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Record W2004986844 · doi:10.4161/psb.2.4.4042

Roles of Iron in Plant Defence and Fungal Virulence

2007· article· en· W2004986844 on OpenAlexaff
David L. Greenshields, Guosheng Liu, Yangdou Wei

Bibliographic record

VenuePlant Signaling & Behavior · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyVirulencePlant defense against herbivoryMicrobiologyBiotechnologyGeneticsGene

Abstract

fetched live from OpenAlex

Iron is an essential component of various proteins and pigments for both plants and pathogenic fungi. However, redox cycling between the ferric and ferrous forms of iron can also catalyse the production of dangerous free radicals and iron homeostasis is therefore tightly regulated. our work has indicated that monocot plants challenged by pathogenic fungi redistribute cellular iron to the apoplast in a controlled manner to activate both intracellular and extracellular defences. In the apoplast, the accumulation of free, reactive ferric iron mediates defensive H(2)O(2) production. Inside the cell, this efflux of iron creates a state of iron depletion, which directs the transcription of pathogenesis-related genes in concert with H(2)O(2). In this addendum, we describe differences between the roles of iron in mediation of the oxidative burst in cereal and Arabidopsis responses to fungal pathogens. Also, we discuss the implications of current work concerning fungal iron uptake on host defence strategies.

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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.225
Teacher spread0.202 · 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

Citations43
Published2007
Admission routes1
Has abstractyes

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