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The nuclear retinoid X receptor-α (RXRα) plays critical roles in cell homeostasis and in many physiological processes mainly through its transcriptional function. However, an N-terminal truncated form of RXRα, tRXRα, was frequently described in various cancer cells and tumor tissues, thus representing a new promising drug target. We recently demonstrated that triptolide (TR01) could target to the oncogenic activity of tRXRα. To improve its tumor selectivity, we developed several TR01 derivatives by introducing different amine ester groups on C-14-hydroxyl site. Interestingly, C-14 modification could differently affect the expression of tRXRα without interfering the level of its full length RXRα. Among the derivatives, TRC4 could strongly reduce tRXRα expression, while TRC5-7 increased it. The capability of inhibiting tRXRα expression was shown to be closely associated with its inactivation of AKT and induction of apoptosis in various cancer cells. Conversely, treatment of cancer cells with the tRXRα-stabilizing compounds TRC5-7 resulted in enhanced AKT activity and apoptosis-resistance. However, although TR01 could strongly reduce tRXRα expression and AKT activity, it also strongly inhibited the expression and transcriptional activity of RXRα in normal cells. Importantly, the tRXRα-selective TRC4 that did not significantly inhibit RXRα transcriptional function retained the most potency of the anticancer effect of TR01 and had no significant effect on the viability of normal cells. In conclusion, our results demonstrated that tRXRα-selective TRC4 will have potential clinical application in terms of drug target and side effects. Our findings will offer new strategies to develop improved triptolide analogs for cancer therapy. [ABSTRACT FROM AUTHOR]

Impulsive processes frequently occur in many fields, such as radar, sonar, communications, audio and speech processing, and biomedical engineering. In this paper, we propose a nonconvex linear regression (NLR) based minimum dispersion beamforming technique for impulsive signals to achieve significant performance improvement over the conventional minimum variance beamformer. The proposed beamformer minimizes the \ellp-norm of the output with p<1 subject to a linear distortionless response constraint, resulting in a difficult nonconvex and nonsmooth optimization problem. The constrained optimization problem is first reduced to a multivariate linear regression via constraint elimination. As a major contribution of this paper, a coordinate descent algorithm (CDA) is devised for solving the resultant NLR problem of \ellp-minimization with p<1 at a computational complexity of \cal O(MN^2), where M is the number of sensors and N is the sample size. At each inner iteration of the CDA, an efficient algorithm is designed to find the global minimum of each subproblem of univariate linear regression. The convergence of the CDA is analyzed. The NLR beamformer with a single constraint is further generalized to the case of multiple linear constraints, which is robust against model mismatch. Simulation results demonstrate the superior performance of nonconvex optimization based beamformer. [ABSTRACT FROM AUTHOR]

Greedy pursuit, which includes matching pursuit (MP) and orthogonal matching pursuit (OMP), is an efficient approach for sparse approximation. However, conventional greedy pursuit algorithms designed for inner product space are not robust against outliers. In this paper, we devise a new definition of correlation in \ell p-space with p>0, called \ell p-correlation, and introduce the concept of orthogonality in \ell p-space. Based on the \ell p-correlation and \ell p-orthogonality, which are generalizations of the absolute inner product and orthogonality of inner product space, respectively, we develop three greedy pursuit algorithms, namely, \ell p-MP, \ell p-OMP, and weak \ell p-MP, for robust sparse approximation in the presence of outliers. The convergence of the three algorithms is proved. In particular, the \ell p-norm of the residual of each algorithm decays exponentially. It is revealed that the exponential decay factor in the worst case is related to the minimal \ell p-correlation of the residual and the dictionary. Numerical examples on sparse signal recovery and harmonic retrieval in impulsive noise demonstrate that the \ell p-greedy pursuit algorithms with 0< p< 2 are more outlier-resistant than their counterparts in inner product space. [ABSTRACT FROM PUBLISHER]

A common approach to time-delay estimation (TDE) and joint delay-Doppler estimation (JDDE) for target localization is matched filtering, which is equivalent to the cross correlation of the received and transmitted signals. The conventional correlation in Hilbert space is statistically optimal in white Gaussian noise. However, its performance significantly degrades in the presence of non-Gaussian noise or clutter. In this paper, two new concepts called \ellp-correlation and \ellp-ambiguity functions, which generalize the conventional matched filtering from Hilbert space to \ellp-space, are proposed. In addition, several important mathematical properties of the \ellp-correlation and \ellp-ambiguity functions are given and proved. Compared with conventional ambiguity function, the time-frequency concentration of the \ellp-ambiguity function is significantly enhanced, i.e., the delay-Doppler resolution is improved and the sidelobes are suppressed. Based on these two newly defined functions, a family of TDE and JDDE algorithms that are robust against impulsive noise or clutter are developed. Furthermore, the Cramér–Rao bounds of TDE and JDDE for non-Gaussian noise with arbitrary probability distribution are derived. Simulation results under several impulsive noise models demonstrate that the performance in terms of robustness, resolution, and estimation accuracy is substantially improved when compared with the conventional matched filtering and fractional lower-order moment based methods. [ABSTRACT FROM AUTHOR]

A quadratically constrained minimum dispersion (QCMD) beamformer that is robust against model uncertainties is devised for non-Gaussian signals. Different from the minimum variance based beamformers, the QCMD beamformer minimizes the \ellp-norm (p\geq 1) of the output while constraining the magnitude response of any steering vector within a spherical uncertainty set to exceed unity. A gradient projection algorithmic framework is proposed to efficiently solve the resulting convex optimization problem instead of directly applying the standard optimization algorithm which has a high computational complexity. In each iteration, the gradient projection updates the solution along the gradient direction and projects it back to the constraint set. Importantly, a closed-form expression of the projection onto the constraint set is derived, which only needs a low complexity of \cal O(M) with M being the sensor number. Therefore, the proposed algorithm is much faster and simpler to implement compared with the standard method. In addition, the robust constant modulus beamformer (RCMB) is also discussed as a special case of the QCMD beamformer. Simulation results demonstrate the efficiency of the gradient projection algorithm and superiority of the QCMD beamformer over several representative robust beamformers, indicating that it can approach the optimal performance bound. [ABSTRACT FROM PUBLISHER]

Abstract: Direction-of-arrival (DOA) estimation is a central problem in array processing and has a variety of applications. In this paper, a new algorithm for finding DOAs of multiple temporally correlated signals is devised. The proposed approach is based on the joint diagonalization structure of a set of spatio-temporal correlation matrices. Unlike the subspace-based DOA estimators, it is not necessary to estimate the noise or signal subspace explicitly. Moreover, the proposed method can provide the spatial spectrum and estimate the DOAs even when the number of sources is not known a priori. Interestingly, it is revealed that the well-known MUSIC method is a special case of our algorithm. Simulation results validate that the developed approach is superior to conventional DOA estimators in terms of resolution capability, estimation accuracy, and robustness against array model errors. [Copyright &y& Elsevier]

A family of algorithms, named \ell p-MUSIC, for direction-of-arrival (DOA) estimation in impulsive noise is proposed. The \ell p-MUSIC estimator adopts the \ell p-norm (1\leq p< 2) of the residual fitting error matrix as the objective function for subspace decomposition, rather than the Frobenius norm that is used in the conventional MUSIC method. Although the matrix \ell p-norm minimization based subspace decomposition will lead to a nonconvex optimization problem, two iterative algorithms are designed for achieving efficient solutions. The first algorithm is the iteratively reweighted singular value decomposition (IR-SVD), where the SVD of a reweighted data matrix is performed in each iteration. The second algorithm solves the nonconvex matrix \ell p-norm minimization by alternating convex optimization. Two complex-valued Newton's methods with optimal step size in each iteration are devised to solve the resulting convex problem. The convergence of the iterative procedure is also proved. Numerical results verify that the \ell p-MUSIC methodology outperforms the standard MUSIC scheme and several existing outlier-resistant DOA estimation approaches in terms of resolution capability and estimation accuracy. [ABSTRACT FROM PUBLISHER]

Acoustic channel estimation is an important problem in various applications. Unlike many existing channel estimation techniques that need known probe or training signals, this paper develops a blind multipath channel identification algorithm. The proposed approach is based on the single-input multiple-output model and exploits the sparse multichannel structure. Three sparse representation algorithms, namely, matching pursuit, orthogonal matching pursuit, and basis pursuit, are applied to the blind sparse identification problem. Compared with the classical least squares approach to blind multichannel estimation, the proposed scheme does not require that the channel order be exactly determined and it is robust to channel order selection. Moreover, the ill-conditioning induced by the large delay spread is overcome by the sparse constraint. Simulation results for deconvolution of both underwater and room acoustic channels confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Conventional time reversal can mitigate multipath delay dispersion by temporal focusing. But it is not applicable to time-varying channels with a Doppler spread. Although recently time reversal communication has been adapted to time-variant channels, the modified technique requires frequent channel updates to track channel variations and cannot handle large Doppler spread, which means that it cannot achieve frequency focusing. In this paper, two time reversal receivers for underwater acoustic communications over doubly spread channels are proposed. The proposed approach, which can be interpreted as time-frequency channel matching, is based on the channel spreading function rather than impulse response adopted by the existing techniques; this leads to much less frequent channel updates. Unlike existing methods that only correct a single Doppler shift, the proposed approach uses a rake-like structure to compensate for multiple Doppler shifts and hence can eliminate severe Doppler spread induced by temporal channel variations. Simulation results verify the effectiveness of the proposed approach, indicating that it can simultaneously counteract delay and Doppler spreads, achieving both temporal and frequency focusing. [ABSTRACT FROM AUTHOR]

The estimation of doubly spread underwater acoustic channels is addressed. By exploiting the sparsity in the delay-Doppler domain, this paper proposes a fast projected gradient method (FPGM) that can handle complex-valued data for estimating the delay-Doppler spread function of a time-varying channel. The proposed FPGM formulates the sparse channel estimation as a complex-valued convex optimization using an ℓ1-norm constraint. Conventional approaches to complex-valued optimization split the complex variables into their real and imaginary parts; this doubles the dimension compared with the original problem and may break the special data structure. Unlike the conventional methods, the proposed method directly handles the complex variables as a whole without splitting them into real numbers; hence the dimension will not increase. By exploiting the block Toeplitz-like structure of the coefficient matrix, the computational complexity of the FPGM is reduced to O(LNlogN), where L is the dimension of the Doppler shift and N is the signal length. Simulation results verify the accuracy and efficiency of the FPGM, indicating that is robust to parameter selection and is orders-of-magnitude faster than standard convex optimization algorithms. The Kauai experimental data processing results are also provided to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Estimation of the parameters of a multipath underwater acoustic channel is of great interest for a variety of applications. This paper proposes a high-resolution method for jointly estimating the multipath time delays, Doppler scales, and attenuation amplitudes of a time-varying acoustical channel. The proposed method formulates the estimation of channel parameters into a sparse representation problem. With the ℓ1-norm as the measure of sparsity, the proposed method makes use of the basis pursuit (BP) criterion to find the sparse solution. The ill-conditioning can be effectively reduced by the ℓ1-norm regularization. Unlike many existing methods that are only applicable to narrowband signals, the proposed method can handle both narrowband and wideband signals. Simulation results are provided to verify the performance and effectiveness of the proposed algorithm, indicating that it has a super-resolution in both delay and Doppler domain, and it is robust to noise. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that there exist two fundamental limitations in the linear discriminant analysis (LDA). One is that it cannot be applied when the within-class scatter matrix is singular, which is caused by the undersampled problem. The other is that it lacks the capability to capture the nonlinearly clustered structure of the data due to its linear nature. In this paper, a new kernel-based nonlinear discriminant analysis algorithm using minimum squared errors criterion (KDA-MSE) is proposed to solve these two problems. After mapping the original data into a higher-dimensional feature space using kernel function, the MSE criterion is used as the discriminant rule and the corresponding dimension reducing transformation is derived. Since the MSE solution does not require the scatter matrices to be nonsingular, the proposed KDA-MSE algorithm is applicable to the undersampled problem. Moreover, the new KDA-MSE algorithm can be applied to multiclass problem, whereas the existing MSE-based kernel discriminant methods are limited to handle twoclass data only. Extensive experiments, including object recognition and face recognition on three benchmark databases, are performed and the results demonstrate that our algorithm is competitive in comparison with other kernel-based discriminant techniques in terms of recognition accuracy. [Copyright &y& Elsevier]

Abstract: This paper studies the stationary points of the multimodulus blind equalization criterion in a noiseless communication channel and proposes two adaptive Newton algorithms. It is shown in this paper that the stationary points of the multimodulus criterion can be grouped into two categories, according to the power of equalizer output. The stationary points having the same equalizer output power with that of the transmitted symbols are desirable global minima, while the stationary points having less equalizer output power than that of the transmitted symbols are saddle points. A pseudo Newton learning algorithm and a full Newton learning algorithm minimizing the multimodulus criterion are proposed. By using the matrix inversion lemma, both Newton algorithms can be efficiently implemented with a computational complexity of , where is the tap length of equalizer. Computer experiment results are presented. It is found that the full Newton algorithm performs well in both static and time-varying communication channels, while the pseudo Newton algorithm performs well only in static communication channels. [Copyright &y& Elsevier]

Abstract: A new near-field multiple source localization algorithm for joint range and direction-of-arrival (DOA) estimation is proposed. Via joint diagonalization of a set of matrices, the array manifold matrix is estimated and the automatically paired DOA and range parameters are obtained subsequently. The proposed algorithm does not require any search over the parameter space or any additional 2-D parameter paring procedure or high-order statistics (HOS) computation compared with the existing near-field source localization methods. Simulation results validate that the root mean square errors (RMSEs) of the estimated DOA and range parameters are close to the Cramer–Rao bounds (CRBs). [Copyright &y& Elsevier]

Objectives: Whether syphilis influences lymphocyte apoptosis has not been reported so far.Goal: The goals of this study were to determine whether syphilis influences lymphocyte apoptosis and to investigate the molecular mechanism of apoptosis.Study Design: Peripheral blood lymphocyte (PBL) immunophenotypes, apoptosis, and expression of Fas (CD95) and Bcl-2 were detected by flow cytometry in 33 patients with secondary early syphilis and 30 healthy subjects.Results: Compared with the control subjects, the percentage of CD4 T cells and the ratio of CD4:CD8 were significantly decreased, and that of CD8 T cells and the apoptotic rates of PBLs and CD4 T cells were obviously increased in syphilitic patients. Fas overexpression and Bcl-2 downexpression in the syphilitic group were observed in PBLs and CD4 cells but not in CD8 and CD19 cells.Conclusions: The increased apoptosis of PBLs and CD4 T cells by Fas-mediated death pathway and downexpression of Bcl-2 protein could account for immune dysfunction in secondary early syphilis, which is responsible for the incomplete clearance of Treponema pallidum from the lesions and the chronic infection. [ABSTRACT FROM AUTHOR]

Phase retrieval aims at recovering a complex-valued signal using the magnitude-only measurements of its linear transformation, and has wide applicability in areas including astronomy, computational biology, crystallography, digital communications, electron microscopy, neutron radiography and optical imaging. However, phase recovery involves solving a system of quadratic equations or minimizing a multivariate fourth-order polynomial, indicating that it is a challenging nonconvex optimization problem. To tackle phase retrieval in an effective and efficient manner, we apply coordinate descent (CD) such that a single unknown is solved at each iteration while all other variables are kept fixed. As a result, only minimization of a univariate quartic polynomial is needed which is easily achieved by finding the closed-form roots of a cubic equation. Three computationally simple algorithms referred to as cyclic, randomized and greedy CDs, based on different updating rules, are devised. It is proved that the three CDs globally converge to a stationary point of the nonconvex problem, and specifically, the randomized CD locally converges to the global minimum and attains exact recovery at a geometric rate with high probability if the sample size is large enough. The cyclic and randomized CDs are also modified via minimization of the ℓ 1 -regularized quartic polynomial for phase retrieval of sparse signals. Furthermore, a novel application of the three CDs, namely, blind equalization in digital communications, is proposed. It is demonstrated that the CD methodology is superior to the state-of-the-art techniques in terms of computational efficiency and/or recovery performance. [ABSTRACT FROM AUTHOR]

This paper addresses robust beamforming in the presence of model uncertainty for constant modulus (CM) signals that are frequently encountered in radar and communication systems. Instead of employing the commonly used minimum variance approach, we utilize a CM criterion that is different from the conventional techniques for blind equalization. The proposed robust beamformer minimizes the CM objective function while constraining the magnitude response of any steering vector within a spherical uncertainty region to exceed unity. We develop two algorithms to solve the resultant nonconvex and nonsmooth optimization problem. The first is subgradient projection method, which is simple but converges slowly and is sensitive to initialization. By introducing auxiliary phase variables, our second scheme reformulates the original nonsmooth problem into a differentiable one, that can be solved by an iterative constrained least squares (ICLS) procedure. At each iteration of the ICLS, a least squares (LS) problem with a second-order cone constraint is solved. More important, a closed-form solution for this constrained LS problem, which has a low computational complexity, is derived. The ICLS has fast convergence and is not sensitive to the initial value. Simulation results demonstrate that the proposed CM beamformer achieves a substantial performance gain compared with several representative robust beamformers. [ABSTRACT FROM AUTHOR]

Abstract:In the fields of scientific computing, big data processing, and artificial intelligence, it is very important to study the relevant application load, analyze the load I/O pattern to reveal the application load change law, etc., which is very important to guide the performance optimization of the cluster storage system. At present, there are many kinds of applications and the applications are updated rapidly and iteratively. The complex environment makes the feature mining of application load full of challenges. To address the above problems, we collected the application log information of five Lustre cluster storages in the production environment for 326 days, explored and analyzed the access and load charateristics of the application load, and verified and supplemented the existing observations. Through horizontal, vertical, and multi-dimensional comparative analysis and information mining of the application log information, we summarize four findings, explore the relationship between the relevant findings and previous research work, and then combine the actual production environment with the corresponding system optimization strategies. Feasible implementation schemes are given, which provide relevant references and suggestions for users, maintainers, upper application developers, multi-tier storage system designers, and other personnel. At the same time, because of the complex practical application environment and time-consuming work of system optimization, a system automation optimization framework (SAOF) is designed and implemented. SAOF can provide functions such as resource reservation and bandwidth limitation for specified application loads. Preliminary tests show that SAOF can provide automatic QoS guarantees for different tasks according to system resources and task load requirements. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an algorithm for sparse channel estimation, called least-absolutes (), and an algorithm for equalization, called linear least-absolutes (LLA), in non-Gaussian impulsive noise are proposed. The proposed approaches are based on the minimization of the absolute error function, rather than the squared error function. By replacing the standard modulus with the of complex numbers, the resulting optimization problem can be efficiently solved through linear programming. The selection of an appropriate regularization parameter is also addressed. Numerical results demonstrate that the proposed algorithms, compared with the classical methods, are more robust to impulsive noise and have a superior accuracy. [Copyright &y& Elsevier]

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-resolution (SR). Recently, visual attention mechanism, which exploits both of the feature importance and contextual cues, has been introduced to image SR and proves to be effective to improve CNN-based SR performance. In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts. We further propose a unified approach called “multi-grained attention networks (MGAN)” which fully exploits the advantages of multi-scale and attention mechanisms in SR tasks. In our method, the importance of each neuron is computed according to its surrounding regions in a multi-grained fashion and then is used to adaptively re-scale the feature responses. More importantly, the “channel attention” and “spatial attention” strategies in previous methods can be essentially considered as two special cases of our method. We also introduce multi-scale dense connections to extract the image features at multiple scales and capture the features of different layers through dense skip connections. Ablation studies on benchmark datasets demonstrate the effectiveness of our method. In comparison with other state-of-the-art SR methods, our method shows the superiority in terms of both accuracy and model size. [ABSTRACT FROM AUTHOR]