The Agro-industrial Byproduct Wheat Bran as an Inducer for Alkaline Protease (ALK-PR23) Production by Pschyrotolerant Lysinibacillus sphaericus Strain AA6 EMCCN3080
Bibliographic record
Abstract
Abstract The current study aims to exploit the zero-cost inducer wheat bran (WB), an agro-industrial byproduct, for production of alkaline protease (ALK-PR23) by the hyper producer psychrotolerant Lysinibacillus sphaericus Strain AA6 EMCCN3080 for the first time ever. Incubation temperature (25 °C), yeast extract concentration, agitation speed (150 rpm), and aeration ratio [1 volume (liquid):5 volume (Erlenmeyer flask)] provoked ALK-PR23 production; OVAT inferences. The pH, yeast extract, and (NH4)2SO4 levels substantively triggered ALK-PR23 production as deduced from Plackett–Burman design. Incubation time (3 days) and WB [2% (w/v)] were the optimal values inducing positive significant influence on ALK-PR23 as conferred from steepest ascent experiments. Yeast extract (0.446% w/v), (NH4)2SO4 (0.339% w/v), and pH (6.872) prompted ALK-PR23 (592.5 U/mL) with an impressive 98-fold enhancement; Box-Behnken design and ridge steepest ascent path implications. The laboratory validation of the model achieved 100% of the predicted value. Laboratory data would present an eco-friendly, cheap, efficient approach towards concurrent WB recycling and massive production of alkaline protease (ALK-PR23) from L. sphaericus Strain AA6 EMCCN3080. Present data would greatly encourage unveiling biochemical characteristics of ALK-PR23 in prospective studies. Graphical Abstract
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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 | 0.000 | 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".