The aim of this work is to develop a prototype system, that is a software tool that contributes to monitor the coastline. In fact it, constituted by a set of techniques and methods for SAR (Synthetic Aperture Radar) image segmentation, can aid to investigate the state of conservation of the coastal environment. The proposed system, called ISC (Interactive System for Coastline detection) and written in JAVA language, is composed by a set of Java classes structured in packages and it is based on the level set method applied to SAR images. In this method, an initial curve defined on the image evolves according to a PDE (Partial Differential Equation) model by means of a velocity whose mathematical expression is related to the characteristics of the image to be segmented. This curve is deformed until it assumes a stable position at the boundary of the area to be extracted from the image. We used images SAR PRI (Precision Image Resolution) acquired during the ERS2 (European Remote Sensing Satellite) mission.

With reference to a defined contribution pension scheme, this paper investigates the computation of suitable risk indicators in a fair valuation context. This subject involves theoretical isuues about the choice of the models for the dynamics of interest and mortality rates. The risk analysis is performed by computing the expected tail loss in a stochastic financial and demographic scenario. Numerical applications illustrate the impact of such evaluations on the reserve quantification in a Monte Carlo simulation framework.

The aim of the paper is to deal with the solvency requirements for defined contribution pension funds. The probability of underfunding is investigated in a stochastic framework by means of the funding ratio, which is the ratio of the market value of the assets to the market value of the liabilities. Demographic and invetment risks are modelled by means of diffusion processes. Their impact on the total riskiness of the fund is analyzed via a quantile approach.

The purpose of neuroimaging is to investigate the brain functionality through the localization of the regions where bioelectric current flows, starting from the measurements of the magnetic field produced in the outer space. Assuming that each component of the current density vector possesses the same sparse representation with respect to a pre-assigned multiscale basis, regularization techniques to the magnetic inverse problem are applied. The linear inverse problem arising can be approximated by iterative algorithms based on gradient steps intertwined with thresholding operations with joint-sparsity constraints. We propose some numerical tests in order to show the features of the numerical algorithm, also regarding the performance in terms of CPU occupancy.

We present a computational method for commodity hardware-based clinical cardiovascular diagnosis based on accurate simulation of cardiovascular blood flow. Our approach leverages the flexibility of the Lattice Boltzmann method to implementation on high-performance, commodity hardware, such as Graphical Processing Units. We developed the procedure for the analysis of real-life cardiovascular blood flow case studies, namely, anatomic data acquisition, geometry and mesh generation, flow simulation and data analysis and visualization. We demonstrate the usefulness of our computational tool through a set of large-scale simulations of the flow patterns associated with the arterial tree of a patient which involves two hundred million computational cells. The simulations show evidence of a very rich and heterogeneous endothelial shear stress pattern (ESS), a quantity of recognized key relevance to the localization and progression of major cardiovascular diseases, such as atherosclerosis, and set the stage for future studies involving pulsatile flows. © 2009 Elsevier B.V. All rights reserved.

We compute the continuum thermohydrodynamical limit of a new formulation of lattice kinetic equations for thermal compressible flows, recently proposed by Sbragaglia [J. Fluid Mech. 628, 299 (2009)]. We show that the hydrodynamical manifold is given by the correct compressible Fourier-Navier-Stokes equations for a perfect fluid. We validate the numerical algorithm by means of exact results for transition to convection in Rayleigh-Beacutenard compressible systems and against direct comparison with finite-difference schemes. The method is stable and reliable up to temperature jumps between top and bottom walls of the order of 50% the averaged bulk temperature. We use this method to study Rayleigh-Taylor instability for compressible stratified flows and we determine the growth of the mixing layer at changing Atwood numbers up to At similar to 0.4. We highlight the role played by the adiabatic gradient in stopping the mixing layer growth in the presence of high stratification and we quantify the asymmetric growth rate for spikes and bubbles for two dimensional Rayleigh-Taylor systems with resolution up to L(x)xL(z)=1664x4400 and with Rayleigh numbers up to Ra similar to 2x10(10). (C) 2010 American Institute of Physics. [doi: 10.1063/1.3392774]

In this paper we present an implementation for the QPACE supercomputer of a Lattice Boltzmann model of a fluid-dynamics flow in 2 dimensions. QPACE is a massively parallel application-driven system powered by the Cell processor. We review the structure of the model, describe in details its implementation on QPACE and finally present performance data and preliminary physics results. (C) 2010 Published by Elsevier Ltd.

A numerical study of turbulence seeded with light particles is presented. We analyze the statistical properties of coherent, small-scale structures by looking at the trapping events of light particles inside vortex filaments. We study the properties of particles attracting set, measuring its fractal dimension and the probability that the separation between two particles remains within the dissipative scale, even for time lapses as long as the large-scale correlation time, T(L). We show how to estimate the vortex lifetime by studying the moment of inertia of bunches of particles, showing the presence of an exponential lifetime distribution, with events up to T(L). (C) 2010 American Institute of Physics. [doi:10.1063/1.3431660]

We present the results of our numerical simulations of the Rayleigh-Taylor turbulence, performed using a recently proposed (Sbragaglia et al 2009 J. Fluid Mech. 628 299, Scagliarini et al 2010 Phys. Fluids 22 055101) lattice Boltzmann method that can describe consistently a thermal compressible flow subjected to an external forcing. The method allowed us to study the system in both the nearly Boussinesq regime and the strongly compressible regime. Moreover, we show that when the stratification is important, the presence of the adiabatic gradient causes the arrest of the mixing process.

Background: Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA). Methodology: Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA. Significance: We find that some nodes in the network that prove to be topologically important, in particular AKT2, IL6, MAPK1 and TP53, are also known to be associated with drugs used for the treatment of RA. Importantly, based on topological consideration, we are also able to suggest CRKL as a novel potentially relevant molecule for the diagnosis or treatment of RA. This type of finding proves the potential of in silico analyses able to produce highly refined hypotheses, based on vast experimental data, to be tested further and more efficiently. As research on RA is ongoing, the present map is in fieri, despite being -at the moment- a reflection of the state of the art. For this reason we make the network freely available in the standardised and easily exportable.xml CellDesigner format at 'www.picb.ac.cn/ClinicalGenomicNTW/temp.html' and 'www.celldesigner.org'. © 2010 Wu et al.

Community structures are found to exist ubiquitously in real-world complex networks. We address here the problem of community detection in directed networks. Most of the previous literature ignores edge directions and applies methods designed for community detection in undirected networks, which discards valuable information and often fails when different communities are defined on the basis of incoming and outgoing edges. We suggest extracting information about edge directions using a PageRank random walk and translating such information into edge weights. After extraction we obtain a new weighted directed network in which edge directions can then be safely ignored. We thus transform community detection in directed networks into community detection in reweighted undirected networks. Such an approach can benefit directly from the large volume of algorithms for the detection of communities in undirected networks already developed, since it is not obvious how to extend these algorithms to account for directed networks and the procedure is often difficult. Validations on synthetic and real-world networks demonstrate that the proposed framework can effectively detect communities in directed networks.

Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based solely on high-throughput gene expression data. The algorithm can infer (i) gene-gene interactions from steady state expression profiles and (ii) mode-of-action of the components that can trigger changes in the system. Results demonstrate that the proposed algorithm can identify both information with high performances, thus overcoming the limitation of current algorithms that can infer reliably only one.

With the expanding availability of sequencing technologies, research previously centered on the human genome can now afford to include the study of humans' internal ecosystem (human microbiome). Given the scale of the data involved in this metagenomic research (two orders of magnitude larger than the human genome) and their importance in relation to human health, it is crucial to guarantee (along with the appropriate data collection and taxonomy) proper tools for data analysis. We propose to adapt the approaches defined for the analysis of gene-expression microarray in order to infer information in metagenomics. In particular, we applied SAM, a broadly used tool for the identification of differentially expressed genes among different samples classes, to a reported dataset on a research model with mice of two genotypes (a high density lipoprotein knockout mouse and its wild-type counterpart). The data contain two different diets (high-fat or normal-chow) to ensure the onset of obesity, prodrome of metabolic syndromes (MS). By using 16S rRNA gene as a genomic diversity marker, we illustrate how this approach can identify bacterial populations differentially enriched among different genetic and dietary conditions of the host. This approach faithfully reproduces highly-relevant results from phylogenetic and standard statistical analyses, used to explain the role of the gut microbiome in relation to obesity. This represents a promising proof-of-principle for using functional genomic approaches in the fast growing area of metagenomics, and warrants the availability of a large body of thoroughly tested and theoretically sound methodologies to this exciting new field.

Community structure has been found to exist ubiquitously in many different kinds of real world complex networks. Most of the previous literature ignores edge directions and applies methods designed for community finding in undirected networks to find communities. Here, we address the problem of finding communities in directed networks. Our proposed method uses PageRank random walk induced network embedding to transform a directed network into an undirected one, where the information on edge directions is effectively incorporated into the edge weights. Starting from this new undirected weighted network, previously developed methods for undirected network community finding can be used without any modification. Moreover, our method improves on recent work in terms of community definition and meaning. We provide two simulated examples, a real social network and different sets of power law benchmark networks, to illustrate how our method can correctly detect communities in directed networks. (C) 2010 Elsevier B.V. All rights reserved.

The representation of real systems with network models is becoming increasingly common and critical to both capture and simplify systems' complexity, notably, via the partitioning of networks into communities. In this respect, the definition of modularity, a common and broadly used quality measure for networks partitioning, has induced a surge of efficient modularity-based community detection algorithms. However, recently, the optimization of modularity has been found to show a resolution limit, which reduces its effectiveness and range of applications. Therefore, one recent trend in this area of research has been related to the definition of novel quality functions, alternative to modularity. In this paper, however, instead of laying aside the important body of knowledge developed so far for modularity-based algorithms, we propose to use a strategy to preprocess networks before feeding them into modularity-based algorithms. This approach is based on the observation that dynamic processes triggered on vertices in the same community possess similar behavior patterns but dissimilar on vertices in different communities. Validations on real-world and synthetic networks demonstrate that network preprocessing can enhance the modularity-based community detection algorithms to find more natural clusters and effectively alleviates the problem of resolution limit.