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Populations are exposed daily to numerous environmental pollutants, particularly through food. To address environmental issues, many agricultural production methods have been developed, including organic farming. To date, there is no exhaustive inventory of the contamination of organic foods as there is for conventional foods. The main objective of this work was to construct a growing and updatable database on chemical substances and their levels in organic foods consumed in Europe. To this end, a literature search was conducted, resulting in a total of 1207 concentration values from 823 food–substances pairs involving 166 food matrices and 209 chemical substances, among which 95% were not authorized in organic farming and 80% were pesticides. The most encountered substance groups are "inorganic contaminants" and "organophosphate", and the most studied food groups are "fruit used as fruit" and "Cereals and cereal primary derivatives". Further studies are needed to continue updating the database with robust and comprehensive data on organic food contamination. This database could be used to study the health risks associated with these contaminants. [ABSTRACT FROM AUTHOR]

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Wild birds share with humans the capacity for moving fast over large distances. During migratory movements, birds carry pathogens that can be transmitted between species at breeding, wintering, and stopover [...]

Diffusion of a particle through an energy and diffusivity landscape is a very general phenomenon in numerous systems of soft and condensed matter. On the one hand, theoretical frameworks such as Langevin and Fokker-Planck equations present valuable accounts to understand these motions in great detail, and numerous studies have exploited these approaches. On the other hand, analytical solutions for correlation functions, as, e.g., desired by experimentalists for data fitting, are only available for special cases. We explore the possibility to use different theoretical methods in the specific picture of time-dependent switching between diffusive states to derive analytical functions that allow to link experimental and simulation results to theoretical calculations. In particular, we present a closed formula for diffusion switching between two states, as well as a general recipe of how to generalize the formula to multiple states. [ABSTRACT FROM AUTHOR]

Abstract Background Tuberculosis (TB), an infectious disease caused by the Mycobacterium tuberculosis is endemic in Madagascar. The capital, Antananarivo is the most seriously affected area. TB had a non-random spatial distribution in this setting, with clustering in the poorer areas. The aim of this study was to explore this pattern further by a Bayesian approach, and to measure the associations between the spatial variation of TB risk and national control program indicators for all neighbourhoods. Methods Combination of a Bayesian approach and a generalized linear mixed model (GLMM) was developed to produce smooth risk maps of TB and to model relationships between TB new cases and national TB control program indicators. The TB new cases were collected from records of the 16 Tuberculosis Diagnostic and Treatment Centres (DTC) of the city from 2004 to 2006. And five TB indicators were considered in the analysis: number of cases undergoing retreatment, number of patients with treatment failure and those suffering relapse after the completion of treatment, number of households with more than one case, number of patients lost to follow-up, and proximity to a DTC. Results In Antananarivo, 43.23% of the neighbourhoods had a standardized incidence ratio (SIR) above 1, of which 19.28% with a TB risk significantly higher than the average. Identified high TB risk areas were clustered and the distribution of TB was found to be associated mainly with the number of patients lost to follow-up (SIR: 1.10, CI 95%: 1.02-1.19) and the number of households with more than one case (SIR: 1.13, CI 95%: 1.03-1.24). Conclusion The spatial pattern of TB in Antananarivo and the contribution of national control program indicators to this pattern highlight the importance of the data recorded in the TB registry and the use of spatial approaches for assessing the epidemiological situation for TB. Including these variables into the model increases the reproducibility, as these data are already available for individual DTCs. These findings may also be useful for guiding decisions related to disease control strategies.

To the Editor: In a recent article (1), Kaplan et al. addressed the problems in detecting a bioterror attack from blood-donor screening. The main point of this comment is the [...]

Highly pathogenic avian influenza (HPAI) risk management requires efficient surveillance of the infection in wild birds for early warning purposes. In this study, our aim was to describe the spread of continent‐wide infection cases using a fireworks model and therefore improve current surveillance systems. The fireworks model is a metaphor illustrating the spread of HPAI as a point source epizootic. The approach is based on early detection of the outbreak seeds (sparks from the fireworks) and uses a predictive model of the probability of the occurrence of new cases following a seed introduction; this then determines the spatiotemporal perimeter for intense surveillance investigations. For a case study, we used surveillance data on HPAI H5N1 in wild birds across Europe between 2005 and 2010 to describe the outbreaks and determine the success of the case detection used to inform management of the disease. The fireworks description assumes simultaneous introductions of 'seeds' of cases, which then 'explode' in local foci but do not merge into a progressive disease wave. This model fits the data well. Using this predictive approach for HPAI cases in EU countries, we found that the investigation radius needed to achieve a detection level of 90% of new cases after an outbreak ranged from 10 km to more than 300 km, depending on the outbreak pattern. Based on these findings, the fireworks approach can be a valuable method for identifying the perimeters and risk areas to be targeted for enhanced surveillance. The rationale of the fireworks approach is quite generic and can easily be adapted to different situations and contexts. [ABSTRACT FROM AUTHOR]