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Record W4311580205 · doi:10.1038/s41467-022-35151-2

Slowest possible replicative life at frigid temperatures for yeast

2022· article· en· W4311580205 on OpenAlexaff
Diederik S. Laman Trip, Théo Maire, Hyun Youk

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsCanadian Institute for Advanced Research
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsYeastDoubling timeSaccharomyces cerevisiaeReactive oxygen speciesCell biologyBiologyBudding yeastGeneCellGenetics

Abstract

fetched live from OpenAlex

Determining whether life can progress arbitrarily slowly may reveal fundamental barriers to staying out of thermal equilibrium for living systems. By monitoring budding yeast's slowed-down life at frigid temperatures and with modeling, we establish that Reactive Oxygen Species (ROS) and a global gene-expression speed quantitatively determine yeast's pace of life and impose temperature-dependent speed limits - shortest and longest possible cell-doubling times. Increasing cells' ROS concentration increases their doubling time by elongating the cell-growth (G1-phase) duration that precedes the cell-replication (S-G2-M) phase. Gene-expression speed constrains cells' ROS-reducing rate and sets the shortest possible doubling-time. To replicate, cells require below-threshold concentrations of ROS. Thus, cells with sufficiently abundant ROS remain in G1, become unsustainably large and, consequently, burst. Therefore, at a given temperature, yeast's replicative life cannot progress arbitrarily slowly and cells with the lowest ROS-levels replicate most rapidly. Fundamental barriers may constrain the thermal slowing of other organisms' lives.

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: 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
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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