High neurocysticercosis (NC) prevalence was recently determined by a computed tomography (CT) scan study in the community of Tepetzitzintla, State of Puebla, Mexico. The aim of the present work was to evaluate the magnitude of fecal and parasite contamination by Taenia spp. in the soil of households of this community during the four seasons of the year. The toilet, backyard, kitchen, washboard, water containers and corrals of 14 to 26 households were sampled during each season. High Taenia spp. egg intensity was found in 24.2% of the sampled areas. The highest percentage was detected in Spring and the lowest in Summer. Significantly higher levels of Taenia spp. eggs were present in kitchen soil samples. A significant correlation was found between the presence of Taenia spp. eggs in household soil during the Summer, and NC diagnoses of the inhabitants by CT scan. Coproparasitological examinations and anti-cysticercal antibodies were determined in a cohort of inhabitants of the sampled households. Antibody levels and coproparasitological results were not associated with NC. Overall, these results illustrate the high degree of fecal contamination of potential risk to human health in rural communities and could be of use for control programmes.
Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:Neurocomputing 163 (2015)
AbstractThe growing interest in big data problems implies the need for unsupervised methods for data visualization and dimensionality reduction. Diffusion Maps (DM) is a recent technique that can capture the lower dimensional geometric structure underlying the sample patterns in a way which can be made to be independent of the sampling distribution. Moreover, DM allows to define an embedding whose Euclidean metric relates to the sample's intrinsic one which, in turn, enables a principled application of k-means clustering. In this work we give a self-contained review of DM and discuss two methods to compute the DM embedding coordinates to new out-of-sample data. Then, we will apply them on two meteorological data problems that involve respectively time and spatial compression of numerical weather forecasts and show how DM is capable to, first, greatly reduce the initial dimension while still capturing relevant information in the original data and, also, how the sample-derived DM embedding coordinates can be extended to new patterns.
Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing. Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study. This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.
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