Micromanaging Freeze Tolerance: The Biogenesis and Regulation of microRNAs in Frozen Frogs
Bibliographic record
Abstract
When temperatures plummet below 0°C, the wood frog (Rana sylvatica) freezes up to 65% of its body water in extracellular ice masses, displaying no measurable brain activity, no breathing, and a flat-lined heart.Various molecular mechanisms including microRNAs, a multifunctional group of short non-coding RNAs, are in place to facilitate freeze tolerance.This thesis provides the first large-scale investigation of microRNA in a freeze tolerant vertebrate.Immunoblotting was used to investigate protein abundance of key microRNA biogenesis factors in brain and liver of control, 24 h frozen, and 8 h thawed R. sylvatica.Biogenesis capacity was reduced in brains and elevated in livers during freezing and thawing.This correlated with RT-qPCR levels of ~110 microRNAs, where the majority of differentially expressed miRNAs were downregulated in brains and upregulated in livers.Bioinformatic miRNA targeting predicted brain miRNAs to play a neuroprotective role, while hepatic miRNAs suppressed energy-expensive pro-growth processes.
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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".