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Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 million smart devices in three major cities in China from January 2020 to March 2021. We find that the human mobility across multi-waves of epidemics presents a surprisingly similar pattern in these three cities, despite their significant gaps in geographic environments and epidemic intensities. In particular, we reveal that the COVID-19 policies and statistics (i.e., confirmed cases) dominate human mobility during the pandemic. Thus, we propose a universal conditional generative adversarial network based framework to estimate human mobility, integrating COVID-19 statistics and policies via a gating fusion module. Extensive numerical experiments demonstrate that our model is generalizable for estimating human mobility dynamics accurately across three cities with multi-waves of COVID-19. Beyond, our model also allows policymakers to better evaluate the potential influences of various policies on human mobility and mitigate the unprecedented and disruptive pandemic.

Chlamydia trachomatis is the most common sexually transmitted pathogen. Due to the latency of the symptoms, Chlamydia trachomatis cannot be diagnosed and treated in the early stage, leading to the development of severe chronic disease with complications and sequelae. Outer membrane complex protein B (OmcB) is the second most abundant outer membrane protein in chlamydia. Because of its abundance and powerful immunogenicity during the infection, OmcB has a very promising prospect in the fields of the diagnosis of CT infection and the development of vaccines. This article reviews the structure and localization of OmcB, and the related mechanisms during Chlamydia trachomatis infection. [ABSTRACT FROM AUTHOR]