Monomodular and multifunctional processive endocellulases: implications for swine nutrition and gut microbiome
Bibliographic record
Abstract
Poor efficiency of dietary fibre utilization not only limits global pork production profit margin but also adversely affects utilization of various dietary nutrients. Poor efficiency of dietary nutrient utilization further leads to excessive excretion of swine manure nutrients and results in environmental impacts of emission of major greenhouse gases (GHG), odor, nitrate leaching and surface-water eutrophication. Emission of the major GHG from intensive pork production contributes to global warming and deteriorates heat stress to pigs in tropical and sub-tropical swine production. Exogenous fibre enzymes of various microbial cellulases, hemicellulases and pectinases have been well studied and used in swine production as the non-nutritive gut modifier feed enzyme additives in the past over two decades. These research efforts have aimed to improve growth performance, nutrient utilization, intestinal fermentation as well as gut physiology, microbiome and health via complementing the porcine gut symbiotic microbial fibrolytic activities towards dietary fibre degradation. The widely reported exogenous fibre enzymes include the singular use of respective cellulases, hemicellulases and pectinases as well as their multienzyme cocktails. The currently applied exogenous fibre enzymes are largely limited by their inconsistent in vivo efficacy likely due to their less defined enzyme stability and limited biochemical property. More recently characterized monomodular, multifunctional and processive endoglucanases have the potential to be more efficaciously used as the next-generation designer fibre biocatalysts. These newly emerging multifunctional and processive endoglucanases have the potential to unleash dietary fibre sugar constituents as metabolic fuels and prebiotics, to optimize gut microbiome, to maintain gut permeability and to enhance performance in pigs under a challenged environment as well as to parallelly unlock biomass to manufacture biofuels and biomaterials.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".