Institution
University of Amsterdam
Education•Amsterdam, Noord-Holland, Netherlands•
About: University of Amsterdam is a education organization based out in Amsterdam, Noord-Holland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 59309 authors who have published 140894 publications receiving 5984137 citations. The organization is also known as: UvA & Universiteit van Amsterdam.
Papers published on a yearly basis
Papers
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Lorenzo Galluzzi1, Lorenzo Galluzzi2, Ilio Vitale3, Stuart A. Aaronson4 +183 more•Institutions (111)
TL;DR: The Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives.
Abstract: Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.
3,301 citations
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TL;DR: In this article, the authors examined the physiological origins and mechanisms of heart rate variability, considered quantitative approaches to measurement, and highlighted important caveats in the interpretation of heart rates variability, and outlined guidelines for research in this area.
Abstract: Components of heart rate variability have attracted considerable attention in psychology and medicine and have become important dependent measures in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability, however, remain complex issues and are fraught with pitfalls. The present report (a) examines the physiological origins and mechanisms of heart rate variability, (b) considers quantitative approaches to measurement, and (c) highlights important caveats in the interpretation of heart rate variability. Summary guidelines for research in this area are outlined, and suggestions and prospects for future developments are considered.
3,273 citations
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TL;DR: Clonogenic assay or colony formation assay is an in vitro cell survival assay based on the ability of a single cell to grow into a colony that can be used to determine cell reproductive death after treatment with ionizing radiation and the effectiveness of other cytotoxic agents.
Abstract: Clonogenic assay or colony formation assay is an in vitro cell survival assay based on the ability of a single cell to grow into a colony. The colony is defined to consist of at least 50 cells. The assay essentially tests every cell in the population for its ability to undergo "unlimited" division. Clonogenic assay is the method of choice to determine cell reproductive death after treatment with ionizing radiation, but can also be used to determine the effectiveness of other cytotoxic agents. Only a fraction of seeded cells retains the capacity to produce colonies. Before or after treatment, cells are seeded out in appropriate dilutions to form colonies in 1-3 weeks. Colonies are fixed with glutaraldehyde (6.0% v/v), stained with crystal violet (0.5% w/v) and counted using a stereomicroscope. A method for the analysis of radiation dose-survival curves is included.
3,244 citations
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Max Planck Society1, McGill University2, University of Toronto3, University of Manchester4, University of Sheffield5, German Aerospace Center6, ASTRON7, University of Amsterdam8, Lebedev Physical Institute9, University of Warwick10, West Virginia University11, University of Virginia12, National Radio Astronomy Observatory13, University of British Columbia14
TL;DR: Pulsar J0348+0432 is only the second neutron star with a precisely determined mass of 2 M☉
Abstract: Many physically motivated extensions to general relativity (GR) predict significant deviations at energies present in massive neutron stars. We report the measurement of a 2.01 \(\pm \) 0.04 solar mass (M\(_\odot \)) pulsar in a 2.46-h orbit around a 0.172 \(\pm \) 0.003 M\(_\odot \) white dwarf. The high pulsar mass and the compact orbit make this system a sensitive laboratory of a previously untested strong-field gravity regime. Thus far, the observed orbital decay agrees with GR, supporting its validity even for the extreme conditions present in the system. The resulting constraints on deviations support the use of GR-based templates for ground-based gravitational wave detection experiments. Additionally, the system strengthens recent constraints on the properties of dense matter and provides novel insight to binary stellar astrophysics and pulsar recycling.
3,224 citations
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09 Sep 2016TL;DR: In this paper, a scalable approach for semi-supervised learning on graph-structured data is presented based on an efficient variant of convolutional neural networks which operate directly on graphs.
Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.
3,200 citations
Authors
Showing all 59759 results
Name | H-index | Papers | Citations |
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Richard A. Flavell | 231 | 1328 | 205119 |
Scott M. Grundy | 187 | 841 | 231821 |
Stuart H. Orkin | 186 | 715 | 112182 |
Kenneth C. Anderson | 178 | 1138 | 126072 |
David A. Weitz | 178 | 1038 | 114182 |
Dorret I. Boomsma | 176 | 1507 | 136353 |
Brenda W.J.H. Penninx | 170 | 1139 | 119082 |
Michael Kramer | 167 | 1713 | 127224 |
Nicholas J. White | 161 | 1352 | 104539 |
Lex M. Bouter | 158 | 767 | 103034 |
Wolfgang Wagner | 156 | 2342 | 123391 |
Jerome I. Rotter | 156 | 1071 | 116296 |
David Cella | 156 | 1258 | 106402 |
David Eisenberg | 156 | 697 | 112460 |
Naveed Sattar | 155 | 1326 | 116368 |