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Record W2230795309 · doi:10.1080/19491034.2015.1095432

Periodicity of nuclear morphology in human fibroblasts

2015· article· en· W2230795309 on OpenAlexaff
Laura Seaman, Walter Meixner, John Snyder, Indika Rajapakse

Bibliographic record

VenueNucleus · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMorphology (biology)Cell biologyBiophysicsBiologyZoology

Abstract

fetched live from OpenAlex

MOTIVATION: Morphology of the cell nucleus has been used as a key indicator of disease state and prognosis, but typically without quantitative rigor. It is also not well understood how nuclear morphology varies with time across different genetic backgrounds in healthy cells. To help answer these questions we measured the size and shape of nuclei in cell-cycle-synchronized primary human fibroblasts from 6 different individuals at 32 time points over a 75 hour period. RESULTS: The nucleus was modeled as an ellipsoid and its dynamics analyzed. Shape and volume changed significantly over this time. Two prominent frequencies were found in the 6 individuals: a 17 hour period consistent with the cell cycle and a 26 hour period. Our findings suggest that the shape of the nucleus changes over time and thus any time-invariant shape property may provide a misleading characterization of cellular populations at different phases of the cell cycle. The proposed methodology provides a general method to analyze morphological change using multiple time points even for non-live-cell experiments.

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 score0.003

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 communication0.0000.000
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 head0.013
GPT teacher head0.242
Teacher spread0.228 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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