Assessing thermal adaptation of a global sample of Aspergillus fumigatus: Implications for climate change effects
Bibliographic record
Abstract
Aspergillus fumigatus is a common environmental mold and a major cause of opportunistic infections in humans. It's distributed among many ecological niches across the globe. A major virulence factor of A. fumigatus is its ability to grow at high temperature. However, at present, little is known about variations among strains in their growth at different temperatures and how their geographic origins may impact such variations. In this study, we analyzed 89 strains from 12 countries (Cameroon, Canada, China, Costa Rica, France, India, Iceland, Ireland, New Zealand, Peru, Saudi Arabia, and USA) representing diverse geographic locations and temperature environments. Each strain was grown at four temperatures and genotyped at nine microsatellite loci. Our analyses revealed a range of growth profiles, with significant variations among strains within individual geographic populations in their growths across the temperatures. No statistically significant association was observed between strain genotypes and their thermal growth profiles. Similarly geographic separation contributed little to differences in thermal adaptations among strains and populations. The combined analyses among genotypes and growth rates at different temperatures in the global sample suggest that most natural populations of A. fumigatus are capable of rapid adaptation to temperature changes. We discuss the implications of our results to the evolution and epidemiology of A. fumigatus under increasing climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".