ensembles of probabilistic principal surfaces and competitive evolution on data two different approaches to data classification

Probabilistic Principal Surfaces (PPS) offer very powerful visualization and classification capabilities and overcome most of the shortcomings of other neural tools such as SOM, GTM, etc. More specifically PPS build a probability density function of a given data set of patterns lying in a D-dimensional space (with D ≫ 3) which can be expressed in terms of a limited number of latent variables laying in a Q-dimensional space (Q is usually 2-3) which can be used to visualize the data in the latent space. PPS may also be arranged in ensembles to tackle very complex classification tasks. Competitive Evolution on Data (CED) is instead an evolutionary system in which the possible solutions (cluster centroids) compete to conquer the largest possible number of resources (data) and thus partition the input data set in clusters. We discuss the application of Spherical-PPS to two data sets coming, respectively, from astronomy (Great Observatory Origins Deep Survey) and from genetics (microarray data from yeast genoma) and of CED to the genetics data only

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

Transitions at CpG Dinucleotides, Geographic Clustering of TP53 Mutations and Food Availability Patterns in Colorectal Cancer

Colorectal cancer is mainly attributed to diet, but the role exerted by foods remains unclear because involved factors are extremely complex. Geography substantially impacts on foods. Correlations between international variation in colorectal cancer-associated mutation patterns and food availabilities could highlight the influence of foods on colorectal mutagenesis. mutations from 12 countries/geographic areas. For food availabilities, we relied on data extracted from the Food Balance Sheets of the Food and Agriculture Organization of the United Nations. Dendrograms for mutation sites, mutation types and food patterns were constructed through Ward's hierarchical clustering algorithm and their stability was assessed evaluating silhouette values. Feature selection used entropy-based measures for similarity between clusterings, combined with principal component analysis by exhaustive and heuristic approaches. hotspots. Pearson's correlation scores, computed between the principal components of the datamatrices for mutation types, food availability and mutation sites, demonstrated statistically significant correlations between transitions at CpGs and both mutation sites and availabilities of meat, milk, sweeteners and animal fats, the energy-dense foods at the basis of “Western” diets. This is best explainable by differential exposure to nitrosative DNA damage due to foods that promote metabolic stress and chronic inflammation

Critical state in Josephson junction arrays as models of a bi-dimensional superconducting granular system

In order to analyze the diamagnetic properties of sintered granular superconductors, a circuital model consisting of a network of identical inductances (L0) and Josephson junctions is developed. In particular, the case of sufficiently high values of the maximum Josephson currents (Ij), such that L0Ij≫Φ0, is considered. Neglecting thermal activation processes, the process of irreversible penetration of flux quanta is studied numerically. A critical state model follows. © 1993

FAST TEMPLATE MATCHING FOR UMTS CODE KEYS WITH GRAPHICS HARDWARE

Template matching is a milestone application in Digital Signal Processing, and sets its roots in fundamental numeric filtering theory, as well as in time and frequency domain analysis. Radio signals, with mass spreading of high bandwidth cellular networks, have become in recent years much more critical to handle in terms of QoS (Quality of Service), QoE (Quality of Experience) and SLA (Service Level Agreement), putting mobile carriers in the need to monitor their network status in a more detailed and efficient way than in past. Here an efficient use case of GPU computing applied to fast signal processing will be illustrated, with particular interest in study and development of a SIMD Linear and FFT-based cross correlation of multiple code keys in air-captured streams for UMTS networks. Developed techniques have been used with success in a commercial available 3G geotagged scanning equipment.