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Record W2001431170 · doi:10.4161/21624046.2014.967611

Microtubule dynamics followed through cell differentiation and tissue biogenesis in<i>C. elegans</i>

2014· article· en· W2001431170 on OpenAlexfundno aff
Benjamin Lacroix, Amy Shaub Maddox

Bibliographic record

VenueWorm · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthCancer Research Society
KeywordsMicrotubuleDynamics (music)BiogenesisCell biologyCellCellular differentiationCaenorhabditis elegansBiologyChemistryPhysicsBiochemistryGene

Abstract

fetched live from OpenAlex

Microtubules (MTs) are cytoskeletal filaments essential for many processes in eukaryotic cells. Assembled of tubulin subunits, MTs are dynamic structures that undergo successive and stochastic phases of polymerization and depolymerization, a behavior called dynamic instability. Dynamic instability has been extensively studied in cultured cells and in vitro using cytoplasmic extracts or reconstituted MTs. However, how MTs behave in intact tissues and how their dynamics are affected by or affect tissue function are poorly understood. Recent advances in high-resolution live imaging have helped overcome technical limitation in order to visualize MTs in intact living organisms including Drosophila or Caenorhabditis elegans. We recently took advantage of the well-characterized development, small size and transparency of C. elegans to monitor MT dynamics throughout tissue biogenesis with high spatial and temporal resolution. Using the sex myoblast lineage that generates the egg-laying muscles from 2 mitotic precursors, we identified selective dynamics in precursor versus differentiated cells, and molecular regulation of MT dynamics changes that occur during cell differentiation. We discuss here how this approach led to novel insights into the regulation of MTs dynamics and organization in vivo.

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 score0.024
Threshold uncertainty score0.631

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 head0.202
Teacher spread0.198 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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