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Record W4322501687 · doi:10.3390/pathogens12030379

The Devastating Rice Blast Airborne Pathogen Magnaporthe oryzae—A Review on Genes Studied with Mutant Analysis

2023· review· en· W4322501687 on OpenAlexafffund
Jinyi Tan, Zhao Haikun, Josh Li, Yihan Gong, Xin Li

Bibliographic record

VenuePathogens · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyAppressoriumVirulenceMutantPathogenMagnaporthePathogenicityGeneGenomeConidiumBiotechnologyGeneticsMicrobiologyMagnaporthe griseaOryza sativa

Abstract

fetched live from OpenAlex

Magnaporthe oryzae is one of the most devastating pathogenic fungi that affects a wide range of cereal plants, especially rice. Rice blast disease causes substantial economic losses around the globe. The M. oryzae genome was first sequenced at the beginning of this century and was recently updated with improved annotation and completeness. In this review, key molecular findings on the fungal development and pathogenicity mechanisms of M. oryzae are summarized, focusing on fully characterized genes based on mutant analysis. These include genes involved in the various biological processes of this pathogen, such as vegetative growth, conidia development, appressoria formation and penetration, and pathogenicity. In addition, our syntheses also highlight gaps in our current understanding of M. oryzae development and virulence. We hope this review will serve to improve a comprehensive understanding of M. oryzae and assist disease control strategy designs in the future.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.054
GPT teacher head0.351
Teacher spread0.297 · 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

Citations34
Published2023
Admission routes2
Has abstractyes

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