Seven donkeys that died due to various health problems or were euthanased on a welfare ground were necropsied and the parasites were recovered and identified to the species level. The study was conducted during the periods 1996-1999.\n\nCoprological examination revealed 99% strongyle, 80% Fasciola, 51%
Parascaris, 30% Gastrodiscus, 11% Strongyloides westeri, 8% cestodes and 2% Oxyuris equi infection prevalence. Over 55% of donkeys had more than 1000 eggs per gram of faeces (epg). Forty two different species of parasites URMC-099 MAPK inhibitor consisting of 33 nematodes, 3 trematodes, 3 cestodes and 3 arthropod larvae were identified from postmortem examined Alvocidib Cell Cycle inhibitor donkeys. Among the nematodes 17 species of Cyathostominae and 7 species of Strongylinae were identified. Other parasites identified include, Habronema muscae, Draschia megastoma, Trichostrongylus axei, Strongyloides westeri, Anoplocephala perfoliata, Anoplocephala magna, Anoplocephaloides (Paranoplocephala) mamillana, Parascaris equorum, Fasciola hepatica, Fasciola gigantica, Gastrodiscus aegyptiacus, Dictyocaulus arnfieldi, Oxyuris equi, Probstmayria vivipara, Gasterophilus intestinalis, Gasterophilus nasalis, Rhinoestrus uzbekistanicus and Setaria equina. This study revealed that working donkeys in Ethiopia are infected with a range of helminths and arthropod
larvae, which are representatives of the important pathogenic parasites found in equids worldwide.”
“Changes in cropland have been the dominating land use changes in Central and Eastern Europe, with cropland abandonment frequently exceeding
cropland expansion. However, surprisingly little is known about the rates, spatial patterns, and determinants of cropland change in Eastern Europe. We study cropland changes between 1995 and 2005 in Arges, County in Pevonedistat price Southern Romania with two distinct modeling techniques. We apply and compare spatially explicit logistic regressions with artificial neural networks (ANN) using an integrated socioeconomic and environmental dataset. The logistic regressions allow identifying the determinants of cropland changes, but cannot deal with non-linear and complex functional relationships nor with collinearity between variables. ANNs relax some of these rigorous assumptions inherent in conventional statistical modeling, but likewise have drawbacks such as the unknown contribution of the parameters to the outcome of interest. We compare the outcomes of both modeling techniques quantitatively using several goodness-of-fit statistics. The resulting spatial predictions serve to delineate hotspots of change that indicate areas that are under more eminent threat of future abandonment.