Mast cell tryptase stimulates myoblast proliferation; a mechanism relying on protease-activated receptor-2 and cyclooxygenase-2

<p>Abstract</p> <p>Background</p> <p>Mast cells contribute to tissue repair in fibrous tissues by stimulating proliferation of fibroblasts through the release of tryptase which activates protease-activated receptor-2 (PAR-2). The possibility that a tryptase/PAR-2 signaling pathway exists in skeletal muscle cell has never been investigated. The aim of this study was to evaluate whether tryptase can stimulate myoblast proliferation and determine the downstream cascade.</p> <p>Methods</p> <p>Proliferation of L6 rat skeletal myoblasts stimulated with PAR-2 agonists (tryptase, trypsin and SLIGKV) was assessed. The specificity of the tryptase effect was evaluated with a specific inhibitor, APC-366. Western blot analyses were used to evaluate the expression and functionality of PAR-2 receptor and to assess the expression of COX-2. COX-2 activity was evaluated with a commercial activity assay kit and by measurement of PGF₂α production. Proliferation assays were also performed in presence of different prostaglandins (PGs).</p> <p>Results</p> <p>Tryptase increased L6 myoblast proliferation by 35% above control group and this effect was completely inhibited by APC-366. We confirmed the expression of PAR-2 receptor <it>in vivo </it>in skeletal muscle cells and in satellite cells and <it>in vitro </it>in L6 cells, where PAR-2 was found to be functional. Trypsin and SLIGKV increased L6 cells proliferation by 76% and 26% above control, respectively. COX-2 activity was increased following stimulation with PAR-2 agonist but its expression remained unchanged. Inhibition of COX-2 activity by NS-398 abolished the stimulation of cell proliferation induced by tryptase and trypsin. Finally, 15-deoxy-Δ-^12,14-prostaglandin J₂(15Δ-PGJ₂), a product of COX-2-derived prostaglandin D₂, stimulated myoblast proliferation, but not PGE₂and PGF₂α.</p> <p>Conclusions</p> <p>Taken together, our data show that tryptase can stimulate myoblast proliferation and this effect is part of a signaling cascade dependent on PAR-2 activation and on the downstream activation of COX-2.</p

EUROGRAPHICS 2006 / E. Gröller and L. Szirmay-Kalos (Guest Editors) Wrinkling Coarse Meshes on the GPU

The simulation of complex layers of folds of cloth can be handled through algorithms which take the physical dynamics into account. In many cases, however, it is sufficient to generate wrinkles on a piece of garment which mostly appears spread out. This paper presents a corresponding fully GPU-based, easy-to-control, and robust method to generate and render plausible and detailed folds. This simulation is generated from an animated mesh. A relaxation step ensures that the behavior remains globally consistent. The resulting wrinkle field controls the lighting and distorts the texture in a way which closely simulates an actually deformed surface. No highly tessellated mesh is required to compute the position of the folds or to render them. Furthermore, the solution provides a 3D paint interface through which the user may bias the computation in such a way that folds already appear in the rest pose

Motion Blur for Textures by Means of Anisotropic Filtering

The anisotropic filtering offered by current graphics hardware can be employed to apply motion blur to textures. The solution proposed here uses a standard texture together with a vertex and a pixel shader acting on a mesh with augmented vertex data. Our method generalizes the usual spatial anisotropic MIP mapping to also include temporal effects. It automatically processes any time series of affine 3D transformations of an object. The application fields include animations containing 2D lettering as well as objects such as spoke wheels that are cookie-cut from large polygons using an alpha channel. We present two different implementations of the technique. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation 1

Abstract A Convolution-Based Algorithm for Animated Water Waves

A non-linear partial differential equation solver is too sophisticated for computer graphics applications if they are only used to render effects like circular waves and ship wakes. We present an approach which simulates waves through a convolution algorithm. It handles both gravity waves and capillary waves; the latter are often neglected even though they dominate small-scale behavior. The algorithm can be integrated into a complete solution architecture: First, standard commercial 3-D software is used to prepare an animated scene with objects traveling on a water surface. Based on the movements of these objects, waves are calculated and added as bump and displacement maps to the 3-D model. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Animation 1

Active Learning of Intuitive Control Knobs for Synthesizers Using Gaussian Processes

Typical synthesizers only provide controls to the low-level parameters of sound-synthesis, such as wave-shapes or filter envelopes. In contrast, composers often want to adjust and express higher-level qualities, such as how ‘scary ’ or ‘steady’ sounds are perceived to be. We develop a system which allows users to directly control abstract, high-level qualities of sounds. To do this, our system learns functions that map from synthesizer control settings to perceived levels of high-level qualities. Given these functions, our system can generate high-level knobs that directly adjust sounds to have more or less of those qualities. We model the functions mapping from control-parameters to the degree of each high-level quality using Gaussian processes, a nonparametric Bayesian model. These models can adjust to the complexity of the function being learned, account for nonlinear interaction between control-parameters, and allow us to characterize the uncertainty about the functions being learned. By tracking uncertainty about the functions being learned, we can use active learning to quickly calibrate the tool, by querying the user about the sounds the system expects to most improve its performance. We show through simulations that this model-based active learning approach learns high-level knobs on certain classes of target concepts faster than several baselines, and give examples of the resulting automaticallyconstructed knobs which adjust levels of non-linear, highlevel concepts