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We evaluated the use of coordinated fallowing as a means to control salmon lice Lepeophtheirus salmonis infestation in farmed Atlantic salmon Salmo salar. In discrete management zones, aquaculture operations such as stocking, fallowing, treatments and harvesting are synchronized at all sites in coordinated areas within the zones. The expected benefit of synchronized generations is to reduce the presence of salmon lice larvae after a period of fallowing, as well as to minimize external infestation pressure from surrounding aquaculture sites. A regression analysis was used to evaluate the effectiveness of coordinated fallowing on the progression of external salmon lice infestation pressure and abundance in Atlantic salmon farming sites in 2 areas (zones) in Norway. The overall results show that external infestation pressure was higher inside than outside the management zones, and the external infestation pressure increased with increasing biomass throughout the production cycle. However, within the zones, the external infestation pressure at the beginning of a production cycle was high and in many cases even higher than the general external infestation pressure in the non-coordinated areas. This suggests that external infestation pressure from the neighboring areas has a considerable effect on the fallowed area. Higher numbers of salmon lice were recorded within the zones than outside and, as the production cycle progressed, this phenomenon became more evident. We conclude that the high infestation pressure from salmon lice at the beginning of the grow-out period after fallowing raises severe doubts about the effectiveness of coordinated fallowing practices.

Salmonid alphavirus (SAV) is the OIE-listed, viral cause of pancreas disease (PD) in farmed Atlantic salmon. SAV is routinely detected by PCR-methods while typical histopathological lesions are additionally used to confirm the diagnosis. Field evaluation of diagnostic test performance is essential to ensure confidence in a test's ability to predict the infection or disease status of a target animal. For most tests used in aquaculture, characteristics like sensitivity (Se) and specificity (Sp) at the analytical level may be known. Few tests are, however, evaluated at the diagnostic level according to the OIE standard. In the present work, we estimated diagnostic test sensitivity (DSe) and diagnostic test specificity (DSp) for five laboratory tests used for SAV detection. As there is no gold standard, the study was designed using Bayesian latent class analysis. Real-time RT-PCR, cell culture, histopathology, virus neutralization test, and immunohistochemistry were compared using samples taken from three different farmed Atlantic salmon populations with different infection status; one population regarded negative, one in an early stage of infection, and one in a later stage of infection. The average fish weight in the three populations was 2.0, 1.6, and 1.5 kg, respectively. The DSe and DSp of real-time RT-PCR is of particular interest due to its common use as a screening tool. The method showed high DSe (≥0.977) and moderate DSp (0.831) in all 3-populations models. The results further suggest that a follow-up test of serum samples in real-time RT-PCR negative populations may be prudent in cases where epidemiological information suggest a high risk of infection and where a false negative result is of high consequence. This study underlines the need to choose a test appropriate for the purpose of the testing. In the case of a weak positive PCR-result, a follow-up test should be conducted to verify the presence of SAV. Cell culture showed high DSe and DSp and may be used to verify viral presence.

Both cloud and GRID are computing paradigms that manage large distributed resources, and currently there is a lot of interest in their integration. An integrated architecture cloud-GRID requires fine-grained access control and identity federation among untrusted distributed domains in the cloud. This paper deals with the trade-off between security and performance in such architectures, comparing the overhead introduced by cloud services with different security levels. The quantitative results obtained in PerfCloud, an existing cloudgrid infrastructure, are presented and discussed.