Physicochemical Changes and Mechanism of Heat-Induced Gelation of Arrowtooth Flounder Myosin
Bibliographic record
Abstract
Physicochemical changes of myosin during heating were investigated to elucidate the mechanism of heat-induced gelation of arrowtooth flounder (ATF) myosin at high ionic strength. Changes in dynamic properties indicated ATF myosin formed a gel in three different stages as shown by the first increase in G' (storage modulus) at 28 degrees C, followed by the decrease at 35 degrees C and the second increase at 42 degrees C. DSC thermogram showed the onset of myosin denaturation at 25 degrees C with two maximum transition temperatures at 30 and 36 degrees C. The decrease in alpha-helical content indicated ATF myosin began to unfold at 15 degrees C and the unfolding continued until it reached 65 degrees C. Turbidity measurement showed myosin began to aggregate at 23 degrees C and the aggregation was complete at 40 degrees C. Surface hydrophobicity increased consistently in the temperature range studied, 20-65 degrees C. Sulfhydryl contents decreased significantly at 20-30 degrees C due to the formation of disulfide linkages but remained constant at temperatures >30 degrees C. ATF myosin was shown to be extremely sensitive to heat, resulting in denaturation at lower temperature than other fish myosin. Denaturation was initiated by unfolding of the alpha-helical region in myosin followed by exposure of hydrophobic and sulfhydryl residues, which are subsequently involved in aggregation and gelation processes.
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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 | 0.000 | 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 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".