In this paper, we present the results of an experiment aiming to compare measurements of atmospheric delay by synthetic aperture radar (SAR) interferometry and GPS techniques to estimates by numerical weather prediction. Maps of the differential atmospheric delay are generated by processing a set of interferometric SAR images acquired by the ENVISAT-ASAR mission over the Lisbon region from April to November 2009. GPS measurements of the wet zenith delay are carried out over the same area, covering the time interval between the first and the last SAR acquisition. The Weather Research and Forecasting (WRF) model is used to model the atmospheric delay over the study area at about the same time of SAR acquisitions. The analysis of results gives hints to devise mitigation approaches of atmospheric artifacts in SAR interferometry applications.

This paper presents an innovative approach to maximally disconnect a given network. More specifically, this work introduces the concept of a Critical Disruption Path, a path between a source and a destination vertex whose deletion minimizes the cardinality of the largest remaining connected component. Network interdiction models seek to optimally disrupt network operations. Existing interdiction models disrupt network operations by removing vertices or edges. We introduce the first problem and formulation that optimally fragments a network via interdicting a path. Areas of study in which this work can be applied include transportation and evacuation networks, surveillance and reconnaissance operations, anti-terrorism activities, drug interdiction, and counter human-trafficking operations. In this paper, we first address the complexity associated with the Critical Disruption Path problem, and then provide a Mixed-Integer Linear Programming formulation for finding its optimal solution. Further, we develop a tailored Branch-and-Price algorithm that efficiently solves the Critical Disruption Path problem. We demonstrate the superiority of the developed Branch-and-Price algorithm by comparing the results found via our algorithm with the results found via the monolith formulation. In more than half of the test instances that can be solved by both the monolith and our Branch-and-Price algorithm, we outperform the monolith by two orders of magnitude. (c) 2013 Elsevier Ltd. All rights reserved.

We present the results of a computational study of the entire left coronary system simulated both at Newtonian level and at red blood cell resolution for a sizeable number of physiological conditions. We analyze the cardiovascular implications of stenotic plaques and show that the standard clinical criterion for surgical or percutaneous intervention, based on the fractional flow reserve (FFR), is significantly affected by system-dependent, local hemodynamic factors. A refined version, based on the new notion of local FFR response to stenotic growth, and accounting for statistical uncertainties due to flow heterogeneity, is suggested and illustrated. © 2013 IPEM.

A model for describing the dynamics of two mutually interacting neurons is considered. In such a context, maintaining statements of the Leaky Integrate-and-Fire framework, we include a random component in the synaptic current, whose role is to modify the equilibrium point of the membrane potential of one of the two neurons when a spike of the other one occurs. We give an approximation for the interspike time interval probability density function of both neurons within any parametric configurations driving the evolution of the membrane potentials in the so-called subthreshold regimen.

The development of high-throughput technology in genome sequencing provide a large amount of raw data to study the regulatory functions of transcription factors (TFs) on gene expression. It is possible to realize a classifier system in which the gene expression level, under a certain condition, is regarded as the response variable and features related to TFs are taken as predictive variables. In this paper we consider the families of Instance-Based (IB) classifiers, and in particular the Prototype exemplar learning classifier (PEL-C), because IB-classifiers can infer a mixture of representative instances, which can be used to discover the typical epigenetic patterns of transcription factors which explain the gene expression levels. We consider, as case study, the gene regulatory system in mouse embryonic stem cells (ESCs). Experimental results show IB-classifier systems can be effectively used for quantitative modelling of gene expression levels because more than 50% of variation in gene expression can be explained using binding signals of 12 TFs; moreover the PEL-C identifies nine typical patterns of transcription factors activation that provide new insights to understand the gene expression machinery of mouse ESCs.

Trust and reputation systems are decision support tools used to drive parties' interactions on the basis of parties' reputation.In such systems, parties rate with each other after each interaction. Reputation scores for each ratee are computed via reputation functions on the basis of collected ratings.We propose a general framework based on Bayesian decision theory for the assessment of such systems, with respect to the number of available ratings.Given a reputation function g and n independent ratings, one is interested in the value of the loss a user may incur by relying on the ratee's reputation as computed by the system.To this purpose, we study the behaviour of both Bayes and frequentist risk of reputation functions with respect to the number of available observations.We provide results that characterise the asymptotic behaviour of these two risks, describing their limits values and the exact exponential rate of convergence.One result of this analysis is that decision functions based on Maximum-Likelihood are asymptotically optimal.We also illustrate these results through a set of numerical simulations.

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters.

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools. First, we define a parametric Klaim specification of a reputation system that can be instantiated with different reputation models. Then, we consider stochastic specification obtained by considering actions with random (exponentially distributed) duration. The resulting specification enables quantitative analysis of properties of the considered system. Feasibility and effectiveness of our proposal is demonstrated by reporting on the analysis of two reputation models.

The signaling Petri net (SPN) simulator, designed to provide insights into the trends of molecules' activity levels in response to an external stimulus, contributes to the systems biology necessity of analyzing the dynamics of large-scale cellular networks. Implemented into the freely available software, BioLayout Express(3D), the simulator is publicly available and easy to use, provided the input files are prepared in the GraphML format, typically using the network editing software, yEd, and standards specific to the software. However, analysis of complex networks represented using other systems biology formatting languages (on which popular software, such as CellDesigner and Cytoscape, are based) requires manual manipulation, a step that is prone to error and limits the use of the SPN simulator in BioLayout Express(3D). To overcome this, we present a Cytoscape plug-in that enables users to automatically convert networks for analysis with the SPN simulator from the standard systems biology markup language. The automation of this step opens the SPN simulator to a far larger user group than has previously been possible.

Microarray and deep sequencing technologies have provided unprecedented opportunities for mapping genome mutations, RNA transcripts, transcription factor binding, and histone modifications at high resolution at the genome-wide level. This has revolutionized the way in which transcriptomes, regulatory networks and epigenetic regulations have been studied and large amounts of heterogeneous data have been generated. Although efforts are being made to integrate these datasets unbiasedly and efficiently, how best to do this still remains a challenge. Here we review major impacts of high-throughput genome-wide data generation, their relevance to human diseases, and various bioinformatics approaches for data integration. Finally, we provide a case study on inflammatory diseases.

Results: Analysis of the CRKL network -available at http://www.picb.ac.cn/ClinicalGenomicNTW/software.html-allows for investigation of the potential effect of perturbing genes of interest. Within the group of genes that are significantly affected by simulated perturbation of CRKL, we are lead to further investigate the importance of PXN. Our results allow us to (1) refine the hypothesis on CRKL as a novel drug target (2) indicate potential causes of side effects in on- going trials and (3) importantly, provide recommendations with impact on on- going clinical studies. Background: Rheumatoid arthritis (RA) is among the most common human systemic autoimmune diseases, affecting approximately 1% of the population worldwide. To date, there is no cure for the disease and current treatments show undesirable side effects. As the disease affects a growing number of individuals, and during their working age, the gathering of all information able to improve therapies -by understanding their and the disease mechanisms of action- represents an important area of research, benefiting not only patients but also societies. In this direction, network analysis methods have been used in previous work to further our understanding of this complex disease, leading to the identification of CRKL as a potential drug target for treatment of RA. Here, we use computational methods to expand on this work, testing the hypothesis in silico.

Results: Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits - when necessary-supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Background: Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps.

Results: We address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers. Background: High-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.

The aim of this chapter is to present an overview of the main results for a well-known optimization problem and an emerging optimization area, as well as introducing a new problem which is related to both of them. The first part of the chapter presents an overview of the main existing results for the classical maximum flow problem. The maximum flow problem is one of the most studied optimization problems in the last decades. Besides its many practical applications, it also arises as a subproblem of several other complex problems (e.g., min cost flow, matching, covering on bipartite graphs). Subsequently, the chapter introduces some problems defined on edge-labeled graphs by reviewing the most relevant results in this field. Edge-labeled graphs are used to model situations where it is crucial to represent qualitative differences (instead of quantitative ones) among different regions of the graph itself. Finally, the maximum flow problem with the minimum number of labels (MF-ML) problem is presented and discussed. The aim is to maximize the network flow as well as the homogeneity of the solution on a capacitated network with logic attributes.

This report describes the development of a numerical optimization procedure for the analysis of marine propellers. The activity is performed by INSEAN in the framework of WP35 of the STREAMLINE Project and the present report is written in fulfilment of Deliverable D35.4. The work described in the report is aimed at developing and integrating numerical optimization tools to build an optimal design framework for the improvement of state-of-art screw propellers in real operating conditions. The optimization problem is recast as a shape optimization study and computational strategies to achieve an efficient and flexible parametrization of propeller blade geometry are reviewed. Classical Free Form Deformation techniques are used as a reference to develop a novel approach, called Conformal Free Form Deformation. The capability of this technique to perform propeller blade shape manipulations is demonstrated through examples. Alternative optimization frameworks are outlined and compared. The importance of introducing a Robust Design Optimization model is clarified in order to make possible the analysis of propeller performance over a representative range of variations of operating conditions instead of considering an isolated design point. In addition to that, different algorithms based on local optimization and global optimization strategies are compared. The impact of introducing design constraints into the procedure is then discussed through simple example describing propeller shape manipulation studies based on a limited number of parameters. Requirements for the selection of a propeller hydrodynamics model to be interfaced with the optimization model are analysed. The Boundary Element Method developed as part of WP34 activities is chosen as an adequate trade-off between computational efficiency and accuracy of numerical predictions of propeller performance. Finally, numerical examples are presented and results of simple propeller optimization studies are discussed to analyse the capabilities of the proposed methodology.

We give a simple convexity-based proof of the following fact: the only eigenfunction of the p-Laplacian that does not change sign is the first one. The method of proof covers also more general nonlinear eigenvalue problems. © 2012 Springer Basel.

Wireless sensor networks involve many different real-world contexts, such as monitoring and control tasks for traffic, surveillance, military and environmental applications, among others. Usually, these applications consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. We want to individuate a set of covers (that is, subsets of sensors that can cover the whole set of targets) and appropriate activation times for each of them in order to maximize the total amount of time in which the monitoring activity can be performed (network lifetime), under the constraint given by the limited power of the battery contained in each sensor. A variant of this problem considers that each sensor can be activated in a certain number of alternative power levels, which determine different sensing ranges and power consumptions. We present some heuristic approaches and an exact approach based on the column generation technique. An extensive experimental phase proves the advantage in terms of solution quality of using adjustable sensing ranges with respect to the classical single range scheme. © 2012 Elsevier B.V. All rights reserved.

In recent years there has been a growing interest in frame based de-noising procedures. The advantage of frames with respect to classical orthonor- mal bases (e.g. wavelet, Fourier, polynomial) is that they can furnish an efficient representation of a more broad class of signals. For example, signals which have fast oscillating behavior as sonar, radar, EEG, stock market, audio and speech are much more well represented by a frame (with similar oscillating characteristic) than by a classical wavelet basis, although the frame representation for such kind of signals can be not properly sparse. In literature the frame based de-noising procedures can be divided into two classes: Bayesian approaches and variational approaches: both types promote sparseness through specific prior hypothesis or penalization term. A new frame based de-noising procedure is presented where no sparseness hypothesis is required on frame coefficients. In particular, the estimator is derived as the empirical version of the Wiener filter general- ized to the frame operator. An analytic expression of it is furnished so no searching strategy is required for the implementation. Results on standard and real test signals are presented.