Embryonic tissues are viscoelastic materials
Bibliographic record
Abstract
Early embryonic development is characterized by spectacular morphogenetic processes such as sorting or spreading of tissues. Analogy between viscoelastic fluids and certain properties of embryonic tissues turned out to be useful in interpreting some aspects of these morphogenetic phenomena. In accordance with the differential adhesion hypothesis, the values of tissue-specific surface tensions have been shown to be consistent with the equilibrium configurations such tissues reach in the course of sorting and spreading. A method to measure tissue surface tension and viscoelastic properties is described. Notions like the Laplace's equation relating surface tension to radii of curvature, or the Kelvin model of viscoelasticity are used to analyze the results of these measurements. The fluid analogy is extended to time-dependent phenomena, in particular, to the analysis of cellular pattern evolution in the course of spreading. On the basis of recent experimental findings, we demonstrate that the kinetics of spreading and nucleation in binary fluids can be analyzed using the same formalism. We illustrate how our results can be used to obtain biologically relevant information on the strength of binding between specific cell adhesion molecules under near physiological conditions. We also suggest a diagnostic application of our method to monitor the metastatic potential of tumors. PACS No.: 03.65Ge
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".