Coupling ALE Technique and Harmonic Parametrization to Describe Concurrent and Successive Elementary Cell Deformations
Bibliographic record
Abstract
We present here an extension of our finite element model of the Drosophila embryo to consider the interdependence of successive morphogenetic movements. A novel approach is used, which couples the Arbitrary Lagrangian Eulerian formulation with the harmonicparametrization. The combination of the two techniques allows to constantly update the deforming embryo geometry and simultaneously build an associated system of curvilinear coordinates. Thus, we are able to exactly describe the elementary cell deformations responsible for each biological event that are then defined with respect to the dynamic middle surface of the embryonic tissue and to their relative reference configuration. Both the active and the passive deformations occurring to the cells are considered through the deformation gradient decomposition. We develop a concurrent simulation of three morphogenetic movements: the ventral furrow invagination, the cephalic furrow formation and the germ band extension. The results show a consistent similarity with respect to the physical phenomena. More generally, the numerical approach that we propose could constitute a powerful tool to rigorously describe and simulate complex shape changes in biological systems.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".