MétaCan
Menu
Back to cohort
Record W3008156371 · doi:10.1126/science.aaw2601

Vertebrate diapause preserves organisms long term through Polycomb complex members

2020· article· en· W3008156371 on OpenAlexafffund
Chi‐Kuo Hu, Wei Wang, Julie Brind’Amour, Param Priya Singh, G. Reeves, Matthew C. Lorincz, Alejandro Sánchez Alvarado, Anne Brunet

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of HealthNIH Office of the DirectorLife Sciences Research FoundationHoward Hughes Medical Institute
KeywordsDiapauseBiologyChromatinVertebrateAdaptation (eye)KillifishGeneEvolutionary biologyCell biologyGeneticsEcologyNeuroscience

Abstract

fetched live from OpenAlex

), we show that diapause preserves complex organisms for extremely long periods of time without trade-offs for subsequent adult growth, fertility, and life span. Transcriptome analyses indicate that diapause is an active state, with dynamic regulation of metabolism and organ development genes. The most up-regulated genes in diapause include Polycomb complex members. The chromatin mark regulated by Polycomb, H3K27me3, is maintained at key developmental genes in diapause, and the Polycomb member CBX7 mediates repression of metabolism and muscle genes in diapause. CBX7 is functionally required for muscle preservation and diapause maintenance. Thus, vertebrate diapause is a state of suspended life that is actively maintained by specific chromatin regulators, and this has implications for long-term organism preservation.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.305
Teacher spread0.273 · 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

Citations115
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueScienceSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207