TY  - JOUR
A1  - Bousquet, J.
A1  - Farrell, J.
A1  - Crooks, G.
A1  - Hellings, P.
A1  - Bel, E. H.
A1  - Bewick, M.
A1  - Chavannes, N. H.
A1  - Correia de Sousa, J.
A1  - Cruz, A. A.
A1  - Haahtela, T.
A1  - Joos, G.
A1  - Khaltaev, N.
A1  - Malva, J.
A1  - Muraro, A.
A1  - Nogues, M.
A1  - Palkonen, S.
A1  - Pedersen, S.
A1  - Robalo-Cordeiro, C.
A1  - Samolinski, B.
A1  - Strandberg, T.
A1  - Valiulis, A.
A1  - Yorgancioglu, A.
A1  - Zuberbier, T.
A1  - Bedbrook, A.
A1  - Aberer, W.
A1  - Adachi, M.
A1  - Agusti, A.
A1  - Akdis, C. A.
A1  - Akdis, M.
A1  - Ankri, J.
A1  - Alonso, A.
A1  - Annesi-Maesano, I.
A1  - Ansotegui, I. J.
A1  - Anto, J. M.
A1  - Arnavielhe, S.
A1  - Arshad, H.
A1  - Bai, C.
A1  - Baiardini, I.
A1  - Bachert, C.
A1  - Baigenzhin, A. K.
A1  - Barbara, C.
A1  - Bateman, E. D.
A1  - Beghé, B.
A1  - Ben Kheder, A.
A1  - Bennoor, K. S.
A1  - Benson, M.
A1  - Bergmann, K. C.
A1  - Bieber, T.
A1  - Bindslev-Jensen, C.
A1  - Bjermer, L.
A1  - Blain, H.
A1  - Blasi, F.
A1  - Boner, A. L.
A1  - Bonini, M.
A1  - Bonini, S.
A1  - Bosnic-Anticevitch, S.
A1  - Boulet, L. P.
A1  - Bourret, R.
A1  - Bousquet, P. J.
A1  - Braido, F.
A1  - Briggs, A. H.
A1  - Brightling, C. E.
A1  - Brozek, J.
A1  - Buhl, R.
A1  - Burney, P. G.
A1  - Bush, A.
A1  - Caballero-Fonseca, F.
A1  - Caimmi, D.
A1  - Calderon, M. A.
A1  - Calverley, P. M.
A1  - Camargos, P. A. M.
A1  - Canonica, G. W.
A1  - Camuzat, T.
A1  - Carlsen, K. H.
A1  - Carr, W.
A1  - Carriazo, A.
A1  - Casale, T.
A1  - Cepeda Sarabia, A. M.
A1  - Chatzi, L.
A1  - Chen, Y. Z.
A1  - Chiron, R.
A1  - Chkhartishvili, E.
A1  - Chuchalin, A. G.
A1  - Chung, K. F.
A1  - Ciprandi, G.
A1  - Cirule, I.
A1  - Cox, L.
A1  - Costa, D. J.
A1  - Custovic, A.
A1  - Dahl, R.
A1  - Dahlen, S. E.
A1  - Darsow, U.
A1  - De Carlo, G.
A1  - De Blay, F.
A1  - Dedeu, T.
A1  - Deleanu, D.
A1  - De Manuel Keenoy, E.
A1  - Demoly, P.
A1  - Denburg, J. A.
A1  - Devillier, P.
A1  - Didier, A.
A1  - Dinh-Xuan, A. T.
A1  - Djukanovic, R.
A1  - Dokic, D.
A1  - Douagui, H.
A1  - Dray, G.
A1  - Dubakiene, R.
A1  - Durham, S. R.
A1  - Dykewicz, M. S.
A1  - El-Gamal, Y.
A1  - Emuzyte, R.
A1  - Fabbri, L. M.
A1  - Fletcher, M.
A1  - Fiocchi, A.
A1  - Fink Wagner, A.
A1  - Fonseca, J.
A1  - Fokkens, W. J.
A1  - Forastiere, F.
A1  - Frith, P.
A1  - Gaga, M.
A1  - Gamkrelidze, A.
A1  - Garces, J.
A1  - Garcia-Aymerich, J.
A1  - Gemicioğlu, B.
A1  - Gereda, J. E.
A1  - González Diaz, S.
A1  - Gotua, M.
A1  - Grisle, I.
A1  - Grouse, L.
A1  - Gutter, Z.
A1  - Guzmán, M. A.
A1  - Heaney, L. G.
A1  - Hellquist-Dahl, B.
A1  - Henderson, D.
A1  - Hendry, A.
A1  - Heinrich, J.
A1  - Heve, D.
A1  - Horak, F.
A1  - Hourihane, J. O’. B.
A1  - Howarth, P.
A1  - Humbert, M.
A1  - Hyland, M. E.
A1  - Illario, M.
A1  - Ivancevich, J. C.
A1  - Jardim, J. R.
A1  - Jares, E. J.
A1  - Jeandel, C.
A1  - Jenkins, C.
A1  - Johnston, S. L.
A1  - Jonquet, O.
A1  - Julge, K.
A1  - Jung, K. S.
A1  - Just, J.
A1  - Kaidashev, I.
A1  - Kaitov, M. R.
A1  - Kalayci, O.
A1  - Kalyoncu, A. F.
A1  - Keil, T.
A1  - Keith, P. K.
A1  - Klimek, L.
A1  - Koffi N’Goran, B.
A1  - Kolek, V.
A1  - Koppelman, G. H.
A1  - Kowalski, M. L.
A1  - Kull, I.
A1  - Kuna, P.
A1  - Kvedariene, V.
A1  - Lambrecht, B.
A1  - Lau, S.
A1  - Larenas‑Linnemann, D.
A1  - Laune, D.
A1  - Le, L. T. T.
A1  - Lieberman, P.
A1  - Lipworth, B.
A1  - Li, J.
A1  - Lodrup Carlsen, K.
A1  - Louis, R.
A1  - MacNee, W.
A1  - Magard, Y.
A1  - Magnan, A.
A1  - Mahboub, B.
A1  - Mair, A.
A1  - Majer, I.
A1  - Makela, M. J.
A1  - Manning, P.
A1  - Mara, S.
A1  - Marshall, G. D.
A1  - Masjedi, M. R.
A1  - Matignon, P.
A1  - Maurer, M.
A1  - Mavale‑Manuel, S.
A1  - Melén, E.
A1  - Melo‑Gomes, E.
A1  - Meltzer, E. O.
A1  - Menzies‑Gow, A.
A1  - Merk, H.
A1  - Michel, J. P.
A1  - Miculinic, N.
A1  - Mihaltan, F.
A1  - Milenkovic, B.
A1  - Mohammad, G. M. Y.
A1  - Molimard, M.
A1  - Momas, I.
A1  - Montilla‑Santana, A.
A1  - Morais‑Almeida, M.
A1  - Morgan, M.
A1  - Mösges, R.
A1  - Mullol, J.
A1  - Nafti, S.
A1  - Namazova‑Baranova, L.
A1  - Naclerio, R.
A1  - Neou, A.
A1  - Neffen, H.
A1  - Nekam, K.
A1  - Niggemann, B.
A1  - Ninot, G.
A1  - Nyembue, T. D.
A1  - O’Hehir, R. E.
A1  - Ohta, K.
A1  - Okamoto, Y.
A1  - Okubo, K.
A1  - Ouedraogo, S.
A1  - Paggiaro, P.
A1  - Pali‑Schöll, I.
A1  - Panzner, P.
A1  - Papadopoulos, N.
A1  - Papi, A.
A1  - Park, H. S.
A1  - Passalacqua, G.
A1  - Pavord, I.
A1  - Pawankar, R.
A1  - Pengelly, R.
A1  - Pfaar, O.
A1  - Picard, R.
A1  - Pigearias, B.
A1  - Pin, I.
A1  - Plavec, D.
A1  - Poethig, D.
A1  - Pohl, W.
A1  - Popov, T. A.
A1  - Portejoie, F.
A1  - Potter, P.
A1  - Postma, D.
A1  - Price, D.
A1  - Rabe, K. F.
A1  - Raciborski, F.
A1  - Radier Pontal, F.
A1  - Repka‑Ramirez, S.
A1  - Reitamo, S.
A1  - Rennard, S.
A1  - Rodenas, F.
A1  - Roberts, J.
A1  - Roca, J.
A1  - Rodriguez Mañas, L.
A1  - et al, 
T1  - Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5)
JF  - Clinical and Translational Allergy
N2  - Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) focuses on the integrated care of chronic diseases. Area 5 (Care Pathways) was initiated using chronic respiratory diseases as a model. The chronic respiratory disease action plan includes (1) AIRWAYS integrated care pathways (ICPs), (2) the joint initiative between the Reference site MACVIA-LR (Contre les MAladies Chroniques pour un VIeillissement Actif) and ARIA (Allergic Rhinitis and its Impact on Asthma), (3) Commitments for Action to the European Innovation Partnership on Active and Healthy Ageing and the AIRWAYS ICPs network. It is deployed in collaboration with the World Health Organization Global Alliance against Chronic Respiratory Diseases (GARD). The European Innovation Partnership on Active and Healthy Ageing has proposed a 5-step framework for developing an individual scaling up strategy: (1) what to scale up: (1-a) databases of good practices, (1-b) assessment of viability of the scaling up of good practices, (1-c) classification of good practices for local replication and (2) how to scale up: (2-a) facilitating partnerships for scaling up, (2-b) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. This strategy has already been applied to the chronic respiratory disease action plan of the European Innovation Partnership on Active and Healthy Ageing.
KW  - EIP on AHA
KW  - European Innovation Partnership on Active and Healthy Ageing
KW  - AIRWAYS ICPs
KW  - MACVIA
KW  - Scaling up
KW  - Chronic respiratory diseases
KW  - ARIA
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-166874
VL  - 6
IS  - 29
ER  - 
TY  - JOUR
A1  - Hudson, Lawrence N.
A1  - Newbold, Tim
A1  - Contu, Sara
A1  - Hill, Samantha L. L.
A1  - Lysenko, Igor
A1  - De Palma, Adriana
A1  - Phillips, Helen R. P.
A1  - Senior, Rebecca A.
A1  - Bennett, Dominic J.
A1  - Booth, Hollie
A1  - Choimes, Argyrios
A1  - Correia, David L. P.
A1  - Day, Julie
A1  - Echeverria-Londono, Susy
A1  - Garon, Morgan
A1  - Harrison, Michelle L. K.
A1  - Ingram, Daniel J.
A1  - Jung, Martin
A1  - Kemp, Victoria
A1  - Kirkpatrick, Lucinda
A1  - Martin, Callum D.
A1  - Pan, Yuan
A1  - White, Hannah J.
A1  - Aben, Job
A1  - Abrahamczyk, Stefan
A1  - Adum, Gilbert B.
A1  - Aguilar-Barquero, Virginia
A1  - Aizen, Marcelo
A1  - Ancrenaz, Marc
A1  - Arbelaez-Cortes, Enrique
A1  - Armbrecht, Inge
A1  - Azhar, Badrul
A1  - Azpiroz, Adrian B.
A1  - Baeten, Lander
A1  - Báldi, András
A1  - Banks, John E.
A1  - Barlow, Jos
A1  - Batáry, Péter
A1  - Bates, Adam J.
A1  - Bayne, Erin M.
A1  - Beja, Pedro
A1  - Berg, Ake
A1  - Berry, Nicholas J.
A1  - Bicknell, Jake E.
A1  - Bihn, Jochen H.
A1  - Böhning-Gaese, Katrin
A1  - Boekhout, Teun
A1  - Boutin, Celine
A1  - Bouyer, Jeremy
A1  - Brearley, Francis Q.
A1  - Brito, Isabel
A1  - Brunet, Jörg
A1  - Buczkowski, Grzegorz
A1  - Buscardo, Erika
A1  - Cabra-Garcia, Jimmy
A1  - Calvino-Cancela, Maria
A1  - Cameron, Sydney A.
A1  - Cancello, Eliana M.
A1  - Carrijo, Tiago F.
A1  - Carvalho, Anelena L.
A1  - Castro, Helena
A1  - Castro-Luna, Alejandro A.
A1  - Cerda, Rolando
A1  - Cerezo, Alexis
A1  - Chauvat, Matthieu
A1  - Clarke, Frank M.
A1  - Cleary, Daniel F. R.
A1  - Connop, Stuart P.
A1  - D'Aniello, Biagio
A1  - da Silva, Pedro Giovani
A1  - Darvill, Ben
A1  - Dauber, Jens
A1  - Dejean, Alain
A1  - Diekötter, Tim
A1  - Dominguez-Haydar, Yamileth
A1  - Dormann, Carsten F.
A1  - Dumont, Bertrand
A1  - Dures, Simon G.
A1  - Dynesius, Mats
A1  - Edenius, Lars
A1  - Elek, Zoltán
A1  - Entling, Martin H.
A1  - Farwig, Nina
A1  - Fayle, Tom M.
A1  - Felicioli, Antonio
A1  - Felton, Annika M.
A1  - Ficetola, Gentile F.
A1  - Filgueiras, Bruno K. C.
A1  - Fonte, Steve J.
A1  - Fraser, Lauchlan H.
A1  - Fukuda, Daisuke
A1  - Furlani, Dario
A1  - Ganzhorn, Jörg U.
A1  - Garden, Jenni G.
A1  - Gheler-Costa, Carla
A1  - Giordani, Paolo
A1  - Giordano, Simonetta
A1  - Gottschalk, Marco S.
A1  - Goulson, Dave
A1  - Gove, Aaron D.
A1  - Grogan, James
A1  - Hanley, Mick E.
A1  - Hanson, Thor
A1  - Hashim, Nor R.
A1  - Hawes, Joseph E.
A1  - Hébert, Christian
A1  - Helden, Alvin J.
A1  - Henden, John-André
A1  - Hernández, Lionel
A1  - Herzog, Felix
A1  - Higuera-Diaz, Diego
A1  - Hilje, Branko
A1  - Horgan, Finbarr G.
A1  - Horváth, Roland
A1  - Hylander, Kristoffer
A1  - Horváth, Roland
A1  - Isaacs-Cubides, Paola
A1  - Ishitani, Mashiro
A1  - Jacobs, Carmen T.
A1  - Jaramillo, Victor J.
A1  - Jauker, Birgit
A1  - Jonsell, Matts
A1  - Jung, Thomas S.
A1  - Kapoor, Vena
A1  - Kati, Vassiliki
A1  - Katovai, Eric
A1  - Kessler, Michael
A1  - Knop, Eva
A1  - Kolb, Annette
A1  - Körösi, Àdám
A1  - Lachat, Thibault
A1  - Lantschner, Victoria
A1  - Le Féon, Violette
A1  - LeBuhn, Gretchen
A1  - Légaré, Jean-Philippe
A1  - Letcher, Susan G.
A1  - Littlewood, Nick A.
A1  - López-Quintero, Carlos A.
A1  - Louhaichi, Mounir
A1  - Lövei, Gabor L.
A1  - Lucas-Borja, Manuel Esteban
A1  - Luja, Victor H.
A1  - Maeto, Kaoru
A1  - Magura, Tibor
A1  - Mallari, Neil Aldrin
A1  - Marin-Spiotta, Erika
A1  - Marhall, E. J. P.
A1  - Martínez, Eliana
A1  - Mayfield, Margaret M.
A1  - Mikusinski, Gregorz
A1  - Milder, Jeffery C.
A1  - Miller, James R.
A1  - Morales, Carolina L.
A1  - Muchane, Mary N.
A1  - Muchane, Muchai
A1  - Naidoo, Robin
A1  - Nakamura, Akihiro
A1  - Naoe, Shoji
A1  - Nates-Parra, Guiomar
A1  - Navarerete Gutierrez, Dario A.
A1  - Neuschulz, Eike L.
A1  - Noreika, Norbertas
A1  - Norfolk, Olivia
A1  - Noriega, Jorge Ari
A1  - Nöske, Nicole M.
A1  - O'Dea, Niall
A1  - Oduro, William
A1  - Ofori-Boateng, Caleb
A1  - Oke, Chris O.
A1  - Osgathorpe, Lynne M.
A1  - Paritsis, Juan
A1  - Parrah, Alejandro
A1  - Pelegrin, Nicolás
A1  - Peres, Carlos A.
A1  - Persson, Anna S.
A1  - Petanidou, Theodora
A1  - Phalan, Ben
A1  - Philips, T. Keith
A1  - Poveda, Katja
A1  - Power, Eileen F.
A1  - Presley, Steven J.
A1  - Proença, Vânia
A1  - Quaranta, Marino
A1  - Quintero, Carolina
A1  - Redpath-Downing, Nicola A.
A1  - Reid, J. Leighton
A1  - Reis, Yana T.
A1  - Ribeiro, Danilo B.
A1  - Richardson, Barbara A.
A1  - Richardson, Michael J.
A1  - Robles, Carolina A.
A1  - Römbke, Jörg
A1  - Romero-Duque, Luz Piedad
A1  - Rosselli, Loreta
A1  - Rossiter, Stephen J.
A1  - Roulston, T'ai H.
A1  - Rousseau, Laurent
A1  - Sadler, Jonathan P.
A1  - Sáfián, Szbolcs
A1  - Saldaña-Vásquez, Romeo A.
A1  - Samnegård, Ulrika
A1  - Schüepp, Christof
A1  - Schweiger, Oliver
A1  - Sedlock, Jodi L.
A1  - Shahabuddin, Ghazala
A1  - Sheil, Douglas
A1  - Silva, Fernando A. B.
A1  - Slade, Eleanor
A1  - Smith-Pardo, Allan H.
A1  - Sodhi, Navjot S.
A1  - Somarriba, Eduardo J.
A1  - Sosa, Ramón A.
A1  - Stout, Jane C.
A1  - Struebig, Matthew J.
A1  - Sung, Yik-Hei
A1  - Threlfall, Caragh G.
A1  - Tonietto, Rebecca
A1  - Tóthmérész, Béla
A1  - Tscharntke, Teja
A1  - Turner, Edgar C.
A1  - Tylianakis, Jason M.
A1  - Vanbergen, Adam J.
A1  - Vassilev, Kiril
A1  - Verboven, Hans A. F.
A1  - Vergara, Carlos H.
A1  - Vergara, Pablo M.
A1  - Verhulst, Jort
A1  - Walker, Tony R.
A1  - Wang, Yanping
A1  - Watling, James I.
A1  - Wells, Konstans
A1  - Williams, Christopher D.
A1  - Willig, Michael R.
A1  - Woinarski, John C. Z.
A1  - Wolf, Jan H. D.
A1  - Woodcock, Ben A.
A1  - Yu, Douglas W.
A1  - Zailsev, Andreys
A1  - Collen, Ben
A1  - Ewers, Rob M.
A1  - Mace, Georgina M.
A1  - Purves, Drew W.
A1  - Scharlemann, Jörn P. W.
A1  - Pervis, Andy
T1  - The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts
JF  - Ecology and Evolution
N2  - Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.
KW  - urban-rural gradient
KW  - instensively managed farmland
KW  - Mexican coffee plantations
KW  - Bombus Spp. Hymenoptera
KW  - bumblebee nest density
KW  - data sharing
KW  - land use
KW  - habitat destruction
KW  - global change
KW  - land-use change
KW  - plant community composition
KW  - Northeastern Costa Rica
KW  - dung beetle coleoptera
KW  - bird species richness
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-114425
VL  - 4
IS  - 24
ER  - 
TY  - JOUR
A1  - Davis, Lea K.
A1  - Yu, Dongmei
A1  - Keenan, Clare L.
A1  - Gamazon, Eric R.
A1  - Konkashbaev, Anuar I.
A1  - Derks, Eske M.
A1  - Neale, Benjamin M.
A1  - Yang, Jian
A1  - Lee, S. Hong
A1  - Evans, Patrick
A1  - Barr, Cathy L.
A1  - Bellodi, Laura
A1  - Benarroch, Fortu
A1  - Berrio, Gabriel Bedoya
A1  - Bienvenu, Oscar J.
A1  - Bloch, Michael H.
A1  - Blom, Rianne M.
A1  - Bruun, Ruth D.
A1  - Budman, Cathy L.
A1  - Camarena, Beatriz
A1  - Campbell, Desmond
A1  - Cappi, Carolina
A1  - Cardona Silgado, Julio C.
A1  - Cath, Danielle C.
A1  - Cavallini, Maria C.
A1  - Chavira, Denise A.
A1  - Chouinard, Sylvian
A1  - Conti, David V.
A1  - Cook, Edwin H.
A1  - Coric, Vladimir
A1  - Cullen, Bernadette A.
A1  - Deforce, Dieter
A1  - Delorme, Richard
A1  - Dion, Yves
A1  - Edlund, Christopher K.
A1  - Egberts, Karin
A1  - Falkai, Peter
A1  - Fernandez, Thomas V.
A1  - Gallagher, Patience J.
A1  - Garrido, Helena
A1  - Geller, Daniel
A1  - Girard, Simon L.
A1  - Grabe, Hans J.
A1  - Grados, Marco A.
A1  - Greenberg, Benjamin D.
A1  - Gross-Tsur, Varda
A1  - Haddad, Stephen
A1  - Heiman, Gary A.
A1  - Hemmings, Sian M. J.
A1  - Hounie, Ana G.
A1  - Illmann, Cornelia
A1  - Jankovic, Joseph
A1  - Jenike, Micheal A.
A1  - Kennedy, James L.
A1  - King, Robert A.
A1  - Kremeyer, Barbara
A1  - Kurlan, Roger
A1  - Lanzagorta, Nuria
A1  - Leboyer, Marion
A1  - Leckman, James F.
A1  - Lennertz, Leonhard
A1  - Liu, Chunyu
A1  - Lochner, Christine
A1  - Lowe, Thomas L.
A1  - Macciardi, Fabio
A1  - McCracken, James T.
A1  - McGrath, Lauren M.
A1  - Restrepo, Sandra C. Mesa
A1  - Moessner, Rainald
A1  - Morgan, Jubel
A1  - Muller, Heike
A1  - Murphy, Dennis L.
A1  - Naarden, Allan L.
A1  - Ochoa, William Cornejo
A1  - Ophoff, Roel A.
A1  - Osiecki, Lisa
A1  - Pakstis, Andrew J.
A1  - Pato, Michele T.
A1  - Pato, Carlos N.
A1  - Piacentini, John
A1  - Pittenger, Christopher
A1  - Pollak, Yehunda
A1  - Rauch, Scott L.
A1  - Renner, Tobias J.
A1  - Reus, Victor I.
A1  - Richter, Margaret A.
A1  - Riddle, Mark A.
A1  - Robertson, Mary M.
A1  - Romero, Roxana
A1  - Rosàrio, Maria C.
A1  - Rosenberg, David
A1  - Rouleau, Guy A.
A1  - Ruhrmann, Stephan
A1  - Ruiz-Linares, Andreas
A1  - Sampaio, Aline S.
A1  - Samuels, Jack
A1  - Sandor, Paul
A1  - Sheppard, Broke
A1  - Singer, Harvey S.
A1  - Smit, Jan H.
A1  - Stein, Dan J.
A1  - Strengman, E.
A1  - Tischfield, Jay A.
A1  - Valencia Duarte, Ana V.
A1  - Vallada, Homero
A1  - Van Nieuwerburgh, Flip
A1  - Veenstra-VanderWeele, Jeremy
A1  - Walitza, Susanne
A1  - Wang, Ying
A1  - Wendland, Jens R.
A1  - Westenberg, Herman G. M.
A1  - Shugart, Yin Yao
A1  - Miguel, Euripedes C.
A1  - McMahon, William
A1  - Wagner, Michael
A1  - Nicolini, Humberto
A1  - Posthuma, Danielle
A1  - Hanna, Gregory L.
A1  - Heutink, Peter
A1  - Denys, Damiaan
A1  - Arnold, Paul D.
A1  - Oostra, Ben A.
A1  - Nestadt, Gerald
A1  - Freimer, Nelson B.
A1  - Pauls, David L.
A1  - Wray, Naomi R.
A1  - Stewart, S. Evelyn
A1  - Mathews, Carol A.
A1  - Knowles, James A.
A1  - Cox, Nancy J.
A1  - Scharf, Jeremiah M.
T1  - Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture
JF  - PLoS Genetics
N2  - The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.
KW  - TIC disorders
KW  - missing heritability
KW  - complex diseases
KW  - neuropsychiatric disorders
KW  - common SNPS
KW  - gilles
KW  - family
KW  - brain
KW  - expression
KW  - autism
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-127377
SN  - 1553-7390
VL  - 9
IS  - 10
ER  - 
TY  - JOUR
A1  - Lindert, J. M.
A1  - Pozzorini, S.
A1  - Boughezal, R.
A1  - Campbell, J. M.
A1  - Denner, A.
A1  - Dittmaier, S.
A1  - Gehrmann-De Ridder, A.
A1  - Gehrmann, T.
A1  - Glover, N.
A1  - Huss, A.
A1  - Kallweit, S.
A1  - Maierhöfer, P.
A1  - Mangano, M. L.
A1  - Morgan, T. A.
A1  - Mück, A.
A1  - Petriello, F.
A1  - Salam, G. P.
A1  - Schönherr, M.
A1  - Williams, C.
T1  - Precise predictions for \(V+\)jets dark matter backgrounds
JF  - European Physical Journal C
N2  - High-energy jets recoiling against missing transverse energy (MET) are powerful probes of dark matter at the LHC. Searches based on large MET signatures require a precise control of the \({Z(ν\overline{ν})}+\) jet background in the signal region. This can be achieved by taking accurate data in control regions dominated by \(Z(ℓ^+ℓ^−)+\) jet, \(W(ℓν)+\) jet and \(γ+\) jet production, and extrapolating to the \({Z(ν\overline{ν})}+\) jet background by means of precise theoretical predictions. In this context, recent advances in perturbative calculations open the door to significant sensitivity improvements in dark matter searches. In this spirit, we present a combination of state-of-the-art calculations for all relevant \(V+\) jets processes, including throughout NNLO QCD corrections and NLO electroweak corrections supplemented by Sudakov logarithms at two loops. Predictions at parton level are provided together with detailed recommendations for their usage in experimental analyses based on the reweighting of Monte Carlo samples. Particular attention is devoted to the estimate of theoretical uncertainties in the framework of dark matter searches, where subtle aspects such as correlations across different \(V+\) jet processes play a key role. The anticipated theoretical uncertainty in the \({Z(ν\overline{ν})}+\) jet background is at the few percent level up to the TeV range.
KW  - Physics
KW  - High energy physics
KW  - High-energy jets
KW  - Dark matter
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172555
VL  - 77
ER  - 
TY  - JOUR
A1  - Mitchell, Jonathan S.
A1  - Li, Ni
A1  - Weinhold, Niels
A1  - Försti, Asta
A1  - Ali, Mina
A1  - van Duin, Mark
A1  - Thorleifsson, Gudmar
A1  - Johnson, David C.
A1  - Chen, Bowang
A1  - Halvarsson, Britt-Marie
A1  - Gudbjartsson, Daniel F.
A1  - Kuiper, Rowan
A1  - Stephens, Owen W.
A1  - Bertsch, Uta
A1  - Broderick, Peter
A1  - Campo, Chiara
A1  - Einsele, Hermann
A1  - Gregory, Walter A.
A1  - Gullberg, Urban
A1  - Henrion, Marc
A1  - Hillengass, Jens
A1  - Hoffmann, Per
A1  - Jackson, Graham H.
A1  - Johnsson, Ellinor
A1  - Jöud, Magnus
A1  - Kristinsson, Sigurdur Y.
A1  - Lenhoff, Stig
A1  - Lenive, Oleg
A1  - Mellqvist, Ulf-Henrik
A1  - Migliorini, Gabriele
A1  - Nahi, Hareth
A1  - Nelander, Sven
A1  - Nickel, Jolanta
A1  - Nöthen, Markus M.
A1  - Rafnar, Thorunn
A1  - Ross, Fiona M.
A1  - da Silva Filho, Miguel Inacio
A1  - Swaminathan, Bhairavi
A1  - Thomsen, Hauke
A1  - Turesson, Ingemar
A1  - Vangsted, Annette
A1  - Vogel, Ulla
A1  - Waage, Anders
A1  - Walker, Brian A.
A1  - Wihlborg, Anna-Karin
A1  - Broyl, Annemiek
A1  - Davies, Faith E.
A1  - Thorsteinsdottir, Unnur
A1  - Langer, Christian
A1  - Hansson, Markus
A1  - Kaiser, Martin
A1  - Sonneveld, Pieter
A1  - Stefansson, Kari
A1  - Morgan, Gareth J.
A1  - Goldschmidt, Hartmut
A1  - Hemminki, Kari
A1  - Nilsson, Björn
A1  - Houlston, Richard S.
T1  - Genome-wide association study identifies multiple susceptibility loci for multiple myeloma
JF  - Nature Communications
N2  - Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls. We confirm all nine known risk loci and discover eight new loci at 6p22.3 (rs34229995, P=1.31 × 10−8), 6q21 (rs9372120, P=9.09 × 10−15), 7q36.1 (rs7781265, P=9.71 × 10−9), 8q24.21 (rs1948915, P=4.20 × 10−11), 9p21.3 (rs2811710, P=1.72 × 10−13), 10p12.1 (rs2790457, P=1.77 × 10−8), 16q23.1 (rs7193541, P=5.00 × 10−12) and 20q13.13 (rs6066835, P=1.36 × 10−13), which localize in or near to JARID2, ATG5, SMARCD3, CCAT1, CDKN2A, WAC, RFWD3 and PREX1. These findings provide additional support for a polygenic model of MM and insight into the biological basis of tumour development.
KW  - Cancer genetics
KW  - Genome-wide association studies
KW  - Myeloma
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-165983
VL  - 7
ER  - 
TY  - JOUR
A1  - van de Donk, Niels W. C. J.
A1  - Palumbo, Antonio
A1  - Johnsen, Hans Erik
A1  - Engelhardt, Monika
A1  - Gay, Francesca
A1  - Gregersen, Henrik
A1  - Hajek, Roman
A1  - Kleber, Martina
A1  - Ludwig, Heinz
A1  - Morgan, Gareth
A1  - Musto, Pellegrino
A1  - Plesner, Torben
A1  - Sezer, Orhan
A1  - Terpos, Evangelos
A1  - Waage, Anders
A1  - Zweegman, Sonja
A1  - Einsele, Hermann
A1  - Sonneveld, Pieter
A1  - Lokhorst, Henk M.
T1  - The clinical relevance and management of monoclonal gammopathy of undetermined significance and related disorders: recommendations from the European Myeloma Network
JF  - Haematologica
N2  - Monoclonal gammopathy of undetermined significance is one of the most common pre-malignant disorders. IgG and IgA monoclonal gammopathy of undetermined significance are precursor conditions of multiple myeloma; light-chain monoclonal gammopathy of undetermined significance of light-chain multiple myeloma; and IgM monoclonal gammopathy of undetermined significance of Waldenstrom's macroglobulinemia and other lymphoproliferative disorders. Clonal burden, as determined by bone marrow plasma cell percentage or M-protein level, as well as biological characteristics, including heavy chain isotype and light chain production, are helpful in predicting risk of progression of monoclonal gammopathy of undetermined significance to symptomatic disease. Furthermore, alterations in the bone marrow microenvironment of monoclonal gammopathy of undetermined significance patients result in an increased risk of venous and arterial thrombosis, infections, osteoporosis, and bone fractures. In addition, the small clone may occasionally be responsible for severe organ damage through the production of a monoclonal protein that has autoantibody activity or deposits in tissues. These disorders are rare and often require therapy directed at eradication of the underlying plasma cell or lymphoplasmacytic clone. In this review, we provide an overview of the clinical relevance of monoclonal gammopathy of undetermined significance. We also give general recommendations of how to diagnose and manage patients with monoclonal gammopathy of undetermined significance.
KW  - multiparameter flow-cytometry
KW  - hematopoietic cell transplantation
KW  - smoldering multiple-myeloma
KW  - venous thromboembolic disease
KW  - bone-mineral density
KW  - population-based cohort
KW  - term-follow-up
KW  - marrow plasma cells
KW  - significance MGUS
KW  - malignant transformation
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-116050
SN  - 0390-6078
VL  - 99
IS  - 6
ER  - 
TY  - JOUR
A1  - Kurrek, Matt M.
A1  - Morgan, Pamela
A1  - Howard, Steven
A1  - Kranke, Peter
A1  - Calhoun, Aaron
A1  - Hui, Joshua
A1  - Kiss, Alex
T1  - Simulation as a New Tool to Establish Benchmark Outcome Measures in Obstetrics
JF  - PLoS ONE
N2  - Background 
There are not enough clinical data from rare critical events to calculate statistics to decide if the management of actual events might be below what could reasonably be expected (i.e. was an outlier). 

Objectives 
In this project we used simulation to describe the distribution of management times as an approach to decide if the management of a simulated obstetrical crisis scenario could be considered an outlier. 

Design 
Twelve obstetrical teams managed 4 scenarios that were previously developed. Relevant outcome variables were defined by expert consensus. The distribution of the response times from the teams who performed the respective intervention was graphically displayed and median and quartiles calculated using rank order statistics. 

Results 
Only 7 of the 12 teams performed chest compressions during the arrest following the 'cannot intubate/cannot ventilate' scenario. All other outcome measures were performed by at least 11 of the 12 teams. Calculation of medians and quartiles with 95% CI was possible for all outcomes. Confidence intervals, given the small sample size, were large. 

Conclusion 
We demonstrated the use of simulation to calculate quantiles for management times of critical event. This approach could assist in deciding if a given performance could be considered normal and also point to aspects of care that seem to pose particular challenges as evidenced by a large number of teams not performing the expected maneuver. However sufficiently large sample sizes (i.e. from a national data base) will be required to calculate acceptable confidence intervals and to establish actual tolerance limits.
KW  - performance
KW  - anesthesiologists
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151646
VL  - 10
IS  - 6
ER  -