Toxicity of environmental and polystyrene plastic particles on the bivalve Corbicula fluminea: focus on the molecular responses.

Among aquatic organisms, filter feeders are particularly exposed to the ingestion of microplastics (MPs) and nanoplastics (NPs). The present study investigates the effect of environmental microplastics (ENV MPs) and nanoplastics (ENV NPs) generated from macro-sized plastic debris collected in the Garonne River (France), and polystyrene NPs (PS NPs) on the freshwater bivalve Corbicula fluminea. Organisms were exposed to plastic particles at three concentrations: 0.008, 10, and 100 µg L-1 for 21 days. Gene expression measurements were conducted in gills and visceral mass at 7 and 21 days to assess the effects of plastic particles on different functions. Our results revealed: (i) an up-regulation of genes, mainly involved in endocytosis, oxidative stress, immunity, apoptosis, and neurotoxicity, at 7 days of exposure for almost all environmental plastic particles and at 21 days of exposure for PS NPs in the gills, (ii) PS NPs at the three concentrations tested and ENV MPs at 0.008 µg L-1 induced strong down-regulation of genes involved in detoxication, oxidative stress, immunity, apoptosis, and neurotoxicity at 7 days of exposure in the visceral mass whereas ENV MPs at 10 and 100 µg L-1 and all ENV NPs induced less pronounced effects, (iii) overall, PS NPs and ENV MPs 0.008 µg L-1 did not trigger the same effects as ENV MPs 10 and 100 µg L-1 and all ENV NPs, either in the gills or the visceral mass at 7 and 21 days of exposure. This study highlighted the need to use MPs and NPs sampled in the environment for future studies as their properties induce different effects at the molecular level to living organisms.

Toxicity assessment of environmental MPs and NPs and polystyrene NPs on the bivalve Corbicula fluminea using a multi-marker approach.

Small plastic particles, microplastics (MPs) and nanoplastics (NPs) represent a major threat in aquatic environments. Freshwater organisms are exposed to MPs and NPs, particularly in industrial and urban areas. The present study aimed to compare the toxicity between polystyrene NPs (PS NPs) and environmental microplastics (ENV MPs) and nanoplastics (ENV NPs) generated from macro-sized debris collected in the Garonne River on the freshwater bivalve C. fluminea. The organisms were exposed to the different plastic particles at three environmentally relevant concentrations: 0.008, 10, and 100 µg L-1 for 21 days. The biological responses of organisms were assessed using a multi-biomarker approach from the sub-individual to the individual level. The results demonstrated that: i) ENV NPs triggered more effects on detoxification processes and immune response, confirming that using manufactured NPs for laboratory exposure can lead to misleading conclusions on the risks posed by plastic particles; ii) effects of ENV MPs were less marked than ENV NPs, emphasizing the importance of testing a size continuum of plastic particles from NPs to MPs; iii) some effects were only observed for the low and/or intermediate concentrations tested, underlining the importance of using environmentally relevant concentrations. In light of these results, laboratory studies should be continued by exposing aquatic species to environmental MPs and NPs. The properties of these particles have to be characterized for a better risk assessment of environmental plastic particles.

Continuum from microplastics to nanoplastics: effects of size and source on the estuarine bivalve Scrobicularia plana.

Plastic has been largely detected in estuarine environments and represents major concern towards aquatic living organisms. The present study evaluates the impact of microplastics (MPs) and nanoplastics (NPs) under realistic exposure conditions. Scrobicularia plana individuals were exposed to low concentrations (0.008, 10, and 100 µg L-1) of environmental MPs and NPs as well as to standard PS NPs, as a comparison condition. The aim of this study was to understand the ecotoxicological effects of environmental plastic particles on S. plana gills and digestive glands but also to compare the effects of plastic polymers size in order to highlight if the size could induce different toxicity profiles within this model organism, at different levels of biological organization. Results showed a differential induction of detoxification enzymes (CAT, GST), immunity (AcP), DNA damage processes as well as a differential effect on behavior and condition index of animals depending upon the type of plastic, the size, the concentration tested, and the type of organ. This study underlines the necessity of testing (i) plastics collected from the environment as compared to standard ones and (ii) the effect of size using plastics coming from the same batch of macrosized plastics. This study concludes on the future need directions that plastic-based studies must take in order to be able to generate a large quantity of relevant data that could be used for future regulatory needs on the use of plastic.

Spatialized epidemiological forecasting applied to Covid-19 pandemic at departmental scale in France.

In this paper, we present a spatialized extension of a SIR model that accounts for undetected infections and recoveries as well as the load on hospital services. The spatialized compartmental model we introduce is governed by a set of partial differential equations (PDEs) defined on a spatial domain with complex boundary. We propose to solve the set of PDEs defining our model by using a meshless numerical method based on a finite difference scheme in which the spatial operators are approximated by using radial basis functions. Such an approach is reputed as flexible for solving problems on complex domains. Then we calibrate our model on the French department of Isère during the first period of lockdown, using daily reports of hospital occupancy in France. Our methodology allows to simulate the spread of Covid-19 pandemic at a departmental level, and for each compartment. However, the simulation cost prevents from online short-term forecast. Therefore, we propose to rely on reduced order modeling to compute short-term forecasts of infection number. The strategy consists in learning a time-dependent reduced order model with few compartments from a collection of evaluations of our spatialized detailed model, varying initial conditions and parameter values. A set of reduced bases is learnt in an offline phase while the projection on each reduced basis and the selection of the best projection is performed online, allowing short-term forecast of the global number of infected individuals in the department. The original approach proposed in this paper is generic and could be adapted to model and simulate other dynamics described by a model with spatially distributed parameters of the type diffusion-reaction on complex domains. Also, the time-dependent model reduction techniques we introduced could be leveraged to compute control strategies related to such dynamics.

Transport effect of COVID-19 pandemic in France.

An extension of the classical pandemic SIRD model is considered for the regional spread of COVID-19 in France under lockdown strategies. This compartment model divides the infected and the recovered individuals into undetected and detected compartments respectively. By fitting the extended model to the real detected data during the lockdown, an optimization algorithm is used to derive the optimal parameters, the initial condition and the epidemics start date of regions in France. Considering all the age classes together, a network model of the pandemic transport between regions in France is presented on the basis of the regional extended model and is simulated to reveal the transport effect of COVID-19 pandemic after lockdown. Using the measured values of displacement of people between cities, the pandemic network of all cities in France is simulated by using the same model and method as the pandemic network of regions. Finally, a discussion on an integro-differential equation is given and a new model for the network pandemic model of each age class is provided.

Model reduction using wavelet multiresolution technique applied to fluorescence diffuse optical tomography.

Fluorescence diffuse optical tomography is a powerful tool for the investigation of molecular events in studies for new therapeutic developments. Here, the stress is put on the mathematical problem of tomography, which can be formulated in terms of an estimation of physical parameters appearing as a set of partial differential equations and solved by the finite element method. This method is well known to be time consuming, and our principal objective is to reduce the model in order to speed up computation. A method based on a wavelet multiresolution technique is presented in detail. A validation study was conducted on synthetic data and experiments.

Application of a wavelet-Galerkin method to the forward problem resolution in fluorescence diffuse optical tomography.

Fluorescence diffuse optical tomography is a powerful tool for the investigation of molecular events in studies for new therapeutic developments. Here, the emphasis is put on the mathematical problem of tomography, which can be formulated in terms of an estimation of physical parameters appearing as a set of Partial Differential Equations (PDEs). The standard polynomial Finite Element Method (FEM) is a method of choice to solve the diffusion equation because it has no restriction in terms of neither the geometry nor the homogeneity of the system, but it is time consuming. In order to speed up computation time, this paper proposes an alternative numerical model, describing the diffusion operator in orthonormal basis of compactly supported wavelets. The discretization of the PDEs yields to matrices which are easily computed from derivative wavelet product integrals. Due to the shape of the wavelet basis, the studied domain is included in a regular fictitious domain. A validation study and a comparison with the standard FEM are conducted on synthetic data.

Model reduction by multiresolution method applied to fluorescence diffuse optical tomography.

This paper is devoted to the Fluorescence Diffuse Optical Tomography. This inverse problem relies on an iterative algorithm based on solutions of partial differential equations. The goal of the paper is to present a multiresolution technique applied to these equations, with the objective of computation complexity reduction. The effectiveness of the approach is then illustrated on a practical example.