The need to inform pig farmers about biological occupational risks is therefore crucial.”
“The purpose of this study was to prepare and characterize virosome containing envelope proteins of the

avian influenza (H5N1) virus. The virosome was prepared by the solubilization of virus with octaethyleneglycol mono (n-dodecyl) ether (C12E8) followed by detergent removal with SM2 Bio-Beads. Biochemical analysis by SDS-PAGE and western blotting, indicated that avian influenza learn more H5N1 virosome had similar characteristics to the parent virus and contained both the hemagglutinin (HA, 60-75 kDa) and neuraminidase (NA, 220 kDa) protein, with preserved biological activity, such as hemagglutination activity. The virosome structure was analyzed by negative

stained transmission electron microscope (TEM) demonstrated that the spherical shapes of vesicles with surface glycoprotein spikes were harbored. In conclusion, the biophysical properties of the virosome selleck chemical were similar to the parent virus, and the use of octaethyleneglycol mono (n-dodecyl) ether to solubilize viral membrane, followed by removal of detergent using polymer beads adsorption (BioBeads SM2) was the preferable method for obtaining avian influenza virosome. The outcome of this study might be useful for further development veterinary virus vaccines. (C) 2013 PVJ. All rights reserved”
“QuestionIncreasing population pressure, socio-economic development and associated natural BI 6727 resource use in savannas are resulting in large-scale land cover changes, which can be mapped using remote sensing. Is a three-dimensional (3D) woody vegetation

structural classification applied to LiDAR (Light Detection and Ranging) data better than a 2D analysis to investigate change in fine-scale woody vegetation structure over 2yrs in a protected area (PA) and a communal rangeland (CR)? LocationBushbuckridge Municipality and Sabi Sand Wildtuin, NE South Africa. MethodsAirborne LiDAR data were collected over 3300ha in April 2008 and 2010. Individual tree canopies were identified using object-based image analysis and classified into four height classes: 1-3, 3-6, 6-10 and bigger than 10m. Four structural metrics were calculated for 0.25-ha grid cells: canopy cover, number of canopy layers present, cohesion and number of height classes present. The relationship between top-of-canopy cover and sub-canopy cover was investigated using regression. Gains, losses and persistence (GLP) of cover at each height class and the four structural metrics were calculated. GLP of clusters of each structural metric (calculated using LISA – Local Indicators of Spatial Association – statistics) were used to assess the changes in clusters of each metric over time. ResultsTop-of-canopy cover was not a good predictor of sub-canopy cover.