Evaluación de la genotoxicidad in vitro de arcillas modificadas con silanos mediante el ensayo de micronúcleos

El diseño de nuevos materiales de envasado con propiedades mecánicas, térmicas y de barrera mejoradas, destinados a incrementar la vida media de los productos alimenticios en el mercado, es una apuesta segura llevada a cabo por la industria alimentaria. Estos envases son conocidos como materiales nanocompuestos y difieren del polímero tradicional en que cuentan en su estructura con arcillas modificadas que presentan láminas de grosor nanométrico. Entre estas arcillas se encuentra Clay3, una arcilla modificada desarrollada por el Instituto Tecnológico de Embalaje, Transporte y Logística de Valencia. Dicha arcilla se basa en la modificación de la montmorillonita con un silano, el 3-aminopropiltrietoxisilano. Aunque las mejoras tecnológicas son bien conocidas, poco se sabe acerca de la toxicidad de estos nuevos materiales, siendo necesario su conocimiento para asegurar la salud de los consumidores y evitar futuros daños por exposición a estas arcillas debido a migraciones del envase al alimento. En el presente trabajo se investiga la genotoxicidad de Clay3 mediante el ensayo in vitro de micronúcleos con bloqueo de la citocinesis (CBMN) en la línea celular hepática humana HepG2. La inducción de micronúcleos (MN) y otras malformaciones nucleares se analizaron tras 24 h de exposición a concentraciones subcitotóxicas de Clay3 (0-250 μg/ml). Los resultados obtenidos no indicaron una alteración notable de las células tratadas con respecto a los grupos control bajo las condiciones de ensayo establecidas. El ensayo de MN forma parte de un conjunto de ensayos obligatorios solicitados por parte de las autoridades competentes en los procesos de autorización de materiales destinados al contacto con alimentos. Por tanto, es necesario ampliar los datos disponibles acerca de la toxicidad de este nuevo material antes de su potencial utilización comercialUniversidad de Sevilla. Grado en Farmaci

A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information

Surface ozone (O3) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology

A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Junta de Andalucía P12-TIC-1728Instituto Ramón y Cajal (RYC) RYC-2012-1198

A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

Estudio comparativo de la influencia de un programa de ejercicio físico en la disminución de medicamentos en adultos mayores de los municipios de Ayutuxtepeque y Mejicanos en el departamento de San Salvador, durante el año 2019

El adulto mayor en El Salvador es una parte de la sociedad el cual es excluido y marginado por diferentes factores, uno de los más principales es que se cree que ya dio todo lo que tenía que dar a la sociedad basta solo esperar al día de su muerte, cosa que es totalmente errona ya que el adulto mayor conforma uno de los principales bastiones de cada sociedad esto debido a que posee mucha experiencia de la cual los jóvenes y niños pueden aprender. La etapa de la vejes conforma una de las etapas más duras del ser humano esto debido a que en esta etapa se acumulan un buen porcentaje de enfermedades de las cuales el ser humano nunca se prepara para esta situación. La vulnerabilidad en la adultez mayor radica en la prevalencia de múltiples patologías, el uso de muchos medicamentos auto-administrados y suplementos o principios activos de origen alternativo a la medicina alopática. Un estudio reciente basado en los datos de la última Encuesta Nacional de Salud, realizada por el [MINSAL] (2017), demostró que existen subgrupos que son especialmente frágiles frente a la administración de fármacos: los mayores de 80 años, mujeres, los que viven en instituciones, los más pobres o con bajo nivel educacional. Así como lo describe anteriormente el párrafo en la etapa de la vejes se consumen un considerable número de fármacos con los cuales se busca controlar en un gran porcentaje las enfermedades, en algunas ocasiones los adultos mayores se automedican sin conocer cuáles serán las consecuencias de estas acciones. En la investigación se buscó indagar las principales enfermedades del adulto mayor, así también los efectos secundarios de la automedicación y lo más importante que beneficios trae la actividad física en esta etapa, y como contribuye a la reducción de medicamentos ingeridos dirimente el resultado de esta investigación fue: a mayor actividad física menor consumo de medicamentos

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.Ministerio de Economía y Competitividad TIN2017-88209-C