Las IDE como evolución natural de los SIG

La IDEE es un proyecto cooperativo, de autoría colectiva, en el que colaboran organismos e instituciones de los tres ámbitos de la Administración (general, regional y local), del entorno universitario y del sector privado. Esta impresionante oferta de información geográfica, junto con las funcionalidades que aportan las tecnologías de Infraestructuras de Datos Espaciales (IDE), permite vislumbrar un abanico de líneas de trabajo, todavía inexploradas, de gran interés para todos los especialistas, técnicos e investigadores que manejan o precisan de cartografía en su quehacer cotidiano, que veremos en el presente artículo

Una nueva etapa: hacia la IDE 2.0

El desarrollo de las Infraestructuras de Datos Espaciales (IDE) en España ha cubierto una primera etapa basada en el despliegue de servicios básicos, aplicaciones de visualización y apertura de geoportales. Una IDE paradigmática de esta primera fase, que podemos llamar convencionalmente IDE 1.0, incluiría: un visualizador con servicios de mapas WMS de ortofotos, imágenes de satélite y cartografía, un catálogo de metadatos (CSW, SRW, otro perfil, o soluciones no estándar), un servicio de Nomenclátor (WFS-G, WFS-MNE o soluciones no estándar) para realizar búsquedas por nombre, un servicio de descarga de datos (basado en WFS), ,y probablemente aplicaciones complementarias no estándar al margen de las specificaciones OGC, como, por ejemplo, utilidades de transformación de coordenadas, o un cliente pesado para realizar vuelos virtuales. En suma, la mayoría de los geoportales disponibles están orientados fundamentalmente a la visualización de datos geográficos

Physical Activity Patterns of the Spanish Population Are Mostly Determined by Sex and Age: Findings in the ANIBES Study

Background Representative data for the Spanish population regarding physical activity (PA) behaviors are scarce and seldom comparable due to methodological inconsistencies. Aim Our objectives were to describe the PA behavior by means of the standardized self-reported International Physical Activity Questionnaire (IPAQ) and to know the proportion of the Spanish population meeting and not meeting international PA recommendations. Material and Methods PA was assessed using the IPAQ in a representative sample of 2285 individuals (males, 50.4%) aged 9–75 years and living in municipalities of at least 2,000 inhabitants. Data were analyzed according to: age groups 9–12, 13–17, 18–64, and 65–75 years; sex; geographical distribution; locality size and educational levels. Results Mean total PA was 868.8±660.9 min/wk, mean vigorous PA 146.4±254.1 min/wk, and mean moderate PA 398.1±408.0 min/wk, showing significant differences between sexes (p<0.05). Children performed higher moderate-vigorous PA than adolescents and seniors (p<0.05), and adults than adolescents and seniors (p<0.05). Compared to recommendations, 36.2%of adults performed <150 min/week of moderate PA, 65.4% <75 min/week of vigorous PA and 27.0%did not perform any PA at all, presenting significant differences between sexes (p<0.05). A total of 55.4%of children and adolescents performed less than 420 min/week of MVPA, being higher in the later (62.6%) than in the former (48.4%). Highest non-compliance was observed in adolescent females (86.5%). Conclusion Sex and age are the main influencing factors on PA in the Spanish population. Males engage in more vigorous and light PA overall, whereas females perform more moderate PA. PA behavior differs between age groups and no clear lineal increase with age could be observed. Twenty-seven percent of adults and 55.4% of children and adolescents do not meet international PA recommendations. Identified target groups should be addressed to increase PA in the Spanish populationCoca-Cola Iberia through Spanish Nutrition Foundation (FEN)Coca-Cola Iberi

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)