Avoidance behaviour of aquatic macroinvertebrates for real-time detection of micropollutant surge in wastewater effluents.

Micropollutants are regularly detected at the outlets of wastewater treatment plants (WWTPs). Across urban and industrial WWTPs, monitoring directives only require assessment for a handful of chemicals via sampling methods that fail to capture the temporal variability in micropollutant discharge. In this study, we develop a biotest for real-time on-line monitoring of micropollutant discharge dynamics in WWTPs effluents. The selected biomonitoring device ToxMate uses videotracking of invertebrate movement, which was used to deduce avoidance behaviour of the amphipod Gammarus fossarum. Organism conditioning was set up to induce a state of minimal locomotor activity in basal conditions to maximise avoidance signal sensitivity to micropollutant spikes. We showed that with a standardised protocol, it was possible to minimise both overall movement and sensitivity to physio-chemical variations typical to WWTP effluents, as well as capture the spikes of two micropollutants upon exposure (copper and methomyl). Spikes in avoidance behaviour were consistently seen for the two chemicals, as well as a strong correlation between avoidance intensity and spiked concentration. A two-year effluent monitoring case study also illustrates how this biomonitoring method is suitable for real-time on-site monitoring, and shows a promising non-targeted approach for characterising complex micropollutant discharge variability at WWTP effluents, which today remains poorly understood.

Comparison of bootstrap resampling methods for 3-D PET imaging.

Two groups of bootstrap methods have been proposed to estimate the statistical properties of positron emission tomography (PET) images by generating multiple statistically equivalent data sets from few data samples. The first group generates resampled data based on a parametric approach assuming that data from which resampling is performed follows a Poisson distribution while the second group consists of nonparametric approaches. These methods either require a unique original sample or a series of statistically equivalent data that can be list-mode files or sinograms. Previous reports regarding these bootstrap approaches suggest different results. This work compares the accuracy of three of these bootstrap methods for 3-D PET imaging based on simulated data. Two methods are based on a unique file, namely a list-mode based nonparametric (LMNP) method and a sinogram based parametric (SP) method. The third method is a sinogram-based nonparametric (SNP) method. Another original method (extended LMNP) was also investigated, which is an extension of the LMNP methods based on deriving a resampled list-mode file by drawings events from multiple original list-mode files. Our comparison is based on the analysis of the statistical moments estimated on the repeated and resampled data. This includes the probability density function and the moments of order 1 and 2. Results show that the two methods based on multiple original data (SNP and extended LMNP) are the only methods that correctly estimate the statistical parameters. Performances of the LMNP and SP methods are variable. Simulated data used in this study were characterized by a high noise level. Differences among the tested strategies might be reduced with clinical data sets with lower noise.