Bibliographic record
Abstract
Mammalian hibernation is an amazing strategy for winter survival. Animals sink into a deep torpor where metabolic rate is < 5% of normal, body temperature falls to 0-5 degrees C, and physiological functions are strongly suppressed. Hibernation is a closely regulated process that includes multiple controls on gene transcription and protein translation, the primary subjects of this review. Recent studies by our lab and others have used multiple techniques of gene discovery, including cDNA array screening, to identify genes that are up-regulated in hibernation and continuing studies are tracing the functions of the encoded proteins and the signal transduction systems that regulate expression. For example, up-regulation of fatty acid binding proteins during hibernation facilitates the switch to a primary dependence on lipid fuels by nearly all organs and new studies have shown that up-regulation is mediated by the PPARgamma transcription factor and its co-activator, PGC-1. Several hypoxia-related genes including HIF-1alpha are also up-regulated during hibernation suggesting a role for this transcription factor in mediating adaptive responses for hibernation. Controls on mRNA translation during hibernation accomplish two goals: a general strong suppression of protein synthesis that contributes to energy savings and the selected synthesis of a few specific proteins. These goals are accomplished by mechanisms that include reversible phosphorylation controls on ribosomal initiation and elongation factors and differential distribution of individual mRNA species between polysome and monosome fractions. Studies of gene expression, protein synthesis regulation, controls on fuel metabolism, and signal transduction pathways are combining to produce an integrated model of the biochemical regulation of hibernation.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".