CHIP regulates bone mass by targeting multiple TRAF family members in bone marrow stromal cells

Carboxyl terminus of Hsp70-interacting protein（CHIP or STUB1） is an E3 ligase and regulates the stability of several proteins which are involved in different cellular functions. Our previous studies demonstrated that Chip deficient mice display bone loss phenotype due to increased osteoclast formation through enhancing TRAF6 activity in osteoclasts. In this study we provide novel evidence about the function of CHIP. We found that osteoblast differentiation and bone formation were also decreased in Chip KO mice. In bone marrow stromal（BMS） cells derived from Chip^-/- mice, expression of a panel of osteoblast marker genes was significantly decreased. ALP activity and mineralized bone matrix formation were also reduced in Chip-deficient BMS cells. We also found that in addition to the regulation of TRAF6, CHIP also inhibits TNFα-induced NF-κB signaling through promoting TRAF2 and TRAF5 degradation. Specific deletion of Chip in BMS cells downregulated expression of osteoblast marker genes which could be reversed by the addition of NF-κB inhibitor. These results demonstrate that the osteopenic phenotype observed in Chip^-/- mice was due to the combination of increased osteoclast formation and decreased osteoblast differentiation. Taken together, our findings indicate a significant role of CHIP in bone remodeling.

Multi-Bernoulli Filter for Tracking Multiple Targets Using Sensor Array

This paper presents a multi-Bernoulli filter for tracking the direction of arrival(DOAs)of time-varying number of targets using sensor array.Our method operates directly on the measurements of sensor array and does not require any detection.Firstly,more information is reserved and compared with the after-detection measurements using a finite set of detected points.It can significantly improve the tracking performance,especially in low signal-to-noise ratio.Secondly,it inherits the advantages of the multi-Bernoulli approximation which models each of the targets individually.This allows more accurate multi-target state estimation,especially when targets cross.The proposed filter does not need clustering step and simulation results showcase the improved performance of the proposed filter.

SIMULTANEOUS MULTIPLE TARGET RECOGNITION USING POLARIZATION AGILE WAVES

A novel matching method for simultaneous multi-target recognition is proposed by jointly considering target's prior scattering knowledge and the polarization parameters of radar echoes. The matching coefficients are calculated for the judgment. MATLAB simulations show that several targets can be accurately recognized simultaneously, and a high recognition probability can be achieved in Monte Carlo simulations. The total execution time can be remarkably reduced in the Field Programmable Gate Array (FPGA) implementation of the matching procedure.

Multiple extended target tracking algorithm based on Gaussian surface matrix

In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

DL0410,a candidate for anti-Alzheimer disease with multiple targets in multiple pathways

OBJECTIVE To find a promising candidate for anti-Alzheimer disease(AD)with multiple targets in multiple pathways.METHODS A series of classifiers were constructed for predicting the active compounds against 51 key targets toward Alzheimer disease(AD)using the multitargetquantitative structure-activity relationships(mt-QSAR)method.While drug screening assays were established to evaluate the predicted active molecules.In addition,various cellular models and animal models related with AD were set up to further study the effects of the active compounds.RESULTS A system for the discovery of Multitarget-Directed Ligands against AD was set up and applied,the predicted active compounds were validated by the drug screening assays,and several active compounds with multiple targets were discovered.Among them,DL0410 exerted high activity on H3R,α7n ACh R,ACh E and ERα,also displayed the most significant effect in improving the ability of memory and learning in several AD animal models.The study on its action mechanisms showed that it′s effect may partially through increasing neurotransmitter,inhibiting oxidative emergency,inhibiting the expression of APP,and promoting long-term potentiation.Besides,DL0410 is of more safety than the first-line clinical medicines.CONCLUSION DL0410 is a promising candidate for further development for AD treatment.

Estimation of detection threshold in multiple ship target situations with HF ground wave radar

A credible method of calculating the detection threshold is presented for the multiple target situations, which appear frequently in the lower Doppler velocity region during the surveillance of sea with HF ground wave radar. This method defines a whole-peak-outlier elimination （WPOE） criterion, which is based on in-peak-samples correlation of each target echo spectra, to trim off the target signals and abnormal disturbances with great amplitude from the complex spectra. Therefore, cleaned background noise samples are obtained to improve the accuracy and reliability of noise level estimation. When the background noise is nonhomogeneous, the detection samples are limited and often occupied heavily with outliers. In this case, the problem that the detection threshold is overvalued can be solved. In applications on experimental data, it is verified that this method can reduce the miss alarm rate of signal detection effectively in multiple target situations as well as make the adaptability of the detector better.

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.

Modeling and simulation of torpedo acoustic homing trajectory with multiple targets

The characteristics of a torpedo＇s acoustic homing trajectory with multiple targets were studied. The differential equations of torpedo motion were presented based on hydrodynamics. The Fourth order Runge-Kutta method was used to solve these equations. Derived from sonar equations and Snell＇ s law, a simple virtual underwater acoustic environment was established for simulating the torpedo homing process. The Newton iteration method was used to calculate homing range and ray tracing was approximated by pieccwise line, which takes into consideration distortions cause by temperature, pressure, and salinity in a given sea area. The influence of some acoustic warfare equipment disturb the torpedo homing process in certain circumstances, including decoys and jammers, was alsotaken into account in simulations. Relative target identification logic and homing control laws were presented. Equal consideration during research was given to the requirements of rcal-timeactivity as well as accuracy. Finally, a practical torpedo homing trajectory simulation program was developed and applied to certain projects.

Multiple Targets Recognition for Highly-Compressed Color Images in a Joint Transform Correlator

In this paper, we are proposing a compression-based multiple color target detection for practical near real-time optical pattern recognition applications. By reducing the size of the color images to its utmost compression, the speed and the storage of the system are greatly increased. We have used the powerful Fringe-adjusted joint transform correlation technique to successfully detect compression-based multiple targets in colored images. The colored image is decomposed into three fundamental color components images (Red, Green, Blue) and they are separately processed by three-channel correlators. The outputs of the three channels are then combined into a single correlation output. To eliminate the false alarms and zero-order terms due to multiple desired and undesired targets in a scene, we have used the reference shifted phase-encoded and the reference phase-encoded techniques. The performance of the proposed compression-based technique is assessed through many computer simulation tests for images polluted by strong additive Gaussian and Salt & Pepper noises as well as reference occluded images. The robustness of the scheme is demonstrated for severely compressed images (up to 94% ratio), strong noise densities (up to 0.5), and large reference occlusion images (up to 75%).

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

ANALYSIS OF CELL-AVERAGING BASED DETECTORS FOR χ^2 FLUCTUATING TARGETS IN MULTITARGET ENVIRONMENTS

The χ^2 family of signal fluctuation distributions represents the main fluctuation models which most radar targets follow it in their reflections. This family can be categorized as fluctuation distribution with two degrees of freedom and those with four degrees of freedom. The first category represents all important class of fluctuation models which when illuminated by a coherent pulse train, return a train of fully correlated pulses （Swerling Ⅰ model） or fully decorrelated pulses （Swerling Ⅱ model）. The detection of this type of fluctuating targets is therefore of great importance. This paper is devoted to the analysis of Cell-Averaging （CA） based detectors for the case where the radar receiver noncoherently integrates M square-law detected pulses and the signal fluctuation obeys 2 statistics with two degrees of freedom. These detectors include the Mean-Of （MO）, the Greatest-Of （GO） and the Smallest-Of（SO） schemes. In these processors, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. Exact formulas for the detection probabilities are derived, in the absence as well as in the presence of spurious targets. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the χ^2 fluctuation model with two degrees of freedom （SWI ＆ SWII）. The numerical results show that the MO version has the best homogeneous performance, the SO scheme has the best multiple-target performance, while the GO procedure does not offer any merits, neither in the absence nor in the presence of outlying targets.

New targets number estimation method under colored noise background

A multiple targets detection method based on spatial smoothing （MTDSS） is proposed to solve the problem of the source number estimation under the colored noise background. The forward and backward smoothing based on auxiliary vectors which are received data on some specific elements is computed. By the spatial smoothing with auxiliary vectors, the correlated signals are decorrelated, and the colored noise is partially alleviated. The correlation matrix formed from the cross correlations between subarray data and auxiliary vectors is computed. By exploring the second-order statistics property of the covariance matrix, a threshold based on Gerschgorin radii of the smoothing correlation matrix is set to estimate the number of sources. Simulations and experimental results validate that MTDSS has an effective performance under the condition of the colored noise background and coherent sources, and MTDSS is robust with the correlated factor of signals and noise.

Signal decomposition of HF radar maneuvering targets by using S^2-method with clutter rejection

Multiple maneuvedng targets signal processing in high frequency radar is challenging due to the following difficulties： the interference between signals is severe because of significant spread of the target Doppler spectrum, the low signal to clutter ratio （SCR） environment degrades the performance of signal process- ing algorithms. This paper addresses this challenging problem by using an S2-method and an adaptive clutter rejection scheme. The proposed S2-method improves the S-method by eliminating inter- ference between signals, and thus it enables multi-target signals to be reconstructed individually. The proposed adaptive clutter rejec- tion scheme is based on an adaptive notch filter, which is designed according to the envelop of the clutter spectrum. Experiments with simulated targets added into radar sea clutter echo and real air target data illustrate the effectiveness of the proposed method.

Imaging algorithm of multi-ship motion target based on compressed sensing

An imaging algorithm based on compressed sensing（CS） for the multi-ship motion target is presented. In order to reduce the quantity of data transmission in searching the ships on a large sea area, both range and azimuth of the moving ship targets are converted into sparse representation under certain signal basis. The signal reconstruction algorithm based on CS at a distant calculation station, and the Keystone and fractional Fourier transform（FRFT） algorithm are used to compensate range migration and obtain Doppler frequency. When the sea ships satisfy the sparsity, the algorithm can obtain higher resolution in both range and azimuth than the conventional imaging algorithm. Some simulations are performed to verify the reliability and stability.

Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ~2-targets

To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity. Moreover, the non-coherent integration of M pulses ameliorates the processor performance either it operates in homogeneous or multi-target environment.

STOCHASTIC NEURAL NETWORK AND ITS APPLICATION TO MULTI－MANEUVERING TARGET TRACKING

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Posterior Cramer-Rao lower bounds for multitarget bearings-only tracking

Usually, only the Cramer-Rao lower bound （CRLB） of single target is taken into consideration in the state estimate of passive tracking systems. As for the case of multitarget, there are few works done due to its complexity. The recursion formula of the posterior Cramer-Rao lower bound （PCRLB） in multitarget bearings-only tracking with the three kinds of data association is presented. Meanwhile, computer simulation is carried out for data association. The final result shows that the accuracy probability of data association has an important impact on the PCRLB.

Distance to Isocenter Directly Affects Margin and Inappropriate Margin Increases the Risk of Local Control Failure in LINAC-Based Single-Isocenter SRS or SRT for Multiple Brain Metastases

=In the advancement of single-isocenter multiple target treatment in the LINAC-based SRS or SRT, the target distance to the isocenter and grouping of multiple targets are the highly concerned and debatable topics in the SRS/SRT field at present. Three failure and success cases of local control in our early practices are presented in this study and it indicated that the target distance to the isocenter directly affects the margin and an inappropriate margin increase the risk of local control failure. The GTV expansion margin should be LINAC-specific and institute-specific. Within the physics and dosimetry scope, the AHARA (as high as reasonably achievable) principle is the first time proposed to the radiation oncology field. Radiobiology and tumor response complexity is beyond this study.

Optimization of Blade Geometry of Savonius Hydrokinetic Turbine Based onGenetic Algorithm

Savonius hydrokinetic turbine is a kind of turbine set which is suitable for low-velocity conditions.Unlike conventional turbines,Savonius turbines employ S-shaped blades and have simple internal structures.Therefore,there is a large space for optimizing the blade geometry.In this study,computational fluid dynamics(CFD)numerical simulation and genetic algorithm(GA)were used for the optimal design.The optimization strategies and methods were determined by comparing the results calculated by CFD with the experimental results.The weighted objective function was constructed with the maximum power coefficient Cp and the high-power coefficient range R under multiple working conditions.GA helps to find the optimal individual of the objective function.Compared the optimal scheme with the initial scheme,the overlap ratioβincreased from 0.2 to 0.202,and the clearance ratioεincreased from 0 to 0.179,the blade circumferential angleγincreased from 0°to 27°,the blade shape extended more towards the spindle.The overall power of Savonius turbines was maintained at a high level over 22%,R also increased from 0.73 to 1.02.In comparison with the initial scheme,the energy loss of the optimal scheme at high blade tip speed is greatly reduced,and this reduction is closely related to the optimization of blade geometry.As R becomes larger,Savonius turbines can adapt to the overall working conditions and meet the needs of its work in low flow rate conditions.The results of this paper can be used as a reference for the hydrodynamic optimization of Savonius turbine runners.

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.