African swine fever virus transmission cycles in Central Europe: evaluation of wild boar-soft tick contacts through detection of antibodies against Ornithodoros erraticus saliva antigen.

BACKGROUND African swine fever (ASF) is one of the most complex viral diseases affecting both domestic and wild pigs. It is caused by ASF virus (ASFV), the only DNA virus which can be efficiently transmitted by an arthropod vector, soft ticks of the genus Ornithodoros. These ticks can be part of ASFV-transmission cycles, and in Europe, O. erraticus was shown to be responsible for long-term maintenance of ASFV in Spain and Portugal. In 2014, the disease has been reintroduced into the European Union, affecting domestic pigs and, importantly, also the Eurasian wild boar population. In a first attempt to assess the risk of a tick-wild boar transmission cycle in Central Europe that would further complicate eradication of the disease, over 700 pre-existing serum samples from wild boar hunted in four representative German Federal States were investigated for the presence of antibodies directed against salivary antigen of Ornithodoros erraticus ticks using an indirect ELISA format. RESULTS Out of these samples, 16 reacted with moderate to high optical densities that could be indicative of tick bites in sampled wild boar. However, these samples did not show a spatial clustering (they were collected from distant geographical regions) and were of bad quality (hemolysis/impurities). Furthermore, all positive samples came from areas with suboptimal climate for soft ticks. For this reason, false positive reactions are likely. CONCLUSION In conclusion, the study did not provide stringent evidence for soft tick-wild boar contact in the investigated German Federal States and thus, a relevant involvement in the epidemiology of ASF in German wild boar is unlikely. This fact would facilitate the eradication of ASF in the area, although other complex relations (wild boar biology and interactions with domestic pigs) need to be considered.

Principal Component Analysis of Results Obtained from Finite-Difference Time-Domain Algorithms

Finite-Differences Time-Domain (FDTD) algorithms are well established tools of computational electromagnetism. Because of their practical implementation as computer codes, they are affected by many numerical artefact and noise. In order to obtain better results we propose using Principal Component Analysis (PCA) based on multivariate statistical techniques. The PCA has been successfully used for the analysis of noise and spatial temporal structure in a sequence of images. It allows a straightforward discrimination between the numerical noise and the actual electromagnetic variables, and the quantitative estimation of their respective contributions. Besides, The GDTD results can be filtered to clean the effect of the noise. In this contribution we will show how the method can be applied to several FDTD simulations: the propagation of a pulse in vacuum, the analysis of two-dimensional photonic crystals. In this last case, PCA has revealed hidden electromagnetic structures related to actual modes of the photonic crystal.

Spatial dynamics of bovine tuberculosis in the Autonomous Community of Madrid, Spain (2010-2012)

Progress in control of bovine tuberculosis (bTB) is often not uniform, usually due to the effect of one or more sometimes unknown epidemiological factors impairing the success of eradication programs. Use of spatial analysis can help to identify clusters of persistence of disease, leading to the identification of these factors thus allowing the implementation of targeted control measures, and may provide some insights of disease transmission, particularly when combined with molecular typing techniques. Here, the spatial dynamics of bTB in a high prevalence region of Spain were assessed during a three year period (2010-2012) using data from the eradication campaigns to detect clusters of positive bTB herds and of those infected with certain Mycobacterium bovis strains (characterized using spoligotyping and VNTR typing). In addition, the within-herd transmission coefficient (β) was estimated in infected herds and its spatial distribution and association with other potential outbreak and herd variables was evaluated. Significant clustering of positive herds was identified in the three years of the study in the same location ("high risk area"). Three spoligotypes (SB0339, SB0121 and SB1142) accounted for >70% of the outbreaks detected in the three years. VNTR subtyping revealed the presence of few but highly prevalent strains within the high risk area, suggesting maintained transmission in the area. The spatial autocorrelation found in the distribution of the estimated within-herd transmission coefficients in herds located within distances <14 km and the results of the spatial regression analysis, support the hypothesis of shared local factors affecting disease transmission in farms located at a close proximity.