Genome wide analysis of the evolution of Senecavirus A from swine clinical material and assembly yard environmental samples
Bibliographic record
Abstract
Senecavirus A (SVA), previously known as Seneca Valley virus, was first isolated in the United States in 2002. SVA was associated with porcine idiopathic vesicular disease in Canada and the USA in 2007 and 2012, respectively. Recent increase in SVA outbreaks resulting in neonatal mortality of piglets and/or vesicular lesions in sows in Brazil, the USA and Canada point to the necessity to study the pathogenicity and molecular epidemiology of the virus. Here, we report the analysis of the complete coding sequences of SVA from 2 clinical cases and 9 assembly yard environmental samples collected in 2015 in Canada, along with 22 previously released complete genomes in the GenBank. With this combined data set, the evolution of the SVA over a 12-month period in 2015/2016 was evaluated. These SVA isolates were characterized by a rapid accumulation of genetic variations driven mainly by a high nucleotide substitution rate and purifying selection. The SVA sequences clustered in clearly defined geographical areas with reported cases of SVA infection. No transmission links were identified between assembly yards, suggesting that point source introductions may have occurred. In addition, 25 fixed non-synonymous mutations were identified across all analyzed strains when compared to the prototype SVA strain (SVV-001). This study highlights the importance of monitoring SVA mutations for their role in increased virulence and impact on SVA diagnostics.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".