Ecompetentis: a tool for evaluation of generic competences

[ES] Uno de los objetivos del proyecto “Desarrollo de la herramienta eCompetentis para la evaluación de competencias transversales” (EA2009‐0040) financiado por el Ministerio de Educación y Ciencia, fue elaborar un portal web (http://www.ecompetentis.es)   que pretende ser una plataforma de apoyo a los docentes universitarios españoles. Su uso puede ayudarles a desarrollar competencias genéricas en sus actividades docentes y a evaluar a sus estudiantes. Respecto a competencias genéricas, en el portal se ofrecen: instrumentos de evaluación, proyectos de innovación e investigación,   experiencias de éxito y otras utilidades.A día de hoy, el portal incluye instrumentos para la evaluación de las competencias genéricas “trabajo en equipo” y “resolución de problemas”. Pero eCompetentis se ha planteado como un espacio colaborativo, y en breve, dispondrá de otros instrumentos, proyectos y experiencias de compañeros que han manifestado su interés en participar.[EN] One of the aims of the project "Development of eCompetentis tool for assessing generic competences" (EA2009‐0040) funded by the Ministry of Education and Science, was to develop a website (http://www.ecompetentis.es) which has been conceived to be a support framework for Spanish professors. This framework could help them develop generic competences regarding their teaching activities and assess their students. As far as generic competences is concerned, this website offers: assessing tools, innovation and investigation projects and successful experiences, among other tools.At present, the website included tools for the assessment of the generic competences “teamwork” and “problem solving”. However, eCompetentis has been presented as a collaboration space and would soon provide some other tools, projects and experiences of colleagues who are interested in participating.Uno de los objetivos del proyecto “Desarrollo de la herramienta eCompetentis para la evaluación de competencias transversales” (EA2009‐0040) financiado por el Ministerio de Educación y Ciencia, fue elaborar un portal web (http://www.ecompetentis.es) que pretende ser una plataforma de apoyo a los docentes universitarios españolesGarcía García, MJ.; Arranz Manso, G.; Blanco Cotano, J.; Edwards Schachter, M.; Hernández Perdomo, W.; Mazadiego Martínez, L.; Piqué, R. (2011). Ecompetentis: una herramienta para la evaluación de competencias genéricas. REDU. Revista de Docencia Universitaria. 8(1):111-120. https://doi.org/10.4995/redu.2010.6220OJS11112081Fernández Ballesteros, R. (1997). "Evaluación psicológica y tests", en A. Cordero (coord..) La evaluación psicológica en el año 2000, Madrid: TEA, pp.1‐26.Gairín, J. y otros (2009) Nuevas funciones de la evaluación. La evaluación como autorregulación. Madrid, MEC.Gully, S. M., Incalcaterra, K. A., Joshi, A., & Beaubien, J. M. (2002). A meta‐analysis of team‐efficacy, potency, and performance: Interdependence and level of analysis as moderators of observed relationships. Journal of Applied Psychology, 87, 819-832. https://doi.org/10.1037/0021-9010.87.5.819Hambleton (1994) Guideline for Adaptating educational and psychological test: a progress report. European Journal of Psychologycal Assessment, 10, pp. 229‐ 244.Heppner, P.P., & Peterson, C.H. (1982). The development and implications of a personal problem‐solving inventory. Journal of Counseling Psychology, 29, p. 66‐75. https://doi.org/10.1037/0022-0167.29.1.66Heppner, P. P.; Witty, T. E. y Dixon, W. A. (2004). Problem‐Solving Appraisal and Human Adjustment. A Review of 20 Years of Research Using the Problem Solving Inventory. The Counselling Psychologist Vol. 32(3):344‐428. https://doi.org/10.1177/0011000003262793Ibarra Sáiz, M.S. et al (2007) EvalCOMIX: Evaluación de competencias en un contexto de aprendizaje mixto. Memoria Programa Estudios y Análisis MEC EA2007‐ 0099.Porter, C. O. L. H. (2005). Goal orientation: Effects on backing up behavior, performance, efficacy, and commitment in teams. Journal of Applied Psychology, 90, 811-818. https://doi.org/10.1037/0021-9010.90.4.811Rey Ardid, R. (1974) Psicología Médica. Espaxs, Barcelona.Tasa, K.; Taggar, S. y Seijts, G. H. (2007). The development of collective efficacy in teams: a multilevel and longitudinal perspective. Journal of Applied Psychology Vol. 92(1):17‐27. https://doi.org/10.1037/0021-9010.92.1.17Tovar Caro, E. et al (2009). Estudio comparativo sobre nivel de desarrollo de competencias transversales en alumnos de nuevo ingreso en enseñanzas de informática. PROYECTO EA2008‐0043. MEC.Villardón Gallego, L. (2006). Evaluación del aprendizaje para promover el desarrollo de competencias. Educatio siglo XXI, 24: 57 - 76.Wiggins, Grant (1990). The case for authentic assessment. Practical Assessment, Research & Evaluation, 2(2). Retrieved November 15, 2010 from http://PAREonline.net/getvn.asp?v=2&n=2

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Desarrollo de un laboratorio virtual en internet como apoyo a las prácticas de Física

Se han desarrollado en la Escuela Técnica Superior de Ingeniería Informática (ETS II)y en la Escuela Universitaria Politécnica (EUP) de la Universidad de VAlladolid (UVA) un conjunto de herramientas y programas informáticas para facilitar la enseñanza de la Física y potencar la eficacia de las prácticas de laboratorio. En concreto se han desarrollado cinco prácticas virtuales accesibles a través de internet. Cada una esta formada por diversas página web y simulaciones en JAVA además se han creado dos páginas web complejas incluyendo APPLEts de JAVA para explicar con ejemplos dos temas completos de teoría. Tercero,se han incluido en estas páginas web también dos programas de simulación para que los alumnos los utilizen para estudiar los fenómenos físicos representados por ellos. Finalmente, también se han grabado dos películas de vídeo en las que profesores participantes en el proyecto explican mediante ejemplos y experimentos algunos fenómenos físicos. Los materiales utilizados han sido, obviamente, diversos ordenadores y editores de páginas web, así como una cámara de vídeo y programas de tratamiento de imágenes. Los resultados se comunicarán en un Congreso Internacional y un Artículo (aún sin publicar).Junta de Castilla y León. Dirección General de Universidades e Investigación. Monasterio Ntra. Sra. del Prado. Autovia Puente Colgante s/n. 47071 Valladolid. Teléfono: 983-41.18.87Castilla y LeónES

Proyecto de creación de un grupo de trabajo en nuevas metodologías docentes en asignaturas de Ingeniería en el ámbito de la convergencia europea

Resumen tomado de la publicaciónSe realiza la formación de un grupo de trabajo que imparte docencia en las titulaciones de Ingeniería interesados en poner en práctica nuevas experiencia docentes, profundización en el estudio y experimentación de nuevas metodologías de enseñanza-aprendizaje que promuevan la consecución de competencias genéricas y específicas junto con los contenidos propios de las titulaciones, diseño de diferentes modelos de asignaturas aplicando estas metodologías, de forma que se promuevan actitudes más participativas por parte de los alumnos, y construyendo nuevos sistemas de evaluación de los conocimientos adquiridos por ellos que tengan en cuenta estos esfuerzos realizados y aplicación de estos modelos a algunas de las asignaturas que se imparten en el primer y segundo cuatrimestre, analizando los resultados. Se lleva a cabo en dos fases: constitución del grupo de trabajo y puesta en común al día sobre las posibles metodologías a emplear y diseño de experiencias de innovación docente, puesta en marcha y evaluación. Los objetivos propuestos fueron cubiertos con éxito. Finalmente se presentan dichos resultados y se evalúa por partes el proyecto.Castilla y LeónConsejería de Educación. Dirección General de Universidades e Investigación; Monasterio de Nuestra Señora de Prado, Autovía Puente Colgante s. n.; 47071 Valladolid; +34983411881; +34983411939;ES

Diez líneas verdes

Se desarrolla un proyecto de innovación educativa que pretende fomentar el respeto y cuidado medioambiental, creando y desarrollando la conciencia y sensibilidad. Se desarrolla a lo largo del curso 2006-2007 para todos los ciclos de Educación Infantil y Educación Primaria, se integra en el currículo y se trabaja por ciclos y asambleas las actividades del seminario. La metodología empleada en la práctica del proyecto se basa en la observación, experimentación y expresión de lo vivido en un clima de sensibilización hacia el medio ambiente. Se realizan talleres escolares, salidas a la naturaleza, fabricación de instrumentos con materiales de desecho, disfraces y plantación de plantas en un huerto entre otras actividades. Se realiza una evaluación continua de las actividades llevadas a acabo y al final se pasa una encuesta a los padres, profesorado y alumnado para realizar un sondeo de las opiniones de las tareas realizadas en el proyecto de innovación educativa. La mayoría del alumnado ha mejorado sus hábitos tanto dentro como fuera del colegio, ha aprendido a reciclar y han transmitido a sus familias estas nuevas conductas aprendidas. El profesorado y los padres opinan que el proyecto es muy útil y positivo para el alumnado. Se ha conseguido implicar a la comunidad educativa, haciendo efectiva su participación en la Red de Centros Comprometidos con el Medio Ambiente. En general el proyecto de innovación educativa es de fácil aplicación en el aula, genera hábitos y cambios de conducta medioambiental y por ello es conveniente darle continuidad en el centro para afianzar los hábitos creados.Castilla y LeónConsejería de Educación. Dirección General de Universidades e Investigación; Monasterio de Nuestra Señora de Prado, Autovía Puente Colgante s. n.; 47071 Valladolid; +34983411881; +34983411939ES

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

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)