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Record W1970436330 · doi:10.5402/2012/513546

An Approach to Visualize the Deformation of the Intermediate Filament Cytoskeleton in Response to Locally Applied Forces

2011· article· en· W1970436330 on OpenAlexafffund
Jiashan Wang, Andrew E. Pelling

Bibliographic record

VenueISRN Cell Biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCytoskeletonVimentinIntermediate filamentProtein filamentActinConfocalDeformation (meteorology)MicrotubuleBiophysicsAnisotropyActin cytoskeletonMaterials sciencePhysicsCell biologyChemistryCellBiologyOpticsComposite material

Abstract

fetched live from OpenAlex

The intermediate filament (IF) cytoskeleton plays an important role in integrating biomechanical pathways associated with the actin and microtubule cytoskeleton. Vimentin is a type III IF protein commonly found in fibroblast cells and plays a role in transmitting forces through the cytoskeleton. Employing simultaneous laser scanning confocal and atomic force microscopy (AFM), we developed a methodology to quantify the deformation of the GFP-vimentin-labeled IF cytoskeleton as a function of time in response to force application by the AFM. Over short times (seconds), IFs deformed rapidly and transmitted force throughout the entire cell in a highly complex and anisotropic fashion. After several minutes, mechanically induced displacements of IFs resemble basal movements. In well-adhered cells the deformation of IFs is highly anisotropic as they tend to deform away from the longitudinal axis of the cell. This study demonstrates that simultaneous AFM and LSCM can be employed to track the deformation and dissipation of force through the IF cytoskeleton.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.252
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2011
Admission routes2
Has abstractyes

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