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Record W2991481733 · doi:10.1128/jvi.01891-19

Identification and Characterization of the Nucleolar Localization Signal of Autographa californica Multiple Nucleopolyhedrovirus LEF5

2019· article· en· W2991481733 on OpenAlexaff
Guoqing Chen, Qing Yan, Haoran Wang, Shufen Chao, Lijuan Wu, Peter J. Krell, Guozhong Feng

Bibliographic record

VenueJournal of Virology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of Guelph
FundersChinese Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsAutographa californicaBiologyIdentification (biology)GeneticsVirologyComputational biologyGene

Abstract

fetched live from OpenAlex

Many viruses, including human and plant viruses, target nucleolar functions as part of their infection strategy. However, nucleolar localization for baculovirus proteins has not yet been characterized. In this study, two nucleolar proteins, SfNS and SfFBL, were identified in Sf9 cells. Our results showed that Autographa californica multiple nucleopolyhedrovirus (AcMNPV) infection resulted in redistribution of the nucleoli of infected cells. We demonstrated that AcMNPV late expression factor 5 (LEF5) could localize to the nucleolus and contains a nucleolar localization signal (NoLS), which is important for nucleolar localization of AcMNPV LEF5 and for production of viral progeny and yield of occlusion bodies.

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 score0.003
Threshold uncertainty score0.006

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 head0.004
GPT teacher head0.214
Teacher spread0.211 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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