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.

Analysis of energetically biased transcripts of viruses and transposable elements

RNA interference (RNAi) is a natural endogenous process by which double-stranded RNA molecules trigger potent and specific gene silencing in eukaryotic cells and is characterized by target RNA cleavage. In mammals, small interfering RNAs (siRNAs) are the trigger molecules of choice and constitute a new class of RNA-based antiviral agents. In an efficient RNAi response, the antisense strand of siRNAs must enter the RNA-induced silencing complex (RISC) in a process mediated by thermodynamic features. In this report, we hypothesize that silent mutations capable of inverting thermodynamic properties can promote resistance to siRNAs. Extensive computational analyses were used to assess whether continuous selective pressure that promotes such mutations could lead to the emergence of viral strains completely resistant to RNAi (i.e., prone to transfer only the sense strands to RISC). Based on our findings, we propose that, although synonymous mutations may produce functional resistance, this strategy cannot be systematically adopted by viruses since the longest RNAi-refractory sequence is only 10 nt long. This finding also suggests that all mRNAs display fluctuating thermodynamic landscapes and that, in terms of thermodynamic features, RNAi is a very efficient antiviral system since there will always be sites susceptible to siRNAs.