Methyl Epigenetic Mechanisms in the Freeze-Tolerance Response of Rana Sylvatica Nervous Tissue
Bibliographic record
Abstract
Wood frog freeze tolerance is a classic example of metabolic rate depression (MRD), which facilitates reprioritization of minimal anaerobic resources to pro-survival pathways.Global gene expression is consequentially suppressed due, in part, to transcriptional controls, but to date, specific mechanisms have received little attention.Methylation of DNA and histone lysine residues are common epigenetic mechanisms that are tightly associated with control of transcription and thus have been implicated in MRD.However, preliminary findings appeared tissue-and species-specific, and considering research into nervous tissues was lacking, further investigation is required.This thesis tracks the expression and activity of some key methyl epigenetic modifiers like lysine/DNA methyltransferases and DNA demethylases, as well as selected putative targets across the wood frog freeze-thaw cycle and associated sub-stresses.This thesis provides strong evidence in favour of roles for H3K9 and DNA hypomethylation during freeze recovery, which are largely correlated with changes in expression of catalyzing enzymes.Some non-histone target roles are also suggested.Alleviation of repressive epigenetic controls likely contribute to the resumption of a permissive transcriptional state and may induce the activity of essential repair pathways during thawing.
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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) | 0.002 | 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".