Characterization and sequence analysis of potential biofertilizer and biocontrol agent <i>Bacillus subtilis</i> strain SEM-9 from silkworm excrement
Bibliographic record
Abstract
Fusarium wilt is a devastating soil-borne disease caused mainly by highly host-specific formae speciales of Fusarium oxysporum. Antagonistic microorganisms play a very important role in Fusarium wilt control, and the isolation of potential biocontrol strains is becoming more and more important. We isolated a bacterial strain (SEM-9) from the high-temperature stage of silkworm excrement composting, which had a marked ability to solubilize phosphorus, promote the growth and increase the yield of the small Chinese cabbage, and which also exhibited considerable antagonistic effect towards Fusarium sambucinum and other fungi. The result of physiological and biochemical analyses, as well as genome sequencing, showed that SEM-9 was a strain of Bacillus subtilis. Through genome annotation and analysis, it was found that SEM-9 contained genes related to the regulation of biofilm formation, which may play an important role in colonization, and gene clusters encoding the biosynthesis of antimicrobials, such as surfactin, bacilysin, fengycin, and subtilosin-A. The production of such antifungal compounds may constitute the basis of the mode-of-action of SEM-9 against Fusarium spp. These data suggested that the SEM-9 strain has potential as both a biofertilizer and a biocontrol agent, with the potential to manage Fusarium wilt disease in crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".