Aleutian disease: Risk factors and ImmunAD strategy for genetic improvement of tolerance in American mink (Neogale vison)
Bibliographic record
Abstract
Aleutian disease (AD) is a devastating infectious disease in American mink (Neogale vison) industry caused by Aleutian mink disease virus (AMDV). Two crucial steps toward controlling infectious diseases in farm animals are: (i) assessment of the infection risk factors to minimize the likelihood of infection and (ii) selection of animals with superior immune responses against pathogens to build tolerant farms. This study aimed to investigate AD risk factors and evaluate a novel "ImmunAD" approach for genetic improvement of AD tolerance. Phenotypic records and pedigree information of 1,366 and 24,633 animals were included in this study. The risk of animal's age, sex, color type, and year of sampling on AMDV infection was assessed using a logistic regression model and counter immune-electrophoresis (CIEP) test results. ImmunAD phenotype was calculated based on AMDVG enzyme-linked immunosorbent assay (ELISA) and CIEP test results, and breeding values for ImmunAD were estimated using an animal model. Animals were classified into high-coordinated (HCIR), average-coordinated (ACIR), and low-coordinated immune responders (LCIR) using ImmunAD's breeding values, and the impact of selection of HCIR on live grade of pelt quality (PQ), harvest weight (HW), and harvest length (HL) breeding values were evaluated. Age of > 1 year, male sex, and year of sampling were identified as significant risk factors of AD (p < 0.05). A moderate-to-high heritability (0.55±0.07) was estimated for ImmunAD, while a higher heritability was observed among the CIEP-positive animals (0.76±0.06). Significantly higher breeding values were observed for PQ and HL among HCIR than those for LCIR and ACIR (p < 0.05). Our findings indicate the critical role of male breeders in AD distribution within mink farms. Regular screening of AD in male breeders before pairing them with females during breeding seasons can help disease control. ImmunAD strategy can be applied to genetic improvement of AD tolerance, with favorable impacts on some growth and production traits. Higher genetic gains can be achieved in populations with higher AD seroprevalences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".