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Record W2528943679 · doi:10.7567/jjap.55.1102a8

Soft matter physics: Tools and mechanical models for living cellular aggregates

2016· article· en· W2528943679 on OpenAlexaff
Nada Khalifat, Grégory Beaune, Usharani Nagarajan, Françoise M. Winnik, Françoise Brochard‐Wyart

Bibliographic record

VenueJapanese Journal of Applied Physics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversité de Montréal
FundersUniversité de Lorraine
KeywordsLiving matterActive matterSoft matterElasticity (physics)PipetteNanotechnologyMechanicsSoft materialsPhysicsLiving systemsBiophysicsClassical mechanicsStatistical physicsBiological systemMaterials scienceChemistryComputer scienceBiologyThermodynamicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Tissues belong to the broad field of active matter, a novel class of non-equilibrium materials composed of many interacting units that individually consume energy and collectively generate motion or mechanical stresses. Active systems span an enormous range of length scales, from individual living cells, to tissues and organisms, to animal groups. We introduce the concept of biological tissues as examples of entangled active matter, where the units (cell) are bound by transient links. We focus here on the mechanical properties (surface tension, elasticity, and viscosity) of cells and tissues derived from measurements performed by the pipette aspiration technique. This approach has been very fruitful in unveiling striking analogies between the physics of inert soft matter (polymer, viscous pastes, and Silly Putty ® ) and the behavior of biological tissues. The results obtained from such analogies suggest important implications in the fields of tissue engineering and development.

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 score0.293
Threshold uncertainty score0.392

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 head0.015
GPT teacher head0.230
Teacher spread0.214 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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