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The Biological Impact of the Hypervariable N-Terminal Region of Potyviral Genomes

2019· review· en· W2957622437 on OpenAlexafffund
Hongguang Cui, Aiming Wang

Bibliographic record

VenueAnnual Review of Virology · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBiologyGenomeGeneticsHypervariable regionPicornaviridaePicornavirusVirologyOpen reading frameFrameshift mutationRNAGenomic organizationPolyproteinsGenePeptide sequenceMutation

Abstract

fetched live from OpenAlex

is the largest family of plant-infecting RNA viruses, encompassing over 30% of known plant viruses. The family is closely related to animal picornaviruses such as enteroviruses and belongs to the picorna-like supergroup. Like all other picorna-like viruses, potyvirids employ polyprotein processing as a gene expression strategy and have single-stranded, positive-sense RNA genomes, most of which are monopartite with a long open reading frame. The potyvirid polyproteins are highly conserved in the central and carboxy-terminal regions. In contrast, the N-terminal region is hypervariable and contains position-specific mutations resulting from transcriptional slippage during viral replication, leading to translational frameshift to produce additional viral proteins essential for viral infection. Some potyvirids even lack one of the N-terminal proteins P1 or helper component-protease and have a genus-specific or species-specific protein instead. This review summarizes current knowledge about the conserved and divergent features of potyvirid genomes and biological relevance and discusses future research directions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.104
GPT teacher head0.371
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations71
Published2019
Admission routes2
Has abstractyes

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