Bacterial antagonists and hexanal-induced systemic resistance of mango fruits against <i>Lasiodiplodia theobromae</i> causing stem-end rot
Bibliographic record
Abstract
Induction of defense-related enzymes, such as phenylalanine ammonia lyase (PAL), peroxidase (PO), polyphenol oxidase (PPO), superoxide dismutase (SOD) and catalase (CAT) due to bacterial antagonists viz., Pseudomonas fluorescens (Pf1) and Bacillus subtilis (EPCO16) and plant-derived lipoxygenase volatile compound hexanal, were studied in mango fruits against Lasiodiplodia theobromae causing stem-end rot disease. The results showed increased induction of all the defense-related enzymes in mango fruits 3–5 days after dipping treatment with combination of bacterial antagonists and hexanal when compared to untreated control treatment and treatment with fungicide carbendazim in storage condition. The increased activity was observed up to 3 days after treatment and thereafter declined. Further, increased expression of specific isoforms of PO, PPO, SOD and CAT were also observed in the treatment effect of P. fluorescens (0.5%) + hexanal (0.02%) treated fruits against L. theobromae. From the results obtained, it is inferred that due to the enhancement of defense-related enzymes via the phenylpropanoid pathway and due to secretion of secondary metabolites that would play significant role in hindering the pathogen quiescence and further invasion in mango fruits and thereby prevent the fruit rot.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".