Institution
University of Ljubljana
Education•Ljubljana, Slovenia•
About: University of Ljubljana is a education organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Population & Liquid crystal. The organization has 17210 authors who have published 47013 publications receiving 1082684 citations. The organization is also known as: Univerza v Ljubljani.
Papers published on a yearly basis
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University of Helsinki1, Semmelweis University2, Hungarian Academy of Sciences3, University of Szeged4, University of Palermo5, University of Porto6, Institute of Molecular Pathology and Immunology of the 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, University of Southern Denmark18, Statens Serum Institut19, 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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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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23 Feb 2020
TL;DR: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper, where a brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
3,111 citations
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01 May 1994TL;DR: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them and is analysed and extended to deal with noisy, incomplete, and multi-class data sets.
Abstract: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem.
2,849 citations
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TL;DR: How and why Relief algorithms work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.
Abstract: Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming.
A broad spectrum of successful uses calls for especially careful investigation of various features Relief algorithms have. In this paper we theoretically and empirically investigate and discuss how and why they work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.
2,651 citations
Authors
Showing all 17388 results
Name | H-index | Papers | Citations |
---|---|---|---|
David Miller | 203 | 2573 | 204840 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
James M. Tour | 143 | 859 | 91364 |
Carmen García | 139 | 1503 | 96925 |
Bernt Schiele | 130 | 568 | 70032 |
Vladimir Cindro | 129 | 1157 | 82000 |
Teresa Barillari | 129 | 984 | 78782 |
Sven Menke | 129 | 1121 | 82034 |
Horst Oberlack | 129 | 985 | 80069 |
Hubert Kroha | 129 | 1126 | 80746 |
Peter Schacht | 129 | 1030 | 80092 |
Siegfried Bethke | 129 | 1266 | 103520 |
Igor Mandić | 128 | 1065 | 79498 |
Stefan Kluth | 128 | 1261 | 84534 |
Andrej Gorišek | 128 | 951 | 67830 |