docs(dynacell): point code links at applications/dynacell on main

applications/dynacell/README.md

@@ -56,7 +56,7 @@ must be installed with `uv`. Install uv first if you don't have it
 (`curl -LsSf https://astral.sh/uv/install.sh | sh`), then:
 
 ```bash
-git clone --branch dynacell-models https://github.com/mehta-lab/VisCy.git
+git clone https://github.com/Biohub/VisCy.git
 cd VisCy
 uv venv -p 3.13
 source .venv/bin/activate
@@ -216,7 +216,7 @@ a bare invocation writes **and** submits.
 
 ## Data and code availability
 
-- **Code** — BSD-3-Clause, [github.com/mehta-lab/VisCy](https://github.com/mehta-lab/VisCy/tree/dynacell-models).
+- **Code** — BSD-3-Clause, [github.com/Biohub/VisCy](https://github.com/Biohub/VisCy/tree/main/applications/dynacell).
 - **Data** — OME-Zarr stores hosted on AWS Open Data. The DynaCell A549 dataset
   is released under CC-BY-4.0; the reprocessed iPSC subset under the Allen
   Institute Terms of Use.

applications/dynacell/examples/hf_demo/cards/checkpoints_README.md

@@ -16,7 +16,7 @@ tags:
 Model checkpoints for the **DynaCell** virtual-staining demo: predict fluorescence
 channels (membrane, nuclei, ER, mitochondria) from label-free **phase-contrast**
 3-D microscopy of live A549 cells. Trained and run with
-[VisCy](https://github.com/mehta-lab/VisCy).
+[VisCy](https://github.com/Biohub/VisCy/tree/main/applications/dynacell).
 
 Live demo: [`biohub/dynacell`](https://huggingface.co/spaces/biohub/dynacell)
 
@@ -66,7 +66,7 @@ ckpt = hf_hub_download("biohub/dynacell-checkpoints", "vscyto3d_caax.ckpt")
 
 Input data must be an OME-Zarr HCS store with `Phase3D` as the source channel.
 See the [demo Space](https://huggingface.co/spaces/biohub/dynacell) and
-[VisCy](https://github.com/mehta-lab/VisCy) for the full prediction config.
+[VisCy](https://github.com/Biohub/VisCy/tree/main/applications/dynacell) for the full prediction config.
 
 ## Training data
 

applications/dynacell/examples/hf_demo/cards/demo_data_README.md

@@ -39,7 +39,7 @@ Each store is a crop of `fov0006` from the DynaCell v1 **test** split,
 (5–21 hpi), the central 32 of 48 Z slices, and the central 512×512 of the 640×960 field
 of view. The crop is recorded in each position's `dynacell_demo` attributes and is
 rebuilt by `build_demo_data.py` in
-[VisCy](https://github.com/mehta-lab/VisCy/tree/dynacell-models/applications/dynacell/examples/hf_demo).
+[VisCy](https://github.com/Biohub/VisCy/tree/main/applications/dynacell/examples/hf_demo).
 ER and mitochondria targets are raw (not deconvolved) fluorescence, as used for
 training and evaluation.
 
@@ -64,7 +64,7 @@ zip_path = hf_hub_download(
 # unzip → open with iohub.ngff.open_ome_zarr
 ```
 
-Read the stores with [iohub](https://github.com/czbiohub-sf/iohub). The demo Space
+Read the stores with [iohub](https://github.com/Biohub/iohub). The demo Space
 loads these automatically via its "Load Demo Data" button.
 
 ## License

applications/dynacell/examples/hf_demo/hf_space/README.md

@@ -41,4 +41,4 @@ is one held-out test field of view per marker, cropped from the DynaCell v1 rele
 ([`biohub/dynacell-checkpoints`](https://huggingface.co/biohub/dynacell-checkpoints)) are the
 released A549-trained models (`s3://dynacell/v1/models/a549/`).
 
-Code: [VisCy `applications/dynacell`](https://github.com/mehta-lab/VisCy/tree/dynacell-models/applications/dynacell).
+Code: [VisCy `applications/dynacell`](https://github.com/Biohub/VisCy/tree/main/applications/dynacell).

applications/dynacell/src/dynacell/croissant/builder.py

@@ -246,7 +246,7 @@ def build_croissant_from_release(
             "@id": "dynacell-code-repo",
             "name": "VisCy — DynaCell application code",
             "description": ("Training, prediction, evaluation, and reporting pipelines. BSD-3-Clause license."),
-            "contentUrl": "https://github.com/mehta-lab/VisCy/tree/modular-viscy-staging/applications/dynacell",
+            "contentUrl": "https://github.com/Biohub/VisCy/tree/main/applications/dynacell",
             "encodingFormat": "application/x-git",
             "license": "https://opensource.org/licenses/BSD-3-Clause",
             "sha256": "",
@@ -258,9 +258,9 @@ def build_croissant_from_release(
         {
             "@type": "cr:FileObject",
             "@id": "dynacell-demo-sample",
-            "name": "DynaCell reviewer-accessible sample",
+            "name": "DynaCell demo subsample",
             "description": (
-                "Reviewer-accessible sample (<4 GB) with a reduced number of "
+                "Demo subsample (<4 GB) with a reduced number of "
                 "FOVs and timepoints, intended for quick inspection without "
                 "downloading the full release."
             ),

applications/dynacell/src/dynacell/croissant/cli.py

@@ -258,7 +258,8 @@ def _a549_static_fields():
             "were cultured and imaged under three conditions: mock-infected, "
             "Zika virus-infected, and Dengue virus-infected (both BSL-2). "
             "Each field of view is a paired 3D time-lapse acquisition combining "
-            "label-free transmitted-light channels (multi-angle phase) and a "
+            "a label-free brightfield channel, from which quantitative phase "
+            "is reconstructed, and a "
             "single fluorescence target marker per store (H2B / nucleus, CAAX / "
             "membrane, SEC61B / endoplasmic reticulum, or TOMM20 / mitochondria). "
             "Plates were imaged at native cadences of 10, 30, or 120 minutes "
@@ -286,20 +287,23 @@ def _a549_static_fields():
             "single fluorescence target marker."
         ),
         rai_data_use_cases=(
-            "The dataset is intended for two tasks: (1) virtual staining — "
-            "predicting fluorescent organelle channels from label-free "
-            "transmitted-light volumes; (2) representation learning of "
-            "organelle morphology and dynamics in 3D live cells. Validated use "
-            "cases (paired test set, see accompanying paper): zero-shot and "
-            "fine-tuned virtual staining of nuclear (H2B), membrane (CAAX), "
-            "endoplasmic reticulum (SEC61B), and mitochondrial (TOMM20) "
-            "targets in A549 cells under mock, ZIKV, and DENV conditions, "
-            "evaluated with PCC, SSIM, and FID. Not validated for: clinical "
-            "or diagnostic decision support; automated drug discovery without "
-            "independent wet-lab validation; cross-cell-line, cross-organism, "
-            "or cross-microscope generalization without retraining or "
-            "recalibration; segmentation of structures other than the four "
-            "targets above."
+            "The dataset is intended for benchmarking 3D virtual staining, "
+            "i.e., predicting fluorescence images of four organelle markers "
+            "(H2B / nucleus, CAAX / membrane, SEC61B / endoplasmic reticulum, "
+            "TOMM20 / mitochondria) from label-free phase volumes, evaluated "
+            "for pixel fidelity, segmentation utility, and single-cell "
+            "phenotype similarity. Validated use cases (see accompanying "
+            "paper): training and evaluating regression, adversarial, and "
+            "generative models on A549, iPSC, or both datasets and testing "
+            "them on both cell types under mock, ZIKV, and DENV conditions; "
+            "assessing whether predictions preserve infection responses and "
+            "remain consistent over time. Possible but not validated: "
+            "self-supervised representation learning of organelle morphology "
+            "and dynamics. Not validated for: clinical or diagnostic decision "
+            "support; automated drug discovery without independent wet-lab "
+            "validation; cell types, perturbations, or microscopes not "
+            "represented in the dataset; segmentation of structures other "
+            "than the four targets above."
         ),
         rai_data_social_impact=(
             "Positive impact: virtual staining reduces phototoxicity and "
@@ -314,8 +318,8 @@ def _a549_static_fields():
             "faithful proxies for biological ground truth in quantitative "
             "measurements where small intensity errors matter (e.g., kinetic "
             "flux, single-molecule counting). Mitigations: the dataset is "
-            "released as an evaluation benchmark with frozen test splits and "
-            "held-out perturbations; full data limitations and bias "
+            "released as an evaluation benchmark with frozen test splits; "
+            "full data limitations and bias "
             "statements are published alongside the data; benchmark code, "
             "splits, and metric implementations are open-source so claims "
             "can be reproduced and audited."
@@ -365,8 +369,8 @@ def _a549_static_fields():
                 "@type": "prov:Activity",
                 "prov:label": "DynaCell preprocessing",
                 "description": (
-                    "Phase reconstruction from multi-angle transmitted-"
-                    "light volumes via the open-source waveorder "
+                    "Phase reconstruction from brightfield volumes via "
+                    "the open-source waveorder "
                     "pipeline; co-registration of label-free and "
                     "fluorescence channels; per-FOV deskewing and stage-"
                     "position metadata correction; channel-wise "
@@ -432,24 +436,26 @@ def _aics_hipsc_static_fields():
             "paired brightfield and 3D confocal fluorescence volumes of "
             "WTC-11 human induced pluripotent stem cells (single donor, "
             "gene-edited line) on a spinning-disk confocal microscope, "
-            "with endogenous fluorescent tags marking organelle-specific "
-            "proteins. DynaCell selects four targets — H2B / nucleus, "
-            "CAAX / cell membrane, SEC61B / endoplasmic reticulum, and "
-            "TOMM20 / mitochondria — and reconstructs the corresponding "
-            "3D phase channel from the brightfield input via the open-"
-            "source waveorder pipeline. 500 FOVs per organelle in the "
-            "training split, 100 in the held-out evaluation split."
+            "with a DNA dye, a cell-membrane dye, and one endogenously "
+            "mEGFP-tagged protein per line. DynaCell selects four targets — "
+            "the DNA dye (Hoechst) for nucleus, the membrane dye (CellMask) "
+            "for cell membrane, SEC61B for endoplasmic reticulum, and TOMM20 "
+            "for mitochondria — and reconstructs the corresponding 3D phase "
+            "channel from the brightfield input via the open-source waveorder "
+            "pipeline. The training split has 500 FOVs each for nucleus, "
+            "membrane, and mitochondria and 423 for ER; the held-out "
+            "evaluation split has 100 FOVs per organelle."
         ),
         rai_data_biases=(
             "The dataset covers a single human induced pluripotent stem "
             "cell line (WTC-11) acquired on a single platform (Allen "
             "Institute spinning-disk confocal). Models trained on this "
             "data may not generalize to other iPSC lines, differentiated "
             "cell types, primary cells, organisms, or microscope "
-            "platforms without additional validation. The endogenous-"
-            "tagging strategy targets specific protein loci within each "
-            "organelle and may not capture morphological variation "
-            "visible with alternative markers."
+            "platforms without additional validation. ER and mitochondria are "
+            "marked by single endogenously tagged proteins (SEC61B, TOMM20) "
+            "and nucleus and membrane by dyes (Hoechst, CellMask); other "
+            "markers may reveal different morphological variation."
         ),
         rai_annotations_per_item=(
             "Each FOV provides paired label-free (brightfield + "
@@ -477,18 +483,17 @@ def _aics_hipsc_static_fields():
             "pipeline; reconstruction quality is bounded by that pipeline."
         ),
         rai_data_use_cases=(
-            "The dataset is intended for two tasks: (1) virtual staining "
-            "— predicting fluorescent organelle channels (nucleus, "
-            "membrane, ER, mitochondria) from label-free transmitted-"
-            "light volumes; (2) representation learning of organelle "
-            "morphology in 3D iPSCs. Validated use cases (held-out 100-"
-            "FOV evaluation split, see accompanying paper): zero-shot "
-            "and fine-tuned virtual staining of the four targets above, "
-            "evaluated with PCC, SSIM, and FID; representation learning "
-            "evaluated against organelle classification benchmarks. Not "
-            "validated for: clinical or diagnostic decision support; "
-            "cross-cell-line, cross-organism, or cross-microscope "
-            "generalization without retraining."
+            "The dataset is intended for benchmarking 3D virtual staining of "
+            "the same four organelles (Hoechst / nucleus, CellMask / "
+            "membrane, SEC61B / ER, TOMM20 / mitochondria) from phase volumes "
+            "reconstructed from brightfield, and as a second cell type and "
+            "microscope for testing cross-dataset generalization. Validated "
+            "use cases (see accompanying paper): training and evaluating "
+            "models on iPSC, A549, or both datasets with the held-out test "
+            "split of 100 FOVs per organelle. Possible but not validated: "
+            "self-supervised representation learning of organelle morphology. "
+            "Not validated for: clinical or diagnostic decision support; cell "
+            "types or microscopes not represented in the dataset."
         ),
         rai_data_social_impact=(
             "Positive impact: virtual staining reduces phototoxicity and "
@@ -543,8 +548,8 @@ def _aics_hipsc_static_fields():
                     "3D paired brightfield + confocal fluorescence "
                     "acquisition of WTC-11 hiPSC lines on the Allen "
                     "Institute spinning-disk confocal microscope. "
-                    "Endogenous fluorescent tags marked organelle-"
-                    "specific proteins."
+                    "Each FOV includes a DNA dye, a cell-membrane dye, "
+                    "and one endogenously mEGFP-tagged protein."
                 ),
                 "prov:wasAttributedTo": [
                     {
@@ -569,7 +574,9 @@ def _aics_hipsc_static_fields():
                     "via the open-source waveorder pipeline; per-FOV "
                     "channel-wise normalization statistics computed and "
                     "stored as zarr attributes; train/test split frozen "
-                    "at FOV level (500 train, 100 test per organelle)."
+                    "at FOV level (500 training FOVs each for nucleus, "
+                    "membrane, and mitochondria and 423 for ER; 100 test "
+                    "FOVs per organelle)."
                 ),
                 "prov:wasAttributedTo": [
                     {