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A survey of Eimeria infections was performed in dairy goats and kids (<6 months old) of six farms from a dry desert area of Gran Canaria Island (Spain). The number of oocysts per gram of faeces (OPG) was determined by a modified McMaster technique over a total of 2,616 individual faecal samples taken from the rectum in monthly intervals. Eimeria oocysts were found in 96.1% of the samples with OPG ranging from 1 x 10(2) to 1.4 x 10(6). Kid goats had significantly (P < 0.001) higher OPG counts (46,496 +/- 5,228) than dairy females (2,225 +/- 287). Eight Eimeria species were identified, with Eimeria ninakohlyakimovae (30.0%), Eimeria arloingi (28.6%) and Eimeria alijevi (20.5%) being the most frequent species followed by Eimeria caprina (9.1%), Eimeria christenseni (4.5%), Eimeria jolchijevi (3.4%), Eimeria caprovina (3.2%) and Eimeria hirci (0.7%). Although significant differences were observed among goat groups and herds, the eight species were present in the six farms in both dairy goats and kids. The intensity of oocysts shedding was related to some factors such as the size of the herd and was further influenced by the prevailing climatic conditions of the area. The highest OPG counts were recorded during the hot season in dairy goats and close to weaning time in kids reared in small farms having no prophylactic treatments against eimeriosis.

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21Haemonchus contortus (H. contortus) is a haematophagous parasite which causes

Abstract.A new parallel algorithm for simultaneous untangling and smoothing of tetrahedral meshes is proposed in this paper. We provide a detailed analysis of its performance on shared-memory many-core computer architectures. This performance analysis includes the evaluation of execution time, parallel scalability, load balancing, and parallelism bottlenecks. Additionally, we compare the impact of three previously published graph coloring procedures on the performance of our parallel algorithm. We use six benchmark meshes with a wide range of sizes. Using these experimental data sets, we describe the behavior of the parallel algorithm for different data sizes. We demonstrate that this algorithm is highly scalable when it runs on two different high-performance many-core computers with up to 128 processors. However, some parallel deterioration is observed. Here, we analyze the main causes of this parallel deterioration.

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