We consider a simple discrete model for screw dislocations in crystals. Using a variational discrete scheme we study the motion of a configuration of dislocations toward low energy configurations. We deduce an effective fully overdamped dynamics that follows the maximal dissipation criterion introduced in Cermelli and Gurtin (1999) and predicts motion along the glide directions of the crystal. (C) 2016 Elsevier Ltd. All rights reserved.

We consider a simple discrete model for screw dislocations in crystals. Using a variational discrete scheme we study the motion of a configuration of dislocations toward low energy configurations. We deduce an effective fully overdamped dynamics that follows the maximal dissipation criterion introduced in Cermelli and Gurtin (1999) and predicts motion along the glide directions of the crystal. (C) 2016 Elsevier Inc. All rights reserved.

Results: We propose to additionally evaluate a microbiome based on its global composition, by automatic annotation of pathogenic genera and statistical assessment of the net varied frequency of harmless versus harmful organisms. This application is intuitive, quantitative and computationally efficient and designed to cope with the currently incomplete species' functional knowledge. Our results, applied to human GI-microbiome data exemplify how this layer of information provides additional insights into treatments' impact on the GI microbiome, allowing to characterize a more physiologic effects of Prednisone versus Methotrexate, two treatments for rheumatoid arthritis (RA) a complex autoimmune systemic disease. Background: Sequencing technologies applied to mammals' microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases' progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated. Typical metagenomic analyses are run to associate to a condition (disease, therapy, diet) a pool of bacteria, whose eubiotic/dysbiotic potential is assessed either by a-diversity, a measure of the varieties populating the microbiome, or by Firmicutes to Bacteroides ratio, associated to systemic inflammation, and finally by manual and direct inspection of bacteria's biological functions, when known. These approaches lead to results sometimes difficult to interpret in terms of the evolution towards a specific microbial composition, harmed by large areas of unknown.

Normalized mutual information (NMI) is a widely used metric for performance evaluation of community detection methods, recently proven to be affected by finite size effects. To overcome this issue, a metric called relative normalized mutual information (rNMI) has been proposed. However, we show here that rNMI is still a biased metric and may lead, under given circumstances, to erroneous conclusions. The bias is an effect of the so-called reverse finite size effect. We discuss different strategies to address this issue, and then propose a new metric, the corrected normalized mutual information (cNMI), symmetric and well normalized, in the form of empirical calculation and closed-form expression. The experiments show that cNMI not only removes the finite size effect of NMI but also the reverse finite size effect of rNMI, and is hence more suitable for performance evaluation of community detection methods and for other approaches typical of the more general clustering context.

Community structure is an important feature of networks, and the correct detection of communities is a fundamental problem in network analysis. Statistical inference has recently been proposed for successful detection, provided the number of communities can be appropriately estimated a priori. In the absence of such information, model selection by determination of the number of communities remains an issue. We show here that correlation between communities from a highly parceled partition can be used to estimate a narrow range of variation for the real number of communities. This range, further elaborated by modularity-based belief propagation, correctly identifies communities. Testing on synthetic networks generated by a stochastic block model and a set of real-world networks shows that our method can alleviate the effects of modularity fluctuations well and enhance the ability of community detection of the bare modularity-based belief propagation method.

Degeneration is a hallmark of autoimmune diseases, whose incidence grows worldwide. Current therapies attempt to control the immune response to limit degeneration, commonly promoting immunodepression. Differently, mechanical stimulation is known to trigger healing (regeneration) and it has recently been proposed locally for its therapeutic potential on severely injured areas. As the early stages of healing consist of altered intra-and inter-cellular fluxes of soluble molecules, we explored the potential of this early signal to spread, over time, beyond the stimulation district and become systemic, to impact on distributed or otherwise unreachable injured areas. We report in a model of arthritis in rats how stimulations delivered in the subcutaneous dorsal tissue result, over time, in the control and healing of the degeneration of the paws' joints, concomitantly with the systemic activation of wound healing phenomena in blood and in correlation with a more eubiotic microbiome in the gut intestinal district.

The anonymous marketplaces ecosystem represents a new channel for black market/goods and services, offering a huge variety of illegal items. For many darknet marketplaces, the overall sales incidence is not (yet) comparable with the correspondent physical market; however, since it represents a further trade channel, providing opportunities to new and old forms of illegal trade with worldwide customers, anonymous trading should be carefully studied, via regular crawling and data analysis, in order to detect new trends in illegal goods and services (physical and digital), new drug substances and sources and alternative paths to import socially dangerous goods (e.g. drugs, weapons). Such markets, based on e-commerce retail leaders model, e.g. Amazon and E-bay, are designed with ease of use in mind, using off-the-shelf web technologies where users have their own profiles and credentials, acting as sellers, posting offers, or buyers, posting reviews or both. This lead to very poor data quality related to market offers and related, possible feedback, increasing the complexity of extraction of reliable data.

Cell motility in higher organisms (eukaryotes) is crucial to biological functions ranging from wound healing to immune response, and also implicated in diseases such as cancer. For cells crawling on hard surfaces, significant insights into motility have been gained from experiments replicating such motion in vitro. Such experiments show that crawling uses a combination of actin treadmilling (polymerization), which pushes the front of a cell forward, and myosin-induced stress (contractility), which retracts the rear. Here we present a simplified physical model of a crawling cell, consisting of a droplet of active polar fluid with contractility throughout, but treadmilling connected to a thin layer near the supporting wall. The model shows a variety of shapes and/or motility regimes, some closely resembling cases seen experimentally. Our work strongly supports the view that cellular motility exploits autonomous physical mechanisms whose operation does not need continuous regulatory effort.

Active systems, or active matter, are self-driven systems that live, or function, far from equilibrium - a paradigmatic example that we focus on here is provided by a suspension of self-motile particles. Active systems are far from equilibrium because their microscopic constituents constantly consume energy from the environment in order to do work, for instance to propel themselves. The non-equilibrium nature of active matter leads to a variety of non-trivial intriguing phenomena. An important one, which has recently been the subject of intense interest among biological and soft matter physicists, is that of the so-called "motility-induced phase separation", whereby self-propelled particles accumulate into clusters in the absence of any explicit attractive interactions between them. Here we review the physics of motility-induced phase separation, and discuss this phenomenon within the framework of the classic physics of phase separation and coarsening. We also discuss theories for bacterial colonies where coarsening may be arrested. Most of this work will focus on the case of run-and-tumble and active Brownian particles in the absence of solvent-mediated hydrodynamic interactions - we will briefly discuss at the end their role, which is not currently fully understood in this context.

We present a continuum theory of self-propelled particles, without alignment interactions, in a momentum-conserving solvent. To address phase separation, we introduce a dimensionless scalar concentration field ? with advective-diffusive dynamics. Activity creates a contribution ? to the deviatoric stress, where is odd under time reversal and d is the number of spatial dimensions; this causes an effective interfacial tension contribution that is negative for contractile swimmers. We predict that domain growth then ceases at a length scale where diffusive coarsening is balanced by active stretching of interfaces, and confirm this numerically. Thus, there is a subtle interplay of activity and hydrodynamics, even without alignment interactions.

Cosa sono le reti? Come funzionano? Lo racconta Paolo Tieri, fisico e ricercatore del CNR che da anni si dedica alla biologia dei sistemi e all'immunologia. Ogni mattina, quando andiamo a scuola, all'università, al lavoro, usiamo la rete stradale o quella dei trasporti urbani. Arrivati a destinazione ci troviamo ad interagire e a collaborare con amici e colleghi della nostra rete amicale. Ogni tanto controlliamo su Facebook cosa fanno gli amici del nostro social network preferito. E poi facciamo la spesa e prenotiamo le vacanze su Internet, usando la "rete delle reti" tecnologica mondiale. Insomma le reti sono ovunque e noi ci siamo immersi.

In remote storage services, delays in the time to retrieve data can cause economic losses to the data owners. In this paper, we address the problem of properly establishing specific clauses in the service level agreement (SLA), intended to guarantee a short and predictable retrieval time. Based on the rationale that the retrieval time mainly depends on the storage media used at the server side, we introduce the concept of Provable Storage Medium (PSM), to denote the ability of a user to efficiently verify that the provider is complying to this aspect of the SLA. We propose PSM as an extension of Provable Data Possession (PDP): embedding challenge-response PDP schemes with measurements of the response time, both properties can be enforced without any need for the user to locally store nor download her data. We describe a realistic implementation of PSM in a scenario where data should be stored both in RAM and HDD. A thorough analysis shows that, even for relatively small challenges, the total time to compute and deliver the response is sensibly affected by the remarkable difference in the access time of the two supports. An extensive simulation campaign confirms the quality and viability of our proposal.

In this paper we face the problem of maximizing the amount of time over which a set of target points, located in a given geographic region, can be monitored by means of a wireless sensor network. The problem is well known in the literature as Maximum Network Lifetime Problem (MLP). In the last few years the problem and a number of variants have been tackled with success by means of different resolution approaches, including exact approaches based on column generation techniques. In this work we propose an exact approach which combines a column generation approach with a genetic algorithm aimed at solving efficiently its separation problem. The genetic algorithm is specifically aimed at the Maximum Network ?-Lifetime Problem (?-MLP), a variant of MLP in which a given fraction of targets is allowed to be left uncovered at all times; however, since ?-MLP is a generalization of MLP, it can be used to solve the classical problem as well. The computational results, obtained on the benchmark instances, show that our approach overcomes the algorithms, available in the literature, to solve both MLP and ?-MLP.

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.

Overcomplete representations such as wavelets and windowed Fourier expansions have become mainstays of modern statistical data analysis. Here we derive expressions for the mean quadratic risk of shrinkage estimators in the context of general finite frames, which include any fullrank linear expansion of vector data in a finite-dimensional setting. We provide several new results and practical estimation procedures that take into account the geometric correlation structure of frame elements. These results motivate aggregation estimators and block thresholding procedures, and reinforce that the correlations induced by frame structure should be explicitly treated to yield improvements in estimation. A simulation study confirms these improvements.

SBMLMechanotransduction Map

Solutions to the n-Laplace equation with a right-hand side f are considered. We exhibit the largest rearrangement-invariant space to which f has to belong for every local weak solution to be continuous. Moreover, we find the optimal modulus of continuity of solutions when f ranges in classes of rearrangement-invariant spaces, including Lorentz, Lorentz-Zygmund and various standard Orlicz spaces.

An important topic in the numerical analysis of Volterra integral equations is the stability theory. The main results known in theliterature have been obtained on linear test equations or, at least, on nonlinear equations with convolution kernel. Here, we considerVolterra integral equations with Hammerstein nonlinearity, not necessarily of convolution type, and we study the error equation forDirect Quadrature methods with respect to bounded perturbations. For a class of Direct Quadrature methods, we obtain conditionson the stepsize h for the numerical solution to behave stably and we report numerical examples which show the robustness of thisnonlinear stability theory.