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Opportunistic fungal infections are a leading cause of death among immune-compromised patients, and there is a pressing need to develop new antifungal therapeutic agents because of toxicity and resistance to the antifungal drugs currently in use. Although C-type lectin receptor- and Toll-like receptor-induced signaling pathways are key activators of host antifungal immunity, little is known about the mechanisms that negatively regulate host immune responses to a fungal infection. Here we found that JNK1 activation suppresses antifungal immunity in mice. We showed that JNK1-deficient mice had a significantly higher survival rate than wild-type control mice in response to Candida albicans infection, and the expression of JNK1 in hematopoietic innate immune cells was critical for this effect. JNK1 deficiency leads to significantly higher induction of CD23, a novel C-type lectin receptor, through NFATc1-mediated regulation of the CD23 gene promoter. Blocking either CD23 upregulation or CD23-dependent nitric oxide production eliminated the enhanced antifungal response found in JNK1-deficient mice. Notably, JNK inhibitors exerted potent antifungal therapeutic effects in both mouse and human cells infected with C. albicans, indicating that JNK1 may be a therapeutic target for treating fungal infection.

Unidirectional two-lane freeway is a typical and the simplest form of freeway. The traffic flow characteristics including safety condition on two-lane freeway is of great significance in planning, design, and management of a freeway. Many previous traffic flow models are able to figure out flow characteristics such as speed, density, delay, and so forth. These models, however, have great difficulty in reflecting safety condition of vehicles. Besides, for the cellular automation, one of the most widely used microscopic traffic simulation models, its discreteness in both time and space can possibly cause inaccuracy or big errors in simulation results. In this paper, a micro-simulation model of two-lane freeway vehicles is proposed to evaluate characteristics of traffic flow, including safety condition. The model is also discrete in time but continuous in space, and it divides drivers into several groups on the basis of their preferences for overtaking, which makes the simulation more aligned with real situations. Partial test is conducted in this study and results of delay, speed, volume, and density indicate the preliminary validity of our model, based on which the proposed safety coefficient evaluates safety condition under different flow levels. It is found that the results of this evaluation coincide with daily experience of drivers, providing ground for effectiveness of the safety coefficient.