Inactivation of pectin methylesterase by immobilized trypsins from cunner fish and bovine pancreas
Bibliographic record
Abstract
Immobilized cunner fish trypsin was used to inactivate pectin methylesterase (PME). The effects of different reaction conditions (e.g., incubation time, PME concentration, and temperature) on PME inactivation and kinetics of inactivation were investigated. Temperature, incubation time, and PME concentration significantly affected the extent of PME inactivation. Generally, higher temperature, longer incubation time, and low PME concentration caused more PME inactivation. The immobilized fish trypsin had higher capacity to inactivate PME than immobilized bovine trypsin. The inactivation efficiency of the immobilized fish trypsin was about 20% higher than that of its bovine counterpart. However, PME inactivated by both trypsins regained partial activity during storage at 4°C, with immobilized fish trypsin-treated PME regaining more of its original activity than the immobilized bovine trypsin-treated PME. Heat-denatured PME was hydrolyzed more extensively by immobilized fish trypsin than by its bovine counterpart. The rate constants increased, whereas the D-values decreased with temperature for both immobilized fish and bovine trypsins. The inactivation rate constants of immobilized fish trypsin at all the temperatures investigated (i.e., 15-35°C) were higher than those of immobilized bovine trypsin. Furthermore, the activation energy (Ea ) of PME inactivation by immobilized fish trypsin was lower than that of immobilized bovine trypsin.
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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 | 0.001 |
| 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".