Institution
Middle East Technical University
Education•Ankara, Ankara, Turkey•
About: Middle East Technical University is a education organization based out in Ankara, Ankara, Turkey. It is known for research contribution in the topics: Large Hadron Collider & Catalysis. The organization has 12919 authors who have published 29448 publications receiving 639350 citations. The organization is also known as: METU & Orta Doğu Teknik Üniversitesi.
Topics: Large Hadron Collider, Catalysis, Population, Turkish, Lepton
Papers published on a yearly basis
Papers
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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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TL;DR: In this paper, results from searches for the standard model Higgs boson in proton-proton collisions at 7 and 8 TeV in the CMS experiment at the LHC, using data samples corresponding to integrated luminosities of up to 5.8 standard deviations.
8,857 citations
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TL;DR: The Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN as mentioned in this paper was designed to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1)
Abstract: The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.
5,193 citations
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University of Helsinki1, Semmelweis University2, Hungarian Academy of Sciences3, University of Szeged4, University of Palermo5, Institute of Molecular Pathology and Immunology of the University of Porto6, University of Porto7, Autonomous University of Barcelona8, Instituto de Biologia Molecular e Celular9, Ikerbasque10, Harvard University11, University of Duisburg-Essen12, Paracelsus Private Medical University of Salzburg13, Salk Institute for Biological Studies14, University of Colorado Denver15, Bilkent University16, Middle East Technical University17, Statens Serum Institut18, University of Southern Denmark19, Ghent University Hospital20, Oslo University Hospital21, University of Belgrade22, University of Ljubljana23, University of Mainz24, Finnish Red Cross25, University of Gothenburg26, Latvian Biomedical Research and Study centre27, University of Applied Sciences and Arts Northwestern Switzerland FHNW28, University of Valencia29, Centro Nacional de Investigaciones Cardiovasculares30, University of Freiburg31, Utrecht University32, Trinity College, Dublin33, Catalan Institution for Research and Advanced Studies34, University of Barcelona35, International University Of Catalonia36, Aarhus University Hospital37
TL;DR: A comprehensive overview of the current understanding of the physiological roles of EVs is provided, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia.
Abstract: In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.
3,690 citations
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Radboud University Nijmegen1, Eindhoven University of Technology2, Utrecht University3, Beth Israel Deaconess Medical Center4, Massachusetts Institute of Technology5, Harvard University6, The Chinese University of Hong Kong7, Munich Business School8, Middle East Technical University9, University of Toronto10, University of Warwick11, Coventry Health Care12, Qatar University13, HTW Berlin - University of Applied Sciences14, Tampere University of Technology15, University of Tampere16, Technische Universität München17, Osaka University18, University of South Florida19, National Academy of Sciences of Belarus20, University of Castilla–La Mancha21, Pontifical Catholic University of Peru22, Pierre-and-Marie-Curie University23
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Abstract: Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884];P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
2,116 citations
Authors
Showing all 13080 results
Name | H-index | Papers | Citations |
---|---|---|---|
P. Chang | 170 | 2154 | 151783 |
Jay Hauser | 155 | 2145 | 132683 |
Y. B. Hsiung | 138 | 1258 | 94278 |
Mehmet Zeyrek | 132 | 1079 | 86827 |
Gulsen Onengut | 131 | 1232 | 84686 |
Erhan Gülmez | 129 | 1071 | 84216 |
Robert McPherson | 128 | 1105 | 80342 |
Ozlem Kaya | 128 | 1168 | 84212 |
Kadri Ozdemir | 128 | 1163 | 83704 |
Francois Corriveau | 128 | 1022 | 75729 |
Candan Dozen | 128 | 1139 | 77011 |
Efe Yazgan | 128 | 986 | 79041 |
Gul Gokbulut | 128 | 1107 | 76822 |
Semiray Girgis | 126 | 1008 | 75554 |
Kerem Cankocak | 125 | 1180 | 81323 |