Variations of Thiaminase I Activity pH Dependencies among Typical Great Lakes Forage Fish and <i>Paenibacillus thiaminolyticus</i>
Bibliographic record
Abstract
The source of thiaminase in the Great Lakes food web remains unknown. Biochemical characterization of the thiaminase I activities observed in forage fish was undertaken to provide insights into potential thiaminase sources and to optimize catalytic assay conditions. We measured the thiaminase I activities of crude extracts from five forage fish species and one strain of Paenibacillus thiaminolyticus over a range of pH values. The clupeids, alewife Alosa pseudoharengus and gizzard shad Dorosoma cepedianum, had very similar thiaminase I pH dependencies, with optimal activity ranges (> or = 90% of maximum activity) between pH 4.6 and 5.5. Rainbow smelt Osmerus mordax and spottail shiner Notropis hudsonius had optimal activity ranges between pH 5.5-6.6. The thiaminase I activity pH dependence profile of P. thiaminolyticus had an optimal activity range between pH 5.4 and 6.3, which was similar to the optimal range for rainbow smelt and spottail shiners. Incubation of P. thiaminolyticus extracts with extracts from bloater Coregonus hoyi (normally, bloaters have little or no detectable thiaminase I activity) did not significantly alter the pH dependence profile of P. thiaminolyticus-derived thiaminase I, such that it continued to resemble that of the rainbow smelt and spottail shiner, with an apparent optimal activity range between pH 5.7 and 6.6. These data are consistent with the hypothesis of a bacterial source for thiaminase I in the nonclupeid species of forage fish; however, the data also suggest different sources of thiaminase I enzymes in the clupeid species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".