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Public Health Public Health

Infodemiological study to understand the community risk perceptions of COVID-19 outbreak in South Korea.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : South Korea is among the best-performing countries in tackling the coronavirus pandemic by utilizing mass drive-through testing, facemasks use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis.

OBJECTIVE : We attempted to explore patterns of community health risk perceptions of COVID-19 in South Korea using Internet search data.

METHODS : Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19-related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access dataset for time period of December 5, 2019 to May 31, 2020. Spearman's rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and Internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, GT, and NAVER RSVs in lag periods (of 3 to 1 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor (VIF) of <5.

RESULTS : Numbers of COVID-19-related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a facemask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763~0.823; p<0.05), and age groups of ≤29 (r=0.726~0.821; p<0.05), 30~44 (r=0.701~0.826; p<0.05), and ≥50 years (r=0.706~0.725; p<0.05). In terms of spatial distribution, GT and NAVER RSVs were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704~0.804; p<0.05) compared to those of desktop searches (r=0.705~0.717; p<0.05), indicating changing behaviors in searching for online health information during the outbreak. Those varied Internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test-related information as being more important than disease-related knowledge. Meanwhile, younger and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19-related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case-based model and potentially be used to predict epidemic curves.

CONCLUSIONS : The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.


Husnayain Atina, Shim Eunha, Fuad Anis, Su Emily Chia-Yu


Pathology Pathology

RCNN for Region of Interest Detection in Whole Slide Images

ArXiv Preprint

Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology service in Western Australia. We used 60 WSIs for training the RCNN model and another 12 WSIs for testing. The model was further tested on a new set of unseen WSIs. The results show that RCNN can be effectively used for ROI detection from WSIs.

A Nugaliyadde, Kok Wai Wong, Jeremy Parry, Ferdous Sohel, Hamid Laga, Upeka V. Somaratne, Chris Yeomans, Orchid Foster


General General

Predicting fine spatial scale traffic noise using mobile measurements and machine learning.

In Environmental science & technology ; h5-index 132.0

Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which has large fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, CA, and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R2 = 0.71, root mean square error (RMSE) 4.54 dB; 5-fold R2 = 0.96, RMSE 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.

Yin Xiaozhe, Fallah-Shorshani Masoud, McConnell Rob, Fruin Scott, Franklin Meredith


General General

Rational Design of Semiconductor-Based Chemiresistors and their Libraries for Next-Generation Artificial Olfaction.

In Advanced materials (Deerfield Beach, Fla.)

Artificial olfaction based on gas sensor arrays aims to substitute for, support, and surpass human olfaction. Like mammalian olfaction, a larger number of sensors and more signal processing are crucial for strengthening artificial olfaction. Due to rapid progress in computing capabilities and machine-learning algorithms, on-demand high-performance artificial olfaction that can eclipse human olfaction becomes inevitable once diverse and versatile gas sensing materials are provided. Here, rational strategies to design a myriad of different semiconductor-based chemiresistors and to grow gas sensing libraries enough to identify a wide range of odors and gases are reviewed, discussed, and suggested. Key approaches include the use of p-type oxide semiconductors, multinary perovskite and spinel oxides, carbon-based materials, metal chalcogenides, their heterostructures, as well as heterocomposites as distinctive sensing materials, the utilization of bilayer sensor design, the design of robust sensing materials, and the high-throughput screening of sensing materials. In addition, the state-of-the-art and key issues in the implementation of electronic noses are discussed. Finally, a perspective on chemiresistive sensing materials for next-generation artificial olfaction is provided.

Jeong Seong-Yong, Kim Jun-Sik, Lee Jong-Heun


artificial olfaction, chemiresistors, electronic noses, gas sensors, oxide semiconductors

General General

Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine.

In Analytical methods : advancing methods and applications

Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 μg mL-1 for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.

Bao Qiwen, Zhao Hang, Han Siqingaowa, Zhang Chen, Hasi Wuliji


Surgery Surgery

A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke - The Stroke Mobility Score (SMS).

In Medical science monitor : international medical journal of experimental and clinical research

BACKGROUND For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS). MATERIAL AND METHODS In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied. RESULTS (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach's alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore "gait speed" are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling. CONCLUSIONS The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.

Raab Dominik, Diószeghy-Léránt Brigitta, Wünnemann Meret, Zumfelde Christina, Cramer Elena, Rühlemann Alina, Wagener Johanna, Gegenbauer Silke, Geu Flores Francisco, Jäger Marcus, Zietz Dörte, Hefter Harald, Kecskemethy Andres, Siebler Mario