Thermal acclimation uncovers a simple genetic basis of adaptation to high temperature in a cosmopolitan pest
Bibliographic record
Abstract
Understanding a population's fitness heterogeneity and genetic basis of thermal adaptation is essential for predicting the responses to global warming. We examined the thermotolerance and genetic adaptation of Plutella xylostella to exposure to hot temperatures. The population fitness parameters of the hot-acclimated DBM strains varied in the thermal environments. Using genome scanning and transcription profiling, we find a number of genes potentially involved in thermal adaptation of DBM. Editing two ABCG transporter genes, PxWhite and PxABCG , confirmed their role in altering cuticle permeability and influencing thermal responses. Our results demonstrate that SNP mutations in genes and changes in gene expression can allow DBM to rapidly adapt to thermal environment. ABCG transporter genes play an important role in thermal adaptation of DBM. This work improves our understanding of genetic adaptation mechanisms of insects to thermal stress and our capacity to predict the effects of rising global temperatures on ectotherms.
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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".