Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Enhanced Precision Therapy of Multiple Myeloma Through Engineered Biomimetic Nanoparticles with Dual Targeting

Multiple myeloma(MM)is the second most prevalent hematological malignancy.Current MM treatment strategies are hampered by systemic toxicity and suboptimal therapeutic efficacy.This study addressed these limitations through the development of a potent MM-targeting chemotherapy strategy,which capitalized on the high binding affinity of alendronate for hydroxyapatite in the bone matrix and the homologous targeting of myeloma cell membranes,termed T-PB@M.The results from our investigations highlight the considerable bone affinity of T-PB@M,both in vitro and in vivo.Additionally,this material demonstrated a capability for drug release triggered by low pH conditions.Moreover,T-PB@M induced the generation of reactive oxygen species and triggered cell apoptosis through the poly(ADP-ribose)polymerase 1(PARP1)-Caspase-3-B-cell lymphoma-2(Bcl-2)pathway in MM cells.Notably,T-PB@M preferentially targeted bone-involved sites,thereby circumventing systemic toxic side effects and leading to prolonged survival of MM orthotopic mice.Therefore,this designed target-MM nanocarrier presents a promising and potentially effective platform for the precise treatment of MM.

Gene signatures to therapeutics:Assessing the potential of ivermectin against t(4;14)multiple myeloma

BACKGROUND Multiple myeloma(MM)is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow.The translocation,(t)(4;14),results in high-risk MM with limited treatment alternatives.Thus,there is an urgent need for identification and validation of potential treatments for this MM subtype.Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets.AIM To elucidate the molecular basis and search for potential effective drugs of t(4;14)MM subtype by employing a comprehensive approach.METHODS The transcriptional signature of t(4;14)MM was sourced from the Gene Expression Omnibus.Two datasets,GSE16558 and GSE116294,which included 17 and 15 t(4;14)MM bone marrow samples,and five and four normal bone marrow samples,respectively.After the differentially expressed genes were identified,the Cytohubba tool was used to screen for hub genes.Then,the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis.Using the STRING database and Cytoscape,protein–protein interaction networks and core targets were identified.Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis,respectively.RESULTS In this study,a total of 258 differentially expressed genes with enriched functions in cancer pathways,namely cytokine receptor interactions,nuclear factor(NF)-κB signaling pathway,lipid metabolism,atherosclerosis,and Hippo signaling pathway,were identified.Ten hub genes(cd45,vcam1,ccl3,cd56,app,cd48,btk,ccr2,cybb,and cxcl12)were identified.Nine drugs,including ivermectin,deforolimus,and isoliquiritigenin,were predicted by the Connectivity Map database to have potential therapeutic effects on t(4;14)MM.In molecular docking,ivermectin showed strong binding affinity to all 10 identified targets,especially cd45 and cybb.Ivermectin inhibited t(4;14)MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro.Furthermore,ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14)MM cells.CONCLUSION Collectively,the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14)MM diagnosis and treatment,with ivermectin emerging as a potential therapeutic alternative.

A simulation method on target strength and circular SAS imaging of Xrudder UUV including multiple acoustic scattering

Target strength(TS)and circular synthetic aperture sonar(CSAS)images provide essential information for active acoustic detection and recognition of non-cooperative unmanned undersea vehicles(UUVs),which pose a significant threat to underwater preset facilities.To access them,we propose an iterative physical acoustics(IPA)-based method to simulate the multiple acoustic scattered fields on rigid surfaces in high-frequency cases.It uses the Helmholtz integral equation with an appropriate Green's function in terms of the Neumann series,and then incorporates the ideas of triangulation and iteration into a numerical implementation.Then two approximate analytic formulae with precise physical meanings are derived to predict the TS and CSAS images of concave targets,respectively.There are no restrictions on the surface's curvature and the order of multiple scattering.The method is validated against the finite element method(FEM)for acoustic scattering from a sphere segment and against an experiment involving an X-rudder UUV's stern.On this basis,we simulate and analyze the TS and CSAS images of an X-rudder UUV.In addition,the influence of the angle of adjacent rudders on the multiple scattering characteristics is discussed.Results show that this method can potentially predict accurate UUV features,especially the multiple scattered features.

Labeled box-particle CPHD filter for multiple extended targets tracking

In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter.Based on our recent work on extended box-particle probability hypothesis density(ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density(ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density(PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targets' tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.

MicroRNAs as disease progression biomarkers and therapeutic targets in experimental autoimmune encephalomyelitis model of multiple sclerosis

Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain,spinal cord and optic nerve leading to demyelination.Focal demyelination is associated with relapsing-remitting multiple sclerosis,while progressive forms of the disease show axonal degeneration and neuronal loss.The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity.MicroRNAs(miRNAs)are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases.A review of recent studies with the experimental autoimmune encephalomyelitis animal model(mostly female mice 6–12 weeks of age)has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset(asymptomatic)stage when assessed in blood plasma and urine exosomes,and spinal cord tissue.The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes,brain and spinal cord tissue,and at the post-peak(chronic)stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue.Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease.Interestingly,experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a,miR-23b,miR-497,miR-26a,and miR-20b,or by suppression of miR-182,miR-181c,miR-223,miR-155,and miR-873.Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course.Additionally,studies should be performed with male mice of a similar age,and with aged male and female mice.

Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.

Multiple Targets Tracking Using Kinematics in Wireless Sensor Networks

Target tracking is considered as one of the cardinal applications of a wireless sensor network. Tracking multiple targets is more challenging than tracking a single target in a wireless sensor network due to targets’ movement in different directions, targets’ speed variations and frequent connectivity failures of low powered sensor nodes. If all the low-powered sensor nodes are kept active in tracking multiple targets coming from different directions of the network, there is high probability of network failure due to wastage of power. It would be more realistic if the tracking area can be reduced so that less number of sensor nodes will be active and therefore, the network will consume less energy. Tracking area can be reduced by using the target’s kinematics. There is almost no method to track multiple targets based on targets’ kinematics. In our paper, we propose a distributed tracking method for tracking multiple targets considering targets’ kinematics. We simulate our method by a sensor network simulator OMNeT++ and empirical results state that our proposed methodology outperforms traditional tracking algorithms.

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.

Should mast cells be considered therapeutic targets in multiple sclerosis?

Mast cells are immune cells of the myeloid lineage that are found throughout the body,including the central nervous system.They perform many functions associated with innate and specific immunity,angiogenesis,and vascular homeostasis.Moreover,they have been implicated in a series of pathologies(e.g.,hypersensitivity reactions,tumors,and inflammatory disorders).In this review,we propose that this cell could be a relevant therapeutic target in multiple sclerosis,which is a central nervous system degenerative disease.To support this proposition,we describe the general biological properties of mast cells,their contribution to innate and specific immunity,and the participation of mast cells in the various stages of multiple sclerosis and experimental autoimmune encephalomyelitis development.The final part of this review is dedicated to an overview of the available mast cells immunomodulatory drugs and their activity on multiple sclerosis and experimental autoimmune encephalomyelitis,including our own experience related to the effect of ketotifen fumarate on experimental autoimmune encephalomyelitis evolution.

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.

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%).

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

Target Tracking Using the Interactive Multiple Model Method

Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.

Fast density peak-based clustering algorithm for multiple extended target tracking

The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.

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.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.