305 research outputs found
“Immunohistochemical Localization of Leptin in Adipose Tissues” in coll.
No full text“Immunohistochemical Localization of Leptin in Adipose Tissues” in coll. R. DeMatteis, M.C. Zingaretti and J. Himms-Hagen. In: Leptin the voice of Adipose Tissue. Ed. by W.F. Blum, W. Kiess, W Rascher . , 199
Why is it so difficult to develop a hepatitis C virus preventive vaccine?
No full textAbstractWith an estimated 3% of the world’s population chronically infected, hepatitis C virus (HCV) represents a major health problem for which an efficient vaccination strategy would be highly desirable. Indeed, chronic hepatitis C is recognized as one of the major causes of cirrhosis, hepatocarcinoma and liver failure worldwide and it is the most common indication for liver transplantation, accounting for 40–50% of liver transplants. Much progress has been made in the prevention of HCV transmission and in therapeutic intervention. However, even if a new wave of directly acting antivirals promise to overcome the problems of low efficacy and adverse effects observed for the current standard of care, which include interferon-a and ribavirin, an effective vaccine would be the only means to definitively eradicate infection and to diminish the burden of HCV-related diseases at affordable costs. Although there is strong evidence that the goal of a prophylactic vaccine could be achieved, there are huge development issues that have impeded reaching this goal and that still have to be addressed. In this article we address the question of whether an HCV vaccine is needed, whether it will eventually be feasible, and why it is so difficult to produce
Comparative analysis of automatic approaches to building detection from multi-source aerial data
No full textAutomatic building detection has been a hot topic since the early 1990’s. Early approaches were based on a single aerial image. Detecting buildings is a difficult task so it can be more effective when multiple sources of information are obtained and fused. The objective of this paper is to provide a comparative analysis of automatic approaches to building detection from multi-source aerial images. We analysed data related to both urban and suburban areas and took into consideration both object based and pixel-based methods. Although many of these methods perform full data classification, we focused only on the detection of building regions. Three measures were used for the evaluation of the performance of each method: number of detected buildings to their total number (detection rate), number of objects wrongly detected as buildings (false positive) and number of missed buildings (false negative) to the number of detected buildings. The data sets we used were RGB and colour infrared (CIR) orthoimages and Digital Surface Models (DSMs) obtained by an airborne laser scanner, which provides a first pulse DSM and a last pulse DSM. In addition, we derived from these data and used other four sources of information: a Digital Terrain Model (DTM) obtained from a filtered version of the last pulse DSM, the height difference between the last pulse and the DTM, the height difference between the first and the last pulse and the Normalized Difference Vegetation Index (NVDI) derived from the red and infrared channels.We analysed results coming from three classification algorithms, namely Bayesian, Dempster-Shafer and AdaBoost, applied to the features extracted both at pixel level and at object level. To obtain a very realistic comparison we used the same training set for all methods, either pixel-based or object-based. Results obtained are interesting and can be synthesised in the need of fusing (the results of) more approaches to yield the best results.Remote SensingAerospace Engineerin
Complete classification of raw LIDAR data and 3D reconstruction of buildings
No full textLIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach
Performance evaluation of automated approaches to building detection in multi-source aerial data
No full textAutomated approaches to building detection in multi-source aerial data are important in many applications, including map updating, city modeling, urban growth analysis and monitoring of informal settlements. This paper presents a comparative analysis of different methods for automated building detection in aerial images and laser data at different spatial resolutions. Five methods are tested in two study areas using features extracted at both pixel level and object level, but with the strong prerequisite of using the same training set for all methods. The evaluation of the methods is based on error measures obtained by superimposing the results on a manually generated reference map of each area. The results in both study areas show a better performance of the Dempster-Shafer and the AdaBoost methods, although these two methods also yield a number of unclassified pixels. The method of thresholding a normalized DSM performs well in terms of the detection rate and reliability in the less vegetated Mannheim study area, but also yields a high rate of false positive errors. The Bayesian methods perform better in the Memmingen study area where buildings have more or less the same heights.Aerospace Engineerin
Degradation of trichloroethylene vapors by micrometric zero-valent Fe-Cu and Fe-Ni bimetals under partially saturated conditions
No full textThe degradation of trichloroethylene (TCE) vapors by zero-valent Iron-Copper (Fe-Cu) and Iron-Nickel (Fe-Ni) bimetals with 1%, 5% and 20% weight content (%wt) of Cu or Ni was tested in anaerobic batch vapor systems carried out at ambient room temperature (20 & PLUSMN; 2 degrees C) under partially saturated conditions. The concentrations of TCE and byproducts were determined at discrete reaction time intervals (4 h-7 days) by analyzing the headspace vapors. In all the experiments, up to 99.9% degradation of TCE in the gas phase was achieved after 2-4 days with zero-order TCE degradation kinetic constants in the range of 134-332 g mair 3d - 1. Fe-Ni showed a higher reactivity towards TCE vapors compared to Fe-Cu, with up to 99.9% TCE dechlorination after 2 days of reaction, i.e., significantly higher than zero-valent iron alone that in previous studies was found to achieve comparable TCE degradation after minimum 2 weeks of reaction. The only detectable byproducts of the reactions were C3-C6 hydrocarbons. Neither vinyl chloride or dichloroethylene peaks were detected in the tested conditions above their method quantification limits that were in the order of 0.01 g mair 3. In view of using the tested bimetals in horizontal permeable reactive barriers (HPRBs) placed in the unsaturated zone to treat chlorinated solvent vapors emitted from contaminated groundwater, the experimental results obtained were integrated into a simple analytical model to simulate the reactive transport of vapors through the barrier. It was found that an HPRB of 20 cm could be potentially effective to ensure TCE vapors reduction
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