Convergent accelerated evolution of mammal-specific conserved non-coding elements in hibernators
Bibliographic record
Abstract
Mammals maintain their body temperature, yet hibernators can temporarily lower their metabolic rate as an energy-saving strategy. It has been proposed that hibernators evolved independently from homeotherms, and it is possible that the convergent evolution of hibernation involved common genomic changes among hibernator-lineages. Since hibernation is a seasonal trait, the evolution of gene regulatory regions in response to changes in season may have been important for the acquisition of hibernation traits. High-frequency accumulation of mutations in conserved non-coding elements (CNEs) could, in principle, alter the expression of neighboring genes and thereby contribute to the acquisition of new traits. To address this possibility, we performed a comparative genomic analysis of mammals to identify accelerated CNEs commonly associated with hibernation. We found that accelerated CNEs are common to hibernator-lineages and could be involved with hibernation. We also found that common factors of genes that located near accelerated CNEs and are differentially expressed between normal and hibernation periods related to gene regulation and cell-fate determination. It suggests that the molecular mechanisms controlling hibernation have undergone convergent evolution. These results help broaden our understanding of the genetic adaptations that facilitated hibernation in mammals and may offer insights pertaining to stress responses and energy conservation.
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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".