Video force microscopy reveals the mechanics of ventral furrow invagination in Drosophila
Bibliographic record
Abstract
The absence of tools for mapping the forces that drive morphogenetic movements in embryos has impeded our understanding of animal development. Here we describe a unique approach, video force microscopy (VFM), that allows detailed, dynamic force maps to be produced from time-lapse images. The forces at work in an embryo are considered to be decomposed into active and passive elements, where active forces originate from contributions (e.g., actomyosin contraction) that do mechanical work to the system and passive ones (e.g., viscous cytoplasm) that dissipate energy. In the present analysis, the effects of all passive components are considered to be subsumed by an effective cytoplasmic viscosity, and the driving forces are resolved into equivalent forces along the edges of the polygonal boundaries into which the region of interest is divided. Advanced mathematical inverse methods are used to determine these driving forces. When applied to multiphoton sections of wild-type and mutant Drosophila melanogaster embryos, VFM is able to calculate the equivalent driving forces acting along individual cell edges and to do so with subminute temporal resolution. In the wild type, forces along the apical surface of the presumptive mesoderm are found to be large and to vary parabolically with time and angular position, whereas forces along the basal surface of the ectoderm, for example, are found to be smaller and nearly uniform with position. VFM shows that in mutants with reduced junction integrity and myosin II activity, the driving forces are reduced, thus accounting for ventral furrow failure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".