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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Statistical semantics in context space: Amrita CEN@author profiling

Statistical semantics in context space: Amrita CEN@author pr...

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2016 Working Notes of Conference and Labs of the Evaluation Forum, CLEF 2016

作者： Barathi Ganesh, H.B. Anand Kumar, M. Soman, K.P. Artificial Intelligence Practice Tata Consultancy Services Kochi Kerala682 042 India Center for Computational Engineering and Networking Amrita School of Engineering Amrita University Coimbatore Amrita Vishwa Vidyapeetham India

Languages shared by people differs due to diversity in their ethnicity, socioeconomic status, gender, language, religion, sexual orien-tation, geographical area, accents, pronunciation and word usages. This eventually fall into hypothesis that they follow unknown hidden pattern. By using this hypothesis, determining the class of a person such as age, gender, their personality and nativity has multiple applications in social media, forensic science, marketing analysis, e-commerce and e-security. This work advances the research on author profiling much further by overcoming existing language dependent, domain dependent and lexicon based author profiling methods by finding user's sociolect aspects based on authors statistical pattern of semantics in context space. It proves to be a domain and language independent method in Author Profiling by nearing constant performance over English, Dutch and Spanish corpus.

关键词： Semantics

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Amrita-CEN at SemEval-2016 task 1: Semantic relation from word embeddings in higher dimension 10

Amrita-CEN at SemEval-2016 task 1: Semantic relation from wo...

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10th International Workshop on Semantic Evaluation, SemEval 2016

作者： Barathi Ganesh, H.B. Anand Kumar, M. Soman, K.P. Artificial Intelligence Practice Tata Consultancy Services Kochi Kerala682 042 India Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita University India

ISBN: (纸本)9781941643952

Semantic Textual Similarity measures similarity between pair of texts, even though the similar context is projected using different words. This work attempted to incorporate the context space of the sentence from that sentence alone. It proposes combination of Word2Vec and Non-Negative Matrix Factorization to represent the sentence as context embedding vector in context space. Distance and correlation values between context embedding vector pairs used as a features for Support Vector Regression to built the domain independent similarity measuring model. The proposed model yielding performance 0.41 in terms of correlation. © 2016 Association for computational Linguistics.

关键词： Semantics

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Variational mode feature-based hyperspectral image classification

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Advances in Intelligent Systems and Computing 2016年 380卷 365-373页

作者： Nechikkat, Nikitha Sowmya, V. Soman, K.P. Center for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham CoimbatoreTamil Nadu India

ISBN: (纸本)9788132225225

Hyperspectral image analysis is considered as a promising technology in the field of remote sensing over the past decade. There are various processing and analysis techniques developed that interpret and extract the maximum information from high-dimensional hyperspectral datasets. The processing techniques significantly improve the performance of standard algorithms. This paper uses variational mode decomposition (VMD) as the processing algorithm for hyperspectral data scenarios followed by classification based on sparse representation. Variational Mode Decomposition decomposes the experimental data set into few different modes of separate spectral bands, which are unknown. These modes are given as raw input to the classifier for performance analysis. Orthogonal matching pursuit (OMP), the sparsity-based algorithm is used for classification. The proposed work is experimented on the standard dataset, namely Indian pines collected by the airborne visible/infrared imaging spectrometer (AVIRIS). The classification accuracy obtained on the hyperspectral data before and after applying Variational Mode Decomposition was analyzed. The experimental result shows that the proposed work leads to an improvement in the overall accuracy from 84.82 to 89.78%, average accuracy from 85.03 to 89.53% while using 40% data pixels for training. © Springer India 2016.

关键词： Variational mode decomposition

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Expressive speech analysis for epoch extraction using zero frequency filtering approach

Expressive speech analysis for epoch extraction using zero f...

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IEEE Students' Technology Symposium (TechSym)

作者： D. Pravena D. Govind Center for Computational Engineering and Networking (CEN) Amrita Vishwa Vidyapeetham Amrita University India

The present work discusses the issues of epoch extraction from expressive speech signals. Epochs represent the accurate glottal closure instants in voiced speech which in turn give the accurate instants of maximum excitation of the vocal tract. Even though, there are many existing methods for epoch extraction, which provide near perfect epoch estimation from clean or neutral speech, these methods show significant drop in the epoch extraction performance for expressive speech signals. The occurrence of uncontrolled and rapid pitch variations in expressive speech signals cause degradation in the epoch extraction performance. The objective of the present work is to improve the epoch extraction performance of the speech signals with various perceptually distinct expressions compared to neutral speech using zero frequency filtering (ZFF) approach. In order to capture the rapid and uncontrolled variations in expressive speech utterances, trend removal is performed on short segments (25 ms) of the output obtained from the cascade of three zero frequency resonators (ZFR). The epoch estimation performance of the proposed method is compared with the conventional ZFF method, existing refined ZFF method proposed for expressive speech and recently proposed zero band filtering (ZBF) approach. The effectiveness of the approach is confirmed by the improved epoch identification rate and reduced miss and false alarm rates compared with that of the existing methods.

关键词： Speech Market research Estimation Spectrogram Filtering Resonant frequency Harmonic analysis

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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Significance of natural elicitation in developing simulated full blown speech emotion databases

Significance of natural elicitation in developing simulated ...

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IEEE Students' Technology Symposium (TechSym)

作者： D. Pravena S. Nandhakumar D. Govind Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita University India

The work presented in this paper investigates the significance of natural elicitation of emotions during the development of simulated full blown emotion speech databases emotion analysis. A subset of primary emotions such as anger, happy and sad emotions along with neutral utterances are used in the present work. The first part of the work discusses the development of a simulated full blown emotion database by selecting 50 emotionally biased prompts for the recording the emotional speech data in Tamil language. For the comparative study, another simulated emotion database is developed by recording 50 neutral utterances for recording the emotion speech from the same speakers. The second part of the work is the comparison of emotion recognition performance of the simulated emotion speech databases using the basic Gaussian mixture model (GMM) based system with mel frequency cepstral coefficients (MFCC). A significant variations in the recognition rates of different emotions are observed for both the databases with emotionally biased utterances and emotionally neutral emotion utterances. Where the emotionally biased utterances observed to be more effective in discriminating emotions than emotionally neutral simulated emotion database. Also, the emotion recognition rates obtained for the simulated emotionally neutral emotion utterances follow the same trend as that of the classical German full blown simulated emotion database.

关键词： Databases Emotion recognition Speech Speech recognition Mel frequency cepstral coefficient Context Feature extraction

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Measuring stock price and trading volume causality among Nifty50 stocks: The Toda Yamamoto method

Measuring stock price and trading volume causality among Nif...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： P Abinaya Varsha Suresh Kumar P Balasubramanian Vijay Krishna Menon Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu IN Amrita School of Business Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University Coimbatore India

ISBN: (纸本)9781509020300

This paper analyzes the existence of a Granger causality relationship between stock prices and trading volume using minute by minute data (transformed from tick by tick data) of Nifty 50 companies traded at the National Stock Exchange, India for the period of one year from July 2014 to June 2015. Since the time series data taken is not integrated in of the same order, the Toda-Yamamoto methodology was applied to test for causality. The results show that 29 companies out 50 companies have two-way (bi-directional) causality between price and volume and 15 companies have one way (unidirectional) causal relationship where price causes volume and volume does not cause price and 6 other companies have no causal relationship in either way. The study suggests that the Efficient Markets Hypothesis does not hold true for these 29 companies during the period of this study.

关键词： Companies Stock markets Reactive power Mathematical model Solid modeling Probability Informatics

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AMRITA_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders 9

AMRITA_CEN@SemEval-2015: Paraphrase Detection for Twitter us...

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Sundaram, Mahalakshmi Shanmuga Madasamy, Anand Kumar Padannayil, Soman Kotti Center for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781941643402

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Semantic Similarity in Twitter. Our paraphrase detection system makes use of phrase-structure parse tree embeddings that are then provided as input to a conventional supervised classification model. We achieve an F1 score of 0.45 on paraphrase identification and a Pearson correlation of 0.303 on computing semantic similarity. © 2015 Association for computational Linguistics

关键词： Semantics

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