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1. A 51.3-TOPS/W, 134.4-GOPS In-Memory Binary Image Filtering in 65-nm CMOS

Author

Deepak Singla, Arindam Basu, and Sumon Kumar Bose

Subjects
FOS: Computer and information sciences, Computer science, business.industry, Binary image, Image and Video Processing (eess.IV), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Latency (audio), Computer Science - Emerging Technologies, Electrical Engineering and Systems Science - Image and Video Processing, Emerging Technologies (cs.ET), CMOS, Neuromorphic engineering, Hardware Architecture (cs.AR), FOS: Electrical engineering, electronic engineering, information engineering, Redundancy (engineering), Median filter, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, Computer Science - Hardware Architecture, business, Throughput (business), Energy (signal processing)
Abstract

Neuromorphic vision sensors (NVS) can enable energy savings due to their event-driven that exploits the temporal redundancy in video streams from a stationary camera. However, noise-driven events lead to the false triggering of the object recognition processor. Image denoise operations require memoryintensive processing leading to a bottleneck in energy and latency. In this paper, we present in-memory filtering (IMF), a 6TSRAM in-memory computing based image denoising for eventbased binary image (EBBI) frame from an NVS. We propose a non-overlap median filter (NOMF) for image denoising. An inmemory computing framework enables hardware implementation of NOMF leveraging the inherent read disturb phenomenon of 6T-SRAM. To demonstrate the energy-saving and effectiveness of the algorithm, we fabricated the proposed architecture in a 65nm CMOS process. As compared to fully digital implementation, IMF enables > 70x energy savings and a > 3x improvement of processing time when tested with the video recordings from a DAVIS sensor and achieves a peak throughput of 134.4 GOPS. Furthermore, the peak energy efficiencies of the NOMF is 51.3 TOPS/W, comparable with state of the art inmemory processors. We also show that the accuracy of the images obtained by NOMF provide comparable accuracy in tracking and classification applications when compared with images obtained by conventional median filtering., 13 pages

Published
2022
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2. NEW PRODUCT DEVELOPMENT IN TEXTILES AND APPAREL INDUSTRY

Author

Arindam Basu

Subjects
Textile industry, Textile, business.industry, Process (engineering), New product development, Sustainability, General Medicine, Natural variability, business, Industrial organization
Abstract

Product development is key to survival of any industry with change of time. This article discusses about the process of new product development in textile and apparel industry. Major points considered during this process are demand of customers, availability of new raw materials, sustainability, economic validity and responsibiiity towards nature. Engineering of products is a complicated issue here due to natural variability of textile materials, limitations of fibre available and use of old conventional machineries in many cases.

Published
2021
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3. Introducing Recurrence in Strong PUFs for Enhanced Machine Learning Attack Resistance

Author

Brojogopal Sapui, Durba Chatterjee, Arindam Basu, Nimesh Shah, and Debdeep Mukhopadhyay

Subjects
021110 strategic, defence & security studies, Hardware security module, Computer science, business.industry, Reliability (computer networking), 020208 electrical & electronic engineering, Spice, 0211 other engineering and technologies, Arbiter, 02 engineering and technology, Machine learning, computer.software_genre, Reduction (complexity), Current mirror, Application-specific integrated circuit, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, Electrical and Electronic Engineering, Field-programmable gate array, business, computer
Abstract

Hardware security circuits based on Physically Unclonable Functions (PUF) are finding widespread use due to increasing adoption of IoT devices. However, existing strong PUFs such as Arbiter-PUF (APUF) and its compositions are susceptible to machine learning (ML) attacks due to a linear relationship between the challenge and its response. In this paper, we present a Recurrence-based PUF (Rec-PUF) which uses feedback and XOR function together to significantly improve ML-attack resistance, without significant reduction in reliability. Our method is generic and works for both analog and digital PUF cores. As proof of the claim, we apply recurrence on an analog PUF using current mirror array validated on ASIC libraries, referred to as Rec-CMAPUF. At the other end, we also design and evaluate a digital PUF fortified with recurrence, called Rec-DAPUF, based on double arbiter logic and prototyped on FPGAs. Our result shows that ML resistance of Rec-CMAPUF is within 62% with 138, 000 CRPs, with reliability of 95%. Likewise, ML resistance of Rec-DAPUF is around 64%, with average reliability of 95.9%. The merit of recurrence wrt. ML attacks can be understood by the fact that without recurrence the CMAPUF/DAPUF can be modeled with 99%/80% accuracy, thus showing the efficacy and also applicability of the technique for various PUFs and platforms. In addition recurrence is suitably traded to ensure acceptable power consumption of $12.3 \mu \text{W}$ with energy/bit of $\approx 0.16$ pJ for Rec-CMAPUF, estimated through SPICE simulations, while an 165.4 pJ/bit consumption for Rec-DAPUF, based on FPGA results.

Published
2021
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4. Halide perovskite memristors as flexible and reconfigurable physical unclonable functions

Author

Sujaya Kumar Vishwanath, Arindam Basu, Rohit Abraham John, Benny Febriansyah, Nimesh Shah, Si En Ng, Metikoti Jagadeeswararao, Nripan Mathews, and Chip-Hong Chang

Subjects
Hardware security module, Exploit, Computer science, Science, General Physics and Astronomy, 02 engineering and technology, Memristor, 010402 general chemistry, 01 natural sciences, Article, General Biochemistry, Genetics and Molecular Biology, law.invention, law, Electronic devices, Electronics, Electronic circuit, Authentication, Key generation, Multidisciplinary, Hardware_MEMORYSTRUCTURES, business.industry, Reconfigurability, General Chemistry, 021001 nanoscience & nanotechnology, Electrical and electronic engineering, 0104 chemical sciences, Embedded system, 0210 nano-technology, business
Abstract

Physical Unclonable Functions (PUFs) address the inherent limitations of conventional hardware security solutions in edge-computing devices. Despite impressive demonstrations with silicon circuits and crossbars of oxide memristors, realizing efficient roots of trust for resource-constrained hardware remains a significant challenge. Hybrid organic electronic materials with a rich reservoir of exotic switching physics offer an attractive, inexpensive alternative to design efficient cryptographic hardware, but have not been investigated till date. Here, we report a breakthrough security primitive exploiting the switching physics of one dimensional halide perovskite memristors as excellent sources of entropy for secure key generation and device authentication. Measurements of a prototypical 1 kb propyl pyridinium lead iodide (PrPyr[PbI3]) weak memristor PUF with a differential write-back strategy reveals near ideal uniformity, uniqueness and reliability without additional area and power overheads. Cycle-to-cycle write variability enables reconfigurability, while in-memory computing empowers a strong recurrent PUF construction to thwart machine learning attacks., Despite the impressive demonstrations with silicon and oxide memristors, realizing efficient roots of trust for resource-constrained hardware remains a challenge. Here, the authors exploit switching behavior in one dimensional perovskite memristors to design security primitives for key generation and device authentication.

Published
2021

5. Telehealth and the COVID-19 Pandemic: International Perspectives and a Health Systems Framework for Telehealth Implementation to Support Critical Response

Author

Araujo Kleber, Sriram Iyengar, Arindam Basu, Vije Kumar Rajput, Inga Hunter, Fernando José Ribeiro Sales, Jose F. Florez-Arango, Craig Kuziemsky, Gumindu Garuka Kulatunga, Shashi Bhushan Gogia, Jai Ganesh Udayasankaran, Sheila John, Oommen John, Kendall Ho, Magdala de Araújo Novaes, Najeeb Al-Shorbaji, and Prasad Ranatunga

Subjects
Telemedicine, Internationality, 020205 medical informatics, telehealth, coronavirus, MEDLINE, Section 3: Clinical Information Systems, 02 engineering and technology, Telehealth, Health informatics, 03 medical and health sciences, 0302 clinical medicine, RA0421, Pandemic, 0202 electrical engineering, electronic engineering, information engineering, Humans, 030212 general & internal medicine, Working Group Contribution, Public engagement, Societies, Medical, Medical education, Government, business.industry, COVID-19, General Medicine, R1, Government Regulation, Health Systems, business, Psychology, RA, Healthcare system
Abstract

Objectives: Telehealth implementation is a complex systems-based endeavour. This paper compares telehealth responses to (COrona VIrus Disease 2019) COVID-19 across ten countries to identify lessons learned about the complexity of telehealth during critical response such as in response to a global pandemic. Our overall objective is to develop a health systems-based framework for telehealth implementation to support critical response. Methods: We sought responses from the members of the International Medical Informatics Association (IMIA) Telehealth Working Group (WG) on their practices and perception of telehealth practices during the times of COVID-19 pandemic in their respective countries. We then analysed their responses to identify six emerging themes that we mapped to the World Health Organization (WHO) model of health systems. Results: Our analysis identified six emergent themes. (1) Government, legal or regulatory aspects of telehealth; (2) Increase in telehealth capacity and delivery; (3) Regulated and unregulated telehealth; (4) Changes in the uptake and perception of telemedicine; (5) Public engagement in telehealth responses to COVID-19; and (6) Implications for training and education. We discuss these themes and then use them to develop a systems framework for telehealth support in critical response. Conclusion: COVID-19 has introduced new challenges for telehealth support in times of critical response. Our themes and systems framework extend the WHO systems model and highlight that telemedicine usage in response to the COVID-19 pandemic is complex and multidimensional. Our systems-based framework provides guidance for telehealth implementation as part of health systems response to a global pandemic such as COVID-19.

Published
2021
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6. A 0.11–0.38 pJ/cycle Differential Ring Oscillator in 65 nm CMOS for Robust Neurocomputing

Author

Arindam Basu, Jyotibdha Acharya, and Xueyong Zhang

Subjects
Materials science, business.industry, Oscillation, 020208 electrical & electronic engineering, Transistor, Ranging, 02 engineering and technology, Ring oscillator, law.invention, Capacitor, CMOS, Neuromorphic engineering, law, Phase noise, Hardware_INTEGRATEDCIRCUITS, 0202 electrical engineering, electronic engineering, information engineering, Optoelectronics, Electrical and Electronic Engineering, business
Abstract

This paper presents a low-area and low-power consumption CMOS differential current controlled oscillator (CCO) for neuromorphic applications. The oscillation frequency is improved over the conventional one by reducing the number of MOS transistors thus lowering the load capacitor in each stage. The analysis shows that for the same power consumption, the oscillation frequency can be increased about 11% compared with the conventional one without degrading the phase noise. Alternatively, the power consumption can be reduced 15% at the same frequency. The prototype structures are fabricated in a standard 65 nm CMOS technology and measurements demonstrate that the proposed CCO operates from 0.7 – 1.2 V supply with maximum frequencies of 80 MHz and energy/cycle ranging from 0.11 – 0.38 pJ over the tuning range. Further, system level simulations show that the nonlinearity in current-frequency conversion by the CCO does not affect its use as a neuron in a Deep Neural Network if accounted for during training.

Published
2021
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7. Power-efficient Spike Sorting Scheme Using Analog Spiking Neural Network Classifier

Author

Arindam Basu, Indrajit Chakrabarti, Anand Kumar Mukhopadhyay, Mrigank Sharad, and Atul Sharma

Subjects
Spiking neural network, business.industry, Computer science, 020208 electrical & electronic engineering, Process (computing), 02 engineering and technology, 03 medical and health sciences, 0302 clinical medicine, Neuromorphic engineering, Spike sorting, Hardware and Architecture, Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, Spike (software development), Electrical and Electronic Engineering, Crossbar switch, business, 030217 neurology & neurosurgery, Software, Computer hardware, Communication channel
Abstract

The method to map the neural signals to the neuron from which it originates is spike sorting. A low-power spike sorting system is presented for a neural implant device. The spike sorter constitutes a two-step trainer module that is shared by the signal acquisition channel associated with multiple electrodes. A low-power Spiking Neural Network (SNN) module is responsible for assigning the spike class. The two-step shared supervised on-chip training module is presented for improved training accuracy for the SNN. Post implant, the relatively power-hungry training module can be activated conditionally based on a statistics-driven retraining algorithm that allows on the fly training and adaptation. A low-power analog implementation for the SNN classifier is proposed based on resistive crossbar memory exploiting its approximate computing nature. Owing to the direct mapping of SNN functionality using physical characteristics of devices, the analog mode implementation can achieve ∼21 × lower power than its fully digital counterpart. We also incorporate the effect of device variation in the training process to suppress the impact of inevitable inaccuracies in such resistive crossbar devices on the classification accuracy. A variation-aware, digitally calibrated analog front-end is also presented, which consumes less than ∼50 nW power and interfaces with the digital training module as well as the analog SNN spike sorting module. Hence, the proposed scheme is a low-power, variation-tolerant, adaptive, digitally trained, all-analog spike sorter device, applicable to implantable and wearable multichannel brain-machine interfaces.

Published
2021
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8. Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action

Author

Andre Kushniruk, Elaine Huesing, Arindam Basu, Xinxin Zhu, Ronald Cornet, Dari Alhuwail, Diane J. Skiba, Craig Kuziemsky, Andrew Georgiou, Romaric Marcilly, Luis Fernandez-Luque, Elizabeth M. Borycki, Najeeb Al-Shorbaji, Charlene Ronquillo, Max Topaz, Chris Paton, Farah Magrabi, Shashi Bhushan Gogia, Shinji Kobayashi, Carolyn Petersen, Tobias Mettler, Kerstin Denecke, Thomas M. Deserno, Linda W. P. Peute, Paulette Lacroix, Christian Nøhr, Vivian Vimarlund, Sriram Iyengar, Elia Gabarron, Hamad Bin Khalifa University (HBKU), Qatar Computing Research Institute [Doha, Qatar] (QCRI), Nanyang Technological University [Singapour], University of Southern Denmark (SDU), Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Centre d'Investigation Clinique - Innovation Technologique de Lille - CIC 1403 - CIC 9301 (CIC Lille), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), CHU Lille, Amsterdam Public Health Research Institute [The Netherlands], Medical Informatics, APH - Health Behaviors & Chronic Diseases, APH - Global Health, APH - Methodology, APH - Digital Health, and APH - Quality of Care

Subjects
medicine.medical_specialty, Evidence-based practice, Health Informatics, Context (language use), Health informatics, Health Information Management, Political science, medicine, Humans, medical informatics, Other Health Sciences, Cooperative Behavior, mobile health, public health, COVID-19, Pandemics, Qualitative Research, Advanced and Specialized Nursing, business.industry, SARS-CoV-2, Public health, Capacity building, Public relations, Special Interest Group, Digital health, Annan hälsovetenskap, Evidence-Based Practice, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Working group, For-Discussion Article
Abstract

Background As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis. Aim In this paper, we describe a joint call for action to use and leverage evidence-based health informatics as the foundation for the COVID-19 response and public health interventions. Tangible examples are provided for how the working groups and special interest groups of the International Medical Informatics Association (IMIA) are helping to build an evidence-based response to this crisis. Methods Leaders of working and special interest groups of the IMIA, a total of 26 groups, were contacted via e-mail to provide a summary of the scientific-based efforts taken to combat COVID-19 pandemic and participate in the discussion toward the creation of this manuscript. A total of 13 groups participated in this manuscript. Results Various efforts were exerted by members of IMIA including (1) developing evidence-based guidelines for the design and deployment of digital health solutions during COVID-19; (2) surveying clinical informaticians internationally about key digital solutions deployed to combat COVID-19 and the challenges faced when implementing and using them; and (3) offering necessary resources for clinicians about the use of digital tools in clinical practice, education, and research during COVID-19. Discussion Rigor and evidence need to be taken into consideration when designing, implementing, and using digital tools to combat COVID-19 to avoid delays and unforeseen negative consequences. It is paramount to employ a multidisciplinary approach for the development and implementation of digital health tools that have been rapidly deployed in response to the pandemic bearing in mind human factors, ethics, data privacy, and the diversity of context at the local, national, and international levels. The training and capacity building of front-line workers is crucial and must be linked to a clear strategy for evaluation of ongoing experiences.

Published
2020
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10. Role of Artificial Intelligence within the Telehealth Domain

Author

Anthony Maeder, Arindam Basu, Oommen John, Shashi Bhushan Gogia, Craig E. Kuziemsky, Márcia Ito, and Sushil Kumar Meher

Subjects
Telemedicine, Quality management, 020205 medical informatics, Scope (project management), Service delivery framework, business.industry, Computer science, MEDLINE, 02 engineering and technology, General Medicine, Telehealth, Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, Health care, 0202 electrical engineering, electronic engineering, information engineering, 030212 general & internal medicine, Artificial intelligence, business
Abstract

Objectives: This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances. Methods: Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth. Results: Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes. Conclusion: Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

Published
2019
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11. Hypertension and diabetes management: a policy perspective from Ghana

Author

Arindam Basu, Pauline Barnett, and Mark Fordjour Owusu

Subjects
medicine.medical_specialty, Policy initiatives, Ghana, Interviews as Topic, 03 medical and health sciences, 0302 clinical medicine, Documentation, Diabetes management, Diabetes mellitus, Diabetes Mellitus, Humans, Medicine, 030212 general & internal medicine, 030505 public health, business.industry, Health Policy, Perspective (graphical), Focus Groups, medicine.disease, Focus group, Health Planning, Content analysis, Key informants, Family medicine, Hypertension, Business, Management and Accounting (miscellaneous), Health Services Research, 0305 other medical science, business, Delivery of Health Care
Abstract

Purpose The purpose of this paper is to assess policy and management of hypertension and diabetes in Ghana. Design/methodology/approach The authors conducted 26 key informant interviews with policy makers, payers, providers and participants from advocacy groups associated with the management of hypertension and diabetes both at national and district levels in Ghana; conducted focus group discussions with a total of 18 hypertensive and diabetic patients; and analyzed documentation detailing activities that have been undertaken in response to the management of hypertension and diabetes in Ghana. The authors then conducted a content analysis after combining the three sources of information. Findings Using a stepwise policy process, the Ghanaian health sector has developed overarching policies and strategies on management of diabetes, hypertension, other non-communicable diseases, tobacco, alcohol and nutrition-related issues. Availability of funds and over-concentration on communicable diseases are the main barriers to the implementation of policies. Besides, response of the health sector to address the problems of hypertension and diabetes is focused more on clinical management than prevention; while the structures and processes to manage diabetes and hypertension is available at all levels of service delivery, more needs to be done on preventive aspects. Originality/value This is the first study in Ghana to combine individual interviews, focus groups and document analysis to review policy development, implementation and response activities on the management of hypertension and diabetes. The authors believe that the evidence from this research will inform future policy initiatives on hypertension and diabetes management in Ghana.

Published
2019
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12. A conceptual introduction to ggplot2

Author

Arindam Basu

Subjects
Information retrieval, Grammar, business.industry, Computer science, media_common.quotation_subject, General Engineering, Data set, Data visualization, ggplot2, Work (electrical), Quality (business), Graphics, business, media_common
Abstract

This is a conceptual introduction to ggplot2 in Rstudio. We take an available data set in R and work step by step to identify patterns and produce publishable quality graphics using the principles of grammar of graphics.

Published
2021
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13. WaC : first results on practical side-channel attacks on commercial machine learning accelerator

Author

Dirmanto Jap, Arindam Basu, Shivam Bhasin, Yoo-Seung Won, Soham Chatterjee, School of Electrical and Electronic Engineering, 5th Workshop on Attacks and Solutions in Hardware Security (ASHES '21), and Temasek Laboratories

Subjects
Edge device, Computer science, business.industry, Low resource, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Machine Learning Accelerator, Inference, Machine learning, computer.software_genre, Physical access, Side channel attack, Artificial intelligence, business, computer, Intel Neural Compute Stick 2, Mathematics::Discrete mathematics::Cryptography [Science]
Abstract

Commercial machine learning accelerators like Intel neural Compute Stick 2 (NCS2) enable efficient inference on otherwise low resource edge devices. However, these accelerators are also ex- posed to new threats leveraging physical access. In this paper, we present the first results demonstrating practical electromagnetic side-channel attack on NCS2, allowing secret weight recovery from executed models National Research Foundation (NRF) This research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme / Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001)

Published
2021

14. Smart electronic yarns and wearable fabrics for human biomonitoring

Author

Arindam Basu

Subjects
Textile, business.industry, Computer science, Conductive materials, Electrical engineering, Wearable computer, Substrate (printing), engineering.material, GeneralLiterature_MISCELLANEOUS, Highly sensitive, Coating, engineering, ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS, Electronics, business
Abstract

With the advent of modern engineered fibers and highly developed electronics a new variety of textile material has been evolved which is known as smart textiles. A smart textile generally has five major functions, that is, sensors, actuators, data processing, energy supply, and communication. Development of thin, flexible, and highly sensitive sensors has made it possible to detect the problems faced by the human during sickness or while participating in sports. This article reviews the development in all five areas and their future directions. Technologies such as printing, coating, lamination, and embroidery have been used for producing thin, flexible sensors on textile substrate, or plastic which helped in producing wearable smart textiles. The textile yarns, fabrics have been converted into conductive materials by chemical methods, mixing metallic fibers and other methods. Human biomonitoring is one of the most important uses of smart textiles, which either reacts or communicates messages depending on the values sensed by the sensors.

Published
2021
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15. Time to leak : cross-device timing attack on edge deep learning accelerator

Author

Dirmanto Jap, Arindam Basu, Yoo-Seung Won, Shivam Bhasin, Soham Chatterjee, School of Electrical and Electronic Engineering, 2021 International Conference on Electronics, Information, and Communication (ICEIC), and Temasek Laboratories

Subjects
Performance Evaluation, Exploit, Computer science, business.industry, Deep learning, Computer science and engineering::Information systems::Information systems applications [Engineering], Static timing analysis, Inference, Adversary, Timing attack, Deep Learning, Computer engineering, Enhanced Data Rates for GSM Evolution, Artificial intelligence, business, Vulnerability (computing)
Abstract

Edge deep learning accelerators are optimised hard ware to enable efficient inference on the edge. The models deployed on these accelerators are often proprietary and thus sensitive for commercial and privacy reasons. In this paper, we demonstrate practical vulnerability of deployed deep learning models to timing side-channel attacks. By measuring the execution time of the inference, the adversary can determine and reconstruct the model from a known family of well known deep learning model and then use available techniques to recover remaining hyperparameters. The vulnerability is validated on Intel Compute Stick 2 for VGG and ResNet family of models. Moreover, the presented attack is quite devastating as it can be performed in a cross-device setting, where adversary profiles constructed on a legally own device can be used to exploit the victim device with a single query and still can achieve near perfect success rate. National Research Foundation (NRF) Accepted version This research is supported in parts by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme / Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001).

Published
2021

16. How to create a summary of findings table with GRADEPro

Author

Arindam Basu

Subjects
Structure (mathematical logic), Actuarial science, business.industry, media_common.quotation_subject, Certainty, Outcome (game theory), Quality of evidence, Quality appraisal, Health care, Table (database), business, Psychology, Construct (philosophy), media_common
Abstract

GRADE provides a structure for clinicians and healthcare analysts to understand the quality of evidence for an outcome. An outcome can be associated with a number of studies, and quality appraisal in terms of whether additional evidence must be needed before a definitive conclusion about an intervention-outcome can be arrived at is done on the basis of conducting GRADE tables. GRADEpro as a software enables construction of grade tables, and has two parts. In part one of this series, we showed how to construct certainty of evidence tables. In this part, we discuss how to create summary of findings tables with GRADEpro for dichotomous outcome variables.

Published
2020
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18. HyNNA: Improved Performance for Neuromorphic Vision Sensor Based Surveillance using Hybrid Neural Network Architecture

Author

Arindam Basu, Andrés Ussa, Lavanya Ramapantulu, Deepak Singla, Bharath Ramesh, and Soham Chatterjee

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Event (computing), Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Object (computer science), Convolutional neural network, Object detection, Machine Learning (cs.LG), Domain (software engineering), Hybrid neural network, Neuromorphic engineering, Digital image processing, FOS: Electrical engineering, electronic engineering, information engineering, business, Computer hardware
Abstract

Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages over traditional imagers in this domain, the existing NVS systems either do not meet the power constraints or have not demonstrated end-to-end system performance. To address this, we improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal and address the low-power requirement for object detection and classification by exploring various convolutional neural network (CNN) architectures. Specifically, we compare the results obtained from our object detection framework against the state-of-the-art low-power NVS surveillance system and show an improved accuracy of 82.16% from 63.1%. Moreover, we show that using multiple bits does not improve accuracy, and thus, system designers can save power and area by using only single bit event polarity information. In addition, we explore the CNN architecture space for object classification and show useful insights to trade-off accuracy for lower power using lesser memory and arithmetic operations., 4 pages, 2 figures

Published
2020
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19. HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses

Author

Roshan Gopalakrishnan, Ashish Jith Sreejith Kumar, Arindam Basu, Yansong Chua, Pengfei Sun, School of Electrical and Electronic Engineering, and Institute for Infocomm Research, A*STAR

Subjects
Computer science, neural network, convolutional neural network, 02 engineering and technology, 01 natural sciences, Convolutional neural network, TrueNorth, lcsh:RC321-571, Software, hardware constraints, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, convolution, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Original Research, computer.programming_language, Neuromorphic Computing, 010302 applied physics, Artificial neural network, business.industry, General Neuroscience, Deep learning, deep learning, Python (programming language), neuromorphic chip, neuromorphic computing, 020202 computer hardware & architecture, Neuromorphic engineering, Computer architecture, crossbar array, Electrical and electronic engineering [Engineering], Neuromorphic Chip, Artificial intelligence, Crossbar switch, business, computer, Neuroscience
Abstract

The hardware-software co-optimization of neural network architectures is a field of research that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth and Intel Loihi. Development of simulation and automated mapping software tools in tandem with the design of neuromorphic hardware, whilst taking into consideration the hardware constraints, will play an increasingly significant role in deployment of system-level applications. This paper illustrates the importance and benefits of co-design of convolutional neural networks (CNN) that are to be mapped onto neuromorphic hardware with a crossbar array of synapses. Toward this end, we first study which convolution techniques are more hardware friendly and propose different mapping techniques for different convolutions. We show that, for a seven-layered CNN, our proposed mapping technique can reduce the number of cores used by 4.9–13.8 times for crossbar sizes ranging from 128 × 256 to 1,024 × 1,024, and this can be compared to the toeplitz method of mapping. We next develop an iterative co-design process for the systematic design of more hardware-friendly CNNs whilst considering hardware constraints, such as core sizes. A python wrapper, developed for the mapping process, is also useful for validating hardware design and studies on traffic volume and energy consumption. Finally, a new neural network dubbed HFNet is proposed using the above co-design process; it achieves a classification accuracy of 71.3% on the IMAGENET dataset (comparable to the VGG-16) but uses 11 times less cores for neuromorphic hardware with core size of 1,024 × 1,024. We also modified the HFNet to fit onto different core sizes and report on the corresponding classification accuracies. Various aspects of the paper are patent pending. Published version

Published
2020
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20. Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics

Author

Arindam Basu, Naveen Tiwari, Joydeep Basu, Rohit Abraham John, Chiara Bartolozzi, Sumon Kumar Bose, Nripan Mathews, Muhammad Iszaki Bin Patdillah, Ankit, Amoolya Nirmal, Sujaya Kumar Vishwanath, Mohit Rameshchandra Kulkarni, Chan Jun Yu, Nidhi Tiwari, School of Materials Science and Engineering, School of Mechanical and Aerospace Engineering, School of Electrical and Electronic Engineering, and Energy Research Institute @ NTU (ERI@N)

Subjects
Nociception, 0301 basic medicine, Transistors, Electronic, Logic, Computer science, Science, Presynaptic Terminals, Action Potentials, General Physics and Astronomy, 02 engineering and technology, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Electronic and spintronic devices, Robustness (computer science), Electronic devices, lcsh:Science, Edge computing, Neuronal Plasticity, Multidisciplinary, Materials [Engineering], business.industry, Signal Processing, Computer-Assisted, Robotics, Fault tolerance, General Chemistry, 021001 nanoscience & nanotechnology, Electrical and electronic engineering, Associative learning, 030104 developmental biology, Neuromorphic engineering, Computer architecture, Signal Processing, Mechanical engineering [Engineering], Robot, lcsh:Q, Artificial intelligence, 0210 nano-technology, business, Robotic arm
Abstract

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness., Sensory information processing in robots relies on a centralized approach with issues of wiring, fault-tolerance and latency. Here, the authors report a decentralized neuromorphic approach with self-healable memristive elements enabling intelligent sensations in a prototypical robotic nervous system.

Published
2020
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21. Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning

Author

Arindam Basu and Jyotibdha Acharya

Subjects
FOS: Computer and information sciences, Patient-Specific Modeling, Computer Science - Machine Learning, Sound (cs.SD), Computer science, Remote patient monitoring, Feature extraction, Biomedical Engineering, Machine Learning (stat.ML), 02 engineering and technology, Machine learning, computer.software_genre, Computer Science - Sound, Data modeling, Machine Learning (cs.LG), Statistics - Machine Learning, Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Humans, Electrical and Electronic Engineering, Respiratory Sounds, Artificial neural network, business.industry, Quantization (signal processing), Deep learning, 020208 electrical & electronic engineering, Signal Processing, Computer-Assisted, Memory footprint, Anomaly detection, Artificial intelligence, Neural Networks, Computer, business, computer, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to reduce the memory footprint for deployment in memory constrained systems such as wearable devices. The proposed hybrid CNN-RNN model achieves a score of $\text{66.31}\%$ on four-class classification of breathing cycles for ICBHI’17 scientific challenge respiratory sound database. When the model is re-trained with patient specific data, it produces a score of $\text{71.81}\%$ for leave-one-out validation. The proposed weight quantization technique achieves ${\approx}4 \times$ reduction in total memory cost without loss of performance. The main contribution of the paper is as follows: Firstly, the proposed model is able to achieve state of the art score on the ICBHI’17 dataset. Secondly, deep learning models are shown to successfully learn domain specific knowledge when pre-trained with breathing data and produce significantly superior performance compared to generalized models. Finally, local log quantization of trained weights is shown to be able to reduce the memory requirement significantly. This type of patient-specific re-training strategy can be very useful in developing reliable long-term automated patient monitoring systems particularly in wearable healthcare solutions.

Published
2020

22. A Spiking Neural Network Model for Category Learning

Author

Arindam Basu and Laxmi R. Iyer

Subjects
Spiking neural network, Range (mathematics), Categorization, Artificial neural network, Computer science, business.industry, Concept learning, Excitatory postsynaptic potential, Artificial intelligence, Inhibitory postsynaptic potential, Prefrontal cortex, business
Abstract

The creation of useful categories from data is an important cognitive ability, and from the extensive research on categorization, it is now known that the brain has distinct systems for category learning. In this paper, we present the first spiking neural network (SNN) model of human category learning. Here categories are combinations of features - such categories are observed in the prefrontal cortex (PFC). The system follows an architecture commonly used to model the cortex - features are arranged in a topological 2D grid with short range excitatory connectivity and long range inhibitory connectivity - however, here, this architecture is used differently from other models to model higher level cognition. Earlier we presented an artificial neural network (ANN) model of category learning; however, here, a simpler model was adequate, as desired functionality emerges from the SNN dynamics. We identified the objectives that had to be fulfilled for the model to achieve the desired functionality, and performed a design space exploration (DSE) to identify the parameter range in which each of the objectives was fulfilled, and the parameter range for which the system exhibits good performance. Finally, we compared triphasic STDP (a variant of spike time dependant plasticity (STDP)) with standard STDP and observed that triphasic STDP exhibited quicker convergence.

Published
2020
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23. Disruptive technologies: Present and future

Author

Magdala de Araújo Novaes and Arindam Basu

Subjects
Computer science, business.industry, Health care, Robot, Virtual reality, business, Medical care, Data science, Telehomecare
Abstract

In this chapter, we provide a smorgasbord of emergent technologies on the watch. We begin with an introduction to telehomecare; continue our discussions on the topic of the use of robots for medical and healthcare applications, artificial intelligence and its usage in health and medical care, and virtual reality and its applications in healthcare and medicine; and conclude with blockchain technology. We discuss these technologies with a view to their present and future potential.

Published
2020
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24. A 75kb SRAM in 65nm CMOS for In-Memory Computing Based Neuromorphic Image Denoising

Author

Sumon Kumar Bose, Arindam Basu, and Vivek Mohan

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer science, business.industry, Binary image, Image and Video Processing (eess.IV), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Electrical Engineering and Systems Science - Image and Video Processing, Kernel (image processing), CMOS, Neuromorphic engineering, In-Memory Processing, Hardware Architecture (cs.AR), Median filter, FOS: Electrical engineering, electronic engineering, information engineering, Static random-access memory, Electrical Engineering and Systems Science - Signal Processing, business, Computer Science - Hardware Architecture, Throughput (business), Computer hardware
Abstract

This paper presents an in-memory computing (IMC) architecture for image denoising. The proposed SRAM based in-memory processing framework works in tandem with approximate computing on a binary image generated from neuromorphic vision sensors. Implemented in TSMC 65nm process, the proposed architecture enables approximately 2000X energy savings (approximately 222X from IMC) compared to a digital implementation when tested with the video recordings from a DAVIS sensor and achieves a peak throughput of 1.25-1.66 frames/us., Comment: 8 pages

Published
2020
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25. Balancing Health Information Exchange and Privacy Governance from a Patient-Centred Connected Health and Telehealth Perspective

Author

Mowafa Househ, Arindam Basu, Carolyn Petersen, Shashi Bhushan Gogia, and Craig E. Kuziemsky

Subjects
Telemedicine, 020205 medical informatics, business.industry, Internet privacy, Privacy laws of the United States, Health information exchange, 02 engineering and technology, General Medicine, Telehealth, Business model, 03 medical and health sciences, 0302 clinical medicine, Health care, Connected health, 0202 electrical engineering, electronic engineering, information engineering, Confidentiality, 030212 general & internal medicine, business
Abstract

Objectives: Connected healthcare is an essential part of patient-centred care delivery. Technology such as telehealth is a critical part of connected healthcare. However, exchanging health information brings the risk of privacy issues. To better manage privacy risks we first need to understand the different patterns of patient-centred care in order to tailor solutions to address privacy risks. Methods: Drawing upon published literature, we develop a business model to enable patient-centred care via telehealth. The model identifies three patient-centred connected health patterns. We then use the patterns to analyse potential privacy risks and possible solutions from different types of telehealth delivery. Results: Connected healthcare raises the risk of unwarranted access to health data and related invasion of privacy. However, the risk and extent of privacy issues differ according to the pattern of patient-centred care delivery and the type of particular challenge as they enable the highest degree of connectivity and thus the greatest potential for privacy breaches. Conclusion: Privacy issues are a major concern in telehealth systems and patients, providers, and administrators need to be aware of these privacy issues and have guidance on how to manage them. This paper integrates patient-centred connected health care, telehealth, and privacy risks to provide an understanding of how risks vary across different patterns of patient-centred connected health and different types of telehealth delivery.

Published
2018
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26. Current Mirror Array: A Novel Circuit Topology for Combining Physical Unclonable Function and Machine Learning

Author

Chip-Hong Chang, Xueyong Zhang, Zheng Wang, Aakash Patil, Arindam Basu, J. Jayabalan, and Yi Chen

Subjects
Engineering, Artificial neural network, business.industry, 020208 electrical & electronic engineering, Physical unclonable function, 02 engineering and technology, 020202 computer hardware & architecture, Current mirror, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, Bit error rate, Overhead (computing), Enhanced Data Rates for GSM Evolution, Electrical and Electronic Engineering, business, Computer hardware, Edge computing, Extreme learning machine
Abstract

Edge analytics support industrial Internet of Things by pushing some data processing capacity to the edge of the network instead of sending the streaming data captured by the sensor nodes directly to the cloud. It is advantageous to endow machine learners for data reduction with suitable security primitives for privacy protection in edge computing devices to conserve area and power consumption. In this paper, we propose a novel physical unclonable function (PUF) based on current mirror array (CMA) circuits that reuses the circuit implementation of a machine learner–the extreme learning machine (ELM), which is a randomized neural network. Seven different challenge activation and response readout schemes are proposed to realize different weak and strong PUF functions from within the same CMA array. ELM endowed with such reconfigurable challenge-response mechanism is more robust and adaptable to different authentication protocols and security functions. Measurement results on $0.35\mu m$ test chips demonstrate that the proposed strong PUF outperforms other state-of-the-art designs with smaller area/bit of $9\times 10^{-36} \mu m^{2}$ and lower native bit error rate (BER) of 0.16% with an added overhead of less than 2.5% power and 2.9% area over the native ELM implementation.

Published
2018
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27. Physical Activity, Mental Health and Wellbeing during the First COVID-19 Containment in New Zealand: A Cross-Sectional Study

Author

Nick Draper, John Batten, Michael J. Hamlin, James Faulkner, Wendy J. O’Brien, Arindam Basu, Danielle Lambrick, Catherine Elliot, and Claire E. Badenhorst

Subjects
Adult, Male, Cross-sectional study, Health, Toxicology and Mutagenesis, Physical fitness, coronavirus, Sitting, wellness, Article, lifestyle behaviour change, Environmental health, Humans, Medicine, physical distancing, Socioeconomic status, Depression (differential diagnoses), exercise, SARS-CoV-2, business.industry, pandemic, Confounding, Public Health, Environmental and Occupational Health, COVID-19, applied_psychology, Middle Aged, anxiety, Mental health, Cross-Sectional Studies, Mental Health, Communicable Disease Control, depression, Anxiety, Female, medicine.symptom, business, New Zealand
Abstract

Strategies implemented worldwide to contain COVID-19 outbreaks varied in severity across different countries, and established a new normal for work and school life (i.e., from home) for many people, reducing opportunities for physical activity. Positive relationships of physical activity with both mental and physical health are well recognised, and therefore the aim was to ascertain how New Zealand’s lockdown restrictions impacted physical activity, mental health and wellbeing. Participants (n = 4007, mean ± SD: age 46.5 ± 14.7 years, 72% female, 80.7% New Zealand European) completed (10–26 April 2020) an online amalgamated survey (Qualtrics): International Physical Activity Questionnaire: Short Form, Depression, Anxiety and Stress Scale-9, World Health Organisation-Five Well-Being Index, Stages of Change Scale. Positive dose–response relationships between physical activity levels and wellbeing scores were demonstrated for estimates that were unadjusted (moderate activity OR 3.79, CI 2.88–4.92, high activity OR 8.04, CI 6.07–10.7) and adjusted (confounding variables: age, gender, socioeconomic status, time sitting and co-morbidities) (moderate activity 1.57, CI 1.11–2.52, high activity 2.85, CI 1.97–4.14). The study results support previous research demonstrating beneficial effects of regular physical activity on mental health and wellbeing. Governments may use these results to promote meeting physical activity guidelines in order to protect mental health and wellbeing during the ongoing COVID-19 restrictions and future pandemics.

Published
2021
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28. Association of HSD11B1 rs12086634 and HSD11B1 rs846910 gene polymorphisms with polycystic ovary syndrome in South Indian women

Author

Nayana Devang, Padmalatha S. Rai, Kapaettu Satyamoorthy, Prabha Adhikari, M. Nandini, and Arindam Basu

Subjects
0301 basic medicine, medicine.medical_specialty, business.industry, Endocrinology, Diabetes and Metabolism, 030209 endocrinology & metabolism, Single-nucleotide polymorphism, Odds ratio, medicine.disease, Obesity, Polycystic ovary, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Endocrinology, Insulin resistance, Internal medicine, Genotype, Internal Medicine, medicine, Gene polymorphism, business, Body mass index
Abstract

The 11beta-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme generates cortisol from cortisone for local glucocorticoid action in hepatocytes and adipocytes. Functional polymorphisms within 11beta-hydroxysteroid dehydrogenase type 1 (HSD11B1) gene have shown an association with various factors including insulin resistance, diabetes, and obesity. In this study, we have assessed a candidate gene association study in order to examine the association of HSD11B1 gene polymorphisms with polycystic ovary syndrome (PCOS). A total of 200 women were investigated in this case-control study. DNA samples from PCOS women (n = 100) and the age-matched control women (n = 100) were analyzed. Genotyping of HSD11B1 rs12086634 and rs846910 variants was performed using tetra-primer amplification refractory mutation system (TETRA-ARMS) PCR. Odds ratio and 95% confidence interval were calculated to determine the association of HSD11B1 gene polymorphisms with polycystic ovary syndrome. The association analysis indicate that HSD11B1 rs12086634 showed positive association with polycystic ovary syndrome (OR = 1.95; 95%CI = 1.11–3.44, p = 0.0195) and women with HSD11B1 rs12086634 TG genotype had significantly higher body mass index (28.05 kg/mt2, p = 0.001), higher waist circumference (89.68 cms, p = 0.019), higher triglycerides (120.37 mg/dl, p = 0.049), and lower HDL levels (45.56 mg/dl, p = 0.004) compared to the women with HSD11B1 TT genotype. HSD11B1 rs846910 did not show any association with PCOS (OR = 0.57; 95%CI = 0.311–1.051, p = 0.072). Our results suggest that HSD11B1 rs12086634 polymorphism is associated with PCOS and HSD11B1 rs846910 gene polymorphism is not associated with PCOS in South Indian women.

Published
2017
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29. Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

Author

Arindam Basu, Camilo Libedinsky, Rosa So, Shoeb Shaikh, and Tafadzwa Sibindi

Subjects
Male, Support Vector Machine, Computer science, Biomedical Engineering, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), Internal Medicine, Animals, 030304 developmental biology, Extreme learning machine, Cerebral Cortex, 0303 health sciences, business.industry, General Neuroscience, Rehabilitation, Discriminant Analysis, Signal Processing, Computer-Assisted, Pattern recognition, Linear discriminant analysis, Ensemble learning, Random forest, Support vector machine, Macaca fascicularis, Brain-Computer Interfaces, Multilayer perceptron, Neural Networks, Computer, Artificial intelligence, business, Classifier (UML), 030217 neurology & neurosurgery, Algorithms, Psychomotor Performance, Decoding methods
Abstract

This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracorticalBrain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels.We have tested the generality of this technique on different base classifiers - linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to ≈ 21%,13%,19%,10% in non-human primate (NHP) A and 7%,9%,7%,9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively.The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to ≈ 24%,11%,22%,9% in NHP A and 14%,19%,7%,28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.

Published
2019
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30. Towards Intelligent Intra-cortical BMI (i2BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters

Author

Camilo Libedinsky, Rosa So, Arindam Basu, Tafadzwa Sibindi, and Shoeb Shaikh

Subjects
Signal processing, business.industry, Computer science, 020208 electrical & electronic engineering, Bandwidth (signal processing), Biomedical Engineering, 02 engineering and technology, Energy consumption, Kalman filter, Chip, Computer engineering, Neuromorphic engineering, 0202 electrical engineering, electronic engineering, information engineering, Wireless, Electrical and Electronic Engineering, business, Algorithm, Decoding methods, Extreme learning machine
Abstract

Fully-implantable wireless intracortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10× is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data-rate is reduced drastically. Earlier work on neuromorphic decoder chips only showed classification of discrete states. We present results for continuous state decoding using a low-power neuromorphic decoder chip termed S pike-input E xtreme L earning Ma chine (SELMA) that implements a nonlinear decoder without memory and its memory-based version with time-delayed bins, SELMA-bins. We have compared SELMA, SELMA-bins against state-of-the-art S teady- S tate K alman F ilter (SSKF), a linear decoder with memory, across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% (20%) or more increase in the fraction of variance accounted for (FVAF) by SELMA (SELMA-bins) over SSKF across the datasets. Estimated energy consumption comparison shows SELMA (SELMA-bins) consuming $\approx$ 9 $nJ/update$ (23 $nJ/update$ ) against SSKF's $\approx$ 7.4 $nJ/update$ for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF while consuming energy in the same range as SSKF whereas SELMA-bins performs the best with moderately increased energy consumption, albeit far less than energy required for raw data transmission. This paves the way for reducing transmission data rates in future scaled iBMI systems.

Published
2019
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31. Quantitative text analysis

Author

Arindam Basu

Subjects
Text mining, business.industry, Computer science, Artificial intelligence, business, computer.software_genre, computer, Natural language processing
Published
2019
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32. A Current Mirror Cross Bar Based 2.86-TOPS/W Machine Learner and PUF with <2.5% BER in 65nm CMOS for IoT Application

Author

Aakash Patil, Zheng Wang, Arindam Basu, and Yi Chen

Subjects
Current mirror, CMOS, Computer science, business.industry, Sensor node, Physical unclonable function, Overhead (computing), business, Computer hardware
Abstract

Energy-efficient machine-learning and physical unclonable function (PUF) becomes popular in Internet-of-Things (IoT) applications for saliency detection and privacy protection at sensor node. A machine-learning and PUF engine for IoT applications is presented in this work with a current mirror cross-bar (CMCB) array being a shared core, reducing silicon area. A novel dimension expansion technique is proposed to increase weight matrix dimension beyond the physically implemented array with small hardware and energy overhead. A signed multiply-and-accumulation is realized in CMCB with differential current path and 2-phase conversion. The proposed engine achieves an error rate of 6.34% on MNIST digit recognition task with an energy efficiency of 2.86 TOPS/W. The PUF achieves a native BER of 2.3% across corners and extremely low area/CRP of 4.17×10–59 µm2/CRP.

Published
2019
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33. Live Demonstration: Autoencoder-Based Predictive Maintenance for IoT

Author

Bapi Kar, Arindam Basu, Mohendra Roy, Sumon Kumar Bose, Pradeep Kumar Gopalakrishnan, School of Electrical and Electronic Engineering, and 2019 IEEE International Symposium on Circuits and Systems (ISCAS)

Subjects
IoT, Artificial neural network, business.industry, Computer science, Feature extraction, Electrical and electronic engineering [Engineering], Predictive Maintenance, Anomaly detection, Layer (object-oriented design), business, Autoencoder, Computer hardware, Predictive maintenance
Abstract

This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection. NRF (Natl Research Foundation, S’pore) Accepted version

Published
2019
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34. Real-time Closed Loop Neural Decoding on a Neuromorphic chip

Author

Camilo Libedinsky, Tafadzwa Sibindi, Shoeb Shaikh, Rosa So, and Arindam Basu

Subjects
FOS: Computer and information sciences, business.industry, Computer science, 020208 electrical & electronic engineering, Feature extraction, Computer Science - Emerging Technologies, 02 engineering and technology, Chip, Emerging Technologies (cs.ET), Neuromorphic engineering, Joystick, Hardware Architecture (cs.AR), 0202 electrical engineering, electronic engineering, information engineering, Wireless, Computer Science - Hardware Architecture, business, Computer hardware, Decoding methods, Brain–computer interface, Neural decoding
Abstract

This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of approximately 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of approximately 85% of those obtained by hand-controlled joystick . These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs., accepted at Neural Engineering Conference (NER), 2019

Published
2019
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35. Spiking Neural Network based Region Proposal Networks for Neuromorphic Vision Sensors

Author

Arindam Basu, Vandana Reddy Padala, and Jyotibdha Acharya

Subjects
FOS: Computer and information sciences, Spiking neural network, Membrane potential, business.industry, Computer science, Region proposal, Computer Science - Neural and Evolutionary Computing, Computer Science - Emerging Technologies, Pattern recognition, 02 engineering and technology, 020202 computer hardware & architecture, Convolution, Synapse, Emerging Technologies (cs.ET), Neuromorphic engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Neural and Evolutionary Computing (cs.NE), Mean-shift, Artificial intelligence, Layer (object-oriented design), Cluster analysis, business
Abstract

This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors. The proposed architecture consists of refractory, convolution and clustering layers designed with bio-realistic leaky integrate and fire (LIF) neurons and synapses. The proposed algorithm is tested on traffic scene recordings from a DAVIS sensor setup. The performance of the region proposal network has been compared with event based mean shift algorithm and is found to be far superior (~50% better) in recall for similar precision (~85%). Computational and memory complexity of the proposed method are also shown to be similar to that of event based mean shift, Accepted in IEEE ISCAS, 2019

Published
2019

36. ADEPOS

Author

Pradeep Kumar Gopalakrishnan, Sumon Kumar Bose, Bapi Kar, Arindam Basu, and Mohendra Roy

Subjects
Event (computing), business.industry, Computer science, 020208 electrical & electronic engineering, Real-time computing, Cloud computing, 02 engineering and technology, Predictive maintenance, 0202 electrical engineering, electronic engineering, information engineering, Bandwidth (computing), 020201 artificial intelligence & image processing, Anomaly detection, business, Edge computing, Energy (signal processing), Data transmission
Abstract

In Industry 4.0, predictive maintenance (PdM) is one of the most important applications pertaining to the Internet of Things (IoT). Machine learning is used to predict the possible failure of a machine before the actual event occurs. However, main challenges in PdM are: (a) lack of enough data from failing machines, and (b) paucity of power and bandwidth to transmit sensor data to cloud throughout the lifetime of the machine. Alternatively, edge computing approaches reduce data transmission and consume low energy. In this paper, we propose Anomaly Detection based Power Saving (ADEPOS) scheme using approximate computing through the lifetime of the machine. In the beginning of the machine's life, low accuracy computations are used when machine is healthy. However, on detection of anomalies as time progresses, system is switched to higher accuracy modes. We show using the NASA bearing dataset that using ADEPOS, we need 8.8X less neurons on average and based on post-layout results, the resultant energy savings are 6.4--6.65X.

Published
2019
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37. A 0.16pJ/bit recurrent neural network based PUF for enhanced machine learning attack resistance

Author

Nimesh Shah, Arindam Basu, Durga Prasad Sahoo, Manaar Alam, and Debdeep Mukhopadhyay

Subjects
Comparator, Computer science, business.industry, Reliability (computer networking), 020208 electrical & electronic engineering, Physical unclonable function, 0211 other engineering and technologies, Arbiter, 02 engineering and technology, Machine learning, computer.software_genre, Power (physics), Reduction (complexity), ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS, Recurrent neural network, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, business, computer, Energy (signal processing), 021106 design practice & management
Abstract

Physically Unclonable Function (PUF) circuits are finding wide-spread use due to increasing adoption of IoT devices. However, the existing strong PUFs such as Arbiter PUFs (APUF) and its compositions are susceptible to machine learning (ML) attacks because the challenge-response pairs have a linear relationship. In this paper, we present a Recurrent-Neural-Network PUF (RNN-PUF) which uses a combination of feedback and XOR function to significantly improve resistance to ML attack, without significant reduction in the reliability. ML attack is also partly reduced by using a shared comparator with offset-cancellation to remove bias and save power. From simulation results, we obtain ML attack accuracy of 62% for different ML algorithms, while reliability stays above 93%. This represents a 33.5% improvement in our Figure-of-Merit. Power consumption is estimated to be 12.3μW with energy/bit of ≈ 0.16pJ.

Published
2019
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38. The Prevalence of Body Mass Index-Associated Chronic Diseases in Diverse Ethnic Groups in New Zealand

Author

Arindam Basu, Andrew Adiguna Halim, and Ray Kirk

Subjects
Adult, Male, Population, Prevalence, Ethnic group, 030209 endocrinology & metabolism, Type 2 diabetes, Overweight, Body Mass Index, 03 medical and health sciences, 0302 clinical medicine, medicine, Ethnicity, Humans, 030212 general & internal medicine, education, education.field_of_study, business.industry, Public Health, Environmental and Occupational Health, nutritional and metabolic diseases, medicine.disease, Obesity, Health Surveys, Diabetes Mellitus, Type 2, Cardiovascular Diseases, Chronic Disease, Hypertension, Female, medicine.symptom, Metabolic syndrome, business, Body mass index, Demography, New Zealand
Abstract

The use of universal body mass index (BMI) cutoffs do not take into account variation in the association between BMI and health risk across diverse ethnic groups. We used the New Zealand Health Survey data collected between 2002/2003 and 2014/2015 to calculate the predictive marginal means of hypertension, cardiovascular diseases, and type 2 diabetes mellitus (T2DM) after adjusting for demographic variables and health-related behaviors. Compared with European group, we found that Pacific had a lower prevalence of hypertension at a BMI of ≥35 kg/m2, and Māori had a higher prevalence of hypertension, T2DM, and cardiovascular diseases at higher BMI intervals. Whereas Asian had a higher T2DM prevalence compared with Māori, Pacific, and European at some BMI intervals. Using universal BMI cutoffs on the ethnically diverse population to compare obesity rates does not address the risk of chronic diseases associated with high BMI and may stigmatize certain ethnic groups.

Published
2019

39. Development of Smart Textiles for Medical Care

Author

Saurab Jain, Arindam Basu, and V. S. Khoiwal

Subjects
Textile, Software, Heartbeat, Human–computer interaction, business.industry, Computer science, Global Positioning System, ComputerApplications_COMPUTERSINOTHERSYSTEMS, business, Medical care, GeneralLiterature_MISCELLANEOUS
Abstract

The advent of electronic sensors and other developments has helped the textile technologists to develop smart/intelligent textiles. In the present study, a smart textile garment has been developed which can sense the heartbeat, body temperature and geographical position of the wearer. The software, developed under this project, sends information to the caregivers and relatives whenever these values cross the threshold limits, which has been fixed based on practical data.

Published
2019
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40. Diffusive and Drift Halide Perovskite Memristive Barristors as Nociceptive and Synaptic Emulators for Neuromorphic Computing

Author

Natalia Yantara, Arindam Basu, Ankit, Mohit Rameshchandra Kulkarni, Si En Ng, Muhammad Iszaki Bin Patdillah, Nripan Mathews, Joydeep Basu, Rohit Abraham John, Subodh Mhaisalkar, Nur Fadilah Jamaludin, School of Materials Science and Engineering, School of Electrical and Electronic Engineering, and Energy Research Institute @ NTU (ERI@N)

Subjects
Artificial Synapses, Materials science, Materials [Engineering], Neuromorphic engineering, Mechanics of Materials, business.industry, Mechanical Engineering, Neuromorphic, Optoelectronics, Halide, General Materials Science, business, Perovskite (structure)
Abstract

With the current research impetus on neuromorphic computing hardware, realizing efficient drift and diffusive memristors are considered critical milestones for the implementation of readout layers, selectors, and frameworks in deep learning and reservoir computing networks. Current demonstrations are predominantly limited to oxide insulators with a soft breakdown behavior. While organic ionotronic electrochemical materials offer an attractive alternative, their implementations thus far have been limited to features exploiting ionic drift a.k.a. drift memristor technology. Development of diffusive memristors with organic electrochemical materials is still at an early stage, and modulation of their switching dynamics remains unexplored. Here, halide perovskite (HP) memristive barristors (diodes with variable Schottky barriers) portraying tunable diffusive dynamics and ionic drift are proposed and experimentally demonstrated. An ion permissive poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate interface that promotes diffusive kinetics and an ion source nickel oxide (NiOx) interface that supports drift kinetics are identified to design diffusive and drift memristors, respectively, with methylammonuim lead bromide (CH3NH3PbBr3) as the switching matrix. In line with the recent interest on developing artificial afferent nerves as information channels bridging sensors and artificial neural networks, these HP memristive barristors are fashioned as nociceptive and synaptic emulators for neuromorphic sensory signal computing. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version NRF‐CRP14‐2014‐03 NRF2018‐ITC001‐001 MOE2016‐T2‐1‐100 MOE2018‐T2‐2‐083

Published
2021
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41. An Online Structural Plasticity Rule for Generating Better Reservoirs

Author

Subhrajit Roy and Arindam Basu

Subjects
FOS: Computer and information sciences, Artificial neural network, Computer science, business.industry, Cognitive Neuroscience, Spike train, Computer Science - Neural and Evolutionary Computing, Pattern recognition, 02 engineering and technology, 03 medical and health sciences, 0302 clinical medicine, Arts and Humanities (miscellaneous), Neuromorphic engineering, Pattern recognition (psychology), Learning rule, 0202 electrical engineering, electronic engineering, information engineering, Unsupervised learning, 020201 artificial intelligence & image processing, Neural and Evolutionary Computing (cs.NE), Artificial intelligence, Representation (mathematics), business, 030217 neurology & neurosurgery, Separation property
Abstract

In this article, a novel neuro-inspired low-resolution online unsupervised learning rule is proposed to train the reservoir or liquid of Liquid State Machine. The liquid is a sparsely interconnected huge recurrent network of spiking neurons. The proposed learning rule is inspired from structural plasticity and trains the liquid through formation and elimination of synaptic connections. Hence, the learning involves rewiring of the reservoir connections similar to structural plasticity observed in biological neural networks. The network connections can be stored as a connection matrix and updated in memory by using Address Event Representation (AER) protocols which are generally employed in neuromorphic systems. On investigating the 'pairwise separation property' we find that trained liquids provide 1.36 $\pm$ 0.18 times more inter-class separation while retaining similar intra-class separation as compared to random liquids. Moreover, analysis of the 'linear separation property' reveals that trained liquids are 2.05 $\pm$ 0.27 times better than random liquids. Furthermore, we show that our liquids are able to retain the 'generalization' ability and 'generality' of random liquids. A memory analysis shows that trained liquids have 83.67 $\pm$ 5.79 ms longer fading memory than random liquids which have shown 92.8 $\pm$ 5.03 ms fading memory for a particular type of spike train inputs. We also throw some light on the dynamics of the evolution of recurrent connections within the liquid. Moreover, compared to 'Separation Driven Synaptic Modification' - a recently proposed algorithm for iteratively refining reservoirs, our learning rule provides 9.30%, 15.21% and 12.52% more liquid separations and 2.8%, 9.1% and 7.9% better classification accuracies for four, eight and twelve class pattern recognition tasks respectively., 45 pages, 13 figures, journal

Published
2016
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42. Reproducible research using minimalist plain text tools

Author

Arindam Basu

Subjects
Publishing, business.industry, Plain text, Computer science, General Engineering, Mindset, computer.file_format, Analysis tools, business, computer, Data science, Biological sciences, Replication (computing)
Abstract

For many studies, particularly in the biomedical disciplines such as Epidemiology and biological sciences, a full replication is impossible. Therefore, if research is reproducible such that the data and code are disseminated widely and openly and made available for everyone to review and re-analyse, this may facilitate discovery of new knowledge and verification of claims. There are several barriers to the implementation of reproducible research: one of the identified barriers is the challenge of changing people's mindset of publishing and dissemination of results. We argue that using simple plain text tools and easy dissemination of research, codes, and data may help. In this article, I will discuss the the role and importance of using simple plain text based writing and data analysis tools can benefit dissemination of reproducible research by lowering the barrier to dissemination of robust research and findings. This article is both a discussion paper and a showcase.

Published
2018
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44. A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition

Author

Aakash Patil, Yi Chen, Jyotibdha Acharya, Arindam Basu, Shih-Chii Liu, Xiaoya Li, University of Zurich, Basu, Arindam, Interdisciplinary Graduate School (IGS), and School of Electrical and Electronic Engineering

Subjects
Computational complexity theory, Speech recognition, Population, Feature extraction, real-time, Silicon Cochlea, neuromorphic, 02 engineering and technology, Neural Engineering Framework, Bin, lcsh:RC321-571, extreme learning machine, 03 medical and health sciences, 0302 clinical medicine, Software, silicon cochlea, 0202 electrical engineering, electronic engineering, information engineering, neural engineering framework, education, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Original Research, 10194 Institute of Neuroinformatics, Extreme learning machine, education.field_of_study, Quantitative Biology::Neurons and Cognition, business.industry, General Neuroscience, 020208 electrical & electronic engineering, 2800 General Neuroscience, Neuromorphic engineering, 570 Life sciences, biology, business, Classifier (UML), 030217 neurology & neurosurgery, Neuroscience
Abstract

Frontiers in Neuroscience, 12, ISSN:1662-453X, ISSN:1662-4548

Published
2018
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45. An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition

Author

Sunil L. Kukreja, Arindam Basu, Nitish V. Thakor, Yi Chen, Mahdi Rasouli, School of Electrical and Electronic Engineering, and VIRTUS IC Design Centre of Excellence

Subjects
Computer science, Interface (computing), Models, Neurological, Biomedical Engineering, Action Potentials, 02 engineering and technology, Pattern Recognition, Automated, Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Humans, Computer vision, Electrical and Electronic Engineering, Neuromorphics, Extreme learning machine, Skin, Signal processing, Artificial neural network, business.industry, 020208 electrical & electronic engineering, Signal Processing, Computer-Assisted, Tactile perception, Neuromorphic engineering, Touch, Pattern recognition (psychology), Electrical and electronic engineering [Engineering], 020201 artificial intelligence & image processing, Artificial intelligence, Neural Networks, Computer, Extreme Learning Machine, business, Tactile sensor, Algorithms
Abstract

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

Published
2018

46. Powering the IoT through embedded machine learning and LoRa

Author

Vignesh Mahalingam Suresh, Arindam Basu, Zhang Lei, Prateek Karkare, Rishi Raj Singh Sidhu, and Aakash Patil

Subjects
Battery (electricity), LPWAN, Power transmission, Edge device, Computer science, business.industry, 020208 electrical & electronic engineering, 020206 networking & telecommunications, 02 engineering and technology, Machine learning, computer.software_genre, Transmission (telecommunications), 0202 electrical engineering, electronic engineering, information engineering, Key (cryptography), Artificial intelligence, business, computer, Edge computing
Abstract

The Internet of Things (IoT) technology is rapidly changing the way we live and the number of connected devices are increasing at an exponential pace. However, two key challenges are the battery life for off-grid IoT applications and the ability of edge devices to communicate over long range. Raw data transmission poses as a very power hungry activity for any device. The conventional cellular wide area networks are power-hungry and incompatible for battery-operated IoT devices. There is a need for low-power edge computing devices that reduce the transmission payload and integrate Low-Power Wide-Area Network (LPWAN) technologies, which offer a wide range connectivity while still providing a long battery life. One of the most promising LPWAN technologies today is LoRa. In this paper, we present a solution that employs machine learning on the edge device and performs low power transmission through LoRa. We demonstrate the use case of our solution through a field trial conducted in China for sow activity classification. By implementing embedded machine learning with LoRa, we could compress the transmitted data by 512 times and extend the battery life by three times. A very low energy expenditure of 5.1 mJ per classification result is achieved.

Published
2018
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47. Ionotronic halide perovskite drift-diffusive synapses for low-power neuromorphic computation

Author

Yan Fong Ng, Arindam Basu, Edoardo Mosconi, Mohit Rameshchandra Kulkarni, Daniele Meggiolaro, Filippo De Angelis, Natalia Yantara, Pradeep Kumar Gopalakrishnan, Nripan Mathews, Rohit Abraham John, Govind Narasimman, Chien A. Nguyen, Subodh Mhaisalkar, School of Electrical and Electronic Engineering, School of Materials Science & Engineering, and Energy Research Institute @ NTU (ERI@N)

Subjects
halide perovskites, ion migration, ionic semiconductors, neuromorphic computing, synaptic plasticity, Materials science, 02 engineering and technology, Memristor, 010402 general chemistry, 01 natural sciences, law.invention, Condensed Matter::Materials Science, Halide Perovskite, law, General Materials Science, Perovskite (structure), Signal processing, Quantitative Biology::Neurons and Cognition, Materials [Engineering], business.industry, Mechanical Engineering, Conductance, Reconfigurability, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Ion Migration, Formamidinium, Neuromorphic engineering, Mechanics of Materials, Optoelectronics, Unsupervised learning, 0210 nano-technology, business
Abstract

Emulation of brain‐like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift‐diffusive ionic kinetics would enable energy‐efficient analog‐like switching of metastable conductance states. Here, ionic–electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short‐ and long‐term plasticity rules like paired‐pulse facilitation and spike‐time‐dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network‐level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy‐efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version

Published
2018

48. A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring

Author

Sumon Kumar Bose, Bapi Kar, Pradeep Kumar Gopalakrishnan, Arindam Basu, Mohendra Roy, School of Electrical and Electronic Engineering, and IEEE Symposium Series on Computational Intelligence (SSCI)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Feature extraction, Machine Learning (stat.ML), 02 engineering and technology, computer.software_genre, Predictive maintenance, Feature Extraction, Machine Learning (cs.LG), Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Audio signal processing, Extreme learning machine, business.industry, 020208 electrical & electronic engineering, Condition monitoring, Pattern recognition, Autoencoder, Electrical and electronic engineering [Engineering], Prognostics, 020201 artificial intelligence & image processing, Anomaly detection, Artificial intelligence, business, computer, Condition Monitoring
Abstract

Condition monitoring is one of the routine tasks in all major process industries. The mechanical parts such as a motor, gear, bearings are the major components of a process industry and any fault in them may cause a total shutdown of the whole process, which may result in serious losses. Therefore, it is very crucial to predict any approaching defects before its occurrence. Several methods exist for this purpose and many research are being carried out for better and efficient models. However, most of them are based on the processing of raw sensor signals, which is tedious and expensive. Recently, there has been an increase in the feature based condition monitoring, where only the useful features are extracted from the raw signals and interpreted for the prediction of the fault. Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data. This of course requires the prior knowledge of the nature of data and related processes. This limits the feature extraction process. However, recent development in the autoencoder based feature extraction method provides an alternative to the traditional handcrafted approaches; however, they have mostly been confined in the area of image and audio processing. In this work, we have developed an automated feature extraction method for on-line condition monitoring based on the stack of the traditional autoencoder and an on-line sequential extreme learning machine(OSELM) network. The performance of this method is comparable to that of the traditional feature extraction approaches. The method can achieve 100% detection accuracy for determining the bearing health states of NASA bearing dataset. The simple design of this method is promising for the easy hardware implementation of Internet of Things(IoT) based prognostics solutions., Comment: This article has been submitted to IEEE-SSCI 2018 conference

Published
2018
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49. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013

Author

Yong Zhao, Hadi Danawi, Bach Xuan Tran, Gene Bukhman, Vasiliki Stathopoulou, Taavi Tillmann, Nelson Alvis-Guzman, Yongmei Li, Jerry Puthenpurakal Abraham, Sudan Prasad Neupane, Jack Caravanos, Ben Schöttker, Rafael Lozano, Damian G Hoy, Yoshihiro Kokubo, Nicholas J K Breitborde, Sergey Soshnikov, Yukito Shinohara, Randall V. Martin, Michael Brainin, Fernando Perez-Ruiz, Yingfeng Zheng, Santosh Mishra, Julio Cesar Montañez Hernandez, Michael Phillips, Belinda J. Gabbe, Hebe N. Gouda, Ziad A. Memish, Rupert R A Bourne, Guoqing Hu, Emmanuel A. Ameh, Abigail McLain, Michelle L. Bell, Christopher Margono, Marissa Iannarone, Wilkister N. Moturi, Donald H. Silberberg, Carl Abelardo T. Antonio, Nataliya Foigt, Anand Dayama, Yanping Wang, Amanda J. Mason-Jones, Bolajoko O. Olusanya, Monica S. Vavilala, Katherine B Gibney, David Tanne, Sidibe S Kany Roseline, Marcella Montico, Abhishek Singh, Sarah Derrett, Alireza Esteghamati, Seok Jun Yoon, Corine Karema, Rakhi Dandona, David M. Pereira, Kazem Rahimi, Gitanjali M. Singh, Vivekanand Jha, John Hornberger, Anne M. Riederer, Kathryn H. Jacobsen, Andrea Pedroza, Lily Alexander, Fiona M. Blyth, Tommi Vasankari, Kyle J Foreman, Rana J. Asghar, Tilahun Nigatu Haregu, Yousef Khader, Rafael Alfonso-Cristancho, Suzanne Barker-Collo, Lydia S. Atkins, Simerjot K. Jassal, Mohammad Ali Sahraian, Peter Scarborough, Hans W. Hoek, E. Ray Dorsey, Muluken Dessalegn, David C. Schwebel, Gavin Shaddick, Thomas D. Fleming, Mohammad Tavakkoli, Dorairaj Prabhakaran, Mohammad H. Forouzanfar, Christopher C. Mapoma, Jost B. Jonas, Erin C Mullany, Gene F. Kwan, Johanna M. Geleijnse, Antonio Luiz Pinho Ribeiro, Roberto Tchio Talongwa, Tolesa Bekele, Jed D. Blore, Gunn Marit Aasvang, Philimon Gona, Miguel Angel Alegretti, Babak Eshrati, Mitsuru Mukaigawara, Richard F. Gillum, Odgerel Chimed-Ochir, Ubai Alsharif, Richard C. Franklin, Felix Masiye, Richard T. Burnett, Sanjay Krishnaswami, Martin McKee, John J Huang, Lucía Cuevas-Nasu, Wagner Marcenes, Walid Ammar, Knud Juel, Joseph R. Zunt, Martha M Téllez Rojo, Mamta Swaroop, Noela M Prasad, Azmeraw T. Amare, Tim Driscoll, Michael Kravchenko, Heresh Amini, Amir Sapkota, Theo Vos, Charlotte Watts, Dennis Odai Laryea, D. Alex Quistberg, Justin Beardsley, Cheng Huang, Adnan M. Durrani, Sarah V Thackway, Rita Van Dingenen, Manami Inoue, Martha Híjar, Honglei Chen, Amany H. Refaat, Yichong Li, Vineet K. Chadha, Wenzhi Wang, Louisa Degenhardt, Kingsley N. Ukwaja, Nayu Ikeda, James D. Wilkinson, Linh N Bui, Maria Hagströmer, Gonghuan Yang, Ann Kristin Knudsen, David J. Margolis, Soewarta Kosen, Hans Kromhout, Atsushi Goto, Man Mohan Mehndiratta, Thomas N. Williams, Michael Soljak, Yun Jin Kim, Hideaki Toyoshima, Jeyaraj D Pandian, Borja del Pozo-Cruz, Soufiane Boufous, Ivy Shiue, Anders Larsson, Guilherme V. Polanczyk, John Powles, Yara A. Halasa, Robin Room, Ratilal Lalloo, Carolina Batis Ruvalcaba, Panniyammakal Jeemon, Elisabete Weiderpass, Jürgen Rehm, Ejaz Ahmad Khan, Alicia Aleman, Zacharie Tsala Dimbuene, Elena Alvarez, Rachelle Buchbinder, Randah R. Hamadeh, Bryan Hubbell, Sadaf G. Sepanlou, Farhad Islami, Costas A. Christophi, Heidi Stöckl, Ismael R. Campos-Nonato, Nigel Bruce, Edward J Mills, Samuel A L Perry, Taavi Lai, Baffour Awuah, Mete Saylan, Karen J. Courville, Arindam Basu, Vanessa De la Cruz-Góngora, Teresita González de Cosío, Naohiro Yonemoto, Frida Namnyak Ngalesoni, Muluken Azage Yenesew, Atte Meretoja, Michael Brauer, Cyrus Cooper, Giorgia Giussani, Valentina S. Arsic Arsenijevic, Vasiliy Victorovich Vlassov, André Karch, Leilei Duan, Matthew M Coates, Omid Ameli, Gelin Xu, Matthias Endres, Ganesan Karthikeyan, Ione Jayce Ceola Schneider, Mohamed Hsairi, Palwasha Anwari, Mazin J. Al Khabouri, Dariush Mozaffarian, Juan R. Sanabria, Pablo M. Lavados, Sumeet S. Chugh, Johan Ärnlöv, Ivo Rakovac, Maurice Giroud, Haidong Kan, Ibrahim Abdelmageem Mohamed Ginawi, José Luis Texcalac-Sangrador, Luigi Naldi, Erica Leigh Slepak, Deena Alasfoor, James E. Saunders, Richard Matzopoulos, Talal Bakfalouni, Stein Emil Vollset, Andrea Werdecker, Lennert J. Veerman, Lorenzo Monasta, Henrica A. F. M. Jansen, Reyna A Gutiérrez, Brittany Wurtz, Luz Maria Sanchez, Lijing L. Yan, M. Patrice Lindsay, Michele Meltzer, Sanjay Basu, Steven van de Vijver, Alaa Badawi, Thomas Claßen, Young-Ho Khang, Brett M. Kissela, Jun Zhu, In-Hwan Oh, Fiona J Charlson, Maria Cecilia Bahit, Dinorah González-Castell, Rosario Cárdenas, Dan Poenaru, Sayed Saidul Alam, Mitchell T. Wallin, Harish Chander Gugnani, James Leigh, Ferrán Catalá-López, Lidia Morawska, Jim van Os, Stephanie J. London, Kaire Innos, Isabelle Romieu, Fiorella Cavalleri, Adrian Davis, Hwee Pin Phua, Chakib Nejjari, Héctor Gómez Dantés, Boris I. Pavlin, Karen Sliwa, Lynne Gaffikin, Constance D. Pond, Michael F. MacIntyre, Blake Thomson, Norberto Perico, Ronny Westerman, Samantha M. Colquhoun, Michael H. Criqui, Ana Maria Nogales Vasconcelos, Wubegzier Mekonnen, Bulat Idrisov, Ana Basto-Abreu, Andrew G. M. Bulloch, Jasvinder A. Singh, Vinod K. Paul, Emin Murat Tuzcu, Svetlana Popova, Hmwe H Kyu, Richard L. Guerrant, Mohammed I. Albittar, Srikanth Mangalam, Steven E. Lipshultz, Lela Sturua, Semaw Ferede Abera, Eduardo Bernabé, George D. Thurston, Bruno F. Sunguya, Tiffany Ku, Alejandra G. Contreras, Abdullah Sulieman Terkawi, Charles Atkinson, Ashkan Afshin, Heidi J. Larson, Abdullatif Husseini, Jose C. Adsuar, Reza Assadi, Ademola Lukman Adelekan, Joshua A. Salomon, Yousef M. Elshrek, Gokalp Kadri Yentur, Devina Nand, Narayanaswamy Venketasubramanian, Graça Maria Ferreira De Lima, Maheswar Satpathy, Fotis Topouzis, Traolach S. Brugha, Hywel C Williams, Coen H. Van Gool, Andrew H. Kemp, Awoke Misganaw, Amado D Quezada, Norito Kawakami, Bert Brunekreef, Peter Burney, Tati S. Warouw, Jongmin Lee, Inga Dora Sigfusdottir, Marcel Tanner, Solveig A. Cunningham, Benjamin O. Anderson, Tariku Jibat Beyene, Lars Barregard, Xia Wan, Giuseppe Remuzzi, Bernadette Thomas, Lilia S Pedraza, Massimo Cirillo, Alina Rodriguez, Ricky Leung, Farshad Pourmalek, K. Srinath Reddy, Charles D.A. Wolfe, Ulrich O Mueller, Neeraj Bedi, Al Artaman, Lucia Hernandez, Itamar S. Santos, C. Arden Pope, Norlinah Mohamed Ibrahim, Carlo Irwin A. Panelo, Selen Begüm Uzun, Miltiadis K. Tsilimbaris, Anwar Rafay, Daniel Dicker, Melvin Barrientos Marzan, Sajjad Ur Rahman, Mohammed O. Basulaiman, Edgar P. Simard, Mohammad T Mashal, Maysaa El Sayed Zaki, Shiwei Liu, Don C. Des Jarlais, Bo Norrving, Salvador Villalpando, Miia Kivipelto, Yang Liu, Carolina Maria Teixeira, Catalina Medina, Sudha Jayaraman, Josep Maria Haro, Diego De Leo, Angel J Paternina Caicedo, Abigail C. McKay, Eric L. Ding, Mukesh Dherani, Ljiljana Pejin Stokic, Vinay Nangia, Sukanta Saha, Juan Liang, Elisabeth Cardis, Zourkaleini Younoussi, José R Nogueira, Braden Te Ao, Vasco Manuel Pedro Machado, Lionel Racapé, Ting Wu Chuang, Shahab Khatibzadeh, E Filipa de Castro, Barthelemy Kuate Defo, Ulises Trujillo, Alan D. Lopez, Soraya Seedat, Lope H Barrero, Linhong Wang, Daniel Pope, Alexandra Brazinova, Faris Lami, Valentina Colistro, G Anil Kumar, Derek F J Fay, Haidong Wang, Hwashin H. Shin, Raimundas Lunevicius, Suzanne Polinder, Dietrich Plass, David Rojas-Rueda, Stephen S Lim, Tania G Sánchez-Pimienta, K.M. Venkat Narayan, Yuantao Hao, Jung-Chen Chang, Corina Benjet, Seyed-Mohammad Fereshtehnejad, Luciano A. Sposato, Stan Biryukov, Kunihiro Matsushita, Beth E. Ebel, Cleusa P. Ferri, Soumya Swaminathan, K. Ryan Wessells, Gustavo Velasquez-Melendez, Leslie T. Cooper, David O. Carpenter, Nancy Lopez, Bryan L. Sykes, Sandra Nolte, Murray B. Stein, Paul N. Jensen, Fabiola Mejía-Rodríguez, Xiaonong Zou, Bradford D. Gessner, Dhruv S. Kazi, Dragos Virgil Davitoiu, Alejandra Jáuregui, Pouria Heydarpour, Megan Bohensky, Harvey Whiteford, Berrak Bora Basara, Zhengming Chen, Gregory R. Wagner, Paul I. Dargan, Hermann Brenner, Nima Hafezi-Nejad, John Nelson Opio, Scott Weichenthal, Deborah Salvo, Jun She, Tea Lallukka, Carolyn C. Gotay, Stephen G. Waller, Christian Kieling, Shivanthi Balalla, Valery L. Feigin, Qing Lan, Matias Trillini, Adam D M Briggs, Sungroul Kim, Niveen M E Abu-Rmeileh, Renata Micha, Sergey Petrovich Ermakov, Ole Frithjof Norheim, Zulfiqar A Bhutta, Paul S. F. Yip, Grant Nguyen, Ralph L. Sacco, Biju Abraham, Ken Takahashi, Jixiang Ma, Peter A. Meaney, Ayse Abbasoglu Ozgoren, Kimberly Cooperrider, M Rifat Kose, Shams Eldin Ali Hassan Khalifa, Rasmus Havmoeller, Alize J. Ferrari, Kebede Deribe, Nadim E. Karam, George A. Mensah, Bongani M. Mayosi, Konrad Pesudovs, Joanna Moschandreas, Ziad Nahas, James Damsere-Derry, Nsanzimana Sabin, Tonatiuh Barrientos-Gutiérrez, Ying Jiang, Andre Pascal Kengne, Peter Allebeck, Jonas Minet Kinge, Shankuan Zhu, Guy B. Marks, Daniel C Casey, Marco A Avila, Anna Roca, Lalit Dandona, Ami R. Moore, Adansi A. Amankwaa, David Gunnell, Andre Keren, Yohannes Adama Melaku, Nhung T Nguyen, Anthony D. Woolf, Mayuree Rao, Peter J. Allen, Christina Papachristou, Karzan Abdulmuhsin Mohammad, Ravi Kumar Balu, Marie Ng, Marcello Tonelli, Maziar Moradi-Lakeh, Maigeng Zhou, Emmanuela Gakidou, Mohammed K. Ali, Amanda W Pain, Dan J. Stein, Kawkab Shishani, Fortuné Gbètoho Gankpé, Howard J. Hoffman, James Scott, Nadine Steckling, Samia Alhabib, Deborah Jarvis, Kara Estep, Arsène Kouablan Adou, Ricardo Orozco, Holly Hagan, K. C. Astha, Reza Malekzadeh, Klara Dokova, Aliya Naheed, Ernst J. Kuipers, Valeria Caso, Derrick A Bennett, Andrea B. Feigl, Uche S. Uchendu, Holly E. Erskine, Shireen Sindi, Arjun Lakshmana Balaji, Francesco Saverio Violante, Monika Sawhney, Alejandra Cantoral, Ketevan Goginashvili, Raghib Ali, Fan Jiang, Robert G. Weintraub, Homie Razavi, Myriam Tobollik, Howard Hu, Emerito Jose A. Faraon, Irma Khonelidze, Patricia M. Riccio, Eun-Kee Park, Julio Cesar Campuzano, Ibrahim Abubakar, Jürgen C Schmidt, Konstantinos Stroumpoulis, Aref A. Bin Abdulhak, Graeme J. Hankey, Natan M. Bornstein, Mouhanad Hammami, Lee Richardson, Rintaro Mori, Alanur Çavlin, Ruth W Kimokoti, Samir Soneji, Mark J. Nieuwenhuijsen, John Q. Wong, Joseph Frostad, Tom Achoki, Rahman Shiri, Ashish Bhalla, Kurt Straif, Simon I. Hay, Scott B. Patten, Kalpana Balakrishnan, Awoke Misganaw Temesgen, Chandrashekhar T Sreeramareddy, Ryan M Barber, Rosana E. Norman, JianLi Wang, Siyi Shangguan, Luke Nyakarahuka, Kovin Naidoo, Charles D. H. Parry, Mercedes Colomar, H. Ross Anderson, Carlos Magis-Rodriguez, Joan M. Nolla, Muhammad Imran Nisar, Karen Devries, Andrew L. Thorne-Lyman, Denis Nash, Marape Marape, Rajiv Chowdhury, Dima M. Qato, Luca Ronfani, Nobhojit Roy, Daniel Kim, Yuichiro Yano, Luke D. Knibbs, Margaret Robinson, Hilda L Harb, Rogelio Pérez-Padilla, Janet L Leasher, Jonathan L. Wright, Peter Brooks, Cristiana Abbafati, Belinda K Lloyd, Victor Aboyans, Nikhil Tandon, Charles R. Newton, Simón Barquera, Ted R. Miller, Kinnari S. Murthy, Habib Benzian, Glen Mola, Paulo A. Lotufo, Burcu Kucuk Bicer, Peggy Pei-Chia Chiang, Alexander Kraemer, Solomon Meseret Woldeyohannes, Saman Fahimi, Lesley Rushton, Kim Moesgaard Iburg, Vafa Rahimi-Movaghar, Logan Sandar, Bruce Neal, Teresa Shamah Levy, Karen M. Tabb, Jeffrey A. Towbin, Christopher J L Murray, Ramesh Sahathevan, Aaron Cohen, Chanda Kulkarni, Van C. Lansingh, François Alla, Tasara T. Mazorodze, Murugesan Raju, Saeid Shahraz, Uchechukwu K.A. Sampson, Rajeev Gupta, Neil Pearce, Mustafa Z. Younis, Veena S. Kulkarni, Francisco A García-Guerra, Amanda G. Thrift, Stefan Ma, Samaya Ismayilova, Evariste Gasana, Amitava Banerjee, Aslam Pervaiz, Emilie Agardh, Abraham D. Flaxman, Farshad Farzadfar, Peter W. Gething, Ileana Heredia-Pi, Boris Bikbov, Wanqing Chen, Saad B. Omer, Ruben Castro, Neeraj Bhala, Sara Sheikhbahaei, Hilton Lam, Urbano Fra Paleo, Lidia Sanchez-Riera, Alicia Elena Beatriz Lawrynowicz, Kristen Delwiche, Richard G. Ellenbogen, Max Petzold, Yuri Y Varakin, Guilherme Borges, Guohong Jiang, Francis Guillemin, Kyle R. Heuton, Yohannes Kinfu, Victoria F Bachman, Joseph A Wagner, Carlos A Castañeda-Orjuela, Leonardo Trasande, Abbas Ali Mahdi, Josef Coresh, Chuanhua Yu, Kenji Shibuya, Berrin Serdar, Laetitia Huiart, Xiaofeng Liang, Jean de Dieu Ngirabega, Takayoshi Ohkubo, Natalie Stephens, Francis Apolinary Mhimbira, Jefferson Traebert, Amiran Gamkrelidze, Kjetil Søreide, Samath D Dharmaratne, Robert P. Dellavalle, George Mugambage Ruhago, Lakshmi Vijayakumar, Joannie Lortet-Tieulent, Dipan Bose, Tania C Aburto, Saleem M Rana, Miriam Levi, Mohammad Taghi Hedayati, Rodolfo S Pagcatipunan, Ron T. Gansevoort, H. D. Hosgood, Michael Burch, Mohsen Naghavi, Vegard Skirbekk, Ayfer Pekericli, Walter Mendoza, Pengpeng Ye, Gabrielle deVeber, Ali H. Mokdad, David M. Broday, Koranteng Adofo, Zewdie Aderaw Alemu, Shifalika Goenka, Carrie Beth Peterson, Nicolas J. C. Stapelberg, Edson Serván-Mori, Anil Kaul, Foad Abd-Allah, Marek Majdan, Rahul Gupta, Giancarlo Logroscino, Kardiyoloji, Peterson, Carrie B., Laboratoire de Physique des Solides (LPS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Wisconsin Division of Public Health, Laboratoire de psychologie sociale et de psychologie cognitive (LAPSCO), Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Erosion torrentielle neige et avalanches (UR ETGR (ETNA)), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), London South Bank University, Metropolitan Police Service, Hong Kong Baptist University (HKBU), Institut für Informatik [München/Munich] (LMU), Ludwig-Maximilians-Universität München (LMU), Neuroépidémiologie Tropicale (NET), Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Chirurgie Thoracique et Vasculaire - Médecine vasculaire [CHU Limoges], CHU Limoges, Insight Centre for Data Analytics [Galway] (INSIGHT), National University of Ireland [Galway] (NUI Galway), Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université Paris Descartes - Paris 5 (UPD5)-Université de Lorraine (UL), Centre d'Investigation Clinique - Epidemiologie Clinique/essais Cliniques Nancy, Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale (INSERM), Karolinska Institute, karolinska institute, Department of Molecular Biosciences, Department of Molecular Biosciences [Oslo], Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO)-Faculty of Mathematics and Natural Sciences [Oslo], University of Oslo (UiO)-University of Oslo (UiO), Centro de Estudios Avanzados en Zonas Aridas (CEAZA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Regional Genetic Service, St Mary's Hospital, Manchester, Laboratoire d'Ingénierie des Matériaux (LIM), Centre National de la Recherche Scientifique (CNRS), Computer Science Department [Bristol], University of Bristol [Bristol], Universität Mannheim [Mannheim], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) (SRI-B), Computational Science and Engineering Department [Daresbury] (STFC), Science & Technologie Facilities Council, Multimedia Research Center (MRC), University of Alberta, Division of Biostatistics (Biostat - MINNEAPOLIS), University of Minnesota [Twin Cities], University of Minnesota System-University of Minnesota System, University of Southampton, Imperial College London, Neurology Department, Ichilov Medical Center, Interactions, transferts, ruptures artistiques et culturels - EA 6301 (InTRu), Université de Tours, Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Julius Center for Health Sciences and Primary Care, University Medical Center [Utrecht], Risk Assessment Sciences Institute, Utrecht University [Utrecht], Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Respiratory Epidemiology and Public Health, Imperial College London-Royal Brompton Hospital-National Heart and Lung Institute [UK], CIBER de Epidemiología y Salud Pública (CIBERESP), STAR laboratory, Stanford University [Stanford], Unité de recherche Virologie et Immunologie Moléculaires (VIM), Institut National de la Recherche Agronomique (INRA), Tennent Institute of Ophthalmology, National University of Singapore (NUS), Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Multidisciplinary Nanotechnology Centre, Swansea University, Cyprus International Institute for the Environment and Public Health, Harvard School of Public Health, Genomics Research Center, Academia Sinica, Neuro Rehab Services LLP [New Delhi], Department of Signal Theory and Communications (TSC), Univ. Politec. de Catalunya, King‘s College London, Dept. Mat. Engn. De Ma, Sao Carlos, Fed. Univ. Sao Carlos UFSCar, RESPEC (RESPEC), RESPEC, Advanced Laboratories on Embedded Systems [Roma] (ALES), Department of Biology [Miami], University of Miami [Coral Gables], Health Care, Minister Of Labour-Ministry of Labor, Health and Social Affairs, Department of Nephrology, University Medical Center, University of Groningen, Division of Human Nutrition, Wageningen University and Research Centre [Wageningen] (WUR), Spatial Ecology and Epidemiology Group, University of Oxford [Oxford], College of Medicine, University of Hail, Saudi Arabia, Laboratory of Neurologic Diseases, Mario Negri Institute, Milan, Department of Civil Engineering [Hamirpur], National Institute of Technology [Hamirpur], GEMMA — Environmental Engineering and Microbiology Research Group, Department of Hydraulic, Maritime and Environmental Engineering, Universitat Politècnica de Catalunya [Barcelona] (UPC), Department of Biosciences and Nutrition, Karolinska Institutet [Stockholm], Institut National de Recherche et d'Analyse Physico-Chimique (INRAP), Institut National de Recherche et d'Analyse Physico-chimique (Ariana, Tunisie) (INRAP), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences [Tehran] (SBUMS), Shahid Beheshti University-Shahid Beheshti University, Center for Applied Medical Research (CIMA), University of Tehran, Secretariat of the Pacific Community, Public Health Division, Sociétés, Acteurs, Gouvernement en Europe (SAGE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), College of Precision Instrument and Optoelectronics Engineering, Tianjin University, George Washington University (GW), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U912 INSERM - Aix Marseille Univ - IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universiteit Gent [Ghent], Washington State University (WSU), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, School of Computer Science - China University of Geosciences (China University of Geosciences (East Area)), Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université Catholique de Louvain (UCL), Fachbereich Physik [Berlin], Freie Universität Berlin, Div Cyclotron & Radiopharmaceut Sci (DRDO, INMAS), Univ New Delhi, School of Physics and Astronomy, University of St Andrews [Scotland], University of Cape Town, Department of Neuroscience, Department of Computer Science and Engineering [Daejeon] (Chungnam National University), Lawrence University, Gastroenterology & Hepatology, Tata Research Development and Design Center (TRDDC), TCS Innovation Labs, University of Helsinki, Google Inc [Mountain View], Research at Google, Swedish Defense Research Agency (FOI), Servicio de Neurologia (SANTIAGO - Neurologie), Universidad del Desarrollo, Novartis Pharmaceutical Corporation, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble [1985-2015] (OSUG [1985-2015]), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Department of Mechanical and Automation Engineering (CAD Laboratory), The Chinese University of Hong Kong [Hong Kong], Università degli studi di Bari, Department of Health and Human Services, National Institutes of Health [Bethesda] (NIH), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), College of Information and Electrical Engineering [Beijing] (CIEE), China Agricultural University (CAU), Thales Research and Technology [Palaiseau], THALES, Department of Rheumatology and Connective Tissue Diseases, Medical University of Lublin, Barts and The London School of Medicine and Dentistry, Queen Mary University of London (QMUL), Centre d'économie de la Sorbonne (CES), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Paris School of Economics (PSE), École supérieure du professorat et de l'éducation - Académie de Grenoble [2013-2019] (ESPE Grenoble [2013-2019]), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Mathematics, University of Iowa [Iowa City], College of Medicine, Alfaisal University, Saudi Ministry of Health, Institut national des recherches agricoles du Bénin, Centre de Recherches agricoles du Sud, Departments of Epidemiology and Nutrition, Unit of Human Nutrition, Agricultural University of Athens, Department of Animal Science, Pennsylvania State University (Penn State), Penn State System-Penn State System, University of Virginia, University of Virginia [Charlottesville], Epidemiology and Biostatistics Unit, Institute for Maternal and Child Health - IRCCS ‘‘Burlo Garofolo', Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Division of Cardiovascular Medicine and Channing Division of Network Medicine, Brigham and Women's Hospital [Boston], American University of Beirut [Beyrouth] (AUB), Department of Chemistry, Scientific Computing Research Unit, Department of dermatology, Milano University-Azienda Ospedaleria Ospedali Riuniti di Bergamo, Mailman School of Public Health, Columbia University [New York], The Georges Institute for International Health, The University of Sydney, Department of epidemiology and Public Health, Faculty of Medicine, Département Optique (OPT), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Universitat Pompeu Fabra [Barcelona], Center for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra [Barcelona]-Catalunya ministerio de salud, Nutriments Lipidiques et Prévention des Maladies Métaboliques, Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université de la Méditerranée - Aix-Marseille 2, Department of Neurology Lunds University Hospital Lund, Unit of Functional Bionanomaterials, School of Biosciences, University of Birmingham [Birmingham], Electrical Engineering and Computer Science Department - Case Western Reserve University, Case Western Reserve University [Cleveland], World Health Organization, Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Nordic School of Public Health, The James Hutton Institute, Horia Hulubei National Institute for Physics and Nuclear Engineering, Department of Pediatrics, University of Washington [Seattle], Institute of Public Health, Department of Physics, Clarendon Laboratory, Center for TeleInFrastruktur (CTIF), Aalborg University [Denmark] (AAU), Physikalisches Institut [Freiburg], Albert-Ludwigs-Universität Freiburg, Savoirs, Textes, Langage (STL) - UMR 8163 (STL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Dept.of Computer Science, Indian Institute of Technology Madras (IIT Madras), Istituto Mario Negri Bergamo, Centro Ricerche e Trapianti Villa Camozzi, Universidade Estadual Paulista Júlio de Mesquita Filho [São José do Rio Preto] (UNESP), Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut geològic de Catalunya (IGC), Institut Geològic de Catalunya-IGC, Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie et Génomique des Poissons (LPGP), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Recherche Agronomique (INRA), Politecnico di Milano [Milan] (POLIMI), Symantec, University of Oviedo, European Microsoft Innovation Center (EMIC), Microsoft Corporation [Redmond, Wash.], Technion - Israel Institute of Technology [Haifa], Laboratoire de Mécanique, Physique et Géosciences (LMPG), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU), Department of Physics, Cavendish Laboratory, University of Cambridge [UK] (CAM), Laboratoire de Probabilités et Modèles Aléatoires (LPMA), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Pierre et Marie Curie - Paris 6 (UPMC), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Departments of Applied Physics [New Haven], Yale University [New Haven], Center for Mathematical Modeling (CMM), Department of Global Health and Development, London School of Hygiene and Tropical Medicine (LSHTM), University of Occupational and Environmental Health [Kitakyushu] (UEOH), Department of Computer Science and Engineering [New Delhi], Indian Institute of Technology Delhi (IIT Delhi), Institut de Recherche sur les Phénomènes Hors Equilibre (IRPHE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), GlaxoSmithKline, Imperial College London-Clinical Imaging Center, Universidade Federal de Pernambuco [Recife] (UFPE), Maclean Building, Benson Lane, Crowmarsh Gifford, Centre for Ecology and Hydrology, Nanoscience Institute (NEST), Dipartimento di Fisica, Università di Pisa, Aristotle University of Thessaloniki, Laboratory Of Immune Cell Biology (LICB), JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Occupational Health Unit, Bologna University Hospital-Sant'Orsola-Malpighi Polyclinic, Royal Institute of Technology [Stockholm] (KTH ), NICTA [Eveleigh], National ICT Australia [Sydney] (NICTA), Division of Solid Mechanics, Lund University [Lund], University of Calgary, BioWare Corp, Manchester Academic Health Sciences Centre, Institut d'Histoire et de Philosophie des Sciences et des Techniques (IHPST), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Ghent University [Belgium] (UGENT), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Children’s Hospital of Philadelphia (CHOP ), Perelman School of Medicine, University of Pennsylvania [Philadelphia], Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Department of Physiology, Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Neurorestoration Group, Wolfson Centre for Age-related Diseases-King‘s College London, Electronic Navigation Research Institute (ENRI), Ministry of Land, Infrastructure, Transport and Tourism, Department of Computer Science [KAIST] (CS), Korea Advanced Institute of Science and Technology (KAIST), Centre for Undergraduate Studies, University of the Punjab, Siemens Corporate Research, Siemens AG [Munich], University of Massachusetts [Boston] (UMass Boston), University of Massachusetts System (UMASS), Department of Materials Science, Sichuan University [Chengdu] (SCU), Natl Engn Res Ctr Vegetables, Key Lab Biol & Genet Improvement Hort Crops N Chi, Beijing Acad Agr & Forestry Sci, University Hospital Puerta de Hierro, Madrid, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Université de Franche-Comté (UFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), Shahid Beheshti University of Medical Sciences, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), MOLTECH-ANJOU (MOLTECH-ANJOU), Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), Universidad de Santiago de Chile [Santiago] (USACH)-Universidad del Desarrollo, Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), École supérieure du professorat et de l'éducation - Académie de Grenoble (ESPE Grenoble), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Grenoble Alpes (UGA), California Institute of Technology (CALTECH)-NASA, Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Sud - Paris 11 (UP11), Université de la Méditerranée - Aix-Marseille 2-Institut National de la Recherche Agronomique (INRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad de Santiago de Chile [Santiago] (USACH), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), King‘s College London-Wolfson Centre for Age-related Diseases, Sichuan University, Universitat de Barcelona, Interne Geneeskunde, Medische Sociologie, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), Psychiatrie & Neuropsychologie, CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5)-Centre National de la Recherche Scientifique (CNRS), Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), RS: MHeNs - R2 - Mental Health, Laboratoire de Physique des Solides ( LPS ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de psychologie sociale et de psychologie cognitive ( LAPSCO ), Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ) -Centre National de la Recherche Scientifique ( CNRS ), Erosion torrentielle neige et avalanches ( UR ETGR ), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Hong Kong Baptist University ( HKBU ), Institut für Informatik [München/Munich] ( LMU ), Ludwig-Maximilians-Universität München, Neuroépidémiologie Tropicale ( NET ), CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Génomique, Environnement, Immunité, Santé, Thérapeutique ( GEIST ), Université de Limoges ( UNILIM ) -Université de Limoges ( UNILIM ), Insight Centre for Data Analytics (National University of Ireland Galway (NUIG)) ( INSIGHT ), Maladies chroniques, santé perçue, et processus d'adaptation ( APEMAC ), Université Paris Descartes - Paris 5 ( UPD5 ) -Université de Lorraine ( UL ), Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale ( INSERM ), University of Oslo ( UiO ) -University of Oslo ( UiO ), Centro de Estudios Avanzados en Zonas Aridas ( CEAZA ), Ecole Polytechnique Fédérale de Lausanne ( EPFL ), Laboratoire d'Ingénierie des Matériaux ( LIM ), Centre National de la Recherche Scientifique ( CNRS ), Lawrence Berkeley National Laboratory [Berkeley] ( LBNL ), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) ( SRI-B ), Computational Science and Engineering Department [Daresbury] ( STFC ), Multimedia Research Center ( MRC ), University of Alberta [Edmonton], Division of Biostatistics ( Biostat - MINNEAPOLIS ), University of Minnesota [Minneapolis], University of Southampton [Southampton], Interactions, transferts, ruptures artistiques et culturels - EA 6301 ( InTRu ), Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), University Medical Center Utrecht, Biologie Fonctionnelle et Adaptative ( BFA ), Imperial College London-Royal Brompton Hospital-National Heart and Lung Institute, Unité de recherche Virologie et Immunologie Moléculaires ( VIM ), Institut National de la Recherche Agronomique ( INRA ), National University of Singapore ( NUS ), Centre de Robotique ( CAOR ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Department of Signal Theory and Communications ( TSC ), RESPEC ( RESPEC ), Advanced Laboratories on Embedded Systems [Roma] ( ALES ), Wageningen University and Research Centre [Wageningen] ( WUR ), Universitat Politècnica de Catalunya [Barcelona] ( UPC ), Institut National de Recherche et d'Analyse Physico-Chimique ( INRAP ), Institut National de Recherche et d'Analyse Physico-chimique (INRAP-Tunisie), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) ( FEMTO-ST ), Université de Franche-Comté ( UFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Ecole Nationale Supérieure de Mécanique et des Microtechniques ( ENSMM ) -Université de Technologie de Belfort-Montbeliard ( UTBM ), Tehran University, Sociétés, Acteurs, Gouvernement en Europe ( SAGE ), Université de Strasbourg ( UNISTRA ) -Centre National de la Recherche Scientifique ( CNRS ), Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale ( SESSTIM - U912 INSERM - AMU - IRD ), Institut de Recherche pour le Développement ( IRD ) -Aix Marseille Université ( AMU ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Washington State University ( WSU ), Laboratoire de Physique de l'ENS Lyon ( Phys-ENS ), École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), Institut des Sciences Chimiques de Rennes ( ISCR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Ecole Nationale Supérieure de Chimie de Rennes-Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de recherche en informatique de Toulouse ( IRIT ), Institut National Polytechnique [Toulouse] ( INP ) -Université Toulouse 1 Capitole ( UT1 ) -Université Toulouse - Jean Jaurès ( UT2J ) -Université Toulouse III - Paul Sabatier ( UPS ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche en Automatique de Nancy ( CRAN ), Université de Lorraine ( UL ) -Centre National de la Recherche Scientifique ( CNRS ), Université Catholique de Louvain ( UCL ), Freie Universität Berlin [Berlin], Div Cyclotron & Radiopharmaceut Sci ( DRDO, INMAS ), Institut de Physique Nucléaire d'Orsay ( IPNO ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Tata Research Development and Design Center ( TRDDC ), Laboratoire MOLTECH-Anjou [Angers] ( MOLTECH ANJOU ), Université d'Angers ( UA ) -Centre National de la Recherche Scientifique ( CNRS ), University of Helsinki [Helsinki], Swedish Defense Research Agency ( FOI ), Servicio de Neurologia ( SANTIAGO - Neurologie ), Universidad de Santiago de Chile [Santiago] ( USACH ) -Universidad del Desarrollo, Novartis Pharmaceutical Corp., East Hanover NJ 07936, USA, Laboratoire de glaciologie et géophysique de l'environnement ( LGGE ), Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Mechanical and Automation Engineering ( CAD Laboratory ), National Institutes of Health ( NIH ), Centre d'étude et de recherche en informatique et communications ( CEDRIC ), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise ( ENSIIE ) -Conservatoire National des Arts et Métiers [CNAM] ( CNAM ), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] ( Heudiasyc ), Université de Technologie de Compiègne ( UTC ) -Centre National de la Recherche Scientifique ( CNRS ), College of Information and Electrical Engineering [Beijing] ( CIEE ), China Agricultural University ( CAU ), Queen Mary University of London ( QMUL ), Centre d'économie de la Sorbonne ( CES ), Université Panthéon-Sorbonne ( UP1 ) -Centre National de la Recherche Scientifique ( CNRS ), Paris School of Economics ( PSE ), École supérieure du professorat et de l'éducation - Académie de Grenoble ( ESPE Grenoble ), Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -Université Grenoble Alpes ( UGA ), PennState University [Pennsylvania] ( PSU ), Jet Propulsion Laboratory ( JPL ), NASA-California Institute of Technology ( CALTECH ), American University of Beirut [Beyrouth], The University of Sydney [Sydney], Département Optique ( OPT ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des signaux et systèmes ( L2S ), Université Paris-Sud - Paris 11 ( UP11 ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Center for Research in Environmental Epidemiology ( CREAL ), Université de la Méditerranée - Aix-Marseille 2-Institut National de la Recherche Agronomique ( INRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Center for TeleInFrastruktur ( CTIF ), Aalborg University [Denmark] ( AAU ), Savoirs, Textes, Langage (STL) - UMR 8163 ( STL ), Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Indian Institute of Technology Madras ( IIT Madras ), Universidade Estadual Paulista Júlio de Mesquita ( UNESP ), Laboratoire de Génie Informatique et Ingénierie de Production ( LGI2P ), IMT - Mines Alès Ecole Mines - Télécom ( IMT - MINES ALES ), Institut geològic de Catalunya ( IGC ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Physiologie et Génomique des Poissons ( LPGP ), Institut National de la Recherche Agronomique ( INRA ) -Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Politecnico di Milano [Milan], European Microsoft Innovation Center ( EMIC ), Laboratoire de Mécanique, Physique et Géosciences ( LMPG ), Université Le Havre Normandie ( ULH ), Normandie Université ( NU ) -Normandie Université ( NU ), University of Cambridge [UK] ( CAM ), Laboratoire de Probabilités et Modèles Aléatoires ( LPMA ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Génétique et de Biologie Moléculaire et Cellulaire ( IGBMC ), Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Center for Mathematical Modeling ( CMM ), Universidad de Santiago de Chile [Santiago] ( USACH ), London School of Hygiene and Tropical Medicine ( LSHTM ), University of Occupational and Environmental Health [Kitakyushu] ( UEOH ), Indian Institute of Technology Delhi ( IIT Delhi ), Institut de Recherche sur les Phénomènes Hors Equilibre ( IRPHE ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), Universidade Federal de Pernambuco [Recife] ( UFPE ), Nanoscience Institute ( NEST ), Laboratory Of Immune Cell Biology ( LICB ), JRC Institute for Environment and Sustainability ( IES ), European Commission - Joint Research Centre [Ispra] ( JRC ), Bonn Universität [Bonn], Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] ( LNCC / MCT ), Royal Institute of Technology [Stockholm] ( KTH ), National ICT Australia [Sydney] ( NICTA ), Institut d'Histoire et de Philosophie des Sciences et des Techniques ( IHPST ), Université Panthéon-Sorbonne ( UP1 ) -Département d'Etudes Cognitives - ENS Paris ( DEC ), École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ), Ghent University [Belgium] ( UGENT ), Children’s Hospital of Philadelphia ( CHOP ), Univeristy of Pennsylvania Medical School, Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique ( LHEEA ), École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche en Génie Civil et Mécanique ( GeM ), Université de Nantes ( UN ) -École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Medical College of Georgia, Electronic Navigation Research Institute ( ENRI ), Department of Computer Science [KAIST] ( CS ), Korea Advanced Institute of Science and Technology ( KAIST ), Laboratoire de l'Accélérateur Linéaire ( LAL ), University of Massachusetts [Boston] ( UMass Boston ), Forouzanfar, Mohammad H, Alexander, Lily, Anderson, H. Ro, Bachman, Victoria F, Biryukov, Stan, Brauer, Michael, Burnett, Richard, Casey, Daniel, Coates, Matthew M, Cohen, Aaron, Delwiche, Kristen, Estep, Kara, Frostad, Joseph J, Kc, Astha, Kyu, Hmwe H, Moradi Lakeh, Maziar, Ng, Marie, Slepak, Erica Leigh, Thomas, Bernadette A, Wagner, Joseph, Aasvang, Gunn Marit, Abbafati, Cristiana, Ozgoren, Ayse Abbasoglu, Abd Allah, Foad, Abera, Semaw F, Aboyans, Victor, Abraham, Biju, Abraham, Jerry Puthenpurakal, Abubakar, Ibrahim, Abu Rmeileh, Niveen M. E, Aburto, Tania C, Achoki, Tom, Adelekan, Ademola, Adofo, Koranteng, Adou, Arsène K, Adsuar, José C, Afshin, Ashkan, Agardh, Emilie E, Al Khabouri, Mazin J, Al Lami, Faris H, Alam, Sayed Saidul, Alasfoor, Deena, Albittar, Mohammed I, Alegretti, Miguel A, Aleman, Alicia V, Alemu, Zewdie A, Alfonso Cristancho, Rafael, Alhabib, Samia, Ali, Raghib, Ali, Mohammed K, Alla, Françoi, Allebeck, Peter, Allen, Peter J, Alsharif, Ubai, Alvarez, Elena, Alvis Guzman, Nelson, Amankwaa, Adansi A, Amare, Azmeraw T, Ameh, Emmanuel A, Ameli, Omid, Amini, Heresh, Ammar, Walid, Anderson, Benjamin O, Antonio, Carl Abelardo T, Anwari, Palwasha, Cunningham, Solveig Argeseanu, Arnlöv, Johan, Arsenijevic, Valentina S. Arsic, Artaman, Al, Asghar, Rana J, Assadi, Reza, Atkins, Lydia S, Atkinson, Charle, Avila, Marco A, Awuah, Baffour, Badawi, Alaa, Bahit, Maria C, Bakfalouni, Talal, Balakrishnan, Kalpana, Balalla, Shivanthi, Balu, Ravi Kumar, Banerjee, Amitava, Barber, Ryan M, Barker Collo, Suzanne L, Barquera, Simon, Barregard, Lar, Barrero, Lope H, Barrientos Gutierrez, Tonatiuh, Basto Abreu, Ana C, Basu, Arindam, Basu, Sanjay, Basulaiman, Mohammed O, Ruvalcaba, Carolina Bati, Beardsley, Justin, Bedi, Neeraj, Bekele, Tolesa, Bell, Michelle L, Benjet, Corina, Bennett, Derrick A, Benzian, Habib, Bernabé, Eduardo, Beyene, Tariku J, Bhala, Neeraj, Bhalla, Ashish, Bhutta, Zulfiqar A, Bikbov, Bori, Abdulhak, Aref A. Bin, Blore, Jed D, Blyth, Fiona M, Bohensky, Megan A, Başara, Berrak Bora, Borges, Guilherme, Bornstein, Natan M, Bose, Dipan, Boufous, Soufiane, Bourne, Rupert R, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J, Brenner, Hermann, Briggs, Adam D. M, Broday, David M, Brooks, Peter M, Bruce, Nigel G, Brugha, Traolach S, Brunekreef, Bert, Buchbinder, Rachelle, Bui, Linh N, Bukhman, Gene, Bulloch, Andrew G, Burch, Michael, Burney, Peter G. J, Campos Nonato, Ismael R, Campuzano, Julio C, Cantoral, Alejandra J, Caravanos, Jack, Cárdenas, Rosario, Cardis, Elisabeth, Carpenter, David O, Caso, Valeria, Castañeda Orjuela, Carlos A, Castro, Ruben E, Catalá López, Ferrán, Cavalleri, Fiorella, Çavlin, Alanur, Chadha, Vineet K, Chang, Jung Chen, Charlson, Fiona J, Chen, Honglei, Chen, Wanqing, Chen, Zhengming, Chiang, Peggy P, Chimed Ochir, Odgerel, Chowdhury, Rajiv, Christophi, Costas A, Chuang, Ting Wu, Chugh, Sumeet S, Cirillo, Massimo, Claßen, Thomas Kd, Colistro, Valentina, Colomar, Mercede, Colquhoun, Samantha M, Contreras, Alejandra G, Cooper, Cyru, Cooperrider, Kimberly, Cooper, Leslie T, Coresh, Josef, Courville, Karen J, Criqui, Michael H, Cuevas Nasu, Lucia, Damsere Derry, Jame, Danawi, Hadi, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Davis, Adrian, Davitoiu, Dragos V, Dayama, Anand, de Castro, E. Filipa, De la Cruz Góngora, Vanessa, De Leo, Diego, de Lima, Graça, Degenhardt, Louisa, Del Pozo Cruz, Borja, Dellavalle, Robert P, Deribe, Kebede, Derrett, Sarah, Jarlais, Don C. De, Dessalegn, Muluken, Deveber, Gabrielle A, Devries, Karen M, Dharmaratne, Samath D, Dherani, Mukesh K, Dicker, Daniel, Ding, Eric L, Dokova, Klara, Dorsey, E. Ray, Driscoll, Tim R, Duan, Leilei, Durrani, Adnan M, Ebel, Beth E, Ellenbogen, Richard G, Elshrek, Yousef M, Endres, Matthia, Ermakov, Sergey P, Erskine, Holly E, Eshrati, Babak, Esteghamati, Alireza, Fahimi, Saman, Faraon, Emerito Jose A, Farzadfar, Farshad, Fay, Derek F. J, Feigin, Valery L, Feigl, Andrea B, Fereshtehnejad, Seyed Mohammad, Ferrari, Alize J, Ferri, Cleusa P, Flaxman, Abraham D, Fleming, Thomas D, Foigt, Nataliya, Foreman, Kyle J, Paleo, Urbano Fra, Franklin, Richard C, Gabbe, Belinda, Gaffikin, Lynne, Gakidou, Emmanuela, Gamkrelidze, Amiran, Gankpé, Fortuné G, Gansevoort, Ron T, García Guerra, Francisco A, Gasana, Evariste, Geleijnse, Johanna M, Gessner, Bradford D, Gething, Pete, Gibney, Katherine B, Gillum, Richard F, Ginawi, Ibrahim A. M, Giroud, Maurice, Giussani, Giorgia, Goenka, Shifalika, Goginashvili, Ketevan, Dantes, Hector Gomez, Gona, Philimon, de Cosio, Teresita Gonzalez, González Castell, Dinorah, Gotay, Carolyn C, Goto, Atsushi, Gouda, Hebe N, Guerrant, Richard L, Gugnani, Harish C, Guillemin, Franci, Gunnell, David, Gupta, Rahul, Gupta, Rajeev, Gutiérrez, Reyna A, Hafezi Nejad, Nima, Hagan, Holly, Hagstromer, Maria, Halasa, Yara A, Hamadeh, Randah R, Hammami, Mouhanad, Hankey, Graeme J, Hao, Yuantao, Harb, Hilda L, Haregu, Tilahun Nigatu, Haro, Josep Maria, Havmoeller, Rasmu, Hay, Simon I, Hedayati, Mohammad T, Heredia Pi, Ileana B, Hernandez, Lucia, Heuton, Kyle R, Heydarpour, Pouria, Hijar, Martha, Hoek, Hans W, Hoffman, Howard J, Hornberger, John C, Hosgood, H. Dean, Hoy, Damian G, Hsairi, Mohamed, Hu, Guoqing, Hu, Howard, Huang, Cheng, Huang, John J, Hubbell, Bryan J, Huiart, Laetitia, Husseini, Abdullatif, Iannarone, Marissa L, Iburg, Kim M, Idrisov, Bulat T, Ikeda, Nayu, Innos, Kaire, Inoue, Manami, Islami, Farhad, Ismayilova, Samaya, Jacobsen, Kathryn H, Jansen, Henrica A, Jarvis, Deborah L, Jassal, Simerjot K, Jauregui, Alejandra, Jayaraman, Sudha, Jeemon, Panniyammakal, Jensen, Paul N, Jha, Vivekanand, Jiang, Fan, Jiang, Guohong, Jiang, Ying, Jonas, Jost B, Juel, Knud, Kan, Haidong, Roseline, Sidibe S. Kany, Karam, Nadim E, Karch, André, Karema, Corine K, Karthikeyan, Ganesan, Kaul, Anil, Kawakami, Norito, Kazi, Dhruv S, Kemp, Andrew H, Kengne, Andre P, Keren, Andre, Khader, Yousef S, Khalifa, Shams Eldin Ali Hassan, Khan, Ejaz A, Khang, Young Ho, Khatibzadeh, Shahab, Khonelidze, Irma, Kieling, Christian, Kim, Daniel, Kim, Sungroul, Kim, Yunjin, Kimokoti, Ruth W, Kinfu, Yohanne, Kinge, Jonas M, Kissela, Brett M, Kivipelto, Miia, Knibbs, Luke D, Knudsen, Ann Kristin, Kokubo, Yoshihiro, Kose, M. Rifat, Kosen, Soewarta, Kraemer, Alexander, Kravchenko, Michael, Krishnaswami, Sanjay, Kromhout, Han, Ku, Tiffany, Defo, Barthelemy Kuate, Bicer, Burcu Kucuk, Kuipers, Ernst J, Kulkarni, Chanda, Kulkarni, Veena S, Kumar, G. Anil, Kwan, Gene F, Lai, Taavi, Balaji, Arjun Lakshmana, Lalloo, Ratilal, Lallukka, Tea, Lam, Hilton, Lan, Qing, Lansingh, Van C, Larson, Heidi J, Larsson, Ander, Laryea, Dennis O, Lavados, Pablo M, Lawrynowicz, Alicia E, Leasher, Janet L, Lee, Jong Tae, Leigh, Jame, Leung, Ricky, Levi, Miriam, Li, Yichong, Li, Yongmei, Liang, Juan, Liang, Xiaofeng, Lim, Stephen S, Lindsay, M. Patrice, Lipshultz, Steven E, Liu, Shiwei, Liu, Yang, Lloyd, Belinda K, Logroscino, Giancarlo, London, Stephanie J, Lopez, Nancy, Lortet Tieulent, Joannie, Lotufo, Paulo A, Lozano, Rafael, Lunevicius, Raimunda, Ma, Jixiang, Ma, Stefan, Machado, Vasco M. P, Macintyre, Michael F, Magis Rodriguez, Carlo, Mahdi, Abbas A, Majdan, Marek, Malekzadeh, Reza, Mangalam, Srikanth, Mapoma, Christopher C, Marape, Marape, Marcenes, Wagner, Margolis, David J, Margono, Christopher, Marks, Guy B, Martin, Randall V, Marzan, Melvin B, Mashal, Mohammad T, Masiye, Felix, Mason Jones, Amanda J, Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M, Mazorodze, Tasara T, Mckay, Abigail C, Mckee, Martin, Mclain, Abigail, Meaney, Peter A, Medina, Catalina, Mehndiratta, Man Mohan, Mejia Rodriguez, Fabiola, Mekonnen, Wubegzier, Melaku, Yohannes A, Meltzer, Michele, Memish, Ziad A, Mendoza, Walter, Mensah, George A, Meretoja, Atte, Mhimbira, Francis Apolinary, Micha, Renata, Miller, Ted R, Mills, Edward J, Misganaw, Awoke, Mishra, Santosh, Ibrahim, Norlinah Mohamed, Mohammad, Karzan A, Mokdad, Ali H, Mola, Glen L, Monasta, Lorenzo, Hernandez, Julio C. Montañez, Montico, Marcella, Moore, Ami R, Morawska, Lidia, Mori, Rintaro, Moschandreas, Joanna, Moturi, Wilkister N, Mozaffarian, Dariush, Mueller, Ulrich O, Mukaigawara, Mitsuru, Mullany, Erin C, Murthy, Kinnari S, Naghavi, Mohsen, Nahas, Ziad, Naheed, Aliya, Naidoo, Kovin S, Naldi, Luigi, Nand, Devina, Nangia, Vinay, Narayan, Km Venkat, Nash, Deni, Neal, Bruce, Nejjari, Chakib, Neupane, Sudan P, Newton, Charles R, Ngalesoni, Frida N, de Dieu Ngirabega, Jean, Nguyen, Grant, Nguyen, Nhung T, Nieuwenhuijsen, Mark J, Nisar, Muhammad I, Nogueira, José R, Nolla, Joan M, Nolte, Sandra, Norheim, Ole F, Norman, Rosana E, Norrving, Bo, Nyakarahuka, Luke, Oh, In Hwan, Ohkubo, Takayoshi, Olusanya, Bolajoko O, Omer, Saad B, Opio, John Nelson, Orozco, Ricardo, Pagcatipunan, Rodolfo S, Pain, Amanda W, Pandian, Jeyaraj D, Panelo, Carlo Irwin A, Papachristou, Christina, Park, Eun Kee, Parry, Charles D, Caicedo, Angel J. Paternina, Patten, Scott B, Paul, Vinod K, Pavlin, Boris I, Pearce, Neil, Pedraza, Lilia S, Pedroza, Andrea, Stokic, Ljiljana Pejin, Pekericli, Ayfer, Pereira, David M, Perez Padilla, Rogelio, Perez Ruiz, Fernando, Perico, Norberto, Perry, Samuel A. L, Pervaiz, Aslam, Pesudovs, Konrad, Peterson, Carrie B, Petzold, Max, Phillips, Michael R, Phua, Hwee Pin, Plass, Dietrich, Poenaru, Dan, Polanczyk, Guilherme V, Polinder, Suzanne, Pond, Constance D, Pope, C. Arden, Pope, Daniel, Popova, Svetlana, Pourmalek, Farshad, Powles, John, Prabhakaran, Dorairaj, Prasad, Noela M, Qato, Dima M, Quezada, Amado D, Quistberg, D. Alex A, Racapé, Lionel, Rafay, Anwar, Rahimi, Kazem, Rahimi Movaghar, Vafa, Rahman, Sajjad Ur, Raju, Murugesan, Rakovac, Ivo, Rana, Saleem M, Rao, Mayuree, Razavi, Homie, Reddy, K. 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Alex A., Rahimi-Movaghar, Vafa, Ur Rahman, Sajjad, Rana, Saleem M., Refaat, Amany H., Ribeiro, Antonio L., Riccio, Patricia M., Rojas-Rueda, David, Ruhago, George M., Sacco, Ralph L., Salomon, Joshua A., Sampson, Uchechukwu K., Sanabria, Juan R., Sánchez-Pimienta, Tania G., Sanchez-Riera, Lidia, Santos, Itamar S., Saunders, James E., Saylan, Mete I., Schmidt, Jürgen C., Schneider, Ione J.C., Schwebel, David C., Scott, James G., Sepanlou, Sadaf G., Servan-Mori, Edson E., Shamah Levy, Teresa, Shin, Hwashin H., Sigfusdottir, Inga D., Silberberg, Donald H., Simard, Edgar P., Singh, Gitanjali M., Singh, Jasvinder A., Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Stapelberg, Nicolas J.C., Stein, Dan J., Stein, Murray B., Sunguya, Bruno F., Sykes, Bryan L., Tabb, Karen M., Talongwa, Roberto T., Te Ao, Braden J., Teixeira, Carolina M., Téllez Rojo, Martha M., Terkawi, Abdullah S., Texcalac-Sangrador, José Lui, Thackway, Sarah V., Thorne-Lyman, Andrew L., Thrift, Amanda G., Thurston, George D., Towbin, Jeffrey A., Tran, Bach X., Tsala Dimbuene, Zacharie, Uchendu, Uche S., Ukwaja, Kingsley N., Uzun, Selen B., Van De Vijver, Steven, Van Gool, Coen H., Van Os, Jim, Varakin, Yuri Y., Vasankari, Tommi J., Vasconcelos, Ana Maria N., Vavilala, Monica S., Veerman, Lennert J., Velasquez-Melendez, Gustavo, Venketasubramanian, N., Violante, Francesco S., Victorovich Vlassov, Vasiliy, Wagner, Gregory R., Waller, Stephen G., Wallin, Mitchell T., Warouw, Tati S., Watts, Charlotte H., Weintraub, Robert G., Whiteford, Harvey A., Wilkinson, James D., Williams, Hywel C., Williams, Thomas N., Woldeyohannes, Solomon M., Wolfe, Charles D.A., Wong, John Q., Woolf, Anthony D., Wright, Jonathan L., Yan, Lijing L., Yentür, Gökalp K., Yoon, Seok-Jun, Younis, Mustafa Z., Zaki, Maysaa E., Zunt, Joseph R., Lopez, Alan D., and Temesgen, A.M.

Subjects
Male, Fine particulate matter, Nutrition and Disease, MESH : Sanitation, Health Behavior, Diseases, MESH: Metabolic Diseases, MESH: Global Health, 030204 cardiovascular system & hematology, MESH: Risk Assessment, Global Health, MESH : Nutritional Status, MESH: Occupational Exposure, 0302 clinical medicine, Unsafe Sex, MESH: Risk Factors, Risk Factors, Voeding en Ziekte, Medicine, Air-pollution, MESH : Female, 030212 general & internal medicine, MESH : Risk Assessment, Sanitation, Wasting, 2. Zero hunger, Factors de risc en les malalties, Medicine (all), [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, MESH : Occupational Diseases, MESH: Nutritional Status, All-cause mortality, MESH : Risk Factors, humanities, Environmental Exposure, Female, Humans, Metabolic Diseases, Nutritional Status, Occupational Diseases, Occupational Exposure, Risk Assessment, Tobacco smoking, 3. Good health, Nutritional Statu, MESH : Occupational Exposure, MESH : Metabolic Diseases, Cohort, medicine.symptom, Risk assessment, Blood-pressure, Human, MESH: Occupational Diseases, Risk factors in diseases, Coronary-heart-disease, MESH : Male, MESH: Health Behavior, MESH: Environmental Exposure, Population health, Body-mass index, 03 medical and health sciences, Household cooking, Cardiovascular-disease, Environmental health, General & Internal Medicine, parasitic diseases, Life Science, MESH: Sanitation, Risk factor, MESH : Health Behavior, VLAG, GBD2013, MESH: Humans, business.industry, Risk Factor, Global Burden of Disease Study, 79 behavioural, environmental and occupational, and metabolic risks, Long-term exposure, MESH : Humans, CAUSE-SPECIFIC MORTALITY, MESH: Male, Metabolic Disease, Occupational Disease, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, MALE BRITISH DOCTORS, Years of potential life lost, Relative risk, Malalties, MESH : Global Health, OUTDOOR AIR-POLLUTION, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH : Environmental Exposure, MESH: Female
Abstract

Summary Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5−89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. Funding Bill & Melinda Gates Foundation. Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the fi rst of a series of annual updates of the GBD. Risk factor quantifi cation, particularly of modifi able risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the fi rst level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular fi ltration rate. For most risks, data for exposure were synthesised with a Bayesian metaregression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5−89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks. Funding Bill & Melinda Gates Foundation.

Published
2015
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50. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013

Author

Meghan D. Mooney, Ken Takahashi, Andrea Stewart, Jonathan C Brown, Shireen Sindi, Amany H Refaat, Ruben Castro, Sara Sheikhbahaei, Kyle R. Heuton, Gillian M. Hansen, Chante Karimkhani, Bryan K. Phillips, Ibrahim Abubakar, Yohannes Kinfu, Victoria F Bachman, Konstantinos Stroumpoulis, Megan Coggeshall, Lucía Cuevas-Nasu, Yichong Li, Vineet K. Chadha, Andrew G. M. Bulloch, Takayoshi Ohkubo, Don C. Des Jarlais, Giancarlo Logroscino, Francis Apolinary Mhimbira, Jefferson G Fernandes, Cheng Huang, Ejaz Ahmad Khan, Fortuné Gbètoho Gankpé, Roderick J Hay, Itamar S. Santos, Zanfina Ademi, Fiona J Charlson, Norlinah Mohamed Ibrahim, Anwar Rafay, Andrew L. Thorne-Lyman, Juanita A. Haagsma, Emmanuel A. Ameh, John J. McGrath, Massimo Cirillo, Wubegzier Mekonnen, Holly Hagan, Naohiro Yonemoto, Frida Namnyak Ngalesoni, Dietrich Plass, Matias Trillini, David Phillips, Braden Te Ao, Wanqing Chen, Yun Jin Kim, David Rojas-Rueda, Christina Papachristou, Andrew E. Moran, Richard A. Gosselin, Maziar Moradi-Lakeh, Soraya Seedat, Janet L Leasher, Belinda K Lloyd, Lorenzo Monasta, Bruno F. Sunguya, Eun-Kee Park, Eduardo A. Undurraga, Mohammad A. AlMazroa, Mohammad H. Forouzanfar, Young-Ho Khang, Vasiliki Stathopoulou, Dima M. Qato, James Scott, Ileana Heredia-Pi, Luca Ronfani, Haidong Kan, Tasara T. Mazorodze, Murugesan Raju, Saeid Shahraz, Taavi Tillmann, Wang Wenzhi, Neil Pearce, Eric Y. Tenkorang, Aliya Naheed, Ferrán Catalá-López, Sudan Prasad Neupane, Emily Dansereau, Michael McKee, Derrick A Bennett, Mazeda Hossain, Paul S. F. Yip, Grant Nguyen, Norberto Perico, Miguel Angel Alegretti, Babak Eshrati, Boris Bikbov, Palwasha Anwari, Guoqing Hu, Amelia Bertozzi-Villa, Peter A. Meaney, Farshad Farzadfar, Svetlana Popova, Tara Templin, Hmwe H Kyu, Uche S. Uchendu, Kebede Deribe, Sergey Soshnikov, Nobhojit Roy, Daniel Kim, Ilana N. Ackerman, Homie Razavi, Leslie T. Cooper, Sandra Nolte, David T. Felson, John J Huang, Yang Liu, Fiorella Cavalleri, Adrian Davis, Héctor Gómez Dantés, Klara Dokova, Yuantao Hao, Catalina Medina, Austin E Schumacher, Stan Biryukov, Jane Rowley, Arindam Basu, Jose C. Adsuar, Rosana E. Norman, Yousef Khader, Rafael Alfonso-Cristancho, Sukanta Saha, Simón Barquera, Diego Gonzalez-Medina, Philip B. Mitchell, Lars Barregard, Haidong Wang, Yongmei Li, Ami R. Moore, Marie Ng, Raghib Ali, Peter T. Serina, Lijing L Yan, Ayse Abbasoglu Ozgoren, Ricky Leung, Michelle L. Bell, Tim Driscoll, Azmeraw T. Amare, Farshad Pourmalek, Tea Lallukka, Benjamin O Anderson, Raimundas Lunevicius, Corine Karema, Robert G. Weintraub, Erin C Mullany, Anders Larsson, Glen Mola, Paulo A. Lotufo, Luke Nyakarahuka, Sayed Saidul Alam, Louisa Degenhardt, Hugh R. Taylor, E. 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Venkat, Nejjari, Chakib, Neupane, Sudan P., Newton, Charles R., Ng, Marie, Ngalesoni, Frida N., Nguyen, Grant, Nisar, Muhammad I., Nolte, Sandra, Norheim, Ole F., Norman, Rosana E., Norrving, Bo, Nyakarahuka, Luke, Oh, In-Hwan, Ohkubo, Takayoshi, Ohno, Summer L., Olusanya, Bolajoko O., Opio, John Nelson, Ortblad, Katrina, Ortiz, Alberto, Pain, Amanda W., Pandian, Jeyaraj D., Panelo, Carlo Irwin A., Papachristou, Christina, Park, Eun-Kee, Park, Jae-Hyun, Patten, Scott B., Patton, George C., Paul, Vinod K., Pavlin, Boris I., Pearce, Neil, Pereira, David M., Perez-Padilla, Rogelio, Perez-Ruiz, Fernando, Perico, Norberto, Pervaiz, Aslam, Pesudovs, Konrad, Peterson, Carrie B., Petzold, Max, Phillips, Michael R., Phillips, Bryan K., Phillips, David E., Piel, Frédéric B., Plass, Dietrich, Poenaru, Dan, Polinder, Suzanne, Pope, Daniel, Popova, Svetlana, Poulton, Richie G., Pourmalek, Farshad, Prabhakaran, Dorairaj, Prasad, Noela M., Pullan, Rachel L., Qato, Dima M., Quistberg, D Alex, Rafay, Anwar, Rahimi, Kazem, Rahman, Sajjad U., Raju, Murugesan, Rana, Saleem M., Razavi, Homie, Reddy, K Srinath, Refaat, Amany, Remuzzi, Giuseppe, Resnikoff, Serge, Ribeiro, Antonio L., Richardson, Lee, Richardus, Jan Hendrik, Roberts, D Allen, Rojas-Rueda, David, Ronfani, Luca, Roth, Gregory A., Rothenbacher, Dietrich, Rothstein, David H., Rowley, Jane T., Roy, Nobhojit, Ruhago, George M., Saeedi, Mohammad Y., Saha, Sukanta, Sahraian, Mohammad Ali, Sampson, Uchechukwu K. A., Sanabria, Juan R., Sandar, Logan, Santos, Itamar S., Satpathy, Maheswar, Sawhney, Monika, Scarborough, Peter, Schneider, Ione J., Schöttker, Ben, Schumacher, Austin E., Schwebel, David C., Scott, James G., Seedat, Soraya, Sepanlou, Sadaf G., Serina, Peter T., Servan-Mori, Edson E., Shackelford, Katya A., Shaheen, Amira, Shahraz, Saeid, Levy, Teresa Shamah, Shangguan, Siyi, She, Jun, Sheikhbahaei, Sara, Shi, Peilin, Shibuya, Kenji, Shinohara, Yukito, Shiri, Rahman, Shishani, Kawkab, Shiue, Ivy, Shrime, Mark G., Sigfusdottir, Inga D., Silberberg, Donald H., Simard, Edgar P., Sindi, Shireen, Singh, Abhishek, Singh, Jasvinder A., Singh, Lavanya, Skirbekk, Vegard, Slepak, Erica Leigh, Sliwa, Karen, Soneji, Samir, Søreide, Kjetil, Soshnikov, Sergey, Sposato, Luciano A., Sreeramareddy, Chandrashekhar T., Stanaway, Jeffrey D., Stathopoulou, Vasiliki, Stein, Dan J., Stein, Murray B., Steiner, Caitlyn, Steiner, Timothy J., Stevens, Antony, Stewart, Andrea, Stovner, Lars J., Stroumpoulis, Konstantino, Sunguya, Bruno F., Swaminathan, Soumya, Swaroop, Mamta, Sykes, Bryan L., Tabb, Karen M., Takahashi, Ken, Tandon, Nikhil, Tanne, David, Tanner, Marcel, Tavakkoli, Mohammad, Taylor, Hugh R., Te Ao, Braden J., Tediosi, Fabrizio, Temesgen, Awoke M., Templin, Tara, Ten Have, Margreet, Tenkorang, Eric Y., Terkawi, Abdullah S., Thomson, Blake, Thorne-Lyman, Andrew L., Thrift, Amanda G., Thurston, George D., Tillmann, Taavi, Tonelli, Marcello, Topouzis, Foti, Toyoshima, Hideaki, Traebert, Jefferson, Tran, Bach X., Trillini, Matia, Truelsen, Thoma, Tsilimbaris, Miltiadi, Tuzcu, Emin M., Uchendu, Uche S., Ukwaja, Kingsley N., Undurraga, Eduardo A., Uzun, Selen B., Van Brakel, Wim H., Van De Vijver, Steven, Van Gool, Coen H., Van Os, Jim, Vasankari, Tommi J., Venketasubramanian, N, Violante, Francesco S., Vlassov, Vasiliy V., Vollset, Stein Emil, Wagner, Gregory R., Wagner, Joseph, Waller, Stephen G., Wan, Xia, Wang, Haidong, Wang, Jianli, Wang, Linhong, Warouw, Tati S., Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G., Wenzhi, Wang, Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey A., Wilkinson, James D., Williams, Thomas N., Wolfe, Charles D., Wolock, Timothy M., Woolf, Anthony D., Wulf, Sarah, Wurtz, Brittany, Xu, Gelin, Yan, Lijing L., Yano, Yuichiro, Ye, Pengpeng, Yentür, Gökalp K., Yip, Paul, Yonemoto, Naohiro, Yoon, Seok-Jun, Younis, Mustafa Z., Yu, Chuanhua, Zaki, Maysaa E., Zhao, Yong, Zheng, Yingfeng, Zonies, David, Zou, Xiaonong, Salomon, Joshua A., Lopez, Alan D., Vos, Theo, Medische Sociologie, MUMC+: Hersen en Zenuw Centrum (3), MUMC+: MA Psychiatrie (3), Psychiatrie & Neuropsychologie, Barber, Ryan M, Foreman, Kyle J, Abd Allah, Foad, Abera, Semaw F, Abraham, Jerry P, Abu Raddad, Laith J, Abu Rmeileh, Niveen M, Ackerman, Ilana N, Adou, Arsène K, Adsuar, José C, Agardh, Emilie E, Albittar, Mohammed I, Alegretti, Miguel A, Alemu, Zewdie A, Alfonso Cristancho, Rafael, Almazroa, Mohammad A, Alvis Guzman, Nelson, Amare, Azmeraw T, Ameh, Emmanuel A, Anderson, Benjamin O, Antonio, Carl Abelardo T, Asghar, Rana J, Atkins, Lydia S, Avila, Marco A, Bachman, Victoria F, Bahit, Maria C, Barker Collo, Suzanne L, Barrero, Lope H, Basulaiman, Mohammed O, Bell, Michelle L, Bennett, Derrick A, Bensenor, Isabela M, Bertozzi Villa, Amelia, Beyene, Tariku J, Bhutta, Zulfiqar A, Blore, Jed D, Blosser, Christopher D, Blyth, Fiona M, Bohensky, Megan A, Bolliger, Ian W, Bornstein, Natan M, Bourne, Rupert R. A, Boyers, Lindsay N, Brayne, Carol E, Breitborde, Nicholas J. K, Briggs, Adam D, Brooks, Peter M, Brown, Jonathan C, Brugha, Traolach S, Buckle, Geoffrey C, Budke, Christine M, Bulloch, Andrew G, Campos Nonato, Ismael R, Carapetis, Jonathan R, Carpenter, David O, Castañeda Orjuela, Carlos A, Castro, Ruben E, Catalá López, Ferrán, Chadha, Vineet K, Chang, Jung Chen, Charlson, Fiona J, Chiang, Peggy P, Chimed Ochir, Odgerel, Christophi, Costas A, Coates, Matthew M, Coffeng, Luc E, Coggeshall, Megan S, Colquhoun, Samantha M, Cooke, Graham S, Cooper, Leslie T, Coppola, Luis M, Criqui, Michael H, Crump, John A, Cuevas Nasu, Lucia, Dargan, Paul I, Davitoiu, Dragos V, Del Pozo Cruz, Borja, Dellavalle, Robert P, Jarlais, Don C. De, Dharmaratne, Samath D, Dherani, Mukesh K, Diaz Torné, Cesar, Ding, Eric L, Dorsey, E. 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Dean, Hotez, Peter J, Hoy, Damian G, Huang, John J, Iannarone, Marissa L, Iburg, Kim M, Jacobsen, Kathryn H, Jarvis, Deborah L, Jassal, Simerjot K, Jensen, Paul N, Jonas, Jost B, Karema, Corine K, Kassebaum, Nicholas J, Kemp, Andrew H, Kengne, Andre P, Khader, Yousef S, Khalifa, Shams Eldin A, Khan, Ejaz A, Khang, Young Ho, Kinge, Jonas M, Knibbs, Luke D, Kuipers, Ernst J, Kulkarni, Veena S, Kumar, G. Anil, Kyu, Hmwe H, Lansingh, Van C, Lawrynowicz, Alicia E. 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Venkat, Neupane, Sudan P, Newton, Charles R, Ngalesoni, Frida N, Nisar, Muhammad I, Norheim, Ole F, Norman, Rosana E, Oh, In Hwan, Ohno, Summer L, Olusanya, Bolajoko O, Pain, Amanda W, Pandian, Jeyaraj D, Panelo, Carlo Irwin A, Park, Eun Kee, Park, Jae Hyun, Patten, Scott B, Patton, George C, Paul, Vinod K, Pavlin, Boris I, Pereira, David M, Perez Padilla, Rogelio, Perez Ruiz, Fernando, Peterson, Carrie B, Phillips, Michael R, Phillips, Bryan K, Phillips, David E, Piel, Frédéric B, Poulton, Richie G, Prasad, Noela M, Pullan, Rachel L, Qato, Dima M, Quistberg, D. Alex, Rahman, Sajjad U, Rana, Saleem M, Reddy, K. Srinath, Ribeiro, Antonio L, Roberts, D. Allen, Rojas Rueda, David, Roth, Gregory A, Rothstein, David H, Rowley, Jane T, Ruhago, George M, Saeedi, Mohammad Y, Sampson, Uchechukwu K. A, Sanabria, Juan R, Santos, Itamar S, Schneider, Ione J, Schumacher, Austin E, Schwebel, David C, Scott, James G, Sepanlou, Sadaf G, Serina, Peter T, Servan Mori, Edson E, Shackelford, Katya A, Shrime, Mark G, Sigfusdottir, Inga D, Silberberg, Donald H, Simard, Edgar P, Singh, Jasvinder A, Sposato, Luciano A, Sreeramareddy, Chandrashekhar T, Stanaway, Jeffrey D, Stein, Dan J, Stein, Murray B, Steiner, Timothy J, Stovner, Lars J, Sunguya, Bruno F, Sykes, Bryan L, Tabb, Karen M, Taylor, Hugh R, Ao, Braden J. Te, Temesgen, Awoke M, Tenkorang, Eric Y, Terkawi, Abdullah S, Thorne Lyman, Andrew L, Thrift, Amanda G, Thurston, George D, Tran, Bach X, Tuzcu, Emin M, Uchendu, Uche S, Ukwaja, Kingsley N, Undurraga, Eduardo A, Uzun, Selen B, Van Brakel, Wim H, van Gool, Coen H, Vasankari, Tommi J, Violante, Francesco S, Vlassov, Vasiliy V, Wagner, Gregory R, Waller, Stephen G, Warouw, Tati S, Weintraub, Robert G, Whiteford, Harvey A, Wilkinson, James D, Williams, Thomas N, Wolfe, Charles D, Wolock, Timothy M, Woolf, Anthony D, Yan, Lijing L, Yentür, Gökalp K, Yoon, Seok Jun, Younis, Mustafa Z, Zaki, Maysaa E, Salomon, Joshua A, Lopez, Alan D, Computational Science and Engineering Department [Daresbury] ( STFC ), Science & Technologie Facilities Council, Neuroépidémiologie Tropicale ( NET ), CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Génomique, Environnement, Immunité, Santé, Thérapeutique ( GEIST ), Université de Limoges ( UNILIM ) -Université de Limoges ( UNILIM ), Service de Chirurgie Thoracique et Vasculaire - Médecine vasculaire [CHU Limoges], CHU Limoges, Weill Cornell Medical College - Qatar, Qatar Foundation - Education City, Doha, Maladies chroniques, santé perçue, et processus d'adaptation ( APEMAC ), Université Paris Descartes - Paris 5 ( UPD5 ) -Université de Lorraine ( UL ), Centre d'Investigation Clinique - Epidemiologie Clinique/essais Cliniques Nancy, Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Karolinska Institute, karolinska institute, Centro de Estudios Avanzados en Zonas Aridas ( CEAZA ), Ecole Polytechnique Fédérale de Lausanne ( EPFL ), Wisconsin Division of Public Health, Regional Genetic Service, St Mary's Hospital, Manchester, Laboratoire d'Ingénierie des Matériaux ( LIM ), Centre National de la Recherche Scientifique ( CNRS ), Computer Science Department [Bristol], University of Bristol [Bristol], Lawrence Berkeley National Laboratory [Berkeley] ( LBNL ), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) ( SRI-B ), Multimedia Research Center ( MRC ), University of Alberta [Edmonton], Division of Biostatistics ( Biostat - MINNEAPOLIS ), University of Minnesota [Minneapolis], Laboratory of Neurologic Diseases, Mario Negri Institute, Milan, University of Southampton [Southampton], Imperial College London, Neurology Department, Ichilov Medical Center, Department of Public Health and Primary Care, Cambridge University, Interactions, transferts, ruptures artistiques et culturels - EA 6301 ( InTRu ), Université de Tours, Institut Jacques Monod ( IJM ), Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), Institute of Parasitology, STAR laboratory, Stanford University [Stanford], Unité de recherche Virologie et Immunologie Moléculaires ( VIM ), Institut National de la Recherche Agronomique ( INRA ), Tennent Institute of Ophthalmology, National University of Singapore ( NUS ), Multidisciplinary Nanotechnology Centre, Swansea University, Department of Computer Sciences [Scheffield], University of Sheffield [Sheffield], Cyprus International Institute for the Environment and Public Health, Harvard School of Public Health, Glasgow Centre for Physical Organic Chemistry, University of Glasgow, King‘s College London, Université Panthéon-Sorbonne - 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UFR Médecine ( UGA UFRM ), Université Grenoble Alpes ( UGA ), Clinical Epidemiology and Aging Research, German Cancer Research Center, Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Symantec, European Microsoft Innovation Center ( EMIC ), Microsoft Corporation [Redmond, Wash.], Laboratoire de Mécanique, Physique et Géosciences ( LMPG ), Université Le Havre Normandie ( ULH ), Normandie Université ( NU ) -Normandie Université ( NU ), Novartis institute for tropical diseases, Institut de Génétique et de Biologie Moléculaire et Cellulaire ( IGBMC ), Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Departments of Anatomy and Cell Biology, Wayne State University School of Medicine, Departments of Ophthalmology, Departments of Applied Physics [New Haven], Yale University [New Haven], Center for Mathematical Modeling ( CMM ), Universidad de Santiago de Chile [Santiago] ( USACH ), Laboratory for Atmospheric and Space Physics [Boulder] ( LASP ), University of Colorado Boulder [Boulder], University of Occupational and Environmental Health [Kitakyushu] ( UEOH ), Department of Computer Science and Engineering [New Delhi], Indian Institute of Technology Delhi ( IIT Delhi ), Institut de Recherche sur les Phénomènes Hors Equilibre ( IRPHE ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), GlaxoSmithKline, Imperial College London-Clinical Imaging Center, Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, Yale School of Medicine-Yale School of Medicine-Yale Stem Cell Center, Maclean Building, Benson Lane, Crowmarsh Gifford, Centre for Ecology and Hydrology, Nanoscience Institute ( NEST ), Dipartimento di Fisica, Università di Pisa, Aristotle University of Thessaloniki, Laboratory Of Immune Cell Biology ( LICB ), National Institutes of Health ( NIH ), Institute of Human Genetics, Bonn Universität [Bonn], Occupational Health Unit, Bologna University Hospital-Sant'Orsola-Malpighi Polyclinic, Royal Institute of Technology [Stockholm] ( KTH ), Institut für Informatik [München/Munich] ( LMU ), Ludwig-Maximilians-Universität München, NICTA [Eveleigh], National ICT Australia [Sydney] ( NICTA ), Manchester Academic Health Sciences Centre, Institut d'Histoire et de Philosophie des Sciences et des Techniques ( IHPST ), Université Panthéon-Sorbonne ( UP1 ) -Département d'Etudes Cognitives - ENS Paris ( DEC ), École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ) -Centre National de la Recherche Scientifique ( CNRS ), Ghent University [Belgium] ( UGENT ), German Research Centre for Geosciences - Helmholtz-Centre Potsdam ( GFZ ), Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique ( LHEEA ), École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche en Génie Civil et Mécanique ( GeM ), Université de Nantes ( UN ) -École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), Neurorestoration Group, King‘s College London-Wolfson Centre for Age-related Diseases, Department of Computer Science [KAIST] ( CS ), Korea Advanced Institute of Science and Technology ( KAIST ), Laboratoire de l'Accélérateur Linéaire ( LAL ), Natl Engn Res Ctr Vegetables, Key Lab Biol & Genet Improvement Hort Crops N Chi, Beijing Acad Agr & Forestry Sci, University Hospital Puerta de Hierro, Madrid, Computational Science and Engineering Department [Daresbury] (STFC), Neuroépidémiologie Tropicale (NET), CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), Weill Cornell Medicine [Qatar], Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université Paris Descartes - Paris 5 (UPD5)-Université de Lorraine (UL), Cancéropôle du Grand Est-Institut National de la Santé et de la Recherche Médicale (INSERM), Centro de Estudios Avanzados en Zonas Aridas (CEAZA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratoire d'Ingénierie des Matériaux (LIM), Centre National de la Recherche Scientifique (CNRS), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Samsung Research &Development Institute India - Bangalore (Groupe Samsung) (SRI-B), Multimedia Research Center (MRC), University of Alberta, Division of Biostatistics (Biostat - MINNEAPOLIS), University of Minnesota [Twin Cities] (UMN), University of Minnesota System-University of Minnesota System, University of Southampton, University of Cambridge [UK] (CAM), Interactions, transferts, ruptures artistiques et culturels - EA 6301 (InTRu), Université de Tours (UT), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Stanford University, Unité de recherche Virologie et Immunologie Moléculaires (VIM (UR 0892)), Institut National de la Recherche Agronomique (INRA), National University of Singapore (NUS), Université Paris 1 Panthéon-Sorbonne - UFR Science Politique (UP1 UFR11), Université Paris 1 Panthéon-Sorbonne (UP1), National Diagnostics Centre (NDC), National University of Ireland [Galway] (NUI Galway), Arthritis Research UK Epidemiology Unit (MANCHESTER - Arthritis Research), Universidade do Porto = University of Porto, Advanced Laboratories on Embedded Systems [Roma] (ALES), Wageningen University and Research [Wageningen] (WUR), University of Oxford, Universitat Politècnica de Catalunya [Barcelona] (UPC), Institut National de Recherche et d'Analyse Physico-Chimique (INRAP), University of Connecticut (UCONN), Norwegian Institute for Air Research (NILU), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), University of Tehran, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Sociétés, Acteurs, Gouvernement en Europe (SAGE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), The George Washington University (GW), Washington State University (WSU), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Université Catholique de Louvain = Catholic University of Louvain (UCL), Div Cyclotron & Radiopharmaceut Sci (DRDO, INMAS), School of Physics and Astronomy [St Andrews], Tata Research Development and Design Center (TRDDC), MOLTECH-Anjou, Université d'Angers (UA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Swedish Defense Research Agency (FOI), Centre de Recherche en Information Biomédicale sino-français (CRIBS), Université de Rennes (UR)-Southeast University [Jiangsu]-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratory of Image Science and Technology [Nanjing] (LIST), Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Mechanical and Automation Engineering (CAD Laboratory), Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), Queen Mary University of London (QMUL), Centre d'économie de la Sorbonne (CES), Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Paris School of Economics (PSE), Université Paris 1 Panthéon-Sorbonne (UP1)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Università della Svizzera italiana = University of Italian Switzerland (USI)-Scuola universitaria professionale della Svizzera italiana = University of Applied Sciences and Arts of Southern Switzerland [Manno] (SUPSI), Pennsylvania State University (Penn State), Penn State System-Penn State System, Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Hong Kong Baptist University (HKBU), Département Optique (OPT), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Département Réseaux et Services Multimédia Mobiles (TSP - RS2M), University of Melbourne, Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), University of Otago [Dunedin, Nouvelle-Zélande], Center for TeleInFrastruktur (CTIF), Aalborg University [Denmark] (AAU), Savoirs, Textes, Langage (STL) - UMR 8163 (STL), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Indian Institute of Technology Madras (IIT Madras), Universidade Estadual Paulista Júlio de Mesquita Filho = São Paulo State University (UNESP), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Université Grenoble Alpes - UFR Médecine (UGA UFRM), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), European Microsoft Innovation Center (EMIC), Laboratoire de Mécanique, Physique et Géosciences (LMPG), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU), Novartis Institute for Tropical Diseases (NITD), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Center for Mathematical Modeling (CMM), Universidad de Chile = University of Chile [Santiago] (UCHILE), Laboratory for Atmospheric and Space Physics [Boulder] (LASP), University of Colorado [Boulder], University of Occupational and Environmental Health [Kitakyushu] (UEOH), Indian Institute of Technology Delhi (IIT Delhi), Institut de Recherche sur les Phénomènes Hors Equilibre (IRPHE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Yale University [New Haven]-Yale School of Medicine [New Haven, Connecticut] (YSM), Nanoscience Institute (NEST), University of Pisa - Università di Pisa, Laboratory Of Immune Cell Biology (LICB), National Institutes of Health [Bethesda] (NIH), Rheinische Friedrich-Wilhelms-Universität Bonn, Royal Institute of Technology [Stockholm] (KTH ), Institut für Informatik [München/Munich] (LMU), Ludwig-Maximilians-Universität München (LMU), National ICT Australia [Sydney] (NICTA), Institut d'Histoire et de Philosophie des Sciences et des Techniques (IHPST), Université Paris 1 Panthéon-Sorbonne (UP1)-Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Universiteit Gent = Ghent University (UGENT), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Science [KAIST] (CS), Korea Advanced Institute of Science and Technology (KAIST), Kardiyoloji, Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Minnesota [Twin Cities], Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Unité de recherche Virologie et Immunologie Moléculaires (VIM), Université Panthéon-Sorbonne - UFR Science Politique (UP1 UFR11), Université Panthéon-Sorbonne (UP1), Wageningen University and Research Centre [Wageningen] (WUR), Institut National de Recherche et d'Analyse Physico-chimique (Ariana, Tunisie) (INRAP), Université de Franche-Comté (UFC)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Technologie de Belfort-Montbeliard (UTBM), George Washington University (GW), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Université Catholique de Louvain (UCL), MOLTECH-ANJOU (MOLTECH-ANJOU), Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), University of Helsinki, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Southeast University [Jiangsu]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Università della Svizzera italiana (USI)-Scuola universitaria professionale della Svizzera italiana [Manno] (SUPSI), California Institute of Technology (CALTECH)-NASA, Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS), Département Réseaux et Services Multimédia Mobiles (RS2M), Universidade Estadual Paulista Júlio de Mesquita Filho [São José do Rio Preto] (UNESP), Université Grenoble Alpes (UGA), Universidad de Santiago de Chile [Santiago] (USACH), Yale University [New Haven]-Yale University School of Medicine, Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), Ghent University [Belgium] (UGENT), Université de Nantes - Faculté des Sciences et des Techniques, Grelier, Elisabeth, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Universidade do Porto, Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Université Toulouse 1 Capitole (UT1), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Università degli studi di Bari Aldo Moro (UNIBA), École des Ponts ParisTech (ENPC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-École des hautes études en sciences sociales (EHESS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Università della Svizzera italiana = University of Italian Switzerland (USI)-Scuola universitaria professionale della Svizzera italiana [Manno] (SUPSI), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Universiteit Gent = Ghent University [Belgium] (UGENT), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-CHU Limoges-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Belfort-Montbeliard (UTBM), Institut de Chimie du CNRS (INC)-Université d'Angers (UA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Nantes (ECN), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Wolfson Centre for Age-related Diseases-King‘s College London, Cell biology, Public Health, Epidemiology, Health Technology Assessment (HTA), Erasmus MC other, Pathology, and Cardiothoracic Surgery

Subjects
Gerontology, Male, CHANGING RELATION, Nutrition and Disease, MESH : Life Expectancy, MESH : Aged, ECONOMIC-DEVELOPMENT, Poison control, MESH: Global Health, Global Health, Socioeconomic Factor, Communicable Disease, MESH : Chronic Disease, Health Transition, Voeding en Ziekte, Quality-Adjusted Life Year, SELF-RATED HEALTH, MESH : Socioeconomic Factors, Medicine, MESH : Female, MESH: Mortality, Premature, 2. Zero hunger, MESH: Aged, education.field_of_study, MESH: Middle Aged, Mortality rate, Medicine (all), GBD2013 diseases, [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, General Medicine, Middle Aged, 3. Good health, MESH : Wounds and Injuries, Epidemiological transition, MESH: Quality-Adjusted Life Years, MESH: Communicable Diseases, NONCOMMUNICABLE DISEASES, Female, Quality-Adjusted Life Years, MESH: Life Expectancy, MESH: Health Transition, Human, MESH: Socioeconomic Factors, ACUTE MYOCARDIAL-INFARCTION, MESH : Male, MORTALITY TRENDS, Population, MESH : Health Transition, Communicable Diseases, Article, Life Expectancy, EUROPEAN-UNION, SDG 3 - Good Health and Well-being, General & Internal Medicine, SYSTEMATIC ANALYSIS, Disability-adjusted life year, Humans, Life Science, MESH : Middle Aged, Mortality, education, Premature, MESH : Mortality, Premature, VLAG, Aged, MESH: Humans, business.industry, Mortality, Premature, MESH: Chronic Disease, MESH : Communicable Diseases, Wounds and Injurie, MESH : Humans, MESH : Quality-Adjusted Life Years, Non-communicable disease, Chronic Disease, Socioeconomic Factors, Wounds and Injuries, medicine.disease, MESH: Male, LOW SOCIOECONOMIC-STATUS, Years of potential life lost, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, MESH: Wounds and Injuries, Life expectancy, RISK-FACTORS, MESH : Global Health, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH: Female, Demography
Abstract

Summary Background The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age–sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development. Methods We used the published GBD 2013 data for age-specific mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specific death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantified patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time. Findings Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6–6·6), from 65·3 years (65·0–65·6) in 1990 to 71·5 years (71·0–71·9) in 2013, HALE at birth rose by 5·4 years (4·9–5·8), from 56·9 years (54·5–59·1) to 62·3 years (59·7–64·8), total DALYs fell by 3·6% (0·3–7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6–29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non–communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age-standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specific non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the five leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional deficiencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specific estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries. Interpretation Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition—in which increasing sociodemographic status brings structured change in disease burden—is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specific assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions. Funding Bill & Melinda Gates Foundation. BACKGROUND: The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age-sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development. METHODS: We used the published GBD 2013 data for age-specific mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specific death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantified patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time. FINDINGS: Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6-6·6), from 65·3 years (65·0-65·6) in 1990 to 71·5 years (71·0-71·9) in 2013, HALE at birth rose by 5·4 years (4·9-5·8), from 56·9 years (54·5-59·1) to 62·3 years (59·7-64·8), total DALYs fell by 3·6% (0·3-7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6-29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non-communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age-standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specific non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the five leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional deficiencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specific estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries. INTERPRETATION: Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition-in which increasing sociodemographic status brings structured change in disease burden-is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specific assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions. FUNDING: Bill & Melinda Gates Foundation.

Published
2015
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