Due to the increase in bacterial resistance, improving the anti-infectious immunity of the host is rapidly becoming a new strategy for the prevention and treatment of bacterial pneumonia. However, the specific lung immune responses and key immune cell subsets involved in bacterial infection are obscure. Actinobacillus pleuropneumoniae (APP) can cause porcine pleuropneumonia, a highly contagious respiratory disease that has caused severe economic losses in the swine industry. Here, using high-dimensional mass cytometry, the major immune cell repertoire in the lungs of mice with APP infection was profiled. Various phenotypically distinct neutrophil subsets and Ly-6C+ inflammatory monocytes/macrophages accumulated post-infection. Moreover, a linear differentiation trajectory from inactivated to activated to apoptotic neutrophils corresponded with the stages of uninfected, onset, and recovery of APP infection. CD14+ neutrophils, which mainly increased in number during the recovery stage of infection, were revealed to have a stronger ability to produce cytokines, especially IL-10 and IL-21, than their CD14- counterparts. Importantly, MHC-II+ neutrophils with antigen-presenting cell features were identified, and their numbers increased in the lung after APP infection. Similar results were further confirmed in the lungs of piglets infected with APP and Klebsiella pneumoniae infection by using a single-cell RNA-seq technique. Additionally, a correlation analysis between cluster composition and the infection process yielded a dynamic and temporally associated immune landscape where key immune clusters, including previously unrecognized ones, marked various stages of infection. Thus, these results reveal the characteristics of key neutrophil clusters and provide a detailed understanding of the immune response to bacterial pneumonia.

In a multimodal public transport network, transfers are inevitable. Planning and managing an efficient transfer connection is thus important and requires an understanding of the factors that influence those transfers. Existing studies on predicting passenger transfer flows have mainly used transit assignment models based on route choice, which need extensive computation and underlying behavioral assumptions. Inspired by studies that use network properties to estimate public transport (PT) demand, this paper proposes to use the network properties of a multimodal PT system to explain transfer flows. A statistical model is estimated to identify the relationship between transfer flow and the network properties in a joint bus and metro network. Apart from transfer time, the number of stops, and bus lines, the most important network property we propose in this study is transfer accessibility. Transfer accessibility is a newly defined indicator for the geographic factors contributing to the possibility of transferring at a station, given its position in a multimodal PT network, based on an adapted gravity-based measure. It assumes that transfer accessibility at each station is proportional to the number of reachable points of interest within the network and dependent on a cost function describing the effect of distance. The R-squared of the regression model we propose is 0.69, based on the smart card data, PT network data, and Points of Interest (POIs) data from the city of Beijing, China. This suggests that the model could offer some decision support for PT planners especially when complex network assignment models are too computationally intensive to calibrate and use.