Medium optimization for hen egg white lysozyme production by recombinant <i>Aspergillus niger</i> using statistical methods
Bibliographic record
Abstract
Statistics-based experimental design was used to investigate the effect of medium components (starch, peptone, ammonium sulfate, yeast extract, and CaCl2.2H2O) on hen's egg white lysozyme production by Aspergillus niger HEWL WT-13-16. A 2(5-1) fractional factorial design augmented with center points revealed that peptone, starch, and ammonium sulfate were the most significant factors, whereas the other factors were not important within the levels tested. The method of steepest ascent was used to approach the proximity of optimum. This task was followed by a central composite design to develop a response surface for medium optimization. The optimum medium composition for lysozyme production was found to be: starch 34 g L-1, peptone 34 g L-1, ammonium sulfate 11.9 g L-1, yeast extract 0.5 g L-1, and CaCl2.2H2O 0.5 g L-1. This medium was projected to produce, theoretically, 212 mg L-1 lysozyme. Using this medium, an experimental maximum lysozyme concentration of 209+/-18 mg L-1 verified the applied methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".