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Record W2974342976 · doi:10.1128/mbio.01841-19

Structure-Function Studies of the Bacillus subtilis Ric Proteins Identify the Fe-S Cluster-Ligating Residues and Their Roles in Development and RNA Processing

2019· article· en· W2974342976 on OpenAlexaff
Felix Adusei-Danso, Faisal Tarique Khaja, Micaela DeSantis, Philip D. Jeffrey, Eugenie Dubnau, Borries Demeler, Matthew B. Neiditch, David Dubnau

Bibliographic record

VenuemBio · 2019
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsUniversity of Lethbridge
FundersNational Institute of Allergy and Infectious DiseasesNational Science FoundationNational Institutes of HealthNational Cancer InstituteUniversity of Texas Health Science Center at San AntonioNational Institute of General Medical SciencesDepartment of Mechanical Engineering, University of Texas at Austin
KeywordsBacillus subtilisBiologyRNAPhenotypeGeneticsFunction (biology)Computational biologyBiochemistryBacteriaChemistryGene

Abstract

fetched live from OpenAlex

Among the phenotypes associated with the inactivation of these proteins are the inability to be genetically transformed or to form biofilms, a decrease in sporulation frequency, and changes in the stability and maturation of multiple RNA species. Despite their importance, the molecular mechanisms of Ric protein activities have not been elucidated and the roles of the two iron-sulfur clusters on the complex of the three proteins are not understood. To unravel the mechanisms of Ric action, molecular characterization of the complex and of its constituent proteins is essential. This report represents a major step toward understanding the structures of the Ric proteins, the arrangement and roles of the Fe-S clusters, and the phenotypes associated with Ric mutations.

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 score0.003

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 integrity0.0000.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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