Optimization of Culture Conditions and Wheat Bran Class Selection in the Production of Bacillus thuringiensis-Based Biopesticides
Bibliographic record
Abstract
Bacillus thuringiensis is the leading microbial-based biopesticide, thanks to its parasporal crystal proteins or δ-endotoxins, which are toxic to insect larvae upon ingestion. Once in the insect larvae midgut, the crystal is solubilized by the alkaline pH and the δ-endotoxins activated by proteolytic cleavage. Thanks to its high efficiency as a biopesticide, several efforts have been made to enhance its growth and δ-endotoxins production, in various types of culture media. In this study, a culture medium based on wheat bran (WB), the by-product of cereal grain milling, was used to grow Bacillus thuringiensis and produce δ-endotoxins. Using the response surface methodology (RSM), the effects of three variables were evaluated: WB particles granulometry, their concentration, and their agitation in a 48-h shake-flask culture at 30 °C. Three response parameters were targeted: δ-endotoxins production, final culture pH, and dry-matter consumption. According to the RSM results, the optimum would be at 3.7 g WB/50 mL, with a granulometry above 680 μm and agitation between 170 and 270 rpm. This study is key to developing natural and cheap culture media that can be used at an industrial level for Bacillus thuringiensis-based biopesticides.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".