Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform Cristo

Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the genera- tion of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since min- imizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

NORA: Scalable OWL reasoner based on NoSQL databasesand Apache Spark

Reasoning is the process of inferring new knowledge and identifying inconsis-tencies within ontologies. Traditional techniques often prove inadequate whenreasoning over large Knowledge Bases containing millions or billions of facts.This article introduces NORA, a persistent and scalable OWL reasoner built ontop of Apache Spark, designed to address the challenges of reasoning over exten-sive and complex ontologies. NORA exploits the scalability of NoSQL databasesto effectively apply inference rules to Big Data ontologies with large ABoxes. Tofacilitatescalablereasoning,OWLdata,includingclassandpropertyhierarchiesand instances, are materialized in the Apache Cassandra database. Spark pro-grams are then evaluated iteratively, uncovering new implicit knowledge fromthe dataset and leading to enhanced performance and more efficient reasoningover large-scale ontologies. NORA has undergone a thorough evaluation withdifferent benchmarking ontologies of varying sizes to assess the scalability of thedeveloped solution.Funding for open access charge: Universidad de Málaga / CBUA This work has been partially funded by grant (funded by MCIN/AEI/10.13039/501100011033/) PID2020-112540RB-C41,AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploita-tion). Antonio Benítez-Hidalgo is supported by Grant PRE2018-084280 (Spanish Ministry of Science, Innovation andUniversities)

SALON ontology for the formal description of Sequence Alignments.

Background. Information provided by high-throughput sequencing platforms allows the collection of content-rich data about bio- logical sequences and their context. Sequence alignment is a bioinformatics approach to identifying regions of similarity in DNA, RNA, or protein sequences. However, there is no consensus about the specific common terminology and representation for sequence alignments. Thus, automatically linking the wide existing knowledge about the sequences with the alignments is challenging. Results. The Sequence Alignment Ontology (SALON) defines a helpful vocabulary for representing and semantically annotating pairwise and multiple sequence alignments. SALON is an OWL 2 ontology that supports automated reasoning for alignments validation and retrieving complementary information from public databases under the Open Linked Data approach. This will reduce the effort needed by scientists to interpret the sequence alignment results. Conclusions. SALON defines a full range of controlled terminology in the domain of sequence alignments. It can be used as a mediated schema to integrate data from different sources and validate acquired knowledge.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Disentangling the attention network test: behavioral, event related potentials, and neural source analyses

Background: The study of the attentional system remains a challenge for current neuroscience. The “Attention Network Test” (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. Results: This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions: The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects.Ministerio de Economía y Competitividad PSI2010- 1682

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.Universidad de Málaga. Andalucía TECH

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020 112540RB-C41 (AEI/FEDER, UE) and Andalusian PAIDI program with grant P18-RT-2799. Funding for open access charge: Universidad de Málaga / CBUA

PREVENÇÃO E INTERVENÇÃO EDUCATIVA SOBRE O BULLYING: A EDUCAÇÃO FÍSICA COMO UMA OPORTUNIDADE

Bullying is a social problem characterized by intentional aggression over time, usually in school contexts and in cyberspace (cyberbullying). There has been growing interest in preventing that phenomenon to reduce its adverse effects. Different studies and reports suggest that Physical Education may be a setting of interest to promote attitudes and behaviors against bullying. This work examines the state of the debate in this area through scientific literature. Based on the analysis of the evidence, we conclude by making recommendations regarding strategies and skills that should be included in Physical Education curricula to prevent bullying and cyberbullying. Among those most widely used, we highlight cooperative methodologies, a teaching attitude that promotes a motivational climate focused on the task, and curricular integration of teaching resources that address skills and protective factors. El acoso escolar es un problema social caracterizado por la agresión intencional que se produce a lo largo del tiempo, generalmente en contextos escolares y en el ciberespacio (ciberacoso). Hay un creciente interés en la prevención de este fenómeno, con el fin de reducir sus efectos adversos. Diferentes estudios e informes sugieren que la Educación Física puede impulsar actitudes y comportamientos contra el acoso escolar. El objetivo de este trabajo es examinar, a través de una revisión de la literatura científica, el estado de este tema en el área. Con base en el análisis de las evidencias, concluimos con recomendaciones sobre estrategias y habilidades que deberían incluirse en los currículos de Educación Física para prevenir el acoso y el ciberacoso. Entre las más utilizadas, destacamos las metodologías cooperativas, una actitud docente que promueve un clima motivacional centrado en la tarea y la integración curricular de recursos didácticos que aborden habilidades y factores de protección. O bullying é um problema social caracterizado pela agressão intencional que ocorre ao longo do tempo, geralmente em contextos escolares e no ciberespaço (cyberbullying). Tem havido um interesse crescente na prevenção deste fenômeno, a fim de reduzir os efeitos adversos. Diferentes estudos e relatórios sugerem que o tema da Educação Física pode ser um cenário de interesse para promover atitudes e comportamentos contra o bullying. O objetivo deste trabalho é examinar o estado da questão nesta área através de uma revisão da literatura científica. Com base na análise das evidências, concluímos fazendo recomendações sobre estratégias e habilidades que devem ser incluídas nos currículos de Educação Física para prevenir o bullying e o cyberbullying. Dentre as mais utilizadas, destacamos as metodologias cooperativas, uma atitude de ensino que promove um clima motivacional focado na tarefa e a integração curricular de recursos didáticos que abordam habilidades e fatores de proteção

jMetalPy: A Python framework for multi-objective optimization with metaheuristics

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.This work has been partially funded by Grants TIN2017-86049-R (Spanish Ministry of Education and Science). José García-Nieto is the recipient of a Post-Doctoral fellowship of “Captación de Talento para la Investigación” Plan Propio at Universidad de Málaga. Javier Del Ser and Izaskun Oregui receive funding support from the Basque Government through the EMAITEK Program (Spain)