Fungal Bioconversion of Lignocellulosic Residues; Opportunities & Perspectives
Bibliographic record
Abstract
The development of alternative energy technology is critically important because of the rising prices of crude oil, security issues regarding the oil supply, and environmental issues such as global warming and air pollution. Bioconversion of biomass has significant advantages over other alternative energy strategies because biomass is the most abundant and also the most renewable biomaterial on our planet. Bioconversion of lignocellulosic residues is initiated primarily by microorganisms such as fungi and bacteria which are capable of degrading lignocellulolytic materials. Fungi such as Trichoderma reesei and Aspergillus niger produce large amounts of extracellular cellulolytic enzymes, whereas bacterial and a few anaerobic fungal strains mostly produce cellulolytic enzymes in a complex called cellulosome, which is associated with the cell wall. In filamentous fungi, cellulolytic enzymes including endoglucanases, cellobiohydrolases (exoglucanases) and beta-glucosidases work efficiently on cellulolytic residues in a synergistic manner. In addition to cellulolytic/hemicellulolytic activities, higher fungi such as basidiomycetes (e.g. Phanerochaete chrysosporium) have unique oxidative systems which together with ligninolytic enzymes are responsible for lignocellulose degradation. This review gives an overview of different fungal lignocellulolytic enzymatic systems including extracellular and cellulosome-associated in aerobic and anaerobic fungi, respectively. In addition, oxidative lignocellulose-degradation mechanisms of higher fungi are discussed. Moreover, this paper reviews the current status of the technology for bioconversion of biomass by fungi, with focus on mutagenesis, co-culturing and heterologous gene expression attempts to improve fungal lignocellulolytic activities to create robust fungal strains.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".