Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in two genes, PKD1 and PKD2. The complexity of these genes, particularly PKD1, has complicated genetic screening, though recent advances have provided new opportunities for amplifying these genes. In the Han Chinese population, no complete mutational analysis has previously been conducted across the entire span of PKD1 and PKD2. Here, we used single-strand conformation polymorphism (SSCP) analysis to screen the entire coding sequence of PKD1 and PKD2 in 85 healthy controls and 72 Han Chinese from 24 ADPKD pedigrees. In addition to 11 normal variants, we identified 17 mutations (12 in PKD1 and 5 in PKD2), 15 of which were novel ones (11 for PKD1 and 4 for PKD2). We did not identify any seeming mutational hot spots in PKD1 and PKD2. Notably, we found several disease-associated C–T or G–A mutations that led to charge or hydrophobicity changes in the corresponding amino acids. This suggests that the mutations cause conformational alterations in the PKD1 and PKD2 protein products that may impact the normal protein functions. Our study is the first report of screenable mutations in the full-length PKD1 and PKD2 genes of the Han Chinese, and also offers a benchmark for comparisons between Caucasian and Han ADPKD pedigrees and patients.

No abstract

No abstract

An approach to automatic modeling of individual human bodies using complex shape and pose information. The aim is to address the need for human shape and pose model generation for markerless motion capture. With multi-view markerless motion capture, three-dimensional morphable models are learned from an existing database of registered body scans in different shapes and poses. We estimate the body skeleton and pose parameters from the visual hull mesh reconstructed from multiple human silhouettes. Pose variation of body shapes is implemented by the defined underlying skeleton. The shape parameters are estimated by fitting the morphable model to the silhouettes. It is done relying on extracted silhouettes only. An error function is defined to measure how well the human model fits the input data, and minimize it to get the good estimate result. Further, experiments on some data show the robustness of the method, where the body shape and the initial pose can be obtained automatically.

Currently, many vision-based motion capture systems require passive markers attached to key locations on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field (MRF) and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences.human motion capture, Markov random field, dynamic graph-cut, color constrains