Image analysis to automatically classify anemia based on Famacha score in sheep using ocular conjunctiva images.

Haemonchus contortus is the most pathogenic blood-feeding parasitic in sheep, causing anemia and consequently changes in the color of the ocular conjunctiva, from the deep red of healthy sheep to shades of pink to practically white of non-healthy sheep. In this context, the Famacha method has been created for detecting sheep unable to cope with the infection by H. contortus, through visual assessment of ocular conjunctiva coloration. Thus, the objectives of this study were (1) to extract ocular conjunctiva image features to automatically classify Famacha score and compare two classification models (multinomial logistic regression-MLR and random forest-RF) and (2) to evaluate the applicability of the best classification model on three sheep farms. The dataset consisted of 1,156 ocular conjunctiva images from 422 animals. RF model was used to segment the images, i.e., to select the pixels that belong to the ocular conjunctiva. After segmentation, the quantiles (1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 99%) of color intensity in each image channel (red, blue, and green) were determined and used as explanatory variables in the classification models, and the Famacha scores 1 (non-anemic) to 5 (severely anemic) were the target classes to be predicted (scores 1 to 5, with 162, 255, 443, 266, and 30 images, respectively). For objective 1, the performance metrics (precision and sensitivity) were obtained using MLR and RF models considering data from all farms randomly split. For objective 2, a leave-one-farm-out cross-validation technique was used to assess prediction quality across three farms (farms A, B, and C, with 726, 205, and 225 images, respectively). The RF provided the best performances in predicting anemic animals, as indicated by the high values of sensitivity for Famacha score 3 (80.9%), 4 (46.2%), and 5 (60%) compared to the MLR model. The precision of the RF was 72.7% for Famacha score 1 and 62.5% for Famacha score 2. These results indicate that is possible to successfully predict Famacha score, especially for scores 2 to 4, in sheep via image analysis and RF model using ocular conjunctiva images collected in farm conditions. As expected, model validation excluding entire farms in cross-validation presented a lower prediction quality. Nonetheless, this setup is closer to reality because the developed models are supposed to be used across farms, including new ones, and with different environments and management conditions.

Identification of copy number variations in the genome of Dairy Gir cattle.

Studying structural variants that can control complex traits is relevant for dairy cattle production, especially for animals that are tolerant to breeding conditions in the tropics, such as the Dairy Gir cattle. This study identified and characterized high confidence copy number variation regions (CNVR) in the Gir breed genome. A total of 38 animals were whole-genome sequenced, and 566 individuals were genotyped with a high-density SNP panel, among which 36 animals had both sequencing and SNP genotyping data available. Two sets of high confidence CNVR were established: one based on common CNV identified in the studied population (CNVR_POP), and another with CNV identified in sires with both sequence and SNP genotyping data available (CNVR_ANI). We found 10 CNVR_POP and 45 CNVR_ANI, which covered 1.05 Mb and 4.4 Mb of the bovine genome, respectively. Merging these CNV sets for functional analysis resulted in 48 unique high confidence CNVR. The overlapping genes were previously related to embryonic mortality, environmental adaptation, evolutionary process, immune response, longevity, mammary gland, resistance to gastrointestinal parasites, and stimuli recognition, among others. Our results contribute to a better understanding of the Gir breed genome. Moreover, the CNV identified in this study can potentially affect genes related to complex traits, such as production, health, and reproduction.

Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep.

This study investigated the feasibility of using easy-to-measure phenotypic traits to predict sheep resistant, resilient, and susceptible to gastrointestinal nematodes, compared the classification performance of multinomial logistic regression (MLR), linear discriminant analysis (LDA), random forest (RF), and artificial neural network (ANN) methods, and evaluated the applicability of the best classification model on each farm. The database comprised 3654 records of 1250 Santa Inês sheep from 6 farms. The animals were classified into resistant (2605 records), resilient (939 records), and susceptible (110 records) according to fecal egg count and packed cell volume. A random oversampling method was performed to balance the dataset. The classification methods were fitted using the information of age class, the month of record, farm, sex, Famacha© degree, body weight, and body condition score as predictors, and the resistance, resilience, and susceptibility to gastrointestinal nematodes as the target classes to be predicted considering data from all farms randomly. An additional leave-one-farm-out cross-validation technique was used to assess prediction quality across farms. The MLR and LDA models presented good performances in predicting susceptible and resistant animals. The results suggest that the use of readily available records and easily measurable traits may provide useful information for supporting management decisions at the farm level.

Estimation of genetic parameters for longevity considering the cow's age at last calving.

The aim of this study was to estimate heritability and predict breeding values for longevity among cows in herds of Nellore breed, considering the trait cow's age at last calving (ALC), by means of survival analysis methodology. The records of 11,791 animals from 22 farms were used. The variable ALC has been used by a criterion that made it possible to include cows not only at their first calving but also at their ninth calving. The criterion used was the difference between the date of each cow's last calving and the date of the last calving on each farm. If this difference was greater than 36 months, the cow was considered to have failed and uncensored. If not, this cow was censored, thus indicating that future calving remained possible for this cow. The survival model used for the analyses was the proportional hazards model, and the base risk was given by a Weibull distribution. The heritability estimate obtained was equal to 0.25. It was found that the ALC variable had the capacity to respond to selection for the purpose of increasing the longevity of the cows in the herds.