University of Graz

About: University of Graz is a education organization based out in Graz, Steiermark, Austria. It is known for research contribution in the topics: Population & Context (language use). The organization has 17934 authors who have published 37489 publications receiving 1110980 citations. The organization is also known as: Carolo Franciscea Graecensis & Karl Franzens Universität.

Automated melanoma recognition

Abstract: A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.

Decrease of substance p in primary afferent neurones and impairment of neurogenic plasma extravasation by capsaicin

Abstract: 1 Rats were pretreated with capsaicin (50 mg/kg, s.c.) on the 2nd, 10th, or 20th day of life. Three months later immunoreactive substance P (I-SP) was determined in skin, sensory nerves and the central nervous system. Neurogenic plasma extravasation was also examined.2 Pretreatment at the age of 2 or 10 days resulted in a decrease (26 to 69%) of I-SP in skin, saphenous and vagus nerve, dorsal roots, dorsal half of the spinal cord, and medulla oblongata. The I-SP content of the ventral half of the spinal cord, of midbrain, hypothalamus, striatum, cortex, and cerebellum remained unchanged. Neurogenic plasma extravasation was inhibited by more than 80%.3 In contrast to this irreversible effect of capsaicin on newborn rats, pretreatment of 20 day old rats led to reversible depletion of I-SP and to reversible impairment of neurogenic plasma extravasation.4 Capsaicin pretreatment of adult rats caused a marked depletion of I-SP in the skin of the hind paw and an impairment of neurogenic plasma extravasation. A similar decrease of I-SP was seen after chronic denervation of the skin.5 Intra-arterial infusion of substance P (threshold dose 5 x 10(-13) mol/min) or physalaemin induced dose-dependent plasma extravasation. Somatostatin, vasoactive intestinal polypeptide, caerulein and the enkephalin-analogue FK 33-824 were ineffective in doses 100 fold higher.6 The results indicate that the action of capsaicin on substance P neurones is restricted to primary sensory neurones. Since in every case a decreased substance P content of the skin was associated with impaired neurogenic plasma extravasation, it is suggested that release of substance P is involved in neurogenic plasma extravasation.

Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models

Abstract: Although regional climate models (RCMs) are powerful tools for describing regional and even smaller scale climate conditions, they still feature severe systematic errors. In order to provide optimized climate scenarios for climate change impact research, this study merges linear and nonlinear empirical-statistical downscaling techniques with bias correction methods and investigates their ability for reducing RCM error characteristics. An ensemble of seven empirical-statistical downscaling and error correction methods (DECMs) is applied to post-process daily precipitation sums of a high-resolution regional climate hindcast simulation over the Alpine region, their error characteristics are analysed and compared to the raw RCM results. Drastic reductions in error characteristics due to application of DECMs are demonstrated. Direct point-wise methods like quantile mapping and local intensity scaling as well as indirect spatial methods as nonlinear analogue methods yield systematic improvements in median, variance, frequency, intensity and extremes of daily precipitation. Multiple linear regression methods, even if optimized by predictor selection, transformation and randomization, exhibit significant shortcomings for modelling daily precipitation due to their linear framework. Comparing the well-performing methods to each other, quantile mapping shows the best performance, particularly at high quantiles, which is advantageous for applications related to extreme precipitation events. The improvements are obtained regardless of season and region, which indicates the potential transferability of these methods to other regions. Copyright © 2010 Royal Meteorological Society