Induction and suppression of <i>Dichomitus squalens</i> and <i>Ceriporiopsis subvermispora</i> peroxidase activity by manganese sulphate in response to carbon and nitrogen sources
Bibliographic record
Abstract
Abstract Delignification is critical in the production of biofuel from a potentially abundant renewable resource, lignocellulosic waste. One of the classes of lignolytic enzymes are peroxidases. Manganese sulphate (Mn2+) has a significant impact on the peroxidase activity of lignolytic fungi. Along with induction, the suppression of peroxidase activity by Mn2+ has been observed. Peroxidase regulation is governed by three critical Mn2+ concentrations: the minimum inductive concentration (MIC), the peak concentration (PC) and the minimum suppressive concentration (MSC). The induction and suppression of enzyme activity were not associated with fungal growth capacity, but with a specific enzyme response to the nutritional conditions. Manipulation of the carbon and nitrogen sources shifted the peroxidase suppression by Mn2+ to high concentrations and, hence, increased the peroxidase tolerance to Mn2+ and, consequently, peak peroxidase activities. The manipulation of carbon and nitrogen sources allowed increasing the peak concentrations of Mn2+ and corresponding peroxidase activity up to 0.5 mg/mL and 180.70 AU (173% increase compared to standard) in C. subvermispora, and up to 0.05 mg/mL and 151.23 AU in D. squalens (91% increase compared to standard). Thus, manipulation of carbon and nitrogen sources is a useful tool in enhancing fungi peroxidase tolerance to Mn2+ and improving the delignification of lignocellulosic biomass by fungi to prepare an easily consumable substrate for economically viable biofuel production.
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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) | 0.000 | 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) | 0.001 | 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".