The Understanding of Human Activities by Computer Vision Techniques

Esta tesis propone nuevas metodologías para el aprendizaje de actividades humanas y su clasificación en categorías. Aunque este tema ha sido ampliamente estudiado por la comunidad investigadora en visión por computador, aún encontramos importantes dificultades por resolver. En primer lugar hemos encontrado que la literatura sobre técnicas de visión por computador para el aprendizaje de actividades humanas empleando pocas secuencias de entrenamiento es escasa y además presenta resultados pobres [1] [2]. Sin embargo, este aprendizaje es una herramienta crucial en varios escenarios. Por ejemplo, un sistema de reconocimiento recién desplegado necesita mucho tiempo para adquirir nuevas secuencias de entrenamiento así que el entrenamiento con pocos ejemplos puede acelerar la puesta en funcionamiento. También la detección de comportamientos anómalos, ejemplos de los cuales son difíciles de obtener, puede beneficiarse de estas técnicas. Existen soluciones mediante técnicas de cruce dominios o empleando características invariantes, sin embargo estas soluciones omiten información del escenario objetivo la cual reduce el ruido en el sistema mejorando los resultados cuando se tiene en cuenta y ejemplos de actividades anómalas siguen siendo difíciles de obtener. Estos sistemas entrenados con poca información se enfrentan a dos problemas principales: por una parte el sistema de entrenamiento puede sufrir de inestabilidades numéricas en la estimación de los parámetros del modelo, por otra, existe una falta de información representativa proveniente de actividades diversas. Nos hemos enfrentado a estos problemas proponiendo novedosos métodos para el aprendizaje de actividades humanas usando tan solo un ejemplo, lo que se denomina one-shot learning. Nuestras propuestas se basan en sistemas generativos, derivadas de los Modelos Ocultos de Markov[3][4], puesto que cada clase de actividad debe ser aprendida con tan solo un ejemplo. Además, hemos ampliado la diversidad de información en los modelos aplicado una transferencia de información desde fuentes externas al escenario[5]. En esta tesis se explican varias propuestas y se muestra como con ellas hemos conseguidos resultados en el estado del arte en tres bases de datos públicas [6][7][8]. La segunda dificultad a la que nos hemos enfrentado es el reconocimiento de actividades sin restricciones en el escenario. En este caso no tiene por qué coincidir el escenario de entrenamiento y el de evaluación por lo que la reducción de ruido anteriormente expuesta no es aplicable. Esto supone que se pueda emplear cualquier ejemplo etiquetado para entrenamiento independientemente del escenario de origen. Esta libertad nos permite extraer vídeos desde cualquier fuente evitando la restricción en el número de ejemplos de entrenamiento. Teniendo suficientes ejemplos de entrenamiento tanto métodos generativos como discriminativos pueden ser empleados. En el momento de realización de esta tesis encontramos que el estado del arte obtiene los mejores resultados empleando métodos discriminativos, sin embargo, la mayoría de propuestas no suelen considerar la información temporal a largo plazo de las actividades[9]. Esta información puede ser crucial para distinguir entre actividades donde el orden de sub-acciones es determinante, y puede ser una ayuda en otras situaciones[10]. Para ello hemos diseñado un sistema que incluye dicha información en una Máquina de Vectores de Soporte. Además, el sistema permite cierta flexibilidad en la alineación de las secuencias a comparar, característica muy útil si la segmentación de las actividades no es perfecta. Utilizando este sistema hemos obtenido resultados en el estado del arte para cuatro bases de datos complejas sin restricciones en los escenarios[11][12][13][14]. Los trabajos realizados en esta tesis han servido para realizar tres artículos en revistas del primer cuartil [15][16][17], dos ya publicados y otro enviado. Además, se han publicado 8 artículos en congresos internacionales y uno nacional [18][19][20][21][22][23][24][25][26]. [1]Seo, H. J. and Milanfar, P. (2011). Action recognition from one example. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5):867–882.(2011) [2]Yang, Y., Saleemi, I., and Shah, M. Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1635–1648. (2013) [3]Rabiner, L. R. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286. (1989) [4]Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA. (2006) [5]Cook, D., Feuz, K., and Krishnan, N. Transfer learning for activity recognition: a survey. Knowledge and Information Systems, pages 1–20. (2013) [6]Schuldt, C., Laptev, I., and Caputo, B. Recognizing human actions: a local svm approach. In International Conference on Pattern Recognition (ICPR). (2004) [7]Weinland, D., Ronfard, R., and Boyer, E. Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding, 104(2-3):249–257. (2006) [8]Gorelick, L., Blank, M., Shechtman, E., Irani, M., and Basri, R. Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):2247–2253. (2007) [9]Wang, H. and Schmid, C. Action recognition with improved trajectories. In IEEE International Conference on Computer Vision (ICCV). (2013) [10]Choi, J., Wang, Z., Lee, S.-C., and Jeon, W. J. A spatio-temporal pyramid matching for video retrieval. Computer Vision and Image Understanding, 117(6):660 – 669. (2013) [11]Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.-C., Lee, J. T., Mukherjee, S., Aggarwal, J. K., Lee, H., Davis, L., Swears, E., Wang, X., Ji, Q., Reddy, K., Shah, M., Vondrick, C., Pirsiavash, H., Ramanan, D., Yuen, J., Torralba, A., Song, B., Fong, A., Roy-Chowdhury, A., and Desai, M. A large-scale benchmark dataset for event recognition in surveillance video. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3153–3160. (2011) [12] Niebles, J. C., Chen, C.-W., and Fei-Fei, L. Modeling temporal structure of decomposable motion segments for activity classification. In European Conference on Computer Vision (ECCV), pages 392–405.(2010) [13]Reddy, K. K. and Shah, M. Recognizing 50 human action categories of web videos. Machine Vision and Applications, 24(5):971–981. (2013) [14]Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T. HMDB: a large video database for human motion recognition. In IEEE International Conference on Computer Vision (ICCV). (2011) [15]Rodriguez, M., Orrite, C., Medrano, C., and Makris, D. One-shot learning of human activity with an map adapted gmm and simplex-hmm. IEEE Transactions on Cybernetics, PP(99):1–12. (2016) [16]Rodriguez, M., Orrite, C., Medrano, C., and Makris, D. A time flexible kernel framework for video-based activity recognition. Image and Vision Computing 48-49:26 – 36. (2016) [17]Rodriguez, M., Orrite, C., Medrano, C., and Makris, D. Extended Study for One-shot Learning of Human Activity by a Simplex-HMM. IEEE Transactions on Cybernetics (Enviado) [18]Orrite, C., Rodriguez, M., Medrano, C. One-shot learning of temporal sequences using a distance dependent Chinese Restaurant Process. In Proceedings of the 23nd International Conference Pattern Recognition ICPR (December 2016) [19]Rodriguez, M., Medrano, C., Herrero, E., and Orrite, C. Spectral Clustering Using Friendship Path Similarity Proceedings of the 7th Iberian Conference, IbPRIA (June 2015) [20]Orrite, C., Soler, J., Rodriguez, M., Herrero, E., and Casas, R. Image-based location recognition and scenario modelling. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications, VISAPP (March 2015) [21]Castán, D., Rodríguez, M., Ortega, A., Orrite, C., and Lleida, E. Vivolab and cvlab - mediaeval 2014: Violent scenes detection affect task. In Working Notes Proceedings of the MediaEval (October 2014) [22]Orrite, C., Rodriguez, M., Herrero, E., Rogez, G., and Velastin, S. A. Automatic segmentation and recognition of human actions in monocular sequences In Proceedings of the 22nd International Conference Pattern Recognition ICPR (August 2014) [23]Rodriguez, M., Medrano, C., Herrero, E., and Orrite, C. Transfer learning of human poses for action recognition. In 4th International Workshop of Human Behavior Unterstanding (HBU). (October 2013) [24]Rodriguez, M., Orrite, C., and Medrano, C. Human action recognition with limited labelled data. In Actas del III Workshop de Reconocimiento de Formas y Analisis de Imagenes, WSRFAI. (September 2013) [25]Orrite, C., Monforte, P., Rodriguez, M., and Herrero, E. Human Action Recognition under Partial Occlusions . Proceedings of the 6th Iberian Conference, IbPRIA (June 2013) [26]Orrite, C., Rodriguez, M., and Montañes, M. One sequence learning of human actions. In 2nd International Workshop of Human Behavior Unterstanding (HBU). (November 2011)This thesis provides some novel frameworks for learning human activities and for further classifying them into categories. This field of research has been largely studied by the computer vision community however there are still many drawbacks to solve. First, we have found few proposals in the literature for learning human activities from limited number of sequences. However, this learning is critical in several scenarios. For instance, in the initial stage after a system installation the capture of activity examples is time expensive and therefore, the learning with limited examples may accelerate the operational launch of the system. Moreover, examples for training abnormal behaviour are hardly obtainable and their learning may benefit from the same techniques. This problem is solved by some approaches, such as cross domain implementations or the use of invariant features, but they do not consider the specific scenario information which is useful for reducing the clutter and improving the results. Systems trained with scarce information face two main problems: on the one hand, the training process may suffer from numerical instabilities while estimating the model parameters; on the other hand, the model lacks of representative information coming from a diverse set of activity classes. We have dealt with these problems providing some novel approaches for learning human activities from one example, what is called a one-shot learning method. To do so, we have proposed generative approaches based on Hidden Markov Models as we need to learn each activity class from only one example. In addition, we have transferred information from external sources in order to introduce diverse information into the model. This thesis explains our proposals and shows how these methods achieve state-of-the-art results in three public datasets. Second, we have studied the recognition of human activities in unconstrained scenarios. In this case, the scenario may or may not be repeated in training and evaluation and therefore the clutter reduction previously mentioned does not happen. On the other hand, we can use any labelled video for training the system independently of the target scenario. This freedom allows the extraction of videos from the Internet dismissing the implicit constrains when training with limited examples. Having plenty of training examples both, generative and discriminative, methods can be used and by the time this thesis has been made the state-of-the-art has been achieved by discriminative ones. However, most of the methods usually fail when taking into consideration long-term information of the activities. This information is critical when comparing activities where the order of sub-actions is important, and may be useful in other comparisons as well. Thus, we have designed a framework that incorporates this information in a discriminative classifier. In addition, this method introduces some flexibility for sequence alignment, useful feature when the activity segmentation is not exact. Using this framework we have obtained state-of-the-art results in four challenging public datasets with unconstrained scenarios

Sistema de realidad aumentada en entornos reales adversos

En este trabajo proponemos una aplicación para plataformas móviles capaz de realizar la proyección de una imagen virtual sobre un entorno real de forma consistente a lo largo de un periodo de tiempo más o menos prolongado. Se realiza una detección del entorno para seleccionar la superficie de proyección de la imagen y, a lo largo de la duración de toda la observación, se mantiene la proyección de forma estable sobre ella. En el escenario seleccionado confluyen diferentes elementos adversos que dificultan las tareas de detección y seguimiento, como son cambios de iluminación, escala, giros y movimientos de la cámara y oclusiones por lo que hemos diseñado nuestro sistema para que sea capaz de gestionar todos estos distractores propios de ciertos escenarios adversos. Adicionalmente, se propone un sistema de reinicialización para aquellos casos en los que los distractores hagan imposible el seguimiento de la superficie y una proyección consistente

The outcome of boosting mitochondrial activity in alcohol-associated liver disease is organ-dependent.

BACKGROUND AND AIMS Alcohol-associated liver disease (ALD) accounts for 70% of liver-related deaths in Europe, with no effective approved therapies. Although mitochondrial dysfunction is one of the earliest manifestations of alcohol-induced injury, restoring mitochondrial activity remains a problematic strategy due to oxidative stress. Here, we identify methylation-controlled J protein (MCJ) as a mediator for ALD progression and hypothesize that targeting MCJ may help in recovering mitochondrial fitness without collateral oxidative damage. APPROACH AND RESULTS C57BL/6 mice [wild-type (Wt)] Mcj knockout and Mcj liver-specific silencing (MCJ-LSS) underwent the NIAAA dietary protocol (Lieber-DeCarli diet containing 5% (vol/vol) ethanol for 10 days, plus a single binge ethanol feeding at day 11). To evaluate the impact of a restored mitochondrial activity in ALD, the liver, gut, and pancreas were characterized, focusing on lipid metabolism, glucose homeostasis, intestinal permeability, and microbiota composition. MCJ, a protein acting as an endogenous negative regulator of mitochondrial respiration, is downregulated in the early stages of ALD and increases with the severity of the disease. Whole-body deficiency of MCJ is detrimental during ALD because it exacerbates the systemic effects of alcohol abuse through altered intestinal permeability, increased endotoxemia, and dysregulation of pancreatic function, which overall worsens liver injury. On the other hand, liver-specific Mcj silencing prevents main ALD hallmarks, that is, mitochondrial dysfunction, steatosis, inflammation, and oxidative stress, as it restores the NAD + /NADH ratio and SIRT1 function, hence preventing de novo lipogenesis and improving lipid oxidation. CONCLUSIONS Improving mitochondrial respiration by liver-specific Mcj silencing might become a novel therapeutic approach for treating ALD.This work was supported by grants from Ministerio de Ciencia e Innovación, Programa Retos-Colaboración RTC2019-007125-1 (for Jorge Simon and Maria Luz Martinez-Chantar); Ministerio de Economía, Industria y Competitividad, Retos a la Sociedad AGL2017- 86927R (for F.M.); Instituto de Salud Carlos III, Proyectos de Investigación en Salud DTS20/00138 and DTS21/00094 (for Jorge Simon and Maria Luz Martinez-Chantar, and Asis Palazon. respectively); Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias co-founded by European Regional Development Fund/European Social Fund, “Investing in your future” PI19/00819, “Una manera de hacer Europa” FIS PI20/00765, and PI21/01067 (for Jose J. G. Marin., Pau Sancho-Bru,. and Mario F. Fraga respectively); Departamento de Industria del Gobierno Vasco (for Maria Luz Martinez-Chantar); Asturias Government (PCTI) co-funding 2018-2023/ FEDER IDI/2021/000077 (for Mario F. Fraga.); Ministerio de Ciencia, Innovación y Universidades MICINN: PID2020-117116RB-I00, CEX2021-001136-S PID2020-117941RB-I00, PID2020-11827RB-I00 and PID2019-107956RA-100 integrado en el Plan Estatal de Investigación Científica y Técnica y Innovación, cofinanciado con Fondos FEDER (for Maria Luz Martinez-Chantar, Francisco J Cubero., Yulia A Nevzorova and Asis Palazon); Ayudas Ramón y Cajal de la Agencia Estatal de Investigación RY2013-13666 and RYC2018- 024183-I (for Leticia Abecia and Asis Palazon); European Research Council Starting Grant 804236 NEXTGEN-IO (for Asis Palazon); The German Research Foundation SFB/TRR57/P04, SFB1382-403224013/ A02 and DFG NE 2128/2-1 (for Francisco J Cubero and Yulia A Nevzorova); National Institute of Health (NIH)/National Institute of Alcohol Abuse and Alcoholism (NIAAA) 1U01AA026972-01 (For Pau Sancho-Bru); Junta de Castilla y León SA074P20 (for Jose J. G. Marin); Junta de Andalucía, Grupo PAIDI BIO311 (for Franz Martin); CIBERER Acciones Cooperativas y Complementarias Intramurales ACCI20-35 (for Mario F. Fraga); Ministerio de Educación, Cultura y Deporte FPU17/04992 (for Silvia Ariño); Fundació Marato TV3 201916-31 (for Jose J. G. Marin.); Ainize Pena-Cearra is a fellow of the University of the Basque Country (UPV/ EHU); BIOEF (Basque Foundation for Innovation and Health Research); Asociación Española contra el Cáncer (Maria Luz Martinez-Chantar and Teresa C. Delgado.); Fundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation) Rare Tumor Calls 2017 (for Maria Luz Martinez-Chantar); La Caixa Foundation Program (for Maria Luz Martinez-Chantar); Proyecto Desarrollo Tecnologico CIBERehd (for Maria Luz Martinez-Chantar); Ciberehd_ISCIII_MINECO is funded by the Instituto de Salud Carlos III.S

Congreso Internacional de Responsabilidad Social Apuestas para el desarrollo regional.

Congreso Internacional de Responsabilidad Social: apuestas para el desarrollo regional [Edición 1 / Nov. 6 - 7: 2019 Bogotá D.C.]El Congreso Internacional de Responsabilidad Social “Apuestas para el Desarrollo Regional”, se llevó a cabo los días 6 y 7 de noviembre de 2019 en la ciudad de Bogotá D.C. como un evento académico e investigativo liderado por la Corporación Universitaria Minuto de Dios -UNIMINUTO – Rectoría Cundinamarca cuya pretensión fue el fomento de nuevos paradigmas, la divulgación de conocimiento renovado en torno a la Responsabilidad Social; finalidad adoptada institucionalmente como postura ética y política que impacta la docencia, la investigación y la proyección social, y cuyo propósito central es la promoción de una “sensibilización consciente y crítica ante las situaciones problemáticas, tanto de las comunidades como del país, al igual que la adquisición de unas competencias orientadas a la promoción y al compromiso con el desarrollo humano y social integral”. (UNIMINUTO, 2014). Dicha postura, de conciencia crítica y sensibilización social, sumada a la experiencia adquirida mediante el trabajo articulado con otras instituciones de índole académico y de forma directa con las comunidades, permitió establecer como objetivo central del evento la reflexión de los diferentes grupos de interés, la gestión de sus impactos como elementos puntuales que contribuyeron en la audiencia a la toma de conciencia frente al papel que se debe asumir a favor de la responsabilidad social como aporte seguro al desarrollo regional y a su vez al fortalecimiento de los Objetivos de Desarrollo Sostenible

Congreso Internacional de Responsabilidad Social Apuestas para el desarrollo regional.

Congreso Internacional de Responsabilidad Social: apuestas para el desarrollo regional [Edición 1 / Nov. 6 - 7: 2019 Bogotá D.C.]El Congreso Internacional de Responsabilidad Social “Apuestas para el Desarrollo Regional”, se llevó a cabo los días 6 y 7 de noviembre de 2019 en la ciudad de Bogotá D.C. como un evento académico e investigativo liderado por la Corporación Universitaria Minuto de Dios -UNIMINUTO – Rectoría Cundinamarca cuya pretensión fue el fomento de nuevos paradigmas, la divulgación de conocimiento renovado en torno a la Responsabilidad Social; finalidad adoptada institucionalmente como postura ética y política que impacta la docencia, la investigación y la proyección social, y cuyo propósito central es la promoción de una “sensibilización consciente y crítica ante las situaciones problemáticas, tanto de las comunidades como del país, al igual que la adquisición de unas competencias orientadas a la promoción y al compromiso con el desarrollo humano y social integral”. (UNIMINUTO, 2014). Dicha postura, de conciencia crítica y sensibilización social, sumada a la experiencia adquirida mediante el trabajo articulado con otras instituciones de índole académico y de forma directa con las comunidades, permitió establecer como objetivo central del evento la reflexión de los diferentes grupos de interés, la gestión de sus impactos como elementos puntuales que contribuyeron en la audiencia a la toma de conciencia frente al papel que se debe asumir a favor de la responsabilidad social como aporte seguro al desarrollo regional y a su vez al fortalecimiento de los Objetivos de Desarrollo Sostenible

Gestión del conocimiento. Perspectiva multidisciplinaria. Volumen 17

El libro “Gestión del Conocimiento. Perspectiva Multidisciplinaria”, Volumen 17 de la Colección Unión Global, es resultado de investigaciones. Los capítulos del libro, son resultados de investigaciones desarrolladas por sus autores. El libro es una publicación internacional, seriada, continua, arbitrada, de acceso abierto a todas las áreas del conocimiento, orientada a contribuir con procesos de gestión del conocimiento científico, tecnológico y humanístico. Con esta colección, se aspira contribuir con el cultivo, la comprensión, la recopilación y la apropiación social del conocimiento en cuanto a patrimonio intangible de la humanidad, con el propósito de hacer aportes con la transformación de las relaciones socioculturales que sustentan la construcción social de los saberes y su reconocimiento como bien público

Spatiotemporal Characteristics of the Largest HIV-1 CRF02_AG Outbreak in Spain: Evidence for Onward Transmissions

Background and Aim: The circulating recombinant form 02_AG (CRF02_AG) is the predominant clade among the human immunodeficiency virus type-1 (HIV-1) non-Bs with a prevalence of 5.97% (95% Confidence Interval-CI: 5.41–6.57%) across Spain. Our aim was to estimate the levels of regional clustering for CRF02_AG and the spatiotemporal characteristics of the largest CRF02_AG subepidemic in Spain.Methods: We studied 396 CRF02_AG sequences obtained from HIV-1 diagnosed patients during 2000–2014 from 10 autonomous communities of Spain. Phylogenetic analysis was performed on the 391 CRF02_AG sequences along with all globally sampled CRF02_AG sequences (N = 3,302) as references. Phylodynamic and phylogeographic analysis was performed to the largest CRF02_AG monophyletic cluster by a Bayesian method in BEAST v1.8.0 and by reconstructing ancestral states using the criterion of parsimony in Mesquite v3.4, respectively.Results: The HIV-1 CRF02_AG prevalence differed across Spanish autonomous communities we sampled from (p < 0.001). Phylogenetic analysis revealed that 52.7% of the CRF02_AG sequences formed 56 monophyletic clusters, with a range of 2–79 sequences. The CRF02_AG regional dispersal differed across Spain (p = 0.003), as suggested by monophyletic clustering. For the largest monophyletic cluster (subepidemic) (N = 79), 49.4% of the clustered sequences originated from Madrid, while most sequences (51.9%) had been obtained from men having sex with men (MSM). Molecular clock analysis suggested that the origin (tMRCA) of the CRF02_AG subepidemic was in 2002 (median estimate; 95% Highest Posterior Density-HPD interval: 1999–2004). Additionally, we found significant clustering within the CRF02_AG subepidemic according to the ethnic origin.Conclusion: CRF02_AG has been introduced as a result of multiple introductions in Spain, following regional dispersal in several cases. We showed that CRF02_AG transmissions were mostly due to regional dispersal in Spain. The hot-spot for the largest CRF02_AG regional subepidemic in Spain was in Madrid associated with MSM transmission risk group. The existence of subepidemics suggest that several spillovers occurred from Madrid to other areas. CRF02_AG sequences from Hispanics were clustered in a separate subclade suggesting no linkage between the local and Hispanic subepidemics

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.S.E.H. and C.A.S. partially supported genotyping through a philanthropic donation. A.F. and D.E. were supported by a grant from the German Federal Ministry of Education and COVID-19 grant Research (BMBF; ID:01KI20197); A.F., D.E. and F.D. were supported by the Deutsche Forschungsgemeinschaft Cluster of Excellence ‘Precision Medicine in Chronic Inflammation’ (EXC2167). D.E. was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). D.E., K.B. and S.B. acknowledge the Novo Nordisk Foundation (NNF14CC0001 and NNF17OC0027594). T.L.L., A.T. and O.Ö. were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 279645989; 433116033; 437857095. M.W. and H.E. are supported by the German Research Foundation (DFG) through the Research Training Group 1743, ‘Genes, Environment and Inflammation’. L.V. received funding from: Ricerca Finalizzata Ministero della Salute (RF-2016-02364358), Italian Ministry of Health ‘CV PREVITAL’—strategie di prevenzione primaria cardiovascolare primaria nella popolazione italiana; The European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project LITMUS- and for the project ‘REVEAL’; Fondazione IRCCS Ca’ Granda ‘Ricerca corrente’, Fondazione Sviluppo Ca’ Granda ‘Liver-BIBLE’ (PR-0391), Fondazione IRCCS Ca’ Granda ‘5permille’ ‘COVID-19 Biobank’ (RC100017A). A.B. was supported by a grant from Fondazione Cariplo to Fondazione Tettamanti: ‘Bio-banking of Covid-19 patient samples to support national and international research (Covid-Bank). This research was partly funded by an MIUR grant to the Department of Medical Sciences, under the program ‘Dipartimenti di Eccellenza 2018–2022’. This study makes use of data generated by the GCAT-Genomes for Life. Cohort study of the Genomes of Catalonia, Fundació IGTP (The Institute for Health Science Research Germans Trias i Pujol) IGTP is part of the CERCA Program/Generalitat de Catalunya. GCAT is supported by Acción de Dinamización del ISCIII-MINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026); the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (2017-SGR 529). M.M. received research funding from grant PI19/00335 Acción Estratégica en Salud, integrated in the Spanish National RDI Plan and financed by ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (European Regional Development Fund (FEDER)-Una manera de hacer Europa’). B.C. is supported by national grants PI18/01512. X.F. is supported by the VEIS project (001-P-001647) (co-funded by the European Regional Development Fund (ERDF), ‘A way to build Europe’). Additional data included in this study were obtained in part by the COVICAT Study Group (Cohort Covid de Catalunya) supported by IsGlobal and IGTP, European Institute of Innovation & Technology (EIT), a body of the European Union, COVID-19 Rapid Response activity 73A and SR20-01024 La Caixa Foundation. A.J. and S.M. were supported by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). A.J. was also supported by national grant PI17/00019 from the Acción Estratégica en Salud (ISCIII) and the European Regional Development Fund (FEDER). The Basque Biobank, a hospital-related platform that also involves all Osakidetza health centres, the Basque government’s Department of Health and Onkologikoa, is operated by the Basque Foundation for Health Innovation and Research-BIOEF. M.C. received Grants BFU2016-77244-R and PID2019-107836RB-I00 funded by the Agencia Estatal de Investigación (AEI, Spain) and the European Regional Development Fund (FEDER, EU). M.R.G., J.A.H., R.G.D. and D.M.M. are supported by the ‘Spanish Ministry of Economy, Innovation and Competition, the Instituto de Salud Carlos III’ (PI19/01404, PI16/01842, PI19/00589, PI17/00535 and GLD19/00100) and by the Andalussian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018, COVID-Premed, COVID GWAs). The position held by Itziar de Rojas Salarich is funded by grant FI20/00215, PFIS Contratos Predoctorales de Formación en Investigación en Salud. Enrique Calderón’s team is supported by CIBER of Epidemiology and Public Health (CIBERESP), ‘Instituto de Salud Carlos III’. J.C.H. reports grants from Research Council of Norway grant no 312780 during the conduct of the study. E.S. reports grants from Research Council of Norway grant no. 312769. The BioMaterialBank Nord is supported by the German Center for Lung Research (DZL), Airway Research Center North (ARCN). The BioMaterialBank Nord is member of popgen 2.0 network (P2N). P.K. Bergisch Gladbach, Germany and the Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany. He is supported by the German Federal Ministry of Education and Research (BMBF). O.A.C. is supported by the German Federal Ministry of Research and Education and is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—CECAD, EXC 2030–390661388. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. This work was supported by grants of the Rolf M. Schwiete Stiftung, the Saarland University, BMBF and The States of Saarland and Lower Saxony. K.U.L. is supported by the German Research Foundation (DFG, LU-1944/3-1). Genotyping for the BoSCO study is funded by the Institute of Human Genetics, University Hospital Bonn. F.H. was supported by the Bavarian State Ministry for Science and Arts. Part of the genotyping was supported by a grant to A.R. from the German Federal Ministry of Education and Research (BMBF, grant: 01ED1619A, European Alzheimer DNA BioBank, EADB) within the context of the EU Joint Programme—Neurodegenerative Disease Research (JPND). Additional funding was derived from the German Research Foundation (DFG) grant: RA 1971/6-1 to A.R. P.R. is supported by the DFG (CCGA Sequencing Centre and DFG ExC2167 PMI and by SH state funds for COVID19 research). F.T. is supported by the Clinician Scientist Program of the Deutsche Forschungsgemeinschaft Cluster of Excellence ‘Precision Medicine in Chronic Inflammation’ (EXC2167). C.L. and J.H. are supported by the German Center for Infection Research (DZIF). T.B., M.M.B., O.W. und A.H. are supported by the Stiftung Universitätsmedizin Essen. M.A.-H. was supported by Juan de la Cierva Incorporacion program, grant IJC2018-035131-I funded by MCIN/AEI/10.13039/501100011033. E.C.S. is supported by the Deutsche Forschungsgemeinschaft (DFG; SCHU 2419/2-1).Peer reviewe

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of a well-characterized cohort of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen (HLA) region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a highly pleiotropic ∼0.9-Mb inversion polymorphism and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.Andre Franke and David Ellinghaus were supported by a grant from the German Federal Ministry of Education and Research (01KI20197), Andre Franke, David Ellinghaus and Frauke Degenhardt were supported by the Deutsche Forschungsgemeinschaft Cluster of Excellence “Precision Medicine in Chronic Inflammation” (EXC2167). David Ellinghaus was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). David Ellinghaus, Karina Banasik and Søren Brunak acknowledge the Novo Nordisk Foundation (grant NNF14CC0001 and NNF17OC0027594). Tobias L. Lenz, Ana Teles and Onur Özer were funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 279645989; 433116033; 437857095. Mareike Wendorff and Hesham ElAbd are supported by the German Research Foundation (DFG) through the Research Training Group 1743, "Genes, Environment and Inflammation". This project was supported by a Covid-19 grant from the German Federal Ministry of Education and Research (BMBF; ID: 01KI20197). Luca Valenti received funding from: Ricerca Finalizzata Ministero della Salute RF2016-02364358, Italian Ministry of Health ""CV PREVITAL – strategie di prevenzione primaria cardiovascolare primaria nella popolazione italiana; The European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project LITMUS- and for the project ""REVEAL""; Fondazione IRCCS Ca' Granda ""Ricerca corrente"", Fondazione Sviluppo Ca' Granda ""Liver-BIBLE"" (PR-0391), Fondazione IRCCS Ca' Granda ""5permille"" ""COVID-19 Biobank"" (RC100017A). Andrea Biondi was supported by the grant from Fondazione Cariplo to Fondazione Tettamanti: "Biobanking of Covid-19 patient samples to support national and international research (Covid-Bank). This research was partly funded by a MIUR grant to the Department of Medical Sciences, under the program "Dipartimenti di Eccellenza 2018–2022". This study makes use of data generated by the GCAT-Genomes for Life. Cohort study of the Genomes of Catalonia, Fundació IGTP. IGTP is part of the CERCA Program / Generalitat de Catalunya. GCAT is supported by Acción de Dinamización del ISCIIIMINECO and the Ministry of Health of the Generalitat of Catalunya (ADE 10/00026); the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (2017-SGR 529). Marta Marquié received research funding from ant PI19/00335 Acción Estratégica en Salud, integrated in the Spanish National RDI Plan and financed by ISCIIISubdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER-Una manera de hacer Europa").Beatriz Cortes is supported by national grants PI18/01512. Xavier Farre is supported by VEIS project (001-P-001647) (cofunded by European Regional Development Fund (ERDF), “A way to build Europe”). Additional data included in this study was obtained in part by the COVICAT Study Group (Cohort Covid de Catalunya) supported by IsGlobal and IGTP, EIT COVID-19 Rapid Response activity 73A and SR20-01024 La Caixa Foundation. Antonio Julià and Sara Marsal were supported by the Spanish Ministry of Economy and Competitiveness (grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36). Antonio Julià was also supported the by national grant PI17/00019 from the Acción Estratégica en Salud (ISCIII) and the FEDER. The Basque Biobank is a hospitalrelated platform that also involves all Osakidetza health centres, the Basque government's Department of Health and Onkologikoa, is operated by the Basque Foundation for Health Innovation and Research-BIOEF. Mario Cáceres received Grants BFU2016-77244-R and PID2019-107836RB-I00 funded by the Agencia Estatal de Investigación (AEI, Spain) and the European Regional Development Fund (FEDER, EU). Manuel Romero Gómez, Javier Ampuero Herrojo, Rocío Gallego Durán and Douglas Maya Miles are supported by the “Spanish Ministry of Economy, Innovation and Competition, the Instituto de Salud Carlos III” (PI19/01404, PI16/01842, PI19/00589, PI17/00535 and GLD19/00100), and by the Andalussian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018, COVID-Premed, COVID GWAs). The position held by Itziar de Rojas Salarich is funded by grant FI20/00215, PFIS Contratos Predoctorales de Formación en Investigación en Salud. Enrique Calderón's team is supported by CIBER of Epidemiology and Public Health (CIBERESP), "Instituto de Salud Carlos III". Jan Cato Holter reports grants from Research Council of Norway grant no 312780 during the conduct of the study. Dr. Solligård: reports grants from Research Council of Norway grant no 312769. The BioMaterialBank Nord is supported by the German Center for Lung Research (DZL), Airway Research Center North (ARCN). The BioMaterialBank Nord is member of popgen 2.0 network (P2N). Philipp Koehler has received non-financial scientific grants from Miltenyi Biotec GmbH, Bergisch Gladbach, Germany, and the Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany. He is supported by the German Federal Ministry of Education and Research (BMBF).Oliver A. Cornely is supported by the German Federal Ministry of Research and Education and is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – CECAD, EXC 2030 – 390661388. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. Genotyping was performed by the Genotyping laboratory of Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki. This work was supported by grants of the Rolf M. Schwiete Stiftung, the Saarland University, BMBF and The States of Saarland and Lower Saxony. Kerstin U. Ludwig is supported by the German Research Foundation (DFG, LU-1944/3-1). Genotyping for the BoSCO study is funded by the Institute of Human Genetics, University Hospital Bonn. Frank Hanses was supported by the Bavarian State Ministry for Science and Arts. Part of the genotyping was supported by a grant to Alfredo Ramirez from the German Federal Ministry of Education and Research (BMBF, grant: 01ED1619A, European Alzheimer DNA BioBank, EADB) within the context of the EU Joint Programme – Neurodegenerative Disease Research (JPND). Additional funding was derived from the German Research Foundation (DFG) grant: RA 1971/6-1 to Alfredo Ramirez. Philip Rosenstiel is supported by the DFG (CCGA Sequencing Centre and DFG ExC2167 PMI and by SH state funds for COVID19 research). Florian Tran is supported by the Clinician Scientist Program of the Deutsche Forschungsgemeinschaft Cluster of Excellence “Precision Medicine in Chronic Inflammation” (EXC2167). Christoph Lange and Jan Heyckendorf are supported by the German Center for Infection Research (DZIF). Thorsen Brenner, Marc M Berger, Oliver Witzke und Anke Hinney are supported by the Stiftung Universitätsmedizin Essen. Marialbert Acosta-Herrera was supported by Juan de la Cierva Incorporacion program, grant IJC2018-035131-I funded by MCIN/AEI/10.13039/501100011033. Eva C Schulte is supported by the Deutsche Forschungsgemeinschaft (DFG; SCHU 2419/2-1).N