ABSTRACT. The microbial community of the reproductive apparatus, when known, can provide information about the health of the host. Metagenomics has been used to characterize and obtain genetic information about microbial communities in various environments and can relate certain diseases with changes in this community composition. In this study, samples of vaginal surface mucosal secretions were collected from five healthy cows and five cows that showed symptoms of reproductive disorders. Following high-throughput sequencing of the isolated microbial DNA, data were processed using the Mothur software to remove low-quality sequences and chimeras, and released to the Ribosomal Database Project for classification of operational taxonomic units (OTUs). Local BLASTn was performed and results were 6519 Vaginal microbiota diversity of healthy and diseased cattle ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (2): 6518-6528 (2015) loaded into the MEGAN program for viewing profiles and taxonomic microbial attributes. The control profile comprised a total of 15 taxa, with Bacteroides, Enterobacteriaceae, and Victivallis comprising the highest representation of OTUs; the reproductive disorder-positive profile comprised 68 taxa, with Bacteroides, Enterobacteriaceae, Histophilus, Victivallis, Alistipes, and Coriobacteriaceae being the taxa with the most OTU representation. A change was observed in both the community composition as well as in the microbial attributes of the profiles, suggesting that a relationship might exist between the pathogen and representative taxa, reflecting the production of metabolites to disease progression.

Besides the failures that cause accidents, there are the ones responsible for preventing the car’s motion capacity. These failures cause inconveniences to the passengers and expose them to the dangers of the road. Although modern vehicles are equipped with a failure detection system, it does not provide an online approach to the drivers. Third-party devices and skilled labor are necessary to manage the data for failure characterization. One of the most common failures in engines is called misfire, and it happens when the spark is weak or inexistent, compromising the whole set. In this work, two algorithms are compared, based on Wavelet Multiresolution Analysis (WMA) and another using an approach performing signal analysis based on Chaos using the density of maxima (SAC-DM) to identify misfare in a combustion engine of a working automotive vehicle. Experimental tests were carried out in a car to validate the techniques for the engine without failure, with failure in one piston, and with two failed pistons. The results made it possible to obtain the failure diagnosis for 100% of the cases for both WMA and SAC-DM methods, but a shorter time window when using the last one.