Search

Objective: A remarkably sensitive, accurate, and efficient ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach was developed as a facile and expeditious method for measuring cilofexor concentration in beagle dogs, the herb-drug interactions between silybinin and cilofexor was explored based on pharmacokinetics.Methods: The plasma sample protein of the beagles were rapidly sedimented with acetonitrile, and cilofexor and tropifexor (internal standard, ISTD) were separated by gradient elution using a 0.1% formic acid aqueous solution and acetonitrile as the mobile phase. The concentrations were detected using positive ion multiple reaction monitoring (MRM) mode. Mass transfer pairs were m/z 587.91→267.91 for cilofexor and m/z 604.08→228.03 for ISTD, respectively. A two-period self-controlled experimental design was adopted for the HDIs experiment. In the first period (Group A), six beagle dogs were orally administered cilofexor at a dose of 1 mg/kg. In the second period (Group B), silybinin (3 mg/kg) was orally administered to the six beagle dogs twice a day for seven consecutive days, after which cilofexor was orally administered. The cilofexor concentration in beagle dogs was determined, and HDIs were evaluated based on their pharmacokinetics.Results: The accuracy and precision of cilofexor were both less than 15%, and the recoveries, matrix effects, and stability met the relevant requirements. The Cmax of cilofexor in group B was 49.62% higher than that in group A, whereas the AUC(0-t) and AUC(0−∞) of cilofexor in group B were 47.85% and 48.52% higher, respectively, than those in group A. Meanwhile, the t1/2 extended from 7.84 h to 9.45 h, CL and Vz decreased in Group B.Conclusion: A novel UPLC-MS/MS approach was successfully applied for the measurement of cilofexor in beagle dog plasma. Silybinin can alter the pharmacokinetics of cilofexor in beagle dogs, thereby increasing plasma exposure to cilofexor.

In the scenarios of non-cooperative wireless communications, automatic modulation recognition (AMR) is an indispensable algorithm to recognize various types of signal modulations before demodulation in many internet of things applications. Convolutional neural network (CNN)-based AMR is considered as one of the most promising methods to achieve good recognition performance. However, conventional CNN-based methods are often unstable and also lack of generalized capabilities under varying noise conditions, because these methods are merely trained on specific dataset and can only work at the corresponding noise condition. Hence, it is hard to apply these methods directly in practical systems. In this paper, we propose a CNN-based robust automatic modulation recognition (RAMR) method to recognize three types of modulation signals, i.e., frequency shift key (FSK), phase shift key (PSK), and quadrature amplitude modulation (QAM). The proposed method is trained on a mixed dataset for extracting common features under varying noise scenarios. Simulation results show that our proposed generalized CNN-based architecture can achieve higher robustness and convenience than conventional ones.

The sign least mean square with reweighted L1-norm constraint (SLMS-RL1) algorithm is an attractive sparse channel estimation method among Gaussian mixture model (GMM) based algorithms for use in impulsive noise environments. The channel sparsity can be exploited by SLMS-RL1 algorithm based on appropriate reweighted factor, which is one of key parameters to adjust the sparse constraint for SLMS-RL1 algorithm. However, to the best of the authors’ knowledge, a reweighted factor selection scheme has not been developed. This paper proposes a Monte-Carlo (MC) based reweighted factor selection method to further strengthen the performance of SLMS-RL1 algorithm. To validate the performance of SLMS-RL1 using the proposed reweighted factor, simulations results are provided to demonstrate that convergence speed can be reduced by increasing the channel sparsity, while the steady-state MSE performance only slightly changes with different GMM impulsive-noise strengths.

Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500) demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results.

This paper studies synthetic aperture radar (SAR) imaging problem which the scatterers are often distributed in block sparse pattern. To exploiting the sparse geometrical feature, a Kronecker constrained SAR imaging algorithm is proposed by combining the block sparse characteristics with the multiway sparse reconstruction framework with Tucker modeling. We validate the proposed algorithm via real data and it shows that the our algorithm can achieve better accuracy and convergence than the reference methods even in the demanding environment. Meanwhile, the complexity is smaller than that of the existing methods. The simulation experiments confirmed the effectiveness of the algorithm as well. [ABSTRACT FROM AUTHOR]

How to identify influential nodes in weighted complex network has drawn much attention in recent years. According to empirical observations, an individual in the real world usually influences its nearest neighbors and next nearest neighbors. The influence of the individual spreads from the center to the surrounds and follows a gradient descent rule. In this paper, we propose a new method to identify influential nodes in complex social networks and name it Diffusion Centrality. This measure describes the influence spread rules of the real world well. Numerical examples are provided to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]