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Two dominant timescales of cytoskeletal crosslinking in the viscoelastic response of the cytoplasm

2022· article· en· W4311704830 on OpenAlexafffund
Ora T. Cohen, Adam G. Hendricks

Bibliographic record

VenuePhysical Review Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMicrorheologyViscoelasticityCytoplasmActinCytoskeletonRelaxation (psychology)BiophysicsIntracellularPhysicsChemistryMaterials scienceMechanicsCellBiologyNeuroscienceThermodynamics

Abstract

fetched live from OpenAlex

Cells precisely regulate their frequency-dependent viscoelastic properties in response to chemical and mechanical cues. We use optical trap-based active microrheology using intracellular probes to measure the cytoplasmic mechanical response of fibroblast and macrophage cells over a broad frequency range ($\ensuremath{\sim}0.02--350$ Hz). Both cell types show similar frequency-dependent behavior, suggesting that the mechanisms that control the cell's mechanical response are general to many cell types. At frequencies above 1 Hz, the cytoplasmic mechanical behavior shows a broad distribution of relaxation timescales consistent with power-law mechanics. At low frequencies ($<1$ Hz), cells exhibit fluidlike behavior with distinct relaxation timescales, similar to that observed in reconstituted networks of transiently crosslinked actin filaments. The response across all frequencies can be captured by a mathematical model combining a power-law term with two crosslinker-unbinding terms. The two unbinding rates required to describe the low-frequency response suggest that the viscoelastic relaxation of the cytoplasm is governed either by multiple dominant crosslinkers or by a single crosslinker with multiple states.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.128
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.001
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 head0.037
GPT teacher head0.412
Teacher spread0.375 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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