TY  - JOUR
A1  - Fages, Antoine
A1  - Hanghoj, Kristian
A1  - Khan, Naveed
A1  - Gaunitz, Charleen
A1  - Seguin-Orlando, Andaine
A1  - Leonardi, Michela
A1  - Constantz, Christian McCrory
A1  - Gamba, Cristina
A1  - Al-Rasheid, Khaled A. S.
A1  - Albizuri, Silvia
A1  - Alfarhan, Ahmed H.
A1  - Allentoft, Morten
A1  - Alquraishi, Saleh
A1  - Anthony, David
A1  - Baimukhanov, Nurbol
A1  - Barrett, James H.
A1  - Bayarsaikhan, Jamsranjav
A1  - Benecke, Norbert
A1  - Bernaldez-Sanchez, Eloisa
A1  - Berrocal-Rangel, Luis
A1  - Biglari, Fereidoun
A1  - Boessenkool, Sanne
A1  - Boldgiv, Bazartseren
A1  - Brem, Gottfried
A1  - Brown, Dorcas
A1  - Burger, Joachim
A1  - Crubezy, Eric
A1  - Daugnora, Linas
A1  - Davoudi, Hossein
A1  - Damgaard, Peter de Barros
A1  - de Chorro y de Villa-Ceballos, Maria de los Angeles
A1  - Deschler-Erb, Sabine
A1  - Detry, Cleia
A1  - Dill, Nadine
A1  - Oom, Maria do Mar
A1  - Dohr, Anna
A1  - Ellingvag, Sturla
A1  - Erdenebaatar, Diimaajav
A1  - Fathi, Homa
A1  - Felkel, Sabine
A1  - Fernandez-Rodriguez, Carlos
A1  - Garcia-Vinas, Esteban
A1  - Germonpre, Mietje
A1  - Granado, Jose D.
A1  - Hallsson, Jon H.
A1  - Hemmer, Helmut
A1  - Hofreiter, Michael
A1  - Kasparov, Aleksei
A1  - Khasanov, Mutalib
A1  - Khazaeli, Roya
A1  - Kosintsev, Pavel
A1  - Kristiansen, Kristian
A1  - Kubatbek, Tabaldiev
A1  - Kuderna, Lukas
A1  - Kuznetsov, Pavel
A1  - Laleh, Haeedeh
A1  - Leonard, Jennifer A.
A1  - Lhuillier, Johanna
A1  - von Lettow-Vorbeck, Corina Liesau
A1  - Logvin, Andrey
A1  - Lougas, Lembi
A1  - Ludwig, Arne
A1  - Luis, Cristina
A1  - Arruda, Ana Margarida
A1  - Marques-Bonet, Tomas
A1  - Silva, Raquel Matoso
A1  - Merz, Victor
A1  - Mijiddorj, Enkhbayar
A1  - Miller, Bryan K.
A1  - Monchalov, Oleg
A1  - Mohaseb, Fatemeh A.
A1  - Morales, Arturo
A1  - Nieto-Espinet, Ariadna
A1  - Nistelberger, Heidi
A1  - Onar, Vedat
A1  - Palsdottir, Albina H.
A1  - Pitulko, Vladimir
A1  - Pitskhelauri, Konstantin
A1  - Pruvost, Melanie
A1  - Sikanjic, Petra Rajic
A1  - Papesa, Anita Rapan
A1  - Roslyakova, Natalia
A1  - Sardari, Alireza
A1  - Sauer, Eberhard
A1  - Schafberg, Renate
A1  - Scheu, Amelie
A1  - Schibler, Jorg
A1  - Schlumbaum, Angela
A1  - Serrand, Nathalie
A1  - Serres-Armero, Aitor
A1  - Shapiro, Beth
A1  - Seno, Shiva Sheikhi
A1  - Shevnina, Irina
A1  - Shidrang, Sonia
A1  - Southon, John
A1  - Star, Bastiaan
A1  - Sykes, Naomi
A1  - Taheri, Kamal
A1  - Taylor, William
A1  - Teegen, Wolf-Rudiger
A1  - Vukicevic, Tajana Trbojevic
A1  - Trixl, Simon
A1  - Tumen, Dashzeveg
A1  - Undrakhbold, Sainbileg
A1  - Usmanova, Emma
A1  - Vahdati, Ali
A1  - Valenzuela-Lamas, Silvia
A1  - Viegas, Catarina
A1  - Wallner, Barbara
A1  - Weinstock, Jaco
A1  - Zaibert, Victor
A1  - Clavel, Benoit
A1  - Lepetz, Sebastien
A1  - Mashkour, Marjan
A1  - Helgason, Agnar
A1  - Stefansson, Kari
A1  - Barrey, Eric
A1  - Willerslev, Eske
A1  - Outram, Alan K.
A1  - Librado, Pablo
A1  - Orlando, Ludovic
T1  - Tracking five millennia of horse management with extensive ancient genome time series
JF  - Cell
N2  - Horse domestication revolutionized warfare and accelerated travel, trade, and the geographic expansion of languages. Here, we present the largest DNA time series for a non-human organism to date, including genome-scale data from 149 ancient animals and 129 ancient genomes (>= 1-fold coverage), 87 of which are new. This extensive dataset allows us to assess the modem legacy of past equestrian civilisations. We find that two extinct horse lineages existed during early domestication, one at the far western (Iberia) and the other at the far eastern range (Siberia) of Eurasia. None of these contributed significantly to modern diversity. We show that the influence of Persian-related horse lineages increased following the Islamic conquests in Europe and Asia. Multiple alleles associated with elite-racing, including at the MSTN "speed gene," only rose in popularity within the last millennium. Finally, the development of modem breeding impacted genetic diversity more dramatically than the previous millennia of human management.
Y1  - 2019
U6  - https://doi.org/10.1016/j.cell.2019.03.049
SN  - 0092-8674
SN  - 1097-4172
VL  - 177
IS  - 6
SP  - 1419
EP  - 1435
PB  - Cell Press
CY  - Cambridge
ER  - 
TY  - JOUR
A1  - Kalkbrenner, T.
A1  - Hakanson, U.
A1  - Schadle, A.
A1  - Burger, S.
A1  - Henkel, Carsten
A1  - Sandoghdar, Vahid
T1  - Optical microscopy via spectral modifications of a nanoantenna
N2  - The existing optical microscopes form an image by collecting photons emitted from an object. Here we report on the experimental realization of microscopy without the need for direct optical communication with the sample. To achieve this, we have scanned a single gold nanoparticle acting as a nanoantenna in the near field of a sample and have studied the modification of its intrinsic radiative properties by monitoring its plasmon spectrum
Y1  - 2005
SN  - 0031-9007
ER  - 
TY  - JOUR
A1  - Bürger, Gerd
A1  - Sobie, S. R.
A1  - Cannon, A. J.
A1  - Werner, A. T.
A1  - Murdock, T. Q.
T1  - Downscaling extremes an intercomparison of multiple methods for future climate
JF  - Journal of climate
N2  - This study follows up on a previous downscaling intercomparison for present climate. Using a larger set of eight methods the authors downscale atmospheric fields representing present (1981-2000) and future (2046-65) conditions, as simulated by six global climate models following three emission scenarios. Local extremes were studied at 20 locations in British Columbia as measured by the same set of 27 indices, ClimDEX, as in the precursor study. Present and future simulations give 2 x 3 x 6 x 8 x 20 x 27 = 155 520 index climatologies whose analysis in terms of mean change and variation is the purpose of this study. The mean change generally reinforces what is to be expected in a warmer climate: that extreme cold events become less frequent and extreme warm events become more frequent, and that there are signs of more frequent precipitation extremes. There is considerable variation, however, about this tendency, caused by the influence of scenario, climate model, downscaling method, and location. This is analyzed using standard statistical techniques such as analysis of variance and multidimensional scaling, along with an assessment of the influence of each modeling component on the overall variation of the simulated change. It is found that downscaling generally has the strongest influence, followed by climate model; location and scenario have only a minor influence. The influence of downscaling could be traced back in part to various issues related to the methods, such as the quality of simulated variability or the dependence on predictors. Using only methods validated in the precursor study considerably reduced the influence of downscaling, underpinning the general need for method verification.
Y1  - 2013
U6  - https://doi.org/10.1175/JCLI-D-12-00249.1
SN  - 0894-8755
VL  - 26
IS  - 10
SP  - 3429
EP  - 3449
PB  - American Meteorological Soc.
CY  - Boston
ER  -