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Record W2099284380 · doi:10.4161/psb.29130

<i>Arabidopsis</i>dual resistance proteins, both RPS4 and RRS1, are required for resistance to bacterial wilt in transgenic<i>Brassica</i>crops

2014· article· en· W2099284380 on OpenAlexaff
Mari Narusaka, Katsunori Hatakeyama, Ken Shirasu, Yoshihiro Narusaka

Bibliographic record

VenuePlant Signaling & Behavior · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsBiologyBacterial wiltRalstonia solanacearumBrassicaceaeArabidopsis thalianaArabidopsisPlant disease resistanceBrassicaGenetically modified cropsR geneEffectorBotanyWilt diseaseTransgeneGenePathogenGeneticsCell biologyMutant

Abstract

fetched live from OpenAlex

Bacterial wilt phytopathogen Ralstonia solanacearum is a serious soil-borne disease that attacks several economically important plants worldwide, including Brassicaceae. Previous studies indicate that recognition of avirulence (Avr)-effector PopP2 by resistance (R) protein, RRS1-R, and physical interaction between RRS1-R and PopP2 in the nucleus are required for resistance. Of late, we showed that a pair of Arabidopsis thaliana TIR-NLR proteins, RRS1 and RPS4, function together in disease resistance against multiple pathogen isolates. Here, we report that dual R proteins, RRS1 and RPS4, from A. thaliana ecotype Wassilewskija confer resistance to bacterial wilt in transgenic Brassica crops. For practical applications, this finding may provide a new strategy for developing disease resistant plants that express R genes from other plants.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.231
Teacher spread0.207 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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