167 Monomodular and Multi-Functional Processive Endocellulases for Improving Fibre Utilization and Reducing Environmental Footprint in Pigs
Bibliographic record
Abstract
Abstract Global emission of the major greenhouse gases (GHG) from food animal production accounts for 15% of the total human-induced GHG with pork production being a significant contributor. Poor efficiency of dietary fibre utilization not only limits global pork production profit margins but also adversely affects dietary crude protein and amino acid utilization; and these collectively lead to excessive manure fibre and nitrogenous compounds, resulting in anaerobic biogenesis and emission of the major GHG from swine manure slurry storage. Two unique glycosyl hydrolase family-5 (GH5) processive endoglucanases, referred to as GH5-tCel5A1 and GH5-, have been reported. The GH5-tCel5A1 was originally identified via targeted genome sequencing of the extremely thermophilic Thermotoga maritima while the GH5-p4818Cel5_2A was screened from the porcine hindgut microbial metagenomic expression library. Both GH5-tCel5A1 and GH5-p4818_2A have been characterized to be monomodular, act as processive β-1,4-endoglucanases and have multi-functionality towards hydrolyzing dietary natural crystalline cellulose and pre-treated cellulosic substrates as well as several hemicelluloses including β-glucans, xylan, xylogulcans, mannans, galactomannans and glucomannans. We have recently overexpressed both GH5-tCel5A1 and GH5-p4818Cel5_2A in the ClearColiBL21(DE3) cell and investigated the susceptibility of both these cellulases to auto-oxidation by air-borne O2. Furthermore, we have also examined these endoglucanases’ stability under the mimicked in vitro porcine gastric and the small intestinal pH and proteases’ conditions. Further pig nutrition and growth performance studies are being conducted to investigate the in vivo efficacy of these two processive endoglucanases as newly emerging potential exogenous fibre enzymes.
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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 | 0.001 |
| 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".