Statistical optimization of medium components and physicochemical parameters to simultaneously enhance bacterial growth and esterase production by <i>Bacillus thuringiensis</i>
Bibliographic record
Abstract
Bacillus thuringiensis is a genus extensively studied because of its high potential for biotechnological application, principally in biocontrol techniques. However, the optimization of esterase production by this strain has been scarcely studied. The aim of this work was to select and optimize the physicochemical and nutritional parameters that significantly influence the growth and esterase production of B. thuringiensis. To this purpose, 6 nutritional factors and 2 physicochemical parameters were evaluated using a Plackett-Burman design. Significant variables were optimized using a Box-Behnken design and through the desirability function to select the levels of the variables that simultaneously maximize microbial growth and esterase production. The optimum conditions resulting from simultaneous optimization of the responses under study were found to be 1 g/L glucose, 15 g/L peptone, and 3.25 g/L NaCl. Under these optimal conditions, it was possible to achieve a 2.5 log CFU/mL increase in bacterial growth and a 113-fold increase in esterase productivity, compared with minimal medium without agitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".