Growth–melt asymmetry in ice crystals under the influence of spruce budworm antifreeze protein
Bibliographic record
Abstract
Here we describe studies of the crystallization behavior of ice in an aqueous solution of spruce budworm antifreeze protein (sbwAFP) at atmospheric pressure. SbwAFP is an ice binding protein with high thermal hysteresis activity, which helps protect Choristoneura fumiferana (spruce budworm) larvae from freezing as they overwinter in the spruce and fir forests of the north eastern United States and Canada. Different types of ice binding proteins have been found in many other species. They have a wide range of applications in cryomedicine and cryopreservation, as well as the potential to protect plants and vegetables from frost damage through genetic engineering. However, there is much to learn regarding the mechanism of action of ice binding proteins. In our experiments, a solution containing sbwAFP was rapidly frozen and then melted back, thereby allowing us to produce small single crystals. These maintained their hexagonal shapes during cooling within the thermal hysteresis gap. Melt– growth–melt sequences in low concentrations of sbwAFP reveal the same shape transitions as are found in pure ice crystals at low temperature (−22 ◦ C) and high pressure (2000 bar) (Cahoon et al 2006 Phys. Rev. Lett. 96 255502); while both growth and melt shapes display faceted hexagonal morphology, they are rotated 30 ◦ relative to one another. Moreover, the initial melt shape and orientation is recovered in the sequence. To visualize the binding of sbwAFP to ice, we labeled the antifreeze protein with enhanced green fluorescent protein (eGFP) and observed the sbwAFP–GFP molecules directly on ice crystals using confocal microscopy. When cooling the ice crystals, facets form on the six primary prism planes (slowest growing planes) that are evenly decorated with sbwAFP–GFP. During melting, apparent facets form on secondary prism planes (fastest melting planes), leaving residual sbwAFP at the six corners of the
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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 | 0.001 |
| 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".