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Record W2513957935 · doi:10.3389/fpls.2016.01306

How Does Host Carbon Concentration Modulate the Lifestyle of Postharvest Pathogens during Colonization?

2016· review· en· W2513957935 on OpenAlexaff
Dov Prusky, Fangcheng Bi, Juan Moral, Shiri Barad

Bibliographic record

VenueFrontiers in Plant Science · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsMinistry of Agriculture
FundersIsrael Science FoundationUnited States - Israel Binational Agricultural Research and Development Fund
KeywordsColonizationPostharvestHost (biology)BiologyMicrobiologyBotanyEcology

Abstract

fetched live from OpenAlex

Postharvest pathogens can penetrate fruit by breaching the cuticle or directly through wounds, and they show disease symptoms only long after infection. During ripening and senescence, the fruit undergo physiological processes accompanied by a decline in antifungal compounds, which allows the pathogen to activate a mechanism of secretion of small effector molecules that modulate host environmental pH. These result in the activation of genes under their optimal pH conditions, enabling the fungus to use a specific group of pathogenicity factors at each particular pH. New research suggests that carbon availability in the environment is a key factor triggering the production and secretion of small pH-modulating molecules: ammonia and organic acids. Ammonia is secreted under limited carbon and gluconic acid under excess carbon. This mini review describes our most recent knowledge of the mechanism of activation of pH-secreted molecules and their contribution to colonization by postharvest pathogens to facilitate the transition from quiescence to necrotrophic lifestyle.

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)0.001
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 score0.938
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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