The full text can be downloaded at http://journal.embnet.org/index.php/embnetjournal/article/view/205/460

In this paper, we study two earthquakes: the April 6th 2009 earthquake of L'Aquila in the re-gion of Abruzzo (Italy) and the 1997 Colfiorito earthquake in the regions of Umbria and Marche (Italy). The data sets of these two earthquakes were analysed in both time and space domains. For time domain we used statistical methods and models both parametric and non-parametric. Concerning the space domain, we used Mathe-matical Morphology filters. The time domain analysis provides evidence of a possible corre-lation between seismic activities and the tides of the crust of the Earth. The results obtained show evidence that the daily number of earthquakes of the sequences proceeding and following the April 6th 2009 earthquake of L'Aquila and that of the sequence following the 1997 Colfiorito earth-quake have a periodic component of occurrence with period of about 7 days. It seems that the maxima of this component occur at a position of the Moon with respect to the Earth and the Sun corresponding to approximately 3 days before the four main Moon phases. The space domain analysis indicates that the foreshock activity in both earthquakes is clustered and concentrated. Furthermore, in each of the two earthquakes the clusters are located at about 3 kilometers from the epicentre of the main shock.

Purpose - The demographic risk is the risk due to the uncertainty in the demographic scenario assumptions by which life insurance products are designed and valued. The uncertainty lies both in the accidental (insurance risk) and systematic (longevity risk) deviations of the number of deaths from the value anticipated for it. This last component gives rise to the risk due to the randomness in the choice of the survival model for valuations (model risk or projection risk). If the insurance risk component can be assumed negligible for well-diversified portfolios, as in the case of pension annuities, longevity risk is crucial in the actuarial valuations. The question is particularly decisive in contexts in which the longevity phenomenon of the population is strong and pension annuity portfolios constitute a meaningful slice of the financial market - both typical elements of Western economies. The paper aims to focus on the solvency appraisal for a portfolio of life annuities, deepening the impact of the demographic risk according to suitable risk indexes apt to describe its evolution in time. Design/methodology/approach - The financial quantity proposed for representing the economic wealth of the life insurance company is the stochastic surplus, and the paper analyses the impact on it of different demographic assumptions by means of risk indicators as the projection risk index, the quantile surplus valuation and the ruin probability. By means of the proposed models, the longevity risk is mainly taken into account in a stochastic scenario for the financial risk component, in order to consider their interactions, too. In order to furnish practical details significant in the portfolio risk management, several numerical applications clarify the practical meaning of the models in the solvency context. Findings - This paper studies the impact on the portfolio surplus of the systematic demographic risk, taking into account their interaction with the financial risk sources. In this order of ideas, the internal risk profile of a life annuity portfolio is deeply investigated by means of suitable risk indexes: in a solvency analysis perspective, some possible scenarios for the evolution of death rates (generated by different survival models) are considered and this paper evaluates the impact on the portfolio surplus caused by different choices of the demographic model. The first index is deduced by a variance decomposition formula, the other ones involve the conditional quantile calculus and the ruin probability. Such indexes constitute benchmarks, whose conjoined use provides useful information to the meeting of the solvency requirements. Originality/value - With respect to the recent actuarial literature, in which the most important contribution on the surplus analysis has been given by Lisenko et al. - where the analysis focuses on the financial aspect applied to portfolios of temporary and endowment contracts - the paper considers life annuity portfolios, taking into account the effect of the systematic demographic risk and its interactions with the financial risk components.

In this paper we analyze the performance of the three MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observation modes that sound the Upper-Troposphere/Lower-Stratosphere (UT/LS) region. The two-dimensional (2-D) tomographic retrieval approach is assumed to derive the atmospheric field of geophysical parameters. For each observation mode we have calculated the 2-D distribution of the information load quantifier relative to the main MIPAS targets. The performance of the observation modes has been evaluated in terms of strength and spatial coverage of the information-load distribution along the full orbit. The indications of the information-load analysis has been validated with simulated retrievals based on the observational parameters of real orbits. In the simulation studies we have assessed the precision and the spatial (both horizontal and vertical) resolution of the retrieval products. The performance of the three observation modes has been compared for the MIPAS main products in both the UT/LS and the extended altitude range. This study shows that the two observation modes that were specifically designed for the UT/LS region are actually competitive with the third one, designed for the whole stratosphere, up to altitudes that far exceed the UT/LS. In the UT/LS the performance of the two specific observation modes is comparable even if the best performance in terms of horizontal resolution is provided by the observation mode that was excluded by the European Space Agency (ESA) from the current MIPAS duty cycle. This paper reports the first application of the information-load analysis and highlights the worthiness of this approach to make qualitative considerations about retrieval potential and selection of retrieval grid.

We present a computational framework for multi-scale simulations of real-life biofluidic problems. The framework allows to simulate suspensions composed by hundreds of millions of bodies interacting with each other and with a surrounding fluid in complex geometries. We apply the methodology to the simulation of blood flow through the human coronary arteries with a spatial resolution comparable with the size of red blood cells, and physiological levels of hematocrit (the red blood cell volume fraction). The simulation exhibits excellent scalability on a cluster of 4000 M2050 Nvidia GPUs and achieves close to 1 Petaflop aggregate performance, which demonstrates the capability to predicting the evolution of biofluidic phenomena of clinical significance. The combination of novel mathematical models, computational algorithms, hardware technology, code tuning and optimization required to achieve these results are presented. Copyright 2011 ACM.

We discuss the optimal evaluation of endothelial shear stress for real-life case studies based on anatomic data acquisition. The fluid dynamic simulations require smoothing of the geometric dataset to avoid major artefacts in the flow patterns, especially in the proximity of bifurcations. A systematic series of simulations at different corrugation levels shows that, below a smoothing length of about 0.5 mm, the numerical data are insensitive to further smoothing. © 2011 The Royal Society.

A special feature of Rayleigh-Taylor systems with chemical reactions is the competition between turbulent mixing and the "burning processes", which leads to a highly non-trivial dynamics. We studied the problem performing high resolution numerical simulations of a 2d system, using a thermal lattice Boltzmann (LB) model. We spanned the various regimes emerging at changing the relative chemical/turbulent time scales, from slow to fast reaction; in the former case we found numerical evidence of an enhancement of the front propagation speed (with respect to the laminar case), providing a phenomenological argument to explain the observed behaviour. When the reaction is very fast, instead, the formation of sharp fronts separating patches of pure phases, leads to an increase of intermittency in the small scale statistics of the temperature field.

We study the statistics of curvature and torsion of Lagrangian trajectories from direct numerical simulations of homogeneous and isotropic turbulence (at Re-lambda approximate to 280) in order to extract informations on the geometry of small-scale coherent structures in turbulent flows. We find that, as previously observed by Braun et al. (W. Braun, F. De Lillo, and B. Eckhardt, Geometry of particle paths in turbulent flows, J. Turbul. 7 (2006), p. 62) and Xu et al. (H. Xu, N.T. Ouellette, and E. Bodenschatz, Curvature of Lagrangian trajectories in turbulence, Phys. Rev. Lett. 98 (2007), p. 050201), the high curvature statistics is dominated by large-scale flow reversals where velocity magnitude assumes very low values. We show that flow-reversal events are characterized by very short correlation times. We introduce both time filtering and threshold in the minimum velocity amplitude in order to disentangle intense curvature events generated from genuine small-scale vortex structures from simple flow-reversal. We present for the first time measurements of torsion statistics in fully developed turbulent flows. By studying the joint statistics of curvature and torsion, we present further evidences that intense and persistent events are dominated by helical-type trajectories.

Regenerative medicine is a novel clinical branch aiming at the cure of diseases by replacement of damaged tissues. The crucial use of stem cells makes this area rich of challenges, given the poorly understood mechanisms of differentiation. One highly needed and yet unavailable technology should allow us to monitor the exact (metabolic) state of stem cells differentiation to maximize the effectiveness of their implant in vivo. This is challenged by the fact that not all relevant metabolites in stem cells differentiation are known and not all metabolites can currently be continuously monitored. To bring advancements in this direction, we propose the enhancement and integration of two available technologies into a general pipeline. Namely, high-throughput biochip for gene expression screening to pre-select the variables that are most likely to be relevant in the identification of the stem cells' state and low-throughput biochip for continuous monitoring of cell metabolism with highly sensitive carbon nanotubes-based sensors. Intriguingly, additionally to the involvement of multidisciplinary expertise (medicine, molecular biology, computer science, engineering, and physics), this whole query heavily relies on biochips: it starts in fact from the use of high-throughput ones, which output, in turn, becomes the base for the design of low-throughput, highly sensitive biochips. Future research is warranted in this direction to develop and validated the proposed device.

Community structure is an important topological phenomenon typical of complex networks. Accurately unveiling communities is thus crucial to understand and capture the many-faceted nature of complex networks. Communities in real world frequently overlap, i.e. nodes can belong to more than one community. Therefore, quantitatively evaluating the extent to which a node belongs to a community is a key step to find overlapping boundaries between communities. Non-negative matrix factorization (NMF) is a technique that has been used to detect overlapping communities. However, previous efforts in this direction present: (i) limitations in the interpretation of meaningful overlaps and (ii) lack of accuracy in predicting the correct number of communities. In this paper, a hybrid method of NMF to overcome both limitations is presented. This approach effectively estimates the number of communities and is more interpretable and more accurate in identifying overlapping communities in undirected networks than previous approaches. Validations on synthetic and real world networks show that the proposed community learning framework can effectively reveal overlapping communities in complex networks.

Results: We use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors. Background: Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.

Community structures are found to exist ubiquitously in a number of systems conveniently represented as complex networks. Partitioning networks into communities is thus important and crucial to both capture and simplify these systems' complexity. The prevalent and standard approach to meet this goal is related to the maximization of a quality function, modularity, which measures the goodness of a partition of a network into communities. However, it has recently been found that modularity maximization suffers from a resolution limit, which prevents its effectiveness and range of applications. Even when neglecting the resolution limit, methods designed for detecting communities in undirected networks cannot always be easily extended, and even less directly applied, to directed networks (for which specifically designed community detection methods are very limited). Furthermore, real-world networks are frequently found to possess hierarchical structure and the problem of revealing such type of structure is far from being addressed. In this paper, we propose a scheme that partitions networks into communities by electing community leaders via message passing between nodes. Using random walk on networks, this scheme derives an effective similarity measure between nodes, which is closely related to community memberships of nodes. Importantly, this approach can be applied to a very broad range of networks types. In fact, the successful validation of the proposed scheme on real and synthetic networks shows that this approach can effectively (i) address the problem of resolution limit and (ii) find communities in both directed and undirected networks within a unified framework, including revealing multiple levels of robust community partitions.

Results: The algorithm was first validated on synthetic and real benchmarks. It was then applied to the reconstruction of the core of the amino acids metabolism in Bifidobacterium longum, an essential, yet poorly known player in the human gut intestinal microbiome, known to be related to the onset of important diseases, such as metabolic syndromes. Our results show how computational approaches can offer effective tools for applications with the identification of potential new biological information. Motivation: The reliable and reproducible identification of gene interaction networks represents one of the grand challenges of both modern molecular biology and computational sciences. Approaches based on careful collection of literature data and network topological analysis, applied to unicellular organisms, have proven to offer results applicable to medical therapies. However, when little a priori knowledge is available, other approaches, not relying so strongly on previous literature, must be used. We propose here a novel algorithm ( based on ordinary differential equations) able to infer the interactions occurring among genes, starting from gene expression steady state data.

This new book examines the latest research in the synthetic biology which refers to both: the design and fabrication of biological components and systems that do not already exist in the natural world the re-design and fabrication of existing biological systems. It also deals with Integrative biology is the study and research of biological systems. It does not simply involve one discipline, but integrates a wide variety of disciplines that work together to find answers to scientific questions.

Different iterative schemes based on collocation methods have been well studied and widely applied to the numerical solution of nonlinear hypersingular integral equations (Capobianco et al. 2005). In this paper we apply Newton's method and its modified version to solve the equations obtained by applying a collocation method to a nonlinear hypersingular integral equation of Prandtl's type. The corresponding convergence results are derived in suitable Sobolev spaces. Some numerical tests are also presented to validate the theoretical results.

Background: In a systems biology perspective, protein-protein interactions (PPI) are encoded in machine-readable formats to avoid issues encountered in their retrieval for the reconstruction of comprehensive interaction maps and biological pathways. However, the information stored in electronic formats currently used doesn't allow a valid automatic reconstruction of biological pathways.Results: We propose a logical model of PPI that takes into account the "state" of proteins before and after the interaction. This information is necessary for proper reconstruction of the pathway.Conclusions: The adoption of the proposed model, which can be easily integrated into existing machine-readable formats used to store the PPI data, would facilitate the automatic or semi-automated reconstruction of biological pathways.Reviewers: This article was reviewed by Dr. Wen-Yu Chung (nominated by Kateryna Makova), Dr. Carl Herrmann (nominated by Dr. Purificación López-García) and Dr. Arcady Mushegian. © 2010 Termanini et al; licensee BioMed Central Ltd.

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information. © 2010 Tieri et al; licensee BioMed Central Ltd.

Human aging and longevity are complex and multi-factorial traits that result from a combination of environmental, genetic, epigenetic and stochastic factors, each contributing to the overall phenotype. The multi-factorial process of aging acts at different levels of complexity, from molecule to cell, from organ to organ systems and finally to organism, giving rise to the dynamic "aging mosaic". At present, an increasing amount of experimental data on genetics, genomics, proteomics and other -omics are available thanks to new highthroughput technologies but a comprehensive model for the study of human aging and longevity is still lacking. Systems biology represents a strategy to integrate and quantify the existing knowledge from different sources into predictive models, to be later tested and then implemented with new experimental data for validation and refinement in a recursive process. The ultimate goal is to compact the new acquired knowledge into a single picture, ideally able to characterize the phenotype at systemic/organism level. In this review we will briefly discuss the aging phenotype in a systems biology perspective, showing four specific examples at different levels of complexity, from a systemic process (inflammation) to a cascade-process pathways (coagulation) and from cellular organelle (proteasome) to single gene-network (PON-1), which could also represent targets for anti-aging strategies. © 2010 Bentham Science Publishers Ltd.