Resilience of High Immune Response (HIR) Genetics in the Context of Climate Change: Effects of Heat Stress on Cattle with Diverse Immune Response Genotypes
Bibliographic record
Abstract
Abstract As the temperature-humidity index (THI) increases, animals are less able to regulate body temperature leading to decreases in production and immune function. The High Immune Response (HIR™) technology classifies animals based on estimated breeding values (EBVs) of their IR capacity as high (H), average (A), or low (L). H-responders with their unique IR genotype have less disease than A or L, but have not been evaluated for adaptation to heat stress. Therefore, rectal temperatures of beef & dairy cows with known IR classification were recorded during THI< 74 (normal) and THI>74 (above normal). Results show that 50% of beef & 65% of dairy cows had body temperature above 39.2 at THI>74. However, cows that ranked H for antibody response were significantly better at regulating temperature and respiration at THI>74, than A or L. In vitro experiments showed variability in PBMC function when cells were heat stressed (42□, 4hrs). PBMC were stimulated with ConA to measure proliferation or LPS to assess nitric oxide (NO) production. HSP70 was measured in control and heat treated cells. Dairy results indicated that H responders had significantly greater cell proliferation across all treatments and greater NO and HSP production with significance depending on the treatment. Beef results indicate that HSP70 from unstimulated PBMC increased after 1 or 2 heat treatments, and the magnitude of the change varied by IR class. Conversely, PBMCs challenged with LPS produced more NO before than after heat treatments. These results indicate that cattle with diverse immune response genotypes vary in their ability to withstand heat stress, and that it should be possible to identify resilient individuals with both greater immunity and adaptation to climate warming.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".