Optimizing glucose utilization in Trichoderma asperellum wild-type(wt) to promote growth and efficiency in treating cashew wounds after pruning
Bibliographic record
Abstract
This study aimed to enhance the glucose utilization of Trichoderma asperellum wild-type (wt) to optimize growth rates and biocontrol efficiency for treating cashew pruning wounds. Evolutionary adaptation techniques were employed in PD broth containing 2%, 2.5%, and 3% (w/v) glucose, leading to the development of the strain T. asperellum TIS-11T. The strain was applied to cashew pruning wounds using a completely randomized design over a 90-day period. The biomass growth rate of T. asperellum TIS-11T increased from 0.29 to 0.50 g/mL, achieving 100% inhibition of pathogenic fungi within five days on fresh PDA media. During the healing process, Rhizopus, Penicillium, and unidentified fungi were observed on the surface of cashew wounds after day 45. When applied to pruning wounds, treatments with T. asperellum wt and T. asperellum TIS-11T reduced wound surface areas to 16.88 ± 8.70% and 3.85 ± 4.50%, respectively, within 90 days. The efficacy of T. asperellum TIS-11T was six times greater than that of T. asperellum wt. Compared to previous studies, T. asperellum TIS-11T exhibited superior antagonistic activity, making it a promising candidate for large-scale applications in protecting and healing cashew pruning wounds. Further studies are required to evaluate the efficacy of T. asperellum TIS-11T on other plant species to obtain broader results. In addition, applying the strain to whole plants is suggested to gain more knowledge. This study highlights the potential of evolutionary adaptation to improve the efficacy of biological fungicides, contributing to sustainable agriculture. The biological fungicide T. asperellum TIS-11T demonstrates a high capacity to protect and eradicate pathogenic microbes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".