Induction of bacterial blight (<i>Xanthomonas oryzae</i> pv. <i>oryzae</i>) resistance in rice by treatment with acibenzolar‐S‐methyl
Bibliographic record
Abstract
Summary The role of the plant defence activator, acibenzolar‐S‐methyl (ASM), in inducing resistance in rice against bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae (Xoo) was studied. Application of ASM induced resistance in rice to infection by Xoo. When the pathogen was clip‐inoculated to the rice plants, it caused bacterial leaf blight symptoms in the untreated control. However, in the rice plants pretreated with ASM, infection was significantly reduced. Induced systemic resistance was found to persist for up to 3 days in the pretreated rice plants. Increased phenolic content and accumulation of pathogenesis‐related (PR) proteins, viz. chitinase, β‐1,3‐glucanase and thaumatin‐like protein (TLP; PR 5) were observed in rice plants pretreated with ASM followed by inoculation with Xoo. Immunoblot analysis using rice TLP and tobacco chitinase antiserum revealed rapid induction and over‐expression of 25 and 35 kDa TLP and chitinase, respectively, in rice in response to pretreatment with ASM followed by Xoo inoculation. Based on these experiments, it is evident that induction of disease resistance in rice was accelerated following treatment with ASM.
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 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".