Effects of Pseudomonas aureofaciens 63-28 on Defense Responses in Soybean Plants Infected by Rhizoctonia solani
Bibliographic record
Abstract
The objective of this work was to investigate the ability of the plant growth-promoting rhizobacterium Pseudomonas aureofaciens 63-28 to induce plant defense systems, including defense-related enzyme levels and expression of defense-related isoenzymes, and isoflavone production, leading to improved resistance to the phytopathogen Rhizoctonia solani AG-4 in soybean seedlings. Seven-dayold soybean seedlings were inoculated with P. aureofaciens 63-28, R. solani AG-4, or P. aureofaciens 63-28 plus R. solani AG-4 (P+R), or not inoculated (control). After 7 days of incubation, roots treated with R. solani AG-4 had obvious damping-off symptoms, but P+R-treated soybean plants had less disease development, indicating suppression of R. solani AG-4 in soybean seedlings. Superoxide dismutase (SOD) and catalase (CAT) activities of R. solani AG-4-treated roots increased by 24.6% and 54.0%, respectively, compared with control roots. Ascorbate peroxidase (APX) and phenylalanine ammonia lyase (PAL) activities of R. solani AG-4-treated roots were increased by 75.1% and 23.6%, respectively. Polyphenol oxidase (PPO) activity in soybean roots challenged with P. aureofaciens 63-28 and P+R increased by 25.0% and 11.6%, respectively. Mn-SOD (S1 band on gel) and Fe-SOD (S2) were strongly induced in P+R-treated roots, whereas one CAT (C1) and one APX (A3) were strongly induced in R. solani AG-4- treated roots. The total isoflavone concentration in P+Rtreated shoots was 27.2% greater than the control treatment. The isoflavone yield of R. solani AG-4-treated shoots was 60.9% less than the control.
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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".