The evidence for groundwater as a source of enteric illness in developing countries: A systematic review of the literature

Groundwater constitutes approximately 30% of freshwater globally and serves as a source of drinking water in many regions. Groundwater sources are subject to contamination with human pathogens (viruses, bacteria and protozoa) from a variety of sources that can cause diarrhea and contribute to the devastating global burden of this disease. To attempt to describe the extent of this public health concern in developing countries, a systematic review of the evidence for groundwater microbially-contaminated at its source as risk factor for enteric illness under endemic (non-outbreak) conditions in these countries was conducted. Epidemiologic studies published in English language journals between January 2000 and January 2011, and meeting certain other criteria, were selected, resulting in eleven studies reviewed. Data were extracted on microbes detected (and their concentrations if reported) and on associations measured between microbial quality of, or consumption of, groundwater and enteric illness; other relevant findings are also reported. In groundwater samples, several studies found bacterial indicators of fecal contamination (total coliforms, fecal coliforms, fecal streptococci, enterococci and E. coli), all in a wide range of concentrations. Rotavirus and a number of enteropathogenic bacteria and parasites were found in stool samples from study subjects who had consumed groundwater, but no concentrations were reported. Consumption of groundwater was associated with increased risk of diarrhea, with odds ratios ranging from 1.9 to 6.1. However, limitations of the selected studies, especially potential confounding factors, limited the conclusions that could be drawn from them. These results support the contention that microbial contamination of groundwater reservoirs—including with human enteropathogens and from a variety of sources—is a reality in developing countries. While microbially-contaminated groundwaters pose risk for diarrhea, other factors are also important, including water treatment, water storage practices, consumption of other water sources, water quantity and access to it, sanitation and hygiene, housing conditions, and socio-economic status. Further understanding of the interrelationships between, and the relative contributions to disease risk of, the various sources of microbial contamination of groundwater can guide the allocation of resources to interventions with the greatest public health benefit. Several recommendations for future research, and for practitioners and policymakers, are presented.^

Disparities in foodborne illness in Harris County, Texas, 2005--2010

Using a retrospective cross-sectional approach, this study quantitatively analyzed foodborne illness data, restaurant inspection data, and census-derived socioeconomic and demographic data within Harris County, Texas between 2005 and 2010. The main research question investigated involved determining the extent to which contextual and regulatory conditions distinguish outbreak and non-outbreak establishments within Harris County. Two groups of Harris County establishments were analyzed: outbreak and non-outbreak restaurants. STATA 11 was employed to determine the average profiles of each category across both the regulatory and socioeconomic (contextual) variables. Cross tabulations of all of the non-quantitative variables were also performed, and finally, a discriminant analysis was conducted to assess how well the variables were able to allocate the restaurants into their respective categories. Contextual and regulatory conditions were found to be minimally associated with the occurrence of foodborne outbreaks within Harris County. Across both the categories (outbreak and non-outbreak establishments), variables included were extremely similar in means, and when possible to observe, distributions. The variables analyzed in this study, both regulatory and contextual, were not found to significantly allocate the establishments into their correct outbreak or non-outbreak categories. The implications of these findings are that regulatory processes and guidelines in place in Harris County do not effectively to distinguish outbreak from non-outbreak restaurants. Additionally, no socioeconomic or racial/ethnic patterns are apparent in the incidence of foodborne disease in the county. ^

Identifying Topics in Social Media Posts using DBpedia

This paper describes a method for identifying topics in text published in social media, by applying topic recognition techniques that exploit DBpedia. We evaluate such method for social media in Spanish and we provide the results of the evaluation performed.

Looking for business models in digital media and content industries

International Conference on Dynamics of the Media and Content Industry. European Forum for Science and Industry.

On the generative equations of fractal self-similarity in granular media and the related PSD models

The physical appearance of granular media suggests the existence of geometrical scale invariance. The paper discuss how this physico-empirical property can be mathematically encoded leading to different generative models: a smooth one encoded by a differential equation and another encoded by an equation coming from a measure theoretical property.

A packing computational method relating fractal particle size distribution and void fraction in granular media

The study of granular systems is of great interest to many fields of science and technology. The packing of particles affects to the physical properties of the granular system. In particular, the crucial influence of particle size distribution (PSD) on the random packing structure increase the interest in relating both, either theoretically or by computational methods. A packing computational method is developed in order to estimate the void fraction corresponding to a fractal-like particle size distribution.

Thermodynamic aproach to particle size distribution in granular media

The study of particulate systems is of great interest in many fields of science and technology. Soil, sediments, powders, granular materials, colloidal and particulate suspensions are examples of systems involving many size particles. For those systems, the statistical description of the particle size distribution (PSD), that is, the mathematical distribution that defines the relative amounts of particles present, sorted according to size, is a crutial issue. The PSD can be important in understanding soil hydraulic properties, the geological origin or sediments or the physical and chemical properties of granular materials and ceramics, among others.

A Packing Computational Method Relating Fractal Particle Size Distribution and Void Fraction in Granular Media

The study of granular systems is of great interest to many fields of science and technology. The packing of particles affects to the physical properties of the granular system. In particular, the crucial influence of particle size distribution (PSD) on the random packing structure increase the interest in relating both, either theoretically or by computational methods. A packing computational method is developed in order to estimate the void fraction corresponding to a fractal-like particle size distribution.

Numerical study of ultrasound induced non-linear shape and size bubble oscillations in viscoelastic media

The theoretical study of forced bubble oscillations is motivated by the importance of cavitation bubbles and oscillating encapsulated microbubbles (i.e. contrast agents) in medical sciences. In more details,theoretical studies on bubble dynamics addressing the sound-bubble interaction phenomenon provide the basis for understanding the dynamics of contrast agent microbubbles used in medical diagnosis and of non-linearly oscillating cavitation bubbles in the case of high-intensity ultrasound therapy. Moreover, the inclusion of viscoelasticity is of vital importance for an accurate theoretical analysis since most biological tissues and fluids exhibit non-Newtonian behavior.

Methods and Techniques for Segmentation of Consumers in Social Media

Los medios sociales han revolucionado la manera en la que los consumidores se relacionan entre sí y con las marcas. Las opiniones publicadas en dichos medios tienen un poder de influencia en las decisiones de compra tan importante como las campañas de publicidad. En consecuencia, los profesionales del marketing cada vez dedican mayores esfuerzos e inversión a la obtención de indicadores que permitan medir el estado de salud de las marcas a partir de los contenidos digitales generados por sus consumidores. Dada la naturaleza no estructurada de los contenidos publicados en los medios sociales, la tecnología usada para procesar dichos contenidos ha menudo implementa técnicas de Inteligencia Artificial, tales como algoritmos de procesamiento de lenguaje natural, aprendizaje automático y análisis semántico. Esta tesis, contribuye al estado de la cuestión, con un modelo que permite estructurar e integrar la información publicada en medios sociales, y una serie de técnicas cuyos objetivos son la identificación de consumidores, así como la segmentación psicográfica y sociodemográfica de los mismos. La técnica de identificación de consumidores se basa en la huella digital de los dispositivos que utilizan para navegar por la Web y es tolerante a los cambios que se producen con frecuencia en dicha huella digital. Las técnicas de segmentación psicográfica descritas obtienen la posición en el embudo de compra de los consumidores y permiten clasificar las opiniones en función de una serie de atributos de marketing. Finalmente, las técnicas de segmentación sociodemográfica permiten obtener el lugar de residencia y el género de los consumidores. ABSTRACT Social media has revolutionised the way in which consumers relate to each other and with brands. The opinions published in social media have a power of influencing purchase decisions as important as advertising campaigns. Consequently, marketers are increasing efforts and investments for obtaining indicators to measure brand health from the digital content generated by consumers. Given the unstructured nature of social media contents, the technology used for processing such contents often implements Artificial Intelligence techniques, such as natural language processing, machine learning and semantic analysis algorithms. This thesis contributes to the State of the Art, with a model for structuring and integrating the information posted on social media, and a number of techniques whose objectives are the identification of consumers, as well as their socio-demographic and psychographic segmentation. The consumer identification technique is based on the fingerprint of the devices they use to surf the Web and is tolerant to the changes that occur frequently in such fingerprint. The psychographic profiling techniques described infer the position of consumer in the purchase funnel, and allow to classify the opinions based on a series of marketing attributes. Finally, the socio-demographic profiling techniques allow to obtain the residence and gender of consumers.