The granulin-epithelin precursor (GEP/PCDGF), a 68-88 kDa secreted glycoprotein, has been shown to be an important growth and survival factor for ovarian cancer cells. Furthermore, GEP expression is a predictor of patient survival in metastatic ovarian cancer cells. Up to this point, however, the molecular mechanisms and clinical relevance of a GEP-mediated prosurvival phenotype remain poorly characterized. We hypothesize that the prosurvival function of GEP is important in ovarian cancer tumor progression and chemoresponse. To explore this hypothesis, we examined the effects of GEP overexpression on migration, invasion and cisplatin (CDDP) chemosensitivity in the ovarian cancer cell line A2780. Full length GEP transfectants demonstrated an increased capacity to migrate and invade their substratum when compared to empty vector controls. In addition, GEP overexpression was associated with CDDP chemoresistance. Finally, GEP overexpression increased tumor formation and protected cells from tumor regression in response to CDDP treatment in vivo. Taken together, these data support a role for GEP in tumor progression and development of drug resistance. ' 2007 Wiley-Liss, Inc.
In recent times fault-tolerant controller is a prime choice because of the handling capability of the multifunction into the closed-loop system irrespective of the faults and the external process disturbances in various engineering applications. This paper presents innovative fault-tolerant control scheme using a neural network, and the main contribution of this work is to design a feedforwarded back propagation neural network for controlling the actuator and system component (leak) faults into the level control process. By adopting the neural network as a fault-tolerant controller the efficacy of the fault-tolerant control scheme is dominating to the conventional scheme proposed by Dutta et al. (Real-time linear quadratic versus sliding mode liquid level control of a coupled tank system"
PurposeThe purpose of this article is about the design of controllers for conical two-tank noninteracting level (CTTNL) system in simulation. Local linearization around the equilibrium point has been done for the nonlinear CTTNL system to obtain a linearized model transfer function.Design/methodology/approachThis article deals with the design of novel optimal fractional-order tilt-integral-derivative (TID) controller using type-1 fuzzy set for the CTTNL prototype system. In this study, type-1 fuzzy TID controller parameters have been optimized through genetic algorithm (GA) and those set of values have been employed for the design of proportional-integral-derivative (PID) controller.FindingsA performance comparison between FTID and PID controller is then investigated. The analysis shows the superiority of FTID controller over PID controller in terms of integral absolute error (IAE), integral square error (ISE), integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE) integral errors. The transient and steady state performance of the FTID controller are superior as compared to conventional PID controller. In future, the FTID controller fault-tolerance capability tested on CTTNL system subject to actuator and system component (leak) faults. The detailed study of robustness in presence of model uncertainties will be incorporated as a scope of further research.Originality/valueA performance comparison between FTID and PID controller is then investigated. The analysis shows the superiority of FTID controller over PID controller in terms of IAE, ISE, ITAE and ITSE integral errors. Additionally, fault-tolerant performance of the proposed controller evaluated with fault-recovery time (Frt) parameter. The transient and steady state performance of the FTID controller are superior as compared to conventional PID controller.
This article suggests passive methods for designing Fault-Tolerant Control (FTC) for nonlinear uncertain systems with actuator and leak faults. To anticipate the Fault-Tolerant Control (FTC) action to overcome the actuator and leak faults, two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) and two-layer Cascade Forward Neural Network (CFNN) have been used, it will also tolerate external process additive disturbances. We employ the passive approach for fault-tolerant control using Proportional Integral Derivative (PID) control methodology to create a fault-tolerant controller without a fault detection mechanism. Further, we use the four residue signal features (i.e., mean, variance, skewness and normalize data of residue signal) to train the neural network in this study to tackle the issue originating from having less faults and uncertainty from residue signal. To show the efficacy of the suggested approach, simulations are run. The measurement of the residue signal was done using a healthy and a faulty uncertain non-linear system model. A comparison of findings utilizing a state of-the-art control methodology provided in (Dutta et al., 2014) was also presented to validate the proposed FTC methodology.
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