The prospects of cellulase-producing bacteria for the bioconversion of lignocellulosic biomass
Bibliographic record
Abstract
Lignocellulosic biomass is a renewable and abundant resource with great potential for bioconversion to value-added bioproducts. However, the biorefining process remains economically unfeasible due to a lack of biocatalysts that can overcome costly hurdles such as cooling from high temperature, pumping of oxygen/stirring, and, neutralization from acidic or basic pH. The extreme environmental resistance of bacteria permits screening and isolation of novel cellulases to help overcome these challenges. Rapid, efficient cellulase screening techniques, using cellulase assays and metagenomic libraries, are a must. Rare cellulases with activities on soluble and crystalline cellulose have been isolated from strains of Paenibacillus and Bacillus and shown to have high thermostability and/or activity over a wide pH spectrum. While novel cellulases from strains like Cellulomonas flavigena and Terendinibacter turnerae, produce multifunctional cellulases with broader substrate utilization. These enzymes offer a framework for enhancement of cellulases including: specific activity, thermalstability, or end-product inhibition. In addition, anaerobic bacteria like the clostridia offer potential due to species capable of producing compound multienzyme complexes called cellulosomes. Cellulosomes provide synergy and close proximity of enzymes to substrate, increasing activity towards crystalline cellulose. This has lead to the construction of designer cellulosomes enhanced for specific substrate activity. Furthermore, cellulosome-producing Clostridium thermocellum and its ability to ferment sugars to ethanol; its amenability to co-culture and, recent advances in genetic engineering, offer a promising future in biofuels. The exploitation of bacteria in the search for improved enzymes or strategies provides a means to upgrade feasibility for lignocellulosic biomass conversion, ultimately providing means to a 'greener' technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".