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

Big Data Optimization: Framework Algorítmico para el análisis de Datos guiado por Semántica

En las últimas décadas el aumento de fuentes de información en diferentes campos de la sociedad desde la salud hasta las redes sociales, ha puesto de manifiesto la necesidad de nuevas técnicas para su análisis, lo que se ha venido a llamar el Big Data. Los problemas clásicos de optimización no son ajenos a este cambio de paradigma, como por ejemplo el problema del viajante de comercio (TSP), ya que se puede beneficiar de los datos que proporciona los diferentes sensores que se encuentran en las ciudades y que podemos acceder a ellos gracias a los portales de Open Data. En esta tesis se ha desarrollado un nuevo framework, jMetalSP, para la optimización de problemas en el ´ ámbito del Big Data permitiendo el uso de fuentes de datos externas para modificar los datos del problema en tiempo real. Por otro lado, cuando estamos realizando análisis, ya sea de optimización o machine learning en Big Data, una de las formas más usada de abordarlo es mediante workflows de análisis. Estos están formados por componentes que hacen cada paso del análisis. El flujo de información en workflows puede ser anotada y almacenada usando herramientas de la Web Semántica para facilitar la reutilización de dichos componentes o incluso el workflow completo en futuros análisis, facilitando así, su reutilización y a su vez, mejorando el procesos de creación de los mismos. Para ello se ha creado la ontología BIGOWL, que permite trazar la cadena de valor de los datos de los workflows mediante semántica y además ayuda al analista en la creación de workflow gracias a que va guiando su composición con la información que contiene por la anotación de algoritmos, datos, componentes y workflows

The feasibility of pulsed light processing in the meat industry

Today, the increasing demand for minimally processed foods that are nutritious, sensorially acceptable, and free from microbial, chemical and physical hazards, challenges research and development to establish alternative methods to reduce the level of bacterial contamination. As one of the newly developing non-thermal methods, pulsed light is a technology for the fast, mild, and residue-free surface decontamination of meat and meat contact materials in the meat processing environment. This review provides specific information on pulsed light technology and the feasibility of its application for unpackaged and packaged meat and meat products as well as meat contact materials. The advantages, limitations and achieved effects of pulsed light on microbial inactivation, lipid peroxidation, sensory quality and color of meat, seafood and meat products are illustrated and discussed in relation to its implementation on the industrial level

Innovative non-thermal technologies affecting potato tuber and fried potato quality

Background: Potatoes are important tubers for human consumption, providing an essential source of energy and great nutritional characteristics for human health. However, before consumption, potato tubers need to be stored and processed. As frying is the most common technique used in potato processing, fried potato is the most important processed potato product. Some food characteristics, provided by the frying process, are considered desirable, but others are harmful to human health and, thereby the main challenge is to reduce the formation of the undesirable characteristics, without compromising the sensorial attributes. Scope and approach: In this review, the origin, economic importance, morphology and composition of potato tubers are presented. Afterwards, some factors affecting potato tuber quality, not only for human consumption, but also for further processing are addressed. Then, potato processing is discussed with a focus on the frying process, including the textural, chemical and nutritional changes induced by frying and the main characteristics affecting the quality of fried potato products. Finally, a special focus is given to the novel emerging non-thermal technologies and a brief review of their effects on potato tuber and fried potato quality is provided. Key findings and conclusions: Irradiation, cold plasma, ultrasounds, pulsed electric fields and high pressure processing are innovative non-thermal technologies with potential to be an alternative for the traditional treatments of potato tubers and to be applied as a frying pre-treatment, improving time and energy for slicing and cooking, and creating improved and healthier fried potatoes. Further studies are needed to better understand the subjacent biochemical mechanisms.publishe

Injecting domain knowledge in multi-objective optimization problems: A semantic approach

In the field of complex problem optimization with me-taheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker's preferences, or algorithms. However, there is a lack of approaches where ontologies are ap-plied in a direct way into the optimization process, with the aim of enhancing it by allowing the systematic incorporation of additional domain knowledge. This is due to the high level of abstraction of ontologies, which makes them difficult to be mapped into the code implementing the problems and/or the specific operators of metaheuristics. In this paper, we present a strategy to inject domain knowledge (by reusing existing ontologies or creating a new one) into a problem implementation that will be optimized using a metaheu-ristic. Thus, this approach based on accepted ontologies enables building and exploiting complex computing systems in optimization problems. We describe a methodology to automatically induce user choices (taken from the ontology) into the problem implementations provided by the jMetal op-timization framework. With the aim of illustrating our proposal, we focus on the urban domain. Concretely, We start from defining an ontology repre-senting the domain semantics for a city (e.g., building, bridges, point of inte-rest, routes, etc.) that allows defining a-priori preferences by a decision ma-ker in a standard, reusable, and formal (logic-based) way. We validate our proposal with several instances of two use cases, consisting in bi-objective formulations of the Traveling Salesman Problem (TSP) and the Radio Net-work Design problem (RND), both in the context of an urban scenario. The results of the experiments conducted show how the semantic specification of domain constraints are effectively mapped into feasible solutions of the tackled TSP and RND scenarios. TUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Blood parameters as biomarkers in a Salmonella spp. disease model of weaning piglets

peer-reviewedBackground The weaning pig is used as an experimental model to assess the impact of diet on intestinal health. Blood parameters (BP) are considered a useful tool in humans, but there is very scarce information of such indicators in the weaning pig. The objective of the present study is to evaluate the use of different BP as indicators in an experimental model of salmonellosis. Methodology Seventy-two 28-day-old piglets were divided into four groups in a 2x2 factorial arrangement, with animals receiving or not a probiotic combination based on B. infantis IM1® and B. lactis BPL6 (109 colony forming units (cfu)/d) and orally challenged or not a week later with Salmonella Typhimurium (5x108 cfu). Blood samples of one animal per pen (N = 24) were taken four days post-inoculation for the evaluation of different BP using an I-stat® System and of plasmatic concentrations of zinc, iron and copper. Principal findings Results reported marginal deficiencies of zinc in piglets at weaning. Moreover, plasmatic zinc, copper and iron presented good correlations with weight gain (r 0.57, r -0.67, r 0.54 respectively; P < 0.01). Blood electrolytes (Na+, Cl- and K+) decreased (P < 0.01) only when the performance of the animals was seriously compromised and clinical symptoms were more apparent. Acid-base balance parameters such as HCO3-, TCO2 and BEecf significantly correlated with weight gain, but only in the challenged animals (r -0.54, r -0.55, and r -0.51, respectively; P < 0.05), suggesting metabolic acidosis depending on Salmonella infection. Glucose was affected by the challenge (P = 0.040), while Htc and Hgb increased with the challenge and decreased with the probiotic (P < 0.05). Furthermore, correlations of Glu, Htc and Hgb with weight gain were observed (P < 0.05). Overall, BP could be regarded as simple, useful indexes to assess performance and health of weaning piglets

Algoritmo Evolutivo Multi-Objectivo para la Toma de Decisiones Interactiva en Optimización Dinámica

Debido al creciente interés en el análisis de datos en streaming en entornos Big Data para la toma de decisiones, cada vez es más común la aparición de problemas de optimización dinámica que involucran dos o más objetivos en conflicto. Sin embargo, los enfoques que combinan optimización dinámica multi-objetivo con la articulación de preferencias para la toma de decisiones son todavía escasos. En este artículo, proponemos un nuevo algoritmo de optimización dinámica multi-objetivo llamado InDM2, que permite incorporar preferencias del experto (humano) de cara a la toma de decisiones para guiar el proceso de búsqueda. Con InDM2, el decisor no solo puede expresar sus preferencias mediante uno o más puntos de referencia (que definen la la región de interés deseada), sino que estos puntos también se pueden modificar de manera interactiva. La propuesta incorpora métodos para mostrar gráficamente las diferentes aproximaciones de la región de interés obtenidas durante el proceso de optimización. El decisor puede así inspeccionar y cambiar, en tiempo de optimización, la región de interés de acuerdo con la información mostrada. Las principales características de InDM2 son descritas y se analiza su funcionamiento mediante casos de uso académicos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Un Framework para Big Data Optimization Basado en jMetal y Spark

Las metaheurísticas multi-objetivo se han convertido en técnicas muy utilizadas para la resolución de problemas complejos de optimización compuestos de varias funciones objetivo en conflicto entre sí. Nos encontramos en la actualidad inmersos en la era del Big Data, por lo que los problemas multi-objetivo que surjan en este contexto cumplirán algunas de las cinco V’s que caracterizan a las aplicaciones Big Data (volumen, velocidad, variedad, veracidad, valor). Como consecuencia, las metaheurísticas deberán ser capaces de resolver problemas dinámicos, que pueden cambiar en el tiempo debido al procesamiento y análisis de diferentes fuentes de datos, que típicamente serán en streaming. En este trabajo presentamos el software jMetalSP, que combina el framework jMetal con Apache Spark. De esta forma, las metaheurísticas disponibles en jMetal se pueden adaptar fácilmente para resolver problemas dinámicos que se alimenten de distintas fuentes de datos en streaming, y que son gestionadas por Spark. Se describe la arquitectura de jMetalSP y se valida mediante un caso de uso realista basado en TSP bi-objetivo con datos abiertos reales de tráfico de la ciudad de Nueva York.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Camellia japonica: a phytochemical perspective and current applications facing its industrial exploitation

In response to the increased popularity of medicinal plants, a number of conservation groups are recommending the investigation on poorly characterized and widely distributed species, as it is the case of camellias. In particular, Camellia japonica L. is a widespread species found in Galicia (NW Spain), where it has been largely exploited with ornamental purposes. Recent findings on its phytochemical characterization showed thousands of bioactive ingredients, mostly represented by phenolic compounds, together with terpenoids, and fatty acids. These molecules present associated biological activities, acting as antioxidant, antimicrobial, anti-inflammatory, and anticancer agents. This review is aimed at describing the main bioactive compounds of C. japonica, as well as the health-enhancing properties attributed to this medicinal plant. Novel strategies are needed to implement an efficient industrialization process for C. japonica, ranging from small-scale approaches to the establishment of large plantations, thus involving important sectors, such as the food, pharmaceutical and cosmetic industries.The research leading to these results was supported by MICINN supporting the Ram´on y Cajal grant for M.A. Prieto (RYC-2017-22891) and and the Juan de la Cierva Incorporaci´on Hui Cao (IJC2020-046055- I); by Xunta de Galicia for supporting the pre-doctoral grant of A.G. Pereira (ED481A-2019/0228); by European Union that supports the work of P. Garcia-Perez through the “Margarita Salas” grant from the “NextGenerationEU” program.info:eu-repo/semantics/publishedVersio