Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-art accuracy on public benchmarks but are restricted to specific types of objects and motions covered by the training datasets. Model-based approaches do not rely on training data but show lower accuracy on these datasets. In this paper, we introduce a model-based method called Structure from Articulated Motion (SfAM), which can recover multiple object and motion types without training on extensive data collections. At the same time, it performs on par with learning-based state-of-the-art approaches on public benchmarks and outperforms previous non-rigid structure from motion (NRSfM) methods. SfAM is built upon a general-purpose NRSfM technique while integrating a soft spatio-temporal constraint on the bone lengths. We use alternating optimization strategy to recover optimal geometry (i.e., bone proportions) together with 3D joint positions by enforcing the bone lengths consistency over a series of frames. SfAM is highly robust to noisy 2D annotations, generalizes to arbitrary objects and does not rely on training data, which is shown in extensive experiments on public benchmarks and real video sequences. We believe that it brings a new perspective on the domain of monocular 3D recovery of articulated structures, including human motion capture.
This paper represents qPCR validation results for the detection of Bacillus anthracis pagA pXO1 plasmid marker. The aim of the work was to transfer, implement and validate anthrax specific pagA qPCR assay for the detection of pagA, the genetic marker of the pXO1 plasmid of Bacillus anthracis. qPCR was conducted using the Applied Biosystems Fast 7500 Real-time PCR system including Applied Biosystem specific reagents (AmpliTaq Gold). Anthrax pXO1 pagA primers (pagA_forward, pagA_reverse) and TaqMan pagA probe. Data analysis and statistical calculations were performed using Microsoft Excel. The limit of detection (probit analysis) was calculated using the Statgraphics software. Robustness of qPCR was adjusted by optimization of amplification parameters (annealing temperature) and concentration of reaction components (MgCl2, primers, probe and Taq polymerase). In order to test the repeatability and precision of the qPCR assay after optimization, the variation within the experiment (Intra-assay variability) and between several independent experiments (Inter-assay variability) was evaluated. Probit analysis with serial dilutions of positive control with five replicates per dilution was carried out to define the 95% limit of detection (LOD). To determine if the CT value correlates with the amount of template DNA, the linearity of qPCR was analyzed. The standard curve was generated and the linear regression line and the coefficient of correlation (R2) were calculated. To define the ability to detect sequence of interest (sensitivity), we tested mixed panel of Bacillus anthracis DNAs. As the result, pagA marker could be detected in all tested strains . To find out the specificity of our assay, we also tested DNA of various strains of B. cereus, B. thuringiensis, B. mycoides, and B. globigii (potential cross-reacting organisms) as well as DNA samples of various pathogenic bacteria and viruses which cause similar clinical symptoms as anthrax (differential diagnosis relevant organisms).
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