The highest-elevation frog provides insights into mechanisms and evolution of defenses against high UV radiation
Bibliographic record
Abstract
Defense against ultraviolet (UV) radiation exposure is essential for survival, especially in high-elevation species. Although some specific genes involved in UV response have been reported, the full view of UV defense mechanisms remains largely unexplored. Herein, we used integrated approaches to analyze UV responses in the highest-elevation frog, Nanorana parkeri . We show less damage and more efficient antioxidant activity in skin of this frog than those of its lower-elevation relatives after UV exposure. We also reveal genes related to UV defense and a corresponding temporal expression pattern in N. parkeri . Genomic and metabolomic analysis along with large-scale transcriptomic profiling revealed a time-dependent coordinated defense mechanism in N. parkeri . We also identified several microRNAs that play important regulatory roles, especially in decreasing the expression levels of cell cycle genes. Moreover, multiple defense genes (i.e., TYR for melanogenesis) exhibit positive selection with function-enhancing substitutions. Thus, both expression shifts and gene mutations contribute to UV adaptation in N. parkeri . Our work demonstrates a genetic framework for evolution of UV defense in a natural environment.
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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 | 0.001 |
| 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".