TY  - CHAP
A1  - Kurbel, Karl
A1  - Nowak, Dawid
A1  - Azodi, Amir
A1  - Jaeger, David
A1  - Meinel, Christoph
A1  - Cheng, Feng
A1  - Sapegin, Andrey
A1  - Gawron, Marian
A1  - Morelli, Frank
A1  - Stahl, Lukas
A1  - Kerl, Stefan
A1  - Janz, Mariska
A1  - Hadaya, Abdulmasih
A1  - Ivanov, Ivaylo
A1  - Wiese, Lena
A1  - Neves, Mariana
A1  - Schapranow, Matthieu-Patrick
A1  - Fähnrich, Cindy
A1  - Feinbube, Frank
A1  - Eberhardt, Felix
A1  - Hagen, Wieland
A1  - Plauth, Max
A1  - Herscheid, Lena
A1  - Polze, Andreas
A1  - Barkowsky, Matthias
A1  - Dinger, Henriette
A1  - Faber, Lukas
A1  - Montenegro, Felix
A1  - Czachórski, Tadeusz
A1  - Nycz, Monika
A1  - Nycz, Tomasz
A1  - Baader, Galina
A1  - Besner, Veronika
A1  - Hecht, Sonja
A1  - Schermann, Michael
A1  - Krcmar, Helmut
A1  - Wiradarma, Timur Pratama
A1  - Hentschel, Christian
A1  - Sack, Harald
A1  - Abramowicz, Witold
A1  - Sokolowska, Wioletta
A1  - Hossa, Tymoteusz
A1  - Opalka, Jakub
A1  - Fabisz, Karol
A1  - Kubaczyk, Mateusz
A1  - Cmil, Milena
A1  - Meng, Tianhui
A1  - Dadashnia, Sharam
A1  - Niesen, Tim
A1  - Fettke, Peter
A1  - Loos, Peter
A1  - Perscheid, Cindy
A1  - Schwarz, Christian
A1  - Schmidt, Christopher
A1  - Scholz, Matthias
A1  - Bock, Nikolai
A1  - Piller, Gunther
A1  - Böhm, Klaus
A1  - Norkus, Oliver
A1  - Clark, Brian
A1  - Friedrich, Björn
A1  - Izadpanah, Babak
A1  - Merkel, Florian
A1  - Schweer, Ilias
A1  - Zimak, Alexander
A1  - Sauer, Jürgen
A1  - Fabian, Benjamin
A1  - Tilch, Georg
A1  - Müller, David
A1  - Plöger, Sabrina
A1  - Friedrich, Christoph M.
A1  - Engels, Christoph
A1  - Amirkhanyan, Aragats
A1  - van der Walt, Estée
A1  - Eloff, J. H. P.
A1  - Scheuermann, Bernd
A1  - Weinknecht, Elisa
ED  - Meinel, Christoph
ED  - Polze, Andreas
ED  - Oswald, Gerhard
ED  - Strotmann, Rolf
ED  - Seibold, Ulrich
ED  - Schulzki, Bernhard
T1  - HPI Future SOC Lab
BT  - Proceedings 2015
N2  - Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie.
Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einige der Schwerpunkte sind Cloud Computing, Parallelisierung und In-Memory Technologien. 
In diesem Technischen Bericht werden die Ergebnisse der Forschungsprojekte des Jahres 2015 vorgestellt.  Ausgewählte Projekte stellten ihre Ergebnisse am 15. April 2015 und 4. November 2015 im Rahmen der Future SOC Lab Tag Veranstaltungen vor.
KW  - Future SOC Lab
KW  - Forschungsprojekte
KW  - Multicore Architekturen
KW  - In-Memory Technologie
KW  - Cloud Computing
KW  - maschinelles Lernen
KW  - künstliche Intelligenz
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-102516
ER  - 
TY  - JOUR
A1  - Wuttke, Matthias
A1  - Li, Yong
A1  - Li, Man
A1  - Sieber, Karsten B.
A1  - Feitosa, Mary F.
A1  - Gorski, Mathias
A1  - Tin, Adrienne
A1  - Wang, Lihua
A1  - Chu, Audrey Y.
A1  - Hoppmann, Anselm
A1  - Kirsten, Holger
A1  - Giri, Ayush
A1  - Chai, Jin-Fang
A1  - Sveinbjornsson, Gardar
A1  - Tayo, Bamidele O.
A1  - Nutile, Teresa
A1  - Fuchsberger, Christian
A1  - Marten, Jonathan
A1  - Cocca, Massimiliano
A1  - Ghasemi, Sahar
A1  - Xu, Yizhe
A1  - Horn, Katrin
A1  - Noce, Damia
A1  - Van der Most, Peter J.
A1  - Sedaghat, Sanaz
A1  - Yu, Zhi
A1  - Akiyama, Masato
A1  - Afaq, Saima
A1  - Ahluwalia, Tarunveer Singh
A1  - Almgren, Peter
A1  - Amin, Najaf
A1  - Arnlov, Johan
A1  - Bakker, Stephan J. L.
A1  - Bansal, Nisha
A1  - Baptista, Daniela
A1  - Bergmann, Sven
A1  - Biggs, Mary L.
A1  - Biino, Ginevra
A1  - Boehnke, Michael
A1  - Boerwinkle, Eric
A1  - Boissel, Mathilde
A1  - Böttinger, Erwin
A1  - Boutin, Thibaud S.
A1  - Brenner, Hermann
A1  - Brumat, Marco
A1  - Burkhardt, Ralph
A1  - Butterworth, Adam S.
A1  - Campana, Eric
A1  - Campbell, Archie
A1  - Campbell, Harry
A1  - Canouil, Mickael
A1  - Carroll, Robert J.
A1  - Catamo, Eulalia
A1  - Chambers, John C.
A1  - Chee, Miao-Ling
A1  - Chee, Miao-Li
A1  - Chen, Xu
A1  - Cheng, Ching-Yu
A1  - Cheng, Yurong
A1  - Christensen, Kaare
A1  - Cifkova, Renata
A1  - Ciullo, Marina
A1  - Concas, Maria Pina
A1  - Cook, James P.
A1  - Coresh, Josef
A1  - Corre, Tanguy
A1  - Sala, Cinzia Felicita
A1  - Cusi, Daniele
A1  - Danesh, John
A1  - Daw, E. Warwick
A1  - De Borst, Martin H.
A1  - De Grandi, Alessandro
A1  - De Mutsert, Renee
A1  - De Vries, Aiko P. J.
A1  - Degenhardt, Frauke
A1  - Delgado, Graciela
A1  - Demirkan, Ayse
A1  - Di Angelantonio, Emanuele
A1  - Dittrich, Katalin
A1  - Divers, Jasmin
A1  - Dorajoo, Rajkumar
A1  - Eckardt, Kai-Uwe
A1  - Ehret, Georg
A1  - Elliott, Paul
A1  - Endlich, Karlhans
A1  - Evans, Michele K.
A1  - Felix, Janine F.
A1  - Foo, Valencia Hui Xian
A1  - Franco, Oscar H.
A1  - Franke, Andre
A1  - Freedman, Barry I.
A1  - Freitag-Wolf, Sandra
A1  - Friedlander, Yechiel
A1  - Froguel, Philippe
A1  - Gansevoort, Ron T.
A1  - Gao, He
A1  - Gasparini, Paolo
A1  - Gaziano, J. Michael
A1  - Giedraitis, Vilmantas
A1  - Gieger, Christian
A1  - Girotto, Giorgia
A1  - Giulianini, Franco
A1  - Gogele, Martin
A1  - Gordon, Scott D.
A1  - Gudbjartsson, Daniel F.
A1  - Gudnason, Vilmundur
A1  - Haller, Toomas
A1  - Hamet, Pavel
A1  - Harris, Tamara B.
A1  - Hartman, Catharina A.
A1  - Hayward, Caroline
A1  - Hellwege, Jacklyn N.
A1  - Heng, Chew-Kiat
A1  - Hicks, Andrew A.
A1  - Hofer, Edith
A1  - Huang, Wei
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Indridason, Olafur S.
A1  - Ingelsson, Erik
A1  - Ising, Marcus
A1  - Jaddoe, Vincent W. V.
A1  - Jakobsdottir, Johanna
A1  - Jonas, Jost B.
A1  - Joshi, Peter K.
A1  - Josyula, Navya Shilpa
A1  - Jung, Bettina
A1  - Kahonen, Mika
A1  - Kamatani, Yoichiro
A1  - Kammerer, Candace M.
A1  - Kanai, Masahiro
A1  - Kastarinen, Mika
A1  - Kerr, Shona M.
A1  - Khor, Chiea-Chuen
A1  - Kiess, Wieland
A1  - Kleber, Marcus E.
A1  - Koenig, Wolfgang
A1  - Kooner, Jaspal S.
A1  - Korner, Antje
A1  - Kovacs, Peter
A1  - Kraja, Aldi T.
A1  - Krajcoviechova, Alena
A1  - Kramer, Holly
A1  - Kramer, Bernhard K.
A1  - Kronenberg, Florian
A1  - Kubo, Michiaki
A1  - Kuhnel, Brigitte
A1  - Kuokkanen, Mikko
A1  - Kuusisto, Johanna
A1  - La Bianca, Martina
A1  - Laakso, Markku
A1  - Lange, Leslie A.
A1  - Langefeld, Carl D.
A1  - Lee, Jeannette Jen-Mai
A1  - Lehne, Benjamin
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Lim, Su-Chi
A1  - Lind, Lars
A1  - Lindgren, Cecilia M.
A1  - Liu, Jun
A1  - Liu, Jianjun
A1  - Loeffler, Markus
A1  - Loos, Ruth J. F.
A1  - Lucae, Susanne
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Magi, Reedik
A1  - Magnusson, Patrik K. E.
A1  - Mahajan, Anubha
A1  - Martin, Nicholas G.
A1  - Martins, Jade
A1  - Marz, Winfried
A1  - Mascalzoni, Deborah
A1  - Matsuda, Koichi
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Metspalu, Andres
A1  - Mikaelsdottir, Evgenia K.
A1  - Milaneschi, Yuri
A1  - Miliku, Kozeta
A1  - Mishra, Pashupati P.
A1  - Program, V. A. Million Veteran
A1  - Mohlke, Karen L.
A1  - Mononen, Nina
A1  - Montgomery, Grant W.
A1  - Mook-Kanamori, Dennis O.
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nalls, Mike A.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - Noordam, Raymond
A1  - Olafsson, Isleifur
A1  - Oldehinkel, Albertine J.
A1  - Orho-Melander, Marju
A1  - Ouwehand, Willem H.
A1  - Padmanabhan, Sandosh
A1  - Palmer, Nicholette D.
A1  - Palsson, Runolfur
A1  - Penninx, Brenda W. J. H.
A1  - Perls, Thomas
A1  - Perola, Markus
A1  - Pirastu, Mario
A1  - Pirastu, Nicola
A1  - Pistis, Giorgio
A1  - Podgornaia, Anna I.
A1  - Polasek, Ozren
A1  - Ponte, Belen
A1  - Porteous, David J.
A1  - Poulain, Tanja
A1  - Pramstaller, Peter P.
A1  - Preuss, Michael H.
A1  - Prins, Bram P.
A1  - Province, Michael A.
A1  - Rabelink, Ton J.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Reilly, Dermot F.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Ridker, Paul M.
A1  - Rivadeneira, Fernando
A1  - Rizzi, Federica
A1  - Roberts, David J.
A1  - Robino, Antonietta
A1  - Rossing, Peter
A1  - Rudan, Igor
A1  - Rueedi, Rico
A1  - Ruggiero, Daniela
A1  - Ryan, Kathleen A.
A1  - Saba, Yasaman
A1  - Sabanayagam, Charumathi
A1  - Salomaa, Veikko
A1  - Salvi, Erika
A1  - Saum, Kai-Uwe
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Ben Schottker, 
A1  - Schulz, Christina-Alexandra
A1  - Schupf, Nicole
A1  - Shaffer, Christian M.
A1  - Shi, Yuan
A1  - Smith, Albert V.
A1  - Smith, Blair H.
A1  - Soranzo, Nicole
A1  - Spracklen, Cassandra N.
A1  - Strauch, Konstantin
A1  - Stringham, Heather M.
A1  - Stumvoll, Michael
A1  - Svensson, Per O.
A1  - Szymczak, Silke
A1  - Tai, E-Shyong
A1  - Tajuddin, Salman M.
A1  - Tan, Nicholas Y. Q.
A1  - Taylor, Kent D.
A1  - Teren, Andrej
A1  - Tham, Yih-Chung
A1  - Thiery, Joachim
A1  - Thio, Chris H. L.
A1  - Thomsen, Hauke
A1  - Thorleifsson, Gudmar
A1  - Toniolo, Daniela
A1  - Tonjes, Anke
A1  - Tremblay, Johanne
A1  - Tzoulaki, Ioanna
A1  - Uitterlinden, Andre G.
A1  - Vaccargiu, Simona
A1  - Van Dam, Rob M.
A1  - Van der Harst, Pim
A1  - Van Duijn, Cornelia M.
A1  - Edward, Digna R. Velez
A1  - Verweij, Niek
A1  - Vogelezang, Suzanne
A1  - Volker, Uwe
A1  - Vollenweider, Peter
A1  - Waeber, Gerard
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Wang, Ya Xing
A1  - Wang, Chaolong
A1  - Waterworth, Dawn M.
A1  - Bin Wei, Wen
A1  - White, Harvey
A1  - Whitfield, John B.
A1  - Wild, Sarah H.
A1  - Wilson, James F.
A1  - Wojczynski, Mary K.
A1  - Wong, Charlene
A1  - Wong, Tien-Yin
A1  - Xu, Liang
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Weihua
A1  - Zonderman, Alan B.
A1  - Rotter, Jerome I.
A1  - Bochud, Murielle
A1  - Psaty, Bruce M.
A1  - Vitart, Veronique
A1  - Wilson, James G.
A1  - Dehghan, Abbas
A1  - Parsa, Afshin
A1  - Chasman, Daniel I.
A1  - Ho, Kevin
A1  - Morris, Andrew P.
A1  - Devuyst, Olivier
A1  - Akilesh, Shreeram
A1  - Pendergrass, Sarah A.
A1  - Sim, Xueling
A1  - Boger, Carsten A.
A1  - Okada, Yukinori
A1  - Edwards, Todd L.
A1  - Snieder, Harold
A1  - Stefansson, Kari
A1  - Hung, Adriana M.
A1  - Heid, Iris M.
A1  - Scholz, Markus
A1  - Teumer, Alexander
A1  - Kottgen, Anna
A1  - Pattaro, Cristian
T1  - A catalog of genetic loci associated with kidney function from analyses of a million individuals
JF  - Nature genetics
N2  - Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
Y1  - 2019
U6  - https://doi.org/10.1038/s41588-019-0407-x
SN  - 1061-4036
SN  - 1546-1718
VL  - 51
IS  - 6
SP  - 957
EP  - +
PB  - Nature Publ. Group
CY  - New York
ER  - 
TY  - GEN
A1  - Gorski, Mathias
A1  - Jung, Bettina
A1  - Li, Yong
A1  - Matias-Garcia, Pamela R.
A1  - Wuttke, Matthias
A1  - Coassin, Stefan
A1  - Thio, Chris H. L.
A1  - Kleber, Marcus E.
A1  - Winkler, Thomas W.
A1  - Wanner, Veronika
A1  - Chai, Jin-Fang
A1  - Chu, Audrey Y.
A1  - Cocca, Massimiliano
A1  - Feitosa, Mary F.
A1  - Ghasemi, Sahar
A1  - Hoppmann, Anselm
A1  - Horn, Katrin
A1  - Li, Man
A1  - Nutile, Teresa
A1  - Scholz, Markus
A1  - Sieber, Karsten B.
A1  - Teumer, Alexander
A1  - Tin, Adrienne
A1  - Wang, Judy
A1  - Tayo, Bamidele O.
A1  - Ahluwalia, Tarunveer S.
A1  - Almgren, Peter
A1  - Bakker, Stephan J. L.
A1  - Banas, Bernhard
A1  - Bansal, Nisha
A1  - Biggs, Mary L.
A1  - Boerwinkle, Eric
A1  - Böttinger, Erwin
A1  - Brenner, Hermann
A1  - Carroll, Robert J.
A1  - Chalmers, John
A1  - Chee, Miao-Li
A1  - Chee, Miao-Ling
A1  - Cheng, Ching-Yu
A1  - Coresh, Josef
A1  - de Borst, Martin H.
A1  - Degenhardt, Frauke
A1  - Eckardt, Kai-Uwe
A1  - Endlich, Karlhans
A1  - Franke, Andre
A1  - Freitag-Wolf, Sandra
A1  - Gampawar, Piyush
A1  - Gansevoort, Ron T.
A1  - Ghanbari, Mohsen
A1  - Gieger, Christian
A1  - Hamet, Pavel
A1  - Ho, Kevin
A1  - Hofer, Edith
A1  - Holleczek, Bernd
A1  - Foo, Valencia Hui Xian
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Josyula, Navya Shilpa
A1  - Kahonen, Mika
A1  - Khor, Chiea-Chuen
A1  - Koenig, Wolfgang
A1  - Kramer, Holly
A1  - Kraemer, Bernhard K.
A1  - Kuehnel, Brigitte
A1  - Lange, Leslie A.
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Loos, Ruth J. F.
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Milaneschi, Yuri
A1  - Mishra, Pashupati P.
A1  - Mononen, Nina
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - O'Donoghue, Michelle L.
A1  - Orho-Melander, Marju
A1  - Pendergrass, Sarah A.
A1  - Penninx, Brenda W. J. H.
A1  - Preuss, Michael H.
A1  - Psaty, Bruce M.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Rosenkranz, Alexander R.
A1  - Rossing, Peter
A1  - Rotter, Jerome
A1  - Sabanayagam, Charumathi
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Schoettker, Ben
A1  - Schulz, Christina-Alexandra
A1  - Sedaghat, Sanaz
A1  - Shaffer, Christian M.
A1  - Strauch, Konstantin
A1  - Szymczak, Silke
A1  - Taylor, Kent D.
A1  - Tremblay, Johanne
A1  - Chaker, Layal
A1  - van der Harst, Pim
A1  - van der Most, Peter J.
A1  - Verweij, Niek
A1  - Voelker, Uwe
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Waterworth, Dawn M.
A1  - White, Harvey D.
A1  - Wilson, James G.
A1  - Wong, Tien-Yin
A1  - Woodward, Mark
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Yan
A1  - Snieder, Harold
A1  - Wanner, Christoph
A1  - Boger, Carsten A.
A1  - Kottgen, Anna
A1  - Kronenberg, Florian
A1  - Pattaro, Cristian
A1  - Heid, Iris M.
T1  - Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline
T2  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät
N2  - Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.
T3  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 19 
KW  - acute kidney injury
KW  - end-stage kidney disease
KW  - genome-wide association
KW  - study
KW  - rapid eGFRcrea decline
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-565379
IS  - 19
ER  - 
TY  - JOUR
A1  - Gorski, Mathias
A1  - Jung, Bettina
A1  - Li, Yong
A1  - Matias-Garcia, Pamela R.
A1  - Wuttke, Matthias
A1  - Coassin, Stefan
A1  - Thio, Chris H. L.
A1  - Kleber, Marcus E.
A1  - Winkler, Thomas W.
A1  - Wanner, Veronika
A1  - Chai, Jin-Fang
A1  - Chu, Audrey Y.
A1  - Cocca, Massimiliano
A1  - Feitosa, Mary F.
A1  - Ghasemi, Sahar
A1  - Hoppmann, Anselm
A1  - Horn, Katrin
A1  - Li, Man
A1  - Nutile, Teresa
A1  - Scholz, Markus
A1  - Sieber, Karsten B.
A1  - Teumer, Alexander
A1  - Tin, Adrienne
A1  - Wang, Judy
A1  - Tayo, Bamidele O.
A1  - Ahluwalia, Tarunveer S.
A1  - Almgren, Peter
A1  - Bakker, Stephan J. L.
A1  - Banas, Bernhard
A1  - Bansal, Nisha
A1  - Biggs, Mary L.
A1  - Boerwinkle, Eric
A1  - Böttinger, Erwin
A1  - Brenner, Hermann
A1  - Carroll, Robert J.
A1  - Chalmers, John
A1  - Chee, Miao-Li
A1  - Chee, Miao-Ling
A1  - Cheng, Ching-Yu
A1  - Coresh, Josef
A1  - de Borst, Martin H.
A1  - Degenhardt, Frauke
A1  - Eckardt, Kai-Uwe
A1  - Endlich, Karlhans
A1  - Franke, Andre
A1  - Freitag-Wolf, Sandra
A1  - Gampawar, Piyush
A1  - Gansevoort, Ron T.
A1  - Ghanbari, Mohsen
A1  - Gieger, Christian
A1  - Hamet, Pavel
A1  - Ho, Kevin
A1  - Hofer, Edith
A1  - Holleczek, Bernd
A1  - Foo, Valencia Hui Xian
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Josyula, Navya Shilpa
A1  - Kahonen, Mika
A1  - Khor, Chiea-Chuen
A1  - Koenig, Wolfgang
A1  - Kramer, Holly
A1  - Kraemer, Bernhard K.
A1  - Kuehnel, Brigitte
A1  - Lange, Leslie A.
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Loos, Ruth J. F.
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Milaneschi, Yuri
A1  - Mishra, Pashupati P.
A1  - Mononen, Nina
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - O'Donoghue, Michelle L.
A1  - Orho-Melander, Marju
A1  - Pendergrass, Sarah A.
A1  - Penninx, Brenda W. J. H.
A1  - Preuss, Michael H.
A1  - Psaty, Bruce M.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Rosenkranz, Alexander R.
A1  - Rossing, Peter
A1  - Rotter, Jerome
A1  - Sabanayagam, Charumathi
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Schoettker, Ben
A1  - Schulz, Christina-Alexandra
A1  - Sedaghat, Sanaz
A1  - Shaffer, Christian M.
A1  - Strauch, Konstantin
A1  - Szymczak, Silke
A1  - Taylor, Kent D.
A1  - Tremblay, Johanne
A1  - Chaker, Layal
A1  - van der Harst, Pim
A1  - van der Most, Peter J.
A1  - Verweij, Niek
A1  - Voelker, Uwe
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Waterworth, Dawn M.
A1  - White, Harvey D.
A1  - Wilson, James G.
A1  - Wong, Tien-Yin
A1  - Woodward, Mark
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Yan
A1  - Snieder, Harold
A1  - Wanner, Christoph
A1  - Boger, Carsten A.
A1  - Kottgen, Anna
A1  - Kronenberg, Florian
A1  - Pattaro, Cristian
A1  - Heid, Iris M.
T1  - Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline
JF  - Kidney international : official journal of the International Society of Nephrology
N2  - Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m(2)/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m(2) at follow-up among those with eGFRcrea 60 mL/min/1.73m(2) or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or (LARP4B). Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.
KW  - acute kidney injury
KW  - end-stage kidney disease
KW  - genome-wide association
KW  - study
KW  - rapid eGFRcrea decline
Y1  - 2020
U6  - https://doi.org/10.1016/j.kint.2020.09.030
SN  - 0085-2538
SN  - 1523-1755
VL  - 99
IS  - 4
SP  - 926
EP  - 939
PB  - Elsevier
CY  - New York
ER  - 
TY  - JOUR
A1  - Scholz, Matthias
A1  - Kaplan, F.
A1  - Guy, C. L.
A1  - Kopka, Joachim
A1  - Selbig, Joachim
T1  - Non-linear PCA : a missing data approach
N2  - Motivation: Visualizing and analysing the potential non-linear structure of a dataset is becoming an important task in molecular biology. This is even more challenging when the data have missing values. Results: Here, we propose an inverse model that performs non-linear principal component analysis (NLPCA) from incomplete datasets. Missing values are ignored while optimizing the model, but can be estimated afterwards. Results are shown for both artificial and experimental datasets. In contrast to linear methods, non-linear methods were able to give better missing value estimations for non-linear structured data. Application: We applied this technique to a time course of metabolite data from a cold stress experiment on the model plant Arabidopsis thaliana, and could approximate the mapping function from any time point to the metabolite responses. Thus, the inverse NLPCA provides greatly improved information for better understanding the complex response to cold stress
Y1  - 2005
SN  - 1367-4803
ER  - 
TY  - THES
A1  - Scholz, Matthias
T1  - Approaches to analyse and interpret biological profile data
T1  - Methoden zur Analyse und Interpretation biologischer Profildaten
N2  - Advances in biotechnologies rapidly increase the number of molecules of a cell which can be observed simultaneously. This includes expression levels of thousands or ten-thousands of genes as well as concentration levels of metabolites or proteins.  Such Profile data, observed at different times or at different experimental conditions (e.g., heat or dry stress), show how the biological experiment is reflected on the molecular level. This information is helpful to understand the molecular behaviour and to identify molecules or combination of molecules that characterise specific biological condition (e.g., disease).  This work shows the potentials of component extraction algorithms to identify the major factors which influenced the observed data. This can be the expected experimental factors such as the time or temperature as well as unexpected factors such as technical artefacts or even unknown biological behaviour.  Extracting components means to reduce the very high-dimensional data to a small set of new variables termed components. Each component is a combination of all original variables. The classical approach for that purpose is the principal component analysis (PCA).  It is shown that, in contrast to PCA which maximises the variance only, modern approaches such as independent component analysis (ICA) are more suitable for analysing molecular data. The condition of independence between components of ICA fits more naturally our assumption of individual (independent) factors which influence the data. This higher potential of ICA is demonstrated by a crossing experiment of the model plant Arabidopsis thaliana (Thale Cress). The experimental factors could be well identified and, in addition, ICA could even detect a technical artefact.  However, in continuously observations such as in time experiments, the data show, in general, a nonlinear distribution. To analyse such nonlinear data, a nonlinear extension of PCA is used. This nonlinear PCA (NLPCA) is based on a neural network algorithm. The algorithm is adapted to be applicable to incomplete molecular data sets. Thus, it provides also the ability to estimate the missing data. The potential of nonlinear PCA to identify nonlinear factors is demonstrated by a cold stress experiment of Arabidopsis thaliana.  The results of component analysis can be used to build a molecular network model. Since it includes functional dependencies it is termed functional network. Applied to the cold stress data, it is shown that functional networks are appropriate to visualise biological processes and thereby reveals molecular dynamics.
N2  - Fortschritte in der Biotechnologie ermöglichen es, eine immer größere Anzahl von Molekülen in einer Zelle gleichzeitig zu erfassen. Das betrifft sowohl die Expressionswerte tausender oder zehntausender Gene als auch die Konzentrationswerte von Metaboliten oder Proteinen.  Diese Profildaten verschiedener Zeitpunkte oder unterschiedlicher experimenteller Bedingungen (z.B. unter Stressbedingungen wie Hitze oder Trockenheit) zeigen, wie sich das biologische Experiment auf molekularer Ebene widerspiegelt. Diese Information kann genutzt werden, um molekulare Abläufe besser zu verstehen und um Moleküle oder Molekül-Kombinationen zu bestimmen, die für bestimmte biologische Zustände (z.B.: Krankheit) charakteristisch sind.  Die Arbeit zeigt die Möglichkeiten von Komponenten-Extraktions-Algorithmen zur Bestimmung der wesentlichen Faktoren, die einen Einfluss auf die beobachteten Daten ausübten. Das können sowohl die erwarteten experimentellen Faktoren wie Zeit oder Temperatur sein als auch unerwartete Faktoren wie technische Einflüsse oder sogar unerwartete biologische Vorgänge.  Unter der Extraktion von Komponenten versteht man die Reduzierung dieser stark hoch-dimensionalen Daten auf wenige neue Variablen, die eine Kombination aus allen ursprünglichen Variablen darstellen und als Komponenten bezeichnet werden. Die Standard-Methode für diesen Zweck ist die Hauptkomponentenanalyse (PCA).  Es wird gezeigt, dass - im Vergleich zur nur die Varianz maximierenden PCA - moderne Methoden wie die Unabhängige Komponentenanalyse (ICA) für die Analyse molekularer Datensätze besser geeignet sind. Die Unabhängigkeit von Komponenten in der ICA entspricht viel besser unserer Annahme individueller (unabhängiger) Faktoren, die einen Einfluss auf die Daten ausüben. Dieser Vorteil der ICA wird anhand eines Kreuzungsexperiments mit der Modell-Pflanze Arabidopsis thaliana (Ackerschmalwand) demonstriert. Die experimentellen Faktoren konnten dabei gut identifiziert werden und ICA erkannte sogar zusätzlich einen technischen Störfaktor.  Bei kontinuierlichen Beobachtungen wie in Zeitexperimenten zeigen die Daten jedoch häufig eine nichtlineare Verteilung. Für die Analyse dieser nichtlinearen Daten wird eine nichtlinear erweiterte Methode der PCA angewandt. Diese nichtlineare PCA (NLPCA) basiert auf einem neuronalen Netzwerk-Algorithmus. Der Algorithmus wurde für die Anwendung auf unvollständigen molekularen Daten erweitert. Dies ermöglicht es, die fehlenden Werte zu schätzen. Die Fähigkeit der nichtlinearen PCA zur Bestimmung nichtlinearer Faktoren wird anhand eines Kältestress-Experiments mit Arabidopsis thaliana demonstriert.  Die Ergebnisse aus der Komponentenanalyse können zur Erstellung molekularer Netzwerk-Modelle genutzt werden. Da sie funktionelle Abhängigkeiten berücksichtigen, werden sie als Funktionale Netzwerke bezeichnet. Anhand der Kältestress-Daten wird demonstriert, dass solche funktionalen Netzwerke geeignet sind, biologische Prozesse zu visualisieren und dadurch die molekularen Dynamiken aufzuzeigen.
KW  - Bioinformatik
KW  - Hauptkomponentenanalyse
KW  - Unabhängige Komponentenanalyse
KW  - Neuronales Netz
KW  - Maschinelles Lernen
KW  - Fehlende Daten
KW  - Ackerschmalwand
KW  - nichtlineare PCA (NLPCA)
KW  - molekulare Netzwerke
KW  - nonlinear PCA (NLPCA)
KW  - molecular networks
Y1  - 2006
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-7839
ER  - 
TY  - JOUR
A1  - Schwarze, Martin
A1  - Schellhammer, Karl Sebastian
A1  - Ortstein, Katrin
A1  - Benduhn, Johannes
A1  - Gaul, Christopher
A1  - Hinderhofer, Alexander
A1  - Toro, Lorena Perdigon
A1  - Scholz, Reinhard
A1  - Kublitski, Jonas
A1  - Roland, Steffen
A1  - Lau, Matthias
A1  - Poelking, Carl
A1  - Andrienko, Denis
A1  - Cuniberti, Gianaurelio
A1  - Schreiber, Frank
A1  - Neher, Dieter
A1  - Vandewal, Koen
A1  - Ortmann, Frank
A1  - Leo, Karl
T1  - Impact of molecular quadrupole moments on the energy levels at organic heterojunctions
JF  - Nature Communications
N2  - The functionality of organic semiconductor devices crucially depends on molecular energies, namely the ionisation energy and the electron affinity. Ionisation energy and electron affinity values of thin films are, however, sensitive to film morphology and composition, making their prediction challenging. In a combined experimental and simulation study on zinc-phthalocyanine and its fluorinated derivatives, we show that changes in ionisation energy as a function of molecular orientation in neat films or mixing ratio in blends are proportional to the molecular quadrupole component along the p-p-stacking direction. We apply these findings to organic solar cells and demonstrate how the electrostatic interactions can be tuned to optimise the energy of the charge-transfer state at the donor-acceptor interface and the dissociation barrier for free charge carrier generation. The confirmation of the correlation between interfacial energies and quadrupole moments for other materials indicates its relevance for small molecules and polymers.
Y1  - 2019
U6  - https://doi.org/10.1038/s41467-019-10435-2
SN  - 2041-1723
VL  - 10
PB  - Nature Publ. Group
CY  - London
ER  - 
TY  - BOOK
A1  - Haas, Maria
A1  - Holzendorf, Ulf
A1  - Künzel, Matthias
A1  - Lenk, Jenny
A1  - Lenk, Michael
A1  - Ordnung, Martina
A1  - Priebe, Ulrike
A1  - Scholz, Beate
A1  - Schubert, Arndt
A1  - Traue, Heidi
ED  - Meier, Bernd
ED  - Schmid, Margarete
T1  - Wirtschaft - Technik - Haushalt/Soziales : Neubearbeitung zum Lehrplan Wirtschaft - Technik - Haushalt/Soziales in Sachsen ; Schülerbuch 10
Y1  - 2010
SN  - 978-3-637-00605-8
VL  - 10
PB  - Oldenbourg Schulbuchverl.
CY  - München
ER  - 
TY  - JOUR
A1  - Meyer, Rhonda C.
A1  - Witucka-Wall, Hanna
A1  - Becher, Martina
A1  - Blacha, Anna Maria
A1  - Boudichevskaia, Anastassia
A1  - Dörmann, Peter
A1  - Fiehn, Oliver
A1  - Friedel, Svetlana
A1  - von Korff, Maria
A1  - Lisec, Jan
A1  - Melzer, Michael
A1  - Repsilber, Dirk
A1  - Schmidt, Renate
A1  - Scholz, Matthias
A1  - Selbig, Joachim
A1  - Willmitzer, Lothar
A1  - Altmann, Thomas
T1  - Heterosis manifestation during early Arabidopsis seedling development is characterized by intermediate gene expression and enhanced metabolic activity in the hybrids
JF  - The plant journal
N2  - Heterosis-associated cellular and molecular processes were analyzed in seeds and seedlings of Arabidopsis thaliana accessions Col-0 and C24 and their heterotic hybrids. Microscopic examination revealed no advantages in terms of hybrid mature embryo organ sizes or cell numbers. Increased cotyledon sizes were detectable 4 days after sowing. Growth heterosis results from elevated cell sizes and numbers, and is well established at 10 days after sowing. The relative growth rates of hybrid seedlings were most enhanced between 3 and 4 days after sowing. Global metabolite profiling and targeted fatty acid analysis revealed maternal inheritance patterns for a large proportion of metabolites in the very early stages. During developmental progression, the distribution shifts to dominant, intermediate and heterotic patterns, with most changes occurring between 4 and 6 days after sowing. The highest incidence of heterotic patterns coincides with establishment of size differences at 4 days after sowing. In contrast, overall transcript patterns at 4, 6 and 10 days after sowing are characterized by intermediate to dominant patterns, with parental transcript levels showing the largest differences. Overall, the results suggest that, during early developmental stages, intermediate gene expression and higher metabolic activity in the hybrids compared to the parents lead to better resource efficiency, and therefore enhanced performance in the hybrids.
KW  - heterosis
KW  - seedlings
KW  - metabolite profiling
KW  - transcript profiling
KW  - morphological analysis
KW  - Arabidopsis thaliana
KW  - biomass
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-313X.2012.05021.x
SN  - 0960-7412
VL  - 71
IS  - 4
SP  - 669
EP  - 683
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - ´Cwiek-Kupczynska, Hanna
A1  - Altmann, Thomas
A1  - Arend, Daniel
A1  - Arnaud, Elizabeth
A1  - Chen, Dijun
A1  - Cornut, Guillaume
A1  - Fiorani, Fabio
A1  - Frohmberg, Wojciech
A1  - Junker, Astrid
A1  - Klukas, Christian
A1  - Lange, Matthias
A1  - Mazurek, Cezary
A1  - Nafissi, Anahita
A1  - Neveu, Pascal
A1  - van Oeveren, Jan
A1  - Pommier, Cyril
A1  - Poorter, Hendrik
A1  - Rocca-Serra, Philippe
A1  - Sansone, Susanna-Assunta
A1  - Scholz, Uwe
A1  - van Schriek, Marco
A1  - Seren, Ümit
A1  - Usadel, Bjorn
A1  - Weise, Stephan
A1  - Kersey, Paul
A1  - Krajewski, Pawel
T1  - Measures for interoperability of phenotypic data: minimum information requirements and formatting
JF  - Plant Methods
N2  - Background: Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse. Results: In this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called "Minimum Information About a Plant Phenotyping Experiment", which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented. Conclusions: Acceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.
KW  - Data standardisation and formatting
KW  - Experimental metadata
KW  - Minimum information recommendations
KW  - Plant phenotyping
KW  - Experiment description
Y1  - 2016
U6  - https://doi.org/10.1186/s13007-016-0144-4
SN  - 1746-4811
VL  - 12
PB  - BioMed Central
CY  - London
ER  -