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Record W3107966501 · doi:10.3389/fpls.2020.600063

Directing Trophic Divergence in Plant-Pathogen Interactions: Antagonistic Phytohormones With NO Doubt?

2020· review· en· W3107966501 on OpenAlexafffund
Shuanglong Huang, Xuehua Zhang, W. G. Dilantha Fernando

Bibliographic record

VenueFrontiers in Plant Science · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJasmonic acidBiologyAuxinTrophic levelAbscisic acidCell biologyBotanySalicylic acidEcologyBiochemistryGene

Abstract

fetched live from OpenAlex

A fundamental process culminating in the mechanisms of plant-pathogen interactions is the regulation of trophic divergence into biotrophic, hemibiotrophic, and necrotrophic interactions. Plant hormones, of almost all types, play significant roles in this regulatory apparatus. In plant-pathogen interactions, two classical mechanisms underlying hormone-dependent trophic divergence are long recognized. While salicylic acid dominates in the execution of host defense response against biotrophic and early-stage hemibiotrophic pathogens, jasmonic acid, and ethylene are key players facilitating host defense response against necrotrophic and later-stage hemibiotrophic pathogens. Evidence increasingly suggests that trophic divergence appears to be modulated by more complex signaling networks. Acting antagonistically or agonistically, other hormones such as auxins, cytokinins, abscisic acid, gibberellins, brassinosteroids, and strigolactones, as well as nitric oxide, are emerging candidates in the regulation of trophic divergence. In this review, the latest advances in the dynamic regulation of trophic divergence are summarized, emphasizing common and contrasting hormonal and nitric oxide signaling strategies deployed in plant-pathogen interactions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.980
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.248
Teacher spread0.223 · 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 teacher head, 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

Citations43
Published2020
Admission routes2
Has abstractyes

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