Multi-scale finite element modeling allows the mechanics of amphibian neurulation to be elucidated
Bibliographic record
Abstract
The novel multi-scale computational approach introduced here makes possible a new means for testing hypotheses about the forces that drive specific morphogenetic movements. A 3D model based on this approach is used to investigate neurulation in the axolotl (Ambystoma mexicanum), a type of amphibian. The model is based on geometric data from 3D surface reconstructions of live embryos and from serial sections. Tissue properties are described by a system of cell-based constitutive equations, and parameters in the equations are determined from physical tests. The model includes the effects of Shroom-activated neural ridge reshaping and lamellipodium-driven convergent extension. A typical whole-embryo model consists of 10,239 elements and to run its 100 incremental time steps requires 2 days. The model shows that a normal phenotype does not result if lamellipodium forces are uniform across the width of the neural plate; but it can result if the lamellipodium forces decrease from a maximum value at the mid-sagittal plane to zero at the plate edge. Even the seemingly simple motions of neurulation are found to contain important features that would remain hidden, they were not studied using an advanced computational model. The present model operates in a setting where data are extremely sparse and an important outcome of the study is a better understanding of the role of computational models in such environments.
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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) | 0.001 | 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 | 0.001 |
| 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".