Integration of Cellular Dynamics and Morphology to Understand Mouse Facial Development
Bibliographic record
Abstract
One common phenotype observed in response to many developmental perturbations is a change in proliferation or apoptosis. Further, it is often predicted that small changes in proliferation or apoptosis can explain the development of a structural birth defect. One flaw with this logic is that little is known about the relationships between proliferation and morphology in the face. Does proliferation actually play a role in the normal directional outgrowth and morphological changes which pattern the developing face? Here, we set out to understand the spatial distribution of proliferation in the developing mouse face and relate regional proliferation to the growth of the face over a small span of developmental time (E10‐E11). We use light sheet microscopy to capture total and proliferating nuclei in 30 E10.5 to 11.5 mouse embryo heads. Cells are quantified using a convolutional neural network methodology that has similar accuracy in cell identification to the between observer error. From these images, we then generate an atlas using linear and non‐linear transformation and perform analysis of embryo morphology and distribution of proliferation relative to total cells. Models of proliferation and its ability to alter morphology are generated in PhysiCell ( www.physicell.org ). We identify regions where there is both change in proliferation and morphology that relates to changes in the number of tail somites. We also use the spatial data gathered from these to inform a model of growth of the maxillary prominence to determine how much proliferation is likely to contribute to the directional growth of the maxilla.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".