Shiny Dashboard for Monitoring the COVID-19 Pandemic in Spain

[Abstract] Real-time monitoring of events such as the recent pandemic caused by COVID-19, as well as the visualization of the effects produced by its expansion, has highlighted the need to join forces in fields already widely used to working hand in hand, such as medicine, biology and information technology. Our dashboard is developed in R and is supported by the Shiny package to generate an attractive visualization tool: COVID-19 Spain automatically produces daily updates from official sources (Carlos III Research Institute and Ministry of Health, Consumer Affairs and Welfare) in cases, deaths, recovered, ICU admissions and accumulated daily incidence. In addition, it shows on a georeferenced map the evolution of active, new and accumulated cases by autonomous community allowing to travel in time from the origin to the last available day, which allows to visualize the expansion of infections and serves as a visual support for epidemiological studies.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431C 2018/4

Desarrollo de nuevos algoritmos neurogliales que modelizan la interacción astrocito-neurona en sistemas de altas prestaciones

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V0[Resumo] Estamos vivindo una era de evolución constante debido aos avances tecnolóxicos. Moitos deles están sendo posibles grazas á Intelixencia Artificial (IA) e a que se están almacenando grandes volumes de datos. Pódense construír ferramentas con componentes intelixentes que están revolucionando diversos campos grazas á gran cantidade de datos que poden ser analizados mediante devanditos modelos intelixentes. Sen embargo, moitas investigación só céntranse na cantidade e a calidade dos datos dos que se dispón, e apenas se realizan esforzos en mellorar as propias técnicas de IA. A proposta desta tese é a mellora de sistemas intelixentes conexionistas que ata hai pouco estaban formados por Redes de Neuronas Artificiais (RNA). Para levar a cabo tan ambiciosa labor, considerouse, do mesmo xeito que ocorreu con investigación noutras áreas, ter en conta como resolve o problema a naturaleza. Para iso, centrarémonos na estructura máis complexa e máis eficiente coñecida polo home, o cerebro humano. Con ese fin, é necesario apoiarse no campo da Neurociencia onde se pode tratar de levar os avances que se descobren ou as hipótesis que se xeran ao campo da IA. O núcleo desta tese vira contorna as investigación que evidencian que as neuronas non son os únicos elementos do cerebro humano que participan no procesamento da información, si non que os astrocitos do Sistema Glial (SG) xogan un papel esencial. De feito, a comunicación sináptica sábese xa que se produce con participación de neuronas e astrocitos, o cal coñécese como sinapsis tripartita. Isto levou á inclusión de novos elementos de procesado nas RNA que simulan o comportamento das células gliales creando as Redes NeuroGliais Artificiais (RNGA). Para demostrar a utilidade dos astrocitos artificiais e colaborar en demostrar a capacidade do SG, leváronse a cabo novos algoritmos de modulación astrocítica que se puxeron a proba en diferentes problemas de clasificación e regresión, obténdose resultados significativos con respecto a redes sen astrocitos. Ademais, desenvolveuse graza a esta tese una aplicación web opensource para que a comunidade científica poida usar estas redes libremente.[Resumen] Estamos viviendo una era de evolución constante debido a los avances tecnológicos. Muchos de ellos están siendo posibles gracias a la Inteligencia Artificial (IA) y a que se están almacenando grandes volúmenes de datos. Se pueden construir herramientas con componentes inteligentes que están revolucionando diversos campos gracias a la gran cantidad de datos que pueden ser analizados mediante dichos modelos inteligentes. Sin embargo, muchas investigaciones solo se centran en la cantidad y la calidad de los datos de los que se dispone, y apenas se realizan esfuerzos en mejorar las propias técnicas de IA. La propuesta de esta tesis es la mejora de sistemas inteligentes conexionistas que hasta hace poco estaban formados por Redes de Neuronas Artificiales (RNA). Para llevar a cabo tan ambiciosa labor, se ha considerado, al igual que ha ocurrido con investigación en otras áreas, tener en cuenta cómo resuelve el problema la naturaleza. Para ello, nos centraremos en la estructura más compleja y más eficiente conocida por el hombre, el cerebro humano. Con ese fin, es necesario apoyarse en el campo de la Neurociencia donde se puede tratar de llevar los avances que se descubren o las hipótesis que se generan al campo de IA. El núcleo de esta tesis gira entorno las investigaciones que evidencian que las neuronas no son los únicos elementos del cerebro humano que participan en el procesamiento de la información, si no que los astrocitos del sistema glial juegan un papel esencial. De hecho, la comunicación sináptica se sabe ya que se produce con participación de neuronas y astrocitos, lo cual se conoce como sinapsis tripartita. Esto llevó a la inclusión de nuevos elementos de procesado en las RNA que simulan el comportamiento de las células gliales creando las Redes NeuroGliales Artificiales (RNGA). Para demostrar la utilidad de los astrocitos artificiales y colaborar en demostrar la capacidad del Sistema Glial (SG), se han llevado a cabo nuevos algoritmos de modulación astrocítica que se han puesto a prueba en diferentes problemas de clasificación y regresión, obteniéndose resultados significativos con respecto a redes sin astrocitos. Además, se ha desarrollado gracias a esta tesis una aplicación web opensource para que la comunidad científica pueda usar estas redes libremente.[Abstract] We are living an era of constant evolution due to technological advances. Many of them are being possible thanks to Artificial Intelligence (AI) and the large volumes of data are being stored. You can build tools with intelligent components that are revolutionizing various fields thanks to a large amount of data that can be analyzed by these intelligent models. However, many investigations only focus on the quantity and quality of the data available, and little effort is made to improve the AI techniques themselves. The proposal of this thesis is the improvement of connectionist intelligent systems that until recently were formed by Artificial Neural Networks (ANN). To carry out such ambitious work, it has been considered, as has happened with research in other areas, to consider how nature solves the problem. For this, we will focus on the most complex and efficient structure known, the human brain. To that end, it is necessary to rely on the field of Neuroscience where one can try to take the advances that are discovered or the hypotheses that are generated in the AI field. The core of this thesis is based on research that shows that neurons are not the only elements of the human brain that participate in the processing of information. Astrocytes of the glial system play an essential role in the treatment of information. In fact, it is known that synaptic communication occurs with the participation of neurons and astrocytes, which is known as tripartite synapses. This led to the inclusion of new elements that simulate the behaviour of glial cells in ANN. The addition of the new elements, artificial astrocytes, originated the Artificial NeuroGial Networks (ANGN). To demonstrate the usefulness of artificial astrocytes and collaborate in demonstrating the capacity of the Glial System (GS), new algorithms of astrocytic modulation have been used. These algorithms have been tested in different classification and regression problems, obtaining significant results with respect to networks that do not use the GS. In addition, an open source web application has been developed thanks to this thesis so that the scientific community can use these networks freely

Intensificación de la producción forrajera en Galicia y evaluación del modelo CERES-Maize

En ensayos de campo realizados en Lugo entre los años 1997 y 2002, se ha comparado el rendimiento en regadío (R; condiciones no limitantes del medio) y en secano (S) de la rotación raigrás italiano alternativo-maíz (RIA-M), de dos cultivos por año, con la de raigrás italiano no alternativo (RINA), prevista para sembrar cada dos años, como base para intensificar la producción de forraje en Galicia. Además, se probaron dos técnicas de siembra en la rotación RIA-M: Laboreo convencional (LC) y siembra directa (SD)

Music Recommendation System Based on Ratings Obtained from Amazon

Cursos e Congresos, C-155[Abstract] In the current context of an era in which a significant portion of people are constantly living online, with various multimedia streaming platforms serving as major sources of entertainment, and with e-commerce playing also a key role, recommender systems are carving out their place as one of the most important and widely used tools for enhancing user experiences on these platforms. This work undertakes a comparative study on some of the techniques used within these systems, mainly focused on those based in collaborative filtering. Multiple recommender systems will be implemented according to each of these methods, taking for this purpose the vinyl records and CDs Amazon’s user ratingsCITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS)

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

[Abstract] The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Ministerio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-350

Probiotic: First Prescriptive Application of Probiotics in Spain

[Abstract] The study of the intestinal microbiota is one of the greatest challenges in today’s clinical environment. Thus, probiotics have been established as a focus for its stability, as they play a key role in its regulation. The development of an automated technique that allows the practitioners the smooth search for the optimal probiotic is postulated as the main objective of this study. Despite the existence of previous attempts at applications for this purpose, they have only been carried out for the countries of origin, preventing them from being used in others such as Spain. Therefore, a system has been developed with open, multi-platform, and free technologies, which manages to locate the optimal probiotic for each pathology.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/2

First Multiplatform Application for Pharmacies in Spain, Which Guides the Prescription of Probiotics According to Pathology

[Abstract] The study of the intestinal microbiota is one of the biggest challenges in the current clinical environment. In this context, probiotics have been a focus of interest to achieve the stability of the intestinal microbiota, due to probiotics’ key role in its regulation. The development of an automated system that allows practitioners to easily search for the optimal probiotic is the main objective of this study. Although it is true that there have been previous attempts of applications with this purpose, only authorized probiotics available in the countries of origin, Canada and the USA, were included. This event was a limitation when looking for those endorsed in other countries such as Spain. Thus, a system has been developed from free and multiplatform technologies that allow its use without any cost, finding, in a simple way, those probiotics that would be ideal for each pathology, either from a browser or from a cell phone.This work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016, the European Regional Development Funds (ERDF)—“A way to build Europe.”, the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). The funding body did not have a role in the experimental design, data collection, analysis and interpretation, and writing of this manuscript. CITIC, as a Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidades” of Xunta de Galicia, 80% co-financed by the ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G 2019/0

Using Genetic Algorithms to Improve Support Vector Regression in the Analysis of Atomic Spectra of Lubricant Oils

[Abstract] Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach – The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings – A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value – The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model