Episodic positive selection accumulates through time in seasonal influenza A, with no climatic signature
Bibliographic record
Abstract
Abstract The haemagglutinin (HA) and neuraminidase (NA) genes of seasonal influenza A evolve under continual immune-driven positive selection. To test whether the tempo of selection has changed over time, we mapped branch- and site-specific episodic diversifying selection (MEME) onto Bayesian relaxed-clock time trees for HA and NA in H1N1 and H3N2, across multiple countries and four sequence-subsampling schemes. We dated each selection episode and tested whether episodes accumulated through time after accounting for the growing number of sampled lineages. Positive-selection episodes increased over time in every gene-subtype combination, at about 2-6% per lineage-year, and rose faster for NA than HA. Episodes were concentrated at a small number of codon sites, especially recurrent sites in H3N2 HA that fell within canonical antigenic regions of the HA1 head. This increase was robust to subsampling scheme and time-bin width, and was driven disproportionately by recent lineages. A detrended spatial analysis found no association with latitude or temperature anomalies. Overall, positive selection on influenza surface antigens appears to be intensifying through time, most likely because of immune escape and expanded surveillance rather than climate warming.
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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 | 0.002 |
| 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".