Effect of fusaric acid and phytoanticipins on growth of rhizobacteria and<i>Fusarium oxysporum</i>
Bibliographic record
Abstract
Suppression of soilborne diseases by biocontrol agents involves complex interactions among biocontrol agents and the pathogen and between these microorganisms and the plant. In general, these interactions are not well characterized. In this work, we studied (i) the diversity among strains of fluorescent Pseudomonas spp., Bacillus spp., and Paenibacillus sp. for their sensitivity to fusaric acid (FAc) and phytoanticipins from different host plants, (ii) the diversity of pathogenic and nonpathogenic Fusarium oxysporum isolates for their sensitivity to phytoanticipins, and (iii) the influence of FAc on the production of pyoverdine by fluorescent Pseudomonas spp. tolerant to this compound. There was a great diversity in the response of the bacterial strains to FAc; however, as a group, Bacillus spp. and Paenibacillus macerans were much more sensitive to FAc than Pseudomonas spp. FAc also affected production of pyoverdine by FAc-tolerant Pseudomonas spp. strains. Phytoanticipins differed in their effects on microbial growth, and sensitivity to a phytoanticipin varied among bacterial and fungal strains. Biochanin A did not affect growth of bacteria, but coumarin inhibited growth of Pseudomonas spp. strains and had no effect on Bacillus circulans and P. macerans. Conversely, tomatine inhibited growth of B. circulans and P. macerans. Biochanin A and tomatine inhibited growth of three pathogenic isolates of F. oxysporum but increased growth of three nonpathogenic F. oxysporum isolates. Coumarin inhibited growth of all pathogenic and nonpathogenic F. oxysporum isolates. These results are indicative of the complex interactions that can occur among plants, pathogens, and biological control agents in the rhizosphere and on the root surface. Also, these results may help to explain the low efficacy of some combinations of biocontrol agents, as well as the inconsistency in achieving disease suppression under field conditions.
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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".