Continuous-time formulation of reaction-diffusion processes on heterogeneous metapopulations

We present the derivation of the continuous-time equations governing the limit dynamics of discrete-time reaction-diffusion processes defined on heterogeneous metapopulations. We show that, when a rigorous time limit is performed, the lack of an epidemic threshold in the spread of infections is not limited to metapopulations with a scale-free architecture, as it has been predicted from dynamical equations in which reaction and diffusion occur sequentially in time

Speed of reaction-diffusion fronts in spatially heterogeneous media

The front speed problem for nonuniform reaction rate and diffusion coefficient is studied by using singular perturbation analysis, the geometric approach of Hamilton-Jacobi dynamics, and the local speed approach. Exact and perturbed expressions for the front speed are obtained in the limit of large times. For linear and fractal heterogeneities, the analytic results have been compared with numerical results exhibiting a good agreement. Finally we reach a general expression for the speed of the front in the case of smooth and weak heterogeneities

Predicting cancer involvement of genes from heterogeneous data

Background: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data. Results: We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature. Conclusion: Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

De-hyping transmit diversity in modern MIMO cellular systems

A contemporary perspective on the tradeoff between transmit antenna diversity and spatial multi-plexing is provided. It is argued that, in the context of modern cellular systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexingoutperform techniques that explicitly sacrifice spatialmultiplexing for diversity. Reaching this conclusion, however, requires that the channel and some key system features be adequately modeled; failure to do so may bring about starkly different conclusions. As a specific example, this contrast is illustrated using the 3GPP Long-Term Evolution system design.

Estudi de la influència de substàncies en la proliferació cel·lular: el·laboració d'un cultiu cel·lular

 Treball de recerca realitzat per un alumne d’ensenyament secundari i guardonat amb un Premi CIRIT per fomentar l'esperit científic del Jovent l’any 2010. L’objectiu principal d'aquest treball de recerca és estudiar la manera com diverses substàncies afecten el creixement cel•lular in vitro. La hipòtesi principal és que algunes substàncies poden tenir un important efecte en el desenvolupament de les cèl•lules, perquè les substàncies modifiquen l'adherència de les cèl•lules a la placa de cultiu. Concretament, en aquest treball s'ha estudiat la possible influència de la poliornitina, el col•lagen i la polilisina en el creixement cel•lular in vitro de cèl•lules PC12 (cèl•lules tumorals de ronyó de rata) i 293 (cèl•lules embrionàries de ronyó humà). Per dur a terme aquest estudi és necessari utilitzar material de laboratori d'un nivell tècnic que sobrepassa els equipaments dels laboratoris dels Centres de Secundària. La cambra de flux laminar, per a poder manipular els cultius de manera estèril, incubadors cel•lulars estèrils i el microscopi invertit són equipaments imprescindibles per a poder treballar amb cultius cel•lulars. El Departament de Bioquímica de la Facultat de Medicina i Infermeria (Universitat de Lleida) ha posat a disposició de l'autor tots aquest equipaments i ha participat directament en la acció tutelar d'aquest estudi. Els resultats d'aquesta recerca han permès confirmar les hipòtesis principals que inicialment s'havien plantejat. S'ha constatat que les substàncies utilitzades tenen efectivament un influència decisiva en el creixement de les cèl•lules estudiades. Inicialment les substàncies utilitzades han afavorit el creixement cel•lular in vitro, exceptuant la polilisina, que ha resultat tòxica per a les cèl•lules 293. Ara bé, si el cultiu sobrecreix i la població sobrepassa un determinat llindar, les cèl•lules inicien la mort per apoptosi.

Equilibrium asset prices and savings of heterogeneous agents in the presence of incomplete markets and portfolio constraints

We study the quantitative properties of a dynamic general equilibrium model in which agents face both idiosyncratic and aggregate income risk, state-dependent borrowing constraints that bind in some but not all periods and markets are incomplete. Optimal individual consumption-savings plans and equilibrium asset prices are computed under various assumptions about income uncertainty. Then we investigate whether our general equilibrium model with incomplete markets replicates two empirical observations: the high correlation between individual consumption and individual income, and the equity premium puzzle. We find that, when the driving processes are calibrated according to the data from wage income in different sectors of the US economy, the results move in the direction of explaining these observations, but the model falls short of explaining the observed correlations quantitatively. If the incomes of agents are assumed independent of each other, the observations can be explained quantitatively.

Heterogeneous information and trade policy

Protectionism enjoys surprising popular support, in spite of deadweight losses. At thesame time, trade barriers appear to decline with public information about protection.This paper develops an electoral model with heterogeneously informed voters whichexplains both facts and predicts the pattern of trade policy across industries. In themodel, each agent endogenously acquires more information about his sector of employment. As a result, voters support protectionism, because they learn more about thetrade barriers that help them as producers than those that hurt them as consumers.In equilibrium, asymmetric information induces a universal protectionist bias. Thestructure of protection is Pareto inefficient, in contrast to existing models. The modelpredicts a Dracula effect: trade policy for a sector is less protectionist when there ismore public information about it. Using a measure of newspaper coverage across industries, I find that cross-sector evidence from the United States bears out my theoreticalpredictions.

Estimating Bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise andpreferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decisionmaking. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisionsover heterogeneous priors. Relative to existing estimation approaches, our \Prior-Based Identification" extends the possible environments which can be estimated,and also substantially improves the accuracy and precision of estimates in thoseenvironments which can be estimated using existing methods.

Heterogeneous life-cycle profiles, income risk and consumption inequality

Was the increase in income inequality in the US due to permanent shocks or merely to an increase in the variance of transitory shocks? The implications for consumption and welfare depend crucially on the answer to this question. We use CEX repeated cross-section data on consumption and income to decompose idiosyncratic changes in income into predictable life-cycle changes, transitory and permanent shocks and estimate the contribution of each to total inequality. Our model fits the joint evolution of consumption and income inequality well and delivers two main results. First, we find that permanent changes in income explain all of the increase in inequality in the 1980s and 90s. Second, we reconcile this finding with the fact that consumption inequality did not increase much over this period. Our results support the view that many permanent changes in income are predictable for consumers, even if they look unpredictable to the econometrician, consistent withmodels of heterogeneous income profiles.

Overconfidence and market efficiency with heterogeneous agents

We study financial markets in which both rational and overconfident agents coexist and make endogenous information acquisition decisions. We demonstrate the following irrelevance result: when a positive fraction of rational agents (endogeneously) decides to become informed in equilibrium, prices are set as if all investors were rational, and as a consequence the overconfidence bias does not aect informational efficiency, price volatility, rational traders expected profits or their welfare. Intuitively, as overconfidence goes up, so does price infornativeness, which makes rational agents cut their information acquisition activities, effectively undoing the standard effect of more aggressive trading by the overconfident.

Solving heterogeneous-agent models by projection and perturbation

The paper proposes a numerical solution method for general equilibrium models with a continuum of heterogeneous agents, which combines elements of projection and of perturbation methods. The basic idea is to solve first for the stationary solutionof the model, without aggregate shocks but with fully specified idiosyncratic shocks. Afterwards one computes a first-order perturbation of the solution in the aggregate shocks. This approach allows to include a high-dimensional representation of the cross-sectional distribution in the state vector. The method is applied to a model of household saving with uninsurable income risk and liquidity constraints. The model includes not only productivity shocks, but also shocks to redistributive taxation, which cause substantial short-run variation in the cross-sectional distribution of wealth. If those shocks are operative, it is shown that a solution method based on very few statistics of the distribution is not suitable, while the proposed method can solve the model with high accuracy, at least for the case of small aggregate shocks. Techniques are discussed to reduce the dimension of the state space such that higher order perturbations are feasible.Matlab programs to solve the model can be downloaded.

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems