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We analysed eight monoclonal antibodies (mAbs) directed against the Mip (macrophage infectivity potentiator) protein, a virulence factor of the intracellular pathogen Legionella pneumophila. Mip belongs to the FK506-binding proteins (FKBPs) and exhibits peptidyl prolyl cis/trans isomerase (PPIase) activity. Five of the mAbs recognised epitopes in the C-terminal, FKBP-homologous domain of Mip, which is highly conserved among all Legionella species. Upon immunological binding to Mip, all but one of these mAbs caused inhibition of the PPIase activity in vitro. mAb binding to the N-terminal domain of Mip did not influence its enzymatic activity. All but one of the PPIase inhibiting mAbs were able to significantly inhibit the early establishment and initiation of an intracellular infection of the bacteria in Acanthamoeba castellanii, the natural host, and in the human phagocytic cell line U937. These data demonstrate for the first time that for the virulence-enhancing property of the L. pneumophila Mip protein, an intact active site of the enzyme is an essential requirement.

It has been challenging to efficiently and accurately reproduce pedestrian head/brain injury, which is one of the most important causes of pedestrian deaths in road traffic accidents, due to the limitations of existing pedestrian computational models, and the complexity of accidents. In this paper, a new coupled pedestrian computational biomechanics model (CPCBM) for head safety study is established via coupling two existing commercial pedestrian models. The head–neck complex of the CPCBM is from the Total Human Model for Safety (THUMS, Toyota Central R&D Laboratories, Nagakute, Japan) (Version 4.01) finite element model and the rest of the parts of the body are from the Netherlands Organisation for Applied Scientific Research (TNO, The Hague, The Netherlands) (Version 7.5) multibody model. The CPCBM was validated in terms of head kinematics and injury by reproducing three cadaveric tests published in the literature, and a correlation and analysis (CORA) objective rating tool was applied to evaluate the correlation of the related signals between the predictions using the CPCBM and the test results. The results show that the CPCBM head center of gravity (COG) trajectories in the impact direction (YOZ plane) strongly agree with the experimental results (CORA ratings: Y = 0.99 ± 0.01; Z = 0.98 ± 0.01); the head COG velocity with respect to the test vehicle correlates well with the test data (CORA ratings: 0.85 ± 0.05); however, the correlation of the acceleration is less strong (CORA ratings: 0.77 ± 0.06). No significant differences in the behavior in predicting the head kinematics and injuries of the tested subjects were observed between the TNO model and CPCBM. Furthermore, the application of the CPCBM leads to substantial reduction of the computation time cost in reproducing the pedestrian head tissue level injuries, compared to the full-scale finite element model, which suggests that the CPCBM could present an efficient tool for pedestrian brain-injury research.

This paper constructs an evaluation system that reflects dangerous driving behavior. The evaluation system has a three-layer structure model of “Evaluation Index-Performance Mode-Driving behavior score.” Verification of the feasibility of the model based on the relationship between the driver and the cause of the accident based on behavioral characteristics. First, the driving return survey data and accident form information of the real traffic accident cases of China In-Depth Accident Study (CIDAS) database are counted, and the character variables are converted into digital variables. Then, a three-tier structure of the dangerous driving behavior evaluation system is built, and the correlation between the driver and the cause of the accident is conducted to verify the feasibility of the model. The research shows that the individual characteristics of drivers with dangerous driving behavior are closely related to the cause of accidents, and the evaluation system constructed in this paper can quantify and describe this relationship effectively.