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arXiv 2021年

作者： Cong, Runmin Zhang, Yumo Fang, Leyuan Li, Jun Zhang, Chunjie Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong College of Electrical and Information Engineering Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518000 China Guangdong Provincial Key Laboratory of Urbanization and Geosimulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively. https://***/proj_***. © 2021, CC BY-NC-SA.

关键词： Object detection

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Transcutaneous Cervical Vagal Nerve Stimulation in Patients with Posttraumatic Stress Disorder (PTSD): A Pilot Study of Effects on PTSD Symptoms and Interleukin-6 Response to Stress

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Journal of Affective Disorders Reports 2021年 6卷 100190-100190页

作者： Bremner, J. Douglas Wittbrodt, Matthew T. Gurel, Nil Z. Shandhi, MdMobashir H. Gazi, Asim H. Jiao, Yunshen Levantsevych, Oleksiy M. Huang, Minxuan Beckwith, Joy Herring, Isaias Murrah, Nancy Driggers, Emily G. Ko, Yi-An Alkhalaf, MhmtJamil L. Soudan, Majd Shallenberger, Lucy Hankus, Allison N. Nye, Jonathon A. Park, Jeanie Woodbury, Anna Mehta, Puja K. Rapaport, Mark H. Vaccarino, Viola Shah, Amit J. Pearce, Bradley D. Inan, Omer T. Department of Psychiatry & Behavioral Sciences Emory University School of Medicine Atlanta Georgia Department of Radiology and Imaging Sciences Emory University School of Medicine Atlanta Georgia Atlanta VA Medical Center Decatur Georgia School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia Department of Epidemiology Rollins School of Public Health Emory University Atlanta Georgia Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University Atlanta Georgia Department of Medicine Renal Division Emory University School of Medicine Atlanta Georgia Department of Anesthesiology Emory University School of Medicine Atlanta Georgia Department of Medicine Division of Cardiology Emory University School of Medicine Atlanta Georgia Huntsman Mental Health Institute Department of Psychiatry University of Utah School of Medicine Salt Lake City Utah United States Coulter Department of Bioengineering Georgia Institute of Technology Atlanta Georgia

Background: Posttraumatic stress disorder (PTSD) is a highly disabling condition associated with alterations in multiple neurobiological systems, including increases in inflammatory and sympathetic function, responsible for maintenance of symptoms. Treatment options including medications and psychotherapies have limitations. We previously showed that transcutaneous Vagus Nerve Stimulation (tcVNS) blocks inflammatory (interleukin (IL)-6) responses to stress in PTSD. The purpose of this study was to assess the effects of tcVNS on PTSD symptoms and inflammatory responses to stress. Methods: Twenty patients with PTSD were randomized to double blind active tcVNS (N=9) or sham (N=11) stimulation in conjunction with exposure to personalized traumatic scripts immediately followed by active or sham tcVNS and measurement of IL-6 and other biomarkers of inflammation. Patients then self administered active or sham tcVNS twice daily for three months. PTSD symptoms were measured with the PTSD Checklist (PCL) and the Clinician Administered PTSD Scale (CAPS), clinical improvement with the Clinical Global Index (CGI) and anxiety with the Hamilton Anxiety Scale (Ham-A) at baseline and one-month intervals followed by a repeat of measurement of biomarkers with traumatic scripts. After three months patients self treated with twice daily open label active tcVNS for another three months followed by assessment with the CGI. Results: Traumatic scripts increased IL-6 in PTSD patients, an effect that was blocked by tcVNS (p<.05). Active tcVNS treatment for three months resulted in a 31% greater reduction in PTSD symptoms compared to sham treatment as measured by the PCL (p=0.013) as well as hyperarousal symptoms and somatic anxiety measured with the Ham-A p<0.05). IL-6 increased from baseline in sham but not tcVNS. Open label tcVNS resulted in improvements measured with the CGI compared to the sham treatment period p<0.05). Conclusions: These preliminary results suggest that tcVNS reduces inf

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Relaxed weight sharing: Effectively modeling time-varying relationships in clinical time-series

arXiv

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arXiv 2019年

作者： Oh, Jeeheh Wang, Jiaxuan Tang, Shengpu Sjoding, Michael Wiens, Jenna Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Institute for Healthcare Policy & Innovation Department of Internal Medicine University of Michigan Ann ArborMI United States

Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part, to their use of parameter sharing over time (i.e., cells are repeated hence the name recurrent). We hypothesize, however, that this trait also contributes to the increased difficulty such models have with learning relationships that change over time. Conditional shift, i.e., changes in the relationship between the input X and the output y, arises if the risk factors for the event of interest change over the course of a patient admission. While in theory, RNNs and gated RNNs (e.g., LSTMs) in particular should be capable of learning timevarying relationships, when training data are limited, such models often fail to accurately capture these dynamics. We illustrate the advantages and disadvantages of complete weight sharing (RNNs) by comparing an LSTM with shared parameters to a sequential architecture with time-varying parameters on three clinically-relevant prediction tasks: Acute respiratory failure (ARF), shock, and in-hospital mortality. In experiments using synthetic data, we demonstrate how weight sharing in LSTMs leads to worse performance in the presence of conditional shift. To improve upon the dichotomy between complete weight sharing vs. no weight sharing, we propose a novel RNN formulation based on a mixture model in which we relax weight sharing over time. The proposed method outperforms standard LSTMs and other state-of-the-art baselines across all tasks. In settings with limited data, relaxed weight sharing can lead to improved patient risk strati Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

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Implementation of Data Mining to Classify the Consumer's Complaints of Electricity Usage Based on Consumer's Locations Using Clustering Method 1

Implementation of Data Mining to Classify the Consumer's Com...

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1st Workshop on Environmental Science, Society, and Technology, WESTECH 2018

作者： Pardede, A.M.H. Sembiring, Yusdiana Br Iskandar, Akbar Pitasari, Dyah Retno Sriadhi, S. Rianita, Dian Arifin, Muhammad Ririhena, Mersy Yoslin Siregar, Nurintan Asyiah Supriyono Sari, Ayu Esteka Tondo, Simson Zarlis, Muhammad Winarno, Edy Tulus STMIK Kaputama Sumatera Utara Binjai Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Law Universitas Halmahera Tobelo Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Public Administrations Universitas Lancang Kuning Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Department of Accounting Universitas Halmahera Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Information System Universitas Muria Kudus Kudus Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Public Administration Universitas Halmahera Tobelo Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia

Data collected by PLN staff on customer's complaints in the usage of electricity are very huge and accumulate. Previously, there was no information about these kinds of complaints in all sub-districts included inside the company, PT. PLN Binjai. On the other hand, each year company experienced problems to classify all complaints in order to obtain customers with information to later be used as a basis for making policy/decision. Data mining used clustering method and K-Means algorithm to classify the data so that important information is obtained from customer's complaints on the electricity usage, using variable: types of complaints, power using by consumer and consumer's region. Data was analyzed using Matlab to produce cluster centers and obtained a relationship between variables obtained with groups with high number of complaints. Results revealed that from 500 customers who have complained, cluster 1 had 218 complaints namely NT Fuse Putus with power used was 1300 watt and was located in Binjai. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Comparison of Gait Speed Estimation of Multiple Sensor-Based Technologies

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2019年第1期8卷 135 - 139页

作者： Plinio P. Morita Adson S. Rocha George Shaker Doojin Lee Jing Wei Brandon Fong Anjali Thatte Amir-Hossein Karimi Lin Lin Xu Avery Ma Alex Wong Jennifer Boger School of Public Health and Health Systems University of WaterlooCentre for Global eHealth Innovation Techna Institute University Health NetworkInstitute of Health Policy Management and Evaluation University of TorontoDepartment of Systems Design Engineering University of WaterlooResearch Institute for Aging University of WaterlooWaterloo Artificial Intelligence Institute University of Waterloo School of Public Health and Health Systems University of WaterlooGoiano Federal Institute Research Institute for Aging University of WaterlooDepartment of Electrical and Computer Engineering University of WaterlooDepartment of Mechanical and Mechatronics Engineering University of WaterlooWaterloo Artificial Intelligence Institute University of Waterloo Department of Mechanical and Mechatronics Engineering University of Waterloo Department of Systems Design Engineering University of Waterloo Department of Systems Design Engineering University of WaterlooResearch Institute for Aging University of WaterlooWaterloo Artificial Intelligence Institute University of Waterloo Department of Systems Design Engineering University of WaterlooResearch Institute for Aging University of Waterloo

In light of our aging population, there is an immediate need for non-obtrusive, continuous, and ubiquitous health monitoring technologies that will enable our population to age with a higher quality of life and independence. Research has demonstrated that gait indicators, such as walking speed, can reflect cognitive and physical functioning. However, gradual changes in such indicators usually go undetected until critical problems arise; being able to detect changes in indicators, such as gait deterioration, of older adults while in their home environments would enable clinicians to tailor more effective and personalized interventions by better understanding user behaviour in real-world settings. Real-world data is essential to enabling our healthcare system to act where patients most need help and to optimize the effect of designed eHealth solutions.

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1929P Dabrafenib and trametinib (D+T) in BRAFV600E differentiated thyroid cancer: A real-world experience in Italy

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Annals of Oncology 2024年 35卷 S1121-S1122页

作者： L.D. Locati E. Colombo L. Fugazzola P. Bossi F. Magri S. Zovato D. Salvatore A. Malovini R. Elisei C. Durante Department of Internal Medicine and Therapeutics University of Pavia Pavia Italy Medical Oncology Dept. Fondazione IRCCS - Istituto Nazionale dei Tumori Milan Italy Endocrinological Department Istituto Auxologico Italiano IRCCS Milan Italy Department of Biomedical Sciences IRCCS Humanitas Research Hospital Milan Italy Hereditary Tumors Unit Azienda Universitaria Ospedaliera di Padova Padua Italy Department of Public Health Università degli Studi di Napoli Federico II - Dipartimento di Sanità Pubblica Naples Italy Department of Electrical Computer and Biomedical Engineering IRCCS Pavia - Fondazione Salvatore Maugeri Pavia Italy Department of Clinical and Experimental Medicine Università degli Studi di Pisa Pisa Italy Department of Translational and Precision Medicine Sapienza Università di Roma Rome Italy

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Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

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Exposome 2024年第1期4卷 osae001页

作者： Ming Kei Chung John S House Farida S Akhtari Konstantinos C Makris Michael A Langston Khandaker Talat Islam Philip Holmes Marc Chadeau-Hyam Alex I Smirnov Xiuxia Du Anne E Thessen Yuxia Cui Kai Zhang Arjun K Manrai Alison Motsinger-Reif Chirag J Patel Department of Biomedical Informatics Harvard Medical School Boston MA USA. School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong China. Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China. Biostatistics and Computational Biology Branch Division of Intramural Research National Institute of Environmental Health Sciences Durham NC USA. Cyprus International Institute for Environmental and Public Health School of Health Sciences Cyprus University of Technology Limassol Cyprus. Department of Electrical Engineering and Computer Science University of TN Knoxville TN USA. Department of Population and Public Health Sciences Keck School of Medicine of the University of Southern CA Los Angeles CA USA. Department of Physics Villanova University Villanova Philadelphia USA. Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Chemistry NC State University Raleigh NC USA. Department of Bioinformatics and Genomics College of Computing and Informatics University of NC at Charlotte Charlotte NC USA. Department of Biomedical Informatics University of CO Anschutz Medical Campus Aurora CO USA. Exposure Response and Technology Branch Division of Extramural Research and Training National Institute of Environmental Health Sciences Durham NC USA. Department of Environmental Health Sciences School of Public Health University at Albany State University of NY Rensselaer NY USA.

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

关键词： Exposome-Wide Association Study (ExWAS) data science epidemiology exposome false discovery rate phenotype

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Improving Quality of Life Based on Electronic Patient Reported Outcomes in Patients with CLL and MDS: The Mypal Study

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Blood 2024年 144卷 2358-2358页

作者： Thomas Chatzikonstantinou Maria Vasilopoulou Dimitrios Kyrou Georgios Karakatsoulis Panagiotis Argyropaidas Sotiria Besikli-Dimou Panos Bonotis Maria Chatzimina Jana Didi Christos Demosthenous Tina Garani-Papadatos Stefan Hoffman Christine Kakalou Eleni Kazantzaki Stephan Kiefer Lefteris Koumakis Eva Hellstrom Lindberg Julie Ling Christos Maramis Marcel Meyerheim Lydia Scarfo Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece Center for Molecular Medicine Central European Institute of Technology Masaryk University Brno Czech Republic Hematology Department - BMT Unit G Papanicolaou Hospital Thessaloniki Greece Department of Public Health Policy School of Public Health University of West Attica Athens Greece Promotion Software GmbH Berlin Germany Department of Hematology University of Crete School of Medicine Heraklion Greece Fraunhofer Institute For Biomedical Engineering (IBMT) Sulzbach Germany Center for Hematology and Regenerative Medicine (HERM) Department of Medicine Huddinge Karolinska Institutet Stockholm Sweden European Association for Palliative Care Vilvoorde Belgium Department of Pediatric Oncology and Hematology Faculty of Medicine Saarland University Homburg Germany Strategic Research Program on CLL IRCCS Ospedale San Raffaele Milano Italy Università Vita-Salute San Raffaele Milan Italy

Introduction: Palliative care is a critical component of care for patients with chronic hematological malignancies. Leveraging eHealth approaches can potentially facilitate the delivery of palliative care, leading to improved quality of life (QoL). Methods: The MyPal study (NCT04370457) is a randomized controlled clinical trial assessing an eHealth intervention on QoL of patients. The study was conducted according to the Declaration of Helsinki. The ethical aspects of the study, given the vulnerability of the patients, were addressed with diligence and approvals were obtained from the appropriate Research Ethics Committees. Patients who were receiving or had previously received treatment for CLL or MDS were randomly assigned (1:1) to access the MyPal platform versus standard of care. The MyPal platform included a smartphone application used to report QoL status (ePRO questionnaires) and symptoms. The primary endpoint was QoL at 12 months, assessed by the European Organization for Research and Treatment of Cancer (EORTC) quality of life QLQ-C30 General Questionnaire and the Euroqol 5-dimension (Euroqol EQ-5D-3L). Secondary endpoints included physical and emotional functioning, measured by the Integrated Palliative Care Outcome Scale (IPOS), satisfaction with care, measured by the EORTC Patient Satisfaction with Cancer Care questionnaire PATSAT-C33 (PATSAT-C33), and overall survival (OS). Additionally, the Edmonton Symptom Assessment System (ESAS), the Brief Pain Inventory (BPI), and the Emotion Thermometers (ET) QoL questionnaires were assessed only in the intervention group. Results: A total of 171 patients (97 and 74 in the control and intervention arms, respectively) who answered multiple questionnaires were included in the analysis. The intervention group reported a significant decrease in pain (β2=-0.48 (-0.77, -0.19), p<0.001) compared to the control group (β1=0.3 (0.09, 0.5), p=0.01) as measured by the EORTC QLQ-C30. Communication and pain as measured by IPOS

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The effect of pulsed electromagnetic field exposure on osteoinduction of human mesenchymal stem cells cultured on nano-TiO2surfaces

arXiv

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arXiv 2020年

作者： Bloise, Nora Petecchia, Loredana Ceccarelli, Gabriele Fassina, Lorenzo Usai, Cesare Bertoglio, Federico Balli, Martina Vassalli, Massimo Gabriella, Maria Angelis, Cusella De Gavazzo, Paola Imbriani, Marcello Visai, Livia Department of Molecular Medicine DMM Centre for Health Technologies INSTM Unit University of Pavia Pavia Department of Occupational Medicine Toxicology and Environmental Risks Istituti Clinici Scientifici Maugeri IRCCS Institute of Biophysics National Research Council Genova Department of Public Health Experimental Medicine and Forensic Centre for Health Technologies Human Anatomy Unit University of Pavia Pavia Department of Electrical Computer and Biomedical Engineering Centre for Health Technologies University of Pavia Pavia Italy

Human bone marrow-derived mesenchymal stem cells (hBM-MSCs) are considered a great promise in the repair and regeneration of bone. Considerable efforts have been oriented towards uncovering the best strategy to promote stem cells osteogenic differentiation. In previous studies, hBM-MSCs exposed to physical stimuli such as pulsed electromagnetic fields (PEMFs) or directly seeded on nanostructured titanium surfaces (TiO2) were shown to improve their differentiation to osteoblasts in osteogenic condition. In the present study, the effect of a daily PEMF-exposure on osteogenic differentiation of hBM-MSCs seeded onto nanostructured TiO2 (with clusters under 100 nm of dimension) was investigated. TiO2- seeded cells were exposed to PEMF (magnetic field intensity: 2 mT;intensity of induced electric field: 5 mV;frequency: 75 Hz) and examined in terms of cell physiology modifications and osteogenic differentiation. Results showed that PEMF exposure affected TiO2- seeded cells osteogenesis by interfering with selective calcium-related osteogenic pathways, and greatly enhanced hBM-MSCs osteogenic features such as the expression of early/late osteogenic genes and protein production (e.g., ALP, COL-I, osteocalcin and osteopontin) and ALP activity. Finally, PEMF-treated cells resulted to secrete into conditioned media higher amounts of BMP-2, DCN and COL-I than untreated cell cultures. These findings confirm once more the osteoinductive potential of PEMF, suggesting that its combination with TiO2nanostructured surface might be a great option in bone tissue engineering applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Titanium dioxide

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Daily rainfall forecast model from satellite image using Convolution neural network 3

Daily rainfall forecast model from satellite image using Con...

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3rd International Conference on Information Technology, InCIT 2018

作者： Boonyuen, Kitinan Kaewprapha, Phisan Srivihok, Patchanok Department of Electrical and Computer Engineering Thammasat University Rangsit Campus Pathum Thani Thailand PTT Public Company Limited Innovation Institute Wangnoi Ayuttaya13170 Thailand

ISBN: (纸本)9789741960323

The purpose of this paper is to investigate the capability of artificial intelligence by using convolutional neural networks (CNN), to forecast daily rainfall. The input of this model were the satellite images of the areas in Asia. The output of the model was daily rainfall prediction. Klong Yai rain station in Rayong province of Thailand was selected as our case study. We chose Inception-v3 model, which is an advance technique in convolutional neural networks. The model got very high accuracy on the ImageNet database which is the largest database of images. We helped the model to focus by reducing the size of the images and divided them into three different datasets. We used our 3 datasets to train the inception-v3 model by using 2 methods, the first method used transfer learning technique where we used a pre-trained model to train our dataset at the last fully connected layer. The second one was done from scratch where we trained all the layers of inception-v3. The training dataset consisted of satellite images of July, August and September 2017. The testing dataset had satellite images of October 2017. The result of forecasting revealed that the models were able to predict today rainfall, 1 day ahead rainfall, 2 days ahead rainfall and 3 days ahead rainfall successfully. © 2018 Mahasarakham University, Faculty of Informatics.

关键词： Rain

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