检索结果-内蒙古大学图书馆

Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Boucharas, Dimitrios G. Androutsos, Christos Gkois, George Tsakanikas, Vassilis D. Pezoulas, Vasileios C. Manousos, Dimitrios Skaramagkas, Vasileios Chatzaki, Chariklia Kontogiannis, Stathis Spandonidis, Christos Pantazis, Alexandros K. Tachos, Nikolaos S. Tsiknakis, Manolis Fotiadis, Dimitrios I. The Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina IoanninaGR-45110 Greece Crete HeraklionGR-70013 Greece Biomedical Informatics and eHealth Laboratory Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Crete HeraklionGR-71004 Greece The Department of Electric and Computer Engineering Hellenic Mediterranean University Crete HeraklionGR-71004 Greece PD Neurotechnology Ltd IoanninaGR-45110 Greece Prisma Electronics SA P.Faliro AthensGR-17564 Greece Crete HeraklionGR-70013 Greece Biomedical Research Institute FORTH IoanninaGR-45110 Greece The Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR-45110 Greece

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson’s disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication protocols enables the straightforward replacement of one of the core components with a relative one (e.g., a different model of insoles), transparently from the end user, without affecting the overall architecture, resulting in a framework with the flexibility to adjust its modularity. © 2022, CC BY.

关键词： computer architecture

Modern deep learning in bioinformatics

学校读者我要写书评

暂无评论

Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

Atomistic Behavior of Deformed Au Thin Film during the Indentation Process

学校读者我要写书评

暂无评论

Atomistic Behavior of Deformed Au Thin Film during the Inden...

Innovative research in Applied Science, engineering and Technology (IRASET),Conference on

作者： Abdelkarim Ou-khouya Hassane Mes-adi Meryem Taoufiki Imane Ait Ibrahim Amin El Harfouf Rachid Herbazi Mohamed Tahiri Process Engineering Computer Science and Mathematics Laboratory Khouribga National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco Faculty of Sciences and Techniques Sustainable Development Laboratory Sultan Moulay Slimane University Beni-Mellal Morocco Departement of Physics Laboratory of Condensed Matter and interdisciplinary Sciences CNRST Labeled Research Unit Faculty of Sciences Mohamed V University Rabat Morocco Department of Physics Polydisciplinary Faculty of Khouribga University of Sultan Moulay Slimane Khouribga Morocco Intelligent Systems and Applications Laboratory (LSIA) EMSI Tangier Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

In this study, we investigate the mechanical properties of gold (Au) materials using molecular dynamics simulations. In this simulation, the nanoindentation process was performed to analyze the deformation defects and hardness properties of Au under different indentation velocities. The results suggest that the deformation behavior of the Au specimen was affected by different indentation velocities. These findings were confirmed by examining the load-displacement curve, which indicated elastic deformation through the appearance of a linear load-displacement relationship at low indentation velocities. In contrast, plastic deformation was identified by the presence of intensified serrations in the load-displacement curve at higher indentation velocities. Under such conditions, the hardness exhibited minimal variation when the indenter velocity increased from 5 mls to 10 mls. However, a significant increase in hardness was noted when the indentation velocity was further raised to 15 mls.

关键词： Deformable models Gold Analytical models Deformation Dynamics Plastics Load modeling

Radiomics-Based Non-Invasive Lymph Node Metastases Prediction in Breast Cancer

学校读者我要写书评

暂无评论

Radiomics-Based Non-Invasive Lymph Node Metastases Predictio...

Annual IEEE Symposium on computer-Based Medical systems

作者： Ermanno Cordelli Rosa Sicilia Domiziana Santucci Carlo de Felice Carlo Cosimo Quattrocchi Bruno Beomonte Zobel Giulio Iannello Paolo Soda Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Unit of Radiology Department of Medicine Università Campus Bio-Medico di Roma Unit of Radiology Department of Medicine Università La Sapienza di Roma

ISBN: (数字)9781728194295

ISBN: (纸本)9781728194301

Breast cancer is the most common tumour in women and it is characterized by a huge variety of clinical and histological scenarios and imaging pattern. The axillary lymph node metastases presence or absence is one of the most important prognostic factors affecting the loco-regional recurrence and the overall survival. The lymph node status is usually determined by an histological exam, an invasive procedure that could result in complications. This work aims to provide a safer and non-invasive prognostic approach by introducing a radiomics-based method that predicts axillary lymph node metastasis. It combines primary tumor histological features and patients clinical data with quantitative measures extracted from the MR images. To compute these latter quantities we determine the convex hull of the ROIs and we introduce the Three Orthogonal Planes-Local Binary Pattern (TOP-LBP). On 99 samples the approach achieves a promising AUC equal to 85.6%.

关键词： Lesions Three-dimensional displays Lymph nodes Magnetic resonance imaging Feature extraction Image segmentation

KNEE ARTHRITIS SEVERITY MEASUREMENT USING DEEP LEARNING: A PUBLICLY AVAILABLE ALGORITHM WITH A MULTI-INSTITUTIONAL VALIDATION SHOWING RADIOLOGIST-LEVEL PERFORMANCE

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Gu, Hanxue Li, Keyu Colglazier, Roy J. Yang, Jichen Lebhar, Michael O'Donnell, Jonathan Jiranek, William A. Mather, Richard C. French, Rob J. Said, Nicholas Zhang, Jikai Park, Christine Mazurowski, Maciej A. The Duke University of Medicine Durham27708 United States The Department of Radiology Radiology Musculoskeletal Imaging Radiology Durham27708 United States The Practice Transformation Unit The Department of Orthopaedic Surgery Duke University Durham27708 United States The Orthopaedic Surgery Orthopaedics Orthopaedics Duke University Durham27708 United States Population Health Sciences Population Health Sciences Basic Science Department Durham27708 United States Duke Clinical Research Institute Institutes and Centers Durham27708 United States Duke-Margolis Center for Health Policy Duke - Margolis Center For Health Policy Initiatives Durham27708 United States Radiology Radiology Clinical Science Department Durham27708 United States Biostatistics and Bioinformatics Biostatistics & Bioinformatics Basic Science Department Durham27708 United States The Department of Electrical and Computer Engineering Electrical and Computer Engineering Pratt School of Engineering Durham27708 United States Computer Science Computer Science Trinity College of Arts & Sciences Durham27708 United States Duke Cancer Institute Institutes and Centers in Duke University Durham27708 United States

The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. © 2022, CC BY-NC-SA.

关键词： Image classification

CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity

学校读者我要写书评

暂无评论

CARDIOCARE: An integrated platform for the management of eld...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： G. Karanasiou L. Koumakis S. Sfakianakis G. Manikis G. Kalliatakis A. Antoniades L. Lakkas D. Mauri C. Cipolla K. Mazzocco A. Papakonstantinou G. Filippatos A. Constantinidou B. Šeruga C. Conti A. Bucur E. Pacella K. Marias M. Tsiknakis D.I. Fotiadis Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Foundation for Research and Technology - Hellas Institute of Computer Science Heraklion Crete Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Greece Stremble Ventures Ltd Limassol Cyprus 2nd Department of Cardiology School of Medicine University of Ioannina Department of Medical Oncology University of Ioannina Greece Cardiology Division European Institute of Oncology Milan Italy Department for Breast Cancer Endocrine Tumors and Sarcoma Theme Cancer Karolinska University Hospital and the Department of Oncology-Pathology Karolinska Institute Stockholm Sweden Department of Cardiology National and Kapodistrian University of Athens School of Medicine Attikon University Hospital Athens Greece Bank of Cyprus Oncology Centre Bank of Cyprus Oncology Centre Nicosia Cyprus Department of Medical Oncology Institute of Oncology Ljubljana Ljubljana Slovenia Istituto di Management Sanitario S.r.l Milano Italy Philips Research Europe Eindhoven Netherlands European Society of Cardiology Brussels Belgium Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute Foundation for Research and Technology – Hellas University Campus of Ioannina Greece

Breast cancer (BC) remains the most diagnosed cancer in women, accounting for 12% of new annual cancer cases in Europe and worldwide. Advances in surgery, radiotherapy and systemic treatment have resulted in improved clinical outcomes and increased survival rates in recent years. However, BC therapy-related cardiotoxicity, may severely impact short- and long-term quality of life and survival. This study presents the CARDIOCARE platform and its main components, which by integrating patient-specific data from different categories, data from patient-oriented eHealth applications and wearable devices, and by employing advanced data mining and machine learning approaches, provides the healthcare professionals with a valuable tool for effectively managing BC patients and preventing or alleviating treatment induced *** Relevance— Through the adoption of CARDIOCARE platform healthcare professionals are able to stratify patients for their risk for cardiotoxicity and timely apply adequate interventions to prevent its onset.

关键词：

Recording provenance of workflow runs with RO-Crate

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Leo, Simone Crusoe, Michael R. Rodríguez-Navas, Laura Sirvent, Raül Kanitz, Alexander De Geest, Paul Wittner, Rudolf Pireddu, Luca Garijo, Daniel Fernández, José M. Colonnelli, Iacopo Gallo, Matej Ohta, Tazro Suetake, Hirotaka Capella-Gutierrez, Salvador de Wit, Renske Kinoshita, Bruno P. Soiland-Reyes, Stian CA Pula Italy Vrije Universiteit Amsterdam Amsterdam Netherlands DTL Projects Netherlands Forschungszentrum Jülich Germany Barcelona Supercomputing Center Barcelona Spain Biozentrum University of Basel Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland VIB Data Core Gent Belgium Faculty of Informatics Masaryk University Brno Czech Republic Institute of Computer Science Masaryk University Brno Czech Republic BBMRI-ERIC Graz Austria Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Computer Science Department Università degli Studi di Torino Torino Italy Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Shizuoka Japan Institute for Advanced Academic Research Chiba University Chiba Japan Sator Incorporated Tokyo Japan Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (research Object Crate) and *** to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. © 2023, CC BY.

关键词： Workflow management

TAp73α drives cancer metastasis via PPI-mediated derepression of the neuronal HDAC2/REST-GABBR2 axis

学校读者我要写书评

暂无评论

Cancer Letters 2025年 628卷 217867页

作者： Nico Murr Christin Richter Shailendra K. Gupta Elke Hammer Nares Trakooljul Anja Stoll Steffen Möller Lukas E. Neumann Brigitte M. Pützer Alf Spitschak Institute of Experimental Gene Therapy and Cancer Research Rostock University Medical Center 18069 Rostock Germany Department of Systems Biology and Bioinformatics University of Rostock 18057 Rostock Germany Department of Biomedical Engineering & Bioinformatics Chhattisgarh Swami Vivekananda Technical University Bhilai Chhattisgarh India DZHK (German Center for Cardiovascular Research) Greifswald Germany Department Functional Genomics Interfaculty Institute of Genetics and Functional Genomics University Medicine Greifswald Greifswald Germany Physiological Genomics Unit Research Institute for Farm Animal Biology 18196 Dummerstorf Germany Department Life Light & Matter University of Rostock 18057 Rostock Germany

Metastasis is the leading cause of death in patients with malignant melanoma, yet the molecular and transcriptional mechanisms remain elusive. This study reveals a crucial role of the p53 homolog, TAp73α, in promoting melanoma metastasis. Using multi-omics approaches combining transcriptomics, proteomics, cistromics and 3D modeling, we discovered a paradigm-shifting mechanism by which TAp73α binds directly to HDAC2, disassembles the HDAC2/REST repressor complex and aberrantly triggers activation of the neuronal receptor GABBR2 in cancer cells. TAp73α-induced derepression of GABBR2 expression leads to upregulation of EMT markers, promotes cancer cell invasiveness and proliferation, and correlates with poor survival outcomes. Our findings redefine the function of p73 in cancer pathogenesis and identify the TAp73α-HDAC2/REST-GABBR2 axis as a novel driver of melanoma progression. These insights could guide future strategies on melanoma treatment.

关键词： p73 HDAC2/REST complex GABBR2 Melanoma metastasis Protein-protein-interaction Transcriptomics 3D modeling

Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms