J Elias 

A Faccinetto

N Nuns

C Pirim

C Focsa

X Mercier

Coupling of ToF-SIMS and Raman Spectroscopy for the Characterization of the Chemical Species Involved in the Soot Nucleation Process

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) and Raman spectroscopy are used to characterize samples extracted from a laboratory methane diffusion flame at different reaction times along the flame axis in order to gain information on the soot nucleation process. Experimental results from the comparison of these techniques allow the chemical characterization of the gas to soot particles phase transition and the quantification of the hydrogen content of the sampled matter. Before soot formation, no phase separation is observed on the samples, while after soot formation, a phase separation appears with two families of PAHs and a difference in the Raman signal.

Introduction

Despite the considerable number of experimental and theoretical studies performed during the last decades, a comprehensive view of the transition from molecular precursors to condensed phase particles (soot nucleation) in flames is still missing [START_REF] Mercier | Combustion-formed nanoparticles[END_REF][START_REF] Wang | Formation of nascent soot and other condensed-phase materials in flames[END_REF].

A common experimental approach to obtain information on soot nucleation relies on the online or exsitu chemical analysis of the stable species adsorbed on the surface of soot particles sampled at various reaction times in flame environments. This approach proved to be highly successful and led in particular to the identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives as main soot precursors [START_REF] Dobbins | The evolution of soot precursor particles in a diffusion flame[END_REF][START_REF] Schulz | Insights into incipient soot formation by atomic force microscopy[END_REF][START_REF] Öktem | Chemical species associated with the early stage of soot growth in a laminar premixed ethylene-oxygen-argon flame[END_REF]. In this work, a set of samples was collected from the axis of a methane laminar diffusion flame and subsequently ex-situ characterized by secondary ion mass spectrometry (ToF-SIMS) and Raman spectroscopy. The results are combined with the aim of characterizing the nature of the molecular precursors involved in the formation of nascent soot particles.

The systematic use of ToF-SIMS for the analysis of the chemical composition of combustion byproducts is relatively recent [START_REF] Irimiea | Unveiling trends in soot nucleation and growth: When secondary ion mass spectrometry meets statistical analysis[END_REF][START_REF] Faccinetto | Evidence on the formation of dimers of polycyclic aromatic hydrocarbons in a laminar diffusion flame[END_REF][START_REF] Ngo | Chemical discrimination of the particulate and gas phases of miniCAST exhausts using a two-filter collection method[END_REF]. While ToF-SIMS is highly sensitive to the surface composition of the samples, and in static mode can provide access to surface layers as thin as a few nm, the post-ionization dissociation and recombination processes are far from being fully understood, and the perspective of quantitative analyses remains challenging to date [START_REF] Popczun | On the SIMS ionization probability of organic molecules[END_REF][START_REF] Wucher | Molecular ionization probability in cluster-SIMS[END_REF]. Therefore, statistical tools are often required to extract the large amount of chemical information contained in the mass spectra.

Raman spectroscopy has long been used to obtain quantitative information on carbon-based materials. For instance, empirical relationships to obtain the H/C ratio of carbon films from the slope of the fluorescence and the intensity of the G band have been proposed as early as two decades ago [START_REF] Casiraghi | Bonding in hydrogenated diamond-like carbon by Raman spectroscopy[END_REF][START_REF] Buijnsters | Hydrogen quantification in hydrogenated amorphous carbon films by infrared, Raman, and x-ray absorption near edge spectroscopies[END_REF]. The intrinsically complex nature of combustion-generated materials and the  Corresponding author: jessy.elias@univ-lille.fr Proceedings of the European Combustion Meeting 2021 existence of hydrocarbons adsorbed on the particle surface further adds to the spectral complexity of soot [START_REF] Minutolo | Characterization of flame-generated 2-D carbon nanodisks[END_REF][START_REF] Commodo | Physicochemical evolution of nascent soot particles in a laminar premixed flame: from nucleation to early growth[END_REF][START_REF] Parent | Nanoscale characterization of aircraft soot: a high-resolution transmission electron microscopy, Raman spectroscopy, X-ray photoelectron and near-edge X-ray absorption spectroscopy study[END_REF][START_REF] Le | Direct observation of aliphatic structures in soot particles produced in low-pressure premixed ethylene flames via online Raman spectroscopy[END_REF].

The main goal of this work is to provide additional information that can be used for the ex-situ characterization of soot particles extracted from a laboratory flame and deposited on metal substrates, that are for all practical purposes treated like the carbon films mentioned above. In particular, we aim to identify experimental parameters that can be used to pinpoint and if possible to characterize the transition from molecular precursors to condensed-phase particles. With respect to our previous investigation [START_REF] Faccinetto | Evidence on the formation of dimers of polycyclic aromatic hydrocarbons in a laminar diffusion flame[END_REF], in which we focused on the zone where nascent soot particles could first be detected by laser induced incandescence (LII), in this work we extend the investigated range to almost the entire flame height.

Experimental

In this work, measurements are performed on soot samples collected from a 120 mm height methane laminar diffusion flame, shown in Figure 1a, stabilized on a Santoro-type burner at atmospheric pressure. This burner consists of a central injector supplied with 0.546 L/min of methane and surrounded by a 86.6 L/min air flow. This flame is characterized by the stratified distribution of PAHs and soot particles through the centerline of the flame that can therefore be targeted independently during sampling [START_REF] Mercier | Dimers of polycyclic aromatic hydrocarbons: the missing pieces in the soot formation process[END_REF]. Species are extracted from the flame using a dilutive quartz microprobe inserted radially and operated with nitrogen, in order to reduce the collision probability in the sampling line and limit their reactivity. The collected species are deposited on titanium wafers using a 1/8" diameter impactor. High velocity impaction of the species (~34 m/s) results in soot particles and other condensed-phase species aggregating at the center of the wafer and the condensable gas scattering all over the wafer surface as shown in Figure 1b. Therefore, ToF-SIMS and Raman ex-situ measurements are performed, as detailed below, on two selected regions of interest (ROIs): the soot deposit (impaction ROIs) and the wafer surface surrounding it (halo ROIs). The mass spectra recorded on the impaction ROIs, where soot particles impacted (only detected for height above the burner HAB > 50 mm, as discussed below), contain the combined contributions of soot and condensable gas, while the mass spectra recorded on the halo ROIs only contain the contribution of condensable gas. ToF-SIMS is a sensitive surface analytical technique used to obtain information on the chemical composition of solid samples. In ToF-SIMS, a primary Bi3 + ion beam is used to sputter and ionize the compounds on the sample surface. The generated secondary ions, are accelerated and analyzed on a time-of-flight mass spectrometer with a mass resolution of m/Δm = 10000. This technique is sensitive to high mass organic compounds (m/z > 200) due to the use of Bi3 + ions having an energy of 25 keV with a very low current intensity of pA in static mode. Mass spectra in positive polarity are recorded at 50 scans/acquisition on a 500 × 500 μm 2 surface with an image resolution of 128 × 128 pixels. Acquisitions are performed on three different zones on each ROI. The collected mass spectra are aligned, calibrated and normalized by the total useful ion count. ToF-SIMS mass spectra contain a significant amount of chemical information difficult to extract and require detailed data treatment. Peaks are identified using mass defect analysis [START_REF] Sleno | The use of mass defect in modern mass spectrometry[END_REF], allowing the assignment of a molecular formula to the selected accurate masses. The vast majority of the attributed molecular formulae are consistent with PAHs or their derivatives. Then, an advanced statistical tool, the principal component analysis (PCA), is used to classify the data and explain the variance of the database. From a theoretical point of view, PCA is a series of linear transformations which project a set of correlated variables onto linearly uncorrelated variables denoted as principal components (PC). PCA is performed on the covariance matrix using the relative ion counts (normalized to the total useful ion count) of individual m/z as variables and ROIs of each sample as observations, in order to find statistically significant data correlations.

Raman spectroscopy analyses are carried out in order to obtain structural information on soot and characterize the gas/particle phase transition. The Raman microscope (InVia Reflex, Renishaw) is equipped with a λ = 514.5 nm diode-pumped solid-state laser. The laser power is reduced to 0.05% corresponding to a laser irradiance equal to 1627 W/cm 2 to avoid structural changes in the sample. The laser beam is focused on the samples with a 20x magnification objective. The Raman spectral intensity and wavelength are calibrated with a silicon wafer by utilizing the pure Si peak at 520 cm -1 . Instrument settings (grating 600 lines per mm and acquisition time 6 s with 80 accumulations) are chosen after a set of test measurements to provide the best signalto-noise ratio for the analysis. About 30 Raman spectra per ROIs are recorded between 700 and 2500 cm -1 for each sample.

Results

The main results of the PCA performed on the mass spectra database after removing the background and fragment ions are shown in Figure 2. The matrix used for the PCA includes 529 peaks (variables) and 150 mass spectra (observations). Together, PC1 and PC2 explain the 74.25% of the total variance of the database. In the PC2 vs. PC1 score plot shown in Figure 1a, at least three different clusters of mass spectra can be identified:

• For HAB ≤ 35mm (yellow datapoints), no statistically significant difference is found between impaction and halo ROIs that appear all clustered together in the score plot and characterized by PC1 < 0.

Implications for the soot formation process

The comparison of ToF-SIMS and Raman measurements provides useful information on the global fraction of H atoms [H] as a function of the HAB in the flame that in turn can be used to gain insights into the soot nucleation process. [H]ToF-SIMS can be calculated by means of the peaks identified through mass defect analysis, where the normalized signal intensity of the mass spectra wi is used to weight the individual contributions of the identified ions N X,i [START_REF] Faccinetto | Evidence on the formation of dimers of polycyclic aromatic hydrocarbons in a laminar diffusion flame[END_REF]:

[H] = N H N H + N C + N O with N X = ∑ N X,i w i i , X = H, C, O and ∑ w i = 1 i
Figure 3a shows that hydrocarbons in halo ROIs (condensable gas only) continue to gain hydrogens all along the flame axis, while hydrocarbons in impaction ROIs (soot and/or condensable gas) undergo a loss in hydrogen between 45-55 mm HAB. This region is particularly significant as previous in-situ LII measurements performed on this flame show that nascent soot particles are detectable in the flame axis above 50 mm HAB [START_REF] Mercier | Dimers of polycyclic aromatic hydrocarbons: the missing pieces in the soot formation process[END_REF]. Here, the change in chemical composition of the impaction and halo ROIs, potentially representing a change of phase, begins at 40 mm HAB, slightly below the previously detected LII signal. However, it must be noted that the experimental LII configuration used in our previous investigation [START_REF] Mercier | Dimers of polycyclic aromatic hydrocarbons: the missing pieces in the soot formation process[END_REF] was not optimized for detecting the smallest particles, and therefore the existence of nascent soot particles between 50 and 55 mm HAB cannot be completely ruled out. This observation, even when considering the uncertainty due to the flame perturbation induced by the sampling microprobe (size of the sampled region < 1 mm, size of the perturbed flame region < 2.5 mm), shows that the change in the chemical composition of the samples occurs immediately before or at the same time as the detection of soot particles.

Experimental OH profiles measured by 1aser induced fluorescence (LIF) in this flame (not shown here) pinpoint the beginning of the soot oxidation region, identified by a sharp increase of the OH radical [H]Raman has been shown to be proportional to log m/I(G), where m is the slope of the linear fit of the fluorescence background (in this work measured between 1000 and 2000 cm -1 ), and I(G) is the intensity of the Raman G band [START_REF] Casiraghi | Bonding in hydrogenated diamond-like carbon by Raman spectroscopy[END_REF][START_REF] Buijnsters | Hydrogen quantification in hydrogenated amorphous carbon films by infrared, Raman, and x-ray absorption near edge spectroscopies[END_REF]. As shown in Figure 3, the trends of [H]ToF-SIMS and log m/I(G) are remarkably consistent up to 65 mm HAB. In particular, although log log m/I(G) of the halo ROIs cannot be calculated due to the absence of the G band in the Raman spectra, log m/I(G) of the impaction ROIs, as shown in Figure 3b, exhibits a change of slope at the same HAB observed by ToF-SIMS. This local change of sign of the slope of [H] vs. HAB immediately before the detection of LII signal might be related to the soot formation process. Specifically, it could point to the formation of species bound by C-C covalent bonds possibly via a sequence of hydrogen abstraction reactions [START_REF] Frenklach | On the mechanism of soot nucleation[END_REF]. Figure 4 compares the data obtained here with two empirical equations (blue dotted line and black dashed line) obtained from the analysis of amorphous hydrogenated carbon films prepared by chemical vapor deposition [START_REF] Casiraghi | Bonding in hydrogenated diamond-like carbon by Raman spectroscopy[END_REF][START_REF] Buijnsters | Hydrogen quantification in hydrogenated amorphous carbon films by infrared, Raman, and x-ray absorption near edge spectroscopies[END_REF]. As shown in the figure, log m/I(G) of the impaction ROIs (red solid line) scales linearly with [H]ToF-SIMS up to 70 mm HAB (R 2 = 0.7580 that approximately corresponds to the beginning of the soot oxidation region mentioned above:

[H] ToF-SIMS = -13.7 + 45.9 log m 𝐼(G) The two empirical equations available in the literature differ significantly from each other. This work indicates a better agreement with the empirical equation proposed by Buijnsters et al. [START_REF] Buijnsters | Hydrogen quantification in hydrogenated amorphous carbon films by infrared, Raman, and x-ray absorption near edge spectroscopies[END_REF]. The deviation with respect to literature can be attributed to the different volume probed by the two techniques: ToF-SIMS in static mode affects a surface layer as thin as a few nm, while the penetration depth of the Vis laser beam used in this work for Raman spectroscopy is in the range of a few m [21]. [ At HAB > 65 mm, a large deviation from linearity is observed, and therefore the HABs in the soot oxidation zone of the flame (70, 80, 90 and 100 mm) are not considered in the fitting procedure shown in Figure 4. This behavior is still unexplained and will require future investigations.

Conclusions and perspectives

In this work, samples extracted at different height above the burner (HAB) from a laboratory methane diffusion flame are analyzed by secondary ion mass spectrometry (ToF-SIMS) and Raman spectroscopy to gain information on the soot nucleation process.

For HAB ≤ 35 mm, well before nascent soot particles are detected by laser induced incandescence (LII), no phase separation is observed on the deposition substrates and all impaction sites have virtually the same chemical composition that includes the highest mass hydrocarbons detected all along the flame HAB. The Raman spectra are characterized by an intense fluorescence background and intense D and G Raman bands detected at 1360 cm -1 and 1580 cm -1 . Downstream from roughly 40 mm HAB, a phase separation and the partition of organic molecules between the two phases are observed. The condensable gas is progressively depleted of high m/z PAHs (their signal in the mass spectra becomes less and less prominent, while the Raman D and G bands disappear), while high m/z PAHs persist when associated to the formation of soot particles. In particular, around 45 mm HAB both ToF-SIMS and Raman detect a change in the sign of the slope of [H] vs. HAB that might be related to the phase transition. Future work include a full deconvolution of the Raman spectra into multiple bands (D4, D1 ' , D1, D3, G and D2) to evaluate the significant spectral parameters (position of the bands, full width at half maximum and ratios of integrated intensity) following the flame central axis. The goal of this work is to expand the interpretation of the results obtained and the comparison with ToF-SIMS analysis in order to characterize the chemical species involved in the soot nucleation process in flame combustion.

Figure 1 .

 1 Figure 1. Pictures of (a) the investigated flame with inserted sampling probe, and (b) the flamesampled materials ready for the chemical analysis.

•

  For 40 mm ≤ HAB ≤ 70 mm (orange-red datapoints), impaction and halo ROIs are split into two groups: impaction ROIs in PC1 < 0 while halo ROIs in PC1 > 0. • For HAB ≥ 80 mm (black datapoints) impaction and halo ROIs are both in PC1 > 0, but they are split into two clusters in PC2. The loading plots of PC1 and PC2, shown in Figure 2b and 2c respectively, give some insights on the variables affecting the PCs. PC1 < 0 is characterized by high loadings of high m/z hydrocarbons, while PC1 > 0 is characterized by high loadings of low m/z hydrocarbons. A sharp change of the PC1 sign is observed around 240 m/z (corresponding to C19 hydrocarbons). High scores in one PC means that the corresponding data are strongly affected by the variables characterizing that same PC. Thus, the mass spectra recorded in the impaction and halo ROIs at HAB ≤ 35 mm and in the impaction ROIs at 40 mm ≤ HAB ≤ 70 mm are characterized by the highest relative ion counts of high m/z hydrocarbons. On the other hand, the mass spectra recorded in the halo ROIs at 40 mm ≤ HAB ≤ 70 mm and in the impaction and halo (a) (b) ROIs at HAB ≥ 80 mm are characterized by the highest relative ion counts of low m/z hydrocarbons. In other words, the transition from the left to the right of the score plot corresponds well to a progressive increase in the relative contribution to the mass spectra of low m/z hydrocarbons. Furthermore, the score plot strongly suggests that the phenomenon represented by the PC1 is HAB-dependent (the mass spectra are also sorted from the left to the right according to the sampling HAB). On the other hand, a clear clustering of the mass spectra on PC2 only occurs at 80, 90 and 100 mm HAB (impaction ROIs have PC2 > 0 and halo ROIs have PC2 < 0). The role of PC2 has not yet been identified. As also shown in Figure 2, all Raman spectra at HAB ≤ 35 mm and the Raman spectra of the impaction ROIs at 40 mm ≤ HAB ≤ 70 mm feature high fluorescence background and intense at 1360 cm -1 and 1580 cm -1 that are characteristic of disordered graphite (D band) and graphitic lattices (G band) respectively [19]. These bands are absent in the halo ROIs at 40 mm ≤ HAB ≤ 70 mm.

Figure 2 .

 2 Figure 2. Results of the PCA on the full mass spectra database and comparison with Raman spectra without baseline correction. (a) PC2 vs. PC1 score plot. (b) PC1 loading plot. (c) PC2 loading plot. (d) Raman spectra on the impaction and halo ROIs before soot formation. (e) Raman spectra on the impaction and halo ROIs after soot formation.

  flame axis, at 70-75 mm HAB that corresponds to the second change of slope of [H]ToF-SIMS vs. HAB.

Figure 3 .

 3 Figure 3. (a) [H]ToF-SIMS vs. HAB. (b) log m/I(G) vs. HAB.

Figure 4 .

 4 Figure 4. log m/I(G) vs. [H]ToF-SIMS and comparison with literature (impaction ROIs only).

Acknowledgments

This work was supported by the Agence Nationale de la Recherche through the LABEX CAPPA (ANR-11-LABX-0005), and the Ministry of Higher Education and Research, Hauts de France Regional Council, and European Regional Development Fund (ERDF) through the Contrat de Projets Etat Region (CPER CLIMIBIO). The authors also thank the Agency for ecological transition (ADEME) for the financial support of this project.


Josquin Harrison

Marco Lorenzi

Benoit Legghe

Xavier Iriart

Hubert Cochet

Maxime Sermesant

Phase-independent Latent Representation for Cardiac Shape Analysis

Keywords: Shape Analysis, Atrial Fibrillation, Thrombosis, Graph Representation, Latent Space Model, Multi-Task Learning, Meta-Learning

Atrial fibrillation (AF) is a complex cardiac disease impacting an ever-growing population and increases 6-fold the risk of thrombus formation. However, image based bio-markers to predict thrombosis in presence of AF are not well known. This lack of knowledge comes from the difficulty to analyse and compare the shape of the Left Atrium (LA) as well as the insufficiency of data that limits the complexity of models we can use. Conducting data analysis in cardiology exacerbates the small dataset problem because the heart cycle renders impossible to compare images taken at systole and diastole time. To address these issues, we first propose a graph representation of the LA, to focus on the impact of pulmonary veins (PV) and LA Appendage (LAA) positions, giving a simple object easy to analyse. Secondly, we propose a meta-learning framework for heterogeneous datasets based on the consistent representation of each dataset in a common latent space. We show that such a model is analogous to a meta-classifier, where each dataset is characterised by specific projection in a common latent space, while sharing the same separating boundary. We apply this model to the graph representation of the LA and interpret the model to give novel time-dependant bio-markers related to PV and LAA configurations for the prediction of thrombosis.

Introduction

Atrial fibrillation (AF) is a complex cardiac disease impacting an ever-growing population, creating a hemodynamic environment prone to clot formation and ischemic stroke. The stratification of stroke risk in AF has significant clinical implications for the management of anticoagulation, which was shown to effectively limit the occurrence of strokes but at the cost of increased risk of bleeding. The net benefit of introducing preventive anticoagulation is currently estimated by computing a score based on patient demographics, clinical condition and past history, but the ability of this score to accurately assess stroke risk in AF patients is largely suboptimal [START_REF] Quinn | Wide variation in reported rates of stroke across cohorts of patients with atrial fibrillation[END_REF]. Thus, to better understand AF, important studies were conducted on LA hemodynamics [START_REF] Watson | Mechanisms of thrombogenesis in atrial fibrillation: Virchow's triad revisited[END_REF] or LAA morphology [START_REF] Yaghi | The left atrial appendage morphology is associated with embolic stroke subtypes using a simple classification system: a proof of concept study[END_REF]. Indeed, as the immense majority of clots occur in the LAA which is known to show high interindividual variability, series of studies have focused on characterizing the LAA shape, demonstrating moderate association with stroke risk [START_REF] Di Biase | Does the left atrial appendage morphology correlate with the risk of stroke in patients with atrial fibrillation? results from a multicenter study[END_REF]. However, those studies are mostly qualitative, tools available to clinician for decision making are still limited and while imaging data availability is growing, it is under-utilised to quantitatively explore novel image-based bio-markers. An important hurdle making it difficult to use is its heterogeneity. In cardiology, images are taken at different times in the heart cycle, using different imaging systems, and for different tasks. Developing analysis methods allowing to integrate information across heterogeneous datasets to enable statistical studies is therefore a necessity.

In this work we follow this idea. Inspired by [START_REF] Antelmi | Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data[END_REF], we propose a multi-channel formulation to merge multiple heterogeneous datasets into a common latent representation. This framework realises a combination of Multi-task Learning (MTL) [START_REF] Ruder | An overview of multi-task learning in deep neural networks[END_REF] and Meta Learning (ML) [START_REF] Hospedales | Meta-learning in neural networks: A survey[END_REF]: the common representation across datasets imposed in the latent space induces homogeneity across latent projections while enriching the amount of data provided to train a classifier for automated image-based diagnosis. In addition, the learned latent distribution of the joint representation is a meta parameter that constitutes an excellent prior for future datasets as it is robust to multiple datasets sharing a common task.

We propose to use such a model to explore underlying links between Thrombosis and Pulmonary Veins (PV), Appendage (LAA) positions and orientations in the Left Atrium. In the following sections we propose a lightweight graph representation of the LA to focus on PV and LAA positions and formulate the classification methodology within a supervised framework where the common representation is improved in terms of Kullback-Leibler divergence. We finally apply the model to the joint analysis of LA Graphs where the data is split in systole and diastole subsets. This constitutes a multi-label classification problem across datasets where the labels are consistent but the dataset is heterogeneous.

Methodology

Pre-processing Pipeline

Our study was performed on 3D Computational Tomography scans (CT-Scans) along with clinical data from 107 patients suffering atrial fibrillation, of which 64 are labelled Thrombus positive, a composite criterium composed of a detection of LAA thrombus on CT scan and/or past history embolism. In particular, our database is composed of 50 patients in systole (of which 27 are Thrombus positive) and 57 patients in diastole (of which 37 are Thrombus positive). Cardiac segmentation was first conducted automatically with a 3D U-Net neural network as proposed by [START_REF] Jia | Automatically segmenting the left atrium from cardiac images using successive 3d u-nets and a contour loss[END_REF], and then hand-corrected by experts. All the data was acquired by the Bordeaux University Hospital.

From the available segmentation masks we use the open-source package MMG4 for meshing the shapes. First we apply a marching cube algorithm, giving a rough meshing of the surface, the mmg3d algorithm is then applied with specific parameters to keep the number of triangles under 2500.

Graph Representation of the LA

To study the impact of PVs and LAA geometry in clot formation, we propose to represent the LA as a graph similar to its centre-line. To do so, we first label automatically the PVs and LAA of every mesh with the help of the LDDMM framework and the varifold representation of shapes (see [START_REF] Pennec | Riemannian Geometric Statistics in Medical Image Analysis[END_REF]). We compute the Atlas, or mean shape of the population, giving us a diffeomorphic registration from the Atlas to every shape. The population can then be fully represented by the Atlas T and a set of deformations {ϕ i } i n . After hand-labeling of the Atlas, we warped the labels through the deformations in order to label every patient.

In practice, due to different types of vein anatomy in the population, the atria were separated in three classes to prevent big deformations from moving the labels too far from the roots of the veins. The analysis was performed with the deformetrica software [START_REF] Bône | Deformetrica 4: an open-source software for statistical shape analysis[END_REF] and the deformation and varifold kernels widths were both set to 10mm. As a result, the variations of each mesh were captured well up to the residual noise on the surface. Therefore, atlas labels were warped to each subject faithfully to the anatomy of the atrium, as shown in Fig. 1.

To achieve the graph representation, we extract the centre of mass of each label (i.e. PVs, LAA and body of LA) as well as the centre of each junction between labels, representing the ostium 5 . These points are the graph nodes, each branch is connected to the centre of the body of the LA.

Finally, to have a unified coordinate system between graphs, we set the body centre as the origin, fix the x-axis as the direction from the centre to the LAA ostium, and chose the left anterior PV ostium as the second direction.

Design of Fusion and Classification Loss Function

Let's denote D = {D k } N k=1 N datasets with respective dimension d k , and (x, y) = {x k , y k } N
k=1 a set of pairs of observation x k and label y k from every dataset. Let z ∈ R d be the latent variable shared by all elements of (x, y), with d the dimension of the latent space such that d inf {d k |1 k N }. We aim at having a common representation across datasets D k , thus for every k a common distribution p(z|x k , y k ).

To do so, we use variational inference by introducing φ = {φ k } N k=1 the inference parameters for each datasets, θ the common generative parameters and density functions q φ (z|x k , y k ) ∈ Q, which we want, on average, to be as close as possible to the common posterior p θ (z|D). By using the Kullback-Leibler divergence, this problem translates to:

argmin q∈Q E N [D KL (q φ (z|x k , y k )||p θ (z|D))] (1)
Because of the intractability of p θ (z|D) we cannot directly solve this optimisation problem. We aim to find a lower bound of (1) by expanding the Kullback-Leibler divergence:

D KL [q φ (z|x k , y k )||p θ (z|D))] = R d q φ (z|x k , y k )[ln q φ (z|x k , y k ) -ln p θ (z|D)]dz (2)
Using Bayes' theorem, we can now rearrange the divergence to:

D KL [q φ (z|x k , y k )||p θ (z|D))] =D KL [q φ (z|x k , y k )||p θ (z|x))] -E z∼q φ k [ln p θ (y|z, x)] + ln p θ (y|x) (3)
Which yields the following evidence lower bound:

ln p θ (y|x) -D KL [q φ (z|x k , y k )||p θ (z|D))] =E z∼q φ k (z|x k ,y k ) [ln p θ (y|z, x)] -D KL [q φ (z|x k , y k )||p θ (z|x))] (4)
We impose this constraint over all datasets, by supposing that every dataset is conditionally independent, we have the following evidence lower bound:

L(x, y, θ, φ) = 1 N N k=1 E z∼q φ k (z|x k ,y k ) N k=1 ln p θ (y k |z, x k ) -D KL [q φ k (z|x k , y k )||p θ (z|x))] (5)
Maximising the lower bound L is therefore equivalent to optimising the initial problem [START_REF] Antelmi | Sparse multi-channel variational autoencoder for the joint analysis of heterogeneous data[END_REF]. The distribution p θ (y k |z, x k ) of shared parameters θ is learned by a common decoder from the latent space, and acts on the labels y k , in this sense the decoder is a classifier on the set of all labels in D. In addition the learned distribution p θ is a meta-parameter that contains information from every datasets in D.

Unlike variational auto-encoders, the reconstruction objective in ( 5) is over the labels y, which transforms the traditional decoder into a classifier. Moreover having more than one encoder impacts the reconstruction loss which becomes a cross reconstruction of the labels from every dataset. This constraint forces the encoders to identify a common latent representation across all the datasets.

In practice, we assume Q is the Gaussian family, parameters θ and φ are initialised randomly, the optimisation is done by stochastic gradient descent, using an adaptive learning rate with an Adam optimiser and back-propagation.

Synthetic Experiments

The aim of our synthetic experiments6 is to highlight the possibilities offered by our method, for the sake of interpretability, we chose to use a relatively simple parameterisation for our model, consisting of a neural network with three fully connected layers as our encoder and 2 fully connected layers for the classifier. All activation functions are ReLU, and a Softmax function is used for classification.

The synthetic data was generated with the make-classification function from the scikit-learn7 library, by generating clusters of points for multi-label classification by sampling from a normal distribution. We then generate similar datasets by applying transformation and adding noise to the initial problem. Thanks to this method, we can generate a high variety of independent datasets where the features have different distributions, but sharing the same target space.

We generate a collection of 5 independent datasets of dimensions varying between 10 and 20, sharing three labels. The latent dimension is set to 4 and we train our model jointly across datasets, as well as on each dataset separately. On 100 experiments, results show a generally increased overall accuracy score on test data when the datasets are trained jointly. With a 90% confidence level, the accuracy for joint training lies in [0.83, 0.99] with a median of 0.92, while the accuracy for separate training lies in [0.73, 0.94] with a median of 0.86, and about the same amount of epochs are required for convergence. Moreover, we can see in fig. 2 that training multiple datasets jointly leads to a coherent latent space shared between labels (fig. 2b), while trying to independently represent the latent spaces of each dataset in a common space leads to poor consistency of the latent space representation across datasets (fig. 2a).

Noisy labels

Because of the shared latent space between the collection of datasets D, some subspace is allocated for labels that may not belong to a given dataset D i . This brings robustness to the classification when there is an uncertainty on the ground truth labels. In fact, if we assign a wrong label to a given dataset D i the model is capable of assigning the observation to another label due to the constraint of obtaining a coherent latent space. To illustrate this point we created four datasets sharing three common possible labels; for one dataset we modified the third label into a fourth label. In figure 3 we show the evolution of the joint latent space throughout training, which highlights the preference for obtaining a coherent latent space rather than high-accuracy, the test accuracy dropped from 0.88 to 0.75 while the fourth label completely disappeared in the predictions. 4 Application to LAA Graphs

Our clinical database is composed of 50 patients in systole (of which 27 are Thrombus positive) and 57 patients in diastole (of which 37 are Thrombus positive), thus we use two encoders in which we feed the nodes of the graphs to enable a joint analysis of the dataset. The labels are 1 if the patient is Thrombus positive and 0 otherwise. As such, the classes are well balanced for both datasets. We train the model with the same architecture and hyper-parameters as for synthetic data. After 10-fold cross-validation, the model yields an average test accuracy of 0.89, for the diastolic set 0.92, and for the systolic set 0.86. In contrast, attempts to classify the subsets independently suffer from mode collapse; even a careful hyper-parameters fine-tuning results in very poor accuracy scores, reaching at best 0.65 in both cases. This highlights the robustness of the model and the clear advantage of joint analysis. Fig. 4b shows the shared latent space with the systole subset as (×) and diastole subset as (•). We see a clear separation of classes as well as a common separator for the subsets, while systole and diastole are well grouped together for each class.

When we attempt to classify the complete dataset without any splitting, disregarding the considerable changes in shape during the cardiac cycle, we obtain slightly worse accuracy results of 0.84. In addition, such a model is less interpretable as important features for a given class can be contradictory in between subsets. To highlight this we investigate important features and show possible bio-markers.

As an additional baseline we performed PCA followed by logistic regression with cross validation and grid search on the number of principal components. We observed a much lower accuracy both on the complete dataset or individual subsets (0.65 at diastole, 0.71 at systole and 0.66 on the complete dataset).

We compare results from three interpretation algorithm (Integrated Gradient [START_REF] Sundararajan | Axiomatic attribution for deep networks[END_REF], DeepLIFT [START_REF] Shrikumar | Learning important features through propagating activation differences[END_REF], and KernelSHAP [START_REF] Lundberg | A unified approach to interpreting model predictions[END_REF]) implemented in captum8 python library. We first feed the tests 100 times during cross validation and compute the feature importance algorithms on samples that are predicted right with more than a 95% certainty. Mean values over all samples are the final feature attribution scores. Fig. 4a highlights the necessity to split the datasets to keep clinical coherence; It shows the Integrated gradient score on the x value of the Right Inferior PV (RIPV) point when the dataset is split (systole and diastole) and when it is trained commonly (i.e not split). We see the model without splitting the data disregards this feature when in fact it is important for the diastole subset. Fig. 5 shows the possible bio-markers, in black are atlases of the population at systole and diastole, in blue are important feature values for control patients, in red are the ones for Thrombus positive patients. At diastole, the model seems to focus on the left PVs; their ostia being closer to and rotated towards the interior compared to the rest of the frame in Thrombus positive cases; In addition, for those cases, all veins intersection with the LA body are more horizontal, this 'fold' could impact the hemodynamic environment of the LA, which plays an important part in clot formations. At systole, the left PV and the angulation of the appendage are the focus. For Thrombus positive cases, PVs are rotated towards the interior with the left interior PV being on top and aligned with the LAA; The LAA ostium tends to be closer to the center and the LAA lower. Finally we see again the PVs tendency to being more 'folded'. While being interpretable, this also highlights the importance of analysing systole and diastole images separately.

Conclusion

In this work, we provided a graph representation of the LA to analyse possible image-based bio-markers. In order to enable joint analysis of systole and diastole graphs we presented a new method at the crossroad between multi-task learning and meta learning to tackle the joint analysis of multiple heterogeneous datasets. By leveraging on the idea that the whole is better than its parts, we proposed a classification scheme with good interpretation properties of the latent space highlighted in the study of LA Graphs. We believe that the coherent latent space inherited from our model makes it possible to have deep neural network as encoders while conserving the interpretability of simpler models. We aim at further exploiting this property by applying the method to joint analysis of datasets containing much more heterogeneity. Finally, we believe the lightweight graph representation can be added in a more complete and multi-disciplinary study of the LA.

Fig. 1 .

 1 Fig. 1. Pipeline for the graph representation of the LA.

Fig. 2 .

 2 Fig. 2. Latent space after convergence on 5 datasets with 3 labels. Our model provides a unified representation (fig. 2b), not achievable when datasets are trained independently (fig. 2a)

Fig. 3 .

 3 Fig. 3. Evolution of joint latent space whith noisy labels. The wrong label (red) disappears completely from predictions to become coherent label (green)

  (a) Integrated Gradient scores of the RSPV center's x-axis for joint (purple) and without split (gray) training. (b) Shared latent space.

Fig. 4 .

 4 Fig. 4. Results of the method. Joint analysis enables better interpretation of biomarkers (4a) as well as successful representation in the latent space (4b).

Fig. 5 .

 5 Fig. 5. Visualisation of important features for predicting presence (red) and absence (blue) of thrombosis, both at diastole and systole.

https://www.mmgtools.org/

The code is available at https://github.com/Inria-Asclepios/mcvc

https://scikit-learn.org/

https://captum.ai/

Acknowledgments

This work is funded by ERACoSysMed PARIS Project, and is in collaboration with the IHU Liryc of Bordeaux and the Pompeu Fabra University in Barcelona. This work has been supported by the French government, through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002, as well as the IHU LIRYC with the reference number ANR-10-IAHU-04.


Vincent Dufour

Laure Wiest

Sylvain Slaby

François Le Cor Ac

Lucile Auger

Olivier Cardoso

Laurence Curtet

Laure Pasquini

Xavier Dauchy

Emmanuelle Vulliet

Damien Banas

Miniaturization of an extraction protocol for the monitoring of pesticides and polar transformation products in biotic matrices

Introduction

Pesticides, or plant protection products (PPPs), are widely used across the world to increase agricultural yields. As only some of the applied dose reaches target organisms, a significant amount of active substances ends up in the environment, depending on the PPPs, environmental conditions, and agricultural practices (Gaillard et al. 2016b). Aquatic ecosystems play the role of final receptacles for organic micropollutants, with concentration levels ranging from ultra-traces to µg.L -1 (Gaillard et al. 2016a;[START_REF] Nowell | Complex mixtures of dissolved pesticides show potential aquatic toxicity in a synoptic study of Midwestern U.S. streams[END_REF]. Gaillard et al. (2016a) estimated PPP concentrations of up to 20 µg.L -1 in streams located in headwater agricultural catchments. Moreover, PPPs can affect the health of wild organisms such as macroinvertebrates (MIs) [START_REF] Macchi | The impact of pesticides on the macroinvertebrate community in the water channels of the Río Negro and Neuquén Valley, North Patagonia (Argentina)[END_REF], amphibians [START_REF] Slaby | Exposures to chemical contaminants: What can we learn from reproduction and development endpoints in the amphibian toxicology literature?[END_REF], fish [START_REF] Clasen | Bioaccumulation and oxidative stress caused by pesticides in Cyprinus carpio reared in a rice-fish system[END_REF], and birds [START_REF] Stanton | Analysis of trends and agricultural drivers of farmland bird declines in North America: A review[END_REF][START_REF] Brain | The agro-enabled urban revolution, pesticides, politics, and popular culture: a case study of land use, birds, and insecticides in the USA[END_REF], and biodiversity in general [START_REF] Mclaughlin | The impact of agricultural practices on biodiversity[END_REF][START_REF] Geiger | Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland[END_REF]. Bioaccumulation and bioamplification along the trophic chain are of particular concern as they may result in increased contamination levels in top predators [START_REF] Konwick | Bioaccumulation, biotransformation, and metabolite formation of fipronil and chiral legacy pesticides in rainbow trout[END_REF]Rostron 2010). To overcome these phenomena, more polar PPPs have been synthesized. However, they can reach aquatic ecosystems and contaminate wild organisms even more easily because of their intrinsic mobility. In addition, PPPs can undergo biotic and abiotic processes in the environment that lead to the generation of transformation products (TPs) being usually more stable, polar, and mobile [START_REF] Reemtsma | Mind the Gap: Persistent and Mobile Organic Compounds-Water Contaminants That Slip Through[END_REF], and sometimes having more effects on organisms [START_REF] Sinclair | Assessing the Ecotoxicity of Pesticide Transformation Products[END_REF][START_REF] Tousova | European demonstration program on the effect-based and chemical identification and monitoring of organic pollutants in European surface waters[END_REF] than their parent compounds. There is a current gap in knowledge on these polar mobile organic chemicals (PMOCs) since, compared to historic PPPs, their nature and dynamics in the environment are poorly known [START_REF] Reemtsma | Mind the Gap: Persistent and Mobile Organic Compounds-Water Contaminants That Slip Through[END_REF] despite their potential toxicity at low concentrations.

Filling this gap would require the use of specific analytical tools and method such as ion chromatography, that are usually not compatible with multi-residues analysis of medium to nonpolar compounds.

The QuEChERS (quick, easy, cheap, effective, rugged, and safe) method [START_REF] Anastassiades | Fast and Easy Multiresidue Method Employing Acetonitrile Extraction/Partitioning and "Dispersive Solid-Phase Extraction" for the Determination of Pesticide Residues in Produce[END_REF] is one of the most universal extraction approach for multi-residue analysis of pesticides, including polar pesticides. It usually ensures good extraction rates (ERs) for a large number of substances [START_REF] Knoll | Trends in sample preparation and separation methods for the analysis of very polar and ionic compounds in environmental water and biota samples[END_REF]. This method was successfully applied to solid matrices with different levels of complexity such as fruit and vegetables [START_REF] Lehotay | Comparison of QuEChERS sample preparation methods for the analysis of pesticide residues in fruits and vegetables[END_REF][START_REF] Koesukwiwat | High throughput analysis of 150 pesticides in fruits and vegetables using QuEChERS and low-pressure gas chromatographytime-of-flight mass spectrometry[END_REF], meat and fish [START_REF] Lazartigues | Multiresidue method to quantify pesticides in fish muscle by QuEChERS-based extraction and LC-MS/MS[END_REF][START_REF] Lichtmannegger | Alternative QuEChERS-based modular approach for pesticide residue analysis in food of animal origin[END_REF][START_REF] Oliveira Fa Da | Multiresidue method for detection of pesticides in beef meat using liquid chromatography coupled to mass spectrometry detection (LC-MS) after QuEChERS extraction[END_REF], or food from animal origin [START_REF] Giroud | Trace level determination of pyrethroid and neonicotinoid insecticides in beebread using acetonitrile-based extraction followed by analysis with ultra-high-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Golge | Validation of a Modified QuEChERS Method for the Determination of 167 Pesticides in Milk and Milk Products by LC-MS/MS[END_REF][START_REF] Song | Determination of 60 pesticides in hen eggs using the QuEChERS procedure followed by LC-MS/MS and GC-MS/MS[END_REF]. However, owing to their nature, more polar substances such as TPs present other analytical challenges. Indeed, they are more soluble in the aqueous phase than in acetonitrile, even if the latter is saturated with buffer, resulting in low or no recoveries. [START_REF] Dufour | Development of a simple multiresidue extraction method for the quantification of a wide polarity range list of pesticides and transformation products in eggs by liquid chromatography and tandem mass spectrometry[END_REF].

Another disadvantage of most current extraction methods is the use of high sample masses, usually greater than 1 g, that can be very difficult to obtain for many environmental matrices. This hinders the study of PPP transfers in food webs for two reasons: (1) it may not be possible to collect enough biomass to analyze pesticides in smaller organisms such as insects or small fish [START_REF] Knoll | Trends in sample preparation and separation methods for the analysis of very polar and ionic compounds in environmental water and biota samples[END_REF]; or (2) individuals must be pooled, which may lead to less accurate results [START_REF] Roche | Rice fields regulate organochlorine pesticides and PCBs in lagoons of the Nature Reserve of Camargue[END_REF]. This is typically the case for MIs (e.g., insect or crustacean larvae), which have small individual masses but play a key role in trophic chains by ingesting plant matter and then being consumed by predators [START_REF] Four | Using stable isotope approach to quantify pond dam impacts on isotopic niches and assimilation of resources by invertebrates in temporary streams: a case study[END_REF]. Developing analytical methods to quantify the contamination of this trophic level is a major challenge for modeling PPP transfers in ecosystems. Moreover, it may also represent an interesting tool for the monitoring of marine mammals, reptiles and apex predators from a biopsy, avoiding euthanasia or invasive surgery to obtain sufficient material for analysis, which is an actual need concerning the assessment of micropollutants in marine wildlife [START_REF] Sanganyado | Toward an integrated framework for assessing micropollutants in marine mammals: Challenges, progress, and opportunities[END_REF].

Providing a robust, reliable, and sensitive analytical method based on low matrix masses could increase the number of studies in that research field and benefit the understanding of the global dynamics of organic micropollutants in the environment. In this context, the aim of this work was to study the effect of the miniaturization of a previously developed extraction protocol [START_REF] Dufour | Development of a simple multiresidue extraction method for the quantification of a wide polarity range list of pesticides and transformation products in eggs by liquid chromatography and tandem mass spectrometry[END_REF] on analytical performance and to test its applicability on two matrices: fish and MIs. As masses of MI samples are very low, the effect of miniaturization was first investigated on fish, and then applied and validated on MIs. Special attention was paid to the validation phase in order to guarantee the reliability and accuracy of the method as it is well known that decreasing the reduction of the sample mass increases the variability of the measurement [START_REF] Han | Use of an Efficient Measurement Uncertainty Approach To Compare Room Temperature and Cryogenic Sample Processing in the Analysis of Chemical Contaminants in Foods[END_REF][START_REF] Lehotay | Use of a quality control approach to assess measurement uncertainty in the comparison of sample processing techniques in the analysis of pesticide residues in fruits and vegetables[END_REF].

Materials and methods

Standards and reagents

Ultrapure water (Fisher Chemical, Geel, BE), acetonitrile (ACN) (Honeywell, Seelze, DE), and heptane (Merck, Darmstadt, DE) were of LC-MS quality. Magnesium sulfate (MgSO4) was purchased from Sigma-Aldrich (Saint Louis, USA). Formic acid (FA) and ammonium sulfate were purchased from Biosolve-chemicals (Dieuze, FR). Fifty-milliliter polypropylene metal-free centrifuge tubes were purchased from VWR (Fontenay-sous-Bois, FR) and 2 mL polypropylene safe-lock centrifuge tubes were obtained from Eppendorf (Montesson, FR). Forty-one analytical standards and 15 isotopically labeled standards (Table S1) were purchased from A2S (Martignas-sur-Jalle, FR), Honeywell (Seelze, DE), HPC standards (Cunnersdorf, DE), LGC group (Teddington, UK), Sigma-Aldrich (Saint-Louis, USA), and TCI Europe (Anvers, BE). All were of high purity (> 96%) with most > 99%. Individual solutions made in acetonitrile (100 mg.L -1 ) were stored at -18°C. They were mixed to obtain a 5 μg.L -1 solution used to develop and validate the extraction protocol. Mix solutions were regularly characterized to check their stability over time and data were compiled in a control chart. Stored at -18°C, they were stable for at least 6 months.

Sampling strategy and pre-treatment

Young tench (Tinca tinca) (n = 13) were provided by the experimental platform of aquaculture (URAFPA, University of Lorraine) and were used as a blank reference matrix for fish. The MI reference matrix consisted in chironomid larvae reared under laboratory conditions, kindly provided by INRAE (ECOTOX team, Centre Lyon-Villeurbanne, FR).

For this purpose, fish and MIs were both sampled in November 2018 in two fishponds of the Dombes region (FR), over 2 distinct days. Those fishponds have watershed strongly influenced by conventional farming with the presence of either grassland or maize, wheat or rape crops. Fish and MI are expected to be exposed to diverse pesticides at important levels, and more especially herbicides like metazachlor and metolachlor, which are often applied to this type of crop, as well as to their TPs. Three fish species were selected for their representativeness of the local production: carp (Cyprinus carpio, n = 6), roach (Rutilus rutilus, n = 6), and rudd (Scardinius erythrophthalmus, n = 5). On average (± SEM), they weighed 247.5 ± 11.2, 58.3 ± 3.5, and 37.6 ± 12.2 g wet weight (ww), respectively. Concerning MIs, organisms were pooled to constitute 19 samples according to taxonomic classification. They were sorted into Anisoptera (n = 2), Chironomidae (n = 5), Corixidae (n = 5), Daphniidae (n = 2), Dystiscidae (n = 1), and Physidae (n = 4) groups. All blank reference matrices and samples (whole organisms) were frozen at -20°C in aluminum tray. Then, they were freeze-dried (Reacti-Vap, PIERCE, Bellafonte, USA), ground, and stored at room temperature until extraction.

Initial protocol

The initial protocol (IP), based on a previous work by [START_REF] Dufour | Development of a simple multiresidue extraction method for the quantification of a wide polarity range list of pesticides and transformation products in eggs by liquid chromatography and tandem mass spectrometry[END_REF] and designed for the analysis of pesticides and their degradation products in high-mass environmental samples (e.g., eggs), is illustrated in Fig. 1. Briefly, 500 mg of sample was placed in a 50 mL centrifuge tube and spiked with 50 µL of a 500 µg.L -1 mix solution containing all standards to constitute artificial samples. The solvent of the solution was evaporated under gentle nitrogen flux for 10 min and the spiked matrix was mixed with 5 mL of LC-MS water at 16 Hz for 10 min (SamplePrep 2010 Geno/Grinder from SPEX, Costa Mesa, USA). Then, 5 mL of heptane and 10 mL of ACN (+ 0.2% FA) were added and the whole sample was mixed again at 16 Hz for 10 min before centrifugation at 9,500 RCF (Relative Centrifugal Force) at 20°C for 5 min. Six milliliters of the lower layer (ACN/water mix) was dehydrated over 2.5 g of MgSO4 in a 50 mL centrifuge tube and manually mixed before being centrifuged again for 2 min (20°C, 9,500 RCF). The final extract was diluted 10 times in ultrapure water + 0.1% FA before injection in LC-MS/MS.

Miniaturized protocol

In order to make the analysis of small organisms (e.g., insect larvae, small fish) possible, a miniaturized protocol (MP) was developed (Fig. 1). For this MP, a 2 mL polypropylene centrifuge tube was filled with 30 mg of matrix spiked with 50 µL of a mix solution containing all analytes (30 µg.L -1 ) to constitute artificial samples (concentrations and volumes used specifically for validation are given in section 2.6). Before extraction, the added solvent was evaporated under gentle nitrogen flux for 10 min. Next, 0.5 mL of LC-MS water was added, and the whole sample was shaken for 10 min at 16 Hz using Geno/Grinder. The tube was then completed with 1 mL of ACN + 0.2% FA and 0.5 mL of heptane before being shaken again for 10 min at 16 Hz. It was centrifuged for 5 min at 20°C and at 9,500 RCF. Then, 0.6 mL of the lower layer (ACN/water mix) was sampled and dehydrated in a new centrifuge tube containing 0.3 g of MgSO4 and 3 zirconium oxide 3 mm grinding balls (Fisher Scientific, Illkirch, FR). Grinding balls were used to break up crystals formed in these small tubes, which slow down the dehydration process and lead to poor recoveries for the most polar analytes (e.g. -ESA or -OXA TPs). The tube was shaken for 1 min at 16 Hz to favor the reaction and was centrifuged again at 9,500 RCF and 20°C for 2 min. Finally, the extract was diluted 10 times in ultrapure water + 0.1% FA before injection in LC-MS/MS.

LC-MS/MS

The development of the LC-MS/MS method was described in [START_REF] Dufour | Development of a simple multiresidue extraction method for the quantification of a wide polarity range list of pesticides and transformation products in eggs by liquid chromatography and tandem mass spectrometry[END_REF]. Analysis of the 41 PPP residues was performed using a 1290 Infinity UPLC system (Agilent Technologies, Massy, FR) coupled with a 5500 QTRAP mass spectrometer (Sciex, Villebon-sur-Yvette, FR) in both positive and negative modes using electrospray ionization. Forty microliters of sample was injected, and separation was performed at 0.4 mL.min -1 and at 50°C on an XSelect® HSS T3 column (2.1 × 150 mm, 2.5 μm, Waters, Saint Quentin-en-Yvelines, FR). The column was protected with a KrudKatcher Ultra filter (Phenomenex, Le Pecq, FR) and an XSelect® HSS T3 precolumn (2.1 × 5 mm, 2.5 μm, Waters, Saint Quentin-en-Yvelines, FR). Ultrapure water + 0.1% FA (A) and ACN + 0.1% FA (B) were used as mobile phases in positive mode, while 1 mM ammonium acetate in ultrapure water (A) and ACN (B) were used in negative mode. The gradient was the same for both methods: it started with 10% B for 1 min, increased to 100% B over 7 min and held for 6 min, then decreased back to 10% B over 1 min and held for 4 min. The temperatures of the mass spectrometer source, the nebulizer gas pressure, and heating gas pressure were set at 550°C, 40 psi, and 50 psi, respectively. Ion spray voltages were 5500 V for positive ionization and -4500 V for negative ionization. Analytes and analytical parameters are available in Table S1.

Method validation

The MP was validated for the 41 molecules (21 PPPs and 20 TPs) for the fish matrix, using matrixmatched calibration and following European guidance recommendations (European Commission DG-SANTE 2019). The reference matrix spiked at 6 different concentration levels, namely 1 (C1), 2 (C2), 5 (C3), 10 (C4), 15 (C5), and 20 (C6) times the initial concentration (C1) (available in Table 1), and a protocol blank (C0), made up with reference matrix only, were extracted 5 times following the whole MP to validate response linearity. The matrix was spiked at C2, C4, and C6 before extraction and the corresponding areas were compared to the matrix spiked after extraction to calculate ERs. The areas in the matrix spiked after extraction were also compared to those of standard solutions of equivalent concentrations to calculate matrix effects (MEs). ER and ME calculations are available in Supplementary information (SI). Inter-day precision was determined over 5 days with the daily extraction in triplicate of one of the following concentration levels: C2 (n = 3), C4 (n = 2), and C6 (n = 3).

Because only low masses of the reference MI matrix were available, the validation of the method for MIs could not follow European guidelines (European Commission DG-SANTE 2019). Two triplicates of matrix spiked at C4 and 2 protocol blanks were extracted on 2 different days to assess protocol performance on this specific matrix. The limits of quantification (LOQs) were estimated to be 10 times the signal-to-noise ratio on C4 samples. No ER estimation could be performed but recoveries (RECs, Eq. 3 in SI), which reflect the trueness of the method, were calculated instead for each molecule on the C4 samples (n = 6), using matrix-matched calibration (C1 to C6).

Chromatograms of both reference matrices spiked with monitored molecules at concentration C6 are available in SI (Fig. S1).

Method application

The developed miniaturized method was applied to 17 fish from 3 different species and 19 MI samples from 6 different taxa (see Section 2.2 for details) to characterize contamination in field samples. Following European guidelines (European Commission DG-SANTE 2019), quantification was performed with a 6-point matrix-matched calibration curve (described in Section 2.6). A protocol blank and a reference matrix spiked at C2 for fish or C4 for MIs were extracted with each sample series to avoid false positives and control RECs of each extraction series. When protocol blanks showed contamination, the LOQs were increased to 3 times their quantified concentrations to avoid false positives.

Statistical analysis

Isotope-labeled standards (D11-acetochlor, D5-acetochlor-ESA, D5-atrazine, D5-atrazine-2hydroxy, D6-bentazone, D4-carbendazim, 13C-fipronil, D4-imidacloprid, D3-isoproturon, D5metazachlor, D6-metazachlor-ESA, D10-simazine, D9-tebuconazole, D5-terbutryn) were introduced into the MP in parallel with the analytes in the artificial samples, or instead of them for the analysis of the field samples (50 µL of a 30 µg.L -1 mix solution). They are too few and do not cover all the chemical families researched to be used as internal standards, or they would probably have led to a decrease of the accuracy and an increase of uncertainties [START_REF] Han | Use of an Efficient Measurement Uncertainty Approach To Compare Room Temperature and Cryogenic Sample Processing in the Analysis of Chemical Contaminants in Foods[END_REF][START_REF] Lehotay | Use of a quality control approach to assess measurement uncertainty in the comparison of sample processing techniques in the analysis of pesticide residues in fruits and vegetables[END_REF].

However, they can be used for evaluating the complexity of the matrices analyzed and tracing matrix effects.

The area ratio between the reference matrix and the field samples was calculated for each isotopically labeled standard. This parameter allowed to evaluate matrix complexity because quantities introduced in all samples were the same. Principal component analyses (PCAs) were performed on this ratio, for both fish and MIs, to evaluate if clusters of matrix complexity could be determined based on species (fish) and taxonomic groups (MIs). PCAs were based on Pearson regression (α = 0.05). All statistical analyses and graphical PCA illustrations were performed using the software R (v. 4.0.0, © The R Foundation, 2020) with the following packages: "ggplot2" [START_REF] Wickham | GGPLOT2: Elegant Graphics for Data Analysis[END_REF], "factoextra" (Kassambara and Mundt 2020), "FactoMineR" [START_REF] Lê | FactoMineR : An R Package for Multivariate Analysis[END_REF], and "RColorBrewer" [START_REF] Neuwirth | RColorBrewer: ColorBrewer Palettes[END_REF].

Results and discussion

Performance comparison between the initial protocol and the miniaturized protocol

The IP and the MP were applied to a fish matrix (n = 5) with a C4 concentration level to assess protocol performance (ERs and LOQs). The results are shown in Fig. 2. ERs ranged from 53% (terbutryn) to 103% (benzamide) with the IP and from 50% (prosulfocarb) to 120% (methsulfuronmethyl) with the MP. According to European guidance recommendations (European Commission DG-SANTE 2019), the ERs were considered good (70% < ERs < 120%) for 39 and 38 molecules, respectively. They were similar for both protocols, with a slight advantage for the MP. The most noticeable increases in ER were observed for desethylterbuthylazine-hydroxy (61% to 97% for IP and MP, respectively), terbuthylazine (73% to 87%), and terbutryn (53% to 69%). The only outlier that did not benefit from the change in protocol was prosulfocarb with respective ERs of 54% and 50%. LOQs ranged from 0.1 (fipronil, fipronil sulfone, metolachlor) to 221.8 ng.g -1 dw (metazachlor-OXA) with the IP and from 0.1 (metolachlor) to 221.2 ng.g -1 dw (metazachlor-OXA) for the MP. Fourteen molecules showed LOQs of up to 10 ng.g -1 dw for the IP, as against 18 molecules for the MP, with 4 and 5 molecules having LOQs of up to 50 ng.g -1 dw, respectively. The LOQs were very similar for both protocols, following a near-perfect linear regression (R² = 0.958). However, they were on average 1.4 times higher with the MP than with the IP. As proportions of reactants were conserved between the two protocols, obtaining similar performance results in terms of LOQ and ER did not come as a surprise. Miniaturizing the IP yielded equivalent ERs and LOQs, and allowed trace-level quantification of pesticide residues using 30 mg instead of 500 mg of matrix.

Performance of the miniaturized protocol in fish

The performance of the MP was fully evaluated for fish, as described in Section 2.6. The results are shown in Table 1. Linear regression was determined for each molecule and R² values were always greater than 0.99. Only 6 chemicals out of the 41 tested had R² values below 0.99: acetochlor-OXA (0.987), chlorotoluron (0.988), fenthion (0.950), flufenacet-ESA (0.987), metolachlor-ESA (0.989), and terbutryn (0.989). However, second-order polynomial regressions were more adapted for those molecules and their R² values were found to be 0.994, 0.996, 0.991, 0.992, 0.999, 0.993, and 0.996, respectively.

Among the 41 targeted substances, 38 had LOQs lower than or equal to 60 ng.g -1 dw, including 19 below 10 ng.g -1 dw. Taking into account the dehydration of the matrix (82% ± 11%), these limits corresponded to about 2 and 10 ng.g -1 ww, respectively. The overall performance was consistent with that reported in the literature [START_REF] Lazartigues | Multiresidue method to quantify pesticides in fish muscle by QuEChERS-based extraction and LC-MS/MS[END_REF][START_REF] Kaczyński | Multiclass pesticide residue analysis in fish muscle and liver on one-step extraction-cleanup strategy coupled with liquid chromatography tandem mass spectrometry[END_REF][START_REF] Inostroza | Chemical activity and distribution of emerging pollutants: Insights from a multi-compartment analysis of a freshwater system[END_REF][START_REF] Barbieri | Analysis of 52 pesticides in fresh fish muscle by QuEChERS extraction followed by LC-MS/MS determination[END_REF]) although some LOQs were higher probably because of the low matrix masses used to perform the extraction (0.9 to 10 g in the literature, as against 0.03 g in the present study).

MEs ranged from -77% (fenthion) to 131% (imidacloprid-desnitro). Signal suppression was observed for 11 molecules and signal enhancement concerned 8 substances (out of a -20/+20 range). This was consistent with other studies reporting that signal suppression is the most commonly observed ME in complex matrices [START_REF] Lazartigues | Multiresidue method to quantify pesticides in fish muscle by QuEChERS-based extraction and LC-MS/MS[END_REF][START_REF] Barbieri | Analysis of 52 pesticides in fresh fish muscle by QuEChERS extraction followed by LC-MS/MS determination[END_REF]. The monitoring of this parameter was important for validating the method even though matrix-matched calibration is supposed to compensate for those effects.

ERs at C2 ranged from 57% (prosulfocarb) to 108% (TCP). According to the European guidelines (European Commission DG-SANTE 2019) 35 of the 42 molecules presented good ERs (between 70% and 120%), including 12 of up to 80%. At C4, acceptable ERs for fish and MI matrices were obtained for 31 and 25 molecules, respectively. The inter-day precision was considered good for 32 chemicals at C2 according to the European guidelines previously mentioned (< 20%). This variability tended to decrease for higher concentration levels as the inter-day precision was lower than or equal to 20% for 31 molecules at C4, which is in agreement with European guidelines (European Commission DG-SANTE 2019). Moreover, if we leave aside the nature of the tested matrix, the inter-day precision is better with IP [START_REF] Dufour | Development of a simple multiresidue extraction method for the quantification of a wide polarity range list of pesticides and transformation products in eggs by liquid chromatography and tandem mass spectrometry[END_REF] than with MP. This was most probably due to the decrease of the sample mass which led to an increase of the measurement uncertainties, as shown by [START_REF] Han | Use of an Efficient Measurement Uncertainty Approach To Compare Room Temperature and Cryogenic Sample Processing in the Analysis of Chemical Contaminants in Foods[END_REF] and [START_REF] Lehotay | Use of a quality control approach to assess measurement uncertainty in the comparison of sample processing techniques in the analysis of pesticide residues in fruits and vegetables[END_REF] on food samples towards pesticide contaminations. The developed analytical method was suitable for accurate quantification, except for fenthion for which inter-day precision was of up to 50% for the 3 tested concentration levels.

A signal-to-noise ratio of more than 3 was observed in protocol blanks for acetochlor-alachlor-ESA, bentazon, benzamide, boscalid, CGA 50267, fipronil, fipronil sulfone, isoproturon, metolachlor, prosulfocarb, and terbutryn. Contamination ranged from 0.1 to 8.8 ng depending on the molecule considered. The quantification frequency in blanks was usually below 50%, but reached 60% for fipronil and prosulfocarb, while metolachlor and terbutryn were systematically quantified. The LOQs were increased to 3 times the contamination levels for these molecules to ensure the reliability of the method.

Performance of the miniaturized protocol in macroinvertebrates

Performance of the MP was good for fish and compatible with trace analysis, so the protocol was tested on MI samples. As very low mass was available for the reference matrix (chironomid larvae), only linearity, RECs, and LOQs were assessed (Fig. 3). The tested concentration domains, ERs, LOQs, and blank contamination of each molecule are shown in Table S2. The quantification of most molecules followed a linear regression with R² values greater than 0.99; only fenthion (0.986), flufenacet (0.943), and TCP (0.961) were slightly below, reaching 0.994, 0.961, and 0.992, respectively, when a second-order polynomial regression was applied. Protocol blanks were clean (n = 4) but traces (≤ 0.8 ng) of dimethenamid-ESA, MCPA, and terbutryn were quantified (quantification frequency below 0.5). LOQs ranged from 0.1 (fipronil, fipronil sulfone, metolachlor) to 356 ng.g -1 dw (metazachlor-OXA); taking into account the dehydration (85% on average for this matrix), the LOQs ranged from 0.015 to 53 ng.g -1 ww. Among the 41 tested molecules, 29 had LOQs below 10 ng.g -1 dw, 14 of which being below 1 ng.g -1 dw. These values were of the same order of magnitude as those reported in the literature [START_REF] Haroune | Liquid chromatography-tandem mass spectrometry determination for multiclass pesticides from insect samples by microwave-assisted solvent extraction followed by a salt-out effect and micro-dispersion purification[END_REF][START_REF] Althakafy | Determination of selected emerging contaminants in freshwater invertebrates using a universal extraction technique and liquid chromatography accurate mass spectrometry[END_REF][START_REF] Miller | Biomonitoring of pesticides, pharmaceuticals and illicit drugs in a freshwater invertebrate to estimate toxic or effect pressure[END_REF]. Most tested molecules had good RECs (80%-120%) and values above 120% were obtained with four of them (i.e., bentazone 122%, boscalid 122%, desethylterbutylazine-hydroxy 121%, and metazachlor-OXA 131%). The latter values were close to the acceptable limits, except for metazachlor-OXA for which a correction was necessary for the quantification of MI field samples. It confirmed that most pesticide residues can be monitored in complex biotic matrices with low matrix masses using a single method that provides equivalent levels of performance.

Field samples

In order to validate the performance of the method, 17 fish (3 species) and 19 MI (6 orders) samples were collected and analyzed with the MP. All samples were spiked before extraction with 14 isotopically labeled standards (see section 2.8). The areas of these molecules were compared between field samples and reference matrices. The average ratio ranged from 0.9 (D5-acetochlor-ESA, D6-bentazone, D6-metazachlor-ESA, D5-atrazine-2-hydroxy, D9-tebuconazole) to 1.4 (D5terbutryn) for fish and from 0.8 (D5-acetochlor-ESA, D6-metazachlor-ESA) to 1.5 (D11-acetochlor) for MIs. The relative standard deviation (%RSD) was lower than 30% for all molecules, except for D5-atrazine (34%) and 13C-fipronil (32%) in fish, and D5-acetochlor-ESA (50%) in MIs. Taking into account that a single matrix was used to quantify 3 different fish species, and another single matrix for 6 different MI groups, these ratios close to 1 confirmed the potential for the protocol to be transposed to other biotic matrices with similar relative levels of fat and protein. PCAs performed on area ratios illustrated 77.6% and 66.5% of total variability for fish and MI matrices, respectively (Fig. 4). Clusters could be identified for fish, showing that differences in matrix complexity exist between the 3 species, probably based on their physiological constitution. However, there was considerable overlap between the clusters, indicating relatively similar responses for internal standards. The same overlap was observed for MIs, confirming that the chosen reference matrix is suitable for matrix-matched calibration. However, hindsight is relatively poor for Anisoptera, Daphniidae, and Dytiscidae as their number of samples is ≤ 2.

Concentrations monitored in field samples are shown in Table 2. Among the 40 targeted molecules (fenthion being excluded because of its unreliability, see Section 3.3), 7 were detected: benzamide, imidacloprid-desnitro, fipronil, imidacloprid, prosulfocarb, tebuconazole, and terbutryn. Benzamide, imidacloprid-desnitro, and prosulfocarb were almost systematically quantified in fish (from 42 to 237 ng.g -1 dw, 3 ng.g -1 dw, and from 30 to 165 ng.g -1 dw, respectively) and in MIs (from 62 to 438 ng.g -1 dw, from 2 to 6 ng.g -1 dw, and from 15 to 29 ng.g -1 dw, respectively). Considering the dehydration ratio, the average quantified concentrations of these three molecules were of 21, <LOQ, and 12 ng.g -1 ww in fish, respectively, and of 26, 0.4, and 3 ng.g -1 ww in MIs, respectively.

Prosulfocarb is a herbicide known to drift away, leading to the contamination of non-target crops and waterbodies [START_REF] Devault | Prosulfocarb at center stage![END_REF]. As it possesses a log P of 4.5, it is likely to accumulate in organisms, which seemed to be the case in this study for MIs and fish. It has already been reported in ponds at concentrations of up to 0.5 µg.L -1 (Gaillard et al. 2016a). Benzamide is a TP of folpet that accumulates in both fish and MIs. Despite having a lower log P value than prosulfocarb (log P = 0.6), it was found in higher concentrations. Imidacloprid-desnitro, a TP of imidacloprid, was quantified in 5% of the fish samples and in 42% of the MI samples, while the quantification frequency of the active substance was 0% and 16%, respectively. There is a lack of data in the literature investigating the toxicity of imidacloprid-desnitro in aquatic organisms. Nevertheless, research revealed that it has lower toxicity than imidacloprid in bees [START_REF] Suchail | Discrepancy between acute and chronic toxicity induced by imidacloprid and its metabolites in Apis mellifera[END_REF]). It should be noted that, in the present study, field sampling was carried out in May 2018. A few months later, in September 2018, the outdoor use of the herbicide was banned by the European Commission, so biotic concentrations are expected to decrease over time. However, the monitoring of TPs remain relevant as they sometimes have higher environmental concentrations than their parent molecules, and there is little evidence for their safety [START_REF] Kassambara | New relevant pesticide transformation products in groundwater detected using target and suspect screening for agricultural and urban micropollutants with LC-HRMS[END_REF].

Conclusion

A miniaturized extraction protocol was successfully developed and applied to two complex matrices: fish and MIs. It allowed a sensitive and consistent analysis of PPPs even when only low sample mass was available. A decrease in the required mass of matrix slightly increased the LOQs of the MP in fish compared to the IP, but final LOQs were still suitable for trace-level analysis with most below 10 ng.g -1 dw. Moreover, ERs were in the range 70%-120% for 35 of the 41 molecules. The transposition of IP to MP did not altered its performances. The application of this protocol in the field has revealed low contamination of aquatic organisms, but it has also shown that they are contaminated by a cocktail of molecules and TPs. The harmlessness of such a combination of contaminants is not guaranteed, and it is important to take it into consideration in order to ensure the good status of aquatic ecosystems. The herbicide prosulfocarb and the transformation product benzamide were almost systematically quantified in both matrices. The MP, developed in this study, represents a potential tool for the characterization of polar pesticides in small complex biotic samples. and limits of quantification (LOQs) obtained for fish blank matrix spiked at C4 (n=5); see Table 1 for the molecules associated with the numbers Fig. 3. Quantification recoveries (%) and limits of quantification (LOQs) (ng.g -1 dw) obtained for macroinvertebrates spiked at C4 (n=6); see Table 1 for the molecules associated with the numbers

Fig. 1 .

 1 Fig. 1. Step comparison between the initial and miniaturized protocol (ACN: acetonitrile, FA: Formic acid)

Fig. 4 .

 4 Fig. 4. Representation of isotope-labeled standard area ratios between reference and field samples for fish and macroinvertebrates; PCA based on Pearson regression (α = 0.05) (USE COLORS FOR THIS FIGURE)

Table 1 .

 1 Analytical performance of the validated method in fish identification of molecules in Fig.2and Fig.3, Domain: range of tested concentrations (ng.g -1 dw), dw: dry weight, LOQ: estimated limit of quantification (ng.g -1 dw), ME: matrix effect (%), ER: extraction rate (%), QF: quantification frequency, C2 to C6: tested concentration levels.

	511
	n° Chemicals	Domain (ng.g -1 dw)	r² ± ‰RSD (n = 5)	LOQ (ng.g -1 dw)	Matrix effect ± SD (%)	Recovery ± %RSD (%) C2 (n = 6) C4 (n = 3) C6 (n = 6)	Inter-day precision (%RSD) C2 (n = 5) C4 (n = 5) C6 (n = 5)	Blanks (n = 5) Min -Max (ng) QF
		Fungicides
	1	Boscalid	60 -1200 0.996 (4)	60	-6 ± 12	80 ± 15	87 ± 4	93 ± 8	27	18	5.1 -5.1	0.2
	2	Tebuconazole	3 -60	0.993 (2)	3	-18 ± 15	69 ± 20 98 ± 17	89 ± 8	26	15	-	-
		Herbicides
	3	Acetochlore	60 -1200 0.995 (4)	60	-2 ± 14	76 ± 12	77 ± 2	80 ± 4	27	20	-	-
	4	Alachlor	30 -600	0.995 (3)	30	-10 ± 12	77 ± 9	79 ± 6	79 ± 7	24	19	-	-
	5	Atrazine	3 -60	0.996 (2)	3	-31 ± 12	74 ± 13	82 ± 5	86 ± 7	22	18	-	-
	6	Bentazon	3 -60	0.992 (8)	3	-8 ± 16	69 ± 33	88 ± 6	88 ± 3	22	17	0.1 -0.1	0.2
	7	Chlorotoluron	6 -120	0.988 (14)	6	-20 ± 16	83 ± 19 86 ± 10	92 ± 7	20	21	-	-
	8	Dimethachlor	6 -120	0.996 (2)	6	-14 ± 17	78 ± 22	89 ± 5	89 ± 3	27	21	-	-
	9	Dimethenamide	6 -120	0.995 (1)	6	-20 ± 14	76 ± 25	86 ± 5	88 ± 10	22	20	-	-
	10 Flufenacet	6 -120	0.996 (4)	6	-6 ± 17	74 ± 18	85 ± 6	91 ± 5	27	18	-	-
	11 Isoproturon	3 -60	0.993 (4)	3	-23 ± 15	77 ± 18	92 ± 9	93 ± 7	22	18	0.1	0.2
	12 MCPA	30 -600	0.993 (4)	30	16 ± 9	72 ± 29	89 ± 1	86 ± 7	26	21	-	-
	13 Metazachlor	3 -60	0.992 (5)	3	-10 ± 16	83 ± 19 90 ± 10	92 ± 9	25	18	-	-
	14 Metolachlor	3 -60	0.997 (3)	3	-6 ± 12	88 ± 8	82 ± 12	83 ± 4	26	18	0.1 -0.2	1.0
	15 Metsulfuron-methyl	30 -600	0.994 (5)	30	48 ± 29	71 ± 31 101 ± 7 106 ± 7	27	21	-	-
	16 Prosulfocarb	30 -600	0.99 (6)	30	-57 ± 16	102 ± 63	36 ± 11 45 ± 13	23	24	0 -8.8	0.6
	17 Terbuthylazine	3 -60	0.994 (4)	3	-41 ± 23	84 ± 24 67 ± 16	74 ± 7	19	17	-	-
	18 Terbutryn	3 -60	0.989 (7)	3	-43 ± 7	86 ± 26 63 ± 10	66 ± 6	26	16	0.1 -0.2	1.0
		Insecticides
	19 Fenthion	20 -400	0.95 (76)	80	-77 ± 21	87 ± 33	82 ± 8	121 ± 88	62	59	-	-
	20 Fipronil	3 -60	0.997 (2)	3	-13 ± 8	74 ± 22	85 ± 5	90 ± 6	23	16	0.1	0.6
	21 Imidacloprid	30 -600	0.993 (8)	30	19 ± 11	101 ± 70	94 ± 6	94 ± 4	27	19	-	-

Acknowledgments

This project was funded by the "Office Français pour la Biodiversité". The authors sincerely thank the fish farmers and owners for granting access to their ponds. They are also grateful to Alain Iurétig (sampling and pre-treatment, Univ. of Lorraine), Pamela Hartmeyer (pre-treatment, Univ. of Lorraine), Aisha Nunoo (extractions, Univ. of Lorraine/ISA), and Maud Dessein-Lepasteur (extractions and analysis, Univ. of Lorraine/ISA) for their work, as well as to Arnaud Chaumot and Laura Garnero from the Ecotoxicology Team of the UR RIVERLY (INRAE, Centre de Lyon-Villeurbanne) for providing chironomid larvae. The authors sincerely thank ABC Translation for the proofreading work done.

Funding Information:

This project was funded by the "Office Français pour la Biodiversité".

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