Combinations of covariance selections for graphical modelling.

We explore the possibility of composing the results of a fixed number of Gaussian graphical model selections on some partially overlapping variables. This appears to be an useful approach in all the research areas where a large amount of data from different sources and types of experiments is available. Therefore the focus is in binding together information coming from heterogeneous studies to improve the understanding of a particular phenomenon of interest. The proposed approach relies on numerical results on artificial and real data

Multi-temporal scale analysis of complementarity between hydro and solar power along an alpine transect

Variable renewable energy sources display different space-time variability driving the availability of energy generated from these sources. Complementarity among variable renewable energies in time and space allows reducing the variability of power supply and helps matching the electricity demand curve. This work investigates the temporal structure of complementarity along an alpine transect in North-East Italy, considering a 100% renewable energy mix scenario composed by photovoltaic and run-of-the-river energy. We analyze the dominant scales of variability of variable renewable energy sources and electricity demand. In addition, we introduce a new metric, the wavelet-based complementarity index, to quantify the potential complementarity between two different energy sources. We show that this index varies at different temporal scales and it helps explaining the discrepancy between demand and supply in the study area. Continuous and discrete wavelet analyses are applied to assess the energy balance variability at multiple temporal scales and to identify the optimal mix of renewable energies, respectively. This work describes therefore an effective approach to investigate the temporal-scale dependency of the variance in the energy balance and can be further extended to different and more complex situations

The impact of heat waves on mortality in 9 European cities: results from the EuroHEAT project

BACKGROUND: The present study aimed at developing a standardized heat wave definition to estimate and compare the impact on mortality by gender, age and death causes in Europe during summers 1990-2004 and 2003, separately, accounting for heat wave duration and intensity. METHODS: Heat waves were defined considering both maximum apparent temperature and minimum temperature and classified by intensity, duration and timing during summer. The effect was estimated as percent increase in daily mortality during heat wave days compared to non heat wave days in people over 65 years. City specific and pooled estimates by gender, age and cause of death were calculated. RESULTS: The effect of heat waves showed great geographical heterogeneity among cities. Considering all years, except 2003, the increase in mortality during heat wave days ranged from + 7.6% in Munich to + 33.6% in Milan. The increase was up to 3-times greater during episodes of long duration and high intensity. Pooled results showed a greater impact in Mediterranean (+ 21.8% for total mortality) than in North Continental (+ 12.4%) cities. The highest effect was observed for respiratory diseases and among women aged 75-84 years. In 2003 the highest impact was observed in cities where heat wave episode was characterized by unusual meteorological conditions. CONCLUSIONS: Climate change scenarios indicate that extreme events are expected to increase in the future even in regions where heat waves are not frequent. Considering our results prevention programs should specifically target the elderly, women and those suffering from chronic respiratory disorders, thus reducing the impact on mortality

A Comprehensive and Universal Method for Assessing the Performance of Differential Gene Expression Analyses

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods

Assessment and prevention of acute health effects of weather conditions in Europe, the PHEWE project: background, objectives, design

<p>Abstract</p> <p>Background</p> <p>The project "Assessment and prevention of acute health effects of weather conditions in Europe" (PHEWE) had the aim of assessing the association between weather conditions and acute health effects, during both warm and cold seasons in 16 European cities with widely differing climatic conditions and to provide information for public health policies.</p> <p>Methods</p> <p>The PHEWE project was a three-year pan-European collaboration between epidemiologists, meteorologists and experts in public health. Meteorological, air pollution and mortality data from 16 cities and hospital admission data from 12 cities were available from 1990 to 2000. The short-term effect on mortality/morbidity was evaluated through city-specific and pooled time series analysis. The interaction between weather and air pollutants was evaluated and health impact assessments were performed to quantify the effect on the different populations. A heat/health watch warning system to predict oppressive weather conditions and alert the population was developed in a subgroup of cities and information on existing prevention policies and of adaptive strategies was gathered.</p> <p>Results</p> <p>Main results were presented in a symposium at the conference of the International Society of Environmental Epidemiology in Paris on September 6^th2006 and will be published as scientific articles. The present article introduces the project and includes a description of the database and the framework of the applied methodology.</p> <p>Conclusion</p> <p>The PHEWE project offers the opportunity to investigate the relationship between temperature and mortality in 16 European cities, representing a wide range of climatic, socio-demographic and cultural characteristics; the use of a standardized methodology allows for direct comparison between cities.</p

Gene set analysis exploiting the topology of a pathway

<p>Abstract</p> <p>Background</p> <p>Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it.</p> <p>Results</p> <p>We propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression.</p> <p>Conclusions</p> <p>The use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation.</p

A novel approach to the clustering of microarray data via nonparametric density estimation

<p>Abstract</p> <p>Background</p> <p>Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.</p> <p>Results</p> <p>Here, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.</p> <p>Conclusions</p> <p>The proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.</p