Association of <i><scp>TLR</scp>4</i> polymorphisms with <i>Mycobacterium avium</i> subspecies <i>paratuberculosis</i> infection status in Canadian Holsteins
Bibliographic record
Abstract
Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic enteritis in cattle that results in substantial financial losses to the cattle industry worldwide. Given that susceptibility to MAP infection is determined in part by genetics, marker-assisted selection may help in the breeding of animals that are more resistant to MAP infection. The toll-like receptor 4 gene (TLR4) was selected as a potential candidate gene because of its role in innate immunity and its involvement in MAP recognition and infection. The objective of this study, therefore, was to identify associations between TLR4 polymorphisms and susceptibility to MAP infection in Canadian Holstein cows. Two biologically relevant SNPs, including c.-226G>C in the 5'-untranslated region and the non-synonymous SNP c.2021C>T in the potential TIR domain, were selected for an association analysis with MAP infection status in 409 Canadian Holsteins. The haplotype C-T from these combined SNPs yielded significant association with susceptibility to MAP infection, supporting the involvement of TLR4 in susceptibility to MAP infection.
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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 | 0.001 | 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".