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Record W1969536112 · doi:10.1021/jf101029a

Metabolite Composition and Bioactivity of<i>Rhizoctonia solani</i>Sclerotial Exudates

2010· article· en· W1969536112 on OpenAlexaff
Konstantinos A. Aliferis, Suha Jabaji

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsRhizoctonia solaniMetaboliteChemistryComposition (language)Fourier transform ion cyclotron resonanceMicroorganismNuclear magnetic resonance spectroscopyMass spectrometryBotanyBiochemistryBiologyChromatographyBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

Sclerotia are vegetative structures that play a major role in survival of fungi under adverse conditions. The sclerotia of the plant pathogen Rhizoctonia solani AG2-2 IIIB exude liquid brown droplets that were evaluated for their bioactivity and toxicity against microorganisms and plant species. Also, their metabolic composition was analyzed by integrating Fourier transform ion cyclotron resonance-mass spectrometry (FT-ICR/MS), gas chromatography-MS (GC/MS), and proton nuclear magnetic resonance ((1)H NMR) spectroscopy. The results showed that exudates are complex mixtures composed of phenolics (17.40%), carboxylic acids (12.79%), carbohydrates (6.08%), fatty acids (3.78%), and amino acids (3.47%). The presence of such metabolites contributed to their antifungal and phytotoxic activities. The biological interpretation of the results highly suggests that the exudates not only have multiple roles in fungal physiology but also are a potential bioactive source with moderate toxicity. Our findings show with certainty that the integration of different analytical platforms is a powerful approach for extracting the maximum and reliable information on the metabolic composition of complex biological samples.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.146

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 head0.007
GPT teacher head0.175
Teacher spread0.167 · 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

Citations53
Published2010
Admission routes1
Has abstractyes

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