Although the transcription factor Krüppel-like factor 5 (KLF5) plays important roles in both inflammation and cancer, the mechanism by which this factor promotes cervical carcinogenesis remains unclear. In this study, we demonstrated a potential role for tumour necrosis factor receptor superfamily member 11a (TNFRSF11a), the corresponding gene of which is a direct binding target of KLF5, in tumour cell proliferation and invasiveness. Coexpression of KLF5 and TNFRSF11a correlated significantly with tumorigenesis in cervical tissues (P < 0.05) and manipulation of KLF5 expression positively affected TNFRSF11a mRNA and protein expression. Functionally, KLF5 promoted cancer cell proliferation, migration and invasiveness in a manner dependent partly on TNFRSF11a expression. Moreover, in vivo functional TNFRSF11a-knockdown mouse studies revealed suppression of tumorigenicity and liver metastatic potential. Notably, tumour necrosis factor (TNF)-α induced KLF5 expression by activating the p38 signalling pathway and high KLF5 and TNFRSF11a expression increased the risk of death in patients with cervical squamous cell carcinoma. Our results demonstrate that KLF5 and TNFRSF11a promote cervical cancer cell proliferation, migration and invasiveness.
The charge of fuel-air explosive (FAE) warhead usually is solid-liquid mixed fuel. The solid component is aluminium powder. To meet the demand of FAE weapon usage and storage safety, in the mixed-fuel medium, there must be gaps where adiabatic compression occurs during launchine overloading of warhead. Adiabatic compression makes the temperature of the medium --in the gaps to rise. High temperature can cause dxplosion of the mixed fuel during launching acceleration of the warhead, which is very dangerous. Because the fuel is a multicomponent mixture, the critical ignitioh temperature can't be determined only by one component. Through experiment, the critical ignition temperature of the mixed fuel is attained, and the changing regularity of the pressure following the temperature is shown in this paper.
With the rapid development of the Internet, malicious domain names pose more and more serious threats to many fields, such as network security and social security, and there have been many research results on malicious domain detection. This article proposes a malicious domain name detection model based on improved deep learning, which can combine the advantages of three different network models, convolutional neural network (CNN), temporal convolutional network (TCN), and long short-term memory network (LSTM) in malicious domain name detection, to obtain a better detection effect than that of the original single or two models. Experiments show that the effect of the improved deep learning model proposed in this article is better than that of the combined model of CNN and LSTM or the combined model of CNN and TCN, and the accuracy and regression rates reached 99.76% and 98.81%, respectively.
The asymptotic stability of the fractional-order neural networks system with uncertainty by sampled-data controller is addressed in the article. First, considering the influence of uncertainty and fractional-order on the system, a new sampled-data controller is designed with alterable sampling period. In the light of the input delay approach, the fractional system is simulated by the delay system. The main purpose of the method presented is to design a sampled-data controller, which the closed-loop fractional-order system can guarantee the asymptotic stability. Then, the fractional-order Razumishin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. A stability conditions are presented in the form of LMIs on the new delay-dependent and order-dependent. Furthermore, the sampling controller can be acquired to promise the stability and stabilization for fractional-order system. A numerical example is gotten to demonstrate the effectiveness and advantages for the provided method.
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