Sondes Melliti

Ben Garali

Inès Sahraoui

Hiba Ben Othman

Abdessalem Kouki

Pablo De

La A Iglesia

Jorge Diogène

Céline Lafabrie

Karl B Andree

Margarita Fernández-Tejedor

Kaouther Mejri

Marouan Meddeb

Olivier Pringault

Asma Sakka Hlaili
email: asma.sakkahlaili@gmail.comasma.sakka@fsb.u-carthage.tn

Capacity of the potentially toxic diatoms Pseudo-nitzschia mannii and Pseudo-nitzschia hasleana to tolerate polycyclic aromatic hydrocarbons

Keywords: P. mannii, P. hasleana, PAH mixtures, Biodegradation, Eco-toxicology

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Chemical pollution of aquatic ecosystems is one of the major environmental problems because of its disastrous impacts on these environments and their resources and services [START_REF] Schwarzenbach | The challenge of micropollutants in aquatic systems[END_REF]. Polycyclic aromatic hydrocarbons (PAHs) are among the chemical contaminants that have required special attention because they are the most commonly encountered at high concentrations in the marine environment and especially in coastal waters [START_REF] Ben Othman | Structural and functional responses of coastal marine phytoplankton communities to PAH mixtures[END_REF]. PAHs are also the most hazardous pollutants because of their strong toxicity, since some of them are known as mutagens and carcinogens [START_REF] Sakari | Characterization, distribution, sources and origins of aliphatic hydrocarbons from surface sediment of Prai Strait, Penang, Malaysia: A widespread anthropogenic input[END_REF][START_REF] Ben Othman | Structural and functional responses of coastal marine phytoplankton communities to PAH mixtures[END_REF]. These pollutants have low water solubility, high hydrophobicity and low biodegradation and thus can be concentrated and bioaccumulated throughout the marine food webs and may seriously harm aquatic and human life. The primary producers represent the entry route for PAHs into the marine food webs [START_REF] Berrojalbiz | Accumulation and cycling of polycyclic aromatic hydrocarbons in zooplankton[END_REF]. Therefore, the ability of phytoplankton to accumulate PAHs can control the fate of these pollutants and their magnification in higher trophic levels.

The high persistence and toxicity of PAHs has led to intensive research on the process of their elimination and degradation. Environmental technology has employed microorganisms, such as bacteria and fungi, to remove PAHs from contaminated waters [START_REF] Gutierrez | Searching for new bacterial species that break down polyaromatic hydrocarbons in coastal and oceanic waters[END_REF][START_REF] Gargouri | Application of a continuously stirred tank bioreactor (CSTR) for bioremediation of hydrocarbon-rich industrial wastewater[END_REF][START_REF] Mansouri | Combined intervention of static magnetic field and growth rate of Microbacterium maritypicum CB7 for Benzo(a)pyrene biodegradation[END_REF]. In recent years, more attention has been paid on the role of microalgae in the biodegradation of PAHs [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF].

Although PAHs can inhibit algal growth and decrease their biomass, through the process of photosynthesis and the enzyme activities of phytoplankton [START_REF] Echeveste | Decrease in the abundance and viability of oceanic phytoplankton due to trace levels of complex mixtures of organic pollutants[END_REF][START_REF] Perez | Use of fast repetition rate fluorometry on detection and assessment of PAH toxicity on microalgae[END_REF][START_REF] Ben Othman | Toxicity of benz(a)anthracene and fluoranthene to marine phytoplankton in culture[END_REF], some microalgal species can resist the effects of these pollutants by accumulating and degrading them [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF]. However, the tolerance to PAHs varies among phytoplankton species and appears to be related to their cell biovolume [START_REF] Ben Othman | Toxicity of benz(a)anthracene and fluoranthene to marine phytoplankton in culture[END_REF]. So, large algae, such as diatoms, are recognized as tolerating organisms, since they are able to maintain growth under contamination by PAHs. Therefore, [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF] showed the capacity of two diatom species (Skeletonema costatum and Nitzschia sp.) in the accumulation and biodegradation of phenanthrene and fluoranthene, although with differing efficiencies. In addition, field experiments conducted in two coastal Mediterranean lagoons (Bizerte and Thau lagoons) revealed the resistance of the diatoms Skeletonema costatum and Chaetoceros sp. to the contamination with a cocktail of PAHs [START_REF] Ben Othman | Structural and functional responses of coastal marine phytoplankton communities to PAH mixtures[END_REF].

Within diatoms, the genus Pseudo-nitzschia has received considerable scientific attention because of the increase of the number of species producing domoic acid [START_REF] Teng | Pseudo-nitzschia kodamae sp. nov.(Bacillariophyceae), a toxigenic species from the Strait of Malacca, Malaysia[END_REF][START_REF] Sakka Hlaili | Toxic and potentially toxic diatom blooms in Tunisian (SW Mediterranean) Waters: Review of Ten Years of Investigations[END_REF]. This neurotoxin is responsible for Amnesic Shellfish Poisoning (ASP) and thus blooms of Pseudo-nitzschia represent serious threats to the environment, aquaculture shellfish industries and human health. Unfortunately, blooms of the toxic diatoms are increasing in frequency and magnitude in several coastal environments [START_REF] Loureiro | The significance of organic nutrients in the nutrition of Pseudo-nitzschia delicatissima (Bacillariophyceae)[END_REF][START_REF] Giménez | Management of domoic acid monitoring in shellfish from the Catalan coast[END_REF]. Since coastal waters can be severely contaminated by many substances, the impact of chemical pollutants on the occurrence of Pseudo-nitzschia species and their toxicity has been suspected. Interestingly, a recent work showed that resuspension of contaminated sediment caused a shift in the phytoplankton community of the Thau lagoon, with high tolerance of Pseudo-nitzschia spp after contamination [START_REF] Ben Othman | Impact of contaminated sediment elutriate on coastal phytoplankton community (Thau lagoon, Mediterranean Sea, France)[END_REF]. Moreover, toxic diatoms are known for their resistance to metal pollutants, such as copper and iron [START_REF] Lelong | Pseudo-nitzschia (Bacillariophyceae) species, domoic acid and amnesic shellfish poisoning: revisiting previous paradigms[END_REF]. These species can use complexation strategy by increasing domoic acid production to avoid metal contaminants.

However, the responses of Pseudo-nitzschia to PAHs and persistent organic pollutants have never been addressed. The PAH response of several diatom species (Nitzschia brevirostris Cyclotella caspia, Phaeodactylum tricornutum and Thalassiosira pseudonana) were reported in field and laboratory studies [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF][START_REF] Croxton | The use of flow cytometric applications to measure the effects of PAHs on growth, membrane integrity, and relative lipid content of the benthic diatom, Nitzschia brevirostris[END_REF][START_REF] Niehus | Miniaturised marine algae test with polycyclic aromatic hydrocarbons -Comparing equilibrium passive dosing and nominal spiking[END_REF].

Nevertheless, there remains a considerable lack on data concerning potentially toxic diatoms.

Therefore, the eco-toxicology impact of the organic pollutants on potentially toxic diatoms must be well understood and described in order to improve our knowledge of the initiating factors of their blooms and their toxicity.

In the lagoon of Bizerte (SW Mediterranean), diatoms of the genus Pseudo-nitzschia showed pronounced diversity and occurrence throughout the year [START_REF] Sahraoui | Toxic and potentially toxic Pseudo-nitzschia populations in Bizerte Lagoon (Tunisia, SW Mediterranean) during 2006-2007, and first report of domoic acid production by Pseudo-nitzschia brasiliana Lundholm, Hasle, Fryxell[END_REF][START_REF] Sahraoui | Rresponse of potentially toxic Pseudo-nitzschia (Bacillariophyceae) populations and domoic acid to environmental conditions in an eutrophied, SW Mediterranean coastal lagoon (Tunisia)[END_REF][START_REF] Sakka Hlaili | Toxic and potentially toxic diatom blooms in Tunisian (SW Mediterranean) Waters: Review of Ten Years of Investigations[END_REF]. In this coastal ecosystem, where PAHs contamination has been reported [START_REF] Lafabrie | Contaminated sediment resuspension induces shifts in the structure and the functioning of phytoplankton in an eutrophic Mediterranean lagoon[END_REF], Pseudo-nitzschia spp. bloomed at high levels in different periods of the year (>10 6 cells l -1 ; Bouchouicha [START_REF] Bouchouicha Smida | Population dynamics of potentially harmful algal blooms in Bizerte Lagoon, Tunisia[END_REF][START_REF] Sakka Hlaili | Toxic and potentially toxic diatom blooms in Tunisian (SW Mediterranean) Waters: Review of Ten Years of Investigations[END_REF], suggesting that this genus could be resistant to PAHs. Furthermore, our previous work has described two species (P. hasleana and P. mannii) for the first time in the south Mediterranean Sea and gave the first report of their potential toxicity in culture (Melliti Ben Garali, 2016). Since the tolerance to PAHs contamination may be a factor contributing to the proliferation of Pseudo-nitzschia spp., our study's purpose was to investigate the responses of the two species recently observed in the Bizerte lagoon to PAHs stress. In polluted marine environment, PAHs are commonly encountered in a mixture and can act in an antagonistic or synergistic manner. Moreover, separate PAHs and PAHs in mixture do not usually induce the same effects and thresholds on algal species [START_REF] Lei | Removal of fluoranthene and pyrene by different microalgal species[END_REF]10 Echeveste et al., 2010).

Thus, the specific objectives of the study are (i) to assess the impact of increasing concentrations of a PAHs mixture on the growth, the photosynthetic parameters and the biovolume of Pseudo-nitzschia mannii and P. hasleana, (ii) to determine if a link between the toxigenic potential of species and PAHs exists, and (iii) to examine the species ability for degradation and accumulation of these pollutants. The PAHs mixture used in this study was formed by 15 molecules, which were chosen based on the actual contamination of the Bizerte lagoon.

Materials and methods

Isolation, identification and culture of species

Using a 20 µm mesh plankton net, strains of diatoms were collected from the lagoon of Bizerte located in the north of Tunisia (SW Mediterranean Sea). The two Pseudo-nitzschia species were purified and cultured in f/2 medium [START_REF] Guillard | Culture of phytoplankton for feeding marine invertebrates[END_REF] under non-axenic conditions. The cultures were maintained in a thermostatic chamber at 22 o C illuminated with cool-white fluorescent tubes at light intensity of 100 µmol photons m -2 s -1 and under a photoperiod of 12 light:12 dark. The identification of species was done using a transmission electron microscopy (TEM). Details were reported in (Melliti Ben Garali et al., 2016).

Preparation of PAH mixtures

A previous record showed the presence of 15 PAHs (anthracene, pyrene, benzo(a)pyrene, benz(a)anthracene, chrysene, fluoranthene, indeno(1,2,3-cd)pyrene, naphthalene, phenanthrene, benzo(b)fluoranthene, benzo(g,h,i)perylene, benz(k)fluoranthene, acenaphthene, fluorene and dibenzo(a,h)anthracène) in the sediments of the Bizerte Lagoon.

The specific in situ concentrations of each PAH were reported in [START_REF] Lafabrie | Contaminated sediment resuspension induces shifts in the structure and the functioning of phytoplankton in an eutrophic Mediterranean lagoon[END_REF].

Based on these concentrations, stock solutions of each type of PAH were prepared by dissolving pure chemicals in reagent grade dimethylsulfoxide (DMSO, 0.05%) without exceeding the solubility threshold.

Then, the various stock solutions were mixed to obtain a first mixture solution containing 4 % of every type of pure PAHs (dissolved concentration in w/v compare to the actual PAH concentrations in sediment in w/w) of every type of pure PAH. Finally, this solution (of 4% of PAHs mixture) was diluted with the DMSO to obtain three other cocktail solutions with 2%, 0.5% and 0.1% of PAHs. These 4%, 2%,0.5% and 0.1% values correspond respectively to a 8/100, 2/100, 1/100 and 0.1/100 (mg/L) of the total PAHs present (mg/kg dry weight) in the Bizerte sediments. The choice of 4 percentages of present PAHs was arbitrary.

The theoretical concentrations, which correspond to every cocktail, were presented in Table 1.

The PAHs solutions were kept in dark and cold (4°C) until the day of their uses during the contamination experiment. All chemicals (DMSO and PAHs; purity>97%) were purchased from Sigma-Aldrich.

Experimental set-up

For each Pseudo-nitzschia species, 12 glass Erlenmeyer flasks, containing 2 L culture medium, were prepared and autoclaved. Before their use the flasks have been rinsed with 10% HCl solution. After sterilization, 3 µl of one of the four PAH cocktails were added to three Erlenmeyer flasks to obtain four contaminated treatments in triplicates; C1, C2, C3 and C4 which received respectively 0.1, 0.5, 1 and 4 % of PAH cocktails. Three flasks without PAHs were used as control treatment (C) and three others have received only the DMSO (at 0.05 % v/v). This control treatment with DMSO (C DMSO ) was prepared to test the toxicity of DMSO at 0.05 % on the diatom species. Pseudo-nitzschia cells reaching exponential growth phase were then inoculated to each flask to obtain an initial concentration of 27 10 7 cells l -1 . The 36 Erlenmeyer flasks, prepared for both species, were finally incubated for 144 h in the thermostatic chamber under the same environmental conditions described above. The light intensity and the temperature within the incubator were checked throughout the experiment using a spherical quantum mini-recorder LI-250 A and Thermometer (Model TH-020), respectively. Sub-samples were taken from all treatments during incubation period for several measurements, as described below.

Sampling and analyses

PAH measurement

PAHs were analyzed in all treatments at the beginning, 72 h and 144 h of the experiment and every day in the treatment C3. Samples were filtered by glass fiber filters and the filtrates were used to analyze the dissolved phase PAHs in Pseudo-nitzschia culture. For this, each filtrate sample was gathered by solid-phase extraction (SPE) with a C18 column (preconditioned with 10 ml DCM, 10 ml methanol and 10 ml ultrapure water two times) at a flow rate of 5 ml•min -1 . Then, PAHs were eluted from the column with 12 ml DCM. The extraction was concentrated to 0.5 ml using Termovap Sample Concentrator in a water bath (45℃) and analyzed with GC-MS. To analyze particulate PAHs, the GF/F glass fiber filters were freeze-dried and weighed. The dried filters were extracted with 150 ml of the mixed solvent n-hexane and DCM (1:1, v/v) for 24 hrs. Extractions were purified by a self-packet silica-alumina column (from top to bottom: anhydrous sodium sulphate, 1 cm; the mixture of silicone and n-hexane, 10 cm; anhydrous sodium sulphate, 1 cm; a few glassfiber). Then, The PAHs eluent was concentrated to 0.5 ml for GC-MS analysis.

Domoic acid measurement

According to the analysis of DA in shellfish and seawater (particulate), certificate calibration solution of DA (CRM-DA-f, 101.8 ± 2.1 μg mL -1 ) was obtained from the Measurement Science and Standards, National Research Council of Canada. HPLC gradient grade and LC-MS hypergrade acetonitrile, methanol and formic acid were purchased from Merck (Darmstadt, Germany). Milli-Q water was obtained from a Millipore water purification system (Bedford, MA, USA).

For particulate domoic acid (pDA), samples (100 ml) were taken at the beginning and the end of incubation and filtered through GF/F filters (Whatman) and processed according to a rapid resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) method (de la [START_REF] De La Iglesia | Determination of dissolved domoic acid in seawater with reversed-phase extraction disks and rapid resolution liquid chromatography tandem mass spectrometry with head column trapping[END_REF], with slight modifications (see Melliti Ben Garali et al., 2016).

2.4.3.Chl a analysis and cell counting

Samples (20 ml) were taken every day and filtered on Whatman GF/F filters. Chl a was extracted at 90% acetone (v/v) for 30 h in the dark at 4 o C. The pigment concentration was measured using the spectrophotometric method and following the procedure given by [START_REF] Parsons | Biological oceanographic, 3rd edn[END_REF].

Other samples (1 ml) were taken every day of the incubation and were fixed with Lugol's acid solution (3% final concentration). Cell counting was performed in triplicate, on aliquots of 5 μl, deposited between slide -coverslip and observed under light microscopy BX-102 A at × 40 magnification [START_REF] Lundholm | Effect of pH on growth and domoic acid production by potentially toxic diatoms of the genera Pseudo-nitzschia and Nitzschia[END_REF].

Photosynthetic efficiency of PSII

Samples (10 ml), collected throughout the experiment, were incubated for 20 min in the dark. The maximum quantum efficiency of PSII (Fv/Fm) was analyzed by Aqua-Pen C (manufactured by Photon System Instruments, www.psi.cz). A blue LED (450 nm) excites a sample of suspended cell by a saturating flash (2000 µE m -2 s -1 ). The induction of chlorophyll fluorescence (680 nm) was then measured for one second at a time scale from 10 to 100 µs, depending on OJIP protocol of the manufacturer. This protocol allows the parameters F0 and Fm to be obtained, which represent, respectively, the minimum and maximum yield of Chl a fluorescence measured on dark-adapted samples. Then, both parameters were used to calculate the potential photosynthetic efficiency (Fv/Fm, with Fv = Fm -F0).

Calculation

Growth rate

For the various treatments, the net growth rates of Pseudo-nitzschia species were estimated using the exponential model of [START_REF] Landry | Estimating the grazing impact of marine microzooplankton[END_REF],

N t = N 0 e μt (1) Ln (N t / N 0 ) = µ t (2)
Where N 0 and N t were the abundance of Pseudo-nitzschia at the beginning and at 72 h of incubation, respectively; μ (d -1 ) is the net cell growth rate; and t (d) is the incubation time. µ is the slope of the line from the linear regression of ln (N t / N 0 ) against time of the form y = a x.

Biovolume

The dimensions of diatoms (at least 50 cells for each species) were measured using a calibrated ocular micrometer and biovolume (V, µm 3 ) were estimated by applying standard geometric formulae to each species, as proposed by [START_REF] Hillebrand | Biovolume calculation for pelagic and benthic microalgae[END_REF]. We assumed that P. mannii and P. hasleana have a shape of a prismatic geometric shape:

V (μm 3 ) = [(length of the cell) / 3 * π * (width /2)] * 2

Percentages of PAHs accumulation, biodegradation and in the medium

The percentages of dissolved PAHs (i.e. in the medium, %PAH diss ) and accumulated in the cells (%PAH acc ) as well as degraded (%PAH deg ) were calculated as follows [START_REF] Chan | Removal and biodegradation of polycyclic aromatic hydrocarbons by Selenastrum capricornutum[END_REF],

Ecotoxicological values

EC50s (concentrations inducing a response halfway between the control and the maximum toxicity effect) were used to compare available data on the toxicity of PAHs mixture to microalgae. They were determined using the REGTOX macro for Excel developed by E. Vindimian [START_REF] Arzul | In vitro test-based comparison of pesticide induced sensitivity in marine and freshwater phytoplankton[END_REF]. Optimal EC50s were selected and 95% confidence limits were given for each calculated value.

Statistical analysis

A two-factor parametric variance analysis (ANOVA) (treatment and time) was used to test the effect of contamination with the PAH cocktails on all variables measured for both species. When the effect was significant, a multiple comparison posterior test (Tukey test) was performed to determine which contamination had a significant difference from the control. When the normality of the data distribution (Kolmogorov-Smirnov test) and/or the homogeneity of the variances (Bartlett-Box test) were not verified, a non-parametric ANOVA analysis (Kuskal-Wallis) was used. All statistical analyzes were performed using SPSS18.0 Software

Results

Impact of PAHs on growth of Pseudo-nitzschia species

The cell abundances of Pseudo-nitzschia species and Chl a concentrations measured throughout the incubation did not vary significantly between the two controls (ANOVA test)

[negative control without DMSO (treatment C) and positive control with DMSO (treatment C DMSO )]. Furthermore, both species showed similar proliferation and growth rates (1.80 -2.04 d -1 , Table 2) between the two control treatments (C and C DMSO ), their abundance increasing gradually throughout the experiment (Fig. 1). This indicates that the 0.05% v/v of DMSO, used to prepare the PAH cocktails, had no toxic effect on the diatoms. Under the various contaminations, the diatoms were able to maintain continuous growth throughout the incubations, but with lower rates compared to the control (Table 2).

For each species, the PAH-treated cells did not totally lose their viability as fluorescence was still observed. However, their biomasses were significantly reduced compared to that of the control (Fig. 1A). In the P. hasleana culture, treatment C1 induced a significant (P <0.05) decrease in the Chl a compared to control after 48 h (reduction of 10.72%). At the end of the incubation, a decrease of 37% was observed (Fig. 1A). The other treatments resulted in a rapid decrease of Chl a (during the first 24 hours), with a reduction of 20 % for C2 and 40% for the contaminations C3 and C4 (Fig. 1A). After 144 h, Chl a concentration in treatments C2, C3 and C4 were 50, 56 and 90% lower than those of the controls, respectively (Fig. 1). A dose-dependent effect was also detected for the P. mannii culture. Indeed, the decrease of Chl a was inversely proportional to the level of contamination (Fig. 1B). At the end of treatments, reductions of 23%, 55%, 67% and 90% were noted in treatments C1, C2, C3 and C4, respectively (Fig. 1B).

For P. hasleana, all PAH cocktails induced reduction (40% of control) of the cell concentrations at the beginning of the experiment. At the end of incubation, the number of cells was reduced by about 53% in the C1 treatment and by about 75% in the other contaminated treatments (Fig. 1C).This species exhibited growth rates 2, 3 and 4 times lower than the control in treatments C1, C2 and C3/C4, respectively (Table 2). The same trend was observed for P. mannii, with 31% reduction in first day of incubation. After 144 h, the PAH contaminations caused decreases of 38, 57, 58 and 79% in the C1, C2, C3 and C4 treatments, respectively (Fig. 1D). Under C1 and C2 treatments, this species proliferated two times as slow as untreated cells, whereas under heavy contamination (C3 and C4), its growth was three times lower than that of the control (Table 2).

Impact of PAHs on the physiology of Pseudo-nitzschia species

As observed for growth, 0.05% DMSO did not have a significant effect on the efficiency of the PSII, since similar values of Fv/Fm (on average 0.4) were measured in control with or without DMSO throughout both experiments (Fig. 2). When P. hasleana was exposed to C1 and C2 contaminations, the Fv/Fm (mean value 0.7) did not show a significant difference (P>0.05) compared to control. On the contrary, under higher contamination (C3, C4), the Fv/Fm dropped abruptly during the first 24 hours, with a 100% reduction compared to the control (Fig. 2A). For P. mannii, only the highest contamination (C4) induced a negative effect on the PSII efficiency. During the first 48 h of this treatment, the Fv/Fm was reduced by 24% relatively to the control, but by 100% after 72 h (Fig. 2B). The EC50s measured for this photosynthesis parameter after 72 h of contamination were 135 and 115 μg PAHs l -1 for P. mannii and P. hasleana, respectively.

The two species showed similar cell biovolume (≈ 210μm 3 ) in both control treatments (C and C DMSO ) at the beginning (0 h) and at the end (144 h) of the incubation. After 144 h of PAH exposure, the biovolumes of both species were significantly enhanced (Fig. 3). This increase was more pronounced in P. mannii (16-35% of the initial volume) than in P. hasleana (11-17% of initial volume). Note that for each species, the biovolume has increased proportionally with the level of contamination.

The strain of each Pseudo-nitzschia species used in the manipulations was tested negative for production of domoic acid. The concentrations of this neuro-toxin measured at the end of the incubation in the different contaminations were below the instrumental limit of quantification (LQA = 0.1 ng ml -1 ).

Fate of PAHs during incubation

The concentrations of PAHs measured in each treatment at the beginning of the experiment were compared with those used theoretically for contamination (Table 1). The results showed that the measured levels were close to 95% of the theoretical concentrations.

In addition, the measured and theoretical concentrations were linear for the concentration ranges of all the samples (n = 12). Therefore, the measured concentrations were used in the remainder of the results.

The daily monitoring of PAHs in the C3 treatment (contaminated with 2% of the PAH cocktail), carried out for both species, showed a gradual drop in dissolved PAH concentrations from the beginning (28 -28.7 μg l -1 ) to the end of incubations (Fig. 4A).

Despite this decrease, the final levels of dissolved PAHs were 2.3 to 5.1 μg l -1 , indicating that both Pseudo-nitzschia species were maintained in contaminated media until the end of the experiments. In contrast, concentrations of particulate PAHs increased throughout the experiments (from 2.1 to 14 μg l -1 for P. hasleana and from 0.8 to 15.5 μg l -1 for P. mannii) (Fig. 4B).

In each treatment, the percentage of dissolved PAHs (%PAH diss ), accumulated PAHs and degraded PAHs varied significantly throughout the incubation time for both species (Figs. 5 and6). The %PAH diss dropped significantly (P<0.05) from the start to the end of incubation, in all treatments. It decreased from 100% to 21 -45% and to 17 -29% for P. hasleana and P. mannii, respectively (Figs. 5B,6B). In all treatments, there were more PAHs accumulated in the cells at 144 h (30 -57% and 25-59%) than at 72 h (12-26%and 10-35%) (Figs. 5A,6A).

The PAH degradation generally increased over time in most treatments and for both species. The highest %PAH deg (51%) was observed for P. mannii contaminated by C4 cocktail at the end of incubation (Figs. 5C, 6C).

Discussion

Impact of PAHs on the growth of Pseudo-nitzschia species

In control microcosms (C and C DMSO ), both species proliferated well with fairly similar growth rates (1.83 -2.04 d -1 , Table 2). Their biomasses and abundances gradually increased from the beginning to end of the experiment (Fig. 1). At 144 h, the concentrations reached by these two species were 10 times higher than the initial densities. This indicates that all the physico-chemical conditions (temperature, light, nutrients, etc.) under which the experiments were conducted were adequate for an optimal growth of diatoms.

The contamination of the two species by the different concentrations of the PAH cocktails induced a decrease in their growth (Table 2) resulting in a significant reduction in their biomasses and abundances (Fig. 1). This result is consistent with other ongoing work on the adverse effect of PAHs in mixture on phytoplankton growth [START_REF] Djomo | Toxic effects of some major polyaromatic hydrocarbons found in crude oil and aquatic sediments on Scenedesmus subspicatus[END_REF][START_REF] Vieira | Multiple stress effects on marine planktonic organisms: influence of temperature on the toxicity of polycyclic aromatic hydrocarbons to Tetraselmis chuii[END_REF] and more on that of diatoms (Nayar et al., 2005). It seems that the silicate present in the frustules of the diatoms allows the adsorption and the retention of the PAHs, which can provoke the alteration of the cell membrane by these molecules and therefore their penetration into the cytoplasm, resulting in a decrease in cell growth.

The negative effect of PAHs was dose-dependent, as both species proliferated with rates that were lower as the level of contamination increased (Table 2). In addition, decrease in Chl a and cell abundance was proportional to the PAH concentrations (Fig. 1). However, the dose-dependent impact of PAHs on the algal growth was different between both species. In the short term (i.e. 24 h after contamination), the treatments C2, C3 and C4 induced more pronounced decrease in Chl a and cell densities for P. hasleana (20 -42% and 37 -40%, respectively) than for P. mannii (20 -30% and 31-35%, respecttively). Moreover, at 144 h, P. hasleana showed more reduced final cell concentration under all contaminations (53 -77%) than P. mannii (38 -75%). The dose-dependent impact of PAHs on microalgae has been previously reported by several authors [START_REF] Echeveste | Decrease in the abundance and viability of oceanic phytoplankton due to trace levels of complex mixtures of organic pollutants[END_REF][START_REF] Ben Othman | Structural and functional responses of coastal marine phytoplankton communities to PAH mixtures[END_REF].

Previous work that examined the impact of increasing concentrations of PAH cocktails on different microalgal species, often reported complete inhibition of phytoplankton growth and even lethal status under high levels of contamination [START_REF] Ben Othman | Structural and functional responses of coastal marine phytoplankton communities to PAH mixtures[END_REF]. This was not observed during our experiment, indeed, both species-maintained growth even under the highest concentration of PAHs tested (i.e. C4). Moreover, after 144 h, there were approximately 50 10 6 ml -1 cells of each species in the C4 microcosms (Fig. 1), suggesting that P.mannii and P. hasleana can tolerate these organic pollutants. However, the first species seemed to be more tolerant than the second. Under the highest contaminations, P mannii proliferated a little more quickly than P. hasleana (Table 2) and as mentioned above, the decreases in final cell concentrations were less pronounced for P. mannii than for P. hasleana (Fig. 1). The tolerance of both Pseudo-nitzschia species to various PAH cocktails and the difference in the degree of this tolerance between them may be related to physiological adaptations that allowed the microalgae to overcome the toxic effects of PAHs.

Impact of PAHs on physiology of Pseudo-nitzschia species

In addition to biomass and growth, our results revealed that PAHs also had an impact on the physiology of both species, as the effectiveness of their photosystem II was also negatively affected by these contaminants. However, the effect was variable among PAH cocktails and between species. The treatments C1 and C2 did not elicit by any significant effect (P> 0.05) on the Fv/Fm variable, suggesting that both species were physiologically unaffected (Fig. 2). In contrast, the higher contaminations (i.e. treatments C3 and C4) resulted in a sharp drop in the maximum quantum yield of the PSII during the first 48 h (Fig. 2). These results are consistent with those of other studies showing that pronounced PAH cocktail concentrations (i.e. 14-75 µg l -1 ) strongly reduce the photosynthetic efficiency of phytoplankton [START_REF] Echeveste | Decrease in the abundance and viability of oceanic phytoplankton due to trace levels of complex mixtures of organic pollutants[END_REF][START_REF] Ben Othman | Structural and functional responses of coastal marine phytoplankton communities to PAH mixtures[END_REF]. Hydrocarbons, known by their hydrophobic character, tend to accumulate in chloroplasts whose structure is rich in lipids. These pollutants can be introduced by altering the membrane of chloroplasts [START_REF] Baker | The effect of oils on plants[END_REF], then that of thylakoids, where the PSII is located [START_REF] Sargian | Nonsynergetic effects of water-soluble crude oil and enhanced ultraviolet-B radiation on a natural plankton assemblage[END_REF]. By subsequently interfering with photosynthetic electron transport [START_REF] Duxbury | Effects of simulated solar radiation on the bioaccumulation of polycyclic aromatic hydrocarbons by the duckweed emnagibba[END_REF], PAHs can affect the efficiency of PSII. Thus, diatom cells are physiologically able to tolerate low PAH concentration, by increasing, for example, the PSII absorption cross-section (increase in the size of the antennas) in order to maintain an efficient photosynthetic function [START_REF] Moore | Phytoplankton photoacclimation and photoadaptation in response to environmental gradients in a shelf sea[END_REF].

For P. mannii, the photosynthetic efficiency was reduced only by the highest PAH level (C4 contamination) and was totally deteriorated after 48 h, whereas for P. hasleana, even the C3 treatment provoked a marked fall and total deterioration in the first 24 h (Fig. 2). In addition, the measured EC50 for Fv/Fm was higher for P. mannii (135 μg PAHs l -1 ) than for P. hasleana (115 μg PAHs l -1 ). These results prove that P. mannii was physiologically better adapted to high levels of PAHs, which explains its higher tolerance to PAHs than P. hasleana, confirming the better proliferation of the first species. The measured EC50 exceed the concentrations found in the water of the Bizerte lagoon. However, high amounts of PAHs could be released from sediment resuspension [START_REF] Cornelissen | Assessing PAH and PCB emissions from the relocation of harbour sediments using equilibrium passive samplers[END_REF], or deposited by atmospheric aerosol [START_REF] Barhoumi | Levels and risk assessment of hydrocarbons and organochlorines in aerosols from a north African coastal city (Bizerte, Tunisia)[END_REF] or loaded by oil spill [START_REF] Wang | Effects and risk evaluation of oil spillage in the sea areas of changxing island[END_REF]. All these phenomena could lead to the contamination of water column by PAHs, which would be toxic for algal species.

In addition to physiological adaptations, cell biovolume has also been identified as a factor in the resistance or sensitivity of species to PAHs [START_REF] Echeveste | Decrease in the abundance and viability of oceanic phytoplankton due to trace levels of complex mixtures of organic pollutants[END_REF]. Small cells, with a high surface to volume ratio, absorb the dissolved elements more and are therefore more exposed and more affected by the contaminants. By opposition, large microalgae (such as diatoms and dinoflagellates) are more resistant to pollutants, such as PAHs [START_REF] Ben Othman | Toxicity of benz(a)anthracene and fluoranthene to marine phytoplankton in culture[END_REF]. Both Pseudo-nitzschia species had at the beginning of the experiment the same biovolume (210 μm 3 ) which kept it almost until the end in the control treatments (Fig. 3).

However, both diatoms have increased their biovolumes after 144 hours' exposure to all concentrations of the PAH cocktails (Fig. 3). Moreover, this increase was all the more important as the level of contamination increased. Thus, it seems that these diatoms have adopted this strategy of increasing size, for example by accumulating certain substances like lipid bodies, to reduce the toxicity of PAHs. In fact, exposing diatoms to abiotic stresses can boost lipid synthesis and yield [START_REF] Zulu | Current trends to comprehend lipid metabolism in diatoms[END_REF]. In addition, the biovolume of P. mannii increased from 16 to 35% compared to the baseline, whereas the increase in P. hasleana was lower (11 -17%). This could therefore reinforce the resistance of P. mannii to PAHs compared to that of the other species. So, this strategy allows diatoms to maintain their growth under high contamination.

Several studies have shown that the production of domoic acid by Pseudo-nitzschia species, can be involved in their resistance against metallic contaminants [START_REF] Lelong | Cell-based measurements to assess physiological status of Pseudo-nitzschia multiseries, a toxic diatom[END_REF]. This amino acid can chelate ETMs and subsequently decrease their bioavailability for diatoms, thereby reducing metal toxicity to cells. Unlike ETMs, Pseudo-nitzschia defense strategies against organic pollutants are not known and the role that DA could play in this resistance is still unknown. Strains of P. mannii and P. hasleana, used in our experiments, were tested negative for the production of DA. The monitoring of this toxin during the incubation showed that the species remained non-toxic throughout the experiment under the different contaminant concentrations. This indicates that either the two species were physiologically and genetically incapable of producing DA, or that even though they had the toxic potential, PAH pollutants did not stimulate it and so the release of domoic acid by the diatoms can't be envisaged as a potential strategy to face PAH contamination.

Accumulation and degradation of PAHs by Pseudo-nitzschia species

Our results clearly showed that the growth and the physiology of both species of Pseudo-nitzschia were affected by the PAH cocktails, with P. mannii being less affected than P. haslena. Both species can reduce the PAH toxicity by increasing their biovolume, but this increase was more pronounced for P. mannii that exhibited a better tolerance to PAHs. The resistance of both diatoms to PAHs could be also related to their ability to eliminate these pollutants, which would be expected to be more important for P. mannii.

The Pseudo-nitzschia species were effectively capable to uptake the PAHs from the medium and accumulating them. Indeed, over time (0 h to 72 h and 72 h to 144 h), the percentages of PAHs remaining in each treatment decreased in parallel with an increase in accumulation percentages (Figs. 5 and6). Furthermore, in cultures contaminated with the C3 cocktail, concentrations of particulate PAHs increased over days in a linear pattern (Fig. 4B). This is in agreement with previous reports of the ability of several microalagal species, including diatoms to accumulate several individual PAHs) or in mixture [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF]Melliti Ben Garali, 2016). After 144 h exposure to C2 contamination, both species were able to accumulate ≈ 60% of PAHs (Figs. 5A, 6A), a percentage exceeding value found for other diatoms, such as Skeletonema costatum (45%; [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF]. This proves the excellent potential of the two species of Pseudo-nitzschia for PAH accumulation. However, the percentages of accumulation were slightly different between the species (Figs. 5A,6A). It is known that the magnitude of bioaccumulation of organic contaminants by microalgae is species-dependent, since for the same PAH, some species may accumulate it more than others [START_REF] Lei | Removal of fluoranthene and pyrene by different microalgal species[END_REF]. The size and shape of species have an important role in the adsorption and accumulation of these molecules. So, P. mannii, by increasing much more its biovolume in all contaminations treatments (Fig. 3), could have a lower area:volume ratio and subsequently lower PAH absorbance, resulting in less accumulated PAHs. The bioaccumulation of PAHs by phytoplankton can also directly be related to the initial cell concentration used in the inoculum. [START_REF] Chan | Removal and biodegradation of polycyclic aromatic hydrocarbons by Selenastrum capricornutum[END_REF] found that the uptake of three PAHs in a mixture (phenanthrene, fluoranthene and pyrene) by Selenastrum capricornutum increased as its initial concentration increased from 5 10 4 to 10 7 cells ml -1 . Nevertheless, in the present study, the cell densities of the two species were the same (5 10 6 cells ml -1 ) at the beginning of incubations, so the concentration of inocula could not contribute to the observed differences in PAH accumulation between P. mannii and P. hasleana.

In the C3 treatment (contaminated with 2% of the PAH cocktail), a gradual drop in dissolved PAH concentrations was observed over time in cultures of both species (Fig. 4A).

Several studies have shown that the decrease in concentrations of dissolved PAHs during contamination experiments may be due to several processes, including volatilization, photooxidation, absorption and biodegradation [START_REF] Lekunberri | Changes in bacterial activity and community composition caused by exposure to a simulated oil spill in microcosm and mesocosms experiments[END_REF]. Besides photochemical decomposition, the activity of P. mannii and P. hasleana could also contribute to PAH degradation. Effectively, bio-degradation of individual or mixed PAHs has been largely demonstrated for some diatoms (Nitzschia and Skeletonema) [START_REF] Hong | Accumulation and biodegradation of phenanthrene and fluoranthene by the algae enriched from a mangrove aquatic ecosystem[END_REF][START_REF] Echeveste | Decrease in the abundance and viability of oceanic phytoplankton due to trace levels of complex mixtures of organic pollutants[END_REF]. In addition to phototrophs, bacteria also have the ability to degrade PAHs [START_REF] Arulazhagan | Role of a moderately halophilic bacterial consortium in the biodegradation of polyaromatic hydrocarbons[END_REF][START_REF] Jiménez | Polyphasic approach for assessing changes in an autochthonous marine bacterial community in the presence of Prestige fuel oil and its biodegradation potential[END_REF]. Since the cultures of the two Pseudo-nitzschia species were not axenic, it is very likely that the heterotrophic activity of bacteria present in the diatom cultures contribute to the biodegradation of PAH during the incubation. Recent studies have shown the presence of hydrocarbonoclastic bacteria intimately associated with diatoms [START_REF] Thompson | Visualisation of the obligate hydrocarbonoclastic bacteria Polycyclovoran salgicola and Algiphilusaroma ticivorans in co-cultures with micro-algae by CARD-FISH[END_REF][START_REF] Severin | The Phytoplankton taxon-dependent oil response and its microbiome: correlation but not causation[END_REF]). It appears that P. mannii maintained in culture was able to degrade PAHs, with a higher percentage of degradation (72 h: 7 -47%, 144 h: 20-51%) than those found in culture of P. hasleana, (72 h: 0.7 -17%, 144 h: 10 -25%) (Figs. 5C,6C). It has been reported that the solubility and molar mass of PAHs significantly affect their bio-degradation. In general, low molecular weight PAHs having 2 to 3 aromatic nuclei rings (such as naphthalene and phenanthrene) are more degraded than those with more than 3 aromatic nuclei rings(such as phenanthrene, anthracene and fluoranthene) [START_REF] Haritash | Biodegradation aspects of polycyclic aromatic hydrocarbons (PAHs): A review[END_REF]. In addition, the presence of one PAH can stimulate the bio-degradation of another. This synergistic effect has been observed for some PAHs (such as pyrene, phenanthrene, fluoranthene and naphtalene) degraded by phytoplankton species [START_REF] Chan | Removal and biodegradation of polycyclic aromatic hydrocarbons by Selenastrum capricornutum[END_REF]. In our experiments, 15 different PAH compounds, with different molar masses, were present in mixture. Some of these molecules may be more susceptible or resistant to degradation than others. Moreover, the interactions between them can contribute to a simultaneous degradation of the mixture of the PAHs used.

Conclusion

The contamination of two species of Pseudo-nitzschia (P. mannii and P. hasleana) by increased concentrations of PAH mixture in culture induced a reduction in cell growth, Chl a concentration and cell density of the two species. This effect was dose-dependent, with a more pronounced decrease as the level of contamination increased. PAH contamination also had a negative effect on the physiology of both diatoms, since their photosynthetic efficiency was altered. Both species of Pseudo-nitzschia did not produce domoic acid in the experimental conditions imposed, irrespective of the PAH concentration added in the culture medium. Therefore, unlike metals, domoic acid does not seem to be involved in their defense mechanisms to face organic pollutants such as PAHs and thus PAH pollution might not be one of the factors controlling the toxicity of Pseudo-nitzschia species. On the other hand, P. mannii and P. hasleana have a morphological capacity to tolerate PAHs, by increasing their biovolume and subsequently their surface:volume ratio decreases which makes them less sensitive to the presence of contaminants outside the cells. The biovolume augmentation can be an effective strategy adopted by microalgae that allows them, maintaining their proliferation in coastal environments contaminated by PAHs. P. mannii showed more biovolume increase and appeared to be more tolerant than P. hasleana. The percentage of accumulation of PAHs by both species, which increased over time, was inversely proportional to PAH cocktail concentrations. This suggests that P. mannii and P. hasleana were able to bio-concentrate and metabolize PAHs with probably the help of the heterotrophic bacteria present in the non-axenic cultures, but the second species appeared to have greater accumulating potency. Both diatoms with associated bacteria in cultures were also able to degrade PAHs, with percentages in P. mannii cultures greater than those found in P. hasleana cultures. The tolerance of Pseudo-nitzschia species to PAHs, related to their morphological defense strategy and their ability to bioaccumulate and degrade PAHs, could help to understand their increasing occurrence and their intense blooms observed in highly PAH contaminated coastal waters, like the Bizerte lagoon.

Figure legends

  %PAH diss = (amount of PAHs remaining in the medium / amount of PAHs added) x 100 %PAH acc = (amount of PAHs accumulated in the cells/amount of PAHs added) x 100 %PAH deg = (amounts of PAHs addedamount of PAHs remaining in the mediumamount of PAHs accumulated in the cells)/amount of PAHs added x 100%

Fig. 1 .

 1 Fig. 1. Evolution of Chl a concentrations (a, b) and cell abundances(c, d) in contaminated treatments [C1: 0.1% (3 μg L -1 ); C2: 0.5% (15 μg L -1 ); C3: 2% (30 μg L -1 ); C4: 4% (120 μg L -1 )] relatively to the control (C) during the experiments performed for P. hasleana and P. mannii (Mean ± SD).

Fig. 2 .

 2 Fig. 2. Evolution of the efficiency of photosystem II (Fv/Fm) in contaminated treatments [C1:0.1% (3 μg L -1 ); C2: 0.5% (15 μg L -1 ); C3: 2% (30 μg L -1 ); C4: 4% (120 μg L -1 )] relatively to the control (C) during the experiments performed for P. hasleanaandP. mannii(Mean ± SD).

Fig. 3 .

 3 Fig. 3. Effects of PAH cocktails on cellular biovolume of P. hasleana(a) andP. mannii (b) in different treatments [C DMSO ; C1: 0.1% (3 μg L -1 ); C2: 0.5% (15 μg L 1 ); C3: 2% (30 μg L -1 ); C4: 4% (120 μg L -1 )](Mean ± SD).

Fig. 4 .

 4 Fig. 4. Concentration of dissolved (a) and particulate PAHs (b) in the treatment C3 contaminated by 2% of PAHs cocktail (means ± 91% confidence intervals, n = 3).

Fig. 5 .

 5 Fig. 5. Percentages of PAHs accumulated in the cells (PAH acc , a), remaining dissolved in the culture medium (PAH diss , b) and degraded (PAH deg , c) in the P. hasleana cultures over time in the different contaminated treatments [C1: 0.1% (3 μg L -1); C2: 0.5% (15 μg L - 1 ); C3: 2% (30 μg L -1 ); C4: 4% (120 μg L -1 )] (Mean ± SD).

Fig. 6 .

 6 Fig. 6. Percentages of PAHs accumulated in the cells (PAH acc , a), remaining dissolved in the culture medium (PAH diss , b) and degraded (PAH deg , c) in the P. mannii cultures over time in the different contaminated treatments [C1: 0.1% (3 μg L -1); C2: 0.5% (15 μg L - 1 ); C3: 2% (30 μg L -1 ); C4: 4% (120 μg L -1 )] (Mean ± SD).

Fig. 1

 1 Fig. 1

Fig

  Fig. 2.

  Fig. 3.

  17

  Fig. 5.

Table 1 32

 1 Theoretical and measured concentrations (µg L -1 ) of PAHs mixtures in Pseudo-nitzschia

	33
	34	cultures (n =3)
	35
				Theoretical concentrations
			C1	C2	C3	C4
			3	15	30	120
		Measured concentrations
		Culture of P. hasleana	2.1 ± 0.1 13.8 ± 0.7	28.0 ± 1.2	117.0 ± 3.5
		Culture of P. mannii	2.4 ± 0.2 13.7 ± 0.5	28.7 ± 2.3	118.6 ± 4.4
	36
	37
	38
	39
	40
	41
	42
	43
	44
	45
	46
	47
	48
	49
	50
	51
	52
	53

Acknowledgements

This work was supported by IRD through the Laboratoire Mixte International (LMI) COSYS-Med (Contaminants et Ecosystèmes Sud Méditerranéens).


Leila Amgoud
email: amgoud@irit.fr

Philippe Besnard
email: besnard@irit.fr

Anthony Hunter
email: anthony.hunter@ucl.ac.uk

Logical Representation and Analysis for RC-Arguments

Keywords:

An argument is seen as reason in favour of a claim. It is made of three parts: a set of premises representing the reason, a conclusion representing the supported claim, and a connection showing how the premises lead to the conclusion.

Arguments are frequently exchanged by human agents in natural language (spoken or written) in discussion, debate, negotiation, persuasion, etc. They may be very different in that their three components may have various forms.

In this paper, we propose a language for representing such arguments. We show that it is general enough to capture the various forms of arguments encountered in natural language, and that it is possible to represent attack and support relations between arguments as formulas of the same language.

I. INTRODUCTION

An argument gives reason to support a claim that is questionable, or open to doubt. It is made of three parts: a set of premises representing the reason, a conclusion representing the supported claim, and a link showing how the premises lead to the conclusion [START_REF] Cottrell | Critical Thinking Skills : Developing Effective Analysis and Argument[END_REF]. The link is hence the logical part of an argument. The notion of argument is very rich and complex. Indeed, the reason (respectively the conclusion) varies from simple statements to combinations of arguments, and the link may be deductive, abductive, inductive, . . . [START_REF] Amgoud | Handling threats, rewards, and explanatory arguments in a unified setting[END_REF]. Let us consider the following example of a natural language argument.

Example 1: The title and first two paragraphs from an article on whether the London Heathrow airport should be expanded with a third runway. The article comes from the BBC website 1 .

claim Heathrow needs more capacity. \claim reason Heathrow runs at close to 100% capacity. With demand for air travel predicted to double in a generation, Heathrow will not be able to cope without a third runway, say those in favour of the plan. \reason reason Because the airport is over-stretched, any problems which arise cause knock-on delays. Heathrow, the argument goes, needs extra capacity if it is to reach the levels of service found at competitors elsewhere in Europe, or it will be overtaken by its rivals. \reason 1 http://news.bbc.co.uk/1/hi/uk/7828694.stm

In the above tagging, we have a single claim, viz "Heathrow needs more capacity", and we have two reasons for this claim. Hence, we appear to have two arguments. Each with the same claim.

However, if we look at the second reason, we see that there are nested arguments, and so we could deconstruct the second paragraph as follows. The first sentence is an argument containing a reason and claim as follows reason Because the airport is over-stretched \reason , claim any problems which arise cause knock-on delays. \claim . Then, we see that the above argument is itself a premise for the following claim claim Heathrow, the argument goes, needs extra capacity if it is to reach the levels of service found at competitors elsewhere in Europe, or it will be overtaken by its rivals. \claim Putting these observations together, we could tag the second paragraph as follows where we have an argument as a nested reason.

reason reason Because the airport is overstretched, any problems which arise cause knockon delays. \reason claim Heathrow, the argument goes, needs extra capacity if it is to reach the levels of service found at competitors elsewhere in Europe, or it will be overtaken by its rivals. \claim \reason So the final paragraph contains an argument (i.e. a reason with claim), this argument is the reason for a claim within the paragraph. Furthermore, the whole paragraph is a reason for the claim in the title of the article.

Recently, there is growing interest in the computational models of argument community and in the computational linguistics community in mining arguments from texts (see for example [START_REF] Cabrio | Generating abstract arguments: a natural language approach[END_REF], [START_REF] Cabrio | A natural language bipolar argumentation approach to support users in online debate interactions[END_REF], [START_REF] Feng | Classifying arguments by scheme[END_REF], [START_REF] Florou | Argument extraction for supporting public policy formulation[END_REF], [START_REF] Moens | Automatic detection of arguments in legal texts[END_REF], [START_REF] Peldszus | From argument diagrams to argumentation mining in texts: a survey[END_REF], [START_REF] Rooney | Applying kernel methods to argumentation mining[END_REF], [START_REF] Schneider | A review of argumentation for the social semantic web[END_REF], [START_REF] Ch | Annotating argument components and relations in persuasive essays[END_REF], [START_REF] Teufel | An annotation scheme for discourse-level argumentation in research articles[END_REF], [START_REF] Wang | Improving agreement and disagreement identification in online discussions with a socially-tuned sentiment lexicon[END_REF], [START_REF] Wyner | Approaches to text mining arguments from legal cases[END_REF], [START_REF] Wyner | Semi-automated argumentative analysis of online product reviews[END_REF]). An interesting challenge that is thus arising is the choice of target formalism for representing the extracted arguments. In computational models of argument, abstract argumentation (as proposed by Dung [START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF]) and logical or structured argumentation (as proposed in [START_REF] Ph | Elements of Argumentation[END_REF], [START_REF] Bondarenko | An abstract, argumentation-theoretic approach to default reasoning[END_REF], [START_REF] García | Defeasible logic programming: an argumentative approach[END_REF], [START_REF] Prakken | An abstract framework for argumentation with structured arguments[END_REF]) are the two key options. Neither is ideal as a target formalism as we outline below.

Abstract argumentation: Each argument is atomic. There is no differentiation between reasons and claims. So there is insufficient structure for a target language for argument mining.

Logical argumentation: Each argument is a set of formulae for premises, and a formula for a claim, where the premises imply the claim using a given consequence operator of a particular monotonic logic. So there is excessive structure for a target language for argument mining since natural language arguments are generally enthymemes, that is some of their premises are unstated.

In previous work [START_REF] Amgoud | Representing and reasoning with arguments mined from texts and dialogues[END_REF], we have proposed a formal language for representing arguments. The language is made of formulae of the form (-)R(y) : (-)C(x). The notation (-)R(y) (resp. (-)C(x)) denotes that there are two cases: -R(y) and R(y) (resp. -C(y) and C(y)). A formula R(y) : C(x) stands for y gives reason to claim x, R(y) : -C(x) stands for y gives reason for not claiming x, and -R(y) : C(x) stands for y is not a reason for claiming x. The latter form is called rejection of argument. The link between the reason and the claim is left implicit. We have shown that the language is general enough to capture a wide range of types of arguments. In this paper, we extend the language in such a way that the reason and/or the conclusion of an argument can also be a combination of arguments. This allows us to capture complex arguments like those conveyed in recommendation letters. Consider a recommendation letter written for someone who is applying for a position. The main claim is that the candidate deserves the position and the reason is a conjunction of several arguments, each of which may be nested. Another contribution of the paper consists of highlighting several forms of attacks and supports between arguments, some of them have never been defined in computational models of arguments. We show that each form can be defined as a formula of the language. This allows us to represent in a unified setting arguments and relations between them. We show also what issues in (computational) argumentation our formalism can address. In particular, we deal with the details of applying our formalism in a range of cases, discussing the significance of various forms of arguments allowed in our formalism.

The paper is organized as follows: Section II introduces the language. Section III shows how to encode attacks and supports between arguments as formulae of the language. Section IV compares our formalism with computational argumentation ones, and Section V describes briefly how the language can be used as a target language for argument mining. The last section concludes.

II. SYNTAX FOR RC FORMULAE

Our formalism for representing arguments is inspired by Apothéloz [START_REF] Apothéloz | The function of negation in argumentation[END_REF] and extends the one proposed in [START_REF] Amgoud | Representing and reasoning with arguments mined from texts and dialogues[END_REF] for representing nested arguments.

The formalism is built upon a classical propositional language L(A) where A is a set of atoms. The formulae of the language L(A) are defined in the usual way from A and the usual classical operators ¬, ∨, ∧, →, ↔. Our formalism also uses two functions R(.) and C(.), a disjunction operator |, a conjunction operator &, and an additional negation operator -. Thus, two negation operators are needed: ¬ for denying propositional formulas (¬x denotes that x is false), andfor denying R(.) and C(.). Please note that ¬¬x is identified with x and --R(.) is identified with R(.) (also, --C(.) is identified with C(.)).

Definition 1 (RC formulae): The set of formulas Arg(L(A)) is the smallest set such that a formula is of the form (-)R(y) : (-)C(x) where x and y are formulae of L(A) ∪ Arg(L(A)) or is a Boolean combination of formulae of Arg(L(A)) with the connectives | and &.

The two operators | and & connect RC formulae as follows: R(y) :

C(x) | R(z) : C(t), R(y) : C(x) & R(z) : C(t), R(y) : C(x) | -R(z) : C(t), R(y) : C(x) & -R(z) : C(t), . . . Remark: Please note that -((-)R(y) : (-)C(x)) is identi- fied with -(-)R(y) : (-)C(x).
Each formula of Arg(L(A)) is either an argument or a rejection of an argument. An argument is a reason for concluding a claim. It has two main parts: premises (the reason) and a conclusion. An argument is interpreted as follows: its conclusion holds because it follows, according to a given notion, from the premises. The notion refers to the nature of the link (e.g., the premises cause the conclusion).

Definition 2 (Argument): An argument is a formula of Arg(L(A)) of the form R(y) : (-)C(x).

The functions R and C play the roles of giving reason and concluding, resp. They thus capture the coupling between a reason and a conclusion. As we will see later, the contents may be true while the functions do not hold and vice versa. The intuitive meaning of the two formal expressions captured by the previous definition is as follows: R(y) : C(x) means that "y is a reason for concluding x" R(y) : -C(x) means that "y is a reason for not concluding x"

The nature of the link between the reason and the conclusion is captured by the colon. There are at least two reasons for leaving the link implicit. First, natural language arguments are generally enthymemes, thus some of their premises are unstated. For instance, in the argument "Paul has DNA because he is human", there is a missing premise which says "All humans have DNA". Actually, it is not always possible to make the link explicit. The second reason is that there are several kinds of links, each of which leads to a particular definition of arguments. In [START_REF] Amgoud | Handling threats, rewards, and explanatory arguments in a unified setting[END_REF], it was shown that arguments of types "threats" and "rewards" are defined in an abductive way, while arguments of type "appeals to prevailing practices" are deductive. Our purpose is to have one general definition of argument in which all the different types can be captured. So far, the negation operator "-" has been used to deny the concluding function. In what follows, the function of giving reason can be denied as well by placing "-" in front of R. What is denied in this case is not the premises but rather the idea that the premises justify the conclusion of the argument. Such a form is called rejection of argument since it has exactly the opposite meaning of an argument.

Definition 3 (Rejection): A rejection of an argument is a formula of Arg(L(A)) of the form -R(y) : (-)C(x).

The intuitive meaning for these formal expressions is as follows:

Example 2: Assume the propositional atoms bird, penguin, damaged.wing (to denote animals with a damaged wing), slightly.damaged.wing (to denote animals with a slightly damaged wing), and egg.laying (to denote animals that lay eggs).

1) R(bird

) : C(f ly) 2) R(penguin) : C(¬f ly) 3) R(bird ∧ damaged.wing) : -C(f ly) 4) -R(bird ∧ slightly.damaged.wing) : -C(f ly) 5) -R(egg.laying) : C(f ly) 6) -R(egg.laying) : C(¬f ly) 7) -R(egg.laying) : -C(f ly)
Arguments can be counterarguments for other arguments. For instance, R(bird) : C(f ly) has R(penguin) : C(¬f ly) and R(bird ∧ damaged.wing) : -C(f ly) as counterarguments. We investigate the notion of a counterargument as RC-formulae in the next section.

III. REPRESENTING ATTACK AND SUPPORT

In structured argumentation, an attack against a given argument consists of presenting another argument denying one of the components of the initial argument (i.e., premises, conclusion, link). Thus, similar to a rejection, the aim of an attack is to undermine an argument. The main difference between the two lies in that the attacker provides a reason for the attack. For instance, to undermine the conclusion x of an argument R(y) : C(x), one should provide another argument justifying why ¬x holds. In contrast, a rejection needs no justification (but it may have one). Every attack between two arguments leads to a rejection of the attacked argument in the following way:

If R(z) : C(w) attacks R(y) : C(x), then R(R(z) : C(w)) : C(-R(y) : C(x)).
Note that the converse is not true, rejections might not be transformed into an attack between a pair of arguments. Consider the following dialogue. Paul: Why are you late? (la). Carla: Because I am late R(la) : C(la)

Paul: This is not a reason -R(la) : C(la) In the example, Paul rejects Carla's argument without justifying why. In fact, he denies the fact that the argument can be circular.

We now turn to showing how to detect attacks between mined arguments, and how the language can be used to capture them within formulae of the language. Recall that an argument R(y) : C(x) may be attacked on one of its components: conclusion, premises, the link.

There are two ways for undermining the conclusion of an argument: a strong way by showing that the negation of the conclusion holds, and a weak way by showing that the conclusion fails.

R(y) : C(x) R(z) : C(¬x) R(R(z) : C(¬x)) : C(-R(y) : C(x)) Strong Rebuttal R(y) : C(x) R(z) : -C(x) R(R(z) : -C(x)) : C(-R(y) : C(x))

Weak Rebuttal

Strong Rebuttal corresponds to the well-known rebuttal in existing argumentation formalisms.

Example 3: Illustration for strong rebuttal: Nixon is a quaker (nq) and Nixon is a republican (nr). Is Nixon a pacifist (np)?

R(nq) : C(np) R(nr) : C(¬np) R(R(nr) : C(¬np)) : C(-R(nq) : C(np))
Weak rebuttal captures somehow the so-called undercutting relation [START_REF] Pollock | How to reason defeasibly[END_REF]. The basic idea is to block the application of a defeasible rule in some cases. Let us consider the following example:

Example 4: The object is red (re) because it looks red (lr). This argument is written in our formalism as R(lr) : C(re). In existing argumentation systems like ASPIC and ASPIC+, the hidden assumption "Objects that look red are indeed red" is encoded as a defeasible rule. If the object is illuminated by red light (il), then undercutting amounts to blocking the application of the rule, thus blocking its conclusion re. In our language, this is simply written as R(il) : -C(re). Unlike Example 3, the reason in the counter-argument (il) needs not command that the negation of the conclusion in the attacked argument (¬re) holds. The premises of an argument may also be undermined in a strong or a weak way as follows:

R(y) : C(x) R(z) : C(¬y) R(R(z) : C(¬y)) : C(-R(y) : C(x))   Strong Premise Attack   R(y) : C(x) R(z) : -C(y) R(R(z) : -C(y)) : C(-R(y) : C(x))   Weak Premise Attack  
Strong Premise Attack amounts to the well-known Assumption-Attack in argumentation literature.

Example 5: Illustration of strong premise attack: The weather is good (gw) so the bbq will be a success (bs) but weather reports predict rain (ra). The last component of an argument is the link between the premises and the claim. As already said, the link concerns the logical part of the argument, that is the inference pattern that is used in order to infer the conclusion from the premises. It can be denied in three ways as follows:

R(z) : C(-R(y) : C(x)) (Strong Reason Attack) R(z) : -C(R(y) : C(x)) (Weak Reason Attack) -R(y) : C(x) (Pure Reason Attack)
Example 6: Consider the following abductive argument. If all dogs are mammals (dm), then all dogs are animals (da). All dogs are animals. Therefore, all dogs are mammals. This argument can be written as: R((dm → da) ∧ da) : C(dm). Note that in spite of the premises and the conclusion all being true, the argument is not valid. Indeed, it uses the inference pattern

B if A then B A .
Of course, it may happen that A is false although B and if A then B are true. In the same spirit, one may reject the initial argument by means of a mere rejection -R((dm → da) ∧ da) : C(dm). There are many circumstances where a rejection can be justified, though. It would here be of the form R(z) : C(-R((dm → da) ∧ da) : C(dm)) with z a case making the previous pattern invalid (e.g. read da as "David is annoyed" and dm as "David moans", let z stand for a situation where David is only slightly annoyed so that da is not enough of a reason for dm).

Example 7: Consider the following argument. 90% of humans are right-handed (hrh), therefore Paul is also righthanded (prh). The argument, written as R(hrh) : C(prh), might be rejected (-R(hrh) : C(prh)) because the link between the premise hrh and the conclusion prh is invalid.

To sum up, with our logic of arguments, we can formalize and manipulate attacks explicitly within the logic (which is not possible in other formal systems of argumentation), and we have a wider range of attacks than are considered in other formal proposals for argumentation.

Unlike attacks which express negative links between arguments, supports express positive links. In existing literature (e.g., [START_REF] Cayrol | Bipolarity in argumentation graphs: towards a better understanding[END_REF]), such links are captured by a binary relation defined on the set of arguments. In our approach, such an external relation is not needed since supports can be expressed as formulae of the language. Let us now look at various forms of support. An argument may support another argument by approving one of its components: premises, claim and link. R(z) :

C(y) R(y) : C(x) R(R(z) : C(y)) : C(R(y) : C(x)) Premise Support R(z) : C(x) R(y) : C(x) R(R(z) : C(x)) : C(R(y) : C(x)) Claim Support R(R(z) : C(t)) : C(R(y) : C(x)) (Reason Support) R(z) : C(R(y) : C(x)) (Reason Support)
The first two relations have been presented in [START_REF] Cayrol | On the acceptability of arguments in bipolar argumentation frameworks[END_REF] but both forms of link (reason) support are new.

Example 8: Consider the following dialogue. Paul: Carl will pass his exams (pe). He is smart (sm). R(sm) : C(pe) John: He is moreover well prepared (wp).

R(wp) : C(pe)

John's argument can also be interpreted in a sense to be captured by the following argument: R(R(wp) : C(pe)) : C(R(sm) : C(pe))

IV. COMPARISON

We have explained the language for our approach, and we now turn to comparing it with existing approaches in the literature.

A. Implicit representation of links

In almost all works on structured argumentation (e.g. [START_REF] Amgoud | Logical limits of abstract argumentation frameworks[END_REF], [START_REF] Amgoud | Towards a consensual formal model: inference part[END_REF], [START_REF] Ph | Elements of Argumentation[END_REF], [START_REF] García | Defeasible logic programming: an argumentative approach[END_REF], [START_REF] Prakken | An abstract framework for argumentation with structured arguments[END_REF], [START_REF] Verheij | Deflog: on the logical interpretation of prima facie justified assumptions[END_REF]), an argument is a set of premises that, using a notion of derivation, will lead to a conclusion (see [START_REF] Hunter | Base logics in argumentation[END_REF] for a comprehensive description of the derivations used in the literature). As argued in the introduction, this definition needs a logical representation of all the premises. This is not viable for arguments mined from texts since they are enthymemes (and therefore lack the explicit representation of premises and/or claim). Moreover, in general, existing definitions capture only one type of argument (viz. deductive arguments) while in text or dialogue, analogical arguments are very common. Our approach captures enthymemes and a wide variety of types of argument (such as abductive and inductive).

B. Capturing links not relying upon inference

As mentioned above, in almost all works on structured argumentation, an argument relies on a notion of derivation linking the premises of the argument to its conclusion. However, there arguably exist arguments that do not involve an inference from premises to claim. An example is:

Smoking is a reason to get cancer. Please observe that this is definitely an argument. Though, there is no default rule to infer "getting cancer" from "smoking", not even a weighted default-like rule: It does not seem right to express that smoking entails getting cancer with likelihood x, whatever x in [0, 1].

In other words, such arguments fall outside the realm of the existing approaches to structured argumentation. Nonetheless, such an argument can be naturally expressed in the RC-formalism as:

R(s) : C(gc)
where of course we use s to mean "smoking" and gc to mean "getting cancer".

C. Arguing explicitly about ignorance

In our approach, it is possible to argue explicitly about ignorance. For instance, the RC-formula R(s) : -C(f e) can represent the argument below:

Since Carl is very smart (s), we cannot conclude that he will fail his exams (f e). Some approaches to structured argumentation (e.g., [START_REF] Amgoud | Towards a consensual formal model: inference part[END_REF], [START_REF] Prakken | An abstract framework for argumentation with structured arguments[END_REF]) prevent the conclusion f e from being deduced in an indirect way, using Pollock's undercutting [START_REF] Pollock | How to reason defeasibly[END_REF] for blocking the application of defeasibles rules as is illustrated next.

Example 9: Consider the facts F = {sm, ¬wh}, the set of strict rules S = {sm → n}, the set of defeasible rules D = {¬wh → f e}, where f e denotes Carl will fail his exams, wh denotes Carl worked hard, sm denotes Carl is smart. Let n refer to the defeasible rule ¬wh → f e and let n denote its non-application. So the strict rule sm → n actually means that ¬wh → f e does not apply in the context of sm. The following four arguments can be built:

• a 1 : (< sm >, sm) • a 2 : (< ¬wh >, ¬wh) • a 3 : (< sm, sm → n >, n) • a 4 : (< ¬wh, ¬wh → f e >, f e)
Argument a 3 is a Pollock undercutting of a 4 . Using Pollock undercutting as the attack relation, {a 1 , a 2 , a 3 } is the only stable extension. So, the set of conclusions drawn from the theory at hand is {sm, ¬wh, n}. As expected, f e is not inferred. There is however no argument expressing that sm is the main reason for not having f e. While this approach is worthwhile in reasoning, it is not natural in dialogues where agents provide arguments for blocking conclusions. Our solution (i.e., using an argument of the form R(sm) : -C(f e)) makes the connection explicit. Instead of using a succinct formula such as R(y) : -C(x) where x and y are propositions, structured argumentation identifies two arguments A 1 and A 2 where the reason (premises) of A 1 includes x, and the claim of A 2 is y. Furthermore, for structured argumentation, the claim of A 1 , and the premises of A 2 , need to be determined in order to have the attack defined (which can be problematic when they are not explicitly represented in the text or dialogue).

Consider the following argument:

Since Carl is at the university (u), he cannot conclude whether his printer is delivered at home (de) that can be represented simply by R(u) : -C(de) in our approach. Contrastedly, in structured argumentation, not even blocking the conclusion de is possible without further information being available. In structured argumentation, not only is there need for an argument A with some explicit premises and claim de, but, in addition, a counterargument B is needed that either attacks the premises of A ′ or the derivation of the claim de from those premises.

D. Complex arguments

Our approach supports the representation of nesting of reasons. This means an argument or a rejection of an argument can be used as a reason or as a claim in an argument or rejection of an argument. The premises and conclusion can also be a conjunction or disjunction of arguments. This provides a rich formalism for representing arguments and rejections of arguments as arising in texts and dialogues.

DefLog [START_REF] Verheij | Deflog: on the logical interpretation of prima facie justified assumptions[END_REF] offers a language for representing arguments that in some respects is similar to our Definition 2. It has reasons and claims. These are atomic, or for claims, they can be nested arguments. So unlike our approach, DefLog does not support Boolean formulae as reasons or claims, and DefLog does not support nested reasons. Also, DefLog does not support rejections of arguments.

In [START_REF] Boella | Metaargumentation modelling I: methodology and techniques[END_REF], [START_REF] Modgil | Metalevel argumentation[END_REF], abstract argumentation has been extended with attacks on attacks, but this form of meta-argumentation does not support differentiation of reasons and claims, and it does not support rejection of arguments. Meta-argumentation has also been proposed in logic-based approaches to argumentation (such as [START_REF] Hunter | Reasoning about the appropriateness of proponents for arguments[END_REF], [START_REF] Wooldridge | On the metalogic of arguments[END_REF]). These allow arguments to appear in the premises or claims of other arguments. However, these approaches assume explicit representation of the logical formulae in the premises by which the claim is derived, and they do not support rejection of arguments. Also, they do not support arguing about ignorance.

E. Rejection of arguments

Our approach incorporates the representation of a rejection of an argument. This is different to a counterargument, as we have argued in Section III. Approaches to structured argumentation (such as [START_REF] Ph | Elements of Argumentation[END_REF], [START_REF] Bondarenko | An abstract, argumentation-theoretic approach to default reasoning[END_REF], [START_REF] García | Defeasible logic programming: an argumentative approach[END_REF], [START_REF] Prakken | An abstract framework for argumentation with structured arguments[END_REF]) represent counterarguments, but there is no proposal that represents rejection of arguments.

F. Explicit representation of attacks

Our approach represents rejections of arguments, which are essential for representing diverse mined arguments. Consequently, an attack is represented as an explicit construct in the language. Attacks can also be justified (e.g. R(x) : C(-R(y) : C(z)). As said before, no other logic-based approach to modelling argumentation provides a language for expressing rejection of arguments and attacks in the object language.

A proposal for introducing support and attacks relations into the language is E-DeLP [START_REF] Cohen | Backing and undercutting in defeasible logic programming[END_REF], but the formalism only allows reasons to support or to attack a claim, and the claim can only be a defeasible rule. It therefore seems unlikely to be a suitable target language for representing mined arguments.

V. TARGET LANGUAGE FOR ARGUMENT MINING

Next we consider how our formalism can be used as a target language for argument mining. Tagging is an important step in developing a natural language processing system (an NLP system). For this, we need to annotate a corpora of items of text that we use for training an NLP system (for instance based on statistical natural language processing and/or machine learning). The aim is that after training, the NLP system can automatically tag previously unseen items of text (i.e. items of text not used for training) correctly. Since there is often a subjective aspect to the tagging. each of text is tagged by a number of people independently.

Example 10: We return to Example 1. We use the tag x 1 to tag a string in the title, we use the tags y 1 and y 2 to tag strings in the first paragraph, and we use the tags z 1 , z 2 , and z 3 to tag strings in the second paragraph. For each tag p, p denotes the start of the string, and \p denotes the end of the string. From the following tagged strings, we can obtained the RC-formulae R(y 1 ) : C(x 1 ) and R(R(R(z 1 ) : C(z 2 )) : C(z 3 )) : C(x 1 ).

• x 1 Heathrow needs more capacity \x 1 • y 1 Heathrow runs at close to 100% capacity. With demand for air travel predicted to double in a generation, Heathrow will not be able to cope without a third runway \y 1 , say those in favour of the plan. • z 1 Because the airport is over-stretched \z 1 , z 2 any problems which arise cause knock-on delays \z 2 . z 3 Heathrow, the argument goes, needs extra capacity if it is to reach the levels of service found at competitors elsewhere in Europe, or it will be overtaken by its rivals \z 3 . So, as a target language, RC-formulae capture the connection between reasons and claims, and as illustrated in Example 10, this connection can be nested. Furthermore, arguments and rejections can be nested as reasons and claims.

VI. CONCLUSIONS

This paper deals with the definition of a formalism for representing natural language arguments. The formalism provides a wide range of benefits including: (1) Target language for arguments, mined from texts or dialogues, that is between abstract and logical argumentation; This paper builds on a previous work [START_REF] Amgoud | Representing and reasoning with arguments mined from texts and dialogues[END_REF] which it extends in the following way. Whereas [START_REF] Amgoud | Representing and reasoning with arguments mined from texts and dialogues[END_REF] focussed on the inference system for RC-formulae, this paper deals with a number of notions in argumentation that can be captured by our approach and which facilitate the representation and reasoning with arguments as arising in AI applications involving text and dialogue. Indeed, representation of attack and support, nature of the reason-claim link and its underlying requirements/conditions, ability to capture mere ignorance, difference between rejections and counter-arguments, are all topics in this paper that were not addressed in [START_REF] Amgoud | Representing and reasoning with arguments mined from texts and dialogues[END_REF].

R

  (gw) : C(bs) R(ra) : C(¬gw) R(R(ra) : C(¬gw)) : C(-R(gw) : C(bs))

  (2) Representation of any type of arguments in a unified setting (threats, rewards, examples, . . .); (3) Representation of arguments in favour of ignorance; (4) Explicit representation of attacks and supports in the object language; (5) Practical representation of enthymemes; (6) Representation of rejections; and (7) Nesting and combinations of arguments and rejections.

ACKNOWLEDGEMENT

This work benefited from the support of AMANDE ANR-13-BS02-0004 project of the French National Research Agency.