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piPSC-ECs derivation is not only potential for the autologous cell transplantation and cardiovascular drug screening, but also for the mechanistic studies on EC differentiation and endothelial dysfunction.Convolutional neural network (CNN) based methods, such as the convolutional encoder-decoder network, offer state-of-the-art results in monaural speech enhancement. In the conventional encoder-decoder network, large kernel size is often used to enhance the model capacity, which, however, results in low parameter efficiency. This could be addressed by using group convolution, as in AlexNet, where group convolutions are performed in parallel in each layer, before their outputs are concatenated. However, with the simple concatenation, the inter-channel dependency information may be lost. To address this, the Shuffle network re-arranges the outputs of each group before concatenating them, by taking part of the whole input sequence as the input to each group of convolution. In this work, we propose a new convolutional fusion network (CFN) for monaural speech enhancement by improving model performance, inter-channel dependency, information reuse and parameter efficiency. First, a new group convolutional fusion unit (GCFU) consisting of the standard and depth-wise separable CNN is used to reconstruct the signal. Second, the whole input sequence (full information) is fed simultaneously to two convolution networks in parallel, and their outputs are re-arranged (shuffled) and then concatenated, in order to exploit the inter-channel dependency within the network. Third, the intra skip connection mechanism is used to connect different layers inside the encoder as well as decoder to further improve the model performance. Extensive experiments are performed to show the improved performance of the proposed method as compared with three recent baseline methods.Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed convolution and readout operations. In many graph classification applications, GCN-based approaches have outperformed traditional methods. However, most of the existing GCNs are inefficient to preserve local information of graphs - a limitation that is especially problematic for graph classification. In this work, we propose a locality-preserving dense GCN with graph context-aware node representations. Specifically, our proposed model incorporates a local node feature reconstruction module to preserve initial node features into node representations, which is realized via a simple but effective encoder-decoder mechanism. To capture local structural patterns in neighborhoods representing different ranges of locality, dense connectivity is introduced to connect each convolutional layer and its corresponding readout with all previous convolutional layers. To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer's readout to form a global context-aware node representation. In addition, a self-attention module is introduced to aggregate layer-wise representations to form the final graph-level representation. Experiments on benchmark datasets demonstrate the superiority of the proposed model over state-of-the-art methods in terms of classification accuracy.The aim of this study was to investigate how methionine enkephalin (MENK) regulates the biological behavior of lung cancer cells and to further explore its anti-lung cancer mechanisms in vitro and in vivo. The results showed that MENK enhanced the expression of opioid receptor (OGFr) and induced apoptosis of lung cancer cells by activating the Bcl-1/Bax/caspase-3 signaling pathway in vitro and in vivo. However, the regulatory effects of MENK disappeared after blockade of the OGFr. This confirmed that a prerequisite for the anti-tumor action of MENK is binding to OGFr. Additionally, we observed that MENK treatment enhanced the immunogenicity of lung cancer by enhancing the exposure of calreticulin and high mobility group box 1, and increasing the expression of NKG2D ligands. Further studies showed that MENK treatment increased the expression of natural killer (NK) cell-related cytokines such as granzyme B and interferon-γ and NK cell activation. Thus, we concluded that MENK might inhibit the proliferation of lung cancer cells by activating the Bcl-2/Bax/caspase-3 signaling pathway and enhancing immunogenicity and NK cell-driven tumor immunity.Feed corruption and poor breeding environment could cause widespread bacterial infection which could cause severe liver inflammation and lead to liver damage, even death. It has been proved that Polysaccharide of Atractylodes macrocephala Koidz (PAMK) could improve the immunity of animal, but the mechanism of its protective effect on hepatitis has been rarely reported. This study investigated the protective effect of PAMK on mouse liver through LPS-induced liver inflammatory. The results showed that LPS caused swelling of hepatocytes, disappearance of hepatic cord structure and infiltration of a large number of inflammatory cells, and LPS could up-regulated mRNA and protein expression levels of TLR4, MyD88, IKBα and NFκB, increased cytokines IL-1β, IL-4, IL-6 and TNF-α levels, enhance the levels of antioxidant enzymes CAT, GSH-PX, SOD, iNOs and MDA. PAMK pretreatment could relieved histopathological damage caused by LPS, and could activate the TLR4-MyD88-NFκB signalling pathway, reduce the levels of IL-1β, IL-6 and TNF-α, increase IL-4 levels, inhibit the levels of GSH-PX and MDA. These results indicate that PAMK could reduce inflammatory damage and oxidative stress in mice and play a protective role in the early stages of LPS invasion of the liver.The chemokine receptor CCR5 has been implicated in COVID-19. CCR5 and its ligands are overexpressed in patients. The pharmacological targeting of CCR5 would improve the COVID-19 severity. We sought to investigate the role of the CCR5-Δ32 variant (rs333) in COVID-19. The CCR5-Δ32 was genotyped in 801 patients (353 in the intensive care unit, ICU) and 660 healthy controls, and the deletion was significantly less frequent in hospitalysed COVID-19 than in healthy controls (p = 0.01, OR = 0.66, 95%CI = 0.49-0.88). Of note, we did not find homozygotes among the patients, compared to 1% of the controls. The CCR5 transcript was measured in leukocytes from 85 patients and 40 controls. [https://www.selleckchem.com/btk.html check details] We found a significantly higher expression of the CCR5 transcript among the patients, with significant difference when comparing the non-deletion carriers (controls = 35; patients = 81; p = 0.01). ICU-patients showed non-significantly higher expression than no-ICU cases. Our study points to CCR5 as a genetic marker for COVID-19. The pharmacological targeting of CCR5 should be a promising treatment for COVID-19.