Phylodynamics of the Emergence of Influenza Viruses after Cross-Species Transmission
Bibliographic record
Abstract
Human populations are constantly exposed to emerging pathogens such as influenza A viruses that result from cross-species transmissions. Generally these sporadic events are evolutionary dead-ends, but occasionally, viruses establish themselves in a new host that offers a novel genomic context to which the virus must adjust to avoid attenuation. However, the dynamics of this process are unknown. Here we present a novel method to characterize the time it takes to G+C composition at third codon positions (GC3 content) of influenza viruses to adjust to that of a new host. We compare the inferred dynamics in two subtypes, H1N1 and H3N2, based on complete genomes of viruses circulating in humans, swine and birds between 1900-2009. Our results suggest that both subtypes have the same fast-adjusting genes, which are not necessarily those with the highest absolute rates of evolution, but those with the most relaxed selective pressures. Our analyses reveal that NA and NS2 genes adjust the fastest to a new host and that selective pressures of H3N2 viruses are relaxed faster than for H1N1. The asymmetric nature of these processes suggests that viruses with the greatest adjustment potential to humans are coming from both birds and swine for H3N2, but only from birds for H1N1.
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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 | 0.001 | 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".