@article{WarringtonBeaumontHorikoshietal.2019,
  author    = {Warrington, Nicole and Beaumont, Robin and Horikoshi, Momoko and Day, Felix R. and Helgeland, {\O}yvind and Laurin, Charles and Bacelis, Jonas and Peng, Shouneng and Hao, Ke and Feenstra, Bjarke and Wood, Andrew R. and Mahajan, Anubha and Tyrrell, Jessica and Robertson, Neil R. and Rayner, N. William and Qiao, Zhen and Moen, Gunn-Helen and Vaudel, Marc and Marsit, Carmen and Chen, Jia and Nodzenski, Michael and Schnurr, Theresia M. and Zafarmand, Mohammad Hadi and Bradfield, Jonathan P. and Grarup, Niels and Kooijman, Marjolein N. and Li-Gao, Ruifang and Geller, Frank and Ahluwalia, Tarunveer Singh and Paternoster, Lavinia and Rueedi, Rico and Huikari, Ville and Hottenga, Jouke-Jan and Lyytik{\"a}inen, Leo-Pekka and Cavadino, Alana and Metrustry, Sarah and Cousminer, Diana L. and Wu, Ying and Thiering, Elisabeth Paula and Wang, Carol A. and Have, Christian Theil and Vilor-Tejedor, Natalia and Joshi, Peter K. and Painter, Jodie N. and Ntalla, Ioanna and Myhre, Ronny and Pitk{\"a}nen, Niina and van Leeuwen, Elisabeth M. and Joro, Raimo and Lagou, Vasiliki and Richmond, Rebecca C. and Espinosa, Ana and Barton, Sheila J. and Inskip, Hazel M. and Holloway, John W. and Santa-Marina, Loreto and Estivill, Xavier and Ang, Wei and Marsh, Julie A. and Reichetzeder, Christoph and Marullo, Letizia and Hocher, Berthold and Lunetta, Kathryn L. and Murabito, Joanne M. and Relton, Caroline L. and Kogevinas, Manolis and Chatzi, Leda and Allard, Catherine and Bouchard, Luigi and Hivert, Marie-France and Zhang, Ge and Muglia, Louis J. and Heikkinen, Jani and Morgen, Camilla S. and van Kampen, Antoine H. C. and van Schaik, Barbera D. C. and Mentch, Frank D. and Langenberg, Claudia and Scott, Robert A. and Zhao, Jing Hua and Hemani, Gibran and Ring, Susan M. and Bennett, Amanda J. and Gaulton, Kyle J. and Fernandez-Tajes, Juan and van Zuydam, Natalie R. and Medina-Gomez, Carolina and de Haan, Hugoline G. and Rosendaal, Frits R. and Kutalik, Zolt{\´a}n and Marques-Vidal, Pedro and Das, Shikta and Willemsen, Gonneke and Mbarek, Hamdi and M{\"u}ller-Nurasyid, Martina and Standl, Marie and Appel, Emil V. R. and Fonvig, Cilius Esmann and Trier, Caecilie and van Beijsterveldt, Catharina E. M. and Murcia, Mario and Bustamante, Mariona and Bon{\`a}s-Guarch, S{\´i}lvia and Hougaard, David M. and Mercader, Josep M. and Linneberg, Allan and Schraut, Katharina E. and Lind, Penelope A. and Medland, Sarah Elizabeth and Shields, Beverley M. and Knight, Bridget A. and Chai, Jin-Fang and Panoutsopoulou, Kalliope and Bartels, Meike and S{\´a}nchez, Friman and Stokholm, Jakob and Torrents, David and Vinding, Rebecca K. and Willems, Sara M. and Atalay, Mustafa and Chawes, Bo L. and Kovacs, Peter and Prokopenko, Inga and Tuke, Marcus A. and Yaghootkar, Hanieh and Ruth, Katherine S. and Jones, Samuel E. and Loh, Po-Ru and Murray, Anna and Weedon, Michael N. and T{\"o}njes, Anke and Stumvoll, Michael and Michaelsen, Kim Fleischer and Eloranta, Aino-Maija and Lakka, Timo A. and van Duijn, Cornelia M. and Kiess, Wieland and Koerner, Antje and Niinikoski, Harri and Pahkala, Katja and Raitakari, Olli T. and Jacobsson, Bo and Zeggini, Eleftheria and Dedoussis, George V. and Teo, Yik-Ying and Saw, Seang-Mei and Montgomery, Grant W. and Campbell, Harry and Wilson, James F. and Vrijkotte, Tanja G. M. and Vrijheid, Martine and de Geus, Eco J. C. N. and Hayes, M. Geoffrey and Kadarmideen, Haja N. and Holm, Jens-Christian and Beilin, Lawrence J. and Pennell, Craig E. and Heinrich, Joachim and Adair, Linda S. and Borja, Judith B. and Mohlke, Karen L. and Eriksson, Johan G. and Widen, Elisabeth E. and Hattersley, Andrew T. and Spector, Tim D. and Kaehoenen, Mika and Viikari, Jorma S. and Lehtimaeki, Terho and Boomsma, Dorret I. and Sebert, Sylvain and Vollenweider, Peter and Sorensen, Thorkild I. A. and Bisgaard, Hans and Bonnelykke, Klaus and Murray, Jeffrey C. and Melbye, Mads and Nohr, Ellen A. and Mook-Kanamori, Dennis O. and Rivadeneira, Fernando and Hofman, Albert and Felix, Janine F. and Jaddoe, Vincent W. V. and Hansen, Torben and Pisinger, Charlotta and Vaag, Allan A. and Pedersen, Oluf and Uitterlinden, Andre G. and Jarvelin, Marjo-Riitta and Power, Christine and Hypponen, Elina and Scholtens, Denise M. and Lowe, William L. and Smith, George Davey and Timpson, Nicholas J. and Morris, Andrew P. and Wareham, Nicholas J. and Hakonarson, Hakon and Grant, Struan F. A. and Frayling, Timothy M. and Lawlor, Debbie A. and Njolstad, Pal R. and Johansson, Stefan and Ong, Ken K. and McCarthy, Mark I. and Perry, John R. B. and Evans, David M. and Freathy, Rachel M.},
  title     = {Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors},
  series = {Nature genetics},
  volume    = {51},
  journal   = {Nature genetics},
  number    = {5},
  publisher = {Nature Publ. Group},
  address   = {New York},
  organization    = {EGG Consortium},
  issn      = {1061-4036},
  pages     = {804 -- +},
  year      = {2019},
  abstract  = {Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.},
  language  = {en}
}
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D. and Suric, T. and Sutcliffe, P. and Sykes, J. and Szanecki, M. and Szepieniec, T. and Szostek, A. and Tagliaferri, G. and Tajima, H. and Takahashi, H. and Takahashi, K. and Takalo, L. and Takami, H. and Talbot, C. and Tammi, J. and Tanaka, M. and Tanaka, S. and Tasan, J. and Tavani, M. and Tavernet, J. -P. and Tejedor, L. A. and Telezhinsky, Igor O. and Temnikov, P. and Tenzer, C. and Terada, Y. and Terrier, R. and Teshima, M. and Testa, V. and Tezier, D. and Thuermann, D. and Tibaldo, L. and Tibolla, O. and Tiengo, A. and Tluczykont, M. and Todero Peixoto, C. J. and Tokanai, F. and Tokarz, M. and Toma, K. and Torii, K. and Tornikoski, M. and Torres, D. F. and Torres, M. and Tosti, G. and Totani, T. and Toussenel, C. and Tovmassian, G. and Travnicek, P. and Trifoglio, M. and Troyano, I. and Tsinganos, K. and Ueno, H. and Umehara, K. and Upadhya, S. S. and Usher, T. and Uslenghi, M. and Valdes-Galicia, J. F. and Vallania, P. and Vallejo, G. and van Driel, W. and van Eldik, C. and Vandenbrouke, J. and Vanderwalt, J. and Vankov, H. and Vasileiadis, G. and Vassiliev, V. and Veberic, D. and Vegas, I. and Vercellone, S. and Vergani, S. and Veyssiere, C. and Vialle, J. P. and Viana, A. and Videla, M. and Vincent, P. and Vincent, S. and Vink, J. and Vlahakis, N. and Vlahos, L. and Vogler, P. and Vollhardt, A. and von Gunten, H. P. and Vorobiov, S. and Vuerli, C. and Waegebaert, V. and Wagner, R. and Wagner, R. G. and Wagner, S. and Wakely, S. P. and Walter, R. and Walther, T. and Warda, K. and Warwick, R. and Wawer, P. and Wawrzaszek, R. and Webb, N. and Wegner, P. and Weinstein, A. and Weitzel, Q. and Welsing, R. and Werner, M. and Wetteskind, H. and White, R. and Wierzcholska, A. and Wiesand, S. and Wilkinson, M. and Williams, D. A. and Willingale, R. and Winiarski, K. and Wischnewski, R. and Wisniewski, L. and Wood, M. and Woernlein, A. and Xiong, Q. and Yadav, K. K. and Yamamoto, H. and Yamamoto, T. and Yamazaki, R. and Yanagita, S. and Yebras, J. M. and Yelos, D. and Yoshida, A. and Yoshida, T. and Yoshikoshi, T. and Zabalza, V. and Zacharias, M. and Zajczyk, A. and Zanin, R. and Zdziarski, A. and Zech, Alraune and Zhao, A. and Zhou, X. and Zietara, K. and Ziolkowski, J. and Ziolkowski, P. and Zitelli, V. and Zurbach, C. and Zychowski, P.},
  title     = {Introducing the CTA concept},
  series = {Astroparticle physics},
  volume    = {43},
  journal   = {Astroparticle physics},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  organization    = {CTA Consortium},
  issn      = {0927-6505},
  doi       = {10.1016/j.astropartphys.2013.01.007},
  pages     = {3 -- 18},
  year      = {2013},
  abstract  = {The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project.},
  language  = {en}
}
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  title     = {Design concepts for the Cherenkov Telescope Array CTA an advanced facility for ground-based high-energy gamma-ray astronomy},
  series = {Experimental astronomy : an international journal on astronomical instrumentation and data analysis},
  volume    = {32},
  journal   = {Experimental astronomy : an international journal on astronomical instrumentation and data analysis},
  number    = {3},
  publisher = {Springer},
  address   = {Dordrecht},
  organization    = {CTA Consortium},
  issn      = {0922-6435},
  doi       = {10.1007/s10686-011-9247-0},
  pages     = {193 -- 316},
  year      = {2011},
  abstract  = {Ground-based gamma-ray astronomy has had a major breakthrough with the impressive results obtained using systems of imaging atmospheric Cherenkov telescopes. Ground-based gamma-ray astronomy has a huge potential in astrophysics, particle physics and cosmology. CTA is an international initiative to build the next generation instrument, with a factor of 5-10 improvement in sensitivity in the 100 GeV-10 TeV range and the extension to energies well below 100 GeV and above 100 TeV. CTA will consist of two arrays (one in the north, one in the south) for full sky coverage and will be operated as open observatory. The design of CTA is based on currently available technology. This document reports on the status and presents the major design concepts of CTA.},
  language  = {en}
}
@article{PenoneAllanSoliveresetal.2019,
  author    = {Penone, Caterina and Allan, Eric and Soliveres, Santiago and Felipe-Lucia, Maria R. and Gossner, Martin M. and Seibold, Sebastian and Simons, Nadja K. and Schall, Peter and van der Plas, Fons and Manning, Peter and Manzanedo, Ruben D. and Boch, Steffen and Prati, Daniel and Ammer, Christian and Bauhus, Juergen and Buscot, Francois and Ehbrecht, Martin and Goldmann, Kezia and Jung, Kirsten and Mueller, Joerg and Mueller, Joerg C. and Pena, Rodica and Polle, Andrea and Renner, Swen C. and Ruess, Liliane and Schoenig, Ingo and Schrumpf, Marion and Solly, Emily F. and Tschapka, Marco and Weisser, Wolfgang W. and Wubet, Tesfaye and Fischer, Markus},
  title     = {Specialisation and diversity of multiple trophic groups are promoted by different forest features},
  series = {Ecology letters},
  volume    = {22},
  journal   = {Ecology letters},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1461-023X},
  doi       = {10.1111/ele.13182},
  pages     = {170 -- 180},
  year      = {2019},
  abstract  = {While forest management strongly influences biodiversity, it remains unclear how the structural and compositional changes caused by management affect different community dimensions (e.g. richness, specialisation, abundance or completeness) and how this differs between taxa. We assessed the effects of nine forest features (representing stand structure, heterogeneity and tree composition) on thirteen above- and belowground trophic groups of plants, animals, fungi and bacteria in 150 temperate forest plots differing in their management type. Canopy cover decreased light resources, which increased community specialisation but reduced overall diversity and abundance. Features increasing resource types and diversifying microhabitats (admixing of oaks and conifers) were important and mostly affected richness. Belowground groups responded differently to those aboveground and had weaker responses to most forest features. Our results show that we need to consider forest features rather than broad management types and highlight the importance of considering several groups and community dimensions to better inform conservation.},
  language  = {en}
}
@article{AllanWeisserFischeretal.2013,
  author    = {Allan, Eric and Weisser, Wolfgang W. and Fischer, Markus and Schulze, Ernst-Detlef and Weigelt, Alexandra and Roscher, Christiane and Baade, Jussi and Barnard, Romain L. and Bessler, Holger and Buchmann, Nina and Ebeling, Anne and Eisenhauer, Nico and Engels, Christof and Fergus, Alexander J. F. and Gleixner, Gerd and Gubsch, Marlen and Halle, Stefan and Klein, Alexandra Maria and Kertscher, Ilona and Kuu, Annely and Lange, Markus and Le Roux, Xavier and Meyer, Sebastian T. and Migunova, Varvara D. and Milcu, Alexandru and Niklaus, Pascal A. and Oelmann, Yvonne and Pasalic, Esther and Petermann, Jana S. and Poly, Franck and Rottstock, Tanja and Sabais, Alexander C. W. and Scherber, Christoph and Scherer-Lorenzen, Michael and Scheu, Stefan and Steinbeiss, Sibylle and Schwichtenberg, Guido and Temperton, Vicky and Tscharntke, Teja and Voigt, Winfried and Wilcke, Wolfgang and Wirth, Christian and Schmid, Bernhard},
  title     = {A comparison of the strength of biodiversity effects across multiple functions},
  series = {Oecologia},
  volume    = {173},
  journal   = {Oecologia},
  number    = {1},
  publisher = {Springer},
  address   = {New York},
  issn      = {0029-8549},
  doi       = {10.1007/s00442-012-2589-0},
  pages     = {223 -- 237},
  year      = {2013},
  abstract  = {In order to predict which ecosystem functions are most at risk from biodiversity loss, meta-analyses have generalised results from biodiversity experiments over different sites and ecosystem types. In contrast, comparing the strength of biodiversity effects across a large number of ecosystem processes measured in a single experiment permits more direct comparisons. Here, we present an analysis of 418 separate measures of 38 ecosystem processes. Overall, 45 \% of processes were significantly affected by plant species richness, suggesting that, while diversity affects a large number of processes not all respond to biodiversity. We therefore compared the strength of plant diversity effects between different categories of ecosystem processes, grouping processes according to the year of measurement, their biogeochemical cycle, trophic level and compartment (above- or belowground) and according to whether they were measures of biodiversity or other ecosystem processes, biotic or abiotic and static or dynamic. Overall, and for several individual processes, we found that biodiversity effects became stronger over time. Measures of the carbon cycle were also affected more strongly by plant species richness than were the measures associated with the nitrogen cycle. Further, we found greater plant species richness effects on measures of biodiversity than on other processes. The differential effects of plant diversity on the various types of ecosystem processes indicate that future research and political effort should shift from a general debate about whether biodiversity loss impairs ecosystem functions to focussing on the specific functions of interest and ways to preserve them individually or in combination.},
  language  = {en}
}
@article{VaidSomaniRussaketal.2020,
  author    = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.},
  title     = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation},
  series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  volume    = {22},
  journal   = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  number    = {11},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/24018},
  pages     = {19},
  year      = {2020},
  abstract  = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.},
  language  = {en}
}