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Progression subtypes in Parkinson's disease: Results of an AI-based multi-cohort analysis

Clinical Neurophysiology 2024年 159卷 e39-e39页

作者： Hähnel, T. Raschka, T. Sapienza, S. Klucken, J. Glaab, E. Corvol, J. Falkenburger, B. Fröhlich, H. Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT University of Bonn Bonn Germany Biomedical Data Science Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Luxembourg Institute of Health (LIH) Strassen Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Sorbonne Université Paris Brain Institute – ICM Inserm CNRS Assistance Publique Hôpitaux de Paris Pitié-Salpêtrière Hospital Department of Neurology Paris France German Center for Neurodegenerative Diseases (DZNE) Tatzberg 41 01307 Dresden Germany

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Parameter Identification in Cyclic Voltammetry of Alkaline Methanol Oxidation 8

Parameter Identification in Cyclic Voltammetry of Alkaline M...

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8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2018

作者： Clees, Tanja Nikitin, Igor Nikitina, Lialia Pott, Sabine Krewer, Ulrike Haisch, Theresa Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Institute of Energy and Process Systems Engineering Technical University Braunschweig Germany

ISBN: (纸本)9789897583230

Alkaline methanol oxidation is an electrochemical process, perspective for the design of efficient high energy density fuel cells. The process involves a large number of elementary reactions, forming a complex reaction graph, and it is described by a system of non-linear differential equations of high order. The purpose of parameter identification is a reconstruction of reaction constants in this model on the basis of available experimental data. Cyclic voltammetry, a measurement of dynamical current-voltage characteristic of the cell, is especially suitable for analysis of electrochemical kinetics. In this paper we present several approaches for parameter identification in cyclic voltammetry of alkaline methanol oxidation. With the aid of global optimization methods and interactive parameter study we find four islands of solutions in parameter space, corresponding to different chemical mechanisms of the process. The main features of solutions are discussed and the reconstructed reaction constants are presented. Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： Differential equations

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The recovery of ridge functions on the hypercube suffers from the curse of dimensionality

arXiv

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arXiv 2019年

作者： Doerr, Benjamin Mayer, Sebastian Ecole Polytechnique CS35003 Palaiseau91120 France Fraunhofer Center for Machine Learning Fraunhofer-Institute for Algo-rithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany

A multivariate ridge function is a function of the form f(x) = g(aT x), where g is univariate and a ∈ Rd. We show that the recovery of an unknown ridge function defined on the hypercube [-1, 1]d with Lipschitz-regular profile g suffers from the curse of dimensionality when the recovery error is measured in the L∞-norm, even if we allow randomized algorithms. If a limited number of components of a is substantially larger than the others, then the curse of dimensionality is not present and the problem is weakly tractable provided the profile g is sufficiently regular. Copyright © 2019, The Authors. All rights reserved.

关键词： Recovery

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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What was the Question? A Systematization of Information Retrieval and NLP Problems

What was the Question? A Systematization of Information Retr...

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Federated Conference on Computer Science and Information Systems

作者： Jens Dorpinghaus Johannes Darms Marc Jacobs Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven

ISBN: (纸本)9781538623718

In this paper we suggest a novel systematization of Information Retrieval and Natural Language Processing problems. Using this rather general description of problems we are able to discuss and proof the equivalence of some problems. We provide reformulations of well-known problems like Named Entity Recognition using our novel description and discuss further research and the expected outcome. We will discuss the relation of two problems, cluster labeling and search query finding. With these results we are able to provide a novel optimization approach to both problems. This novel systematization approach provides a yet unknown view generating new classes of problems in NLP. It brings application and algorithmic approaches together and offers a better description with concepts of theoretical computer science.

关键词： information retrieval Systematization Natural Language Processing NINL gene entity identification theoretical computer science New classes

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Evaluating digital device technology in Alzheimer’s disease via artificial intelligence

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Alzheimer's & Dementia 2021年第S6期17卷

作者： Meemansa Sood Robbert Harms Maximilian Bügler Ioannis Tarnanas Neva Coello Holger Fröhlich Bonn-Aachen International Center for IT (b-it) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Altoida Inc. Houston TX USA Altoida AG Hirschengraben Lucerne Switzerland Global Brain Health Institute Trinity College Dublin Ireland Hellenic Initiative Against Alzheimer's Disease Johns Hopkins Precision Medicine Center Baltimore MD USA Novartis Basel Switzerland

Background Digital Measures (DMs) derived from mobile devices and smartphone applications have received a strongly increasing attention during the last years, because they could allow for an accurate, quantitative monitoring of disease symptoms, even outside clinics. In addition, DMs may help to diagnose Alzheimer’s Disease (AD) in a pre-symptomatic stage and thus increase the success chances of therapeutic interventions. However, before any use in clinical routine, DMs have to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. In this regard the IMI project RADAR-AD ( *** ) has the ambition to evaluate a broad panel of digital technologies with respect to their potential for early disease diagnosis while focusing on functional activities of daily living. Method An example of a panel of digital technology RADAR-AD uses is a smartphone based virtual reality game resulting into an assessment of cognitive impairment. In our work we analyzed connections between digital readouts and cognitive features like MMSE (Mini Mental State Examination) via one of our recently developed Artificial Intelligence (AI) approaches called Variational Autoencoder Modular Bayesian Networks (VAMBN). Going one step further we also tested the possibility to accurately predict MMSE scores from DMs and vice versa via machine learning. Based on this finding we then simulated DMs within the ADNI cohort and re-ran VAMBN. Result Application of VAMBN on the data from virtual reality game resulted into a network comprising DMs, MMSE sub-item scores and demographic features (Figure 1). It thus allowed to disentangle and quantify the relationship between DMs and established clinical scores. The simulation of DM’s and application of VAMBN in the ADNI cohort allowed us to further predict connections of DMs with FAQ (Functional Activity Questionnaire) and even molecular mechanisms. Conclusion Our results indicate t

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Assessment of RMTs for Discriminating Stages of Alzheimer’s Disease

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Alzheimer's & Dementia 2023年第S22期19卷

作者： Manuel Lentzen Srinivasan Vairavan Jelena Curcic Alankar Atreya Chris Hinds Marijn Muurling Pauline Conde Margarita Grammatikopoulou Ioulietta Lazarou Spiros Nikolopoulos Neva Coello Casper de Boer Dag Aarsland Anna-Katharine Brem Holger Fröhlich RADAR-AD Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Bonn-Aachen International Center for IT (b-it) Bonn Germany Janssen Research and Development LLC Titusville NJ USA Novartis Institutes for BioMedical Research Basel Switzerland University of Oxford Oxford United Kingdom VU University Medical Center Amsterdam Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands King’s College London London United Kingdom Centre for Research & Technology Hellas Thessaloniki Greece Amsterdam Neuroscience Neurodegeneration Amsterdam Noord-Holland Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Noord-Holland Netherlands

Background RADAR-AD is a European project in the context of the Innovative Medicine Initiative (IMI) focusing on the earlier identification of patients at risk for developing Alzheimer’s Disease (AD) via a panel of remote monitoring technologies (RMTs), including smartphone apps and wearable devices. Method We examined the ability of 6 RMTs (Altoida, Axivity, Banking app, Fitbit, Physilog, and Mezurio) to distinguish between healthy controls (HC) and disease stages of preclinical (PreAD), prodromal (ProAD), and mild to moderate Alzheimer’s disease (MildAD) based on 175 patients (interim analysis). We trained three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) in a pairwise setting (HC vs. PreAD, HC vs. ProAD, HC vs. MildAD, PreAD vs. ProAD, and ProAD vs. MildAD). Since the interim dataset is still limited, we performed repeated, stratified nested cross-validation to get a robust performance estimate. Each classifier was trained with the features of the different devices and a set of baseline variables. The latter include a patient’s gender, age, years of education, and body mass index (BMI) when physical conditions might play a role (Axivitiy, Fitbit, Physilog). In addition, we checked whether specific patterns of the study groups allowed discrimination of the different study groups based on the baseline variables alone. Therefore, we trained one Logistic Regression model with these variables and compared the performance of the other three models with this baseline. The models trained with the baseline and questionnaire-based data served as the reference value in our benchmark that represents how well the discrimination of the different groups works with clinical tests. Result Our preliminary data show that RMTs can identify patients already in a prodromal disease stage (AUC ∼69%, Figure 1). Furthermore, the pairwise combination of data from a banking app and an app monitoring functional cognitive abilities via an augmented reality g

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Pseudoentropic derivation of the regularized lattice Boltzmann method

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Physical Review E 2019年第2期100卷 023302-023302页

作者： Andreas Krämer Dominik Wilde Knut Küllmer Dirk Reith Holger Foysi Institute of Technology Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Grantham-Allee 20 53757 Sankt Augustin Germany National Heart Lung and Blood Institute National Institutes of Health Bethesda Maryland 20892 United States Department of Mechanical Engineering University of Siegen Paul-Bonatz-Straße 9-11 57076 Siegen-Weidenau Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 53754 Sankt Augustin Germany

The lattice Boltzmann method (LBM) facilitates efficient simulations of fluid turbulence based on advection and collision of local particle distribution functions. To ensure stable simulations on underresolved grids, the collision operator must prevent drastic deviations from local equilibrium. This can be achieved by various methods, such as the multirelaxation time, entropic, quasiequilibrium, regularized, and cumulant schemes. Complementing a part of a unified theoretical framework of these schemes, the present work presents a derivation of the regularized lattice Boltzmann method (RLBM), which follows a recently introduced entropic multirelaxation time LBM by Karlin, Bösch, and Chikatamarla (KBC). It is shown that both methods can be derived by locally maximizing a quadratic Taylor expansion of the entropy function. While KBC expands around the local equilibrium distribution, the RLBM is recovered by expanding entropy around a global equilibrium. Numerical tests were performed to elucidate the role of pseudoentropy maximization in these models. Simulations of a two-dimensional shear layer show that the RLBM successfully reproduces the largest eddies even on a 16×16 grid, while the conventional LBM becomes unstable for grid resolutions of 128×128 and lower. The RLBM suppresses spurious vortices more effectively than KBC. In contrast, simulations of the three-dimensional Taylor-Green and Kida vortices show that KBC performs better in resolving small scale vortices, outperforming the RLBM by a factor of 1.8 in terms of the effective Reynolds number.

关键词： Flow instability Shear flows Turbulence

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DARE: A reflective platform designed to enable agile data-driven research on the cloud 15

DARE: A reflective platform designed to enable agile data-dr...

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15th IEEE International Conference on eScience, eScience 2019

作者： Klampanos, Iraklis Davvetas, Athanasios Gemund, Andre Atkinson, Malcolm Koukourikos, Antonis Filgueira, Rosa Krause, Amrey Spinuso, Alessandro Charalambidis, Angelos Magnoni, Federica Casarotti, Emanuele Page, Christian Lindner, Mike Ikonomopoulos, Andreas Karkaletsis, Vangelis Institute of Informatics and Telecommunications NCSR Demokritos Greece Fraunhofer Institute for Algorithms and Scientific Computing Germany School of Informatics University of Edinburgh United Kingdom EPCC University of Edinburgh United Kingdom Netherlands Istituto Nazionale Geofisica e Vulcanologia [INGV Italy Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique [CERFACS France Karlsruher Institut fur Technologie [KIT Germany Institute of Nuclear and Radiological Sciences and Technology Energy and Safety NCSR Demokritos Greece

ISBN: (纸本)9781728124513

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development. © 2019 IEEE.

关键词： Semantics

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RDM-stars as sources of fast radio bursts

arXiv

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arXiv 2018年

作者： Nikitin, Igor Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany

In this work we consider a recently formulated model of a black hole coupled with radially directed flows of dark matter (RDM-star). In this model, a cutoff by quantum gravity (QG) creates a core of Planck density, filled with a gas of Planck mass particles, inside the star. Further, in this model, a fast radio burst (FRB) can be generated via the following mechanism. An object of an asteroid mass falls onto an RDM-star. Due to large gravitational forces available in the interior, the RDM-star works as a powerful accelerator, boosting the nucleons composing the object to extremely high energies. Upon collision with the core, the nucleons enter in deeply inelastic reactions with the Planck particles composing the core. This process is followed by stimulated emission of highly energetic photons. On the way outside, these photons are subjected to a strong redshift factor, downscaling their frequencies to the radio diapason, as a result producing an FRB. A straightforward computation gives a characteristic upper FRB frequency of 0.6GHz. With attenuation factors, it is stretched to a range 0.35-8GHz, fitting well with the observable FRB frequencies. We also discuss the reconstruction of other FRB parameters in frames of the model. Copyright © 2018, The Authors. All rights reserved.

关键词： Stars

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