Floriane Demailly
email: floriane.demailly@irstea.fr

Imane Elfeky

Laura Malbezin

Marina Le Guédard

Mélissa Eon

Jean-Jacques Bessoule

Agnès Feurtet-Mazel

François Delmas

Nicolas Mazzella

Patrice Gonzalez

Soizic Morin

Impact of diuron and S-metolachlor on the freshwater diatom Gomphonema gracile: complementarity between fatty acid profiles and different kinds of ecotoxicological impact-endpoints

Keywords: Microbial ecotoxicology, Microalgae, Bacillariophycae, Fatty acid, Herbicides, Toxicity

Impact of diuron and S-metolachlor on the freshwater diatom Gomphonema gracile: Complementarity between fatty acid profiles and different kinds of ecotoxicological impact-endpoints

Introduction

The intensive use of pesticides for crop protection causes many environmental problems, making pesticides major pollutants of aquatic ecosystems [START_REF] Aydinalp | The effects of pesticides in water resource[END_REF]Porca 2004, Vidal et al. 2012). Indeed, whereas the use of these pesticides takes place in the terrestrial environment, by aerial spraying, leaching, runoff or accidental spills, the final receptacles of these chemicals are aquatic environments [START_REF] Vidal | Acute and chronic toxicity of Betanal((R))Expert and its active ingredients on nontarget aquatic organisms from different trophic levels[END_REF]. In France, many different herbicides are present in rivers. For example, the tributaries of Arcachon Bay are contaminated by diverse mobile and persistent herbicides, such as diuron and S-metolachlor [START_REF] Fauvelle | Use of Mixed-Mode Ion Exchange Sorbent for the Passive Sampling of Organic Acids by Polar Organic Chemical Integrative Sampler (POCIS)[END_REF]).

The herbicide diuron 3-(3, 4-dichlorophenyl)-1, 1-dimethylurea, is a compound of the substituted urea family and is resistant to photolysis and hydrolysis [START_REF] Okamura | Photodegradation of the antifouling compounds Irgarol 1051 and Diuron released from a commercial antifouling paint[END_REF]. This herbicide is an inhibitor of photosystem II (PSII) which inhibits photosynthesis by preventing the electron transport chain [START_REF] Rutherford | Herbicide-induced oxidative stress in photosystem II[END_REF] and stopping oxygen formation [START_REF] Moisset | Genetic and physiological responses of three freshwater diatoms to realistic diuron exposures[END_REF]. Furthermore, diuron is known to generate reactive oxygen species (ROS) which induce a gap in the balance between prooxidative and antioxidative reactions [START_REF] Geoffroy | Effect of oxyfluorfen and diuron alone and in mixture on antioxidative enzymes of Scenedesmus obliquus[END_REF]. Moreover, it is also demonstrated that diuron inhibits polyunsaturated fatty acid synthesis by inhibiting the desaturation process [START_REF] Troton | Modifications of thylakoid lipids in Euglena gracilis during diuron-adaptation[END_REF]). S-metolachlor is a compound of the chloroacetamides family and is a molecule that is potentially dangerous to the environment [START_REF] Liu | A comparative study of rac-and S-metolachlor toxicity to Daphnia magna[END_REF]. This herbicide was developed in order to control grass weeds following pre-emergence application [START_REF] Neves | Biochemical and populational responses of an aquatic bioindicator species, Daphnia longispina , to a commercial formulation of a herbicide (Primextra® Gold TZ) and its active ingredient ( S -metolachlor)[END_REF]. It was shown that this pesticide inhibits synthesis of chlorophyll, proteins, fatty acids, lipids and finally, growth of the organism [START_REF] Fuerst | Understanding the mode of action of the chloroacetamide and thiocarbamate herbicides[END_REF][START_REF] Rivard | Environmental fate of metolachlor[END_REF]. In fact, S-metolachlor inhibits elongases involved in the elongation of highly unsaturated fatty acid (HUFA) and especially very long-chain fatty acids (VLCFA) [START_REF] Trenkamp | Specific and differential inhibition of verylong-chain fatty acid elongases from Arabidopsis thaliana by different herbicides[END_REF]). Furthermore, its toxicity is linked with the oxidation of components of cells increasing the production of reactive oxygen species (ROS) and starting lipid peroxidation [START_REF] Maronić | S-metolachlor promotes oxidative stress in green microalga Parachlorella kessleri-A potential environmental and health risk for higher organisms[END_REF].

Many microorganisms co-occur in watercourses, especially periphytic communities and particularly diatoms which, at certain times of the year, represent the bulk of periphyton [START_REF] Moisset | Genetic and physiological responses of three freshwater diatoms to realistic diuron exposures[END_REF]. Among primary producers in watercourses, freshwater diatoms are the dominant phototrophic organisms and play an important role in ecological processes such as primary production [START_REF] Potapova | Benthic diatoms in USA rivers: distributions along spatial and environmental gradients[END_REF]Charles 2002, Rimet 2012). Diatoms are relevant bioindicators because of their adaptation capacities and their tolerance of environmental factors and aquatic pollution [START_REF] Potapova | Benthic diatoms in USA rivers: distributions along spatial and environmental gradients[END_REF].

Several monitoring tools based on diatom community composition (such as the Biological Diatom Index, BDI; [START_REF] Coste | Improvements of the Biological Diatom Index (BDI): Description and efficiency of the new version (BDI-2006)[END_REF] have been developed to assess the ecological quality of watercourses, but unfortunately these indices are not suitable to detect the impacts of water pollution by pesticides on diatoms [START_REF] Lenoir | Development of a practical diatom index of overall water quality applicable to the French national water board network[END_REF][START_REF] Prygiel | Diatoms and diatom indices in the networks for quality measurement of French watercourses : Short history and future[END_REF][START_REF] Dorigo | Seasonal changes in the sensitivity of river microalgae to atrazine and isoproturon along a contamination gradient[END_REF]).

It was shown in the literature that the growth and physiology of diatoms can be impacted by herbicide pollution [START_REF] Delorenzo | Toxicity of pesticides to aquatic microorganisms: A review[END_REF][START_REF] Debenest | Effects of Pesticides on Freshwater Diatoms[END_REF]. Differential gene expression proved to be an early and sensitive biomarker of toxicant exposure [START_REF] Kim Tiam | Development of q-PCR approaches to assess water quality: effects of cadmium on gene expression of the diatom Eolimna minima[END_REF][START_REF] Moisset | Genetic and physiological responses of three freshwater diatoms to realistic diuron exposures[END_REF][START_REF] Kim Tiam | Morphological, physiological and molecular responses of Nitzschia palea under cadmium stress[END_REF]), compared to more global endpoints.

Chemical pollution may impact organisms at the biomolecular and biochemical levels being sensitive and quick-responding bioindicators [START_REF] Adams | Ecotoxicological indicators of water quality: Using multi-response indicators to assess the health of aquatic ecosystems[END_REF]. Alteration in FA profiles is a sensitive early bioindicator of environmental and chemical stress in zooplankton, fish, crustaceans and diatoms, as demonstrated by recent studies [START_REF] Maazouzi | Chronic copper exposure and fatty acid composition of the amphipod Dikerogammarus villosus: Results from a field study[END_REF][START_REF] Gonçalves | Fatty acid acid profiling reveals seasonal and spatial shifts in zooplankton diet in a temperate estuary[END_REF][START_REF] Ramírez | Aquafeed imprint on bogue (Boops boops) populations and the value of fatty acids as indicators of aquaculture-ecosystem interaction: Are we using them properly?[END_REF][START_REF] Sánchez-Muros | Effects of chemical and handling exposure on fatty acids, oxidative stress and morphological welfare indicators in gilt-head sea bream (Sparus aurata)[END_REF], Gonçalves et al. 2018).

Fatty acids contribute to the fluidity, production and permeability of cell membranes, and they are the main components of lipids that play multiple roles in the cell. Moreover, saturated and monounsaturated fatty acids (SFA and MUFA) are the principal forms of stored energy [START_REF] Olofsson | Seasonal variation of lipids and fatty acids of the microalgae Nannochloropsis oculata grown in outdoor large-scale photobioreactors[END_REF], whereas polyunsaturated fatty acids (PUFA with > 1 double bonds) and highly unsaturated fatty acids (HUFA) (with ≥ 20 carbons and ≥ 3 double bonds) play a significant role in the organism, being major constituents of cellular membranes [START_REF] Gonçalves | Fatty Acids' Profiles of Aquatic Organisms: Revealing the Impacts of Environmental and Anthropogenic Stressors[END_REF]. PUFA and HUFA take part in the regulation of membrane fluidity and serve as precursors of many hormones [START_REF] Brett | The role of highly unsaturated fatty acids in aquatic foodweb processes[END_REF]. PUFA and HUFA are only synthesized by photosynthetic organisms and are thus the most important molecules to be transferred in the trophic chain [START_REF] Dalsgaard | Fatty acid trophic markers in the pelagic marine environment[END_REF][START_REF] Allan | Determining spatial changes in the diet of nearshore suspension-feeders along the South African coastline: Stable isotope and fatty acid signatures[END_REF].

HUFA such as eicosapentaenoic acid (20:5n3, EPA) and docosahexaenoic acid (22:6n3, DHA) are key nutritional compounds of zooplankton diet because of their energetic efficiency [START_REF] Perhar | Examination of the role of detritus food quality, phytoplankton intracellular storage capacity, and zooplankton stoichiometry on planktonic dynamics[END_REF]. They are used as fuel in all metabolic systems for all organisms, having an important role on physiological response [START_REF] Arts | Lipids in Aquatic Ecosystems[END_REF]. Some studies have demonstrated the impact of pesticides on fatty acid profiles of marine microalgae [START_REF] Robert | Metolachlor-mediated selection of a microalgal strain producing novel polyunsaturated fatty acids[END_REF], Filimonova et al. 2016[START_REF] Filimonova | Effects of a herbicide and copper mixture on the quality of marine plankton[END_REF]. For example, Filimonova et al. (2016) observed that exposure to the Primextra® herbicide decreased the percentage of PUFA and particularly of HUFA but increased the percentage of SFA in the marine diatom Thalassiosira weissflogii. Likewise, [START_REF] Robert | Metolachlor-mediated selection of a microalgal strain producing novel polyunsaturated fatty acids[END_REF] demonstrated that the herbicide metolachlor decreased the percentage of HUFA in the microalga Melosira moniliformis.

Moreover, few studies have demonstrated the effect of herbicides on freshwater microalgae, such as green algae [START_REF] Weisshaar | Chloroacetamide inhibition of fatty acid synthesis[END_REF][START_REF] El-Sheekh | Effect of atrazine herbicide on growth, photosynthesis, protein synthesis, and fatty acid composition in the unicellular green alga Chlorella kessleri[END_REF], Böger et al. 2000, Chalifour Author produced version of the article published in Science of the Total Environment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016Doi : 10. /j.scitotenv.2019Doi : 10. .06.347 et al. 2014)). For instance, an increase of MUFA and a decrease of PUFA and HUFA content were found on the green algae Scenedesmus and Chlamydomonas reinhardtii after respectively chloroacetamide treatment; norflurazon and fluridone exposure [START_REF] Böger | Towards the primary target of chloroacetamides-new findings pave the way[END_REF][START_REF] Chalifour | Combined effect of temperature and bleaching herbicides on photosynthesis, pigment and fatty acid composition of Chlamydomonas reinhardtii[END_REF]).

To our knowledge, no study has yet focused on the impact of pesticides on fatty acid profiles of freshwater diatoms, and on the variable impacts of distinctly-acting toxic compounds such as diuron and S-metolachlor. We therefore tested the effects environmentally realistic concentrations of both pesticides (10 µg.L -1 each) [START_REF] Coquillé | Use of diatom motility features as endpoints of metolachlor toxicity[END_REF][START_REF] Moisset | Genetic and physiological responses of three freshwater diatoms to realistic diuron exposures[END_REF] on FA profiles and classical endpoints in the freshwater diatom G. gracile at 20.5°C for seven days. The aims of our study were: 1) to evaluate the impact of these herbicides on "classical" global descriptors such as growth rate, photosynthesis, pigment contents and to investigate the subsequent expression levels of the genes involved in mitochondrial metabolism (cox1 (cytochrome C oxidase subunit I), 12S (mitochondrial ribosomal RNA 12S) and nad5 (NADH dehydrogenase subunit 5)), photosynthetic activity (d1 (D1 protein) and psA (PsaA protein)), oxidative stress response (cat (catalase) and gpx (glutathione peroxidase)) and frustule synthesis (sil1 (silaffin-1 protein)) known to be putatively differentially expressed during pesticide exposure; 2) to explore the impact of two herbicides with contrasting cellular targets (diuron and S-metolachlor) on fatty acid profiles of freshwater diatoms, hypothesizing that diatom fatty acid responses would be similar to those of green algae; and finally, 3) to compare fatty acid profiles and "classical descriptors" responses in order to show their complementarity based on physiological and ecological responses. G. gracile Ehrenberg (1838) (Bacillariophyceae) is a benthic diatom found preferentially in biofilms from slightly acidic waters [START_REF] Coste | Improvements of the Biological Diatom Index (BDI): Description and efficiency of the new version (BDI-2006)[END_REF]. It is a pennate diatom and has a bidirectional movement, as most raphid species [START_REF] Cohn | Chapter 13 Photo-stimulated effects on diatom motility[END_REF]. According to the Specific Pollution Sensitivity Index [START_REF] Cemagref | Etude des Méthodes Biologiques Quantitatives d'appréciation de la qualité des eaux -Rapport Division Qualité des Eaux Lyon[END_REF], this diatom is considered to be sensitive to macropollutants.

Materials and methods

Diatom culture: Gomphonema gracile

The freshwater diatom culture was isolated in December 2013 from a field sample (collected from an upstream section of the Leyre river, which is the main tributary of Arcachon Bay (South West France)) by micromanipulation under an inverted microscope [START_REF] Coquillé | Use of diatom motility features as endpoints of metolachlor toxicity[END_REF]. The diatom culture was maintained non axenic in the laboratory and was cultured in sterile Dauta medium [START_REF] Dauta | Conditions for phytoplankton development, a comparative study of the behaviour of eight species in culture. I. Determination of growth parameters in relation to light and temperature[END_REF]) at 20.5°C in a thermostatic chamber 610 XAP (LMS LTD®, UK) at 45 µmol photons.m -2 .s -1 with a dark: light cycle of 8:16h, in 1-L erlenmeyers.

Every week, the culture was renewed with new sterile Dauta medium for the purpose of keeping diatoms in an exponential growth phase.

Experimental design

The experiment was carried out in round, 100 mL borosilicate sterile glass flasks previously heated to 550°C for 4h. One week before pesticide contamination, glass flasks were inoculated with 40 mL of stock culture in exponential growth phase and 40 mL of Dauta culture medium. This step allowed a one-week adaptation of the culture to the glass flasks.

The contamination experiment lasted 7 days. For the first day of contamination, glass flasks were inoculated with 300,000 cell.mL -1 using adapted cultures in exponential growth phase and Dauta medium (total volume = 80 mL). Three treatments were run: a control treatment (no pesticide) and two pesticide treatments: diuron and S-metolachlor at 10 µg.L -1 . Controls and herbicide-exposed cultures were carried out in quadruplicate. Chemical (nutrients and pesticides) analyses were carried out at the beginning (t0) and at the end (t7) of the experiment, following the protocols described in Supplementary Material (SI 1 and 2). Detailed environmental conditions in the cultures are provided in Table SI 1.

Biological parameters

Photosystem II effective quantum yield -Photosynthesis

The analysis of inhibition of photosynthesis was carried out using a PHYTO-PAM (Pulse-Amplitude Modulated (PAM)) (Heinz Walz, GmbH, Germany) every day of the experiment.

PHYTO-PAM is a device used to evaluate the physiological state of algae. It is based on the quantitative relationship between the efficiency of photosynthetic energy conversion and chlorophyll fluorescence [START_REF] Corcoll | The use of photosynthetic fluorescence parameters from autotrophic biofilms for monitoring the effect of chemicals in river ecosystems. Emerging and priority pollutants in rivers[END_REF]) and its use for determining pesticide effects on photosynthesis [START_REF] Dorigo | A pulse-amplitude modulated fluorescence-based method for assessing the effects of photosystem II herbicides on freshwater periphyton[END_REF].

The PSII effective quantum yield (Φ' M ) was determined using PHYTO-PAM with an emitterdetector unit (PHYTO-ED), in the "brown algae" mode. Measurements were performed in 1cm-wide quartz cuvettes using 2 mL of diatom culture. Five Φ' M measurements were performed for each condition. Φ' M is described by the following equation [START_REF] Genty | The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence[END_REF]:

Φ' M = (F' M -F) / F' M
With: -F being the minimum fluorescence determined after exposure to a weak far red modulated light -F' M being the maximum level of fluorescence measured during a saturating white light pulse Cellular concentrations of diatoms were quantified every day of the experiment from 1mL of each sample fixed in Lugol (4%) and stored at 4°C until counting. Cell density was determined by counting using a Nageotte counting chamber (Marienfield, Germany) and following the protocol below. Each fixed sample was homogenized with a vortex. After homogenization, 125 μL of sample was dropped into the counting chamber. The number of cells was identified, under light microscopy, in ten columns of the counting area (1.25 μL each, 0.5 mm depth). The distinction between live and dead organisms was estimated by the observation of the color of the chloroplasts and by the observation of their turgescence [START_REF] Morin | Recovery potential of periphytic communities in a river impacted by a vineyard watershed[END_REF].

Growth rate

Cellular concentration was noted in number of individuals per milliliter, and then average growth rates were calculated to optimally fit the daily counting over the exponential growth phase, as the slope of the regression line of Ln (cell.mL -1 ) over time (days).

Chlorophyll a and carotenoid contents

Chlorophyll a and carotenoid content analyses were performed at t0 and t7 of the experiment.

An aliquot of 20 mL (t0) and another of approximately 10 mL (t7) of each sample were filtered on Whatman® GF/C filters and frozen at -20°C until the time of analysis. Samples

were then defrosted at room temperature before adding 10 mL of 80% acetone (v/v). Each tube was wrapped with aluminum (to induce darkness) and shaken. The tubes were placed in a beaker containing water (to ensure stability) and ultrasonicated for 20 min before being left to settle over a 10 min period, in the dark. Each sample was filtered on Whatman® GF/C filters and the filters were recovered in the filter cone. The tubes were rinsed with 80% acetone and the filters were pressed. The filtrates were collected in the initial tube and the final volume was noted (graduated tube). Chlorophyll a content was determined with a Shimadzu UV-1800 spectrophotometer (Shimadzu Inc., Kyoto, Japan). Absorbance measurements were made at the following wavelengths: 470 nm, 663.2 nm and 648.8 nm. The chlorophyll a and carotenoid contents were calculated with Lichtenthaler equations [START_REF] Lichtenthaler | Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents[END_REF]Wellburn 1983, Iummato et al. 2017) and expressed as milligrams per liter (mg.L -1 ).

Genetic analyses

Four milliliters, with a cell concentration of 576400 ± 140224 cells.mL -1 , were sampled at t7

and were centrifuged at 1.500g during 5 min in order to the pellet cells and thus remove culture medium. To avoid degradation of RNA, 200 µL of RNA later buffer were added to the pellet. Samples were stored one night at 4°C and then at -80°C until the genetic analysis. The level of expression of each gene was determined in relation to that of the reference gene (-actin) whose expression was stable and did not show any variation depending on the experimental treatments applied. Indeed, the reference gene rpl7 was not stable and so was not retained.

The detailed primers for each gene are provided in Table SI 3.

Fatty acid profiles

Twenty milliliters were sampled for each condition at t0 and t7. In order to remove medium culture samples were centrifuged at 1,500 g for 5 min and then 1 mL of methanol containing 2.5% H 2 SO 4 (v/v) was added to the pellet. The fatty acid methyl esters were obtained according to the protocol described by Le [START_REF] Le Guédard | Development of a biomarker for metal bioavailability: the lettuce fatty acid composition[END_REF].

Separation of FAMEs was carried out using gas chromatography (Agilent 7890A) on a DB-WAX column (15 m x 1 µm x 0.53 mm, Agilent) and with flame ionization detection. The oven temperature was 150°C for 1 min, followed by a 5°C.min -1 ramp to 230°C. This last temperature was maintained for a further 8min (total run time = 25 min). The limit of detection was 0.25µg of fatty acid.

Identification of fatty acids was performed by comparing their retention times with PUFA No3 standard (Sigma, Aldrich). For each fatty acids or class of fatty acids (SFA, MUFA, PUFA, HUFA) results were expressed in percentage of total fatty acids.

Statistical analyses

To detect significant differences between the beginning and the end of the experiment (FA profiles), and the control conditions and herbicide-exposed cultures (all descriptors), a nonparametric Wilcoxon-Mann-Whitney test was performed because of the non-normal distribution of the data (with a p-value <0.05 considered as statistically significant). All statistical analyses were carried out with the R studio software (RStudioTeam 2016).

Principal Component Analyses (PCA) were performed for the purpose of highlighting correlations between different variables such as photosynthesis activity, growth rate, contents of chlorophyll a and carotenoid, the percentage of the four classes of fatty acids (SFA, MUFA, PUFA and HUFA), the percentage of each fatty acid (14:0, 16:0, 16:1 etc.), the repression or induction factor of each gene of interest, nutrient concentrations and treatment with pesticides. In order to show significant difference between groups of individuals a PERMANOVA test was performed (with a p-value <0.05 considered as statistically significant) and three confidence ellipses were represented on the PCA plot.

Results

"Classical descriptors"

Cellular concentration, growth rate, photosynthesis and pigment contents

Control cultures grew during the seven days of the experiment with an average growth rate of 0.11 div.day -1 . The cellular concentration increased between t1 and t7 from 373,000 cells.mL - 1 to 576,400 cells.mL -1 . However, the chlorophyll a and carotenoid contents were stable over the time. Chlorophyll a content was in average of 0.84 ± 0.36 mg.L -1 at t0 and 1.49 ± 0.61 mg.L -1 at t7 and carotenoid content was 0.19 ± 0.05 mg.L -1 at t0 and 0.42 ± 0.17 mg.L -1 at t7.

Moreover, the photosynthetic activity was stable over time, with an average effective quantum yield of 0.54 ± 0.01.

S-metolachlor and diuron significantly decreased the growth of G. gracile (pvalue<0.05) with a decrease in growth rate from 0.11 div.day -1 to 0.07 div.day -1 and to 0.02 div.day -1 , respectively. The cellular concentration of G. gracile was significantly lower in both Smetolachlor and diuron-treated cultures than in the controls (pvalue<0.05), being 427,600 ± 26,368 cells.mL -1 and 308,600 ± 57,189 cells.mL -1 , respectively.

No significant difference in photosynthesis was observed between control and S-metolachlor conditions, whereas diuron decreased photosynthesis with an average effective quantum yield of 0.40 ± 0.02 (pvalue<0.05 for all dates). However, it was interesting to note that photosynthesis at t7 under diuron treatment was significantly higher than t1, t2, t5 and t6 with values of 0.37 ± 0.01, 0.37 ± 0.00, 0.36 ± 0.01 and 0.35 ± 0.02, respectively.

Chlorophyll a concentration significantly decreased with the two pesticide treatments from 1.49 ± 0.61 mg.L -1 to 0.80 ± 0.17 mg.L -1 for diuron (pvalue<0.05) and from 1.49 ±0.61 mg.L - 1 to 0.78 ± 0.19 mg.L -1 for S-metolachlor (pvalue<0.05), while carotenoid contents were not impacted by either herbicide.

Genetic analyses

The expression of the photosynthesis gene psaA and the mitochondrial metabolism gene 12s

were identical with that of the control after seven days of exposure to both pesticides (Table 1). The results of the oxidative stress gene gpx and the frustule gene sil1 were not exploitable in this study.

Conversely, the oxidative stress gene cat and photosynthetic gene d1 were induced by 18.58 and 2.83 respectively with diuron treatment, while S-metolachlor induced the photosynthetic gene d1, the mitochondrial metabolism genes cox1 and nad5 and the oxidative stress gene cat respectively by 6.31, 4.99, 5.08 and 86.57 (Table 1).

Fatty acid profiles

Variability of fatty acid profiles with time

During the seven days of culture without herbicides, changes in the fatty acid composition of diatoms were observed. At the beginning of the experiment (t0), G. gracile was composed mainly of seven fatty acids: EPA (26.17 ± 0.08%); 16:1 (18.63 ± 0.18%); 16:0 (17.57 ± 0.22%); 16:3n4 (9.45 ± 0.06%); 14:0 (7.14 ± 0.12%); 18:1n9 (5.71 ± 0.12%) and 20:4n6 (5.63 ± 0.01%). At the end of the experiment (t7), the six major fatty acids were 20.5n3 (28.49 ± 0.53%); 16:1 (18.21 ± 1.99%); 16:0 (16.57 ± 0.50%), 16:3n4 (11.16 ± 0.52%); 14:0 (7.01 ± 0.33%) and 20:4n6 (5.67 ± 0.27%). The rest of fatty acids that were not described previously had content lower than 5%. The percentage in 16:0 and 18:1n9 significantly decreased (pvalue<0.05) respectively from 17.57 ± 0.22% to 16.57 ± 0.50% and from 5.71 ± 0.12% to 3.11 ± 1.78% between the beginning and the end of the experiment.

SFA were affected by the seven days of culture with a significant decrease (pvalue<0.05) in their percentage between t0 and t7 from 24.71 ± 0.32% to 23.58 ± 0.48%. In the same way, the percentage in MUFA significantly decreased (pvalue<0.05) between t0 and t7 from 24.34 ± 0.25% to 21.32 ± 1.21%. The percentage in polyunsaturated fatty acids 16:3n4 and 18:4n3 significantly increased (pvalue<0.05) respectively from 9.45 ± 0.06% to 11.16 ± 0.52% and from 0 ± 0% to 0.56 ± 0.02% and significantly decreased (pvalue<0.05) for 18:2n6 from 1.64 ± 0.02% to 1.44 ± 0.05% between t0 and t7. Overall, the percentage in PUFA significantly increased (pvalue<0.05) from 16.42 ± 0.12% to 18.78 ± 0.94%. The percentage in highly unsaturated fatty acid EPA significantly increased (pvalue<0.05) from 26.17 ± 0.08% to 28.49 ± 0.53% and the percentage in 22:5n3 significantly decreased (pvalue<0.05) from 2.42 ± 0.04% to 1.85 ± 0.20%. The overall percentage in HUFA significantly increased (pvalue<0.05) from 34.51 ± 0.63% to 36.31 ± 1.13%.

Variability of fatty acid profiles with herbicide exposure

Under diuron treatment, the major fatty acid classes were HUFA (31.12%); MUFA (28.84%) (Figure 1) and the major fatty acids were EPA (24.88%); 16:1 (20.64%); 16:0 (14.97%); 16:3n4 (12.73%); 18:1n9 (8.20%) and 14:0 (6.41%) (Figure 2). Diuron significantly increased the percentage in MUFA from 21.32% to 28.84% and significantly decreased SFA and HUFA percentage from 23.58% to 21.38% and from 36.31% to 31.12%, respectively (Figure 2).

Furthermore, a significant increase in the percentage of 18:1n9, 16:2n4 and 16:3n4 was noted between control and diuron conditions from 3.11% to 8.20%, from 3.51% to 3.86% and from 11.16% to 12.73%, respectively. A significant decrease in the percentage of 14:0, 16:0, 18:2n6, 18:4n3 and EPA was observed from 7.01% to 6.41%, from 16.57% to 14.97%, from 1.44% to 0.29%, from 0.56% to 0% and from 28.49% to 24.88%, respectively. Moreover, a non-significant decrease in the percentage of 22:5n3 and 22:6n3 was noted between control and diuron treatment from 1.85% to 0.50% and from 0.30% to 0%, respectively. Also, the percentage of 16:1 increased in a non-significant way from 18.21% to 20.64%.

Under S-metolachlor contamination, no significant impact was observed on fatty acid classes.

However, a significant increase in the percentage of 16:0 from 16.57% to 17.25% and a significant decrease in the percentage of 18:2n6, 18:4n3 and 20:4n6 from 1.44% to 1.28%, from 0.56% to 0.52% and from 5.67% to 5.12%, respectively, were observed (Figure 3).

Correlations between « classical descriptors » and fatty acid profiles

The results obtained at the end of the experiment (t7) for "classical descriptors" and fatty acid profiles were correlated using a Principal Component Analysis (Figure 4). Percentages of saturated (SFA) and highly unsaturated fatty acids (HUFA), photosynthetic activity (Photo_t7) and growth rate were positively correlated with the axis 1 while monounsaturated fatty acid (MUFA) was negatively correlated with this axis. Control and S-metolachlor treatments were correlated with the axis 1 while diuron treatment was negatively correlated with this axis.

These results permitted to conclude that : 1) SFA, MUFA HUFA, photosynthetic activity and growth rate was impacted by herbicide pollutions conversely to pigment contents; 2) Diuron seemed more impacted on "classical descriptors" previously mentioned, conversely to Smetolachlor; 3) Diuron seemed more toxic than S-metolachlor in this study.

Discussion

"Classical descriptors"

With chemical contamination such as pesticides, reactive centers of diatom photosystems are saturated faster, and light is used less efficiently by diatoms. Therefore, potential damages and the physiological state of the organism can be reflected by the effective quantum yield [START_REF] Laviale | Effet des polluants sur les communautés périphytiques naturelles: Apport des mesures de fluorescence chlorophyllienne en lumiere modulée (PAM), Lille 1[END_REF][START_REF] Bonnineau | Light history modulates antioxidant and photosynthetic responses of biofilms to both natural (light) and chemical (herbicides) stressors[END_REF][START_REF] Coquillé | Use of diatom motility features as endpoints of metolachlor toxicity[END_REF][START_REF] Moisset | Genetic and physiological responses of three freshwater diatoms to realistic diuron exposures[END_REF]. G. gracile cultures exposed to environmental concentration of diuron showed that this herbicide targets photosynthesis and thus cell development. [START_REF] Larras | The effect of temperature and a herbicide mixture on freshwater periphytic algae[END_REF] observed the sensitivity of benthic diatoms to diuron with an establishment of an EC50 value of 9.50 µg.L -1 based on the 96h growth rate of the population. Diuron acts by blocking electron transport between PSII and PSI and as a consequence, it is considered to be one of the most potent photosynthetic inhibitors [START_REF] Hall | An ecological risk assessment for the use of Irgarol 1051 as an algaecide for antifoulant paints[END_REF][START_REF] Thomas | Increased persistence of antifouling paint biocides when associated with paint particles[END_REF].

With S-metolachlor contamination, no impact was observed on photosynthetic activity of G.

gracile culture exposed to environmental concentrations. [START_REF] Coquillé | Use of diatom motility features as endpoints of metolachlor toxicity[END_REF] demonstrated the same result on photosynthetic activity of G. gracile with this pesticide at 1, 10 and 100 µg.L -1 .

However, environmental concentration of S-metolachlor impacted growth rate of G. gracile.

Previous findings were obtained at 100 µg.L -1 of S-metolachlor [START_REF] Coquillé | Use of diatom motility features as endpoints of metolachlor toxicity[END_REF] and 5 and 30 µg.L -1 [START_REF] Debenest | Sensitivity of freshwater periphytic diatoms to agricultural herbicides[END_REF]) of this pesticide. These observations can be explained by the target of S-metolachlor: Carder and Hoagland (1998) noted that chloroacetamides disrupt fatty acid synthesis and finally inhibit cell division. The inhibition of diatom cell division can result in a decrease of chlorophyll content [START_REF] Debenest | Sensitivity of freshwater periphytic diatoms to agricultural herbicides[END_REF].

In diatoms, pigment content is also an indicator of cell physiology: in order to estimate the influence of contaminants, many studies use pigment content as a biomarker (Teisseire andVernet 2001, Geoffroy et al. 2002). [START_REF] Silkina | Antifouling activity of macroalgal extracts on Fragilaria pinnata (Bacillariophyceae): a comparison with Diuron[END_REF] showed a decrease in pigment contents in the diatom Staurosirella pinnata under diuron at 1mg.L -1 . The results of the present study showed the same observations for chlorophyll a content conversely to carotenoid content. The applied concentration, more environmentally realistic, was 100-fold lower than the above-mentioned one and could explain this lack of carotenoid content decrease.

As found here, [START_REF] Coquillé | Use of diatom motility features as endpoints of metolachlor toxicity[END_REF] demonstrated a decrease in chlorophyll a content under S-metolachlor at 100µg.L -1 .

It is also well-known that pesticides can provoke oxidative stress resulting in DNA breakage, mitochondrial and/or photosynthetic metabolism dysfunction and apoptotic mechanisms induction, leading to the death of the cell [START_REF] Rutherford | Herbicide-induced oxidative stress in photosystem II[END_REF][START_REF] Slaninova | REVIEWS Oxidative stress in fish induced by pesticides[END_REF][START_REF] Fukuyama | Apoptosis in immunocytes induced by several types of pesticides[END_REF].

ROS are one of the earliest responses to chemical stress. When present in low quantities, ROS are rapidly converted to a less reactive form [START_REF] Lauritano | Key genes as stress indicators in the ubiquitous diatom Skeletonema marinoi[END_REF]. In order to maintain healthy equilibrium of ROS, effective enzymatic mechanisms such as catalase (CAT) were developed by diatoms (Gonçalves et al. 2018, Gonçalves et al. 2018). However, at abnormally high quantities, they may provoke an alteration of the normal metabolism of the cell impacting nucleic acids, proteins and photosynthetic pigments resulting in an inactivation of enzymes, membrane damage and lipid peroxidation and affecting diverse physiological processes such as viability of cells [START_REF] Arora | Oxidative stress and antioxidative system in plants[END_REF][START_REF] Jaganjac | Pathophysiology of neutrophilmediated extracellular redox reactions[END_REF]. This could explain differences in results observed between diuron and s-metolachlor used at the same environmental concentration.

Under S-metolachlor contamination, photosynthetic defense mechanism (gene d1) seemed efficient since no impact was noted on photosynthetic activity: D1 protein activity was activated increasing PSII reaction centers and allowed countering toxicant effects of this pesticide. The induction of the genes cox1, nad5 suggests an increase of ATP production to counter the effects of a toxicant [START_REF] Kim Tiam | Development of q-PCR approaches to assess water quality: effects of cadmium on gene expression of the diatom Eolimna minima[END_REF], since these genes are involved in the functioning of the respiratory chain and PS II. Moreover, the increase in ATP demand linked to the significant decrease of growth rate in G. gracile suggests that energy in the cell was reallocated to detoxification mechanisms to fight against the molecular effects of Smetolachlor [START_REF] Kim Tiam | Morphological, physiological and molecular responses of Nitzschia palea under cadmium stress[END_REF].

However, G. gracile culture exposed to diuron showed a lower level of d1 and cat than those exposed at the same environmental concentration to S-metolachlor. The antioxidative defense mechanisms of G. gracile exposed to diuron seem to be not sufficiently effective to protect cells against ROS since, unlike the response with S-metolachlor, a decrease in photosynthesis was observed.

Fatty acid profiles

Variability of fatty acid profiles with time

Culture parameters are important to consider when analyzing biochemical composition of microalgae. It has been demonstrated in many studies that the percentage of fatty acid and particularly EPA content of algal strains is not only associated with heredity, but also influenced by different growing conditions [START_REF] Teshima | Effects of water temperature and salinity on eicosapentaenoic acid level of marine Chlorella [Chlorella saccharophila[END_REF][START_REF] Thompson | Influence of irradiance on the fatty acid composition of phytoplankton 1[END_REF][START_REF] Renaud | Effect of light intensity on the proximate biochemical and fatty acid composition of Isochrysis sp. and Nannochloropsis oculata for use in tropical aquaculture[END_REF][START_REF] Yongmanitchai | Growth of and omega-3 fatty acid production by Phaeodactylum tricornutum under different culture conditions[END_REF][START_REF] Alonso | Isolation of clones of Isochrysis galbana rich in eicosapentaenoic acid[END_REF][START_REF] Reitan | Effect of nutrient limitation on fatty acid and lipid content of marine microalgae[END_REF][START_REF] Renaud | Effect of temperature on the growth, total lipid content and fatty acid composition of recently isolated tropical microalgae Isochrysis sp., Nitzschia closterium, Nitzschia paleacea, and commercial species Isochrysis sp. (clone T.ISO)[END_REF][START_REF] Tan | Screening of diatoms for heterotrophic eicosapentaenoic acid production[END_REF] and by the growth stage of the algae [START_REF] Chu | The fatty acid composition of three unicellular algal species used as food sources for larvae of the American oyster (Crassostrea virginica)[END_REF][START_REF] Fernández-Reiriz | Biomass production and variation in the biochemical profile (total protein, carbohydrates, RNA, lipids and fatty acids) of seven species of marine microalgae[END_REF][START_REF] Brown | Effects of harvest stage and light on the biochemical composition of the diatom Thalassiosira pseudonana[END_REF]). In the present study, the fatty acid profile of G. gracile was modified with the sampling time, especially with a decrease of SFA and MUFA and an increase of PUFA and HUFA. necessary for the growth, the surplus of neutral lipid accumulation was reduced in diatoms at the exponential growth phase. Finally the proportion of PUFA and HUFA, which are fatty acids of membrane lipids and structural components, are increased in diatoms during the exponential growth phase [START_REF] Kates | Lipid components of diatoms[END_REF].

Concerning microalgae, it was noted that Cylindrotheca strains accumulated a high proportion of PUFA and HUFA at the exponential phase (29.5%-42.9%), particularly EPA and 20:4n6 [START_REF] Ying | Effects of harvest stage on the total lipid and fatty acid composition of four Cylindrotheca strains[END_REF]. A study carried out on the diatom Thalassiosira pseudonana highlighted that the proportion of total lipid content was higher in the late stationary phase of the culture, whereas PUFA and HUFA decreased during this later phase or in the early stationary phase.

The proportion of PUFA and HUFA was higher in the exponential phase [START_REF] Brown | Effects of harvest stage and light on the biochemical composition of the diatom Thalassiosira pseudonana[END_REF].

Variability of fatty acid profiles with herbicide exposure

Numerous studies demonstrated that marine and freshwater microalgae confronted to acute exposition to pesticides (not only herbicides) responded by a modification of their fatty acid profiles with a decrease of PUFA and HUFA content, particularly EPA and PUFA with 18-C, such as linoleic acid (18:2), linolenic acid (18:3); but also, with an increase of MUFA content with palmitoleic acid (16:1) and oleic acid (18:1) (Sicko-Goad et al. 1989, Sicko-Goad et al. 1989[START_REF] Robert | Metolachlor-mediated selection of a microalgal strain producing novel polyunsaturated fatty acids[END_REF].

MUFA

The increase in the percentage of MUFA showed under diuron treatment can be explained by the inhibition of photosynthetic activity by this pesticide. Without stress factors and under normal growth conditions, ATP and NADPH, which are products of photosynthesis, are consumed to generate biomass. Various studies have highlighted that when photosystem II is altered and proliferation is finally inhibited because of stress factors such as a lack of nutrients, photoinhibition, or other sorts of damages, the major electron acceptor for photosynthesis can become depleted and NADPH is consumed in the biosynthesis of fatty acids. The cells deposit fatty acids in triacylglycerol (TAG), which are reserve lipids and contained majority of MUFA, because there is no need to produce new membrane compounds [START_REF] Thompson | Lipids and membrane function in green algae[END_REF][START_REF] Hu | Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances[END_REF][START_REF] Converti | Effect of temperature and nitrogen concentration on the growth and lipid content of Nannochloropsis oculata and Chlorella vulgaris for biodiesel production[END_REF]. Finally, higher neutral lipid content in algae can be a biological reaction to physiological stress and results observed in the present study are likely the result of a modification in lipid metabolism from the synthesis of membrane lipids to storage of neutral lipids [START_REF] Hu | Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances[END_REF]). To confirm this explanation, it could be relevant in future studies to carry out lipid measurement in diatoms using TAG dosage [START_REF] Lupette | The architecture of lipid droplets in the diatom Phaeodactylum tricornutum[END_REF] or fluorescent Nile red and BODIPY, for example (Rumin et al. 2015).

PUFA and HUFA

With herbicide contamination, it was suggested that the process of fatty acid desaturation is inhibited by the direct inhibition of the desaturase enzymes involved in PUFA and HUFA synthesis (Filimonova et al. 2016). For example, it was shown in the literature that the substituted pyridazinone herbicide inhibited the desaturation process and especially the synthesis of omega-3 fatty acids, decreasing the 18:3n3 content in the glycolipids of algae. In the same way, the herbicide norflurazon inhibited desaturation system and particularly the omega 6 fatty acids [START_REF] Cohen | Potential use of substituted pyridazinones for selecting polyunsaturated fatty acid overproducing cell lines of algae[END_REF]. Filimonova et al. (2016) observed a decrease of HUFA content of the marine diatom T. weissfloggi from 16.48% to 12.32% under Primextra® herbicide treatment.

Under diuron treatment, the decrease of HUFA in G. gracile can thus be explained by the inhibition of the desaturation process. [START_REF] Troton | Modifications of thylakoid lipids in Euglena gracilis during diuron-adaptation[END_REF] demonstrated identical results on the species Euglena gracilis with the decrease of PUFA induced by 5.8 mg.L -1 of diuron. The authors explained this result by the inhibition of desaturation and the activation of elongation in the process of fatty acid synthesis with diuron contamination. In future studies, it could be relevant to explore genetic responses of desaturase genes concurrent with fatty acid profiles in order to compare their results.

Surprisingly, no effect was observed on fatty acid classes under S-metolachlor treatment.

However, a significant decrease of 18:2n6, 18:4n3 and 20:4n6 was observed (pvalue<0.05). It has been shown that the chloroacetamide herbicide metolachlor modifies the synthesis of very long chain fatty acids by inhibiting the action of the enzyme involved in the condensation of malonyl-CoA and acyl-CoA to produce CO 2 and 3-ketoacyl-CoA and impacts fatty acid elongase (FAE) in Melosira cf. moniliformis with the decrease of the fatty acid content of 18:0, 18:1n9, 18:4 and 20:5 [START_REF] Thakkar | Comparative responses of two species of marine phytoplankton to metolachlor exposure[END_REF]. The marine diatom Melosira cf.

moniliformis contains a FAE-type that converts 16:0-ACP into 18:0-ACP (ACP: Acyl Carrier

Protein is an important compound in the process of fatty acid synthesis [START_REF] Berg | Biochemistry (Loose-Leaf)[END_REF])), and 18:1 acid is then produced by n9-desaturation. Moreover, the inhibition of n3 and n6desaturation induces a decrease of linoleic and linolenic acids content in diatoms (Filimonova et al. 2016). In order to confirm these hypotheses, it could be relevant to contrast genetic responses of desaturase and elongase genes at the same time.

Fatty acid relevance as biomarker of water contamination

Complementarity between "classical descriptors" and fatty acid profiles

The induction of the oxidative stress gene cat under diuron treatment suggests that alteration of HUFA content synthesis may be related to ROS production. To respond to oxidative stress, cells can activate enzymatic antioxidants including catalase (CAT), but when antioxidative defenses fail, HUFA are attacked by the free radicals on their bisallylic site resulting in lipid peroxidation [START_REF] Maronić | S-metolachlor promotes oxidative stress in green microalga Parachlorella kessleri-A potential environmental and health risk for higher organisms[END_REF]. Conversely, with S-metolachlor the gene cat was induced much more importantly than diuron and as a consequence HUFA are less impacted, even not impacted. Thus, this response could be explained by the efficiency of the antioxidant mechanism.

Studies on higher plants showed that fatty acids impacted by the organic and inorganic contaminants were the most unsaturated fatty acids and were those present in the chloroplast lipids and necessary for photosynthetic activity. Reductions of these lipids resulted in the dismantling of the photosynthetic membranes and prevented PSI and PSII activity [START_REF] Kobayashi | Role of membrane glycerolipids in photosynthesis, thylakoid biogenesis and chloroplast development[END_REF]. It was shown in the literature that the fatty acids 18:4 and 20:5 were mainly linked with MGDG (monogalactosyldiacylglycerol) and DGDG (digalactosyldiacylglycerols), which are mostly chloroplastic lipids (80%) [START_REF] Nakamura | Lipids in plant and algae development[END_REF]Li-Beisson 2016, Zulu et al. 2018), which can explain the decline of these fatty acids along with photosynthesis in this study.

Finally, a decrease in photosynthesis leads to a reduction of the growth rate, that it is consistent with the ACP results in this study. The decrease of photosynthesis activity despite the induction of d1 gene under diuron treatment could be explained previous by the decrease of HUFA content. Photosynthetic yield was not impacted by S-metolachlor and this might be the result of the efficiency of the d1 gene against contaminant exposure but also due to very low effect observed on HUFA.

The growth rate was damaged under S-metolachlor treatment but in lower proportion than under diuron stress; it could be explained by the induction of mitochondrial metabolism genes and, at the same time, by the very low effect on HUFA which are membrane structure compounds needful for diatom growth.

Finally, S-metolachlor contamination seemed to be less toxic than diuron at the same environmental concentration. Several hypotheses can be formulated: 1) contrary to other studies, the herbicide was applied alone and without adjuvant, making the toxicity of compounds lower in the present case. Filimonova et al. (2016) studied the impact on the marine diatom Thalassiosira weissflogii of the commercial compound Primextra® Gold TZ, which is produced by Syngenta AG and is made-up of two main active ingredients: 17.75%

(w/w) of terbuthylazine (TBA) and 30.2% (w/w) of S-metolachlor; 2) the applied concentration was 10 µg.L -1 , a much lower concentration compared to other studies enabling the defense mechanisms to be efficient. For example, [START_REF] Robert | Metolachlor-mediated selection of a microalgal strain producing novel polyunsaturated fatty acids[END_REF] studied the impact of metolachlor but the concentrations applied were 5.7; 7.09 and 8.5 mg.L -1 ; 3) the high concentrations of nutrients provided by the Dauta medium may have stimulated diatoms metabolism and masked, at least partially, pesticide toxicity [START_REF] Guasch | Community composition and sensitivity of periphyton to atrazine in flowing waters: the role of environmental factors[END_REF][START_REF] Guasch | Phosphate limitation influences the sensitivity to copper in periphytic algae[END_REF]); 4) the species G. gracile is less sensitive than those used in the previous studies. [START_REF] Wood | How benthic diatoms within natural communities respond to eight common herbicides with different modes of action[END_REF] showed that the mode of action of herbicide did not change the most sensitive benthic diatom response used in their study, except for G. gracile. In fact, G. gracile was the third most sensitive to the PSII inhibitors but was ranked as less sensitive to herbicide with another mode of action. [START_REF] Larras | Using bioassays and species sensitivity distributions to assess herbicide toxicity towards benthic diatoms[END_REF] reported the same results: PSII inhibitors are more phytotoxic to diatoms than herbicides with other modes of action. In the current study, the finding of diuron being more toxic than S-metolachlor could so be an artefact of the test organism G. gracile.

Finally and according to the results of the PCA analysis, it was shown that fatty acid profiles provide complementary information of herbicide pollution compared to pigment content responses where no impacts were found. Conversely, fatty acid profile responses were correlated with photosynthesis and growth rate responses. However, fatty acid profiles give supplementary information of mode of action of herbicide with especially induction of ROS species, conversely to growth rate and photosynthesis descriptors.

Ecological relevance of fatty acid profiles

Fatty acid profiles analyses were often used to identify relationships in trophic chain [START_REF] El-Sabaawi | Characterizing dietary variability and trophic positions of coastal calanoid copepods: insight from stable isotopes and fatty acids[END_REF][START_REF] Van Den Meersche | Autochthonous and allochthonous contributions to mesozooplankton diet in a tidal river and estuary: Integrating carbon isotope and fatty acid constraints[END_REF][START_REF] Allan | Determining spatial changes in the diet of nearshore suspension-feeders along the South African coastline: Stable isotope and fatty acid signatures[END_REF][START_REF] Kelly | Fatty acids as dietary tracers in benthic food webs[END_REF]. HUFA and especially EPA are necessary in the health and function of all animals at all trophic levels, including plankton invertebrates, fish and humans. Animals are able to convert one form of HUFA to another using elongation and desaturation process but very few can synthesize HUFA de novo [START_REF] Brett | The role of highly unsaturated fatty acids in aquatic foodweb processes[END_REF]. Moreover, it was shown in the literature that bacillariophyceae as diatoms had high contents of EPA and thus were excellent quality food resources for zooplankton [START_REF] Taipale | Fatty acid composition as biomarkers of freshwater microalgae: analysis of 37 strains of microalgae in 22 genera and in seven classes[END_REF]).

However, lipid components and fatty acid profiles are very sensitive to stressors and environmental modifications [START_REF] Arts | Lipids in Aquatic Ecosystems[END_REF]. Filimonova et al. (2016) noted modifications in the FA profiles of the primary producer T. weissflogii and primary consumers A. tonsa and A. franciscana under Primextra® herbicide treatment with the increase of SFA and decrease of PUFA and especially HUFA contents. Thus, it was suggested that pesticide could affect the nutritive value of primary producers and primary consumers impacting the entire trophic chain.

Conversely to "classical descriptors", FA profiles could provide information of nutritional quality of diatom. In the way where diuron decreased HUFA and especially EPA content of G. gracile, it could be decreased the nutritional quality of G. gracile and consequently the trophic chain could be impacted, conversely to S-metolachlor where no impact where found on EPA. In this context, it could be pertinent in future studies to explore the impact of diuron and S-metolachlor at the same concentration on fatty acid profiles of a consumer of G. gracile contaminated by trophic and direct ways.

In summary, while pigment content did not capture pesticide effects at environmentally realistic concentrations, a combination of classical endpoints such as photosynthesis or growth rate reduction and FA profiles is recommended for future studies. The latter endpoints were able to capture pesticide effects on diatoms, with FA profiles yielding additional information on the underlying mode of action (e.g. ROS formation) and the ecological relevance (e.g. trophic impact) of pesticide pollution.

Conclusions

The impact of diuron and S-metolachlor at 10 µg.L -1 was investigated on the freshwater diatom G. gracile. At the same concentration, these two pesticides had different effects on "classical descriptors" of G. gracile. Fatty acid profiles of G. gracile were also affected by these two pesticides, with a considerably stronger response to diuron, suggesting that this pesticide was more toxic to G. gracile in this study than S-metolachlor. Both pesticides seemed to induce ROS formation but according to impacted fatty acids, it could be possible that diuron also inactivated desaturation enzymes. The low but environmentally realistic concentrations tested in the present study did not allow to show a high effect of S-metolachlor on fatty acid profiles and it is difficult to conclude on its mode of action of G. gracile's fatty acid profiles. However, considering the target specificity of herbicides, the fatty acids profiles used to assess chemical toxicity might provide additional information on the mode of action and as consequence on the type of pesticide studied.

Moreover, this study proves the complementarity between the fatty acid profiles and the "classical descriptors" since FA indicate herbicide effects conversely to pigment contents and provide additional nutritive values involving ecological information particularly on the trophic chain.

  The protocols of RNA extraction, Reverse Transcription of RNA and Real-Time q-PCR were based on the protocol of Kim[START_REF] Kim Tiam | Development of q-PCR approaches to assess water quality: effects of cadmium on gene expression of the diatom Eolimna minima[END_REF] and carried out at the end of the experiment. Ten genes were studied; three involved in mitochondrial metabolism (cox1 (cytochrome C oxidase subunit I), 12S (mitochondrial ribosomal RNA 12S) and nad5 (NADH dehydrogenase subunit 5)), two involved in photosynthetic activity (d1 (D1 protein) and psA (PsaA protein)), two involved in oxidative stress (cat (catalase) and gpx (glutathione peroxidase)) and one involved

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

  Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

	Li et al. (2014) noted this variability with the accumulation of neutral lipids rich in SFA (14:0,

16:0) and MUFA (16:1) during a stationary growth phase. These types of lipids are used to store excess carbon accumulated during photosynthesis. Since structural components are

Author produced version of the article published in Science of the TotalEnvironment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.com/science/article/pii/S0048969719329201 Doi : 10.1016/j.scitotenv.2019.06.347

Author produced version of the article published in Science of the Total Environment, 2019, 688, 960-969 The original publication is available at https https://www.sciencedirect.


Zhongliang Zhao
email: zhao@inf.unibe.ch

Jose Carrera
email: carrera@inf.unibe.ch

Joel Niklaus
email: joel.niklaus@students.unibe.ch

Torsten Braun
email: braun@inf.unibe.ch

Machine Learning-based Real-Time Indoor Landmark Localization

Keywords: Machine learning, indoor localization, real-time landmark detection

Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones' indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones' locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning.

Introduction

High localization accuracy within buildings would be very useful -in particular, large complex buildings like shopping malls, airports and hospitals would be well served by this feature. It would make orientation within these highly complicated structures much easier and would diminish the need for big floor maps scattered all around these buildings. However, walls, roofs, windows and doors of the buildings greatly reduce the GPS signals carried by radio waves, which leads to a severe accuracy loss of GPS inside buildings.

Different solutions already exist for indoor localization of mobile devices such as Pedestrian Dead Reckoning (PDR) and Wi-Fi fingerprinting based methods [START_REF] He | Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons[END_REF] [START_REF] Ouyang | Chiang Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning[END_REF]. In PDR the future location of a smartphone user is predicted based on the current location, and the movement information derived from the inertial sensor measurements. In Wi-Fi fingerprinting, the Received Signal Strength (RSS) values of several access points in range are collected and stored together with the coordinates of the location. A new set of RSS values is then compared with the stored fingerprints and the location of the closest match is returned.

In contrast to outdoors, building interiors normally have a large number of different Wi-Fi access points constantly emitting signals. By scanning the area around the device, we can measure the received signal strength of each of the nearby access points. Because there are typically so many of them, we presume that the list of all these values combined is unique at every distinct point in the building. Furthermore, we can strongly assume that these values are also constant over time as the access points are fixed in place and are constantly emitting signals of the same strength. Of course, there may be occasional changes, for instance if the network is remodeled, but we expect these changes to be infrequent.

In this way, we can collect lots of labeled location data of the building. However, because each data point may contain a very large number of Wi-Fi access point RSS measurements and magnetic field measurements, the data is very complex. Therefore, we propose using supervised Machine Learning (ML) methods to process this large amount of collected data. By training a classifier (supervised learning algorithm such as K-Nearest-Neighbor) on the collected labeled data, rules can be extracted. Feeding in the actual live data (RSS values, magnetic field values, illuminance level, etc.) of a moving user, the trained classifier can then predict the user's location on a coarse-grained level. We propose to apply machine learning methods, both individual predictors and ensemble predictors, to solve this task due to the large amount of features that are available in indoor environments, such as Wi-Fi RSS values, magnetic field values, and other sensor data. We expect that ensemble predictors can outperform the individual machine learning algorithms to discover patterns in the data, which can then be used to differentiate between different rooms and regions within the detected rooms.

The rest of the paper is organized as follows. In Section 2 we present some related work in indoor localization and landmark detection. Section 3 describes the used machine learning models, including the individual and ensemble ones, as well as the considered features to conduct the indoor landmark localization task. Section 4 presents implementation and experiment details. Section 5 discusses the performance results of our approach. Section 6 concludes the paper.

Related Work

Various machine learning-based approaches that use fingerprinting to estimate user indoor locations have been proposed. Machine learning-based indoor localization can be classified into generative or discriminative methods, which build the machine learning model using a joint probability or conditional probability respectively [START_REF] He | Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons[END_REF] [START_REF] Ouyang | Chiang Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning[END_REF]. K-Nearest-Neighbor (KNN) is the most basic and popular discriminative technique. Based on a similarity measure such as a distance function, the KNN algorithm determines the k closest matches in the signal space to the target. Then, the location of the target can be estimated by the average of the coordinates of the k neighbors [START_REF] Lakmali | Implementation & Testing of Positioning Techniques in Mobile Networks[END_REF]. Generative localization methods apply statistical approaches, e.g., Hidden Markov Model [START_REF] Ghahramani | An Introduction to Hidden Markov Models and Bayesian Networks[END_REF], Bayesian Inference [START_REF] Bousquet | Bayesian Inference: An Introduction to Principles and Practice in Machine Learning[END_REF], Gaussian Processes [START_REF] Ferris | Fox Gaussian Processes for Signal Strength-based Location Estimation[END_REF], on the Wi-Fi fingerprint database. Thus, the accuracy can obviously be improved by adding more measurements. In [START_REF] Ferris | Fox Gaussian Processes for Signal Strength-based Location Estimation[END_REF] for instance, Gaussian Processes are used to estimate the signal propagation model through an indoor environment. There is a limited number of works that have focused in reducing off-line efforts in learning-based approaches for indoor localization [START_REF] Chai | Reducing the calibration Effort for Probabilistic Indoor Location Estimation[END_REF] [8] [START_REF] Liu | A Low-Cost and Accurate Indoor Localization Algorithm Using Label Propagation based Semi Supervised Learning[END_REF]. These approaches reduce the off-line effort by reducing either the number of samples collected at each survey point or the number of survey points or both of them. Then, a generative model is applied to reinforce the sample collection data. In [START_REF] Chai | Reducing the calibration Effort for Probabilistic Indoor Location Estimation[END_REF] for instance, a linear interpolation method is used. In [START_REF] Madigan | Bayesian Indoor Positioning Systems[END_REF], a Bayesian model is applied. In [START_REF] Liu | A Low-Cost and Accurate Indoor Localization Algorithm Using Label Propagation based Semi Supervised Learning[END_REF], authors propose a propagation method to generate data from collected samples. In [START_REF] Ouyang | Chiang Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning[END_REF], authors combine characteristics of generative and discriminative models in a hybrid model. Although this hybrid model reduces offline efforts, it still relies on a number of samples collected from fixed survey points (i.e., labeled samples) along the environment. Therefore, to maintain high accuracy, the number of survey points shall be increased in larger environments. Thus, collecting samples from numerous survey points will become a demanding process, which makes the system unsuitable to large environments. In [START_REF] Mascharka | LIPS: Learning Based Indoor Positioning System using mobile phone-based sensors[END_REF], authors validated the performance of different individual machine learning approaches for indoor positioning systems. However, they rather compare the results without any deep analysis of the performance difference. Moreover, they did not discuss how ensemble learning approaches could be used to enhance system performance.

In this work we present and analyze the performance of different individual predictors as well as ensemble predictors for the indoor landmark localization problem. This work could also be used as a basis of indoor tracking systems to firstly locate the target with a coarse-grained accuracy using indoor landmark localization, which then triggers the real-time localization algorithm to locate the object around the detected landmarks. The located landmark can also be used to correct the localization failures like the kidnapped robot problem [START_REF] Carrera | A real-time robust indoor tracking system in smartphones[END_REF].

Machine Learning-based Indoor Landmark Localization

An indoor landmark is defined as a small area within a room. The aim of the indoor landmark localization system presented in this work is to improve the accuracy of indoor landmark recognition using machine learning approaches. We do this by excluding all the possible locations of the user within the room if the system predicts the others by using landmarks. Thus, when a landmark has been recognized, the indoor positioning system can use the identified coarse-grained locations to optimize the positioning accuracy, such as revising positioning errors.

Algorithms

In this section, we shortly describe the machine learning algorithms that are used in this work to perform the room landmark localizations.

Naive Bayes (NB) classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Multilayer Perceptron (MLP) is a class of feed-forward artificial neural network. An MLP consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function.

K-Nearest

Voting is one of the simplest ensemble predictors. It combines the predictions from multiple individual machine learning algorithms. It works by first creating two or more standalone prediction models from the training dataset. A Voting classifier can then be used to wrap the models and average the predictions of the sub-models when asked to make predictions for new data.

Features

In a machine learning-based classification task, the attributes of the classes are denoted as features. Each feature is describing an aspect of the classes. In our case features are our measurements, for instance an RSS value. To deliver good machine learning prediction accuracy it is very important to select the right attributes/features and to also modify certain features or even create new features out of existing features.

Wi-Fi RSS values provide the core data as they contribute the most to the performance of the ML methods. The smartphone scans the surrounding Wi-Fi access points, obtains and registers the RSS values of each access point. Wi-Fi RSS values depend on the distance between the smartphone and the Wi-Fi access points. Normally, the Wi-Fi RSS values in our datasets were between -20 dBm and -90 dBm.

Magnetic Field (MF) The device's sensors measure the magnetic field in the device's coordinate system. As the user walks around, the orientation of the device may change all the time. Therefore, we have to collect all possible values from every orientation in every point in the training phase. This would result in a huge amount of data and the training performance would be inaccurate.

Light sensors might also be helpful to identify rooms. For instance, a room facing a window will clearly be brighter than one surrounded by walls only. As shown in Section 5 this does improve the prediction accuracy. However, these assumptions are not stable, as the illuminance level might change over time. Therefore, it is better to work with light differences instead of absolute values.

Implementation and Experiments

This section explains how the indoor room landmarks are defined and presents details about how to make the room landmark localization using ML algorithms.

Room and Landmark Recognition

A room landmark is defined as a small area within a room, and room landmark fingerprint database inlcudes the Wi-FI RSS, MF measurements, and illuminance level data measured within that small area. In the room recognition phase we distinguish several rooms on the same floor. In the landmark recognition phase we distinguish several landmarks inside the detected room. Therefore, we define two landmarks in a small room with size of 3x3 meters, and four landmarks in a normal office-sized room (5x5 meters). In a big room (7x7 meters) we define five landmarks, one in each corner and one in the center, as shown in Figure 1. Considering that the landmark detection accuracy can be influenced by some environmental parameters, we conduct some experiments to determine how parameters such as AP position or number of APs influence the accuracy of the Wi-Fi-based fingerprinting landmark detection approach. Additionally, we perform experiments to show how the accuracy is improved by considering additional features such as magnetic field (MF) values and light illuminance level readings. As shown in Figure 3, we define 9 wall separated areas in our experiment environment. Hereafter, we refer to these areas as rooms. In our experiments, we do not need to know the locations of the APs, while only the fingerprints of Wi-Fi RSSI, MF readings, and illuminance level readings are recorded during the data collection process.

System Architecture

Parameters of learning-based algorithms are optimized from training data. Additionally, certain algorithms also have parameters that are not optimized during the training process. These parameters are called hyperparameters, which have significant impact on the performance of the learning-based algorithm. Therefore, we use a nested cross validation technique to adjust them. The nested cross validation technique defines an inner and outer cross validation. The inner cross validation is intended to select the model with optimized hyperparameters, whereas outer cross validation is used to obtain an estimation of the generalization error. Ten-fold cross validation was applied on both inner and outer cross validation. The classifiers were optimized over a set of hyperparameters. We optimized the global blend percentage ratio hyperparameter for KNN, kernel type function for SVM, number of hidden layers and neurons per layer for MLP. Based on the parameter optimization process, we established the optimal hyperparameter values for the classifiers as follows: global blend percent ratio of 30% for KNN, single order polynomial kernel, c = 1, γ = 0.0 for SVM, and single hidden layer with 10 neurons for MLP.

Datasets

To test the room landmark detection performance, we performed experiments on the third floor of the Computer Science building of the University of Bern, as shown in Fig. 3. During the experiments, we collected 14569 data points in total, 3061 data points were collected from the biggest room (1) and 514 data points were from the smallest room (4). Collecting the training dataset takes around 50 minutes. With the collected data, we build models with different data: the first one builds the fingerprint using only collected Wi-Fi RSSI data, the second one using Wi-Fi RSS together with MF readings, and the third one with Wi-Fi RSS, MF readings, and illuminance level readings.

As described before, to build the landmark fingerprint database, we ask a person to walk randomly around each room holding the phone in his/her hand. Landmark fingerprint database entries must be collected equally distributed along the whole area in each room. The data collection rate is only constrained by computational capabilities of the Wi-Fi sensor of the smartphone. Thus, in our experiments every data measurement was collected at a rate of 3 entries/second. Because our approach does not need to predefine any survey point, the time needed to build the landmark fingerprint database is proportional to the number of collected instances multiplied by the instance collection rate.

Results

Indoor Landmark Localization Accuracy

This section discusses the accuracy of the landmark detection model when different classifiers and features are used. When comparing their performance, it is impossible to define a single metric that provides a fair comparison in all possible applications. We focus on the metrics of prediction accuracy, which refers to the percentages of correct room recognition and landmark localization within the detected room. Landmark definition is described in Section 4.1.

At first we use only Wi-Fi RSS values as inputs to machine learning algorithms. Figure 4 shows the classification accuracy of different predictors when different numbers of Wi-Fi RSS values are used. As we can see, starting from 5 RSS values, more RSS inputs increase the prediction accuracy for most of the predictors. Nevertheless, after 7 Wi-Fi RSS values are used, the improvement of adding more RSS values is almost negligible in all tested classifiers, and some of the predictors even got reduced accuracy when additional RSS values are considered. We think that the signal interferences may be the reason for the worse performance when more than 7 Wi-Fi RSS values are utilized. Therefore, we take 7 Wi-Fi RSS as the default configuration for the following experiments. Next, we compare the classification accuracy when using only Wi-Fi RSS, Wi-Fi RSS plus MF, and Wi-Fi RSS plus MF and illuminance levels. Figure 5 shows the performance evaluation of the selected classifiers obtained with different feature combinations. The best performance is reached by the Naive Bayes classifier, which achieves 90.13% of instances correctly classified if the fingerprint is composed by Wi-Fi RSS, MF readings, and illuminance levels. By using Wi-Fi RSS, MF readings, and illuminance levels in the room landmark recognition, the accuracy is improved in all tested classifiers. As mentioned before, hyperparameters have significant impacts on the performance of the learning-based algorithm. Figure 6 shows the performance of the selected classifiers with the hyperparameters optimized. The classifiers are all fed with Wi-Fi RSS plus MF and illuminance levels. As we can see, compared to results in Figure 5, all the classifiers have improved performance, and MLP even reaches an accuracy of 92.08%. We also include the results of Voting, which combines the prediction results of MLP, Naive Bayes, KNN, and SVM using majority vote. It shows that Voting can reach an accuracy of 94.04%.

Result Analysis

In indoor environments, Wi-Fi RSSI and MF measurement vary dependent on locations. However, these values will remain similar on nearby positions. For example, on locations close to landmark borders, high similarities will be observed on the RSS values. These similarities could lead to misclassification problems. From Figure 5 we can see that KNN and SVM outperform others in terms of accuracy when Wi-Fi RSS and MF readings are used. This is because KNN is an instance-based learning algorithm, which uses entropy as a distance measure to determine how similar two instances are. Thus, this method is more sensitive to slight variations upon the instance as unity. J48 builds the classification model by parsing the entropy of information at attribute level. It means that J48 measures entropy in the attribute domain to decide which attribute goes into a decision node. Therefore, the classification model is prone to misclassification in the landmark detection problem. When the illuminance level is included as input feature to predictors, Naive Bayes outperforms others. This is because the feature of illuminance level is completely independent from other radio signal measurements, which fits with Naive Bayes' strong assumptions about the independence of each input variable.

To further explain how the Voting predictor improves the performance of individual predictors, we show the confusion matrix of room recognition using MLP, SVM, Naive Bayes (NB), and Voting in Tables 123. We can observe that room 2 is correctly identified 527 times by MLP, 632 times by SVM and 393 times by NB. As a consequence, SVM seems to be better in predicting room 2 as compared to other two predictors. Furthermore, NB does not seem to have less misclassification of class b compared to other two predictors. Analyzing the results from the above-mentioned tables, MLP has misclassified class b 138 times, NB 272 times, and SVM only 33 times. From the confusion matrix of Voting, as shown in Table 4, we can see that the Voting ensemble predictor adopts behaviors of different individual predictors. For instance, it adopts the good behavior of MLP and Naive Bayes, which leads to a much better prediction accuracy for room 2. This can be observed from the only two misclassifications of room 2 as room 9 as shown in Table 4. Unfortunately, it still has problems in some classifications. For instance, there are 116 misclassification of room 8 as room 4, which is probably due to a higher weight assigned to MLP instead of SVM. In general, it can be observed that the Voting ensemble predictor improves the accuracy, while there are still difficulties to distinguish small rooms that are next to each other, as room 8 and 4 depicted in Figure 3. This work analyzes the performance of 5 common individual predictors and 1 ensemble predictor in indoor landmark localization to distinguish rooms on a floor and special landmarks using machine learning methods. We have validated the performance of the system using different smartphone sensor measurements, such as Wi-Fi RSS, MF readings, and illuminance levels. Evaluation results show that the Voting ensemble predictor achieves the best indoor landmark localization accuracy of 94%. In the future, we will further optimize the hyperparameter cross-validation procedure and integrate this work with an indoor tracking system to firstly locate the target with a coarse-grained accuracy, which then triggers the tracking algorithm to track the object around the located landmarks.

  Neighbors (KNN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. Support Vector Machine (SVM) is a supervised learning model with associated learning algorithms that analyze data used for classification and regression. Given a set of training examples, each is marked as belonging to one or the other category. An SVM training algorithm builds a model that assigns new data measurements to one category or the other, making it a non-probabilistic binary linear classifier.

Fig. 1 .

 1 Fig. 1. Five landmarks and the collection of red points (location examples) predicted by the indoor landmark localization system.

Figure 2 Fig. 2 .

 22 Figure 2 shows the data flow and the different components of our developed Android app. Sensor and Wi-Fi RSS values are measured by the smartphone and received by the app. We then perform the data training process offline in a PC to pass the collected data to the Model Training component, which applies different machine learning algorithms to build the models. The trained models are then optimized and transfered to the app on the smartphone for online experiments.

Fig. 3 .

 3 Fig. 3. Experiment scenario and data collection path.

Fig. 4 .

 4 Fig. 4. Landmark prediction performance with different numbers of Wi-Fi RSS values.

Fig. 5 .

 5 Fig. 5. Landmark prediction performance when using different features.

Fig. 6 .

 6 Fig. 6. Landmark prediction performance of individual predictors with optimized hyperparameters and Voting ensemble predictor.

Acknowledgements

This work was supported by the Swiss National Science Foundation #154458.