Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders.

In the United States, dairy calves are typically housed individually due to the perception of reduced risk of spreading infectious diseases between calves and the ability to monitor health on an individual calf basis. However, automated milk feeders (AMF) can provide individual monitoring of group-housed calves while allowing them to express more natural feeding behaviors and interact with each other. Research has shown that feeding behaviors recorded by AMF can be a helpful screening tool for detecting disease in dairy calves. Altogether, there is an opportunity to use the data from AMF to create a more robust and efficient model to predict disease, reducing the need for visual observation. Therefore, the objective of this observational study was to predict disease in preweaning dairy calves using AMF feeding behavior data and machine learning (ML) algorithms. This study was conducted on a dairy farm located in the Upper Midwest United States and visited weekly from July 2018 to May 2019. During farm visits, AMF data and calves' treatment records were collected, and calves were visually health-scored for attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, and cough score. The final datasets used for the analyses consisted of 740 and 741 calves, with 1,007 (healthy = 594 and sick = 413) and 1,044 (healthy = 560 and sick = 484) observations (health events) for Data 1 and Data 2, respectively. Data 1 included only the weekly calf health scores observed by research personnel, whereas Data 2 included primarily daily calf treatment records by farm staff in addition to weekly health scores. Calf visit-level feeding behaviors from AMF data included milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per d), and the interval between visits (min). Three approaches were used to predict health status: generalized linear model, random forest, and gradient boosting machine. A total of 16 models were built using different combinations of behavior parameters, including the number of rewarded visits, number of unrewarded visits, visit duration, the interval between visits, intake, intake divided by rewarded visits, drinking speed, and drinking speed divided by rewarded visits, and also calf age at the sick day as predictor variables. Of all algorithms, random forest and gradient boosting had the best performance predicting the health status of dairy calves. The results indicated that weekly health scores were not enough to predict calf health status. However, daily treatment records and AMF data were sufficient for creating predictive algorithms (e.g., F1-scores of 0.775 and 0.784 for random forest and gradient boosting, Data 2). This study suggests that ML was effective in determining the specific visit-level feeding behaviors that can be used to predict disease in group-housed preweaning dairy calves. Implementing these ML algorithms could reduce the need for visual calf observation on farms, minimizing labor time and improving calf health.

Feeding behaviors collected from automated milk feeders were associated with disease in group-housed dairy calves in the Upper Midwest United States.

Automated milk feeders (AMF) are an attractive option for producers interested in adopting practices that offer greater behavioral freedom for calves and can potentially improve labor management. These feeders give farmers the opportunity to have a more flexible labor schedule and more efficiently feed group-housed calves. However, housing calves in group systems can pose challenges for monitoring calf health on an individual basis, potentially leading to increased morbidity and mortality. Feeding behavior recorded by AMF software could potentially be used as an indicator of disease. Therefore, the objective of this observational study was to investigate the association between feeding behaviors and disease in preweaning group-housed dairy calves fed with AMF. The study was conducted at a dairy farm located in the Upper Midwest United States and included a final data set of 599 Holstein heifer calves. The farm was visited on a weekly basis from May 2018, to May 2019, when calves were visually health scored and AMF data were collected. Calf health scores included calf attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, cough score, and rectal temperatures. Generalized additive mixed models (GAMM) were used to identify associations between feeding behavior and disease. The final quasibinomial GAMM included the fixed (main and interactions) effects of feeding behavior at calf visit-level including milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per day), and interval between visits (min), as well as the random effects of calf age in regard to their relationship with calf health status. Total milk intake (mL/d), drinking speed (mL/min), interval between visits (min) to the AMF, calf age (d), and rewarded visits were significantly associated with dairy calf health status. These results indicate that as total milk intake and drinking speed increased, the risk of calves being sick decreased. In contrast, as the interval between visits and age increased, the risk of calves being sick also increased. This study suggests that AMF data may be a useful screening tool for detecting disease in dairy calves. In addition, GAMM were shown to be a simple and flexible approach to modeling calf health status, as they can cope with non-normal data distribution of the response variable, capture nonlinear relationships between explanatory and response variables and accommodate random effects.

Exon size affects competition between splicing and cleavage-polyadenylation in the immunoglobulin mu gene.

The alternative RNA processing of microseconds and microns mRNAs from a single primary transcript depends on competition between a cleavage-polyadenylation reaction to produce microseconds mRNA and a splicing reaction to produce microns mRNA. The ratio of microseconds to microns mRNA is regulated during B-cell maturation; relatively more spliced microns mRNA is made in B cells than in plasma cells. The balance between the efficiencies of splicing and cleavage-polyadenylation is critical to the regulation. The mu gene can be modified to either reduce or improve the efficiency of each reaction and thus alter the ratio of the two RNAs produced. However, as long as neither reaction is so strong that it totally dominates, expression of the modified mu genes is regulated in B cells and plasma cells. The current experiments reveal a relationship between the C mu 4 exon size and the microseconds/microns expression ratio. The shorter the distance between the C mu 4 5' splice site and the nearest upstream 3' splice site, the more spliced microns mRNA was produced. Conversely, when this exon was expanded, more microseconds mRNA was produced. Expression from these mu genes with altered exon sizes were regulated between B cells and plasma cells. Since RNA processing in the mu gene can be considered a competition between defining the C mu 4 exon as an internal exon (in microns mRNA) versus a terminal exon (in microseconds mRNA), exon size may affect the competition among factors interacting with this exon.