PD-L1 Expression Is Increased in LPS-Induced Acute Respiratory Distress Syndrome by PI3K-AKT-Egr-1/C/EBPδ Signaling Pathway.

The role of programmed death ligand 1 (PD-L1) has been extensively investigated in adaptive immune system. However, increasing data show that innate immune responses are also affected by the immune checkpoint molecule. It has been demonstrated that regulation of PD-L1 signaling in macrophages may be a potential therapeutic method for acute respiratory distress syndrome (ARDS). However, the PD-L1 expression pattern in local macrophages and whole lung tissues remains mysterious, hindering optimization of the potential treatment program. Therefore, we aim to determine the PD-L1 expression pattern during ARDS. Our findings show that PD-L1 levels are markedly increased in lipopolysaccharide (LPS)-stimulated lung tissues, which might be attributable to an increase in the gene expression by immune cells, including macrophages and neutrophils. In vitro experiments are performed to explore the mechanism involved in LPS-induced PD-L1 production. We find that PD-L1 generation is controlled by transcription factors early growth response 1 (Egr-1) and CCAAT/enhancer binding protein delta (C/EBPδ). Strikingly, PD-L1 production is enhanced by phosphoinositide-3 kinase (PI3K)-protein kinase B (AKT) signaling pathway via up-regulation of Egr-1 and C/EBPδ expressions. Additionally, we observe that expressions of Egr-1 and C/EBPδ mutually reinforce each other. Moreover, we observe that PD-L1 is protective for ARDS due to its regulatory role in macrophage-associated inflammatory response. In summary, during LPS-induced ARDS, PD-L1 expression, which is beneficial for the disease, is increased via the PI3K-AKT1-Egr-1/C/EBPδ signaling pathway, providing theoretical basis for application of methods controlling PD-L1 signaling in macrophages for ARDS treatment in clinic.

An interpretable approach for automatic aesthetic assessment of remote sensing images.

The increase of remote sensing images in recent decades has resulted in their use in non-scientific fields such as environmental protection, education, and art. In this situation, we need to focus on the aesthetic assessment of remote sensing, which has received little attention in research. While according to studies on human brain's attention mechanism, certain areas of an image can trigger visual stimuli during aesthetic evaluation. Inspired by this, we used convolutional neural network (CNN), a deep learning model resembling the human neural system, for image aesthetic assessment. So we propose an interpretable approach for automatic aesthetic assessment of remote sensing images. Firstly, we created the Remote Sensing Aesthetics Dataset (RSAD). We collected remote sensing images from Google Earth, designed the four evaluation criteria of remote sensing image aesthetic quality-color harmony, light and shadow, prominent theme, and visual balance-and then labeled the samples based on expert photographers' judgment on the four evaluation criteria. Secondly, we feed RSAD into the ResNet-18 architecture for training. Experimental results show that the proposed method can accurately identify visually pleasing remote sensing images. Finally, we provided a visual explanation of aesthetic assessment by adopting Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important image area that influenced model's decision. Overall, this paper is the first to propose and realize automatic aesthetic assessment of remote sensing images, contributing to the non-scientific applications of remote sensing and demonstrating the interpretability of deep-learning based image aesthetic evaluation.