Association of polymorphism in the promotor area of the caprine BMPR1B gene with litter size and body measurement traits in Damani goats.

This study aimed to explore polymorphisms in the promoter region of the caprine BMPR1B (Bone morphogenetic protein receptor 1 beta) gene and its association with body measurement and litter size traits in Damani does. A total of 53 blood samples were collected to analyze the association between the BMPR1B gene polymorphism and 11 phenotypic traits in Damani female goats. The results revealed that three novel SNPs were identified in the promoter region of the caprine BMPR1B gene, including g.67 A > C (SNP1), g.170 G > A(SNP2), and g.501A > T (SNP3), among which the SNP1 and SNP2 were significantly (p < 0.05) associated with litter size and body measurement traits in Damani goats. In SNP1 the AC genotype could be used as a marker for litter size, and the CC genotype for body weight in Damani goats. In SNP2, the genotype GG was significantly (p < 0.05) associated with ear and head length. Therefore, we can conclude from the present study, that genetic variants AC and CC of the caprine BMPR1B gene could be used as genetic markers for economic traits through marker-assisted selection for the breed improvement program of the Damani goat.

Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier.

Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called "Stacking Classifier" in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.