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Record W3170939697 · doi:10.1096/fba.2021-00040

Plant extracellular vesicles: Trojan horses of cross‐kingdom warfare

2021· review· en· W3170939697 on OpenAlexfundno aff
Baoye He, Rachael Hamby, Hailing Jin

Bibliographic record

VenueFASEB BioAdvances · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchNational Institutes of HealthNational Science Foundation
KeywordsTrojanExtracellular vesiclesKingdomVesicleBiologyCell biologyComputer securityComputer scienceBotanyBiochemistryMembrane

Abstract

fetched live from OpenAlex

Plants communicate with their interacting microorganisms through the exchange of functional molecules. This communication is critical for plant immunity, for pathogen virulence, and for establishing and maintaining symbioses. Extracellular vesicles (EVs) are lipid bilayer-enclosed spheres that are released by both the host and the microbe into the extracellular environment. Emerging evidence has shown that EVs play a prominent role in plant-microbe interactions by safely transporting functional molecules, such as proteins and RNAs to interacting organisms. Recent studies revealed that plant EVs deliver fungal gene-targeting small RNAs into fungal pathogens to suppress infection via cross-kingdom RNA interference (RNAi). In this review, we focus on the recent advances in our understanding of plant EVs and their role in plant-microbe 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 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.041
GPT teacher head0.342
Teacher spread0.302 · 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

Citations64
Published2021
Admission routes1
Has abstractyes

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