Liver fibrosis and its endstage, cirrhosis, represent a major public health problem worldwide. Activation of hepatic stellate cells (HSCs) is a central event in hepatic fibrosis. However, the proteins that control HSC activation are incompletely understood. Here we show that (6aS, 10S, 11aR, 11bR, 11cS)-10-methylamino-dodecahydro-3a, 7a-diaza-benzo [de]anthracene-8-thione (MASM) exhibits potent inhibitory activity against liver fibrosis in vitro and in vivo associated with the reduction of Akt phosphorylation. Furthermore, ribosomal protein S5 (RPS5) was identified as a direct target of MASM, which stabilized RPS5 in cultured HSCs and in the liver of experimental animals after dimethylnitrosamine (DMN) or bile duct ligation (BDL). Functional studies revealed that RPS5 could prevent HSC activation. RPS5 overexpression in HSCs resulted in Akt dephosphorylation at both Ser473 and Thr308, and led to subsequent dephosphorylation of GSK3β or P70S6K. Progression of DMN- and BDL-induced hepatic fibrosis was aggravated by Rps5 knockdown and alleviated by RPS5 overexpression, which correlated with the modulation of Akt phosphorylation and HSC number in the fibrotic livers. Moreover, RPS5 was substantially reduced in the transdifferentiated HSCs, experimental fibrotic livers, and human cirrhosis samples. Conclusion: These results demonstrate that RPS5 is implicated in hepatic fibrogenesis and may represent a promising target for potential therapeutic intervention in liver fibrotic diseases. (Hepatology 2014;60:648–660)

A new efficient root-propagator method based on a uniform linear antenna array is proposed to estimate the directions of arrival (DOAs) and ranges of near-field sources with-out a priori knowledge of the source number. A non-Hermitian matrix is firstly designed which allows the applications of rooting methods. Then by constructing two propagators, the parameter estimation for near-field sources can be achieved through rooting methods in a decoupled way without eigenvalue decomposition (EVD) and spectrum searches. At last, the importance of the priori source number to source localization problems is studied, and specifically, an improvement is proposed to get rid of this dependence. Numerical examples are carried out to show the effectiveness and validate the theoretical analysis of the proposed root-propagator method. The results show that the proposed method provides more superior performance for near-field source localization than other existing similar methods. Even in the case without a priori knowledge of the source number, the performance of the proposed method is still satisfactory.

Subspace clustering aims to separate the data into clusters under the hypothesis that the samples within the same cluster will lie in the same low-dimensional subspace. Due to the tough pairwise constraints, $k$ -subspace clustering is sensitive to outliers and initialization. In this letter, we present a novel deep architecture for $k$ -subspace clustering to address this issue, called as Deep Weighted $k$ -Subspace Clustering (DWSC). Specifically, our framework consists of autoencoder and weighted $k$ -subsapce network. We first use the autoencoder to non-linearly compress the samples into the low-dimensional latent space. In the weighted $k$ -subspace network, we feed the latent representation into the assignment network to output soft assignments which represent the probability of data belonging to the according subspace. Subsequently, the optimal $k$ subspaces are identified by minimizing the projection residuals of the latent representations to all subspaces, using the learned soft assignments as a weighting vector. Finally, we jointly optimize the representation learning and clustering in a unified framework. Experimental results show that our approach outperforms the state-of-the-art subspace clustering methods on two benchmark datasets.

Time synchronization is a fundamental and paramount technology for a wireless network when a cooperative mission is performed. In this paper, the problem of how to achieve time synchronization and ranging for a mobile wireless network is addressed. The case of all nodes are in motion, equipped with a drifting clock, and capable of communicating with each other under unknown positions and velocities is handled. A network-wide model for synchronizing time-varying clocks and measuring relative distance is presented, from which the clock skews, clock offsets, and virtual polynomial coefficients related to the relative distances could be estimated. The Cram´er-Rao Lower Bound (CRLB) is derived for the proposed model. The simulation results show that the proposed method is close to the best result of the existing available estimators and meets the CRLB. Furthermore, compared with the existing estimators, the proposed method is able to reduce the overall communication overheads and streamline the process.

A dual-core high-performance processor for Elliptic Curve Cryptography (ECC) in GF( ${p}$ ) over Generic Weierstrass Curves was presented in this brief. Firstly, an optimized radix-128 Montgomery modular multiplication algorithm was proposed. It takes 6 clock cycles to calculate modular multiplication, which promotes the speed of elliptic curve point-addition (PA) and elliptic curve point-doubling (PD). Secondly, a dual-core hardware architecture which includes PD core and PA core to calculate elliptic curve point-multiplication (PM) was developed. It can speed up the PM by adopting parallelism with different levels and optimizing the scheduling. Finally, a data transfer architecture between PA and PD cores was proposed which requires only a 1024-bit register. It improves the speed of PM. The proposed design consumes 996k gates and compute the PM in 0.017 ms on ASIC with a 90 nm standard cell library. Moreover, the proposed design is also implemented on FPGA (Virtex-6 platform), which consumes 182k LUTs and compute the PM in 0.056 ms.