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 (Mn 2+ ) has a significant impact on the peroxidase activity of lignolytic fungi. Along with induction, the suppression of peroxidase activity by Mn 2+ has been observed. Peroxidase regulation is governed by three critical Mn 2+ 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 Mn 2+ to high concentrations and, hence, increased the peroxidase tolerance to Mn 2+ and, consequently, peak peroxidase activities. The manipulation of carbon and nitrogen sources allowed increasing the peak concentrations of Mn 2+ 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 Mn 2+ 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 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".