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To estimate the up-to-date prevalence of chronic kidney disease among the health check-up population in economically developed areas of China using estimated glomerular filtration rate, urinary albumin creatinine ratio, and kidney ultrasound. Healthcare data from 38,093 subjects in 10 megalopolises of China who had an annual health check-up in 2021 were used. The overall and stratified prevalence of chronic kidney disease by sex, age, region and comorbidity group was reported. The association between chronic kidney disease and covariates of demographics, and comorbidities were analyzed in the multivariable-adjusted logistic regression model. A total of 3837 CKD cases were detected meeting any of the three CKD diagnostic criteria, with a crude prevalence of 10.1% in the study population. Using one criterion of decreased glomerular filtration rate, albuminuria and kidney structural abnormalities alone detected 204 (5.3%), 3289 (85.7%) and 563 (14.7%) cases, respectively. The addition of kidney ultrasound detected 427 (11.1%) structural abnormality cases without decreased GFR and albuminuria. The most common abnormalities were renal masses, hydronephrosis due to obstruction and congenital anomalies of kidney and urinary tract. Female, older age, low city-tier, hypertension, diabetes, obesity, hypertriglyceridemia as well as early disease stages such as pre-hypertension, impaired fasting glucose and overweight were significantly associated with chronic kidney disease. Kidney ultrasound helps to amplify the detection of CKD patients, which is a supplement to kidney function and urine protein. [ABSTRACT FROM AUTHOR]

In biomedical and epidemiological studies, gene-environment (G-E) interactions have been shown to importantly contribute to the etiology and progression of many complex diseases. Most existing approaches for identifying G-E interactions are limited by the lack of robustness against outliers/contaminations in response and predictor spaces. In this study, we develop a novel robust G-E identification approach using the trimmed regression technique under joint modelling. A robust data-driven criterion and stability selection are adopted to determine the trimmed subset which is free from both vertical outliers and leverage points. An effective penalization approach is developed to identify important G-E interactions, respecting the ‘main effects, interactions’ hierarchical structure. Extensive simulations demonstrate the better performance of the proposed approach compared to multiple alternatives. Interesting findings with superior prediction accuracy and stability are observed in the analysis of The Cancer Genome Atlas data on cutaneous melanoma and breast invasive carcinoma. [ABSTRACT FROM AUTHOR]

As a significant application of CO2geological storage technology, CO2-enhanced oil recovery (CO2-EOR) considers impacts on ecosystems due to the risk of CO2leakage from oil-producing wells. It is critical to clarify the relationship between CO2leakage and vegetation growth, because large numbers of wells are situated in the midst of cropland. In this study, we calculated the net primary productivity (NPP) of crops to examine the correlation between oil-producing wells and crop growth using Pleiades’ high-spatial resolution remote-sensing imaging in a CO2-EOR experimental area in Shandong Province, China, basing our methods on the improved Carnegie–Ames–Stanford approach (CASA) model. Atmospheric CO2concentrations at varying distances from one of the producing wells were collected by wireless sensors and hand-held equipment, and analysis was conducted in regard to the impact of CO2leakage from producing wells on crop growth according to a simple sampling method. The results indicated that increased CO2concentration resulting from leakage from producing wells has a slightly positive influence on crop productivity, with the influence ranges correlated with wind direction and velocity. The variance of NPP resulting from increased CO2concentration and wind direction ranged from 5.0 to 33.7%. [ABSTRACT FROM PUBLISHER]

Academic libraries are a vital space for collaboration, socialization, research, and quiet study. To support these needs, they must offer a variety of environments, noise levels, and furniture to support varying student needs. However, it can be difficult to understand these specific needs and properly balance the types of spaces and seating alternatives to optimally meet them. This article describes a space usage study undertaken to determine patron needs and preferences and the resulting visualization of the data using Tableau Public. The project provided important insights into patron space preferences and use, and the methodology can be replicated at other institutions. [ABSTRACT FROM AUTHOR]