Exploring the interplay between climate, population immunity and SARS-CoV-2 transmission dynamics in Mediterranean countries

The relationship between SARS-CoV-2 transmission and environmental factors has been analyzed in numerous studies since the outbreak of the pandemic, resulting in heterogeneous results and conclusions. This may be due to differences in methodology, considered variables, confounding factors, studied periods and/or lack of adequate data. Furthermore, previous works have reported that the lack of population immunity is the fundamental driver in transmission dynamics and can mask the potential impact of environmental variables. In this study, we aimed to investigate the association between climate variables and COVID-19 transmission considering the influence of population immunity. We analyzed two different periods characterized by the absence of vaccination (low population immunity) and a high degree of vaccination (high level of population immunity), respectively. Although this study has some limitations, such us the restriction to a specific climatic zone and the omission of other environmental factors, our results indicate that transmission of SARSCoV-2 may increase independently of temperature and specific humidity in periods with low levels of population immunity while a negative association is found under conditions with higher levels of population immunity in the analyzed regions

DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes

Meta-analysis techniques allow researchers to jointly analyse different studies to determine common effects. In the field of transcriptomics, these methods have gained popularity in recent years due to the increasing number of datasets that are available in public repositories. Despite this, there is a limited number of statistical software packages that implement proper meta-analysis functionalities for this type of data. This article describes DExMA, an R package that provides a set of functions for performing gene expression meta-analyses, from data downloading to results visualization. Additionally, we implemented functions to control the number of missing genes, which can be a major issue when comparing studies generated with different analytical platforms. DExMA is freely available in the Bioconductor repository.Teaching Staff Programme by the Ministerio de Universidades FPU19/01999 MCIN/AEI PID2020119032RB-I00FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades P20_00335 B-CTS-40-UGR20'Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades' (CTEICU)European Union through the European Social Fund (ESF) named 'Andalucia se mueve con Europa" European Union-NextGenerationEU, Ministerio de Universidades (Spain's Government)Recovery, Transformation and Resilience Plan, through a call from the University of Granad

Functional Enrichment Analysis of Regulatory Elements

This work has been partially supported by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento/(grant CV20-36723), grant PID2020-119032RB-I00, MCIN/AEI/10.13039/501100011033 and FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades (Grant P20_00335).Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by theWallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento CV20-36723MCIN/AEI PID2020-119032RB-I00FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades P20_0033

A comprehensive database for integrated analysis of omics data in autoimmune diseases

This work is partially funded by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento (Grant CV20-36723), Consejeria de Salud (Grant PI-0173-2017) and by EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS (115565). JMM is partially funded by Ministerio de Economia, Industria y Competitividad. None of the funding bodies played any role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript.Background: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Results: Here, we present Autoimmune Diseases Explorer (https:// adex. genyo. es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. Conclusions: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento CV20-36723Consejeria de Salud PI-0173-2017EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565Ministerio de Economia, Industria y Competitivida

Práctica 10. Regresión Lineal Simple y Correlación en R

Regresión Lineal Simple y Correlación en R. Simple Linear Regression and Correlation in R.Todo el material para el conjunto de actividades de este curso ha sido elaborado y es propiedad intelectual del grupo BioestadisticaR formado por: Juan de Dios Luna del Castillo, Pedro Femia Marzo, Miguel Ángel Montero Alonso, Christian José Acal González, Pedro María Carmona Sáez, Juan Manuel Melchor Rodríguez, José Luis Romero Béjar, Manuela Expósito Ruíz, Juan Antonio Villatoro García, Juan Manuel Praena Fernández, Miguel Ángel Luque Fernández, Francisco Javier Arnedo Fernández. Todos los integrantes del grupo han participado en todas las actividades, en su elección, construcción, correcciones o en su edición final, no obstante, en cada una de ellas, aparecerán uno o más nombres correspondientes a las personas que han tenido la máxima responsabilidad de su elaboración junto al grupo de BioestadisticaR. Todos los materiales están protegidos por la Licencia Creative Commons CC BY-NC-ND que permite "descargar las obras y compartirlas con otras personas, siempre que se reconozca su autoría, pero no se pueden cambiar de ninguna manera ni se pueden utilizar comercialmente"

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

DatAC: A visual analytics platform to explore climate and air quality indicators associated with the COVID-19 pandemic in Spain.

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented global health crisis, with several countries imposing lockdowns to control the coronavirus spread. Important research efforts are focused on evaluating the association of environmental factors with the survival and spread of the virus and different works have been published, with contradictory results in some cases. Data with spatial and temporal information is a key factor to get reliable results and, although there are some data repositories for monitoring the disease both globally and locally, an application that integrates and aggregates data from meteorological and air quality variables with COVID-19 information has not been described so far to the best of our knowledge. Here, we present DatAC (Data Against COVID-19), a data fusion project with an interactive web frontend that integrates COVID-19 and environmental data in Spain. DatAC is provided with powerful data analysis and statistical capabilities that allow users to explore and analyze individual trends and associations among the provided data. Using the application, we have evaluated the impact of the Spanish lockdown on the air quality, observing that NO2, CO, PM2.5, PM10 and SO2 levels decreased drastically in the entire territory, while O3 levels increased. We observed similar trends in urban and rural areas, although the impact has been more important in the former. Moreover, the application allowed us to analyze correlations among climate factors, such as ambient temperature, and the incidence of COVID-19 in Spain. Our results indicate that temperature is not the driving factor and without effective control actions, outbreaks will appear and warm weather will not substantially limit the growth of the pandemic. DatAC is available at https://covid19.genyo.es

Libreria de funciones que automatizan algunos procedimientos analíticos propios de la Bioestadística.

BioestadisticaR 2.1.1 es un paquete de funciones de R que automatiza algunas rutinas propias del análisis estadístico ofreciendo informes de resultados. La motivación para la elaboración de este paquete tiene carácter docente. Está pensado para impartir docencia de Bioestadística en titulaciones como son Medicina o las propias de las Ciencias de la Salud, que precisan de la inferencia estadística como una parte esencial en su faceta de carácter investigador.Todo el material para el conjunto de actividades de este curso ha sido elaborado y es propiedad intelectual del grupo BioestadisticaR formado por: Juan de Dios Luna del Castillo, Pedro Femia Marzo, Miguel Ángel Montero Alonso, Christian José Acal González, Pedro María Carmona Sáez, Juan Manuel Melchor Rodríguez, José Luis Romero Béjar, Manuela Expósito Ruíz, Juan Antonio Villatoro García. Todos los integrantes del grupo han participado en todas las actividades, en su elección, construcción, correcciones o en su edición final, no obstante, en cada una de ellas, aparecerán uno o más nombres correspondientes a las personas que han tenido la máxima responsabilidad de su elaboración junto al grupo de BioestadisticaR. Todos los materiales están protegidos por la Licencia Creative Commons CC BY-NC-ND que permite "descargar las obras y compartirlas con otras personas, siempre que se reconozca su autoría, pero no se pueden cambiar de ninguna manera ni se pueden utilizar comercialmente"

Un paquete de Bioestadística para R

Paquete de R con funciones que automatizan procedimientos propios de la Bioestadística. La motivación de la elaboración del paquete es la de facilitar la docencia en asignaturas de Bioestadística (Grados en Medicina, Enfermería, Fisioterapia). No obstante, esta automatización también permite realizar de forma eficiente un análisis estadístico avanzado. R-Package with functions that automate procedures of Bioestatistics. The motivation for the preparation of the package is to facilitate teaching in subjects of this area (e.g. degrees in Medicine, Biology, Nursing, Physiotherapy, etc.). On the other hand, this automation helps perform a statistical analysis at an advanced level