Screening of Primer Sets for Amplification Cyclic Lipopeptide Gene of <i>Bacillus</i> Against Banana Fusarium Wilt
Bibliographic record
Abstract
Cyclic lipopeptides (CLPs) are a class of natural agricultural antibiotics produced by Bacillus , it plays an important role in the biological control using of Bacillus . In this study, 20 strains of Bacillus , including Bacillus amyloliquefaciens , Bacillus subtilis , Bacillus siamese , and Bacillus cereus were determined the antagonistic ability against to pathogenic fungi such as Foc 4 . PCR amplification was performed on 20 strains of Bacillus and 7 soil samples, by using the designed 14 pairs of primers for CLPs-related synthetase genes, including Bacillomycin, Fengycins, Iturins, Surfactin etc, screen CLPs primers that can be used for Bacillus and soil monitoring. The results showed that: a total of 16 Bacillus strains have the antagonistic activity against pathogens, and microscopic observation of the crude lipopeptide extract of fermentation broth of these Bacillus strains can destroy the biofilm system and cell wall of Foc4 , causing the mycelium and meristems to expand and deform; 12 pairs primers of this study ( BamD-F/R, BamA-F/R, FenA-F/R, FenB-F/R, 61FenA-F/R, 61FenB-F/R, ItuA-F/R, ItuB-F/R, ItuC-F/R, ItuD-F/R, SrfAA-F/R, SrfAB-F/R ) could effective amplify CLPs gene, and find that all of test Bacillus strains has the ItuC gene, there is a positive relationship between the antagonistic ability of Bacillus and the diversity and richness of CLPs genes, through PCR amplification of total soil DNA, it was found that the designed CLPs primers were feasible to monitor Bacillus in soil environment. It would be used as an important supplement to conventional plate counting and high-throughput sequencing methods, monitor and guide the application of Bacillus control soil-borne diseases of plants to provide theoretical support for further field.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".