ALBERT

Description: A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4486207/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4486207/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Whelan, Robert -- Watts, Richard -- Orr, Catherine A -- Althoff, Robert R -- Artiges, Eric -- Banaschewski, Tobias -- Barker, Gareth J -- Bokde, Arun L W -- Buchel, Christian -- Carvalho, Fabiana M -- Conrod, Patricia J -- Flor, Herta -- Fauth-Buhler, Mira -- Frouin, Vincent -- Gallinat, Juergen -- Gan, Gabriela -- Gowland, Penny -- Heinz, Andreas -- Ittermann, Bernd -- Lawrence, Claire -- Mann, Karl -- Martinot, Jean-Luc -- Nees, Frauke -- Ortiz, Nick -- Paillere-Martinot, Marie-Laure -- Paus, Tomas -- Pausova, Zdenka -- Rietschel, Marcella -- Robbins, Trevor W -- Smolka, Michael N -- Strohle, Andreas -- Schumann, Gunter -- Garavan, Hugh -- IMAGEN Consortium -- MH082116/MH/NIMH NIH HHS/ -- P20 GM103644/GM/NIGMS NIH HHS/ -- P20GM103644/GM/NIGMS NIH HHS/ -- P50 DA036114/DA/NIDA NIH HHS/ -- P50DA036114/DA/NIDA NIH HHS/ -- Medical Research Council/United Kingdom -- Wellcome Trust/United Kingdom -- England -- Nature. 2014 Aug 14;512(7513):185-9. doi: 10.1038/nature13402. Epub 2014 Jul 2.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Department of Psychiatry, University of Vermont, Burlington, Vermont 05401, USA [2] Department of Psychology, University College Dublin, Dublin 4, Ireland. ; Department of Radiology, University of Vermont, Burlington, Vermont 05401, USA. ; Vermont Center for Children, Youth, and Families, University of Vermont, Burlington, Vermont 05401, USA. ; 1] Department of Pediatrics, University of Vermont, Burlington, Vermont 05401, USA [2] Department of Psychology, University of Vermont, Burlington, Vermont 05401, USA. ; 1] Institut National de la Sante et de la Recherche Medicale, INSERM CEA Unit 1000 "Imaging &Psychiatry", University Paris Sud, 91400 Orsay, France [2] Department of Psychiatry, Orsay Hospital, 4 place du General Leclerc, 91400 Orsay, France. ; Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany. ; Institute of Psychiatry, King's College London, London SE5 8AF, UK. ; Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland. ; 1] Department of Systems Neuroscience, Universitatsklinikum Hamburg Eppendorf, 20246 Hamburg, Germany [2] Department of Psychology, Stanford University, Stanford, California 94305, USA. ; 1] Institute of Psychiatry, King's College London, London SE5 8AF, UK [2] Department of Psychiatry, Universite de Montreal, CHU Ste Justine Hospital, Montreal H3T 1C5, Canada. ; 1] Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany [2] Department of Addictive Behaviour and Addiction Medicine, Heidelberg University, 68159 Mannheim, Germany. ; 14 CEA, DSV, I2BM, Neurospin bat 145, 91191 Gif-Sur-Yvette, France. ; 1] Department of Systems Neuroscience, Universitatsklinikum Hamburg Eppendorf, 20246 Hamburg, Germany [2] Department of Psychiatry and Psychotherapy, Campus Charite Mitte, Charite-Universitatsmedizin Berlin 10117, Germany. ; Department of Psychiatry and Neuroimaging Center, Technische Universitat Dresden, 01062 Dresden, Germany. ; School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK. ; Department of Psychiatry and Psychotherapy, Campus Charite Mitte, Charite-Universitatsmedizin Berlin 10117, Germany. ; Physikalisch-Technische Bundesanstalt (PTB), 10587 Berlin, Germany. ; School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK. ; 1] Institut National de la Sante et de la Recherche Medicale, INSERM CEA Unit 1000 "Imaging &Psychiatry", University Paris Sud, 91400 Orsay, France [2] AP-HP Department of Adolescent Psychopathology and Medicine, Maison de Solenn, University Paris Descartes, 75006 Paris, France. ; 1] Department of Psychiatry, University of Vermont, Burlington, Vermont 05401, USA [2] Neuroscience Graduate Program, University of Vermont, Burlington, Vermont 05401, USA. ; 1] Department of Psychiatry and Psychotherapy, Campus Charite Mitte, Charite-Universitatsmedizin Berlin 10117, Germany [2] AP-HP Department of Adolescent Psychopathology and Medicine, Maison de Solenn, University Paris Descartes, 75006 Paris, France. ; 1] Rotman Research Institute, University of Toronto, Toronto, Ontario M5R 0A3, Canada [2] Montreal Neurological Institute, McGill University, H3A 2B4, Canada. ; The Hospital for Sick Children, University of Toronto, Toronto, Ontario M5G 0A4, Canada. ; Behavioural and Clinical Neuroscience Institute and Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK. ; 1] Institute of Psychiatry, King's College London, London SE5 8AF, UK [2] MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, London WC2R 2LS, UK. ; 1] Department of Psychiatry, University of Vermont, Burlington, Vermont 05401, USA [2] Department of Psychology, University of Vermont, Burlington, Vermont 05401, USA [3] Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25043041" target="_blank"〉PubMed〈/a〉

Description: ACS Sustainable Chemistry & Engineering DOI: 10.1021/acssuschemeng.5b00967

Description: We compare measurements of the turbulent and radiative surface energy fluxes from an automatic weather station (AWS) on Larsen C Ice Shelf, Antarctica with corresponding fluxes from three high-resolution atmospheric models over a one-month period during austral summer. All three models produce a reasonable simulation of the (relatively small) turbulent energy fluxes at the AWS site. However, biases in the modelled radiative fluxes, which dominate the surface energy budget, are significant. There is a significant positive bias in net shortwave radiation in all three models, together with a corresponding negative bias in net longwave radiation. In two of the models, the longwave bias only partially offsets the positive shortwave bias, leading to an excessive amount of energy available for heating and melting the surface, while, in the third, the negative longwave bias exceeds the positive shortwave bias, leading to a deficiency in calculated surface melt. Biases in shortwave and longwave radiation are anticorrelated, suggesting that they both result from the models simulating too little cloud (or clouds that are too optically thin). We conclude that, while these models may be able to provide some useful information on surface energy fluxes, absolute values of modelled melt rate are significantly biased and should be used with caution. Efforts to improve model simulation of melt should initially focus on the radiative fluxes and, in particular, on the simulation of the clouds that control these fluxes.

Description: A better understanding of regional-scale precipitation patterns in the Himalayan region is required to increase our knowledge of the impacts of climate change on downstream water availability. This study examines the impact of four cloud microphysical schemes (Thompson, Morrison, WRF Single-Moment 5-class, and WRF Double-Moment 6-class) on summer monsoon precipitation in the Langtang Valley in the central Nepalese Himalayas, as simulated by the Weather Research and Forecasting (WRF) model at 1-km grid spacing for a 10-day period in July 2012. The model results are evaluated through a comparison with surface precipitation and radiation measurements made at two observation sites. Additional understanding is gained from a detailed examination of the microphysical characteristics simulated by each scheme, which are compared with measurements using a spaceborne radar/lidar cloud product. Also examined are the roles of large and small-scale forcing. In general the schemes are able to capture the timing of surface precipitation better than the actual amounts in the Langtang Valley, which are predominately underestimated, with the Morrison scheme showing the best agreement with the measured values. The schemes all show a large positive bias in incoming radiation. Analysis of the radar/lidar cloud product and hydrometeors from each of the schemes suggests that ‘cold-rain’ processes are a key precipitation formation mechanism, which is also well represented by the Morrison scheme. As well as microphysical structure, both large-scale and localised forcing is also important.