Background
The urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong.
Method
This study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth.
Result
Our results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases.
Conclusion
In this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention.
Background. Many breakthroughs have been made in the clinical treatment of liver cancer, but there are still many liver cancer patients with limited treatment methods. Therefore, it is very important to find targets for early diagnosis and specific treatment of liver cancer. Methods. During the operation, 32 pairs of tumor tissues and corresponding normal liver tissues were acquired from patients. The mRNA expression was measured by qPCR. The protein expression was evaluated via Western blot. Flow cytometry assay was performed to measure the cells apoptosis. CCK-8 assay was performed to detect cell proliferation. Transwell chamber assay was applied to detect migration and invasion of SNU-449 cells. Results. BAP31 was upregulated in liver cancer tissues and cells. Knockdown of BAP31 repressed cell proliferation and enhanced cell apoptosis of liver cancer. Knockdown of BAP31 apparently upregulated apoptosis-related proteins (Bax and Caspase-3), while it downregulated antiapoptotic proteins (Bcl-2). Knockdown of BAP31 repressed migration and invasion of SNU-449 cells. In contrast with the control and si-NC group, protein expression of MMP-2 and MMP-9 was obviously lower after si-BAP31 transfection of cells. Knockdown of BAP31 repressed PI3K/AKT signaling pathways in liver cancer cells. Conclusion. Knockdown of BAP31 repressed cell proliferation, migration, and invasion in liver cancer by suppressing PI3K/AKT/mTOR signaling pathways.
Improving perceptions of service quality is a promising planning strategy for increasing the attractiveness and retaining the ridership of public transport systems. The widespread use of social media presents an opportunity to investigate the performance of transport services from the customer’s perspective. This study proposes a framework for integrating quantitative and qualitative analyses to investigate the perceptions of transport services by mining data from social media. We utilise Sina Weibo data related to the Shenzhen metro system to illustrate a text mining process categorised by semantic, spatial and temporal information. On the semantic front, in addition to identifying service attributes and sentiment polarity consistent with previous literature, we find attributes specific to the Chinese context. We also identify clear temporal variations among different service attributes by visualising the number of corresponding microblogs across varying time scales such as hours, days and weekdays. The spatial variations reveal five main clusters around central business districts and transport hubs, which produce the highest density of reports about crowdedness, waiting times, reliability and frequency. However, microblogs that report on perceptions of safety and personnel behaviour present a different spatial pattern. This research offers insights into the ways in which we can use social media data to identify key service areas for immediate improvement and to monitor and manage metro systems more effectively over the long term.
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