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Record W2527241049 · doi:10.31018/jans.v8i2.836

Partial characterization of toxins associated with stem end rot of mango caused by Lasiodiplodia theobromae

2016· article· en· W2527241049 on OpenAlexfundno aff
P. Mohammed Faisal, K. Prabakar

Bibliographic record

VenueJournal of Applied and Natural Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersInternational Development Research CentreGovernment of Canada
KeywordsLasiodiplodia theobromaeWiltingToxinBioassayToxicityFood scienceBiologyChemistryMicrobiologyBotanyHorticulture

Abstract

fetched live from OpenAlex

In this study, the toxicity of liquid culture media from different isolates of Lasiodiplodia theobromae was characterized and some properties of the toxic metabolites were distinguished. In this work toxin producing ability of L. theobromae was revealed by studying the physical parameters viz., osmotic potential, toxin concentration, pH, temperature and biological parameter like host specificity and wilting index. The obtained results showed that the optimal toxin-production conditions for L. theobromae in potato dextrose broth under pH 6.0, at 25-35°C for 7 days. The liquid culture from all isolates were toxic to mango plants and induced the rapid wilting. The toxin obtained from the liquid culture has thermal, acid base stability and a broad range of toxicity to main host and non-host plants. Moreover, the direct bioassay for two components of the liquid filtrates precipitated by ethanol showed that the active ingredient of the toxin is a kind of non protein substance, which was further endorsed by the papain hydrolysis analysis. Our results confirmed the chemical nature of toxic compound elucidating the favorable environmental conditions for toxin production of L. theobromae and proved potential role of toxic metabolites in the mechanism of disease development.

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 score0.135
Threshold uncertainty score0.128

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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