Arnout Standaert

Modelling the individual cell lag phase. Isolating single cells: protocol development.

Aims: To develop a protocol to isolate single cells in wells of a microtitre plate, having a high certainty of individual cells, combined with a sufficient yield.

The Aeroflex: A Bicycle for Mobile Air Quality Measurements

Fixed air quality stations have limitations when used to assess peoples real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification.

Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes

Aims:  The effect of temperature (2–30°C), pH (4·8–7·4) and water activity (0·946–0·995) on the relationship between optical density (OD) at 600 nm and the plate count (CFU ml−1) was investigated for Listeria monocytogenes.

Single cell variability of L. monocytogenes grown on liver pâté and cooked ham at 7°C: comparing challenge test data to predictive simulations

Aims:  The variability in growth between individual Listeria monocytogenes cells was investigated on liver pâté and cooked ham. These results were compared to Monte Carlo simulations based on data collected previously in broths (Francois et al., submitted for publication).

Concepts and tools for predictive modeling of microbial dynamics

Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.

Physical Activity through Sustainable Transport Approaches (PASTA): a study protocol for a multicentre project

Introduction Only one-third of the European population meets the minimum recommended levels of physical activity (PA). Physical inactivity is a major risk factor for non-communicable diseases. Walking and cycling for transport (active mobility, AM) are well suited to provide regular PA. The European research project Physical Activity through Sustainable Transport Approaches (PASTA) pursues the following aims: (1) to investigate correlates and interrelations of AM, PA, air pollution and crash risk; (2) to evaluate the effectiveness of selected interventions to promote AM; (3) to improve health impact assessment (HIA) of AM; (4) to foster the exchange between the disciplines of public health and transport planning, and between research and practice. Methods and analysis PASTA pursues a mixed-method and multilevel approach that is consistently applied in seven case study cities. Determinants of AM and the evaluation of measures to increase AM are investigated through a large scale longitudinal survey, with overall 14 000 respondents participating in Antwerp, Barcelona, London, Örebro, Rome, Vienna and Zurich. Contextual factors are systematically gathered in each city. PASTA generates empirical findings to improve HIA for AM, for example, with estimates of crash risks, factors on AM-PA substitution and carbon emissions savings from mode shifts. Findings from PASTA will inform WHOs online Health Economic Assessment Tool on the health benefits from cycling and/or walking. The studys wide scope, the combination of qualitative and quantitative methods and health and transport methods, the innovative survey design, the general and city-specific analyses, and the transdisciplinary composition of the consortium and the wider network of partners promise highly relevant insights for research and practice. Ethics and dissemination Ethics approval has been obtained by the local ethics committees in the countries where the work is being conducted, and sent to the European Commission before the start of the survey. The PASTA website (http://www.pastaproject.eu) is at the core of all communication and dissemination activities.

Wearable Sensors for Personal Monitoring and Estimation of Inhaled Traffic-Related Air Pollution: Evaluation of Methods

Physical activity and ventilation rates have an effect on an individuals dose and may be important to consider in exposure-response relationships; however, these factors are often ignored in environmental epidemiology studies. The aim of this study was to evaluate methods of estimating the inhaled dose of air pollution and understand variability in the absence of a true gold standard metric. Five types of methods were identified: (1) methods using (physical) activity types, (2) methods based on energy expenditure, METs (metabolic equivalents of task), and oxygen consumption, (3) methods based on heart rate or (4) breathing rate, and (5) methods that combine heart and breathing rate. Methods were compared using a real-life data set of 122 adults who wore devices to track movement, black carbon air pollution, and physiological health markers for 3 weeks in three European cities. Different methods for estimating minute ventilation performed well in relative terms with high correlations among different methods, but in absolute terms, ignoring increased ventilation during day-to-day activities could lead to an underestimation of the daily dose by a factor of 0.08-1.78. There is no single best method, and a multitude of methods are currently being used to approximate the dose. The choice of a suitable method for determining the dose in future studies will depend on both the size and the objectives of the study.

Physical activity and sedentary behaviour in daily life: A comparative analysis of the Global Physical Activity Questionnaire (GPAQ) and the SenseWear armband

Reduction of sedentary time and an increase in physical activity offer potential to improve public health. However, quantifying physical activity behaviour under real world conditions is a major challenge and no standard of good practice is available. Our aim was to compare the results of physical activity and sedentary behaviour obtained with a self-reported instrument (Global Physical Activity Questionnaire (GPAQ)) and a wearable sensor (SenseWear) in a repeated measures study design. Healthy adults (41 in Antwerp, 41 in Barcelona and 40 in London) wore the SenseWear armband for seven consecutive days and completed the GPAQ on the final day. This was repeated three times. We used the Wilcoxon signed rank sum test, Spearman correlation coefficients, mixed effects regression models and Bland-Altman plots to study agreement between both methods. Mixed models were used to assess the effect of personal characteristics on the absolute and relative difference between estimates obtained with the GPAQ and SenseWear. Moderate to vigorous energy expenditure and duration derived from the GPAQ were significantly lower (p<0.05) compared to the SenseWear, yet these variables showed significant correlations ranging from 0.45 to 0.64. Estimates of vigorous-intensity physical activity in particular showed high similarity (r>0.59). Results for sedentary behaviour did not differ, yet were poorly correlated (r<0.25). The differences between all variables were reproducible across repeated measurements. In addition, we observed a relationship between these differences and BMI, body fat and physical activity domain. Due to the lack of a standardized protocol, results from different studies measuring physical activity and sedentary behaviour are difficult to compare. Therefore, we suggested an easy-to-implement approach for future studies adding the GPAQ to the wearable of choice as a basis for comparisons.

A semi-probabilistic modelling approach for the estimation of dietary exposure to phthalates in the Belgian adult population

In this study, a semi-probabilistic modelling approach was applied for the estimation of the long-term human dietary exposure to phthalates--one of worlds most used families of plasticisers. Four phthalate compounds were considered: diethyl phthalate (DEP), di-n-butyl phthalate (DnBP), benzylbutyl phthalate (BBP) and di(2-ethylhexyl) phthalate (DEHP). Intake estimates were calculated for the Belgian adult population and several subgroups of this population for two considered scenarios using an extended version of the EN-forc model. The highest intake rates were found for DEHP, followed by DnBP, BBP and DEP. In the Belgian adult population, men and young adults generally had the highest dietary phthalate intake estimates. Nevertheless, predicted dietary intake rates for all four investigated phthalates were far below the corresponding tolerable daily intake (TDI) values (i.e. P99 intake values were 6.4% of the TDI at most), which is reassuring because adults are also exposed to phthalates via other contamination pathways (e.g. dust ingestion and inhalation). The food groups contributing most to the dietary exposure were grains and grain-based products for DEP, milk and dairy products for DnBP, meat and meat products or grains and grain-based products (depending on the scenario) for BBP and meat and meat products for DEHP. Comparison of the predicted intake results based on modelled phthalate concentrations in food products with intake estimates from other surveys (mostly based on measured concentrations) showed that the extended version of the EN-forc model is a suitable semi-probabilistic tool for the estimation and evaluation of the long-term dietary intake of phthalates in humans.

Assessing multimedia/multipathway exposures to inorganic arsenic at population and individual level using MERLIN-Expo

In this study, we report on model simulations performed using the newly developed exposure tool, MERLIN-Expo, in order to assess inorganic arsenic (iAs) exposure to adults resulting from past emissions by non-ferrous smelters in Belgium (Northern Campine area). Exposure scenarios were constructed to estimate external iAs exposure as well as the toxicologically relevant As (tAs, i.e., iAs, MMA and DMA) body burden in adults living in the vicinity of the former industrial sites as compared to adults living in adjacent areas and a reference area. Two scenarios are discussed: a first scenario studying exposure to iAs at the aggregated population level and a second scenario studying exposure at the individual level for a random sub-sample of subjects in each of the three different study areas. These two scenarios only differ in the type of human related input data (i.e., time-activity data, ingestion rates and consumption patterns) that were used, namely averages (incl. probability density functions, PDFs) in the simulation at population level and subject-specific values in the simulation at individual level. The model predictions are shown to be lower than the corresponding biomonitoring data from the monitoring campaign. Urinary tAs levels in adults, irrespective of the area they lived in, were under-predicted by MERLIN-Expo by 40% on average. The model predictions for individual adults, by contrast, under-predict the biomonitoring data by 7% on average, but with more important under-predictions for subjects at the upper end of exposure. Still, average predicted urinary tAs levels from the simulations at population level and at individual level overlap, and, at least for the current case, lead to similar conclusions. These results constitute a first and partial verification of the model performance of MERLIN-Expo when dealing with iAs in a complex site-specific exposure scenario, and demonstrate the robustness of the modelling tool for these situations.