Insect cold hardiness: metabolic, gene, and protein adaptation<sup>1</sup>This review is part of a virtual symposium on recent advances in understanding a variety of complex regulatory processes in insect physiology and endocrinology, including development, metabolism, cold hardiness, food intake and digestion, and diuresis, through the use of omics technologies in the postgenomic era.
Bibliographic record
Abstract
Winter survival for thousands of species of insects relies on adaptive strategies for cold hardiness. Two basic mechanisms are widely used (freeze avoidance by deep supercooling and freeze tolerance where insects endure ice formation in extracellular fluid spaces), whereas additional strategies (cryoprotective dehydration, vitrification) are also used by some polar species in extreme environments. This review assesses recent research on the biochemical adaptations that support insect cold hardiness. We examine new information about the regulation of cryoprotectant biosynthesis, mechanisms of metabolic rate depression, role of aquaporins in water and glycerol movement, and cell preservation strategies (chaperones, antioxidant defenses and metal binding proteins, mitochondrial suppression) for survival over the winter. We also review the new information coming from the use of genomic and proteomic screening methods that are greatly widening the scope for discovery of genes and proteins that support winter survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".