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Instrumented indentation is a versatile method of extracting hyper-elastic material parameters, particularly useful for applications where stress-strain data are difficult to be in-situ measured. Because the analytical force-displacement relation is still unavailable for the indentation of hyper-elastic materials, identifying hyper-elastic parameters often requires an iterative optimization strategy that fits finite element simulations with experimental data. However, the optimization strategy is burdened by heavy computation and its prediction accuracy is greatly influenced by the choice of optimization algorithm. To address these challenges in this study, a bidirectional long short-term memory (BLSTM) neural network is presented that directly predicts hyper-elastic material parameters from indentation load-displacement data, focusing on Mooney-Rivlin hyper-elasticity as an example. To improve the predication accuracy, the condition numbers for the inverse identification of the hyper-elastic parameters are investigated. And, a normalization procedure is proposed to treat the input data, which can guarantee the BLSTM network is well-conditioned. During evaluation, the trained BLSTM network significantly outperforms the iterative optimization strategy using a genetic algorithm. Furthermore, the effect of the normalization procedure is demonstrated. [ABSTRACT FROM AUTHOR]

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Simultaneous silencing of multiple up-regulated genes is an attractive and viable strategy to treat many incurable diseases including cancer. Herein we used dual gene targeted siRNA (DGT siRNA) conjugate composed of NET-1 and VEGF siRNA sequences in the same backbone could inhibit growth and angiogenesis HCC. DGT siRNA showed a further down regulation on VEGF mRNA and protein levels compared with NET-1 siRNA or VEGF siRNA, but not on NET-1 expression. It also exhibited greater suppression on proliferation and trigger of apoptosis in HepG2 cells than NET-1 siRNA or VEGF siRNA; this could be explained by the significant down regulation of cyclin D1 and Bcl-2. A lower level of ANG2 mRNA and protein was detected in HUVEC cultured with supernatant of HepG2 cells treated with DGT siRNA than that of VEGF siRNA or NET-1 siRNA, resulting in much more inhibited angiogenesis of HUVEC. Tumor growth was inhibited and microvessel density dropped in the xenograft tumor models compared to the untreated controls. NET-1 and VEGF silencing play a key role in inhibiting hepatocellular cell proliferation, promoting apoptosis, and reducing angiogenesis. Simultaneous silencing of NET-1 and VEGF using DGT siRNA construct may provide an advantageous alternative in development of therapeutics for Hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]