Bilateral Ureteral Stenosis with Hydronephrosis as First Manifestation of Granulomatosis with Polyangiitis (Wegener's Granulomatosis): A Case Report and Review of the Literature.

Ureteral stenosis is a rare manifestation of granulomatosis with polyangiitis (formerly known as Wegener's granulomatosis). We report the case of a 76-year-old woman with progressive renal failure in which bilateral hydronephrosis due to ureteral stenosis was the first manifestation of the disease. Our patient also had renal involvement with pauci-immune crescentic glomerulonephritis associated with high titers of anti-proteinase 3 c-ANCAs, but no involvement of the upper or lower respiratory tract. The hydronephrosis and renal function rapidly improved under immunosuppressive therapy with high-dose corticosteroids and intravenous pulse cyclophosphamide. We reviewed the literature and found only ten other reported cases of granulomatosis with polyangiitis/Wegener's granulomatosis and intrinsic ureteral stenosis: in two cases, the presenting clinical manifestation was unilateral hydronephrosis and in only two others was the hydronephrosis bilateral, but this complication developed during a relapse of the disease. This case emphasizes the importance of including ANCA-related vasculitis in the differential diagnosis of unusual cases of unilateral or bilateral ureteral stenosis

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects.

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours.

We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. The approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined in a space of multiple, arbitrarily shaped objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data

No widespread induction of cell death genes occurs in pure motoneurons in an amyotrophic lateral sclerosis mouse model

To identify candidate genes that may be involved in motoneuron degeneration, we combined laser capture microdissection with microarray technology. Gene expression in motoneurons was analyzed during the progression of the disease in transgenic SOD1G93A mice that develop motoneuron loss. Three major observations were made: first, there was only a small number of genes that were differentially expressed in motoneurons at a pre-symptomatic age (27 out of 34 000 transcripts). Secondly, there is an early specific up-regulation of the gene coding for the intermediate filament vimentin that is increased even further during disease progression. Using in situ hybridization and immunohistochemical analysis, we show that vimentin expression was not only elevated in motoneurons but that the protein formed inclusions in the motoneuron cytoplasm. Thirdly, a time-course analysis of the motoneurons at a symptomatic age (90 and 120 days) showed a modest de-regulation of only a few genes associated with cell death pathways; however, a massive up-regulation of genes involved in cell growth and/or maintenance was observed. This is the first description of the gene profile of SOD1G93A motoneurons during disease progression and unexpectedly, no widespread induction of cell death-associated genes was detected in motoneurons of SOD1G93A mic

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours

We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. The approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined in a space of multiple, arbitrarily shaped objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data

A Q-Ising model application for linear-time image segmentation

A computational method is presented which efficiently segments digital grayscale images by directly applying the Q-state Ising (or Potts) model. Since the Potts model was first proposed in 1952, physicists have studied lattice models to gain deep insights into magnetism and other disordered systems. For some time, researchers have realized that digital images may be modeled in much the same way as these physical systems (i.e., as a square lattice of numerical values). A major drawback in using Potts model methods for image segmentation is that, with conventional methods, it processes in exponential time. Advances have been made via certain approximations to reduce the segmentation process to power-law time. However, in many applications (such as for sonar imagery), real-time processing requires much greater efficiency. This article contains a description of an energy minimization technique that applies four Potts (Q-Ising) models directly to the image and processes in linear time. The result is analogous to partitioning the system into regions of four classes of magnetism. This direct Potts segmentation technique is demonstrated on photographic, medical, and acoustic images.Comment: 7 pages, 8 figures, revtex, uses subfigure.sty. Central European Journal of Physics, in press (2010