Analysis of the Effects of Model Mismatch and Flat MMF for Estimating Particle Motion

In this report, we analyze the performance degradation due to three classes of model mismatch: parameter jumping, undermodeling and overmodeling, in estimating the particle motion by using the orthogonal polynomials to model the trajectory. We find that these model mismatches make the \u27optimal estimator\u27 to have large bias and mean squared error. For the case of undermodeling, the estimation error increases, in general, without a bound as the observation interval increases. We then propose the Finite Lifetime Alternately Triggered Multiple Model Filter (FLAT MMF), as a solution. FLAT MMF is a filter composed of a set of K identical conventional state estimation filters, each triggered alternately. After the last filter is triggered, the oldest one is triggered again and so on. The structure of Multiple Model Filter is used to combine these estimates optimally, in the sense of minimum mean squared error. We find that the ratio of weightings in FLAT MMF are related to some independent non-central χ2 random variables. Consequently, we show that the FLAT MMF can provide an estimate that follows abrupt changes in the trajectory and has the small bias for undermodeling. For the case of overmodeling or the case that the trajectory model matches to the actual motion, the estimate does not degrade significantly. A number of simulations are conducted to illustrate the estimation performance degradation due to the model mismatches for the conventional Kalman filter and the performance improvement as the proposed FLAT MMF is used

Estimation of General Rigid Body Motion From a Long Sequence of Images

In estimating the 3-D rigid body motion and structure from time-varying images, most of previous approaches which exploit a large number of frames assume that the rotation, and the translation in some case, are constant. For a long sequence of images, this assumption in general is not valid. In this paper, we propose a new state estimation formulation for the general motion in which the 3-D translation and rotation are modeled as the polynomials of arbitrary order. Extended Kalman filter is used to find the estimates recursively from noisy images. A number of simulations including the Monte Carlo analysis are conducted to illustrate the performance of the proposed formulation

Estimation of 3-D Motion and Structure Based on a Temporally-Oriented Approach With the Method of Regression

In this paper we argue that the 3-D velocity of a single point up to a scalar factor can be recovered from its 2-D trajectory under the perspective projection. We then extend the idea to the recovery of 3-D motion of rigid objects. In both cases measurements are collected through temporal axis first, while keeping the amount of measurements in each frame minimal. We may use multiple features to get a more accurate estimate if they are available. This approach called temporally oriented approach requires us to introduce the explicit model for the evolution of 3-D motion. Our analysis is based on the assumption that the 3-D motion is smooth so that its 3-D velocity can be approximated as a truncated Taylor series. Regression relations between unknown motion parameters and measurements for a single point and rigid body are derived. The method of Maximum Likelihood is used to estimate the motion. The uniqueness of determining the 3-D motion of a single point is discussed. Experimental results obtained from simulated data and real images are given to illustrate the robustness of this approach

Repressive Effects of Resveratrol on Androgen Receptor Transcriptional Activity

The chemopreventive effects of resveratrol (RSV) on prostate cancer have been well established; the androgen receptor (AR) plays pivotal roles in prostatic tumorigenesis. However, the exact underlying molecular mechanisms about the effects of RSV on AR have not been fully elucidated. A model system is needed to determine whether and how RSV represses AR transcriptional activity.The AR cDNA was first cloned into the retroviral vector pOZ-N and then integrated into the genome of AR-negative HeLa cells to generate the AR(+) cells. The constitutively expressed AR was characterized by monitoring hormone-stimulated nuclear translocation, DNA binding, and transcriptional activation, with the AR(-) cells serving as controls. AR(+) cells were treated with RSV, and both AR protein levels and AR transcriptional activity were measured simultaneously. Chromatin immunoprecipitation (ChIP) assays were used to detect the effects of RSV on the recruitment of AR to its cognate element (ARE).AR in the AR (+) stable cell line functions in a manner similar to that of endogenously expressed AR. Using this model system we clearly demonstrated that RSV represses AR transcriptional activity independently of any effects on AR protein levels. However, neither the hormone-mediated nucleus translocation nor the AR/ARE interaction was affected by RSV treatment.We demonstrated unambiguously that RSV regulates AR target gene expression, at least in part, by repressing AR transcriptional activity. Repressive effects of RSV on AR activity result from mechanisms other than the affects of AR nuclear translocation or DNA binding

Estimation of three-dimensional motion and structure from images by using a temporally oriented approach

This dissertation explores the problem of estimating the 3-D motion and the structure of an object from video images using a new approach which looks for the temporal information prior to the spatial information. Since motion is observed over an extended period of time, we can reduce the number of features required by the conventional approach and improve significantly the estimation performance. In recovering the motion of a single particle or a rigid body, we prove that, under some conditions, the solution is unique. Regression relations between the unknown motion parameters and the projective trajectories are obtained for general particle motion and constant rigid motion. The method of maximum likelihood is used to estimate the motion. Using the nonlinear state estimation formulation, the extended Kalman filter is applied to obtain the estimate recursively. We propose an approach to estimate a general non-constant rigid motion in which the orders of translation and rotation can be arbitrary. We also show that for some special angular velocities, non-constant rigid motion has closed-form evolution, and discuss how to reduce the number of unknowns for a planar surface. We have addressed the problem of model mismatches for parameter jumping, undermodeling and overmodeling. We find that the model error makes the conventional approach break down. In order to solve this problem, we develop a new filter called Finite Lifetime Alternately Triggered Multiple Model Filter FLAT MMF). FLAT MMF is a filter composed of a number of identical conventional state estimation filters, each triggered alternately. After the last filter is triggered, the oldest one is triggered again and, so on. Experiments on the simulated trajectory and the real images show that FLAT MMF is quite effective in suppressing the model errors. For the particle motion without a depth change, we obtain the analytic, closed-form estimate for cases of model match and mismatch. We show that the filter that provides the best estimates dominates the final estimate. Finally, we show the potential of FLAT MMF for real-time object tracking

Array Comparative Genomic Hybridization Identifies a Heterozygous Deletion of Exon 3 of the RYR2 Gene

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a heritable cardiac disorder characterized by life-threatening ventricular tachycardia caused by exercise or acute emotional stress. The standard diagnostic screening involves Sanger-based sequencing of 45 of the 105 translated exons of the RYR2 gene, and copy number changes of a limited number of exons that are detected using multiplex ligation-dependent probe amplification (MLPA)

The zebrafish model system in cardiovascular research: A tiny fish with mighty prospects

The zebrafish Danio rerio, a tropical freshwater fish, belongs to the family of cyprinidae, which in the last 30 years has developed into a very popular model organism for studies of embryonic development and human diseases. Initially the zebrafish species has been selected on the basis of its small size of approximately 3-5 cm, its transparency during development and its high fertility, qualities first identified by George Stresinger, the founding father of zebrafish research [1]. The ability to house thousands of small fishes and the ease of screening mutations in the translucent embryos made it feasible to perform large-scale forward genetic screens in a vertebrate model organism. The abundance of eggs obtained, approximately 200 eggs per female per week, is ideal for genetic and statistical analysis. The mutagenesis screens performed in the early 1990s have led to the identification of genes important in vertebrate organogenesis in an unbiased fashion [2-3]. Many of the isolated mutants have now been fully characterized and the mutated genes mapped, as the zebrafish genome sequencing completes. The knowledge derived has led to a better understanding of the underlying genetic networks governing vertebrate development. More sophisticated phenotype-based screens have since been developed to screen for mutations in defined biological processes [4]