Effect of lactic acid bacteria on the control of<i>Fusarium oxysporum</i>and<i>Ralstonia solanacearum</i>on singly infected and co-infected tomato plants
Bibliographic record
Abstract
AIM: To evaluate the effect of lactic acid bacteria (LAB) on the control of Fol59 and Rs on singly infected and co-infected tomato plants and to address molecular pathways that may be involved in this interaction. METHODS AND RESULTS: To assess the development of the disease, individual infection and coinfection were stimulated in plants under controlled conditions, at two concentrations of Rs and Fol59 applied at two different moments. Additionally, the antagonistic activity of LAB against Rs and Fol59 in vitro and its biocontrol efficacy in planta were evaluated. Preliminary results indicate that inoculation with 1 × 106 microconidia ml-1 of Fol59 and 1 × 108 cfu ml-1 of Rs may be a reliable synchronous coinfection method. Of the 68 LAB strains evaluated in vitro, AC13, AC40, and AC49 had an antagonistic effect on both pathogens, with AC40 showing the highest efficacy rate after submerging the seeds in suspension and sowing them in substrate. Finally, gene expression experiments confirmed the AC40 effect on the expression of PR-1a, ERF1, and LoxA genes. CONCLUSION: The delayed appearance of symptoms and the reduced severity of the disease may be associated with the expression of PR-1a, ERF1, and LoxA genes related to salicylic acid, ethylene, and jasmonic acid pathways respectively.
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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".