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Record W2103593616 · doi:10.1083/jcb.201502092

Mitotic chromosome length scales in response to both cell and nuclear size

2015· article· en· W2103593616 on OpenAlexafffund
Anne-Marie Ladouceur, Jonas F. Dorn, Paul S. Maddox

Bibliographic record

VenueThe Journal of Cell Biology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBiologyMitosisCell biologyChromosomeCell sizeCell divisionCellChromosome segregationGenetics

Abstract

fetched live from OpenAlex

Multicellular development requires that cells reduce in size as a result of consecutive cell divisions without increase in embryo volume. To maintain cellular integrity, organelle size adapts to cell size throughout development. During mitosis, the longest chromosome arm must be shorter than half of the mitotic spindle for proper chromosome segregation. Using high-resolution time-lapse microscopy of living Caenorhabditis elegans embryos, we have quantified the relation between cell size and chromosome length. In control embryos, chromosome length scaled to cell size. Artificial reduction of cell size resulted in a shortening of chromosome length, following a trend predicted by measurements from control embryos. Disturbing the RAN (Ras-related nuclear protein)-GTP gradient decoupled nuclear size from cell size and resulted in chromosome scaling to nuclear size rather than cell size; smaller nuclei contained shorter chromosomes independent of cell size. In sum, quantitative analysis relating cell, nuclear, and chromosome size predicts two levels of chromosome length regulation: one through cell size and a second in response to nuclear size.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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 designObservational
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

Citations48
Published2015
Admission routes2
Has abstractyes

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