Isolation of <i>Bacillus subtilis</i> strain SEM-2 from silkworm excrement and characterisation of its antagonistic effect against <i>Fusarium</i> spp.
Bibliographic record
Abstract
Fusarium wilt is a devastating soil-borne disease mainly caused by highly host-specific formae speciales of Fusarium spp. Antagonistic microorganisms play a very important role in Fusarium wilt control. Isolation of potential biocontrol strains has become increasingly important. Bacterial strain SEM-2 was isolated from the high-temperature stage of silkworm excrement composting. SEM-2 exhibited a considerable antagonistic effect against Fusarium graminearum mycelial growth and spore germination. The results of pot experiments suggested that SEM-2 has a better inhibitory effect on the early stage of disease occurrence. The green fluorescent protein labelled SEM-2 coated on the surface of tomato seeds colonised the roots of tomato plants in 15 days. Genome sequencing identified SEM-2 as a new strain of Bacillus subtilis, and genome annotation and analysis determined gene clusters related to the biosynthesis of antimicrobials, such as bacillaene, fengycin, bacillibactin, subtilosin A, surfactin, and bacilysin. Interestingly, liquid chromatography – quadrupole time-of-flight mass spectrometry revealed that metabolites in pathways associated with the synthesis of secondary metabolites and antibiotics were highly differentially expressed. These findings may help to explain the mode of action of B. subtilis SEM-2 against Fusarium spp.
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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 | 0.001 |
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".