Andrea Paluselli

Vincent Fauvelle

Natascha Schmidt

François Galgani

Sopheak Net

Richard Sempéré
email: richard.sempere@mio.osupytheas.fr

Thomas Kevin

Distribution of phthalates in Marseille Bay (NW Mediterranean Sea)

Keywords:

Estimation of DMP, DEP, DiBP, DnBP, BzBP and DEHP concentration at

Introduction

Phthalates or phthalic acid esters (PAEs) are widely used in the manufacture and processing of plastic products such as plasticizers in a very broad range of industrial applications [START_REF] Serôdio | Considerations on ultra-trace analysis of phthalates in drinking water[END_REF]Net et al., 2015a). PAEs account for approximately 92% of produced plasticizers and represent the most produced and consumed plasticizers worldwide [START_REF] Rahman | The plasticizer market: an assessment of traditional plasticizers and research trends to meet new challenges[END_REF][START_REF] He | Spatio-temporal distributions and the ecological and health risks of phthalate esters (PAEs) in the surface water of a large, shallow Chinese lake[END_REF] since these compounds are used to improve the flexibility of polyvinyl chloride (PVC) resins [START_REF] Kimber | An assessment of the ability of phthalates to influence immune and allergic responses[END_REF]Net et al., 2015b). Other industrial applications include the manufacturing of cosmetics, insect repellents, insecticide carriers and propellants (Net et al., 2015a;Net et al., 2015b;[START_REF] Xie | Occurrence and air-sea exchange of phthalates in the Arctic[END_REF]. PAEs of lower molecular weight (LMW-PAEs: C3-C6) are used as essential components of solvents, adhesives, waxes, pharmaceutical products, insecticide materials and cosmetics, whereas PAEs of higher molecular weight (HMW-PAEs: C7-C13) are instead used as additives to improve the flexibility and handling of industrial materials [START_REF] Holahan | Phthalates and neurotoxic effects on hippocampal network plasticity[END_REF][START_REF] Iarc | Some industrial chemicals, di(2-ethylhexyl) phthalate[END_REF][START_REF] Halden | Plastics and health risks[END_REF]. These compounds are suspected to enter the environment directly as emissions from household and industrial products, as released by wastewater from production and processing activity or the use and disposal of materials [START_REF] Staples | The environmental fate of phthalate esters: a literature review[END_REF] as well as from plastic polymer migration [START_REF] Andrady | Microplastics in the marine environment[END_REF].

Previous studies have shown that PAEs are endocrine-disrupting chemicals with the capability of inducing significant effects on the reproduction of various organisms, including protozoans, mollusks, crustaceans, fishes and invertebrates [START_REF] Andrady | Microplastics in the marine environment[END_REF], on the ecosystem functioning [START_REF] Kolena | Phthalate exposure and health-related outcomes in specific types of work environment[END_REF] and on human obesity and cancer development [START_REF] Okamoto | Potential risks of phthalates esters: acquisition of endocrine-disrupting activity during environmental and metabolic processing[END_REF][START_REF] Howdeshell | Mechanisms of action of phthalate esters, individually and in combination, to induce abnormal reproductive development in male laboratory rats[END_REF][START_REF] Buckley | Prenatal phthalate exposures and childhood fat mass in a New York City cohort[END_REF][START_REF] Wang | Phthalate exposure in association with serum hormone levels, sperm DNA damage and spermatozoa apoptosis: a cross-sectional study in China[END_REF]. Therefore, PAEs have become a matter of concern for their potential risk to health and the environment. Six PAEs (dimethyl phthalate (DMP), diethyl phthalate (DEP), di-n-butyl phthalate (DnBP), benzylbutyl phthalate (BzBP), diethylhexyl phthalate (DEHP) and di-n-octyl phthalate (DnOP)) have been included as priority pollutants of the US-EPA (United States Environmental Protection Agency, 2014), the European Union (EU) (Commission Staff Working Document on the Implementation of the Community Strategy for Endocrine Disrupters-A Range of Substances Suspected of Interfering with the Hormone System of Humans and Wildlife, 2007) and the Chinese water list (National Standard of the People's Republic of China, 2006). Accordingly, monitoring PAEs in the major environmental matrices has become a priority. After their introduction in the environment, they can reach marine water by wastewater and riverine inputs, and end up in the sediment via association with humic acid and adsorption onto particles and finally end up in the sediment [START_REF] Bauer | Chemodynamics, transport behavior and treatment of phthalic acid esters in municipal landfill leachates[END_REF]. Due to the physiochemical properties and biogeochemical cycling of PAEs, they have been detected in food, air, water, soil and sediment (Net et al., 2015b;[START_REF] Turner | The behaviour of di-(2-ethylhexyl) phthalate in estuaries[END_REF][START_REF] Fromme | Occurrence of phthalates and bisphenol A and F in the environment[END_REF][START_REF] Horn | Plasticizer metabolites in the environment[END_REF][START_REF] Brossa | Occurrence of twenty-six endocrinedisrupting compounds in environmental water samples from Catalogna, Spain. Environ[END_REF][START_REF] Teil | Occurrence of phthalates diesters in river of the Paris district (France)[END_REF][START_REF] Xie | Atmospheric concentrations and air-sea exchanges of phthalates in the North Sea (German Bight)[END_REF][START_REF] Xie | Development and validation of a method for the determination of trace alkylphenols and phthalates in the atmosphere[END_REF][START_REF] Zeng | Occurrence of phthalate esters in water and sediment of urban lakes in a subtropical city, Guangzhou, South China[END_REF][START_REF] Ferreira | Ex-situ bioremediation of Brazilian soil contaminated with plasticizers process wastes[END_REF][START_REF] Fu | Photochemical and other source of organic compounds in the Canadian high Artic aerosol pollution during winter-spring[END_REF][START_REF] Fu | Organic molecular composition of marine aerosols over the Arctic Ocean in summer: contributions of primary emission and secondary aerosol formation[END_REF]. However, there is a lack of data related to their sources, distribution in the whole water column as well as their transfer mechanisms from the molecular state to different living species.

The Mediterranean Sea being a semi-enclosed basin with slow turnover time of ~80 years implies a sensitive response to anthropogenic impact [START_REF] Mermex | Marine ecosystems responses to climatic and anthropogenic forcings in the Mediterranean[END_REF]. Mediterranean rivers and primarily the Rhone River supply large amounts of freshwater, particles and organic carbon to the Mediterranean Sea [START_REF] Sempéré | Carbon inputs of the Rhône river to the Mediterranean Sea: biogeochemical implications[END_REF][START_REF] Panagiotopoulos | The composition and flux of particulate and dissolved carbohydrates from the Rhone River into the Mediterranean Sea[END_REF]. There is extensive urbanization particularly along the coastline as well as a number of highly industrialized spots all along the Mediterranean basin that are concentrated mainly in the northwestern Mediterranean Sea. All these activities as well as agricultural and domestic activities [START_REF] Mermex | Marine ecosystems responses to climatic and anthropogenic forcings in the Mediterranean[END_REF]UNEP Chemicals, 2002) generate a large volume of wastewater, which provides marine litter, microplastics [START_REF] Deudero | Mediterranean marine biodiversity under threat: reviewing influence of marine litter on species[END_REF][START_REF] Collignon | Neustonic microplastic and zooplankton in the North Western Mediterranean Sea[END_REF][START_REF] Cozar | Plastic accumulation in the Mediterranean Sea[END_REF][START_REF] Faure | Plastic pollution in Swiss surface waters: nature and concentrations, interaction with pollutants[END_REF][START_REF] Pedrotti | Changes in the floating plastic pollution of the Mediterranean Sea in relation to the distance to land[END_REF][START_REF] Suaria | The Mediterranean plastic soup: synthetic polymers in Mediterranean surface waters[END_REF][START_REF] Ruiz-Orejon | Floating plastic debris in the Central and Western Mediterranean Sea[END_REF]Schmidt et al., submitted), anthropogenic molecules [START_REF] Sicre | Poly-aromatic hydrocarbon (PAH) inputs from the Rhone River to the Mediterranean Sea in relation with the hydrological cycle: impact of floods[END_REF]Sempéré et al., in press), persistent organic pollutants (POPs) and related contaminants such as polycyclic aromatic hydrocarbons (PAHs) and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) (The [START_REF] Mermex | Marine ecosystems responses to climatic and anthropogenic forcings in the Mediterranean[END_REF][START_REF] Berrojalbiz | Biogeochemical and physical controls on concentrations of polycyclic aromatic hydrocarbons in water and plankton of the Mediterranean and Black Sea[END_REF][START_REF] Castro-Jimenez | Atmospheric occurrence and deposition of polychlorinated dibenzo-p-dioxins and dibenofurans (PCDD/Fs) in the open Mediterranean Sea[END_REF] that impact the coastal marine ecosystems in unknown proportion. However, only a few studies have reported the occurrence of individual phthalates in the Mediterranean Sea [START_REF] Brossa | Occurrence of twenty-six endocrinedisrupting compounds in environmental water samples from Catalogna, Spain. Environ[END_REF][START_REF] Sanchez-Avila | Organic micropollutants in coastal waters from NW Mediterranean Sea: Sources distribution and potential risk[END_REF], and there is only one study dealing with the distribution of a series of PAEs in the Mediterranean basin (Paluselli et al., in press). The objectives of the present study are to investigate the composition and distribution of PAEs in the NW Mediterranean coastal seawater column and to ascertain their temporal trends on an annual basis.

Materials and methods

Study area, sampling, and dissolved organic carbon analyses

Seawater samples (n = 72) were collected with R/V Antedon from the SUNMEX station (Fig. 1) in the northwestern Mediterranean Sea in Marseille Bay (43°18′N, 05°22′E) from December 2013 to November 2014 at 1.5, 5, 15 and 30 m (with a bottom depth of 32-33 m) with a 12-L GO-FLO© (GENERAL OCEANICS) bottle previously rinsed with 1% hydrochloric acid and ultrapure water to avoid contamination. Immediately after sampling, the seawater samples were directly transferred from the GO-FLO© into 5-L precombusted (450 °C for 6 h) glass bottles closed with PTFE (polytetrafluoroethylene) lined screw caps, wrapped with aluminum foil and brought back to the laboratory within 4 h for processing. Four samples collected in October 2014 at Marseille Bay were already published (Paluselli et al., in press). The samples were filtrated through precombusted (450 °C for 6 h) GFF filters (47-mm filter diameter, rinsed with 2 L of Milli-Q water and 150 mL of sample prior to filtration) in a glass apparatus directly and transferred into 1-L glass bottles. After filtration, duplicate subsamples (10 mL) were collected for dissolved organic carbon (DOC) analyses with precombusted Pasteur pipettes, transferred into precombusted glass vials, poisoned with sulfuric acid to pH ~2 to avoid any biological activity, closed with PTFE-lined screw caps and stored in the dark at 4 °C. DOC concentrations were measured using a Shimadzu TOC-5000 carbon analyzer [START_REF] Sempéré | Distribution and bacterial availability of dissolved neutral sugars in the South East Pacific[END_REF]. The nominal analytical precision of the procedure was within 2%. The accuracy of the instrument and the system blank were determined by analyzing reference materials (D. Hansell, Rosenstiel School of Marine and Atmospheric Science, Miami, USA), including the Deep Seawater reference (DSR) and low carbon water (LCW) reference materials. The average DOC concentrations in the DSR and LCW reference standards were 45 ± 2 μM C, n = 24 and 1 ± 0.3 μM C, n = 24, respectively.

All glassware including Pasteur pipettes, glass bottles and glass filtration apparatus was previously cleaned in 1% hydrochloric acid bath, rinsed with ultrapure water and combusted at 450 °C for 6 h. All the GC/MS and DOC seawater analyses were conducted in duplicate in the MIO laboratory in Marseille within 6 months. Filtration of samples was conducted in the MIO ISO class 6 chemistry cleanroom (temperature: 22 °C; SAS pressure: +15 Pa; SAS brewing rate: 30 vol/h; lab pressure: +30 Pa; brewing rate: 50 vol/h).

Phthalates analyses

Seven phthalates were studied including dimethyl phthalate (DMP), diethyl phthalate (DEP), di-isobutyl phthalate (DiBP), di-n-butyl phthalate (DnBP), benzylbutyl phthalate (BzBP), diethylhexyl phthalate (DEHP) and di-n-octyl phthalate (DnOP) (Fig. 2). The deuterated internal standards were: DEP-d4, DnBP-d4, DEHP-d4. All native and labeled standards were of high purity grade (N 98%, 2000 μg mL -1 Supelco). Stock solutions were prepared in hexane and stored in the dark at 4 °C. Working solutions (unlabeled and labeled standards) were prepared by dilution of these solutions at 20 mg L -1 . All solvents were glass-distilled grade and supplied by Rathburn Chemicals Ltd. (Walkerburn, UK). Milli-Q water was produced on-site on a Milli-Q system, Millipore (Molsheim, France) with resistivity higher than 18.2 MΩ cm (25 °C) and Total Organic Carbon b 2 μg L -1 .

The extractions were performed following the method optimized for seawater PAE detection at trace levels (Paluselli et al., in press). PAEs were extracted by solid phase extraction (SPE) from seawater using a precombusted 6-mL glass reaction tube containing 200 mg of Oasis® HLB sorbent (Waters Corporation, 30 μm, 100 g). SPE was conducted on a Visiprep vacuum manifold from Sigma-Aldrich (Saint Quentin Fallavier, France). Before sample extraction, SPE cartridges were three times sequentially cleaned with 5 mL of acetone, 5 mL of dichloromethane, conditioned with 5 mL of ethyl acetate, 5 mL of acetone and 5 mL of ultrapure water prior sample processing. Seawater samples were spiked (for recovery estimate) with DEP-d4, DnBP-d4 and DEHP-d4 at 50, 200 and 400 ng L -1 , respectively, prior to loading onto SPE cartridges under Northern current is the main general circulation feature influencing the Gulf of Lyon with a geostrophic flux (calculated to 700 dbar) that varies throughout the year in a range of 0.9-1.8 Sv with its maximum in November or December [START_REF] Sempéré | Carbon inputs of the Rhône river to the Mediterranean Sea: biogeochemical implications[END_REF]. The Rhone River is the main fresh water supplier of the Mediterranean Sea with an average flow rate of 54 ± 12 km 3 year -1 with maximum values in the fall period [START_REF] Sempéré | Carbon inputs of the Rhône river to the Mediterranean Sea: biogeochemical implications[END_REF]. In the studied period, the Rhone River flux ranged from 1300 to 2800 m3 s -1 . vacuum at a flow rate of 5 to 10 mL min -1 . Then, 5 mL of ultrapure water was percolated to remove the remaining salt and air-dried for around 1 h under vacuum. PAEs were eluted into precombusted 10 mL-vials by a 2-step percolation of 3 mL of ethyl acetate. Ethyl acetate was gently evaporated to a final volume of 200 μL at room temperature under a gentle stream of nitrogen (purity N99,995%). Then, samples were immediately closed with PTFE lined screw caps rubbed with PTFE ribbon and stored before injection in the dark at -20 °C.

Analysis was performed using an Agilent Technologies 6850 GC system coupled to an Agilent Technologies 5975C mass spectrometer (GC/ MS) operated with electron impact ionization (70 eV). Chromatographic separation was achieved on an Agilent HP-5MS capillary column (30 m × 0.25 mm, 0.25 μm film thickness) with the GC oven programmed at 70 °C for 1 min and then up to 230 °C at 30 °C min -1 , to 250 °C at 8 °C min -1 and to 280 °C at 30 °C min -1 with a final isotherm hold for 7.50 min. Helium was used as a carrier gas at a flow rate of 1.1 mL min -1 . Samples were manually injected (2 μL) on a splitless mode injector with a temperature set at 250 °C. The injector (Merlin Microseal system) was used as a low carryover septum and a gas purifier (Charcoal, CP17972) to prevent contamination during the injection. Data were acquired in single ion monitoring (SIM) mode for increased sensitivity with a dwell time of 100 ms. Data were collected and analyzed with the Agilent ChemStation software.

Quality control and quality assurance

For each batch of 12 samples, a method blank, a spiked blank and a sample duplicate were processed. The retention time and the response factors of GC/MS were assessed for each analytical sequence from daily control standards to ensure the most appropriate quantification. The average recoveries of PAEs spiked in seawater samples showed acceptable recovery for all phthalates, ranging from 97% ± 3% for DEP to 110% ± 7% for DiBP. Internal standard recoveries were estimates as 94% ± 2% for DEP-d4, 77% ± 3% for DnBP-d4 and 14% ± 2% for DEHP-d4. All material was produced in borosilicate glass previously kept in an acidic bath overnight (10% hydrochloric acid), rinsed with ultrapure water, combusted at 450 °C for 6 h and rinsed with methanol and dichloromethane before use. Extractions were carried out in controlled air conditions in an ISO class 6 chemistry cleanroom (temperature: 22 °C; SAS pressure: + 15 Pa; SAS brewing rate: 30 vol/h; lab pressure: +30 Pa; brewing rate: 50 vol/h). Although precautions were considered to prevent sample contamination during the protocol, DEP, DiBP and occasionally DnBP were detected in the blanks at levels that remained below 0.07 ± 0.02 ng L -1 , 0.8 ± 0.3 ng L -1 and 0.7 ± 0.2 ng L -1 , respectively. DEHP was occasionally detected in blanks between the method detection limit (MDL) and the quantification detection limit (QDL). Limits of detection (LOD) were derived from the blanks and quantified as mean blanks plus three times the standard deviation of blanks, and they ranged from 0.11 ng L -1 for DMP to 1.67 ng L -1 for DEHP.

Statistics

Principal component analysis (PCA) was applied to reduce the multidimensional nature of the dataset and to evaluate the interrelationships among the PAEs, sites and sampling periods. PCA was performed using the individual phthalate concentrations (i.e. DEHP, DiBP) as PCA parameters. This data set was transformed in two smaller matrices that are linear combinations of the original data set. The number of observations in this study is 216, which permits the use of this statistical approach. The PCA statistics were performed using the statistical package XLSTAT 2010.2 (Addinsoft). Time series maps were made using the latest Ocean Data View version: ODV 4.7.4.

Results and discussion

General characteristics of the water column

The hydrological data (Fig. 3) are in line with previous well-known features in Marseille Bay and the local coastal area indicating that [START_REF] Serôdio | Considerations on ultra-trace analysis of phthalates in drinking water[END_REF] the water column was well mixed during winter and fall periods, most of the time under the action of wind [START_REF] Fraysse | Intrusion of Rhone River diluted water into the Bay of Marseille: generation processes and impacts on ecosystem functioning[END_REF][START_REF] Gatti | The Rhone River dilution zone present in the northeastern shelf of the Gulf of Lion in December 2003[END_REF][START_REF] Barrier | Strong intrusions of the Northern Mediterranean Current on the eastern Gulf of Lion: insights from in-situ observations and high resolution numerical modeling[END_REF], whereas (Net et al., 2015a) the water began warming in May (16-17 °C) causing water stratification. It is noteworthy that lower salinity was observed in October-November 2014 probably in association with freshwater intrusion (Fig. 3b). The DOC distribution (Fig. 3e) showed a clear seasonal trend with a concentration range of 62.5-72.5 μM in winter-spring and 72.5-90 μM during summer-fall. Higher DOC values (79-81 μM) were observed in the spring water column as well as above the bottom in winter. As a byproduct of primary production, the DOC distribution highlighted higher concentrations in spring-summer following phytoplankton growth (Fig. 3d). The high DOC concentrations near the bottom and in the whole water column during winter might instead be explained by resuspension of rich organic sediment and subsequent mixing under the action of wind as previously reported from CDOM (colored dissolved organic matter) and DOC time series studies [START_REF] Para | Fluorescence and absorption properties of chromophoric dissolved organic matter (CDOM) in coastal surface waters of the northwestern Mediterranean Sea, influence of the Rhône Rive not completer[END_REF][START_REF] Sempéré | Variability of Solar radiation and CDOM in Surface Coastal Waters of the Northwestern Mediterranean Sea[END_REF]. Such low DOC concentrations were comparable to those previously reported in open waters of the Mediterranean Sea [START_REF] Doval | Dissolved and particulate organic carbon and nitrogen in the Northwestern Mediterranean[END_REF][START_REF] Dafner | Total organic carbon distribution and budget through the Strait of Gibraltar in April 1998[END_REF][START_REF] Santinelli | Organic carbon dynamics in the Mediterranean Sea: an integrated study[END_REF][START_REF] Sempéré | Total organic dynamics in the Aegean Sea[END_REF][START_REF] Sempéré | Distribution and cycling of total organic carbon across the Almeria-Oran Front in the Mediterranean Sea: implications for carbon cycling in the western basin[END_REF], with a stable annual mean of 67 ± 7 μMC at 2 m and 63 ± 6 μMC at 5 m.

Concentration and relative abundance of PAEs in Marseille Bay

All targeted PAEs were detected in all samples collected at SUNMEX station except DnOP. During the studied period, the sum of the 6 PAEs' concentrations in the water column (ƩPAEs) ranged from 130 to 1330 ng L -1 (av. 520 ng L -1 ; Table 1; Fig. 3f) in Marseille Bay. The highest ƩPAEs concentrations were observed all along the well mixed water column in winter 2013 as well as near the bottom in the May-August and September-December 2014 periods (Fig. 3). By contrast, the lowest ƩPAEs concentrations were found in shallower waters between 0.5 and 15 m by the end of winter and in October-November 2014 where low salinities were observed (Fig. 3b). Note that the October-November 2014 period corresponds to possible freshwater intrusion into Marseille Bay [START_REF] Gatti | The Rhone River dilution zone present in the northeastern shelf of the Gulf of Lion in December 2003[END_REF] as suggested by lower salinity values (Fig. 3b). In Marseille Bay, for 62 out of 72 samples, DEHP (9.3-91.6%) was the most abundant PAE, followed by DiBP (3.8-78%) and DnBP (2.1-44.8%) (Figs. 45; Table 1). By contrast, DMP, DEP and BzBP were the least abundant (0.2-2%) and can be considered minor species here.

Very limited data are available for the occurrence and fate of PAEs as a simultaneous series in marine waters and more specifically in both the Mediterranean Sea and river waters, making any comparison of ƩPAEs data with existing studies difficult. However we found that our data are consistent with literature. Indeed, examination of already published individual PAEs, indicated that our DEHP concentrations (20-920 ng L -1 ; Table 1) were in the same range as values reported for NW Spanish coastal seawater (30-620 ng L -1 ; (Sanchez-Avila et al., 2012)) and the southern coast of the UK (100-2200 ng L -1 ; [START_REF] Turner | The behaviour of di-(2-ethylhexyl) phthalate in estuaries[END_REF]) but one order of magnitude lower than that for the Northern Mediterranean Spanish coast [START_REF] Brossa | Occurrence of twenty-six endocrinedisrupting compounds in environmental water samples from Catalogna, Spain. Environ[END_REF]. Noteworthy lower values were also reported in the North Sea for DEHP (0.5-5.3 ng L -1 ), DMP as well as DEP (0.02-4.0 ng L -1 ) [START_REF] Teil | Occurrence of phthalates diesters in river of the Paris district (France)[END_REF][START_REF] Dargnat | Phthalate removal throughout wastewater treatment plant: case study of Marne Aval station (France)[END_REF]. Similar concentrations of DEHP, 161-314 and 323-779 ng L -1 , were found in the Seine River whereas DMP concentrations were reported in the range of 2.5-5.5 ng L -1 in six Spanish rivers including Ebro River [START_REF] Sanchez-Avila | Organic micropollutants in coastal waters from NW Mediterranean Sea: Sources distribution and potential risk[END_REF]. DEP was detected in different studies in the Seine River (France) in a range slightly higher than our data, from 46 to 208 ng L -1 [START_REF] Teil | Occurrence of phthalates diesters in river of the Paris district (France)[END_REF] to 71-181 ng L -1 . [START_REF] Dargnat | Phthalate removal throughout wastewater treatment plant: case study of Marne Aval station (France)[END_REF] as well as 52-284 ng L -1 in 6 Spanish rivers [START_REF] Sanchez-Avila | Organic micropollutants in coastal waters from NW Mediterranean Sea: Sources distribution and potential risk[END_REF]. BzBP was also reported at low concentration in the range of 4.8-23 ng L -1 (Sanchez- [START_REF] Sanchez-Avila | Organic micropollutants in coastal waters from NW Mediterranean Sea: Sources distribution and potential risk[END_REF][START_REF] Dargnat | Phthalate removal throughout wastewater treatment plant: case study of Marne Aval station (France)[END_REF]. Limited data are available for the distribution of PAEs in coastal seawater.

The contribution of carbon from PAEs to the DOC pool (PAE-C) accounted for 10-60 ppm. A comparison with other organic compounds within the DOC pool identified in the coastal Mediterranean Sea indicated that our PAE concentrations are in the same order of magnitude as the reported values of dissolved water phase PAHs (3-120 ng L -1 ; [START_REF] Berrojalbiz | Biogeochemical and physical controls on concentrations of polycyclic aromatic hydrocarbons in water and plankton of the Mediterranean and Black Sea[END_REF]), one of the most abundant classes of organic contaminants in the marine environment. Lower concentration range was reported for other organic pollutant as PFCs (0.07-4.55 ng L -1 ; [START_REF] Sanchez-Avila | Organic micropollutants in coastal waters from NW Mediterranean Sea: Sources distribution and potential risk[END_REF][START_REF] Ahrens | Sources of polyfluoroalkyl compounds in the North Sea, Baltic Sea and Norwegian Sea: evidence from their spatial distribution in surface water[END_REF]) in NW Mediterranean Sea, PCB (2.2-82.4 pg L -1 ; [START_REF] Berrojalbiz | Biogeochemical and physical controls on concentrations of polycyclic aromatic hydrocarbons in water and plankton of the Mediterranean and Black Sea[END_REF]) in surface water of Mediterranean Sea and PBDE, another group of compounds used as additives in polymer, detected in Aegean Sea with concentration range of 4-6 pg L -1 [START_REF] Lammel | Air and seawater pollution and air-sea gas exchange of persistent toxic substances in the Aegean Sea: spatial trendes of PAHs, PCBs, OCPs and PBDEs[END_REF]. PCDD/Fs was reported with two orders of magnitude lower than PAEs concentration (42.5-64.0 fg L -1 ) [START_REF] Castro-Jimenez | PCDD/F and PCB multi-media ambient concentrations, congener patterns and occurrence in a Mediterranean coastal lagoon (Etang de Thau, France)[END_REF]. However, PAE concentrations are nevertheless two orders of magnitude lower than the sum of α,ω-dicarboxylic acids (20.7 ± 10.6 μg L -1 ) and ω-oxoacids (11.7 ± 6.2 μg L -1 ) in the Marseille coastal area [START_REF] Tedetti | Determination of low molecular weight dicarboxylic and ketocarboxylic acids in seawater samples[END_REF] or in the Rhone River close to the Marseille Bay (Sempéré et al., in press).

PCA analysis

The grouping of samples by depth and season using principal component analysis (PCA) formed several clusters (samples scores, Fig. 6a) over PC1 and PC2 that represent 79.7% of the total information. Samples collected at Marseille Bay formed five well-identified clusters (Fig. 6a) including the fall samples in the lower left quadrant, the spring in the central left, the summer in the upper left, winter samples in the upper right quadrant and the bottom samples in the right quadrant. Of the biomarkers plotted, DMP, DEP and DnBP being positive on PC1 and most negative on PC2 form a compact cluster on the central-right quadrant and separated from DEHP located in the upper quadrant (positive on PC1 and PC2 factors) and from DiBP in the lower quadrant (positive on PC1 and negative on PC2). DEHP is a plasticizer used in the manufacture of PVC and other plastic products. On the other hand, the presence of LMW-PAEs in the same group is related to their common use as components of solvents, adhesives, pharmaceutical products, insecticide materials and cosmetics.

In brief, the PCA score plot therefore highlights that i) summer and winter groups have a similar pattern i.e. highest concentrations of DEHP, DMP and DEP, together with lowest concentrations of DiBP; ii) fall and spring groups are also very close on Fig. 6a: their intensity on both PC1 and PC2 axis are low and negative, meaning low Σ PAEs concentration rather than a specific PAE mix; and iii) bottom samples are mainly driven by DiBP concentration, and are thus clearly separated from upper water layers whatever the season.

Potential sources and sinks of PAEs in the water column

The averaged concentrations of PAEs found in Marseille Bay (522.7 ± 313.3 ng L -1 ) samples are significantly higher than that previously found offshore (maximum depth 2500 m) (135 ± 74 ng L -1 . (Paluselli et al., in press)) suggesting that the freshwater input and urban area provide a significant amount of PAEs to Marseille Bay. Our results also indicated i) significant seasonal variations of ƩPAEs in Marseille Bay suggesting variability in sources, degradation processes of PAEs occurring in coastal seawaters, and/or partitioning of PAEs onto particulate matter; ii) heterogeneous vertical distribution by the end of winter/spring and homogenous concentration in the whole water column in the winter period with a large ƩPAEs increase of concentration above the sea floor at different periods of the year.

Seasonal variation

Concerning seasonal variation in Marseille Bay, it is interesting to note that elevated concentration of PAEs (Fig. 3f) in summer coastal seawater nearby the Marseille urban area is concomitant with [START_REF] Serôdio | Considerations on ultra-trace analysis of phthalates in drinking water[END_REF] the intense recreational coastal activities that reach their maximum during the summer on the beaches of Marseille, as well as (Net et al., 2015a) the elevated maritime traffic occurring during the tourist season between the ports of the coastal study area. As DEHP is predominant during the summer period , it is likely that such summer activities preferentially generate inputs of DEHP-rich PAE into coastal seawater. It should be noticed that DEHP is the most abundant plasticizer for PVC, PVA and rubber materials that are widely used at sea through surfing, diving and swimming or left on the beaches such as food packaging, bags and sunscreen containers. The high turnover of cruise ships could also represent a significant source of DEHP for the coastal area. In Marseille Bay, the large variations of PAE concentrations in the first 25 m of the water column could be connected to variation of input and degradation processes as well. Few PAE-degrading bacteria have been isolated from marine environments [START_REF] Gu | Degradation of the endocrine-disrupting dimethyl phthalate carboxylic ester by Sphingomonas yanoikuyae DOS01 isolated from the South China Sea and the biochemical pathway[END_REF] and a recent study suggests that the ability to degrade phthalate and its monoesters is widespread in Japanese seawater from bacteria related to 11 different genera [START_REF] Iwaki | Isolation and characterization of marine bacteria capable of utilizing phthalate[END_REF]. It is likely that prokaryotic degradation processes regulate the PAE concentration in the water column as well.

Higher concentration near the bottom

Interestingly, we found that PAE concentrations are quite elevated above the bottom being, in most cases, higher than the corresponding concentration on the sea surface (Figs. 3f-7), with the exclusion of winter 2013 and September for which PAE concentrations are similar in the whole water column probably because of winter mixing under the action of Mistral wind [START_REF] Fraysse | Intrusion of Rhone River diluted water into the Bay of Marseille: generation processes and impacts on ecosystem functioning[END_REF]. Such an accumulation of ƩPAEs near the bottom has already been reported in a previous study in offshore Mediterranean seawater at 2000 m depth (Paluselli et al., in press). A gradient in the PAE concentration might be either due to i) preferential photochemical and/or prokaryotic degradation in the surface water column rather than in deeper seawater, giving rise to accumulation of PAEs above the seafloor, or ii) PAE input directly from the bottom by pure chemical diffusion and/or advection phenomena (resuspension of sediment or bioturbation).

Surface water column photochemical degradation is certainly more effective in the first meters of the water column whereas prokaryotic degradation is likely to be more efficient above the sediment. Although PAE-degrading bacteria have been isolated from marine environments [START_REF] Gu | Degradation of the endocrine-disrupting dimethyl phthalate carboxylic ester by Sphingomonas yanoikuyae DOS01 isolated from the South China Sea and the biochemical pathway[END_REF][START_REF] Iwaki | Isolation and characterization of marine bacteria capable of utilizing phthalate[END_REF], preferential biodegradation in the water column rather than above the bottom is very unlikely. Therefore, only photochemical degradation may explain such PAE concentration gradient along the vertical gradient. This vertical gradient might also be connected to a concentration of plastic debris above the sea floor that may releases various chemical additives including phthalates into surrounding waters during their degradation as stated before. Indeed, accumulation of debris with a large proportion of plastic material has been observed several times, by trawling sampling and by video quantification based on recordings at the sea floor of the French Mediterranean coast including Marseille Bay [START_REF] Galgani | Distribution and abundance of debris on the continental shelf of the north-western Mediterranean Sea[END_REF][START_REF] Galgani | Accumulation of debris on the deep sea floor off the French Mediterranean coast[END_REF][START_REF] Galgani | Litter on the sea floor along European coasts[END_REF], the Rhone River plume area as well as canyons and continental slopes in the northwestern Mediterranean [START_REF] Fabri | Megafauna of vulnerable marine ecosystems in French mediterranean submarine canyons: Spatial distribution and anthropogenic impacts[END_REF]. In most cases, plastic bags accounted for N 90% of the total debris [START_REF] Galgani | Distribution and abundance of debris on the continental shelf of the north-western Mediterranean Sea[END_REF] and in particular in the eastern canyons of the Gulf of Lion [START_REF] Galgani | Litter on the sea floor along European coasts[END_REF] close to Marseille. Such debris are likely PAE-producer candidates. Chemical diffusion from sediment, or sediment resuspension/ bioturbation could though explain higher PAE concentration close to the sediment. Resuspension episodes of PAE-rich sediment in the water column (originating from wind-driven turbulence and hydrodynamic features [START_REF] Lapouyade | Durrieu de Madron, X. Seasonal variability of the advective transport of particulate matter and organic carbon in the Gulf of Lion (NW Mediterranean)[END_REF]) such as upwelling above the seafloor as previously reported for CDOM and DOC in Marseille Bay [START_REF] Para | Fluorescence and absorption properties of chromophoric dissolved organic matter (CDOM) in coastal surface waters of the northwestern Mediterranean Sea, influence of the Rhône Rive not completer[END_REF]) are possible. However, higher DOC concentrations were not observed close to the sediment (Fig. 3e), supporting poor physical transport from the sediment toward the water column. Transport by chemical diffusion would therefore be predominant, in case PAEs input came from the sediment.

Interestingly, in Marseille Bay bottom samples, DiBP and DnBP relative abundance values were predominant over DEHP or higher (compared to surface waters) and match the period of high concentration of total PAEs. Indeed, in surface water samples, the relative abundance of DEHP, DnBP and DiBP averaged 66 ± 18%, 11 ± 6% and 21 ± 15%, respectively, whereas these values averaged 48 ± 21%, 21 ± 12% and 32 ± 14%, respectively, in bottom samples during the year (Fig. 5). A possible explanation would be a higher degradation rate of LMW-PAEs close to the atmosphere/water interface due to higher organisms' density, as suggested by [START_REF] Yuan | Biodegradation of dibutyl phthalate and di-(2ethylhexyl) phthalate and microbial community changes in mangrove sediment[END_REF]. The predominance (or higher relative abundance) of DnBP and DiBP versus DEHP in deepwater samples is in line with previous observations at a deep offshore station (z = 2400 m) (Paluselli et al., in press). The variation of PAE relative abundance near the bottom could also be explained considering the higher hydrophobicity of DEHP (logK ow = 7.54, SPARC) compared with DnBP and DEP (logK ow = 4.63 and 2.51, respectively, SPARC), which could determine different grades of PAE repartition between sediment and water, making available a smaller dissolved fraction of DEHP and suggesting that DEP and even DnBP mainly exist under freely dissolved phase compared with DEHP. The higher desorption rate of these phthalate, with the exception of DEHP and DMP, in the interface sediment/water was already observed in a previous study [START_REF] Mackintosh | Sorption of phthalate esters and PCBs in a marine ecosystem[END_REF]. Surprisingly, a significant decrease of DEHP concentration and relative abundance was observed after the summer (October/November 2014) (Figs. 45). The change in relative abundance during fall, even on the surface, was due to an increase of DiBP and to a lesser extent DnBP and could be associated with potential resuspension as we observed only high PAE concentration in bottom samples. Note that the variable intensity of the resuspension processes might be favored by the Northern Current entering into the Gulf of Lyon and being particularly high from October to May [START_REF] Lapouyade | Durrieu de Madron, X. Seasonal variability of the advective transport of particulate matter and organic carbon in the Gulf of Lion (NW Mediterranean)[END_REF] intensifying the inputs and shelf/slope exchange processes including turbulent mixing and vertical flux on the continental slope [START_REF] Lapouyade | Durrieu de Madron, X. Seasonal variability of the advective transport of particulate matter and organic carbon in the Gulf of Lion (NW Mediterranean)[END_REF][START_REF] Monaco | Origin and variability of downward biogeochemical fluxes on the Rhone continental margin (NW Mediterranean)[END_REF]. In these circumstances exchange with the deep bottom, already observed to be rich in plastic debris, could represent a seasonal source of PAEs for the Bay of Marseille at the end of the stratification season, especially in the eastern canyons close to Marseille. This area was already noticed to accumulate most of the debris in the Gulf of Lyon, especially plastic bags [START_REF] Galgani | Distribution and abundance of debris on the continental shelf of the north-western Mediterranean Sea[END_REF][START_REF] Galgani | Accumulation of debris on the deep sea floor off the French Mediterranean coast[END_REF]. Our data together with others studies, suggest sediment resuspension as an important process explaining the heterogeneous vertical distribution. However, additional specific experiments are needed to confirm such hypothesis.

Conclusion

This study provides the first complete data on PAEs concentration levels in the Bay of Marseille (NW Mediterranean Sea). In addition, the first estimation of the PAEs annual occurrence and PAEs vertical distribution were reported here. The Marseille Bay, which is highly populated, displays levels of contamination of the same order of magnitude as other coastal area (NW Spanish coast, UK southern coast, Seine River; [START_REF] Turner | The behaviour of di-(2-ethylhexyl) phthalate in estuaries[END_REF][START_REF] Brossa | Occurrence of twenty-six endocrinedisrupting compounds in environmental water samples from Catalogna, Spain. Environ[END_REF][START_REF] Teil | Occurrence of phthalates diesters in river of the Paris district (France)[END_REF][START_REF] Sanchez-Avila | Organic micropollutants in coastal waters from NW Mediterranean Sea: Sources distribution and potential risk[END_REF][START_REF] Dargnat | Phthalate removal throughout wastewater treatment plant: case study of Marne Aval station (France)[END_REF]). DEHP was detected as the predominant PAE congener in spring/summer season at Marseille area in the whole water column, whereas DiBP and DnBP were found to be abundant when close to the seafloor and especially in fall/winter season in the study areas. DMP, DEP and BzBP represent a minor fraction of PAEs pool and DnOP was never detected in the analyzed samples. The highest PAEs concentrations were detected close to the bottom and we have hypothesized a connection with the plastic debris accumulated over the seafloor and with PAEs in the sediment.

More research regarding PAEs input from plastic, and PAEs presence in the sediment should be implemented in the future to better identify the sources of the PAEs in the water column. Furthermore, more investigation is needed on the level of PAEs transfer to food web, for a better risk assessment and management policy.

Fig. 1 .

 1 Fig.1. Area of study. The map (adapted from[START_REF] Fraysse | Intrusion of Rhone River diluted water into the Bay of Marseille: generation processes and impacts on ecosystem functioning[END_REF] shows the sampling stations in the Gulf of Lyon: SUNMEX (43°18′N, 05°22′E; depth 32 m) in the Bay of Marseille (December 2013-November 2014). Northern current is the main general circulation feature influencing the Gulf of Lyon with a geostrophic flux (calculated to 700 dbar) that varies throughout the year in a range of 0.9-1.8 Sv with its maximum in November or December[START_REF] Sempéré | Carbon inputs of the Rhône river to the Mediterranean Sea: biogeochemical implications[END_REF]. The Rhone River is the main fresh water supplier of the Mediterranean Sea with an average flow rate of 54 ± 12 km 3 year -1 with maximum values in the fall period[START_REF] Sempéré | Carbon inputs of the Rhône river to the Mediterranean Sea: biogeochemical implications[END_REF]. In the studied period, the Rhone River flux ranged from 1300 to 2800 m3 s -1 .

Fig. 2 .

 2 Fig. 2. Chemical structure of the 6 detected PAEs. Dimethyl phthalate (DMP; MW = 194.18), Diethyl phthalate (DEP; MW = 222.24), Dibutyl phthalate (DnBP; MW = 278.34), Diisobutyl phthalate (DiBP; MW = 278.34), Benzylbutyl phthalate (BzBP; MW = 312.36) and Diethylhexyl phthalate (DEHP; MW = 390.56). Figure adapted from Xie et al., 2007.

Fig. 3 .

 3 Fig. 3. a) Temperature (°C), b) salinity (PSU), c) oxygen (ml L -1 ), d) chlorophyll A (μg L -1 ), e) DOC (μM) and f) ∑PAEs (ng L -1 ) time-series at SUNMEX station (Bay of Marseille) from December 2013 to November 2014. Samples were collected at 1.5, 5, 15 and 30 m.

Fig. 4 .

 4 Fig. 4. Relative abundances for DEHP (a), DnBP (b), DiBP (c), DEP (d), DMP (e) and BzBP (f) at SUNMEX station in Marseille Bay from December 2013 to November 2014.

Fig. 5 .

 5 Fig. 5. Relative abundance of PAEs in surface (0.5 m; a) and bottom (30 m; b) seawater samples collected at SUNMEX station in Marseille Bay from December 2013 to November 2014.

Fig. 6 .

 6 Fig. 6. Principal components analysis (PCA) for PAEs in the study area was applied to reduce the multidimensional nature of the dataset and to evaluate the interrelationships among the PAEs, sites and sampling periods. The PCA statistics were performed using the statistical package XLSTAT 2010.2 (Addinsoft). In (a) the sample scores, whereas in (b) the variable loadings of the PCA. The variance accounted for by each principal component is shown in parentheses after the axis label.

Table 1

 1 Concentration ranges, average concentrations and relative abundance of PAEs in Marseille Bay (n = 72) from December 2013 to November 2014 (samples were collected at 0.5, 5, 15 and 30 m depth).

		Depth (m)	Concentrations (ng L -1 )		Relative abundance (%)
			Range	Av. ± SD	Range	Av. ± SD
			Bay of Marseilles (Dec 2013-Nov 2014)
	Dimethyl phthalate (DMP)	0.5	0.9-6.9	2.8 ± 2.2	0.2-1.4	0.6 ± 0.3
		5	0.8-6.8	2.9 ± 2.0	0.3-1.1	0.7 ± 0.4
		15	0.8-7.7	2.7 ± 2.3	0.2-0.9	0.6 ± 0.3
		30	2.1-11.9	4.5 ± 2.1	0.2-1.2	0.7 ± 0.3
		Total	0.8-11.9	3.2 ± 2.4	0.2-1.4	0.6 ± 0.3
	Diethyl phthalate (DEP)	0.5	3.4-25.7	11.8 ± 7.6	1.1-4.9	2.6 ± 1.4
		5	6.3-27.2	11.9 ± 7.9	1.1-10.2	3.4 ± 2.7
		15	3.3-45.5	13.9 ± 13.4	0.9-9.9	3.3 ± 2.7
		30	8.9-50.0	25.3 ± 12.6	0.9-5.1	3.8 ± 1.3
		Total	3.3-50.0	15.7 ± 12.5	0.9-10.2	3.3 ± 2.1
	Diisobutyl phthalate (DiBP)	0.5	34.2-215.7	89.5 ± 70.3	4.3-68.0	24.3 ± 21.9
		5	30.1-155.3	62.0 ± 37.9	3.8-78.0	23.3 ± 23.1
		15	29.4-155.1	71.1 ± 39.6	4.7-64.2	20.2 ± 18.6
		30	50.8-383.4	188.5 ± 124.8	5.0-42.5	28.0 ± 11.5
		Total	27.5-383.4	102.7 ± 90.5	3.8-78.0	23.9 ± 18.7
	Dibutyl phthalate (DnBP)	0.5	12.0-314.6	76.7 ± 66.7	2.6-26.3	11.1 ± 7.9
		5	14.0-196.5	58.3 ± 50.9	2.7-26.1	11.1 ± 7.3
		15	12.0-178.6	61.4 ± 48.9	2.8-42.1	14.2 ± 12.0
		30	21.0-596.0	173.8 ± 165.0	2.1-44.8	19.4 ± 13.7
		Total	12.0-596.0	83.2 ± 75.2	2.1-44.8	13.9 ± 10.7
	Benzylbutyl phthalates (BzBP)	0.5	2.7-4.9	3.7 ± 0.7	0.4-2.0	1.0 ± 0.6
		5	2.6-4.6	3.6 ± 0.6	0.5-1.8	1.1 ± 0.5
		15	2.6-4.6	3.6 ± 0.6	0.5-1.8	1.0 ± 0.4
		30	3.6-6.1	4.4 ± 0.9	0.4-1.5	0.8 ± 0.4
		Total	2.6-6.1	3.8 ± 0.7	0.4-2.0	0.9 ± 0.5
	Diethylhexyl phthalate (DEHP)	0.5	42.3-802.0	331.4 ± 257.1	18.5-89.3	61.0 ± 23.8
		5	15.8-714.0	317.0 ± 274.1	9.3-90.2	61.1 ± 24.7
		15	51.3-525.0	291.9 ± 221.5	21.2-91.1	61.4 ± 24.1
		30	130.0-923.8	328.6 ± 257.9	18.3-91.6	48.1 ± 23.9
		Total	15.8-923.8	317.2 ± 242.7	9.3-91.6	57.9 ± 23.8
	Total PAEs		130.8-1330.7	522.7 ± 313.3	100

Acknowledgments

This study was conducted as part of the project PLASTOX-JPI-Ocean, PlasticMicro-EC2CO and MERMEX/MISTRALS LAbex-OTMed projects and supported by 'Pole Mer Méditerranée'. This study is a contribution to the international SOLAS and LOICZ projects. We acknowledge the financial support from the PACA region, which provided a PhD scholarship for A. Paluselli. The authors are grateful to the crews of the R/V Antedon II and SAM-M I O platform for operation at sea as well as Patrick Raimbault, Nagib Bhairy for hydrological data and sampling and Anne Delmont for help in sampling and DOC analyses. Reviewers are kindly acknowledged for improving the revised version of the MS. The project leading to this publication has received funding from European FEDER Fund under project 1166-39417. A PhD scholarship for N. Schmidt was provided by Agence de l'Eau.


Omar Mohammed
email: omar-samir.mohammed@grenoble-inp.fr

Gérard Bailly

Damien Pellier

Style Transfer and Extraction for the Handwritten Letters Using Deep Learning

Keywords: Generative models, Deep Learning, Online Handwriting, Style Extraction

How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we see that it separates consistently writing styles.

Introduction

One aspect of a successful human-machine interface (e.g. human-robot interaction, chatbots, speech, handwriting . . . ) is the ability to have a personalized interaction. This affects the overall human experience, and allow for a more fluent interaction. At the moment, there is a lot of work that uses machine learning in order to learn to model for such interactions. However, most of these models do not address the issue of personalized behavior: they try to average over the different examples from different people in the training set. Identifying the human styles during the training and inference time open the possibility of biasing the models output to take into account the human preference. In this paper, we focus the problem of styles in the context of handwriting.

However, defining and extracting handwriting styles is a challenging problem, since there is no formal definition for these styles (i.e. it is an ill-posed problem). A style is both social -depends on writer's training, especially at middle school -and idiosyncratic -depends on the writer's shaping (letter roundness, sharpness, size, slope . . . ) and force distribution across time. To add to the problem, till recently, there were no metrics to assess the quality of handwriting generation.

There are two questions: what is the task itself? and what is the style used to achieve this task?. In handwriting, the task space is well defined (i.e. which letter we want to write), thus, allowing us to focus on the second part, of extracting styles for achieving this task.

In this paper, we address the problem of style extraction by using an conditioned-temporal deep autoencoder model. The conditioning is on the letter identity. The reason we use an autoencoder is that there is no explicit way that we know about to evaluate the quality of the handwriting styles other than using them to generate handwriting, and evaluate this generation. [START_REF] Mohammed | Handwriting styles: benchmarks and evaluation metrics[END_REF] introduced benchmarks and evaluation metrics in order to assess the quality of generating handwritten letters. In comparison to the those benchmarks and metrics, we achieve higher performance, while extracting a meaningful latent space.

We also hypothesize that the latent space of styles is generic, i.e. that it will generalize over unseen writers, thus achieving a "transfer of style". To test this hypothesis, we assess our model on 30 new writers. We compare the tracings generated by this model to a benchmark model already proposed for online handwriting generation.

In addition, we explore the latent space of our model for each letter separately. This revealed that there is a limited number of 'unique' styles per letter, categorical as well as continuous. We report our analysis for some of the letters, since a full analysis is out of the scope for this paper.

Thus, our contributions in this paper are the following:

• We test and compare our deep conditioned autoencoder with the state of the art benchmarks. We show that this model greatly improves the generation performance over a state of the art benchmark model.

• We experiment on performing style transfer on new writers using this model achieves, and we show that it achieves much better results than the benchmark model.

• Finally, and maybe most interestingly, we further analyze the extracted the latent space from our model to show that there is a limited number of styles for each letter and that the style manifold is not a continuous space.

2 Related work

Generative models

Recent advances in deep learning [START_REF] Goodfellow | Deep Learning[END_REF] architectures and optimization methods led to remarkable results in the area of generative models. For static data, like images, the mainstream research builds on the advances in Variational Autoencoders [START_REF] Kingma | Auto-encoding variational bayes[END_REF] and Generative Adversarial Networks [START_REF] Goodfellow | Generative adversarial nets[END_REF].

For generating sequences, the problem is more difficult: the model generates one frame at a time, and the final result must be coherent over long sequences. Recent recurrent neural networks architectures, like Long-Short Term Memory (LSTM) [START_REF] Hochreiter | Long short-term memory[END_REF] and Gated Recurrent Units (GRU) [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF], achieve unprecedented performance in handling long sequences.

Theses architectures has been used in many applications, like learning language models [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF], image captioning [START_REF] Vinyals | Show and tell: A neural image caption generator[END_REF], music generation [START_REF] Briot | Music generation by deep learning-challenges and directions[END_REF] and speech synthesis [START_REF] Oord | Wavenet: A generative model for raw audio[END_REF].

Focus was dedicated to use these powerful tool in order to extract meaningful latent space. One such work that inspired the investigation in this paper is [START_REF] Ha | A neural representation of sketch drawings[END_REF]. In their work, they investigated the problem of sketch drawing (Google, 2017) using a Variational Autoencoder. The latent space emerged encoded meaningful semantic information about these drawings. In our work, we simple a similar architecture, without the variational part, showing that similar behaviour.

Data Representation

For handwriting, a continuous coordinate representation (e.g. continuous X, Y) seems the natural option. However, generating continuous data is not straightforward. Traditionally, in neural networks, when we want to output a continuous value, a simple linear or Tanh activation function is used in the output layer of the neural network.

However, Bishop [START_REF] Bishop | Mixture density networks[END_REF] studied the limitations of these functions and showed that they can not model rich distributions. In particular, when the input can have multiple outputs (one-to-many), these functions will average over all the outputs. He proposed the use of Gaussian Mixture Model (GMM) as the final activation function of a neural network. The alliance of neural networks and GMMs is called Mixture Density Network (MDN). The training consists in optimizing the GMM parameters (means, covariances). The inference is done by sampling from the GMM distribution.

To simplify the process, and focus our study on investigating of styles, we extract two features for the tracings: directions and speed (explained in section 3), and we quantize these features. Thus, we can model each point in the letter tracings as a categorical distribution, and use a simple SoftMax function as the output of the network, which is much simpler than MDN. This was inspired by the studies done in [START_REF] Oord | Wavenet: A generative model for raw audio[END_REF], where they report impressive results on originally continuous data, using suitable quantization policy. A categorical distribution is more flexible and generic than continuous ones.

Evaluation metrics

The objective evaluation of a generative model is a challenging task, since there is no consensus for objective evaluation metrics. In many cases, a subjective evaluation is performed to overcome this problem. For handwriting of Chinese letters, [START_REF] Chang | Generating handwritten chinese characters using cyclegan[END_REF] proposed two metrics: Content accuracy and Style discrepancy. In the first metric, a classifier is trained to determine the type of the letter on the reference letters, then it is used to evaluate the generated letters. However, it is not clear how to reliably use the classifier trained on one distribution (reference letters) to evaluate new distribution (the generated letters). The second metric is not applicable to our case, since it assumes the use of Convolution Neural Network (CNN) on the image of the letter, while we us the pen sequence of drawing the letter (i.e., temporal data) with RNNs. [START_REF] Mohammed | Handwriting styles: benchmarks and evaluation metrics[END_REF]) also addressed the problem of evaluation of handwriting generation. They used the BLEU score [START_REF] Papineni | Bleu: a method for automatic evaluation of machine translation[END_REF] (a metric widely used in text translation and image captioning) and the End of Sequence (EoS) analysis (both metrics are explained in section 5). They showed that these metrics correlate with the quality of the generated letter. We use these metric in our experiments.

Dataset

In this study, we use the IRON-OFF Cursive Handwriting Dataset [START_REF] Viard-Gaudin | The ireste on/off (ironoff) dual handwriting database[END_REF], which contains isolated handwritten letters. To summarize this dataset:

• Around 700 writers in total. We use the 412 writers who have written isolated letters. • 10,685 isolated lower case letters, 10,679 isolated upper case letters, 4,086 isolated digits and 410 euro signs. • The gender, handiness (left or right handed), age and nationality of the writers. • For each example (letter, digit, euro sign), we have that example's image -with size around 167x214 pixels, and a resolution of 300 dpi -, pen movement timed sequence comprising continuous X, Y and pen pressure, and also discrete pen state. This data is sampled at 100 points per seconds on a Wacom UltraPad A4. We focused on the uppercase letters only, and we did not use the pen state or the pen pressure. The idea was to limit number the possible style factors, so that we can better study them. 90% of the data is used for training, and 10% for validation.

One challenging issue with this dataset however is that we have only one example for each writer-letter combination. This makes the task more difficult, because it is hard to extract a writer style using very few items (the 26 letters/writer in this case).

We represent each letter tracing by two features: directions and speed of the pen between each two consecutive points. Each feature is quantized into 16 levels and represented as a one-hot encoded vector. Freeman codes [START_REF] Freeman | On the encoding of arbitrary geomet-Figure 11: Examples of generated letters. The blue mark is the starting point. The traces in green is the ground truth, and the red is the generated ones by our model. ric configurations[END_REF] is used in order to encode the direction feature. It belongs to a family of compression algorithms called Chain Codes. We can use N freeman codes (where N are the number of directions), depending on the needed resolution.

Model architecture

The model architecture is illustrated in figure 1. The trace of the letter is first fed to encoder module.

The final hidden state of that module summarizes the letter. In order to allow this module to focus on learning the style embedding, we complement this last hidden state with the one-hot encoding of the letter identity, and use a projection of them as the bias input to the generator. Thus, we decouple the task space -the letter -from the style space: the encoder is free from the need to learn the letter identity, and can focus learning additional information that enables the generator to better approximate the ground truth tracings.

In the decoder, we follow the framework proposed by [START_REF] Vinyals | Show and tell: A neural image caption generator[END_REF] in order to bias the model: we create an extra time step at the beginning, which has the information we want to bias the model with. In this case, this time step is the projection of the encoder last hidden state and the letter encoder. This has a much lower dimension than encoder hidden state (the hyperparameters are discussed in section 4.1). This further encourage the model to learn only necessary style information, as suggested in [START_REF] Skerry-Ryan | Towards end-to-end prosody transfer for expressive speech synthesis with tacotron[END_REF].

Hyper-parameter tuning

We ran random hyper-parameter search for a wide range of parameters (learning rate, size and the number of layers for the encoder and the decoder, dropout percentage, etc). GRU layers [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF] is being used in this model. We use Adam [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] optimizer. In order to allow for faster exploration of different hyper-parameters, we use an early stopping of 20 epochs (no improvement happens during these epochs).

Training

The encoder and the decoder parts have the target of modeling the next time step in the sequence, x t+1 , given the previous time steps, or in other words, P(x t+1 |x 1 , x 2 , ..., x T ), where x t is the tracing point at time t, and T is the length of the input sequence. To achieve this, the model is given the ground truth input of points x1, x2, ..., x T -1 and is asked to output the sequence x2, x3, ..., x T .

The model is trained to minimize the negative log likelihood loss of the correct point at each time step. For each feature (speed and freeman codes), it is calculated as in equation 1. The final loss is the average loss of the two feature, as in equation 2. (2)

Loss = -log T ∏ t=1 p(x t |x 1 , x 2 , ..., x t-1 ) = - T ∑ t=1 log p(x t |x 1 , x 2 , ..., x t-1 ) (1)
During the training, the output of the model at each time step is the:

x g t+1 = argmax x p(x|x t , h t ) (3)
where x g t+1 is the generated/predicted next time step by the model, x t is the ground truth input at the current time step t, and h t is the hidden state of the GRU at the current time step. To sample from the model, we used the Temperature Sampling strategy from he Softmax output.

Evaluation metrics

Evaluation is a challenging problem when using generative models. We want metrics to capture the distance between the generated and the ground truth distributions. Similar to the work done in [START_REF] Mohammed | Handwriting styles: benchmarks and evaluation metrics[END_REF], we use the same two evaluation metrics in our model:

• BLEU score [START_REF] Papineni | Bleu: a method for automatic evaluation of machine translation[END_REF] It is a well known metric to evaluate text generation applications, like image captioning [START_REF] Vinyals | Show and tell: A neural image caption generator[END_REF] and machine translation [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF]. Since we discretized the letter drawings, this fits nicely within our work. The general intuition is the following: if we take a segment from the generated letter, did this segment happen in the ground truth letter? We keep doing this for segments of increasing length (the length of the segment here is the number of grams used in the BLEU score).

For our work, we report the results on segments from 1 to 3 time steps. Each part of the letter has two parallel segments: freeman codes and speed, thus, we report the BLEU score for both of them. The equation to compute the BLEU score is the following:

BLEU N = ∑ C∈G ∑ N∈C Count Clipped (N) ∑ C∈G ∑ N∈C Count(N) (4) Score N = min (0, 1 - L R L G ) N ∏ n=1 BLEU n (5)
where: G is all the generated sequences, N is the total number of N-grams we want to consider.

Count Clipped is clipped N-grams count (if the number of N-grams in the generate sequence is larger than the reference sequence, the count is limited to the number in the reference sequence only), L R is the length of the reference sequence, L G is the length of the generated sequence. The term min(0, 1 -L R L G ) is added in order to penalize short generated sequences (shorter than the reference sequence), which will deceptively achieve high scores.

• End of Sequence (EoS) The length letter is another aspect of the style. The distribution of length in the generated examples should follow the ground truth examples. In order to perform this analysis, we compute Pearson correlation coefficient between the generated examples and the ground truth data.

6 Experiments and results

Letter generation with style preservation

The objective here to compare the quality of the generated letters to the state-of-the-art benchmarks. As mentioned earlier, we compare using the BLEU score metric and the EoS analysis. The BLEU score results can be seen in table 1, and the results for EoS analysis results are in table 3. We can see that the BLEU-3 score results of our model achieves 32.3% accuracy in Speed feature and 38.7% accuracy in Freeman feature, compared to 25.1% and 28.3% accuracy using the benchmark model on both features respectively. The same goes for the EoS analysis. In comparing the Person Coefficient, our model achieves 0.99 score compared to 0.55 for the benchmark model (the highest score is 1.0). This is a support that our model capture the style of handwriting better than the benchmark.

Examples for the generated letters can be found in figure 11.

Style transfer across writers

One of the hypotheses we want to test is whether there is a limited number of styles needed, to generalize over new writers. To achieve this, the learned representation for styles should extract generic information about the styles.

In order to test this hypothesis, we expose our model to 30 writers that have not been seen before. We compare our model performance on these writers with a model is biased by the writer and letter identities (the benchmark model). The latter model was not constrained from seeing those writers (thus, the reported results of the comparison overestimates the actual performance of that model).

The BLEU scores can be seen in table 2. Our model achieves on BLEU-3 score 32.2% and 42.1% accuracy on the Speed and Freeman code features, compared to 25.3% and 27.7% on the benchmark model for the same features respectively.

The EoS analysis can be seen in table 4. Our model achieves a coefficient value of 0.99, compared to 0.5 for the benchmark. Thus, the new model clearly outperform the current benchmarks on the transfer task, on both BLEU score and EoS analysis.

Styles per letters

One of the nice consequences of using our model is that we can have a better look at the styles. We explore the latent space for multiple letters, and see that we can uncover interesting writing styles. A full scale analysis is beyond the scope of this paper. We project the latent space using Principal Components Analysis (PCA) [START_REF] Jolliffe | Principal component analysis[END_REF] and t-SNE [START_REF] Maaten | Visualizing data using t-sne[END_REF].

As a start, we take a look at letter X. Beforehand, we identified a style feature in letter X: some writer draw X clockwise, and some draw it anti-clockwise. We manually annotated the whole dataset for this feature; the result can be seen in figure 2. Almost half of the writers draw the letter X clockwise, and the other half draw it anti-clockwise. If our assumption is correct, our model should be able to capture this feature. We project the latent of the model using PCA on all the letter X, which can be seen in figure 3. The model latent space clusters almost perfectly based on rotation. Examples for letters from both clusters are in figure 4.

Encouraged by the results on letter X, we explored more letters. For letter C, we can see the latent space project in figure 5. It can be seen that there are at least two main clusters. Examples from this cluster in the red ellipse are in figure 7. The indicated cluster represents the Edwardian handwriting style. The rest of the writers (in the big cluster) have a very similar style (this is expected, since the drawing of the letter C is quite simple).

For letter A, our model latent space create two main clusters, figure 6. We give examples from those two in figure 8, where we can see clear difference in the style. Some people start drawing the letter from down-left, other writers start from the top of letter A, move down, then continue drawing of the letter.

Another example is for letter S bottleneck, figure 9. There are three resulting clusters which we investigated. The indicated cluster (in red) is clearly different from the other two clusters (not indicated). Examples can be seen in figure 10. The indicated cluster is again for people with Edwardian handwriting style. We did not find a clear difference between the other two clusters though, but this is an expected outcome of using t-SNE (since it does not have the clear objective of clustering styles).

These examples show is that we can use our model to extract verbose style information. Figure 3: Projection for latent space for letter X using PCA.

The colors show the ground truth of the X rotation: blue is counter clockwise, orange is clockwise, and the few red points are undefined.

Conclusions and future work

In this paper, we explored the concepts of styles of handwriting, using a deep neural network paradigm. We have approached the problem systematically. First, we compared our generation results to the benchmark reported in the state-of-the-art on this problem, and we show that our model outperforms the benchmark. Second, we explore the ability to perform style transfer, by testing the model's performance on 30 new writers. We hypothesize that there is a limited number of style components that describe handwriting, and a writers. Our plan is to investigate style transfer while changing the task (e.g., learn style on uppercase letters, and transfer them to the lowercase writers).

Based on the results of the latent space analysis, our next objective is to build an latent space structure and objective function that disentangle the style manifold. So far, we used multiple projection techniques in order to explore the style information in the latent space. We would like this to emerge on its own in the latent space. This step is usually known as Knowledge Restructuring, which enable the addressing of several interesting questions, like: What are all the different styles available for different letters? Can we use the styles from those different letters to build a footprint for each writer (i.e. style embedding for the writer)? If so, how good is this embedding in learning to generate letters using it as a prior knowledge only?

Figure 1 :

 1 Figure 1: Schematic diagram of the model we used. Both the encoder and the decoder have 2 layers, with size of 128. A dropout of 0.2 is used for the decoder. Learning rate selected is 0.001. During the training time, the input to the model is always the ground truth. During the inference time however, the input to the decoder (generator) part at each time step is its own predication in the previous time step.

Figure 2 :

 2 Figure 2: Results of the manual annotation for the rotation of letter X drawings over the whole dataset. Almost half the writers drew X clockwise, the other half anti-clockwise. The undefined styles were unclear to determine.

Figure 4 :

 4 Figure 4: Examples for writing of letter X. Starting point is marked with the blue mark. Each raw is randomly sampled from each cluster in the bottleneck. The clusters shows that almost half the writers draw the letter clockwise (first row, first cluster), and the other half draw it anti-clockwise (second row, second cluster).

Figure 5 :

 5 Figure 5: Projection for latent space for letter C using t-SNE. The cluster surrounded by the red circle has a clear interpretation, where writers have a cursive style.

Figure 6 :

 6 Figure 6: Projection for latent space for letter A using PCA.

Figure 7 :

 7 Figure 7: Examples for writing of letter C from the selected cluster (first row) versus the rest of the letter drawings (second row). Starting point is marked with the blue mark. The drawings from the selected cluster show people with Edwardian style of handwriting.

Figure 8 :

 8 Figure 8: Examples for writing of letter A from the selected clusters. Starting point is marked with the blue mark. Each row is from one cluster. The first row show people who start drawing the letter from the top, going down, and then continue the drawing of the letter. The second row show people who start drawing from down directly.

Figure 9 :

 9 Figure9: Projection for latent space for letter S using t-SNE. We manage to interpret the indicated cluster as the Edwardian style in drawing. The other two clusters (not indicated) did not show clear difference in the style, but this is an expected behavior from using the t-SNE algorithm, since it does not try to cluster styles as an objective.

Figure 10 :

 10 Figure 10: Examples for writing of letter S from the selected cluster (first row) versus the other two clusters (second row).Starting point is marked with the blue mark. The drawings from the selected cluster is always Edwardian style.

Table 1 :

 1 BLEU scores for different models for known writers.

	Aspect/Feature		Speed	Freeman
	Model / B-score	B-1 B-2 B-3	B-1 B-2 B-3
	Letter + Writer bias 51.5 41.4 25.1 56.7 39.4 28.3
	Style Extractor	71	51.7 32.3 65.6 51.5 38.7
	Aspect/Feature		Speed	Freeman
	Model / B-score	B-1 B-2 B-3	B-1 B-2 B-3
	Letter + Writer bias 55.4 39.6 25.3 50.2 38.6 27.7
	Style Extractor	72.4 52.4 32.2 70.4 55.6 42.1

Table 2 :

 2 BLEU scores for different models for style extraction for 30 new writers (style transfer).

	Models	Pearson coefficient
	Letter + Writer bias	0.55
	Style Extractor	0.99

Table 3 :

 3 Pearson correlation coefficients for the End-Of-Sequence (EoS) distributions for the different models on the normal generation scenario

	Models	Pearson coefficient
	Letter + Writer bias	0.50
	Style Extractor	0.93

Table 4 :

 4

Pearson correlation coefficients for the End-Of-Sequence (EoS) distributions for the different models on 30 new writers (style transfer).

Acknowledgements

This work is supported by PERSYVAL (ANR-11-LABX-0025) via the project-action RHUM.