Characterization and antifungal properties against <i>Botrytis cinerea</i> of bacteria isolated from hemp seed oil
Bibliographic record
Abstract
Botrytis cinerea is a pathogen infecting Cannabis sativa L. plants, causing economic losses, and can develop resistance to chemical fungicides, the use of which is restricted in cannabis production. Thus, developing biocontrol methods is imperative. Seven bacterial strains were isolated from hemp seed oil, characterized, and examined for the potential to control a Botrytis cinerea isolate from cannabis. Three isolates, Bacillus mojavensis HOB3, Paenibacillus sp. HOB6, and Bacillus subtilis HOB7 exhibited significant inhibition of Botrytis cinerea. These isolates were further evaluated for their biosurfactant activity using two liquid media, Lysogeny Broth (LB) and hydrocarbon-amended Bushnell and Haas (BH). The oil-spreading and drop-collapse assays revealed growth-medium-dependent variation in surface activity associated with biosurfactant presence. The BH cell-free extract (BH-CFE) of Bacillus subtilis HOB7 showed the highest estimated biosurfactant presence and antifungal activity against Botrytis cinerea, but both activities were absent when using the LB cell-free extract (LB-CFE) of Bacillus subtilis HOB7. Thus, a potential relationship between antifungal activity and biosurfactant production was suggested. Genome mining of the strains identified gene clusters encoding compounds with antifungal activity, including the biosurfactants polymyxin B, fusaricidin B, fengycin, and surfactin. To our knowledge, this is the first report of the isolation of hemp seed oil bacteria with potential biocontrol properties against fungal phytopathogens.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".