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Record W3093026727 · doi:10.1126/science.abb2169

Apical stress fibers enable a scaling between cell mechanical response and area in epithelial tissue

2020· article· en· W3093026727 on OpenAlexfundno aff
Jesús M. López-Gay, Hayden Nunley, Meryl Spencer, Florencia di Pietro, Boris Guirao, Floris Bosveld, Olga Markova, Isabelle Gaugué, Stéphane Pelletier, David K. Lubensky, Yohanns Bellaı̈che

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsnot available
FundersInstitut CurieCentre National de la Recherche ScientifiqueFondation pour la Recherche MédicaleNational Science FoundationInstitut national de la recherche scientifiqueFondation ARC pour la Recherche sur le CancerH2020 European Research CouncilAgence Nationale de la Recherche
KeywordsCell biologyCellEpitheliumTissue engineeringEpithelial tissueTissue remodelingScalingBiologyChemistryBiophysicsImmunologyGeometryInflammationBiochemistry

Abstract

fetched live from OpenAlex

Fiber tension enables tissue scaling Tissue development, homeostasis, and repair require cells to sense mechanical forces. Although many molecular actors implicated in cell mechanosensitivity have been extensively studied, the basis by which cells adapt their mechanical responses to their geometry remains poorly defined. López-Gay et al. now identify how two fundamental epithelial structures—stress fibers and tricellular junctions—endow Drosophila cells with an internal ruler to scale their mechanical response with their area. This work explains how cells of different sizes within an epithelial tissue collectively adapt their mechanical response to control tissue shape and proliferation. Scaling of biological properties with size is a core property of other biological systems. Science , this issue p. eabb2169

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 score0.010
Threshold uncertainty score0.290

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)0.0000.000

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 head0.017
GPT teacher head0.260
Teacher spread0.244 · 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

Citations90
Published2020
Admission routes1
Has abstractyes

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