A thermodynamic perspective on mammalian neural crest ingression
Bibliographic record
Abstract
The ingression of neural crest cells from an ectodermal to a mesodermal layer is regulated by instructive, directional cues and potentially stochastic, biophysical parameters such as differential cell adhesion and tension heterogeneity. However, a cohesive framework in which to consider how various influences contribute to ingression remains elusive. Here, we observe the cell behaviors of the murine neural crest in three dimensions over time and apply a free energy framework to more wholly understand why cells ingress. Guided by work on granular matter that provides a path by which to define the roles of stochastic mechanisms in nonequilibrium systems, we measured and manipulated biophysical parameters in vivo. The data suggest that an energy barrier to cell ingression is overcome by a combination of relatively favorable cell adhesion energies, high cell shape fluctuations, and entropic cell packing configurations. Under those conditions, cell ingression may proceed spontaneously. Recognized biophysical cues likely tilt these parameters to make the process more robust. The results imply that dissipative mechanisms which transiently disorder tissue may underlie some morphogenetic events. Variations of a thermodynamic framework can potentially be applied to integrate various inputs that drive morphogenesis in different contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".