Forest tree extracts induce resistance to Pseudomonas syringae pv. tomato in Arabidopsis
Bibliographic record
Abstract
The widespread use of conventional pesticides for plant pathogen control poses significant risks to human health and the environment, and it is therefore crucial to develop environmentally friendly, human-safe alternatives to these products that offer a sustainable approach for crop protection. Here, we examined the potential of ethanolic extracts from four forest tree species for their antibacterial activity against the bacterial pathogen Pseudomonas syringae pv. tomato (Pst) and their ability to trigger effective defense responses in the model plant Arabidopsis thaliana. The extracts exhibited direct toxic effects against Pst and triggered the expression of defense-related genes naturally induced by oxidative stress cues or the defense elicitor salicylic acid in leaf tissue. The direct antibacterial effects of the tree extracts, together with their defense gene-inducing effects in planta, resulted in a strong host plant-protecting effect against Pst. These findings suggest the eventual effectiveness of forest tree extracts as plant protectants against the bacterial pathogen Pst. They also suggest the potential of these extracts as a sustainable, eco-friendly alternative to conventional pesticides for the management of economically important plant pathogens.
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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".