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Record W2966311990 · doi:10.1016/j.celrep.2019.06.089

Nanoscale Architecture of the Cortical Actin Cytoskeleton in Embryonic Stem Cells

2019· article· en· W2966311990 on OpenAlexaff
Shumin Xia, Ying Bena Lim, Zhen Zhang, Yilin Wang, Shan Zhang, Chwee Teck Lim, Evelyn K. F. Yim, Pakorn Kanchanawong

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Waterloo
FundersUniversity of QueenslandUniversity of New South WalesNational Heart, Lung, and Blood InstituteMinistry of Education - SingaporeNational Research Foundation Singapore
KeywordsEmbryonic stem cellCell biologyCytoskeletonNanoscopic scaleStem cellActinActin cytoskeletonBiologyNeuroscienceNanotechnologyMaterials scienceGeneticsCellGene

Abstract

fetched live from OpenAlex

Mechanical cues influence pluripotent stem cell differentiation, but the underlying mechanisms are not well understood. Mouse embryonic stem cells (mESCs) exhibit unusual cytomechanical properties, including low cell stiffness and attenuated responses to substrate rigidity, but the underlying structural basis remains obscure. Using super-resolution microscopy to investigate the actin cytoskeleton in mESCs, we observed that the actin cortex consists of a distinctively sparse and isotropic network. Surprisingly, the architecture and mechanics of the mESC actin cortex appear to be largely myosin II-independent. The network density can be modulated by perturbing Arp2/3 and formin, whereas capping protein (CP) negatively regulates cell stiffness. Transient Arp2/3-containing aster-like structures are implicated in the organization and mechanical homeostasis of the cortical network. By generating a low-density network that physically excludes myosin II, the interplay between Arp2/3, formin, and CP governs the nanoscale architecture of the actin cortex and prescribes the cytomechanical properties of mESCs.

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 score0.047
Threshold uncertainty score0.292

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 head0.004
GPT teacher head0.206
Teacher spread0.201 · 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

Citations105
Published2019
Admission routes1
Has abstractyes

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