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Purpose: Studying abroad is an important way for universities to cultivate talent with international vision and global competitiveness. Based on its analysis of a U.S. university's study in China program, this study provides policy recommendations to promote the overseas study and exchange of U.S. students in China. Design/Approach/Methods: This study examines the Beijing Overseas Learning Program of L University to examine the actual operations of a study abroad program in China. More specifically, this study examines the cooperative institution where the U.S. study program is located in China to analyze the operation mechanism and underlying logical framework of the program. Findings: This study provides a comprehensive analysis of the logical factors as well as internal and external reasons behind U.S. students' decisions to study in China. This study demonstrates that the "peer factor" has a decisive influence on students' decisions to study in China. Originality/Value: Study abroad programs constitute an integral part of China's initiatives to enhance people-to-people exchanges with other countries. This study improves our understanding of the practical dynamics of study abroad programs in China, thereby enhancing abilities to attract U.S. students to study in China.

Background and Objectives Older Chinese immigrants are at risk for depression due to acculturative stress and language barriers. Residential segregation with respect to language use plays an important role in the mental health of historically marginalized populations. Previous research provided mixed evidence about the segregation effect among older Latino and Asian immigrants. Guided by a model of social processes, we examined the direct and indirect effects of residential segregation on depressive symptoms via multiple mechanisms of acculturation, discrimination, social network, social support, social strain, and social engagement. Research Design and Methods Four waves of depressive symptoms were assessed in the Population Study of Chinese Elderly (2011–19, N = 1,970), and linked to the 2010–14 American Community Survey estimates of neighborhood context. Residential segregation was measured by the Index of Concentrations at the Extremes which simultaneously assesses Chinese and English language use within a given census tract. Latent growth curve models with adjusted cluster robust standard errors were estimated after controlling for individual-level factors. Results Residents of segregated Chinese-speaking neighborhoods had fewer baseline depressive symptoms but a slower rate of symptom reduction than those living in neighborhoods segregated with English-only speakers. Racial discrimination, social strain, and social engagement partially mediated the association between segregation and baseline depressive symptoms; social strain and social engagement partially mediated the association with long-term reduction in depressive symptoms. Discussion and Implications This study demonstrates the importance of residential segregation and social processes in shaping mental well-being among older Chinese immigrants and suggests potential mechanisms to alleviate mental health risks. [ABSTRACT FROM AUTHOR]

Emissions of ozone (O3) precursors in the United States have decreased in recent decades, and near-surface O3 concentrations showed a significant decrease in summer but an increase in winter. In this study, an O3 source tagging technique is utilized in a chemistry–climate model to investigate the source contributions to O3 mixing ratios in the US from various emitting sectors and regions of nitrogen oxides (NOx) and reactive carbon species during 1995–2019. We show that domestic emission reductions from energy and surface transportation are primarily responsible for the decrease in summertime O3 during 1995–2019. However, in winter, the emission control also weakens the NOx titration process, resulting in considerable increases in O3 levels from natural sources. Additionally, increases in aviation and shipping emissions and transpacific transport of O3 from Asia largely contribute to the winter O3 increase. We also found that changes in large-scale circulation favoring O3 transport from the upper atmosphere and foreign transport from Asia also explain 15 % of the increase in the US near-surface O3 levels in winter. [ABSTRACT FROM AUTHOR]

Objectives Although a number of studies have documented cognitive health among older immigrants in the United States, little is known about how the life-course immigration experiences are associated with cognitive trajectories among older Chinese immigrants. We assess patterns of cognitive functioning and change over time and examine whether age at migration, reasons for migration, acculturation, perceived discrimination, and preferred dialects are related to cognitive trajectories. Methods The sample comprised 2,075 participants from the Population Study of Chinese Elderly (PINE), who completed a battery of cognitive tests at four time points (2011–2019). Latent class growth analysis and multinomial logistic regression were utilized. Results Three latent classes of cognitive trajectories were identified: the low functioning with the fastest decline (LCF, 12%), the moderate functioning with a medium decline rate (MCF, 39%), and the high functioning with the slowest decline (HCF, 48%). Perceiving more discrimination reduced, whereas speaking Taishanese increased the odds of being in the LCF and MCF. High acculturation only distinguished MCF from HCF after controlling for the known factors of cognitive health such as age, education, and social engagement. Discussion This study identifies a group of older Chinese immigrants who are especially vulnerable to cognitive impairment and indicates that the risk of cognitive decline appears to be elevated with lower levels of acculturation and unidentified racial discrimination. More research is needed to fully understand the underlying mechanisms that link the life-course immigration experiences to cognitive health outcomes in later life. [ABSTRACT FROM AUTHOR]

Social isolation has been recognized as a critical public health problem. As the most vulnerable population, older adults are disproportionately affected by social isolation during the COVID-19 pandemic. The purposes of this study were to examine the association between social isolation and loneliness among U.S. older adults and to explore the moderating effect of solitary activity by using data from the Health and Retirement Study (HRS). Social isolation was measured by six indicators, including marital status, living arrangement, social participation in any clubs or social organizations, and the frequency of social contact with children, family members, and friends. Loneliness was assessed by eleven questions derived from the UCLA Loneliness Scale. Solitary activity included 11 types of activities that respondents could perform alone with limited or no social interaction. Results from the multivariate regression analyses indicated that unmarried status and lower frequency of social contact were associated with more perceived loneliness. Solitary activity significantly moderated the negative effects of the low frequency of social contact with family members on loneliness. The findings implicate that social work programs and interventions can aim to expand social networks and provide more opportunities for solitary activities, particularly for isolated older adults. [ABSTRACT FROM AUTHOR]

This study attempted to analyse the differences between Chinese and American thought patterns by using quantitative methods based on the commentaries in theNew York TimesandPeople’s Dailyfor 9 months in 2010 considering climate change issues as samples; the thought patterns were based on six aspects: synthetic, analytical, imagery, logical, dialectical, and contradictory thinking. This study applied thet-test, chi-square test, and ANOVA to the coding analysis. It was observed that the thought patterns of synthetic and analytical thinking were similar, whereas the other four patterns had considerable differences. Moreover, the reliability of the coders was tested. [ABSTRACT FROM PUBLISHER]

An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy. [ABSTRACT FROM AUTHOR]

Background and Objectives: Social isolation has been recognized as a social problem with negative health consequences. Using data from 3 waves of the Health and Retirement Study, this study aimed to examine the long-term impact of social isolation on loneliness and depressive symptoms and to explore the moderating effect of resilience., Research Design and Methods: This study comprised 3,681 U.S. adults aged 60 and older at the baseline wave. Social isolation index was constructed using 5 indicators, including not married or cohabitating with a partner, no social participation, and less than monthly contacts with children, family members, or friends. Loneliness was measured by the University of California Los Angeles (UCLA) Loneliness Scale and depressive symptoms were measured by the Center for Epidemiological Studies-Depression scale. The moderator of resilience was assessed by the simplified resilience score. Latent growth curve models with robust standard errors were estimated., Results: The results of latent growth curve models showed that social isolation was significantly associated with more initial loneliness and depressive symptoms. However, social isolation was associated with a slower increasing rate of loneliness, but no significant relationship with the change rate of depressive symptoms. Furthermore, resilience significantly buffered the negative effect of social isolation on the initial level of depressive symptoms., Discussion and Implications: The findings underscore the importance of enacting strategies and interventions targeting resilience to address social isolation and its negative consequences among older adults., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The study focuses on the association between serum carotenoids and cancer-related death. Using data from the National Health and Nutrition Examination Survey (2001-2006 and 2017-2018), the study encompasses 10,277 participants older than age 20 years, with recorded baseline characteristics and serum carotenoid concentrations (including α-carotene, trans-β-carotene, cis-β-carotene, β-cryptoxanthin, trans-lycopene, and lutein/zeaxanthin). We hypothesized that serum carotenoid concentrations were negatively associated with cancer-related death. The weighted chi-square analyses indicate significant negative correlations between higher serum concentrations of α-carotene, β-cryptoxanthin, trans-lycopene, and total carotenoids, and the risk of cancer-related deaths. Using weighted Cox regression analysis, this study confirms that α-carotene, β-cryptoxanthin, trans-lycopene, and total carotenoids, as continuous or categorical variables, are inversely related to cancer mortality (P < .0001). Furthermore, considering competitive risk events, lower concentrations of serum β-cryptoxanthin (Fine-Gray P = 1.12e-04), trans-lycopene (P = 5.68e-14), and total carotenoids (P = .03) are associated with an increased risk of cancer-related deaths. The research reveals a crucial inverse relationship between serum carotenoid concentrations and cancer-related death., Competing Interests: Author Declarations The authors declare no competing interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Attention deficit/hyperactivity disorder (ADHD) has been identified as a risk factor for obesity in both children and adolescents. However, the mechanisms underlying the relationship between ADHD and obesity are still unclear. This study aimed to test a theoretical model of whether anxiety/depression is an intermediary factor in the ADHD-obesity relationship., Methods: Data were derived from the National Health Interview Survey (NHIS), a principal source of information on the health of the civilian noninstitutionalized population of the United States. A total of 35,108 adolescents aged 12-17 years old from 2010-2015 NHIS and 2016-2018 NHIS representing 46,550,729 individuals in the weighted population, had a parent-reported previous ADHD diagnosis, emotional problems, and height and weight data. Mediation analyses were used to explore whether anxiety/depression is an intermediary factor in the relationship between ever having ADHD and obesity. Mediation analyses were performed using multiple logistic regressions., Results: The findings showed that ADHD was a predictor of obesity. This relationship was partially mediated by depression(2010-2015: β=0.28, 95%CI:0.13-0.43; 2016-2018: β=0.26, 95%CI:0.03-0.49), as well as anxiety (2010-2015: β=0.28, 95%CI:0.18-0.38)., Conclusions: Our study suggests the hypothetical role of depression and anxiety as underlying mechanisms in the association between ever having ADHD and obesity in adolescents. When treating children with ADHD, clinicians need to be particularly attentive to whether they show emotional problems and use interventions to eliminate anxiety/depression to protect against obesity., (© 2023. The Author(s).)

Purpose: The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models., Methods: We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model., Results: A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models., Conclusion: XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death., Competing Interests: The authors have declared that no competing interests exist.

The cases of stomach cancer (SC) incidence are increasing per year and the SC burden has remained very high in some countries. We aimed to evaluate the global geographical variation in SC incidence and temporal trends from 1978 to 2007, with an emphasis on the effect of birth cohort. Joinpoint regression and age-period-cohort model were applied. From 2003 to 2007, male rate were 1.5- to 3-fold higher than female in all countries. Rates were highest in Eastern Asian and South American countries. Except for Uganda, all countries showed favorable trends. Pronounced cohort-specific increases in risk for recent birth cohorts were seen in Brazil, Colombia, Iceland, New Zealand, Norway, Uganda and US white people for males and in Australia, Brazil, Colombia, Costa Rica, Czech Republic, Ecuador, Iceland, India, Malta, New Zealand, Norway, Switzerland, United Kingdom, Uganda, US black and white people for females. The cohort-specific ratio for male significantly decreased in Japan, Malta and Spain for cohorts born since 1950 and in Austria, China, Croatia, Ecuador, Russia, Switzerland and Thailand for cohorts born since 1960 and for female in Japan for cohorts born since 1950 and in Canada, China, Croatia, Latvia, Russia and Thailand for cohorts born since 1960. Disparities in incidence and carcinogenic risk persist worldwide. The favorable trends may be due to changes in environmental exposure and lifestyle, including decreased Helicobacter pylori prevalence, increased intake of fresh fruits and vegetables, the availability of refrigeration and decreased intake of salted and preserved food and smoking prevalence., (© 2017 UICC.)