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Triggered by a territorial dispute as well as historical animosity, a nationwide civilian boycott of Japanese products took place in China in the summer of 2012. Using detailed data on vehicle sales in four major Chinese cities (Beijing, Tianjin, Shanghai, and Nanjing), this study investigates the impact of boycotts on sales and advertising effectiveness of products from different countries of origin. The boycott dramatically reduced the market share of Japanese brands and benefitted Chinese and non-Japanese foreign brands. Consumer switching to other brands accounted for the majority of the loss in sales. Advertising became less effective for Japanese brands during the boycott but more effective for non-Japanese brands, especially at the parent-brand level (e.g., Toyota) than sub-brand level (e.g., Camry). The sales impacts were strongest in Nanjing, the city that had the most atrocious war experience with Japan during the Battle of Nanjing in 1937, and weakest in Beijing, where local government agencies explicitly discouraged public demonstrations. Finally, although the decline in sales of Japanese brands did not vary by quality or production location, competing non-Japanese brands that were had better quality or were manufactured locally benefitted more. This paper was accepted by Juanjuan Zhang, marketing. [ABSTRACT FROM AUTHOR]

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Background: Previous studies on the effects of lipotoxicity and oxidative stress on islet beta cell function mainly focused on patients with diabetes, whereas studies on normal glucose tolerance (NGT) are few. The aim of this study was to explore the relationships among triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), oxidative stress indicators, insulin resistance, and beta cell function in populations with different glucose and lipid metabolism states. Methods: A total of 517 individuals were recruited from a rural community in Beijing, China. Glucose metabolism status was defined according to the results of a 75-g oral glucose tolerance test (OGTT). Dyslipidemia was defined as abnormal TG, HDL-c, or LDL-c levels. The population was divided into four groups: individuals with normal glucose and lipid levels (group A, n = 62); those with dyslipidemia alone (group B, n = 82); those with dysglycemia alone (group C, n = 121); and those with dysglycemia and dyslipidemia (group D, n = 247). Oxidative stress indicators, including superoxide dismutase (SOD), glutathione reductase (GR) and 8-hydroxydeoxyguanosine (8-OHdG), were measured. Homeostasis model assessment of insulin resistance (HOMA-IR) and glucose disposition index (DI30, DI120) were calculated to assess insulin resistance and islet beta cell function, respectively. Stratified multiple linear regression analysis was used to explore relationships between TG, HDL-c, LDL-c, oxidative stress indicators, and insulin resistance (natural log transformation of HOMA-IR, LnHOMA-IR) and beta cell function (natural log transformation of DI30, Ln DI30). Results: Compared with the control group, populations with dyslipidemia and/or dysglycemia showed significantly increased insulin resistance. Dyslipidemia aggravated insulin resistance and beta cell dysfunction in individuals with dysglycemia. Stratified regression analysis showed that TG positively correlated with LnHOMA-IR in individuals with normal glucose levels (beta = 0.321, 0.327, P = 0.011, 0.003 in groups A and B, respectively) and negatively correlated with LnDI30 in participants with dyslipidemia (beta = − 0.225, − 0.122, P = 0.035, 0.048 in groups B and D, respectively). Reduced serum SOD levels in individuals with dysglycemia plus dyslipidemia were observed, and a negative association between TG and SOD levels was found (r = − 0.461, P < 0.001). Conclusion: TG correlated with both insulin resistance and beta cell function in individuals with dyslipidemia alone. SOD negatively correlated with TG, indicating a close relationship between oxidative stress and glucose-lipid metabolism. Due to the adverse effect of hypertriglyceridemia on insulin sensitivity and islet beta cell function, more attention should be paid to the detection and management of hypertriglyceridemia. [ABSTRACT FROM AUTHOR]

Taking a case study approach and utilizing Hoggan's transformative learning (TL) typology and criteria as a framework, we examined the self-reported TL experiences of 12 local school leaders in Beijing, China. These local school leaders gained cross-cultural learning from a school leadership development program that was designed to prepare school leaders for education internationalization. Our data analysis indicated that these 12 local school leaders had experienced significant changes in their educational epistemology, self-identity, and school leadership practices. Further examining these changes at the intersection of the Confucian and Western cultural heritages, we discovered that these school leaders had developed an integrated approach to education, started to consciously reflect on what their social- and self-identities entail, and began the process of transitioning from conservative to appreciative school leadership practices. [ABSTRACT FROM AUTHOR]

The increasing prevalence of hyperuricemia has been recognized as an emerging public health concern in both developed and developing countries. Hyperuricemia is a metabolic condition characterized by an elevated serum uric acid, and associated with renal damage, diabetes, autoimmune disorders, and cardiovascular diseases. Although human genetic variation has been recognized as a factor, posttranslational cellular processes and glycan biomarkers have not been studied extensively for susceptibility to hyperuricemia. We evaluated whether immunoglobulin (Ig)G N-glycans play a role in hyperuricemia in the general population. This cross-sectional study enrolled 635 participants (208 men and 427 women), ages ≥18 years, from a community-based population in Beijing, China. The IgG N-glycan composition of serum was analyzed by an ultraperformance liquid chromatography method. The prevalence of hyperuricemia observed in this sample was 5.98% (14.9% in men and 1.6% in women). Serum uric acid level was positively correlated with glycan peaks (GP)1, GP2, GP4, GP6, GP10, and GP11, whereas it was negatively correlated with GP12, GP13, GP14, GP15, GP18, and GP20. The combination of GP9, GP10, body mass index, and gender distinguished individuals with hyperuricemia from subjects without hyperuricemia, with an area under the curve value of 0.849 (95% confidence interval: 0.784–0.915). These findings collectively suggest a possible link between hyperuricemia and IgG N-glycans, which might be potentially mediated through inflammation-related mechanisms. Additional research on glycan biomarkers in independent and community-based population samples might allow the development of glycan diagnostics for hyperuricemia and gout in the future. [ABSTRACT FROM AUTHOR]

There is increasingly concern that PM 2.5 constituents play a significant role in PM 2.5 -related cardiovascular outcomes. However, little is known about the associations between specific constituents of PM 2.5 and risk for cardiovascular health. To evaluate the exposure to specific chemicals of PM 2.5 from various sources and their cardiac effects, a longitudinal investigation was conducted with four repeated measurements of elderly participants' HRV and PM 2.5 species in urban Beijing. Multiple chemicals in PM 2.5 (metals, ions and PAHs) were characterized for PM 2.5 source apportionment and personalized exposure assessment. Five sources were finally identified with specific chemicals as the indicators: oil combustion (1.1%, V & PAHs), secondary particle (11.3%, SO 4 2− & NO 3 −), vehicle emission (1.2%, Pd), construction dust (28.7%, Mg & Ca), and coal combustion (57.7%, Se & As). As observed, each IQR increase in exposure to oil combustion (V), vehicle emission (Pd), and coal combustion (Se) significantly decreased rMSSD by 13.1% (95% CI: −25.3%, −1.0%), 27.4% (95% CI: −42.9%, −7.6%) and 24.7% (95% CI: −39.2%, −6.9%), respectively, while those of PM 2.5 mass with decreases of rMSSD by 11.1% (95% CI: −19.6%, −1.9%) at lag 0. Elevated exposures to specific sources/constituents of PM 2.5 disrupt cardiac autonomic function in elderly and have more adverse effects than PM 2.5 mass. In the stratified analysis, medication and gender modify the associations of specific chemicals from variable sources with HRV. The findings of this study provide evidence on the roles of influential constituents of ambient air PM 2.5 and their sources in terms of their adverse cardiovascular health effects. • First study to extensively examine PM 2.5 constituents and elderly's cardiac risk. • Elevated exposures to PM 2.5 specific chemicals disrupt cardiac autonomic function. • Fossil fuel combustion sources have more adverse cardiac effects than PM 2.5 mass. • rMSSD, pNN50 and VLF are key and early biomarkers of PM 2.5 related cardiac events. • Medication and gender modify the source specific PM 2.5 on cardiac effects. [ABSTRACT FROM AUTHOR]