The erythropoietin-producing hepatocellular carcinoma (Eph) receptors and their Eph receptor-interacting (ephrin) ligands together constitute a vital cell communication system with diverse roles. Experimental evidence revealed Eph receptor bidirectional signaling with both tumor-promoting and tumor-suppressing activities in different cancer types and surrounding environment. Eph receptor B2 (EphB2), an important member of the Eph receptor family, has been proved to be aberrantly expressed in many cancer types, such as colorectal cancer, gastric cancer and hepatocellular carcinoma, resulting in tumor occurrence and progression. However, there are no reviews focusing on the dual roles of EphB2 in cancer. Thus, in this paper we systematically summarize and discuss the roles of EphB2 in cancer. Firstly, we review the main biological features and the related signaling regulatory mechanisms of EphB2, and then we summarize the roles of EphB2 in cancer through current studies. Finally, we put forward our viewpoint on the future prospects of cancer research focusing on EphB2, especially with regard to the effects of EphB2 on tumor immunity.
In the classical GM(1,1) model, an accumulated generating operation is made on the original non-negative sequence to obtain a monotone increasing 1-AGO sequence, and the forecasting model is established based on the 1-AGO sequence. A great number of scholars have improved the accuracy of grey model prediction through better developed background value and the equation for the time response. In this work, we reconstruct the background value based on a new developed monotonicity-preserving piecewise cubic interpolations spline, and thereby establish a new GM(1,1) model. Numerical examples show that the new GM(1,1) model has better prediction quality of data than the original GM(1,1) model and improves the precision of prediction in practice.
In view of the flow instability problem caused by the strong shock wave and secondary flow in the channel of the high-load counter-rotating compressor, this paper adopts the design method of coupling aerodynamic optimization technology and CFD and establishes a three-dimensional aerodynamic optimization design platform for the blade channel based on an artificial neural network and genetic algorithm. The aerodynamic optimization design and internal flow-field diagnosis of a high-load counter-rotating compressor with a 1/2 + 1 aerodynamic configuration are carried out. The research indicates that the optimized blade channel can drive and adjust the flow better, and the expected supercharging purpose and efficient energy conversion process are achieved by controlling the intensity of the shock wave and secondary flow in the channel. The total pressure ratio at the design point of the compressor exceeds 2.9, the adiabatic efficiency reaches 87%, and the aerodynamic performance is excellent at the off-design condition, which is on the advanced design level of the same type of axial compressor. The established aerodynamic optimization design platform has important practical engineering applications for the development of high thrust-to-weight ratio aero-engine compression systems.
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