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Record W2076650069 · doi:10.4161/psb.27563

The rise of the undead

2014· article· en· W2076650069 on OpenAlexaff
Jennifer D. Lewis, Timothy Lo, Patrick Bastedo, David S. Guttman, Darrell Desveaux

Bibliographic record

VenuePlant Signaling & Behavior · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEffectorBiologyPseudomonas syringaeArabidopsisImmunityArabidopsis thalianaPathogenCell biologyImmune systemMicrobiologyGeneticsGeneMutant

Abstract

fetched live from OpenAlex

Pathogens use effector proteins to suppress host immunity and promote infection. However, plants can recognize specific effectors and mount an effector-triggered immune response that suppresses pathogen growth. The YopJ/HopZ family of type III secreted effector proteins is broadly distributed in bacterial pathogens of both animals and plants. These effectors can either suppress host immunity or elicit defense responses depending on the host genotype. In a recent report, we identified an Arabidopsis thaliana pseudokinase ZED1 that is required for the recognition of the Pseudomonas syringae HopZ1a effector. Here we discuss the role of ZED1 in HopZ1a recognition, and present models of effector recognition in plants. We draw parallels between HopZ1a and YopJ effector proteins, and between ZED1 and other immunity-related kinases that can be targeted by pathogen effectors.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.024
GPT teacher head0.224
Teacher spread0.200 · 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 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

Citations19
Published2014
Admission routes1
Has abstractyes

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