Simultaneous production of biopesticide and alkaline proteases by Bacillus thuringiensis using sewage sludge as a raw material
Bibliographic record
Abstract
The simultaneous production of Bacillus thuringiensis (Bt) based biopesticide and proteases was studied using synthetic medium and wastewater sludge as a raw material. The studies were conducted in shake flask and computer controlled 15-L capacity fermentors. Measuring viable cell and spore counts, entomotoxicity and protease activity monitored the progress of the biopesticide production process. A higher viable cell count and spore count was observed in synthetic Soya medium, however, higher entomotoxicity and protease activity were observed in wastewater sludge medium. Thus, the wastewater sludge is a better raw material than commercial Soya medium for the biopesticides and enzyme production. The maximum entomotoxicity and protease activity observed in the fermentor was 9,332 IU/microL and 4.58 IU/mL, respectively. The proteases produced by Bt were also characterised. Two types of proteases were detected; neutral proteases with pH optimum 7.0 and alkaline proteases with pH optimum 10-11. Further, two types of alkaline proteases were detected; one having a pH and temperature optimum at 10 and 50 degrees C while the other at 11 and 70 degrees C. The protease thermal stability was found to increase in the presence of CaCl2, indicating the proteases were metalloproteases.
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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 | 0.000 | 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 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".