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Record W2947495774 · doi:10.1126/sciadv.aav7999

Control of antiviral innate immune response by protein geranylgeranylation

2019· article· en· W2947495774 on OpenAlexaff
Shigao Yang, Alfred T. Harding, Catherine Sweeney, David Miao, Gregory Swan, Connie J. Zhou, Zhaozhao Jiang, Katherine A. Fitzgerald, Gianna Hammer, Martin O. Bergö, Heather K. Kroh, D. Borden Lacy, Chunxiang Sun, Michael Glogauer, Loretta G. Que, Nicholas S. Heaton, Donghai Wang

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Institute of Allergy and Infectious DiseasesU.S. Department of Veterans Affairs
KeywordsInnate immune systemGeranylgeranylationBiologyCell biologyUbiquitin ligaseImmune systemUbiquitinPrenylationImmunologyBiochemistry

Abstract

fetched live from OpenAlex

to the MAVS signalosome and the subsequent cleavage of Ripk1 that terminates MAVS signaling. Consistently, mice with myeloid deficiency of protein geranylgeranylation showed improved survival upon influenza A virus infection. Our work revealed a critical role of protein geranylgeranylation in regulating antiviral innate immune response.

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)0.001
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 score0.025
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.247
Teacher spread0.243 · 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

Citations67
Published2019
Admission routes1
Has abstractyes

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