Examination of<i>Mycobacterium avium</i>subspecies<i>paratuberculosis</i>mixed genotype infections in dairy animals using a whole genome sequencing approach
Bibliographic record
Abstract
Many pathogenic mycobacteria are known to cause severe disease in humans and animals.M. aviumsubspeciesparatuberculosis(Map) is the causative agent of Johne’s disease—a chronic wasting disease affecting ruminants such as cattle and sheep, responsible for significant economic losses in the dairy and beef industries. Due to the lack of treatment options or effective vaccines, mitigating losses can be difficult. In addition, the early stages ofMapinfection may occur in asymptomatic hosts that continue to shed viable bacteria in their faeces, leading to the infection of other healthy animals. Using multi-locus short sequence repeat (ML-SSR) analysis we previously reported that individual Johne’s positive dairy cattle from farms across the island of Newfoundland were infected byMapwith multiple SSR-types simultaneously. The occurrence of multiple mixed genotype infections has the potential to change pathogen and disease dynamics as well as reduce the efficacy of treatments and vaccines. Therefore, we conducted whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) analysis on a subset of these isolates for a more in-depth examination. We also implemented a PCR assay using two discriminatory SNPs and demonstrated the incidence of a mixed infection by three genotypically diverseMapisolates in a single animal. In addition, results show that WGS and SNP analysis can provide a better understanding of the relationship betweenMapisolates from individual and different animals. In the future such studies on the occurrence of mixed genotype infections could potentially lead to the identification of variable pathogenicity of different genotypes and allow for better tracking ofMapisolates for epidemiological studies.
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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 | 0.000 |
| 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".