TY  - JOUR
A1  - Bora, Sanjay Singh
A1  - Cotton, Fabrice Pierre
A1  - Scherbaum, Frank
A1  - Edwards, Benjamin
A1  - Traversa, Paola
T1  - Stochastic source, path and site attenuation parameters and associated variabilities for shallow crustal European earthquakes
JF  - Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering
N2  - We have analyzed the recently developed pan-European strong motion database, RESORCE-2012: spectral parameters, such as stress drop (stress parameter, Delta sigma), anelastic attenuation (Q), near surface attenuation (kappa(0)) and site amplification have been estimated from observed strong motion recordings. The selected dataset exhibits a bilinear distance-dependent Q model with average kappa(0) value 0.0308 s. Strong regional variations in inelastic attenuation were also observed: frequency-independent Q(0) of 1462 and 601 were estimated for Turkish and Italian data respectively. Due to the strong coupling between Q and kappa(0), the regional variations in Q have strong impact on the estimation of near surface attenuation kappa(0). kappa(0) was estimated as 0.0457 and 0.0261 s for Turkey and Italy respectively. Furthermore, a detailed analysis of the variability in estimated kappa(0) revealed significant within-station variability. The linear site amplification factors were constrained from residual analysis at each station and site-class type. Using the regional Q(0) model and a site-class specific kappa(0), seismic moments (M-0) and source corner frequencies f (c) were estimated from the site corrected empirical Fourier spectra. Delta sigma did not exhibit magnitude dependence. The median Delta sigma value was obtained as 5.75 and 5.65 MPa from inverted and database magnitudes respectively. A comparison of response spectra from the stochastic model (derived herein) with that from (regional) ground motion prediction equations (GMPEs) suggests that the presented seismological parameters can be used to represent the corresponding seismological attributes of the regional GMPEs in a host-to-target adjustment framework. The analysis presented herein can be considered as an update of that undertaken for the previous Euro-Mediterranean strong motion database presented by Edwards and Fah (Geophys J Int 194(2):1190-1202, 2013a).
KW  - Stochastic model
KW  - Attenuation
KW  - Stress parameter
KW  - Kappa
KW  - Crustal earthquakes
Y1  - 2017
U6  - https://doi.org/10.1007/s10518-017-0167-x
SN  - 1570-761X
SN  - 1573-1456
VL  - 15
SP  - 4531
EP  - 4561
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Marquer, Laurent
A1  - Gaillard, Marie-Jose
A1  - Sugita, Shinya
A1  - Poska, Anneli
A1  - Trondman, Anna-Kari
A1  - Mazier, Florence
A1  - Nielsen, Anne Birgitte
A1  - Fyfe, Ralph M.
A1  - Jonsson, Anna Maria
A1  - Smith, Benjamin
A1  - Kaplan, Jed O.
A1  - Alenius, Teija
A1  - Birks, H. John B.
A1  - Bjune, Anne E.
A1  - Christiansen, Jorg
A1  - Dodson, John
A1  - Edwards, Kevin J.
A1  - Giesecke, Thomas
A1  - Herzschuh, Ulrike
A1  - Kangur, Mihkel
A1  - Koff, Tiiu
A1  - Latalowa, Maligorzata
A1  - Lechterbeck, Jutta
A1  - Olofsson, Jorgen
A1  - Seppa, Heikki
T1  - Quantifying the effects of land use and climate on Holocene vegetation in Europe
JF  - Quaternary science reviews : the international multidisciplinary research and review journal
N2  - Early agriculture can be detected in palaeovegetation records, but quantification of the relative importance of climate and land use in influencing regional vegetation composition since the onset of agriculture is a topic that is rarely addressed. We present a novel approach that combines pollen-based REVEALS estimates of plant cover with climate, anthropogenic land-cover and dynamic vegetation modelling results. This is used to quantify the relative impacts of land use and climate on Holocene vegetation at a sub-continental scale, i.e. northern and western Europe north of the Alps. We use redundancy analysis and variation partitioning to quantify the percentage of variation in vegetation composition explained by the climate and land-use variables, and Monte Carlo permutation tests to assess the statistical significance of each variable. We further use a similarity index to combine pollen based REVEALS estimates with climate-driven dynamic vegetation modelling results. The overall results indicate that climate is the major driver of vegetation when the Holocene is considered as a whole and at the sub-continental scale, although land use is important regionally. Four critical phases of land-use effects on vegetation are identified. The first phase (from 7000 to 6500 BP) corresponds to the early impacts on vegetation of farming and Neolithic forest clearance and to the dominance of climate as a driver of vegetation change. During the second phase (from 4500 to 4000 BP), land use becomes a major control of vegetation. Climate is still the principal driver, although its influence decreases gradually. The third phase (from 2000 to 1500 BP) is characterised by the continued role of climate on vegetation as a consequence of late-Holocene climate shifts and specific climate events that influence vegetation as well as land use. The last phase (from 500 to 350 BP) shows an acceleration of vegetation changes, in particular during the last century, caused by new farming practices and forestry in response to population growth and industrialization. This is a unique signature of anthropogenic impact within the Holocene but European vegetation remains climatically sensitive and thus may continue to respond to ongoing climate change. (C) 2017 Elsevier Ltd. All rights reserved.
KW  - Climate
KW  - Holocene
KW  - Human impact
KW  - Land use
KW  - LPJ-GUESS
KW  - Europe
KW  - Pollen
KW  - REVEALS
KW  - Vegetation composition
Y1  - 2017
U6  - https://doi.org/10.1016/j.quascirev.2017.07.001
SN  - 0277-3791
VL  - 171
SP  - 20
EP  - 37
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Bora, Sanjay Singh
A1  - Scherbaum, Frank
A1  - Kühn, Nicolas
A1  - Stafford, Peter
A1  - Edwards, Benjamin
T1  - Development of a Response Spectral Ground-Motion Prediction Equation (GMPE) for Seismic-Hazard Analysis from Empirical Fourier Spectral and Duration Models
JF  - Bulletin of the Seismological Society of America
N2  - Empirical ground-motion prediction equations (GMPEs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This article presents a holistic framework for the development of a response spectral GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain. The approach for developing a response spectral GMPE is unique, because it combines the predictions of empirical models for the two model components that characterize the spectral and temporal behavior of the ground motion. Essentially, as described in its initial form by Bora et al. (2014), the approach consists of an empirical model for the Fourier amplitude spectrum (FAS) and a model for the ground-motion duration. These two components are combined within the random vibration theory framework to obtain predictions of response spectral ordinates. In addition, FAS corresponding to individual acceleration records are extrapolated beyond the useable frequencies using the stochastic FAS model, obtained by inversion as described in Edwards and Fah (2013a). To that end, a (oscillator) frequency-dependent duration model, consistent with the empirical FAS model, is also derived. This makes it possible to generate a response spectral model that is easily adjustable to different sets of seismological parameters, such as the stress parameter Delta sigma, quality factor Q, and kappa kappa(0). The dataset used in Bora et al. (2014), a subset of the RESORCE-2012 database, is considered for the present analysis. Based upon the range of the predictor variables in the selected dataset, the present response spectral GMPE should be considered applicable over the magnitude range of 4 <= M-w <= 7.6 at distances <= 200 km.
Y1  - 2015
U6  - https://doi.org/10.1785/0120140297
SN  - 0037-1106
SN  - 1943-3573
VL  - 105
IS  - 4
SP  - 2192
EP  - 2218
PB  - Seismological Society of America
CY  - Albany
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  -