Expression and Characterization of the Novel Gene<i>fr47</i>during Freezing in the Wood Frog,<i>Rana sylvatica</i>
Bibliographic record
Abstract
The wood frog, Rana sylvatica, has numerous adaptations that allow it to survive freezing of up to 65% of its total body water during the winter. Such adaptations have been found to include the expression of novel freeze responsive genes that are thought to be important for adaptation and survival. In this study, the tissue-specific stress responsive expression of one novel gene, fr47, was assessed in seven wood frog tissues. In response to freezing, the transcript expression of fr47 increased significantly in six tissues: heart, lung, liver, skeletal muscle, kidney, and testes. The expression of fr47 was also strongly upregulated by component stresses of freezing, namely, anoxia and dehydration. A dynamic change in fr47 expression was also observed during tadpole development, with expression low in embryonic stages (Gosner stages 14-20), increasing through intermediate (stages 26-43) and transformation phases (stages 44-45). These results indicated that fr47 potentially has a role to play in development and metamorphosis, in addition to freeze, anoxia, and dehydration tolerance. De novo analysis of FR47 protein structure revealed a likelihood of membrane associated function and possible GRB2 association. It is hypothesized that this interaction may influence inositol 1,4,5-trisphosphate production, known to increase during wood frog freezing.
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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) | 0.000 | 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".