Antifungal activity and potential mechanism of 3-phenylpropionic acid against pear Valsa canker
Bibliographic record
Abstract
Pear Valsa canker disease, caused by Valsa pyri, is among the diseases with highest prevalence, representing a significant threat to pear production. Alternative tools are urgently needed to supplement the chemical pesticide-based approaches in plant disease control. In this study, we explored the efficacy and underlying mechanism of 3-phenylpropionic acid (3PPA) against V. pyri. The results showed that 3PPA had significant inhibition activity on V. pyri. After 3PPA treatment at different concentrations, the average diameters of mycelial colony all significantly reduced compared with control. Treatment with 3PPA at the concentration of 300 mg L−1 completely inhibited V. pyri growth. Moreover, 3PPA treatment destroyed hyphal cell wall and membrane integrity of V. pyri. In addition, 3PPA significantly inhibited V. pyri-induced Valsa canker disease development on pear twigs. After treatment with 200 or 300 mg L−1 of 3PPA, the disease incidence and average lesion length markedly decreased. Transcriptome analysis showed that 3PPA modulated the expression of genes involved in cell wall and membrane biosynthesis and metabolism, as well as amino acid metabolism and secondary metabolism in V. pyri. Taken together, this study demonstrates 3PPA as a potential novel candidate control agent of pear Valsa canker and reveals the underlying inhibition mechanism.
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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".