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Record W6948468743 · doi:10.5061/dryad.3j9kd51sh

Mass spectrometry of axonemes from Tetrahymena thermophila CU428 and acetylation mutants

2024· dataset· en· W6948468743 on OpenAlexafffund

Bibliographic record

VenueOpen MIND · 2024
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsAcetylationTetrahymenaMicrotubuleTubulinMutantCiliumLysine

Abstract

fetched live from OpenAlex

Acetylation of α-tubulin at the lysine 40 residue (αK40) by the ATAT1/MEC-17 acetyltransferase influences the properties of microtubules and is a widespread phenomenon in eukaryotic cells. Previous research indicates that microtubules that undergo acetylation at αK40 are more stable and resilient to damage. Notably, αK40 acetylation represents the sole identified post-translational modification site within the microtubule lumen, suggesting its role in regulating the lateral interactions among protofilaments within the microtubule structure. This investigation focuses on evaluating the impact of tubulin acetylation on doublet microtubules present in the cilia of Tetrahymena thermophila, employing mass spectrometry analysis. Cilia samples derived from Tetrahymena wild type, acetylation mutants (K40R and MEC17-Knockout), and non-acetylation mutants (RIB72B-Knockout and RIB72AB-Knockout) underwent comparative mass spectrometry analysis. The results from mass spectrometry revealed a correlation between αK40 acetylation and phosphorylation within the ciliary structures.

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)0.001
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.025
GPT teacher head0.311
Teacher spread0.285 · 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
GenreDataset

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

Citations0
Published2024
Admission routes2
Has abstractyes

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Same venueOpen MINDSame topicBiomedical Text Mining and OntologiesFrench-language works237,207