Towards a goal-oriented agent-based simulation framework for high-performance computing

Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

Biana: a software framework for compiling biological interactions and analyzing networks

<p>Abstract</p> <p>Background</p> <p>The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.</p> <p>Results</p> <p>We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from <url>http://sbi.imim.es/web/BIANA.php</url>.</p> <p>Conclusions</p> <p>BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.</p

ArchDB 2014:Structural classification of loops in proteins

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability—raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses

Social network data analysis for event detection

Cities concentrate enough Social Network (SN) activity to empower rich models. We present an approach to event discovery based on the information provided by three SN, minimizing the data properties used to maximize the total amount of usable data. We build a model of the normal city behavior which we use to detect abnormal situations (events). After collecting half a year of data we show examples of the events detected and introduce some applications.Peer ReviewedPostprint (published version

A system on chip based electroencephalogram acquisition system

La mayoría de las implementaciones actuales de interfaces cerebro computadora (BCI) consisten en una etapa de adquisición de biopotenciales y una PC, donde se realiza el procesamiento de las señales. Como estas han demostrado su potencialidad para mejorar la calidad de vida de personas con movilidad reducida y pérdida del habla se plantea la necesidad de migrar a un dispositivo de tamaño reducido, eliminando la PC del esquema. Los estrictos requerimientos de procesamiento en tiempo real de las BCI justifican la elección de un sistema embebido heterogéneo para este propósito. En este trabajo se presenta un adquisidor de señales de electroencefalograma (EEG) basado en un sistema SoC (System on Chip) DE10-nano de bajo costo, provisto por Altera. Se describe el sistema por completo y se muestran registros de EEG de un ritmo alfa, correspondientes a un usuario durante un periodo de relajación. Mediante el procesamiento off line de la señal se obtiene su espectro de frecuencias, el cual tiene un máximo de amplitud en 12,8 Hz, característico de una señal de este tipo. La validación del sistema de adquisición implementado constituye un punto de partida sólido para el desarrollo de una BCI completamente funcional en el futuro, así como otro tipo de aplicaciones con restricciones temporales más demandantes.Current Brain computer interfaces (BCI) are usually implemented by a biopotential acquisition board and a computer (PC), where the signal processing is performed. Since these have demonstrated their potential to improve the quality of life of people with reduced mobility and speech losses, it is time to migrate to smaller devices, eliminating the PC from the scheme. The strict real time processing requirements of BCI justify the use of a System on Chip (SoC) for this propose. This work presents an electroencephalogram (EEG) acquisition system based on a low-cost DE10-nano SoC provided by Altera. The complete system is described and EEG records of an alpha rhythm, corresponding to a user during a period of relaxation, are shown. Through offline processing of the signal, its frequency spectrum is obtained, which has a maximum amplitude at 12.8 Hz, characteristic for a signal of this type. The validation of the acquisition system constitutes a solid starting point for the development of a fully functional BCI in the future, as well as other applications with more demanding time constraints.an electroencephalogram (EEG) acquisition system based on a low-cost DE10-nano SoC provided by Altera. The complete system is described and EEG records of an alpha rhythm, corresponding to a user during a period of relaxation, are shown. Through offline processing of the signal, its frequency spectrum is obtained, which has a maximum amplitude at 12.8 Hz, characteristic for a signal of this type. The validation of the acquisition system constitutes a solid starting point for the development of a fully functional BCI in the future, as well as other applications with more demanding time constraints.Fil: Oliva, Matias Javier. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Garcia, Pablo Andres. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Spinelli, Enrique Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentin

On the mechanisms of protein interactions : predicting their affinity from unbound tertiary structures

Motivation: The characterization of the protein–protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein–protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures. Results: We present a new approach that relies on the unbound protein structures and protein docking to predict protein–protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol. Availability and implementation: The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock

Making smart cities smarter using artificial intelligence techniques for smarter mobility

The term Smart City is tipically applied to urban and metropolitan areas where Information and Communication Technologies provide ways to enable social, cultural and urban development, improving social and political capacities and/or efficiency. In this paper we will show the potential of Artificial Intelligence techniques for augmenting ICT solutions to both increase the cities competiveness but also the active participation of citizens in those processes, making Smart Cities smarter. As example we will describe the usage of Artificial Intellgence techniques to provide Smart Mobility in the context of the SUPERHUB Project.Postprint (published version

InteractoMIX:A suite of computational tools to exploit interactomes in biological and clinical research

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com)