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Structure, function, and mechanism of the TNFAIP8 (TIPE) family of proteins in cancer and inflammation
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作者 zipeng lin CHUXI TANG +5 位作者 LE KANG GUANXI LAI SHIWEN LIU YIXIANG WU HUIQUN TIAN SONG LIU 《BIOCELL》 SCIE 2023年第10期2217-2232,共16页
The multiple roles of the tumor necrosis factor(TNF)-α-inducible protein 8(TNFAIP8),also named TIPE family of proteins have been shown in tumor and inflammation progression and regulation of cellular autophagy and ap... The multiple roles of the tumor necrosis factor(TNF)-α-inducible protein 8(TNFAIP8),also named TIPE family of proteins have been shown in tumor and inflammation progression and regulation of cellular autophagy and apoptosis.In this review,we found that the TIPE family showed highly homologous sequences and conserved functional domains,such as the death effector domain(DED)-like domain but displayed different roles and mechanisms in different biological activities.For example,while TIPE is primarily associated with tumor progression and antitumor drug resistance,TIPE1 suppresses tumor progression in most instances.TIPE2 has multiple roles in tumor progression regulation,and antitumor drug resistance.Moreover,TIPE2 was also involved in inflammatory response regulation,tumor typing,and staging.A few studies reported that TIPE3 was engaged in tumor development by activating the phosphatidylinositol-3-kinase(PI3K)/protein kinase B(AKT)signaling pathway.The structure,function,and mechanism of the TIPE family in cancer and inflammation have been summarized in this review.This might serve as a reference for further research on the TIPE family and shed new light on the crosstalk among antitumor responses,inflammation,and immunology. 展开更多
关键词 TIPE TIPE1 TIPE2 TIPE3 DED-like domain
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Physics-Aware Deep Learning on Multiphase Flow Problems 被引量:1
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作者 zipeng lin 《Communications and Network》 2021年第1期1-11,共11页
In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is... In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is even challenging for numerical solvers due to the instability. We display the preciseness of the solution domain delivered by deep learning models and the low cost of deploying this model for complex physics problems, showing the versatile character of this method and bringing it to new areas. This will require more allocation points and more careful design of the deep learning model architectures and residual neural network can be a potential candidate. 展开更多
关键词 Deep Learning Neural Network MULTI-PHASE Oil Incompressible Fluid Physics Partial Differential Equation
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