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Record W3045602409 · doi:10.3389/fmolb.2020.00161

Elastic Tethers Between Separating Anaphase Chromosomes Regulate the Poleward Speeds of the Attached Chromosomes in Crane-Fly Spermatocytes

2020· article· en· W3045602409 on OpenAlexafffund
Arthur Forer, Michael W. Berns

Bibliographic record

VenueFrontiers in Molecular Biosciences · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaDavid and Lucile Packard Foundation
KeywordsAnaphaseBiologyChromosomeCell biologyGenetics

Abstract

fetched live from OpenAlex

Elastic "tethers" connect separating anaphase chromosomes in most (or all) animal cells. We tested whether tethers are involved in coordinating movements of separating anaphase chromosomes in crane-fly spermatocytes. In these cells the coupled movements of separating chromosomes become uncoupled after the tethers are severed by laser microbeam irradiation of the interzone region between the chromosomes (Sheykhani et al., 2017). While this strongly suggests that tethers are involved with coordinating the poleward chromosome movements, the experiments are open to another interpretation: laser irradiations that cut the tethers also might damage something else in the interzone, and those non-tether components might regulate chromosome movements. In the experiments reported herein we distinguish between those two possibilities by disabling the tethers without cutting the interzone. We cut the arms from individual chromosomes, thereby severing the mechanical connection between separating chromosomes, disconnecting them, without damaging components in the interzone. Disabling tethers in this way uncoupled the movements of the separating chromosomes. We thus conclude that tethers are involved in regulating the speeds of separating anaphase chromosomes in crane-fly spermatocytes.

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 score0.026
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.253
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 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

Citations10
Published2020
Admission routes2
Has abstractyes

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