Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Molecular characterization of group A bovine rotavirus in southeastern and central-western Brazil, 2009-2010

Rotavirus is an important cause of neonatal diarrhea in humans and several animal species, including calves. A study was conducted to examine 792 fecal samples collected from calves among 65 dairy and beef herds distributed in two of Brazil's major livestock producing regions, aiming to detect the occurrence of rotavirus and perform a molecular characterization of the rotavirus according to G and P genotypes in these regions. A total of 40 (5.05%) samples tested positive for rotavirus by the polyacrylamide gel electrophoresis (PAGE) technique. The molecular characterization was performed by multiplex semi-nested RT-PCR reactions, which indicated that the associations of genotypes circulating in herds in Brazil's southeastern region were G6P[11], G10P[11], G[-]P[5] + [11], G[-]P[6] in the state of Sao Paulo and G6P[11], G8P[5], G11P[11], G10P[11] in the state of Minas Gerais. In the central-western region, the genotypes G6P[5] + [11], G6P[5], G8P[-], G6P[11], G [-] P[1], G[-] P[11], and G[-] P[5] were detected in the state of Goias, while the genotypes G6P[5], G8[P11], G6[P11], G8[P1], G8[P5], G6[P1] were circulating in herds in the state of Mato Grosso do Sul. The genotypic diversity of bovine rotavirus found in each region under study underlines the importance of characterizing the circulating samples in order to devise the most effective prophylactic measures.