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Record W2949747921 · doi:10.1091/mbc.e19-01-0075

Choice between 1- and 2-furrow cytokinesis in <i>Caenorhabditis elegans</i> embryos with tripolar spindles

2019· article· en· W2949747921 on OpenAlexfundno aff
Tomo Kondo, Akatsuki Kimura

Bibliographic record

VenueMolecular Biology of the Cell · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceNational Institutes of HealthSumitomo FoundationUniversity of TokyoInstitute of GeneticsNaito Foundation
KeywordsBiologyCytokinesisCell biologyCentrosomeMitosisSpindle apparatusCaenorhabditis elegansMultipolar spindlesSpindle pole bodyCell divisionCellCell cycleGenetics

Abstract

fetched live from OpenAlex

Excessive centrosomes often lead to multipolar spindles, and thus probably to multipolar mitosis and aneuploidy. In Caenorhabditis elegans, ∼70% of the paternal emb-27 APC6 mutant embryonic cells contained more than two centrosomes and formed multipolar spindles. However, only ~30% of the cells with tripolar spindles formed two cytokinetic furrows. The rest formed one furrow, similar to normal cells. To investigate the mechanism via which cells avoid forming two cytokinetic furrows even with a tripolar spindle, we conducted live-cell imaging in emb-27 APC6 mutant cells. We observed that the chromatids were aligned on only two of the three sides of the tripolar spindle, and the angle of the tripolar spindle relative to the long axis of the cell correlated with the number of cytokinetic furrows. Our numerical modeling showed that the combination of cell shape, cortical pulling forces, and heterogeneity of centrosome size determines whether cells with a tripolar spindle form one or two cytokinetic furrows.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.202
Teacher spread0.199 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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