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Due to the severe damage and huge loss caused by natural disasters to road networks, the protection of the road network is essential. Edge importance identification can help preserve the road network by protecting key edges. This paper proposes a new network performance measure method and introduces a new edge load redistribution method in cascading failure model. To identify different edge importance in the network, this paper proposes three edge importance evaluation metrics, including information entropy of degree values, information entropy of iterative factors and two-dimensional evaluation metric based on the Pareto non-dominated set which combines two single metrics. A case study of highway road in Sichuan province with 204 nodes and 322 edges which was affected by Luding Earthquake is conducted to demonstrate the best one of the three metrics, including data from the Department of Transport of Sichuan Province. The final results of the chi-square test and Kendall’s correlation coefficient comparing the importance ranking of the three metrics with the ranking derived from the network performance assessment model indicate that the two-dimensional evaluation metrics have the best performance and that the road network tends to collapse at the same time when attacked against the road network under different edge rankings, suggesting that the effect of cascading failures should be limited early.

Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus–subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience. [ABSTRACT FROM AUTHOR]

The immune status of tumor‐infiltrating lymphocytes (TILs) is essential for the effectiveness of cancer immunotherapies. However, due to the diversity of immune status in TILs, cellular heterogeneity, and the applicability to the clinic, it is still lacking effective strategies to meet clinical needs. We developed a novel immuno‐recognition‐induced method based on rolling circle amplification (RCA), namely immunoRCA, to in situ visualize the immune status of TILs in actual clinical samples. This developed immunoRCA method, in which, feature mRNAs were used as the biomarkers for the immune status of TILs, has a low fluorescence background, high sensitivity, and specificity. The immunoRCA was able to efficiently evaluate the immune status of CD8+ T cells regulated by activating or inhibiting factors, track the T cell type and immune status during in vitro expansion, and in situ visualize the number, location, and immune status of TILs in clinical specimens. [ABSTRACT FROM AUTHOR]