Improved Gaussian mixture probability hypothesis density for tracking closely spaced targets

Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

AbstractThe probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches

Cryptic t(15;17) acute promyelocytic leukemia with a karyotype of add(11)(p15) and t(13,20)- A case report with a literature review

Most acute promyelocytic leukemia (APL) are characterized by reciprocal translocations t(15;17)(q22;21), which results in the fusion of PML gene at 15q22 with RARα gene at 17q21. However, several complex variant translocations also have been reported. Here we report a 62-year-old man with typical morphology and clinical features of APL with a complex karyotype including add(11)(p15) and t(13,20)(q12;q11.2) without typical t(15;17) assayed by the G-banding analysis. FISH with a PML/RARα dual-color DNA probe showed an atypical fusion signal, RT-qPCR analysis showed PML/RARα fusion transcripts, and NGS detected FLT3, WT1, and KRAS mutations. The patient achieved complete remission after treatment with conventional chemotherapy combined ATRA and ATO. Although the mechanism of this kind of cryptic variant remains unknown, we conclude that the cryptic PML/RARα fusion with add(11)(p15), t(13,20)(q12;q11.2) seems not to alter the effectiveness of chemotherapy combined with ATRA and ATO

Covariation in Plant Functional Traits and Soil Fertility within Two Species-Rich Forests

The distribution of plant species along environmental gradients is expected to be predictable based on organismal function. Plant functional trait research has shown that trait values generally vary predictably along broad-scale climatic and soil gradients. This work has also demonstrated that at any one point along these gradients there is a large amount of interspecific trait variation. The present research proposes that this variation may be explained by the local-scale sorting of traits along soil fertility and acidity axes. Specifically, we predicted that trait values associated with high resource acquisition and growth rates would be found on soils that are more fertile and less acidic. We tested the expected relationships at the species-level and quadrat-level (20×20 m) using two large forest plots in Panama and China that contain over 450 species combined. Predicted relationships between leaf area and wood density and soil fertility were supported in some instances, but the majority of the predicted relationships were rejected. Alternative resource axes, such as light gradients, therefore likely play a larger role in determining the interspecific variability in plant functional traits in the two forests studied