Municipal solid waste incinerator (MSWI) not only is deemed one of the uppermost sources of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs), but also produces substantial amount of polybrominated dibenzo-p-dioxins/furans (PBDD/Fs) owing to the existence of brominated flame retardants (BFRs) in the waste. So far, however, PBDD/Fs in the vicinal environments of MSWI and their associated risks remain rarely studied. Based on a one-year passive air sampling (PAS) scheme, we investigated airborne PBDD/Fs and PCDD/Fs around a large-scale MSWI that has been operated for multi-years. Both the concentrations of PBDD/Fs and PCDD/Fs showed spatially decreasing trends with the distance away from the MSWI, confirming the influence of the MSWI on the dioxin levels in its ambient air. But its influence on PBDD/Fs was less because PBDD/Fs exhibit lower volatility and therefore lower gaseous concentrations than PCDD/Fs. Compared to the existing global data of airborne PCDD/Fs and PBDD/Fs, our data of the MSWI vicinity were at medium levels, despite PAS samples only represent the concentrations of gaseous dioxins in theory. The seasonal data suggest that meteorological conditions exerted apparent influences over the concentrations and sources of airborne dioxins around the MSWI. As for PCDD/Fs, the MSWI was diagnosed as their uppermost source, followed by local traffic and volatilization/deposition. Whereas the top three PBDD/F sources were related to PBDEs, bromophenol/bromobenzene, and traffic vehicles, respectively. The bioassay-derived TEQs based on the aryl hydrocarbon receptor activation of airborne dioxins around the MSWI were one or two orders of magnitudes higher than their concentration-based TEQs, and the corresponding carcinogenic risks at some MSWI-vicinal sites exceeded the acceptable threshold proposed by the U. S. EPA (10-6 ∼ 10-4) and deserve continuous attention.

With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling methods randomly sample or sample only for a particular area, which affects the classification results. To solve the above limitations, this study proposes an imbalanced data oversampling method, SD-KMSMOTE, based on the spatial distribution of minority samples. A filter noise pre-treatment is added, the category information of the near-neighbouring samples is considered, and the existing minority class sample noise is removed. These conditions lead to the design of a new sample synthesis method, and the rules for calculating the weight values are constructed on this basis. The spatial distribution of minority class samples is considered comprehensively; they are clustered, and the sub-clusters that contain useful information are assigned larger weight values and more synthetic sample numbers. The experimental results show that the experimental results outperform existing methods in terms of precision, recall, F1 score, G-mean, and area under the curve values when the proposed method is used to expand the imbalanced dataset in the field of medicine and other fields.

Respondent-driven sampling (RDS) is a method of sampling specifically for hidden populations, often applied to transgender women, female sex workers, men who have sex with men, and other groups that are difficult to identify and contact because of stigma and legal constraints. However, this method is gradually applied to the general population. With the continuous improvement of RDS, studies find that the network size of RDS samples can be weighted to estimate the overall situation and population size. This article summarizes the current application progress of RDS in population size estimation and provides ideas for the development of RDS and the use of RDS to carry out relevant research.
受访者驱动抽样（RDS）是一种专门针对隐匿人群的抽样方法，常用于跨性别者、暗娼、MSM等因耻辱感和法律制度的约束难以识别和接触的人群，并逐渐应用于一般人群。随着RDS的不断改进和完善，研究发现对RDS样本的社交网络规模赋予权重可以估计总体的情况及人群规模。本文对目前RDS在人群规模估计中的应用进展进行综述，为RDS的发展及使用该方法开展相关研究提供思路。.

Xinjiang is one of the areas in China with extremely severe iodine deficiency. The health of Xinjiang residents has been endangered for a long time. In order to provide reasonable suggestions for scientific iodine supplementation and improve the health and living standards of the people in Xinjiang, it is necessary to understand the spatial distribution of iodine content in drinking water and explore the influencing factors of spatial heterogeneity of water iodine content distribution.
The data of iodine in drinking water arrived from the annual water iodine survey in Xinjiang in 2017. The distribution of iodine content in drinking water in Xinjiang is described from three perspectives: sampling points, districts/counties, and townships/streets. ArcGIS was used for spatial auto-correlation analysis, mapping the distribution of iodine content in drinking water and visualizing the distribution of Geographically Weighted Regression (GWR) model parameter. Kriging method is used to predict the iodine content in water at non-sampling points. GWR software was used to build GWR model in order to find the factors affecting the distribution of iodine content in drinking water.
There are 3293 sampling points in Xinjiang. The iodine content of drinking water ranges from 0 to 128 μg/L, the median is 4.15 μg/L. The iodine content in 78.6% of total sampling points are less than 10 μg/L, and only that in the 3.4% are more than 40 μg/L. Among 1054 towns\' water samples in Xinjiang, 88.9% of the samples\' water iodine content is less than 10 μg/L. Among the 94 studied areas, the median iodine content in drinking water in 87 areas was less than 10 μg/L, those values in 7 areas were between 10-40 μg/L, and the distribution of water iodine content in Xinjiang shows clustered. The GWR model established had found that the effects of soil type and precipitation on the distribution of iodine content in drinking water were statistically significant.
The iodine content of drinking water in Xinjiang is generally low, but there are also some areas which their drinking water has high iodine content. Soil type and precipitation are the factors affecting the distribution of drinking water iodine content, and are statistically significant (P<0.05).

Severe fever with thrombocytopenia syndrome (SFTS) is a novel tick-borne infectious disease caused by a new type of SFTS virus (SFTSV). Here, a longitudinal sampling study is conducted to explore the differences in transcript levels after SFTSV infection, and to characterize the transcriptomic and epigenetic profiles of hospitalized patients. The results reveal significant changes in the mRNA expression of certain genes from onset to recovery. Moreover, m6A-seq reveals that certain genes related with immune regulation may be regulated by m6A. Besides the routine tests such as platelet counts, serum ALT and AST levels testing, distinct changes in myocardial enzymes, coagulation function, and inflammation are well correlated with the clinical data and sequencing data, suggesting that clinical practitioners should monitor the above indicators to track disease progression and guide personalized treatment. In this study, the transcript changes and RNA modification may lend a fresh perspective to our understanding of the SFTSV and play a significant role in the discovery of drugs for effective treatment of this disease.

Evidence indicates that age-related macular degeneration (AMD) is associated with the prior presence of allergic diseases; however, large-scale studies in the literature are limited. A case-control study was conducted to describe the relationship between premorbid allergic diseases and AMD using Taiwan\'s National Health Insurance database. Eligibility criteria for inclusion of new adult AMD cases from 2000 to 2013 were set up. We defined the year of diagnosis as the index year. Age-, gender-, index year- matched controls who were drawn from the same database. The case control ratio was 1:4. For all participants, all premorbid conditions staring 1996 to index year were documented. Binary logistic regression was used to describe factors related to AMD occurrence. The AMD group consisted of 10,911 patients, and the comparison group consisted of 43,644 individuals. Patients with AMD showed significant associations with premorbid allergic diseases (aOR 1.54, 95% CI 1.47-1.61), specifically with allergic conjunctivitis (aOR 2.07, 95% CI 1.94-2.20), allergic rhinitis (aOR 1.32, 95% CI 1.25-1.39), asthma (aOR 0.99, 95% CI 0.93-1.06), and atopic dermatitis (aOR 1.04, 95% CI 0.94-1.17). Further analyses indicated that patients with more concurrent allergic diseases have higher associations with AMD than those with fewer concurrent diseases. Patients with more annual medical visits for their allergic diseases also showed higher associations with AMD than those with fewer visits. AMD is significantly associated with premorbid allergic diseases. The underlying mechanisms must be further investigated.

The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing\'anling region is expressed by the regression equation of y = -35.51x1 + 11206.813 (R2 = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing\'anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.

Outcome-dependent sampling designs such as the case-control or case-cohort design are widely used in epidemiological studies for their outstanding cost-effectiveness. In this article, we propose and develop a smoothed weighted Gehan estimating equation approach for inference in an accelerated failure time model under a general failure time outcome-dependent sampling scheme. The proposed estimating equation is continuously differentiable and can be solved by the standard numerical methods. In addition to developing asymptotic properties of the proposed estimator, we also propose and investigate a new optimal power-based subsamples allocation criteria in the proposed design by maximizing the power function of a significant test. Simulation results show that the proposed estimator is more efficient than other existing competing estimators and the optimal power-based subsamples allocation will provide an ODS design that yield improved power for the test of exposure effect. We illustrate the proposed method with a data set from the Norwegian Mother and Child Cohort Study to evaluate the relationship between exposure to perfluoroalkyl substances and women\'s subfecundity.

In the face of the global pandemic of coronavirus disease-2019 (COVID-19), people\'s adherence to precautionary behavioral measures (e.g. social distancing) largely influences the effectiveness of those measures in containing the spread of the coronavirus. The present study aims at testing the applicability of the health belief model (HBM) and generalised social beliefs (i.e. social axioms) to explore strategies for promoting adherence to COVID-19 precautionary measures.
We conducted a telephone survey with a two-step stratified random sampling method and obtained a probability sample of 616 adults in Macao, China (18-87 years old; 60.9% women) in April 2020.
Our participants showed stronger adherence to some COVID-19 precautionary measures (e.g. face mask wearing; 96.4%) but not others (e.g. social distancing; 42.3%). Their adherence to those measures was found to be significantly associated with four HBM factors and two social axioms, after controlling for gender, age, and years of education.
The HBM and the generalised social beliefs of social cynicism and reward for application can be applied to understanding adherence to precautionary measures against COVID-19. Strategies based on beliefs were proposed to facilitate the promotion of precautionary measures.

Since its outbreak in January 2020, COVID-19 has quickly spread worldwide and has become a global pandemic. Social media platforms have been recognized as important tools for health-promoting practices in public health, and the use of social media is widespread among the public. However, little is known about the effects of social media use on health promotion during a pandemic such as COVID-19.
In this study, we aimed to explore the predictive role of social media use on public preventive behaviors in China during the COVID-19 pandemic and how disease knowledge and eHealth literacy moderated the relationship between social media use and preventive behaviors.
A national web-based cross-sectional survey was conducted by a proportionate probability sampling among 802 Chinese internet users (\"netizens\") in February 2020. Descriptive statistics, Pearson correlations, and hierarchical multiple regressions were employed to examine and explore the relationships among all the variables.
Almost half the 802 study participants were male (416, 51.9%), and the average age of the participants was 32.65 years. Most of the 802 participants had high education levels (624, 77.7%), had high income >¥5000 (US $736.29) (525, 65.3%), were married (496, 61.8%), and were in good health (486, 60.6%). The average time of social media use was approximately 2 to 3 hours per day (mean 2.34 hours, SD 1.11), and the most frequently used media types were public social media (mean score 4.49/5, SD 0.78) and aggregated social media (mean score 4.07/5, SD 1.07). Social media use frequency (β=.20, P<.001) rather than time significantly predicted preventive behaviors for COVID-19. Respondents were also equipped with high levels of disease knowledge (mean score 8.15/10, SD 1.43) and eHealth literacy (mean score 3.79/5, SD 0.59). Disease knowledge (β=.11, P=.001) and eHealth literacy (β=.27, P<.001) were also significant predictors of preventive behaviors. Furthermore, eHealth literacy (P=.038) and disease knowledge (P=.03) positively moderated the relationship between social media use frequency and preventive behaviors, while eHealth literacy (β=.07) affected this relationship positively and disease knowledge (β=-.07) affected it negatively. Different social media types differed in predicting an individual\'s preventive behaviors for COVID-19. Aggregated social media (β=.22, P<.001) was the best predictor, followed by public social media (β=.14, P<.001) and professional social media (β=.11, P=.002). However, official social media (β=.02, P=.597) was an insignificant predictor.
Social media is an effective tool to promote behaviors to prevent COVID-19 among the public. Health literacy is essential for promotion of individual health and influences the extent to which the public engages in preventive behaviors during a pandemic. Our results not only enrich the theoretical paradigm of public health management and health communication but also have practical implications in pandemic control for China and other countries.