
+  #     url: 'http://localhost:8888'
+  #     token: '512ac78f14e1141db1fac17e8b4099c1e5bc7d589518b38c'
   open_access: true
   license: CC-BY-4.0
   banner: banner.png
-  thumbnail: thumbnail.webp
+  thumbnail: thumbnail.gif
   date: 2025-09-26
   requirements:
     - 'Dockerfile'
@@ -48,9 +49,29 @@ project:
           ror: 05bnh6r87
       roles:
         - Conceptualization
-        - Project administration
+        - Data Curation
+        - Formal Analysis
+        - Investigation
+        - Resources
+        - Software
+        - Visualization
         - Writing - original draft
-        - Data collection, analysis and visualizations
+        - Writing - review & editing
+    - id: ajbrodrick
+      name: Andrew James Brodrick
+      email: ajb64@umd.edu
+      orcid: 0009-0000-6389-0643
+      affiliations:
+        - id: maryland
+          department: 
+          institution: University of Maryland
+          address: 
+          ror: 047s2c258
+      roles:
+        - Conceptualization
+        - Methodology
+        - Software
+        - Visualization
     - id: jslparker
       name: John Stuart Leslie Parker
       email: jsp7@cornell.edu
@@ -58,10 +79,11 @@ project:
       affiliations:
         - id: cornell
       roles:
-        - Funding acquisition
+        - Funding Acquisition
+        - Supervision
+        - Project Administration
+        - Writing - review & editing
   abbreviations:
-    Barkley: Russell JR Barkley
-    Parker: John SL Parker
     ER: endoplasmic reticulum
     ROI: region of interest
     T1L: type 1 Lang
@@ -70,8 +92,41 @@ project:
     MOI: multiplicity of infection
     DAPI: 4',6-Diamidino-2-phenylindole
     DNA: deoxyribonucleic acid
-    MSE: mean squared error
     PBS: phosphate buffered saline
     PFA: paraformaldehyde
+    DZI: deep zoom image
+    ZDC: Z-drift compensation
+  toc:
+    - file: 00_index.md
+    - file: 01_introduction.md
+    - title: Microscopy Maps
+      children:
+        - file: 02_approach.md
+        - file: 03_model.md
+        - file: 04_plasmids.md
+        - file: 05_virus.md
+        - title: Antibodies
+          children:
+            - file: 06_antibody_ER.md
+            - file: 07_antibody_reovirus.md
+        - file: 08_results.md
+    - file: 09_discussion.md
+    - title: Supplemental
+      children:
+        - file: 10_oser.md
+        - file: 11_video.md
+        - title: Replication Attempts
+          children:
+            - file: 12_replicate_one.md
+            - file: 13_replicate_two.md
+            - file: 14_replicate_three.md
+            - file: 15_replicate_four.md
+        - title: Repetition Experiments
+          children:
+            - file: 16_antibody_reovirus.md
+            - file: 17_saturated_maps.md
+            - file: 18_discarded_maps.md
+        - file: 19_archived_maps.md
+        - file: 20_metadata.md
 site:
   template: book-theme
diff --git a/notebooks/experiment1_vizarr.ipynb b/notebooks/experiment1_vizarr.ipynb
deleted file mode 100644
index c786b4d2a..000000000
--- a/notebooks/experiment1_vizarr.ipynb
+++ /dev/null
@@ -1,83 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a76a9c97-3ed2-458d-811b-7d6d647f8df4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "541e2b11d4f14addb75c9c9894627293",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "GridspecLayout(children=(<vizarr._widget.Viewer object at 0x000002405BAFFA10>, <vizarr._widget.Viewer object a…"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import vizarr\n",
-    "from ipywidgets import GridspecLayout\n",
-    "\n",
-    "grid = GridspecLayout(2, 2, height='1000px', width='60%')\n",
-    "\n",
-    "# Create viewers\n",
-    "viewer1 = vizarr.Viewer()\n",
-    "viewer1.add_image(\n",
-    "    source='https://barkley-replication.s3.us-east-2.amazonaws.com/experiment1/sample1.zarr/',\n",
-    ")\n",
-    "\n",
-    "viewer2 = vizarr.Viewer()\n",
-    "viewer2.add_image(\n",
-    "    source='https://barkley-replication.s3.us-east-2.amazonaws.com/experiment1/sample2.zarr/',\n",
-    ")\n",
-    "\n",
-    "viewer3 = vizarr.Viewer()\n",
-    "viewer3.add_image(\n",
-    "    source='https://barkley-replication.s3.us-east-2.amazonaws.com/experiment1/sample3.zarr/',\n",
-    ")\n",
-    "\n",
-    "viewer4 = vizarr.Viewer()\n",
-    "viewer4.add_image(\n",
-    "    source='https://barkley-replication.s3.us-east-2.amazonaws.com/experiment1/sample4.zarr/',\n",
-    ")\n",
-    "\n",
-    "# Add viewers to grid\n",
-    "grid[0, 0] = viewer1\n",
-    "grid[0, 1] = viewer2\n",
-    "grid[1, 0] = viewer3\n",
-    "grid[1, 1] = viewer4\n",
-    "\n",
-    "grid"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig1b_cellbrowser_nucleusnet10k.ipynb b/notebooks/fig1b_cellbrowser_nucleusnet10k.ipynb
deleted file mode 100644
index d1e2c0b6e..000000000
--- a/notebooks/fig1b_cellbrowser_nucleusnet10k.ipynb
+++ /dev/null
@@ -1,274 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b559f93f-ae4b-4e8d-b083-20888d9c19b4",
-   "metadata": {},
-   "source": [
-    "# Cell Browser - Random selection from NucleusNet-10K"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "013791ba-e3b2-45d2-968f-d81d8010b4b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c8a3071c4401497bb65f4ab57af261ba",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating train split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2d04af44ce84aacbadf614eabcf297b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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' width=650.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: nucleusnet10k_cellbrowser\n",
-    "\n",
-    "%matplotlib widget\n",
-    "\n",
-    "import os, logging\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.widgets import Slider, RangeSlider, Button\n",
-    "from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar\n",
-    "import matplotlib.font_manager as fm\n",
-    "import fsspec\n",
-    "from tifffile import imread as tiff_imread\n",
-    "from urllib.parse import urlparse\n",
-    "\n",
-    "# ── Quiet mode: suppress HF/fsspec progress bars & noisy logs ────────────\n",
-    "os.environ[\"HF_HUB_DISABLE_PROGRESS_BARS\"] = \"1\"\n",
-    "os.environ[\"HF_HUB_DISABLE_SYMLINKS_WARNING\"] = \"1\"\n",
-    "os.environ[\"TQDM_DISABLE\"] = \"1\"\n",
-    "try:\n",
-    "    from huggingface_hub.utils import disable_progress_bars\n",
-    "    disable_progress_bars()\n",
-    "except Exception:\n",
-    "    pass\n",
-    "for _name in (\"fsspec\", \"huggingface_hub\", \"urllib3\", \"datasets\"):\n",
-    "    logging.getLogger(_name).setLevel(logging.ERROR)\n",
-    "\n",
-    "# ── Config ───────────────────────────────────────────────────────────────\n",
-    "REPO_ID         = \"RussellBarkley/msa-em-figures\"\n",
-    "BRANCH          = \"main\"\n",
-    "HF_GLOBS = [\n",
-    "    f\"hf://datasets/{REPO_ID}@{BRANCH}/**/*.tif\",\n",
-    "    f\"hf://datasets/{REPO_ID}@{BRANCH}/**/*.tiff\",\n",
-    "]\n",
-    "K_SAMPLES       = 50\n",
-    "SEED            = None\n",
-    "USE_SIMPLECACHE = True\n",
-    "\n",
-    "# Scale bar\n",
-    "PX_PER_UM = 8.0453\n",
-    "SCALER_LENGTH_UM = 1.0\n",
-    "\n",
-    "rng = np.random.default_rng(SEED)\n",
-    "fs  = fsspec.filesystem(\"hf\")\n",
-    "\n",
-    "def _ensure_hf_uri(p: str) -> str:\n",
-    "    return p if p.startswith(\"hf://\") else (\"hf://\" + p.lstrip(\"/\"))\n",
-    "\n",
-    "def _maybe_cache(uri: str) -> str:\n",
-    "    return f\"simplecache::{uri}\" if USE_SIMPLECACHE else uri\n",
-    "\n",
-    "def _basename_from_uri(uri: str) -> str:\n",
-    "    return os.path.basename(urlparse(uri).path)\n",
-    "\n",
-    "def _list_all_tiff_paths():\n",
-    "    paths = []\n",
-    "    for pat in HF_GLOBS:\n",
-    "        try:\n",
-    "            for p in fs.glob(pat):\n",
-    "                paths.append(_ensure_hf_uri(p))\n",
-    "        except Exception:\n",
-    "            pass\n",
-    "    return sorted(set(paths))\n",
-    "\n",
-    "ALL_PATHS = _list_all_tiff_paths()\n",
-    "MODE = \"files\" if len(ALL_PATHS) > 0 else \"dataset\"\n",
-    "\n",
-    "def _load_gray_from_uri(uri: str):\n",
-    "    with fsspec.open(_maybe_cache(uri), \"rb\") as f:\n",
-    "        arr = tiff_imread(f)\n",
-    "    if arr.ndim == 3:\n",
-    "        arr = arr[..., 0]\n",
-    "    name = _basename_from_uri(uri)\n",
-    "    return name, arr\n",
-    "\n",
-    "def _sample_and_load_files(k=K_SAMPLES):\n",
-    "    k = min(k, len(ALL_PATHS))\n",
-    "    idx = rng.choice(len(ALL_PATHS), size=k, replace=False)\n",
-    "    chosen = [ALL_PATHS[i] for i in idx]\n",
-    "    recs = [_load_gray_from_uri(u) for u in chosen]\n",
-    "    recs.sort(key=lambda t: t[0])\n",
-    "    filenames = [r[0] for r in recs]\n",
-    "    images    = [r[1] for r in recs]\n",
-    "    return filenames, images\n",
-    "\n",
-    "def _sample_and_load_dataset(k=K_SAMPLES):\n",
-    "    from datasets import load_dataset\n",
-    "    ds = load_dataset(REPO_ID, split=\"train\", keep_in_memory=False)\n",
-    "    k = min(k, len(ds))\n",
-    "    idx = rng.choice(len(ds), size=k, replace=False)\n",
-    "    subset = ds.select(sorted(idx.tolist()))\n",
-    "    filenames, images = [], []\n",
-    "    for ex in subset:\n",
-    "        arr = np.asarray(ex[\"image\"])\n",
-    "        if arr.ndim == 3:\n",
-    "            arr = arr[..., 0]\n",
-    "        name = ex.get(\"filename\", f\"sample_{len(filenames):05d}.tif\")\n",
-    "        filenames.append(name)\n",
-    "        images.append(arr)\n",
-    "    return filenames, images\n",
-    "\n",
-    "def _sample_and_load(k=K_SAMPLES):\n",
-    "    return _sample_and_load_files(k) if MODE == \"files\" else _sample_and_load_dataset(k)\n",
-    "\n",
-    "# ── Initial load (random 50) ─────────────────────────────────────────────\n",
-    "filenames, images = _sample_and_load()\n",
-    "if len(images) == 0:\n",
-    "    raise RuntimeError(\"No images found in repo/dataset.\")\n",
-    "n = len(images)\n",
-    "\n",
-    "# ── Viewer ──────────────────────────────────────────────────────────────\n",
-    "img0 = images[0]\n",
-    "if np.issubdtype(img0.dtype, np.integer):\n",
-    "    lo0, hi0 = 0, int(np.iinfo(img0.dtype).max)\n",
-    "else:\n",
-    "    lo0, hi0 = float(np.nanmin(img0)), float(np.nanmax(img0))\n",
-    "if not np.isfinite(lo0): lo0 = 0.0\n",
-    "if not np.isfinite(hi0): hi0 = lo0 + 1.0\n",
-    "if hi0 <= lo0: hi0 = lo0 + 1.0\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(6.5, 6.5))\n",
-    "plt.subplots_adjust(bottom=0.30, top=0.95)\n",
-    "\n",
-    "im = ax.imshow(img0, cmap='gray', vmin=lo0, vmax=hi0)\n",
-    "ax.set_axis_off()\n",
-    "title = ax.set_title(filenames[0], fontsize=10)  # ← repo name removed\n",
-    "\n",
-    "# Scalebar\n",
-    "bar_px = SCALER_LENGTH_UM * PX_PER_UM\n",
-    "fontprops = fm.FontProperties(size=10, weight='bold')\n",
-    "scalebar = AnchoredSizeBar(\n",
-    "    ax.transData, bar_px, f\"{SCALER_LENGTH_UM:g} μm\",\n",
-    "    loc='lower right', pad=0.5, color='red',\n",
-    "    frameon=False, size_vertical=1, fontproperties=fontprops\n",
-    ")\n",
-    "ax.add_artist(scalebar)\n",
-    "\n",
-    "# Contrast slider\n",
-    "contrast_ax = fig.add_axes([0.15, 0.12, 0.70, 0.03])\n",
-    "step = max(1, int((hi0 - lo0) // 1024)) if np.issubdtype(img0.dtype, np.integer) else None\n",
-    "contrast_slider = RangeSlider(\n",
-    "    ax=contrast_ax, label='Min/max',\n",
-    "    valmin=float(lo0), valmax=float(hi0),\n",
-    "    valinit=(float(lo0), float(hi0)), valstep=step,\n",
-    ")\n",
-    "def update_contrast(_):\n",
-    "    lo, hi = contrast_slider.val\n",
-    "    im.set_clim(lo, hi)\n",
-    "    fig.canvas.draw_idle()\n",
-    "contrast_slider.on_changed(update_contrast)\n",
-    "\n",
-    "# Index slider\n",
-    "idx_ax = fig.add_axes([0.15, 0.06, 0.70, 0.03])\n",
-    "idx_slider = Slider(ax=idx_ax, label='Index', valmin=0, valmax=n-1, valinit=0, valstep=1, valfmt='%0.0f')\n",
-    "\n",
-    "def update_image(_):\n",
-    "    idx = int(idx_slider.val)\n",
-    "    im.set_data(images[idx])\n",
-    "    lo, hi = contrast_slider.val\n",
-    "    im.set_clim(lo, hi)\n",
-    "    title.set_text(filenames[idx])  # ← repo name removed\n",
-    "    fig.canvas.draw_idle()\n",
-    "idx_slider.on_changed(update_image)\n",
-    "\n",
-    "# Resample (new random 50)\n",
-    "btn_ax = fig.add_axes([0.15, 0.18, 0.18, 0.04])\n",
-    "resample_btn = Button(btn_ax, \"Resample\")\n",
-    "def on_resample(event=None):\n",
-    "    global filenames, images, n\n",
-    "    filenames, images = _sample_and_load()\n",
-    "    n = len(images)\n",
-    "    idx_slider.valmax = n - 1\n",
-    "    idx_slider.set_val(0)\n",
-    "    im.set_data(images[0])\n",
-    "    lo, hi = contrast_slider.val\n",
-    "    im.set_clim(lo, hi)\n",
-    "    title.set_text(filenames[0])  # ← repo name removed\n",
-    "    fig.canvas.draw_idle()\n",
-    "resample_btn.on_clicked(on_resample)\n",
-    "\n",
-    "# Toolbar back/forward → prev/next index\n",
-    "tb = getattr(fig.canvas, \"toolbar\", None)\n",
-    "if tb is not None:\n",
-    "    def _back_override(*args, **kwargs):\n",
-    "        cur = int(idx_slider.val)\n",
-    "        if cur > 0:\n",
-    "            idx_slider.set_val(cur - 1)\n",
-    "    def _forward_override(*args, **kwargs):\n",
-    "        cur = int(idx_slider.val)\n",
-    "        if cur < n - 1:\n",
-    "            idx_slider.set_val(cur + 1)\n",
-    "    tb.back = _back_override\n",
-    "    tb.forward = _forward_override\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2ab_mnist_replication_example.ipynb b/notebooks/fig2ab_mnist_replication_example.ipynb
deleted file mode 100644
index 8647077d8..000000000
--- a/notebooks/fig2ab_mnist_replication_example.ipynb
+++ /dev/null
@@ -1,270 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4ff4dda1",
-   "metadata": {},
-   "source": [
-    "# MNIST literature reproduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2f9a07c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "%matplotlib widget\n",
-    "\n",
-    "import os, gzip, struct, urllib.request\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# MNIST IDX mirror + cache\n",
-    "DATA_DIR = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"mnist_idx\")\n",
-    "USE_MIRROR = \"https://storage.googleapis.com/cvdf-datasets/mnist/\"\n",
-    "FILES = {\n",
-    "    \"train_images\": \"train-images-idx3-ubyte.gz\",\n",
-    "    \"train_labels\": \"train-labels-idx1-ubyte.gz\",\n",
-    "    \"test_images\":  \"t10k-images-idx3-ubyte.gz\",\n",
-    "    \"test_labels\":  \"t10k-labels-idx1-ubyte.gz\",\n",
-    "}\n",
-    "\n",
-    "LABEL_OF_INTEREST = 4\n",
-    "GEOMED_MAX_ITERS = 1000\n",
-    "GEOMED_TOL = 1e-7\n",
-    "GEOMED_SUBSAMPLE = None  # set to e.g. 3000 if geomedian is slow\n",
-    "\n",
-    "def _download_if_needed():\n",
-    "    os.makedirs(DATA_DIR, exist_ok=True)\n",
-    "    for fn in FILES.values():\n",
-    "        path = os.path.join(DATA_DIR, fn)\n",
-    "        if not os.path.exists(path):\n",
-    "            urllib.request.urlretrieve(USE_MIRROR + fn, path)\n",
-    "\n",
-    "def _read_idx_images(path_gz):\n",
-    "    with gzip.open(path_gz, \"rb\") as f:\n",
-    "        magic, n, rows, cols = struct.unpack(\">IIII\", f.read(16))\n",
-    "        if magic != 2051:\n",
-    "            raise ValueError(f\"Invalid magic for images: {magic}\")\n",
-    "        arr = np.frombuffer(f.read(rows * cols * n), dtype=np.uint8)\n",
-    "        return arr.reshape(n, rows, cols)\n",
-    "\n",
-    "def _read_idx_labels(path_gz):\n",
-    "    with gzip.open(path_gz, \"rb\") as f:\n",
-    "        magic, n = struct.unpack(\">II\", f.read(8))\n",
-    "        if magic != 2049:\n",
-    "            raise ValueError(f\"Invalid magic for labels: {magic}\")\n",
-    "        return np.frombuffer(f.read(n), dtype=np.uint8)\n",
-    "\n",
-    "def load_mnist_all():\n",
-    "    _download_if_needed()\n",
-    "    paths = {k: os.path.join(DATA_DIR, v) for k, v in FILES.items()}\n",
-    "    x_tr = _read_idx_images(paths[\"train_images\"]).astype(np.float32) / 255.0\n",
-    "    y_tr = _read_idx_labels(paths[\"train_labels\"])\n",
-    "    x_te = _read_idx_images(paths[\"test_images\"]).astype(np.float32) / 255.0\n",
-    "    y_te = _read_idx_labels(paths[\"test_labels\"])\n",
-    "    X = np.concatenate([x_tr, x_te], axis=0)\n",
-    "    y = np.concatenate([y_tr, y_te], axis=0)\n",
-    "    return X, y\n",
-    "\n",
-    "def geometric_median_weiszfeld(X, max_iters=1000, tol=1e-7):\n",
-    "    X = np.asarray(X, dtype=np.float64)\n",
-    "    x = np.median(X, axis=0)\n",
-    "    if not np.all(np.isfinite(x)):\n",
-    "        x = np.mean(X, axis=0)\n",
-    "    eps = 1e-12\n",
-    "    for _ in range(max_iters):\n",
-    "        diffs = X - x\n",
-    "        dists = np.linalg.norm(diffs, axis=1)\n",
-    "        hit = np.where(dists < eps)[0]\n",
-    "        if hit.size:\n",
-    "            return X[hit[0]].copy()\n",
-    "        w = 1.0 / np.maximum(dists, eps)\n",
-    "        x_new = (w[:, None] * X).sum(axis=0) / w.sum()\n",
-    "        if np.linalg.norm(x_new - x) < tol:\n",
-    "            return x_new\n",
-    "        x = x_new\n",
-    "    return x\n",
-    "\n",
-    "def svd_basis(A):\n",
-    "    U, s, Vt = np.linalg.svd(A, full_matrices=False)\n",
-    "    tol = s[0] * max(A.shape) * np.finfo(float).eps if s.size > 0 else 0.0\n",
-    "    r = int(np.sum(s > tol)) if s.size > 0 else 0\n",
-    "    if r == 0 and s.size > 0:\n",
-    "        r = 1\n",
-    "    return U[:, :r], s[:r], Vt[:r]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "935a60c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Compute theory reps for label 4 and choose practical reps\n",
-    "X_all, y_all = load_mnist_all()\n",
-    "X4 = X_all[y_all == LABEL_OF_INTEREST]        # (N, 28, 28)\n",
-    "N, H, W = X4.shape\n",
-    "X4_flat = X4.reshape(N, H*W)                  # (N, 784)\n",
-    "\n",
-    "# Theoretical centers (pixel space)\n",
-    "mean_vec   = np.mean(X4_flat, axis=0)\n",
-    "median_vec = np.median(X4_flat, axis=0)\n",
-    "if GEOMED_SUBSAMPLE is not None and GEOMED_SUBSAMPLE < X4_flat.shape[0]:\n",
-    "    rng = np.random.default_rng(123)\n",
-    "    take = rng.choice(X4_flat.shape[0], size=GEOMED_SUBSAMPLE, replace=False)\n",
-    "    X_geo = X4_flat[take]\n",
-    "else:\n",
-    "    X_geo = X4_flat\n",
-    "geomed_vec = geometric_median_weiszfeld(X_geo, max_iters=GEOMED_MAX_ITERS, tol=GEOMED_TOL)\n",
-    "\n",
-    "theory = {\n",
-    "    \"mean\": mean_vec.reshape(H, W),\n",
-    "    \"median\": median_vec.reshape(H, W),\n",
-    "    \"geom_median\": geomed_vec.reshape(H, W)\n",
-    "}\n",
-    "\n",
-    "# SVD basis over all '4's\n",
-    "A = X4_flat.T                               # (784, N) columns are images\n",
-    "U_r, s_r, Vt_r = svd_basis(A)               # U_r: (784, r)\n",
-    "C = U_r.T @ X4_flat.T                       # (r, N) coordinates of all real images\n",
-    "\n",
-    "def nearest_in_basis(xT_vec):\n",
-    "    cT = U_r.T @ xT_vec                     # (r,)\n",
-    "    dists = np.linalg.norm(C.T - cT[None, :], axis=1)\n",
-    "    idx = int(np.argmin(dists))\n",
-    "    return idx, float(dists[idx])\n",
-    "\n",
-    "chosen = {}\n",
-    "for name, vec in {\"mean\": mean_vec, \"median\": median_vec, \"geomedian\": geomed_vec}.items():\n",
-    "    idx, dist = nearest_in_basis(vec)\n",
-    "    chosen[name] = (idx, dist)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3226805e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae06bdb7442f48c99490c4af3974780a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                </div>\n",
-       "                <img 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' width=880.0000000000001/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "#| label: fig2a_data\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(8.8, 3))\n",
-    "for ax, key, ttl in zip(\n",
-    "    axes,\n",
-    "    [\"mean\", \"median\", \"geom_median\"],\n",
-    "    [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "):\n",
-    "    ax.imshow(np.clip(theory[key], 0, 1), cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(ttl, fontsize=10)\n",
-    "    ax.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "23dba138",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "31638eda55f9455180bf953a5678af5a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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-       "                </div>\n",
-       "                <img src='data:image/png;base64,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' width=880.0000000000001/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "#| label: fig2b_data\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(8.8, 3))\n",
-    "for ax, key, ttl in zip(\n",
-    "    axes,\n",
-    "    [\"mean\", \"median\", \"geomedian\"],\n",
-    "    [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "):\n",
-    "    idx, _ = chosen[key]\n",
-    "    ax.imshow(X4[idx], cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(ttl, fontsize=10)\n",
-    "    ax.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2cd_mnist_autoencoder_example.ipynb b/notebooks/fig2cd_mnist_autoencoder_example.ipynb
deleted file mode 100644
index a26a3bb4e..000000000
--- a/notebooks/fig2cd_mnist_autoencoder_example.ipynb
+++ /dev/null
@@ -1,444 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "da41d574",
-   "metadata": {},
-   "source": [
-    "# MNIST autoencoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0278101b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow: 2.20.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib widget\n",
-    "\n",
-    "import os, math, random\n",
-    "import numpy as np\n",
-    "import h5py\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.layers import Input, Dense, Reshape, Conv2DTranspose, ReLU\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from pathlib import Path\n",
-    "\n",
-    "np.random.seed(42)\n",
-    "tf.random.set_seed(42)\n",
-    "\n",
-    "print(\"TensorFlow:\", tf.__version__)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0c77339f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RESULTS_DIR: ..\\data\\mnist-ae\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ─────────────────────────────────────────────────────────\n",
-    "# CONFIG — set this back to your original style/path\n",
-    "# ─────────────────────────────────────────────────────────\n",
-    "RESULTS_DIR       = Path(\"..\") / \"data\" / \"mnist-ae\"   # ← change to your actual folder if needed\n",
-    "LATENTS_H5        = os.path.join(RESULTS_DIR, \"latents.h5\")\n",
-    "DECODER_WEIGHTS   = os.path.join(RESULTS_DIR, \"decoder_weights.h5\")\n",
-    "\n",
-    "# Must match training\n",
-    "N_LAYERS      = 2\n",
-    "BASE_FILTERS  = 16\n",
-    "LATENT_DIM    = 64\n",
-    "\n",
-    "# Geometric median settings\n",
-    "GEOMED_MAX_ITERS = 1000\n",
-    "GEOMED_TOL       = 1e-7\n",
-    "GEOMED_SUBSAMPLE = None\n",
-    "\n",
-    "print(\"RESULTS_DIR:\", RESULTS_DIR)\n",
-    "assert os.path.exists(LATENTS_H5),      f\"Missing file: {LATENTS_H5}\"\n",
-    "assert os.path.exists(DECODER_WEIGHTS), f\"Missing file: {DECODER_WEIGHTS}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "416ac830",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded Z: (70000, 64), labels: (70000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "with h5py.File(LATENTS_H5, \"r\") as hf:\n",
-    "    Z      = hf[\"z\"][:].astype(np.float32)     # (N, D)\n",
-    "    labels = hf[\"labels\"][:].astype(np.int64)   # (N,)\n",
-    "\n",
-    "N, D = Z.shape\n",
-    "print(f\"Loaded Z: {Z.shape}, labels: {labels.shape}\")\n",
-    "assert D == LATENT_DIM, f\"LATENT_DIM mismatch: expected {LATENT_DIM}, got {D}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9db62815",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ALL_IMAGES: (70000, 28, 28, 1) ALL_LABELS: (70000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "x_train = np.expand_dims(x_train.astype(\"float32\") / 255.0, -1)\n",
-    "x_test  = np.expand_dims(x_test.astype(\"float32\")  / 255.0, -1)\n",
-    "\n",
-    "ALL_IMAGES = np.concatenate([x_train, x_test], axis=0)\n",
-    "ALL_LABELS = np.concatenate([y_train, y_test], axis=0)\n",
-    "assert ALL_IMAGES.shape[0] == N, \"Mismatch between number of latents and images\"\n",
-    "\n",
-    "print(\"ALL_IMAGES:\", ALL_IMAGES.shape, \"ALL_LABELS:\", ALL_LABELS.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f5e3045d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Subset size for label 4: 6824 latents\n"
-     ]
-    }
-   ],
-   "source": [
-    "target_label = 4\n",
-    "idx_subset = np.where(labels == target_label)[0]\n",
-    "\n",
-    "if idx_subset.size == 0:\n",
-    "    raise ValueError(f\"No samples found with label == {target_label}\")\n",
-    "\n",
-    "Z_sub   = Z[idx_subset]\n",
-    "IM_sub  = ALL_IMAGES[idx_subset]\n",
-    "LBL_sub = labels[idx_subset]\n",
-    "\n",
-    "print(f\"Subset size for label {target_label}: {Z_sub.shape[0]} latents\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bb09f689",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Decoder loaded from: ..\\data\\mnist-ae\\decoder_weights.h5\n"
-     ]
-    }
-   ],
-   "source": [
-    "def build_decoder(n_layers=2, base_filters=16, latent_dim=64):\n",
-    "    latent_in = Input((latent_dim,), name=\"z_sampling\")\n",
-    "    spatial   = 28 // (2**n_layers)  # 28 -> 7 for 2 layers\n",
-    "    channels  = base_filters * (2**(n_layers-1))\n",
-    "\n",
-    "    x = Dense(spatial * spatial * channels)(latent_in)\n",
-    "    x = Reshape((spatial, spatial, channels))(x)\n",
-    "\n",
-    "    for i in reversed(range(n_layers)):\n",
-    "        f = base_filters * (2**i)\n",
-    "        x = Conv2DTranspose(f, 3, strides=(2,2), padding=\"same\")(x)\n",
-    "        x = ReLU()(x)\n",
-    "\n",
-    "    out = tf.keras.layers.Conv2D(1, 3, padding=\"same\", activation=\"sigmoid\", name=\"decoder_output\")(x)\n",
-    "    return Model(latent_in, out, name=\"decoder\")\n",
-    "\n",
-    "decoder = build_decoder(N_LAYERS, BASE_FILTERS, LATENT_DIM)\n",
-    "decoder.load_weights(DECODER_WEIGHTS)\n",
-    "print(\"Decoder loaded from:\", DECODER_WEIGHTS)\n",
-    "\n",
-    "_ = decoder.predict(np.zeros((1, LATENT_DIM), dtype=np.float32), verbose=0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fb3b0062",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Central latents computed for label==4 subset.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def geometric_median(points, tol=1e-7, max_iters=1000):\n",
-    "    P = points.astype(np.float64)\n",
-    "    x = P.mean(axis=0)\n",
-    "    eps = 1e-12\n",
-    "    for _ in range(max_iters):\n",
-    "        diff = P - x\n",
-    "        d    = np.linalg.norm(diff, axis=1) + eps\n",
-    "        w    = 1.0 / d\n",
-    "        x_new = (w[:, None] * P).sum(axis=0) / w.sum()\n",
-    "        if np.linalg.norm(x_new - x) <= tol:\n",
-    "            return x_new.astype(np.float32)\n",
-    "        x = x_new\n",
-    "    return x.astype(np.float32)\n",
-    "\n",
-    "mean_z   = Z_sub.mean(axis=0)\n",
-    "median_z = np.median(Z_sub, axis=0)\n",
-    "\n",
-    "if GEOMED_SUBSAMPLE is None or GEOMED_SUBSAMPLE >= Z_sub.shape[0]:\n",
-    "    gm_input = Z_sub\n",
-    "else:\n",
-    "    sel = np.random.choice(Z_sub.shape[0], GEOMED_SUBSAMPLE, replace=False)\n",
-    "    gm_input = Z_sub[sel]\n",
-    "\n",
-    "geom_z = geometric_median(gm_input, tol=GEOMED_TOL, max_iters=GEOMED_MAX_ITERS)\n",
-    "print(\"Central latents computed for label==4 subset.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "166a095a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reconstructions: (3, 28, 28)\n"
-     ]
-    }
-   ],
-   "source": [
-    "central_stack = np.stack([mean_z, median_z, geom_z], axis=0).astype(np.float32)\n",
-    "recons = decoder.predict(central_stack, verbose=0)  # (3, 28, 28, 1)\n",
-    "recons = np.squeeze(recons, axis=-1)                # -> (3, 28, 28)\n",
-    "print(\"Reconstructions:\", recons.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9420572d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Closest (global) indices: [3639, 3639, 3639]\n",
-      "Distances: ['2.435483', '2.347138', '2.327151']\n",
-      "Labels   : [4, 4, 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def nearest_idx(vec, Z):\n",
-    "    diff = Z - vec[None, :]\n",
-    "    d2   = np.sum(diff*diff, axis=1)\n",
-    "    idx  = int(np.argmin(d2))\n",
-    "    dist = float(np.sqrt(d2[idx]))\n",
-    "    return idx, dist\n",
-    "\n",
-    "mean_i_sub,   mean_dist   = nearest_idx(mean_z,   Z_sub)\n",
-    "median_i_sub, median_dist = nearest_idx(median_z, Z_sub)\n",
-    "geom_i_sub,   geom_dist   = nearest_idx(geom_z,   Z_sub)\n",
-    "\n",
-    "mean_idx_global   = int(idx_subset[mean_i_sub])\n",
-    "median_idx_global = int(idx_subset[median_i_sub])\n",
-    "geom_idx_global   = int(idx_subset[geom_i_sub])\n",
-    "\n",
-    "closest_global_indices = [mean_idx_global, median_idx_global, geom_idx_global]\n",
-    "closest_dists          = [mean_dist, median_dist, geom_dist]\n",
-    "closest_labels         = [int(labels[i]) for i in closest_global_indices]  # should all be 4\n",
-    "closest_images         = [ALL_IMAGES[i, ..., 0] for i in closest_global_indices]\n",
-    "\n",
-    "print(\"Closest (global) indices:\", closest_global_indices)\n",
-    "print(\"Distances:\", [f\"{d:.6f}\" for d in closest_dists])\n",
-    "print(\"Labels   :\", closest_labels)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "13226f21",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2fc6a826b3a4317a0f31a5177cb4088",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=900.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: fig2c_data\n",
-    "\n",
-    "titles = [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "fig1, axes1 = plt.subplots(1, 3, figsize=(9, 3), constrained_layout=True)\n",
-    "for ax, img, title in zip(axes1, recons, titles):\n",
-    "    ax.imshow(img, cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(title)\n",
-    "    ax.axis(\"off\")\n",
-    "fig1.canvas.header_visible = False\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8050b87a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "642efc305a434829a02c914e0735c21f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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WWVNsj6paMLzzzjtRUVFR/fjmzZtrLQy3ph9A42vatGkMGDAgFi5cGGvWrKkRhrp37x4RUSs4VP0fPnbs2DjzzDPrHLdr164R8flzfs2aNbUer3pzp6qxdlZ5eXkUCoV49tlnq4PIF9W1bVuKWR/uDWuoL/bbrdW1bWsPP/xw9OvXL+67774a29evX78j06VIAuFO2rJlSzz00EPRuXPnmDp1aq3H582bF3fddVc88cQTDfoQ+T3hFY4hQ4bE7NmzY8uWLfV+jMa2rk5urbS0NPr27RuPPvpo3HrrrY3WlIsxcODAKCkpiddffz3OOuusbdYVCoVIKdVq5FOnTo0tW7bs6mkCEVFRURHXXHNNvPrqq3HeeefVWVNsj6r6IPsZM2bE8ccfX7395z//eWzevHm789APYNcYO3ZsPPHEE3HJJZfEnDlz6r1dsmvXrvHVr341Xnzxxfjxj3+83dpTTz01Hn/88Vi9enWNdymdPn167L///o32UQdDhgyJ2267Ld5+++0YPnz4dmuLXSdtz96whuratWu0b98+Zs2aFX/3d39XHXRXrVoVzz33XK13jd1aoVCo1W9feumlWLp0aa1bgNl5AuFOeuKJJ2L16tUxYcKE6sXGFx1zzDHxj//4jzFt2rQGBcIePXpERMSkSZPivPPOi2bNmkXXrl13+Ycyf9GIESNixowZMWjQoLjiiivihBNOiGbNmsXvfve7WLx4cZxxxhkxbNiw6vnOnj07HnnkkTjiiCOiRYsW1eewtYkTJ8Y3vvGN+NrXvhbXX399dOnSJdauXRu//OUvY8qUKbvsHCsrK+NHP/pR/OAHP4jf/va3cdppp0Xr1q1j7dq1sWzZsmjZsmXcfPPN0apVqzjllFPijjvuiPLy8qisrIwlS5bEtGnT4qCDDtolcwNqu+2227b7eLE9qlu3bnHOOefE3XffHc2aNYu/+Iu/iJdffjnuvPPOGp91WBf9AHaNk046KX7605/GmDFjolevXnHRRRfF0UcfHU2aNIk1a9bE3LlzIyJqPEenTJkS3/zmN2PgwIExatSoqKioiPfffz9eeeWVeOGFF+LRRx+NiIhx48bFvHnzon///nHjjTdGmzZtYsaMGTF//vy4/fbb48ADD2y0c7joooti9OjRsXz58jjllFOiZcuWsWbNmvi3f/u36NGjR1x66aUR8fk66bHHHov77rsvjj/++GjSpEn8+Z//+Q4fc09fQzVp0iRuueWW+P73vx/Dhg2LCy+8MNatWxc33XRTnbeRbm3IkCFxyy23xLhx46Jv377x2muvxY9+9KPo1KlTvS/gseMEwp00bdq02G+//WL06NF1Pl5eXh7Dhg2LOXPmVN93vSP69esXY8eOjYceeih+9rOfxWeffRaLFy+uM3zuKk2bNo1f/vKXMWnSpPinf/qn+MlPfhIlJSVx2GGHRd++fWsEvptvvjnWrFkTF154Yaxfvz46duy4zfvEjz322Fi2bFmMGzcuxo4dG+vXr4927drFgAEDtvm3QI1l7Nix0b1795g0aVLMmjUrPv3002jXrl307t07Lrnkkuq6mTNnxhVXXBHXXnttbN68OU466aRYtGhRrT+6BnafHelR06ZNi7Zt28aDDz4Y99xzT/Ts2TPmzp1b/YZg26MfwK5xySWXRJ8+fWLSpEnx93//97F69eooFApx2GGHxYknnhhPPfVUDBgwoLq+f//+sWzZsrj11lvjyiuvjD/84Q9x8MEHR/fu3WtcoevatWs899xzccMNN8Tll18eGzdujG7dusUDDzwQo0aNatRzmDJlSnz961+PKVOmxL333hufffZZdOjQIU466aQ44YQTquuuuOKKWLFiRdxwww3xwQcfRPr8Hf93+Hh7wxrqggsuiIiICRMmxJlnnhmVlZVxww03xJIlS+p9o50f/OAHsWHDhpg2bVrcfvvt0b1795g8eXI8/vjjDXozQ7avkBryWwgAAMBez8dOAAAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYEQgAAgEwJhAAAAJkSCAEAADJV0tAdC4VCY84DyEhK6Us9nn4FNJR+BewtGtqvXCEEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYEQgAAgEwJhAAAAJkSCAEAADIlEAIAAGRKIAQAAMiUQAgAAJApgRAAACBTAiEAAECmBEIAAIBMCYQAAACZEggBAAAyJRACAABkSiAEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYEQgAAgEwJhAAAAJkSCAEAADIlEAIAAGRKIAQAAMiUQAgAAJApgRAAACBTAiEAAECmBEIAAIBMCYQAAACZEggBAAAyVbK7JwB7qqOPPrremgULFhQ11tSpU4uqu/nmm4uqA/gi/QrYW+hXex5XCAEAADIlEAIAAGRKIAQAAMiUQAgAAJApgRAAACBTAiEAAECmBEIAAIBMCYQAAACZ8sH0sA133nlnvTXt27cvaqyVK1fu7HQAtkm/AvYW+tWexxVCAACATAmEAAAAmRIIAQAAMiUQAgAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYEQgAAgEyV7O4J0HAtWrQoqq5p06ZF1X388cc7M529RmlpaVF1HTp0qLdm3bp1RY21YMGCoupgX6VfNYx+BV8+/aph9Ku9lyuEAAAAmRIIAQAAMiUQAgAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYEQgAAgEwJhAAAAJkq2d0ToOGuv/76ouoGDRpUVN3gwYPrrXn33XeLGmtPNmrUqKLqjjnmmHprzjnnnKLGeu+994qqg32VftUw+hV8+fSrhtGv9l6uEAIAAGRKIAQAAMiUQAgAAJApgRAAACBTAiEAAECmBEIAAIBMCYQAAACZEggBAAAy5YPp90CXX355UXUDBw4sqm7Lli1F1VVUVNRbsyd/cGox84+IOOWUUxrtmJ07d260sWBvpF81jH4FXz79qmH0q32fK4QAAACZEggBAAAyJRACAABkSiAEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmSqklFKDdiwUGnsuWWjTpk29Nc8880xRYx1++OFF1b300ktF1fXt27eouj1Vy5Yti6r7zW9+U1TdJ598Um/NaaedVtRYb7/9dlF1uWhg22kw/aph9KtdR7/ae+hXewf9atfRr/YeDe1XrhACAABkSiAEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZKpkd09gX1FRUVFU3eOPP15vzdFHH13UWOPGjSuqbvz48UXV7e1Gjx5dVF2nTp2KqhszZky9NW+//XZRY8GeRL/a/fQrKI5+tfvpV/s+VwgBAAAyJRACAABkSiAEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMlVIKaUG7VgoNPZc9mo/+9nPiqo799xz661ZsWJFUWP169evqLoPP/ywqLo9VbHnOXfu3KLqDjzwwKLqDj300Hpr3n///aLGoqYGtp0G069q0q92Hf1q36Nf7V761a6jX+17GtqvXCEEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZKpkd09gT1fsh2z+1V/9VVF1TZs2rbfmjjvuKGqsvf0DUSOK+3DSiy++uKixiv1ZLVy4sKi61q1b11vjg1PZk+hXu5Z+BY1Hv9q19Ct2hCuEAAAAmRIIAQAAMiUQAgAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYEQgAAgEwJhAAAAJkqpJRSg3YsFBp7Lnuk/v37F1W3aNGiRjvmpk2biqpbuXJlox0zIuLJJ5+st+b1118vaqzLLrusqLpWrVrVW1NRUVHUWMW6/PLLi6qbMmVKox6XP2lg22kw/aom/aom/Yrt0a92Df2qJv2KxtDQfuUKIQAAQKYEQgAAgEwJhAAAAJkSCAEAADIlEAIAAGRKIAQAAMiUQAgAAJApgRAAACBTAiEAAECmSnb3BPZ0PXr0KKquUCg02jGbN29eVF337t0b7ZiNPV6TJsW91vDZZ5812jGL9eyzz37px4Qvg37VMPoVfPn0q4bRr9gVXCEEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZMoH09dj8uTJRdW1bdu2qLrOnTvXW9OlS5eixlq5cmVRdXuyioqKemv69OlT1FgvvvhiUXWrVq0qqg72NvrVrqVfQePRr3Yt/Yod4QohAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZEogBAAAyJRACAAAkCmBEAAAIFMCIQAAQKYKKaXUoB0LhcaeC/9faWlpUXUbN27cxTNpuEMPPbSoun/4h3+ot6a8vLyosa699tqi6n79618XVceu08C202D61a6jX9WkX+179Kt9h35Vk36172lov3KFEAAAIFMCIQAAQKYEQgAAgEwJhAAAAJkSCAEAADIlEAIAAGRKIAQAAMiUQAgAAJApgRAAACBTJbt7AtS2cePG3T2FnXbssccWVXfWWWfVW/PYY48VNdavf/3rouqAxqNf1aRfwZ5Lv6pJv6KKK4QAAACZEggBAAAyJRACAABkSiAEAADIlEAIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmSrZ3RNg3zR06NBGG2vWrFmNNhbA1vQrYG+hX7EruEIIAACQKYEQAAAgUwIhAABApgRCAACATAmEAAAAmRIIAQAAMiUQAgAAZEogBAAAyFQhpZQatGOh0NhzYR/y3nvvFVW3aNGiemtGjBixs9NhD9PAttN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-       "                </div>\n",
-       "                <img src='data:image/png;base64,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' width=900.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: fig2d_data\n",
-    "\n",
-    "titles2 = [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "fig2, axes2 = plt.subplots(1, 3, figsize=(9, 3), constrained_layout=True)\n",
-    "for ax, img, title in zip(axes2, closest_images, titles2):\n",
-    "    ax.imshow(img, cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(title)\n",
-    "    ax.axis(\"off\")\n",
-    "fig2.canvas.header_visible = False\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7003ab06",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subset_size_label4': 6824,\n",
-       " 'mean': {'global_index': 3639, 'distance': 2.4354825019836426, 'label': 4},\n",
-       " 'median': {'global_index': 3639, 'distance': 2.3471381664276123, 'label': 4},\n",
-       " 'geom': {'global_index': 3639, 'distance': 2.32715106010437, 'label': 4}}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary = {\n",
-    "    \"subset_size_label4\": int(Z_sub.shape[0]),\n",
-    "    \"mean\":   {\"global_index\": int(closest_global_indices[0]), \"distance\": float(closest_dists[0]), \"label\": int(closest_labels[0])},\n",
-    "    \"median\": {\"global_index\": int(closest_global_indices[1]), \"distance\": float(closest_dists[1]), \"label\": int(closest_labels[1])},\n",
-    "    \"geom\":   {\"global_index\": int(closest_global_indices[2]), \"distance\": float(closest_dists[2]), \"label\": int(closest_labels[2])},\n",
-    "}\n",
-    "summary\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2e_mnist_replication_counterexample.ipynb b/notebooks/fig2e_mnist_replication_counterexample.ipynb
deleted file mode 100644
index 4e2e97e0c..000000000
--- a/notebooks/fig2e_mnist_replication_counterexample.ipynb
+++ /dev/null
@@ -1,267 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4ff4dda1",
-   "metadata": {},
-   "source": [
-    "# MNIST literature reproduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2f9a07c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "%matplotlib widget\n",
-    "\n",
-    "import os, gzip, struct, urllib.request\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# MNIST IDX mirror + cache\n",
-    "DATA_DIR = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"mnist_idx\")\n",
-    "USE_MIRROR = \"https://storage.googleapis.com/cvdf-datasets/mnist/\"\n",
-    "FILES = {\n",
-    "    \"train_images\": \"train-images-idx3-ubyte.gz\",\n",
-    "    \"train_labels\": \"train-labels-idx1-ubyte.gz\",\n",
-    "    \"test_images\":  \"t10k-images-idx3-ubyte.gz\",\n",
-    "    \"test_labels\":  \"t10k-labels-idx1-ubyte.gz\",\n",
-    "}\n",
-    "\n",
-    "LABEL_OF_INTEREST = 4\n",
-    "GEOMED_MAX_ITERS = 1000\n",
-    "GEOMED_TOL = 1e-7\n",
-    "GEOMED_SUBSAMPLE = None  # set to e.g. 3000 if geomedian is slow\n",
-    "\n",
-    "def _download_if_needed():\n",
-    "    os.makedirs(DATA_DIR, exist_ok=True)\n",
-    "    for fn in FILES.values():\n",
-    "        path = os.path.join(DATA_DIR, fn)\n",
-    "        if not os.path.exists(path):\n",
-    "            urllib.request.urlretrieve(USE_MIRROR + fn, path)\n",
-    "\n",
-    "def _read_idx_images(path_gz):\n",
-    "    with gzip.open(path_gz, \"rb\") as f:\n",
-    "        magic, n, rows, cols = struct.unpack(\">IIII\", f.read(16))\n",
-    "        if magic != 2051:\n",
-    "            raise ValueError(f\"Invalid magic for images: {magic}\")\n",
-    "        arr = np.frombuffer(f.read(rows * cols * n), dtype=np.uint8)\n",
-    "        return arr.reshape(n, rows, cols)\n",
-    "\n",
-    "def _read_idx_labels(path_gz):\n",
-    "    with gzip.open(path_gz, \"rb\") as f:\n",
-    "        magic, n = struct.unpack(\">II\", f.read(8))\n",
-    "        if magic != 2049:\n",
-    "            raise ValueError(f\"Invalid magic for labels: {magic}\")\n",
-    "        return np.frombuffer(f.read(n), dtype=np.uint8)\n",
-    "\n",
-    "def load_mnist_all():\n",
-    "    _download_if_needed()\n",
-    "    paths = {k: os.path.join(DATA_DIR, v) for k, v in FILES.items()}\n",
-    "    x_tr = _read_idx_images(paths[\"train_images\"]).astype(np.float32) / 255.0\n",
-    "    y_tr = _read_idx_labels(paths[\"train_labels\"])\n",
-    "    x_te = _read_idx_images(paths[\"test_images\"]).astype(np.float32) / 255.0\n",
-    "    y_te = _read_idx_labels(paths[\"test_labels\"])\n",
-    "    X = np.concatenate([x_tr, x_te], axis=0)\n",
-    "    y = np.concatenate([y_tr, y_te], axis=0)\n",
-    "    return X, y\n",
-    "\n",
-    "def geometric_median_weiszfeld(X, max_iters=1000, tol=1e-7):\n",
-    "    X = np.asarray(X, dtype=np.float64)\n",
-    "    x = np.median(X, axis=0)\n",
-    "    if not np.all(np.isfinite(x)):\n",
-    "        x = np.mean(X, axis=0)\n",
-    "    eps = 1e-12\n",
-    "    for _ in range(max_iters):\n",
-    "        diffs = X - x\n",
-    "        dists = np.linalg.norm(diffs, axis=1)\n",
-    "        hit = np.where(dists < eps)[0]\n",
-    "        if hit.size:\n",
-    "            return X[hit[0]].copy()\n",
-    "        w = 1.0 / np.maximum(dists, eps)\n",
-    "        x_new = (w[:, None] * X).sum(axis=0) / w.sum()\n",
-    "        if np.linalg.norm(x_new - x) < tol:\n",
-    "            return x_new\n",
-    "        x = x_new\n",
-    "    return x\n",
-    "\n",
-    "def svd_basis(A):\n",
-    "    U, s, Vt = np.linalg.svd(A, full_matrices=False)\n",
-    "    tol = s[0] * max(A.shape) * np.finfo(float).eps if s.size > 0 else 0.0\n",
-    "    r = int(np.sum(s > tol)) if s.size > 0 else 0\n",
-    "    if r == 0 and s.size > 0:\n",
-    "        r = 1\n",
-    "    return U[:, :r], s[:r], Vt[:r]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "935a60c8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Compute theory reps on the GLOBAL set (no labels) and choose practical reps\n",
-    "X_all, y_all = load_mnist_all()\n",
-    "N, H, W = X_all.shape\n",
-    "X_flat = X_all.reshape(N, H*W)\n",
-    "\n",
-    "# Theoretical centers (pixel space)\n",
-    "mean_vec   = np.mean(X_flat, axis=0)\n",
-    "median_vec = np.median(X_flat, axis=0)\n",
-    "if GEOMED_SUBSAMPLE is not None and GEOMED_SUBSAMPLE < X_flat.shape[0]:\n",
-    "    rng = np.random.default_rng(123)\n",
-    "    take = rng.choice(X_flat.shape[0], size=GEOMED_SUBSAMPLE, replace=False)\n",
-    "    X_geo = X_flat[take]\n",
-    "else:\n",
-    "    X_geo = X_flat\n",
-    "geomed_vec = geometric_median_weiszfeld(X_geo, max_iters=GEOMED_MAX_ITERS, tol=GEOMED_TOL)\n",
-    "\n",
-    "theory = {\n",
-    "    \"mean\": mean_vec.reshape(H, W),\n",
-    "    \"median\": median_vec.reshape(H, W),\n",
-    "    \"geom_median\": geomed_vec.reshape(H, W)\n",
-    "}\n",
-    "\n",
-    "# SVD basis over all '4's\n",
-    "A = X_flat.T                               # (784, N) columns are images\n",
-    "U_r, s_r, Vt_r = svd_basis(A)               # U_r: (784, r)\n",
-    "C = U_r.T @ X_flat.T                       # (r, N) coordinates of all real images\n",
-    "\n",
-    "def nearest_in_basis(xT_vec):\n",
-    "    cT = U_r.T @ xT_vec                     # (r,)\n",
-    "    dists = np.linalg.norm(C.T - cT[None, :], axis=1)\n",
-    "    idx = int(np.argmin(dists))\n",
-    "    return idx, float(dists[idx])\n",
-    "\n",
-    "chosen = {}\n",
-    "for name, vec in {\"mean\": mean_vec, \"median\": median_vec, \"geomedian\": geomed_vec}.items():\n",
-    "    idx, dist = nearest_in_basis(vec)\n",
-    "    chosen[name] = (idx, dist)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3226805e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be89d957cfb94a43914d8f388af7a96b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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5fvPNN3e7zddee00TJkzQd77zHUlSbW1tmz98AmDvFBYW6swzz9Svf/1rXXXVVbttdoYOHapPPvlEmZmZ6T8S8kXl5eV6/fXXdeGFF6YfW7x4scrLy/dpnUE1grS9SdmbGmvgwIFKpVJ69dVX2/xkVZiGDh2qFStW7PI1lJeXK5lMaunSpTr22GMlbf8psN3NxFy0aJFKS0t12223pR9bt25dqOvG3qOB6yBz585VVVWVLrnkEhUUFLR6bty4cZo5c+YeNXBlZWWaN2+eKisrVVhY2CZnmPr166fvf//7uvDCCzV16lQNGTJEmzdv1iuvvKKBAwfqrLPOUllZmWpra7VgwQINHjxY8Xi8zY8hnHLKKTr55JN17rnnatq0aerbt6/ef/99RSKRPZqJF+T222/X2WefrZKSEp133nnKyMjQu+++q3/961+6++671adPHyWTST344IMaPXq0Fi1apBkzZoS2fQDBotGoVq5cmf73zjp16qQbbrhB1113nVKplE488URt3bpVixcvVl5eni666CJdfvnlmjp1qn7605/qsssu07Jly9r8Vd8v6tu3r2bPnq3Ro0crEolo0qRJ/M8xEKLf/OY3OuGEE1RRUaHJkydr0KBBysjI0JIlS/T+++/r6KOPliSNGDFCxx9/vMaOHat7771X/fv318cff6wXXnhBY8eOVUVFhW688UaNHz9eQ4cO1RlnnKE5c+Zo9uzZ+zyKIKhGkLbXWAsWLNAJJ5ygWCzW6iekdqesrEwXXXSRLr74Yv3qV7/S4MGDtW7dOm3cuFHjx4/fp3Xv7Oabb9awYcP04x//WBMnTlQikdDKlSs1f/58Pfjgg+rfv79GjRqliRMn6re//a0yMzN17bXX7vYOYd++fbV+/Xo99dRTOuaYY/T888/rmWeeCW3N2DeMEeggM2fO1IgRI7600Tr33HO1fPlyvf322+Z8EydOVP/+/VVRUaGioiItWrQozOW2MWvWLF144YW6/vrr1b9/f33729/WP/7xD5WUlEiShg8frssvv1znn3++ioqKdN99931pnr/85S865phj9N3vfleHH364brrpptDvJI4cOVJz587V/Pnzdcwxx2jYsGGaNm2aSktLJUlHHXWUpk2bpnvvvVdHHnmknnzySd1zzz2hrgFAsPz8/Da/X7HDXXfdpdtvv1333HOPysvLNXLkSM2ZM0eHHXaYpO0/JvSXv/xFc+bM0eDBgzVjxgxNmTJlt9ubPn26unTpouHDh2v06NEaOXKkhg4dGvrrAg5Wffr00TvvvKMRI0bolltu0eDBg1VRUaEHH3xQN9xwQ3rgdSQS0QsvvKCTTz5ZF198sfr166cLLrhAa9euTf++19ixY/XLX/5S999/v4444gg98sgjmjVrlk499dR9WmNQjSBJU6dO1fz589uMN7F4+OGHNW7cOF155ZUaMGCAJk6c2OrP+4dh0KBBevXVV/XBBx/opJNO0pAhQzRp0qT03z2QttdtJSUlOuWUU3TOOeekRzbsypgxY3TdddfpJz/5iY466igtXrxYkyZNCnXd2HsRxy/xAAAAAIAXuAMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeCIzzGSRSCTMdAC+gvbX6EnOPwCCcP4B0FHCPP9wBw4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeyOzoBaD9RCKRUOPaO1eYnHOhxIS5PQAAEL6OqGuof8LPhc9xBw4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCQd4dyDLkMcyBkRkZtn7dEmfNFY1GA2PCHHaZSqVCi2tpaQktl3Vd7T1gEwCA9hbmdT/MmsWyLktdY91mmPvBWhtYahtrzWLJZV2XdZvYjjtwAAAAAOAJGjgAAAAA8AQNHAAAAAB4ggYOAAAAADxBAwcAAAAAnqCBAwAAAABP0MABAAAAgCdo4AAAAADAEzRwAAAAAOCJzI5egG8ikUgoMZKUkRHcP2dm2t6irKyswJhYLGbKZYnLyckJLZdlP1g1Nzeb4hoaGgJj6uvrQ8vV1NRkypVMJgNjUqmUKZc1DgCAMFiv55a4aDRqymWpf7Kzs025LDWLtZay1EnW1+icC4yx1hmNjY2BMZa6xhpnrcuof/YMd+AAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnGOT9/zpi+LZlsKR1YHYikQiMycvLM+UqKCgIjMnPzzflsmzTOmDTMpjROnx7y5YtgTFVVVWmXFu3bg2MqampMeWyrL8jhoIDAL6awqx/why+ba1/4vF4YIylRpJs9U/nzp1NuSzrsg4Fb+/6p7q62pQrzPrnQB0KfqDiDhwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeCKzoxfQHiKRSGBMRoatl83MDN5lsVjMlCsejwfG5OXlmXJ17tw5MKawsNCUq2vXrqHESLb1Z2dnm3Ilk8nAmPr6elOu6urqwBjL+yPZ1m/5DEqScy6UmI7IBYTBcjxVVFSYclnirOcyy7llxYoVplyvv/56YIzlHAWEwXp9ikajgTHW63lubm5gTKdOnUy58vPzA2O6dOliytWtW7fAGOs5w7L+rKwsU65UKhUYU1dXZ8pVVVUVGGN5fyRbTRwm6p/PcQcOAAAAADxBAwcAAAAAnqCBAwAAAABP0MABAAAAgCdo4AAAAADAEzRwAAAAAOAJGjgAAAAA8AQNHAAAAAB4gkHe/886yNsydDEnJ8eUyzLk2jow0jKku3v37qZcloGX1gGbln1hGQ4qSS0tLYEx1iGils+EZXCmJDU1NQXGNDQ0mHJZ4pqbm025LIPPwxwwDuzOIYccYoobPnx4YMyRRx5pymU551nPGZbjznJOl2xDcufPn2/KxcBv7I6ltrFegy1Dm631TyKRCIyxDOiWpKKiosAYy4BuyVZLFRQUmHJZ9oV1ELbleh5m/WOptySpsbExMMZa/1hyWeot6eCof7gDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACesI2AP0BZp6hb4qLRqClXZmbwLovFYqZciUQiMKagoMCUq2vXroEx+fn5plw5OTmBMalUypSrrq4utFwZGcH/32DNZXm/s7OzTbks+8uay/L5sn7ugfZiObccccQRplyHHHJIYExjY6Mp1/r16wNjrOcMy3FnzWU593fr1s2Uq7q62hSHr5Yw6x/LtVWyXZ+s1zrLMdC5c2dTLkv9Y82Vm5sbGGM9zhsaGkLL1d71j7WOtcRlZWWZclnWRf3zOe7AAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT3g9yNvKMvjPOhzQMmjQOrTQMgA6Ho+bclmGT1qHddbX1wfGWAfpNjc3m+IsLPvVOnzSwjkXWq4wWddlGep5oL5G+MUySNcyBFiSKisrA2M2b95syrVly5bAGMs5XZK6dOkSGFNUVGTKZTnumpqaTLmAfRVm/WM9zi3XaktdY42zrssyfNt6bCaTycAY67631D/WIeoHam1g2aZ1XWHmOlBxBw4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPGEbTY+0SCQSGJORYeuLs7KyQomRbOtqaGgw5aqrqwuMqa2tNeVKpVKBMdbXGI/HA2Ms+0GyvUfNzc2mXE1NTYExyWTSlKulpSUwxrJPJck5Z4oD9lU0Gg2M+fe//23K9cknnwTGVFVVmXJZjqfMTNtlsKioKDDGesxZznlbtmwx5QIOJGHWP7FYzJQrzPqnvr4+MGbbtm2mXJbzQXZ2tilXbm6uKS4slrrGGmetpah/9gx34AAAAADAEzRwAAAAAOAJGjgAAAAA8AQNHAAAAAB4ggYOAAAAADxBAwcAAAAAnqCBAwAAAABP0MABAAAAgCcOikHeYQ70swyMtA6GtQyytOayDDe0Dma0DOm2DsW0DPi1DrK05LLESLaBkdbhk42NjYEx1n1vWVeYn+eDYdgl9j/L59s6mHrz5s37upw9Yh0Mm0gkAmPi8bgpl2VYcE1NjSkX0F4s9Y/1Gmypbaz1j4W1ZrEcd5ZrvmSr8Sz7VLLtC+u+TyaTgTHW+sdy7u+I+scS53v9wx04AAAAAPAEDRwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPBEeGPuO0CYE9mtIpFIYExmpm23WuKi0agpVyqVCoxJJpOh5crOzjblisfjgTGdOnUy5UokEoExWVlZplx1dXWBMc3NzaZcTU1NgTFh7nvgQGP5fNfW1rbDSlqznK+//vWvm3INHjw4MKagoMCUa+XKlYExnAuwO2HWP9ZcluPJWrNYrtXWXJb1W6/BllyxWMyUKy8vLzAmzPrHur9qamoCY6z1T2NjY2AM9c/+wR04AAAAAPAEDRwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgCa8HeYfJMqBSsg1KDHOQt3VdYbIMqbQMlZRsg20twy4l2/DwlpYWU676+vrQclnirAMqLe+39TPREZ8dfLVkZNj+j886qNXCcpwXFxebcvXp0ycw5sQTTzTl6tq1a2CM5bwiSR988IEpDmgP1muF5XzQEfVPmAOgLfWPtWbp3LlzYEx+fr4pl2V/WYdvWwZ5W8/pljjroPgwHQz1D3fgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATwaPdvwIsE9kzMmy9bDQaDS2XRUtLiykuzHUlEonAmMLCQlOugoKCwJjc3FxTrlQqFRhTV1cXWi5LjGT7fFneH2suSwwQBusx0NzcHBiTk5NjytWtW7fAmOOPP96U69hjjw2MOeSQQ0y5GhsbA2M+/vhjUy7L/gLay4Fa/zjnQouzXoMt56mioiJTrs6dOwfGWOufZDIZSowUbv1jeb+tNUuYuQ4G3IEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeOCgGeVtYh0+GOWjQMijROsjbMsAxzEGW1qG8WVlZpjgLyyBdS4xkG6RrHSLa3oMsw2Rdl3Vf4OBUX18fGNOpUydTLssg7169eplyWQbuWgfWVldXB8Z88sknplxVVVWmOKA9dMT1yVLbNDU1mXJZ1pWZaSt3w6x/srOzTXEWln1hrX8sucKsf6y1p+VzyCDvz3EHDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeIIGDgAAAAA8YRtN7znL5HbLNHlrXEtLiylXU1NTYExDQ4Mpl2Wb2dnZplzJZDIwxrquaDQaGGN5fySpsbExMKa+vt6Uy7LvrSyfCevnyzm3r8sB2p3l2LQe59XV1YExa9euNeWKx+OBMdZjc+PGjYExq1evNuWqqqoyxQH7Ksz6x5LLeg1rbm4OjLHWGRaxWMwUF2b9k5kZXGJbz4t1dXWhxEi2+ifMWsT6Gi06Yl0Hal3GHTgAAAAA8AQNHAAAAAB4ggYOAAAAADxBAwcAAAAAnqCBAwAAAABP0MABAAAAgCdo4AAAAADAEzRwAAAAAOAJBnn/P+sgyzBZBllaB1NbBnmnUilTLgvrAETLuizDvq25LAOFrbnCZN33loGRYb6PQHuxDr/94IMPAmOsg1VramoCYxKJhCmXZf2bNm0KLRcQhvauf6zXVsswaWv9Y7kmWq+blv1lrVks27Tue0u96Hv9Y4k7UIdqdwTuwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJzI7egH7IhKJmOIsk+4tMXsSZ2GZOp9MJk25otFoaLmampoCYxobG025srKyAmOys7NNuZxzpjgLy/to3Z5lv1rea0lqaWkJjLGuK8z9BbQXyzGwefNmU64uXboExvTs2dOUy8J6fbCeD4BdCbP+CTOXVZj1T2ZmcCkbZv3T0NBgymWpfyxrl2z7K8xrvjWXZV3W812Yr/FgqH+4AwcAAAAAnqCBAwAAAABP0MABAAAAgCdo4AAAAADAEzRwAAAAAOAJGjgAAAAA8AQNHAAAAAB4ggYOAAAAADxxUAzytsSFOcjSMlRbsg1wtA65tsRZhkpac3XEuiyDGa3DOsMcItrc3BwYYxkOat2mdSjmwTDIEl89lvNBt27dTLmKi4sDY4qKiky5LAPGP/vsM1MuyzkDCIOltrEO6LbEWQdTW+KstYElriPqnzBfo+WcYa1jLTWE9RxlqW2suSznWGv9Y+F7jcQdOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwRPCY+INEmBPZI5GIKS4WiwXGxONxUy5LXHZ2dmi5rOvKzc01xVk0NjYGxrS0tJhyNTQ0BMbU1NSYcm3bti2U7UlSc3NzYIz1NVo+02F+7nHwysgI/r/A7t27m3KVlZUFxpSXl5tylZaWmuIsNm/eHBhTW1trylVXV7evywFCY70OWGqbaDRqymWpR6x1RiKRCIyx1iKWbebl5Zly5eTkmOIsLNf9ZDJpylVfXx8YY6lrrLkstZtkW38qlTLlOhhqG+7AAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT3g9yNs6qM8y+M86ANESF+ZQzDCHb1uHT1pyWQdUWgb8Woc8WoZhV1dXm3J99tlnoeWyDO8Nc5A3gyxxoOnVq1dgTEVFhSlXnz59AmN69OhhyhWLxQJjNm3aZMq1bt26wJgVK1aYcgHtxXK9sAyJlmz1jzWXpTYIs/7p1KmTKVeYQ8Hbu/7ZunWrKVdVVVVouSwDv62vkfpnz3AHDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgCRo4AAAAAPAEDRwAAAAAeIIGDgAAAAA8kdnRC2gPLS0tgTFNTU2mXPX19YExlsn0kpSbmxsYk0gkTLms0+nD0tzcHFpcTU2NKdfmzZsDY/73v/+Zcm3atCkwprq62pSrtrY2MKahocGUK5lMBsZY32vnnCkO2Fdbt24NjLGchyUpEomEsj3Jdmy+9dZbplyvvfZaYEx7n4dx8LKe3y2fSev13HIds17rLHHWdVn2heW8Yo2zXKcl2/q3bNliyhVm/WPJZV1XXV1dYIy1vrZcI6h/PscdOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AmvB3mHOcjSOmjQMhg2TNahhZbBko2NjaZcOTk5gTHWfW8Z1mkdGPnZZ5+FEmPdpmVApWTbr9bBn5b3m2HBONBYjrtnnnnGlOvQQw8NjMnIsP3fo2WwrfXcD/gozEHelmuidWB2mCz1iPU437ZtW2CM9TXW19cHxlRXV5tyffrpp4ExHVH/WPZrmPXPwTCg24o7cAAAAADgCRo4AAAAAPAEDRwAAAAAeIIGDgAAAAA8QQMHAAAAAJ6ggQMAAAAAT9DAAQAAAIAnaOAAAAAAwBM0cAAAAADgiYgLcay5dTr9gci69mg0GhiTmZlpypWTkxMYE4/HTbkscdZcWVlZpjiL5ubmwJi6ujpTroaGhsCY+vp6U67GxsbAGMvaJSmZTAbGpFIpU64QD8cD1v56jT6ffwC0D84/bVnXnpER/H/+1vqhveufRCJhyhVm/dPU1BQYY61/LLVNR9Q/LS0tgTHUP58L8zVyBw4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCQd57yPIaLcMuJdtQcEuMZBsebs1lWb/1Y2OJswyCtMZZc1kGS1qHT1riDoYBlVYM0gXQUTj/7L0Dtf6xDN/uiPrHUht0RP3T3sO3qX8+xyBvAAAAADgI0cABAAAAgCdo4AAAAADAEzRwAAAAAOAJGjgAAAAA8AQNHAAAAAB4ggYOAAAAADxBAwcAAAAAnqCBAwAAAABPRFyIY8EjkUhYqbxm3Q+WuAM1l5Xl42X9CB6oubBn9td+5fwDIAjnn/3rQK1ZMjLa/37FgVqzUP90nDD3K3fgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeoIEDAAAAAE/QwAEAAACAJzI7egFfRWEOUwQAAPAB9Q/QPrgDBwAAAACeoIEDAAAAAE/QwAEAAACAJ2jgAAAAAMATNHAAAAAA4AkaOAAAAADwBA0cAAAAAHiCBg4AAAAAPEEDBwAAAACeiDjnXEcvAgAAAAAQjDtwAAAAAOAJGjgAAAAA8AQNHAAAAAB4ggYOAAAAADxBAwcAAAAAnqCBAwAAAABP0MABAAAAgCdo4AAAAADAEzRwAAAAAOAJGjgAAAAA8AQNHAAAAAB4ggYOAAAAADxBAwcAAAAAnqCBAwAAAABP/B9iIT+SZPJybgAAAABJRU5ErkJggg==",
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-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=880.0000000000001/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "#| label: fig2e_data\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(8.8, 3))\n",
-    "for ax, key, ttl in zip(\n",
-    "    axes,\n",
-    "    [\"mean\", \"median\", \"geom_median\"],\n",
-    "    [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "):\n",
-    "    ax.imshow(np.clip(theory[key], 0, 1), cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(ttl, fontsize=10)\n",
-    "    ax.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "23dba138",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "53e5e2b315694578809c1262ba675593",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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-       "                </div>\n",
-       "                <img src='data:image/png;base64,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' width=880.0000000000001/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(8.8, 3))\n",
-    "for ax, key, ttl in zip(\n",
-    "    axes,\n",
-    "    [\"mean\", \"median\", \"geomedian\"],\n",
-    "    [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "):\n",
-    "    idx, _ = chosen[key]\n",
-    "    ax.imshow(X_all[idx], cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(ttl, fontsize=10)\n",
-    "    ax.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2f_mnist_autoencoder_counterexample.ipynb b/notebooks/fig2f_mnist_autoencoder_counterexample.ipynb
deleted file mode 100644
index a22bf2fde..000000000
--- a/notebooks/fig2f_mnist_autoencoder_counterexample.ipynb
+++ /dev/null
@@ -1,347 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "da41d574",
-   "metadata": {},
-   "source": [
-    "# MNIST autoencoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0278101b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TensorFlow: 2.20.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib widget\n",
-    "\n",
-    "import os, math, random\n",
-    "import numpy as np\n",
-    "import h5py\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.layers import Input, Dense, Reshape, Conv2DTranspose, ReLU\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from pathlib import Path\n",
-    "\n",
-    "np.random.seed(42)\n",
-    "tf.random.set_seed(42)\n",
-    "\n",
-    "print(\"TensorFlow:\", tf.__version__)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0c77339f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RESULTS_DIR: ..\\data\\mnist-ae\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ─────────────────────────────────────────────────────────\n",
-    "# CONFIG — set this back to your original style/path\n",
-    "# ─────────────────────────────────────────────────────────\n",
-    "RESULTS_DIR       = Path(\"..\") / \"data\" / \"mnist-ae\"   # ← change to your actual folder if needed\n",
-    "LATENTS_H5        = os.path.join(RESULTS_DIR, \"latents.h5\")\n",
-    "DECODER_WEIGHTS   = os.path.join(RESULTS_DIR, \"decoder_weights.h5\")\n",
-    "\n",
-    "# Must match training\n",
-    "N_LAYERS      = 2\n",
-    "BASE_FILTERS  = 16\n",
-    "LATENT_DIM    = 64\n",
-    "\n",
-    "# Geometric median settings\n",
-    "GEOMED_MAX_ITERS = 1000\n",
-    "GEOMED_TOL       = 1e-7\n",
-    "GEOMED_SUBSAMPLE = None\n",
-    "\n",
-    "print(\"RESULTS_DIR:\", RESULTS_DIR)\n",
-    "assert os.path.exists(LATENTS_H5),      f\"Missing file: {LATENTS_H5}\"\n",
-    "assert os.path.exists(DECODER_WEIGHTS), f\"Missing file: {DECODER_WEIGHTS}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "416ac830",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded Z: (70000, 64), labels: (70000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "with h5py.File(LATENTS_H5, \"r\") as hf:\n",
-    "    Z      = hf[\"z\"][:].astype(np.float32)     # (N, D)\n",
-    "    labels = hf[\"labels\"][:].astype(np.int64)   # (N,)\n",
-    "\n",
-    "N, D = Z.shape\n",
-    "print(f\"Loaded Z: {Z.shape}, labels: {labels.shape}\")\n",
-    "assert D == LATENT_DIM, f\"LATENT_DIM mismatch: expected {LATENT_DIM}, got {D}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9db62815",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ALL_IMAGES: (70000, 28, 28, 1) ALL_LABELS: (70000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "x_train = np.expand_dims(x_train.astype(\"float32\") / 255.0, -1)\n",
-    "x_test  = np.expand_dims(x_test.astype(\"float32\")  / 255.0, -1)\n",
-    "\n",
-    "ALL_IMAGES = np.concatenate([x_train, x_test], axis=0)\n",
-    "ALL_LABELS = np.concatenate([y_train, y_test], axis=0)\n",
-    "assert ALL_IMAGES.shape[0] == N, \"Mismatch between number of latents and images\"\n",
-    "\n",
-    "print(\"ALL_IMAGES:\", ALL_IMAGES.shape, \"ALL_LABELS:\", ALL_LABELS.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f5e3045d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using GLOBAL set: 70000 latents\n"
-     ]
-    }
-   ],
-   "source": [
-    "# UPDATED: Use the entire dataset (GLOBAL) independent of labels\n",
-    "Z_sub   = Z\n",
-    "IM_sub  = ALL_IMAGES\n",
-    "LBL_sub = labels\n",
-    "\n",
-    "print(f\"Using GLOBAL set: {Z_sub.shape[0]} latents\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bb09f689",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Decoder loaded from: ..\\data\\mnist-ae\\decoder_weights.h5\n"
-     ]
-    }
-   ],
-   "source": [
-    "def build_decoder(n_layers=2, base_filters=16, latent_dim=64):\n",
-    "    latent_in = Input((latent_dim,), name=\"z_sampling\")\n",
-    "    spatial   = 28 // (2**n_layers)  # 28 -> 7 for 2 layers\n",
-    "    channels  = base_filters * (2**(n_layers-1))\n",
-    "\n",
-    "    x = Dense(spatial * spatial * channels)(latent_in)\n",
-    "    x = Reshape((spatial, spatial, channels))(x)\n",
-    "\n",
-    "    for i in reversed(range(n_layers)):\n",
-    "        f = base_filters * (2**i)\n",
-    "        x = Conv2DTranspose(f, 3, strides=(2,2), padding=\"same\")(x)\n",
-    "        x = ReLU()(x)\n",
-    "\n",
-    "    out = tf.keras.layers.Conv2D(1, 3, padding=\"same\", activation=\"sigmoid\", name=\"decoder_output\")(x)\n",
-    "    return Model(latent_in, out, name=\"decoder\")\n",
-    "\n",
-    "decoder = build_decoder(N_LAYERS, BASE_FILTERS, LATENT_DIM)\n",
-    "decoder.load_weights(DECODER_WEIGHTS)\n",
-    "print(\"Decoder loaded from:\", DECODER_WEIGHTS)\n",
-    "\n",
-    "_ = decoder.predict(np.zeros((1, LATENT_DIM), dtype=np.float32), verbose=0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fb3b0062",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Central latents computed for label==4 subset.\n"
-     ]
-    }
-   ],
-   "source": [
-    "def geometric_median(points, tol=1e-7, max_iters=1000):\n",
-    "    P = points.astype(np.float64)\n",
-    "    x = P.mean(axis=0)\n",
-    "    eps = 1e-12\n",
-    "    for _ in range(max_iters):\n",
-    "        diff = P - x\n",
-    "        d    = np.linalg.norm(diff, axis=1) + eps\n",
-    "        w    = 1.0 / d\n",
-    "        x_new = (w[:, None] * P).sum(axis=0) / w.sum()\n",
-    "        if np.linalg.norm(x_new - x) <= tol:\n",
-    "            return x_new.astype(np.float32)\n",
-    "        x = x_new\n",
-    "    return x.astype(np.float32)\n",
-    "\n",
-    "mean_z   = Z_sub.mean(axis=0)\n",
-    "median_z = np.median(Z_sub, axis=0)\n",
-    "\n",
-    "if GEOMED_SUBSAMPLE is None or GEOMED_SUBSAMPLE >= Z_sub.shape[0]:\n",
-    "    gm_input = Z_sub\n",
-    "else:\n",
-    "    sel = np.random.choice(Z_sub.shape[0], GEOMED_SUBSAMPLE, replace=False)\n",
-    "    gm_input = Z_sub[sel]\n",
-    "\n",
-    "geom_z = geometric_median(gm_input, tol=GEOMED_TOL, max_iters=GEOMED_MAX_ITERS)\n",
-    "print(\"Central latents computed for label==4 subset.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "166a095a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reconstructions: (3, 28, 28)\n"
-     ]
-    }
-   ],
-   "source": [
-    "central_stack = np.stack([mean_z, median_z, geom_z], axis=0).astype(np.float32)\n",
-    "recons = decoder.predict(central_stack, verbose=0)  # (3, 28, 28, 1)\n",
-    "recons = np.squeeze(recons, axis=-1)                # -> (3, 28, 28)\n",
-    "print(\"Reconstructions:\", recons.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "9420572d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def nearest_idx(vec, Z):\n",
-    "    diff = Z - vec[None, :]\n",
-    "    d2   = np.sum(diff*diff, axis=1)\n",
-    "    idx  = int(np.argmin(d2))\n",
-    "    dist = float(np.sqrt(d2[idx]))\n",
-    "    return idx, dist\n",
-    "\n",
-    "# UPDATED: compute nearest matches against the full latent set (GLOBAL)\n",
-    "mean_i_global,   mean_dist   = nearest_idx(mean_z,   Z)\n",
-    "median_i_global, median_dist = nearest_idx(median_z, Z)\n",
-    "geom_i_global,   geom_dist   = nearest_idx(geom_z,   Z)\n",
-    "\n",
-    "closest_global_indices = [int(mean_i_global), int(median_i_global), int(geom_i_global)]\n",
-    "closest_dists  = [float(mean_dist), float(median_dist), float(geom_dist)]\n",
-    "closest_images = ALL_IMAGES[closest_global_indices]\n",
-    "closest_labels = labels[closest_global_indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "13226f21",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a125244aa23d4622808e4760061411cc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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3AR+bX2Ltsdzf7wcNhD/Xq93VV7l+3qtddekxEjRsiLL77Y6vLa2tq9OVwoUQj3UzAYlFdffVV69uwps2bNavP5oqIieeaZZ+T999/fp5PIHwq/4RgzZozMnz9fgsGg62k0dvfq5K4SEhJk+PDh8uabb8qjjz4asUVZ44wzzhCPxyM//vijXHjhhbvNOY4jxpg2C/msWbMkGAwe6MMEICJdu3aVO+64Q9asWSNXXHFFuxntGtVyIvs5c+bICSecEL78jTfekEAgsMfjYD0ADozJkyfL+++/L9dff70sWLDA9e2SBQUFcvTRR8s333wjjz322B6zp59+urzzzjtSWlra6q+Uzp49WxITEyN2qoMxY8bIE088IZs3b5Zx48btMavdJ+3J4bCHKigokJycHJk3b5785S9/CRfdjRs3ytKlS9v81dhdOY7TZr399ttvpbi4uM1bgLH/KIT76f3335fS0lKZOnVqeLPxa8cee6z87W9/k8LCwn0qhP379xcRkenTp8sVV1whMTExUlBQcMBPyvxr48ePlzlz5sjZZ58tt9xyiwwZMkRiYmJk06ZNsnjxYjnvvPNk7Nix4eOdP3++vP7669KjRw+Jj48P34ZdTZs2TU455RQ58cQT5e6775ZevXpJRUWFvPfeezJz5swDdhvz8/PloYceknvvvVfWr18vZ555pqSmpkpFRYUsW7ZMvF6vPPjgg5KUlCSnnXaaPPXUU5KRkSH5+fmyZMkSKSwslJSUlANybADaeuKJJ/b4ee0a1bdvX7n00kvl2WeflZiYGPntb38r3333nTz99NOtznXYHtYD4MAYNmyYPP/883LzzTfLoEGD5Nprr5VjjjlGoqKipKysTN566y0RkVbP0ZkzZ8pZZ50lZ5xxhkycOFG6du0qVVVVsnr1almxYoW8+eabIiIyZcoUKSoqkpEjR8r9998vaWlpMmfOHFm4cKE8+eSTkpycHLHbcO2118qkSZNk+fLlctppp4nX65WysjL5z3/+I/3795cbbrhBRH7ZJ7399tvy4osvygknnCBRUVHym9/8Zq+v81DfQ0VFRcnDDz8sV199tYwdO1auueYaqampkQceeKDdt5HuasyYMfLwww/LlClTZPjw4bJ27Vp56KGHpHv37q6/wMPeoxDup8LCQomNjZVJkya1+/mMjAwZO3asLFiwIPy+670xYsQImTx5srz66qvy97//XUKhkCxevLjd8nmgREdHy3vvvSfTp0+Xf/zjH/L444+Lx+OR3NxcGT58eKvC9+CDD0pZWZlcc801UltbK3l5ebt9n/jxxx8vy5YtkylTpsjkyZOltrZWsrOzZdSoUbv9v0CRMnnyZOnXr59Mnz5d5s2bJ42NjZKdnS2DBw+W66+/PpybO3eu3HLLLXLnnXdKIBCQYcOGyUcffdTmP10D6Dh7s0YVFhZKVlaWvPLKK/Lcc8/JgAED5K233gr/QbA9YT0ADozrr79ehg4dKtOnT5e//vWvUlpaKo7jSG5urpx88sny8ccfy6hRo8L5kSNHyrJly+TRRx+VW2+9VaqrqyU9PV369evX6hW6goICWbp0qdxzzz1y0003SX19vfTt21defvllmThxYkRvw8yZM+Wkk06SmTNnygsvvCChUEi6dOkiw4YNkyFDhoRzt9xyi6xatUruuece2b59u5hf/uL/Xl/f4bCHuuqqq0REZOrUqXLBBRdIfn6+3HPPPbJkyRLXP7Rz7733it/vl8LCQnnyySelX79+MmPGDHnnnXf26Y8ZYs8csy/fhQAAAACAwx6nnQAAAAAAS1EIAQAAAMBSFEIAAAAAsBSFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALAUhRAAAAAALOXZ1y90HCeSxwHAIsaYg3p9rFcA9hXrFYDDxb6uV7xCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApT0cfAA48x3FUuejoaNdMRkaGapY2l56e7pqJj49XzQoEAqrcxo0bVbmysjLXjN/vV80yxqhygO0iuV5lZmaqZuXk5EQsl5KSopoVCoVUuZUrV6pyGzZscM3s3LlTNYv1CtDRrldRUe6vv6SmpqpmRXIf5vV6VbO0+yvNOiSi21/V19erZrFeRQavEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlnLMPp7RUXsyTuw97X0bFxenyvXo0UOV69atm2tm1KhRqlldunRR5TTffj///LNqlsfjUeVqa2tVudWrV7tm/ve//6lmbdy4UZWz5QSrB/t2sl4dONr7NjY2VpXr2rWrKpeXl+ea+f3vf6+a1bNnT1WusbHRNfPTTz+pZmnvN811ioisWrXKNfP555+rZmlOGm0T1iv7aPcTubm5qlz37t1dM6eddppqVq9evVS56Oho10xJSYlqlvb+qK6uVuU069Xy5ctVszZt2qTK2WJf1yteIQQAAAAAS1EIAQAAAMBSFEIAAAAAsBSFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALAUhRAAAAAALEUhBAAAAABLeTr6ANBWp06dVLlRo0apcqeffroq17lzZ9fM9u3bVbNWrlypyvl8PtdMSUmJalZaWpoqFx8fr8odddRRrpmdO3eqZlVUVKhy9fX1qhxwMDiO45rRrleDBw9W5U466SRVrkePHq6ZpqYm1ayvvvpKlYuKcv8d6vr161WzsrKyVDntuqbJde3aVTWrsrJSlWtublblgEOF9uf/kCFDVLkzzjhDlSsoKHDN1NXVqWatXbtWlUtMTHTNbNiwQTUrJSVFlfN6vapcXl6ea0a739y6dasq19jYqMrZilcIAQAAAMBSFEIAAAAAsBSFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALAUhRAAAAAALEUhBAAAAABLUQgBAAAAwFKejj4A23g87nd5jx49VLPS09NVuZUrV6pyXq/XNWOMUc3aunWrKuf3+10zUVG631vs3LlTlQsGg6qc5ti094fmcQcONdHR0a4Z7XqVmZmpyv3888+qnOb57jiOalZZWZkqV1NT45rRPtc1962IyPbt21W5yspKVU5Du+YChxLN921ubq5qVl5enipXXl6uymn2Ew0NDapZW7ZsUeXq6upcM9rn+o4dO1Q57Zobyf2V9jqxZ6z6AAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClOFv2QZacnOya0Z7AWXsSU+2JjRMSElwzzc3NqlnaEz1r5sXGxqpmpaamqnKax0BEd7JT7UldtSdYBQ4lPp/PNaN9Pmlt27ZNlQuFQq4Z7Xq1ceNGVa6pqck1o7nPRETi4+NVOe39qz3RPXCk0jynMjIyVLO0P9tLSkpUOc3+qr6+XjVr8+bNqlwgEHDNaNch7f5Ku3/VHJt2j4vI4BVCAAAAALAUhRAAAAAALEUhBAAAAABLUQgBAAAAwFIUQgAAAACwFIUQAAAAACxFIQQAAAAAS1EIAQAAAMBSFEIAAAAAsJSnow/gSOE4jiqXmJjomsnNzVXNio2NVeV+/vlnVa66uto1U1dXp5rV0NCgysXExLhm4uPjVbOMMapcQkKCKldRUeGaqaqqUs0KBoOqHHAwaNeruLg410y3bt1Us9LT01U57XMqks/P+vp6VU6zXoVCIdUszXorovuZISJSWVnpmqmtrVXN0q6lwMGgXa80e4X8/HzVrJSUFFWupKREldu2bZtrpqamRjVLu7/S3B8eT2RrgHZ/5ff7XTPsrw4uXiEEAAAAAEtRCAEAAADAUhRCAAAAALAUhRAAAAAALEUhBAAAAABLUQgBAAAAwFIUQgAAAACwFIUQAAAAACxFIQQAAAAAS3k6+gCOFI7jqHJxcXGumaysLNUsY4wqFxMTo8qFQiHXjOb4RUTi4+NVuYSEBNdM7969VbO8Xq8qFxsbq8qtW7fONVNbW6uapblvgUON5vnZqVMn1azk5GRVLhgMqnL19fWumUAgoJqluZ3aXLdu3VSztKKjo1U5zW1tbm7e38MBDlk+n881k5mZqZqVkpKiynk8um20Zl3T7psSExNVOc2eqE+fPqpZ2mPT5jRrkd/vV83S7oWxZ7xCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJbydPQB2Mbjcb/L6+vrVbM2bdqkypWVlalymmOLitL9DqGurk6V83q9rpmqqirVrB07dqhy0dHRqlxzc7MqpxEKhSI2CzgcrVu3TpXbvHmzKmeM2Z/D2adZjuNEbJZ2nW9qalLlNGuz5vhFRILBoCoHHEo0zwHtz+Lvv/9elSstLVXlNPsO7f5Ku3YEAgHXTE1NjWqW9n6LiYlR5TTHpl1L2V9FBq8QAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACW4sT0EaI94a/mZJyNjY2qWdoTtmtPdhobG+uaycrKUs3Snii0oKDANZOenq6aVVdXp8qVl5ercprHVHsC50ieRBvYX9r1SsPv96ty2uedlsfj/uOrc+fOqlmZmZmqXP/+/V0zPp9PNWvTpk2qnPbE15H82cJ6hUNJJNerhoYGVa66ulqV05xwXkQkPj7eNaNdr7Q0+yvt2rdt2zZVrrKyUpXT7BG1+ytEBq8QAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKU8HX0AR4ro6GhVLiMjI2LX2dTUpMrFxsaqcsnJya6Z3Nxc1ayGhgZVTnO/+f1+1azS0lJVrq6uTpUzxqhyGo7jRGwWsL+ionS/C9SsV9pZwWBQlUtMTFTlOnXq5Jrp06ePapZ2/dbchtraWtWsyspKVS4QCKhymnU+FAqpZrFe4VCi/X5MS0uL2HU2Njaqctr9VVJSkmtGu7/SHptmbdbur8rLy1U57f5K+3MDBw+PCAAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFjK09EHYJtQKOSaMcaoZvXs2VOVcxxHlcvKynLNdOnSRTVr48aNqlxKSoprprm5WTXL5/OpcnFxcapcTU2NayY5OVk1a8uWLaqc9rYC+0O7xjQ0NETsOnv37q3KRUXpfk/ZuXNn10xOTo5qVklJiSqnWWO0z+GkpKSIXaeISGVlpWsmJiZGNQs4HDU1NblmNHswEZG8vDxV7nDfX2nvj4SEBFUuPj5eldPsr7R7Ne1jgD3jFUIAAAAAsBSFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALAUhRAAAAAALEUhBAAAAABLUQgBAAAAwFKcmD5CtCdT1tixY4cqt3PnTlVOcwJQ7fX++OOPqll1dXWqXGJiomtGe6Jn7e30er2qnOYkt1rBYDBis4D9pT2Rr8fj/iMiEAioZtXW1qpy27ZtU+Wqq6tdM+vWrVPN0q65mpMua+8P7e1MTk5W5RobG10znMAZhyPt9210dLRrRvtz3e/3q3KadUhEtz9Zu3atapb22DT7K2OMataWLVtUOe16pdkTaR937W3AnvEKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFjK09EHYJvGxkbXjN/vV80qLy9X5bZt26bKVVVVuWaSk5NVszIzM1W57Oxs10xTU5NqVkxMjCoXCoVUuc2bN7tmqqurVbOCwaAqBxxKAoGAa6ahoUE1S7telZWVqXKadS0lJUU1Kz09XZXTzGtublbN0ty3IiJxcXGqXGVlpWumrq5ONUu7RgIHg+M4qpzm56x2f7V161ZVTruuafZXPp9PNSsrK0uV69Kli2tGuzfRrgnafdj69etdM7W1tapZxhhVDnvGK4QAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKU9HH8CRIjY2VpVLS0tzzcTFxalmhUIhVc7j0T3M27dvd800NTVF9DobGxtdM4mJiapZtbW1qlxmZqYql56e7pqpqKhQzdI+VsDBEB0drcolJye7ZpKSklSzSktLVTnHcVS5qqoq10wgEFDNio+PV+U087xer2pWVJTu97GdOnVS5VJTU10zmzdvVs0yxqhywMGgXa98Pp9rRruf0D4HtHudmpoa14xmPySi329q9mva9bu+vl6V096/mnWtpKRENYv1KjJ4hRAAAAAALEUhBAAAAABLUQgBAAAAwFIUQgAAAACwFIUQAAAAACxFIQQAAAAAS1EIAQAAAMBSFEIAAAAAsBSFEAAAAAAs5enoAzhSBAIBVa6+vt41s2PHDtWsnJwcVc7r9apyO3fudM306tVLNeuoo45S5fx+v2smGAyqZiUlJalyGRkZqtzatWtdM3V1dapZxhhVDjgYQqGQKtfY2OiaqaqqUs1KTU1V5aqrq1W5uLg418zAgQNVs/r166fK1dbWqnIamp8FIiKO46hyy5Ytc81oHk/gUKPdAzQ0NLhmtOuVdp+gpdmHafdXeXl5qpxmvYqOjlbNio2NVeU8Hl2tqKmpcc00NTWpZiEyeIUQAAAAACxFIQQAAAAAS1EIAQAAAMBSFEIAAAAAsBSFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALAUJ6aPkEieeFw7q6ysTJULBAKqnOZk1doTG69cuVKV05yEuk+fPqpZmhNVi4gsXbpUlVu/fr1rRnMiXOBQoz0xveb7u7m5WTVLu15pT8SuOaGy9oTWK1asUOUqKytdM8cdd5xqlvaE0EuWLFHlSkpKXDPanwXAoUS7J9J8f0d6f6U9ebpmzdXOWr16tSqn2V8de+yxqllRUbrXj7744gtVbvPmza4Z7fqNyOAVQgAAAACwFIUQAAAAACxFIQQAAAAAS1EIAQAAAMBSFEIAAAAAsBSFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALCUp6MP4EjR3NysytXV1blmRowYoZpVWlqqyjU0NKhyjuO4ZjIzM1WzysvLVbm8vDzXTP/+/VWz3n77bVVuzZo1qlxtba1rxhijmgUcSkKhkCpXVVXlmjnppJNUs9LT01W5pqYmVS42NtY107lzZ9WsTZs2qXJ9+/Z1zRQUFKhmvf7666rcunXrVDntOg8cbrTrld/vd8307t1bNUvz8197nSK6/ZV2vdLur3Jzc10zmjVNROSNN95Q5TZs2KDKsV4deniFEAAAAAAsRSEEAAAAAEtRCAEAAADAUhRCAAAAALAUhRAAAAAALEUhBAAAAABLUQgBAAAAwFIUQgAAAACwFIUQAAAAACzl6egDOFIEg0FVrrKy0jVTW1urmpWamqrK7dixQ5VLSUlxzSQmJqpm9erVS5WLinL/ncRHH32kmrVixQpVrqamRpUzxqhywOFG+72tWYsCgYBqlna9am5uVuXS0tJcM16vVzWroKBAlaurq3PNFBUVqWZp1yvNdQJHslAopMpVV1e7ZhoaGlSzfD6fKqddS5OTk10z2v3VsGHDVDnNsf3rX/9Szfr6669VOdarwxevEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlnLMPp5923GcSB8L/j/tCVG7dOmiyh1zzDGqXI8ePVwzTU1Nqlnr169X5VavXu2aKSsrU83SnnCWE853vIP9GLBeHTjx8fGqXG5urio3cOBAVa5Xr16umfr6etWsdevWqXIrV650zZSXl6tmNTc3q3KsVx2P9erI4fV6Vbm8vDxVbsiQIapcz549XTPaPcwPP/ygyi1fvtw1s3nzZtWsxsZGVY71quPt62PAK4QAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKcfs4yntHceJ9LHgANE+VpF8TEOhUMRm4cizj8vOPmO9Onx0xGN1sL8fcXhhvcLuREXpXlfpiP0V65qd9vVx5xVCAAAAALAUhRAAAAAALEUhBAAAAABLUQgBAAAAwFIUQgAAAACwFIUQAAAAACxFIQQAAAAAS1EIAQAAAMBSFEIAAAAAsJSnow8AB54xJqI5ADhQWIcAHC5CoVBHHwIQEbxCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFiKQggAAAAAlqIQAgAAAIClKIQAAAAAYCkKIQAAAABYikIIAAAAAJaiEAIAAACApSiEAAAAAGApCiEAAAAAWIpCCAAAAACWohACAAAAgKUohAAAAABgKQohAAAAAFjKMcaYjj4IAAAAAMDBxyuEAAAAAGApCiEAAAAAWIpCCAAAAACW+n8FP+2xRktf9QAAAABJRU5ErkJggg==' width=900.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: fig2f_data\n",
-    "\n",
-    "titles = [\"Arithmetic mean\", \"Median\", \"Geometric median\"]\n",
-    "fig1, axes1 = plt.subplots(1, 3, figsize=(9, 3), constrained_layout=True)\n",
-    "for ax, img, title in zip(axes1, recons, titles):\n",
-    "    ax.imshow(img, cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_title(title)\n",
-    "    ax.axis(\"off\")\n",
-    "fig1.canvas.header_visible = False\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2g_ae1m_lossplot.ipynb b/notebooks/fig2g_ae1m_lossplot.ipynb
deleted file mode 100644
index 151b082d7..000000000
--- a/notebooks/fig2g_ae1m_lossplot.ipynb
+++ /dev/null
@@ -1,173 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c8d93c9c-e3a2-422b-8bd8-31d6826f5623",
-   "metadata": {},
-   "source": [
-    "# Plotting the training log."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "acb4391a-9c63-4d5e-ab1d-d0dd32880d25",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[i] Loaded CSV: ..\\data\\nucleus-ae\\training_log.csv\n",
-      "[i] Columns found: ['epoch', 'loss', 'mse', 'val_loss', 'val_mse']\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "602033581ff944268dd166220ae69b9b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                </div>\n",
-       "                <img 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' width=750.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: ae1m-loss-plot\n",
-    "import os, csv\n",
-    "from pathlib import Path\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Optional interactive backend (ok to keep)\n",
-    "try:\n",
-    "    from IPython import get_ipython\n",
-    "    ip = get_ipython()\n",
-    "    if ip is not None:\n",
-    "        ip.run_line_magic(\"matplotlib\", \"widget\")\n",
-    "except Exception:\n",
-    "    pass\n",
-    "\n",
-    "# Point to the DIRECTORY that contains training_log.csv\n",
-    "LOG_PATH = Path(\"..\") / \"data\" / \"nucleus-ae\"\n",
-    "\n",
-    "def _pick_csv_from_dir(d: Path) -> Path:\n",
-    "    \"\"\"Return <dir>/training_log.csv or raise if missing.\"\"\"\n",
-    "    p = d / \"training_log.csv\"\n",
-    "    if not p.is_file():\n",
-    "        raise FileNotFoundError(f\"'training_log.csv' not found in: {d}\")\n",
-    "    return p\n",
-    "\n",
-    "def _resolve_log_path(p: Path) -> Path:\n",
-    "    \"\"\"\n",
-    "    Only allow:\n",
-    "      - a directory containing training_log.csv\n",
-    "      - OR the file itself named exactly training_log.csv\n",
-    "    \"\"\"\n",
-    "    if p.is_dir():\n",
-    "        return _pick_csv_from_dir(p)\n",
-    "    if p.is_file():\n",
-    "        if p.name != \"training_log.csv\":\n",
-    "            raise FileNotFoundError(f\"Expected 'training_log.csv', got: {p.name}\")\n",
-    "        return p\n",
-    "    raise FileNotFoundError(f\"Path does not exist: {p}\")\n",
-    "\n",
-    "def load_log(path_like):\n",
-    "    csv_path = _resolve_log_path(Path(path_like))\n",
-    "    with open(csv_path, \"r\", newline=\"\", encoding=\"utf-8-sig\") as f:\n",
-    "        reader = csv.DictReader(f)\n",
-    "        rows = list(reader)\n",
-    "        fieldnames = reader.fieldnames or []\n",
-    "\n",
-    "    if not rows:\n",
-    "        raise ValueError(f\"CSV is empty: {csv_path}\")\n",
-    "\n",
-    "    def col(names):\n",
-    "        \"\"\"Try multiple possible column names; return first match as float array or None.\"\"\"\n",
-    "        for name in (names if isinstance(names, (list, tuple)) else [names]):\n",
-    "            if name in fieldnames:\n",
-    "                vals = []\n",
-    "                for r in rows:\n",
-    "                    v = r.get(name, \"\")\n",
-    "                    try:\n",
-    "                        vals.append(float(v))\n",
-    "                    except Exception:\n",
-    "                        vals.append(np.nan)\n",
-    "                return np.asarray(vals, dtype=float)\n",
-    "        return None\n",
-    "\n",
-    "    # Common variants\n",
-    "    epoch    = col([\"epoch\"])\n",
-    "    loss     = col([\"loss\", \"mse\", \"train_loss\", \"training_loss\"])\n",
-    "    val_loss = col([\"val_loss\", \"val_mse\", \"validation_loss\"])\n",
-    "\n",
-    "    epochs = (epoch.astype(int) + (0 if epoch is not None and epoch.min() >= 1 else 1)) \\\n",
-    "             if epoch is not None else np.arange(1, len(rows) + 1)\n",
-    "\n",
-    "    print(f\"[i] Loaded CSV: {csv_path}\")\n",
-    "    print(f\"[i] Columns found: {fieldnames}\")\n",
-    "    return epochs, loss, val_loss\n",
-    "\n",
-    "# ----- load + plot -----\n",
-    "epochs, loss, val_loss = load_log(LOG_PATH)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(7.5, 4.5), constrained_layout=True)\n",
-    "plotted = False\n",
-    "if loss is not None:\n",
-    "    ax.plot(epochs, loss, label=\"training_loss\"); plotted = True\n",
-    "if val_loss is not None:\n",
-    "    ax.plot(epochs, val_loss, label=\"validation_loss\"); plotted = True\n",
-    "\n",
-    "if not plotted:\n",
-    "    raise ValueError(\"No recognizable loss columns found \"\n",
-    "                     \"(looked for: loss/mse/train_loss/training_loss and val_loss/val_mse/validation_loss).\")\n",
-    "\n",
-    "ax.set_xlabel(\"Epoch\")\n",
-    "ax.set_ylabel(\"Mean squared error reconstruction loss\")\n",
-    "ax.grid(True, alpha=0.3)\n",
-    "ax.legend()\n",
-    "fig.canvas.toolbar_visible = True\n",
-    "fig.canvas.header_visible = False\n",
-    "fig.canvas.footer_visible = False\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2h_latent_interpolation_nucleusnet10k.ipynb b/notebooks/fig2h_latent_interpolation_nucleusnet10k.ipynb
deleted file mode 100644
index 3ca015a16..000000000
--- a/notebooks/fig2h_latent_interpolation_nucleusnet10k.ipynb
+++ /dev/null
@@ -1,393 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "60c3d42f-05b8-4f79-b216-e860f9c108a3",
-   "metadata": {},
-   "source": [
-    "# Decoded latent interpolations between pairs from NucleusNet-10K."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b8894e88-92a0-448f-983d-a3fd3a331123",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ab99936e7a00436fb98a82adb904bb5c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(Button(button_style='info', description='Resample pairs', style=ButtonStyle()), …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: nucleusnet10k-interpolation\n",
-    "\n",
-    "%matplotlib widget\n",
-    "\n",
-    "import os, random, math, logging\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import ipywidgets as W\n",
-    "from IPython.display import display\n",
-    "from tifffile import imread as tiff_imread\n",
-    "from urllib.parse import urlparse\n",
-    "import fsspec\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# Quiet HF logs\n",
-    "os.environ[\"HF_HUB_DISABLE_PROGRESS_BARS\"] = \"1\"\n",
-    "os.environ[\"HF_HUB_DISABLE_SYMLINKS_WARNING\"] = \"1\"\n",
-    "os.environ[\"TQDM_DISABLE\"] = \"1\"\n",
-    "for _name in (\"fsspec\", \"huggingface_hub\", \"urllib3\", \"datasets\"):\n",
-    "    logging.getLogger(_name).setLevel(logging.ERROR)\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.layers import (\n",
-    "    Input, Conv2D, Conv2DTranspose, AveragePooling2D,\n",
-    "    Flatten, Dense, Reshape, ReLU\n",
-    ")\n",
-    "\n",
-    "# ── Config ──────────────────────────────────────────────────────────────\n",
-    "REPO_ID    = \"RussellBarkley/msa-em-figures\"\n",
-    "BRANCH     = \"main\"\n",
-    "WEIGHT_DIRS = [\n",
-    "    Path(\"..\") / \"data\" / \"nucleus-ae\",\n",
-    "]\n",
-    "ENC_NAME    = 'encoder_weights.h5'\n",
-    "DEC_NAME    = 'decoder_weights.h5'\n",
-    "LATENT_DIM  = 512\n",
-    "\n",
-    "N_PAIRS   = 50\n",
-    "T_VALUES  = np.round(np.linspace(0.0, 1.0, 6), 2)\n",
-    "\n",
-    "# ── Build models to match your AE ───────────────────────────────────────\n",
-    "def build_encoder(latent_dim=LATENT_DIM):\n",
-    "    inp = Input((256,256,1), name='encoder_input')\n",
-    "    x = inp\n",
-    "    for filters in [16, 32, 64, 128]:\n",
-    "        x = Conv2D(filters, 3, padding='same')(x); x = ReLU()(x)\n",
-    "        x = Conv2D(filters, 3, padding='same')(x); x = ReLU()(x)\n",
-    "        x = AveragePooling2D(pool_size=2)(x)\n",
-    "    flat = Flatten()(x)\n",
-    "    z = Dense(latent_dim, name='z')(flat)\n",
-    "    return Model(inp, z, name='encoder')\n",
-    "\n",
-    "def build_decoder(latent_dim=LATENT_DIM):\n",
-    "    z_in = Input((latent_dim,), name='z_sampling')\n",
-    "    x = Dense(16 * 16 * 128)(z_in)\n",
-    "    x = Reshape((16, 16, 128))(x)\n",
-    "    for filters in [128, 64, 32, 16]:\n",
-    "        x = Conv2DTranspose(filters, 3, strides=2, padding='same')(x); x = ReLU()(x)\n",
-    "        x = Conv2D(filters, 3, padding='same')(x); x = ReLU()(x)\n",
-    "    out = Conv2D(1, 3, padding='same', activation='sigmoid', name='decoder_output')(x)\n",
-    "    return Model(z_in, out, name='decoder')\n",
-    "\n",
-    "# Tame GPU memory spikes\n",
-    "try:\n",
-    "    for g in tf.config.list_physical_devices(\"GPU\"):\n",
-    "        tf.config.experimental.set_memory_growth(g, True)\n",
-    "except Exception:\n",
-    "    pass\n",
-    "\n",
-    "encoder = build_encoder()\n",
-    "decoder = build_decoder()\n",
-    "\n",
-    "# Try to find and load weights (silent)\n",
-    "enc_path = dec_path = None\n",
-    "for d in WEIGHT_DIRS:\n",
-    "    e = os.path.join(d, ENC_NAME)\n",
-    "    c = os.path.join(d, DEC_NAME)\n",
-    "    if os.path.isfile(e) and os.path.isfile(c):\n",
-    "        enc_path, dec_path = e, c\n",
-    "        break\n",
-    "\n",
-    "latent_ready = False\n",
-    "if enc_path and dec_path:\n",
-    "    try:\n",
-    "        encoder.load_weights(enc_path)\n",
-    "        decoder.load_weights(dec_path)\n",
-    "        latent_ready = True\n",
-    "    except Exception:\n",
-    "        pass  # stay silent\n",
-    "# else: stay silent\n",
-    "\n",
-    "# ── HF file/dataset discovery ───────────────────────────────────────────\n",
-    "USE_SIMPLECACHE = True\n",
-    "fs_hf = fsspec.filesystem(\"hf\")\n",
-    "\n",
-    "def _ensure_hf_uri(p: str) -> str:\n",
-    "    return p if p.startswith(\"hf://\") else (\"hf://\" + p.lstrip(\"/\"))\n",
-    "\n",
-    "def _maybe_cache(uri: str) -> str:\n",
-    "    return f\"simplecache::{uri}\" if USE_SIMPLECACHE else uri\n",
-    "\n",
-    "def _basename_from_uri(uri: str) -> str:\n",
-    "    return os.path.basename(urlparse(uri).path)\n",
-    "\n",
-    "def _list_all_tiff_paths():\n",
-    "    globs = [\n",
-    "        f\"hf://datasets/{REPO_ID}@{BRANCH}/**/*.tif\",\n",
-    "        f\"hf://datasets/{REPO_ID}@{BRANCH}/**/*.tiff\",\n",
-    "    ]\n",
-    "    paths = []\n",
-    "    for pat in globs:\n",
-    "        try:\n",
-    "            for p in fs_hf.glob(pat):\n",
-    "                paths.append(_ensure_hf_uri(p))\n",
-    "        except Exception:\n",
-    "            pass\n",
-    "    return sorted(set(paths))\n",
-    "\n",
-    "ALL_PATHS = _list_all_tiff_paths()\n",
-    "MODE = \"files\" if len(ALL_PATHS) > 0 else \"dataset\"\n",
-    "\n",
-    "DS_OBJ = None\n",
-    "DS_LEN = 0\n",
-    "if MODE == \"dataset\":\n",
-    "    from datasets import load_dataset\n",
-    "    DS_OBJ = load_dataset(REPO_ID, split=\"train\", keep_in_memory=False)\n",
-    "    DS_LEN = len(DS_OBJ)\n",
-    "\n",
-    "# ── Helpers ─────────────────────────────────────────────────────────────\n",
-    "def normalize01(x):\n",
-    "    x = x.astype(np.float32)\n",
-    "    return x/255.0 if x.max() > 1.0 else x\n",
-    "\n",
-    "def load_img_file_hf(uri: str):\n",
-    "    with fsspec.open(_maybe_cache(uri), \"rb\") as f:\n",
-    "        img = tiff_imread(f)\n",
-    "    if img.ndim == 3:\n",
-    "        if img.shape[-1] == 1:    img = img[...,0]\n",
-    "        elif img.shape[-1] == 3:  img = 0.2126*img[...,0] + 0.7152*img[...,1] + 0.0722*img[...,2]\n",
-    "        else:                     img = img[...,0]\n",
-    "    if img.shape != (256,256):\n",
-    "        raise ValueError(f\"{_basename_from_uri(uri)} has shape {img.shape}, expected 256x256.\")\n",
-    "    return normalize01(img)\n",
-    "\n",
-    "def load_img_dataset_idx(idx: int):\n",
-    "    ex = DS_OBJ[int(idx)]\n",
-    "    arr = np.asarray(ex[\"image\"])\n",
-    "    if arr.ndim == 3:\n",
-    "        if arr.shape[-1] == 1:    arr = arr[...,0]\n",
-    "        elif arr.shape[-1] == 3:  arr = 0.2126*arr[...,0] + 0.7152*arr[...,1] + 0.0722*arr[...,2]\n",
-    "        else:                     arr = arr[...,0]\n",
-    "    if arr.shape != (256,256):\n",
-    "        raise ValueError(f\"sample_{idx} has shape {arr.shape}, expected 256x256.\")\n",
-    "    return normalize01(arr)\n",
-    "\n",
-    "def encode_img(img01):\n",
-    "    return encoder.predict(img01[None, ..., None], batch_size=8, verbose=0)[0]\n",
-    "\n",
-    "def decode_many(z_batch):\n",
-    "    rec = decoder.predict(z_batch, batch_size=min(len(z_batch), 16), verbose=0)[..., 0]\n",
-    "    return np.clip(rec, 0.0, 1.0)\n",
-    "\n",
-    "# ── Pair construction from FULL dataset (no 1k pre-sample) ──────────────\n",
-    "rng_py = random.Random()\n",
-    "\n",
-    "def make_pairs_from_full(n_pairs=N_PAIRS):\n",
-    "    pairs = []\n",
-    "    if MODE == \"files\":\n",
-    "        N = len(ALL_PATHS)\n",
-    "        if N < 2:\n",
-    "            return []\n",
-    "        m = min(2*n_pairs, N)\n",
-    "        idxs = list(range(N))\n",
-    "        rng_py.shuffle(idxs)\n",
-    "        chosen = idxs[:m]\n",
-    "        for i in range(0, m - (m % 2), 2):\n",
-    "            pairs.append((ALL_PATHS[chosen[i]], ALL_PATHS[chosen[i+1]]))\n",
-    "        while len(pairs) < n_pairs:\n",
-    "            a = ALL_PATHS[rng_py.randrange(N)]\n",
-    "            b = ALL_PATHS[rng_py.randrange(N)]\n",
-    "            pairs.append((a, b))\n",
-    "    else:\n",
-    "        N = DS_LEN\n",
-    "        if N < 2:\n",
-    "            return []\n",
-    "        m = min(2*n_pairs, N)\n",
-    "        idxs = list(range(N))\n",
-    "        rng_py.shuffle(idxs)\n",
-    "        chosen = idxs[:m]\n",
-    "        for i in range(0, m - (m % 2), 2):\n",
-    "            pairs.append((chosen[i], chosen[i+1]))  # store indices\n",
-    "        while len(pairs) < n_pairs:\n",
-    "            a = rng_py.randrange(N)\n",
-    "            b = rng_py.randrange(N)\n",
-    "            pairs.append((a, b))\n",
-    "    return pairs\n",
-    "\n",
-    "# ── Widgets ─────────────────────────────────────────────────────────────\n",
-    "w_rescan = W.Button(description=\"Resample pairs\", button_style=\"info\")\n",
-    "w_pair   = W.IntSlider(value=0, min=0, max=N_PAIRS-1, step=1, description=\"Pair #\", readout=True, continuous_update=False)\n",
-    "w_prev   = W.Button(description=\"Prev\")\n",
-    "w_next   = W.Button(description=\"Next\")\n",
-    "w_status = W.HTML(\"\")\n",
-    "\n",
-    "# ── Figure (single small row, ~4x2 in; titles above each image) ─────────\n",
-    "_was_interactive = plt.isinteractive()\n",
-    "plt.ioff()\n",
-    "\n",
-    "K = len(T_VALUES)\n",
-    "n_cols, n_rows = K, 1\n",
-    "\n",
-    "FIG_W_IN, FIG_H_IN, DPI = 4.4, 2.0, 150\n",
-    "fig = plt.figure(figsize=(FIG_W_IN, FIG_H_IN), dpi=DPI, constrained_layout=False)\n",
-    "gs  = fig.add_gridspec(n_rows, n_cols)\n",
-    "\n",
-    "axes, ims = [], []\n",
-    "for k in range(K):\n",
-    "    ax = fig.add_subplot(gs[0, k])\n",
-    "    im = ax.imshow(np.zeros((256, 256)), cmap=\"gray\", vmin=0, vmax=1)\n",
-    "    ax.set_xticks([]); ax.set_yticks([]); ax.set_frame_on(False)\n",
-    "    ax.set_title(f\"t={T_VALUES[k]:.2f}\", fontsize=6, pad=2)  # ← title above the image\n",
-    "    axes.append(ax); ims.append(im)\n",
-    "\n",
-    "# Tight spacing; no forced 100% width so it won't zoom/stretch\n",
-    "fig.subplots_adjust(left=0.01, right=0.99, bottom=0.02, top=0.88, wspace=0.02)\n",
-    "fig.canvas.header_visible  = False\n",
-    "fig.canvas.toolbar_visible = False\n",
-    "fig.canvas.layout.width    = \"auto\"\n",
-    "fig.canvas.layout.height   = \"auto\"\n",
-    "\n",
-    "if _was_interactive:\n",
-    "    plt.ion()\n",
-    "\n",
-    "\n",
-    "# ── State & cache ───────────────────────────────────────────────────────\n",
-    "pairs = []   # files mode: list[(uriA, uriB)], dataset mode: list[(idxA, idxB)]\n",
-    "pair_cache = {}  # idx -> dict('imgs', 'A','B','A_z','B_z')\n",
-    "\n",
-    "def set_status(msg, ok=True):\n",
-    "    # keep a minimal status line; still helpful for errors\n",
-    "    color = \"#1b5e20\" if ok else \"#b71c1c\"\n",
-    "    w_status.value = f\"<span style='color:{color}'>{msg}</span>\"\n",
-    "\n",
-    "def _handle_to_name(h):\n",
-    "    if MODE == \"files\":\n",
-    "        return _basename_from_uri(h)\n",
-    "    else:\n",
-    "        return f\"sample_{int(h):06d}.tif\"\n",
-    "\n",
-    "def _load_handle(h):\n",
-    "    if MODE == \"files\":\n",
-    "        return load_img_file_hf(h)\n",
-    "    else:\n",
-    "        return load_img_dataset_idx(int(h))\n",
-    "\n",
-    "def show_frames(framesK, pair_idx):\n",
-    "    for k, im in enumerate(ims):\n",
-    "        im.set_data(framesK[k])\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "\n",
-    "def compute_pair(idx):\n",
-    "    if idx in pair_cache:\n",
-    "        return pair_cache[idx][\"imgs\"]\n",
-    "    a_h, b_h = pairs[idx]\n",
-    "    A = _load_handle(a_h)\n",
-    "    B = _load_handle(b_h)\n",
-    "    A_z = encode_img(A)\n",
-    "    B_z = encode_img(B)\n",
-    "    Z = np.stack([(1.0 - t) * A_z + t * B_z for t in T_VALUES], axis=0)  # (K, latent_dim)\n",
-    "    frames = decode_many(Z)                                              # (K, 256, 256)\n",
-    "    pair_cache[idx] = {\"imgs\": frames, \"A\": a_h, \"B\": b_h, \"A_z\": A_z, \"B_z\": B_z}\n",
-    "    return frames\n",
-    "\n",
-    "def on_rescan_clicked(_):\n",
-    "    if not latent_ready:\n",
-    "        set_status(\"Weights not loaded; cannot interpolate in latent space.\", ok=False)\n",
-    "        return\n",
-    "    if MODE == \"files\" and len(ALL_PATHS) < 2:\n",
-    "        set_status(\"No TIFFs found in repo.\", ok=False)\n",
-    "        return\n",
-    "    if MODE == \"dataset\" and DS_LEN < 2:\n",
-    "        set_status(\"Dataset is empty.\", ok=False)\n",
-    "        return\n",
-    "\n",
-    "    global pairs, pair_cache\n",
-    "    pairs = make_pairs_from_full(N_PAIRS)\n",
-    "    pair_cache = {}\n",
-    "    if not pairs:\n",
-    "        set_status(\"Could not build pairs from the repository.\", ok=False)\n",
-    "        return\n",
-    "\n",
-    "    w_pair.max = len(pairs)-1\n",
-    "    w_pair.value = 0\n",
-    "    # no verbose banner; just build silently and show first pair\n",
-    "    try:\n",
-    "        frames = compute_pair(0)\n",
-    "        show_frames(frames, 0)\n",
-    "    except Exception as ex:\n",
-    "        set_status(f\"Failed pair 1: {ex}\", ok=False)\n",
-    "\n",
-    "def on_pair_change(change):\n",
-    "    idx = int(change['new'])\n",
-    "    if not pairs:\n",
-    "        return\n",
-    "    try:\n",
-    "        frames = compute_pair(idx)\n",
-    "        show_frames(frames, idx)\n",
-    "    except Exception as ex:\n",
-    "        set_status(f\"Failed pair {idx+1}: {ex}\", ok=False)\n",
-    "\n",
-    "def on_prev(_):\n",
-    "    if pairs:\n",
-    "        w_pair.value = max(0, w_pair.value - 1)\n",
-    "\n",
-    "def on_next(_):\n",
-    "    if pairs:\n",
-    "        w_pair.value = min(w_pair.max, w_pair.value + 1)\n",
-    "\n",
-    "w_rescan.on_click(on_rescan_clicked)\n",
-    "w_pair.observe(on_pair_change, names='value')\n",
-    "w_prev.on_click(on_prev)\n",
-    "w_next.on_click(on_next)\n",
-    "\n",
-    "# ── Display ──────────\n",
-    "container = W.VBox([\n",
-    "    W.HBox([w_rescan, w_prev, w_next, w_pair]),\n",
-    "    fig.canvas\n",
-    "], layout=W.Layout(width=\"100%\"))\n",
-    "display(container)\n",
-    "\n",
-    "# Kick off an initial build\n",
-    "on_rescan_clicked(None)\n",
-    "\n",
-    "None\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2i_theoretical_average_images.ipynb b/notebooks/fig2i_theoretical_average_images.ipynb
deleted file mode 100644
index cb53bc5b2..000000000
--- a/notebooks/fig2i_theoretical_average_images.ipynb
+++ /dev/null
@@ -1,221 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "42e34ece-1ce4-411d-9019-b63becda572f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── 0) Interactive backend & quiet TF logs ─────────────────────────────\n",
-    "try:\n",
-    "    get_ipython().run_line_magic(\"matplotlib\", \"widget\")\n",
-    "except Exception:\n",
-    "    pass\n",
-    "\n",
-    "import os\n",
-    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"  # quiet TensorFlow logs\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import ipywidgets as W\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.layers import Input, Dense, Reshape, Conv2D, Conv2DTranspose, ReLU\n",
-    "from tensorflow.keras.models import Model\n",
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "bd33f58c-8d51-406a-a2d3-775ee03e5bf6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── 1) Paths (edit if needed) ──────────────────────────────────────────\n",
-    "RESULTS_DIR       = Path(\"..\") / \"data\" / \"nucleus-ae\" \n",
-    "CENTERS_NPZ       = os.path.join(RESULTS_DIR, \"latent_centers.npz\")\n",
-    "DECODER_WEIGHTS   = os.path.join(RESULTS_DIR, \"decoder_weights.h5\")\n",
-    "\n",
-    "# model constants (must match training)# ── 2) GPU memory growth (safe even if no GPU) ─────────────────────────\n",
-    "try:\n",
-    "    gpus = tf.config.list_physical_devices(\"GPU\")\n",
-    "    for g in gpus:\n",
-    "        tf.config.experimental.set_memory_growth(g, True)\n",
-    "except Exception:\n",
-    "    pass\n",
-    "IMG_SHAPE   = (256, 256, 1)\n",
-    "LATENT_SIDE = 16     # after 4× AveragePooling2D from 256 → 16\n",
-    "BASE_CH     = 128\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5c75327d-faf3-497d-b87c-c85442a48095",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── 2) GPU memory growth (safe even if no GPU) ─────────────────────────\n",
-    "try:\n",
-    "    gpus = tf.config.list_physical_devices(\"GPU\")\n",
-    "    for g in gpus:\n",
-    "        tf.config.experimental.set_memory_growth(g, True)\n",
-    "except Exception:\n",
-    "    pass\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "af58dc4f-5b96-4cf2-9245-27c863afbed8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── 3) Rebuild decoder to match your training script ───────────────────\n",
-    "def build_decoder(latent_dim: int) -> Model:\n",
-    "    z_in = Input((latent_dim,), name=\"z_sampling\")\n",
-    "    x = Dense(LATENT_SIDE * LATENT_SIDE * BASE_CH)(z_in)\n",
-    "    x = Reshape((LATENT_SIDE, LATENT_SIDE, BASE_CH))(x)\n",
-    "    for f in [BASE_CH, 64, 32, 16]:\n",
-    "        x = Conv2DTranspose(f, 3, strides=2, padding=\"same\")(x); x = ReLU()(x)\n",
-    "        x = Conv2D(f, 3, padding=\"same\")(x); x = ReLU()(x)\n",
-    "    out = Conv2D(1, 3, padding=\"same\", activation=\"sigmoid\", name=\"decoder_output\")(x)\n",
-    "    return Model(z_in, out, name=\"decoder\")\n",
-    "\n",
-    "# Load latent centers\n",
-    "npz = np.load(CENTERS_NPZ)\n",
-    "z_mean     = npz[\"mean\"].astype(np.float32)\n",
-    "z_median   = npz[\"median\"].astype(np.float32)\n",
-    "z_geomed   = npz[\"geomedian\"].astype(np.float32)\n",
-    "latent_dim = int(z_mean.shape[0])\n",
-    "\n",
-    "# Build and load decoder weights\n",
-    "decoder = build_decoder(latent_dim)\n",
-    "decoder.load_weights(DECODER_WEIGHTS)\n",
-    "decoder.trainable = False\n",
-    "\n",
-    "# Decode all three\n",
-    "def _decode(z_vec):\n",
-    "    z_vec = np.asarray(z_vec, dtype=np.float32)[None, ...]  # (1, D)\n",
-    "    x = decoder.predict(z_vec, verbose=0)[0, ..., 0]        # in [0,1]\n",
-    "    x = (x * 255.0).astype(np.float32)                      # now 0–255 float\n",
-    "    return x\n",
-    "\n",
-    "img_mean   = _decode(z_mean)\n",
-    "img_median = _decode(z_median)\n",
-    "img_geomed = _decode(z_geomed)\n",
-    "\n",
-    "imgs = [img_mean, img_median, img_geomed]\n",
-    "titles = [\"Mean\", \"Median\", \"Geometric median\"]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4fe453ca-2a52-4cf4-8216-18af2efc688a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "131782b50ff64121abfb6bba69531f47",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(IntRangeSlider(value=(0, 255), description='Intensity', layout=Layout(width='600px'), max=255, …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd3db491fa5f47d990c6513f4d30b715",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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-       "                <img 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' width=1000.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: ae1m-theoretical\n",
-    "\n",
-    "# ── 4) Interactive figure with 0–255 Intensity slider (non-editable readout) ──\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(10, 3.4), constrained_layout=True)\n",
-    "ims = []\n",
-    "for ax, arr, title in zip(axes, imgs, titles):\n",
-    "    # 'nearest' = nearest-neighbor (no smoothing)\n",
-    "    im = ax.imshow(arr, cmap=\"gray\", vmin=0.0, vmax=255.0, interpolation=\"nearest\")\n",
-    "    ax.set_title(title, fontsize=11)\n",
-    "    ax.set_xticks([]); ax.set_yticks([])\n",
-    "    ims.append(im)\n",
-    "\n",
-    "# Non-editable range display\n",
-    "rng = W.IntRangeSlider(\n",
-    "    value=[0, 255],\n",
-    "    min=0, max=255, step=1,\n",
-    "    description=\"Intensity\",\n",
-    "    continuous_update=True,\n",
-    "    readout=False,  # hide the editable textbox\n",
-    "    layout=W.Layout(width=\"600px\")\n",
-    ")\n",
-    "val_label = W.Label(f\"range: {rng.value[0]} – {rng.value[1]}\")\n",
-    "\n",
-    "def on_range_change(change):\n",
-    "    vmin, vmax = change[\"new\"]\n",
-    "    if vmax <= vmin:\n",
-    "        return\n",
-    "    for im in ims:\n",
-    "        im.set_clim(vmin=float(vmin), vmax=float(vmax))\n",
-    "    val_label.value = f\"range: {vmin} – {vmax}\"\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "rng.observe(on_range_change, names=\"value\")\n",
-    "\n",
-    "display(W.HBox([rng, val_label]))\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/fig2j_practical_average_images.ipynb b/notebooks/fig2j_practical_average_images.ipynb
deleted file mode 100644
index caaecde63..000000000
--- a/notebooks/fig2j_practical_average_images.ipynb
+++ /dev/null
@@ -1,294 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d5a37d68-3d8e-42c6-a05d-6ea2a5032531",
-   "metadata": {},
-   "source": [
-    "# Representative microscopy images of the cell nucleus."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "74c0f06d-3de1-4907-a5b3-d6dd90c12a15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d21e5c24be40418c9a2d784baa679b96",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(ToggleButtons(description='Distance metric:', layout=Layout(width='auto'), optio…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3afca0fc17f54655bffbd3df9d159590",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=500.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: ae1m-practical\n",
-    "\n",
-    "# ── Config ───────────────────────────────────────────────────────────────\n",
-    "HF_REPO = \"RussellBarkley/representative_nuclei\"  # datasets repo name\n",
-    "HF_ROOT = f\"hf://datasets/{HF_REPO}\"\n",
-    "\n",
-    "# ── Imports & backend ───────────────────────────────────────────────────\n",
-    "try:\n",
-    "    get_ipython().run_line_magic(\"matplotlib\", \"widget\")\n",
-    "except Exception:\n",
-    "    pass\n",
-    "\n",
-    "import os, re\n",
-    "from io import BytesIO\n",
-    "from functools import lru_cache\n",
-    "from typing import List\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import ipywidgets as W\n",
-    "import fsspec\n",
-    "from tifffile import imread as tiff_imread\n",
-    "\n",
-    "# ── Constants ────────────────────────────────────────────────────────────\n",
-    "CENTERS = [\"mean\", \"median\", \"geomedian\"]\n",
-    "METRICS = [\"euclidean\", \"cosine\"]\n",
-    "EXTS    = (\".tif\", \".tiff\", \".png\")\n",
-    "rank_pat = re.compile(r\"^(\\d{1,4})__\")  # e.g., '001__...tif'\n",
-    "\n",
-    "# ── Filesystem (quiet—no prints) ────────────────────────────────────────\n",
-    "fs = fsspec.filesystem(\"hf\")\n",
-    "\n",
-    "def _natural_sort_key(path: str):\n",
-    "    base = os.path.basename(path)\n",
-    "    m = rank_pat.match(base)\n",
-    "    if m:\n",
-    "        try:\n",
-    "            return (0, int(m.group(1)), base.lower())\n",
-    "        except Exception:\n",
-    "            pass\n",
-    "    return (1, base.lower())\n",
-    "\n",
-    "def list_ranked_images(center: str, metric: str) -> List[str]:\n",
-    "    \"\"\"List and rank-sort up to 100 files under <HF_ROOT>/<center>/<metric>/ (quiet)\"\"\"\n",
-    "    folder = f\"{HF_ROOT}/{center}/{metric}\"\n",
-    "    try:\n",
-    "        files = fs.glob(f\"{folder}/*\")\n",
-    "    except FileNotFoundError:\n",
-    "        files = []\n",
-    "    files = [p for p in files if os.path.splitext(p)[1].lower() in EXTS]\n",
-    "    files.sort(key=_natural_sort_key)\n",
-    "    return files[:100]\n",
-    "\n",
-    "# Build the index used by the viewer code (quiet)\n",
-    "repo_index = {(c, m): list_ranked_images(c, m) for c in CENTERS for m in METRICS}\n",
-    "\n",
-    "# ── Loader with small cache ─────────────────────────────────────────────\n",
-    "@lru_cache(maxsize=64)\n",
-    "def load_image_bytes(hf_path: str) -> np.ndarray:\n",
-    "    \"\"\"Read image from HF as grayscale float32 0..255.\"\"\"\n",
-    "    with fs.open(hf_path, \"rb\") as f:\n",
-    "        data = f.read()\n",
-    "    arr = tiff_imread(BytesIO(data))\n",
-    "\n",
-    "    if arr.ndim > 2:\n",
-    "        arr = arr[..., 0]\n",
-    "    arr = np.asarray(arr)\n",
-    "\n",
-    "    if np.issubdtype(arr.dtype, np.floating):\n",
-    "        a = arr.astype(np.float32)\n",
-    "        if a.max() <= 1.0 + 1e-6:\n",
-    "            a *= 255.0\n",
-    "    else:\n",
-    "        a = arr.astype(np.float32)\n",
-    "\n",
-    "    a = np.clip(a, 0, 255)\n",
-    "    return a\n",
-    "\n",
-    "# ── Helper to trim \"NNN__\" prefix from titles ───────────────────────────\n",
-    "def _display_name(path: str) -> str:\n",
-    "    b = os.path.basename(path)\n",
-    "    return b[5:] if len(b) > 5 else b  # remove first 5 chars like '001__'\n",
-    "\n",
-    "# ── UI widgets (renamed + wider spacing) ────────────────────────────────\n",
-    "CENTERS = [\"mean\", \"median\", \"geomedian\"]\n",
-    "METRICS = [\"euclidean\", \"cosine\"]\n",
-    "\n",
-    "w_metric = W.ToggleButtons(\n",
-    "    options=METRICS, value=\"euclidean\",\n",
-    "    description=\"Distance metric:\", button_style=\"\",\n",
-    "    layout=W.Layout(width=\"auto\")\n",
-    ")\n",
-    "w_center = W.ToggleButtons(\n",
-    "    options=CENTERS, value=\"mean\",\n",
-    "    description=\"Measure of central tendency:\", button_style=\"\",\n",
-    "    layout=W.Layout(width=\"auto\")\n",
-    ")\n",
-    "\n",
-    "# Rank control with Prev/Next buttons\n",
-    "w_prev = W.Button(description=\"◀ Prev\", layout=W.Layout(width=\"80px\"))\n",
-    "w_next = W.Button(description=\"Next ▶\", layout=W.Layout(width=\"80px\"))\n",
-    "w_rank = W.IntSlider(\n",
-    "    value=1, min=1, max=100, step=1, description=\"Rank:\", readout=False,\n",
-    "    continuous_update=True, layout=W.Layout(width=\"320px\"))\n",
-    "w_rank_label = W.Label(f\"{w_rank.value}/100\", layout=W.Layout(width=\"60px\"))\n",
-    "rank_row = W.HBox([w_prev, w_rank, w_rank_label, w_next],\n",
-    "                   layout=W.Layout(align_items=\"center\", gap=\"8px\"))\n",
-    "\n",
-    "# Intensity range\n",
-    "w_rng = W.IntRangeSlider(\n",
-    "    value=[0, 255], min=0, max=255, step=1, description=\"Intensity:\",\n",
-    "    readout=False, continuous_update=True, layout=W.Layout(width=\"480px\"))\n",
-    "w_rng_label = W.Label(f\"{w_rng.value[0]} – {w_rng.value[1]}\")\n",
-    "\n",
-    "# Put metric and center side-by-side with extra gap\n",
-    "row1 = W.HBox([w_metric, w_center],\n",
-    "              layout=W.Layout(justify_content=\"flex-start\", gap=\"28px\"))\n",
-    "row2 = rank_row\n",
-    "row3 = W.HBox([w_rng, w_rng_label], layout=W.Layout(align_items=\"center\", gap=\"8px\"))\n",
-    "\n",
-    "ui = W.VBox([row1, row2, row3], layout=W.Layout(gap=\"10px\"))\n",
-    "display(ui)\n",
-    "\n",
-    "# ── Figure (title shows filename without numeric prefix) ────────────────\n",
-    "fig, ax = plt.subplots(figsize=(5.0, 5.0), constrained_layout=True)\n",
-    "im = ax.imshow(np.zeros((10,10), dtype=np.float32), cmap=\"gray\",\n",
-    "               vmin=0, vmax=255, interpolation=\"nearest\")\n",
-    "ax.set_xticks([]); ax.set_yticks([])\n",
-    "title = ax.set_title(\"\", fontsize=12)\n",
-    "\n",
-    "def _current_files():\n",
-    "    return repo_index.get((w_center.value, w_metric.value), [])\n",
-    "\n",
-    "def _clamp_rank(n):\n",
-    "    files = _current_files()\n",
-    "    if not files:\n",
-    "        return 1\n",
-    "    return int(np.clip(n, 1, len(files)))\n",
-    "\n",
-    "def update_image(*_):\n",
-    "    files = _current_files()\n",
-    "    if not files:\n",
-    "        im.set_data(np.zeros((10,10), dtype=np.float32))\n",
-    "        title.set_text(\"\")  # no filename if missing\n",
-    "        fig.canvas.draw_idle()\n",
-    "        return\n",
-    "\n",
-    "    w_rank.value = _clamp_rank(w_rank.value)\n",
-    "    idx = w_rank.value - 1\n",
-    "    path = files[idx]\n",
-    "    arr = load_image_bytes(path)\n",
-    "\n",
-    "    vmin, vmax = w_rng.value\n",
-    "    im.set_data(arr)\n",
-    "    im.set_clim(vmin=float(vmin), vmax=float(vmax))\n",
-    "\n",
-    "    # trimmed filename (remove first 5 chars like '001__')\n",
-    "    title.set_text(_display_name(path))\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "def on_intensity_change(change):\n",
-    "    vmin, vmax = change[\"new\"]\n",
-    "    if vmax <= vmin:\n",
-    "        return\n",
-    "    w_rng_label.value = f\"{vmin} – {vmax}\"\n",
-    "    im.set_clim(vmin=float(vmin), vmax=float(vmax))\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "def on_rank_change(change):\n",
-    "    w_rank.value = _clamp_rank(change[\"new\"])\n",
-    "    files = _current_files()\n",
-    "    total = len(files) if files else 100\n",
-    "    w_rank_label.value = f\"{w_rank.value}/{total}\"\n",
-    "    update_image()\n",
-    "\n",
-    "def on_prev_clicked(_):\n",
-    "    w_rank.value = _clamp_rank(w_rank.value - 1)\n",
-    "\n",
-    "def on_next_clicked(_):\n",
-    "    w_rank.value = _clamp_rank(w_rank.value + 1)\n",
-    "\n",
-    "# ── Bind toolbar arrows (Back/Forward) and keyboard arrows ──────────────\n",
-    "def _bind_toolbar_arrows(fig):\n",
-    "    tb = getattr(fig.canvas, \"toolbar\", None)\n",
-    "    if tb is None:\n",
-    "        return\n",
-    "    try:\n",
-    "        # Monkey-patch toolbar navigation to use our index navigation\n",
-    "        tb.back = lambda *a, **k: on_prev_clicked(None)\n",
-    "        tb.forward = lambda *a, **k: on_next_clicked(None)\n",
-    "    except Exception:\n",
-    "        pass\n",
-    "\n",
-    "def _on_key(event):\n",
-    "    if event.key in (\"left\", \"a\"):\n",
-    "        on_prev_clicked(None)\n",
-    "    elif event.key in (\"right\", \"d\"):\n",
-    "        on_next_clicked(None)\n",
-    "\n",
-    "# wire events\n",
-    "w_metric.observe(update_image, names=\"value\")\n",
-    "w_center.observe(update_image, names=\"value\")\n",
-    "w_rank.observe(on_rank_change, names=\"value\")\n",
-    "w_rng.observe(on_intensity_change, names=\"value\")\n",
-    "w_prev.on_click(on_prev_clicked)\n",
-    "w_next.on_click(on_next_clicked)\n",
-    "\n",
-    "# Activate toolbar + keyboard hooks\n",
-    "fig.canvas.mpl_connect(\"key_press_event\", _on_key)\n",
-    "_bind_toolbar_arrows(fig)\n",
-    "\n",
-    "update_image()\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/sfig1a_scatterplot.ipynb b/notebooks/sfig1a_scatterplot.ipynb
deleted file mode 100644
index 1715355b0..000000000
--- a/notebooks/sfig1a_scatterplot.ipynb
+++ /dev/null
@@ -1,203 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a53d7651-cb6d-4eb2-b439-d2e5f6baec69",
-   "metadata": {},
-   "source": [
-    "# Supplementary Figure 1A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f6dae75d-1c5f-4e00-95a1-c4c9d8f0d742",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The ipympl module is not an IPython extension.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d57a1e49daaa44a4b2cfabc53f19999c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                </div>\n",
-       "                <img 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' width=800.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: scatter-plot\n",
-    "\n",
-    "%load_ext ipympl\n",
-    "\n",
-    "%matplotlib widget\n",
-    "\n",
-    "import os\n",
-    "import csv\n",
-    "import numpy as np\n",
-    "from scipy.spatial import cKDTree\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pathlib import Path\n",
-    "\n",
-    "# ────────────────────────────────────────────────────────────────────────\n",
-    "# CONFIGURATION\n",
-    "# ────────────────────────────────────────────────────────────────────────\n",
-    "BASE_DIR        = Path(\"..\") / \"data\" / \"nucleus-ae\"\n",
-    "CSV_NAME        = 'results.csv'\n",
-    "CSV_PATH        = os.path.join(BASE_DIR, CSV_NAME)\n",
-    "\n",
-    "# Your precomputed centroid:\n",
-    "CENTROID_ROIS   = 694.49\n",
-    "CENTROID_MEAN   = 79.0141\n",
-    "# ────────────────────────────────────────────────────────────────────────\n",
-    "\n",
-    "def _to_float(x):\n",
-    "    try:\n",
-    "        return float(x)\n",
-    "    except Exception:\n",
-    "        return np.nan\n",
-    "\n",
-    "def load_csv_rows(csv_path):\n",
-    "    \"\"\"\n",
-    "    Returns (images: list[str], num_rois: np.ndarray, mean: np.ndarray, std: np.ndarray)\n",
-    "    Filters out rows with NaNs so array indices align.\n",
-    "    \"\"\"\n",
-    "    images, xs, ys, cs = [], [], [], []\n",
-    "    with open(csv_path, newline='', encoding='utf-8') as f:\n",
-    "        reader = csv.DictReader(f)\n",
-    "        # Expected columns: image, num_rois, mean_intensity, std_intensity\n",
-    "        for row in reader:\n",
-    "            img = row.get('image', '')\n",
-    "            x   = _to_float(row.get('num_rois', ''))\n",
-    "            y   = _to_float(row.get('mean_intensity', ''))\n",
-    "            c   = _to_float(row.get('std_intensity', ''))\n",
-    "            if img and np.isfinite(x) and np.isfinite(y) and np.isfinite(c):\n",
-    "                images.append(img)\n",
-    "                xs.append(x); ys.append(y); cs.append(c)\n",
-    "    return images, np.asarray(xs), np.asarray(ys), np.asarray(cs)\n",
-    "\n",
-    "def plot_interactive(csv_path, centroid_rois, centroid_mean, hover_px=8.0):\n",
-    "    images, x, y, c = load_csv_rows(csv_path)\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "    sc = ax.scatter(x, y, c=c, cmap='viridis', edgecolors='k')\n",
-    "    cbar = fig.colorbar(sc, ax=ax)\n",
-    "    cbar.set_label('Standard deviation Intensity')\n",
-    "\n",
-    "    # Centroid\n",
-    "    ax.scatter([centroid_rois], [centroid_mean], marker='x', s=200,\n",
-    "               c='black', linewidths=3, label='Centroid')\n",
-    "    ax.annotate('Centroid',\n",
-    "                xy=(centroid_rois, centroid_mean),\n",
-    "                xytext=(10, -10), textcoords='offset points',\n",
-    "                color='black', fontsize=10, fontweight='bold')\n",
-    "\n",
-    "    ax.set_xlabel('Number of ROIs')\n",
-    "    ax.set_ylabel('Mean Intensity')\n",
-    "    ax.legend(loc='upper right')\n",
-    "    fig.tight_layout()\n",
-    "\n",
-    "    # Tooltip annotation (initially hidden)\n",
-    "    annot = ax.annotate(\n",
-    "        \"\", xy=(0, 0), xytext=(10, 10), textcoords=\"offset points\",\n",
-    "        bbox=dict(boxstyle=\"round\", fc=\"w\", ec=\"0.5\", alpha=0.9),\n",
-    "        arrowprops=dict(arrowstyle=\"->\")\n",
-    "    )\n",
-    "    annot.set_visible(False)\n",
-    "\n",
-    "    # We’ll build a KD-tree in display (pixel) coordinates for fast hover lookup\n",
-    "    tree = None\n",
-    "    disp_xy = None\n",
-    "\n",
-    "    def build_tree(event=None):\n",
-    "        nonlocal tree, disp_xy\n",
-    "        # Transform data coords -> display (pixel) coords\n",
-    "        data_xy = np.column_stack((x, y))\n",
-    "        disp_xy = ax.transData.transform(data_xy)\n",
-    "        tree = cKDTree(disp_xy)\n",
-    "\n",
-    "    def on_draw(event):\n",
-    "        # Recompute the KD-tree whenever the figure is drawn (resize, zoom, etc.)\n",
-    "        build_tree()\n",
-    "\n",
-    "    def on_move(event):\n",
-    "        if tree is None or not event.inaxes:\n",
-    "            if annot.get_visible():\n",
-    "                annot.set_visible(False)\n",
-    "                fig.canvas.draw_idle()\n",
-    "            return\n",
-    "        if event.x is None or event.y is None:\n",
-    "            return\n",
-    "\n",
-    "        # Query nearest point in pixel space\n",
-    "        dist, idx = tree.query([event.x, event.y], k=1)\n",
-    "        if np.isfinite(dist) and dist <= hover_px:\n",
-    "            # Update annotation\n",
-    "            px, py = x[idx], y[idx]\n",
-    "            annot.xy = (px, py)\n",
-    "            annot.set_text(str(images[idx]))\n",
-    "            if not annot.get_visible():\n",
-    "                annot.set_visible(True)\n",
-    "            fig.canvas.draw_idle()\n",
-    "        else:\n",
-    "            if annot.get_visible():\n",
-    "                annot.set_visible(False)\n",
-    "                fig.canvas.draw_idle()\n",
-    "\n",
-    "    # Initial tree, then connect events\n",
-    "    build_tree()\n",
-    "    cid_draw = fig.canvas.mpl_connect('draw_event', on_draw)\n",
-    "    cid_move = fig.canvas.mpl_connect('motion_notify_event', on_move)\n",
-    "\n",
-    "    return fig, ax\n",
-    "\n",
-    "fig, ax = plot_interactive(CSV_PATH, CENTROID_ROIS, CENTROID_MEAN)\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/sfig2a_mnist_autoencoder_lossplot.ipynb b/notebooks/sfig2a_mnist_autoencoder_lossplot.ipynb
deleted file mode 100644
index 7ce33a720..000000000
--- a/notebooks/sfig2a_mnist_autoencoder_lossplot.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "76e58ba7",
-   "metadata": {},
-   "source": [
-    "# Supplemental Figure 1A: MNIST AE loss plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ca05bd3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib: 3.10.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib widget\n",
-    "\n",
-    "import os, csv\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pathlib import Path\n",
-    "\n",
-    "print(\"Matplotlib:\", plt.matplotlib.__version__)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ff04a8e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LOG_CSV: ..\\data\\mnist-ae\\training_log.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ─────────────────────────────────────────────\n",
-    "# CONFIG — set to your results directory\n",
-    "# ─────────────────────────────────────────────\n",
-    "RESULTS_DIR = Path(\"..\") / \"data\" / \"mnist-ae\"   # change if needed\n",
-    "LOG_CSV     = os.path.join(RESULTS_DIR, \"training_log.csv\")\n",
-    "\n",
-    "print(\"LOG_CSV:\", LOG_CSV)\n",
-    "assert os.path.exists(LOG_CSV), f\"Missing file: {LOG_CSV}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ef416874",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "rows=100, train finite=100, val finite=100\n"
-     ]
-    }
-   ],
-   "source": [
-    "def load_training_log(csv_path):\n",
-    "    epochs, loss, val_loss = [], [], []\n",
-    "    with open(csv_path, \"r\", newline=\"\") as f:\n",
-    "        reader = csv.DictReader(f)\n",
-    "        has_val = \"val_loss\" in reader.fieldnames if reader.fieldnames else False\n",
-    "        for row in reader:\n",
-    "            e = row.get(\"epoch\", None)\n",
-    "            if e is not None:\n",
-    "                try:\n",
-    "                    epochs.append(int(e))\n",
-    "                except:\n",
-    "                    epochs.append(len(epochs))\n",
-    "            else:\n",
-    "                epochs.append(len(epochs))\n",
-    "            loss.append(float(row[\"loss\"]) if \"loss\" in row and row[\"loss\"] != \"\" else np.nan)\n",
-    "            if has_val:\n",
-    "                v = row.get(\"val_loss\", \"\")\n",
-    "                val_loss.append(float(v) if v != \"\" else np.nan)\n",
-    "    return np.array(epochs), np.array(loss), (np.array(val_loss) if val_loss else None)\n",
-    "\n",
-    "epochs, train_loss, val_loss = load_training_log(LOG_CSV)\n",
-    "print(f\"rows={len(epochs)}, train finite={np.isfinite(train_loss).sum()}, val finite={np.isfinite(val_loss).sum() if val_loss is not None else 0}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6d995ccc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2beb820def8c448493ca34041184207d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
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-       "                    Figure\n",
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-       "                <img 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' width=700.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: sfig2a_data\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(7, 4))\n",
-    "fig.canvas.header_visible = False\n",
-    "\n",
-    "tmask = np.isfinite(train_loss)\n",
-    "ax.plot(epochs[tmask], train_loss[tmask], label=\"training_loss\")\n",
-    "\n",
-    "if val_loss is not None:\n",
-    "    vmask = np.isfinite(val_loss)\n",
-    "    if vmask.any():\n",
-    "        ax.plot(epochs[vmask], val_loss[vmask], label=\"validation_loss\")\n",
-    "\n",
-    "ax.set_xlabel(\"Epoch\")\n",
-    "ax.set_ylabel(\"Loss\")\n",
-    "ax.set_title(\"Loss plot\")\n",
-    "ax.legend()\n",
-    "ax.grid(True, alpha=0.3)\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/sfig2b_mnist_autoencoder_reconstructions.ipynb b/notebooks/sfig2b_mnist_autoencoder_reconstructions.ipynb
deleted file mode 100644
index 274bbec02..000000000
--- a/notebooks/sfig2b_mnist_autoencoder_reconstructions.ipynb
+++ /dev/null
@@ -1,268 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e1759548",
-   "metadata": {},
-   "source": [
-    "# Supplemental Figure 1B: MNIST autoencoder reconstructions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5c81c577",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib: 3.10.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib widget\n",
-    "\n",
-    "import os, glob, re\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.widgets import Slider\n",
-    "from functools import lru_cache\n",
-    "from pathlib import Path\n",
-    "\n",
-    "from PIL import Image\n",
-    "from tifffile import imread as tiff_imread\n",
-    "\n",
-    "print(\"Matplotlib:\", plt.matplotlib.__version__)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "fdd45c0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ORIG_DIR : ..\\data\\mnist-ae\\recon_train\\originals\n",
-      "RECON_DIR: ..\\data\\mnist-ae\\recon_train\\reconstructions\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ─────────────────────────────────────────────────────────────\n",
-    "# CONFIG — point to your MNIST AE results\n",
-    "# ─────────────────────────────────────────────────────────────\n",
-    "RESULTS_DIR = Path(\"..\") / \"data\" / \"mnist-ae\"     # change if needed\n",
-    "SPLIT       = \"recon_train\"         # \"recon_train\" or \"recon_val\"\n",
-    "\n",
-    "ORIG_DIR  = os.path.join(RESULTS_DIR, \"recon_train\" if SPLIT==\"recon_train\" else \"recon_val\", \"originals\")\n",
-    "RECON_DIR = os.path.join(RESULTS_DIR, \"recon_train\" if SPLIT==\"recon_train\" else \"recon_val\", \"reconstructions\")\n",
-    "\n",
-    "print(\"ORIG_DIR :\", ORIG_DIR)\n",
-    "print(\"RECON_DIR:\", RECON_DIR)\n",
-    "assert os.path.isdir(ORIG_DIR),  f\"Not a directory: {ORIG_DIR}\"\n",
-    "assert os.path.isdir(RECON_DIR), f\"Not a directory: {RECON_DIR}\"\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b56705a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found epochs: 100. First epoch 1 has 100 indices.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pat = re.compile(r\"^(orig|recon)_epoch(\\d+)_(\\d+)\\.(?:png|tif|tiff)$\", re.IGNORECASE)\n",
-    "\n",
-    "def parse_epoch_index(directory, prefix):\n",
-    "    data = {}\n",
-    "    for ext in (\"png\", \"tif\", \"tiff\"):\n",
-    "        for p in glob.glob(os.path.join(directory, f\"{prefix}_epoch*_*.\" + ext)):\n",
-    "            m = pat.match(os.path.basename(p))\n",
-    "            if not m:\n",
-    "                continue\n",
-    "            _, ep_str, idx_str = m.groups()\n",
-    "            ep, idx = int(ep_str), int(idx_str)\n",
-    "            data.setdefault(ep, {})[idx] = p\n",
-    "    return data\n",
-    "\n",
-    "orig = parse_epoch_index(ORIG_DIR, \"orig\")\n",
-    "reco = parse_epoch_index(RECON_DIR, \"recon\")\n",
-    "if not orig or not reco:\n",
-    "    raise RuntimeError(\"No matching files found. Check ORIG_DIR/RECON_DIR and filename pattern.\")\n",
-    "\n",
-    "epochs = sorted(set(orig).intersection(reco))\n",
-    "indices_by_epoch = {e: sorted(set(orig[e]).intersection(reco[e])) for e in epochs}\n",
-    "epochs = [e for e in epochs if indices_by_epoch[e]]\n",
-    "if not epochs:\n",
-    "    raise RuntimeError(\"No overlapping (epoch, index) pairs between originals and reconstructions.\")\n",
-    "\n",
-    "current_epoch = epochs[0]\n",
-    "current_index = indices_by_epoch[current_epoch][0]\n",
-    "\n",
-    "print(f\"Found epochs: {len(epochs)}. First epoch {current_epoch} has {len(indices_by_epoch[current_epoch])} indices.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "739b82f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@lru_cache(maxsize=1024)\n",
-    "def read_img(path):\n",
-    "    ext = Path(path).suffix.lower()\n",
-    "    if ext in {\".tif\", \".tiff\"}:\n",
-    "        img = tiff_imread(path)\n",
-    "    else:\n",
-    "        img = np.array(Image.open(path))\n",
-    "    if img.ndim == 3 and img.shape[-1] == 1:\n",
-    "        img = img[..., 0]\n",
-    "    elif img.ndim == 3 and img.shape[-1] >= 3:\n",
-    "        img = img[..., 0]\n",
-    "    return img\n",
-    "\n",
-    "def vmin_vmax(arr):\n",
-    "    if np.issubdtype(arr.dtype, np.integer):\n",
-    "        info = np.iinfo(arr.dtype)\n",
-    "        return float(info.min), float(info.max)\n",
-    "    return float(np.nanmin(arr)), float(np.nanmax(arr)) if np.isfinite(arr).all() else (0.0, 1.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "abff870b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cf5bb793162a4bf381b372df6282e88a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=850.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#| label: sfig2b_data\n",
-    "\n",
-    "fig, (ax_o, ax_r) = plt.subplots(1, 2, figsize=(8.5, 4.8))\n",
-    "plt.subplots_adjust(bottom=0.22, wspace=0.25)\n",
-    "\n",
-    "img_o = read_img(orig[current_epoch][current_index])\n",
-    "img_r = read_img(reco[current_epoch][current_index])\n",
-    "v0, V0 = vmin_vmax(img_o)\n",
-    "v1, V1 = vmin_vmax(img_r)\n",
-    "\n",
-    "im_o = ax_o.imshow(img_o, cmap=\"gray\", vmin=v0, vmax=V0)\n",
-    "ax_o.set_title(\"Original\")\n",
-    "ax_o.axis(\"off\")\n",
-    "\n",
-    "im_r = ax_r.imshow(img_r, cmap=\"gray\", vmin=v1, vmax=V1)\n",
-    "ax_r.set_title(\"Reconstruction\")\n",
-    "ax_r.axis(\"off\")\n",
-    "\n",
-    "# Sliders\n",
-    "ax_epoch = plt.axes([0.15, 0.10, 0.70, 0.03])\n",
-    "ax_index = plt.axes([0.15, 0.06, 0.70, 0.03])\n",
-    "\n",
-    "epoch_slider = Slider(ax_epoch, \"Epoch\",\n",
-    "                      valmin=min(epochs), valmax=max(epochs),\n",
-    "                      valinit=current_epoch, valstep=1, valfmt=\"%0.0f\")\n",
-    "\n",
-    "idxs = indices_by_epoch[current_epoch]\n",
-    "index_slider = Slider(ax_index, \"Index\",\n",
-    "                      valmin=idxs[0], valmax=idxs[-1],\n",
-    "                      valinit=current_index, valstep=1, valfmt=\"%0.0f\")\n",
-    "\n",
-    "def update_images(ep, idx):\n",
-    "    o = read_img(orig[ep][idx])\n",
-    "    r = read_img(reco[ep][idx])\n",
-    "    im_o.set_data(o); im_o.set_clim(*vmin_vmax(o))\n",
-    "    im_r.set_data(r); im_r.set_clim(*vmin_vmax(r))\n",
-    "    # Titles remain simple (no epoch/index)\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "def on_epoch_change(val):\n",
-    "    ep = int(val)\n",
-    "    idxs = indices_by_epoch.get(ep, [])\n",
-    "    if not idxs:\n",
-    "        return\n",
-    "    cur = int(index_slider.val)\n",
-    "    cur = min(max(cur, idxs[0]), idxs[-1])\n",
-    "    index_slider.eventson = False\n",
-    "    index_slider.valmin, index_slider.valmax = idxs[0], idxs[-1]\n",
-    "    index_slider.ax.set_xlim(idxs[0], idxs[-1])\n",
-    "    index_slider.set_val(cur)\n",
-    "    index_slider.eventson = True\n",
-    "    update_images(ep, cur)\n",
-    "\n",
-    "def on_index_change(val):\n",
-    "    idx = int(val)\n",
-    "    ep = int(epoch_slider.val)\n",
-    "    if idx in orig.get(ep, {}) and idx in reco.get(ep, {}):\n",
-    "        update_images(ep, idx)\n",
-    "\n",
-    "epoch_slider.on_changed(on_epoch_change)\n",
-    "index_slider.on_changed(on_index_change)\n",
-    "\n",
-    "fig.canvas.toolbar_visible = True\n",
-    "fig.canvas.header_visible = False\n",
-    "fig.canvas.footer_visible = False\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/upload_NucleusNet10K_huggingface.ipynb b/notebooks/upload_NucleusNet10K_huggingface.ipynb
deleted file mode 100644
index 998332cb1..000000000
--- a/notebooks/upload_NucleusNet10K_huggingface.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3ce7206c-f11e-4c6b-b588-8d31e470e72d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset({\n",
-      "    features: ['image', 'filename'],\n",
-      "    num_rows: 10000\n",
-      "})\n",
-      "{'image': Image(mode=None, decode=True), 'filename': Value('string')}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "943dd17c894341b0a06ddd675bab94fd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "README.md:   0%|          | 0.00/24.0 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\Program_Files\\Anaconda3_2024.10-1\\envs\\cell_browser\\Lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Work\\.cache\\huggingface\\hub\\datasets--RussellBarkley--msa-em-figures. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
-      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
-      "  warnings.warn(message)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/datasets/RussellBarkley/msa-em-figures/commit/654b7f771e0e4948390ad0bb16334e3480217339', commit_message='Upload dataset', commit_description='', oid='654b7f771e0e4948390ad0bb16334e3480217339', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/RussellBarkley/msa-em-figures', endpoint='https://huggingface.co', repo_type='dataset', repo_id='RussellBarkley/msa-em-figures'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Upload a local folder of .tif files to a Hugging Face *dataset* repo (no sharding)\n",
-    "\n",
-    "# If not already installed in this env:\n",
-    "# !pip install -U datasets huggingface_hub pillow\n",
-    "\n",
-    "import os\n",
-    "from datasets import load_dataset, Image\n",
-    "from datasets.utils.logging import disable_progress_bar, set_verbosity_error\n",
-    "\n",
-    "# ── Silence logs/progress ────────────────────────────────────────────────\n",
-    "disable_progress_bar()\n",
-    "set_verbosity_error()\n",
-    "os.environ[\"HF_HUB_DISABLE_SYMLINKS_WARNING\"] = \"1\"\n",
-    "\n",
-    "# ── Auth (choose ONE of these) ───────────────────────────────────────────\n",
-    "# Option A: If you've already run `huggingface-cli login` in this environment, skip this.\n",
-    "# Option B: Use an access token (recommended for notebooks). Paste your write token:\n",
-    "# from huggingface_hub import login\n",
-    "# login(\"<YOUR_WRITE_TOKEN>\")  # e.g. 'hf_xxx...'  ← your access code with write perms\n",
-    "\n",
-    "# ── Local data & target repo ─────────────────────────────────────────────\n",
-    "DATA_DIR = r\"C:\\Users\\Work\\Desktop\\NucleusNet-10K\"   # 10,000 .tif files in a flat folder\n",
-    "REPO_ID  = \"RussellBarkley/msa-em-figures\"          # target HF *dataset* repo\n",
-    "\n",
-    "# ── Build the dataset from a folder of images ────────────────────────────\n",
-    "# Note: For a flat folder (no class subfolders), \"imagefolder\" will still create a \"label\" column.\n",
-    "# We'll drop it so the dataset has just {\"image\", \"filename\"}.\n",
-    "ds = load_dataset(\n",
-    "    \"imagefolder\",\n",
-    "    data_dir=DATA_DIR,\n",
-    "    split=\"train\",\n",
-    "    keep_in_memory=False,\n",
-    ")\n",
-    "\n",
-    "# Remove dummy label column if present\n",
-    "if \"label\" in ds.column_names:\n",
-    "    ds = ds.remove_columns(\"label\")\n",
-    "\n",
-    "# Attach the filename while avoiding image decoding during the map\n",
-    "ds = ds.cast_column(\"image\", Image(decode=False))\n",
-    "\n",
-    "def add_filename(ex):\n",
-    "    return {\"filename\": os.path.basename(ex[\"image\"][\"path\"])}\n",
-    "\n",
-    "ds = ds.map(add_filename, batched=False)\n",
-    "\n",
-    "# Re-enable decode for sanity checks / downstream usage\n",
-    "ds = ds.cast_column(\"image\", Image())\n",
-    "\n",
-    "print(ds)\n",
-    "print(ds.features)  # {'image': Image(...), 'filename': Value('string')}\n",
-    "\n",
-    "# ── Push to hub without sharding ─────────────────────────────────────────\n",
-    "# Set max_shard_size to something larger than your dataset size so it stays as ONE .arrow file.\n",
-    "# (If your 10K TIFFs total, say, < 8–10 GB, \"50GB\" is plenty.)\n",
-    "ds.push_to_hub(\n",
-    "    REPO_ID,\n",
-    "    private=True,            # set False if you want it public\n",
-    "    max_shard_size=\"50GB\",   # force a single shard for this small dataset\n",
-    "    # revision=\"main\",       # optional: target branch\n",
-    ")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/upload_NucleusNet_huggingface.ipynb b/notebooks/upload_NucleusNet_huggingface.ipynb
deleted file mode 100644
index 17eb03a30..000000000
--- a/notebooks/upload_NucleusNet_huggingface.ipynb
+++ /dev/null
@@ -1,2115 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9f909b67-0226-444a-84eb-f7f9fd2b4dcc",
-   "metadata": {},
-   "source": [
-    "(huggingface-upload)=\n",
-    "# Hugging Face upload"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "275eab52-4dcb-4bcd-9506-2f510f650e7f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7c1cf74c1054e518dcd19cac951301f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Resolving data files:   0%|          | 0/1061277 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset({\n",
-      "    features: ['image', 'filename'],\n",
-      "    num_rows: 1061277\n",
-      "})\n",
-      "{'image': Image(mode=None, decode=True), 'filename': Value('string')}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, Image\n",
-    "import os\n",
-    "\n",
-    "DATA_DIR = r\"D:\\Confocal_imaging_nuclei_tif\\MIST_Fused_Images\\ROIs\"\n",
-    "\n",
-    "ds = load_dataset(\"imagefolder\", data_dir=DATA_DIR, split=\"train\", keep_in_memory=False)\n",
-    "\n",
-    "# (Optional) remove a dummy label if present\n",
-    "if \"label\" in ds.column_names:\n",
-    "    ds = ds.remove_columns(\"label\")\n",
-    "\n",
-    "# Add filename\n",
-    "ds = ds.cast_column(\"image\", Image(decode=False))\n",
-    "def add_filename(ex): return {\"filename\": os.path.basename(ex[\"image\"][\"path\"])}\n",
-    "ds = ds.map(add_filename, batched=False)\n",
-    "ds = ds.cast_column(\"image\", Image())\n",
-    "\n",
-    "print(ds)           # quick check\n",
-    "print(ds.features)  # should show {'image': Image(...), 'filename': Value('string')}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "08c29690-49ac-43e2-95c5-698ad2f432e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6bba793033346828f536b05430ab16a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Uploading the dataset shards:   0%|          | 0/70 [00:00<?, ? shards/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "069862836f8d4bf39a21207be51241b4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c59ffe472b2c49d78ac2a67ca4f16599",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Creating parquet from Arrow format:   0%|          | 0/10 [00:00<?, ?ba/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e703d3b0a33640a6b5c34a4c407de2cd",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-    },
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-    },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\Program_Files\\Anaconda3_2024.10-1\\envs\\cell_browser\\Lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\Work\\.cache\\huggingface\\hub\\datasets--RussellBarkley--NucleusNet. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
-      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
-      "  warnings.warn(message)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/datasets/RussellBarkley/NucleusNet/commit/3293cf6a8883cb3214744a7110b6e21627c49cc0', commit_message='Upload dataset', commit_description='', oid='3293cf6a8883cb3214744a7110b6e21627c49cc0', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/RussellBarkley/NucleusNet', endpoint='https://huggingface.co', repo_type='dataset', repo_id='RussellBarkley/NucleusNet'), pr_revision=None, pr_num=None)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "REPO_ID = \"RussellBarkley/NucleusNet\"  # or \"RussellBarkley/NucleusNet-v1\" etc.\n",
-    "ds.push_to_hub(REPO_ID, private=True, max_shard_size=\"1GB\")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/upload_representatives_huggingface.ipynb b/notebooks/upload_representatives_huggingface.ipynb
deleted file mode 100644
index e8ecc755b..000000000
--- a/notebooks/upload_representatives_huggingface.ipynb
+++ /dev/null
@@ -1,257 +0,0 @@
-{
- "cells": [
-  {
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-   "id": "74c0f06d-3de1-4907-a5b3-d6dd90c12a15",
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-   "outputs": [
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-       "VBox(children=(HBox(children=(ToggleButtons(description='Distance metric:', layout=Layout(width='auto'), optio…"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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zO+l/p8RgtKAfJW9605tsbm7O7rzzzp7Xb7vtNtu2bZtdcsklVq1W7aqrrrKvfvWrNjs7m2/zxBNP2L333mtvfvObT/Sw+7Jhwwb75V/+Zdu1a1e+oG3evNkajYb9zd/8Tc+2f/iHf/hcDPGImJubs0cffTTqQ2c+8IEP2Lnnnmu//Mu/3POZDcvtt9/e8+//+T//p33ve9/Lo35f9apX2ejoqH3pS1/q2e6pp57KzeWgn+Ks1+vR11/zmtfYN77xjXwBB7//+79vzWbziH3Jx5vLLrvMzMz+83/+zz2vH621KDVPR8qLXvQi+8AHPmDf/e53e8b6Mz/zMzY6Omr/9t/+W5ufnw/2e//7328HDhzIA8KONffdd591u10799xz89eG+Y0ys2PyO3Ws51msDQXFHSWve93r7IorrrB//a//tR06dMjOPfdc+/KXv2x/8id/Yl/60pfy4JSbbrrJXvGKV9gb3vAG+7mf+zlbWlqyj3zkI7Zx40Z73/ve13PMBx98MDfLHTp0yLIsy6tIveIVr7Dt27cf9+t617veZf/xP/5H+w//4T/YT/3UT9nk5KS9853vtM9//vN2zjnn2Mte9jL79re/3RPQdSR873vfswceeMDMDkfZmll+rWeffXZPsMx9991ne/bsMTOzTqdj3/ve9/JtL7vssp6I/G63m6ukbrdrTz/9tH3qU5+yAwcOJKuueUZGRuwTn/iEXX311XbLLbes+Qf4wQcftBtuuMHe9ra32ZNPPmkf+tCH7PTTT7ef/MmfNDOz6elp+/CHP2wf/OAH7V3vepe9/e1vt3379tlNN91kjUbDPvrRj+bHuvDCC83ssFvgda97nVUqFbvooousVqvZhRdeaN/85jftj/7oj2zr1q02MTFhL37xi+2jH/2ofe1rX7Mf/MEftI985CO2fv16u/322+3uu++2X/3VX+0JiDuZeO1rX2uXXnqpve9977NDhw7Z93//99uOHTvs93//983syFMXU/N0NLz//e+3z33uc3bTTTfZ1VdfbZVKxc455xz74he/aO94xzvsFa94hf3sz/6svfjFL7Zdu3bZ5z//efvjP/5je//7328//uM/flTn/trXvma/8zu/Y2984xtt+/bttrKyYg8++KB98pOftHPPPdduuOGGfNthf6PM1vY7Va1W7bLLLuuJ9zge8yzWwHOaBV8QZmdns/e+973Zli1bslqtll100UU9hSPAgw8+mL3mNa/Jms1mNjk5mf3Yj/1Y9uijjwbbXXPNNZmZRf+79dZbj+nYY4VlAApS3HTTTVmWHS6mccMNN2SbN2/OxsbGsquuuip7/PHHMzPLPvrRj+b7pQp+oOjKY489FrwW+48LVFx22WXJbe+99958u9j8nXbaadlll12W3XXXXT3HHFTU45JLLsnWrVuXzczM9J9Iup4//dM/zf7Fv/gX2fT0dDY6OppdeeWV2d///d8H2//u7/5udtFFF2W1Wi2bmprKfvRHfzR7+OGHe7ZZXl7ObrjhhmzTpk1ZqVTqmcP//b//d3bppZdmzWYzM7OeIkTf/e53s6uuuiqbmprKarVa9rKXvSy4f1LXnyqQs5ZiLiBV6Afvbd++vee1/fv3Z9ddd102PT2dNZvN7Iorrsjuv//+zMyyW265ZeBYYvdZv3kaRKywDPjMZz6TmVl222239bz+8MMPZ9dcc012xhlnZCMjI9n69euz1772tdndd98dHONICss88sgj2Vvf+tZs+/btWaPRyBqNRvaP/tE/ym688cZs3759wfbD/kZl2fC/U7F5TM0zrtF/T49HEabnO6UsUz90IcTJzR133GHveMc77C//8i/t1a9+9XM9HCFOSrSgCyFOKr785S/b008/bRdeeKGVy2W7//777dd+7dfsH//jf5yntQkhQuRDPwXJssw6nU7fbUqlUrITFN4/mqIxzzeGmfNKpfK8ytPtdrs9Ed4xuDrZMExMTNhXvvIVu/nmm21+ft62bt1q1157rd18881HOtQoHPXNlMvl56QM88k6LnHyo7viFOS+++7Li1yk/jvnnHP6vu+jqMVgbrvttoFz/nxTj9dff/3AOTkS3vCGN9iDDz5oMzMztrKyYk888YR96lOfGiqPfS0MGvuRVgk8Gh5//PGB4/r4xz9+wsclTg1kcj8FmZ2dtb/927/tu838/Hy0OQRQ9Ona2Ldv38CCIS9+8YttYmLiBI3ouefxxx8f2Iv8ZC7pOahn/caNG4+4V/uR0mq1gtRQZtu2bXmBICE8WtCFEEKIAiCTuxBCCFEAtKALIYQQBeCIoty73a7t3LnTJiYmnldRvUIIIcTxJMsym52dtW3btq05m+GIFvSdO3cek05BQgghhAh58skn7YwzzljTPkdkcn8+RfIKIYQQJ5ojWWePaEGXmV0IIYQ4fhzJOqugOCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAaEEXQgghCoAWdCGEEKIAVJ/rAQgh0lQqlfz/S6WSmZllWdbz17+P13jbfqxl23L5sAbodrt9X+f3hRDHHyl0IYQQogBIoQtxAoGShSpmUq+bxRW5f93vO0htx7blc8csAqy8sQ1ex7blcjl5LbytEOLYIIUuhBBCFAApdCGOgpiPm1Wrh1Urq9R+ajt1XK+kU6qYj+F93uyTx3v9SKn51L9j5+bz+n3kgxdi7WhBFyICL2qpxTq2iHY6neRxefEaZpHmffuRCnBLBctlWZZcYPsdH+PleRrG/J96gPDbVavVvmOKPTD0m3chng/I5C6EEEIUACl08bwFypAD1TqdTqBkWUXC1O4VLlTroOCy2DbDmKEH4dPW+Dx8jd4iMCgozs8Ljzd1jHK5HCjmYVQ2b9vvevg9fCbYpt1uJ48vRBGRQhdCCCEKgBS6KDTVajXp74WCTPmvPf0U8yDl6d8f5K8exn/N+GMOOl6/sbI1ImZFGCaIL3UdvM0wQYP9zpOKP0gF95VKpcBaIr+7KBJS6EIIIUQBkEIXhQLR0R5WcuwbHgZWnD7KnX3xKcVfLpeDoiqDCswMk4oW822nlHi/6PlUaVmv2HEun67Xb5/Y8VPzH0tbS/nq+6n7YawQwMdCeJQ2J05FpNCFEEKIAiCFLk56oKJi6pBV8Vqak6Si072i43PjPf96KiKeVV63202q4H5KfVDJV/966lpSarifEo1ZNjg/PLWP91entmF17K81pZxjx0zdA/3y3AfdH9VqNd9mZWWlZ0zyu4uTFSl0IYQQogBIoYuTDs4nZtUWi3RO/bufQk+9F/MvpxRjuVzOFSCUW8pnnNrf/2V12S93vV/VtliJ19g1DtOkpd9+KYXfb9ypffzrayklm8oYYD9/zNrR7/jYtl6vR8etJjPiZEMKXQghhCgAUujiOaVSqeQKJ1W/O+YDHUYZYt+1tBLFv4fxwfNYBkWJxyLW1xJdzceFAvXWg9RYUlHj3u/O5+mnrHHOflH1KWtDqkreWqrXxc6Zws/JsDEM/rPi+AnO08+yTH51cVKgBV2cUDitzP9wDgqA8tulOoLFfrD7PRjE9l1L8ZiYabnfosELCBZGBF4NMpX7bXiRHqbwyyAzdb9tvXthmDK3gwrVDBOYlxpbzC3CxD4z3j/1mfsudIPmzt+PfBwt9OJEIpO7EEIIUQCk0MVxhU2zIKawWHGyQoK6b7fbyQCotZQT5ff7qeJhgqkGqfhYMB8rOFaDlUolGWwHYuPFa6kCMP1IqXxvAUiZzf35U59JymLij5EKEowFzrG1oF8/90Fqvl9gYD8XAp87NYcqWCOOJ1LoQgghRAGQQhfHDK+ga7Vaz3scWOQVKLe5TAVweT8zK6DU8WMM8on61p+8bayISUoRrkXxp3yvpVJpYPGc2PljKVvD0i9obVBAG1tT/Ht8/H7Wg0FFaXj7GDGVnCoO00+Zp1IWY3EIqWuNFdNRe1dxrJFCF0IIIQqAFLo4aljB1Ov1pF8z5k+N+VD98Vg9eVU8yCcZSxVjYirTWxv43H4fP/5h/MupyO9UalQs2rpfPEIKfEYxVTjI1x1Li+MxxHzevD9bGDiyfxh8bEAqCr2fSk4V9uGx+XiBtRQMSrXojd0TsGK1Wq3BFy7EEEihCyGEEAVACl2sGS5owv7IdrudjEAGsRzeYfPF/esp/3UsAnlQoROvyHBc9vMysdzylOL0Pmi2APQ7T+r4rLpj1ohUpP0wDUtiyp2tMf3y2VOFcPh+8SqZy/sOun9i15q6Hn8dw9xzqfHGLAGp8Q4Ty4F7ga0f8rGLtSKFLoQQQhQAKXQxNGhS4RWhJ+bXTvlnYxXF2Gc5TKQzGLYMqN+W/dgxn2jKH+5V36CqdbFKbvg7MjKSPC6OMcjHDIXnlWRKicdUIc6FzzXlH/djwT6sLvlv7JpwzfAd+7GlxhuLMOfGNgw3zeH/77etjxcAw/j6U9affvdYKg6ErThCDEILuhiKarU6MCDKw4sziJlFBwXFxczGw5ZxjZmUU+bQfmb6lDl3ZGRkYKqSDxbkH2cERqVM2Z1OJygPy+Vzcf5ms2lLS0v5uMxW53RxcbHnPMDPeaPR6Bk3FlwcKzYfg+qc+234GP3qyqe28eOO1VQ3W50PnDfWhW5Q2qN3X/BDTKxefqooUr8Fvl+qnD+P/8xUSlb0QyZ3IYQQogBIoYseWDFy2pYnFQBUrVaD/dik6fdJqV+QMpH7/08V/SiVSoHZP6W8YkqOO8DFTPDYBnOHa2cl7bdJHR9gnkZGRnJXBxQzuzbwt16v29TUVM+2PBZWrz4Qjcv0QrF7UzwHii0vL/f8G9fnlSSOA+sBn8cf01+3nxc/Xr9PbO5AKtUwxjAKOrV/zO2SCsT05xsUOOfPy/e+zPAihhS6EEIIUQCk0IWZDVaiZqsqKZVi5feJNevweOU1bPBbLOiOVU6/86bSvth/3e12A9XN+3hLw+joaHR8HDjmfa1jY2M970FRQ83Ozc3l70Ohz87O9uzDfuxqtZofF38PHTpkZqv+d/yFsvbxCGxhYHU/NTUVKEMcn+enVCoFQXY4PltEvKUE4+N5xmfjr51jClL+ZX9vsJWGFX8snTIVLOiPm/repFS+LxjE23KwX6fTCZT+sIWVxPMLKXQhhBCiAEihP8+BamK1wOUoy+VyUknHlFY/n7b/2y+amCOGvSruFynt9xkZGQnO7aO2/b+9WvN+abzmx4B9arVafnwodY5Kh+r258Br2JfnGwrbj39ycjI6H1Coo6Oj+XFZrWK8mAN87t4/CzjeAft6dY4xrFu3rmdfr6g5ypzTHTG3mJOlpaUgnY8tF/7zxWvYH9viuJh/r8pTaXGxWA8eS6ogjk+9TMV0xO7TlH89doxUnABbawCuXTy/kEIXQgghCoAU+vMUbm86SGH495h+rUtjvkP/N3buQfnilUolmYfO/v1qtZr0nfO//XhwXKhA3gaqzb/O6hfb4Bi1Wi33XeM9KKnTTjut5xjYbmlpKVdhPG5+vdFo2MLCgsXAGDBvPuqdc9S5AAwsD+12O49UT/ndse/CwkKQFw4LQ4qRkZH8eJwhgOPjOlqtVn7fYfwgVZq4Wq0G0f/97kv2ZXPMglfYgxrcDFOUJhUZH4u4T1m1vBVKKv35hxS6EEIIUQCk0J+HjIyMJJ/sU/4833BlGAZF4XrlPkx0sqdWqyUrirHfs1arDVS2nKPdarXy48GXDXBc7FOtVoM2mBwDAIXr4wXwGvvzU+rebDWPm5VXs9kM/h9KHf5kjBdjglrudDo2Pj6ejw+vma2qWH/N3vduZrZx40YzM9u3b5+ZraplX7WOlTL2Zd98tVq1+fn5nn2wzf79+3u2bTQa+Xs8hzz/+NtqtfJ54WvkugmdTieY51RlOv//qYp5/ZrYpCowAl+rgb+3fhv+q/aszz+0oD+PwCLS6XSSRVb6/fDwD3K/fVPpO8AvwINSz3gB8AVOUkFxPpiNg8d4sYz1c8c+OA4H1PkFGYvEpk2bzCwM/vI/wvwjmyqq4wvE4PgIQOMCM7EUNH4Q4UJB3iyNa0Va3IYNG8wsfHAYGRnJF1wc59lnn+2Zl1jBFzafp1wTtVotKJyCv5hv77LAfKTM8/7hAn8xL0gL5NQ8PIS02+18nnFOvq7Y4poq9cqBgX6R5off2HcnVlTJnyd2fn6AVTGa4iOTuxBCCFEApNCfB3A5Tl+qMpUCBbwCYCWbKqnq1UFKmXtlPSiFCOfhsrT+/3kbXzqUVR+UuTdn+/dHR0eT6UBQ7NPT02Z2WL3hOKwiuWgJFLB/Dfuw2d/PARQmuwjwOoq7+IA4nkNsy8VuSqXVhiiYO6hwqFlvCeDPCPOAuYOqnZiYCKwbqdKv/nOYmJjo2ZbnwytoThdkFwH2wfGr1Wo+R7A+cMoiLCOdTiffdlCBmW63myyRnEp1K5VWSxJjLPyd8QWJUq4wPq5X6Dx3MsEXHyl0IYQQogBIoRcYLrwRaz/KwTWpVpdmYXATHxfE0tZY3cfKxPJfH3hm1usX5zKiHETl1Rq2hZJjfzhfT71e7/HrmoXq0vfyxnGhgjm9CeNuNpt58BgXruFUKNBsNgPfJ6eDeetErP2qmdnMzEzPXHo1iGuFOmbfMM7vU+jwFwF1UPXwv/vXMFccmMd+6263m48F1gEuN+ytM5zihmuG4uX7yd8LCOaDCsc14npmZmaCz5OLAPlUPY5RYJXNcQ+lUin4zAelovltQcoS5v+dUv7yqRcPKXQhhBCiAEihFxBWqTEFwG00U8VXvGJPKXJWJ7HjpaLqfWlTwIqafceNRiOwMPj3/BgnJiaS8QEpn2un0+nxu5qtqkhWjjiHPw7GD/Xnfeq4JhwP73GZUkRwj46O5ufCPhwT4CP7OSocY2BVz6rZb7N+/XozW1XYGFuz2ewp7GK2mk7mi+fgfbZCcElZHv/8/Lxt2bLFzNJpa7guP15OVcRfzK2P9WCLFMbG8RS1Wi1IO8RcYs4wL0tLS8F4cW7O0PD3Tb9yyPx+qvRr6nvlG8ak9uXiRuLURwpdCCGEKABS6AWCFSiI5a5yjior6dSTP/b39ItyZ9h/CoXk38NrUL5MtVoNFDnAv33kMPtYudgKR+1Xq9WgRCuiuRFRjmOMjo7myhnHYd8ulOjs7Gx+Dm5Mgr9Qgz6HGmqYC+TgL6wGpVLJDhw4YGarap6tDxiLLxDD2QNQojiGLzyDedi7d28+Pr8PrqvVauXXgr98HRgLjjE2NpYsxYp7AZ+vL/fKCp2VNV5fXFwMIvc5AwSfb6PRCAoFcUtdzE+j0Qja0/J8A/+9SLWR5euKFV5KlUX2DNtitVqtyp9eEKTQhRBCiAIghV4QvC86VUqyXzvGflWq8H6qKhvwUcVckpVViFc7qfKqUIZQD1B/tVotaJXJ+ec+RgDbsm+eFbWPOcB7UOoYI+eNl0ql/PhQjVCxUJPeCoEKZay+Yi1ozQ77jlnVA/ZFLy8vJ5U5iGUZcB463oNyxnmzLAuUOPt9MV/j4+P5+LgFqlfx/t8rKyu5wk3FPWAuNmzYEETA8+fJ8QIbN27Mx8/+cPZxdzqdwA/ODY38HOD/uXQtV+bjCHk/7lgNAryfKvnKkesxH/0gpd7tdhX5XhCk0IUQQogCIIV+iuMjh2MVrDyx/PBBT+/ev5xS/rHGEOybT+WWl8vlIDo8FYmMhiK1Wi1QtqijjnxrKKFmsxmoSW71CXxFMK68xdXTfO1yKHO+DlgYoAZrtVr+GudKQw2z0lpYWMhVN6tWzkOvVCq5rxnXDJXJfmbM5ezsbD5XqbHh2tevXx/kU8PiwKp+06ZNtnv3bjMLK/OhGhty8rHvxMREoGhxbj6Pnw/O2ECOOY6Bz6fdbgd57lytzee5Yy4xXowFnwf+eisCW5N8dUCzXssYf49S6tjXcmdrROy4Zr214lOWu1j0+7B+d3FyogX9FCX2xU6ZzWOkAnFArGkI/xCkFussy5KmUix2fgFjczmb3NmsWK/Xgx9xLOSc4uaLofAPHDc3wb5TU1P5DzwvMHw9vtMXpwliIfEBaBy45XuZexCc1el0gsUNJn1c18GDB82s90eYg984UM+bu9kc7B+G/L4LCwvBgo5t8Flh29nZ2bzIDBZjzCk+KzZhHzx4MOgLj2vGAunvEW6aggUW5+MHlKWlpZ4UPH8dwC/w/PDJrhr/PcA2eJjAuDn10s9lKmCOH1T8wzRI9VmPFZrhoE8ev3eRAS3spyYyuQshhBAFQAr9FCOlrGNKO/UUX6lUBhZ+YRXr30s9vcfKTXIQGdSHV46slrAPB2nh3/V6PVdaqWv0qVhsqoZSgcLjEq0rKyu5EuUgMw6impubC1KfoM5g1obJfffu3UEAIAekxQL1WF2jyAqUum+Ly8FdnKqHf/tGLjzvHGjo54IL17D6A2eccUYwXg5yZAuMPwYsFLyN3xb3Dd8DnHbnr4/vKQ7axNhWVlYCCwCPBecfGRnJrSSA0yphNfCvc2ncfmo4dZ/3K7ucCrYDMdN7yvompX5qIIUuhBBCFAAp9FOEVDAM6BdsEwuO4aAyPj4/kfu0NfYHeh8xnxOqhtPV8Hq1Wg2Cpjg9i5VSqVRKlm1ltefVPAcxsZ/c+14xPyiDiuAmvI5/V6vVnnKkfhuv4s0OK2oOUoOKx9yxj3fz5s3BZ8JWAx8LwMrcB4T5a4eKbTabyXQpLvSztLSUn5MVP8btU9Jw3WeffXbPNe3Zs6fn3/7e42vD+DkQ0N83uP9gCcA2mGvf1IctCZz6BnzpV97WB0SaHZ5bBNBBdWNeuGWvj0FgiwKnIfqyzDy+lEUtpuTX4mdPlaGNlYIWJx9S6EIIIUQBkEI/yeEn81SkuW/HCIZ5ak/517jMaKVSSUZoc+GTarUaKHLsC6UCRZNlWf7/+MutSnEMKLqlpaWgqUys5KvZ4fQklCnFtUJF4RjY16cApnyTuA4fec++WvbRe4uDb7vqx8ClR+Ef91H0mB+Ud4WS4yYwseOyEvXqmP3iULoA8z89PR1cKzfD8aqeo/7x2Zx++uk94wbLy8s91gA/bi4b61uhcpYC4hRwfKj8VquVpzeyxQWpaT7Wg61Y3JwI59+zZ09wbr5PMBeY21KpFGQ2pD4rX+YWpNqm+jFyu9pUsya/Df+bI/F9TIo4+ZBCF0IIIQqAFPpJSip3ul9BiVSpV1bbvpRkSqnzPrVaLfCxssLwCp0VNEf9et8itmUlwsofqs0sVPwMrn3//v35tfG27CPG+bMsS8YJsEL1LTnxHnKp8W8fpQ6fPBQngzFCXU5OTubjgu/Zz4Mfy/Lych6bgGviPH325W7cuDFX/Cj0ws1a2Epktqrw2XfuW7xyGV78m9uyInd7eXk5KCHrLTl8/FRJ2VjWAq4Lx+WcexzD57lz7AXf996ywUV++HuEKHhvcWHLEMBx/f3DrXN9S14PH8vvE3uPGeZ48qefvEihCyGEEAVACv0kpFarJaPZQT+/+KAn8ljOeirCGZTL5cBHzH5lH3XMvlWfQ+73rdfr+f9DLfHxOLfaLGx92i8fHSoP/mkuMcvH9+oZ+/Dxvdpk/ynUE+c8T05OBlXfuDQowHajo6P5thgvK09fbY592bgm9sl7hYpIeyhDvg5fCRAWBm4XyiVl2+12Pi68BisB1yJAjEO73Q7iGtg/jmPNzc0Ffl6o4NR96ceE6+AIc28h4Dx3bhzj72lsi7FwRT3+PrdarcD6w61ofYwAV4/jSoxsYet2u0HlOZDyv/c7HvD7qKHLyYcW9JOQbrebLNjBZVCBT+VKde6KmU5TDwFsjq7X60E9cP5B8q9jfywOqTSzTqcTmOO5S1aq85Y/LrbBjyKbJv1rXATEFxMxO7zwrFu3ruc9DlbzJk+ukc3Bg35BwALODy/4bGBa9WlQWNDZZcDBbBMTE/lY8B4WSy4X6x+iUgFQsQI/qdRFzL83wfM8ICAN18gFVcbHx+2ZZ57pGQuX2PVuHczHzp07e87NNei9mwHHwbZ8rf4hj10GbI5GIN2BAweCxZHnA9ezZcuWfGx4jx8suZRyuVwOAuRw/3FgIYg9tPcrCNWvQI3f1287TIlpcWKRyV0IIYQoAFLoJymp/sexADdslzKlxZ7MB5njcR6vFrgvNNQSArG8ahuUKuMVEqfXcGEZbsQCFe63hfLl+Wq320FDEhQ84cYoPtUtVUqWO7UtLS0lA/NiBT34NVapuDa8vry8nJuHkfIEZQhgcfBWFO/SMAvdCjiP7xcfK7FrtqoCp6eng4AtXDsr/2q1ml8TN6nB62zCXlhYCMy4+IywDwL3ZmZmAmsPm7thHcJ5V1ZWkqVfMQYo30ajkR8f1g22xOA+8nAzGQ7sRADixo0b8/05YI7HWCqVelwN/m8qPdEHvrJbql/a2qAe6rFiNOLkQQpdCCGEKABS6CcRnHZjFioUEGuBmOp/zk/qsUA6Vjvss2w2m8ne3bFSmD49x58bSpqVnf9/9jtCkUJ5zc7O5qoP6oktAt4ywGleXPyDfZheSXO6F84DH7e3AGAb+NuZ+fn5wMqBlK1du3b1bIsx+eAvnAcqEH5+4IPKMN8Ypw+y81Qqlfzc7Hvmkr4rKyu5MoQ/nC1GXn2zEoeFAdeBz9UXX8EcQpFCkeMzxNhgFfL7w5KAa2Zr0MjISJBOyeP3TWegnNkqwc2E/D2McWNMGG8s5oDL/AK2XNTr9fyaUvExsCxgjFmWJQPz+jVyAqlmLDHlrwYuJw9S6EIIIUQBkEI/CeCUF7N0k5RU04RSqTSwKIRX7BzNzoqa/dqtVitQqVAzeN032eDIYL4ef3woCSgibMsR5b5cKvuRuemIL+bC0c/cICZWOpV9rNzeFL5Qs9AqwNHKfo65nCgsFrgHoIBhgRgZGcm34eIqmDdYUdrtdmAJ4QIw+KygHM3CWAJcB/u+O51OEC/B6WoYm28/is+K4x0w77HIeMwD3z8+VY998hs2bOgZ07PPPtuz78TERD4+jIXLxHp/PCvQVIMUH4fA9xRbyfB5NBqNwFfOzYN8YyNOUeQ2vLHsCbbicRYM8Na9QW2Z+zV9URrbc48UuhBCCFEApNCfQ1L+ca+gmVShhyzLkk0kWCVUKpWgwItXtGarKsf7m6HI2QfNUenef8qFQmINZHBuVo+scrz/kNUv+919OdRUJDZHUuP9xcXF/Fpxjaw6fI58zC/t8Qodx4GaRDlXjtD25UrhP8V84PjYFn7mZrMZfG7c6MPnceMYXKQH58Pr2MdbMHw0uNmqusR1NJvN3OLCTWr4nvPFXLhGAOeH+9dx3Rz/gbHAwuOL9/g4D7PVOUXcg1e+XIQnlSfe7Xbzc8KywJkVXBjGZ4LwtXGuvK9LwaWUuc6DL6PMDYCY2G9HqrS0v6cHHU88d0ihCyGEEAVACv05gJVJzF/OqjSltmN+sVhFOL9PlmX5flx9zfv4/L99u0rOaeZo8ZWVlWgEsP+3vy72D3IOr1fO2Jf9jRwF7S0EHE3M8x/zXXLkvj+32Wrp0G63mx8fShTn4Yp3U1NT+Zxx9DbGwk1byuVyrjCxLVQZjuHvDbaMYGzwcUORwj/r88UxflbmvmEJ5oF90VCm3g+PeUamwFlnnZW/568Vx/TWJ74/OSbDV7jD+HAetnz52gHcWARzCzWPMc/PzwdR4tgG95i3WPD4OGODvwetViu3PnCrWHw2/vPFcZHZgPnGtXPzlkqlklsdOG4jVRI2Bm8bi2RPxfWoecuJRwpdCCGEKABS6M8B7OMe1GDEv5bKAe3XRpVzy81C9QW1ACXBvnTv/+WxQKn4mugYD/tNuSKXV01ce5vzmFlB+m24Ip2Ho5RZZXBMwOzsbB7Fjm0xP7gefwxWclCcUMW4nv379wc+eY5AjuVJ8+fJFgd/33B+vs+rNutty2p2WJEiGpxVJOdQeysNjsfH91kRbD3CefA6xuCjozkjge/drVu3mtnhOcZc4fOD/53jK7wC5iwAROBzq1RfnQ2f79NPPx291kajEViX2ErDjWNqtVrQ2IYzBxDL4H30XL0P+8Ry2TEvrN75fvL3Mlt4hslhBz7LxW+rqPcThxb054B+jQ/8X///qfSR2JeU3+NjVavVaLMLfxwuRVoul4MfEeyLBQsBXiMjI/m5Yfbj8qq+YQxfIxdO4UUOi4p/j9N4/I8tm3hxHO645R8gMD/8483FSnz6F5v0U8cwC3/kuByt76ONRRrXymlr3kTOP6Iw3/IPNZifn8/Hu23bNjNbTcnjQkL+HvD7+zHhc8iyLHgo4vQ4dnU0m81gIcHxcG/5ByBOOeP5Bn5h9B0B/Vi4R3ulUsk/Exw/1YnwwIEDQdonm8QxBv/gwO4bfFf4oabdbgfFbLgQDo+t2+0GZX4xFr6ffMOV1P3uSaW4gdjvmDgxyOQuhBBCFAAp9OeAQarbLN1HORVI55unsJmSFUW1Wg2ar0Cx4IkfT/VeWbACSqnwSqWSDJjj4jS+tCmXvuVyrj49LlZoxP/16oMLv+DaEETFJnhv4mTXA4+t0+kEhXUAK2qzUBly4BhUt0+r8o1U/HnYytJqtfL5wPzguDAt43rgvhgZGcnN8wjC4mAtX8qWrTPsFvHuEk5lw3vYF2Zo3GO+sAwfDyVyfTofVDC7AaCo8W8fRIjjsbmb26n675NvxOOvB8cfHx8PPmv/mfgxYt594CXOjXmHgsa+CwsL+WcEuOwvPleffsqNZzjg0qtxLhnM5nO/baql89EE34ljgxS6EEIIUQCk0E8geIrm4KxYKkiqWQKr+liKGityVnSdTidIT/M+Z/9vKDzfUjRVltYrd18Uwx8Pvkpf5IXVPBf94OtptVq52uCiMVAovr0kK4hUWVefYsSNQ7zf3o9106ZNgUoF3MK00WgESgiNUTiIClQqlSAFKhU4Vq1We8q0+vP4FCv//vr16wOfNvbB6/gcqtVqrhQR4MY89dRT+TWnCuxw4KVv/Ynx83cEY8K8HzhwIJjfVDwF5mtqaiootwo/Pz5vWAu8VYL90zi+v1/wPYHK5gIwPLe+3XGqDLO30uHaYKnA/Y1jQNWDWq2Wf35sWcN3hxvT+G1ScT7lcjkZxJsK8h0ZGQm+G+L4IIUuhBBCFAAp9BOEb8wxDKwmBzWK8OVQuTUkK9FyuZw/vbNfk0vAAu9D53KrAEqu0WgEPm6OdPYtOn2ZVn/tUEkYm49WZl80q0mv8FJqCcfllLQDBw7kkcdcKpQbllQqlaCsbaqhTrvdDtp/cnR0rLwuUqxSRXr88bksLMbG5VdxHT79C/PC94Rv48l+a7aM+LavuEZWjzgeF6MplUr5//O2UMD4XCYmJvJ7i321/D3AdVUqlSAan7MW8DmPjo4GliNsy/eY2WopXy5bjDngMsM+u4C/K1xUZ2pqKr8HuCARLCbYFnEJnU4n2USJ27O2Wq3gnuV72d97qSj2VLlqv79S2I4vUuhCCCFEAZBCP0FkWZaMaudoVN+whH2hHHnrfVus2NhX6SOrUTSDn+K53Kp/cucIch4b1MLi4mKgyL11wP+NXRPUJOes4zwbN24Misxw61KvhDh2AePmpiTY9/TTTw+ixaGQWLXV6/WgUQZbYnwEPucCQ9lBDUPhYbtGo5Gr7VRLWvjf9+/fn4+FG8bw54zPo91u5/MMNcnzgX0OHjyYLEnMis5nOqTaePq2qbhWVqdepfox+f3x2eDfmzdvNrNVFct512arFgx/T/lj+eOxsuVSrZ1OJ6jJALhgU6zIDUefs3Vubm4uPy6Oh+/a448/bmar956vK8GR6rByxGIYBuWU+98BtoDw/divroY4vkihCyGEEAVACv04wz5Ss/CJlUtk+mYMKb8UK11fIpR93Vy5rF6vB4qN1T374c1W1SNHfkO5QGksLy8HedvcVtKrZqiL/fv3R8cPvNWAm4+wGvQ+dvw/cnc5T5nLgJbL5SBOgMvRwkIwOjoa+Nkxd75qGs7LMRCpfHcc48wzz8wjxzFuVnD4u3HjxjzugPOJsQ2uw/ttOSeeq5D5DAtW6JgHWHwQ/d5oNJLR7Hx873/nOeQ6A7hHxsbGeirM+XGyhQRq1lsYWD36WgE4Fls5eGw+RgXzASsHN3Dham3VajWIieDPzPvdEY2Pc+N4sEZwDEmr1Qqq1fFn7xU8f4/4vvFKPaW4+6l8thrKl3580IJ+nOlX+5hN7bGayikzPS/A3W43CGDBgoK/MO/6c3IwHJvPfLlUPj6XHvULGX4gUw8kvmQlFlSYPXEeLE748uMHqtvt2mmnndYzBv4B8g8UHNDGPbe5gE21Wg2Ow0GIPkDP/0j7bTGHGLcPLORSu/w54N9PP/10T8les8OpcmZhrfXJycl8XtklwQ8X+AHftGlTT1qXWW+BID/GhYWF/Hi4fiyw/mEIY+NSvTynmG+YgsfHx4P0QB/QxvOE+4T7BeB6uCTs9PR08CDI5mM//2yi5oc6XzIYx8PDAwf8Af/9TY0f+LQ4fG74TgwKYsuyLKj7jutgd1un0wn6tvOCGytHDfh+59+1Uqk0VC14cfTI5C6EEEIUACn04wSXPK1UKoHqTpV3xfZ+f8DbesWHfThNJdavmEt3spnS97UGMCdyOhB3W6vX64ES4gIwvvwntoWChmkc18PBT5OTk0G/dpgkoZC8YoeS5a5oeB2WC+zbbreDQiCcnuXhdC/sg+uBOvad5WCNgHriz8ybk7n0LcaAv5gXTg8z6y016sfiU/fYkoB7gq0JmzdvDkqmYu7wbw6sM0u7Q7At2LdvX/7Zp1wcvoMaGrbAOoBtuHwuzre4uJhvwx32vLsIr3Ovdx6LL92K+cSYWOli3LAOTU9P5595rCufZ3l5ObCa4DhbtmzpGZN3f7FLAvcJ3z9eNfvuipgz3obN8uy+iLmEYsWzxLFHCl0IIYQoAFLoxxlO7fCkgkt8QxFOrUr5/HyxGA68gr/TF7ngp3dWQBw4trKyEoyJfaw+vSfVCIJ7qI+NjQX+PygJPh96YY+MjASpXFB22NaXfmVrAUCRErYA+BRAnCcVbOZb0fLxMe8+4A1qjC0wmA8u6jI+Pp5bDqDcOH3KjxWfJ/tEuT+9t5Sk2o9CBfpiNzguLCLw58fKonIzE07546JA69evz4+Pa969e3fPmHxgJN9/mEPMO8Cx9uzZk8+3D67z+4JyuRyku6UK1vigMi5di+vBNeP9gwcPBkV/cB70XfcNddgKhONy+1p/X3IgJMeKYEyTk5P554lr5vucUz3N0ulqgC2D/P/i2COFLoQQQhQAKfTjRKxxQaqZwTDH4ZQ0LtTilSKUOPuZY8VKONo95VMvlUr50zs/4bMK8f/P6oyjfmu1Wq4QoaxShXHAyspKtFiOH7/fl9toQs1w+VLM28LCQlA8hIu6+BgJ/jx5vF6ZspJihcvNVKampvL5YWXO5xsdHc0tCTx3XOrUW1nYKoPPDPvAcuFTDDl1C/cA7pGJiYl827179/bMHebjmWeeMbPeqHpuCsRFjNDMZmxsLEgNYwsD9sH4JyYm8nHj+NxUxacTptoR4/sEf7kv4wpSrXR9GWOUceXYAnwPMG8+BZDnEuA6vHUO9wusQmwN8vcNXuO/scj7fgWx/D7+no61YY1dhzg6pNCFEEKIAiCFfoxhleALMsQaqvhtQCx3m9UCF9zw6pjVPJSnj25NRfCyBcC3MsWTODecwLG8Yk8VruExwr9pFrcKePx5sT/UBzcw8aoP78EnzH7BmP8d6pJjDbjZzOzsbO6LZ+WM8+I6pqamguyBVDlXzGmr1crnEGoe1wFVjPM2m818nHiNi7dg/D7KG+fibAXcGz5qHH5dbqCD40Fddjqd4P7DPhyR7X3IOBeXZuU891arFWRBcPYCW2I6nU7+WeG+wXkwB77ELz4r+PGxL6vXLMvy4+D68Ze/tz4mI1VKFtflo/dZxWMftvj4+AEu4cvfV9zbBw4cyO8Tri3BsTC++E+qMA43y/G/byk/+1oaV4k0UuhCCCFEAZBCP06w6vZqc1DTAp+3yeU9ubqUf0rmqGH8hV/T+9vZX8rn4Uj5UqkU+JxZ0fl9fZS22apq4mjgmZmZfD9u18nlbXHe6enpXD3Cp8jKE/vs3bs3V2NcOhXbsIL37V9ByuJQqVTy6/dR8mahMhoZGcnHgDFxLjCOgeuYn5/v8QH7bbklp59DfDa4Xzj328dMcJYC/LHYBqo/y7L8XByhzZXROp1Orh7Zb8/X6Nt4clMgzt/Gdfh4AZybc8txrd4KwvcA56yz5cTM8qqE+B7B980NU/ycAbbK+XuZ7zGOYPdzy7ELHBmPsWHOvVLnvPBYtgE+Y1w/V4r0v1mpam/8+pHsI44OLejHCTbr+huXzU68eGZZlqxjzoubr9POaTy8sPgFxtd1N1stdIL0lVi6EwfS4QeI06f82DEm/GAAnH9paSlZzpbNcKgT3ul0cvOzL03rx+v7lvOPIP+I8I9upVJJPkDxtr7bGpfUxGeD188666x8EeLAMF+W12z1AeW0005Lpk/xournEniXiZkFaWHj4+NBXXkuberdPBg/rolLyfoAQxwXwWPbtm3rGT+uy5vxfSqV2erDBc/7M888k4/XBzP6eWK30rp164IgTX448u+nFmP+Pvl6+GyixsKKa/Vlabn4FB56/b2L60iVgOYHCO/iS5naMd/YFuc1W3144dr2oFqtBp0I2VUA+hXOUinY44NM7kIIIUQBkEI/xnD/YK+WOQCKlbQ3XbNaBaysfUoNd46CcuHgFd8YAufmbk6sGnwwDD/xc6qY74XNlgYoGW8O9T2o/TasQH3wHbskOH3K9yAHMFtykBZUp+/dzmoM58PxYXFoNptBdywO7MLn8dRTTwXqxlsq/D441vLyck+qkx8/PhuYo/09BvBvNiXj33v37s3nH2Pg9CxfbhVzBlcHzM04Hv69sLCQK8yU6mOlHlN/XM7WN6SBIgcYNz5PKFF87gcPHgwKpnBXPa/Q8Z1g1wAHAOJ8/vo5XZADAsvlcn5OLoSDexqf69LSUn5cbIvjcCEbnM8sbMqCa+MgPH+PcVAcjykWvDYoBc0rdP49U9rasUUKXQghhCgAUujHiFSb0GECQ/oF0LF/ndt5el86/I7s42PFGEtvAlxkBU/kS0tLuaKAvxfjZuWeZVkQdMT+NV9uNNXPmq8DY1tcXAxK36aYn58PLCMcgIbrwDUfOHAgjykAqbKZvnRqrIe82ep8+eI8UI/cIpbjEXw5WU6bwrjRE3tmZiY4Ds7NCnTnzp1mdljRcZAm1CA+B28B4FgIKGZOc/QFSHhMbGHAtU5NTeXzAR8/mo/gs3viiSfy43OQIMcuIHjNNz/hNDX+7nl/Mn+OOC77y31xIfzF2HDN+Nxx34yNjeWfI87D5WfxOfjfDv4+pdJB/TgB+/F9fAxbyXzRGb9tu90O4iaYVClY/x7Plzg2SKELIYQQBUAK/RjBT8qxtqcpBcfKMVYmlp928aSP15vNZv40DQWEJ2g+hk/fwXvcvpOjxxcWFvJt2N8LfIqbLwDi54ej/0dGRgI1w6VOOR2pVCrl20J9Aa+wGPaDc8wBorCr1WpPe1c/LxxVvLCwEEQn49pZHddqtfz/oR6huqHYEfWO85qt+qA5Up0b1CwvL+fHwVjw18dCmPXeEzyHGCOnr/nPG8flxh+4rvHx8bwBCsdE4NrYJ728vBzcN3wv+Ih7VoqcAeLvXbPDmRxcIIhjL3zqJBQ5x8VgLn1WAzfX4Uh1fCd9mWSoYi4wwymAlUol/1zxmbCCBj42BlYOHI9LQMe+K/yZ4y8XTcK4MFf+Pc448b99KUXOxxJHhhS6EEIIUQCk0I8R/FQaexKNqaPYMcrlcqA2+AkWx8KTvz8W1AE/vfsxcRQ4lAMrJKiHkZGRoJUrR8r73F6oAY5q5yhuPya2YHDJWlxPvV7vadXqx4BtfXGOVMlanAf+ciiZer0eWAA4I4H9s2bxoh7+dd/wgwuCcC4+FFm32w1afLKP0kewQxVDfbEFBmPxUfz4rKAYMXdcZ2B+fj4/F97jVqu+9CvGwLnl+MzwF+er1Wo9qtRsVSH66Ha8zt8fbMt5+5jThYWFIHaEiyXh37t3785LAg9qPlKpVILcfWyLY3C8Sb1e7ynY49/j71m73c6zCvx94ecU5Wl9KWWOh8H9zd+9hYWFoIERtuU59tZDnhfOO+ffO/9a7Lji6JFCF0IIIQqAFPoxAk+9qWh3/x77k9in7p9WOZ8bKoSVxcjISBAR7yOOsY3Z4adkbkvJzRigBKCMyuVy7l+DimLrga+CBXyDCX8+H9kLBYHxQeWxgsPT/djYWH5uHI+bzOD1PXv25L5xjrZm/68vBcs58FBaOAbmf35+PmhOw01CfAVAjI9VKueA+yhs3zDEn5v9zH5bqFT43/n+hH9/fn4+tzZABXIrWq8kMR84N/bl1rTtdjv/jDiSn3O1fUYEYgoAl37188MZB3w8nBfX02w283sNf1N++Ha7HbQf5dxv5KnXarX8NcwDx2fgr89tx/i59WmsDoP/nput3o/4zPCd8fclwHnwOULFY5t9+/YF312eS39vc20AbiwEvNVykFVSCv3YIIUuhBBCFAAp9GNMzG9k1lufPVXNyyvsVP45ntr5GCMjI0FUbson548bi7A3W/Wn+rasrDq4Upw/Fs4JZcWRx74GPRQQnvw5yjeWm41zc/Qz59Vv3749Pw6uiRUQWxpqtVquXrAP1Bm3l11eXs7HzdXfsA+ONTo6mqskjongtqy+ihhXbuO4CT82zidGvrmfb//XR3MjlsC3+PTU6/XAD4t9uOKar+bH0f+cbeAVJFtYOJPCV2l77LHHeo6LzxNWIY5LqNVq+Vg4ngLn8VkN/H3C8fA54LhLS0uBwuTmO5yDPzIyElRf48ZFvvcAR+yz4uXMlvHx8Z4a/35MfA80Go38ODg+W1xwL4yMjOTzzHEaIKbYU41bmFi1QzE8WtCPkthiada/YEI/05TZ4Zuav+wctMOFPJaXl4NzcllI39Ah1ZzCd8vyrKys5GPwQUY4nt8ny7IgMIl/bH2nNvxYcAARl1n1P9C4Nv6B8wsVxub7YfuxAFw7d+cyWzVl4oce5/GFZ7hoCAfd8WLqX/OFe/zY/OcCMy3mnU20/nPnfvC+bKvZqrnbm/w5mIwLvviua/zZs7vFN7HhksBcqjXW9Q4NeNjkC3xwHzcD4qAs3Dc+kAzXxi4aHAsmf1+QiLsL8kLmg1gxh/ww40vi4vVU6dpU6qh/jb/b/LuwsrKSHx+Fh7igkg/M5DQ+Ph4+14WFheC+44dqEPsNHNRpUhwdMrkLIYQQBUAK/SgZpMz9+ylzPAfJxUxVrBbwxMwBRh5uMAK8MuBALhBLK+Ne1WwSx+sTExM9gWD+PDg3B53543BJSTb1+3Q8bpXJCnR+fj4oisE9w6HCvbWDlSdbC5588sl8DNzchRt/QA36gjVo/5oy63rXAffdTpk6/blTZlaoe3wO09PTgeUFAXNc0nd8fDxXdz5A0Y/R9+X2jUjMVuedS+7662D3CuYb8+QDsththONxYxR8hl7tcylcvqd9ESDMIRfYwRx2u92g6QuuEQFouC4EsZXL5cDlwCmkPlCP08rYXcTXOjc3F5S35e+0LwEbswz5sfjAQ56zYVJ2QSoITiVgjw1S6EIIIUQBkEI/RgxqzuIDWwY90WZZFqi8WPEWPhaeojnAituPekWRSuHCvl5BpgLbYo1E2D/KBTd8sBPOzf7vVFOV+fn5XK1CweH4nHI1NjaWK2i2duB88J37Iizw5XLQEc7j06jg++dr5HKgfq6g4HzDGX89HPdgFhaFgWUBxWTa7Xbgc4YiZCuNV998r2EO+Z7es2dP/hlzGhXfn+Pj47k1wLck9dfm4ygAB7Thc+ZgvyzLcksF5oHvab6HG41GfhweE8ca+NKy2CfVGtX7oPmexb3Hc7uwsBCoYP7u+c8F312ck68N1+HvW7YyYSy4Dry/fv36wMrECtpfF8bCc5gqKBOzOHJQn/et8++YGB4pdCGEEKIASKEfJfyEyfgoeE7HYLXNatwsrYDYj9VsNoPWjRxR6n2lXJSDC2ywr75Wq+Xqhf28vmkKxsrFbHAcjA3qzRed4Ih+VjA+Ch2qFEVjoFw42rjT6QQpgBwdjbHhmN6vj/HjeOw/HRsby/+f5wNKyH+ubIXAubAtN15pNBqB+oOaQvSyTzHkiHpOA4PvHp/3wsJCsC2XEfXqlUumYpz4XH3pYL4vOPUPfzHv5XI5yFrgxjwY9/79+wPLDrcY9TERmAMuDJRqw/vkk08GMSOwCLAVpFQqBZkBnD4JywOuzzei4fudM078a6myq5x62G63g2JCnPLqv08YHzIqEPfB35lSqRS0efXz4P96pc7Wh37lYaXMjxwpdCGEEKIASKEfJWuJ1ky1CIz50GPNHMzCFpE+X5RzmqGauAVluVwOrAOA8+qhPHzbUH4Ch9/XKyFWfRzZ7H11GB9UOx+P1V+n08kVBVsqcF4oUd+cBeqDo3yhEHGeUqmU5yPjPbZyQB37xhapJhVciMePFxHUUEt79uwxj8+3BpwhAMXui6FApeLcXO7TN+7A/GDe2RqEsbbb7TxegPO5ORfZNx/BHHIxIMQh+FgMn/PurwP4z5t9rbjHuBgTrB+dTic/HsbNLVzBxMREoCLZUuIzODiOBPc07lNcK8boiy+lWg377zg+P460xzaYYx+5jjkE3PiGG8r442Ff/k0ZGRkJPutUkSpvVWGFnioBK44OKXQhhBCiAEihHyNYncUitFPtFwHnMfv9uYkHK5jZ2dlAmXAlKn9+bubASpcrR62srATRuOx39MdgPxsULVfd8vm+XD6Uc26hdnbt2pUrB19W1Y8fY5mfnw/KtXLVLihcnLfRaAQ51Bxj4CtqcWQ9N7rx/kdunsG+eSh0H3MAFQ8VBjB+3+bTK2Q/TvyFMsUc+MYf7KflHHxv2cHxcB/hvvTVx7haIEdbc8yILy0L2BrhfbqYSzSI8VHb/ri+1CzHMADMF5S0h1uuAn//pNr4cr67z+fmbAguQeytczgXXkPTHSh+9u9v2LAh8Mlz+10fRc/+dtwfsBzhM1tYWAhiXlK/Zx72i8eqHJrFLS9ieLSgHyOG6RqUChqJmaFjgWxmYdlS4NOSuLgKn9d3kuIApVTQyvLycr4NLwocLDc1NRUUIOF9fLEPDvzzpmmz1UVv165d+Wsc4MPBO/gB3LhxY566xeZQTqHzZnxejPlz9Qsxd/vC+HE8/zDGpUD5hxgLJM5fr9fzRZ6D+3AdWOgnJyeTtb1hsuWSuT5NiB+c4KLgvuv+WjkNDPjSr2zC57Ki/uGMH+a405m/bxC8h/c4hZFdXJ1OJ1iE8Ln6ewvX5wMHPXzvtdvtnp7x/vjsMvMPyPwAyMFrYHl5OUhB5fsT/8bDTLVaTaY58oPO7OxsEDwJ8LDku+jxfchzGQt0S5nUYyZ4LeRHjkzuQgghRAGQQj9KUmlrbDbz5RtBTDmb9aoDNk2xSdm/zgFoUIEcnGUWpsPxkz4/fW/cuDF4AmfzqzdDQ2WwqRHBal6ZcrARpyGx9aBSqQRmXLZycOqSh1OXtm/fbmarnckajUYQFMTKiNO3/ByefvrpPdv68rTcyIXnHSoK17Fv376g/zQ30ADz8/P5nEGpcUlcXLu3VrBVBueD8vdKl03WXFbXN1XBazBjQ22zNcVfF6dl8T3hC9qwtcO7qsxW5xSWhunp6eBaYTmKlUlmqwCulRsM+YYlXJSHXUK+wRArdC7qgvnqdDq5lQDj48YtsMjgenyzJlg7vKXCj7/RaAQlmmHVAn7+U90iU79v/jvISp3dLqVSSQr9KJBCF0IIIQqAFPpRwilL/DcWMMJPslzAIsuyQBVwb2fgi1Dg/7k8JPtA/VMyK1jvuzVbfYqfmZmJtkk1C/s3d7vdIH0K54H1AOrDq0r2N7Lf0KsI9uP5dqBmqz5j7yPG8ZEexz5vKOt9+/b19KL2x4VS8i1d8RqsDzg+5sm374Ra5aYyscA/jJEL33AvbxQTWVxcDCw4KV83LAHNZjO/FrwHvymnfR06dCgImvRFbfwxVlZW8nHxtvwZIcBrZmYmaA+KuTzjjDPMbFWB+j7xHMSHOeYYEt90B3/ZEuPvI/w/B4hxP/darRY0MUnFpPh+7JgrLinL9+m6deuCtsb8u4Lj+u8iB5dxmVsfrMkphBwM5xU6f6dTDamwnU9bS+GDCvm3RwyPFLoQQghRAKTQjxJOT0sVTPApRaljeJ96rDmHh9NhVlZW8qd4TvVh1WMWNlThSFVW6t5Hz2Uscc1QWt7nC18uKxf83bx5c08ErR8n1AH79b1/FmAf+EuhMMrlcj4+qDG2crAS8NYOVnRcVndmZiaIIsZ7mCfvN+cmKZw2CN+lV2Icuc7+X6+0OKKc70eoQSiyer2e74/X2BcN2u12oNC5YA2YnJzMP79UsxpODWw0Gklf9DPPPNNzXRs2bAjiNLj0MUeC12q1IPWP40F8XAjuF1bQuF9xr/mYF98Ixs8TrgPz5eMFONaFyyWvrKwEn2PMKuZfL5VKgYUF8/PUU0/1XM/GjRvz64YVhVNUuRGLPx7DsQCxaHd+z8fnxOJexHBIoQshhBAFQAr9KOFIdX6dI0Jj+3A0t28owk+wHM3t/W7sZ4tFh5sdfuJnvzXUHkfweh8alCb+psqA1uv1oN0oxwl4RcRNNjAmKCTkGyM2oF6vJ/OJueWlL7GJ8/DYMNfwHdfr9cB3zn+hAqenp3NlC5/nli1bzMwCf/m5554bFPnANSHCnv3CvpAH+25jcIEdnneOfF5YWMjHzVYf/IUijSl3vIa5xbFiUdZQj2x98u1fOfebc7L99wn3IfaBouUMDnzejz/+eOAr52JDXinyPcUR4DhftVoNsl24EA7n4Fer1cBCgfnAfe/jKHANOB7mlC0A3pqF7zLXi8CceksP/h/nxvcnlt3Bkemp+8NbLfm3jhsxpQr+iLUhhS6EEEIUACn0oyRV9jDmFwecz83H4v/3cItOX/ENT+KAfVner8qlUxF1zr5137Ak5ZeFWvB+VCg1qDz4Dlld+idy39zF/4Vv3udq41yYD4wB5/H5xVzmlqPGoVhwjEqlEsQWcK4z9oUKMgujoLk158GDB4NoZ1xzqimJbxrCbTyxLT47X2cglTEA5YU5aTQauS+YlS2O52MnsD/g68ExfMldbIP5SUWCt9vtfH45kh/X4b9PXAuA86u5auDo6Gi+DatWbmQyOzsb+I/9cfx52u128LnhL7bBZ+StFDgOf5e5imPMR++zRPw+/tpRYRBzibnjjIpDhw7l856qeeBLwrKPm61kXIK60+kE0f4c6zFM+VgxGM2eEEIIUQCk0I8SVtusimPwEy3737yCBr5xiNnqk7rPwU21ZfXtI7Ett5r0Fb789eCJf2RkJIg+B/xE7v3WiHJnfycU4tzcXKC+cI2pZhhbtmzJ9+EqeOxvXr9+fZCrzhYAgPMuLCwEY4IqQdwA/t1qtfLGM6wyOH5gcXExHycsGOwbxeveL4xxYQ5ZlcH3Xy6Xg0p5HBnPc+EtGNwAhKOkd+3aFUSfI04gFpnMvlXOoMDrmOvt27cHzVFgaeFx+yYenEuO43M0fbvdDlQ2rgNq1mcqYBuuac8qf3FxMVDIOA73YsD1rVu3Lh8/Pleu6IZ7eHx8PJ9L/EVcCfbl35SxsbF8XrnWwZNPPtkzb1u3bs2Px1Yl/g52u93Ab8/fEc748VUyU1XmQLlcVnOWo0AL+lHCBTxSC7lvuMJBJWzKrlQqQfEHLrPKjRfK5XJQXALg3/gC+2IuHLjECz2+VJOTk/nx8cPAP5z+IcCndfn3uCiNN29zsBQXcznzzDPzf/MCxfPi04a4SA8eMrA4x8yuMKlzIxH8YPvgQXYj8LXifV/EBUVX2OSIOcb75XI5X7A5KJHvvXK5HDwg4NowBphq/cONL3zj9wFYVGu1Wo8Lxmz1s+HUv5GRkWBxwPH9QujHf/DgweBewDacordp06ageBE/sOHB038f8H3BfYn3sIjg2r3JHZ8Zf67+gQXzgMUTnw0+D77H5ubm8nnHtfF32pcF5vRYbMtz6YP88FnxAyAKKOGan3766SAAkosB+XuMHx7YtRIrT82/Nykx488t1o5M7kIIIUQBkEI/xsSKKYBUIxfARTvMwgCiVJnFLMsCU3Ksp7nZ4adttihw0A2e/KEiSqVSoKh88Rb/+vLycqD0uQ84N5vw1w84jQf77t69u6dNpD8OLA0+iAfH5fc4zQZKqdFoBKqVU+ugmn3AEv/lz9ubK3EcqCiYYqF0fdAcN0DhRit+3nzqoJ9DXDuX8ty8eXP+WfD9g7H53vU4N3++2NbPC6cLsiWDzdD1ej2/RowzFRzq23iyuZ8b0IDZ2dngNU4n83PATY5Srht/H+Fa2YLBbgzv7sH9jTkEvg0sF47B/cFpbKDb7QZBcYB7vy8tLfWUx/XXiDHgr0/nA3y/8+vdbjdZUCb2HRFHjhS6EEIIUQCk0I8x/CTqX081bompbm6/iqdqqDVWYCMjI0GzB/hLY2ll2B9P3jgO0r6gkHxhEm4fCTUI0IZ0bm4u8PWl/INLS0uBFQLXAQWBsSF9zZeH5PKf3Ib0zDPPzIOauMQs5gD+VJ+iw/58br7hfdWYQy5WguOD+fn5/FyYQ/azxwqGcJwAt/r0RUzwGuIDoDJZfeMYhw4dCmIsALbBZ9lsNnNliPHC1497waeD4V7CPOB+5GBBny7nrQFmoXLzVqBYqWE/bn692+32FNTBnPkx+nuaC+zg3DiGDwbDfchBfDgux2BMTEwE3wlWyT4FDcfj1E623GG+nnrqqSAgla0DvrwzB93it4JjeNrtdmAZ4r8xONAN4+Ryz1mWBU1qxPBIoQshhBAFQAr9GMFP8TFYtaeKKJRKpaCxBDetYF+s2aoqwNM1q8sNGzaYWW9kNrbh8qVoBer9qzg+XoP6gwpBScxyuZyrD4wFqsY3EsG+OA6sAlCXOB+e/DHG+fn5oKCMT2fy8/Xss8/mSiXlF/SR/LjWVLta70vE3KY+R1gGvL9/27ZtZhaqR4Bt8Tn4zzfVrAaqbXR0NL/HUEp269at+TX5v+DgwYNB0RPOIMD7e/fuDVQYFCkX4vEtP7m0MRfgAbOzsz0ZAX5fjkZvt9u5jxiwmoyVguVSspxy5RsZwcrDWRKc+dBqtXLLBDdnYasKxthsNvN4A4wBqWOcNeLnDPcfx4Pw59psNvPj8ncQx/IxNpyl4Fuf+uN76xh/fmx59FYu7JOyUvp2xVLmR44UuhBCCFEApNCPE6nIz9g2HPHp22AC9s9yi0WzsBEEVCB8mV65sAoDeIrH07fP//XNKDycn1utVu3ss882s9UiFlA3XP7Tt0KFVYDbVLI1oVwuB3nzUDXsF5+YmMiPB8XMagr57Xifr8Us9Gv63OaYksU4zXob4OD/8R586ch7xxhwjLGxsSDGgI8Ly8umTZvyOcL+mDtu5erzpFPZEBzh32g0gsh1buwC6vV6cD9CKWJfjozPsiw/J7fBxWeGe3nv3r35cTnbgtW9V4fsF+eyuT6jglUqN6LBNdfr9SCCn+9dWJ389xf3Hb5jGBOsKk888UR+fq5/gPseVivMgVfavr2rB9eBfavVamBRY8sL8NkFbMXiVq4+Dz1VYyLVqEocGVLoQgghRAGQQj9O8JOmz8Xsl0uOv3iS5+hVVgK+PCc3u4CKhV/W+39ZbXMTj1hTCY6QhprhsWVZlitEVu948odiMQsVD5eS5cYi3W438DOi/Cq3pKxWq0E+O46L8UK1emsHR/9zMwzg20nyZ8OvNxqNINYCvm4oJLwO9drpdPI2mlxxDfMO1frss88GSoizItgfvG7duvz6cZ+wUsd5Jicn88+A87b5PqrX6z0lV81WLTDAl6wFXOmP6xVgvg4dOpTPET57LgOMfaBEfa0GzCH88PhcveWL7z+uwIjPtdPp5HOF7xzew2fD/uaDBw/m1wgLi69S5/F+axyXs0gwfsyljxXgKnB8H5mt3m/cVClW1pUbOHGdBC6D7X/7OMaF6VcyWwxGC/oxJtWfO3YDp27uUqnUU0M9tg+fp91uB7Wz2dTs63BzGk2qDC1MebVaLT8OB2xhH/xgt1qtoKc5pyj5tDw20eFHFvtymlalUgl+pPgHDSwtLQUBRGzW5RSjSqUSPKzwj5Y3/+NHFgsim8TxAz02NpabTHFuLlKCByH8yHtSgUrPPvusmR1ecDEu/EBjLlOlcf198/TTT/fMA5tqR0ZG8gUV54bZGA8dHq5BD3A+TvFqNBpB0Br3rPcuIowB9yPM8nhYfOqpp8xs9R5fWFgIzMXsyvL3D3ccxPyz2b7RaOTbInBxy5YtZha6j/B3cnIyCGLlPuW43+fm5oKHUFwzf1/xALGwsBDUkffHwzY4BsbC5n9OrfNFbvgvB8N5uAgPPyhwAKw4MmRyF0IIIQqAFPoxJlUW0b+eKnfIxTP8a2wmi6UhcXEIPh6eijudTv4kjqd2qEiYL/E+FOPExEQyMA/7cqEZf04fWOWPUa/X83QdLnDCShdKywcJsbmeLQBZluXjY/WK83Aqmp9/KE/uPe6bh3BqIStcr0qgsHBOwKVrvWkYqssH1/lrx/mmp6fz4DpOk+IudyDLsqCJB86NojG+IA+un1PEuCiQ77CFscDEDmCt8K4K7ujHqW8+qJJTnjB3sEp4V4HZ4bnkxkg+sM2fp91uB9eEzwzHwzE45cvPGeYWx8A1N5vN/L5kKw3+7TvCpYoi8RhwbywuLgZzxy4U//uD+U5Zorw1kVU0p+bx75lvTMXNmUDsd1KsHSl0IYQQogBIoR8nYiVgY77yQfsADoSKFV/gJ33AfmDvp+QUIvbT+p7QrEB5nDh/q9XqKSvpx89BWvV6PVdWXAYSxFJZoFpZiXLf9Wazmb/HTUigkKBEof5mZ2eD93BcnA9qdnx8PEgTTDW+WVpaCoLJMC+cmubLfrJq4uNjLE8//XT+eXFLUZ/G5+e0XC7n88Ln4QItPiAKapt9z75oDKfMccMS9qf6+xbXhOPH7ne2znC6IKwqeN2nxeFacR8B/z6nZSGugQMMfXGhVLtdvl9nZmaCxkW4HnyGUNt+LnmciMXgBjjLy8tB4xb+TvvgO+zHVoNY2hrfu4CthrEgYP4d45Rdvk6xNqTQhRBCiAIghX6ciDVg4WjQVER8t9sNinJw+0VWpl49cWlGKACotEajETRswRM+q3iokU6nk7+HMUEB4fjeV83+TW5U4stSpp7sOfLe++45XYr39f79VEnKM844o2deMNalpaVcaWLcPlLarDcSmRUVj9urZsw3lDKOB2UC/69vh8mR3oz31WOcXNCEU6L8XGJ/+Ia5WIy/n2L+UX+tIFa8iOM/uFjPyspK/hlhznAf+mY4OAZHvnNDI04RjLXx5PvfZzxwFge2hSWGW9P642IufdyH38fPD0fPY96Rbre4uNizn9nq58elj+ELbzabQXEofF+5Re/c3FwwBm4C4++Ffi2iPT5WiFPdVEDm+CCFLoQQQhQAKfRjDJf2jD3NskqIPa1yBDyrSygu0Gq18qd2bk/JisXnkqby5PHE7tUmnq45ap6LorRarfy4XCCH/fxjY2OBkuM2raxiY1HurHag8CqVSjC/GAOXWcX4x8fH8/9nn6hva4rzcNMajhuAKl5aWgquH9tiG+/Hx/HZn8zWDyiviYmJfFtW5hzv4POiOcKb4wV8gRX292KbWEEYbu3pfc5moeIdHR3tmVd/bihe7yuGH5/nwTdY8XgLAGCfsW+40q9OhB/b+Ph4cP2x4ipmvfc9/p+bHfE8+ZgU7AMlzpHlvi0y7gG8h31wDFgAOp1O4LvmaHSfHZBqnsK/UV6Vsy+ex82/Z+LIkEIXQgghCoAU+nGin4+I1RLnevp9WR2waoB/bHp6OlD8nAuLSNvFxcVAOXB0O0ewxo6PJ3weU61WC57AWfVhbKVSKVBSnP/L1fIqlUrQRAZ/Oae6Uqnkx2cLAMaCCm++VCn8yMjr9vnDnv379wf5+GwZ8T57WAM4JxifTSz/l6ubpXJ4fVlarn7HVhRfuYtLnHIUN46/vLwc+I9xHswhagqMjo7mapU/P+8zN+v15+N4UJOILGcfrG9xy1kLfD5YPcxW70McH+/hGPgejI2NBT55KGm+X5eWluz00083s9Xofy4ly1Hos7OzSesVtxH252Nfd+qeKJfLQUljbmGKe3r37t1BYxUu/+tjJFKxP/1KW8caUMXGLY4OLejHidiNmuosFFvg+T022flgNbyPHxEufhKrkY4fGu6sxT9WMH36oDv8cHJBCb+Ic/cqXkS9CZGLzZx22mk9+2Au8AO0b9++nhr2/jp8FzGzww8d3NsdD0HYBu/jR+3AgQNBOhn/0GEO/AMX95/H8ZD65l0RODcHq/EPqDfT848i7gHMwfz8fP6gg0UB/+aUKN/hjAv4cLlSH0zFwZp48ME1+tKjDJfT9cFqvA3Gi20wbixSnU4nuFf54Q5z4L9D+Gw44I0fqpeWloJyqzgfvmf+PkXZXIDr54BJzN/ExESwKKcKN62srOTXwG4vvg6f+skP01w4CQ/kS0tLgVuOC9n4+eFUQr5n+UHInxtoQT8+yOQuhBBCFAAp9BOEL384yPzkFTqbr7xy8/vU6/Xc7JlS874sKj/p+3H6Y/i+zpyGBfiJ3ZtmOdUt1mQDcAlVNm/7Tm3c3AHXDjWFfUdHR/PmJRg3ytuy2vRd0VhFsqvAm/Q5IAqqmJvCtFqtXBVx+hGX8vTzxSmGUPe4Ru+q4fRDn6potqqg4Zo4ePBgfi9wSVlYDaA2p6eng4I1fJ/6oDBOVeRr4+CpZrOZdDE988wzPft4kzu7BrAP5glKvdVqRdWjWVjYp1wuJwuz4N726X3s3uLvNv4NM3qr1RpY+tV3R+PvOz577m/v3TzcGAmfL9/DS0tLgXUDsGsuy7LAfcaBc1wwqFqtqmDMCUIKXQghhCgAUujHCQ4Ui/nFU0ElXs2n/ODA+7w41cwXvDBbVRSVSiVXEqxEOXDGt7bk1BKopq1bt/aMP9asggPdfIlQbrXKRVz4mldWVvJgKVwT1Aen5ExMTCTLqnKw07Zt28ysN6CL25zG0pN4vrEtn2diYiIoscsNbVjpzs/PB8GSPs3LX3O3280tFRx8x0rdB+NhLFyalXuSxwqQsB8b+JKyGAPGjWP4BjrYjmMj2NrkW96ypQj7cnlkWGR2794dxIrwfe/jE7i0LFtTvGWHLV1sncH96L8jqSY7vvEPxsa+clbm2AdxJr6tLytzvud8O2K2pHF6q08DTQX3xkrDpuKGVGDm2CKFLoQQQhQAKfQTyKB0Nb9dLA3F/+Un3uXl5UBlsHrCE/vGjRuDJiOp1Dnvd+dWmVu2bDGzVTUIxTQ1NRX4zvFvKGucf2FhIVfmHFHr0+zMeiPZuYQqjs9qqt1uB61VEZ2M+WCfom+5mioQ4q0QrDpYNXlfMqwZvmiImQXR6bjmycnJoFAQ5oV9r+122/7X//pf+X5+DOyH961keZzcptX7V7n5C8B4vUpmqwarbm7p6kuEAo4w9y1FMV5WnDguN53xShfnZGuSzwTBvHKEOsYIZd1sNnMF7gsC4Zx+Xvwx8Rorc1hTcIz9+/fn78Gyxb50vvd8IShuWMT+9pWVlWAeUgraW8u4WAzg3xAuC+zfU5T7sUUKXQghhCgAUujHGa/wUgVB+N+lUilZOpb97t7nxeUhOa/YRxBjXFByUDGcd+2fsvFEnirL6Qt6cCEN9l36JhxQGew75HKuXiFhvFBhUBisopaWlnJF7hvC+PFy7u3KykrSN8x+2263G3xGODeK0vjcalaPmDsoaJzX+5u5OQ3GAGUHv/Ly8nJQcpcjkFlV7du3L4gOZz+2L4eK+cV73JoWc1mv1/PPBMfjrAW87+91trD4dqP+WGar1gwch5ul+HHjWFxil8vb+gI2Pufdv8dtT70P3Wed+H8DfFa+iI4v1+qP61sbxzJVzFZ981yq+ODBg4Hlj+NMfFZKKq6Hf4f8MblIEW/DMRKx+UiVkRVHhhS6EEIIUQCk0I8z/ITL/+/x/ir2O/HTLj/ZlkqlwI+Jv1wtzFeRgiKCakXVNPa/1Wq1/D3OI+bGJTMzM4FShDpDFK73NXLuNFfFilkPuAkL+2eR75tlWZCbzj5iVl7z8/OBz5Oj230ePI6LfHfMN6sqf93YHwqd7xPsc+jQoXwesC+AysScLi4uBhXocO3wu7Nf0/vCufIfPjtUlfPbY55x7RzZ3Ol08nnlinBsvQGNRiOwHCE+g8fmVTG38eVcdh+bwSVrcX/7xkL4N48zZSWrVCpBJgY+Py6V62MMuIJbqrpivV4PMhxwbyEWgD9PXx+B2ynzb0u1Wk02feLfKr9vynrI8T5+G46eF8cWKXQhhBCiAEihH2diLRgH5V7691O+Lf4be42jf307T66VDUUEnxz7w9rtdtDIBdtC/eEpf8OGDYGq5hroULGVSiXwVXKOMNetrtVqQTMZqFj4IeE391HTqXrV2BdKd35+PmjZis+PfaW+FjpXWMPxvI8UigrnfPzxx83M7Oyzz+45D1TazMxMvo+PwPbX4TMeWFHhM/PV3vy+Xklz4xLk5ftoelaTsRalZr2xBmwR4YYx3tLD+ebAtyrF9bAPmjM1uLpfvV4PlL73+fvXq9VqkEnC8+/975wfzrESmCefMRA7p1l4/5TL5TxOAOdhvz7m2/92YL45G4K/275VLFd5A/68PM88L2wJiP1GpSLkxdGhBf0kJWXeY7wJCz9OKROzX4xSwUf8Q+2/yNyhCkVMAJsZ/TnZpOl/WL2p22x1MeagKuzrC7TgRws/ghgTXl+/fn3QuIWbwfCPTKPRCAIMeRsf1Ac4rS/Wuxo/8GhmgnHDrI2HJJ9mlvoR5KDEcrkcXFNqsfYPTTgup4HxQwA6qZmF9wA+O18kBYsy38sYExeyGR8fD5ql4DPzRZHwl10/+OvTM/217927Nx8vdxzDHPoHOX6Yw7+5fzn2NVs1gfNiF3OHxUoO+338/c8ma4Dj4XuLz9ebzvH58vfAm715DHw+/zfl9uPPzj8IpVJ0xbFFJnchhBCiAEihnyC63W4Q2JYq3uALy8T6Y3u8GZrbJXJzBt/2FEpiz549ZraqVNgs7S0DUDdcOAXqktWh2aqSYyUBRbB+/fpcFbBi4eYePlCMVR4ULcYGdegtALh+31jFnxfHqtVqgUJk64c35adaW2L+fStRLiWLawf4965du3rmyx+XG9/gs6vVaj2FV/y2qYCl0dHR/Nqg7lhhYd7POOOM4HhPPfWUma0WDMKcLiws5J8JpxZysRVcx549e/Lj4NxcHtW7GTgdk4NBcT0+OJH7wuPzRECj/47ifub7HnPgFTVbC3DNODe3uvXHYesG5tZ/9pwuifsHqZE4P4IIq9VqYPrmgDQfGMvKP2Yuxz6p/uf8PfKpgBxMKY4PUuhCCCFEAZBCP4Hwk3JMmYNUmgf7C33LRU4dwpM/B87MzMwEwUGsnjjIZ8uWLflTNrbF8aDE8ETufb2+vKTZajoZqFaruX8UTTSgTPCXg/F8kRsubsFzfPDgwUBtc4oO1BSU2NjYWBDMx+lr/jrwnk8f8/MExsbGAqXF1gO8j+PPzMwEcQ1c9McHgWF/KDUoXC6cgn194Rq26Ph0Oxwfr3HjHFwrFLbfn5Ut4M9qZGQk8O/yZ+ZTJFk9Yp5gFeImKlNTU4FfnBWjTw9jHz8XRfLBZuyD5iY8UNK+vDCnKAKcx88tx5mwSubgvna7HVhGuBiV/43h9zjV0MeOpOJ5Uu2Iq9WqmrGcIKTQhRBCiAIghX4SEHt6TT3RslIHPqWIy07Cl4j3TzvttPw9Lp7Bx4Xq9Oll3HgF6gDR0N5vzVGzwKeqcWEXqAGMN9bu0UdGm4VlRL1qRRQylA+npGG+fDQ6+xBxHihpr/QwPlgY2KeLOSyVSvn+7Mtlte1T+DBeqD6oPeD98JwC6NMD/fH9/cVtdgH7233JXR+74cH1ZVmWjwufDeYZ52MLTKlUyuMxcK2sFL0FidMPOUqfsz1WVlaCRisYC7ILfBEZLuYCdYx9/D2COXryySd7xs9NVPx9wxkmrJL9/Y/7j9NLuXGRHyvHdvAcxto1p4rPxH6PeNy+bC6/n4qMF8cWKXQhhBCiAEihnwSsxZce2wawbxhP2Xi6hzqZnJwM8s6hINjH6yNuua1pyodZq9UC3zxHc+Npfu/evT0lNP0+7H/318ElKrmNpPdfc/lNVrE4ri/OwdfGFgzvU8e1cfQw8rYxX81mM1eR2Idzg5F14DMGOAsAYA6efvppMzusCtmHy01NMAfYd3JyMn8N6hh+cM5zX15eDiwLXK4X8zM9PZ1fGxQtxgT/Pvtnd+7cmbfkBVy0x38POI6Ci/KgnoFX5fisudUwF3mJNSxh9ervOb5fuCUq3od1BePw14bPCu/52BfOJccc8tj8GDkzg/HWmlQhGc6O8PuxVWlQRo44/kihCyGEEAVACv05IFYO1hNr0pLys+H1TqcTPE2zT8sfE2qAG4hwC1C8vn///txHzIrXV8oyO6wooIq4xCZHLWdZlvsDub0mxsBRuqVSKR839sF5cF4oocnJyaCNJsYNZQQlDWUKX6m/foDxIwp9bm4uV6BcfQ/n800yOC+Zy4hytbNut5uPl33RnE8/MjISKEBsi8+Ic6h9DAOuiS092KfdbgfNQTj+wW/rc/XNVmMXUuq+Xq8H/mr2RXtLD+aM88QxX/hcvY+dy9Die8QKPebrxWfFcRT1ej2oJod7gO8NP8dcohnb8ryZrd7PbDXgtrX+XuPc+FRZYB8DwHEmbAHwv02szAHnp2dZJt/5CUIL+klK7Evh/w3YDOu3wY8If6F37drVU4zELOwpzelx3gyNH1ssEvhBwpfWm9y59jenmZVKpXwh5XQ1/DjiPL6nOszjnD6F4+NHvtPp5OPDj6kfp58Xb9bEjyv/QHPxnuXl5Xzx5B+2WLcxXBvPM7sZwMTERB6s5xdLvy/O67uxsTkaD3DYxnfiS3XG8wVxMGYssFx4iB98fClijBPXAXgs/r7BedgFhM9jZmYmWIRTBVRw/ywtLeXzATcId1vz6Xype5hL+y4vLwfBgQAPFfzA3Gq18ocTfkjC98vfE/ywwg+LfP/4RTqWZhebJ7NwIQf+Xkst8ql0Ni3mJw6Z3IUQQogCIIX+HNLP9J4qfMFpZf6pmNUvK2pfqpJVE5u7Oc3G96rmAjL499atW83ssHqCYkBgEpQKP62Pjo7mKoPTvWJdoQCuEdcElYxtfKENNtlD/XFvcl9ohs2frM7w97TTTsuvDVaBVJONTqcTWBI4OCumqvia+B7wyhf7wUTL88PBWr6ACgKt0Pee08GWl5eDAEC+x/zr3MyH3SMc9GgWWgVYhfu0L4yT3UMcXOYtOzt37jSz1XuAy8+yK8csNIlzSt3MzExQ6hXX7gs/+fny/88Bb5zitbS0FFjiMCYOgPXBoBwox78p3kWX6uIYC3BL9UHn5kRcWEkcf6TQhRBCiAJQylKOjz4cOnQo98GKo4cD3nxRC34a5m29uk8VeuCAq0qlkqsNvIdWk74Iilk8UIcD3qDaoFBbrVbQZhQ+Rlb3vkQl9/3mAhVeLaDJCc7J/mTvs+e55DKZsWYYbDVhFQXLw4YNG4LWmJgzqDSvYtlPyo0sWKV5pctBgggmxL579+4NGtpgH24ZCwXa6XSCUqMAFgwcc2VlJen3BRhbt9vNP1dOWeSgQVzH6OhoUKwIKhv7+AYmmDvcA/g3t4j1/8a8chObWG95LkDkW/Ji7swOz+X3vve9nvFinOwP9zErGAOun61xsJj4QkccsMixL/i3LxfLhWpibU7ZGsb0K36VCrYTR8fBgweDgONBSKELIYQQBUA+9JOAWEpICo5+jx2HlWisvCireMB+Lzzlz83N5ZHBrCZxXiiaxcXFXH3wkz+XaPVR7t5X64+PY/nXYVFgpZvy6/nj8ba4LvjC/X7sF8S4cd6VlZX8PS4Py6VNy+VyrrpwXFwHX7NX/RwTwU1yQLPZTDbzwXj5nuh2u7llhH2up59+es95lpeXc3UKXzQ3B/HtTrm9LhdxwXG9D5ytG9xgxKdKYn4RL8A+elax8/Pz+bVC+WDcHANQKpWCRkXsJ/epf768rFmYdRFrOoN5Yf83K1xf0IdjCnDv8efrU8X4t4IzLGJ+ff4exYpdpf4tnjuk0IUQQogCIIV+EhHzofN7/dQ8+9VTJSsrlUqgSAA36oDiGh8fz9UHR8Rznu7y8nJQZAVwG8y5ubkg2hdKCGOBWvVqk0uNYgxcTMfn8OI9jB9qh5ud+IYoGCf7dv0xUo1KuO2pj/7HuaHmuUSuL9jiW2KahYVxfKwB9ks1leHyqJ1OJ38P8404CrZo+Ah0VqL4i+POzs4GJVJTWRK4rpmZmdwag2vjsrd4v9VqBe1RWV0izsLPF871zDPP9IyN4yBarVYQf8DFbTC3Bw4cyOcM5+Q4kH6tYjE+LtOLbVdWVgIrGN8//FnFWtGyr5t9635/3jZW+pWRUn/ukUIXQgghCoAU+kmIL/fJajtVZtFvGzsev8+tFPEXSpEjZBuNRq44uZQs8GqWfau8jT8PKwhWdNgWvuPx8fF8XD5P3l+jVzns64eKgo8YSsxHRafa1GIb+GInJiby47EKYwU9MjKSV6sDHNUey13nEqlsEYCyMwtz6jlDgCPOvY8efzn3HtaccrkcVCgDHLdRLpeDnHdcK1Ql+5nXrVuXK9CU1cPfVzgXW5UwNq5AWKlUeuIBzFYVP/vAsyzLx89tWDlTIcuyfJzsd8e5fRU/XDOsX3wPcMVE/33rl9fu56fb7QYxEf0SmlKWv1ReeoxY1UpxYpFCF0IIIQqAFPopSuxpe1CTBP83FfHto5TNVlVUuVzOn+KhULiRi/8L1cRKhaNyO51OkMsMuBocmJ+fDywXXBcckevz8/O5wkJlMYyNfaKYk8nJySBKH1HX7PddWlrKt4VP1fs+zVYVtFfzmDv2zfuGHzi/9xubmT377LP58fz5Zmdn8+vmNqoYC973tepZXSISn+u2Ly4u9jSC8X+5ml+j0QjmirMB2BfrK6LhmlnNc8tbjMvDkf5+rjE+rq3O1iDfPhXbwLoSqw2B9zBeXDPXjMffcrmc31Nci559215tA66l36+pDEhVfyuXy8H3qR/D9JMQzw1a0E9SUqYu/sIN0zsd8A+GWWgOTY2j3W4nTb/Am/Z27dplZqsmUw4u8wsBxoBFiMuhxopn+N7lZuEDg08J4oAiblbBZnCf1seFd7CPTxnjH1M82CA1Ctc8Pz8fLIjYlxuveLAt9kWJXS4XOzIykl8rF/thU7AP+GKTMq4RD3e+iBSb2jE2LjQzPj4emI5TD1C+kQzGhQcfdkX48+H/OXhtUP9vs9UHGnYxYd+xsbF8XHiNC+3gePPz8/bUU0+Z2erDG+aDCzPhQdY/lLK7i7+L7XY7eHjmBT6WZsb7pL7jfj+mX/qaCsicfMjkLoQQQhQAKfSTlFRrwn7BKanyjbEAO2ybaqDAKUa+HSYrZ4DXR0dHg8I1sUIvZocVDCtQbtrBPZ8bjUbQV5zVh0+jQloal5hl5YV5qtfrQdtOHr+fa7aIcLESHKtSqQTmTm5zylYKvy3MtxgvF9XxTTbYZcAtP3GMDRs2BIqTQWnScrkcBB+yGvZphN4sHrtW7kXebrfzfTggDNeK9yuVSnAvcYoYX7sPnsO4Y73H8T4H6EF1s6XBl4nF8WDd4CYq/jOLuQT8X3+vpAoGpUzvKysrgSmf7z3/25KyAHJhHH88cfIhhS6EEEIUACn0kxxOhVpLeVhW835fPOGnSqVyyotPpeMgpFiZSy5Ggn3gr0ZKUbvdTrZ1hJLmYhrLy8vBPHCgm1fJUKusVLgVKI5frVaDtpqcJoRjNJvNfBv24bJvempqKlDv/NnEFCMHLHJ7Wa8YcVyMCX58LovqC/ywosW/4Tv3DUHYx88WAR8PwfMNuMSpt5hwmiAXwvHzxOmIHEQZ++6wusS2CAT0waAYF+YU9yGu2Vt+OHjPB02ahRakbrcbxIbgs8d948fKwZPDpLHy58mfQ6xFM/8esNVG6vzkRgpdCCGEKABS6KcIXNo0loKSalASa5uYKgPJUcU+spzLfELBxaLfuXUmVCVUJhRXq9WKpi+Zrfpu4Tv2hTi4mAjv+4IXvCA/BreWRLoQrgN+We/bRbQyXzMrSO/LxT4p/+yGDRuCUq+I/oda8o1KzA6rV27jyUVRvGrevHlzzzViH0T9+5KyZod96Xzf4PiYY3xWvuGHT2c0W7UE4DNaWFjIz81NZbitLxRwvV7PX8O5cT7Mk4/t4DLCsAJxq1EobR+hz1YmTvPrdrtBChpbnTCnBw8eDBrO4Fr5e+vVN1s7Uj712HHYmsXXHFPq/O/Y70Eqq0bK/NRACl0IIYQoAFLopxiskPxTPfvZU75p3s9vy8rLP8VzOVco6H5P/BwNDSUEVTY3N5e/xn5ILoXpfd9QWOynhlKEz7Jer+fHTVkCOC968+bNQcQ0joHz+fNgnFBunLuOeXrmmWfyufP+ej82HAtKt1QqBRYQbocLNT42NhZkImBfWFNgjYCSzLIs+Ow5pz+WSZBqXoMxtVqtqNL08wIfNK5neno6aFIDvEUBcKlgXDuOy4V+zFYVOI8Xr8PK4tU8WxhiTU+4pCzn/fMxKpVK8H1lP3usWAx/vqkI9kqlkmzoFFPhqdKvyjU/tZBCF0IIIQqAFPopCj9JV6vVaI50ap9UqUfe17/PyoSP6/2IHC2Pbbj5iPdJsyLn3GnfcIRzgr0i92Ot1+uB/xXqj/Pocfx9+/blig3jg7+alTvUsX8tVZmu3W7nx92zZ4+ZhT50/Jubefht2FKCGIOlpaVcAWKcyCbg8q7e0sPV9rAtv+7LxGLuUiVHG41GrnaxD+YQpWs5HsRX0uNsBd+aF+flsr/YFiV+YQXyrV655DH+DVXvK7lx9TiOWI+1H+VsCI5K99Hqqe8e7+utb4C//xwHEauPkPKD+zx0KfRTGy3opygx09ug4jP+36kvN/8IxI6V+uEBvhcz4P7NOH+tVgsKdzD8EFAul4PSqZw65xcjfjjhtC8c1wd/weTNpnGucb+yspIMmOOUvUqlEgSE8eLGxVZarVY+l1wz3ndxMzu8CGHc3DENQWa+8xj25UWUFxq/gHEHNTYxYzHdv39/UOcd4DpwLJ8yhn3woARXAV7HA0q1Ws3Lw2IR5nrvuA6cZ2FhISh9y73IcX9Wq9V8kQf8QOsD4PDQwos94H/HCsuwSdzPH89hv3Q1Hi/o993mz14L+amJTO5CCCFEAZBCP8WJ9e2O9T9PwcE0KdXg/z9V/MObv9lcy4rIm0HZlBk7nmd8fDxo7MEKCOrGm/9TRUWwLafaxY4Pte3nia0FUEZe8ZsdLqrDvdJ5fnxTGbPDihGKHCob28B878ulclAglKM3m/tj+XOxuR7boBjQzMxMror5WrGttxBgLFCyULyYF4wJLgM/L5zyh/NAuS8sLOTzjvOwqwZjeeaZZ/JjcAon/8V5Zmdn83Hz8dht0e12e7oHeths790M/UzgZmHgW2ybVPGYWNMm0O87rvS0UxspdCGEEKIASKEXhJWVlUAFg1hThpTfLkU/P1usuAWnw0H9crpdu91OKmcukoF/z83NBf57bnbirRRQhOxbhdJFO1IfUJeycmBfKN9KpZKrM05V4h7kfo45II/7oUPx1mq1wI8P2Erhx4u2qVDFOAYH3Xn/LPe1x+uYJ2954SYkPMZarRYE8XExIIzFx1BwEBksIrHPkNMROeANY/O+Yg6g478Y//LyclDIhwMxgb9HMBZc26D2x55+fvJB7U1Tx4iNgV+XKi8OUuhCCCFEAZBCLxBcdAbEVEKqyQv7r716Yz87q1evvlNNZdiK4McBhcVtTaHgoHoajUau4jkdC9v6tp6cEoZzokwqR2pXq9WgsAkXt4EinZmZSc4lrBKI6j506FCu1lMFgrjt6bp165LlROFPhoWgVqvl73HaG4/Rz1PqvoFvHhHgWZblnx+Pk4+/uLiY3wM4F/ZhtervIz5OLGURcBEebIOoefzbp5fhNU4pjJVd5RQ0zirA5xH7/NlaECvqMsiSFotCB8Nko6SsAqlUQ3HqI4UuhBBCFAAp9ALCjRqYWCGJ1NO8j0pPtVyNHZ+LuTBebXJJU47cx/V4peoVuN+W21fGyt3iL/yyXCTF+6pZIWJbqMANGzYE+dZQ95xXX61WA0WObdmS4ZuqcGtS39jGj7FWqwUtOfE5+Na2fp/Z2dngM8K4YYXwsROs6rjZi1e1OAci4xGNzvMcKy6E/HxW9VDWzWYz/3+OMMf1cHOYdrsd3Bepe9lvC9hX71U4f36D4k38a6kx+DGy5YzV/FqQMi8uUuhCCCFEAZBCLzCs1GPqYFAUt3+aH7SPL1XJSiiVU1sqlXJFyH72lP+93W7n6gxAtUINgkajkatWjrRnXyLO8+yzz9pZZ53V8x5fc79cdfwbTUh8DjjnbQPMEyLuoVC9JYLnied0ZmYmaDfK8QNcQS51Tf64PhIcx0HpWoDYgFh5YRyXfc4cZX3gwIHc2sHR52hwg7iBWPYC7s+UYi+Xyz1R7GarnxXHESwvLwfql3PY/Tylvht8DF+pLlXOldV8LOZlmFKwqSwUUVy0oD8PiBVdGVRIJhXUYxaa/WKBRfwDlwq2a7fbwQKLhRc/7ly7vN1uB+Za/NBz0N3CwkJgOmbzsw+2MztsGua0qZRp/NChQ/nCgkWNzc9+IfBlWv1YuKa4T5ViUzXGzYtStVoNFnsuBcuf68TERDC/CKhD4RqwcePG/Lg7d+40s7CjGa51aWkpeIjgDnnALzzccY/dRt7s7evo+/Nwupl/SMX1471UN7SVlZUg9Sz1AOgflLnAC/+bXUKe1CIdS1+LPRj797vdbrLbnSguMrkLIYQQBUAK/XmID3BjUoFuse5NqX/7Tlh83Fh6DVQRB5elSsHG3mPTsi99yuZlmLu5KA3U5sLCQm6637Jli5mFRUp8f3fsj/QuHJ/TnqrVaq5SuVMaXud0v0qlEhQ0YVM28GV02Vwe6/xmdjjojNU75hDXCAuHbwCEMUDpctnVVquVv8Yd02LpZPjLVhNYMjAWLuLjYZM+u5yWlpaSwYdsDarVasE5himzyvd9rDQzSAXQxb6Dg4JWY3DJZFF8pNCFEEKIAiCF/jwlVfglVgCDYaXCqs0HAnFJzVi6DavG1Pm84uWAHyg64FOj4GuF0sT52I8K5Tg+Pp6rdQSn4T00KgHdbjcoWMNlXeEDn52dDawDUPUASg5jrdfruV8Z14ptoNx9mhk3kcFfXA9U/WOPPWZmh/3kOBcXz4FqxRgXFxd7St76uQO+PS728/ES/jyxoK1UWhyO6+8B32LWH5/vYf96atyx2BGOA2F17I/B6potR/0aovAx+sFjYSsZNxoSzy+k0IUQQogCIIX+PIdVCCsJEGvF2q/IRapwDR+3UqkEEfdcwGbQ2MxCawG3rfT/j0hyLgDjVY+PHPfHx75Q3Y1GI1ecUN/YZ2pqysxWI/APHToU+HfZz4lrhNKqVqtBQxVYAOBP9u1NsT8UGsaAfRAL4K+dr5Wj0X00Pccu8Lh9Ghin83EqWiz6mv3u3HbU78MR/JxWhjn0+6TuIZzXF0Tie4rvR87YiM0Hb+vvsWEbrniLFL/HGQP8b/H8QgpdCCGEKABS6MLM0o1dvK89lRPMObY+Gpr97exL9yqF1auPxPbbtlqtoIQqnw8K1I8FPlefK43jmfVGd+O9VHlYr6xZyQKUh/VqjUu+QknzPPmofWzD6ov95CsrK/l4sT+i9fkaYxHVwxRSwf/jPDyXXqGzouUod+DrBLC1hz8z4LflfH8evy9Ww/cLWxp8QSRW4Cm1HWueAjginq/BM0wcSz+rgBBS6EIIIUQBkEIXPQxTVYr91DEFk1IfsZzbVAtLVsN+n1QDGt7HWxagoNlHj2N5Bcw+bmwLa4FX49iGS8uyKqtWq/k54NtO5Yd7pQiFiWtjvzLG4tuDcotVzJ0vnWp2WLHjPY5yjzXY4Uj1VAOdhYWFpG+b/dU+sp3vP45Uj/mSU3nhHF3vSalhzo2PHS+mjge1Ko3VZUh9J2JjU9U3MQxS6EIIIUQBkEIXQ+FrQ/vXzOItIlPVr2I56yk/OFsAvBJLVciK5adzJDlHzbPq8y1duTkLN/NYWlrKX+P642w9qFarue8cEdqsxnhslUolPw784dysBjnmMzMzgb+dx4Z9sN3i4mIwl7g2+Oaxr2/PmqpC5mMEOGKdo89Zdfr2rCk/ckw5p1R2rDZ6KnshpvxTvQr6kWpcFPs3j7ufmpcyF8NQyoapZkAcOnQoNxmK5x/4MRymDGUqeM2/nip5mUqdiz1ccDEUb7JOpczx6wjAarfbQSMOn0Zm1mv6ZBM7z483p2NbXtQAH7/RaOTb+uC32Ly0Wq3ARcCFd7j4TalU6inX6s+Nbbh5jX8PxIoDDZuW5cfCDzaDAt/6HTe2wPNnnkpNix23X0Ba6t5KHTdWSpkfHLSIP785ePBgnh47LDK5CyGEEAVAJnexZlLlOUHMJN7P9MgqPhV81M8c2q9xRkpZ+fKw/rq8Qsc+UK/4tw/o4vlImeBj6XasyFmJ+jakqd7dXqGz8kwV7fHqj5U57+PTBdlkzeZ0fy9wv/ZUcRRYRlZWVqLlWv0YYqmRqYC0fqZs3zAH5/aUy+VkgF7qfLHX+jVP4fnge1iItSKFLoQQQhQAKXRx1HADllhKTqx0LN5P+UVThTy83519rTH1mlJWsYYcqTGx8vV+31QKHavN2DVxkBk39SiVSsn2sviLVDo/vlixH7+Pf53VcKqYy9LSUqDIhyl0wtuwr5jnP7Yvq/3Y8fopZ7yfCrLjfWLBdyliliPeN+ZLH8Y3L8RakEIXQgghCoAUujhmeCUKNQkGqWT//6mIYVCtVqPpb35br0BjZUNj+/i/qYYcvM/IyEhgHUj5UX3pV06tYiUXa+MJpc5z6NU9++bZShArXpIqzML4uYy1Pk3ty58fto213+V4DN4ndtxUQRa+Lv+58j4c2+AZ5BeP3S8pYgVxhDhWSKELIYQQBUAKXRwXoBqh1FnJsb/ZLO1DTPmBzeJlPf3rPuI+de6YkgMpX2ssIn5Q3IAvnJJS5KxQfZMT0C96nOMCYkV/+G/K98+lcrMsC87dr4AQR6YzsflO+bK5zkC/NqEp64qP7Ri0bafTSfrm+407VXxJ/nFxIpBCF0IIIQqAFLo4rqRKhIJ+PtdUDnK5XE5GRrOC9IoX6i6VF+2VdUqJAt94ZFCeu/ebs682paR9wxs+N/Zhy4KvhDYocjpmRUhZKmL+dvb9syUjltXA1xGzlKTiHYDP00+VZk3FYsSOy9YVEMuOSO0by4qQIhfPBVrQxXOCN5X365HuiS3S/GMeWxBSCy0TK0aTKkvrHwpSiwN3JOt2uz3pbv64HFjnHwb61SbnMaYW8n6pXanULeAL5fC2wxRZSRV8GabqND8U8TEwrmGuwwevDTvGfq/FXEBayMVziUzuQgghRAGQQhfPOVyYBqRMnj7dB+9xKVKvpljlsZKOmWxTXbNiQWapVKiYOZe3TQXq+eOz8uzX1CPlgkh1lvNpcexmiLkMBjUd6TeWfkVWeBseE6vjmKtgUGBa7Hj9zOqpc0uFi5MVKXQhhBCiAEihi5OGVAparNjIINXtW5qyCk75WL0vNqX6hlGZqcYiZmExkZTvPOU7HsSgIjqpscbGGyt80q+Eb+o8g/zg/UqnpoiloPFxY8RS2VL7pO5HIU5WpNCFEEKIAiCFLk56WCl5lZiKso750GPFQ/q97kkp3FjhGsaXZmV/fQp/LFbtgJV07PgpCwNfu39v2HF5+pXPTbVw5fMO0+SEP6sY/N4wUeiDPkMhTgWk0IUQQogCIIUuTjm63W6uwrjELPBKLqVs2X89TCES/nen00mqxZjPeJh8Zx5Lv/KqZvHYgJQvvp/vf1BTnH7+8VgkeMp6wkrdK/lB8+HHMCiKPlZ/gN8b1mIixKmAFLoQQghRAKTQxSkJ+9W5xGy9Xjez/qovpdK84h6UtxyrPsb44w1qQMMKslKp9ETse/iaq9VqPi+pqmnDKHMeayxnfVDsgrdGpEqx9vOdDxp3v5LBKWJR68PEDQhxqiCFLoQQQhQAKXRRSFAzPQbUez8VmFKesUpsqVrrsUYrrMRT+e3+vKkocVbuscjyVPORGIMq6fmI/lTluRipOvKxfVMWgGHz02PXI8TzBS3o4nlHv8UecDGblCnYB8WlypPGzNCgnykfr6cC3LiITqzEaeqBod8+vLDjr+/NPqjBin+4GFTcxu+TWvxVflWIwcjkLoQQQhQAKXQhIiCACn9rtZqZxYPNWF1D/XKRkljDkpQp2R+Dz4l/I1XPN09Jmay5sYtXuIPUtm94k0qVSxW0iV1jKlXMH18FXoRYO1LoQgghRAGQQhdiCFqtVvI9qFUoWaST4XXvix62cEosYCxlAfD+8kFFdBhvNUgVkomNgUvH9kv/4mtCfIL84EIcW6TQhRBCiAIghS7EUZLy9+L1I/EH92s+woraK/VhFLlZ2CDFE4vkP5aoLakQxwcpdCGEEKIASKELcRJyJCp2LT5p+a+FKB5S6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQB0IIuhBBCFAAt6EIIIUQBOKIFPcuyYz0OIYQQQvx/jmSdPaIFfXZ29kh2E0IIIcQQHMk6W8qO4DGg2+3azp07bWJiwkql0ppPKoQQQoiQLMtsdnbWtm3bZuXy2jT3ES3oQgghhDi5UFCcEEIIUQD+H9vBCywCnS/VAAAAAElFTkSuQmCC",
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' width=500.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ── Config ───────────────────────────────────────────────────────────────\n",
-    "HF_REPO = \"RussellBarkley/representative_nuclei\"  # datasets repo name\n",
-    "HF_ROOT = f\"hf://datasets/{HF_REPO}\"\n",
-    "\n",
-    "# ── Imports & backend ───────────────────────────────────────────────────\n",
-    "try:\n",
-    "    get_ipython().run_line_magic(\"matplotlib\", \"widget\")\n",
-    "except Exception:\n",
-    "    pass\n",
-    "\n",
-    "import os, re\n",
-    "from io import BytesIO\n",
-    "from functools import lru_cache\n",
-    "from typing import List\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import ipywidgets as W\n",
-    "import fsspec\n",
-    "from tifffile import imread as tiff_imread\n",
-    "\n",
-    "# ── Constants ────────────────────────────────────────────────────────────\n",
-    "CENTERS = [\"mean\", \"median\", \"geomedian\"]\n",
-    "METRICS = [\"euclidean\", \"cosine\"]\n",
-    "EXTS    = (\".tif\", \".tiff\", \".png\")\n",
-    "rank_pat = re.compile(r\"^(\\d{1,4})__\")  # e.g., '001__...tif'\n",
-    "\n",
-    "# ── Filesystem (quiet—no prints) ────────────────────────────────────────\n",
-    "fs = fsspec.filesystem(\"hf\")\n",
-    "\n",
-    "def _natural_sort_key(path: str):\n",
-    "    base = os.path.basename(path)\n",
-    "    m = rank_pat.match(base)\n",
-    "    if m:\n",
-    "        try:\n",
-    "            return (0, int(m.group(1)), base.lower())\n",
-    "        except Exception:\n",
-    "            pass\n",
-    "    return (1, base.lower())\n",
-    "\n",
-    "def list_ranked_images(center: str, metric: str) -> List[str]:\n",
-    "    \"\"\"List and rank-sort up to 100 files under <HF_ROOT>/<center>/<metric>/ (quiet)\"\"\"\n",
-    "    folder = f\"{HF_ROOT}/{center}/{metric}\"\n",
-    "    try:\n",
-    "        files = fs.glob(f\"{folder}/*\")\n",
-    "    except FileNotFoundError:\n",
-    "        files = []\n",
-    "    files = [p for p in files if os.path.splitext(p)[1].lower() in EXTS]\n",
-    "    files.sort(key=_natural_sort_key)\n",
-    "    return files[:100]\n",
-    "\n",
-    "# Build the index used by the viewer code (quiet)\n",
-    "repo_index = {(c, m): list_ranked_images(c, m) for c in CENTERS for m in METRICS}\n",
-    "\n",
-    "# ── Loader with small cache ─────────────────────────────────────────────\n",
-    "@lru_cache(maxsize=64)\n",
-    "def load_image_bytes(hf_path: str) -> np.ndarray:\n",
-    "    \"\"\"Read image from HF as grayscale float32 0..255.\"\"\"\n",
-    "    with fs.open(hf_path, \"rb\") as f:\n",
-    "        data = f.read()\n",
-    "    arr = tiff_imread(BytesIO(data))\n",
-    "\n",
-    "    if arr.ndim > 2:\n",
-    "        arr = arr[..., 0]\n",
-    "    arr = np.asarray(arr)\n",
-    "\n",
-    "    if np.issubdtype(arr.dtype, np.floating):\n",
-    "        a = arr.astype(np.float32)\n",
-    "        if a.max() <= 1.0 + 1e-6:\n",
-    "            a *= 255.0\n",
-    "    else:\n",
-    "        a = arr.astype(np.float32)\n",
-    "\n",
-    "    a = np.clip(a, 0, 255)\n",
-    "    return a\n",
-    "\n",
-    "# ── UI widgets (renamed + wider spacing) ────────────────────────────────\n",
-    "CENTERS = [\"mean\", \"median\", \"geomedian\"]\n",
-    "METRICS = [\"euclidean\", \"cosine\"]\n",
-    "\n",
-    "w_metric = W.ToggleButtons(\n",
-    "    options=METRICS, value=\"euclidean\",\n",
-    "    description=\"Distance metric:\", button_style=\"\",\n",
-    "    layout=W.Layout(width=\"auto\")\n",
-    ")\n",
-    "w_center = W.ToggleButtons(\n",
-    "    options=CENTERS, value=\"mean\",\n",
-    "    description=\"Measure of central tendency:\", button_style=\"\",\n",
-    "    layout=W.Layout(width=\"auto\")\n",
-    ")\n",
-    "\n",
-    "# Rank control with Prev/Next buttons (unchanged)\n",
-    "w_prev = W.Button(description=\"◀ Prev\", layout=W.Layout(width=\"80px\"))\n",
-    "w_next = W.Button(description=\"Next ▶\", layout=W.Layout(width=\"80px\"))\n",
-    "w_rank = W.IntSlider(\n",
-    "    value=1, min=1, max=100, step=1, description=\"Rank:\", readout=False,\n",
-    "    continuous_update=True, layout=W.Layout(width=\"320px\"))\n",
-    "w_rank_label = W.Label(f\"{w_rank.value}/100\", layout=W.Layout(width=\"60px\"))\n",
-    "rank_row = W.HBox([w_prev, w_rank, w_rank_label, w_next],\n",
-    "                   layout=W.Layout(align_items=\"center\", gap=\"8px\"))\n",
-    "\n",
-    "# Intensity range (unchanged)\n",
-    "w_rng = W.IntRangeSlider(\n",
-    "    value=[0, 255], min=0, max=255, step=1, description=\"Intensity:\",\n",
-    "    readout=False, continuous_update=True, layout=W.Layout(width=\"480px\"))\n",
-    "w_rng_label = W.Label(f\"{w_rng.value[0]} – {w_rng.value[1]}\")\n",
-    "\n",
-    "# Put metric and center side-by-side with extra gap\n",
-    "row1 = W.HBox([w_metric, w_center],\n",
-    "              layout=W.Layout(justify_content=\"flex-start\", gap=\"28px\"))\n",
-    "row2 = rank_row\n",
-    "row3 = W.HBox([w_rng, w_rng_label], layout=W.Layout(align_items=\"center\", gap=\"8px\"))\n",
-    "\n",
-    "ui = W.VBox([row1, row2, row3], layout=W.Layout(gap=\"10px\"))\n",
-    "display(ui)\n",
-    "\n",
-    "# ── Figure (title shows filename only) ──────────────────────────────────\n",
-    "fig, ax = plt.subplots(figsize=(5.0, 5.0), constrained_layout=True)\n",
-    "im = ax.imshow(np.zeros((10,10), dtype=np.float32), cmap=\"gray\",\n",
-    "               vmin=0, vmax=255, interpolation=\"nearest\")\n",
-    "ax.set_xticks([]); ax.set_yticks([])\n",
-    "title = ax.set_title(\"\", fontsize=12)\n",
-    "\n",
-    "def _current_files():\n",
-    "    return repo_index.get((w_center.value, w_metric.value), [])\n",
-    "\n",
-    "def _clamp_rank(n):\n",
-    "    files = _current_files()\n",
-    "    if not files:\n",
-    "        return 1\n",
-    "    return int(np.clip(n, 1, len(files)))\n",
-    "\n",
-    "def update_image(*_):\n",
-    "    files = _current_files()\n",
-    "    if not files:\n",
-    "        im.set_data(np.zeros((10,10), dtype=np.float32))\n",
-    "        title.set_text(\"\")  # no filename if missing\n",
-    "        fig.canvas.draw_idle()\n",
-    "        return\n",
-    "\n",
-    "    w_rank.value = _clamp_rank(w_rank.value)\n",
-    "    idx = w_rank.value - 1\n",
-    "    path = files[idx]\n",
-    "    arr = load_image_bytes(path)\n",
-    "\n",
-    "    vmin, vmax = w_rng.value\n",
-    "    im.set_data(arr)\n",
-    "    im.set_clim(vmin=float(vmin), vmax=float(vmax))\n",
-    "\n",
-    "    # filename only\n",
-    "    title.set_text(os.path.basename(path))\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "def on_intensity_change(change):\n",
-    "    vmin, vmax = change[\"new\"]\n",
-    "    if vmax <= vmin:\n",
-    "        return\n",
-    "    w_rng_label.value = f\"{vmin} – {vmax}\"\n",
-    "    im.set_clim(vmin=float(vmin), vmax=float(vmax))\n",
-    "    fig.canvas.draw_idle()\n",
-    "\n",
-    "def on_rank_change(change):\n",
-    "    w_rank.value = _clamp_rank(change[\"new\"])\n",
-    "    files = _current_files()\n",
-    "    total = len(files) if files else 100\n",
-    "    w_rank_label.value = f\"{w_rank.value}/{total}\"\n",
-    "    update_image()\n",
-    "\n",
-    "def on_prev_clicked(_):\n",
-    "    w_rank.value = _clamp_rank(w_rank.value - 1)\n",
-    "\n",
-    "def on_next_clicked(_):\n",
-    "    w_rank.value = _clamp_rank(w_rank.value + 1)\n",
-    "\n",
-    "# wire events\n",
-    "w_metric.observe(update_image, names=\"value\")\n",
-    "w_center.observe(update_image, names=\"value\")\n",
-    "w_rank.observe(on_rank_change, names=\"value\")\n",
-    "w_rng.observe(on_intensity_change, names=\"value\")\n",
-    "w_prev.on_click(on_prev_clicked)\n",
-    "w_next.on_click(on_next_clicked)\n",
-    "\n",
-    "update_image()\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..4f19540ea
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,26 @@
+[project]
+name = "cell-browser"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "cmasher>=1.9.2",
+    "colorspacious>=1.1.2",
+    "datasets>=2.20.0",
+    "fsspec>=2025.10.0",
+    "h5py>=3.15.1",
+    "huggingface-hub>=1.2.4",
+    "ipympl>=0.9.8",
+    "ipywidgets>=8.1.8",
+    "jupyterlab>=4.5.1",
+    "jupyterlab-myst>=2.6.0",
+    "matplotlib>=3.10.8",
+    "numpy>=2.4.0",
+    "pillow>=12.1.0",
+    "scipy>=1.16.3",
+    "tensorflow>=2.20.0",
+    "tifffile>=2025.12.20",
+    "tqdm>=4.67.1",
+    "vizarr>=0.1.1",
+]
diff --git a/references.bib b/references.bib
index b88dbc552..3aa1949e7 100644
--- a/references.bib
+++ b/references.bib
@@ -6,18 +6,6 @@ @book{Goodfellow-et-al-2016
     year={2016}
 }
 
-@inproceedings{NIPS2012_c399862d,
- author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
- booktitle = {Advances in Neural Information Processing Systems},
- editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
- pages = {},
- publisher = {Curran Associates, Inc.},
- title = {ImageNet Classification with Deep Convolutional Neural Networks},
- url = {https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf},
- volume = {25},
- year = {2012}
-}
-
 @article{JMLR:v9:vandermaaten08a,
   author  = {Laurens van der Maaten and Geoffrey Hinton},
   title   = {Visualizing Data using t-SNE},
diff --git a/requirements.in b/requirements.in
index 5a548c67d..7a941803d 100644
--- a/requirements.in
+++ b/requirements.in
@@ -9,6 +9,4 @@ ipympl
 jupyterlab
 jupyterlab-myst
 cmasher
-tifffile
-tensorflow
-pillow
\ No newline at end of file
+vizarr
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 000000000..7b6ee9fbd
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2359 @@
+# This file was autogenerated by uv via the following command:
+#    uv export -o requirements.txt
+absl-py==2.3.1 \
+    --hash=sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9 \
+    --hash=sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d
+    # via
+    #   keras
+    #   tensorboard
+    #   tensorflow
+aiohappyeyeballs==2.6.1 \
+    --hash=sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558 \
+    --hash=sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8
+    # via aiohttp
+aiohttp==3.13.3 \
+    --hash=sha256:01ad2529d4b5035578f5081606a465f3b814c542882804e2e8cda61adf5c71bf \
+    --hash=sha256:042e9e0bcb5fba81886c8b4fbb9a09d6b8a00245fd8d88e4d989c1f96c74164c \
+    --hash=sha256:05861afbbec40650d8a07ea324367cb93e9e8cc7762e04dd4405df99fa65159c \
+    --hash=sha256:084911a532763e9d3dd95adf78a78f4096cd5f58cdc18e6fdbc1b58417a45423 \
+    --hash=sha256:0add0900ff220d1d5c5ebbf99ed88b0c1bbf87aa7e4262300ed1376a6b13414f \
+    --hash=sha256:10b47b7ba335d2e9b1239fa571131a87e2d8ec96b333e68b2a305e7a98b0bae2 \
+    --hash=sha256:1449ceddcdbcf2e0446957863af03ebaaa03f94c090f945411b61269e2cb5daf \
+    --hash=sha256:2712039939ec963c237286113c68dbad80a82a4281543f3abf766d9d73228998 \
+    --hash=sha256:27234ef6d85c914f9efeb77ff616dbf4ad2380be0cda40b4db086ffc7ddd1b7d \
+    --hash=sha256:2b8d8ddba8f95ba17582226f80e2de99c7a7948e66490ef8d947e272a93e9463 \
+    --hash=sha256:2e41b18a58da1e474a057b3d35248d8320029f61d70a37629535b16a0c8f3767 \
+    --hash=sha256:2eb752b102b12a76ca02dff751a801f028b4ffbbc478840b473597fc91a9ed43 \
+    --hash=sha256:2fc82186fadc4a8316768d61f3722c230e2c1dcab4200d52d2ebdf2482e47592 \
+    --hash=sha256:2fff83cfc93f18f215896e3a190e8e5cb413ce01553901aca925176e7568963a \
+    --hash=sha256:34749271508078b261c4abb1767d42b8d0c0cc9449c73a4df494777dc55f0687 \
+    --hash=sha256:34bac00a67a812570d4a460447e1e9e06fae622946955f939051e7cc895cfab8 \
+    --hash=sha256:37239e9f9a7ea9ac5bf6b92b0260b01f8a22281996da609206a84df860bc1261 \
+    --hash=sha256:3d9908a48eb7416dc1f4524e69f1d32e5d90e3981e4e37eb0aa1cd18f9cfa2a4 \
+    --hash=sha256:3dd4dce1c718e38081c8f35f323209d4c1df7d4db4bab1b5c88a6b4d12b74587 \
+    --hash=sha256:4021b51936308aeea0367b8f006dc999ca02bc118a0cc78c303f50a2ff6afb91 \
+    --hash=sha256:425c126c0dc43861e22cb1c14ba4c8e45d09516d0a3ae0a3f7494b79f5f233a3 \
+    --hash=sha256:44531a36aa2264a1860089ffd4dce7baf875ee5a6079d5fb42e261c704ef7344 \
+    --hash=sha256:48e377758516d262bde50c2584fc6c578af272559c409eecbdd2bae1601184d6 \
+    --hash=sha256:49a03727c1bba9a97d3e93c9f93ca03a57300f484b6e935463099841261195d3 \
+    --hash=sha256:568f416a4072fbfae453dcf9a99194bbb8bdeab718e08ee13dfa2ba0e4bebf29 \
+    --hash=sha256:5dff64413671b0d3e7d5918ea490bdccb97a4ad29b3f311ed423200b2203e01c \
+    --hash=sha256:5e1d8c8b8f1d91cd08d8f4a3c2b067bfca6ec043d3ff36de0f3a715feeedf926 \
+    --hash=sha256:5f8ca7f2bb6ba8348a3614c7918cc4bb73268c5ac2a207576b7afea19d3d9f64 \
+    --hash=sha256:693781c45a4033d31d4187d2436f5ac701e7bbfe5df40d917736108c1cc7436e \
+    --hash=sha256:697753042d57f4bf7122cab985bf15d0cef23c770864580f5af4f52023a56bd6 \
+    --hash=sha256:6de499a1a44e7de70735d0b39f67c8f25eb3d91eb3103be99ca0fa882cdd987d \
+    --hash=sha256:6fc0e2337d1a4c3e6acafda6a78a39d4c14caea625124817420abceed36e2415 \
+    --hash=sha256:7a4a94eb787e606d0a09404b9c38c113d3b099d508021faa615d70a0131907ce \
+    --hash=sha256:7b5e8fe4de30df199155baaf64f2fcd604f4c678ed20910db8e2c66dc4b11603 \
+    --hash=sha256:7bfdc049127717581866fa4708791220970ce291c23e28ccf3922c700740fdc0 \
+    --hash=sha256:7f9120f7093c2a32d9647abcaf21e6ad275b4fbec5b55969f978b1a97c7c86bf \
+    --hash=sha256:8057c98e0c8472d8846b9c79f56766bcc57e3e8ac7bfd510482332366c56c591 \
+    --hash=sha256:82611aeec80eb144416956ec85b6ca45a64d76429c1ed46ae1b5f86c6e0c9a26 \
+    --hash=sha256:8542f41a62bcc58fc7f11cf7c90e0ec324ce44950003feb70640fc2a9092c32a \
+    --hash=sha256:87797e645d9d8e222e04160ee32aa06bc5c163e8499f24db719e7852ec23093a \
+    --hash=sha256:87b9aab6d6ed88235aa2970294f496ff1a1f9adcd724d800e9b952395a80ffd9 \
+    --hash=sha256:90455115e5da1c3c51ab619ac57f877da8fd6d73c05aacd125c5ae9819582aba \
+    --hash=sha256:90751b8eed69435bac9ff4e3d2f6b3af1f57e37ecb0fbeee59c0174c9e2d41df \
+    --hash=sha256:96d604498a7c782cb15a51c406acaea70d8c027ee6b90c569baa6e7b93073679 \
+    --hash=sha256:9ae8dd55c8e6c4257eae3a20fd2c8f41edaea5992ed67156642493b8daf3cecc \
+    --hash=sha256:9bf9f7a65e7aa20dd764151fb3d616c81088f91f8df39c3893a536e279b4b984 \
+    --hash=sha256:a19884d2ee70b06d9204b2727a7b9f983d0c684c650254679e716b0b77920632 \
+    --hash=sha256:a1e53262fd202e4b40b70c3aff944a8155059beedc8a89bba9dc1f9ef06a1b56 \
+    --hash=sha256:a45530014d7a1e09f4a55f4f43097ba0fd155089372e105e4bff4ca76cb1b168 \
+    --hash=sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88 \
+    --hash=sha256:add1da70de90a2569c5e15249ff76a631ccacfe198375eead4aadf3b8dc849dc \
+    --hash=sha256:b04be762396457bef43f3597c991e192ee7da460a4953d7e647ee4b1c28e7046 \
+    --hash=sha256:b0d95340658b9d2f11d9697f59b3814a9d3bb4b7a7c20b131df4bcef464037c0 \
+    --hash=sha256:b1a6102b4d3ebc07dad44fbf07b45bb600300f15b552ddf1851b5390202ea2e3 \
+    --hash=sha256:b556c85915d8efaed322bf1bdae9486aa0f3f764195a0fb6ee962e5c71ef5ce1 \
+    --hash=sha256:b903a4dfee7d347e2d87697d0713be59e0b87925be030c9178c5faa58ea58d5c \
+    --hash=sha256:b928f30fe49574253644b1ca44b1b8adbd903aa0da4b9054a6c20fc7f4092a25 \
+    --hash=sha256:bb4f7475e359992b580559e008c598091c45b5088f28614e855e42d39c2f1033 \
+    --hash=sha256:bbe7d4cecacb439e2e2a8a1a7b935c25b812af7a5fd26503a66dadf428e79ec1 \
+    --hash=sha256:c014c7ea7fb775dd015b2d3137378b7be0249a448a1612268b5a90c2d81de04d \
+    --hash=sha256:c19b90316ad3b24c69cd78d5c9b4f3aa4497643685901185b65166293d36a00f \
+    --hash=sha256:c685f2d80bb67ca8c3837823ad76196b3694b0159d232206d1e461d3d434666f \
+    --hash=sha256:d32764c6c9aafb7fb55366a224756387cd50bfa720f32b88e0e6fa45b27dcf29 \
+    --hash=sha256:d60ac9663f44168038586cab2157e122e46bdef09e9368b37f2d82d354c23f72 \
+    --hash=sha256:e3531d63d3bdfa7e3ac5e9b27b2dd7ec9df3206a98e0b3445fa906f233264c57 \
+    --hash=sha256:ea37047c6b367fd4bd632bff8077449b8fa034b69e812a18e0132a00fae6e808 \
+    --hash=sha256:f76c1e3fe7d7c8afad7ed193f89a292e1999608170dcc9751a7462a87dfd5bc0 \
+    --hash=sha256:fc290605db2a917f6e81b0e1e0796469871f5af381ce15c604a3c5c7e51cb730 \
+    --hash=sha256:fc353029f176fd2b3ec6cfc71be166aba1936fe5d73dd1992ce289ca6647a9aa
+    # via fsspec
+aiosignal==1.4.0 \
+    --hash=sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e \
+    --hash=sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7
+    # via aiohttp
+anyio==4.12.1 \
+    --hash=sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703 \
+    --hash=sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c
+    # via
+    #   httpx
+    #   jupyter-server
+anywidget==0.9.21 \
+    --hash=sha256:78c268e0fbdb1dfd15da37fb578f9cf0a0df58a430e68d9156942b7a9391a761 \
+    --hash=sha256:b8d0172029ac426573053c416c6a587838661612208bb390fa0607862e594b27
+    # via vizarr
+appnope==0.1.4 ; sys_platform == 'darwin' \
+    --hash=sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee \
+    --hash=sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c
+    # via ipykernel
+argon2-cffi==25.1.0 \
+    --hash=sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1 \
+    --hash=sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741
+    # via jupyter-server
+argon2-cffi-bindings==25.1.0 \
+    --hash=sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99 \
+    --hash=sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6 \
+    --hash=sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44 \
+    --hash=sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a \
+    --hash=sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f \
+    --hash=sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2 \
+    --hash=sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0 \
+    --hash=sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f \
+    --hash=sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623 \
+    --hash=sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b \
+    --hash=sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44 \
+    --hash=sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98 \
+    --hash=sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500 \
+    --hash=sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94 \
+    --hash=sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6 \
+    --hash=sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d \
+    --hash=sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85 \
+    --hash=sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92 \
+    --hash=sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d \
+    --hash=sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a \
+    --hash=sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb
+    # via argon2-cffi
+arrow==1.4.0 \
+    --hash=sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205 \
+    --hash=sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7
+    # via isoduration
+asciitree==0.3.3 \
+    --hash=sha256:4aa4b9b649f85e3fcb343363d97564aa1fb62e249677f2e18a96765145cc0f6e
+    # via zarr
+asttokens==3.0.1 \
+    --hash=sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a \
+    --hash=sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7
+    # via stack-data
+astunparse==1.6.3 \
+    --hash=sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872 \
+    --hash=sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8
+    # via tensorflow
+async-lru==2.0.5 \
+    --hash=sha256:481d52ccdd27275f42c43a928b4a50c3bfb2d67af4e78b170e3e0bb39c66e5bb \
+    --hash=sha256:ab95404d8d2605310d345932697371a5f40def0487c03d6d0ad9138de52c9943
+    # via jupyterlab
+attrs==25.4.0 \
+    --hash=sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11 \
+    --hash=sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373
+    # via
+    #   aiohttp
+    #   jsonschema
+    #   referencing
+babel==2.17.0 \
+    --hash=sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d \
+    --hash=sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2
+    # via jupyterlab-server
+beautifulsoup4==4.14.3 \
+    --hash=sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb \
+    --hash=sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86
+    # via nbconvert
+bleach==6.3.0 \
+    --hash=sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22 \
+    --hash=sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6
+    # via nbconvert
+certifi==2026.1.4 \
+    --hash=sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c \
+    --hash=sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+cffi==2.0.0 \
+    --hash=sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb \
+    --hash=sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b \
+    --hash=sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f \
+    --hash=sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9 \
+    --hash=sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c \
+    --hash=sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75 \
+    --hash=sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e \
+    --hash=sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e \
+    --hash=sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25 \
+    --hash=sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe \
+    --hash=sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b \
+    --hash=sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91 \
+    --hash=sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592 \
+    --hash=sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187 \
+    --hash=sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1 \
+    --hash=sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94 \
+    --hash=sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba \
+    --hash=sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529 \
+    --hash=sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca \
+    --hash=sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6 \
+    --hash=sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4 \
+    --hash=sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d \
+    --hash=sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b \
+    --hash=sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205 \
+    --hash=sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27 \
+    --hash=sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512 \
+    --hash=sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d \
+    --hash=sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c \
+    --hash=sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037 \
+    --hash=sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c \
+    --hash=sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8 \
+    --hash=sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9 \
+    --hash=sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775 \
+    --hash=sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc \
+    --hash=sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062 \
+    --hash=sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13 \
+    --hash=sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26 \
+    --hash=sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b \
+    --hash=sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6 \
+    --hash=sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c \
+    --hash=sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef \
+    --hash=sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5 \
+    --hash=sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18 \
+    --hash=sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad \
+    --hash=sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3 \
+    --hash=sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2 \
+    --hash=sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5
+    # via
+    #   argon2-cffi-bindings
+    #   pyzmq
+charset-normalizer==3.4.4 \
+    --hash=sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394 \
+    --hash=sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86 \
+    --hash=sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f \
+    --hash=sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8 \
+    --hash=sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152 \
+    --hash=sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72 \
+    --hash=sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e \
+    --hash=sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3 \
+    --hash=sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c \
+    --hash=sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2 \
+    --hash=sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44 \
+    --hash=sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26 \
+    --hash=sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede \
+    --hash=sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf \
+    --hash=sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc \
+    --hash=sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed \
+    --hash=sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133 \
+    --hash=sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e \
+    --hash=sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef \
+    --hash=sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14 \
+    --hash=sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0 \
+    --hash=sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828 \
+    --hash=sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f \
+    --hash=sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328 \
+    --hash=sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090 \
+    --hash=sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c \
+    --hash=sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb \
+    --hash=sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a \
+    --hash=sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec \
+    --hash=sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc \
+    --hash=sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac \
+    --hash=sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525 \
+    --hash=sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894 \
+    --hash=sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a \
+    --hash=sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9 \
+    --hash=sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14 \
+    --hash=sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25 \
+    --hash=sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1 \
+    --hash=sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3 \
+    --hash=sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e \
+    --hash=sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6 \
+    --hash=sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15 \
+    --hash=sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191 \
+    --hash=sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd \
+    --hash=sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2 \
+    --hash=sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794 \
+    --hash=sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838 \
+    --hash=sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d \
+    --hash=sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490 \
+    --hash=sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9
+    # via requests
+click==8.3.1 \
+    --hash=sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a \
+    --hash=sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6
+    # via typer-slim
+cmasher==1.9.2 \
+    --hash=sha256:2fe45fde06051050dda5c023a527ba9066ca21f161c793f22839a6ebe6e4bbbb \
+    --hash=sha256:6c90c2cec87146e3210d3e7f2321fceee38ac7d87c136cd0de25160a14f57ff5
+    # via cell-browser
+colorama==0.4.6 ; sys_platform == 'win32' \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+    # via
+    #   click
+    #   ipython
+    #   tqdm
+colorspacious==1.1.2 \
+    --hash=sha256:5e9072e8cdca889dac445c35c9362a22ccf758e97b00b79ff0d5a7ba3e11b618 \
+    --hash=sha256:c78befa603cea5dccb332464e7dd29e96469eebf6cd5133029153d1e69e3fd6f
+    # via
+    #   cell-browser
+    #   cmasher
+comm==0.2.3 \
+    --hash=sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971 \
+    --hash=sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417
+    # via
+    #   ipykernel
+    #   ipywidgets
+contourpy==1.3.3 \
+    --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \
+    --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \
+    --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \
+    --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \
+    --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \
+    --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \
+    --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \
+    --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \
+    --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \
+    --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \
+    --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \
+    --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \
+    --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \
+    --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \
+    --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \
+    --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \
+    --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \
+    --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \
+    --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \
+    --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \
+    --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \
+    --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \
+    --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \
+    --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \
+    --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \
+    --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \
+    --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \
+    --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \
+    --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \
+    --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \
+    --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \
+    --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \
+    --hash=sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659 \
+    --hash=sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4 \
+    --hash=sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f \
+    --hash=sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20 \
+    --hash=sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36 \
+    --hash=sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb \
+    --hash=sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d \
+    --hash=sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8 \
+    --hash=sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0 \
+    --hash=sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b \
+    --hash=sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7 \
+    --hash=sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe \
+    --hash=sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77 \
+    --hash=sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd \
+    --hash=sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1 \
+    --hash=sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216 \
+    --hash=sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13 \
+    --hash=sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae \
+    --hash=sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae \
+    --hash=sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77 \
+    --hash=sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3 \
+    --hash=sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f \
+    --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \
+    --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a
+    # via matplotlib
+cycler==0.12.1 \
+    --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \
+    --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c
+    # via matplotlib
+datasets==4.4.2 \
+    --hash=sha256:6f5ef3417504d9cd663c71c1b90b9a494ff4c2076a2cd6a6e40ceee6ad95befc \
+    --hash=sha256:9de16e415c4ba4713eac0493f7c7dc74f3aa21599297f00cc6ddab409cb7b24b
+    # via cell-browser
+debugpy==1.8.19 \
+    --hash=sha256:0601708223fe1cd0e27c6cce67a899d92c7d68e73690211e6788a4b0e1903f5b \
+    --hash=sha256:1e8c4d1bd230067bf1bbcdbd6032e5a57068638eb28b9153d008ecde288152af \
+    --hash=sha256:360ffd231a780abbc414ba0f005dad409e71c78637efe8f2bd75837132a41d38 \
+    --hash=sha256:6599cab8a783d1496ae9984c52cb13b7c4a3bd06a8e6c33446832a5d97ce0bee \
+    --hash=sha256:66e3d2fd8f2035a8f111eb127fa508469dfa40928a89b460b41fd988684dc83d \
+    --hash=sha256:85016a73ab84dea1c1f1dcd88ec692993bcbe4532d1b49ecb5f3c688ae50c606 \
+    --hash=sha256:8e19a725f5d486f20e53a1dde2ab8bb2c9607c40c00a42ab646def962b41125f \
+    --hash=sha256:91e35db2672a0abaf325f4868fcac9c1674a0d9ad9bb8a8c849c03a5ebba3e6d \
+    --hash=sha256:b605f17e89ba0ecee994391194285fada89cee111cfcd29d6f2ee11cbdc40976 \
+    --hash=sha256:bccb1540a49cde77edc7ce7d9d075c1dbeb2414751bc0048c7a11e1b597a4c2e \
+    --hash=sha256:c30639998a9f9cd9699b4b621942c0179a6527f083c72351f95c6ab1728d5b73 \
+    --hash=sha256:d40c016c1f538dbf1762936e3aeb43a89b965069d9f60f9e39d35d9d25e6b809 \
+    --hash=sha256:e9c68d9a382ec754dc05ed1d1b4ed5bd824b9f7c1a8cd1083adb84b3c93501de \
+    --hash=sha256:eea7e5987445ab0b5ed258093722d5ecb8bb72217c5c9b1e21f64efe23ddebdb
+    # via ipykernel
+decorator==5.2.1 \
+    --hash=sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360 \
+    --hash=sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a
+    # via ipython
+defusedxml==0.7.1 \
+    --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \
+    --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61
+    # via nbconvert
+deprecated==1.3.1 \
+    --hash=sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f \
+    --hash=sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223
+    # via numcodecs
+dill==0.4.0 \
+    --hash=sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0 \
+    --hash=sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049
+    # via
+    #   datasets
+    #   multiprocess
+executing==2.2.1 \
+    --hash=sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4 \
+    --hash=sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017
+    # via stack-data
+fasteners==0.20 ; sys_platform != 'emscripten' \
+    --hash=sha256:55dce8792a41b56f727ba6e123fcaee77fd87e638a6863cec00007bfea84c8d8 \
+    --hash=sha256:9422c40d1e350e4259f509fb2e608d6bc43c0136f79a00db1b49046029d0b3b7
+    # via zarr
+fastjsonschema==2.21.2 \
+    --hash=sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463 \
+    --hash=sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de
+    # via nbformat
+filelock==3.20.2 \
+    --hash=sha256:a2241ff4ddde2a7cebddf78e39832509cb045d18ec1a09d7248d6bfc6bfbbe64 \
+    --hash=sha256:fbba7237d6ea277175a32c54bb71ef814a8546d8601269e1bfc388de333974e8
+    # via
+    #   datasets
+    #   huggingface-hub
+flatbuffers==25.12.19 \
+    --hash=sha256:7634f50c427838bb021c2d66a3d1168e9d199b0607e6329399f04846d42e20b4
+    # via tensorflow
+fonttools==4.61.1 \
+    --hash=sha256:10d88e55330e092940584774ee5e8a6971b01fc2f4d3466a1d6c158230880796 \
+    --hash=sha256:11f35ad7805edba3aac1a3710d104592df59f4b957e30108ae0ba6c10b11dd75 \
+    --hash=sha256:15acc09befd16a0fb8a8f62bc147e1a82817542d72184acca9ce6e0aeda9fa6d \
+    --hash=sha256:17d2bf5d541add43822bcf0c43d7d847b160c9bb01d15d5007d84e2217aaa371 \
+    --hash=sha256:2180f14c141d2f0f3da43f3a81bc8aa4684860f6b0e6f9e165a4831f24e6a23b \
+    --hash=sha256:21e7c8d76f62ab13c9472ccf74515ca5b9a761d1bde3265152a6dc58700d895b \
+    --hash=sha256:41a7170d042e8c0024703ed13b71893519a1a6d6e18e933e3ec7507a2c26a4b2 \
+    --hash=sha256:41ed4b5ec103bd306bb68f81dc166e77409e5209443e5773cb4ed837bcc9b0d3 \
+    --hash=sha256:497c31ce314219888c0e2fce5ad9178ca83fe5230b01a5006726cdf3ac9f24d9 \
+    --hash=sha256:4c1b526c8d3f615a7b1867f38a9410849c8f4aef078535742198e942fba0e9bd \
+    --hash=sha256:4d7092bb38c53bbc78e9255a59158b150bcdc115a1e3b3ce0b5f267dc35dd63c \
+    --hash=sha256:4f5686e1fe5fce75d82d93c47a438a25bf0d1319d2843a926f741140b2b16e0c \
+    --hash=sha256:5fade934607a523614726119164ff621e8c30e8fa1ffffbbd358662056ba69f0 \
+    --hash=sha256:64102ca87e84261419c3747a0d20f396eb024bdbeb04c2bfb37e2891f5fadcb5 \
+    --hash=sha256:664c5a68ec406f6b1547946683008576ef8b38275608e1cee6c061828171c118 \
+    --hash=sha256:6675329885c44657f826ef01d9e4fb33b9158e9d93c537d84ad8399539bc6f69 \
+    --hash=sha256:75da8f28eff26defba42c52986de97b22106cb8f26515b7c22443ebc9c2d3261 \
+    --hash=sha256:8c56c488ab471628ff3bfa80964372fc13504ece601e0d97a78ee74126b2045c \
+    --hash=sha256:91669ccac46bbc1d09e9273546181919064e8df73488ea087dcac3e2968df9ba \
+    --hash=sha256:9b666a475a65f4e839d3d10473fad6d47e0a9db14a2f4a224029c5bfde58ad2c \
+    --hash=sha256:9cfef3ab326780c04d6646f68d4b4742aae222e8b8ea1d627c74e38afcbc9d91 \
+    --hash=sha256:a75c301f96db737e1c5ed5fd7d77d9c34466de16095a266509e13da09751bd19 \
+    --hash=sha256:aed04cabe26f30c1647ef0e8fbb207516fd40fe9472e9439695f5c6998e60ac5 \
+    --hash=sha256:b148b56f5de675ee16d45e769e69f87623a4944f7443850bf9a9376e628a89d2 \
+    --hash=sha256:b501c862d4901792adaec7c25b1ecc749e2662543f68bb194c42ba18d6eec98d \
+    --hash=sha256:b931ae8f62db78861b0ff1ac017851764602288575d65b8e8ff1963fed419063 \
+    --hash=sha256:c33ab3ca9d3ccd581d58e989d67554e42d8d4ded94ab3ade3508455fe70e65f7 \
+    --hash=sha256:d9203500f7c63545b4ce3799319fe4d9feb1a1b89b28d3cb5abd11b9dd64147e \
+    --hash=sha256:dc492779501fa723b04d0ab1f5be046797fee17d27700476edc7ee9ae535a61e \
+    --hash=sha256:e6bcdf33aec38d16508ce61fd81838f24c83c90a1d1b8c68982857038673d6b8 \
+    --hash=sha256:e76ce097e3c57c4bcb67c5aa24a0ecdbd9f74ea9219997a707a4061fbe2707aa \
+    --hash=sha256:f3cb4a569029b9f291f88aafc927dd53683757e640081ca8c412781ea144565e \
+    --hash=sha256:fa646ecec9528bef693415c79a86e733c70a4965dd938e9a226b0fc64c9d2e6c \
+    --hash=sha256:fff4f534200a04b4a36e7ae3cb74493afe807b517a09e99cb4faa89a34ed6ecd
+    # via matplotlib
+fqdn==1.5.1 \
+    --hash=sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f \
+    --hash=sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014
+    # via jsonschema
+frozenlist==1.8.0 \
+    --hash=sha256:0325024fe97f94c41c08872db482cf8ac4800d80e79222c6b0b7b162d5b13686 \
+    --hash=sha256:032efa2674356903cd0261c4317a561a6850f3ac864a63fc1583147fb05a79b0 \
+    --hash=sha256:03ae967b4e297f58f8c774c7eabcce57fe3c2434817d4385c50661845a058121 \
+    --hash=sha256:06be8f67f39c8b1dc671f5d83aaefd3358ae5cdcf8314552c57e7ed3e6475bdd \
+    --hash=sha256:073f8bf8becba60aa931eb3bc420b217bb7d5b8f4750e6f8b3be7f3da85d38b7 \
+    --hash=sha256:07cdca25a91a4386d2e76ad992916a85038a9b97561bf7a3fd12d5d9ce31870c \
+    --hash=sha256:0c18a16eab41e82c295618a77502e17b195883241c563b00f0aa5106fc4eaa0d \
+    --hash=sha256:0f96534f8bfebc1a394209427d0f8a63d343c9779cda6fc25e8e121b5fd8555b \
+    --hash=sha256:102e6314ca4da683dca92e3b1355490fed5f313b768500084fbe6371fddfdb79 \
+    --hash=sha256:119fb2a1bd47307e899c2fac7f28e85b9a543864df47aa7ec9d3c1b4545f096f \
+    --hash=sha256:154e55ec0655291b5dd1b8731c637ecdb50975a2ae70c606d100750a540082f7 \
+    --hash=sha256:168c0969a329b416119507ba30b9ea13688fafffac1b7822802537569a1cb0ef \
+    --hash=sha256:21900c48ae04d13d416f0e1e0c4d81f7931f73a9dfa0b7a8746fb2fe7dd970ed \
+    --hash=sha256:229bf37d2e4acdaf808fd3f06e854a4a7a3661e871b10dc1f8f1896a3b05f18b \
+    --hash=sha256:28bd570e8e189d7f7b001966435f9dac6718324b5be2990ac496cf1ea9ddb7fe \
+    --hash=sha256:294e487f9ec720bd8ffcebc99d575f7eff3568a08a253d1ee1a0378754b74143 \
+    --hash=sha256:29548f9b5b5e3460ce7378144c3010363d8035cea44bc0bf02d57f5a685e084e \
+    --hash=sha256:2c5dcbbc55383e5883246d11fd179782a9d07a986c40f49abe89ddf865913930 \
+    --hash=sha256:2dc43a022e555de94c3b68a4ef0b11c4f747d12c024a520c7101709a2144fb37 \
+    --hash=sha256:2f05983daecab868a31e1da44462873306d3cbfd76d1f0b5b69c473d21dbb128 \
+    --hash=sha256:33139dc858c580ea50e7e60a1b0ea003efa1fd42e6ec7fdbad78fff65fad2fd2 \
+    --hash=sha256:33f48f51a446114bc5d251fb2954ab0164d5be02ad3382abcbfe07e2531d650f \
+    --hash=sha256:34187385b08f866104f0c0617404c8eb08165ab1272e884abc89c112e9c00746 \
+    --hash=sha256:342c97bf697ac5480c0a7ec73cd700ecfa5a8a40ac923bd035484616efecc2df \
+    --hash=sha256:3462dd9475af2025c31cc61be6652dfa25cbfb56cbbf52f4ccfe029f38decaf8 \
+    --hash=sha256:39ecbc32f1390387d2aa4f5a995e465e9e2f79ba3adcac92d68e3e0afae6657c \
+    --hash=sha256:3e0761f4d1a44f1d1a47996511752cf3dcec5bbdd9cc2b4fe595caf97754b7a0 \
+    --hash=sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad \
+    --hash=sha256:3ef2d026f16a2b1866e1d86fc4e1291e1ed8a387b2c333809419a2f8b3a77b82 \
+    --hash=sha256:405e8fe955c2280ce66428b3ca55e12b3c4e9c336fb2103a4937e891c69a4a29 \
+    --hash=sha256:4314debad13beb564b708b4a496020e5306c7333fa9a3ab90374169a20ffab30 \
+    --hash=sha256:433403ae80709741ce34038da08511d4a77062aa924baf411ef73d1146e74faf \
+    --hash=sha256:44389d135b3ff43ba8cc89ff7f51f5a0bb6b63d829c8300f79a2fe4fe61bcc62 \
+    --hash=sha256:494a5952b1c597ba44e0e78113a7266e656b9794eec897b19ead706bd7074383 \
+    --hash=sha256:4970ece02dbc8c3a92fcc5228e36a3e933a01a999f7094ff7c23fbd2beeaa67c \
+    --hash=sha256:4e0c11f2cc6717e0a741f84a527c52616140741cd812a50422f83dc31749fb52 \
+    --hash=sha256:50066c3997d0091c411a66e710f4e11752251e6d2d73d70d8d5d4c76442a199d \
+    --hash=sha256:517279f58009d0b1f2e7c1b130b377a349405da3f7621ed6bfae50b10adf20c1 \
+    --hash=sha256:54b2077180eb7f83dd52c40b2750d0a9f175e06a42e3213ce047219de902717a \
+    --hash=sha256:5500ef82073f599ac84d888e3a8c1f77ac831183244bfd7f11eaa0289fb30714 \
+    --hash=sha256:581ef5194c48035a7de2aefc72ac6539823bb71508189e5de01d60c9dcd5fa65 \
+    --hash=sha256:5c1c8e78426e59b3f8005e9b19f6ff46e5845895adbde20ece9218319eca6506 \
+    --hash=sha256:5d63a068f978fc69421fb0e6eb91a9603187527c86b7cd3f534a5b77a592b888 \
+    --hash=sha256:6da155091429aeba16851ecb10a9104a108bcd32f6c1642867eadaee401c1c41 \
+    --hash=sha256:74c51543498289c0c43656701be6b077f4b265868fa7f8a8859c197006efb608 \
+    --hash=sha256:776f352e8329135506a1d6bf16ac3f87bc25b28e765949282dcc627af36123aa \
+    --hash=sha256:778a11b15673f6f1df23d9586f83c4846c471a8af693a22e066508b77d201ec8 \
+    --hash=sha256:78f7b9e5d6f2fdb88cdde9440dc147259b62b9d3b019924def9f6478be254ac1 \
+    --hash=sha256:8009897cdef112072f93a0efdce29cd819e717fd2f649ee3016efd3cd885a7ed \
+    --hash=sha256:878be833caa6a3821caf85eb39c5ba92d28e85df26d57afb06b35b2efd937231 \
+    --hash=sha256:8b7b94a067d1c504ee0b16def57ad5738701e4ba10cec90529f13fa03c833496 \
+    --hash=sha256:8d92f1a84bb12d9e56f818b3a746f3efba93c1b63c8387a73dde655e1e42282a \
+    --hash=sha256:908bd3f6439f2fef9e85031b59fd4f1297af54415fb60e4254a95f75b3cab3f3 \
+    --hash=sha256:92db2bf818d5cc8d9c1f1fc56b897662e24ea5adb36ad1f1d82875bd64e03c24 \
+    --hash=sha256:96153e77a591c8adc2ee805756c61f59fef4cf4073a9275ee86fe8cba41241f7 \
+    --hash=sha256:96f423a119f4777a4a056b66ce11527366a8bb92f54e541ade21f2374433f6d4 \
+    --hash=sha256:97260ff46b207a82a7567b581ab4190bd4dfa09f4db8a8b49d1a958f6aa4940e \
+    --hash=sha256:974b28cf63cc99dfb2188d8d222bc6843656188164848c4f679e63dae4b0708e \
+    --hash=sha256:b2a095d45c5d46e5e79ba1e5b9cb787f541a8dee0433836cea4b96a2c439dcd8 \
+    --hash=sha256:b3210649ee28062ea6099cfda39e147fa1bc039583c8ee4481cb7811e2448c51 \
+    --hash=sha256:b4dec9482a65c54a5044486847b8a66bf10c9cb4926d42927ec4e8fd5db7fed8 \
+    --hash=sha256:bac9c42ba2ac65ddc115d930c78d24ab8d4f465fd3fc473cdedfccadb9429806 \
+    --hash=sha256:bf0a7e10b077bf5fb9380ad3ae8ce20ef919a6ad93b4552896419ac7e1d8e042 \
+    --hash=sha256:c4c800524c9cd9bac5166cd6f55285957fcfc907db323e193f2afcd4d9abd69b \
+    --hash=sha256:cb89a7f2de3602cfed448095bab3f178399646ab7c61454315089787df07733a \
+    --hash=sha256:cba69cb73723c3f329622e34bdbf5ce1f80c21c290ff04256cff1cd3c2036ed2 \
+    --hash=sha256:cee686f1f4cadeb2136007ddedd0aaf928ab95216e7691c63e50a8ec066336d0 \
+    --hash=sha256:cf253e0e1c3ceb4aaff6df637ce033ff6535fb8c70a764a8f46aafd3d6ab798e \
+    --hash=sha256:d1eaff1d00c7751b7c6662e9c5ba6eb2c17a2306ba5e2a37f24ddf3cc953402b \
+    --hash=sha256:d3bb933317c52d7ea5004a1c442eef86f426886fba134ef8cf4226ea6ee1821d \
+    --hash=sha256:d6a5df73acd3399d893dafc71663ad22534b5aa4f94e8a2fabfe856c3c1b6a52 \
+    --hash=sha256:db1e72ede2d0d7ccb213f218df6a078a9c09a7de257c2fe8fcef16d5925230b1 \
+    --hash=sha256:e25ac20a2ef37e91c1b39938b591457666a0fa835c7783c3a8f33ea42870db94 \
+    --hash=sha256:eaa352d7047a31d87dafcacbabe89df0aa506abb5b1b85a2fb91bc3faa02d822 \
+    --hash=sha256:eab8145831a0d56ec9c4139b6c3e594c7a83c2c8be25d5bcf2d86136a532287a \
+    --hash=sha256:ec3cc8c5d4084591b4237c0a272cc4f50a5b03396a47d9caaf76f5d7b38a4f11 \
+    --hash=sha256:eefdba20de0d938cec6a89bd4d70f346a03108a19b9df4248d3cf0d88f1b0f51 \
+    --hash=sha256:f21f00a91358803399890ab167098c131ec2ddd5f8f5fd5fe9c9f2c6fcd91e40 \
+    --hash=sha256:f6292f1de555ffcc675941d65fffffb0a5bcd992905015f85d0592201793e0e5 \
+    --hash=sha256:f833670942247a14eafbb675458b4e61c82e002a148f49e68257b79296e865c4 \
+    --hash=sha256:fb30f9626572a76dfe4293c7194a09fb1fe93ba94c7d4f720dfae3b646b45027 \
+    --hash=sha256:fe3c58d2f5db5fbd18c2987cba06d51b0529f52bc3a6cdc33d3f4eab725104bd
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec==2025.10.0 \
+    --hash=sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d \
+    --hash=sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59
+    # via
+    #   cell-browser
+    #   datasets
+    #   huggingface-hub
+gast==0.7.0 \
+    --hash=sha256:0bb14cd1b806722e91ddbab6fb86bba148c22b40e7ff11e248974e04c8adfdae \
+    --hash=sha256:99cbf1365633a74099f69c59bd650476b96baa5ef196fec88032b00b31ba36f7
+    # via tensorflow
+google-pasta==0.2.0 \
+    --hash=sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed \
+    --hash=sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e
+    # via tensorflow
+grpcio==1.76.0 \
+    --hash=sha256:04bbe1bfe3a68bbfd4e52402ab7d4eb59d72d02647ae2042204326cf4bbad280 \
+    --hash=sha256:06c3d6b076e7b593905d04fdba6a0525711b3466f43b3400266f04ff735de0cd \
+    --hash=sha256:1c9b93f79f48b03ada57ea24725d83a30284a012ec27eab2cf7e50a550cbbbcc \
+    --hash=sha256:2229ae655ec4e8999599469559e97630185fdd53ae1e8997d147b7c9b2b72cba \
+    --hash=sha256:25a18e9810fbc7e7f03ec2516addc116a957f8cbb8cbc95ccc80faa072743d03 \
+    --hash=sha256:26ef06c73eb53267c2b319f43e6634c7556ea37672029241a056629af27c10e2 \
+    --hash=sha256:3e2a27c89eb9ac3d81ec8835e12414d73536c6e620355d65102503064a4ed6eb \
+    --hash=sha256:45d59a649a82df5718fd9527ce775fd66d1af35e6d31abdcdc906a49c6822958 \
+    --hash=sha256:45e0111e73f43f735d70786557dc38141185072d7ff8dc1829d6a77ac1471468 \
+    --hash=sha256:479496325ce554792dba6548fae3df31a72cef7bad71ca2e12b0e58f9b336bfc \
+    --hash=sha256:490fa6d203992c47c7b9e4a9d39003a0c2bcc1c9aa3c058730884bbbb0ee9f09 \
+    --hash=sha256:4baf3cbe2f0be3289eb68ac8ae771156971848bb8aaff60bad42005539431980 \
+    --hash=sha256:5e8571632780e08526f118f74170ad8d50fb0a48c23a746bef2a6ebade3abd6f \
+    --hash=sha256:615ba64c208aaceb5ec83bfdce7728b80bfeb8be97562944836a7a0a9647d882 \
+    --hash=sha256:61f69297cba3950a524f61c7c8ee12e55c486cb5f7db47ff9dcee33da6f0d3ae \
+    --hash=sha256:6a15c17af8839b6801d554263c546c69c4d7718ad4321e3166175b37eaacca77 \
+    --hash=sha256:747fa73efa9b8b1488a95d0ba1039c8e2dca0f741612d80415b1e1c560febf4e \
+    --hash=sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73 \
+    --hash=sha256:81fd9652b37b36f16138611c7e884eb82e0cec137c40d3ef7c3f9b3ed00f6ed8 \
+    --hash=sha256:83d57312a58dcfe2a3a0f9d1389b299438909a02db60e2f2ea2ae2d8034909d3 \
+    --hash=sha256:922fa70ba549fce362d2e2871ab542082d66e2aaf0c19480ea453905b01f384e \
+    --hash=sha256:931091142fd8cc14edccc0845a79248bc155425eee9a98b2db2ea4f00a235a42 \
+    --hash=sha256:980a846182ce88c4f2f7e2c22c56aefd515daeb36149d1c897f83cf57999e0b6 \
+    --hash=sha256:9f8f757bebaaea112c00dba718fc0d3260052ce714e25804a03f93f5d1c6cc11 \
+    --hash=sha256:b331680e46239e090f5b3cead313cc772f6caa7d0fc8de349337563125361a4a \
+    --hash=sha256:c088e7a90b6017307f423efbb9d1ba97a22aa2170876223f9709e9d1de0b5347 \
+    --hash=sha256:d388087771c837cdb6515539f43b9d4bf0b0f23593a24054ac16f7a960be16f4 \
+    --hash=sha256:f92f88e6c033db65a5ae3d97905c8fea9c725b63e28d5a75cb73b49bda5024d8 \
+    --hash=sha256:f9f7bd5faab55f47231ad8dba7787866b69f5e93bc306e3915606779bbfb4ba8 \
+    --hash=sha256:fd5ef5932f6475c436c4a55e4336ebbe47bd3272be04964a03d316bbf4afbcbc \
+    --hash=sha256:ff8a59ea85a1f2191a0ffcc61298c571bc566332f82e5f5be1b83c9d8e668a62
+    # via
+    #   tensorboard
+    #   tensorflow
+h11==0.16.0 \
+    --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \
+    --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86
+    # via httpcore
+h5py==3.15.1 \
+    --hash=sha256:121b2b7a4c1915d63737483b7bff14ef253020f617c2fb2811f67a4bed9ac5e8 \
+    --hash=sha256:25c8843fec43b2cc368aa15afa1cdf83fc5e17b1c4e10cd3771ef6c39b72e5ce \
+    --hash=sha256:28a20e1a4082a479b3d7db2169f3a5034af010b90842e75ebbf2e9e49eb4183e \
+    --hash=sha256:316dd0f119734f324ca7ed10b5627a2de4ea42cc4dfbcedbee026aaa361c238c \
+    --hash=sha256:59b0d63b318bf3cc06687def2b45afd75926bbc006f7b8cd2b1a231299fc8599 \
+    --hash=sha256:59b25cf02411bf12e14f803fef0b80886444c7fe21a5ad17c6a28d3f08098a1e \
+    --hash=sha256:5f4fb0567eb8517c3ecd6b3c02c4f4e9da220c8932604960fd04e24ee1254763 \
+    --hash=sha256:61d5a58a9851e01ee61c932bbbb1c98fe20aba0a5674776600fb9a361c0aa652 \
+    --hash=sha256:8a33bfd5dfcea037196f7778534b1ff7e36a7f40a89e648c8f2967292eb6898e \
+    --hash=sha256:954e480433e82d3872503104f9b285d369048c3a788b2b1a00e53d1c47c98dd2 \
+    --hash=sha256:99d374a21f7321a4c6ab327c4ab23bd925ad69821aeb53a1e75dd809d19f67fa \
+    --hash=sha256:9c73d1d7cdb97d5b17ae385153472ce118bed607e43be11e9a9deefaa54e0734 \
+    --hash=sha256:a308fd8681a864c04423c0324527237a0484e2611e3441f8089fd00ed56a8171 \
+    --hash=sha256:a6d8c5a05a76aca9a494b4c53ce8a9c29023b7f64f625c6ce1841e92a362ccdf \
+    --hash=sha256:ab2219dbc6fcdb6932f76b548e2b16f34a1f52b7666e998157a4dfc02e2c4123 \
+    --hash=sha256:b51469890e58e85d5242e43aab29f5e9c7e526b951caab354f3ded4ac88e7b76 \
+    --hash=sha256:c256254a8a81e2bddc0d376e23e2a6d2dc8a1e8a2261835ed8c1281a0744cd97 \
+    --hash=sha256:c8440fd8bee9500c235ecb7aa1917a0389a2adb80c209fa1cc485bd70e0d94a5 \
+    --hash=sha256:c86e3ed45c4473564de55aa83b6fc9e5ead86578773dfbd93047380042e26b69 \
+    --hash=sha256:d8cb02c3a96255149ed3ac811eeea25b655d959c6dd5ce702c9a95ff11859eb5 \
+    --hash=sha256:dea78b092fd80a083563ed79a3171258d4a4d307492e7cf8b2313d464c82ba52 \
+    --hash=sha256:e02fe77a03f652500d8bff288cbf3675f742fc0411f5a628fa37116507dc7cc0 \
+    --hash=sha256:f4a016df3f4a8a14d573b496e4d1964deb380e26031fc85fb40e417e9131888a \
+    --hash=sha256:fa8df5267f545b4946df8ca0d93d23382191018e4cda2deda4c2cedf9a010e13 \
+    --hash=sha256:fd125c131889ebbef0849f4a0e29cf363b48aba42f228d08b4079913b576bb3a
+    # via
+    #   cell-browser
+    #   keras
+    #   tensorflow
+hf-xet==1.2.0 ; platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' \
+    --hash=sha256:10bfab528b968c70e062607f663e21e34e2bba349e8038db546646875495179e \
+    --hash=sha256:210d577732b519ac6ede149d2f2f34049d44e8622bf14eb3d63bbcd2d4b332dc \
+    --hash=sha256:27df617a076420d8845bea087f59303da8be17ed7ec0cd7ee3b9b9f579dff0e4 \
+    --hash=sha256:293a7a3787e5c95d7be1857358a9130694a9c6021de3f27fa233f37267174382 \
+    --hash=sha256:29c8fc913a529ec0a91867ce3d119ac1aac966e098cf49501800c870328cc090 \
+    --hash=sha256:2a212e842647b02eb6a911187dc878e79c4aa0aa397e88dd3b26761676e8c1f8 \
+    --hash=sha256:30e06daccb3a7d4c065f34fc26c14c74f4653069bb2b194e7f18f17cbe9939c0 \
+    --hash=sha256:3651fd5bfe0281951b988c0facbe726aa5e347b103a675f49a3fa8144c7968fd \
+    --hash=sha256:46740d4ac024a7ca9b22bebf77460ff43332868b661186a8e46c227fdae01848 \
+    --hash=sha256:4c1428c9ae73ec0939410ec73023c4f842927f39db09b063b9482dac5a3bb737 \
+    --hash=sha256:66e159cbfcfbb29f920db2c09ed8b660eb894640d284f102ada929b6e3dc410a \
+    --hash=sha256:6de1fc44f58f6dd937956c8d304d8c2dea264c80680bcfa61ca4a15e7b76780f \
+    --hash=sha256:7d40b18769bb9a8bc82a9ede575ce1a44c75eb80e7375a01d76259089529b5dc \
+    --hash=sha256:9c91d5ae931510107f148874e9e2de8a16052b6f1b3ca3c1b12f15ccb491390f \
+    --hash=sha256:a55558084c16b09b5ed32ab9ed38421e2d87cf3f1f89815764d1177081b99865 \
+    --hash=sha256:a8c27070ca547293b6890c4bf389f713f80e8c478631432962bb7f4bc0bd7d7f \
+    --hash=sha256:b70218dd548e9840224df5638fdc94bd033552963cfa97f9170829381179c813 \
+    --hash=sha256:cd3a6027d59cfb60177c12d6424e31f4b5ff13d8e3a1247b3a584bf8977e6df5 \
+    --hash=sha256:ceeefcd1b7aed4956ae8499e2199607765fbd1c60510752003b6cc0b8413b649 \
+    --hash=sha256:d06fa97c8562fb3ee7a378dd9b51e343bc5bc8190254202c9771029152f5e08c \
+    --hash=sha256:e6584a52253f72c9f52f9e549d5895ca7a471608495c4ecaa6cc73dba2b24d69 \
+    --hash=sha256:f182f264ed2acd566c514e45da9f2119110e48a87a327ca271027904c70c5832
+    # via huggingface-hub
+httpcore==1.0.9 \
+    --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \
+    --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8
+    # via httpx
+httpx==0.28.1 \
+    --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \
+    --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad
+    # via
+    #   datasets
+    #   huggingface-hub
+    #   jupyterlab
+huggingface-hub==1.2.4 \
+    --hash=sha256:2db69b91877d9d34825f5cd2a63b94f259011a77dcf761b437bf510fbe9522e9 \
+    --hash=sha256:7a1d9ec4802e64372d1d152d69fb8e26d943f15a2289096fbc8e09e7b90c21a5
+    # via
+    #   cell-browser
+    #   datasets
+idna==3.11 \
+    --hash=sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea \
+    --hash=sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902
+    # via
+    #   anyio
+    #   httpx
+    #   jsonschema
+    #   requests
+    #   yarl
+ipykernel==7.1.0 \
+    --hash=sha256:58a3fc88533d5930c3546dc7eac66c6d288acde4f801e2001e65edc5dc9cf0db \
+    --hash=sha256:763b5ec6c5b7776f6a8d7ce09b267693b4e5ce75cb50ae696aaefb3c85e1ea4c
+    # via jupyterlab
+ipympl==0.9.8 \
+    --hash=sha256:4a03612f77d92c9e2160c9e0d2a80b277e30387126399088f780dba9622247be \
+    --hash=sha256:6d7230d518384521093f3854f7db89d069dcd9c28a935b371e9c9f126354dee1
+    # via cell-browser
+ipython==9.9.0 \
+    --hash=sha256:48fbed1b2de5e2c7177eefa144aba7fcb82dac514f09b57e2ac9da34ddb54220 \
+    --hash=sha256:b457fe9165df2b84e8ec909a97abcf2ed88f565970efba16b1f7229c283d252b
+    # via
+    #   ipykernel
+    #   ipympl
+    #   ipywidgets
+ipython-pygments-lexers==1.1.1 \
+    --hash=sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81 \
+    --hash=sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c
+    # via ipython
+ipywidgets==8.1.8 \
+    --hash=sha256:61f969306b95f85fba6b6986b7fe45d73124d1d9e3023a8068710d47a22ea668 \
+    --hash=sha256:ecaca67aed704a338f88f67b1181b58f821ab5dc89c1f0f5ef99db43c1c2921e
+    # via
+    #   anywidget
+    #   cell-browser
+    #   ipympl
+isoduration==20.11.0 \
+    --hash=sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9 \
+    --hash=sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042
+    # via jsonschema
+jedi==0.19.2 \
+    --hash=sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0 \
+    --hash=sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9
+    # via ipython
+jinja2==3.1.6 \
+    --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \
+    --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67
+    # via
+    #   jupyter-server
+    #   jupyterlab
+    #   jupyterlab-server
+    #   nbconvert
+json5==0.13.0 \
+    --hash=sha256:9a08e1dd65f6a4d4c6fa82d216cf2477349ec2346a38fd70cc11d2557499fbcc \
+    --hash=sha256:b1edf8d487721c0bf64d83c28e91280781f6e21f4a797d3261c7c828d4c165bf
+    # via jupyterlab-server
+jsonpointer==3.0.0 \
+    --hash=sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942 \
+    --hash=sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef
+    # via jsonschema
+jsonschema==4.26.0 \
+    --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \
+    --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce
+    # via
+    #   jupyter-events
+    #   jupyterlab-server
+    #   nbformat
+jsonschema-specifications==2025.9.1 \
+    --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \
+    --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d
+    # via jsonschema
+jupyter-client==8.7.0 \
+    --hash=sha256:3357212d9cbe01209e59190f67a3a7e1f387a4f4e88d1e0433ad84d7b262531d \
+    --hash=sha256:3671a94fd25e62f5f2f554f5e95389c2294d89822378a5f2dd24353e1494a9e0
+    # via
+    #   ipykernel
+    #   jupyter-server
+    #   nbclient
+jupyter-core==5.9.1 \
+    --hash=sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508 \
+    --hash=sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+    #   jupyterlab
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+jupyter-events==0.12.0 \
+    --hash=sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb \
+    --hash=sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b
+    # via jupyter-server
+jupyter-lsp==2.3.0 \
+    --hash=sha256:458aa59339dc868fb784d73364f17dbce8836e906cd75fd471a325cba02e0245 \
+    --hash=sha256:e914a3cb2addf48b1c7710914771aaf1819d46b2e5a79b0f917b5478ec93f34f
+    # via jupyterlab
+jupyter-server==2.17.0 \
+    --hash=sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5 \
+    --hash=sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f
+    # via
+    #   jupyter-lsp
+    #   jupyterlab
+    #   jupyterlab-myst
+    #   jupyterlab-server
+    #   notebook-shim
+jupyter-server-terminals==0.5.3 \
+    --hash=sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa \
+    --hash=sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269
+    # via jupyter-server
+jupyterlab==4.5.1 \
+    --hash=sha256:09da1ddfbd9eec18b5101dbb8515612aa1e47443321fb99503725a88e93d20d9 \
+    --hash=sha256:31b059de96de0754ff1f2ce6279774b6aab8c34d7082e9752db58207c99bd514
+    # via cell-browser
+jupyterlab-myst==2.6.0 \
+    --hash=sha256:1502e776153a43588eeb26ce04fb20a0a401805e20edbc57090c9ac932bd42e0 \
+    --hash=sha256:9a0e0ffb318798998be91b7303ffca8a5f15c29bcf339c968c0c3edbc1926265
+    # via cell-browser
+jupyterlab-pygments==0.3.0 \
+    --hash=sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d \
+    --hash=sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780
+    # via nbconvert
+jupyterlab-server==2.28.0 \
+    --hash=sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c \
+    --hash=sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968
+    # via jupyterlab
+jupyterlab-widgets==3.0.16 \
+    --hash=sha256:423da05071d55cf27a9e602216d35a3a65a3e41cdf9c5d3b643b814ce38c19e0 \
+    --hash=sha256:45fa36d9c6422cf2559198e4db481aa243c7a32d9926b500781c830c80f7ecf8
+    # via ipywidgets
+keras==3.13.0 \
+    --hash=sha256:096793e2be6230816f3f7e030370e66c0f4a89707c59bf2d8fad3ca33869bd1c \
+    --hash=sha256:ec51ad2ffcef086d0e3077ac461fa9e3bc54f91d94b49b7c9a84c9af7f54cf5e
+    # via tensorflow
+kiwisolver==1.4.9 \
+    --hash=sha256:0749fd8f4218ad2e851e11cc4dc05c7cbc0cbc4267bdfdb31782e65aace4ee9c \
+    --hash=sha256:0763515d4df10edf6d06a3c19734e2566368980d21ebec439f33f9eb936c07b7 \
+    --hash=sha256:0856e241c2d3df4efef7c04a1e46b1936b6120c9bcf36dd216e3acd84bc4fb21 \
+    --hash=sha256:0ab74e19f6a2b027ea4f845a78827969af45ce790e6cb3e1ebab71bdf9f215ff \
+    --hash=sha256:0e4e2bf29574a6a7b7f6cb5fa69293b9f96c928949ac4a53ba3f525dffb87f9c \
+    --hash=sha256:1a12cf6398e8a0a001a059747a1cbf24705e18fe413bc22de7b3d15c67cffe3f \
+    --hash=sha256:1b11d6a633e4ed84fc0ddafd4ebfd8ea49b3f25082c04ad12b8315c11d504dc1 \
+    --hash=sha256:1fa333e8b2ce4d9660f2cda9c0e1b6bafcfb2457a9d259faa82289e73ec24891 \
+    --hash=sha256:2327a4a30d3ee07d2fbe2e7933e8a37c591663b96ce42a00bc67461a87d7df77 \
+    --hash=sha256:2489e4e5d7ef9a1c300a5e0196e43d9c739f066ef23270607d45aba368b91f2d \
+    --hash=sha256:24c175051354f4a28c5d6a31c93906dc653e2bf234e8a4bbfb964892078898ce \
+    --hash=sha256:2635d352d67458b66fd0667c14cb1d4145e9560d503219034a18a87e971ce4f3 \
+    --hash=sha256:2c1a4f57df73965f3f14df20b80ee29e6a7930a57d2d9e8491a25f676e197c60 \
+    --hash=sha256:3b3115b2581ea35bb6d1f24a4c90af37e5d9b49dcff267eeed14c3893c5b86ab \
+    --hash=sha256:40092754720b174e6ccf9e845d0d8c7d8e12c3d71e7fc35f55f3813e96376f78 \
+    --hash=sha256:497d05f29a1300d14e02e6441cf0f5ee81c1ff5a304b0d9fb77423974684e08b \
+    --hash=sha256:4a2899935e724dd1074cb568ce7ac0dce28b2cd6ab539c8e001a8578eb106d14 \
+    --hash=sha256:4a48a2ce79d65d363597ef7b567ce3d14d68783d2b2263d98db3d9477805ba32 \
+    --hash=sha256:52a15b0f35dad39862d376df10c5230155243a2c1a436e39eb55623ccbd68185 \
+    --hash=sha256:5656aa670507437af0207645273ccdfee4f14bacd7f7c67a4306d0dcaeaf6eed \
+    --hash=sha256:5a0f2724dfd4e3b3ac5a82436a8e6fd16baa7d507117e4279b660fe8ca38a3a1 \
+    --hash=sha256:60c439763a969a6af93b4881db0eed8fadf93ee98e18cbc35bc8da868d0c4f0c \
+    --hash=sha256:61874cdb0a36016354853593cffc38e56fc9ca5aa97d2c05d3dcf6922cd55a11 \
+    --hash=sha256:67bb8b474b4181770f926f7b7d2f8c0248cbcb78b660fdd41a47054b28d2a752 \
+    --hash=sha256:72d0eb9fba308b8311685c2268cf7d0a0639a6cd027d8128659f72bdd8a024b4 \
+    --hash=sha256:767c23ad1c58c9e827b649a9ab7809fd5fd9db266a9cf02b0e926ddc2c680d58 \
+    --hash=sha256:77937e5e2a38a7b48eef0585114fe7930346993a88060d0bf886086d2aa49ef5 \
+    --hash=sha256:7a08b491ec91b1d5053ac177afe5290adacf1f0f6307d771ccac5de30592d198 \
+    --hash=sha256:7cf974dd4e35fa315563ac99d6287a1024e4dc2077b8a7d7cd3d2fb65d283134 \
+    --hash=sha256:858e4c22fb075920b96a291928cb7dea5644e94c0ee4fcd5af7e865655e4ccf2 \
+    --hash=sha256:85bd218b5ecfbee8c8a82e121802dcb519a86044c9c3b2e4aef02fa05c6da370 \
+    --hash=sha256:92a2f997387a1b79a75e7803aa7ded2cfbe2823852ccf1ba3bcf613b62ae3197 \
+    --hash=sha256:9928fe1eb816d11ae170885a74d074f57af3a0d65777ca47e9aeb854a1fba386 \
+    --hash=sha256:9af39d6551f97d31a4deebeac6f45b156f9755ddc59c07b402c148f5dbb6482a \
+    --hash=sha256:a30fd6fdef1430fd9e1ba7b3398b5ee4e2887783917a687d86ba69985fb08748 \
+    --hash=sha256:a31d512c812daea6d8b3be3b2bfcbeb091dbb09177706569bcfc6240dcf8b41c \
+    --hash=sha256:a5d0432ccf1c7ab14f9949eec60c5d1f924f17c037e9f8b33352fa05799359b8 \
+    --hash=sha256:a60ea74330b91bd22a29638940d115df9dc00af5035a9a2a6ad9399ffb4ceca5 \
+    --hash=sha256:ac5a486ac389dddcc5bef4f365b6ae3ffff2c433324fb38dd35e3fab7c957999 \
+    --hash=sha256:aedff62918805fb62d43a4aa2ecd4482c380dc76cd31bd7c8878588a61bd0369 \
+    --hash=sha256:b34e51affded8faee0dfdb705416153819d8ea9250bbbf7ea1b249bdeb5f1122 \
+    --hash=sha256:b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098 \
+    --hash=sha256:bb4ae2b57fc1d8cbd1cf7b1d9913803681ffa903e7488012be5b76dedf49297f \
+    --hash=sha256:bdd1a81a1860476eb41ac4bc1e07b3f07259e6d55bbf739b79c8aaedcf512799 \
+    --hash=sha256:bfc08add558155345129c7803b3671cf195e6a56e7a12f3dde7c57d9b417f525 \
+    --hash=sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d \
+    --hash=sha256:cc9617b46837c6468197b5945e196ee9ca43057bb7d9d1ae688101e4e1dddf64 \
+    --hash=sha256:ce6a3a4e106cf35c2d9c4fa17c05ce0b180db622736845d4315519397a77beaf \
+    --hash=sha256:d0005b053977e7b43388ddec89fa567f43d4f6d5c2c0affe57de5ebf290dc552 \
+    --hash=sha256:d4188e73af84ca82468f09cadc5ac4db578109e52acb4518d8154698d3a87ca2 \
+    --hash=sha256:d75aa530ccfaa593da12834b86a0724f58bff12706659baa9227c2ccaa06264c \
+    --hash=sha256:d84cd4061ae292d8ac367b2c3fa3aad11cb8625a95d135fe93f286f914f3f5a6 \
+    --hash=sha256:d8aacd3d4b33b772542b2e01beb50187536967b514b00003bdda7589722d2a64 \
+    --hash=sha256:d8fc5c867c22b828001b6a38d2eaeb88160bf5783c6cb4a5e440efc981ce286d \
+    --hash=sha256:d976bbb382b202f71c67f77b0ac11244021cfa3f7dfd9e562eefcea2df711548 \
+    --hash=sha256:dba5ee5d3981160c28d5490f0d1b7ed730c22470ff7f6cc26cfcfaacb9896a07 \
+    --hash=sha256:dd0a578400839256df88c16abddf9ba14813ec5f21362e1fe65022e00c883d4d \
+    --hash=sha256:e2ea9f7ab7fbf18fffb1b5434ce7c69a07582f7acc7717720f1d69f3e806f90c \
+    --hash=sha256:e6b93f13371d341afee3be9f7c5964e3fe61d5fa30f6a30eb49856935dfe4fc3 \
+    --hash=sha256:ed0fecd28cc62c54b262e3736f8bb2512d8dcfdc2bcf08be5f47f96bf405b145 \
+    --hash=sha256:efb3a45b35622bb6c16dbfab491a8f5a391fe0e9d45ef32f4df85658232ca0e2 \
+    --hash=sha256:f2ba92255faa7309d06fe44c3a4a97efe1c8d640c2a79a5ef728b685762a6fd2 \
+    --hash=sha256:f6008a4919fdbc0b0097089f67a1eb55d950ed7e90ce2cc3e640abadd2757a04 \
+    --hash=sha256:f68208a520c3d86ea51acf688a3e3002615a7f0238002cccc17affecc86a8a54 \
+    --hash=sha256:f68e4f3eeca8fb22cc3d731f9715a13b652795ef657a13df1ad0c7dc0e9731df
+    # via matplotlib
+lark==1.3.1 \
+    --hash=sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905 \
+    --hash=sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12
+    # via rfc3987-syntax
+libclang==18.1.1 \
+    --hash=sha256:0b2e143f0fac830156feb56f9231ff8338c20aecfe72b4ffe96f19e5a1dbb69a \
+    --hash=sha256:3f0e1f49f04d3cd198985fea0511576b0aee16f9ff0e0f0cad7f9c57ec3c20e8 \
+    --hash=sha256:4dd2d3b82fab35e2bf9ca717d7b63ac990a3519c7e312f19fa8e86dcc712f7fb \
+    --hash=sha256:54dda940a4a0491a9d1532bf071ea3ef26e6dbaf03b5000ed94dd7174e8f9592 \
+    --hash=sha256:69f8eb8f65c279e765ffd28aaa7e9e364c776c17618af8bff22a8df58677ff4f \
+    --hash=sha256:6f14c3f194704e5d09769108f03185fce7acaf1d1ae4bbb2f30a72c2400cb7c5 \
+    --hash=sha256:83ce5045d101b669ac38e6da8e58765f12da2d3aafb3b9b98d88b286a60964d8 \
+    --hash=sha256:a1214966d08d73d971287fc3ead8dfaf82eb07fb197680d8b3859dbbbbf78250 \
+    --hash=sha256:c533091d8a3bbf7460a00cb6c1a71da93bffe148f172c7d03b1c31fbf8aa2a0b \
+    --hash=sha256:cf4a99b05376513717ab5d82a0db832c56ccea4fd61a69dbb7bccf2dfb207dbe
+    # via tensorflow
+markdown==3.10 \
+    --hash=sha256:37062d4f2aa4b2b6b32aefb80faa300f82cc790cb949a35b8caede34f2b68c0e \
+    --hash=sha256:b5b99d6951e2e4948d939255596523444c0e677c669700b1d17aa4a8a464cb7c
+    # via tensorboard
+markdown-it-py==4.0.0 \
+    --hash=sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147 \
+    --hash=sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3
+    # via rich
+markupsafe==3.0.3 \
+    --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \
+    --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \
+    --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \
+    --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \
+    --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \
+    --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \
+    --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \
+    --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \
+    --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \
+    --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \
+    --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \
+    --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \
+    --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \
+    --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \
+    --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \
+    --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \
+    --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \
+    --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \
+    --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \
+    --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \
+    --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \
+    --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \
+    --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \
+    --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \
+    --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \
+    --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \
+    --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \
+    --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \
+    --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \
+    --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \
+    --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \
+    --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \
+    --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \
+    --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \
+    --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \
+    --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \
+    --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \
+    --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \
+    --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \
+    --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \
+    --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \
+    --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \
+    --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \
+    --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \
+    --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \
+    --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \
+    --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \
+    --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \
+    --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \
+    --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \
+    --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \
+    --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \
+    --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \
+    --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \
+    --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \
+    --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50
+    # via
+    #   jinja2
+    #   nbconvert
+    #   werkzeug
+matplotlib==3.10.8 \
+    --hash=sha256:0a33deb84c15ede243aead39f77e990469fff93ad1521163305095b77b72ce4a \
+    --hash=sha256:113bb52413ea508ce954a02c10ffd0d565f9c3bc7f2eddc27dfe1731e71c7b5f \
+    --hash=sha256:15d30132718972c2c074cd14638c7f4592bd98719e2308bccea40e0538bc0cb5 \
+    --hash=sha256:2299372c19d56bcd35cf05a2738308758d32b9eaed2371898d8f5bd33f084aa3 \
+    --hash=sha256:24d50994d8c5816ddc35411e50a86ab05f575e2530c02752e02538122613371f \
+    --hash=sha256:25d380fe8b1dc32cf8f0b1b448470a77afb195438bafdf1d858bfb876f3edf7b \
+    --hash=sha256:2c1998e92cd5999e295a731bcb2911c75f597d937341f3030cc24ef2733d78a8 \
+    --hash=sha256:2cf5bd12cecf46908f286d7838b2abc6c91cda506c0445b8223a7c19a00df008 \
+    --hash=sha256:32f8dce744be5569bebe789e46727946041199030db8aeb2954d26013a0eb26b \
+    --hash=sha256:3a48a78d2786784cc2413e57397981fb45c79e968d99656706018d6e62e57958 \
+    --hash=sha256:3ab4aabc72de4ff77b3ec33a6d78a68227bf1123465887f9905ba79184a1cc04 \
+    --hash=sha256:3c624e43ed56313651bc18a47f838b60d7b8032ed348911c54906b130b20071b \
+    --hash=sha256:3f2e409836d7f5ac2f1c013110a4d50b9f7edc26328c108915f9075d7d7a91b6 \
+    --hash=sha256:3f5c3e4da343bba819f0234186b9004faba952cc420fbc522dc4e103c1985908 \
+    --hash=sha256:41703cc95688f2516b480f7f339d8851a6035f18e100ee6a32bc0b8536a12a9c \
+    --hash=sha256:495672de149445ec1b772ff2c9ede9b769e3cb4f0d0aa7fa730d7f59e2d4e1c1 \
+    --hash=sha256:4cf267add95b1c88300d96ca837833d4112756045364f5c734a2276038dae27d \
+    --hash=sha256:56271f3dac49a88d7fca5060f004d9d22b865f743a12a23b1e937a0be4818ee1 \
+    --hash=sha256:595ba4d8fe983b88f0eec8c26a241e16d6376fe1979086232f481f8f3f67494c \
+    --hash=sha256:5f62550b9a30afde8c1c3ae450e5eb547d579dd69b25c2fc7a1c67f934c1717a \
+    --hash=sha256:646d95230efb9ca614a7a594d4fcacde0ac61d25e37dd51710b36477594963ce \
+    --hash=sha256:64fcc24778ca0404ce0cb7b6b77ae1f4c7231cdd60e6778f999ee05cbd581b9a \
+    --hash=sha256:83d282364ea9f3e52363da262ce32a09dfe241e4080dcedda3c0db059d3c1f11 \
+    --hash=sha256:99eefd13c0dc3b3c1b4d561c1169e65fe47aab7b8158754d7c084088e2329466 \
+    --hash=sha256:a0a7f52498f72f13d4a25ea70f35f4cb60642b466cbb0a9be951b5bc3f45a486 \
+    --hash=sha256:ad3d9833a64cf48cc4300f2b406c3d0f4f4724a91c0bd5640678a6ba7c102077 \
+    --hash=sha256:b44d07310e404ba95f8c25aa5536f154c0a8ec473303535949e52eb71d0a1565 \
+    --hash=sha256:b53285e65d4fa4c86399979e956235deb900be5baa7fc1218ea67fbfaeaadd6f \
+    --hash=sha256:b5a2b97dbdc7d4f353ebf343744f1d1f1cca8aa8bfddb4262fcf4306c3761d50 \
+    --hash=sha256:b9a5ca4ac220a0cdd1ba6bcba3608547117d30468fefce49bb26f55c1a3d5c58 \
+    --hash=sha256:c108a1d6fa78a50646029cb6d49808ff0fc1330fda87fa6f6250c6b5369b6645 \
+    --hash=sha256:d9050fee89a89ed57b4fb2c1bfac9a3d0c57a0d55aed95949eedbc42070fea39 \
+    --hash=sha256:dd80ecb295460a5d9d260df63c43f4afbdd832d725a531f008dad1664f458adf \
+    --hash=sha256:e8ea3e2d4066083e264e75c829078f9e149fa119d27e19acd503de65e0b13149 \
+    --hash=sha256:eb3823f11823deade26ce3b9f40dcb4a213da7a670013929f31d5f5ed1055b22 \
+    --hash=sha256:f89c151aab2e2e23cb3fe0acad1e8b82841fd265379c4cecd0f3fcb34c15e0f6
+    # via
+    #   cell-browser
+    #   cmasher
+    #   ipympl
+matplotlib-inline==0.2.1 \
+    --hash=sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76 \
+    --hash=sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe
+    # via
+    #   ipykernel
+    #   ipython
+mdurl==0.1.2 \
+    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+    # via markdown-it-py
+mistune==3.2.0 \
+    --hash=sha256:708487c8a8cdd99c9d90eb3ed4c3ed961246ff78ac82f03418f5183ab70e398a \
+    --hash=sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1
+    # via nbconvert
+ml-dtypes==0.5.4 \
+    --hash=sha256:0d2ffd05a2575b1519dc928c0b93c06339eb67173ff53acb00724502cda231cf \
+    --hash=sha256:11942cbf2cf92157db91e5022633c0d9474d4dfd813a909383bd23ce828a4b7d \
+    --hash=sha256:14a4fd3228af936461db66faccef6e4f41c1d82fcc30e9f8d58a08916b1d811f \
+    --hash=sha256:2314892cdc3fcf05e373d76d72aaa15fda9fb98625effa73c1d646f331fcecb7 \
+    --hash=sha256:2b857d3af6ac0d39db1de7c706e69c7f9791627209c3d6dedbfca8c7e5faec22 \
+    --hash=sha256:304ad47faa395415b9ccbcc06a0350800bc50eda70f0e45326796e27c62f18b6 \
+    --hash=sha256:35f29491a3e478407f7047b8a4834e4640a77d2737e0b294d049746507af5175 \
+    --hash=sha256:3bbbe120b915090d9dd1375e4684dd17a20a2491ef25d640a908281da85e73f1 \
+    --hash=sha256:4381fe2f2452a2d7589689693d3162e876b3ddb0a832cde7a414f8e1adf7eab1 \
+    --hash=sha256:531eff30e4d368cb6255bc2328d070e35836aa4f282a0fb5f3a0cd7260257298 \
+    --hash=sha256:533ce891ba774eabf607172254f2e7260ba5f57bdd64030c9a4fcfbd99815d0d \
+    --hash=sha256:5a0f68ca8fd8d16583dfa7793973feb86f2fbb56ce3966daf9c9f748f52a2049 \
+    --hash=sha256:6a0df4223b514d799b8a1629c65ddc351b3efa833ccf7f8ea0cf654a61d1e35d \
+    --hash=sha256:805cef3a38f4eafae3a5bf9ebdcdb741d0bcfd9e1bd90eb54abd24f928cd2465 \
+    --hash=sha256:8ab06a50fb9bf9666dd0fe5dfb4676fa2b0ac0f31ecff72a6c3af8e22c063453 \
+    --hash=sha256:8c6a2dcebd6f3903e05d51960a8058d6e131fe69f952a5397e5dbabc841b6d56 \
+    --hash=sha256:8c760d85a2f82e2bed75867079188c9d18dae2ee77c25a54d60e9cc79be1bc48 \
+    --hash=sha256:9ad459e99793fa6e13bd5b7e6792c8f9190b4e5a1b45c63aba14a4d0a7f1d5ff \
+    --hash=sha256:9bad06436568442575beb2d03389aa7456c690a5b05892c471215bfd8cf39460 \
+    --hash=sha256:a174837a64f5b16cab6f368171a1a03a27936b31699d167684073ff1c4237dac \
+    --hash=sha256:a7f7c643e8b1320fd958bf098aa7ecf70623a42ec5154e3be3be673f4c34d900 \
+    --hash=sha256:bfc534409c5d4b0bf945af29e5d0ab075eae9eecbb549ff8a29280db822f34f9 \
+    --hash=sha256:c1a953995cccb9e25a4ae19e34316671e4e2edaebe4cf538229b1fc7109087b7 \
+    --hash=sha256:cb73dccfc991691c444acc8c0012bee8f2470da826a92e3a20bb333b1a7894e6 \
+    --hash=sha256:ce756d3a10d0c4067172804c9cc276ba9cc0ff47af9078ad439b075d1abdc29b \
+    --hash=sha256:f21c9219ef48ca5ee78402d5cc831bd58ea27ce89beda894428bc67a52da5328
+    # via
+    #   keras
+    #   tensorflow
+multidict==6.7.0 \
+    --hash=sha256:040f393368e63fb0f3330e70c26bfd336656bed925e5cbe17c9da839a6ab13ec \
+    --hash=sha256:05047ada7a2fde2631a0ed706f1fd68b169a681dfe5e4cf0f8e4cb6618bbc2cd \
+    --hash=sha256:0591b48acf279821a579282444814a2d8d0af624ae0bc600aa4d1b920b6e924b \
+    --hash=sha256:07f5594ac6d084cbb5de2df218d78baf55ef150b91f0ff8a21cc7a2e3a5a58eb \
+    --hash=sha256:08d4379f9744d8f78d98c8673c06e202ffa88296f009c71bbafe8a6bf847d01f \
+    --hash=sha256:0934f3843a1860dd465d38895c17fce1f1cb37295149ab05cd1b9a03afacb2a7 \
+    --hash=sha256:09929cab6fcb68122776d575e03c6cc64ee0b8fca48d17e135474b042ce515cd \
+    --hash=sha256:123e2a72e20537add2f33a79e605f6191fba2afda4cbb876e35c1a7074298a7d \
+    --hash=sha256:171b73bd4ee683d307599b66793ac80981b06f069b62eea1c9e29c9241aa66b0 \
+    --hash=sha256:18706cc31dbf402a7945916dd5cddf160251b6dab8a2c5f3d6d5a55949f676b3 \
+    --hash=sha256:19a1d55338ec1be74ef62440ca9e04a2f001a04d0cc49a4983dc320ff0f3212d \
+    --hash=sha256:2049be98fb57a31b4ccf870bf377af2504d4ae35646a19037ec271e4c07998aa \
+    --hash=sha256:2090d3718829d1e484706a2f525e50c892237b2bf9b17a79b059cb98cddc2f10 \
+    --hash=sha256:2397ab4daaf2698eb51a76721e98db21ce4f52339e535725de03ea962b5a3202 \
+    --hash=sha256:27e0b36c2d388dc7b6ced3406671b401e84ad7eb0656b8f3a2f46ed0ce483718 \
+    --hash=sha256:28b37063541b897fd6a318007373930a75ca6d6ac7c940dbe14731ffdd8d498e \
+    --hash=sha256:29fe6740ebccba4175af1b9b87bf553e9c15cd5868ee967e010efcf94e4fd0f1 \
+    --hash=sha256:2a7baa46a22e77f0988e3b23d4ede5513ebec1929e34ee9495be535662c0dfe2 \
+    --hash=sha256:2d2cfeec3f6f45651b3d408c4acec0ebf3daa9bc8a112a084206f5db5d05b754 \
+    --hash=sha256:2f67396ec0310764b9222a1728ced1ab638f61aadc6226f17a71dd9324f9a99c \
+    --hash=sha256:30d193c6cc6d559db42b6bcec8a5d395d34d60c9877a0b71ecd7c204fcf15390 \
+    --hash=sha256:31bae522710064b5cbeddaf2e9f32b1abab70ac6ac91d42572502299e9953128 \
+    --hash=sha256:394fc5c42a333c9ffc3e421a4c85e08580d990e08b99f6bf35b4132114c5dcb3 \
+    --hash=sha256:3b29b980d0ddbecb736735ee5bef69bb2ddca56eff603c86f3f29a1128299b4f \
+    --hash=sha256:3bab1e4aff7adaa34410f93b1f8e57c4b36b9af0426a76003f441ee1d3c7e842 \
+    --hash=sha256:3d7b6ccce016e29df4b7ca819659f516f0bc7a4b3efa3bb2012ba06431b044f9 \
+    --hash=sha256:3da4fb467498df97e986af166b12d01f05d2e04f978a9c1c680ea1988e0bc4b6 \
+    --hash=sha256:3e8bfdd0e487acf992407a140d2589fe598238eaeffa3da8448d63a63cd363f8 \
+    --hash=sha256:44b546bd3eb645fd26fb949e43c02a25a2e632e2ca21a35e2e132c8105dc8599 \
+    --hash=sha256:490dab541a6a642ce1a9d61a4781656b346a55c13038f0b1244653828e3a83ec \
+    --hash=sha256:4a0df7ff02397bb63e2fd22af2c87dfa39e8c7f12947bc524dbdc528282c7e34 \
+    --hash=sha256:4ef089f985b8c194d341eb2c24ae6e7408c9a0e2e5658699c92f497437d88c3c \
+    --hash=sha256:53a42d364f323275126aff81fb67c5ca1b7a04fda0546245730a55c8c5f24bc4 \
+    --hash=sha256:5aa873cbc8e593d361ae65c68f85faadd755c3295ea2c12040ee146802f23b38 \
+    --hash=sha256:654030da3197d927f05a536a66186070e98765aa5142794c9904555d3a9d8fb5 \
+    --hash=sha256:6b4c3d199f953acd5b446bf7c0de1fe25d94e09e79086f8dc2f48a11a129cdf1 \
+    --hash=sha256:716133f7d1d946a4e1b91b1756b23c088881e70ff180c24e864c26192ad7534a \
+    --hash=sha256:749a72584761531d2b9467cfbdfd29487ee21124c304c4b6cb760d8777b27f9c \
+    --hash=sha256:7516c579652f6a6be0e266aec0acd0db80829ca305c3d771ed898538804c2036 \
+    --hash=sha256:79dcf9e477bc65414ebfea98ffd013cb39552b5ecd62908752e0e413d6d06e38 \
+    --hash=sha256:7a0222514e8e4c514660e182d5156a415c13ef0aabbd71682fc714e327b95e99 \
+    --hash=sha256:7b022717c748dd1992a83e219587aabe45980d88969f01b316e78683e6285f64 \
+    --hash=sha256:7bf77f54997a9166a2f5675d1201520586439424c2511723a7312bdb4bcc034e \
+    --hash=sha256:7ef6b61cad77091056ce0e7ce69814ef72afacb150b7ac6a3e9470def2198159 \
+    --hash=sha256:803d685de7be4303b5a657b76e2f6d1240e7e0a8aa2968ad5811fa2285553a12 \
+    --hash=sha256:8891681594162635948a636c9fe0ff21746aeb3dd5463f6e25d9bea3a8a39ca1 \
+    --hash=sha256:8a19cdb57cd3df4cd865849d93ee14920fb97224300c88501f16ecfa2604b4e0 \
+    --hash=sha256:8a3862568a36d26e650a19bb5cbbba14b71789032aebc0423f8cc5f150730184 \
+    --hash=sha256:92abb658ef2d7ef22ac9f8bb88e8b6c3e571671534e029359b6d9e845923eb1b \
+    --hash=sha256:94218fcec4d72bc61df51c198d098ce2b378e0ccbac41ddbed5ef44092913288 \
+    --hash=sha256:9600082733859f00d79dee64effc7aef1beb26adb297416a4ad2116fd61374bd \
+    --hash=sha256:960c60b5849b9b4f9dcc9bea6e3626143c252c74113df2c1540aebce70209b45 \
+    --hash=sha256:9b2fd74c52accced7e75de26023b7dccee62511a600e62311b918ec5c168fc2a \
+    --hash=sha256:9c0359b1ec12b1d6849c59f9d319610b7f20ef990a6d454ab151aa0e3b9f78ca \
+    --hash=sha256:9fb0211dfc3b51efea2f349ec92c114d7754dd62c01f81c3e32b765b70c45c9b \
+    --hash=sha256:9fe04da3f79387f450fd0061d4dd2e45a72749d31bf634aecc9e27f24fdc4b3f \
+    --hash=sha256:9ff96e8815eecacc6645da76c413eb3b3d34cfca256c70b16b286a687d013c32 \
+    --hash=sha256:a027ec240fe73a8d6281872690b988eed307cd7d91b23998ff35ff577ca688b5 \
+    --hash=sha256:a35c5fc61d4f51eb045061e7967cfe3123d622cd500e8868e7c0c592a09fedc4 \
+    --hash=sha256:a37bd74c3fa9d00be2d7b8eca074dc56bd8077ddd2917a839bd989612671ed17 \
+    --hash=sha256:a60a4d75718a5efa473ebd5ab685786ba0c67b8381f781d1be14da49f1a2dc60 \
+    --hash=sha256:a6ef16328011d3f468e7ebc326f24c1445f001ca1dec335b2f8e66bed3006394 \
+    --hash=sha256:ad9ce259f50abd98a1ca0aa6e490b58c316a0fce0617f609723e40804add2c00 \
+    --hash=sha256:b284e319754366c1aee2267a2036248b24eeb17ecd5dc16022095e747f2f4304 \
+    --hash=sha256:b2d7f80c4e1fd010b07cb26820aae86b7e73b681ee4889684fb8d2d4537aab13 \
+    --hash=sha256:b3bc26a951007b1057a1c543af845f1c7e3e71cc240ed1ace7bf4484aa99196e \
+    --hash=sha256:b3e34f3a1b8131ba06f1a73adab24f30934d148afcd5f5de9a73565a4404384e \
+    --hash=sha256:b4121773c49a0776461f4a904cdf6264c88e42218aaa8407e803ca8025872792 \
+    --hash=sha256:b6234e14f9314731ec45c42fc4554b88133ad53a09092cc48a88e771c125dadb \
+    --hash=sha256:b8512bac933afc3e45fb2b18da8e59b78d4f408399a960339598374d4ae3b56b \
+    --hash=sha256:ba672b26069957ee369cfa7fc180dde1fc6f176eaf1e6beaf61fbebbd3d9c000 \
+    --hash=sha256:bee7c0588aa0076ce77c0ea5d19a68d76ad81fcd9fe8501003b9a24f9d4000f6 \
+    --hash=sha256:c04a328260dfd5db8c39538f999f02779012268f54614902d0afc775d44e0a62 \
+    --hash=sha256:c1dcc7524066fa918c6a27d61444d4ee7900ec635779058571f70d042d86ed63 \
+    --hash=sha256:c6e99d9a65ca282e578dfea819cfa9c0a62b2499d8677392e09feaf305e9e6f5 \
+    --hash=sha256:ca43bdfa5d37bd6aee89d85e1d0831fb86e25541be7e9d376ead1b28974f8e5e \
+    --hash=sha256:caf53b15b1b7df9fbd0709aa01409000a2b4dd03a5f6f5cc548183c7c8f8b63c \
+    --hash=sha256:cc41db090ed742f32bd2d2c721861725e6109681eddf835d0a82bd3a5c382827 \
+    --hash=sha256:cd240939f71c64bd658f186330603aac1a9a81bf6273f523fca63673cb7378a8 \
+    --hash=sha256:d1bed1b467ef657f2a0ae62844a607909ef1c6889562de5e1d505f74457d0b96 \
+    --hash=sha256:d1d964afecdf3a8288789df2f5751dc0a8261138c3768d9af117ed384e538fad \
+    --hash=sha256:d4393e3581e84e5645506923816b9cc81f5609a778c7e7534054091acc64d1c6 \
+    --hash=sha256:dd32a49400a2c3d52088e120ee00c1e3576cbff7e10b98467962c74fdb762ed4 \
+    --hash=sha256:e011555abada53f1578d63389610ac8a5400fc70ce71156b0aa30d326f1a5064 \
+    --hash=sha256:e93a0617cd16998784bf4414c7e40f17a35d2350e5c6f0bd900d3a8e02bd3762 \
+    --hash=sha256:ea3334cabe4d41b7ccd01e4d349828678794edbc2d3ae97fc162a3312095092e \
+    --hash=sha256:efbb54e98446892590dc2458c19c10344ee9a883a79b5cec4bc34d6656e8d546 \
+    --hash=sha256:f0feece2ef8ebc42ed9e2e8c78fc4aa3cf455733b507c09ef7406364c94376c6 \
+    --hash=sha256:f844a1bbf1d207dd311a56f383f7eda2d0e134921d45751842d8235e7778965d \
+    --hash=sha256:f8a93b1c0ed2d04b97a5e9336fd2d33371b9a6e29ab7dd6503d63407c20ffbaf \
+    --hash=sha256:fbafe31d191dfa7c4c51f7a6149c9fb7e914dcf9ffead27dcfd9f1ae382b3885 \
+    --hash=sha256:fbd18dc82d7bf274b37aa48d664534330af744e03bccf696d6f4c6042e7d19e7
+    # via
+    #   aiohttp
+    #   yarl
+multiprocess==0.70.18 \
+    --hash=sha256:5aa6eef98e691281b3ad923be2832bf1c55dd2c859acd73e5ec53a66aae06a1d \
+    --hash=sha256:60c194974c31784019c1f459d984e8f33ee48f10fcf42c309ba97b30d9bd53ea \
+    --hash=sha256:871743755f43ef57d7910a38433cfe41319e72be1bbd90b79c7a5ac523eb9334 \
+    --hash=sha256:9b78f8e5024b573730bfb654783a13800c2c0f2dfc0c25e70b40d184d64adaa2 \
+    --hash=sha256:dbf705e52a154fe5e90fb17b38f02556169557c2dd8bb084f2e06c2784d8279b \
+    --hash=sha256:e78ca805a72b1b810c690b6b4cc32579eba34f403094bbbae962b7b5bf9dfcb8 \
+    --hash=sha256:f9597128e6b3e67b23956da07cf3d2e5cba79e2f4e0fba8d7903636663ec6d0d
+    # via datasets
+namex==0.1.0 \
+    --hash=sha256:117f03ccd302cc48e3f5c58a296838f6b89c83455ab8683a1e85f2a430aa4306 \
+    --hash=sha256:e2012a474502f1e2251267062aae3114611f07df4224b6e06334c57b0f2ce87c
+    # via keras
+nbclient==0.10.4 \
+    --hash=sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9 \
+    --hash=sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440
+    # via nbconvert
+nbconvert==7.16.6 \
+    --hash=sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b \
+    --hash=sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582
+    # via jupyter-server
+nbformat==5.10.4 \
+    --hash=sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a \
+    --hash=sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b
+    # via
+    #   jupyter-server
+    #   nbclient
+    #   nbconvert
+nest-asyncio==1.6.0 \
+    --hash=sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe \
+    --hash=sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c
+    # via ipykernel
+notebook-shim==0.2.4 \
+    --hash=sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef \
+    --hash=sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb
+    # via jupyterlab
+numcodecs==0.15.1 \
+    --hash=sha256:1d471a1829ce52d3f365053a2bd1379e32e369517557c4027ddf5ac0d99c591e \
+    --hash=sha256:1dfdea4a67108205edfce99c1cb6cd621343bc7abb7e16a041c966776920e7de \
+    --hash=sha256:a34f0fe5e5f3b837bbedbeb98794a6d4a12eeeef8d4697b523905837900b5e1c \
+    --hash=sha256:a4f7bdb26f1b34423cb56d48e75821223be38040907c9b5954eeb7463e7eb03c \
+    --hash=sha256:b0a9d9cd29a0088220682dda4a9898321f7813ff7802be2bbb545f6e3d2f10ff \
+    --hash=sha256:c3a09e22140f2c691f7df26303ff8fa2dadcf26d7d0828398c0bc09b69e5efa3 \
+    --hash=sha256:daed6066ffcf40082da847d318b5ab6123d69ceb433ba603cb87c323a541a8bc \
+    --hash=sha256:e3d82b70500cf61e8d115faa0d0a76be6ecdc24a16477ee3279d711699ad85f3 \
+    --hash=sha256:eeed77e4d6636641a2cc605fbc6078c7a8f2cc40f3dfa2b3f61e52e6091b04ff
+    # via zarr
+numpy==2.4.0 \
+    --hash=sha256:009bd0ea12d3c784b6639a8457537016ce5172109e585338e11334f6a7bb88ee \
+    --hash=sha256:02a2038eb27f9443a8b266a66911e926566b5a6ffd1a689b588f7f35b81e7dc3 \
+    --hash=sha256:0804a8e4ab070d1d35496e65ffd3cf8114c136a2b81f61dfab0de4b218aacfd5 \
+    --hash=sha256:1889b3a3f47a7b5bee16bc25a2145bd7cb91897f815ce3499db64c7458b6d91d \
+    --hash=sha256:1d8f9fde5f6dc1b6fc34df8162f3b3079365468703fee7f31d4e0cc8c63baed9 \
+    --hash=sha256:1ee32359fb7543b7b7bd0b2f46294db27e29e7bbdf70541e81b190836cd83ded \
+    --hash=sha256:20c115517513831860c573996e395707aa9fb691eb179200125c250e895fcd93 \
+    --hash=sha256:23a3e9d1a6f360267e8fbb38ba5db355a6a7e9be71d7fce7ab3125e88bb646c8 \
+    --hash=sha256:2a8b6bb8369abefb8bd1801b054ad50e02b3275c8614dc6e5b0373c305291037 \
+    --hash=sha256:2e284ca13d5a8367e43734148622caf0b261b275673823593e3e3634a6490f83 \
+    --hash=sha256:355354388cba60f2132df297e2d53053d4063f79077b67b481d21276d61fc4df \
+    --hash=sha256:35ddc8f4914466e6fc954c76527aa91aa763682a4f6d73249ef20b418fe6effb \
+    --hash=sha256:36667db4d6c1cea79c8930ab72fadfb4060feb4bfe724141cd4bd064d2e5f8ce \
+    --hash=sha256:36cbfb13c152b1c7c184ddac43765db8ad672567e7bafff2cc755a09917ed2e6 \
+    --hash=sha256:39699233bc72dd482da1415dcb06076e32f60eddc796a796c5fb6c5efce94667 \
+    --hash=sha256:39b19251dec4de8ff8496cd0806cbe27bf0684f765abb1f4809554de93785f2d \
+    --hash=sha256:3d857f55e7fdf7c38ab96c4558c95b97d1c685be6b05c249f5fdafcbd6f9899e \
+    --hash=sha256:3de19cfecd1465d0dcf8a5b5ea8b3155b42ed0b639dba4b71e323d74f2a3be5e \
+    --hash=sha256:40483b2f2d3ba7aad426443767ff5632ec3156ef09742b96913787d13c336471 \
+    --hash=sha256:44aba2f0cafd287871a495fb3163408b0bd25bbce135c6f621534a07f4f7875c \
+    --hash=sha256:49ff32b09f5aa0cd30a20c2b39db3e669c845589f2b7fc910365210887e39344 \
+    --hash=sha256:4d1cfce39e511069b11e67cd0bd78ceff31443b7c9e5c04db73c7a19f572967c \
+    --hash=sha256:5f48cb3e88fbc294dc90e215d86fbaf1c852c63dbdb6c3a3e63f45c4b57f7344 \
+    --hash=sha256:5fe44e277225fd3dff6882d86d3d447205d43532c3627313d17e754fb3905a0e \
+    --hash=sha256:60e8c196cd82cbbd4f130b5290007e13e6de3eca79f0d4d38014769d96a7c475 \
+    --hash=sha256:65167da969cd1ec3a1df31cb221ca3a19a8aaa25370ecb17d428415e93c1935e \
+    --hash=sha256:680060061adb2d74ce352628cb798cfdec399068aa7f07ba9fb818b2b3305f98 \
+    --hash=sha256:6bbaebf0d11567fa8926215ae731e1d58e6ec28a8a25235b8a47405d301332db \
+    --hash=sha256:6c05483c3136ac4c91b4e81903cb53a8707d316f488124d0398499a4f8e8ef51 \
+    --hash=sha256:6dc8b7e2f4eb184b37655195f421836cfae6f58197b67e3ffc501f1333d993fa \
+    --hash=sha256:6e504f7b16118198f138ef31ba24d985b124c2c469fe8467007cf30fd992f934 \
+    --hash=sha256:74623f2ab5cc3f7c886add4f735d1031a1d2be4a4ae63c0546cfd74e7a31ddf6 \
+    --hash=sha256:77f0d13fa87036d7553bf81f0e1fe3ce68d14c9976c9851744e4d3e91127e95f \
+    --hash=sha256:85eef4cb5625c47ee6425c58a3502555e10f45ee973da878ac8248ad58c136f3 \
+    --hash=sha256:8cfa5f29a695cb7438965e6c3e8d06e0416060cf0d709c1b1c1653a939bf5c2a \
+    --hash=sha256:9198f447e1dc5647d07c9a6bbe2063cc0132728cc7175b39dbc796da5b54920d \
+    --hash=sha256:92190db305a6f48734d3982f2c60fa30d6b5ee9bff10f2887b930d7b40119f4c \
+    --hash=sha256:98e81648e0b36e325ab67e46b5400a7a6d4a22b8a7c8e8bbfe20e7db7906bf95 \
+    --hash=sha256:9a818668b674047fd88c4cddada7ab8f1c298812783e8328e956b78dc4807f9f \
+    --hash=sha256:a152d86a3ae00ba5f47b3acf3b827509fd0b6cb7d3259665e63dafbad22a75ea \
+    --hash=sha256:a899699294f28f7be8992853c0c60741f16ff199205e2e6cdca155762cbaa59d \
+    --hash=sha256:b1f5b45829ac1848893f0ddf5cb326110604d6df96cdc255b0bf9edd154104d4 \
+    --hash=sha256:b48e35f4ab6f6a7597c46e301126ceba4c44cd3280e3750f85db48b082624fa4 \
+    --hash=sha256:ba0cb30acd3ef11c94dc27fbfba68940652492bc107075e7ffe23057f9425681 \
+    --hash=sha256:bb50ce5fb202a26fd5404620e7ef820ad1ab3558b444cb0b55beb7ef66cd2d63 \
+    --hash=sha256:c95eb6db2884917d86cde0b4d4cf31adf485c8ec36bf8696dd66fa70de96f36b \
+    --hash=sha256:d0ffd9e2e4441c96a9c91ec1783285d80bf835b677853fc2770a89d50c1e48ac \
+    --hash=sha256:d57b5046c120561ba8fa8e4030fbb8b822f3063910fa901ffadf16e2b7128ad6 \
+    --hash=sha256:d9e6a7664ddd9746e20b7325351fe1a8408d0a2bf9c63b5e898290ddc8f09544 \
+    --hash=sha256:dc578891de1db95b2a35001b695451767b580bb45753717498213c5ff3c41d63 \
+    --hash=sha256:e0434aa22c821f44eeb4c650b81c7fbdd8c0122c6c4b5a576a76d5a35625ecd9 \
+    --hash=sha256:e493962256a38f58283de033d8af176c5c91c084ea30f15834f7545451c42059 \
+    --hash=sha256:ecb0019d44f4cdb50b676c5d0cb4b1eae8e15d1ed3d3e6639f986fc92b2ec52c \
+    --hash=sha256:f935c4493eda9069851058fa0d9e39dbf6286be690066509305e52912714dbb2
+    # via
+    #   cell-browser
+    #   cmasher
+    #   colorspacious
+    #   contourpy
+    #   datasets
+    #   h5py
+    #   ipympl
+    #   keras
+    #   matplotlib
+    #   ml-dtypes
+    #   numcodecs
+    #   pandas
+    #   scipy
+    #   tensorboard
+    #   tensorflow
+    #   tifffile
+    #   zarr
+opt-einsum==3.4.0 \
+    --hash=sha256:69bb92469f86a1565195ece4ac0323943e83477171b91d24c35afe028a90d7cd \
+    --hash=sha256:96ca72f1b886d148241348783498194c577fa30a8faac108586b14f1ba4473ac
+    # via tensorflow
+optree==0.18.0 \
+    --hash=sha256:02d9999840fabef85a6b22e757f336d5591f712f99c710d8b232d52e53115314 \
+    --hash=sha256:057b983a9526645133553184bed7090bb07855df986abd9e99c456922045c6bc \
+    --hash=sha256:07c5f64783ad0f0f80e61c25f276ce79b47deda83ed7956a4a9af6385fe8f60d \
+    --hash=sha256:0959bac58631e64e2ac6349cc284b37872c24f353b3d73b4682202a431f07d76 \
+    --hash=sha256:0d25941de1acba176305dbdeb931dea6143b30d64ebdc5bfea2bfc12ef9e2b0a \
+    --hash=sha256:1b75e083137f361377ff8d70df885ab3a1cf8980e4019e3f311237579adadb64 \
+    --hash=sha256:1db0a6497203a13063a8f044ae751dd5d8253cb815359270c38de0e4c9f8bed5 \
+    --hash=sha256:1f674e34202383f8b42fa9335f13bedfb6b6f019c66e1f41034929e4be203423 \
+    --hash=sha256:20536964ba2458f166c1e8ab25951e3fc0a5056b651bd08f16be99bb3ffed54a \
+    --hash=sha256:27611c6c122745a003b5be7aedba49ef86e9fef46d743c234596de0bde6dc679 \
+    --hash=sha256:27b1d0cadcf4627c98abbbdce912dbc2243f5687f3c7df39963b793c89321c65 \
+    --hash=sha256:289b184cc41dfc400a30db6207ec997884d14540aae2cba10cb88dc7ebaae2a1 \
+    --hash=sha256:2b5cfb5fc643f16d3a7d957807e55a937dce07566c49ccc4aa71b01064c56758 \
+    --hash=sha256:3014537ff7e4e091ee46e57976f7d95c52f66a0e3eb5ebcbe0de0d924504b58e \
+    --hash=sha256:30a2636279bdc805c8e154a0f346bcf704626b831ff44724d305fb72c90b7389 \
+    --hash=sha256:328857d7a35129904b21164f6b0c2ff1d728ad1f5838589c5f437a16c94213c8 \
+    --hash=sha256:3804fb6ddc923855db2dc4805b4524c66e00f1ef30b166be4aadd52822b13e06 \
+    --hash=sha256:385bd727cc7bd3c01bd6204028ac2adce8a8f622c296053d9df434aa0e30b01f \
+    --hash=sha256:446c46c53cb8f13abcc0d7dd1989d59bb059953c122fe9901ef53de7fb38b33e \
+    --hash=sha256:4eb146711d4cd0876bf93e0118d3e74050b6f633d756c269ce7cda907281b499 \
+    --hash=sha256:51e2cd9ac7fecfd5f6f56ce69f4f805553c226a2744810175959eb408101513c \
+    --hash=sha256:571b732229d7b2e7a2215f57586f8ec0140e07c0faea916e456cbbfa819e56cb \
+    --hash=sha256:5b126c34b459ef4f10f3a4d7d222416d9102b3c5a76b39f346c611792f144821 \
+    --hash=sha256:5b75e32c191e4b8cf42a8aa854ed264df82936136c0bcad77be44605da41cdfc \
+    --hash=sha256:5bc1221068a58175e0ad62afc199893f77c653206673a5552992a604c66fb77e \
+    --hash=sha256:5e669f98b9af9f66144c7ae09912d0367ac3182abe016f67cdd15cb45e13c923 \
+    --hash=sha256:6fc9f8acde3bb561b2034e96079507fbe6d4624058fe204161eb8ef29f961296 \
+    --hash=sha256:7172b16e87c87160475275e4bfaa6e4067ccde184d2cca65ba25a402a8ed7758 \
+    --hash=sha256:71ca2fcad8972ba56d6cfffbcd962f45f5d4bc04182f23d66154b38c2eb37de3 \
+    --hash=sha256:79bbe14d6cad81f5840958589daa1b836864ada40031712a446dce8129917efd \
+    --hash=sha256:7ae6945f68771b1389ee46a1778e779f4ad76bca9306f3e39eb397f9a0dd2753 \
+    --hash=sha256:80d971060c888c3989132b7e75dfb50848636d41bc931af1b93fe2019fba469c \
+    --hash=sha256:81e755124b77e766166c9d05206b90c68f234f425ad2e3c8a6c96f0db548c67b \
+    --hash=sha256:895f23a4cd8aee2c2464efdad2d9bde28a2aaabee634c96423a933f40e74a67e \
+    --hash=sha256:89d5156f8a0a3792701e1c31473eb307f0b45696f48dc51d721f1bfe0c3a950f \
+    --hash=sha256:89e81afb11792d13d3777b503c6f21ec17b1a3b7de69cde1ae2c5471bcdcd4a0 \
+    --hash=sha256:8a2003fab79694e04b5f260628511e441c248b46a9fc46138e2424038ac04ada \
+    --hash=sha256:8a4ca121b6fc6b04300fa225fe6c31897e424db0d92691875af326f8c4e1cead \
+    --hash=sha256:8b9ad4a01a1346b11acc574b7f932dea1a7c7ab31d93546a7540a1f02b3e724a \
+    --hash=sha256:8d88c00c70b5914904feaf8f505f3512c2f3f4493dbbd93951fcdddc85dcfe8c \
+    --hash=sha256:9104fc8915890e7292e5833fc677e4749607c67aa3cf8884677267078201c2f3 \
+    --hash=sha256:9460cba62e941626beb75c99a803373b38a52136d5f1932fcdfdcede1df6f2ef \
+    --hash=sha256:94983b3aa31ee401d2ac77ba570a3157d83f9508cfbb006095a48770e0a1c5ca \
+    --hash=sha256:9d4b9d8c7e9335120ecf222d817699d17de743ad118080fb40467c367f009143 \
+    --hash=sha256:a479fa25b6e2430e530d00f0c27a55e15ecb9de8ad2d0aec3d40b680e2d6df64 \
+    --hash=sha256:a5c213a291c798139ed9ff80aec4bfcd2ac8f001bc015a9cdeb78457e9687dd3 \
+    --hash=sha256:a63df296fec376c5cd08298a85109db4a130f4cc8df15916fc92d44ef6068937 \
+    --hash=sha256:a74c45f04def041504bd21682eaf7f359f1a50dc7cf42b548b6f19aab50596bd \
+    --hash=sha256:b4da3223c5b4cf694822752d0fbb6bf34c3f41648af1bd1b443cc3d68cc55106 \
+    --hash=sha256:b8adc912ecb6e4fd9df227ded66efaa6702f46a98e1403554be3c9c51d0ca920 \
+    --hash=sha256:c017539e1196ea08f20aea3a4c473f758149b851678edd3d15773b4326decf83 \
+    --hash=sha256:c1f20e8754abe312a701ee00d071ddd8502e9d97ca38fbc56204d14a9ffcb41c \
+    --hash=sha256:c8841d44f3648b0662e99fc39ef8c248726ddfb4d1bfce4bdba982e51bb7e3f8 \
+    --hash=sha256:cbb083a15ea968ad99e7da17d24632348d69e26534e83c69941f3020ed7536eb \
+    --hash=sha256:cde70c97e4cc4e997e8fda2266e40a9bff7679c72ab4af6e15e81748a12882cc \
+    --hash=sha256:cfa2e16993ba47e671a4e7ee1ad805f67b8d6744eb30a9d27ea0b07b3b7a22ed \
+    --hash=sha256:d2844478690b5892159df0b2500e9d146dc8d3aa5b44e4564d05787b7330eca3 \
+    --hash=sha256:d569730b2647c51a5ee68d67198aa9a78c7a55563d57b8cc1ca8d8c8377e7621 \
+    --hash=sha256:db00c604c1ae452f6092293bf230984d4f6cbb3ad905a9991e8cf680fd7d1523 \
+    --hash=sha256:e28024e6e343353285cf99ae9c74210f0e89e47b2f0f3af7c72c4a9e89dc3ebc \
+    --hash=sha256:e4a468ae1541614b5aa7b4f00254bce005ab7572fbb1fc764af4ee17d90fde7b \
+    --hash=sha256:f02faeda66d531dc5f5356589afcf2a6bc41c8d00bc903efab60f9a2182b140d \
+    --hash=sha256:f5197f864630162f008f5dfad3fceef32553c0fa7639eee1b8e280d924ed678e \
+    --hash=sha256:fa8e3878a1857761d64f08a23b32140d29754a53f85f7c87186ced2b5b1b49cb
+    # via keras
+packaging==25.0 \
+    --hash=sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 \
+    --hash=sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f
+    # via
+    #   datasets
+    #   huggingface-hub
+    #   ipykernel
+    #   jupyter-events
+    #   jupyter-server
+    #   jupyterlab
+    #   jupyterlab-server
+    #   keras
+    #   matplotlib
+    #   nbconvert
+    #   tensorboard
+    #   tensorflow
+pandas==2.3.3 \
+    --hash=sha256:0242fe9a49aa8b4d78a4fa03acb397a58833ef6199e9aa40a95f027bb3a1b6e7 \
+    --hash=sha256:1611aedd912e1ff81ff41c745822980c49ce4a7907537be8692c8dbc31924593 \
+    --hash=sha256:1b07204a219b3b7350abaae088f451860223a52cfb8a6c53358e7948735158e5 \
+    --hash=sha256:2462b1a365b6109d275250baaae7b760fd25c726aaca0054649286bcfbb3e8ec \
+    --hash=sha256:2e3ebdb170b5ef78f19bfb71b0dc5dc58775032361fa188e814959b74d726dd5 \
+    --hash=sha256:318d77e0e42a628c04dc56bcef4b40de67918f7041c2b061af1da41dcff670ac \
+    --hash=sha256:371a4ab48e950033bcf52b6527eccb564f52dc826c02afd9a1bc0ab731bba084 \
+    --hash=sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87 \
+    --hash=sha256:3fd2f887589c7aa868e02632612ba39acb0b8948faf5cc58f0850e165bd46f35 \
+    --hash=sha256:4e0a175408804d566144e170d0476b15d78458795bb18f1304fb94160cabf40c \
+    --hash=sha256:56851a737e3470de7fa88e6131f41281ed440d29a9268dcbf0002da5ac366713 \
+    --hash=sha256:6253c72c6a1d990a410bc7de641d34053364ef8bcd3126f7e7450125887dffe3 \
+    --hash=sha256:6435cb949cb34ec11cc9860246ccb2fdc9ecd742c12d3304989017d53f039a78 \
+    --hash=sha256:6d21f6d74eb1725c2efaa71a2bfc661a0689579b58e9c0ca58a739ff0b002b53 \
+    --hash=sha256:6d2cefc361461662ac48810cb14365a365ce864afe85ef1f447ff5a1e99ea81c \
+    --hash=sha256:74ecdf1d301e812db96a465a525952f4dde225fdb6d8e5a521d47e1f42041e21 \
+    --hash=sha256:75ea25f9529fdec2d2e93a42c523962261e567d250b0013b16210e1d40d7c2e5 \
+    --hash=sha256:900f47d8f20860de523a1ac881c4c36d65efcb2eb850e6948140fa781736e110 \
+    --hash=sha256:93c2d9ab0fc11822b5eece72ec9587e172f63cff87c00b062f6e37448ced4493 \
+    --hash=sha256:a16dcec078a01eeef8ee61bf64074b4e524a2a3f4b3be9326420cabe59c4778b \
+    --hash=sha256:a21d830e78df0a515db2b3d2f5570610f5e6bd2e27749770e8bb7b524b89b450 \
+    --hash=sha256:a45c765238e2ed7d7c608fc5bc4a6f88b642f2f01e70c0c23d2224dd21829d86 \
+    --hash=sha256:a68e15f780eddf2b07d242e17a04aa187a7ee12b40b930bfdd78070556550e98 \
+    --hash=sha256:b3d11d2fda7eb164ef27ffc14b4fcab16a80e1ce67e9f57e19ec0afaf715ba89 \
+    --hash=sha256:bdcd9d1167f4885211e401b3036c0c8d9e274eee67ea8d0758a256d60704cfe8 \
+    --hash=sha256:c46467899aaa4da076d5abc11084634e2d197e9460643dd455ac3db5856b24d6 \
+    --hash=sha256:c4fc4c21971a1a9f4bdb4c73978c7f7256caa3e62b323f70d6cb80db583350bc \
+    --hash=sha256:d051c0e065b94b7a3cea50eb1ec32e912cd96dba41647eb24104b6c6c14c5788 \
+    --hash=sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b \
+    --hash=sha256:e32e7cc9af0f1cc15548288a51a3b681cc2a219faa838e995f7dc53dbab1062d \
+    --hash=sha256:ecaf1e12bdc03c86ad4a7ea848d66c685cb6851d807a26aa245ca3d2017a1908 \
+    --hash=sha256:ee15f284898e7b246df8087fc82b87b01686f98ee67d85a17b7ab44143a3a9a0 \
+    --hash=sha256:ee67acbbf05014ea6c763beb097e03cd629961c8a632075eeb34247120abcb4b \
+    --hash=sha256:f8bfc0e12dc78f777f323f55c58649591b2cd0c43534e8355c51d3fede5f4dee
+    # via datasets
+pandocfilters==1.5.1 \
+    --hash=sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e \
+    --hash=sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc
+    # via nbconvert
+parso==0.8.5 \
+    --hash=sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a \
+    --hash=sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887
+    # via jedi
+pexpect==4.9.0 ; sys_platform != 'emscripten' and sys_platform != 'win32' \
+    --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \
+    --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f
+    # via ipython
+pillow==12.1.0 \
+    --hash=sha256:00162e9ca6d22b7c3ee8e61faa3c3253cd19b6a37f126cad04f2f88b306f557d \
+    --hash=sha256:079af2fb0c599c2ec144ba2c02766d1b55498e373b3ac64687e43849fbbef5bc \
+    --hash=sha256:0c27407a2d1b96774cbc4a7594129cc027339fd800cd081e44497722ea1179de \
+    --hash=sha256:0ddedfaa8b5f0b4ffbc2fa87b556dc59f6bb4ecb14a53b33f9189713ae8053c0 \
+    --hash=sha256:0deedf2ea233722476b3a81e8cdfbad786f7adbed5d848469fa59fe52396e4ef \
+    --hash=sha256:0ed07dca4a8464bada6139ab38f5382f83e5f111698caf3191cb8dbf27d908b4 \
+    --hash=sha256:0fde7ec5538ab5095cc02df38ee99b0443ff0e1c847a045554cf5f9af1f4aa82 \
+    --hash=sha256:15c794d74303828eaa957ff8070846d0efe8c630901a1c753fdc63850e19ecd9 \
+    --hash=sha256:1a949604f73eb07a8adab38c4fe50791f9919344398bdc8ac6b307f755fc7030 \
+    --hash=sha256:277518bf4fe74aa91489e1b20577473b19ee70fb97c374aa50830b279f25841b \
+    --hash=sha256:27b9baecb428899db6c0de572d6d305cfaf38ca1596b5c0542a5182e3e74e8c6 \
+    --hash=sha256:29a4cef9cb672363926f0470afc516dbf7305a14d8c54f7abbb5c199cd8f8179 \
+    --hash=sha256:3413c2ae377550f5487991d444428f1a8ae92784aac79caa8b1e3b89b175f77e \
+    --hash=sha256:414b9a78e14ffeb98128863314e62c3f24b8a86081066625700b7985b3f529bd \
+    --hash=sha256:43aca0a55ce1eefc0aefa6253661cb54571857b1a7b2964bd8a1e3ef4b729924 \
+    --hash=sha256:43b4899cfd091a9693a1278c4982f3e50f7fb7cff5153b05174b4afc9593b616 \
+    --hash=sha256:461f9dfdafa394c59cd6d818bdfdbab4028b83b02caadaff0ffd433faf4c9a7a \
+    --hash=sha256:4f9f6a650743f0ddee5593ac9e954ba1bdbc5e150bc066586d4f26127853ab94 \
+    --hash=sha256:565c986f4b45c020f5421a4cea13ef294dde9509a8577f29b2fc5edc7587fff8 \
+    --hash=sha256:5c5ae0a06e9ea030ab786b0251b32c7e4ce10e58d983c0d5c56029455180b5b9 \
+    --hash=sha256:5cb7bc1966d031aec37ddb9dcf15c2da5b2e9f7cc3ca7c54473a20a927e1eb91 \
+    --hash=sha256:5fee4c04aad8932da9f8f710af2c1a15a83582cfb884152a9caa79d4efcdbf9c \
+    --hash=sha256:609e89d9f90b581c8d16358c9087df76024cf058fa693dd3e1e1620823f39670 \
+    --hash=sha256:6258f3260986990ba2fa8a874f8b6e808cf5abb51a94015ca3dc3c68aa4f30ea \
+    --hash=sha256:65b80c1ee7e14a87d6a068dd3b0aea268ffcabfe0498d38661b00c5b4b22e74c \
+    --hash=sha256:6741e6f3074a35e47c77b23a4e4f2d90db3ed905cb1c5e6e0d49bff2045632bc \
+    --hash=sha256:681088909d7e8fa9e31b9799aaa59ba5234c58e5e4f1951b4c4d1082a2e980e0 \
+    --hash=sha256:6b7a9d1db5dad90e2991645874f708e87d9a3c370c243c2d7684d28f7e133e6b \
+    --hash=sha256:7315f9137087c4e0ee73a761b163fc9aa3b19f5f606a7fc08d83fd3e4379af65 \
+    --hash=sha256:742aea052cf5ab5034a53c3846165bc3ce88d7c38e954120db0ab867ca242661 \
+    --hash=sha256:7b5dd7cbae20285cdb597b10eb5a2c13aa9de6cde9bb64a3c1317427b1db1ae1 \
+    --hash=sha256:7d6daa89a00b58c37cb1747ec9fb7ac3bc5ffd5949f5888657dfddde6d1312e0 \
+    --hash=sha256:808b99604f7873c800c4840f55ff389936ef1948e4e87645eaf3fccbc8477ac4 \
+    --hash=sha256:80941e6d573197a0c28f394753de529bb436b1ca990ed6e765cf42426abc39f8 \
+    --hash=sha256:896866d2d436563fa2a43a9d72f417874f16b5545955c54a64941e87c1376c61 \
+    --hash=sha256:8e178e3e99d3c0ea8fc64b88447f7cac8ccf058af422a6cedc690d0eadd98c51 \
+    --hash=sha256:907bfa8a9cb790748a9aa4513e37c88c59660da3bcfffbd24a7d9e6abf224551 \
+    --hash=sha256:9212d6b86917a2300669511ed094a9406888362e085f2431a7da985a6b124f45 \
+    --hash=sha256:935b9d1aed48fcfb3f838caac506f38e29621b44ccc4f8a64d575cb1b2a88644 \
+    --hash=sha256:97e9993d5ed946aba26baf9c1e8cf18adbab584b99f452ee72f7ee8acb882796 \
+    --hash=sha256:983976c2ab753166dc66d36af6e8ec15bb511e4a25856e2227e5f7e00a160587 \
+    --hash=sha256:a332ac4ccb84b6dde65dbace8431f3af08874bf9770719d32a635c4ef411b18b \
+    --hash=sha256:a6dfc2af5b082b635af6e08e0d1f9f1c4e04d17d4e2ca0ef96131e85eda6eb17 \
+    --hash=sha256:a786bf667724d84aa29b5db1c61b7bfdde380202aaca12c3461afd6b71743171 \
+    --hash=sha256:aa0c9cc0b82b14766a99fbe6084409972266e82f459821cd26997a488a7261a7 \
+    --hash=sha256:b17fbdbe01c196e7e159aacb889e091f28e61020a8abeac07b68079b6e626988 \
+    --hash=sha256:b63e13dd27da389ed9475b3d28510f0f954bca0041e8e551b2a4eb1eab56a39a \
+    --hash=sha256:bc11908616c8a283cf7d664f77411a5ed2a02009b0097ff8abbba5e79128ccf2 \
+    --hash=sha256:bdec5e43377761c5dbca620efb69a77f6855c5a379e32ac5b158f54c84212b14 \
+    --hash=sha256:bef9768cab184e7ae6e559c032e95ba8d07b3023c289f79a2bd36e8bf85605a5 \
+    --hash=sha256:c990547452ee2800d8506c4150280757f88532f3de2a58e3022e9b179107862a \
+    --hash=sha256:d0a7735df32ccbcc98b98a1ac785cc4b19b580be1bdf0aeb5c03223220ea09d5 \
+    --hash=sha256:d70534cea9e7966169ad29a903b99fc507e932069a881d0965a1a84bb57f6c6d \
+    --hash=sha256:db44d5c160a90df2d24a24760bbd37607d53da0b34fb546c4c232af7192298ac \
+    --hash=sha256:e115c15e3bc727b1ca3e641a909f77f8ca72a64fff150f666fcc85e57701c26c \
+    --hash=sha256:e2479c7f02f9d505682dc47df8c0ea1fc5e264c4d1629a5d63fe3e2334b89554 \
+    --hash=sha256:e5dcbe95016e88437ecf33544ba5db21ef1b8dd6e1b434a2cb2a3d605299e643 \
+    --hash=sha256:e6bdb408f7c9dd2a5ff2b14a3b0bb6d4deb29fb9961e6eb3ae2031ae9a5cec13 \
+    --hash=sha256:efdc140e7b63b8f739d09a99033aa430accce485ff78e6d311973a67b6bf3208 \
+    --hash=sha256:f188d580bd870cda1e15183790d1cc2fa78f666e76077d103edf048eed9c356e \
+    --hash=sha256:f45bd71d1fa5e5749587613037b172e0b3b23159d1c00ef2fc920da6f470e6f0 \
+    --hash=sha256:f61333d817698bdcdd0f9d7793e365ac3d2a21c1f1eb02b32ad6aefb8d8ea831
+    # via
+    #   cell-browser
+    #   ipympl
+    #   matplotlib
+    #   tensorboard
+platformdirs==4.5.1 \
+    --hash=sha256:61d5cdcc6065745cdd94f0f878977f8de9437be93de97c1c12f853c9c0cdcbda \
+    --hash=sha256:d03afa3963c806a9bed9d5125c8f4cb2fdaf74a55ab60e5d59b3fde758104d31
+    # via jupyter-core
+prometheus-client==0.23.1 \
+    --hash=sha256:6ae8f9081eaaaf153a2e959d2e6c4f4fb57b12ef76c8c7980202f1e57b48b2ce \
+    --hash=sha256:dd1913e6e76b59cfe44e7a4b83e01afc9873c1bdfd2ed8739f1e76aeca115f99
+    # via jupyter-server
+prompt-toolkit==3.0.52 \
+    --hash=sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855 \
+    --hash=sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955
+    # via ipython
+propcache==0.4.1 \
+    --hash=sha256:0013cb6f8dde4b2a2f66903b8ba740bdfe378c943c4377a200551ceb27f379e4 \
+    --hash=sha256:005f08e6a0529984491e37d8dbc3dd86f84bd78a8ceb5fa9a021f4c48d4984be \
+    --hash=sha256:031dce78b9dc099f4c29785d9cf5577a3faf9ebf74ecbd3c856a7b92768c3df3 \
+    --hash=sha256:05674a162469f31358c30bcaa8883cb7829fa3110bf9c0991fe27d7896c42d85 \
+    --hash=sha256:060b16ae65bc098da7f6d25bf359f1f31f688384858204fe5d652979e0015e5b \
+    --hash=sha256:120c964da3fdc75e3731aa392527136d4ad35868cc556fd09bb6d09172d9a367 \
+    --hash=sha256:15932ab57837c3368b024473a525e25d316d8353016e7cc0e5ba9eb343fbb1cf \
+    --hash=sha256:17612831fda0138059cc5546f4d12a2aacfb9e47068c06af35c400ba58ba7393 \
+    --hash=sha256:204483131fb222bdaaeeea9f9e6c6ed0cac32731f75dfc1d4a567fc1926477c1 \
+    --hash=sha256:2ad890caa1d928c7c2965b48f3a3815c853180831d0e5503d35cf00c472f4717 \
+    --hash=sha256:333ddb9031d2704a301ee3e506dc46b1fe5f294ec198ed6435ad5b6a085facfe \
+    --hash=sha256:35c3277624a080cc6ec6f847cbbbb5b49affa3598c4535a0a4682a697aaa5c75 \
+    --hash=sha256:381914df18634f5494334d201e98245c0596067504b9372d8cf93f4bb23e025e \
+    --hash=sha256:3f7124c9d820ba5548d431afb4632301acf965db49e666aa21c305cbe8c6de12 \
+    --hash=sha256:405aac25c6394ef275dee4c709be43745d36674b223ba4eb7144bf4d691b7367 \
+    --hash=sha256:41a89040cb10bd345b3c1a873b2bf36413d48da1def52f268a055f7398514874 \
+    --hash=sha256:43eedf29202c08550aac1d14e0ee619b0430aaef78f85864c1a892294fbc28cf \
+    --hash=sha256:473c61b39e1460d386479b9b2f337da492042447c9b685f28be4f74d3529e566 \
+    --hash=sha256:49a2dc67c154db2c1463013594c458881a069fcf98940e61a0569016a583020a \
+    --hash=sha256:4c3c70630930447f9ef1caac7728c8ad1c56bc5015338b20fed0d08ea2480b3a \
+    --hash=sha256:4d3df5fa7e36b3225954fba85589da77a0fe6a53e3976de39caf04a0db4c36f1 \
+    --hash=sha256:4d7af63f9f93fe593afbf104c21b3b15868efb2c21d07d8732c0c4287e66b6a6 \
+    --hash=sha256:501d20b891688eb8e7aa903021f0b72d5a55db40ffaab27edefd1027caaafa61 \
+    --hash=sha256:521a463429ef54143092c11a77e04056dd00636f72e8c45b70aaa3140d639726 \
+    --hash=sha256:5558992a00dfd54ccbc64a32726a3357ec93825a418a401f5cc67df0ac5d9e49 \
+    --hash=sha256:55c72fd6ea2da4c318e74ffdf93c4fe4e926051133657459131a95c846d16d44 \
+    --hash=sha256:564d9f0d4d9509e1a870c920a89b2fec951b44bf5ba7d537a9e7c1ccec2c18af \
+    --hash=sha256:580e97762b950f993ae618e167e7be9256b8353c2dcd8b99ec100eb50f5286aa \
+    --hash=sha256:5a103c3eb905fcea0ab98be99c3a9a5ab2de60228aa5aceedc614c0281cf6153 \
+    --hash=sha256:5c3310452e0d31390da9035c348633b43d7e7feb2e37be252be6da45abd1abcc \
+    --hash=sha256:671538c2262dadb5ba6395e26c1731e1d52534bfe9ae56d0b5573ce539266aa8 \
+    --hash=sha256:678ae89ebc632c5c204c794f8dab2837c5f159aeb59e6ed0539500400577298c \
+    --hash=sha256:6f6ff873ed40292cd4969ef5310179afd5db59fdf055897e282485043fc80ad0 \
+    --hash=sha256:6f8b465489f927b0df505cbe26ffbeed4d6d8a2bbc61ce90eb074ff129ef0ab1 \
+    --hash=sha256:74c1fb26515153e482e00177a1ad654721bf9207da8a494a0c05e797ad27b992 \
+    --hash=sha256:824e908bce90fb2743bd6b59db36eb4f45cd350a39637c9f73b1c1ea66f5b75f \
+    --hash=sha256:8326e144341460402713f91df60ade3c999d601e7eb5ff8f6f7862d54de0610d \
+    --hash=sha256:8873eb4460fd55333ea49b7d189749ecf6e55bf85080f11b1c4530ed3034cba1 \
+    --hash=sha256:89eb3fa9524f7bec9de6e83cf3faed9d79bffa560672c118a96a171a6f55831e \
+    --hash=sha256:8e57061305815dfc910a3634dcf584f08168a8836e6999983569f51a8544cd89 \
+    --hash=sha256:929d7cbe1f01bb7baffb33dc14eb5691c95831450a26354cd210a8155170c93a \
+    --hash=sha256:92d1935ee1f8d7442da9c0c4fa7ac20d07e94064184811b685f5c4fada64553b \
+    --hash=sha256:990f6b3e2a27d683cb7602ed6c86f15ee6b43b1194736f9baaeb93d0016633b1 \
+    --hash=sha256:9a0bd56e5b100aef69bd8562b74b46254e7c8812918d3baa700c8a8009b0af66 \
+    --hash=sha256:9f302f4783709a78240ebc311b793f123328716a60911d667e0c036bc5dcbded \
+    --hash=sha256:a78372c932c90ee474559c5ddfffd718238e8673c340dc21fe45c5b8b54559a0 \
+    --hash=sha256:ab08df6c9a035bee56e31af99be621526bd237bea9f32def431c656b29e41778 \
+    --hash=sha256:ab2943be7c652f09638800905ee1bab2c544e537edb57d527997a24c13dc1455 \
+    --hash=sha256:ab4c29b49d560fe48b696cdcb127dd36e0bc2472548f3bf56cc5cb3da2b2984f \
+    --hash=sha256:af223b406d6d000830c6f65f1e6431783fc3f713ba3e6cc8c024d5ee96170a4b \
+    --hash=sha256:af2a6052aeb6cf17d3e46ee169099044fd8224cbaf75c76a2ef596e8163e2237 \
+    --hash=sha256:bcc9aaa5d80322bc2fb24bb7accb4a30f81e90ab8d6ba187aec0744bc302ad81 \
+    --hash=sha256:c07fda85708bc48578467e85099645167a955ba093be0a2dcba962195676e859 \
+    --hash=sha256:c0d4b719b7da33599dfe3b22d3db1ef789210a0597bc650b7cee9c77c2be8c5c \
+    --hash=sha256:c0ef0aaafc66fbd87842a3fe3902fd889825646bc21149eafe47be6072725835 \
+    --hash=sha256:c2b5e7db5328427c57c8e8831abda175421b709672f6cfc3d630c3b7e2146393 \
+    --hash=sha256:c80ee5802e3fb9ea37938e7eecc307fb984837091d5fd262bb37238b1ae97641 \
+    --hash=sha256:c9b822a577f560fbd9554812526831712c1436d2c046cedee4c3796d3543b144 \
+    --hash=sha256:cae65ad55793da34db5f54e4029b89d3b9b9490d8abe1b4c7ab5d4b8ec7ebf74 \
+    --hash=sha256:cb2d222e72399fcf5890d1d5cc1060857b9b236adff2792ff48ca2dfd46c81db \
+    --hash=sha256:cd547953428f7abb73c5ad82cbb32109566204260d98e41e5dfdc682eb7f8403 \
+    --hash=sha256:cfc27c945f422e8b5071b6e93169679e4eb5bf73bbcbf1ba3ae3a83d2f78ebd9 \
+    --hash=sha256:d472aeb4fbf9865e0c6d622d7f4d54a4e101a89715d8904282bb5f9a2f476c3f \
+    --hash=sha256:d62cdfcfd89ccb8de04e0eda998535c406bf5e060ffd56be6c586cbcc05b3311 \
+    --hash=sha256:d8f353eb14ee3441ee844ade4277d560cdd68288838673273b978e3d6d2c8f36 \
+    --hash=sha256:dee69d7015dc235f526fe80a9c90d65eb0039103fe565776250881731f06349f \
+    --hash=sha256:e153e9cd40cc8945138822807139367f256f89c6810c2634a4f6902b52d3b4e2 \
+    --hash=sha256:e35b88984e7fa64aacecea39236cee32dd9bd8c55f57ba8a75cf2399553f9bd7 \
+    --hash=sha256:ec17c65562a827bba85e3872ead335f95405ea1674860d96483a02f5c698fa72 \
+    --hash=sha256:ecef2343af4cc68e05131e45024ba34f6095821988a9d0a02aa7c73fcc448aa9 \
+    --hash=sha256:ed5a841e8bb29a55fb8159ed526b26adc5bdd7e8bd7bf793ce647cb08656cdf4 \
+    --hash=sha256:ee17f18d2498f2673e432faaa71698032b0127ebf23ae5974eeaf806c279df24 \
+    --hash=sha256:f048da1b4f243fc44f205dfd320933a951b8d89e0afd4c7cacc762a8b9165207 \
+    --hash=sha256:f48107a8c637e80362555f37ecf49abe20370e557cc4ab374f04ec4423c97c3d \
+    --hash=sha256:f7ee0e597f495cf415bcbd3da3caa3bd7e816b74d0d52b8145954c5e6fd3ff37 \
+    --hash=sha256:f95393b4d66bfae908c3ca8d169d5f79cd65636ae15b5e7a4f6e67af675adb0e \
+    --hash=sha256:fd0858c20f078a32cf55f7e81473d96dcf3b93fd2ccdb3d40fdf54b8573df3af
+    # via
+    #   aiohttp
+    #   yarl
+protobuf==6.33.2 \
+    --hash=sha256:1f8017c48c07ec5859106533b682260ba3d7c5567b1ca1f24297ce03384d1b4f \
+    --hash=sha256:56dc370c91fbb8ac85bc13582c9e373569668a290aa2e66a590c2a0d35ddb9e4 \
+    --hash=sha256:7636aad9bb01768870266de5dc009de2d1b936771b38a793f73cbbf279c91c5c \
+    --hash=sha256:87eb388bd2d0f78febd8f4c8779c79247b26a5befad525008e49a6955787ff3d \
+    --hash=sha256:8cd7640aee0b7828b6d03ae518b5b4806fdfc1afe8de82f79c3454f8aef29872 \
+    --hash=sha256:b5d3b5625192214066d99b2b605f5783483575656784de223f00a8d00754fc0e \
+    --hash=sha256:d9b19771ca75935b3a4422957bc518b0cecb978b31d1dd12037b088f6bcc0e43 \
+    --hash=sha256:fc2a0e8b05b180e5fc0dd1559fe8ebdae21a27e81ac77728fb6c42b12c7419b4
+    # via
+    #   tensorboard
+    #   tensorflow
+psutil==7.2.1 \
+    --hash=sha256:05cc68dbb8c174828624062e73078e7e35406f4ca2d0866c272c2410d8ef06d1 \
+    --hash=sha256:08a2f175e48a898c8eb8eace45ce01777f4785bc744c90aa2cc7f2fa5462a266 \
+    --hash=sha256:0d67c1822c355aa6f7314d92018fb4268a76668a536f133599b91edd48759442 \
+    --hash=sha256:2ceae842a78d1603753561132d5ad1b2f8a7979cb0c283f5b52fb4e6e14b1a79 \
+    --hash=sha256:35630d5af80d5d0d49cfc4d64c1c13838baf6717a13effb35869a5919b854cdf \
+    --hash=sha256:3fce5f92c22b00cdefd1645aa58ab4877a01679e901555067b1bd77039aa589f \
+    --hash=sha256:494c513ccc53225ae23eec7fe6e1482f1b8a44674241b54561f755a898650679 \
+    --hash=sha256:5e38404ca2bb30ed7267a46c02f06ff842e92da3bb8c5bfdadbd35a5722314d8 \
+    --hash=sha256:81442dac7abfc2f4f4385ea9e12ddf5a796721c0f6133260687fec5c3780fa49 \
+    --hash=sha256:923f8653416604e356073e6e0bccbe7c09990acef442def2f5640dd0faa9689f \
+    --hash=sha256:93f3f7b0bb07711b49626e7940d6fe52aa9940ad86e8f7e74842e73189712129 \
+    --hash=sha256:99a4cd17a5fdd1f3d014396502daa70b5ec21bf4ffe38393e152f8e449757d67 \
+    --hash=sha256:ab2b98c9fc19f13f59628d94df5cc4cc4844bc572467d113a8b517d634e362c6 \
+    --hash=sha256:b1b0671619343aa71c20ff9767eced0483e4fc9e1f489d50923738caf6a03c17 \
+    --hash=sha256:b2e953fcfaedcfbc952b44744f22d16575d3aa78eb4f51ae74165b4e96e55f42 \
+    --hash=sha256:ba9f33bb525b14c3ea563b2fd521a84d2fa214ec59e3e6a2858f78d0844dd60d \
+    --hash=sha256:cfbe6b40ca48019a51827f20d830887b3107a74a79b01ceb8cc8de4ccb17b672 \
+    --hash=sha256:d34d2ca888208eea2b5c68186841336a7f5e0b990edec929be909353a202768a \
+    --hash=sha256:ea46c0d060491051d39f0d2cff4f98d5c72b288289f57a21556cc7d504db37fc \
+    --hash=sha256:f7583aec590485b43ca601dd9cea0dcd65bd7bb21d30ef4ddbf4ea6b5ed1bdd3 \
+    --hash=sha256:f78baafb38436d5a128f837fab2d92c276dfb48af01a240b861ae02b2413ada8
+    # via ipykernel
+psygnal==0.15.1 \
+    --hash=sha256:07b4c4e03bbf4e8cad7e25f4fbc1ba9575fb9c3d14991bc7edfeb8b09c8d6d54 \
+    --hash=sha256:0c85b7d05b92ccbec47c75ab8a5545eda462e81a492c82424aba5ab81a3ad89d \
+    --hash=sha256:0f3bf68ca42569dfdce20c6cf915d34b78b9e3ddddacb9f78728224fda6946b4 \
+    --hash=sha256:0febcf757a1323d9b8bd75735ee3569213d8110012a7bf0f478e85c5ab459fc6 \
+    --hash=sha256:1ac399566852fe4354ce26a1acbe12319232e8c2b615fe5ad1e114c547095cf6 \
+    --hash=sha256:2087aadc9404f007f79c2899e329932869e362c50de58b90631c5f49b4768cc5 \
+    --hash=sha256:4221140e633e45b076953c64bcb9b41a744833527f9a037c1ca98bc270798cbf \
+    --hash=sha256:4f0ce91b9c18e92281bf2c3fc4bb4e808d90f0b023d0a37b302d354188520338 \
+    --hash=sha256:4f53b4b83355b0a785b745987fd04e59bbf169a9028ed81a68ca7e05fb76d458 \
+    --hash=sha256:803fc33c4280c822c6f4b22e6c3ea7c4483e190f3cc69e69350098b3799476f3 \
+    --hash=sha256:8d56f0f35eaf4a21f660de76885222faf9e8c7112454528d3394d464f3d4d1a3 \
+    --hash=sha256:ac0e693b29e0a429e97315a52313321855bef6140e9975b7ae78b4d93c8fbb42 \
+    --hash=sha256:b5e4837dfbfa4974dabe0795e32be9aadcd87603adf734738ce1114f72238a05 \
+    --hash=sha256:bcbca12190f5aa65c1f8fb04a81fa6f4463c5f5dde25cd74c3a56ceff6f37b02 \
+    --hash=sha256:d3a03055f331ce91d44581c71edb79938ccc133a94af2ce7ad3a18fa57ac7be5 \
+    --hash=sha256:e9fca977f5335deea39aed22e31d9795983e4f243e59a7d3c4105793adb7693d \
+    --hash=sha256:f64f62dee2306fc1c22050a59b6c6cdad126e04b0cf50e393ff858a1da719096
+    # via anywidget
+ptyprocess==0.7.0 ; os_name != 'nt' or (sys_platform != 'emscripten' and sys_platform != 'win32') \
+    --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \
+    --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220
+    # via
+    #   pexpect
+    #   terminado
+pure-eval==0.2.3 \
+    --hash=sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0 \
+    --hash=sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42
+    # via stack-data
+pyarrow==22.0.0 \
+    --hash=sha256:001ea83a58024818826a9e3f89bf9310a114f7e26dfe404a4c32686f97bd7901 \
+    --hash=sha256:0c34fe18094686194f204a3b1787a27456897d8a2d62caf84b61e8dfbc0252ae \
+    --hash=sha256:12fe549c9b10ac98c91cf791d2945e878875d95508e1a5d14091a7aaa66d9cf8 \
+    --hash=sha256:252be4a05f9d9185bb8c18e83764ebcfea7185076c07a7a662253af3a8c07941 \
+    --hash=sha256:334f900ff08ce0423407af97e6c26ad5d4e3b0763645559ece6fbf3747d6a8f5 \
+    --hash=sha256:3d600dc583260d845c7d8a6db540339dd883081925da2bd1c5cb808f720b3cd9 \
+    --hash=sha256:3e739edd001b04f654b166204fc7a9de896cf6007eaff33409ee9e50ceaff754 \
+    --hash=sha256:44729980b6c50a5f2bfcc2668d36c569ce17f8b17bccaf470c4313dcbbf13c9d \
+    --hash=sha256:4c19236ae2402a8663a2c8f21f1870a03cc57f0bef7e4b6eb3238cc82944de80 \
+    --hash=sha256:6dda1ddac033d27421c20d7a7943eec60be44e0db4e079f33cc5af3b8280ccde \
+    --hash=sha256:6f9762274496c244d951c819348afbcf212714902742225f649cf02823a6a10f \
+    --hash=sha256:7388ac685cab5b279a41dfe0a6ccd99e4dbf322edfb63e02fc0443bf24134e91 \
+    --hash=sha256:7a820d8ae11facf32585507c11f04e3f38343c1e784c9b5a8b1da5c930547fe2 \
+    --hash=sha256:84378110dd9a6c06323b41b56e129c504d157d1a983ce8f5443761eb5256bafc \
+    --hash=sha256:854794239111d2b88b40b6ef92aa478024d1e5074f364033e73e21e3f76b25e0 \
+    --hash=sha256:92843c305330aa94a36e706c16209cd4df274693e777ca47112617db7d0ef3d7 \
+    --hash=sha256:9bddc2cade6561f6820d4cd73f99a0243532ad506bc510a75a5a65a522b2d74d \
+    --hash=sha256:a4893d31e5ef780b6edcaf63122df0f8d321088bb0dee4c8c06eccb1ca28d145 \
+    --hash=sha256:a9d9ffdc2ab696f6b15b4d1f7cec6658e1d788124418cb30030afbae31c64746 \
+    --hash=sha256:ac93252226cf288753d8b46280f4edf3433bf9508b6977f8dd8526b521a1bbb9 \
+    --hash=sha256:b883fe6fd85adad7932b3271c38ac289c65b7337c2c132e9569f9d3940620730 \
+    --hash=sha256:ba95112d15fd4f1105fb2402c4eab9068f0554435e9b7085924bcfaac2cc306f \
+    --hash=sha256:bba208d9c7decf9961998edf5c65e3ea4355d5818dd6cd0f6809bec1afb951cc \
+    --hash=sha256:bea79263d55c24a32b0d79c00a1c58bb2ee5f0757ed95656b01c0fb310c5af3d \
+    --hash=sha256:c064e28361c05d72eed8e744c9605cbd6d2bb7481a511c74071fd9b24bc65d7d \
+    --hash=sha256:c3200cb41cdbc65156e5f8c908d739b0dfed57e890329413da2748d1a2cd1a4e \
+    --hash=sha256:c6c791b09c57ed76a18b03f2631753a4960eefbbca80f846da8baefc6491fcfe \
+    --hash=sha256:c6ec3675d98915bf1ec8b3c7986422682f7232ea76cad276f4c8abd5b7319b70 \
+    --hash=sha256:ce20fe000754f477c8a9125543f1936ea5b8867c5406757c224d745ed033e691 \
+    --hash=sha256:cedb9dd9358e4ea1d9bce3665ce0797f6adf97ff142c8e25b46ba9cdd508e9b6 \
+    --hash=sha256:e0a15757fccb38c410947df156f9749ae4a3c89b2393741a50521f39a8cf202a \
+    --hash=sha256:e6e95176209257803a8b3d0394f21604e796dadb643d2f7ca21b66c9c0b30c9a \
+    --hash=sha256:e70ff90c64419709d38c8932ea9fe1cc98415c4f87ea8da81719e43f02534bc9 \
+    --hash=sha256:ec1a15968a9d80da01e1d30349b2b0d7cc91e96588ee324ce1b5228175043e95 \
+    --hash=sha256:f633074f36dbc33d5c05b5dc75371e5660f1dbf9c8b1d95669def05e5425989c \
+    --hash=sha256:f7fe3dbe871294ba70d789be16b6e7e52b418311e166e0e3cba9522f0f437fb1
+    # via datasets
+pycparser==2.23 ; implementation_name != 'PyPy' \
+    --hash=sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2 \
+    --hash=sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934
+    # via cffi
+pygments==2.19.2 \
+    --hash=sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887 \
+    --hash=sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b
+    # via
+    #   ipython
+    #   ipython-pygments-lexers
+    #   nbconvert
+    #   rich
+pyparsing==3.3.1 \
+    --hash=sha256:023b5e7e5520ad96642e2c6db4cb683d3970bd640cdf7115049a6e9c3682df82 \
+    --hash=sha256:47fad0f17ac1e2cad3de3b458570fbc9b03560aa029ed5e16ee5554da9a2251c
+    # via matplotlib
+python-dateutil==2.9.0.post0 \
+    --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
+    --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
+    # via
+    #   arrow
+    #   jupyter-client
+    #   matplotlib
+    #   pandas
+python-json-logger==4.0.0 \
+    --hash=sha256:af09c9daf6a813aa4cc7180395f50f2a9e5fa056034c9953aec92e381c5ba1e2 \
+    --hash=sha256:f58e68eb46e1faed27e0f574a55a0455eecd7b8a5b88b85a784519ba3cff047f
+    # via jupyter-events
+pytz==2025.2 \
+    --hash=sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3 \
+    --hash=sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00
+    # via pandas
+pywinpty==3.0.2 ; os_name == 'nt' \
+    --hash=sha256:1505cc4cb248af42cb6285a65c9c2086ee9e7e574078ee60933d5d7fa86fb004 \
+    --hash=sha256:18f78b81e4cfee6aabe7ea8688441d30247b73e52cd9657138015c5f4ee13a51 \
+    --hash=sha256:28297cecc37bee9f24d8889e47231972d6e9e84f7b668909de54f36ca785029a \
+    --hash=sha256:34b55ae9a1b671fe3eae071d86618110538e8eaad18fcb1531c0830b91a82767 \
+    --hash=sha256:663383ecfab7fc382cc97ea5c4f7f0bb32c2f889259855df6ea34e5df42d305b \
+    --hash=sha256:99fdd9b455f0ad6419aba6731a7a0d2f88ced83c3c94a80ff9533d95fa8d8a9e
+    # via
+    #   jupyter-server
+    #   jupyter-server-terminals
+    #   terminado
+pyyaml==6.0.3 \
+    --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \
+    --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \
+    --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \
+    --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \
+    --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \
+    --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \
+    --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \
+    --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \
+    --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \
+    --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \
+    --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \
+    --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \
+    --hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \
+    --hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \
+    --hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \
+    --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \
+    --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \
+    --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \
+    --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \
+    --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \
+    --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \
+    --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \
+    --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \
+    --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \
+    --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \
+    --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \
+    --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \
+    --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \
+    --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \
+    --hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \
+    --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \
+    --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \
+    --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \
+    --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \
+    --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \
+    --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \
+    --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \
+    --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6 \
+    --hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0
+    # via
+    #   datasets
+    #   huggingface-hub
+    #   jupyter-events
+pyzmq==27.1.0 \
+    --hash=sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d \
+    --hash=sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05 \
+    --hash=sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28 \
+    --hash=sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea \
+    --hash=sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0 \
+    --hash=sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113 \
+    --hash=sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92 \
+    --hash=sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd \
+    --hash=sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233 \
+    --hash=sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31 \
+    --hash=sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc \
+    --hash=sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd \
+    --hash=sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e \
+    --hash=sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128 \
+    --hash=sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96 \
+    --hash=sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f \
+    --hash=sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c \
+    --hash=sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2 \
+    --hash=sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146 \
+    --hash=sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97 \
+    --hash=sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5 \
+    --hash=sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf \
+    --hash=sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540 \
+    --hash=sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db \
+    --hash=sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39 \
+    --hash=sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a \
+    --hash=sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a \
+    --hash=sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856 \
+    --hash=sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9 \
+    --hash=sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7 \
+    --hash=sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7 \
+    --hash=sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496 \
+    --hash=sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+referencing==0.37.0 \
+    --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \
+    --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+    #   jupyter-events
+requests==2.32.5 \
+    --hash=sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6 \
+    --hash=sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf
+    # via
+    #   datasets
+    #   jupyterlab-server
+    #   tensorflow
+rfc3339-validator==0.1.4 \
+    --hash=sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b \
+    --hash=sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa
+    # via
+    #   jsonschema
+    #   jupyter-events
+rfc3986-validator==0.1.1 \
+    --hash=sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 \
+    --hash=sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055
+    # via
+    #   jsonschema
+    #   jupyter-events
+rfc3987-syntax==1.1.0 \
+    --hash=sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f \
+    --hash=sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d
+    # via jsonschema
+rich==14.2.0 \
+    --hash=sha256:73ff50c7c0c1c77c8243079283f4edb376f0f6442433aecb8ce7e6d0b92d1fe4 \
+    --hash=sha256:76bc51fe2e57d2b1be1f96c524b890b816e334ab4c1e45888799bfaab0021edd
+    # via keras
+rpds-py==0.30.0 \
+    --hash=sha256:0a59119fc6e3f460315fe9d08149f8102aa322299deaa5cab5b40092345c2136 \
+    --hash=sha256:0d08f00679177226c4cb8c5265012eea897c8ca3b93f429e546600c971bcbae7 \
+    --hash=sha256:0ed177ed9bded28f8deb6ab40c183cd1192aa0de40c12f38be4d59cd33cb5c65 \
+    --hash=sha256:1ab5b83dbcf55acc8b08fc62b796ef672c457b17dbd7820a11d6c52c06839bdf \
+    --hash=sha256:1f3587eb9b17f3789ad50824084fa6f81921bbf9a795826570bda82cb3ed91f2 \
+    --hash=sha256:2771c6c15973347f50fece41fc447c054b7ac2ae0502388ce3b6738cd366e3d4 \
+    --hash=sha256:27f4b0e92de5bfbc6f86e43959e6edd1425c33b5e69aab0984a72047f2bcf1e3 \
+    --hash=sha256:2e6ecb5a5bcacf59c3f912155044479af1d0b6681280048b338b28e364aca1f6 \
+    --hash=sha256:39c02563fc592411c2c61d26b6c5fe1e51eaa44a75aa2c8735ca88b0d9599daa \
+    --hash=sha256:3d4a69de7a3e50ffc214ae16d79d8fbb0922972da0356dcf4d0fdca2878559c6 \
+    --hash=sha256:3e62880792319dbeb7eb866547f2e35973289e7d5696c6e295476448f5b63c87 \
+    --hash=sha256:3e8eeb0544f2eb0d2581774be4c3410356eba189529a6b3e36bbbf9696175856 \
+    --hash=sha256:4559c972db3a360808309e06a74628b95eaccbf961c335c8fe0d590cf587456f \
+    --hash=sha256:46e83c697b1f1c72b50e5ee5adb4353eef7406fb3f2043d64c33f20ad1c2fc53 \
+    --hash=sha256:47e77dc9822d3ad616c3d5759ea5631a75e5809d5a28707744ef79d7a1bcfcad \
+    --hash=sha256:47f236970bccb2233267d89173d3ad2703cd36a0e2a6e92d0560d333871a3d23 \
+    --hash=sha256:47f9a91efc418b54fb8190a6b4aa7813a23fb79c51f4bb84e418f5476c38b8db \
+    --hash=sha256:4c5f36a861bc4b7da6516dbdf302c55313afa09b81931e8280361a4f6c9a2d27 \
+    --hash=sha256:4e7fc54e0900ab35d041b0601431b0a0eb495f0851a0639b6ef90f7741b39a18 \
+    --hash=sha256:51a1234d8febafdfd33a42d97da7a43f5dcb120c1060e352a3fbc0c6d36e2083 \
+    --hash=sha256:5965af57d5848192c13534f90f9dd16464f3c37aaf166cc1da1cae1fd5a34898 \
+    --hash=sha256:5ba103fb455be00f3b1c2076c9d4264bfcb037c976167a6047ed82f23153f02e \
+    --hash=sha256:5d4c2aa7c50ad4728a094ebd5eb46c452e9cb7edbfdb18f9e1221f597a73e1e7 \
+    --hash=sha256:61046904275472a76c8c90c9ccee9013d70a6d0f73eecefd38c1ae7c39045a08 \
+    --hash=sha256:613aa4771c99f03346e54c3f038e4cc574ac09a3ddfb0e8878487335e96dead6 \
+    --hash=sha256:626a7433c34566535b6e56a1b39a7b17ba961e97ce3b80ec62e6f1312c025551 \
+    --hash=sha256:669b1805bd639dd2989b281be2cfd951c6121b65e729d9b843e9639ef1fd555e \
+    --hash=sha256:68f19c879420aa08f61203801423f6cd5ac5f0ac4ac82a2368a9fcd6a9a075e0 \
+    --hash=sha256:692bef75a5525db97318e8cd061542b5a79812d711ea03dbc1f6f8dbb0c5f0d2 \
+    --hash=sha256:6abc8880d9d036ecaafe709079969f56e876fcf107f7a8e9920ba6d5a3878d05 \
+    --hash=sha256:6bdfdb946967d816e6adf9a3d8201bfad269c67efe6cefd7093ef959683c8de0 \
+    --hash=sha256:73c67f2db7bc334e518d097c6d1e6fed021bbc9b7d678d6cc433478365d1d5f5 \
+    --hash=sha256:74a3243a411126362712ee1524dfc90c650a503502f135d54d1b352bd01f2404 \
+    --hash=sha256:76fec018282b4ead0364022e3c54b60bf368b9d926877957a8624b58419169b7 \
+    --hash=sha256:7cee9c752c0364588353e627da8a7e808a66873672bcb5f52890c33fd965b394 \
+    --hash=sha256:7e6ecfcb62edfd632e56983964e6884851786443739dbfe3582947e87274f7cb \
+    --hash=sha256:806f36b1b605e2d6a72716f321f20036b9489d29c51c91f4dd29a3e3afb73b15 \
+    --hash=sha256:9027da1ce107104c50c81383cae773ef5c24d296dd11c99e2629dbd7967a20c6 \
+    --hash=sha256:922e10f31f303c7c920da8981051ff6d8c1a56207dbdf330d9047f6d30b70e5e \
+    --hash=sha256:945dccface01af02675628334f7cf49c2af4c1c904748efc5cf7bbdf0b579f95 \
+    --hash=sha256:95f0802447ac2d10bcc69f6dc28fe95fdf17940367b21d34e34c737870758950 \
+    --hash=sha256:9a4e86e34e9ab6b667c27f3211ca48f73dba7cd3d90f8d5b11be56e5dbc3fb4e \
+    --hash=sha256:9cf69cdda1f5968a30a359aba2f7f9aa648a9ce4b580d6826437f2b291cfc86e \
+    --hash=sha256:a090322ca841abd453d43456ac34db46e8b05fd9b3b4ac0c78bcde8b089f959b \
+    --hash=sha256:a1010ed9524c73b94d15919ca4d41d8780980e1765babf85f9a2f90d247153dd \
+    --hash=sha256:a161f20d9a43006833cd7068375a94d035714d73a172b681d8881820600abfad \
+    --hash=sha256:a1d0bc22a7cdc173fedebb73ef81e07faef93692b8c1ad3733b67e31e1b6e1b8 \
+    --hash=sha256:a4796a717bf12b9da9d3ad002519a86063dcac8988b030e405704ef7d74d2d9d \
+    --hash=sha256:a8fa71a2e078c527c3e9dc9fc5a98c9db40bcc8a92b4e8858e36d329f8684b51 \
+    --hash=sha256:ac98b175585ecf4c0348fd7b29c3864bda53b805c773cbf7bfdaffc8070c976f \
+    --hash=sha256:acd7eb3f4471577b9b5a41baf02a978e8bdeb08b4b355273994f8b87032000a8 \
+    --hash=sha256:ad1fa8db769b76ea911cb4e10f049d80bf518c104f15b3edb2371cc65375c46f \
+    --hash=sha256:b40fb160a2db369a194cb27943582b38f79fc4887291417685f3ad693c5a1d5d \
+    --hash=sha256:b4dc1a6ff022ff85ecafef7979a2c6eb423430e05f1165d6688234e62ba99a07 \
+    --hash=sha256:ba81a9203d07805435eb06f536d95a266c21e5b2dfbf6517748ca40c98d19e31 \
+    --hash=sha256:c77afbd5f5250bf27bf516c7c4a016813eb2d3e116139aed0096940c5982da94 \
+    --hash=sha256:ca28829ae5f5d569bb62a79512c842a03a12576375d5ece7d2cadf8abe96ec28 \
+    --hash=sha256:cdc62c8286ba9bf7f47befdcea13ea0e26bf294bda99758fd90535cbaf408000 \
+    --hash=sha256:d948b135c4693daff7bc2dcfc4ec57237a29bd37e60c2fabf5aff2bbacf3e2f1 \
+    --hash=sha256:d96c2086587c7c30d44f31f42eae4eac89b60dabbac18c7669be3700f13c3ce1 \
+    --hash=sha256:dbd936cde57abfee19ab3213cf9c26be06d60750e60a8e4dd85d1ab12c8b1f40 \
+    --hash=sha256:dc824125c72246d924f7f796b4f63c1e9dc810c7d9e2355864b3c3a73d59ade0 \
+    --hash=sha256:dd8ff7cf90014af0c0f787eea34794ebf6415242ee1d6fa91eaba725cc441e84 \
+    --hash=sha256:e5d3e6b26f2c785d65cc25ef1e5267ccbe1b069c5c21b8cc724efee290554419 \
+    --hash=sha256:e7536cd91353c5273434b4e003cbda89034d67e7710eab8761fd918ec6c69cf8 \
+    --hash=sha256:eb0b93f2e5c2189ee831ee43f156ed34e2a89a78a66b98cadad955972548be5a \
+    --hash=sha256:eb2c4071ab598733724c08221091e8d80e89064cd472819285a9ab0f24bcedb9 \
+    --hash=sha256:ec7c4490c672c1a0389d319b3a9cfcd098dcdc4783991553c332a15acf7249be \
+    --hash=sha256:ee454b2a007d57363c2dfd5b6ca4a5d7e2c518938f8ed3b706e37e5d470801ed \
+    --hash=sha256:f14fc5df50a716f7ece6a80b6c78bb35ea2ca47c499e422aa4463455dd96d56d \
+    --hash=sha256:f251c812357a3fed308d684a5079ddfb9d933860fc6de89f2b7ab00da481e65f \
+    --hash=sha256:f83424d738204d9770830d35290ff3273fbb02b41f919870479fab14b9d303b2 \
+    --hash=sha256:f8d1736cfb49381ba528cd5baa46f82fdc65c06e843dab24dd70b63d09121b3f \
+    --hash=sha256:fe5fa731a1fa8a0a56b0977413f8cacac1768dad38d16b3a296712709476fbd5
+    # via
+    #   jsonschema
+    #   referencing
+scipy==1.16.3 \
+    --hash=sha256:01e87659402762f43bd2fee13370553a17ada367d42e7487800bf2916535aecb \
+    --hash=sha256:03192a35e661470197556de24e7cb1330d84b35b94ead65c46ad6f16f6b28f2a \
+    --hash=sha256:0553371015692a898e1aa858fed67a3576c34edefa6b7ebdb4e9dde49ce5c203 \
+    --hash=sha256:062246acacbe9f8210de8e751b16fc37458213f124bef161a5a02c7a39284304 \
+    --hash=sha256:0c3b4dd3d9b08dbce0f3440032c52e9e2ab9f96ade2d3943313dfe51a7056959 \
+    --hash=sha256:0c623a54f7b79dd88ef56da19bc2873afec9673a48f3b85b18e4d402bdd29a5a \
+    --hash=sha256:16b8bc35a4cc24db80a0ec836a9286d0e31b2503cb2fd7ff7fb0e0374a97081d \
+    --hash=sha256:1fb2472e72e24d1530debe6ae078db70fb1605350c88a3d14bc401d6306dbffe \
+    --hash=sha256:21d9d6b197227a12dcbf9633320a4e34c6b0e51c57268df255a0942983bac562 \
+    --hash=sha256:2b71d93c8a9936046866acebc915e2af2e292b883ed6e2cbe5c34beb094b82d9 \
+    --hash=sha256:2d1ae2cf0c350e7705168ff2429962a89ad90c2d49d1dd300686d8b2a5af22fc \
+    --hash=sha256:3a4c460301fb2cffb7f88528f30b3127742cff583603aa7dc964a52c463b385d \
+    --hash=sha256:3d4a07a8e785d80289dfe66b7c27d8634a773020742ec7187b85ccc4b0e7b686 \
+    --hash=sha256:4aff59800a3b7f786b70bfd6ab551001cb553244988d7d6b8299cb1ea653b353 \
+    --hash=sha256:50a3dbf286dbc7d84f176f9a1574c705f277cb6565069f88f60db9eafdbe3ee2 \
+    --hash=sha256:56edc65510d1331dae01ef9b658d428e33ed48b4f77b1d51caf479a0253f96dc \
+    --hash=sha256:57d01cb6f85e34f0946b33caa66e892aae072b64b034183f3d87c4025802a119 \
+    --hash=sha256:5803c5fadd29de0cf27fa08ccbfe7a9e5d741bf63e4ab1085437266f12460ff9 \
+    --hash=sha256:6020470b9d00245926f2d5bb93b119ca0340f0d564eb6fbaad843eaebf9d690f \
+    --hash=sha256:63d3cdacb8a824a295191a723ee5e4ea7768ca5ca5f2838532d9f2e2b3ce2135 \
+    --hash=sha256:663b8d66a8748051c3ee9c96465fb417509315b99c71550fda2591d7dd634234 \
+    --hash=sha256:72d1717fd3b5e6ec747327ce9bda32d5463f472c9dce9f54499e81fbd50245a1 \
+    --hash=sha256:7dc1360c06535ea6116a2220f760ae572db9f661aba2d88074fe30ec2aa1ff88 \
+    --hash=sha256:7f68154688c515cdb541a31ef8eb66d8cd1050605be9dcd74199cbd22ac739bc \
+    --hash=sha256:81fc5827606858cf71446a5e98715ba0e11f0dbc83d71c7409d05486592a45d6 \
+    --hash=sha256:875555ce62743e1d54f06cdf22c1e0bc47b91130ac40fe5d783b6dfa114beeb6 \
+    --hash=sha256:8b3c820ddb80029fe9f43d61b81d8b488d3ef8ca010d15122b152db77dc94c22 \
+    --hash=sha256:8d09d72dc92742988b0e7750bddb8060b0c7079606c0d24a8cc8e9c9c11f9079 \
+    --hash=sha256:96491a6a54e995f00a28a3c3badfff58fd093bf26cd5fb34a2188c8c756a3a2c \
+    --hash=sha256:9b9c9c07b6d56a35777a1b4cc8966118fb16cfd8daf6743867d17d36cfad2d40 \
+    --hash=sha256:a8a26c78ef223d3e30920ef759e25625a0ecdd0d60e5a8818b7513c3e5384cf2 \
+    --hash=sha256:aadd23f98f9cb069b3bd64ddc900c4d277778242e961751f77a8cb5c4b946fb0 \
+    --hash=sha256:b7c5f1bda1354d6a19bc6af73a649f8285ca63ac6b52e64e658a5a11d4d69800 \
+    --hash=sha256:b81c27fc41954319a943d43b20e07c40bdcd3ff7cf013f4fb86286faefe546c4 \
+    --hash=sha256:bb61878c18a470021fb515a843dc7a76961a8daceaaaa8bad1332f1bf4b54657 \
+    --hash=sha256:c5192722cffe15f9329a3948c4b1db789fbb1f05c97899187dcf009b283aea70 \
+    --hash=sha256:c97176013d404c7346bf57874eaac5187d969293bf40497140b0a2b2b7482e07 \
+    --hash=sha256:cd13e354df9938598af2be05822c323e97132d5e6306b83a3b4ee6724c6e522e \
+    --hash=sha256:d2ec56337675e61b312179a1ad124f5f570c00f920cc75e1000025451b88241c \
+    --hash=sha256:d3837938ae715fc0fe3c39c0202de3a8853aff22ca66781ddc2ade7554b7e2cc \
+    --hash=sha256:d9f48cafc7ce94cf9b15c6bffdc443a81a27bf7075cf2dcd5c8b40f85d10c4e7 \
+    --hash=sha256:da7763f55885045036fabcebd80144b757d3db06ab0861415d1c3b7c69042146 \
+    --hash=sha256:e1d27cbcb4602680a49d787d90664fa4974063ac9d4134813332a8c53dbe667c \
+    --hash=sha256:e5d42a9472e7579e473879a1990327830493a7047506d58d73fc429b84c1d49d \
+    --hash=sha256:e7efa2681ea410b10dde31a52b18b0154d66f2485328830e45fdf183af5aefc6 \
+    --hash=sha256:eab43fae33a0c39006a88096cd7b4f4ef545ea0447d250d5ac18202d40b6611d \
+    --hash=sha256:f2622206f5559784fa5c4b53a950c3c7c1cf3e84ca1b9c4b6c03f062f289ca26 \
+    --hash=sha256:f379b54b77a597aa7ee5e697df0d66903e41b9c85a6dd7946159e356319158e8 \
+    --hash=sha256:f667a4542cc8917af1db06366d3f78a5c8e83badd56409f94d1eac8d8d9133fa \
+    --hash=sha256:fb4b29f4cf8cc5a8d628bc8d8e26d12d7278cd1f219f22698a378c3d67db5e4b \
+    --hash=sha256:ffa6eea95283b2b8079b821dc11f50a17d0571c92b43e2b5b12764dc5f9b285d
+    # via cell-browser
+send2trash==2.0.0 \
+    --hash=sha256:1761421da3f9930bfe51ed7c45343948573383ad4c27e3acebc91be324e7770d \
+    --hash=sha256:e70d5ce41dbb890882cc78bc25d137478330b39a391e756fadf82e34da4d85b8
+    # via jupyter-server
+setuptools==80.9.0 \
+    --hash=sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922 \
+    --hash=sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c
+    # via
+    #   jupyterlab
+    #   tensorboard
+    #   tensorflow
+shellingham==1.5.4 \
+    --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \
+    --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de
+    # via huggingface-hub
+six==1.17.0 \
+    --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \
+    --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81
+    # via
+    #   astunparse
+    #   google-pasta
+    #   python-dateutil
+    #   rfc3339-validator
+    #   tensorflow
+soupsieve==2.8.1 \
+    --hash=sha256:4cf733bc50fa805f5df4b8ef4740fc0e0fa6218cf3006269afd3f9d6d80fd350 \
+    --hash=sha256:a11fe2a6f3d76ab3cf2de04eb339c1be5b506a8a47f2ceb6d139803177f85434
+    # via beautifulsoup4
+stack-data==0.6.3 \
+    --hash=sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9 \
+    --hash=sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695
+    # via ipython
+tensorboard==2.20.0 \
+    --hash=sha256:9dc9f978cb84c0723acf9a345d96c184f0293d18f166bb8d59ee098e6cfaaba6
+    # via tensorflow
+tensorboard-data-server==0.7.2 \
+    --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \
+    --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \
+    --hash=sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530
+    # via tensorboard
+tensorflow==2.20.0 \
+    --hash=sha256:1590cbf87b6bcbd34d8e9ad70d0c696135e0aa71be31803b27358cf7ed63f8fc \
+    --hash=sha256:197f0b613b38c0da5c6a12a8295ad4a05c78b853835dae8e0f9dfae3ce9ce8a5 \
+    --hash=sha256:25265b0bc527e0d54b1e9cc60c44a24f44a809fe27666b905f0466471f9c52ec \
+    --hash=sha256:2bfbfb3dd0e22bffc45fe1e922390d27753e99261fab8a882e802cf98a0e078f \
+    --hash=sha256:47c88e05a07f1ead4977b4894b3ecd4d8075c40191065afc4fd9355c9db3d926 \
+    --hash=sha256:52b122f0232fd7ab10f28d537ce08470d0b6dcac7fff9685432daac7f8a06c8f \
+    --hash=sha256:5fa3729b0126f75a99882b89fb7d536515721eda8014a63e259e780ba0a37372 \
+    --hash=sha256:c25edad45e8cb9e76366f7a8c835279f9169028d610f3b52ce92d332a1b05438
+    # via cell-browser
+termcolor==3.3.0 \
+    --hash=sha256:348871ca648ec6a9a983a13ab626c0acce02f515b9e1983332b17af7979521c5 \
+    --hash=sha256:cf642efadaf0a8ebbbf4bc7a31cec2f9b5f21a9f726f4ccbb08192c9c26f43a5
+    # via tensorflow
+terminado==0.18.1 \
+    --hash=sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 \
+    --hash=sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e
+    # via
+    #   jupyter-server
+    #   jupyter-server-terminals
+tifffile==2025.12.20 \
+    --hash=sha256:bc0345a20675149353cfcb3f1c48d0a3654231ee26bd46beebaab4d2168feeb6 \
+    --hash=sha256:cb8a4fee327d15b3e3eeac80bbdd8a53b323c80473330bcfb99418ee4c1c827f
+    # via cell-browser
+tinycss2==1.4.0 \
+    --hash=sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7 \
+    --hash=sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289
+    # via bleach
+tornado==6.5.4 \
+    --hash=sha256:053e6e16701eb6cbe641f308f4c1a9541f91b6261991160391bfc342e8a551a1 \
+    --hash=sha256:1768110f2411d5cd281bac0a090f707223ce77fd110424361092859e089b38d1 \
+    --hash=sha256:2d50f63dda1d2cac3ae1fa23d254e16b5e38153758470e9956cbc3d813d40843 \
+    --hash=sha256:50ff0a58b0dc97939d29da29cd624da010e7f804746621c78d14b80238669335 \
+    --hash=sha256:6076d5dda368c9328ff41ab5d9dd3608e695e8225d1cd0fd1e006f05da3635a8 \
+    --hash=sha256:6eb82872335a53dd063a4f10917b3efd28270b56a33db69009606a0312660a6f \
+    --hash=sha256:9c86b1643b33a4cd415f8d0fe53045f913bf07b4a3ef646b735a6a86047dda84 \
+    --hash=sha256:a22fa9047405d03260b483980635f0b041989d8bcc9a313f8fe18b411d84b1d7 \
+    --hash=sha256:d1cf66105dc6acb5af613c054955b8137e34a03698aa53272dbda4afe252be17 \
+    --hash=sha256:d6241c1a16b1c9e4cc28148b1cda97dd1c6cb4fb7068ac1bedc610768dff0ba9 \
+    --hash=sha256:e5fb5e04efa54cf0baabdd10061eb4148e0be137166146fff835745f59ab9f7f \
+    --hash=sha256:fa07d31e0cd85c60713f2b995da613588aa03e1303d75705dca6af8babc18ddc
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+    #   jupyterlab
+    #   terminado
+tqdm==4.67.1 \
+    --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \
+    --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2
+    # via
+    #   cell-browser
+    #   datasets
+    #   huggingface-hub
+traitlets==5.14.3 \
+    --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \
+    --hash=sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f
+    # via
+    #   ipykernel
+    #   ipympl
+    #   ipython
+    #   ipywidgets
+    #   jupyter-client
+    #   jupyter-core
+    #   jupyter-events
+    #   jupyter-server
+    #   jupyterlab
+    #   matplotlib-inline
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+typer-slim==0.21.1 \
+    --hash=sha256:6e6c31047f171ac93cc5a973c9e617dbc5ab2bddc4d0a3135dc161b4e2020e0d \
+    --hash=sha256:73495dd08c2d0940d611c5a8c04e91c2a0a98600cbd4ee19192255a233b6dbfd
+    # via huggingface-hub
+typing-extensions==4.15.0 \
+    --hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \
+    --hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548
+    # via
+    #   aiosignal
+    #   anyio
+    #   anywidget
+    #   beautifulsoup4
+    #   grpcio
+    #   huggingface-hub
+    #   optree
+    #   referencing
+    #   tensorflow
+    #   typer-slim
+tzdata==2025.3 \
+    --hash=sha256:06a47e5700f3081aab02b2e513160914ff0694bce9947d6b76ebd6bf57cfc5d1 \
+    --hash=sha256:de39c2ca5dc7b0344f2eba86f49d614019d29f060fc4ebc8a417896a620b56a7
+    # via
+    #   arrow
+    #   pandas
+uri-template==1.3.0 \
+    --hash=sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7 \
+    --hash=sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363
+    # via jsonschema
+urllib3==2.6.3 \
+    --hash=sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed \
+    --hash=sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4
+    # via requests
+vizarr==0.1.1 \
+    --hash=sha256:a3d2ed7d681eeb93575c535359bef96e74a29d00b59ae2aba89d3960d955203d \
+    --hash=sha256:aa5812be5fa8b5f0b07ea9a1ecd5f2faca979c87537a9fe717e3cf6d061a2f43 \
+    --hash=sha256:b1e5f6a7e0e7c14b2574b2f5c989132062c5bc5eedef276c2721850912496d16
+    # via cell-browser
+wcwidth==0.2.14 \
+    --hash=sha256:4d478375d31bc5395a3c55c40ccdf3354688364cd61c4f6adacaa9215d0b3605 \
+    --hash=sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1
+    # via prompt-toolkit
+webcolors==25.10.0 \
+    --hash=sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d \
+    --hash=sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf
+    # via jsonschema
+webencodings==0.5.1 \
+    --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \
+    --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923
+    # via
+    #   bleach
+    #   tinycss2
+websocket-client==1.9.0 \
+    --hash=sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98 \
+    --hash=sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef
+    # via jupyter-server
+werkzeug==3.1.4 \
+    --hash=sha256:2ad50fb9ed09cc3af22c54698351027ace879a0b60a3b5edf5730b2f7d876905 \
+    --hash=sha256:cd3cd98b1b92dc3b7b3995038826c68097dcb16f9baa63abe35f20eafeb9fe5e
+    # via tensorboard
+wheel==0.45.1 \
+    --hash=sha256:661e1abd9198507b1409a20c02106d9670b2576e916d58f520316666abca6729 \
+    --hash=sha256:708e7481cc80179af0e556bbf0cc00b8444c7321e2700b8d8580231d13017248
+    # via astunparse
+widgetsnbextension==4.0.15 \
+    --hash=sha256:8156704e4346a571d9ce73b84bee86a29906c9abfd7223b7228a28899ccf3366 \
+    --hash=sha256:de8610639996f1567952d763a5a41af8af37f2575a41f9852a38f947eb82a3b9
+    # via ipywidgets
+wrapt==2.0.1 \
+    --hash=sha256:17fb85fa4abc26a5184d93b3efd2dcc14deb4b09edcdb3535a536ad34f0b4dba \
+    --hash=sha256:1fdbb34da15450f2b1d735a0e969c24bdb8d8924892380126e2a293d9902078c \
+    --hash=sha256:2879af909312d0baf35f08edeea918ee3af7ab57c37fe47cb6a373c9f2749c7b \
+    --hash=sha256:2afa23318136709c4b23d87d543b425c399887b4057936cd20386d5b1422b6fa \
+    --hash=sha256:2da620b31a90cdefa9cd0c2b661882329e2e19d1d7b9b920189956b76c564d75 \
+    --hash=sha256:35cdbd478607036fee40273be8ed54a451f5f23121bd9d4be515158f9498f7ad \
+    --hash=sha256:3793ac154afb0e5b45d1233cb94d354ef7a983708cc3bb12563853b1d8d53747 \
+    --hash=sha256:386fb54d9cd903ee0012c09291336469eb7b244f7183d40dc3e86a16a4bace62 \
+    --hash=sha256:3cd1a4bd9a7a619922a8557e1318232e7269b5fb69d4ba97b04d20450a6bf970 \
+    --hash=sha256:3d32794fe940b7000f0519904e247f902f0149edbe6316c710a8562fb6738841 \
+    --hash=sha256:3d366aa598d69416b5afedf1faa539fac40c1d80a42f6b236c88c73a3c8f2d41 \
+    --hash=sha256:3e271346f01e9c8b1130a6a3b0e11908049fe5be2d365a5f402778049147e7e9 \
+    --hash=sha256:47b0f8bafe90f7736151f61482c583c86b0693d80f075a58701dd1549b0010a9 \
+    --hash=sha256:4811e15d88ee62dbf5c77f2c3ff3932b1e3ac92323ba3912f51fc4016ce81ecf \
+    --hash=sha256:4ae879acc449caa9ed43fc36ba08392b9412ee67941748d31d94e3cedb36628c \
+    --hash=sha256:4b55cacc57e1dc2d0991dbe74c6419ffd415fb66474a02335cb10efd1aa3f84f \
+    --hash=sha256:4d2ce1bf1a48c5277d7969259232b57645aae5686dba1eaeade39442277afbca \
+    --hash=sha256:4e54bbf554ee29fcceee24fa41c4d091398b911da6e7f5d7bffda963c9aed2e1 \
+    --hash=sha256:5a4939eae35db6b6cec8e7aa0e833dcca0acad8231672c26c2a9ab7a0f8ac9c8 \
+    --hash=sha256:5e53b428f65ece6d9dad23cb87e64506392b720a0b45076c05354d27a13351a1 \
+    --hash=sha256:61c4956171c7434634401db448371277d07032a81cc21c599c22953374781395 \
+    --hash=sha256:641e94e789b5f6b4822bb8d8ebbdfc10f4e4eae7756d648b717d980f657a9eb9 \
+    --hash=sha256:6c72328f668cf4c503ffcf9434c2b71fdd624345ced7941bc6693e61bbe36bef \
+    --hash=sha256:6d2d947d266d99a1477cd005b23cbd09465276e302515e122df56bb9511aca1b \
+    --hash=sha256:7164a55f5e83a9a0b031d3ffab4d4e36bbec42e7025db560f225489fa929e509 \
+    --hash=sha256:7b219cb2182f230676308cdcacd428fa837987b89e4b7c5c9025088b8a6c9faf \
+    --hash=sha256:7d539241e87b650cbc4c3ac9f32c8d1ac8a54e510f6dca3f6ab60dcfd48c9b10 \
+    --hash=sha256:83ce30937f0ba0d28818807b303a412440c4b63e39d3d8fc036a94764b728c92 \
+    --hash=sha256:8639b843c9efd84675f1e100ed9e99538ebea7297b62c4b45a7042edb84db03e \
+    --hash=sha256:908f8c6c71557f4deaa280f55d0728c3bca0960e8c3dd5ceeeafb3c19942719d \
+    --hash=sha256:9219a1d946a9b32bb23ccae66bdb61e35c62773ce7ca6509ceea70f344656b7b \
+    --hash=sha256:949520bccc1fa227274da7d03bf238be15389cd94e32e4297b92337df9b7a349 \
+    --hash=sha256:98d873ed6c8b4ee2418f7afce666751854d6d03e3c0ec2a399bb039cd2ae89db \
+    --hash=sha256:9c9c635e78497cacb81e84f8b11b23e0aacac7a136e73b8e5b2109a1d9fc468f \
+    --hash=sha256:9ca66b38dd642bf90c59b6738af8070747b610115a39af2498535f62b5cdc1c3 \
+    --hash=sha256:a453257f19c31b31ba593c30d997d6e5be39e3b5ad9148c2af5a7314061c63eb \
+    --hash=sha256:a52f93d95c8d38fed0669da2ebdb0b0376e895d84596a976c15a9eb45e3eccb3 \
+    --hash=sha256:ad3ee9d0f254851c71780966eb417ef8e72117155cff04821ab9b60549694a55 \
+    --hash=sha256:aea9c7224c302bc8bfc892b908537f56c430802560e827b75ecbde81b604598b \
+    --hash=sha256:b4c2e3d777e38e913b8ce3a6257af72fb608f86a1df471cb1d4339755d0a807c \
+    --hash=sha256:b89ef9223d665ab255ae42cc282d27d69704d94be0deffc8b9d919179a609684 \
+    --hash=sha256:be9e84e91d6497ba62594158d3d31ec0486c60055c49179edc51ee43d095f79c \
+    --hash=sha256:bfb5539005259f8127ea9c885bdc231978c06b7a980e63a8a61c8c4c979719d0 \
+    --hash=sha256:c1c91405fcf1d501fa5d55df21e58ea49e6b879ae829f1039faaf7e5e509b41e \
+    --hash=sha256:c235095d6d090aa903f1db61f892fffb779c1eaeb2a50e566b52001f7a0f66ed \
+    --hash=sha256:c5ef2f2b8a53b7caee2f797ef166a390fef73979b15778a4a153e4b5fedce8fa \
+    --hash=sha256:c654eafb01afac55246053d67a4b9a984a3567c3808bb7df2f8de1c1caba2e1c \
+    --hash=sha256:c8d60527d1ecfc131426b10d93ab5d53e08a09c5fa0175f6b21b3252080c70a9 \
+    --hash=sha256:c9e850f5b7fc67af856ff054c71690d54fa940c3ef74209ad9f935b4f66a0233 \
+    --hash=sha256:cbeb0971e13b4bd81d34169ed57a6dda017328d1a22b62fda45e1d21dd06148f \
+    --hash=sha256:d67956c676be5a24102c7407a71f4126d30de2a569a1c7871c9f3cabc94225d7 \
+    --hash=sha256:d7b822c61ed04ee6ad64bc90d13368ad6eb094db54883b5dde2182f67a7f22c0 \
+    --hash=sha256:e042d653a4745be832d5aa190ff80ee4f02c34b21f4b785745eceacd0907b815 \
+    --hash=sha256:e2f84e9af2060e3904a32cea9bb6db23ce3f91cfd90c6b426757cf7cc01c45c7 \
+    --hash=sha256:e3612dc06b436968dfb9142c62e5dfa9eb5924f91120b3c8ff501ad878f90eb3 \
+    --hash=sha256:e505629359cb5f751e16e30cf3f91a1d3ddb4552480c205947da415d597f7ac2 \
+    --hash=sha256:e60690ba71a57424c8d9ff28f8d006b7ad7772c22a4af432188572cd7fa004a1 \
+    --hash=sha256:e76e3f91f864e89db8b8d2a8311d57df93f01ad6bb1e9b9976d1f2e83e18315c \
+    --hash=sha256:eb7cffe572ad0a141a7886a1d2efa5bef0bf7fe021deeea76b3ab334d2c38218 \
+    --hash=sha256:fa4184e74197af3adad3c889a1af95b53bb0466bced92ea99a0c014e48323eec \
+    --hash=sha256:fe21b118b9f58859b5ebaa4b130dee18669df4bd111daad082b7beb8799ad16b \
+    --hash=sha256:fec0d993ecba3991645b4857837277469c8cc4c554a7e24d064d1ca291cfb81f
+    # via
+    #   deprecated
+    #   tensorflow
+xxhash==3.6.0 \
+    --hash=sha256:01262da8798422d0685f7cef03b2bd3f4f46511b02830861df548d7def4402ad \
+    --hash=sha256:01362c4331775398e7bb34e3ab403bc9ee9f7c497bc7dee6272114055277dd3c \
+    --hash=sha256:016e9190af8f0a4e3741343777710e3d5717427f175adfdc3e72508f59e2a7f3 \
+    --hash=sha256:0226aa89035b62b6a86d3c68df4d7c1f47a342b8683da2b60cedcddb46c4d95b \
+    --hash=sha256:0444e7967dac37569052d2409b00a8860c2135cff05502df4da80267d384849f \
+    --hash=sha256:0e4edbfc7d420925b0dd5e792478ed393d6e75ff8fc219a6546fb446b6a417b1 \
+    --hash=sha256:1244460adc3a9be84731d72b8e80625788e5815b68da3da8b83f78115a40a7ec \
+    --hash=sha256:1cf9dcc4ab9cff01dfbba78544297a3a01dafd60f3bde4e2bfd016cf7e4ddc67 \
+    --hash=sha256:2577b276e060b73b73a53042ea5bd5203d3e6347ce0d09f98500f418a9fcf799 \
+    --hash=sha256:25915e6000338999236f1eb68a02a32c3275ac338628a7eaa5a269c401995679 \
+    --hash=sha256:2aa5ee3444c25b69813663c9f8067dcfaa2e126dc55e8dddf40f4d1c25d7effa \
+    --hash=sha256:39be8e4e142550ef69629c9cd71b88c90e9a5db703fecbcf265546d9536ca4ad \
+    --hash=sha256:3cd01fa2aa00d8b017c97eb46b9a794fbdca53fc14f845f5a328c71254b0abb7 \
+    --hash=sha256:3ed0df1b11a79856df5ffcab572cbd6b9627034c1c748c5566fa79df9048a7c5 \
+    --hash=sha256:40c391dd3cd041ebc3ffe6f2c862f402e306eb571422e0aa918d8070ba31da11 \
+    --hash=sha256:418daf3db71e1413cfe211c2f9a528456936645c17f46b5204705581a45390ae \
+    --hash=sha256:42c36dd7dbad2f5238950c377fcbf6811b1cdb1c444fab447960030cea60504d \
+    --hash=sha256:457b8f85dec5825eed7b69c11ae86834a018b8e3df5e77783c999663da2f96d6 \
+    --hash=sha256:49e03e6fe2cac4a1bc64952dd250cf0dbc5ef4ebb7b8d96bce82e2de163c82a2 \
+    --hash=sha256:4b54219177f6c6674d5378bd862c6aedf64725f70dd29c472eaae154df1a2e89 \
+    --hash=sha256:4f6f72232f849eb9d0141e2ebe2677ece15adfd0fa599bc058aad83c714bb2c6 \
+    --hash=sha256:50fc255f39428a27299c20e280d6193d8b63b8ef8028995323bf834a026b4fbb \
+    --hash=sha256:51a73fb7cb3a3ead9f7a8b583ffd9b8038e277cdb8cb87cf890e88b3456afa0b \
+    --hash=sha256:568a6d743219e717b07b4e03b0a828ce593833e498c3b64752e0f5df6bfe84db \
+    --hash=sha256:5851f033c3030dd95c086b4a36a2683c2ff4a799b23af60977188b057e467119 \
+    --hash=sha256:599e64ba7f67472481ceb6ee80fa3bd828fd61ba59fb11475572cc5ee52b89ec \
+    --hash=sha256:5c1343d49ac102799905e115aee590183c3921d475356cb24b4de29a4bc56518 \
+    --hash=sha256:5f059d9faeacd49c0215d66f4056e1326c80503f51a1532ca336a385edadd033 \
+    --hash=sha256:6105ef7e62b5ac73a837778efc331a591d8442f8ef5c7e102376506cb4ae2729 \
+    --hash=sha256:627f0af069b0ea56f312fd5189001c24578868643203bca1abbc2c52d3a6f3ca \
+    --hash=sha256:63275a8aba7865e44b1813d2177e0f5ea7eadad3dd063a21f7cf9afdc7054063 \
+    --hash=sha256:653a91d7c2ab54a92c19ccf43508b6a555440b9be1bc8be553376778be7f20b5 \
+    --hash=sha256:6f2580ffab1a8b68ef2b901cde7e55fa8da5e4be0977c68f78fc80f3c143de42 \
+    --hash=sha256:6fb5f5476bef678f69db04f2bd1efbed3030d2aba305b0fc1773645f187d6a4e \
+    --hash=sha256:757320d45d2fbcce8f30c42a6b2f47862967aea7bf458b9625b4bbe7ee390392 \
+    --hash=sha256:78e7f2f4c521c30ad5e786fdd6bae89d47a32672a80195467b5de0480aa97b1f \
+    --hash=sha256:794fe9145fe60191c6532fa95063765529770edcdd67b3d537793e8004cabbfd \
+    --hash=sha256:7d14a6cfaf03b1b6f5f9790f76880601ccc7896aff7ab9cd8978a939c1eb7e0d \
+    --hash=sha256:7d8b8aaa30fca4f16f0c84a5c8d7ddee0e25250ec2796c973775373257dde8f1 \
+    --hash=sha256:843b52f6d88071f87eba1631b684fcb4b2068cd2180a0224122fe4ef011a9374 \
+    --hash=sha256:858dc935963a33bc33490128edc1c12b0c14d9c7ebaa4e387a7869ecc4f3e263 \
+    --hash=sha256:8cb2f4f679b01513b7adbb9b1b2f0f9cdc31b70007eaf9d59d0878809f385b11 \
+    --hash=sha256:9176dcaddf4ca963d4deb93866d739a343c01c969231dbe21680e13a5d1a5bf0 \
+    --hash=sha256:93f107c673bccf0d592cdba077dedaf52fe7f42dcd7676eba1f6d6f0c3efffd2 \
+    --hash=sha256:9b3222c686a919a0f3253cfc12bb118b8b103506612253b5baeaac10d8027cf6 \
+    --hash=sha256:a40a3d35b204b7cc7643cbcf8c9976d818cb47befcfac8bbefec8038ac363f3e \
+    --hash=sha256:a42e633d75cdad6d625434e3468126c73f13f7584545a9cf34e883aa1710e702 \
+    --hash=sha256:a54844be970d3fc22630b32d515e79a90d0a3ddb2644d8d7402e3c4c8da61405 \
+    --hash=sha256:a756fe893389483ee8c394d06b5ab765d96e68fbbfe6fde7aa17e11f5720559f \
+    --hash=sha256:a881851cf38b0a70e7c4d3ce81fc7afd86fbc2a024f4cfb2a97cf49ce04b75d3 \
+    --hash=sha256:aa912c62f842dfd013c5f21a642c9c10cd9f4c4e943e0af83618b4a404d9091a \
+    --hash=sha256:b1e420ef35c503869c4064f4a2f2b08ad6431ab7b229a05cce39d74268bca6b8 \
+    --hash=sha256:b45fad44d9c5c119e9c6fbf2e1c656a46dc68e280275007bbfd3d572b21426db \
+    --hash=sha256:b465afd7909db30168ab62afe40b2fcf79eedc0b89a6c0ab3123515dc0df8b99 \
+    --hash=sha256:b7b2df81a23f8cb99656378e72501b2cb41b1827c0f5a86f87d6b06b69f9f204 \
+    --hash=sha256:b9c6df83594f7df8f7f708ce5ebeacfc69f72c9fbaaababf6cf4758eaada0c9b \
+    --hash=sha256:ba284920194615cb8edf73bf52236ce2e1664ccd4a38fdb543506413529cc546 \
+    --hash=sha256:bb79b1e63f6fd84ec778a4b1916dfe0a7c3fdb986c06addd5db3a0d413819d95 \
+    --hash=sha256:bd17fede52a17a4f9a7bc4472a5867cb0b160deeb431795c0e4abe158bc784e9 \
+    --hash=sha256:bec91b562d8012dae276af8025a55811b875baace6af510412a5e58e3121bc54 \
+    --hash=sha256:c0f2ab8c715630565ab8991b536ecded9416d615538be8ecddce43ccf26cbc7c \
+    --hash=sha256:c1ce4009c97a752e682b897aa99aef84191077a9433eb237774689f14f8ec152 \
+    --hash=sha256:c5294f596a9017ca5a3e3f8884c00b91ab2ad2933cf288f4923c3fd4346cf3d4 \
+    --hash=sha256:c5aa639bc113e9286137cec8fadc20e9cd732b2cc385c0b7fa673b84fc1f2a93 \
+    --hash=sha256:c6e193e9f56e4ca4923c61238cdaced324f0feac782544eb4c6d55ad5cc99ddd \
+    --hash=sha256:d597acf8506d6e7101a4a44a5e428977a51c0fadbbfd3c39650cca9253f6e5a6 \
+    --hash=sha256:d706dca2d24d834a4661619dcacf51a75c16d65985718d6a7d73c1eeeb903ddf \
+    --hash=sha256:dc94790144e66b14f67b10ac8ed75b39ca47536bf8800eb7c24b50271ea0c490 \
+    --hash=sha256:eae5c13f3bc455a3bbb68bdc513912dc7356de7e2280363ea235f71f54064829 \
+    --hash=sha256:ec44b73a4220623235f67a996c862049f375df3b1052d9899f40a6382c32d746 \
+    --hash=sha256:f01375c0e55395b814a679b3eea205db7919ac2af213f4a6682e01220e5fe292 \
+    --hash=sha256:f0162a78b13a0d7617b2845b90c763339d1f1d82bb04a4b07f4ab535cc5e05d6 \
+    --hash=sha256:f205badabde7aafd1a31e8ca2a3e5a763107a71c397c4481d6a804eb5063d8bd \
+    --hash=sha256:f22927652cba98c44639ffdc7aaf35828dccf679b10b31c4ad72a5b530a18eb7 \
+    --hash=sha256:f7f99123f0e1194fa59cc69ad46dbae2e07becec5df50a0509a808f90a0f03f0 \
+    --hash=sha256:fba27a198363a7ef87f8c0f6b171ec36b674fe9053742c58dd7e3201c1ab30ee
+    # via datasets
+yarl==1.22.0 \
+    --hash=sha256:01e73b85a5434f89fc4fe27dcda2aff08ddf35e4d47bbbea3bdcd25321af538a \
+    --hash=sha256:078a8aefd263f4d4f923a9677b942b445a2be970ca24548a8102689a3a8ab8da \
+    --hash=sha256:0b5bcc1a9c4839e7e30b7b30dd47fe5e7e44fb7054ec29b5bb8d526aa1041093 \
+    --hash=sha256:0d6e6885777af0f110b0e5d7e5dda8b704efed3894da26220b7f3d887b839a79 \
+    --hash=sha256:0dd9a702591ca2e543631c2a017e4a547e38a5c0f29eece37d9097e04a7ac683 \
+    --hash=sha256:131a085a53bfe839a477c0845acf21efc77457ba2bcf5899618136d64f3303a2 \
+    --hash=sha256:1380560bdba02b6b6c90de54133c81c9f2a453dee9912fe58c1dcced1edb7cff \
+    --hash=sha256:139718f35149ff544caba20fce6e8a2f71f1e39b92c700d8438a0b1d2a631a02 \
+    --hash=sha256:1834bb90991cc2999f10f97f5f01317f99b143284766d197e43cd5b45eb18d03 \
+    --hash=sha256:1e7ce67c34138a058fd092f67d07a72b8e31ff0c9236e751957465a24b28910c \
+    --hash=sha256:1e8fbaa7cec507aa24ea27a01456e8dd4b6fab829059b69844bd348f2d467124 \
+    --hash=sha256:22965c2af250d20c873cdbee8ff958fb809940aeb2e74ba5f20aaf6b7ac8c70c \
+    --hash=sha256:22b029f2881599e2f1b06f8f1db2ee63bd309e2293ba2d566e008ba12778b8da \
+    --hash=sha256:243dda95d901c733f5b59214d28b0120893d91777cb8aa043e6ef059d3cddfe2 \
+    --hash=sha256:2ca6fd72a8cd803be290d42f2dec5cdcd5299eeb93c2d929bf060ad9efaf5de0 \
+    --hash=sha256:31f0b53913220599446872d757257be5898019c85e7971599065bc55065dc99d \
+    --hash=sha256:334b8721303e61b00019474cc103bdac3d7b1f65e91f0bfedeec2d56dfe74b53 \
+    --hash=sha256:33e32a0dd0c8205efa8e83d04fc9f19313772b78522d1bdc7d9aed706bfd6138 \
+    --hash=sha256:34b36c2c57124530884d89d50ed2c1478697ad7473efd59cfd479945c95650e4 \
+    --hash=sha256:3b7c88eeef021579d600e50363e0b6ee4f7f6f728cd3486b9d0f3ee7b946398d \
+    --hash=sha256:42188e6a615c1a75bcaa6e150c3fe8f3e8680471a6b10150c5f7e83f47cc34d2 \
+    --hash=sha256:433885ab5431bc3d3d4f2f9bd15bfa1614c522b0f1405d62c4f926ccd69d04fa \
+    --hash=sha256:4398557cbf484207df000309235979c79c4356518fd5c99158c7d38203c4da4f \
+    --hash=sha256:45c2842ff0e0d1b35a6bf1cd6c690939dacb617a70827f715232b2e0494d55d1 \
+    --hash=sha256:47743b82b76d89a1d20b83e60d5c20314cbd5ba2befc9cda8f28300c4a08ed4d \
+    --hash=sha256:4792b262d585ff0dff6bcb787f8492e40698443ec982a3568c2096433660c694 \
+    --hash=sha256:47d8a5c446df1c4db9d21b49619ffdba90e77c89ec6e283f453856c74b50b9e3 \
+    --hash=sha256:47fdb18187e2a4e18fda2c25c05d8251a9e4a521edaed757fef033e7d8498d9a \
+    --hash=sha256:50678a3b71c751d58d7908edc96d332af328839eea883bb554a43f539101277a \
+    --hash=sha256:594fcab1032e2d2cc3321bb2e51271e7cd2b516c7d9aee780ece81b07ff8244b \
+    --hash=sha256:59c189e3e99a59cf8d83cbb31d4db02d66cda5a1a4374e8a012b51255341abf5 \
+    --hash=sha256:5a3bf7f62a289fa90f1990422dc8dff5a458469ea71d1624585ec3a4c8d6960f \
+    --hash=sha256:5c401e05ad47a75869c3ab3e35137f8468b846770587e70d71e11de797d113df \
+    --hash=sha256:5d0fcda9608875f7d052eff120c7a5da474a6796fe4d83e152e0e4d42f6d1a9b \
+    --hash=sha256:68986a61557d37bb90d3051a45b91fa3d5c516d177dfc6dd6f2f436a07ff2b6b \
+    --hash=sha256:6a635ea45ba4ea8238463b4f7d0e721bad669f80878b7bfd1f89266e2ae63da2 \
+    --hash=sha256:6c5010a52015e7c70f86eb967db0f37f3c8bd503a695a49f8d45700144667708 \
+    --hash=sha256:70dfd4f241c04bd9239d53b17f11e6ab672b9f1420364af63e8531198e3f5fe8 \
+    --hash=sha256:719ae08b6972befcba4310e49edb1161a88cdd331e3a694b84466bd938a6ab10 \
+    --hash=sha256:7861058d0582b847bc4e3a4a4c46828a410bca738673f35a29ba3ca5db0b473b \
+    --hash=sha256:8009b3173bcd637be650922ac455946197d858b3630b6d8787aa9e5c4564533e \
+    --hash=sha256:8218f4e98d3c10d683584cb40f0424f4b9fd6e95610232dd75e13743b070ee33 \
+    --hash=sha256:852863707010316c973162e703bddabec35e8757e67fcb8ad58829de1ebc8590 \
+    --hash=sha256:8884d8b332a5e9b88e23f60bb166890009429391864c685e17bd73a9eda9105c \
+    --hash=sha256:8dee9c25c74997f6a750cd317b8ca63545169c098faee42c84aa5e506c819b53 \
+    --hash=sha256:939fe60db294c786f6b7c2d2e121576628468f65453d86b0fe36cb52f987bd74 \
+    --hash=sha256:9d7672ecf7557476642c88497c2f8d8542f8e36596e928e9bcba0e42e1e7d71f \
+    --hash=sha256:9f6d73c1436b934e3f01df1e1b21ff765cd1d28c77dfb9ace207f746d4610ee1 \
+    --hash=sha256:9fb17ea16e972c63d25d4a97f016d235c78dd2344820eb35bc034bc32012ee27 \
+    --hash=sha256:a49370e8f711daec68d09b821a34e1167792ee2d24d405cbc2387be4f158b520 \
+    --hash=sha256:b0748275abb8c1e1e09301ee3cf90c8a99678a4e92e4373705f2a2570d581273 \
+    --hash=sha256:b4f15793aa49793ec8d1c708ab7f9eded1aa72edc5174cae703651555ed1b601 \
+    --hash=sha256:b790b39c7e9a4192dc2e201a282109ed2985a1ddbd5ac08dc56d0e121400a8f7 \
+    --hash=sha256:b8a0588521a26bf92a57a1705b77b8b59044cdceccac7151bd8d229e66b8dedb \
+    --hash=sha256:bca03b91c323036913993ff5c738d0842fc9c60c4648e5c8d98331526df89784 \
+    --hash=sha256:bebf8557577d4401ba8bd9ff33906f1376c877aa78d1fe216ad01b4d6745af71 \
+    --hash=sha256:bec03d0d388060058f5d291a813f21c011041938a441c593374da6077fe21b1b \
+    --hash=sha256:bf4a21e58b9cde0e401e683ebd00f6ed30a06d14e93f7c8fd059f8b6e8f87b6a \
+    --hash=sha256:c0232bce2170103ec23c454e54a57008a9a72b5d1c3105dc2496750da8cfa47c \
+    --hash=sha256:c4647674b6150d2cae088fc07de2738a84b8bcedebef29802cf0b0a82ab6face \
+    --hash=sha256:c7044802eec4524fde550afc28edda0dd5784c4c45f0be151a2d3ba017daca7d \
+    --hash=sha256:ca1f59c4e1ab6e72f0a23c13fca5430f889634166be85dbf1013683e49e3278e \
+    --hash=sha256:cfebc0ac8333520d2d0423cbbe43ae43c8838862ddb898f5ca68565e395516e9 \
+    --hash=sha256:d3e32536234a95f513bd374e93d717cf6b2231a791758de6c509e3653f234c95 \
+    --hash=sha256:d77e1b2c6d04711478cb1c4ab90db07f1609ccf06a287d5607fcd90dc9863acf \
+    --hash=sha256:d947071e6ebcf2e2bee8fce76e10faca8f7a14808ca36a910263acaacef08eca \
+    --hash=sha256:de6b9a04c606978fdfe72666fa216ffcf2d1a9f6a381058d4378f8d7b1e5de62 \
+    --hash=sha256:e1651bf8e0398574646744c1885a41198eba53dc8a9312b954073f845c90a8df \
+    --hash=sha256:e1b51bebd221006d3d2f95fbe124b22b247136647ae5dcc8c7acafba66e5ee67 \
+    --hash=sha256:e340382d1afa5d32b892b3ff062436d592ec3d692aeea3bef3a5cfe11bbf8c6f \
+    --hash=sha256:e4b582bab49ac33c8deb97e058cd67c2c50dac0dd134874106d9c774fd272529 \
+    --hash=sha256:e51ac5435758ba97ad69617e13233da53908beccc6cfcd6c34bbed8dcbede486 \
+    --hash=sha256:e5542339dcf2747135c5c85f68680353d5cb9ffd741c0f2e8d832d054d41f35a \
+    --hash=sha256:ea70f61a47f3cc93bdf8b2f368ed359ef02a01ca6393916bc8ff877427181e74 \
+    --hash=sha256:ebd4549b108d732dba1d4ace67614b9545b21ece30937a63a65dd34efa19732d \
+    --hash=sha256:efb07073be061c8f79d03d04139a80ba33cbd390ca8f0297aae9cce6411e4c6b \
+    --hash=sha256:f1e09112a2c31ffe8d80be1b0988fa6a18c5d5cad92a9ffbb1c04c91bfe52ad2 \
+    --hash=sha256:f3d7a87a78d46a2e3d5b72587ac14b4c16952dd0887dbb051451eceac774411e \
+    --hash=sha256:f4afb5c34f2c6fecdcc182dfcfc6af6cccf1aa923eed4d6a12e9d96904e1a0d8 \
+    --hash=sha256:f6d2cb59377d99718913ad9a151030d6f83ef420a2b8f521d94609ecc106ee82 \
+    --hash=sha256:f87ac53513d22240c7d59203f25cc3beac1e574c6cd681bbfd321987b69f95fd \
+    --hash=sha256:ff86011bd159a9d2dfc89c34cfd8aff12875980e3bd6a39ff097887520e60249
+    # via aiohttp
+zarr==2.18.7 \
+    --hash=sha256:ac3dc4033e9ae4e9d7b5e27c97ea3eaf1003cc0a07f010bd83d5134bf8c4b223 \
+    --hash=sha256:b2b8f66f14dac4af66b180d2338819981b981f70e196c9a66e6bfaa9e59572f5
+    # via vizarr
diff --git a/data/mnist-ae/latents.h5 b/thumbnail.gif
similarity index 55%
rename from data/mnist-ae/latents.h5
rename to thumbnail.gif
index 4222f4fb9..e8c5851c5 100644
Binary files a/data/mnist-ae/latents.h5 and b/thumbnail.gif differ
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 000000000..0456ef7c8
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3498 @@
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