Coevolution of Thermoreceptor Genes of the TRP Family in 15 Populations of the Altai-Sayan Region, Western Siberia, and the Far East
Bibliographic record
Abstract
Abstract The analysis of the TRPV3 thermoreceptor gene (rs322937) was carried out in 15 population samples (1503 people) belonging to different language groups living in the regions of the Altai-Sayan Highlands, Western Siberia, the Far East, and Canada. The highest frequency of the rare allele was found in the Yakuts (42.9%), and the lowest in the Eskimos (10.7%) and Evenks (10.8%). Deviations from the Hardy–Weinberg equilibrium were found in the population of Siberian Tatars. The greatest interpopulation differences were found between the Yakuts and Evenks, and the smallest were found between the Kazakhs, Telengits, and Khakass, the Telengits and Tatars, the Khakass and Nanai, and the Tuvans and Northern Altaians. Transassociation of polymorphic loci of the TRPV1, TRPA1, TRPM8, and TRPV3 genes was carried out using the method of regression analysis. Of the six pairs, a positive correlation was found in three: TRPA1/TRPV3, TRPV3/TRPV1, and TRPV1/TRPM8. Thus, the TRPV3 gene studied by us is positively correlated with TRPV1 and TRPA1 and negatively with TRPM8. The results obtained may indicate different selective pressure in different geographical areas determined by climatic factors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".