No abstract
The dysregulation of ROS production and osteoclastogenesis is involved in the progress of osteoporosis. To identify novel and effective targets to treat this disease, it is important to explore the underlying mechanisms. In our study, we firstly tested the effect of the Nrf2 activator RTA-408, a novel synthetic triterpenoid under clinical investigation for many diseases, on osteoclastogenesis. We found that it could inhibit osteoclast differentiation and bone resorption in a time- and dose-dependent manner. Further, RTA-408 enhanced the expression and activity of Nrf2 and significantly suppressed RANKL-induced reactive oxygen species (ROS) production. Nrf2 regulates the STING expression and STING induces the production of IFN-β. Here, we found that RTA-408 could suppress STING expression, but that it does not affect Ifnb1 expression. RANKL-induced degradation of IκBα and the nuclear translocation of P65 was suppressed by RTA-408. Although this compound was not found to influence STING–IFN-β signaling, it suppressed the RANKL-induced K63-ubiquitination of STING via inhibiting the interaction between STING and the E3 ubiquitin ligase TRAF6. Further, adenovirus-mediated STING overexpression rescued the suppressive effect of RTA-408 on NF-κB signaling and osteoclastogenesis. In vivo experiments showed that this compound could effectively attenuate ovariectomy (OVX)-induced bone loss in C57BL/6 mice by inhibiting osteoclastogenesis. Collectively, we show that RTA-408 inhibits NF-κB signaling by suppressing the recruitment of TRAF6 to STING, in addition to attenuating osteoclastogenesis and OVX-induced bone loss in vivo, suggesting that it could be a promising candidate for treating osteoporosis in the future.
Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F1-measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
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