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Record W1984581162 · doi:10.1139/p00-035

Embryonic tissues are viscoelastic materials

2000· article· en· W1984581162 on OpenAlexvenueno aff
D. Beysens, Gabor Forgács, James A. Glazier

Bibliographic record

VenueCanadian Journal of Physics · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsViscoelasticityPhysicsCurvatureSurface tensionMechanicsClassical mechanicsThermodynamicsGeometry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.219
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2000
Admission routes1
Has abstractyes

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