Immunohistochemical localization of osteoclastogenic cell mediators in feline tooth resorption and healthy teeth

Tooth resorption is among the most common and most challenging problems in feline dentistry It is a progressive disease eventually leading to tooth loss and often root replacement. The etiology of moth resorption remains obscure and to date no effective therapeutic approach is known. The present study is aimed at assessing the reliability of radiographic imaging and addressing the possible involvement of receptor activator of NF kappa B (RANK), its ligand (RANKL), and osteoprotegerin (OPG) in the process of tooth resorption. Teeth from 8 cats were investigated by means of radiographs and paraffin sections followed by immunolabeling. Six cats were diagnosed with tooth resorption based on histopathologic and radiographic findings. Samples were classified according to a four-stage diagnostic system. Radiologic assessment of tooth resorption correlated very strongly with histopathologic findings. Tooth resorption was accompanied by a strong staining with all three antibodies used, especially with anti-RANK and anti-RANKL antibodies. The presence of OPG and RANKL at the resorption site is indicative of repair attempts by fibroblasts and stromal cells. These findings should be extended by further investigations in order to elucidate the pathophysiologic processes underlying tooth resorption that might lead to prophylactic and/or therapeutic measures. J Vet Dent 27(2); 75 - 83, 2010

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.