Institution
University of Luxembourg
Education•Luxembourg, Luxembourg•
About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.
Papers published on a yearly basis
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TL;DR: This review discusses the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates, proteins, plant polyphenols, bile acids, and vitamins, and the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism.
Abstract: The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.
1,294 citations
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TL;DR: The present paper analyzes in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects and illustrates the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.
Abstract: The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.
1,224 citations
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Ghent University1, Forschungszentrum Jülich2, Aalto University3, Åbo Akademi University4, Vienna University of Technology5, Duke University6, University of Grenoble7, École Polytechnique Fédérale de Lausanne8, Durham University9, International School for Advanced Studies10, Max Planck Society11, Uppsala University12, Humboldt University of Berlin13, Fritz Haber Institute of the Max Planck Society14, Technical University of Denmark15, National Institute of Standards and Technology16, University of Udine17, Université catholique de Louvain18, University of Basel19, Harvard University20, University of California, Davis21, Rutgers University22, University of York23, Wake Forest University24, Science and Technology Facilities Council25, University of Oxford26, University of Vienna27, Dresden University of Technology28, Leibniz Institute for Neurobiology29, Radboud University Nijmegen30, University of Tokyo31, Centre national de la recherche scientifique32, University of Cambridge33, Royal Holloway, University of London34, University of California, Santa Barbara35, University of Luxembourg36, Los Alamos National Laboratory37, Harbin Institute of Technology38
TL;DR: A procedure to assess the precision of DFT methods was devised and used to demonstrate reproducibility among many of the most widely used DFT codes, demonstrating that the precisionof DFT implementations can be determined, even in the absence of one absolute reference code.
Abstract: The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. We report the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements.
1,141 citations
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Daniel J. Klionsky1, Amal Kamal Abdel-Aziz2, Sara Abdelfatah3, Mahmoud Abdellatif4 +2980 more•Institutions (777)
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
1,129 citations
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TL;DR: SchNet as mentioned in this paper is a deep learning architecture specifically designed to model atomistic systems by making use of continuous-filter convolutional layers, where the model learns chemically plausible embeddings of atom types across the periodic table.
Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.
1,104 citations
Authors
Showing all 4893 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jun Wang | 166 | 1093 | 141621 |
Leroy Hood | 158 | 853 | 128452 |
Andreas Heinz | 108 | 1078 | 45002 |
Philippe Dubois | 101 | 1098 | 48086 |
John W. Berry | 97 | 351 | 52470 |
Michael Müller | 91 | 333 | 26237 |
Bart Preneel | 82 | 844 | 25572 |
Bjorn Ottersten | 81 | 1058 | 28359 |
Sander Kersten | 79 | 246 | 23985 |
Alexandre Tkatchenko | 77 | 271 | 26863 |
Rudi Balling | 75 | 238 | 19529 |
Lionel C. Briand | 75 | 380 | 24519 |
Min Wang | 72 | 716 | 19197 |
Stephen H. Friend | 70 | 184 | 53422 |
Ekhard K. H. Salje | 70 | 581 | 19938 |