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TL;DR: The number of adults with raised blood pressure increased from 594 million in 1975 to 1·13 billion in 2015, with the increase largely in low-income and middle-income countries, and the contributions of changes in prevalence versus population growth and ageing to the increase.
1,573 citations
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TL;DR: The Large Ion Collider Experiment (ALICE) as discussed by the authors is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model.
Abstract: ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 161626 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008.
1,218 citations
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TL;DR: In this paper, the production of mesons containing strange quarks (KS, φ) and both singly and doubly strange baryons (,, and − + +) are measured at mid-rapidity in pp collisions at √ s = 0.9 TeV with the ALICE experiment at the LHC.
1,176 citations
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TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Abstract: What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
991 citations
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University of Leeds1, California Institute of Technology2, University of California, Irvine3, University of Washington4, Durham University5, University of Grenoble6, Goddard Space Flight Center7, University of Bristol8, University of Colorado Boulder9, Geological Survey of Denmark and Greenland10, University at Buffalo11, National Space Institute12, University of South Florida13, University of Texas at Austin14, University College London15, Dresden University of Technology16, Georgia Institute of Technology17, University of Lincoln18, University of Arizona19, Alfred Wegener Institute for Polar and Marine Research20, Technische Universität München21, Danish Meteorological Institute22, Memorial University of Newfoundland23, National Institute of Geophysics and Volcanology24, University of Magallanes25, Bergen University College26, Remote Sensing Center27, Newcastle University28, University of Toronto29, University of Bonn30, Delft University of Technology31, Seoul National University32, University of Urbino33, University of Stuttgart34
TL;DR: This work combines satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that the Antarctic Ice Sheet lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6‚¬3.9 millimetres.
Abstract: The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.
725 citations
Authors
Showing all 1318 results
Name | H-index | Papers | Citations |
---|---|---|---|
David Richards | 95 | 578 | 47107 |
Kristin Fanebust Hetland | 91 | 456 | 28164 |
Bjarte Kileng | 90 | 438 | 27681 |
Johan Alme | 87 | 456 | 25059 |
Jørgen André Lien | 83 | 414 | 21756 |
Ketil Røed | 83 | 382 | 23041 |
Lars Bo Andersen | 80 | 442 | 40858 |
Håvard Helstrup | 77 | 339 | 22057 |
Hege Austrheim Erdal | 71 | 105 | 14978 |
Mark Peyrot | 66 | 204 | 17579 |
Lars E. Kristensen | 59 | 268 | 10408 |
Bengt Fridlund | 57 | 425 | 12222 |
Ulla Waldenström | 56 | 147 | 9626 |
Karen Søgaard | 56 | 289 | 10839 |
Niels Wedderkopp | 55 | 213 | 12693 |