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Temporal Dietary Patterns Are Associated with Body Mass Index, Waist Circumference and Obesity

Current Developments in Nutrition 2020年第SUPPL 2期4卷 4140518-4140518页

作者： Eicher-Miller, Heather Aqeel, Marah Guo, Jiaqi Gelfand, Saul Delp, Edward Bhadra, Anindya Richards, Elizabeth Hennessy, Erin Lin, Luotao Department of Nutrition Science Purdue University School of Electrical and Computer Engineering Purdue University Department of Statistics Purdue University School of Nursing Purdue University Friedman School of Nutrition Science and Policy Tufts University

AbstractObjectivesThe integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDP), have been linked with dietary quality but links to health outcomes are unknown. TDP were created with the objective to examine their association with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure, and chronic diseases obesity, diabetes, and metabolic syndrome in US adults 20–65 years. MethodsThe first-day 24-hour dietary recall from 1627 non-pregnant US adult participants of the cross-sectional National Health and Nutrition Examination Survey 2003–2006 was used to determine energy intake (kcal), time of intake (min), and sequence of intake occasions throughout the 24-hour day. Modified dynamic time warping coupled with kernel k-means algorithm, clustered participants into four groups representing distinct TDP. Multivariate regression models determined associations between TDP clusters and all outcomes, controlling for potential confounders, energy misreporting, and adjusting for multiple comparisons and the complex survey design ( P < 0.05/6). ResultsThe cluster representing a TDP with proportionally equivalent average energy at three main eating occasions from 8:00 to 23:00 with peaks reaching 175 kcal at 9:00, 13:00, and 19:00, had statistically significant and clinically meaningful lower BMI ( P < 0.0001), WC ( P < 0.0001) and 75% lower odds of obesity compared to three other clusters representing distinct patterns of much higher average peak energy intake of 500 kcal at 13:00 (odds ratio (OR): 4.4; 95% confidence interval (CI)): 2.5, 7.9), 530 kcal at 18:00 (OR: 5.3; 95% CI: 2.8, 10.1), and 550 kcal at 20:00 (OR: 6.7; 95%CI: 3.9, 11.6). ConclusionsA positive association of the TDP of moderate energy intake throughout the day with healthy weight outcomes supports previous findings of hi

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Universal research index: An inclusive metric to quantify scientific research output

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Journal of Academic Librarianship 2023年第3期49卷

作者： Keshavarz-Fathi, Mahsa Yazdanpanah, Niloufar Kolahchi, Sajad Ziaei, Heliya Darmstadt, Gary L. Dorigo, Tommaso Dochy, Filip Levin, Lisa Thongboonkerd, Visith Ogino, Shuji Chen, Wei-Hsin Perc, Matjaz Tremblay, Mark S. Olusanya, Bolajoko O. Rao, Idupulapati M. Hatziargyriou, Nikos Moradi-Lakeh, Maziar Bella, Federico Rosivall, Laszlo Gandomi, Amir H. Sorooshian, Armin Gupta, Manoj Gal, Ciprian Lozano, Andres M. Weaver, Connie Tanzer, Michael Poggi, Alessandro Sepanlou, Sadaf G. Weiskirchen, Ralf Jambrak, Anet Režek Torres, Pedro J. Capanoglu, Esra Barba, Francisco J. Ernest, Chua Kian Jon Sigman, Mariano Pluchino, Stefano Gharehpetian, Gevork B. Fereshtehnejad, Seyed-Mohammad Yang, Muh-Hwa Thomas, Sabu Cai, Wenju Comini, Elisabetta Scolding, Neil J. Myles, Paul S. Nieto, Juan J. Perry, George Sedikides, Constantine Rezaei, Nima School of Medicine Tehran University of Medical Sciences Tehran Iran Universal Scientific Education and Research Network (USERN) Iran Department of Pediatrics Stanford University School of Medicine Stanford CA United States Istituto Nazionale di Fisica Nucleare (INFN) Via Francesco Marzolo Sezione di Padova Italy High Impact Learning Academy Learning and Development Department Brussels Belgium Center for Marine Biodiversity and Conservation Integrative Oceanography Division Scripps Institution of Oceanography University of California San Diego San Diego CA United States Medical Proteomics Unit Office for Research and Development Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital and Harvard Medical School Boston MA United States Broad Institute of MIT and Harvard Cambridge MA United States Department of Aeronautics and Astronautics National Cheng Kung University Tainan Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung 407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung 411 Taiwan Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Complexity Science Hub Vienna Vienna Austria Alma Mater Europaea Maribor Slovenia Children's Hospital of Eastern Ontario Research Institute Department of Pediatrics University of Ottawa Ottawa ON Canada Centre for Healthy Start Initiative Ikoyi Lagos Nigeria Alliance of Bioversity International International Center for Tropical Agriculture Cali Colombia International Centre of Insect Physiology and Ecology (ICIPE) Nairobi Kenya School of Electrical and Computer Engineering Natio

Scientometrics and bibliometrics, the subfields of library and information science, deal with the quantity and quality of research outputs. Currently, various scientometric indices are being used to quantify and compare research outputs. The most widely known is the h-index. However, this index and its derivatives suffer from dependence on the mere count of a scholar's highly cited publications. To remedy this deficiency, we developed a novel index, the Universal Research Index (UR-Index) (https://***/UR-Index/) by which every single publication has its own impact on the total score. We developed this index by surveying international top 1 % cited scientists in various disciplines and included additional component variables such as publication type, leading role of a scholar, co-author count, and source metrics to this scientometric index. We acknowledge that unconscious biases built into the component variables included in the UR-Index might put research from specific groups at a disadvantage, thus continued efforts to improve equitable scholarly impact in science and academia are encouraged. © 2023 Elsevier Inc.

关键词： Citations CiteScore h-index Impact factor Leading author Research impact Scholarly impact Scholarly output Scientometrics UR-Index USERN

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Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach

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Journal of medical Internet research 2023年 25卷 e55010页

作者： Elham Dolatabadi Diana Moyano Michael Bales Sofija Spasojevic Rohan Bhambhoria Junaid Bhatti Shyamolima Debnath Nicholas Hoell Xin Li Celine Leng Sasha Nanda Jad Saab Esmat Sahak Fanny Sie Sara Uppal Nirma Khatri Vadlamudi Antoaneta Vladimirova Artur Yakimovich Xiaoxue Yang Sedef Akinli Kocak Angela M Cheung Faculty of Health School of Health Policy and Management York University Toronto ON Canada. Vector Institute Toronto ON Canada. Department of Medicine and Joint Department of Medical Imaging University of Toronto Toronto ON Canada. Hoffmann-La Roche Ltd Toronto ON Canada. Electrical and Computer Engineering Queen's University Kingston ON Canada. Manulife Toronto ON Canada. Deloitte Toronto ON Canada. TELUS Health Montreal QC Canada. Department of Pediatrics Faculty of Medicine University of British Columbia Vancouver BC Canada. Roche Information Solutions San Francisco CA United States. Hoffmann-La Roche Ltd Munich Germany. University Health Network Toronto ON Canada.

[This corrects the article DOI: 10.2196/45767.].

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using computer Simulation Technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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Meta-Learning Techniques to Analyze the Raman Data for Optical Diagnosis of Oral Cancer Detection

Meta-Learning Techniques to Analyze the Raman Data for Optic...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Mukta Sharma Lokesh Sharma Ming-Jer Jeng Liann-Be Chang Shiang-Fu Huang Shih-Lin Wu Department of Electronic Engineering Chang Gung University Taoyuan Taiwan AI Innovation Research Center Chang Gung University Taoyuan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou Taiwan Department of Public Health Chang Gung University Taoyuan Taiwan Department of Cardiology Chang Gung Memorial Hospital Taoyuan Department of Electrical Engineering Ming Chi University of technology New Taipei City Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9781728152103

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.

关键词： biomedical optical imaging cancer image classification learning (artificial intelligence) medical image processing tumours Biomedical optical imaging Medical image processing Image classification Tumours Mouth Neoplasms Malignant Oral Cavity Tumor cancer test OPTICAL DIAGNOSTICS Boost bagging PURSE

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Identification of cognitive resilience through neuropsychological tests in the Framingham Heart Study

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Phillip H Hwang Samad Amini Cody Karjadi Honghuang Lin Stacy L. Andersen Ioannis Paschalidis Rhoda Au Jesse B. Mez Boston University School of Medicine Boston MA USA Division of Systems Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University Alzheimer’s Disease Research Center Boston MA USA

Background To examine the predictive abilities of 1) traditional neuropsychological (NP) test summary scores, and 2) error and process scores in separating cognitively resilient individuals from those who exhibit cognitive decline. Method A total of 69 participants from the Framingham Heart Study (FHS) Offspring and Omni 1 cohorts who had ≥1 NP assessment from 2011–2014 and at high risk for dementia were included in the sample. Participants also had to be ≥60 years of age and dementia-free at the time of NP assessment. Being at high risk for dementia was determined using a cutoff score of ≥23 based on a published FHS dementia risk score algorithm. Dementia diagnosis was adjudicated based on DSM-IV criteria by consensus. Participants completed 10 NP tests, which were scored using 11 traditional scores and 75 error and process scores. Supervised machine learning algorithms were used to classify individuals who were cognitively resilient, defined as not progressing to dementia within ≥5 years after the NP evaluation. Result Among the 69 participants in our sample, 10 were cognitively resilient while 59 developed dementia. Average area under the curve when using traditional NP metrics to predict cognitive resilience ranged from 90.5% to 95.7% across each supervised machine learning algorithm (logistic regression, random forest, support vector machines, and light gradient boosting machine) (Figure 1). Average accuracy ranged from 85.3% to 92.6%, while average sensitivity was 100% across all models and average specificity ranged from 82.7% to 91.4% (Table 1). Average area under the curve when using the error and process scores to predict cognitive resilience ranged from 93.2% to 98.3% across each supervised machine learning algorithm (Figure 2). Average accuracy ranged from 89.9% to 97.0%, while average sensitivity was also 100% across all models and average specificity ranged from 88.2% to 96.5% (Table 2). All performance metrics were calculated based on five-fold cross-

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Detecting flu outbreaks based on spatiotemporal information from urban systems - Designing a novel study

Detecting flu outbreaks based on spatiotemporal information ...

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2018 Geoinformation for Disaster Management Conference, Gi4DM 2018

作者： Grottenberg, L.O. Njå, O. Tøssebro, E. Braut, G.S. Tønnessen, R. Grøneng, G.M. Department of Safety Economics and Planning Faculty of Science and Technology University of Stavanger Norway Department of Electrical Engineering and Computer Science Faculty of Science and Technology University of Stavanger Norway Stavanger University Hospital Norway Department of Influenza Norwegian Institute of Public Health Norway Department of Infectious Diseases Epidemiology and Modelling Norwegian Institute of Public Health Norway

This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal flu epidemics from a spatial perspective. We believe that combining travel data with epidemiological data will be the first step to develop a tool to predict future epidemics and to better understand the effects that these outbreaks have on societal functions over time. Real-time data-streams provide a powerful, yet underutilised tool when it comes to monitoring and detecting changes to the daily behaviour of inhabitants. In this paper, we describe and discuss the design of the geospatial project, in which we will draw upon data sources available from the Norwegian cities of Oslo and Bergen. Historical datasets from public transport and road traffic will serve as an initial indication of whether changes in daily transport patterns corresponds to seasonal flu data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly flu activity and that these differences can be measured through geostatistical analysis. Conceptually one could be able to monitor changes in human behaviour and activity in nearly true time by using indicators derived from outside the clinical health services. This type of more up-to-date and geographically precise information could contribute to earlier detection of flu outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks. © Authors 2018. CC BY 4.0 License.

关键词： Disease control

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Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

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CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY 2019年 18卷 16-22页

作者： Christopherson, Kaitlin M. Ghosh, Alokananda Sherif, Abdallah Mohamed, Radwan Kamal, Mona Fuller, David Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Department of Biostatistics and Data Science University of Texas School of Public Health Houston TX United States Department of Clinical Oncology University of Alexandria Alexandria Egypt Department of Emergency Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Baylor College of Medicine Houston TX United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA United States Department of Radiation Oncology The University of Texas Medical Branch Galveston TX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer Center Houston TX United States Department of Thoracic & Head and Neck Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston TX United States Department of Computer Science University of Illinois at Chicago Chicago IL United States Department of Electrical and Computer Engineering The University of Iowa Iowa City IA United States

Objectives: We sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. Methods: We reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at >= 12 months after IMRT. Doses to autosegmented regions of interest (ROIs;inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50-59, 60-69, or >70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. Results: Chronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose-response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 +] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD. Conclusions: Age at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.

关键词： Oropharynx IMRT Radiation Toxicity Presbyphagia

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Coordinate: A Spreadsheet-Programmable Augmented Reality Framework for Immersive Map-Based Visualizations

Coordinate: A Spreadsheet-Programmable Augmented Reality Fra...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Aashiq Shaikh Linda Nguyen Alireza Bahremand Hannah Bartolomea Frank Liu Van Nguyen Derrick Anderson Robert LiKamWa School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA School of Arts Media and Engineering Arizona State University Tempe AZ USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Department of Psychology Arizona State University Tempe AZ USA School of Public Affairs Arizona State University Tempe AZ USA

ISBN: (数字)9781728156040

ISBN: (纸本)9781728156057

Augmented reality devices are opening up a new design space for immersive visualizations. 3D spatial content can be overlaid onto existing physical visualizations for new insights into the data. We present Coordinate, a collaborative analysis tool for augmented reality visualizations of map-based data designed for mobile devices. Coordinate leverages a spreadsheet-programmable web interface paired with a contemporary augmented reality infrastructure for an easy-to-use tool that can provide spatial information to multiple users. It also offers an immersive visualization experience that seeks to enrich presentations for business, educational, and scientific discussions.

关键词： Conferences Artificial intelligence Virtual reality 6G mobile communication Integrated circuits

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