@article{ChanJaladankiSomanietal.2021,
  author    = {Chan, Lili and Jaladanki, Suraj K. and Somani, Sulaiman and Paranjpe, Ishan and Kumar, Arvind and Zhao, Shan and Kaufman, Lewis and Leisman, Staci and Sharma, Shuchita and He, John Cijiang and Murphy, Barbara and Fayad, Zahi A. and Levin, Matthew A. and B{\"o}ttinger, Erwin and Charney, Alexander W. and Glicksberg, Benjamin and Coca, Steven G. and Nadkarni, Girish N.},
  title     = {Outcomes of patients on maintenance dialysis hospitalized with COVID-19},
  series = {Clinical journal of the American Society of Nephrology : CJASN},
  volume    = {16},
  journal   = {Clinical journal of the American Society of Nephrology : CJASN},
  number    = {3},
  publisher = {American Society of Nephrology},
  address   = {Washington},
  organization    = {Mount Sinai Covid I},
  issn      = {1555-9041},
  doi       = {10.2215/CJN.12360720},
  pages     = {452 -- 455},
  year      = {2021},
  language  = {en}
}
@article{ChanChaudharySahaetal.2021,
  author    = {Chan, Lili and Chaudhary, Kumardeep and Saha, Aparna and Chauhan, Kinsuk and Vaid, Akhil and Zhao, Shan and Paranjpe, Ishan and Somani, Sulaiman and Richter, Felix and Miotto, Riccardo and Lala, Anuradha and Kia, Arash and Timsina, Prem and Li, Li and Freeman, Robert and Chen, Rong and Narula, Jagat and Just, Allan C. and Horowitz, Carol and Fayad, Zahi and Cordon-Cardo, Carlos and Schadt, Eric and Levin, Matthew A. and Reich, David L. and Fuster, Valentin and Murphy, Barbara and He, John C. and Charney, Alexander W. and B{\"o}ttinger, Erwin and Glicksberg, Benjamin and Coca, Steven G. and Nadkarni, Girish N.},
  title     = {AKI in hospitalized patients with COVID-19},
  series = {Journal of the American Society of Nephrology : JASN},
  volume    = {32},
  journal   = {Journal of the American Society of Nephrology : JASN},
  number    = {1},
  publisher = {American Society of Nephrology},
  address   = {Washington},
  organization    = {Mt Sinai COVID Informatics Ct},
  issn      = {1046-6673},
  doi       = {10.1681/ASN.2020050615},
  pages     = {151 -- 160},
  year      = {2021},
  abstract  = {Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46\%) patients; 347 (19\%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39\%, 19\%, and 42\%, respectively. A total of 976 (24\%) patients were admitted to intensive care, and 745 (76\%) experienced AKI. Of the 435 patients with AKI and urine studies, 84\% had proteinuria, 81\% had hematuria, and 60\% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50\% among patients with AKI versus 8\% among those without AKI (aOR, 9.2; 95\% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35\% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36\%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30\% survived with recovery of kidney function by the time of discharge.},
  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}
}
@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}
}
@article{RotheZhaoKewesetal.2019,
  author    = {Rothe, Martin and Zhao, Yuhang and Kewes, G{\"u}nter and Kochovski, Zdravko and Sigle, Wilfried and van Aken, Peter A. and Koch, Christoph and Ballauff, Matthias and Lu, Yan and Benson, Oliver},
  title     = {Silver nanowires with optimized silica coating as versatile plasmonic resonators},
  series = {Scientific reports},
  volume    = {9},
  journal   = {Scientific reports},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-019-40380-5},
  pages     = {12},
  year      = {2019},
  abstract  = {Metal nanoparticles are the most frequently used nanostructures in plasmonics. However, besides nanoparticles, metal nanowires feature several advantages for applications. Their elongation offers a larger interaction volume, their resonances can reach higher quality factors, and their mode structure provides better coupling into integrated hybrid dielectric-plasmonic circuits. It is crucial though, to control the distance of the wire to a supporting substrate, to another metal layer or to active materials with sub-nanometer precision. A dielectric coating can be utilized for distance control, but it must not degrade the plasmonic properties. In this paper, we introduce a controlled synthesis and coating approach for silver nanowires to fulfill these demands. We synthesize and characterize silver nanowires of around 70 nm in diameter. These nanowires are coated with nm-sized silica shells using a modified Stober method to achieve a homogeneous and smooth surface quality. We use transmission electron microscopy, dark-field microscopy and electron-energy loss spectroscopy to study morphology and plasmonic resonances of individual nanowires and quantify the influence of the silica coating. Thorough numerical simulations support the experimental findings showing that the coating does not deteriorate the plasmonic properties and thus introduce silver nanowires as usable building blocks for integrated hybrid plasmonic systems.},
  language  = {en}
}
@article{KhiderEmileGeayMcKayetal.2019,
  author    = {Khider, D. and Emile-Geay, J. and McKay, N. P. and Gil, Y. and Garijo, D. and Ratnakar, V and Alonso-Garcia, M. and Bertrand, S. and Bothe, O. and Brewer, P. and Bunn, A. and Chevalier, M. and Comas-Bru, L. and Csank, A. and Dassie, E. and DeLong, K. and Felis, T. and Francus, P. and Frappier, A. and Gray, W. and Goring, S. and Jonkers, L. and Kahle, M. and Kaufman, D. and Kehrwald, N. M. and Martrat, B. and McGregor, H. and Richey, J. and Schmittner, A. and Scroxton, N. and Sutherland, E. and Thirumalai, Kaustubh and Allen, K. and Arnaud, F. and Axford, Y. and Barrows, T. and Bazin, L. and Birch, S. E. Pilaar and Bradley, E. and Bregy, J. and Capron, E. and Cartapanis, O. and Chiang, H-W and Cobb, K. M. and Debret, M. and Dommain, R{\´e}ne and Du, J. and Dyez, K. and Emerick, S. and Erb, M. P. and Falster, G. and Finsinger, W. and Fortier, D. and Gauthier, Nicolas and George, S. and Grimm, E. and Hertzberg, J. and Hibbert, F. and Hillman, A. and Hobbs, W. and Huber, M. and Hughes, A. L. C. and Jaccard, S. and Ruan, J. and Kienast, M. and Konecky, B. and Le Roux, G. and Lyubchich, V and Novello, V. F. and Olaka, L. and Partin, J. W. and Pearce, C. and Phipps, S. J. and Pignol, C. and Piotrowska, N. and Poli, M-S and Prokopenko, A. and Schwanck, F. and Stepanek, C. and Swann, G. E. A. and Telford, R. and Thomas, E. and Thomas, Z. and Truebe, S. and von Gunten, L. and Waite, A. and Weitzel, N. and Wilhelm, B. and Williams, J. and Winstrup, M. and Zhao, N. and Zhou, Y.},
  title     = {PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data},
  series = {Paleoceanography and paleoclimatology},
  volume    = {34},
  journal   = {Paleoceanography and paleoclimatology},
  number    = {10},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2572-4517},
  doi       = {10.1029/2019PA003632},
  pages     = {1570 -- 1596},
  year      = {2019},
  abstract  = {The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.},
  language  = {en}
}
@article{KongDeuberKittilaeetal.2018,
  author    = {Kong, Xiang-Zhao and Deuber, Claudia A. and Kittil{\"a}, Anniina and Somogyv{\´a}ri, M{\´a}rk and Mikutis, Gediminas and Bayer, Peter and Stark, Wendelin J. and Saar, Martin O.},
  title     = {Tomographic Reservoir Imaging with DNA-Labeled Silica Nanotracers: The First Field Validation},
  series = {Environmental science \& technology},
  volume    = {52},
  journal   = {Environmental science \& technology},
  number    = {23},
  publisher = {American Chemical Society},
  address   = {Washington},
  issn      = {0013-936X},
  doi       = {10.1021/acs.est.8b04367},
  pages     = {13681 -- 13689},
  year      = {2018},
  abstract  = {This study presents the first field validation of using DNA-labeled silica nanoparticles as tracers to image subsurface reservoirs by travel time based tomography. During a field campaign in Switzerland, we performed short-pulse tracer tests under a forced hydraulic head gradient to conduct a multisource-multireceiver tracer test and tomographic inversion, determining the two-dimensional hydraulic conductivity field between two vertical wells. Together with three traditional solute dye tracers, we injected spherical silica nanotracers, encoded with synthetic DNA molecules, which are protected by a silica layer against damage due to chemicals, microorganisms, and enzymes. Temporal moment analyses of the recorded tracer concentration breakthrough curves (BTCs) indicate higher mass recovery, less mean residence time, and smaller dispersion of the DNA-labeled nanotracers, compared to solute dye tracers. Importantly, travel time based tomography, using nanotracer BTCs, yields a satisfactory hydraulic conductivity tomogram, validated by the dye tracer results and previous field investigations. These advantages of DNA-labeled nanotracers, in comparison to traditional solute dye tracers, make them well-suited for tomographic reservoir characterizations in fields such as hydrogeology, petroleum engineering, and geothermal energy, particularly with respect to resolving preferential flow paths or the heterogeneity of contact surfaces or by enabling source zone characterizations of dense nonaqueous phase liquids.},
  language  = {en}
}
@misc{LichtDupontNivetPullenetal.2016,
  author    = {Licht, Alexis and Dupont-Nivet, Guillaume and Pullen, Alex and Kapp, Paul and Abels, Hemmo A. and Lai, Zulong and Guo, ZhaoJie and Abell, Jordan and Giesler, Dominique},
  title     = {Resilience of the Asian atmospheric circulation shown by paleogene dust provenance},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1114},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43638},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-436381},
  pages     = {8},
  year      = {2016},
  abstract  = {The onset of modern central Asian atmospheric circulation is traditionally linked to the interplay of surface uplift of the Mongolian and Tibetan-Himalayan orogens, retreat of the Paratethys sea from central Asia and Cenozoic global cooling. Although the role of these players has not yet been unravelled, the vast dust deposits of central China support the presence of arid conditions and modern atmospheric pathways for the last 25 million years (Myr). Here, we present provenance data from older (42-33 Myr) dust deposits, at a time when the Tibetan Plateau was less developed, the Paratethys sea still present in central Asia and atmospheric pCO(2) much higher. Our results show that dust sources and near-surface atmospheric circulation have changed little since at least 42 Myr. Our findings indicate that the locus of central Asian high pressures and concurrent aridity is a resilient feature only modulated by mountain building, global cooling and sea retreat.},
  language  = {en}
}
@article{HorikoshiYaghootkarMookKanamorietal.2013,
  author    = {Horikoshi, Momoko and Yaghootkar, Hanieh and Mook-Kanamori, Dennis O. and Sovio, Ulla and Taal, H. Rob and Hennig, Branwen J. and Bradfield, Jonathan P. and St Pourcain, Beate and Evans, David M. and Charoen, Pimphen and Kaakinen, Marika and Cousminer, Diana L. and Lehtimaki, Terho and Kreiner-Moller, Eskil and Warrington, Nicole M. and Bustamante, Mariona and Feenstra, Bjarke and Berry, Diane J. and Thiering, Elisabeth and Pfab, Thiemo and Barton, Sheila J. and Shields, Beverley M. and Kerkhof, Marjan and van Leeuwen, Elisabeth M. and Fulford, Anthony J. and Kutalik, Zoltan and Zhao, Jing Hua and den Hoed, Marcel and Mahajan, Anubha and Lindi, Virpi and Goh, Liang-Kee and Hottenga, Jouke-Jan and Wu, Ying and Raitakari, Olli T. and Harder, Marie N. and Meirhaeghe, Aline and Ntalla, Ioanna and Salem, Rany M. and Jameson, Karen A. and Zhou, Kaixin and Monies, Dorota M. and Lagou, Vasiliki and Kirin, Mirna and Heikkinen, Jani and Adair, Linda S. and Alkuraya, Fowzan S. and Al-Odaib, Ali and Amouyel, Philippe and Andersson, Ehm Astrid and Bennett, Amanda J. and Blakemore, Alexandra I. F. and Buxton, Jessica L. and Dallongeville, Jean and Das, Shikta and de Geus, Eco J. C. and Estivill, Xavier and Flexeder, Claudia and Froguel, Philippe and Geller, Frank and Godfrey, Keith M. and Gottrand, Frederic and Groves, Christopher J. and Hansen, Torben and Hirschhorn, Joel N. and Hofman, Albert and Hollegaard, Mads V. and Hougaard, David M. and Hyppoenen, Elina and Inskip, Hazel M. and Isaacs, Aaron and Jorgensen, Torben and Kanaka-Gantenbein, Christina and Kemp, John P. and Kiess, Wieland and Kilpelainen, Tuomas O. and Klopp, Norman and Knight, Bridget A. and Kuzawa, Christopher W. and McMahon, George and Newnham, John P. and Niinikoski, Harri and Oostra, Ben A. and Pedersen, Louise and Postma, Dirkje S. and Ring, Susan M. and Rivadeneira, Fernando and Robertson, Neil R. and Sebert, Sylvain and Simell, Olli and Slowinski, Torsten and Tiesler, Carla M. T. and Toenjes, Anke and Vaag, Allan and Viikari, Jorma S. and Vink, Jacqueline M. and Vissing, Nadja Hawwa and Wareham, Nicholas J. and Willemsen, Gonneke and Witte, Daniel R. and Zhang, Haitao and Zhao, Jianhua and Wilson, James F. and Stumvoll, Michael and Prentice, Andrew M. and Meyer, Brian F. and Pearson, Ewan R. and Boreham, Colin A. G. and Cooper, Cyrus and Gillman, Matthew W. and Dedoussis, George V. and Moreno, Luis A. and Pedersen, Oluf and Saarinen, Maiju and Mohlke, Karen L. and Boomsma, Dorret I. and Saw, Seang-Mei and Lakka, Timo A. and Koerner, Antje and Loos, Ruth J. F. and Ong, Ken K. and Vollenweider, Peter and van Duijn, Cornelia M. and Koppelman, Gerard H. and Hattersley, Andrew T. and Holloway, John W. and Hocher, Berthold and Heinrich, Joachim and Power, Chris and Melbye, Mads and Guxens, Monica and Pennell, Craig E. and Bonnelykke, Klaus and Bisgaard, Hans and Eriksson, Johan G. and Widen, Elisabeth and Hakonarson, Hakon and Uitterlinden, Andre G. and Pouta, Anneli and Lawlor, Debbie A. and Smith, George Davey and Frayling, Timothy M. and McCarthy, Mark I. and Grant, Struan F. A. and Jaddoe, Vincent W. V. and Jarvelin, Marjo-Riitta and Timpson, Nicholas J. and Prokopenko, Inga and Freathy, Rachel M.},
  title     = {New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism},
  series = {Nature genetics},
  volume    = {45},
  journal   = {Nature genetics},
  number    = {1},
  publisher = {Nature Publ. Group},
  address   = {New York},
  organization    = {MAGIC, Early Growth Genetics EGG},
  issn      = {1061-4036},
  doi       = {10.1038/ng.2477},
  pages     = {76 -- U115},
  year      = {2013},
  abstract  = {Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood(1). Previous genome-wide association studies of birth weight identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes and a second variant, near CCNL1, with no obvious link to adult traits(2). In an expanded genome-wide association metaanalysis and follow-up study of birth weight (of up to 69,308 individuals of European descent from 43 studies), we have now extended the number of loci associated at genome-wide significance to 7, accounting for a similar proportion of variance as maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes, ADRB1 with adult blood pressure and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.},
  language  = {en}
}
@article{NitschkeWangSchmiederetal.2013,
  author    = {Nitschke, Felix and Wang, Peixiang and Schmieder, Peter and Girard, Jean-Marie and Awrey, Donald E. and Wang, Tony and Israelian, Johan and Zhao, XiaoChu and Turnbull, Julie and Heydenreich, Matthias and Kleinpeter, Erich and Steup, Martin and Minassian, Berge A.},
  title     = {Hyperphosphorylation of glucosyl C6 carbons and altered structure of glycogen in the neurodegenerative epilepsy lafora disease},
  series = {Cell metabolism},
  volume    = {17},
  journal   = {Cell metabolism},
  number    = {5},
  publisher = {Cell Press},
  address   = {Cambridge},
  issn      = {1550-4131},
  doi       = {10.1016/j.cmet.2013.04.006},
  pages     = {756 -- 767},
  year      = {2013},
  abstract  = {Laforin or malin deficiency causes Lafora disease, characterized by altered glycogen metabolism and teenage-onset neurodegeneration with intractable and invariably fatal epilepsy. Plant starches possess small amounts of metabolically essential monophosphate esters. Glycogen contains similar phosphate amounts, which are thought to originate from a glycogen synthase error side reaction and therefore lack any specific function. Glycogen is also believed to lack monophosphates at glucosyl carbon C6, an essential phosphorylation site in plant starch metabolism. We now show that glycogen phosphorylation is not due to a glycogen synthase side reaction, that C6 is a major glycogen phosphorylation site, and that C6 monophosphates predominate near centers of glycogen molecules and positively correlate with glycogen chain lengths. Laforin or malin deficiency causes C6 hyperphosphorylation, which results in malformed long-chained glycogen that accumulates in many tissues, causing neurodegeneration in brain. Our work advances the understanding of Lafora disease pathogenesis and suggests that glycogen phosphorylation has important metabolic function.},
  language  = {en}
}