scholarly journals Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Guokang Zhu ◽  
Jia Li ◽  
Zi Meng ◽  
Yi Yu ◽  
Yanan Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Detection Algorithm ◽  
Wearable Devices ◽  
Health Surveillance ◽  
Model Verification ◽  
Wearable Device ◽  
Central China ◽  
Sensor Data ◽  
South Central

The coronavirus disease 2019 (COVID-19) pandemic has triggered a new response involving public health surveillance. The popularity of personal wearable devices creates a new opportunity for tracking and precaution of spread of such infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. In addition to a physiological anomaly detection algorithm defined based on data from wearable devices, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. Four models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The anonymised sensor data from approximately 1.3 million wearable device users are used for model verification. Our experiment's results indicate that the prediction models can be utilized to alert to an outbreak of COVID-19 in advance, which suggests there is potential for a health surveillance system utilising wearable device data.

scholarly journals Automated Feature Extraction from Population Wearable Device Data Identified Novel Loci Associated with Sleep and Circadian Rhythms

2020 ◽  
Author(s):  
Xinyue Li ◽  
Hongyu Zhao
Keyword(s):  
Feature Extraction ◽  
Circadian Rhythms ◽  
Large Scale ◽  
Population Studies ◽  
Wearable Devices ◽  
Wearable Device ◽  
Genome Wide Association Studies ◽  
Physical Activity Monitoring ◽  
Scale Population ◽  
Shared Genetic

AbstractWearable devices have been increasingly used in research to provide continuous physical activity monitoring, but how to effectively extract features remains challenging for researchers. To analyze the generated actigraphy data in large-scale population studies, we developed computationally efficient methods to derive sleep and activity features through a Hidden Markov Model-based sleep/wake identification algorithm, and circadian rhythm features through a Penalized Multi-band Learning approach adapted from machine learning. Unsupervised feature extraction is useful when labeled data are unavailable, especially in large-scale population studies. We applied these two methods to the UK Biobank wearable device data and used the derived sleep and circadian features as phenotypes in genome-wide association studies. We identified 53 genetic loci with p<5×10-8 including genes known to be associated with sleep disorders and circadian rhythms as well as novel loci associated with Body Mass Index, mental diseases and neurological disorders, which suggest shared genetic factors of sleep and circadian rhythms with physical and mental health. Further cross-tissue enrichment analysis highlights the important role of the central nervous system and the shared genetic architecture with metabolism-related traits and the metabolic system. Our study demonstrates the effectiveness of our unsupervised methods for wearable device data when additional training data cannot be easily acquired, and our study further expands the application of wearable devices in population studies and genetic studies to provide novel biological insights.

scholarly journals Clinical Characteristics of 1378 Inpatients with Spinal Tuberculosis in General Hospitals in South-Central China

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zheng Liu ◽  
Jun Wang ◽  
Gong-Zhou Chen ◽  
Wei-Wei Li ◽  
Yun-Qi Wu ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Large Scale ◽  
Spinal Tuberculosis ◽  
Public Health Problem ◽  
Neurological Impairment ◽  
Radicular Pain ◽  
Central China ◽  
General Hospitals ◽  
South Central ◽  
Set Up

In this retrospective study, charts of inpatients with spinal tuberculosis (STB) treated in large-scale general hospitals in Changsha, Hunan, China, between 2007 and 2016 were reviewed to investigate their clinical characteristics. Demographic, epidemiological and clinical features, imaging findings, treatment methods, and prognosis were summarized and analyzed. There were 1378 patients, 805 males and 573 females, with a mean age of 43.7 years. The mean interval between symptom onset and diagnosis was 16.0 months (range 15 days–240 months). The incidence of back pain, radicular pain and symptoms of systemic toxicity was 92.5%, 40.1%, and 32.1%, respectively. The rate of neurological impairment was 49.9 %. STB was present in two or more vertebrae in 91.1% of patients, with two adjacent vertebrae being involved in 67.9% of them. The lumbar segment (38.2%) was the most frequently affected, followed by the thoracic spine (35.7%). The sacrococcygeal area was the least frequently involved (0.8%). Abscesses were detected in 65.5% of patients. One thousand patients (72.6%) were managed with surgery and 378 (27.4%) with anti-TB drugs only. Cure was achieved in 1215 patients (88.2%), whereas 49 (3.5 %) had relapses. Concomitant pulmonary TB (PTB) was diagnosed in 366 patients (26.6%) and 63 (4.6%) had concomitant diabetes. Compared with the previous five years, the number of older patients, urban patients, and medical staff with STB had increased by 6.1%, 5.2%, and 1.3%, respectively in the five years studied. STB remains a severe public health problem that cannot be ignored. Most of the patients ignored early symptoms and therefore received untimely treatment. Thus, surveillance for and treatment of STB in South-central China requires strengthening. In addition to the current China-wide database of patients with PTB, a China-wide database of patients with STB should also be set up.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices

2021 ◽  
Vol 141 (2) ◽  
pp. 89-96
Author(s):  
Hsin-Yen Yen ◽  
Hao-Yun Huang
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Short Form ◽  
Wearable Devices ◽  
Wearable Device ◽  
World Health ◽  
World Health Organization Quality ◽  
Quality Of Life Scale

Aims: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. Methods: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire–Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. Results: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. Conclusion: Wearable devices inspire users’ motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

scholarly journals A New Identification Method Reveals the Resistance of an Extensive-Source Radish Collection to Plasmodiophora brassicae Race 4

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 792
Author(s):  
Haohui Yang ◽  
Yuxiang Yuan ◽  
Xiaochun Wei ◽  
Xiaohui Zhang ◽  
Haiping Wang ◽  
...  
Keyword(s):  
Plasmodiophora Brassicae ◽  
Central China ◽  
Clubroot Disease ◽  
Cruciferous Vegetable ◽  
Inoculation Methods ◽  
Identification Method ◽  
South Central ◽  
Disease Occurrence ◽  
Resistant Varieties ◽  
The Stability

Raphanus sativus, an important cruciferous vegetable, has been increasingly affected by clubroot disease. Establishing a stable and accurate resistance identification method for screening resistant germplasms is urgently needed in radish. In this study, the influence of inoculum concentration, inoculation methods, and pH of the substrate on disease occurrence was studied. The result showed that the disease index (DI) was highest at 2 × 108 spores/mL, the efficiency of two-stage combined inoculation methods was higher than others, and pH 6.5 was favorable for the infection of P. brassicae. By using this new method, DIs of 349 radish germplasms varying from 0.00 to 97.04, presented significantly different levels of resistance. Analysis showed that 85.06% germplasms from China were susceptible to P. brassicae, whilst 28 accessions were resistant and mainly distributed in east, southwest, northwest, and south-central China. Most of the exotic germplasms were resistant. Repeated experiments verified the stability and reliability of the method and the identity of germplasm resistance. In total, 13 immune, 5 highly resistant and 21 resistant radish accessions were identified. This study provides an original clubroot-tolerance evaluation technology and valuable materials for the development of broad-spectrum resistant varieties for sustainable clubroot management in radish and other cruciferous crops.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

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