The mechanics of neurulation: Insights from a whole‐embryo computational model
Bibliographic record
Abstract
The mechanics of amphibian neurulation is explored using a new whole‐embryo finite element model. The initial geometry of the model was generated from 3D surface reconstructions of live embryos obtained using a robotic microscope and from serial sections of fixed embryos. Material properties were obtained from micro‐mechanical testing, and observed regional and temporal variations in these properties were assumed to be the consequence of gene expression. These mechanical properties along with active driving forces generated by convergent extension and other mechanisms were represented through a system of cell‐based constitutive equations. The model was implemented using custom‐written software and as it ran, it predicted how an embryo with a particular initial geometry, specific base mechanical properties and given patterns of gene expression would deform over time. The model shows that regions where Shroom, chordin and PCP are expressed have distinctive mechanical properties and that these properties are crucial to neural tube closure. The model also reveals that neurulation can be disrupted by relatively minor changes in gene expression and by a number of other physiologically relevant factors. This work was funded by the Canadian Institutes of Health Research (CIHR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".