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Transit-oriented development (TOD) mode refers to the integrated development of high-density and multi-functional land in the vicinity of core public transportation stations to increase public transportation rates and address problems such as traffic congestion and land shortages. Therefore, it is crucial to comprehend the relationship between TOD areas and the travel behavior of rail transit residents. However, the income level and occupation of residents have a significant impact on their travel behavior, as people tend to choose their residential, work, and entertainment areas based on their economic characteristics. This paper focuses primarily on two aspects: how to distinguish TOD areas from non-TOD areas (specifically, rail stations) and the variations in the travel behavior of the people residing in these areas. Rail stations were first classified via cluster analysis according to the selected TOD indexes; then, a propensity score matching method was applied to control the influence of self-selection behavior. Based on this, the matched results were analyzed to study the difference in the travel behavior characteristics of residents in TOD and non-TOD areas. The results indicate that residents in TOD areas are more likely to travel by public transportation than those from non-TOD areas. The findings of this study promote a people-oriented urban planning concept and would have practical implications for applications of TOD modes on urban public transportation systems. [ABSTRACT FROM AUTHOR]

Human carboxylesterase 2 (hCE2), one of the most principal drug-metabolizing enzymes, catalyzes the hydrolysis of a variety of endogenous esters, anticancer agents, and environmental toxicants. The significant roles of hCE2 in both endobiotic and xenobiotic metabolism sparked great interest in the discovery and development of efficacious and selective inhibitors. However, the safe and effective inhibitors of hCE2 are scarce, due to the lack of efficient screening and evaluation systems for complex biological systems. To offer a solution to this problem, a high-content analysis (HCA)–based cell imaging and multiparametric assay method was constructed for evaluating the inhibitory effect and safety of hCE2 inhibitors in living cell system. In this study, we first established a cell imaging–based method for identifying hCE2 inhibitors at the living cell level with hCE2 fluorescent probe NCEN. Meanwhile, two nuclear probes, Hoechst 33342 and PI, were integrated to evaluate the potential cytotoxicity of compounds simultaneously. Then, the accuracy of the HCA-based method was verified by the LC-FD-based method with a positive inhibitor BNPP, and the results showed that the HCA-based method exhibited excellent precision, robustness, and reliability. Finally, the newly established HCA-based multiparametric assay panel was successfully applied to re-evaluate a series of reported hCE2 inhibitors in living cells. In summary, the HCA-based multiparametric method could serve as an efficient tool for the accuracy measurement inhibitory effect and cytotoxicity of compounds against hCE2 in living cell system. [ABSTRACT FROM AUTHOR]

The development of new routes and stations, as well as changes in land use, can have significant impacts on public transit ridership. Thus, transport departments and governments should seek to determine the level and spatio-temporal dependency of these impacts with the aim of adjusting services or improving planning. However, existing studies primarily focus on predicting ridership, and pay relatively little attention to analyzing the determinants of ridership from temporal and spatial perspectives. Consequently, no comprehensive cognition of the spatio-temporal relationship between station ridership and the built environment can be obtained from previous models, which makes them unable to facilitate the optimization of transportation demands and services. To rectify this problem, we have employed a Bayesian negative binomial regression model to identify the significant impact factors associated with entry/exit ridership at different periods of the day. Based on this model, we formulated geographically weighted models to analyze the spatial dependency of these impacts over different periods. The spatio-temporal relationship between station ridership and the built environment was analyzed using data from Beijing. The results reveal that the temporal impacts of most ridership determinants are related to the passenger trip patterns. Furthermore, the spatial impacts correspond with the determinants' spatial distribution, and the results give some implications on urban and transportation planning. This analysis gives a common analytical framework analyzing impacts of urban characteristics on ridership, and extending researches on how we capture the impacts of urban and other factors on ridership from a comprehensive perspective. [ABSTRACT FROM AUTHOR]