RhoB regulates the function of macrophages in the hypoxia-induced inflammatory response

Immune cells, particularly macrophages, play critical roles in the hypoxia-induced inflammatory response. The small GTPase RhoB is usually rapidly induced by a variety of stimuli and has been described as an important regulator of cytoskeletal organization and vesicle and membrane receptor trafficking. However, it is unknown whether RhoB is involved in the hypoxia-induced inflammatory response. Here, we investigated the effect of hypoxia on the expression of RhoB and the mechanism and significance of RhoB expression in macrophages. We found that hypoxia significantly upregulated the expression of RhoB in RAW264.7 cells, mouse peritoneal macrophages, and the spleen of rats. Hypoxia-induced expression of RhoB was significantly blocked by a specific inhibitor of hypoxia-inducible factor-1α （HIF-1α）, c-Jun N-terminal kinase （JNK）, or extracellular-signal regulated protein kinase （ERK）, indicating that hypoxia-activated HIF-1α, JNK, and ERK are involved in the upregulation of RhoB by hypoxia. Knockdown of RhoB expression not only significantly suppressed basal production of interleukin-1 beta （IL-1β）, interleukin 6 （IL-6）, and tumor necrosis factor alpha （TNF-α） in normoxia but also more markedly decreased the hypoxia-stimulated production of these cytokines. Furthermore, we showed that RhoB increased nuclear factor-kappa B （NF-κB） activity, and the inhibition of NF-κB transcriptional activity significantly decreased the RhoB-increased mRNA levels of IL-1β, IL-6, and TNF-α. Finally, we demonstrated that RhoB enhanced cell adhesion and inhibited cell migration in normoxia and hypoxia. Taken together, these results suggest that RhoB plays an important role in the hypoxia-induced activation of macrophages and the inflammatory response.

A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles

Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.