Data mining for monitoring and managing systems and networks

Guest Editorial

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

The Brazilian Seismographic Integrated Systems (BRASIS): Infrastructure and data management

In geophysics and seismology, raw data need to be processed to generate useful information that can be turned into knowledge by researchers. The number of sensors that are acquiring raw data is increasing rapidly. Without good data management systems, more time can be spent in querying and preparing datasets for analyses than in acquiring raw data. Also, a lot of good quality data acquired at great effort can be lost forever if they are not correctly stored. Local and international cooperation will probably be reduced, and a lot of data will never become scientific knowledge. For this reason, the Seismological Laboratory of the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of São Paulo (IAG-USP) has concentrated fully on its data management system. This report describes the efforts of the IAG-USP to set up a seismology data management system to facilitate local and international cooperation. © 2011 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved.

Microcomputer technology to extend the use of population data in developing countries: multidisciplinary databases, Geographic Information Systems and REDATAM

Includes bibliography

Evaluating sustainability and food security of peasant farming systems in Mesoamerican highlands

En las últimas décadas, la agricultura sostenible ha sido objeto de gran interés y debate académico, no sólo en términos conceptuales, sino también en términos metodológicos. La persistencia de la inseguridad alimentaria y el deterioro de los recursos naturales en muchas regiones del mundo, ha provocado el surgimiento de numerosas iniciativas centradas en revitalizar la agricultura campesina así como renovadas discusiones sobre el rol que juega la agricultura como motor de desarrollo y principal actividad para alivio de la pobreza. Por ello, cuando hablamos de evaluar sistemas campesinos de montaña, debemos considerar tanto la dimensión alimentaria como las especificidades propias de los sistemas montañosos como base fundamental de la sostenibilidad. Al evaluar la contribución que han hecho alternativas tecnológicas y de manejo en la mejora de la sostenibilidad y la seguridad alimentaria de los sistemas campesinos de montaña en Mesoamérica, surgen tres preguntas de investigación: • ¿Se está evaluando la sostenibilidad de los sistemas campesinos teniendo en cuenta la variabilidad climática, la participación de los agricultores y las dinámicas temporales? • ¿Podemos rescatar tendencias comunes en estos sistemas y extrapolar los resultados a otras zonas? • ¿Son inequívocamente positivas las alternativas propuestas que se han llevado a cabo? En este trabajo se presentan tres evaluaciones de sostenibilidad que tratan de poner de manifiesto cuáles son los retos y oportunidades que enfrentan actualmente los sistemas campesinos de montaña. En primer lugar, se evalúan tres sistemas de manejo agrícola bajo dos años meteorológicamente contrastantes. Se determinó que durante el año que experimentó lluvias abundantes y temperaturas moderadas, los sistemas de bajos insumos, basados en el uso de abonos orgánicos y rotación de cultivos, obtuvieron los mejores resultados en indicadores ecológicos y similares resultados en los económicos y sociales que el sistema de altos insumos químicos. En el segundo año, con heladas tempranas y sequía invernal, la productividad se redujo para todos los sistemas pero los sistemas más diversificados (en variedades de maíz y/o siembra de otros cultivos) pudieron resistir mejor los contratiempos climáticos. En segundo lugar, se evalúa el grado de conocimiento (percepción) campesino para determinar los factores claves que determinan la sostenibilidad de sus sistemas y su seguridad alimentaria. Se determinó que los principales puntos críticos identificados por los campesinos (tamaño de parcela y pendiente del terreno) afectan de forma significativa a cuestiones de índole económica, pero no son capaces de explicar los desequilibrios alimenticios existentes. Realizando un análisis comparativo entre comunidades que presentaban buenos y malos resultados en cuanto a aporte energético y proteico, se determinó que la seguridad alimentaria estaba relacionada con la sostenibilidad de los sistemas y que concretamente estaba ligada a los atributos de equidad y autonomía. Otro resultado destacable fue que las comunidades más marginales y con mayor dificultad de acceso mostraron mayores niveles de inseguridad alimentaria, pero la variabilidad intergrupal fue muy alta. Eso demuestra que la seguridad alimentaria y nutricional forma parte de un complejo sistema de estrategias de autoabastecimiento ligada a la idiosincrasia misma de cada uno de los hogares. En tercer lugar, se evaluó el desempeño de las escuelas de campo de agricultores (ECAs) en la mejora de la sostenibilidad y la seguridad alimentaria de un sistema campesino de montaña. Para ver el efecto del impacto de estas metodologías a largo plazo, se estudiaron tres comunidades donde se habían implementado ECAs hace 8, 5 y 3 años. Encontramos que el impacto fue progresivo ya que fue la comunidad más antigua la que mejores valores obtuvo. El impacto de las ECAs fue rápido y persistente en los indicadores relacionados con la participación, el acceso a servicios básicos y la conservación de los recursos naturales. El estudio demostró un claro potencial de las ECAs en la mejora general de la sostenibilidad y la seguridad alimentaria de estos sistemas, sin embargo se observó una relación directa entre el aumento de producción agrícola y el uso de insumos externos, lo que puede suponer un punto crítico para los ideales sostenibles. ABSTRACT During the last decades, sustainable agriculture has been the subject of considerable academic interest and debate, not only in conceptual terms, but also in methodological ones. The persistence of high levels of environmental degradation and food insecurity in many regions has led to new initiatives focused on revitalizing peasant agriculture and renewed discussions of the role of sustainable agriculture as an engine for development, environmental conservation and poverty alleviation. Therefore, to assess mountain farming systems, we must consider food dimension and taking into account the specificities of the mountain systems as the foundation of sustainability. When evaluating contribution of technological and management alternative proposals in achieving sustainability and food security for peasant farming systems in Mesoamerican highlands, three research questions arise: • Is sustainability of peasant-farming systems being evaluated taking into account climate variability, participation of farmers and temporal dynamics? • Can we rescue common trends in these systems and extrapolate the results to other areas? • What alternative proposals that have been conducted are unequivocally positives? In this document, we present three evaluations of sustainability that try to highlight the challenges and opportunities that currently face mountain farming systems in Mesoamerica. First, we evaluate the sustainability of three agricultural management systems in two contrasting weather years. We determined that during the first year that exposed heavy rains and moderate temperatures, low-input systems, which are based on the use of organic fertilizers and crop rotation, provided better results in terms of ecological indicators and equal results in terms of economic and social indicators than those achieved using a high chemical input system. In the second year, which featured early frosts and a winter drought, productivity declined in all systems; however, the most diversified systems (in terms of the maize varieties grown and the sowing of other crops) more successfully resisted these climatic adversities. Second, we evaluate the farmers’ perception to determine the key drivers for achieving their sustainability and food and nutritional security. We determined that the key factors identified by farmers (landholding size and slope of cropland) exerted significant impacts on economic disparities but did not explain the malnutrition levels. We compared two contrasting hamlets according to their energy and protein supply, one namely Limón Timoté (LT), which did not present food problems and Limón Peña Blanca (LP), which did exhibit food insecurity. The results showed that FNS is linked to sustainability, and it is primarily related to the sustainability attributes of self-reliance and equity. Although the more marginated and inaccessible community exhibited more food insecurity, food and nutritional security depend upon a complex array of self-sufficiency strategies that remain linked to individual household idiosyncrasies. Third, we evaluated the impact of farmer field schools for improving the sustainability and food security of peasant mountain systems. In order to appreciate the long-term impact, we studied three communities where FFSs were implemented eight, five and three years ago, respectively. We found that FFSs have a gradual impact, as the community that first implemented FFSs scores highest. The impact of FFSs was broad and long-lasting for indicators related to participation, access to basic services and conservation of natural resources. This study demonstrates the potential of FFSs, but more attention will have to be paid to critical indicators in order to scale up their potential in the future. We observed a direct relationship between the increase in agricultural production and the use of external inputs, which is a critical point for sustainable ideals.

A Finite Element Numerical Algorithm for Modelling and Data Fitting in Complex Systems

Numerical modelling methodologies are important by their application to engineering and scientific problems, because there are processes where analytical mathematical expressions cannot be obtained to model them. When the only available information is a set of experimental values for the variables that determine the state of the system, the modelling problem is equivalent to determining the hyper-surface that best fits the data. This paper presents a methodology based on the Galerkin formulation of the finite elements method to obtain representations of relationships that are defined a priori, between a set of variables: y = z(x1, x2,...., xd). These representations are generated from the values of the variables in the experimental data. The approximation, piecewise, is an element of a Sobolev space and has derivatives defined in a general sense into this space. The using of this approach results in the need of inverting a linear system with a structure that allows a fast solver algorithm. The algorithm can be used in a variety of fields, being a multidisciplinary tool. The validity of the methodology is studied considering two real applications: a problem in hydrodynamics and a problem of engineering related to fluids, heat and transport in an energy generation plant. Also a test of the predictive capacity of the methodology is performed using a cross-validation method.