Effects of Tween 60 and Tween 80 on Protease Activity, Thiol Group Reactivity, Protein Adsorption, and Cellulose Degradation by Rumen Microbial Enzymes
Bibliographic record
Abstract
Microbial enzymes extracted from mixed ruminal microorganisms were incubated for 2 h with casein and Tween 60 or Tween 80 at 10 concentrations ranging from 0 to 2.0% (vol/vol) to determine the effects of these nonionic surfactants on protease activation and thiol reactivity (unmasking of thiol groups). Rate and extent of protein adsorption to cellulosic substrate (barley straw) was measured in the presence of 0, 0.05, 0.10, 0.25, and 0.50% (vol/vol) Tween 80. Degradation of cellulose by a rumen bacterial fraction was measured over 48 h of incubation with and without Tween 60 or Tween 80 at 0.25% (vol/vol). Maximum accelerations of protease activity achievable with Tween 60 and Tween 80 (calculated from a Michaelis-Menten kinetics model) were 99.2 and 166.8%, respectively. Concentrations of Tween 60 and Tween 80 at which half the maximal velocities were attained were 0.28 and 0.20% (vol/vol), respectively. Tween 80 increased (P < 0.05) the rate and extent of adsorption of microbial protein to barley straw, and the effect was related to concentration of Tween 80 up to 0.10% (vol/vol). Initial rates of cellulose degradation with no surfactant, 0.25% Tween 60, or 0.25% Tween 80 were 0.60, 0.87, and 1.04 micrograms/ml per h, respectively. These nonionic surfactants were effective for enhancing rumen microbial protease and cellulase activities. Thus, further study is warranted to determine their potential for improving ruminant feeding.
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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 | 0.000 |
| 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) | 0.001 | 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".