A small-data-driven model for predicting adsorption properties in polymeric thin films.

Artificial intelligence allowing data-driven prediction of physicochemical properties of polymers is rapidly emerging as a powerful tool for advancing material science. Here, we developed a methodology to use polymer adsorption data as predictable data by analyzing causal relationships between polymer properties and experimental results instead of using big polymer data.

Correction: Data integration of National Dose Registry and survey data using multivariate imputation by chained equations.

[This corrects the article DOI: 10.1371/journal.pone.0261534.].

Data integration of National Dose Registry and survey data using multivariate imputation by chained equations.

INTRODUCTION: Data integration is the process of merging information from multiple datasets generated from different sources, which can obtain more information in comparison to to one data source. All diagnostic medical radiation workers were enrolled in National Dose Registry (NDR) from 1996 to 2011, linked with mortality and cancer registry data. (https://kdca.go.kr/) Survey was conducted during 2012-2013 using self-reported questionnaire on occupational radiation practices among diagnostic medical radiation workers. METHODS: Data integration of NDR and Survey was performed using the multivariate imputation by chained equations (MICE) algorithm. RESULTS: The results were compared by sex and type of job because characteristics of target variables for imputation depend on these variables. There was a difference between the observed and pooled mean for the frequency of interventional therapy for nurses due to different type of medical facility distribution between observed and completed data. Concerning the marital status of males and females, and status of pregnancy for females, there was a difference between observed and pooled mean because the distribution of the year of birth was different between the observed and completed data. For lifetime status of smoking, the percentage of smoking experience was higher in the completed data than in the observed data, which could be due to reasons, such as underreporting among females and the distribution difference in the frequency of drinking between the observed and completed data for males. CONCLUSION: Data integration can allow us to obtain survey information of NDR units without additional surveys, saving us time and costs for the survey.

COVID-19: Were Public Health Interventions and the Disclosure of Patients' Contact History Effective in Upholding Social Distancing? Evidence from South Korea.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has prompted a global-scale public health response. Social distancing, along with intensive testing and contact tracing, has been considered an effective vehicle to reduce new infections. In this study, we aimed to estimate the effect of South Korean public health measures on behavioral changes with respect to social distancing without a nationwide lockdown. The results of this study may provide insights to countries who are planning to relax their aggressive restrictions though still having an unflattened curve of infections. METHODS: To estimate how the closure of educational and social welfare facilities and the disclosure of confirmed patients' contact history affected social distancing behaviors, we analyzed public transportation data in Seoul, Korea. For the modeling analysis, we used linear mixed-effects estimation. RESULTS: Our estimation showed that the average daily number of bus passengers decreased by 21.8% in February 2020 as compared to the previous year with an additional decrease observed in the areas around educational and social welfare facilities. The highest drop in the daily number of passengers was observed in areas with religious facilities. We also found that individuals avoided areas that were proximate to or within the locations that constituted the confirmed patients' contact history. CONCLUSION: Our results demonstrate that public health measures can lead people to practice social distancing. Among them, the measures that strongly encourage voluntary social distancing behaviors would play a critical role in suppressing the infections as it becomes increasingly difficult to continue imposing aggressive restrictions due to practical and economic reasons.

Cost-Effective Extreme Case-Control Design Using a Resampling Method.

Nested case-control sampling design is a popular method in a cohort study whose events are often rare. The controls are randomly selected with or without the matching variable fully observed across all cohort samples to control confounding factors. In this article, we propose a new nested case-control sampling design incorporating both extreme case-control design and a resampling technique. This new algorithm has two main advantages with respect to the conventional nested case-control design. First, it inherits the strength of extreme case-control design such that it does not require the risk sets in each event time to be specified. Second, the target number of controls can only be determined by the budget and time constraints and the resampling method allows an under sampling design, which means that the total number of sampled controls can be smaller than the number of cases. A simulation study demonstrated that the proposed algorithm performs well even when we have a smaller number of controls compared with the number of cases. The proposed sampling algorithm is applied to a public data collected for "Thorotrast Study."

Correlation estimation with singly truncated bivariate data.

Correlation coefficient estimates are often attenuated for truncated samples in the sense that the estimates are biased towards zero. Motivated by real data collected in South Sudan, we consider correlation coefficient estimation with singly truncated bivariate data. By considering a linear regression model in which a truncated variable is used as an explanatory variable, a consistent estimator for the regression slope can be obtained from the ordinary least squares method. A consistent estimator of the correlation coefficient is then obtained by multiplying the regression slope estimator by the variance ratio of the two variables. Results from two limited simulation studies confirm the validity and robustness of the proposed method. The proposed method is applied to the South Sudanese children's anthropometric and nutritional data collected by World Vision. Copyright © 2017 John Wiley & Sons, Ltd.