Bovine<i>PGLYRP1</i>polymorphisms and their association with resistance to<i>Mycobacterium avium</i>ssp.<i>paratuberculosis</i>
Bibliographic record
Abstract
Mycobacterium avium ssp. paratuberculosis (MAP) causes a chronic, granulomatous inflammatory condition of the intestines in ruminants and wild-type species. It causes significant economic losses to the dairy and beef industries owing to reduced productivity, premature culling and mortality. Bovine peptidoglycan recognition protein 1 is an important pattern recognition molecule that is capable of directly killing microorganisms. The goal of this study was to identify single nucleotide polymorphisms (SNPs) in the gene encoding bovine peptidoglycan recognition protein 1 and to assess their association with susceptibility to MAP infection in dairy cattle. Blood and milk samples were collected from Holsteins in Southwestern and Eastern Ontario and tested for MAP infection using blood and milk ELISAs. A resource population consisting of 197 infected (S/P > 0.25) and 242 healthy (S/P < 0.10) cattle was constructed. Sequencing of pooled DNA was used to identify three SNPs (c.102G>C, c.480G>A and c.625C>A) that were genotyped in the resource population. Statistical analysis was performed using a logistic regression model fitting the additive and dominance effects of each SNP in the model. SNP c.480G>A (P = 0.054) was found to be associated with susceptibility to MAP infection. Cows with a copy of the major allele 'G' at this locus had an odds ratio of 1.51 (95% CI: 0.99-2.31) for being infected with MAP.
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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 | 0.000 | 0.000 |
| 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".