16 research outputs found
Symmetrische und asymmetrische Chelatliganden für die Modellierung der Tyrosinase-Reaktion mit biomimetischen Kupferkomplexen : spektroskopische und quantenchemische Studien
Die vorliegende Arbeit beschäftigt sich mit verschiedenen binuklearen Kupfer-Komplexen, die durch Modellierung der Tyrosinase-Reaktion zum besseren Verständnis dieses ubiquitären Enzyms beitragen
Vietnam’s rice sector is the key to meeting methane reduction targets
Rice production contributes almost half of Vietnam’s total methane emissions and is the center of action for reducing the powerful greenhouse gas. At the COP 26 Glasgow, Vietnamese Prime Minister Pham Minh Chinh joined more than 100 countries in signing a methane emission reduction pledge. While the numbers are pointing towards the target, this op-ed piece tackled the challenge of introducing a set of mitigation options to millions of farmers across Vietnam
Greenhouse Gas Mitigation Potential of Alternate Wetting and Drying for Rice Production at National Scale—A Modeling Case Study for the Philippines
Worldwide, rice production contributes about 10% of total greenhouse gas (GHG) emissions from the agricultural sector, mainly due to CH emissions from continuously flooded fields. Alternate Wetting and Drying (AWD) is a promising crop technology for mitigating CH emissions and reducing the irrigation water currently being applied in many of the world\u27s top rice-producing countries. However, decreased emissions of CH may be partially counterbalanced by increased NO emissions. In this case study for the Philippines, the national mitigation potential of AWD is explored using the process-based biogeochemical model LandscapeDNDC. Simulated mean annual CH emissions under conventional rice production for the time period 2000–2011 are estimated as 1,180 ± 163 Gg CH yr. During the cropping season, this is about +16% higher than a former estimate using emission factors. Scenario simulations of nationwide introduction of AWD in irrigated landscapes suggest a considerable decrease in CH emissions by −23%, while NO emissions are only increased by +8%. Irrespective of field management, at national scale, the radiative forcing of irrigated rice production is always dominated by CH (>95%). The reduction potential of GHG emissions depends on, for example, number of crops per year, residue management, amount of applied irrigation water, and sand content. Seasonal weather conditions also play an important role since the mitigation potential of AWD is almost double as high in dry as compared to wet seasons. Furthermore, this study demonstrates the importance of temporal continuity, considering off-season emissions and the long-term development of GHG emissions across multiple years
Activity data on crop management define uncertainty of CH and NO emission estimates from rice: A case study of Vietnam
Background: Globally, rice systems are a major source of atmospheric CH and for major rice-producing countries, such as Vietnam, CH as well as NO emissions from agricultural land used for rice production may represent about one-fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national-scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.
Aims: Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH and NO from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of NO to the total non-CO (CH+NO) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management .
Methods: The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH and NO emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH and NO fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions.
Results: Our study shows that CH and NO fluxes from rice systems in Vietnam are highly seasonal, with national CH and NO emissions totaling to about 2600 Gg CH y and 42 Gg NO y, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield-scaled emissions varied largely in a range of 300–3000 kg CO-eq Mg y, with CH emissions during the rice season(s) dominating (>82%) the total annual non-CO GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH and NO emission inventory.
Conclusions: Our study shows that Tier 3 approaches, that is, process-oriented model approaches combined with GIS databases, for estimating national-scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows to identify regions with elevated emissions, thereby accounting not only for CH, but as well for NO emissions. However, our study also shows that specifically better information on land management is required to narrowing uncertainties
NDC Analysis for Vietnam’s agriculture sector targets by 2030
Production of rice is essential to Vietnam’s economy but paddy rice production also contributes significantly to the nation’s greenhouse gas (GHG) emissions. Rice production emitted 45 million tons of carbon dioxide equivalent (MtCO2e) in 2010, equating to 18% of total national GHG emissions (Tran et al., 2019). A variety of options to reduce GHG emissions during the production of rice must be implemented to achieve Vietnam’s Nationally Determined Contribution (NDC) and green growth strategies. One of the most promising options is alternate wetting and drying (AWD), an irrigation technique in which fields are irrigated and then allowed to dry out to a certain point before irrigation commences. This technique can reduce methane emissions by as much as 50% on average without a reduction in yield (Carrijo et al. 2017). We provide multiple project scenarios (MARD, 2016; Mai & Ngo, 2020; and Tran et al., 2019) to achieve this target under differing technology adoption baselines and with low to high infrastructure investment prospects
Investment guide for low-emission rice in the Mekong Delta, Vietnam
Rice production is an integral commodity to global food security, but it also contributes considerably to greenhouse
gas emissions (GHG). There are a variety of options to mitigate GHG emissions from paddy rice production but the
impacts and opportunities to finance such activities are not well-known or well-understood for global investors, and
much remains to be done to ensure that international climate finance can be used to attract private sector
investments into sustainable agriculture. This guide provides promising GHG mitigation options and analyses of cost and benefits to develop an investment plan for Mekong Delta in Vietnam. Investment de-risking strategies are also included in this publication
Straw Management Adoption Capacity in Dong Thap Province
The study aimed to evaluate the straw management adoption capacity in Dong Thap province. Key informant interview (KII) with district-level DARD officials in Dong Thap province were conducted to determine the straw management adoption capacity in Winter-Spring, Summer-Autumn, and Autumn-Winter season
Straw Management Adoption Capacity in Long An Province
The study aims to evaluate the straw management adoption capacity in Long An province. Key informant interview (KII) with district-level DARD officials in Long An province were conducted to determine the straw management adoption capacity in Winter-Spring, Summer-Autumn, and Autumn-Winter season
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