Helena Guasch
email: helena.guasch@udg.edu

Marta Ricart

Julio Lopez-Doval

Chloé Bonnineau

Lorenzo Proia

Soizic Morin

Isabel Munoz

Anna M Romani

Sergi Sabater

Influence of grazing on triclosan toxicity to stream periphyton

Keywords: algae, ecotoxicology, herbivores, periphyton, triclosan toxicity, trophic interaction

1. Interactions between emerging contaminants and other drivers of community structure and function are poorly known. We used laboratory microcosms to investigate the single and combined effects of grazing and triclosan toxicity on the structure and function of stream periphyton.

2. Grazing alone strongly reduced algal biomass, but also reduced oxidative stress and increased periphyton productivity and phosphorus (P) uptake capacity. This suggests that grazed algal communities can compensate for reduced biomass by enhanced growth after being released from grazing.

3. Triclosan exposure (11.6 ± 1.1 µg L -1 ) reduced the detoxification capacity and P-uptake capacity of periphyton and altered diatom taxonomic composition. This indicates that triclosan at environmentally relevant concentrations affects the capacity of periphyton to remove dissolved nutrients and to cope with toxicant mixtures commonly occurring in streams.

4. Triclosan exposure and grazing pressure had negative synergistic effects on algal size-class distribution and diatom mortality, since the effects of triclosan were higher than expected when periphyton was subject to grazing.

5. Periphyton exposed to toxic substances such as triclosan had a lower capacity to cope with grazing than unexposed communities, because toxicity can limit algal regrowth after release from grazing and promote the loss of less abundant species. This synergism may have important implications because grazing pressure will magnify the negative effects of toxicants on community structure and ecosystem functions such as primary production and nutrient cycling.

Introduction

In aquatic ecosystems, communities are commonly exposed to multiple stresses, including chemical contamination [START_REF] Piggott | Climate warming and agricultural stressors interact to determine stream periphyton community composition[END_REF] and biotic interactions [START_REF] Liess | Invited review: direct and indirect effects in herbivore -Periphyton interactions[END_REF]. Those multiple stressors can individually structure communities and influence their function. In addition, the presence of one stressor can also modify the community response or sensibility to another one. Thus, the combination of two stressors can often induce a synergistic effect stronger than the sum of the individual effects. For instance, natural biotic stressors such as food limitation or parasitism can modify or amplify the negative effects of chemicals on organisms [START_REF] Heugens | A review of the effects of multiple stressors on aquatic organisms and analysis of uncertainty factors for use in risk assessment[END_REF][START_REF] Holmstrup | Interactions between effects of environmental chemicals and natural stressors: a review[END_REF][START_REF] Laskowski | Interactions between toxic chemicals and natural environmental factors: a meta-analysis and case studies[END_REF][START_REF] Coninck | Interactive effects of a bacterial parasite and the insecticide carbaryl to life-history and physiology of two Daphnia magna clones differing in carbaryl sensitivity[END_REF]. Although both contamination and biotic interactions occur simultaneously in stream ecosystems, the effects of toxicants on biotic interactions are not well understood [START_REF] Barranguet | Copper-induced modifications of the trophic relations in riverine algal-bacterial biofilms[END_REF][START_REF] Real | The effect of copper exposure on a simple aquatic food chain[END_REF][START_REF] Evans-White | Direct and indirect effects of a potential aquatic contaminant on grazer-algae interactions[END_REF].

To explore the interaction between chemical contamination and biotic interactions, this study focused on periphyton, an association of autotrophic and heterotrophic micro-organisms embedded in a polymer matrix and attached to submerged surfaces of aquatic ecosystems. Periphyton is an important energy source for aquatic consumers, an active site of nutrient cycling and an important place for the adsorption and accumulation of chemicals [START_REF] Guasch | Metal ecotoxicology in fluvial biofilms: potential influence of water scarcity[END_REF]. Since changes in periphyton community structure and biotic interactions caused by chemicals can reveal indirect effects that would not be detected by standard toxicity tests with single species, periphyton is considered an ecologically relevant model in community ecotoxicology [START_REF] Ricart | Effects of low concentrations of the phenylurea herbicide diuron on biofilm algae and bacteria[END_REF].

Periphyton community structure is influenced by various abiotic and biotic factors, one of which is grazing. Grazing effects may differ, however, depending on periphyton productivity [START_REF] Liess | Light, nutrients and grazing interact to determine diatom species richness via changes to productivity, nutrient state and grazer activity[END_REF]. High productivity reduces the probability of species to disappear, since populations can compensate for grazing losses, whereas any factor that limits biomass accrual (e.g. nutrient limitation or low light availability) increases the probability of species loss. In this situation, synergistic interactions between grazing and a growth-limiting factor might be expected. Similarly, grazing may exacerbate the negative effects on periphyton exerted by contaminants, which limit the ability of periphyton to recover from grazing pressure [START_REF] Munoz | Effects of atrazine on periphyton under grazing pressure[END_REF].

The antimicrobial agent triclosan, 5-chloro-2-(2,4-dichlorophenoly)-phenol, is a broad-spectrum antimicrobial agent that is extremely widespread in medical, household and personal care products such as shampoos, toothpaste, deodorants, medical skin creams and detergents [START_REF] Bhargava | Triclosan: applications and safety[END_REF][START_REF] Daughton | Pharmaceutical and personal care products in the environment: agents of subtle change ?[END_REF][START_REF] Schweizer | Triclosan: a widely used biocide and its link to antibiotics[END_REF]. Most of the triclosan used in this vast array of products ends up in wastewater treatment plants [START_REF] Singer | Triclosan: occurrence and fate of a widely used biocide in the aquatic environment: field measurements in wastewater treatment plants, surface waters, and lake sediments[END_REF][START_REF] Boyd | Pharmaceuticals and personal care products (PPCPs) insurface and treated waters of Louisiana, USA and Ontario, Canada[END_REF]. However, triclosan is not degraded during conventional wastewater treatment [START_REF] Adolfsson-Erici | Triclosan, a commonly used bactericide found in human milk and in the aquatic environment in Sweden[END_REF], resulting in discharges to surface waters, where concentrations of up to 2.3 µg L -1 are reached [START_REF] Chalew | Environmental exposure of aquatic and terrestrial biota to triclosan and triclocarban[END_REF][START_REF] Dann | Triclosan: environmental exposure, toxicity and mechanisms of action[END_REF]. Triclosan is highly toxic to periphytic bacteria (no-effect concentration of 0.21 µg L -1 ; Ricart et2010), periphytic algae [START_REF] Franz | What contributes to the sensitivity of microalgae to triclosan ?[END_REF]Ricart et2010), periphyton architecture [START_REF] Lawrence | Comparative microscale analysis of the effects of triclosan and triclocarban on the structure and function of river biofilm communities[END_REF] and grazers feeding on periphyton [START_REF] Canesi | Effects of triclosan on Mytilus galloprovincialis hemocyte function and digestive gland enzyme activities: possible modes of action on non target organisms[END_REF]Binelli et al.,2009a). As a bactericide, triclosan inhibits the biosynthesis of fatty acids at the level of the enoyl-acyl carrier protein reductase (FabI) [START_REF] Heath | Mechanisms of triclosan inhibition of bacterial fatty acid synthesis[END_REF].

Our aim in this study was to assess effects of grazing pressure and triclosan on periphyton community structure and ecosystem functions such as primary production and nutrient cycling. We experimentally determined the effects of these two factors on a suite of functional and structural endpoints relating to diatoms, all autotrophic periphyton and the entire periphyton community, to test for both the individual and combined effects of

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Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012. . The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

triclosan and grazing and to determine whether any interactions are synergistic or antagonistic.

Methods

Experimental design

Four treatments were applied to periphyton exposed to triclosan and grazing by snails in four replicate crystallising dishes per treatment. Control dishes received neither triclosan nor snails (Ctrl); grazing treatments received snails but no triclosan (S); triclosan treatments received triclosan but no snails (TCS); and triclosan plus grazing treatments received both triclosan and snails (TCS+S). The four treatments were randomly assigned to the dishes. The experiment was run for 48 h at 20 °C and under a light-dark cycle of 12:12 h at 130-150 µmol photons m -2 s -1 .

Periphyton

Periphyton was grown for 3 weeks on sandblasted glass substrata in 16 crystallising dishes. The dishes were filled with 1.5 L of artificial fresh water (Na2SO4, Na2SiO3, CaCl2 9 2H2O, KCl, MgSO4 9 7 H2O and NaHCO3 in deionised water, neutral pH) resembling that of the River Llemena in northeastern Spain in terms of ionic composition (Table 1; [START_REF] Bonnineau | Fluvial biofilms: a pertinent tool to assess b-blockers toxicity[END_REF]. Once a week, an aliquot of a microbenthic community, scraped from three colonised cobbles from the River Llemena (small tributary of the Ter River, northeastern Spain) was resuspended in artificial fresh water and added to the dishes. The water was renewed three times per week and water samples were collected and filtered (nylon membrane filters, 0.2-lm pore size, Whatman, Maidstone, UK) both before and after each water renewal. Soluble reactive phosphorus (SRP) was measured according to [START_REF] Murphy | A modified single solution method for the determination of phosphate in natural waters[END_REF] and Cl-,NO3-, SO42+, Na+, K+, Ca2+ and Mg2+ were analysed by ion chromatography (761 Compact IC, Metrohm Ltd., Herisau). Conductivity, pH and dissolved oxygen were measured with WTW probes (Weilheim).

Grazers

Snails (Radix ovata, Lymnaeidae) were collected from the River Llemena and transported to the laboratory (<3 h travel time) in plastic containers filled with stream water. The plastic containers were placed in a bigger container with stream water and cooler packs to maintain temperature at around 15 °C. The snails were placed in glass aquaria filled with dechlorinated tap water and fed with TetraMin fish food during a 2-day acclimatisation period before starting the experiment. Forty-eight individuals were used in the experiment and 15 additional individuals were directly frozen in liquid N2, later defrosted and the soft body removed from the shell, lyophilised and weighed to the nearest lg. Maximum shell height was measured with digital callipers. The mean shell length was 12.2 mm with a standard deviation (SD) of 0.6 mm, corresponding to a mean dry mass and SD of the soft body tissue of 21.0 ± 4.2 mg.

Experimental set-up

Each dish contained 40 colonised glass substrata. Triclosan was added from a stock solution in methanol. The nominal triclosan concentration was 15 µg L -1 and the final methanol concentration was 0.03%. Control dishes received methanol only. The triclosan concentration was within the range reported from polluted effluents and high enough to affect periphyton in the short term. Water of all dishes was renewed every 3 h to maintain the triclosan concentrations at the target level. On both days of the experiment, physical and chemical variables were measured once as described in the Periphyton section above.

Six snails were added in the grazing treatments [START_REF] Lopez-Doval | Does grazing pressure modify diuron toxicity in a biofilm community?[END_REF]. This density was intended to avoid food limitation of and resource competition among the snails. Dead snails were counted and removed after 1 day and at the end of the experiment after 2 days. To ensure the same grazing pressure on periphyton in all crystallising dishes, the snails removed after the first day were replaced by individuals of the same size (shell length). These individuals were marked and excluded from the analysis of endpoints.

Endpoints for periphyton and snails

Colonised glass substrata from each dish were randomly collected and used to determine all endpoints at both the start and end of the experiment. In vivo fluorescence of chlorophyll a (chl a) was measured with a PhytoPAM fluorometer (Heinz Walz GmbH, Effeltrich, Germany) in a dark chamber at room temperature after 30 min of acclimation to darkness [START_REF] Corcoll | Chl-a fluorescence parameters as biomarkers of metal toxicity in fluvial biofilms: an experimental study[END_REF]. The samples were exposed to a saturating pulse of light to induce maximal fluorescence yield (Fm). The maximum quantum yield (photosynthetic capacity), Ymax, was then calculated as (Fm -F0)/Fm, where F0 is the minimum fluorescence yield of dark-adapted cells [START_REF] Genty | The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence[END_REF]. Photosynthetic capacity was used as an indicator of photosynthetic performance, and thus productivity, and F0 was used as a proxy for algal biomass [START_REF] Corcoll | Chl-a fluorescence parameters as biomarkers of metal toxicity in fluvial biofilms: an experimental study[END_REF].

Algal biomass was also estimated by extracting chl a with 90% acetone for 12 h in the dark at 4 °C. Samples were sonicated (40 W, 40 kHz; SELECTA, Abrera, Spain) for 2 min to improve extraction efficiency. Chl-a concentrations were subsequently determined on a Lambda UV/VIS spectrophotometer (Perkin-Elmer, Waltham) by measuring absorbance at 430 and 665 nm, without phaeophytin correction [START_REF] Humphrey | New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton[END_REF].

Antioxidant Enzyme Activities (AEAs) were determined to provide information on the stress status and the ability of periphyton to cope with oxidative stress [START_REF] Bonnineau | The use of antioxidant enzymes in freshwater biofilms: temporal variability vs. toxicological responses[END_REF]. We measured the activity of catalase (CAT) and ascorbate peroxidase (APX), two antioxidant enzymes directly involved in the scavenging of ROS, as well as the activity of glutathione-S-transferase (GST), a class II detoxification enzyme. By catalysing the conjugation of electrophilic chemicals (such as oxidative toxicants) with glutathione, GST inactivates oxidative toxicants and facilitates their excretion. CAT activity was determined according to [START_REF] Aebi | Catalase in vitro[END_REF], APX activity according to [START_REF] Nakano | Hydrogen peroxide is scavenged by ascorbate-specific peroxidase in spinach chloroplasts[END_REF] and GST activity according to [START_REF] Grant | Quantitative kinetic assays for glutathione S-transferase and general esterase in individual mosquitoes using an EIA reader[END_REF]. Periphyton was removed from the glass substrata using polyethylene cell lifters (Corning Inc.) and collected into an Eppendorf tube. Samples were centrifuged for 5 min at 10 °C and 5000 g. The pellets were immediately frozen in liquid nitrogen and stored at -80 °C for later protein analysis following [START_REF] Bonnineau | Catalase in fluvial biofilms: a comparison between different extraction methods and example of application in a metal-polluted river[END_REF]. Briefly, proteins were extracted in a bead beater after the addition of extraction buffer (100 mM Na2HPO4/KH2PO4, pH 7.4, 100 mM KCl, 1 mM EDTA) and quantified with the Bradford (1976) method using bovine serum albumin as a standard. The assays were performed in microplates and absorbance was measured in a microtiter plate reader Synergy4 (BioTek , Winooski).

The capacity of periphyton to take up phosphorus was determined by measuring the rate of disappearance of soluble reactive phosphorus (SRP) added to the crystallising dishes with a known area of periphyton on the glass slides [START_REF] Proia | Drought episode modulates the response of river biofilms to triclosan[END_REF]. The dishes were spiked with 12 µL of 1Na2 PO4 (1.41 g L -1 ) to quadruple the background concentration of SRP (3.7 ± 1.9 µg L -1 ) and incubated for 120 min in an illuminated incubator under controlled temperature (21 °C) and light (130-150 lmol photons m -2 s -1 ) conditions (Radiber AGP-570).

Suspensions of periphyton scraped off the slides were preserved with a drop of formalin and diluted with water (4%) from the respective crystallising dish (5 mL final volume). Diatoms were subsequently identified and counted and their live-dead ratio [START_REF] Morin | Effects of a bactericide on the structure and survival of benthic diatom communities[END_REF] determined in a Nageotte chamber at a magnification of 109. The presence of chloroplasts was used as a criterion to distinguished live and dead cells. Samples were digested in oxygen peroxide to prepare permanent slides for diatom identification. A minimum of 400 individuals was counted per sample [START_REF] Morin | Effects of a bactericide on the structure and survival of benthic diatom communities[END_REF]. Diatom taxonomy was used to assess treatment effects on the structure of the community (in terms of species composition and traits) and the ratio between live and dead diatom cells to assess effects on diatom viability. In addition, four different growth forms of the periphyton were distinguished: solitary unattached, solitary stalked and heap-forming cells as well as linear colonies. The size distribution of diatom cells was also determined, based on theoretical biovolumes (http://hydrobio-dce.irstea.fr/telecharger/diatomees-ibd).

Snail mortality was recorded after 24 and 48 h. Oxygen consumption by the snails was measured at the start and at the end of the experiment with an Oxygen Measurement Controller (Model 10, Rank Brothers, Cambridge). A total of three (beginning) or four (end) snails were used for each treatment. Only one snail from each replicate was used. The measuring chamber of the oxygen meter was filled with 15 mL of air-saturated bubblefree water at a constant temperature (20 ± 1 °C) and a snail placed inside for 10 min. The difference between the initial and final concentration was assumed to represent the oxygen consumption by the snail. Rates were normalised by snail dry mass determined at the end of the experiment. Snails were marked to recognise them at the end of the experiment and calculate their individual dry mass.

Triclosan analysis

Triclosan concentrations were measured with the magnetic particle-based immunoassay using the triclosan IA kit developed and supplied by Abraxis LLC (Warminster) as described by [START_REF] Kantiani | Triclosan and methyl-triclosan monitoring study in the northeast of Spain using a magnetic particle enzyme immunoassay and confirmatory analysis by gas chromatography-mass spectrometry[END_REF]. One water sample was collected from all replicate crystallising dishes 30 min after triclosan addition. In addition, one triclosan-treated dish was sampled every 20 min between two consecutive water exchanges. Water samples were adjusted with methanol to a concentration of 25% (v/v) prior to analysis and diluted to fall within the calibration range. Suspended antibody-coupled paramagnetic particles supplied with the kit were mixed in a glass test tube and incubated at room temperature for 30 min. Next, 250 µL of HRP-4-[3-bromo-4-(2,4dibromophenoxy) phenoxy] butyrate was added and the mixture was incubated for another 30 min at room temperature. A magnetic field was applied to hold the paramagnetic particles and bound reagents in the tube.

Unbound reagent was removed before washing the particles with water. A chromogenic substance (3,30 ,5,50 -tetramethylbenzidine; TMB) and hydrogen peroxide (500 µL) were then added to the particles and the mixture was incubated for 20 min at room temperature. The colour reaction was stopped by adding 500 µL of 2% sulphuric acid and absorbance at 450 nm was read within 15 min. Standard curves were based on eight triclosan concentrations between 20 and 1000 ng L -1 . The standard curves were obtained by nonlinear curve fitting to a four-parameter equation [START_REF] Kantiani | Triclosan and methyl-triclosan monitoring study in the northeast of Spain using a magnetic particle enzyme immunoassay and confirmatory analysis by gas chromatography-mass spectrometry[END_REF].

Data analysis

Treatment effects on periphyton endpoints determined at the end of the experiment were tested by analysis of variance (ANOVA) with two factors, triclosan exposure and grazing by snails. All endpoints were log10transformed prior to statistical analyses to meet assumptions of normality and homogeneity of variance. Statistically significant differences in the two main factors were interpreted as a positive or negative effect, depending on the sign of changes (see De [START_REF] Coninck | Interactive effects of a bacterial parasite and the insecticide carbaryl to life-history and physiology of two Daphnia magna clones differing in carbaryl sensitivity[END_REF]. Statistically significant triclosan 9 grazing interactions are interpreted as statistically significant deviations from the Independent Action model of joint stressor effects [START_REF] Jonker | Safety evaluation of chemical mixtures and combinations of chemical and non-chemical stressors[END_REF][START_REF] Coninck | Interactive effects of a bacterial parasite and the insecticide carbaryl to life-history and physiology of two Daphnia magna clones differing in carbaryl sensitivity[END_REF]. The interaction was synergistic when the effect observed in the combined treatment was higher than the sum of the effects of the individual factors and antagonistic when the effect observed in the combined treatment was smaller than the sum of the individual effects. Treatment effects on snail mortality and oxygen consumption were tested by one-way ANOVA.

Differences between the start and end of the experiment were also assessed by one-way ANOVA to test for changes during the experiment independent of the treatment applied. All ANOVAs were performed using SPSS v.17.0.

Non-metric multi-dimensional scaling (nMDS) was performed using the PRIMER 6 program to visualise the similarity/dissimilarity between diatom communities exposed to different treatments. The analysis was based on a Bray-Curtis distance matrix created from square-root-transformed relative abundance data.

Results

Physical and chemical variables varied relatively little during the 3-week colonisation period (Table 1) and the 2-day experiment. SRP concentrations decreased over time because P was taken up by periphyton between water exchanges, but it was never depleted during the colonisation period. Water temperature during the experiment was 20.8 ± 0.3 °C, pH was 7.9 ± 0.1, conductivity was 150 ± 3 µS cm -1 and the dissolved oxygen concentration was 8.8 ± 0.3 mg L -1 (mean ± SE, n = 16 in all cases). Triclosan concentration in the triclosanexposed dishes was 11.6 ± 1.1 lg L -1 (n = 8).

No significant differences in any of the endpoints were detected between treatments at the beginning of the experiment (data not shown). However, oxygen consumption by snails increased from 0.35 ± 0.04 g O2 g -1 snail dry mass (mean ± SD) at the start of the experiment to 0.64 ± 0.18 and 0.59 ± 0.18 g O2 g -1 snail dry mass at the end in the absence and presence of triclosan respectively (F = 6.78, P = 0.03).

Grazing effects

Several effects of grazing on periphyton were observed at the end of the experiment (Fig. 1, Table 2). Algal biomass (chl a) was three times lower in the presence of snails, photosynthetic capacity and phosphorus uptake capacity increased and activities of the antioxidant enzymes CAT and APX decreased (Fig. 1, Table 2). Diatom abundance was also strongly reduced (Fig. 1) and the size-class distribution was changed (Fig. 2, Table 2). Specifically, grazing led to a decrease in the relative abundance of small diatoms and an increase in mediumsized cells (Fig. 2). The live/dead ratio of diatoms also increased (Fig. 1, Table 2), whereas the number of diatom taxa and diatom community composition were unaffected (Figs 1 & 3).

Triclosan effects

Triclosan affected both snails and algae. Snail mortality was higher in triclosan treatments (F = 8.93, P = 0.03).

The mean number of dead individuals in controls (1.3 ± 0.58 individuals) was 2.3 times lower than in dishes exposed to TCS (3.0 ± 0.8 dead individuals). GST activity of periphyton and P-uptake capacity were also reduced (Fig. 1, Table 2). A lower live/dead ratio of diatoms indicated increased diatom mortality, and diatom richness also decreased to some extent (Table 2, Fig. 2). nMDS separated diatom communities exposed to triclosan from those that were not exposed (Fig. 3), indicating that triclosan was the main factor determining diatom community composition. The relative abundance of some species was affected by triclosan (Table 2); Achnanthidium pyrenaicum increased from 6% to 10% and Nitzschia palea from 0.4% to more than 1%.

The distribution of biovolume classes and types of growth form were also affected (Fig. 2). Large species like Ulnaria ulna, Gomphonema truncatum and Gyrosigma attenuatum decreased in abundance more than twofold when periphyton was exposed to triclosan, leading to a lower proportion of large diatom cells (Fig. 2, Table 2). Similarly, linear colonies, associated particularly with Fragilariaceae and Encyonema species, were the diatom growth form affected most (Fig. 2b, Table 2).

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Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012. The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

Freshwater Biology 2016Biology doi10.1111/fwb.12797 /fwb.12797 Interactive grazer and triclosan effects Three of the measured endpoints indicated statistically significant interactions of grazing pressure and triclosan exposure (Fig. 1, Table 2). Two of these were synergistic (live/dead ratio of diatoms, abundance of mediumsized diatoms) and one was antagonistic (GST).

Table 1 Physical and chemical conditions during the periphyton colonisation period. Mean and standard error (in parentheses) are given (n=21).

Variable

Before water exchange After water exchange pH 8.3 (0.1) 7.6 (0.1) Conductivity (μS cm -1 ) 189 ( 6) 136 ( 5) Oxygen (mg L -1 ) 9.3 (0.1) 8.73 (0.1) Temperature (ºC)

19.9 (0.2) 21.9 (0.4) Cl -(mg L -1 ) 28.7 (2.1) 24.1 (1.7) NO3 -(mg L -1 ) 1.0 (0.2) 1.9 (0.1) SO4 2+ (mg L -1 ) 9.8 (0.9) 9.0 (0.8) Na + (mg L -1 ) 12.6 (3.0) 10.8 (2.4) K + (mg L -1 )

1.1 (0.4) 0.9 (0.3) Ca 2+ (mg L -1 ) 20.7 (6.0) 13.4 (4.8) Mg 2+ (mg L -1 ) 0.4 (0.1) 0.3 (0.1) SRP (µg L -1 ) 7.1 (0.8) 14.9 (3.6)

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Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012. The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

Table 2 Two-way ANOVA results of periphyton endpoints: phosphorus uptake capacity, antioxidant enzyme activities of ascorbate peroxidase (APX), catalase (CAT) and glutathione-S-transferase (GST), photosynthetic capacity (Ymax), chl-a concentration, diatom relative abundance (%), diatom richness (number of taxa), relative abundance (%) of Achnanthidium pyrenaicum and Nitzschia palea, ratio between live and dead diatoms (live-dead ratio), diatom biovolume class and growth forms.

Endpoint Grazing effect TCS effect TCS × S effect Whole periphyton

Phosphorus uptake F = 25.0, P < 0.01 Fig. 1 Mean and standard error (n = 4) of periphyton endpoints measured after 48 h of exposure to triclosan (TCS), grazers (S) or both. Endpoints specific to autotrophic periphyton: (a) Chl-a concentration (µg cm -2 ) and (b) photosynthetic capacity (relative fluorescence); endpoints of periphyton as a whole: (c) phosphorus uptake capacity (µg P cm -2 h -1 ), antioxidant enzyme activities (d) APX (nmol Na ascorbate min -1 µg -1 protein), (e) CAT (µmol H2O2 min -1 µg -1 protein) and (f) the enzyme GST (µmol CDNB conjugate min -1 µg -1 protein) and diatom endpoints: (g) diatom abundance (number of cells mm -2 ), (h) diatom taxon richness and (i) diatom viability assessed as the ratio between live and dead cells. ANOVA results are presented in Table 2.

F =

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Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012. The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

Fig. 2 Mean and standard error (n = 4) of diatom biovolume classes (a) and growth forms (b) in periphyton communities after 48 h of exposure to triclosan (TCS) and grazers (S). ANOVA results are presented in Table 2.

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Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012. The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

Fig. 3 Nonmetric multi-dimensional scaling (nMDS) ordinationdiatom communities after 48 h of triclosan exposure, based on square-root-transformed relative abundances and Bray-Curtis similarities.

Discussion

Bactericides like triclosan can influence the phosphate uptake capacity, antioxidant activities and community structure of periphyton, and these effects are aggravated by grazing.

Grazing effects on periphyton

Grazing was very intense, reducing algal biomass and shifting algal community structure to early stages of succession, as has been previously reported [START_REF] Denicola | Temporal patterns of grazer-periphyton interactions in laboratory streams[END_REF][START_REF] Underwood | Grazing interactions between pulmonate snails and epiphytic algae and bacteria[END_REF][START_REF] Steinman | Effects of grazers on freshwater benthic algae[END_REF][START_REF] Sabater | The effect of biological factors on the efficiency of river biofilms in improving water quality[END_REF]. Specifically, exposure to grazers reduced the abundance of diatoms as a whole, as generally observed for periphytic algae [START_REF] Munoz | Effects of atrazine on periphyton under grazing pressure[END_REF]. The proportion of small diatoms (biovolume < 100 lm3) also decreased under grazing pressure, whereas the proportion of medium-sized cells (biovolumes between 100 and 500 lm3) increased. Photosynthetic capacity and the viability of diatoms were also higher in grazed periphyton than in controls. These observations together with the observed reduction in CAT and APX activities in grazed periphyton might be due to a rejuvenating effect of grazing, since increases in these AEAs have been previously attributed to community ageing during periphyton succession [START_REF] Bonnineau | The use of antioxidant enzymes in freshwater biofilms: temporal variability vs. toxicological responses[END_REF]. The stimulation of P-uptake capacity of periphyton by grazing is also consistent with positive effects of herbivores on nutrient cycling related to nutrient remineralisation, which may decrease nutrient spiralling length in streams [START_REF] Mulholland | Role of nutrient cycling in streams[END_REF]. Thus, although greatly reducing algal biomass, grazing clearly had a positive effect on periphyton activity.

Triclosan effects

Triclosan affected both the snails and periphyton, but the impact was greater on the diatom community than on the grazers. Indeed, our nMDS analysis showed that although described as a bactericide, triclosan clearly affected the structure and function of periphytic algae as non-target organisms. Increased diatom mortality [START_REF] Ricart | Triclosan persistence through wastewater treatment plants and its potential toxic effects on river biofilms[END_REF][START_REF] Proia | Resistance and recovery of river biofilms receiving short pulses of triclosan and diuron[END_REF] as observed in this study, a decline in diatom richness [START_REF] Wilson | Effects of three pharmaceutical and personal care products on natural freshwater algal assemblages[END_REF] and changes in periphyton architecture as a result of shifts in the relative abundance of species and growth forms [START_REF] Lawrence | Comparative microscale analysis of the effects of triclosan and triclocarban on the structure and function of river biofilm communities[END_REF] have also been found in previous investigations. In our study, the relative decrease in large cells following triclosan exposure could be related to structural damage of the cell membrane, whose surface area increases with cell size. This interpretation is consistent with a marked effect of triclosan on filamentous green algae observed in periphyton [START_REF] Proia | Drought episode modulates the response of river biofilms to triclosan[END_REF]. However, it is unclear whether the TCSinduced decline in the P-uptake capacity of periphyton in our study was effectively caused by toxicity to algae, because similar effects have been observed in studies focusing on the bacterial component of periphyton [START_REF] Proia | Resistance and recovery of river biofilms receiving short pulses of triclosan and diuron[END_REF][START_REF] Proia | Drought episode modulates the response of river biofilms to triclosan[END_REF].

Exposure to triclosan also affected the antioxidant defence response of periphyton. Given that CAT and APX, two enzymes involved in scavenging hydrogen peroxide, were unaffected by triclosan exposure, the 30% decrease of GST in our study suggests that the mechanism of triclosan toxicity did not involve the generation of free radicals. By catalysing the conjugation of glutathione to electrophilic compounds, such as some xenobiotics and cell constituents damaged by ROS, GST can reduce toxicity and facilitate the excretion of toxicants from cells [START_REF] Hoque | Induction of glutathione S-transferase in biofilms and germinating spores of Mucor hiemalis strain EH5 from cold sulfidic spring waters[END_REF]. Thus, the reduced GST activity in our study suggests that the detoxification capacity of periphyton was reduced by triclosan exposure. This observation contrasts with data on mussels [START_REF] Canesi | Effects of triclosan on Mytilus galloprovincialis hemocyte function and digestive gland enzyme activities: possible modes of action on non target organisms[END_REF] and zebra fish larvae [START_REF] Oliveira | Effects of triclosan on zebrafish early-life stages and adults[END_REF], which showed an increase in GST activity after exposure to triclosan for 24 and 96 h respectively. In animals, triclosan may be degraded by phase II enzymes (such as GST) and thus be detoxified (Wang, [START_REF] Falany | Triclosan as a substrate and inhibitor of 30 -phosphoadenosine 50-phosphosulfate-sulfotransferase and udp-glucuronosyl transferase in human liver fractions[END_REF]. In algae or bacteria, however, triclosan sulphonation or glucuronation has not yet been demonstrated and different degradation mechanisms have been suggested (e.g. oxidation by laccase in green algae; [START_REF] Otto | Laccase-like enzyme activities from chlorophycean green algae with potential for bioconversion of phenolic pollutants[END_REF]. These differences in degradation pathways might account for the opposite responses of periphyton and animals. Nevertheless, our study indicates that triclosan decreases the detoxification capacity of periphyton. This is likely to have repercussions on the capacity of periphyton to purify water and to cope with toxicant mixtures that commonly occur in streams [START_REF] Sabater | The effect of biological factors on the efficiency of river biofilms in improving water quality[END_REF].

Triclosan also significantly increased snail mortality up to 50%. However, since snails also died in dishes without TCS (20%), this result needs to be interpreted with caution. Indeed, mortality of freshwater snails at low triclosan concentrations has not been previously detected, and the few studies reporting triclosan effects on other molluscs [START_REF] Canesi | Effects of triclosan on Mytilus galloprovincialis hemocyte function and digestive gland enzyme activities: possible modes of action on non target organisms[END_REF][START_REF] Coogan | Snail bioaccumulation of triclocarban, triclosan, and methyltriclosan in a North Texas, USA, stream affected by wastewater treatment plant runoff[END_REF]Binelli et al., 2009b) did not assess mortality. TCS killed the fish Pimephales promelas (Cyprinidae) at a concentration of 260 µg L -1 after 4 days of exposure [START_REF] Orvos | Aquatic toxicity of triclosan[END_REF] and also produced a dose-dependent toxic effect between 29 and 87 µg L -1 on zebra mussels (Dreissenidae, Binelli et al., 2009a). Furthermore, in vitro experiments with tissue extracts of different organs of the mussel Mytilus galloprovincialis (Mytilidae) found effects on kinase-mediated cell signalling, lysosomal membranes and redox balance at 289 µg triclosan L -1 [START_REF] Canesi | Effects of triclosan on Mytilus galloprovincialis hemocyte function and digestive gland enzyme activities: possible modes of action on non target organisms[END_REF]. However, genotoxicity in mollusc cells exposed to TCS for 24 h was found even at a concentration of 0.29 µg L -1 (Binelli et al., 2009b). These results suggest that snails in our study might have suffered sublethal physiological damage that was undetectable by the endpoints used in our study. However, although oxygen consumption is considered to be a sensitive endpoint for toxicity screening (e.g. [START_REF] Brodeur | Inhibition of oxygen consumption by pentachlorophenol and tetrachloroguaiacol in rainbow trout (Oncorhynchus mykiss)[END_REF][START_REF] Khangarot | Effects of copper on respiration, reproduction, and some biochemical parameters of water flea Daphnia magna Straus[END_REF], no difference in oxygen consumption was observed between treatment and control conditions, suggesting that any physiological stress of triclosan on snails was limited in our experiment.

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Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012. The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

Interactive effects of triclosan and grazing

As initially hypothesised, triclosan effects on periphyton resembled those expected in response to other factors limiting algal growth, such as nutrient or light supply. The combined effects of triclosan and grazing on diatom viability were synergistic, since the live/dead ratio in the TCS+S treatment was lower than predicted based on triclosan or snail effects alone, indicating that triclosan toxicity prevented the recovery of periphyton subject to grazing [START_REF] Burns | Potential for biofilms as biological indicators in Australian riverine systems[END_REF]. The capacity for P uptake showed a similar response pattern, possibly indicating an interactive effect of triclosan and grazing on this ecosystem function, although the interaction was not statistically significant.

A synergistic interaction was also apparent for medium-sized diatom cells. Their relative increase in the presence of both snails and triclosan was higher than predicted based on responses of periphyton to either snail or triclosan exposure. This finding, together with the synergistic interaction on diatom viability described above, indicates that triclosan not only affects the ability of periphyton to recover from grazing pressure but also exerts a selective grazing pressure on particular species. In contrast to these synergistic interactions, the detoxification capacity of periphyton exposed to triclosan and grazing simultaneously was lower than predicted when assessed as GST activity, indicating an antagonistic response. This could imply mitigation of toxicity by grazing, although firm conclusions to this effect are premature as long as the mechanistic basis of how the glutathione cycle in periphyton responds to toxicants remains poorly understood.

Overall, our results indicate complex responses of periphyton simultaneously exposed to grazing and triclosan.

In particular, triclosan exposure suppressed the rejuvenating effect of grazing on periphyton by decreasing the capacity to recover when periphyton was released from grazing. Both synergistic and antagonistic interactions were observed, although insufficiently understood, suggesting that detailed mechanistic studies of toxicant effects on species interactions are required to assess the risks of toxicant release into natural ecosystems.

  Author-produced version of the article published in FreshwaterBiology, 2016Biology, , 61 (12), 2002Biology, -2012.The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

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Author-produced version of the article published in FreshwaterBiology, 2016Biology, , 61 (12), 2002Biology, -2012.The original publication is available at https://onlinelibrary.wiley.com/doi/abs/10.1111/fwb.12797 doi : 10.1111/fwb.12797

Acknowledgements

This study was supported by the European Commission through the project Modelkey (Project 511237-2 GOCE). Additional funds were provided by the European Keybioeffects Project (MRTN-CT-2006-035695), VIECO (009/RN08/011), SCARCE (Consolider-Ingenio 2010, CSD2009-00065) and Fluvialmultistress (CTM2009-14111-CO2-01) and MPCUdG2016/120.

Author-produced version of the article published in Freshwater Biology, 2016Biology, , 61 (12), 2002Biology, -2012


Nima Hatami
email: nima.hatami@emse.fr

Yann Gavet
email: gavet@emse.fr

Johan Debayle
email: debayle@emse.fr

Classification of Time-Series Images Using Deep Convolutional Neural Networks

Keywords: Convolutional Neural Networks (CNN), Time-Series Classification (TSC), Deep Learning, Recurrence Plots (RP)

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

INTRODUCTION

A time-series is a sequence of data points (measurements) which has a natural temporal ordering. Many important real-world pattern recognition tasks deal with time-series analysis. Biomedical signals (e.g. EEG and ECG), financial data (e.g. stock market and currency exchange rates), industrial devices (e.g. gas sensors and laser excitation), biometrics (e.g. voice, signature and gesture), video processing, music mining, forecasting and weather are examples of application domains with time-series nature. [START_REF] Wang | Bag-of-words representation for biomedical time series classification[END_REF][START_REF] Hatami | Classifiers with a reject option for early time-series classification[END_REF][START_REF] Wang | Pooling sax-bop approaches with boosting to classify multivariate synchronous physiological time series data[END_REF] The time-series analysis motivations and tasks are mainly divided into curve fitting, function approximation, prediction and forecasting, segmentation, classification and clustering categories. In a univariate time-series classification, x n → y n so that n-th series of length l: x n = (x n 1 , x n 2 , ..x n l ) is associated with a class label y n ∈ {1, 2, .., c}. It is worth noting that although this paper is mainly focused on time-series classification problem, the proposed method can be easily adapted to the other tasks such as clustering and anomaly detection.

The existing Time-Series Classification (TSC) methods may be categorized from different perspectives. Regarding the feature types, "frequency-domain" methods include spectral analysis and wavelet analysis; while "time-domain" methods include auto-correlation, auto-regression and cross-correlation analysis. Regarding the classification strategy, it can also be divided into "instance-based" and "feature-based" methods. The former measures similarity between any incoming test sample and the training set; and assigns a label to the most similar class (the euclidean distance based 1-Nearest Neighbor (1-NN) and Dynamic Time Wrapping (DTW) are two popular and widely used methods of this category. [START_REF] Jeong | Weighted dynamic time warping for time series classification[END_REF][START_REF] Xing | Early prediction on time series: A nearest neighbor approach[END_REF] The latter first transforms the time-series into the new space and extract more discriminative and representative features in order to be used by a pattern classifier, which aims of the optimum classification boundaries. [START_REF] Eads | Grammar-guided feature extraction for time series classification[END_REF][START_REF] Nanopoulos | Feature-based classification of time-series data[END_REF][START_REF] Rodriguez | Interval and dynamic time warping-based decision trees[END_REF] Figure 1: From time-series signal to recurrence plot. Left: A simple example of time-series signal (x) with 12 data points. Middle: The 2D phase space trajectory is constructed from x by the time delay embedding (τ = 1). States in the phase space are shown with bold dots: s 1 : (x 1 , x 2 ), s 2 : (x 2 , x 3 ), ..., s 11 : (x 11 , x 12 ). Right: The recurrence plot R is a 11 × 11 square matrix with R i,j = dist(s i , s j ).

(CNN) is one of the most popular DL models. Unlike the traditional "feature-based" classification framework, CNN does not require hand-crafted features. Both feature learning and classification parts are unified in one model and are learned jointly. Therefore, their performances are mutually enhanced. Multiple layers of different processing units (e.g. convolution, pooling, sigmoid/hyperbolic tangent squashing, rectifier and normalization) are responsible to learn (represent) a hierarchy of features from low-level to high-level. This paper investigates the performance of Recurrence Plots (RP) [START_REF] Eckmann | Recurrence plots of dynamical systems[END_REF] within the deep CNN model for TSC. RP provides a way to visualize the periodic nature of a trajectory through a phase space and enables us to investigate certain aspects of the m-dimensional phase space trajectory through a 2D representation. Because of the recent outstanding results by CNN on image recognition, we first encode time-series signals as 2D plots, and then treat TSC problem as texture recognition task. A CNN model with 2 hidden layers followed by a fully connected layer is used.

RELATED WORK

This section briefly reviews the recent deep learning contributions on the TSC task. Application of deep learning on TSC has not been fully explored until recently. There are two main types of approaches, when it comes to application of CNN on TSC: some modify the traditional CNN architecture and use 1D time-series signals as an input, while some others first transform 1D signals into 2D matrices and then apply CNN, similar to the traditional CNN for image recognition. A time delay neural network (TDNN) model is adopted for EEG classification. [START_REF] Haselsteiner | Using time-dependent neural networks for EEG classification[END_REF] However, their model has only one single hidden layer and is not deep enough to learn hierarchical features. Lee et al. [START_REF] Lee | Unsupervised feature learning for audio classification using convolutional deep belief networks[END_REF] explored convolutional deep belief network (CDBN) for audio classification. They applied this unsupervised feature learning on frequency domain rather than in time domain. A multi-channel CNN has been proposed to deal with multivariate time-series. [START_REF] Zheng | Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks[END_REF] Each time series signal is fed into a separate CNN, and afterwards outputs of all CNNs are concatenated and fed into a fully connected MLP classifier. Instead of using the raw signal and learn features automatically, Dalto [START_REF] Dalto | Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting[END_REF] feed CNN with variables post-processed using input variable selection (IVS) algorithm. In 19 audio signals are transformed to time-frequency domain and fed into CNN. In another similar work by the same authors, CNN is applied to speech recognition within the framework of hybrid NN-HMM model. [START_REF] Abdel-Hamid | Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition[END_REF] A multi-scale CNN is proposed for TSC in [START_REF] Cui | Multi-scale convolutional neural networks for time series classification[END_REF] . In order to extract features at different scales and frequencies, it transforms signals by down-sampling and smoothing operators, followed by a local convolution stage. A deep CNN is applied on multichannel time-series signals of human activities. [START_REF] Yang | Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition[END_REF] A sliding window strategy is adopted to put time-series segments into a collection of short pieces of signals. This way, a 2D representation of a 1D time-series signal is obtained and a CNN model applied on 2D matrices (treating them similar to images).

Perhaps [START_REF] Wang | Imaging Time-Series to Improve Classification and Imputation[END_REF][START_REF] Wang | Encoding time series as images for visual inspection and classification using tiled convolutional neural networks[END_REF] is the most similar research to our work here. The Gramian Angular Fields (GAF) and Markov Transition Fields (MTF) are used to encode time-series signals as images. Afterwards, a Tiled CNN model is applied to classify the time-series images. Another similar work applied visual descriptors such as Gabor and Local Binary Patterns (LBP) on RP to extract texture features from time series, and then used SVM classifiers. [START_REF] Souza | Extracting Texture Features for Time Series Classification[END_REF][START_REF] Souza | Time Series Classification Using Compression Distance of Recurrence Plots[END_REF] The difference between the latter and our work is that they used a traditional classification framework with hand-crafted features and separated feature extraction and classification steps. While our proposed CNN-based framework, automatically learns the texture features that are useful for the classification layer. The joint learning of feature representation and classifier offered by CNN increases the classification performance.

METHODOLOGY

Time-Series to Image Encoding

Time-series can be characterized by a distinct recurrent behavior such as periodicities and irregular cyclicities. Additionally, the recurrence of states is a typical phenomenon for dynamic nonlinear systems or stochastic processes that time-series are generated in. The RP [START_REF] Eckmann | Recurrence plots of dynamical systems[END_REF] is a visualization tool that aims to explore the mdimensional phase space trajectory through a 2D representation of its recurrences. The main idea is to reveal in which points some trajectories return to a previous state and it can be formulated as:

R i,j = θ( -s i -s j ), s(.) ∈ R m , i, j = 1, ..., K (1)
where K is the number of considered states s, is a threshold distance, . a norm and θ(.) the Heaviside function. The R-matrix contains both texture which are single dots, diagonal lines as well as vertical and horizontal lines; and typology information which are characterized as homogeneous, periodic, drift and disrupted. For instance, fading to the upper left and lower right corners means that the process contains a trend or drift; or vertical and horizontal lines/clusters show that some states do not change or change slowly for some time and this can be interpreted as laminar states. [START_REF] Marwan | Recurrence plots for the analysis of complex systems[END_REF] Obviously, there are patterns and information in RP that are not always very easy to visually see and interpret.

Figure 1 shows the step-by-step instructions for calculating RP for a simple time-series signal. First the 2D phase space trajectory (m = 2) is constructed from the time-series. Then, the R-matrix is calculated based on the closeness of the states in the phase space. It is worth noting that the resulting R-matrix by Formula 1 has only {0,1} values, that caused by thresholding parameter . In order to avoid information loss by binarization of the R-matrix, this paper skips the thresholding step and uses the gray-level texture images. Inspired by the unique texture images obtained from the R-matrices, this paper proposed a TSC pipeline based on the CNN model. First the raw 1D time-series signals x n are transformed into 2D recurrence texture images, and then both features and classifier are jointly learned in one unified model.

CNN for Time-Series Image Classification

There are two aspects of any CNN model that should be considered carefully: i) designing an appropriate architecture, and ii) choosing the right learning algorithm. Both architecture and learning rules should be chosen in a way that they are not only compatible with each other, but also fit the data and the application appropriately.

Architecture. A 2-stage deep CNN model is applied here with 1-channel input of size 28 × 28 and the output layer with c neurons. Each feature learning stage is representing a different feature-level and consists of convolution (filter), activation, and pooling operators, respectively. The input and output of each layer are called feature maps. A filter layer convolves its input with a set of trainable kernels. The convolutional layer is the core building block of a CNN and exploits spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers. The connections are local, but always extend along the entire depth of the input volume in order to produce the strongest response to a spatially local input pattern. The activation function (such as sigmoid and tanh) introduces non-linearity into the networks and allows them to learn complex models. Here we applied ReLU (Rectified Linear Units) because it trains the neural networks several times faster 28 without a significant penalty to generalisation accuracy. Pooling (a.k.a. subsampling) reduces the resolution of input and make it robust to small variations for previously learned features. It combines the outputs of i-1 th layer into a single input in i th layer over a range of local neighborhood.

At the end of the 2-stage feature extraction, the feature maps are flatten and fed into a fully connected (FC) layer for classification. FC layers connect every neuron in one layer to every neuron in another layer, which in principle are the same as the traditional multi-layer perceptron (MLP). The proposed pipeline for TSC is shown in Figure 2.

Learning. Training the above CNN architecture is similar to the MLPs. Gradient-based optimization method (error back-propagation algorithm) is utilized to estimate parameters of the model. For faster convergence, the stochastic gradient descent (SGD) is used for updating the parameters. The training phase has two main steps: propagation and weight update. Each propagation involves feedforward and error back-propagation passes. Former determines the feature maps on input vector by passing from layer to layer until reaching the output (left to right in Figure 2). Latter, calculates the propagation errors according to the loss function for the predicted output (error propagates from right to left in Figure 2). Predicted error on each layer is used for calculating the derivatives by taking advantage of chain-rule of derivative. Once the derivatives of parameters obtained, the weight is updated as follows: the weight's output delta and input activation are multiplied to find the gradient of the weight. And then, a ratio (learning rate) of the weight's gradient is subtracted from the weight. This learning cycle is repeated until the network reaches a satisfactory validation error. More details on CNN architecture and learning algorithm can be found in. [START_REF] Lecun | Gradient-based learning applied to document recognition[END_REF][START_REF] Bouvrie | Notes on convolutional neural networks[END_REF][START_REF] Lecun | Efficient backprop[END_REF]

EXPERIMENTS

In order to evaluate the performance of the proposed method, the UCR time-series classification archive is used. [START_REF] Chen | The ucr time series classification archive[END_REF] For having a comprehensive evaluation of the classification algorithms, this repository contains 85 time- We have selected the same 20 datasets that the results of the other state-the-art algorithms are also reported for them (particularly considering [START_REF] Cui | Multi-scale convolutional neural networks for time series classification[END_REF][START_REF] Wang | Imaging Time-Series to Improve Classification and Imputation[END_REF][START_REF] Wang | Encoding time series as images for visual inspection and classification using tiled convolutional neural networks[END_REF] algorithms that we need to compare our results with -see Table 1). The training and testing sets are provided separate to make sure that the results of different algorithms of different studies are comparable. Furthermore, the error rates of "1-NN Euclidean Distance", "1-NN DTW(r) where r is the percentage of time series length", and "1-NN DTW (no Warping Window)" are reported as the base-line methods. Only the results of 1 -N N DT W is included in the table, because it obtains the lower error rates compared to the other two. [START_REF] Chen | The ucr time series classification archive[END_REF] Application of RP (with the phase space dimension m = 3, and embedding time delay τ = 4) time-series to image encoding on first sample of five different datasets from the UCR archive are shown in Figure 3.

For comparison purposes, "Number of Wins" and "Average Rank" are calculated for each algorithm. Number of Wins counts number of datasets that a specific algorithm obtains the lowest error rates, while Average Rank is the mean of the error-rate ranking over entire datasets. Since some error rates are missing for some datasets for some algorithms, we normalized the measures for each algorithm. An algorithm outperforms the rest if it has a highest number of wins and lowest average rank.

All experiments are carried out with Python * (using Keras) on a PC with 2.4GHz×32 CPU and 32GB memory. For training of the CNN a fixed size window as input layer is required. In the experiments 28×28, 56×56 and 64×64 pixel input, depend on the data, are used. Both layers contain 32 feature maps with 3×3 convolution (32×3×3), MaxPooling of size 2×2 and Dropout = 0.25. The fully connected neural layer contains 128 hidden neurons and c output neurons with Dropout = 0.5. A standard way to represent this architecture is C1(size)-S1-C2(size)-S2-H-O, where C1 and C2 are number of filters in first and seconds layers, Size denotes * The code for the proposed model is available on: http://sites.google.com/site/nimahatami/projects , respectively and based on their performance on the validation set. Since finding the optimal CNN parameters is still an open problem, we followed the rules of thumb to choose the parameters. [START_REF] Lecun | Efficient backprop[END_REF] Visualization of kernels in the 1st and 2nd representation layers from a CNN trained for "TwoPattern" data is given in Figure 4.

Error rate of the proposed model is given in Table 1. Besides comparing with 1-N N DT W , three state-of-the art TSC algorithms are also reported: Fast-Shapelets, [START_REF] Rakthanmanon | Fast shapelets: A scalable algorithm for discovering time series shapelets[END_REF] Bag-of-Patterns (BoP also known as BoF or BoW), [START_REF] Lin | Rotation-invariant similarity in time series using bag-of-patterns representation[END_REF][START_REF] Oates | Exploiting representational diversity for time series classification[END_REF] and SAX-VSM. [START_REF] Senin | Sax-vsm: Interpretable time series classification using sax and vector space model, 1175-1180[END_REF] Not all the performances are reported on the selected data. Therefore, there are some missing elements (shown with "-") in the Table . We have also compared our results with two recent deep CNN models, i.e. Multi-scale CNN(MCNN) [START_REF] Cui | Multi-scale convolutional neural networks for time series classification[END_REF] and GAF-MTF. [START_REF] Wang | Imaging Time-Series to Improve Classification and Imputation[END_REF][START_REF] Wang | Encoding time series as images for visual inspection and classification using tiled convolutional neural networks[END_REF] The former performs various transformations of the timeseries which extract features of different frequency and time scales embedded in a CNN framework. The latter, which has the similar pipeline to ours, first encodes time-series to images using GAF and MTF, and then applies a Tiled CNN. As shown, the proposed model obtains 10 out of 20 wins and average rank of 2.15. The second most accurate algorithm is MCNN (6 wins out of 19, and Ave.Rank = 2.36), followed by SAX-VSM (6 wins out of 19, and Ave.Rank = 3.0). The other deep CNN model that uses time-series images (GAF-MTF) ranks 5th. It highlights the fact that choosing a right time-series encoding is crucial for CNN model. Furthermore, it is observable that TFRP algorithm [START_REF] Souza | Extracting Texture Features for Time Series Classification[END_REF][START_REF] Souza | Time Series Classification Using Compression Distance of Recurrence Plots[END_REF] (using RP images in a traditional classification framework with different texture descriptors followed by a SVM classifier) ranks better than GAF-MTF. It can be explained that the RP introduces more discriminant and informative features than the GAF-MTF transform for TSC. Additionally, comparing the proposed method with TFRP highlights the fact that for TSC of RP images the CNN model is more effective than the traditional classification frameworks.

CONCLUSIONS

A novel pipeline for TSC is proposed. Taking advantage of the CNN's high performance on image classification, time-series signals are first transformed into texture images (using RP) and then handled by a deep CNN model. This pipeline offers the following advantages: i) RP enables us to visualize certain aspects of the m-dimensional phase space trajectory through a 2D images, and ii) CNN automatically learns different levels of time-series features and classification jointly and in a supervised manner. Experimental results demonstrate the superiority of the proposed pipeline. In particular, comparing with models using RP with the traditional classification framework (e.g. SIFT, Gabor and LBP features with SVM classifier [START_REF] Souza | Extracting Texture Features for Time Series Classification[END_REF][START_REF] Souza | Time Series Classification Using Compression Distance of Recurrence Plots[END_REF] ) and other CNN-based time-series image classification (e.g. GAF-MTF images with CNN [START_REF] Wang | Imaging Time-Series to Improve Classification and Imputation[END_REF][START_REF] Wang | Encoding time series as images for visual inspection and classification using tiled convolutional neural networks[END_REF] ) demonstrates that using RP images with CNN in our proposed model obtains the better results.

As future work, CNN architecture with more feature representation layers should be investigated for more difficult TSC tasks (preferably with more data samples available). Large datasets are needed in order to train a deeper architectures. Therefore, adopting the proposed pipeline for TSC with small sample sizes can be another interesting future direction. Exploring different ensemble learning methods [START_REF] Hatami | Some proposal for combining ensemble classifiers[END_REF] for CNN can be also interesting. We will particularly be investigating application of the output coding [START_REF] Hatami | Thinned-ecoc ensemble based on sequential code shrinking[END_REF][START_REF] Hatami | Ecoc-based training of neural networks for face recognition[END_REF][START_REF] Armano | Error-Correcting Output Codes for Multi-Label Text Categorization[END_REF] for CNNs.

Figure 2 :

 2 Figure 2: The proposed 2-stages CNN architecture for TSC. RP images are resized to 28x28, 56x56 or 64x64 (depend on the data) and fed into CNN model. This architecture 32(5)-2-32(5)-2-125-c consists of 2 convolution, 2 pooling, and 2 fully-connected layers.

Figure 3 :

 3 Figure 3: Application of RP (m = 3, τ = 4) time-series to image encoding on five different datasets from the UCR archive: 50words, TwoPatterns, FaceAll, OliveOil and Yoga data (from left to right, respectively).

Figure 4 :

 4 Figure 4: Visualization of kernels in the 1st (left) and 2nd (right) hidden layers from a CNN trained for "TwoPattern" data from the UCR archive.

Table 1 :

 1 Performance (in terms of the error rates) of the proposed method compared to the state-of-the art TSC algorithms on 20 selected data from the UCR archive.

	Dataset	c	Ntr	Nte	l	1-NN DTW	Shapelet BoP	SAX-VSM	TFRP MCNN GAF-MTF ours
	50words	50 450	455	270 0.31	0.44	0.46	-	0.43	0.19	0.30	0.26
	Adiac	37 390	391	176 0.39	0.51	0.43	0.38	0.20	0.23	0.37	0.28
	Beef	5	30	30	470 0.36	0.44	0.43	0.033	0.36	0.36	0.23	0.08
	CBF	3	30	900	128 0.003	0.05	0.01	0.02	-	0.002	0.009	0.005
	Coffee	2	28	28	286 0	0.06	0.03	0	0.03	0.036	0	0
	ECG200	2	100	100	96	0.23	0.22	0.14	0.14	0.17	-	0.09	0
	FaceAll	14 560	1690 131 0.19	0.40	0.21	0.20	0.29	0.23	0.23	0.19
	Face4	4	24	88	350 0.17	0.09	0.023 0	0.21	0	0.06	0
	Fish	7	175	175	463 0.17	0.19	0.074 0.017	0.12	0.05	0.114	0.085
	GunPoint	2	50	150	150 0.093	0.061	0.027 0.007	0.02	0	0.08	0
	Lightning2	2	60	61	637 0.13	0.29	0.16	0.19	0.04	0.16	0.11	0
	Lightning7	7	70	73	319 0.27	0.40	0.46	0.30	0.31	0.21	0.26	0.26
	OliveOil	4	30	30	570 0.16	0.21	0.13	0.10	0.13	0.13	0.2	0.11
	OSULeaf	6	200	242	427 0.40	0.35	0.23	0.107	0.07	0.27	0.35	0.29
	SwedishLeaf 15 500	625	128 0.20	0.27	0.19	0.25	0.04	0.066	0.06	0.06
	SyntControl 6	300	300	60	0.007	0.08	0.03	0.25	-	0.003	0.007	0
	Trace	4	100	100	275 0	0.002	0	0	-	0	0	0
	TwoPattern 4	1000 4000 128 0	0.11	0.12	0.004	-	0.002	0.09	0.17
	Wafer	2	1000 6174 152 0.02	0.004	0.003 0.0006	0.002	0.002	0	0
	Yoga	2	300	3000 426 0.16	0.24	0.17	0.16	0.14	0.11	0.19	0
	# wins	-	-	-	-	4	0	1	6	3	6	3	10
	Ave.Rank	-	-	-	-	4.05	5.75	4.15	3.0	3.37	2.36	3.40	2.15

series datasets with different characteristics i.e. the number of classes 2 ≤ c ≤ 60, number of training samples 16 ≤ N tr ≤ 8926 and time-series length 24 ≤ l ≤ 2709. The datasets are obtained from varieties of different real-world applications, ranging from EEG signals to food/beverage flavors, and electric devices to phonemes signals.

ACKNOWLEDGMENTS

This research is partially supported by the French national research agency (ANR) under the PANDORE grant with reference number ANR-14-CE28-0027.