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Institution

University of Luxembourg

EducationLuxembourg, Luxembourg
About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Population & European union. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, a deep tensor neural network is used to predict atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure.
Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.

1,083 citations

Journal ArticleDOI
TL;DR: Subthalamic stimulation was superior to medical therapy in patients with Parkinson's disease and early motor complications and time with good mobility and no dyskinesia.
Abstract: A B S T R AC T BACKGROUND Subthalamic stimulation reduces motor disability and improves quality of life in patients with advanced Parkinson’s disease who have severe levodopa-induced motor complications. We hypothesized that neurostimulation would be beneficial at an earlier stage of Parkinson’s disease. METHODS In this 2-year trial, we randomly assigned 251 patients with Parkinson’s disease and early motor complications (mean age, 52 years; mean duration of disease, 7.5 years) to undergo neurostimulation plus medical therapy or medical therapy alone. The primary end point was quality of life, as assessed with the use of the Parkinson’s Disease Questionnaire (PDQ-39) summary index (with scores ranging from 0 to 100 and higher scores indicating worse function). Major secondary outcomes included parkinsonian motor disability, activities of daily living, levodopa-induced motor com plications (as assessed with the use of the Unified Parkinson’s Disease Rating Scale, parts III, II, and IV, respectively), and time with good mobility and no dyskinesia. RESULTS For the primary outcome of quality of life, the mean score for the neurostimulation group improved by 7.8 points, and that for the medical-therapy group worsened by 0.2 points (between-group difference in mean change from baseline to 2 years, 8.0 points; P = 0.002). Neurostimulation was superior to medical therapy with respect to motor disability (P<0.001), activities of daily living (P<0.001), levodopa-induced motor complications (P<0.001), and time with good mobility and no dyskinesia (P = 0.01). Serious adverse events occurred in 54.8% of the patients in the neuro stimulation group and in 44.1% of those in the medical-therapy group. Serious adverse events related to surgical implantation or the neurostimulation device oc curred in 17.7% of patients. An expert panel confirmed that medical therapy was consistent with practice guidelines for 96.8% of the patients in the neurostimula tion group and for 94.5% of those in the medical-therapy group. CONCLUSIONS Subthalamic stimulation was superior to medical therapy in patients with Parkinson’s disease and early motor complications. (Funded by the German Ministry of Research and others; EARLYSTIM ClinicalTrials.gov number, NCT00354133.)

1,059 citations

Book ChapterDOI
01 Jan 2012
TL;DR: In this article, the authors argue that success lies in being able to communicate, share, and use information to solve complex problems, in adapting and innovating in response to new demands and changing circumstances, in marshaling and expanding the power of technology to create new knowledge, and in expanding human capacity and productivity.
Abstract: As the previous chapter indicates, there has been a significant shift in advanced economies from manufacturing to information and knowledge services. Knowledge itself is growing ever more specialized and expanding exponentially. Information and communication technology is transforming the nature of how work is conducted and the meaning of social relationships. Decentralized decision making, information sharing, teamwork, and innovation are key in today’s enterprises. No longer can students look forward to middle class success in the conduct of manual labor or use of routine skills – work that can be accomplished by machines. Rather, whether a technician or a professional person, success lies in being able to communicate, share, and use information to solve complex problems, in being able to adapt and innovate in response to new demands and changing circumstances, in being able to marshal and expand the power of technology to create new knowledge, and in expanding human capacity and productivity.

1,056 citations

Journal ArticleDOI
TL;DR: Recon 2, a community-driven, consensus 'metabolic reconstruction', is described, which is the most comprehensive representation of human metabolism that is applicable to computational modeling and has improved topological and functional features.
Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

1,002 citations

Journal ArticleDOI
TL;DR: The results indicate that a number of core symptoms of Internet addiction appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions.
Abstract: In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.

974 citations


Authors

Showing all 4893 results

NameH-indexPapersCitations
Jun Wang1661093141621
Leroy Hood158853128452
Andreas Heinz108107845002
Philippe Dubois101109848086
John W. Berry9735152470
Michael Müller9133326237
Bart Preneel8284425572
Bjorn Ottersten81105828359
Sander Kersten7924623985
Alexandre Tkatchenko7727126863
Rudi Balling7523819529
Lionel C. Briand7538024519
Min Wang7271619197
Stephen H. Friend7018453422
Ekhard K. H. Salje7058119938
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202360
2022250
20211,671
20201,776
20191,710
20181,663