Screening of effective biocontrol agents against postharvest litchi downy blight caused by Peronophythora litchii
Bibliographic record
Abstract
Biocontrol agents (BCAs) could be used for the control of postharvest decay of fruit. In this study, biocontrol bacteria were isolated from litchi soil, leaves and fruit tissues, and their efficacy on the control of postharvest litchi downy blight, caused by Peronophythora litchii, were determined. After evaluating the ability of 188 bacterial isolates to produce certain enzymes and metabolites, and their antagonistic activity in vitro against P. litchii, as well as preliminary identification of 82 representative isolates based on 16 S rDNA sequencing, five isolates including Bacillus amyloliquefaciens PP19 and LI24, Exiguobacterium acetylicum SI17, B. pumilus PI26, and B. licheniformis HS10 were selected for further assessments in several trials in 2016 and 2017. In comparison with control treatment, isolates PP19, SI17 and PI26 could delay the disease development of postharvest litchi downy blight. Furthermore, isolates PP19 and SI17 were able to colonize fruit pericarp without affecting fruit quality. Additionally, the colonization of PP19 changed the microbial community composition on litchi pericarp as demonstrated by pericarp microbiome sequencing. This is the first report of an E. acetylicum acted as a BCA against a phytopathogenic oomycete P. litchii. We conclude that PP19 and SI17 can be used as effective BCAs against postharvest litchi downy blight, especially applied during preharvest stage.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".