Analysis of the Variability in the Non-Coding Regions of Influenza A Viruses
Bibliographic record
Abstract
The genomes of influenza A viruses (IAVs) comprise eight negative-sense single-stranded RNA segments. In addition to the protein-coding region, each segment possesses 5' and 3' non-coding regions (NCR) that are important for transcription, replication and packaging. The NCRs contain both conserved and segment-specific sequences, and the impacts of variability in the NCRs are not completely understood. Full NCRs have been determined from some viruses, but a detailed analysis of potential variability in these regions among viruses from different host groups and locations has not been performed. To evaluate the degree of conservation in NCRs among different viruses, we sequenced the NCRs of IAVs isolated from different wild bird host groups (ducks, gulls and seabirds). We then extended our study to include NCRs available from the National Center for Biotechnology Information (NCBI) Influenza Virus Database, which allowed us to analyze a wider variety of host species and more HA and NA subtypes. We found that the amount of variability within the NCRs varies among segments, with the greatest variation found in the HA and NA and the least in the M and NS segments. Overall, variability in NCR sequences was correlated with the coding region phylogeny, suggesting vertical coevolution of the (coding sequence) CDS and NCR regions.
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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 | 0.001 |
| 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) | 0.001 | 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".