Institution
Stockholm University
Education•Stockholm, Sweden•
About: Stockholm University is a education organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Population & Context (language use). The organization has 21052 authors who have published 62567 publications receiving 2725859 citations. The organization is also known as: University of Stockholm & Stockholms universitet.
Topics: Population, Context (language use), Galaxy, Supernova, Catalysis
Papers published on a yearly basis
Papers
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TL;DR: Pfam as discussed by the authors is a widely used database of protein families, containing 14 831 manually curated entries in the current version, version 27.0, and has been updated several times since 2012.
Abstract: Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.
9,415 citations
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TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.
9,282 citations
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Stockholm Environment Institute1, Stockholm University2, Australian National University3, University of Alaska Fairbanks4, Université catholique de Louvain5, University of East Anglia6, Wageningen University and Research Centre7, Royal Swedish Academy of Sciences8, Potsdam Institute for Climate Impact Research9, University of Oxford10, James Cook University11, Arizona State University12, Royal Institute of Technology13, University of Minnesota14, University of Vermont15, Stockholm International Water Institute16, California State University San Marcos17, Goddard Institute for Space Studies18, Commonwealth Scientific and Industrial Research Organisation19, University of Arizona20, Max Planck Society21
TL;DR: Identifying and quantifying planetary boundaries that must not be transgressed could help prevent human activities from causing unacceptable environmental change, argue Johan Rockstrom and colleagues.
Abstract: Identifying and quantifying planetary boundaries that must not be transgressed could help prevent human activities from causing unacceptable environmental change, argue Johan Rockstrom and colleagues.
8,837 citations
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TL;DR: SignalP 4.0 was the best signal-peptide predictor for all three organism types but was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal- peptide correlation when there are no transmembrane proteins present.
Abstract: We benchmarked SignalP 4.0 against SignalP 3.0 and ten other signal peptide prediction algorithms (Fig. 1). We compared prediction performance using the Matthews correlation coefficient16, for which each sequence was counted as a true or false positive or negative. To test SignalP 4.0 performance, we did not use data that had been used in training the networks or selecting the optimal architecture, and the test data did not contain homologs to the training and optimization data (Supplementary Methods). The test set for SignalP 3.0 was also independent of the training set because we removed sequences used to construct SignalP 3.0 and their homologs from the benchmark data. For other algorithms more recent than SignalP 3.0, the benchmark data may include data used to train the methods, possibly leading to slight overestimations of their performance. Our results show that SignalP 4.0 was the best signal-peptide predictor for all three organism types (Fig. 1). This comes at a price, however, because SignalP 4.0 was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal-peptide correlation when there are no transmembrane proteins present (Supplementary Results). An ideal method would have the best SignalP 4.0: discriminating signal peptides from transmembrane regions
8,370 citations
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TL;DR: The review as discussed by the authors summarizes much of particle physics and cosmology using data from previous editions, plus 3,283 new measurements from 899 Japers, including the recently discovered Higgs boson, leptons, quarks, mesons and baryons.
Abstract: The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,283 new measurements from 899 Japers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as heavy neutrinos, supersymmetric and technicolor particles, axions, dark photons, etc. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as Supersymmetry, Extra Dimensions, Particle Detectors, Probability, and Statistics. Among the 112 reviews are many that are new or heavily revised including those on: Dark Energy, Higgs Boson Physics, Electroweak Model, Neutrino Cross Section Measurements, Monte Carlo Neutrino Generators, Top Quark, Dark Matter, Dynamical Electroweak Symmetry Breaking, Accelerator Physics of Colliders, High-Energy Collider Parameters, Big Bang Nucleosynthesis, Astrophysical Constants and Cosmological Parameters.
7,337 citations
Authors
Showing all 21326 results
Name | H-index | Papers | Citations |
---|---|---|---|
Hongjie Dai | 197 | 570 | 182579 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Richard S. Ellis | 169 | 882 | 136011 |
Stanley B. Prusiner | 168 | 745 | 97528 |
Anders Björklund | 165 | 769 | 84268 |
Yang Yang | 164 | 2704 | 144071 |
Tomas Hökfelt | 158 | 1033 | 95979 |
Bengt Winblad | 153 | 1240 | 101064 |
Zhenwei Yang | 150 | 956 | 109344 |
Marvin Johnson | 149 | 1827 | 119520 |
Jan-Åke Gustafsson | 147 | 1058 | 98804 |
Markus Ackermann | 146 | 610 | 71071 |
Hans-Olov Adami | 145 | 908 | 83473 |
Markku Kulmala | 142 | 1487 | 85179 |
Kjell Fuxe | 142 | 1479 | 89846 |