Silencing lncRNA KCNQ1OT1 reduced hepatic ischemia reperfusion injury-induced pyroptosis by regulating miR-142a-3p/HMGB1 axis.

BACKGROUND: Based on pre-existing evidence, KCNQ1OT1 has been pointed out to be closely related to myocardial and cerebral ischemia reperfusion injury diseases. Herein, the objective of our study is to probe into the potential function as well as the underlying mechanism of KCNQ1OT1 on hepatic ischemia reperfusion injury (HIRI). METHODS: Using C57BL/6 J mice and primary mouse hepatocytes were conducted to establish HIRI model in vivo and in vitro. Cell viability was examined using CCK-8 assay and EdU assay. Flow cytometric analysis was performed to evaluate the pyroptosis. Dual-luciferase reporter assay was employed to verify the interaction relationships. qRT-PCR and Western blot were adopted to analyze the mRNA and protein level. Histopathological alteration of liver tissue was evaluated by HE staining. Immunohistochemistry (IHC) was performed to measure NLRP3 and caspase 1. RESULTS: Our data revealed that KCNQ1OT1 expression was ascending in hepatic tissue of HIRI mouse. Moreover, deprivation of KCNQ1OT1 mitigated I/R-induced hepatic injury and pyroptosis in vivo. Further experiments demonstrated that silencing KCNQ1OT1 promoted proliferation and inhibited pyroptosis in hypoxia/reoxygenation (H/R)-induced primary mouse hepatocytes. Mechanistically, KCNQ1OT1 functioned as a competing endogenous RNA which sponged miR-142a-3p, therefore promoted HMGB1 expression to activate TLR4/NF-κB signaling pathway in HIRI. CONCLUSION: LncRNA KCNQ1OT1 elevated HMGB1 expression through binding to miR-142a-3p, thereby promoting pyroptosis in HIRI.

Physics-informed neural network for polarimetric underwater imaging.

Utilizing the polarization analysis in underwater imaging can effectively suppress the scattered light and help to restore target signals in turbid water. Neural network-based solutions can also boost the performance of polarimetric underwater imaging, while most of the existing networks are pure data driven which suffer from ignoring the physical mode. In this paper, we proposed an effective solution that informed the polarimetric physical model and constrains into the well-designed deep neural network. Especially compared with the conventional underwater imaging model, we mathematically transformed the two polarization-dependent parameters to a single parameter, making it easier for the network to converge to a better level. In addition, a polarization perceptual loss is designed and applied to the network to make full use of polarization information on the feature level rather than on the pixel level. Accordingly, the network was able to learn the polarization modulated parameter and to obtain clear de-scattered images. The experimental results verified that the combination of polarization model and neural network was beneficial to improve the image quality and outperformed other existing methods, even in a high turbidity condition.

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition.

As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.