A Study on the Hyperactive Antifreeze Proteins from the Insect <i>Tenebrio molitor</i>
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are class of proteins that protect organisms from damages caused by freezing, either by preventing freezing or minimizing frost damages.AFPs effectively lower the temperature at which water freezes.They are classified by the depression of the freezing temperature compared to the melting temperature, i.e.Thermal Hysteresis activity (TH): moderately active AFPs and hyperactive AFPs.It is still unknown what makes some AFPs hyperactive compared to the much less active classes of AFPs.Previous studies showed that fusion proteins of fish type III AFP bind independently to ice.This conclusion was derived from experiments with bulky proteins that were fused to this moderately AFP.One possible explanation for the increased activity of the hyperactive AFPs is that they might function cooperatively.To investigate this, the hyperactive AFP from the mealworm, Tenebrio molitor (TmAFP), was linked to bulky proteins.In this thesis, these fusion proteins were assayed by a nanoliter osmometer, a device that has been designed to measure TH of AFPs.The results indicate that the addition of large molecules to the TmAFP does not induce any loss of thermal hysteresis activity; these fusion proteins were rather more active than free TmAFP at almost all concentrations.Further, ice crystal morphologies obtained by the fusion proteins were the same as the ones of free TmAFP.Therefore, it is concluded that TmAFPs independently bind to ice and their enhanced thermal hysteresis activity does not result from cooperativity.
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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) | 0.001 | 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".