Cross-tolerance and Cross-talk in the Cold: Relating Low Temperatures to Desiccation and Immune Stress in Insects
Bibliographic record
Abstract
Multiple stressors, both abiotic and biotic, often are experienced simultaneously by organisms in nature. Responses to these stressors may share signaling pathways ("cross-talk") or protective mechanisms ("cross-tolerance"). Temperate and polar insects that must survive the winter experience low temperatures accompanied by additional abiotic stressors, such as low availability of water. Cold and desiccation have many similar effects at a cellular level, and we present evidence that the cellular mechanisms that protect against cold stress also protect against desiccation, and that the responses to cold and dehydration likely evolved as cross-tolerance. By contrast, there are several lines of evidence suggesting that low temperature stress elicits an upregulation of immune responses in insects (and vice versa). Because there is little mechanistic overlap between cold stress and immune stress at the cellular level, we suggest that this is cross-talk. Both cross-talk and cross-tolerance may be adaptive and likely evolved in response to synchronous stressors; however, we suggest that cross-talk and cross-tolerance may lead to different responses to changes in the timing and severity of multiple stress interactions in a changing world. We present a framework describing the potentially different responses of cross-tolerance and cross-talk to a changing environment and describe the nature of these impacts using interaction of cold-desiccation and cold-immunity in overwintering insects as an example.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".