43 research outputs found
Multi-step building energy model calibration process based on measured data
Building energy models are a key element in regulatory compliance calculations. These energy
performance calculations often do not accurately reflect actual operating conditions. Therefore, evalua-
tion of energy performance comparing actual energy use of a building with the outcome of dynamic sim-
ulation models can be misleading, this difference is also known as the energy performance gap. The
reduction of the gap is an important task aimed to provide confidence in the use of models for evaluation
of energy efficiency. This paper is focused on reducing the technical issues (e.g. poorly adjusted thermal
parameters in the envelope, inefficient boiler operator or lack of adjustment in parameters of heat pumps,
baseboard radiators or air handling units) which are one of the main causes of the energy performance
gap. The application of a multi-step, optimization-based, calibration methodology performed in a
white-box simulation environment (EnergyPlus) using three months of ten minute time-step data to
adjust HVAC parameter values with a genetic algorithm software (Jeplus) is validated on a real test site.
Resulting in a BEM that fits the building’s hourly performance benchmark into international standards on
three key levels: indoor temperature by Thermal Zone (TZ), heat production and electric consumption
from heat pumps, which comprise all the components of a building energy model. A batch of 1500 h
of heating operation, obtained from the building management system, has been used to calibrate the
model. The results complied with the requirements of the American Society of Heating, Refrigerating
and Air-Conditioning Engineers (ASHRAE) Guideline 14–2002 at hourly interval, with NMBE 6–10%,Cv
(RMSE) 630% and R2 P75% and with the International Performance Measurement and Verification
Protocol (EVO) for Cv(RMSE) 620% and R2 P75% in the three aforementioned levels, which can be con-
sidered a step forward in the area of calibrating white box models. In addition, to prove the strength and
robustness of the results, the model has been checked in a long testing and independent period of 2.500 h
of heating operations with the same level of compliance. The demonstrator is the library of a school
located in Denmark. The HVAC system is composed of four air–water heat pumps that deliver heating
to the whole compound with the backup support of a gas boiler. The library is heated with baseboard
radiators system with the support of an air handling unit used for ventilation purposes
Towards a new generation of building envelope calibration
Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change
21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simpli¿ed past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model
Validation of calibrated energy models: Common errors
Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy ¿eld, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models,
and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Veri¿cation Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated
Probabilistic load forecasting for building energy models
In the current energy context of intelligent buildings and smart grids, the use of load
forecasting to predict future building energy performance is becoming increasingly relevant.
The prediction accuracy is directly influenced by input uncertainties such as the weather forecast,
and its impact must be considered. Traditional load forecasting provides a single expected value for
the predicted load and cannot properly incorporate the effect of these uncertainties. This research
presents a methodology that calculates the probabilistic load forecast while accounting for the
inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting
approach has increased in importance in the literature but it is mostly focused on black-box models
which do not allow performance evaluation of specific components of envelope, HVAC systems, etc.
This research fills this gap using a white-box model, a building energy model (BEM) developed in
EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation
(KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load
forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring
system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated
with different prediction intervals. The map provides an overview of different prediction intervals for
each hour, along with the probability that the load forecast error is less than a certain value. This map
can then be applied to the forecast load that is provided by the BEM by applying the prediction
intervals with their associated probabilities to its outputs. The methodology was implemented and
evaluated in a real school building in Denmark. The results show that the percentage of the real
values that are covered by the prediction intervals for the testing month is greater than the confidence
level (80%), even when a small amount of data are used for the creation of the uncertainty map;
therefore, the proposed method is appropriate for predicting the probabilistic expected error in load
forecasting due to the use of weather forecast data
Ground characterization of building energy models
The calibration of building energy models is crucial for their use in some applications that depend on
their accuracy for adequate performance, such as demand response and model predictive control
(MPC). In general, energy models offer many possibilities/strategies when characterizing a construction
system, and such a characterization is key when analyzing both its thermal behavior and its energy
impact. This research analyzes the different ways to characterize the thermal interaction of the building
energy model (BEM) with the ground, comparing conventional approaches with new approaches based
on both optimization of the former and dynamic ground characterizations. Using a model adjusted to
a real case study, each of the existing options are analyzed, in which a different control of the ground
temperature both in terms of its temporal oscillation and its location in the building (based on thermal
zones) is taken into account. Exhaustive monitoring of a real building and measuring the ground and
ground floor surface temperatures have made establishing which EnergyPlus components/objects best
characterize the ground-slab interaction possible, both in terms of the simplicity of modeling and the cost
(economic and technical) required for each of them. As will be seen, there are objects with an excellent
cost/effectiveness ratio when characterizing the groun
Model predictive control optimization via genetic algorithm using a detailed building energy model
There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations¿computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution
Methodology for the quantification of the impact of weather forecasts in predictive simulation models
The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building¿s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on..
Industrialized Sunspace Prototype with Solar Heat Storage. Assessment of Post-Occupational Behaviour in Adaptive Facades.
The thermal performance of two passive solar components has been investigated. An attached sunspace with horizontal heat storage and another one with vertical thermal storage were designed in order to optimize the use of solar gains and its storage and distribution in an industrialized component.
These sunspaces have been tested under real conditions, comparing their thermal performance with two commonly used components in residential buildings in Spain: a window and a double window making up an attached sunspace. Different series of experimental measurements were conducted in two test-cells exposed to outdoor conditions in Pamplona (Northern Spain).
As a result, nine scenarios during winter 2011 and six during summer 2012 have been carried out, comparing all of the prototypes two by two with different use modes.
Results show that a sunspace with heat storage takes advantage of the solar energy and improves the indoor thermal performance of the adjacent room during winter in a better way than a window or a simple sunspace, and that it also offers better performance in summer. The best results in winter and summer were obtained when an appropriate use of the component was performed, in concordance with outdoor conditions. Several thermal control keys for the use of these components are suggested
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