Occurrence of Human Enteric Viruses in Commercial Mussels at Retail Level in Three European Countries

In this study, the prevalence of different enteric viruses in commercial mussels was evaluated at the retail level in three European countries (Finland, Greece and Spain). A total of 153 mussel samples from different origins were analysed for human norovirus (NoV) genogroups I and II, hepatitis A virus (HAV) and hepatitis E virus (HEV). Human adenovirus (HAdV) was also tested as an indicator of human faecal contamination. A full set of controls (such as sample process control, internal amplification controls, and positive and negative controls) were implemented during the process. The use of a sample process control allowed us to calculate the efficiencies of extraction, which ranged from 79 to 0.5?%, with an average value of 10?%. Samples were positive in 41?% of cases, with HAdV being the most prevalent virus detected (36?%), but no significant correlation was found between the presence of HAdV and human NoV, HAV and HEV. The prevalences of human norovirus genogroup II, HEV and human NoV genogroup I were 16, 3 and 0.7?%, respectively, and HAV was not detected. The estimated number of PCR detectable units varied between 24 and 1.4?×?10³?g^?1 of digestive tract. Interestingly, there appeared to be a significant association between the type of mussel species (M. galloprovincialis) and the positive result of samples, although a complete overlap between country and species examined required this finding to be confirmed including samples of both species from all possible countries of origin.     

Identifying pathways to visions of future land use in Europe

Regional Environmental Change - Plausible scenarios of future land use derived from model projections may differ substantially from what is actually desired by society, and identifying such...     

Degradation of biochanin A in soil

Degradation of biochanin A, 400 μg (1.41 μmol) per gram of dry soil, was studied and the initial degradation products, dihydrobiochanin A, pratensein and genistein were identified and quantified. The maximum concentrations of these compounds were determined as 166 pmol/g dry soil after 2 days of incubation, 175 pmol/g dry soil after 1 day and 1,236 pmol/g dry soil after 1 day, respectively. Biochanin A and the initially formed products degrade too fast to be responsible for clover soil sickness (DT₉₀ <20 days), but could be precursors for the responsible compounds. The immediate degradation products from biochanin A are all biologically active and are the prime candidates for a reduction of weed pressure caused by clover.     

Evolving social influence in large populations

Darwinian studies of collective human behaviour, which deal fluently with change and are grounded in the details of social influence among individuals, have much to offer “social” models from the physical sciences which have elegant statistical regularities. Although Darwinian evolution is often associated with selection and adaptation, “neutral” models of drift are equally relevant. Building on established neutral models, we present a general, yet highly parsimonious, stochastic model, which generates an entire family of real-world, right-skew socio-economic distributions, including exponential, winner-take-all, power law tails of varying exponents, and power laws across the whole data. The widely used Barabási and Albert (1999) Science 286: 509-512 “B-A” model of preferential attachment is a special case of this general model. In addition, the model produces the continuous turnover observed empirically within these distributions. Previous preferential attachment models have generated specific distributions with turnover using arbitrary add-on rules, but turnover is an inherent feature of our model. The model also replicates an intriguing new relationship, observed across a range of empirical studies, between the power law exponent and the proportion of data represented in the distribution.     

Model-driven development of covariances for spatiotemporal environmental health assessment

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998–1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country.