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This report presents the results of a feasibility study for improving transparency and accessibility of the models implemented in the Monte Carlo Risk Assessment (MCRA) web platform for assessing the risk of combined exposure to multiple chemicals via multiple exposure routes. It provides an overview of the history and current use of the web platform and its scientific and regulatory context. The technical infrastructure of the current MCRA web platform and the modular design of the various models of MCRA are described. Based on selected user stories, user requirements are established, and suggestions are made for possible designs of an open source collection of MCRA models. Technical options are listed for code organisation, programming language, interfacing and containerisation, data organisation, storage and licensing.Also, the role of the MCRA web platform is discussed. The report ends with a suggested approach and next steps, includinga discussion on the need for a governance structure for sustainable development of Open MCRA.

International audience; Physiologically-based toxicokinetic (PBTK) models are important tools for in vitro to in vivo or inter-species extrapolations in health risk assessment of foodborne and non-foodborne chemicals. Here we present a generic PBTK model implemented in the EuroMix toolbox, MCRA 9 and predict internal kinetics of nine chemicals (three endocrine disrupters, three liver steatosis inducers, and three developmental toxicants), in data-rich and data-poor conditions, when increasingly complex levels of parametrization are applied. At the first stage, only QSAR models were used to determine substance-specific parameters, then some parameter values were refined by estimates from substance-specific or high-throughput in vitro experiments. At the last stage, elimination or absorption parameters were calibrated based on available in vivo kinetic data. The results illustrate that parametrization plays a capital role in the output of the PBTK model, as it can change how chemicals are prioritized based on internal concentration factors. In data-poor situations, estimates can be far from observed values. In many cases of chronic exposure, the PBTK model can be summarized by an external to internal dose factor, and interspecies concentration factors can be used to perform interspecies extrapolation. We finally discuss the implementation and use of the model in the MCRA risk assessment platform.

The bisphenols S, F, and AF (BPS, BPF, and BPAF) are used to replace the endocrine disrupting chemical bisphenol A (BPA) while exerting estrogenic effects of comparable potency. We assessed the cumulative risk for the aforementioned BPs in Europe and compared the risk before and after the year 2011, which was when the first BPA restrictions became effective. For this, we probabilistically modeled external exposures from food, personal care products (PCPs), thermal paper, and dust (using the tools MCRA and PACEM for exposures from food and PCPs, respectively). We calculated internal concentrations of unconjugated BPs with substance-specific PBPK models and cumulated these concentrations normalized by estrogenic potency. The resulting mean internal cumulative exposures to unconjugated BPs were 3.8 and 2.1 ng/kg bw/day before and after restrictions, respectively. This decline was mainly caused by the replacement of BPA by BPS in thermal paper and the lower dermal uptake of BPS compared to BPA. However, the decline was not significant: the selected uncertainty intervals overlapped (P2.5–P97.5 uncertainty intervals of 2.7–4.9 and 1.3–6.3 ng/kg bw/day before and after restrictions, respectively). The upper uncertainty bounds for cumulative exposure were higher after restrictions, which reflects the larger uncertainty around exposures to substitutes compared to BPA., Environmental Science & Technology, 53 (15), ISSN:0013-936X, ISSN:1520-5851

Mixtures of substances to which humans are exposed may lead to cumulative exposure and health effects. To study their effects, it is first necessary to identify a cumulative assessment group (CAG) of substances for risk assessment or hazard testing. Excluding substances from consideration before there is sufficient evidence may underestimate the risk. Conversely, including everything and treating the inevitable uncertainties using conservative assumptions is inefficient and may overestimate the risk, with an unknown level of protection. An efficient, transparent strategy is described to retain a large group, quantifying the uncertainty of group membership and other uncertainties. Iterative refinement of the CAG then focuses on adding information for the substances with high probability of contributing significantly to the risk. Probabilities can be estimated using expert opinion or derived from data on substance properties. An example is presented with 100 pesticides, in which the retain step identified a single substance to target refinement. Using an updated hazard characterisation for this substance reduced the mean exposure estimate from 0.43 to 0.28 μg kg-bw−1 day−1 and reduced the 99.99th percentile exposure from 24.9 to 5.1 μg kg-bw−1 day−1. Other retained substances contributed little to the risk estimates, even after accounting for uncertainty.

International audience; A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates.

Populations are exposed to mixtures of pesticides through their diet on a daily basis. The question of which substances should be assessed together remains a major challenge due to the complexity of the mixtures. In addition, the associated risk is difficult to characterise. The EuroMix project (European Test and Risk Assessment Strategies for Mixtures) has developed a strategy for mixture risk assessment. In particular, it has proposed a methodology that combines exposures and hazard information to identify relevant mixtures of chemicals belonging to any cumulative assessment group (CAG) to which the European population is exposed via food. For the purposes of this study, food consumption and pesticide residue data in food and drinking water were obtained from national surveys in nine European countries. Mixtures of pesticides were identified by a sparse non-negative matrix underestimation (SNMU) applied to the specific liver steatosis effect in children from 11 to 15 years of age, and in adults from 18 to 64 years of age in nine European countries. Exposures and mixtures of 144 pesticides were evaluated through four different scenarios: (1) chronic exposure with a merged concentration dataset in the adult population, (2) chronic exposure with country-specific concentration datasets in the adult population, (3) acute exposure with a merged concentration dataset in the adult population, and (4) chronic exposure with a merged concentration dataset in the paediatric population. The relative potency factors of each substance were calculated to express their potency relative to flusilazole, which was chosen as the reference compound. The selection of mixtures and the evaluation of exposures for each country were carried out using the Monte Carlo Risk Assessment (MCRA) software. Concerning chronic exposure, one mixture explained the largest proportion of the total variance for each country, while in acute exposure, several mixtures were often involved. The results showed that there were 15 main pesticides in the mixtures, with a high contribution of imazalil and dithiocarbamate. Since the concentrations provided by the different countries were merged in the scenario using merged concentration data, differences between countries result from differences in food consumption behaviours. These results support the approach that using merged concentration data to estimate exposures in Europe seems to be realistic, as foods are traded across European borders. The originality of the proposed approach was to start from a CAG and to integrate information from combined exposures to identify a refined list of mixtures with fewer components. As this approach was sensitive to the input data and required significant resources, efforts should continue regarding data collection and harmonisation among the different aspects within the pesticides regulatory framework, and to develop methods to group substances and mixtures to characterise the risk.

Human exposure to pesticide mixtures can occur from the diet and other sources. Realistic exposure and risk assessments should include multiple sources and compounds and include the relative hazards of the different compounds. The EU-funded Euromix project is developing new web-based tools to facilitate these calculations. A case study is presented that exemplifies their use for a population of UK residents, including exposure from crop-spraying. A UK pesticide usage survey provided information on real pesticide combinations applied to crops of wheat, potatoes, sugar beet and dessert apples. This information was combined with outputs from two alternative simulation models of spray drift to estimate dermal, oral and inhalation exposures of residents. These non-dietary exposures were combined with dietary exposure estimates using the Monte Carlo Risk Assessment software to produce a distribution of aggregated and cumulative exposures. Compounds are weighted by relative potency to generate a measure of overall risk. Uncertainty quantification was also included in the distribution of exposures. These tools are flexible to allow diverse sources of exposure and can provide important information to decision-makers and help to prioritise testing of pesticide mixtures. Including non-dietary sources changed the prioritisation of pesticide mixtures, when compared to dietary exposure alone.

Accounting for substitution of foods is inevitable when evaluating health impact of dietary changes. But substitution behavior and the associated health impact may vary between individuals. We therefore propose the use of probabilistic methods to model substitution and assess health impact distributions in risk-benefit assessment (RBA) of foods. We investigated the health impact of substituting red and processed meat with fish in the Danish adult population and the variability in health impact. We applied probabilistic approaches in modeling the substitution to reflect variability between individual substitution behaviors. Furthermore, when multiple intake scenarios are compared, we propose a method for adjusting intake differences for individual day-to-day variability. We estimated that 134 (95% UI: 102; 169) Disability-Adjusted Life Years/100,000 were averted per year by the substitution. The health impact varied considerably by age and sex, with the largest health benefit of the substitution observed for young women in the child-bearing age and for the older generation, mainly men. This study provides further insight in how the health impact of substituting meat by fish varies between individuals and suggests a framework to be applied in RBAs of other food substitutions. Our results are relevant for policy makers in defining targeted public health strategies.

In the context of the second framework partnership agreement between the National Institute for Public Health and the Environment of the Netherlands (RIVM) and the European Food Safety Authority (EFSA)acute cumulative dietary exposure assessments were performed for two cumulative assessment groups (CAGs) of pesticidesthat affect the nervous system: pesticides associated with brain and/or erythrocyte AChE inhibition (CAG-NAN, 47 pesticides) and pesticides associated with functional alterations of the motor division (CAG-NAM, 100 pesticides). The exposure assessments used pesticide monitoring data collected by Member States under their official monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Exposure estimates were obtained for each group of pesticides by means of a 2-dimensional Monte Carlo simulation, which was implemented in the Monte Carlo Risk Assessment (MCRA) software.The scope of the assessment and the parameters to be used for cumulative exposure assessment were discussed and agreed by the Standing Committee on Plants, Animals, Food and Feed (SC PAFF). Based on those discussions, a very conservative tier I modelling approach and a refined, but still conservative tier II modelling approach were used. In these assessments, common risk assessment practice was followed and the cumulative exposure was calculated as total margin of exposure (MOET) at the 50th, 90th, 95th, 99th and 99.9th percentiles of the exposure distribution. Five sensitivity analyses aiming to address major uncertainties were performed. The exposure estimates obtained in this report are used in EFSA's scientific report on the cumulative dietary risk characterisation of pesticides that have acute effects on the nervous system.

Through their diet, populations are exposed to mixtures of chemicals which can cause health diseases. Due to the complexity of mixtures, the question of which substances should be assessed together...

Human populations are exposed to many chemicals that may cause the same health effect. In a risk assessment, exposures coming from dietary and non-dietary routes (e.g. oral, dermal and inhalation) ...

Bisphenol A (BPA) is used in the production of polycarbonate plastics and epoxy resins and for the color development in thermal paper (TP). In the EU, since 2011 its use in plastic materials and ar...

Portion size estimation is expected to be one of the largest sources of uncertainty in dietary assessment of the individual. Therefore, we demonstrated a method to quantify uncertainty due to portion size estimation in the usual intake distributions of vegetables, fruit, bread, protein, and potassium. Dutch participants of the European Food Consumption Validation study completed 2 nonconsecutive 24-h recall interviews. In short, the uncertainty analysis consists of Monte Carlo simulations drawing values for portion size from lognormal uncertainty distributions. The uncertainty of the usual intake distribution and accompanying parameters (IQR and the shrinkage factor) were estimated. For the food groups, portion size uncertainty had the greatest effect for vegetables and the least for fruit: the relative 95% uncertainty interval (UI) of the IQR of the usual intake distribution was 0.61-1.35 for vegetables, 0.77-1.24 for bread, and 0.99-1.10 for fruit. For protein and potassium, the resulting relative width of the UI of the IQR for portion size uncertainty are similar 0.88-1.14 for protein and 0.86-1.14 for potassium. Furthermore, a sensitivity analysis illustrated the importance of the specified uncertainty distributions, The examples show that uncertainty in portion sizes may be more important for some foods such as vegetables. This may reflect differential quantification errors by food groups that deserve further consideration. In conclusion, the presented methodology allows the important quantification of portion size uncertainty and extensions to include other sources of uncertainty is straightforward. J. Nutr. 141: 1396-1401, 2011.

Translocation of a green fluorescent protein (GFP)-tagged Dickeya sp. from stems or from leaves to underground parts of potato plants was studied in greenhouse experiments. Thirty days after stem inoculation, 90% of plants expressed symptoms at the stem base and 95% of plants showed browning of internal stem tissue. The GFP-tagged Dickeya sp. was detected by dilution plating in extracts of the stem interiors (100%), stem bases (90%), roots (80%), stolons (55%), and progeny tubers (24%). In roots, the GFP-tagged Dickeya sp. was found inside and between parenchyma cells whereas, in stems and stolons, the GFP-tagged Dickeya sp. was found in the xylem vessels and protoxylem cells. In progeny tubers, this strain was detected in the stolon end. Thirty days after leaf inoculation, the GFP-tagged Dickeya sp. was detected in extracts of 75% of the leaves, 88% of the petioles, 63% of the axils, and inside 25% of the stems taken 15 cm above the ground level. UV microscopy confirmed the presence of the GFP-tagged Dickeya sp. inside petioles and in the main leaf veins. No blackleg or aerial stem rot and no translocation of the GFP-tagged Dickeya sp. to underground plant parts was observed. The implications for contamination of progeny tubers are discussed.

Various models exist for estimating the usual intake distribution from dietary intake data. In this paper, we compare two of these models, the Iowa State University Foods (ISUF) model and the betabinomial-normal (BBN) model and apply them to three different datasets. Intake data are obtained by aggregating over multiple food products and are often non-normal. The ISUF and BBN model both address non-normality. While the two models have similar structures, they show some differences. The ISUF model includes an additional spline transformation for improving the normality of the intake amount distribution, while the BBN model includes the possibility of addressing covariates, such as age or sex. Our analyses showed that for two of the example datasets both models produced similar estimates of the higher percentiles of the usual intake distribution. However, for the third dataset, where the intake amount distribution appear to be multimodal, both models produced different percentile estimates.

Pesticide risk assessment is hampered by worst-case assumptions leading to overly pessimistic assessments. On the other hand, cumulative health effects of similar pesticides are often not taken into account. This paper describes models and a web-based software system developed in the European research project ACROPOLIS. The models are appropriate for both acute and chronic exposure assessments of single compounds and of multiple compounds in cumulative assessment groups. The software system MCRA (Monte Carlo Risk Assessment) is available for stakeholders in pesticide risk assessment at mcra.rivm.nl. We describe the MCRA implementation of the methods as advised in the 2012 EFSA Guidance on probabilistic modelling, as well as more refined methods developed in the ACROPOLIS project. The emphasis is on cumulative assessments. Two approaches, sample-based and compound-based, are contrasted. It is shown that additional data on agricultural use of pesticides may give more realistic risk assessments. Examples are given of model and software validation of acute and chronic assessments, using both simulated data and comparisons against the previous release of MCRA and against the standard software DEEM-FCID used by the Environmental Protection Agency in the USA. It is shown that the EFSA Guidance pessimistic model may not always give an appropriate modelling of exposure.

Exposures to plant protection products (PPPs) are assessed using risk analysis methods to protect public health. Traditionally, single sources, such as food or individual occupational sources, have been addressed. In reality, individuals can be exposed simultaneously to multiple sources. Improved regulation therefore requires the development of new tools for estimating the population distribution of exposures aggregated within an individual. A new aggregate model is described, which allows individual users to include as much, or as little, information as is available or relevant for their particular scenario. Depending on the inputs provided by the user, the outputs can range from simple deterministic values through to probabilistic analyses including characterisations of variability and uncertainty. Exposures can be calculated for multiple compounds, routes and sources of exposure. The aggregate model links to the cumulative dietary exposure model developed in parallel and is implemented in the web-based software tool MCRA. Case studies are presented to illustrate the potential of this model, with inputs drawn from existing European data sources and models. These cover exposures to UK arable spray operators, Italian vineyard spray operators, Netherlands users of a consumer spray and UK bystanders/residents. The model could also be adapted to handle non-PPP compounds.

Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this novel method to explore the relation between the toxicity of aquatic sediments, as observed in bioassays with Daphnia magna, Chironomus riparius and Vibrio fischeri, and the degree of contamination of the sediments. Despite the low signal-to-noise ratio in the data, some interesting thresholds and (non)linear effects were found. The method has added value compared to the linear multivariate methods applied earlier to these data. Percentages of explained variance remained low, but could be doubled by diminishing the effect of local variability.

Risk assessments for human exposures to plant protection products (PPPs) have traditionally focussed on single routes of exposure and single compounds. Extensions to estimate aggregate (multi-source) and cumulative (multi-compound) exposure from PPPs present many new challenges and additional uncertainties that should be addressed as part of risk analysis and decision-making. A general approach is outlined for identifying and classifying the relevant uncertainties and variabilities. The implementation of uncertainty analysis within the MCRA software, developed as part of the EU-funded ACROPOLIS project to address some of these uncertainties, is demonstrated. An example is presented for dietary and non-dietary exposures to the triazole class of compounds. This demonstrates the chaining of models, linking variability and uncertainty generated from an external model for bystander exposure with variability and uncertainty in MCRA dietary exposure assessments. A new method is also presented for combining pesticide usage survey information with limited residue monitoring data, to address non-detect uncertainty. The results show that incorporating usage information reduces uncertainty in parameters of the residue distribution but that in this case quantifying uncertainty is not a priority, at least for UK grown crops. A general discussion of alternative approaches to treat uncertainty, either quantitatively or qualitatively, is included.

GC-MS data on veterinary drug residues in bovine urine are used for controlling the illegal practice of fattening cattle. According to current detection criteria, peak patterns of preferably four ions should agree within 10 or 20% from a corresponding standard pattern. These criteria are rigid, rather arbitrary and do not match daily practice. A new model, based on multivariate modeling of log peak abundance ratios, provides a theoretical basis for the identification of analytes and optimizes the balance between the avoidance of false positives and false negatives. The performance of the model is demonstrated on data provided by five laboratories, each supplying GC-MS measurements on the detection of clenbuterol, dienestrol and 19 beta-nortestosterone in urine. The proposed model shows a better performance than confirmation by using the current criteria and provides a statistical basis for inspection criteria in terms of error probabilities.

The chemometric analysis of low-level analytical data is hampered by the common presence of interfering compounds, by the frequent absence of measurement signals and by a non-constant measurement variability which is related to concentration level in a non-linear way. A model is presented to handle this type of data in the context of the practical problem of multivariate detection from gas chromatography/mass spectrometry (GC-MS) data. The model, based on log ratio modelling, is compared with previous approaches to parts of the problem. The basic idea behind the model is to define for the multivariate detection problem a null hypothesis for the values of log ratio measurements and to estimate variability as a function of total measured intensity. In practice it is often impossible to anticipate all kinds of interference which may occur. Therefore we propose to use expert assessments of the probability that certain expected peak ratios are generated by the analyte rather than by interferences. These expert assessments can then be used to define a proper null hypothesis for the multivariate detection test. The application of the model is illustrated for the detection of the illegal growth promoter clenbuterol in urine by selected ion-monitoring GC-MS.

Nutrient recommendations in use today are often derived from relatively old data of few studies with few individuals. However, for many nutrients, including vitamin B-12, extensive data have now become available from both observational studies and randomized controlled trials, addressing the relation between intake and health-related status biomarkers. The purpose of this article is to provide new methodology for dietary planning based on dose-response data and meta-analysis. The methodology builds on existing work, and is consistent with current methodology and measurement error models for dietary assessment. The detailed purposes of this paper are twofold. Firstly, to define a Population Nutrient Level (PNL) for dietary planning in groups. Secondly, to show how data from different sources can be combined in an extended meta-analysis of intake-status datasets for estimating PNL as well as other nutrient intake values, such as the Average Nutrient Requirement (ANR) and the Individual Nutrient Level (INL). For this, a computational method is presented for comparing a bivariate lognormal distribution to a health criterion value. Procedures to meta-analyse available data in different ways are described. Example calculations on vitamin B-12 requirements were made for four models, assuming different ways of estimating the dose-response relation, and different values of the health criterion. Resulting estimates of ANRs and less so for INLs were found to be sensitive to model assumptions, whereas estimates of PNLs were much less sensitive to these assumptions as they were closer to the average nutrient intake in the available data.

Background: Many randomized controlled trials (RCTs) and observational studies have provided information on the association between vitamin B-12 intake and biomarkers. The use of these data to estimate dose-response relations provides a useful means to summarize the body of evidence. Objective: We systematically reviewed studies that investigated vitamin B-12 intake and biomarkers of vitamin B-12 status and estimated dose-response relations with the use of a meta-analysis. Design: This systematic review included all RCTs, prospective cohort studies, nested case-control studies, and cross-sectional studies in healthy adult populations published through January 2010 that supplied or measured dietary vitamin B-12 intake and measured vitamin B-12 status as serum or plasma vitamin B-12, methylmalonic acid (MMA), or holotranscobalamin. We calculated an intake-status regression coefficient () for each individual study and calculated the overall pooled and SE () by using random-effects meta-analysis on a double-log scale. Results: The meta-analysis of observational studies showed a weaker slope of dose-response relations than the meta-analysis of RCTs. The pooled dose-response relation of all studies between vitamin B-12 intake and status indicated that a doubling of the vitamin B-12 intake increased vitamin B-12 concentrations by 11% (95% CI: 9.4%, 12.5%). This increase was larger for studies in elderly persons (13%) than in studies in adults (8%). The dose-response relation between vitamin B-12 intake and MMA concentrations indicated a decrease in MMA of 7% (95% CI: -10%, -4%) for every doubling of the vitamin B-12 intake. The assessment of risk of bias within individual studies and across studies indicated risk that was unlikely to seriously alter these results. Conclusion: The obtained dose-response estimate between vitamin B-12 intake and status provides complementary evidence to underpin recommendations for a vitamin B-12 intake of populations.

Seed and leaf treatments with natural compounds having a low risk profile (LRP) were evaluated for their potential to induce resistance in cabbage plants (Brassica oleracea) against Peronospora parasitica, causal organism of downy mildew. The selection of 34 LRP compounds comprised micronutrients, organic compounds such as proline, riboflavin, oligogalacturonides, aminolignosulfonates, bacterial lipopolysaccharides, and bacterial and fungal extracts. Treatments with the synthetic chemical inducers 2,6-dichloroisonicotinic acid (INA), d,l-s-aminobutyric acid, salicylic acid, benzothiadiazole and the fungicide Previcur™ were included as controls. After seed treatment a maximum reduction of 27% diseased leaf area was found with an extract of a Lysobacter strain, compared to a reduction of 99% for INA, the most effective synthetic inducer. Seed treatments with extracts of Pectobacterium carotovorum subsp. carotovorum, Bacillus macerans, Pseudomonas syringae, Streptomyces and Xanthomonas campestris strains also reduced downy mildew infection significantly. After leaf treatment, a maximum reduction of 85% was again found with the Lysobacter extract, compared to a reduction of 99% for INA, the most effective synthetic inducer. Leaf treatments with CuSO4 (=1 mM), MnCl2 (=10 mM), K2HPO4 (100 mM), and extracts of P. syringae, P. carotovorum subsp. carotovorum, Streptomyces, X. campestris and B. macerans strains also reduced the diseased leaf area, but CuSO4 was highly phytotoxic. For seed and leaf treatments with Lysobacter extract, proline, MnCl2 and INA the effect on the induction of chitinase and glucanase activity was tested, using two pathogenesis-related proteins as markers for induced resistance. For seed treatments only INA and for leaf treatments INA, proline and MnCl2 treatments resulted in increased activity of both enzymes. The rate of enzyme activity induced by INA was dependent on the time seeds were exposed to the compound. Highlights ? Seed treatments with isonicotinic acid protects Brassica seedlings from Peronospora infections. ? Treatments of seedlings with extracts of Lysobacter protects against Peronospora infections. ? Effect of seed treatments is dependent on the time of incubation with the elicitor

Colonization of potato plants by soilborne, green fluorescent protein (GFP)-tagged Dickeya sp. IPO2254 was investigated by selective plating, epifluorescence stereo microscopy (ESM), and confocal laser scanning microscopy (CLSM). Replicated experiments were carried out in a greenhouse using plants with an intact root system and plants from which ca. 30% of the lateral roots was removed. One day after soil inoculation, adherence of the pathogen on the roots and the internal colonization of the plants were detected using ESM and CLSM of plant parts embedded in an agar medium. Fifteen days post-soil inoculation, Dickeya sp. was found on average inside 42% of the roots, 13% of the stems, and 13% of the stolons in plants with undamaged roots. At the same time-point, in plants with damaged roots, Dickeya sp. was found inside 50% of the roots, 25% of the stems, and 25% of the stolons. Thirty days postinoculation, some plants showed true blackleg symptoms. In roots, Dickeya sp. was detected in parenchyma cells of the cortex, both inter- and intracellularly. In stems, bacteria were found in xylem vessels and in protoxylem cells. Microscopical observations were confirmed by dilution spread-plating the plant extracts onto agar medium directly after harvest. The implications of infection from soilborne inoculum are discussed.

Probabilistic dietary acute exposure assessments of captan and tolylfluanid were performed for the populations of the Czech Republic, Denmark, Italy, the Netherlands and Sweden. The basis for these assessments was national databases for food consumption and pesticide concentration data harmonised at the level of raw agricultural commodity. Data were obtained from national food consumption surveys and national monitoring programmes and organised in an electronic platform of databases connected to probabilistic software. The exposure assessments were conducted by linking national food consumption data either (1) to national pesticide concentration data or (2) to a pooled database containing all national pesticide concentration data. We show that with this tool national exposure assessments can be performed in a harmonised way and that pesticide concentrations of other countries can be linked to national food consumption surveys. In this way it is possible to exchange or merge concentration data between countries in situations of data scarcity. This electronic platform in connection with probabilistic software can be seen as a prototype of a data warehouse, including a harmonised approach for dietary exposure modelling.

Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this novel method to explore the relation between the toxicity of aquatic sediments, as observed in bioassays with Daphnia magna, Chironomus riparius and Vibrio fischeri, and the degree of contamination of the sediments. Despite the low signal-to-noise ratio in the data, some interesting thresholds and (non)linear effects were found. The method has added value compared to the linear multivariate methods applied earlier to these data. Percentages of explained variance remained low, but could be doubled by diminishing the effect of local variability.