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Predicting Time-to-conversion for Dementia of Alzheimer’s Type using Multi-modal Deep Survival Analysis

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arXiv 2022年

作者： Mirabnahrazam, Ghazal Ma, Da Beaulac, Cédric Lee, Sieun Popuri, Karteek Lee, Hyunwoo Cao, Jiguo Galvin, James E. Wang, Lei Beg, Mirza Faisal School of Engineering Simon Fraser University BurnabyBC Canada School of Medicine Wake Forest University Winston-SalemNC United States Department of Mathematics and Statistics University of Victoria VictoriaBC Canada Mental Health & Clinical Neurosciences School of Medicine University of Nottingham Nottingham United Kingdom Department of Computer Science Memorial University of Newfoundland St. John’sNL Canada Division of Neurology Department of Medicine University of British Columbia VancouverBC Canada Department of Statistics and Actuarial Science Simon Fraser University BurnabyBC Canada Comprehensive Center for Brain Health Department of Neurology University of Miami Miller School of Medicine MiamiFL United States Psychiatry and Behavioral Health Ohio State University Wexner Medical Center ColumbusOH United States

Dementia of Alzheimer’s Type (DAT) is a complex disorder influenced by numerous factors, but it is unclear how each factor contributes to disease progression. An in-depth examination of these factors may yield an accurate estimate of time-to-conversion to DAT for patients at various disease stages. We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data modalities in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We used a deep learning-based survival analysis model that extends the classic Cox regression model to predict time-to-conversion to DAT. Our findings showed that genetic features contributed the least to survival analysis, while CDC features contributed the most. Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features. Consequently, our study demonstrated that using the current clinical procedure, which includes gathering cognitive test results, can outperform survival analysis results produced using costly genetic or CSF data. © 2022, CC BY-NC-ND.

关键词： Neurodegenerative diseases

A mathematical model of platelet aggregation in an extravascular injury under flow. ∗

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arXiv 2020年

作者： Link, Kathryn G. Sorrells, Matthew G. Danes, Nicholas A. Neeves, Keith B. Leiderman, Karin Fogelson, Aaron L. Department of Mathematics University of Utah Salt Lake CityUT Department of Chemical and Biological Engineering Colorado School of Mines GoldenCO Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO Departments of Bioengineering and Pediatrics Hemophilia and Thrombosis Center University of Colorado Denver Anschutz Medical Campus AuroraCO Department of Biomedical Engineering University of Utah Salt Lake CityUT

We present the first mathematical model of flow-mediated primary hemostasis in an extravascular injury which can track the process from initial deposition to occlusion. The model consists of a system of ordinary differential equations (ODE) that describe platelet aggregation (adhesion and cohesion), soluble-agonist-dependent platelet activation, and the flow of blood through the injury. The formation of platelet aggregates increases resistance to flow through the injury, which is modeled using the Stokes-Brinkman equations. Data from analogous experimental (microfluidic flow) and partial differential equation models informed parameter values used in the ODE model description of platelet adhesion, cohesion, and activation. This model predicts injury occlusion under a range of flow and platelet activation conditions. Simulations testing the effects of shear and activation rates resulted in delayed occlusion and aggregate heterogeneity. These results validate our hypothesis that flow-mediated dilution of activating chemical ADP hinders aggregate development. This novel modeling framework can be extended to include more mechanisms of platelet activation as well as the addition of the biochemical reactions of coagulation, resulting in a computationally efficient high throughput screening *** Codes 92B05, 76S99, 93A30 Copyright © 2020, The Authors. All rights reserved.

关键词： Aggregates

The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

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The Importance of Gender Specification for Detection of Driv...

Biomedical engineering International Conference (BMEiCON)

作者： Mohammad Shahbakhti Matin Beiramvand Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology the Human Phenome Institute Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9781665489041

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

关键词： Training Support vector machines Computational modeling Transforms Fatigue Brain modeling Electroencephalography

Hierarchy of magnon mode entanglement in antiferromagnets

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Physical Review B 2020年第22期102卷 224418-224418页

作者： Vahid Azimi Mousolou Andrey Bagrov Anders Bergman Anna Delin Olle Eriksson Yuefei Liu Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Physics and Astronomy Uppsala University Box 516 SE-751 20 Uppsala Sweden Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan 81746-73441 Iran School of Mathematics Institute for Research in Fundamental Sciences (IPM) P. O. Box 19395-5746 Tehran Iran Theoretical Physics and Applied Mathematics Department Ural Federal University Ekaterinburg 620002 Russia Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology AlbaNova University Center SE-10691 Stockholm Sweden Swedish e-Science Research Center (SeRC) KTH Royal Institute of Technology SE-10044 Stockholm Sweden School of Science and Technology Örebro University SE-701 82 Örebro Sweden

Continuous variable entanglement between magnon modes in Heisenberg antiferromagnets with Dzyaloshinskii-Moriya (DM) interaction is examined. Different bosonic modes are identified, which allows us to establish a hierarchy of magnon entanglement. We argue that entanglement between magnon modes is determined by a simple lattice-specific parameter, together with the ratio of the strengths of the DM and Heisenberg exchange interactions, and that magnon entanglement can be detected by means of quantum homodyne techniques. As an illustration of the relevance of our findings for possible entanglement experiments in the solid state, a typical antiferromagnet with the perovskite crystal structure is considered, and it is shown that long wave length magnon modes have a maximal degree of entanglement.

关键词： Magnons Quantum entanglement Antiferromagnets

Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter

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Proceedings of the Association for Information Science and Technology 2020年第1期57卷 e313页

作者： Fan, Lizhou Yu, Huizi Yin, Zhanyuan Program in Digital Humanities University of California Los AngelesCA United States Department of Statistics University of California Los AngelesCA United States Department of Mathematics University of California Los AngelesCA United States Department of Economics University of California Los AngelesCA United States

As the COVID-19 pandemic has unfolded, Hate Speech on social media about China and Chinese people has encouraged social stigmatization. For the historical and humanistic purposes, this history-in-the-making needs to be archived and analyzed. Using the query "china+and+coronavirus" to scrape from the Twitter API, we have obtained 3,457,402 key tweets about China relating to COVID-19. In this archive, in which about 40% of the tweets are from the U.S., we identify 25,467 Hate Speech occurrences and analyze them according to lexicon-based emotions and demographics using machine learning and network methods. The results indicate that there are substantial associations between the amount of Hate Speech and demonstrations of sentiments, and state demographics factors. Sentiments of surprise and fear associated with poverty and unemployment rates are prominent. This digital archive and the related analyses are not simply historical, therefore. They play vital roles in raising public awareness and mitigating future crises. Consequently, we regard our research as a pilot study in methods of analysis that might be used by other researchers in various fields. 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Coronavirus

Comparison of random forest algorithm, support vector machine and neural network for classification of student satisfaction towards higher education services

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AIP Conference Proceedings 2022年第1期2578卷 060003页

作者： Didi Supriyadi Purwanto Purwanto Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang 50275 Indonesia Department of Information System Faculty of Informatics Institut Teknologi Telkom Purwokerto Banyumas 53147 Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang 50275 Indonesia Department of Statistics Faculty of Sciences and Mathematics Diponegoro University Semarang 50275 Indonesia

Students are customers of a higher education institution. Various types of services and facilities are provided by universities for students, such as academic services, student services, and infrastructure including information technology services. Student satisfaction with the quality of the university services is one of the factors that affect the academic performance of the university. This study aims to compare the level of accuracy between the Random Forest method, Support Vector Machine and Neural Network for the classification of student satisfaction with the services provided by Higher Education. The dataset consisted of 430 respondents from private universities in Vietnam taken and adapted from previous research. The training and testing dataset uses a split proportion of 80:20 of the total available datasets. Model performance measurements using matrix confusion include accuracy, sensitivity (recall), and precision. The results showed that the Random Forest method can produce the best accuracy rate of 76.47%, followed by Neural Network and Support Vector Machine algorithms with an accuracy rate of 74.12% on each.

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Influence of PEG on the clustering of active janus colloids

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arXiv 2021年

作者： Kalil, Mohammed A. Baumgartner, Nicky R. Issa, Marola W. Ryan, Shawn D. Wirth, Christopher L. Department of Chemical and Biomedical Engineering Washkewicz College of Engineering Cleveland State University 2121 Euclid Avenue ClevelandOH44115 United States Department of Mathematics and Statistics College of Science and Health Professions Cleveland State University 2121 Euclid Avenue ClevelandOH44115 United States Department of Chemical and Biomolecular Engineering Case School of Engineering Case Western Reserve University ClevelandOH44106 United States Center for Applied Data Analysis and Modeling Cleveland State University ClevelandOH44115 United States

Micrometer scale "colloidal" particles that propel in a deterministic fashion in response to local environmental cues are useful analogs to self-propelling entities found in nature. Both natural and synthetic active colloidal systems are often near boundaries or are located in crowded environments. Herein, we describe experiments in which we measured the influence of hydrogen peroxide concentration and dispersed polyethylene glycol (PEG) on the clustering behavior of 5 μm catalytic active Janus particles at low concentration. We found the extent to which clustering occurred in ensembles of active Janus particles grew with hydrogen peroxide concentration in the absence of PEG. Once PEG was added, clustering was slightly enhanced at low PEG volume fractions, but was reduced at higher PEG volumes fractions. The region in which clustering was mitigated at higher PEG volume fractions corresponded to the region in which propulsion was previously found to be quenched. Complementary agent based simulations showed that clustering grew with nominal speed. These data support the hypothesis that growth of living crystals is enhanced with increases in propulsion speed, but the addition of PEG will tend to mitigate cluster formation as a consequence of quenched propulsion at these conditions. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational methods

Sausage, kink, and fluting MHD wave modes identified in solar magnetic pores by Solar Orbiter/PHI

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arXiv 2024年

作者： Jafarzadeh, S. Schiavo, L.A.C.A. Fedun, V. Solanki, S.K. Stangalini, M. Calchetti, D. Verth, G. Jess, D.B. Grant, S.D.T. Ballai, I. Gafeira, R. Keys, P.H. Fleck, B. Morton, R.J. Browning, P.K. Silva, S.S.A. Appourchaux, T. Gandorfer, A. Gizon, L. Hirzberger, J. Kahil, F. Suárez, D. Orozco Schou, J. Strecker, H. del Toro Iniesta, J.C. Valori, G. Volkmer, R. Woch, J. Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany School of Physics & Astronomy The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Plasma Dynamics Group Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom ASI Italian Space Agency Via del Politecnico snc RomeI-00133 Italy Plasma Dynamics Group School of Mathematics and Statistics The University of Sheffield Hicks Building Hounsfield Road SheffieldS3 7RH United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queen’s University Belfast BelfastBT7 1NN United Kingdom Department of Physics and Astronomy California State University Northridge NorthridgeCA91330 United States Instituto de Astrofísica e Ciências do Espaço Department of Physics University of Coimbra Coimbra3040-004 Portugal ESA Science and Operations Department c/o NASA Goddard Space Flight Center GreenbeltMD20771 United States Univ. Paris-Sud Institut d’Astrophysique Spatiale UMR 8617 CNRS Bâtiment 121 Orsay91405 Cedex France Apartado de Correos 3004 GranadaE-18080 Spain Leibniz-Institut für Sonnenphysik Schöneckstr. 6 FreiburgD-79104 Germany Institut für Astrophysik und Geophysik Georg-August-Universität Göttingen Friedrich-Hund-Platz 1 Göttingen37077 Germany

Solar pores are intense concentrations of magnetic flux that emerge through the Sun’s photosphere. When compared to sunspots, they are much smaller in diameter and hence can be impacted and buffeted by neighbouring granular activity to generate significant magnetohydrodynamic (MHD) wave energy flux within their confines. However, observations of solar pores from ground-based telescope facilities may struggle to capture subtle motions synonymous with higher-order MHD wave signatures due to seeing effects produced in the Earth’s atmosphere. Hence, we have exploited timely seeing-free and high-quality observations of four small magnetic pores from the High Resolution Telescope (HRT) of the Polarimetric and Helioseismic Imager (PHI) on board the Solar Orbiter spacecraft, during its first close perihelion passage in March 2022 (at a distance of 0.5 au from the Sun). Through acquisition of data under stable observing conditions, we have been able to measure the area fluctuations and horizontal displacements of the solar pores. Cross correlations between perturbations in intensity, area, line-of-sight velocity, and magnetic fields, coupled with the first-time application of novel Proper Orthogonal Decomposition (POD) techniques on the boundary oscillations, provide a comprehensive diagnosis of the embedded MHD waves as sausage and kink modes. Additionally, the previously elusive m = 2 fluting mode is identified in the most magnetically isolated of the four pores. An important consideration lies in how the identified wave modes contribute towards the transfer of energy into the upper solar atmosphere. We find that the four pores examined have approximately 56%, 72%, 52%, and 34% of their total wave energy associated with the identified sausage modes, and around 23%, 17%, 39%, and 49% to their kink modes, respectively, while the first pore also has around an 11% contribution linked to the fluting mode. This study marks the first-time identification of concurrent sausage, kin

关键词： Magnetohydrodynamics

An improved algorithm to handle noise objects in the process of clustering

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An improved algorithm to handle noise objects in the process...

作者： Pathberiya, Hasanthi A. Tilakaratne, Chandima D. Hansen, Liwan L. Department of Statistics University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Statistics University of Colombo Colombo Sri Lanka School of Computing Engineering and Mathematics Western Sydney University Locked Bay 1797 PenrithNSW2751 Australia

Cluster analysis is considered as an approach for unsupervised learning. It tends to recognise hidden grouping structure in a set of objects using a predefined set of rules. Objects occupying unusual characteristics add noise to the data space. As a result, complexities and misinterpretation in clustering structures will arise. This study aims at proposing a novel iterative approach to eradicate the effect of noise objects in the process of deriving clusters of data. Performance of the proposed approach is tested on partitioning, hierarchical and neural network based clustering algorithms using both simulated and standard datasets supplemented with noise. An improvement in the quality of clustering structure resulted from the proposed approach is witnessed, compared to that of conventional clustering algorithms. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Cluster analysis