Marie Viel
email: marie.viel@univ-nantes.fr

Florence Collet
email: florence.collet@univ-rennes1.fr

Christophe Lanos
email: christophe.lanos@univ-rennes1.fr

Development and characterization of thermal insulation materials from renewable resources

Keywords: Agricultural waste valuation, Hemp shiv, Corn cob, Green binder, Hygrothermal characterization, Mechanical properties

The present study has investigated the scope for valuation of agro-resources by-products as aggregates and as binding material to produce rigid fully bio-based composite panels. Two types of aggregates: hemp shiv and corn cob residues (obtained after alkali treatment on the corn cob), and six types of green binders are investigated. Specimens are produced to verify the gluing effect, to characterize mechanical, thermal and hygric properties of developed composites and to identify the best aggregate-binder mixture.

They show interesting thermal conductivity ranging from 67 to 148 mW/(m.K) at dry state, excellent hygric properties (MBV > 2 g/(m 2 .%RH)) and high enough mechanical properties to be self bearing.

These results suggest that developed composites can be used as building materials but not for the same types of use. In fact, some composites would be more suitable for thermal insulating products and others would be better suited for indoor facing panels.

Introduction

Nowadays, the building sector is one of the three most energy consuming sectors with industry and transport, particularly because of heating, ventilation and air conditioning systems that ensure indoor thermal comfort. So, building well insulating buildings is important to save energy more efficiently, in particular by reducing heat transfer through the envelopes [START_REF] Adamczyk | The impact of thermal insulation investments on sustainability in the construction sector[END_REF][START_REF] Liu | The development history and prospects of biomass-based insulation materials for buildings[END_REF][START_REF] Papadopoulos | State of the art in thermal insulation materials and aims for future develop-ments[END_REF]. Hence, there is a high demand for renewable, environmentally friendly, low cost and high thermal resistance insulation materials [START_REF] Liu | The development history and prospects of biomass-based insulation materials for buildings[END_REF]. One way to address these objectives is the development of green insulating materials to replace conventional ones [START_REF] Torres-Rivas | Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk[END_REF].

A bio-based material is a material obtained from raw material of mainly biological origin, preferably requiring very little processing. Fossil resources are excluded, so materials from renewable biomass animal or plant are mainly considered. This type of material can lead to excellent hygrothermal performance. They have the ability to moderate humidity of the indoor air by adsorbing and desorbing water vapor which allows to reduce the ventilation rate and thus, the need for heating in winter and for air conditioning in summer [START_REF] Palumbo | Bio-based insulation materials and their hygrothermal performance in a building envelope system (ETICS)[END_REF][START_REF] Zhang | Moisture buffering phenomenon and its impact on building energy consumption[END_REF]. Moreover, they are renewable and environmentally friendly unlike some traditional thermal insulators such as mineral wool which have poor environmental performance. Indeed, various pollutants such as CO x , NO x , SO x , volatile organic compounds and particles are emitted during their energy-intensive production [START_REF] Liu | The development history and prospects of biomass-based insulation materials for buildings[END_REF][START_REF] Torres-Rivas | Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk[END_REF]. The most commonly used bio-based materials are wood, straw, hemp, corn, or sheep wool. Even if wood is the most developed and able to compete with traditional insulation materials, other materials are used more and more, such as cellulose wadding or hemp concrete [START_REF] Liu | The development history and prospects of biomass-based insulation materials for buildings[END_REF][START_REF] Palumbo | Bio-based insulation materials and their hygrothermal performance in a building envelope system (ETICS)[END_REF][START_REF] Torres-Rivas | Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk[END_REF].

This study investigates the development of fully bio-based composites to be used to produce rigid insulating panels. Firstly, six green binders are used. Two bio-binders are developed, they are obtained by the extraction process on corn cobs and on flax fines. Others come from the industry such as black liquor (by-product from the paper industry), molasses (by-product from the sugar industry), commercial lignin (by-product from the wood industry) and the PLA (thermoplastic binder from renewable resources).

Secondly, two types of aggregates: hemp shiv and corn cob residues (obtained after alkali treatment on the corn cob) for their good hygric property, are considered. Then, specimens are produced to verify the gluing effect, to qualify the mechanical properties and the hygrothermal performances of developed composites. The aim of these characterizations, in link with the objectives in terms of reduction of the energy needs of buildings and in terms of hygrothermal comfort of users, is to identify the best aggregate-binder mixture.

Materials and methods

Raw materials 2.1.1. Binders

Interesting gluing effect is quoted in the case of compressed straw panels, where no additional binder is needed to provide a minimum of cohesion. The raw material is just cleaned and compressed between two hot plates where it undergoes a hydrothermal treatment at 200 • C (as the STRAMIT Process [START_REF] Glassco | Modular building construction and method of building assembly[END_REF]). The cohesion of the obtained material is then ensured by the released lignin (between 8 and 17 %), hemicellulose (between 28 and 33 %) and cellulose (between 33 and 42 %) from wheat straw [START_REF] Sun | Appendix 1: Essential guides for isolation/purification of polysaccharides[END_REF].

Based on this observation, it is possible to use the components contained in the bio-based aggregates to formulate a green binder. Initial work was done to evaluate the ability of hemp shiv to be bonded by wheat straw using similar process with hydrothermal treatment and compression. Hemp shiv was mixed with wheat straw chopped with a lab blender to obtain bio-based composites. Several compositions were tested; it was shown that wheat straw ensured a good cohesion to the composite when the dry mix included at least 15 w% of chopped wheat straw [START_REF] Collet | Hemp-Straw Composites: Thermal And Hygric Performances[END_REF].

Another process is investigated in this study to obtain bio-binder by extraction of soluble components.

The extraction process consists in infusing wheat straw in solvent for several hours. Then, wheat straw is pressed in order to collect all the solvent. This solvent is partially evaporated in order to control the concentration of the solution (Figure 1). • The grain size of aggregate;

• The nature of solvent;

• The solvent concentration.

From these trials, the optimum identified conditions are a maceration in alkali solvent during 4 hours at 90 • C. The best solvent types and concentrations are deduced. This process is then applied to several raw materials: hemp shiv and fines, flax shiv and fines, rape straw, wheat straw and corn cobs. These raw materials are supplied by CAVAC, industrial partner of ISOBIO project, and are presented in other paper [START_REF] Viel | Chemical and multi-physical characterization of agro-resources' byproduct as a possible raw building material[END_REF]). Two raw materials allow to have extracts with good gluing properties and a satisfactory extraction yield (over 30 %): corn cobs and flax fines. The weight loss of agro-resources due to the extraction in the alkali solvent, is 39.51 % for the corn cobs and is 24.27 % for the flax fines. The Van Soest method described in [START_REF] Viel | Chemical and multi-physical characterization of agro-resources' byproduct as a possible raw building material[END_REF], gives the chemical composition including weight loss of the corn cobs and the flax fines before and after extraction (Table 1 and Figure 65 2). The materials after extraction are called residues, the binders obtained after extraction are named corn cob extract and flax fine extract. For corn cobs, the alkali treatment leads to substantial removal of cellulose (36.8 % before against 27.1 % after), hemicellulose (38.8 % before against 13.9 % after) and lignin (3.3 % before against 0.7 % after) but only modest dissolution of solubles content (19.3 % before against 17.5 % after) and ash content (0.5 % before against 0.4 % after). For flax fines, the alkali treatment leads to substantial removal of cellulose (28.5 % before against 16.2 % after), hemicellulose (15.8 % before against 5.8 % after) and lignin (18.1 % before against 7.7 % after), only modest dissolution of ash content (4.2 % before against 2.9 % after) but a significant increase of solubles (29.1 % before against 42.9 % after).

These differences can be explained by the weight loss after the treatments. Indeed, the main advantage of alkali treatment is efficient extraction of hemicellulose, lignin and pectin which allows to increase the exposed surface area for the reaction sites for further polymerization. However, this type of treatment has the disadvantage of forming salt and generating degradation if the time of treatment is too long. These degradations can lead to glycosidic bond (O between two aromatic rings in cellulose, hemicellulose and pectins) break which may change the structure with the repolymerization of released monomer units, the cellulose swelling and its partial decrystallization (Figure 3) [START_REF] Martelli-Tosi | Chemical treatment and characterization of soybean straw and soybean protein isolate/straw composite films[END_REF][START_REF] Mosier | Features of promising technologies for pretreatment of lignocellulosic biomass[END_REF][START_REF] Song | Processing bulk natural wood into a high-performance structural material[END_REF][START_REF] Tian | Review of the pretreatment and bioconversion of lignocellulosic biomass from wheat straw materials[END_REF]. Thus, solutions of alkaline extract from the corn cobs and the flax fines are composed of cellulose, of hemicellulose, of lignin, of solubles and of ash or of their monomer units, repolymerized or not, following the alkali treatment. These components play the role of binder during composite curing (2 hours at 190 • C) which allows to initiate repolymerization reactions.

Three other binders are selected as bio-binders coming from the industry: black liquor (waste from the paper industry), molasses (by-product from the sugar industry) and commercial lignin (by-product from the wood industry, Biochoice R powder provided by Domtar).

The last selected binder is a biodegradable thermoplastic from renewable resources: the Poly-Lactic Acid (PLA provided by Galactic -Belgium). This polymer is characterized by very high mechanical properties (flexural strength of 17.8 MPa and compression strength higher than 50 MPa, elastic modulus of 3500 MPa) and glass transition temperature around 180 • C. PLA is marketed in granular form. To be used, it is reduced in chips.

Finally, six types of binders are considered in this study : two green binders which are specifically developed here and four binders coming from the industry.

Aggregates

Two types of aggregates are considered in this study (Figure 4).

The hemp shiv is a commercial product (Biofibat -CAVAC, France) commonly used to produce hemp concrete. Its bulk density is about 100 to 110 kg/m 3 . The average width of aggregates (W 50 ) is 2.2 mm and the average length (L 50 ) is 8 mm. The maximal width is 5 mm and the maximal length is 19 mm.

The corn cob is the part of the ear on which kernels grow and is thus, a by-product coming from corn cultivation. It is processed to obtain aggregates. The bulk density is about 390 kg/m 3 . The average width of aggregates is 3.78 mm and the average length is 5.15 mm. The maximal width is 4.77 mm and the maximal length is 6.47 mm. This aggregate is used to make a green binder with its soluble components in the alkali solvent and the corn cob residues obtained are used as aggregates in the composite formulations. After the extraction, the bulk density of the corn cob residues is about 365 kg/m 3 . This study investigates the effect of formulation on multi-physical properties of composites. Two types of aggregates and six types of binders are tested. To get a large amount of information, screening design is used: Hadamard matrix. This experience design will allow to understand the effects of factors on the composites properties (thermal conductivity and moisture buffer value) [START_REF] Rabier | Modélisation par la méthode des plans d'expériences du comportement dynamique d'un module IGBT utilisé en traction ferroviaire[END_REF]. One factor has 2 levels, for aggregates (hemp shiv or corn cob residues), and one other factor has 7 levels, for binders (without binder, corn cob extract, flax fine extract, black liquor, BioChoice R lignin, Molasses or PLA).

Then, this experimental design is converted in experiment matrix, which is a mathematical entity. It includes as many lines (noted n) as formulations and as many columns (noted p) as unknown coefficients in the model. This experimental design can be rewritten in the form of the following equation:

{Y} = {B}[X] + {e} (1)
with:

• {Y}: response vector;

• [X]: matrix of the model;

• {B}: coefficient vector;

• {e}: vector of the gaps.

The mathematical analysis allows to estimate the coefficients {B} and the residues {e} by the least squares method. The effects of the factors on the responses are calculated as:

{B} = [ t XX] -1 [ t X]{Y} (2)
where [ t XX] -1 is a dispersion matrix and [ t X] is the transpose of the matrix. Once the B i coefficients are determined, they are used in the following equation to predict the responses y i .

y i = B 0 + B 1 .x 1 + B 2 .x 2 + B 3 .x 3 + B 4 .x 4 + B 5 .x 5 + [...] + ∆ + (3) with:
• y i : response;

• x i : level of the factor;

• B 0 : theoretical average value of the response (constant);

• B i : effect of the factor;

• ∆: the lack of fit;

• : random error.

The robustness of the defined model is tested with two tools: F-test for the significance of the model and the t-test for significance of the coefficients.

The analysis of variance (ANOVA) studies the differences of average between the experimental and theoretical responses. It determines if the defined model is significant or not. The total variation in component (regression sum of squares, SSR) and the other is the residual component (error sum of squares, SSE). The first is tested in comparison with the second. These components are the sum of the squared deviations and their equations are summarized in the following analysis of variance table (Table 2).

Regression p SSR M SR = SSR p F * = M SR M SE Residual error n -p -1 SSE M SE = SSE n -p -1 Total n -1 SST O with: SSR = (y i -ŷi ) 2 (4) SSE = (ŷ i -ȳi ) 2 (5)
SST O = SSR + SSE (6)
where y i are the experimental responses, ŷi are the theoretical responses and ȳi is the average 145 response. Then the F * ratio is compared to a critical variable taken in F-table for α = 5 % (risk). Thus, if F * is higher than the considered critical level, the model is considered to be statistically significant.

Another statistical analysis is performed on the coefficients (Table 3).

B i σ i = √ M SE × c ii t exp,i= = B i σi t exp > t th
with:

• σ i : standard deviation of B i ;

• c ii : diagonal term of the level i of the [ t XX] -1 dispersion matrix.

Then the t exp is compared to a critical variable from the t-distribution for α = 5 % (risk). Thus, if t exp is higher than t th , the factor effect is considered to be statistically significant. If a coefficient is considered not relevant, it is possible to eliminate it but its impact on the adjusted determination coefficient R 2 A will need to be evaluated.

In the presence of several variables, the determination coefficient R 2 is not suitable to compare the descriptive quality of the different models. The use of the adjusted coefficient of determination R 2 A is required. This coefficient takes into account the number of variables present in the model. Its calculation is given by the equation:

R 2 A = R 2 × n -1 DF = 1 - (y 2 i ) (n -1) × var(y i ) × n -1 n -p -1 (7)
The closer to 1 the R 2 A is, the closer to experimental values the calculated values will be [START_REF] Goupy | Introduction aux plans d'expériences[END_REF][START_REF] Oehlert | A first course in design and analysis of experiments[END_REF].

Finally, the interaction graph and the path diagram give the synthetic analysis of the results.

Composite production process

Table 4 shows the formulation of the produced composites.

The binder is dissolved in water and then the bio-aggregates are moistened with the solution, except for PLA where PLA chips are mixed with bio-aggregates and the mix is then moistened with water.

To ensure a good cohesion, a content of 15 % by weight of dry binder is used. In order to produce three specimens (100 × 100 × 100 mm 3 ) for each composite, the mix is divided into three equal parts and each part is introduced in one of the three cells of the mold. After, in order to reach an efficient compaction of composite, each part undergoes 5 compression cycles at 0.25 MPa in the mold. At the end of the 5 cycles, the specimen remains compacted and the pressure is considered constant at 0.25 MPa.

The whole is then placed in an oven at 190 • C for 2 hours for thermal curing. The three specimens are demolded after free cooling (Figure 5). The density is calculated from size and weighted of specimens. The three dimensions are measured with an electronic caliper (0.1 mm) and weight with an analytical balance (readability = 0.01 g, reproducibility = 0.01 g, linearity = 0.02 g). Each dimension is the average of four values. This method follows the recommendations of the standard NF EN ISO 12570 [START_REF] Afnor | NF EN ISO 12570 -Performance hygrothermique des matériaux et produits pour le bâtiment -Détermination du taux d'humidité par séchage à chaud[END_REF] which are a measure of volume at close to 1 % and a measure of mass at close to 0.1 % to calculate the apparent density of composites.

Skeleton density

The skeleton density ρ s is measured with pycnometers [START_REF] Astm | D854 -Standard test methods for specific gravity of soil solids by water pycnometer[END_REF]. The dry and crumbled composites are placed in pycnometers. Then, they are immersed in toluene and regularly shaken until there is no air remaining. The pycnometers are completely filled with toluene. The successive weighing of pycnometers (m 1 ), pycnometers with dry samples (m 2 ), pycnometers with dry samples and toluene-filled (m 3 ) and water-filled pycnometers (m 4 ) leads to the mass of the samples and their volume (Figure 7). The density of toluene is also measured by pycnometer, filling it with toluene and water. Three pycnometers of about 600 ml are used for each composite.

ρ s = m sample V sample = m sample V pycno -V toluene = m 2 -m 1 m 4 ρ water - m 3 -m 2 ρ toluene (8)
Where the density ρ toluene corresponds to the density of the toluene and the density ρ water corresponds to the density of water. The densities ρ toluene and ρ water are temperature-dependent.

Total porosity

The total porosity n t of composite is the sum of closed and open porosity and intergranular macroporosity. Assuming that the entire porosity has been accessed during pycnometer measurement, the total porosity results in the equation [START_REF] Collet | Hemp-Straw Composites: Thermal And Hygric Performances[END_REF].

n t = ρ s -ρ app ρ s (9)
with ρ s which corresponds to the the skeleton density of the sample and ρ app to its apparent density.

Surface Morphology by Scanning Electron Microscopy

SEM is used to view the gluing between aggregates and binder. Some aggregates with binder are manually removed from a composite, glued with araldite glue and coated with a layer of palladium (thickness about 30 nm) before the characterization. Scanning electron microscopy (SEM) is performed with a JSM 7100F (Jeol) equipped with Everhart-Thornley secondary electron detector and Schottky field emission.

Mechanical characterization

Compressive tests are performed with a Zwick/Roell ProLine testing machine fitted with a 20 kN XForce load cell (load up to 0.02 % of its full capacity and 0.05 % readability) in order to check that the composites are self-bearing. The tests are carried out in displacement with a cross-head speed equal to 0.05 mm.s -1 . The loading is monotonous (no loading cycles) with testing direction parallel to the compression direction during the production. The samples are placed between two steel plates in order to guarantee a homogeneous displacement and pressure. The load is applied by the displacement of the upper plate. The test is performed on 3 samples for each formulation.

The results of the mechanical tests are analyzed using stress-strain curves, according to the NF EN 826 standard [START_REF]Thermal insulating products for building applications -Determination of compression behaviour[END_REF]. The stress is assessed by reporting the load to the initial surface of the sample and the deformation is relative to the initial height of the sample. The origin of the stress-strain curve is adjusted in order to free itself from the contact effects between the plates and the surface of the samples, which is not perfectly flat.

Thermal characterization

The measurement of thermal conductivity is performed with a transient method: Hot Wire, following the method described by Collet and Pretot [START_REF] Collet | Thermal conductivity of hemp concretes: Variation with formulation, density and water content[END_REF]. The measurement is realized with the commercial CT Meter device equipped with a five-centimeter long hot wire. The power is 142 mW (n • 1 to 4) or 205 mW

Hygric characterization

The hygric performance is characterized by the measurement of the moisture buffer value (MBV) of composites. This value characterizes the ability of the materials to moderate the variations of indoor humidity in buildings.

The moisture buffer value is performed following the Nordtest protocol [START_REF] Rode | Moisture buffering of building materials[END_REF]. After the stabilization of specimens at 23 • C, 50 %RH and their sealing on all their surfaces except one (the one that has been compressed during the production of composites), specimens are exposed for 8 hours at 75 %RH and for 16 hours at 33 %RH during 5 days in a climate chamber (Vötsch VC4060). The specimens are regularly ). Then, the moisture buffer value is determined according to the following equation:

M BV = ∆m A.(RH high -RH low ) ( 10
)
Where MBV is the moisture buffer value (g/(m 2 .%RH)), ∆m is the moisture uptake/release during the period (g), A is the open surface area (m 2 ), RH high/low is the high/low relative humidity level (%).

For each formulation, the MBV is the average of the values obtained for the three specimens.

Results

Apparent and skeleton densities and total porosity

Except for composite n The skeleton density of composites ranges from 1124.6 to 1211.0 kg/m 3 for hemp shiv composites and from 1238.8 to 1310.0 kg/m 3 for corn cob residues composites. The highest values of corn cob residues composites are due to higher skeleton density of aggregate (about 1350 versus 1550 kg/m 3 ).

The variation of skeleton density with binder shows the same trend for hemp shiv composites and corn cob residues composites. However, it does not vary only with the proportion of each component due to chemical reaction and volatilization during the production of composites.

The composites based on hemp shiv have very close total porosities ranging from 84.2 % to 87.5 % except the one made with PLA. Indeed, it has the lowest total porosity (77.5 %) of the composites made with hemp shiv. However, the composites made with corn cob residues have lower total porosity than the composites made with hemp shiv. Indeed, they have very close porosities ranging from 60.0 % to 65.5 %.

Surface Morphology by Scanning Electron Microscopy

Figure 9 presents SEM micrographs at the interface between the aggregates and the binder. For all composites, SEM analysis evidences good adhesion at the interface showing several hemp shiv well coated and glued together. There are micro-structural differences at the interface between the aggregates and the different types of binders.

The surface of composites n • 1 and 2 and 3 (Figures 9.a to 9.d) are similar. The binders lead to a grain deposit on the hemp shiv.

For composite n • 4 (Figure 9.e), the hemp shiv are well coated with lignin in some places. The lignin coats the hemp shiv with a more or less thin smooth film depending on the location. Thus, the adhesion between aggregates and binder is good but the increased thickness of the binder may seal the hemp shiv pores in some places.

For composite n • 5 (Figure 9.f), the hemp shiv are well coated with molasses. The molasses coat the hemp shiv with a thin rough film.

For composite n • 6 (Figure 9.g), the hemp shiv are well coated with PLA in some places. A few large spots of PLA are visible between the hemp shiv so, the PLA coats the hemp shiv with a thick smooth film. Thus, the adhesion between aggregates and binder is good but the thickness of the binder seals the hemp shiv pores and fills the inter-particular space. Indeed, the total porosity is 77.5 % while it is around 85.5 % for other composites made with hemp shiv.

The surface of composites n • 7 and 8 (Figures 9.h to 9.j) are similar but different from composites made with hemp shiv for a same binder. It can be explained by the difference in the composition of agro-resources. Indeed, the corn cob residues have been previously treated with an alkaline solution at 90 • C. Thus, their surface is more reactive [START_REF] Mosier | Features of promising technologies for pretreatment of lignocellulosic biomass[END_REF][START_REF] Tian | Review of the pretreatment and bioconversion of lignocellulosic biomass from wheat straw materials[END_REF] than that of hemp shiv for a same binder. The corn cob residues are coated with a thin smooth layer in some areas but several fracture zones are visible above. More, the composite surfaces include sodium silicate crystals for composites n • 7 and 8. A similar For composite n • 9 (Figure 9.k), the corn cob residues are well coated with black liquor. The black liquor coats the corn cob residues with a thick rough film. The roughness probably corresponds to mineral salts containing silicates. The adhesion between the corn cob residues and the black liquor seems to be less good than with the extracts.

For composite n • 10 (Figure 9.l), the corn cob residues are well coated with lignin. The lignin coats the corn cob residues with a thick rough film which includes several fracture zones. The roughness probably corresponds to mineral salts containing silicates.

Mechanical characterization

Two types of strain-stress curve are obtained.

Curves with a continuous increase in the stress versus strain correspond to compacting behavior (Figure 10.a). For such behavior, the mechanical performance is given by the compressive strength σ 10% obtained for longitudinal strain = 10 % (Figure 10.a) [START_REF]Thermal insulating products for building applications -Determination of compression behaviour[END_REF]. Such curves are obtained for hemp shiv composites and corn cob residues composites with extract binders.

Curves with a peak in the stress-strain curve correspond to ductile behavior (Figure 10.b). For such behavior, the mechanical performance is given by the maximal compressive strength σ m obtained for deformations m under 10 % (Figure 10.b) [START_REF]Thermal insulating products for building applications -Determination of compression behaviour[END_REF]. Such curves are obtained for corn cob residues composites with black liquor and BioChoice R lignin.

This behavior difference is mainly explained by the shape of the aggregates (rectangular for hemp shiv and ovoid for corn cob residues) as well as the total porosity of the composites (about 84.2 % for those made with hemp shiv compared to about 62.4 % for those made with corn cob residues).

The mechanical properties of composites are presented in Figure 11 and Table 6. Experimental values are closer to each other formulation for the composites made with the hemp shiv than those made with the corn cob residues. As shown on Figure 11, the compressive strength for the composites made with the hemp shiv and extracts is around 239 kPa whereas the other composites made with the hemp shiv have a better compressive strength due to a better adhesion between the hemp shiv and the binders (range from 227 to 421 kPa). The composite made with hemp shiv and PLA has the highest compressive strength of composites made with hemp shiv. Compressive strength at 10 % deformation, varies between 492 and 696 kPa for the two specimens made with corn cob residues and extract. Composites n • 7 have the highest compression strength. Thus, the corn cob residues have a good adhesion with the extracts (corn cob extract and flax fine extract) although it is better with the corn cob extract. However, the corn cob residues have a poor adhesion with the other binders (black liquor and BioChoice R lignin)

because the maximal compressive strengths are 202 and 32 kPa respectively for a deformation lower than 7.4 %.

The compressive strength at 10 % deformation of composites is higher than 225 kPa except composites n • 9 and n • 10 (corn cob residues/black liquor and corn cob residues/BioChoice R lignin). According to the composite densities, for stress corresponding to 3 meters in height, the obtained deformations ( h=3m ) are lower than 0.50 %. So, the mechanical properties are sufficient for an application as insulation panels without risk of compaction. Compared with compressive strength at 10 % deformation obtained in the literature, these values are lower. Indeed, Nguyen et al [START_REF] Nguyen | Hygrothermal properties of bio-insulation building materials based on bamboo fibers and bio-glues[END_REF], who studied composites made with bamboo fibers and bio-glues have obtained better results as they range from 2700 to 14200 kPa (densities from 311 to 538 kg/m 3 ).

Beside, the hemp-starch composites developed by Bourdot et al [START_REF] Bourdot | Characterization of a hemp-based agro-material: Influence of starch ratio and hemp shive size on physical, mechanical, and hygrothermal properties[END_REF] have similar mechanical properties except the one made with corn cob residues and corn cob extract which have better properties (around 700 kPa for density around 125 kg/m 3 at 10 % deformation).

Compared with maximal compressive strength obtained by Ratiarisoa et al [START_REF] Ratiarisoa | Assessment of distilled lavender stalks as bioaggregate for building materials: Hygrothermal properties, mechanical performance and chemical interactions with mineral pozzolanic binder[END_REF] for the composites made with residues of lavender and mineral pozzolanic binder this value (220 kPa for density around 620 kg/m 3 at dry state) is slightly better than this obtained for the composites made with corn cob residues and black liquor (203 kPa).

Thermal characterization

To validate thermal conductivity measurement, an infrared thermography picture is taken on each specimen immediately after the measurement. For all specimens, all the volume influenced by the probe is included in the specimen volume, as shown as examples on Figure 12 for composites n • 5 (left) and n • 8 (right). The thermal footprint shows that x: the heat flow remains in the sample during the measurement and that y: the probe volume is representative of the material. Thus, the measurements are representative of the studied materials. Table 7 and Figure 13 show the thermal conductivity for the different formulations developed in this study. At dry state, the thermal conductivity of developed composites ranges from 67.5 to 78.6 mW/(m.K)

for hemp shiv composites and from 128.4 and 147.9 mW/(m.K) for corn cob residues composites. The lowest values of hemp shiv composites are due to lower thermal conductivity of aggregate (53.5 versus 85.1 mW/(m.K)).

The composite with the PLA has a thermal conductivity slightly higher than the others made with the hemp shiv. The composite with the BioChoice R lignin has a thermal conductivity slightly lower than the others made with the corn cob residues.

As shown on Figure 13.a, the thermal conductivity of the composites increases linearly with density.

The correlation coefficient of the fitting curve is very close to 1.The slope of the regression curve for the composites (yellow curve), is more important than the slope of the regression curve for the aggregates (green curve corresponding to bio-aggregates studied in [START_REF] Viel | Chemical and multi-physical characterization of agro-resources' byproduct as a possible raw building material[END_REF]). Thus, the thermal conductivity increases more quickly with the apparent density in the case of the composites.

As shown on Figure 13.b, the thermal conductivity of the composites is higher at (23 • C; 50 %RH) than at (23 • C; dry). The regression lines of the thermal conductivity versus the apparent density at dry state and at 23 • C, 50 %RH are almost parallel but the intercept of the regression line for the composites at dry state (yellow curve), is lower than the intercept of the regression line for the composites at 23 • C, 50 %RH (orange curve). Indeed, the apparent density of the composites increases with the increased ambient humidity (including an increase in water content), resulting in the increase in the thermal conductivity.

To obtain additional information, the thermal conductivity values obtained at dry state, are exploited through the design of experiment. Following the F-test (analysis of variance), the model with 6

coefficients is significant and has the best adjusted determination coefficient (R 2 a = 0.9319) and determination coefficient (R 2 = 0.9437). Thus, the equation to predict the thermal conductivity at dry state is the following:

Figure 14 shows the interactions between the aggregates and the binders. The slope of the lines for the composites, whatever the binder, is more important than the slope of the line in the case of the 375 bulk. The interaction between the hemp shiv and the binders is the same except for the PLA where the interaction is more important. The lines of the corn cob extract and the black liquor are confused.

Their impact is the same on the thermal conductivity for these two aggregates. The interaction between the corn cob residues and the binders is not the same. Indeed, it is more important for the flax fine extract and less important for the BioChoice R lignin.

380

For an identical production process, Figure 15 shows that thermal conductivity increases when the hemp shiv are replaced by the corn cob residues (B1 coefficient) and when the bulk is converted into composites (coefficients B2, B3, B4 and B5). Indeed, the density of the composites increases with the use of corn cob residues as aggregates. The binder with the least important impact is the lignin (coefficient B5). The corn cob extract (coefficient B2) and the black liquor (coefficient B4) both have high impact on the thermal conductivity (nearly the same). The path diagram gives the flax fine extract the highest impact. However, the impact of flax fine extract is much higher on corn cob composites than on hemp shiv composites. Indeed, the impact induced by flax fine extract is not only due to the type of binder but also to its effect on composite apparent density. Compared with other bio-based composites, the hemp composites have higher thermal conductivity 390 than commercial soft hemp insulation materials in the UK. Indeed, the thermal conductivity of the commercial products ranges from 38 to 43 mW/(m.K) for density around 50 kg/m 3 at dry state [START_REF] Latif | Hygric properties of hemp[END_REF].

Thus, this difference is mainly explained by the lowest density of the composites found in literature.

However, hemp-starch composites have a similar thermal conductivity. Indeed, the thermal conductivity of hemp-starch ranges from 48 to 74 mW/(m.K) for density around 125 kg/m 3 at dry state [START_REF] Bourdot | Characterization of a hemp-based agro-material: Influence of starch ratio and hemp shive size on physical, mechanical, and hygrothermal properties[END_REF].

The corn cob residues composites have higher thermal conductivity than the composites made with bamboo fibers and bio-glues and similar to the composites made with residues of lavender and mineral pozzolanic binder. Indeed, the thermal conductivity ranges from 55 for low density (311 kg/m 3 ) to 88 mW/(m.K) for high density (538 kg/m 3 ) at 25 • C and 57 %RH for the composites made with bamboo fibers and bio-glues [START_REF] Nguyen | Hygrothermal properties of bio-insulation building materials based on bamboo fibers and bio-glues[END_REF] whereas the thermal conductivity ranges from 142 to 162 mW/(m.K) for density around 620 kg/m 3 at dry state for the composites made with residues of lavender and mineral pozzolanic binder [START_REF] Ratiarisoa | Assessment of distilled lavender stalks as bioaggregate for building materials: Hygrothermal properties, mechanical performance and chemical interactions with mineral pozzolanic binder[END_REF].

Hygric characterization

Figure 16 shows the ambient relative humidity and temperature in the climate chamber during the test. The average value of relative humidity is slightly lower than 75 % during absorption (about 71.4 %)and slightly higher than 33 % during desorption (about 35.5 %) due to the fact that the door of the climate chamber is regularly opened to weigh specimens (peak on the curve). As shown on Figures 18 and19, the composites made with corn cob residues have a better MBV than the composites made with hemp shiv for a same binder. That makes sense because in bulk, the corn cob residues already have a better MBV than the hemp shiv.

The composites made from similar binders (corn cob extract, flax fine extract and black liquor)

and hemp shiv, have close MBV around 3.14 g/(m 2 .%RH). These composites have the best MBV of composites made with hemp shiv probably due to a grain deposit of the binder which increases the specific surface area available for moisture adsorption. Composite n • 6, which has the lowest MBV (1.86 g/(m 2 .%RH)), is made with hemp shiv and PLA. This is probably due to the fact that PLA reduces the accessible porosity. The others composites made from hemp shiv have a MBV slightly higher around 2.05 g/(m 2 .%RH). The composite made from similar binders (corn cob extract, flax fine extract and black liquor) and corn cob residues also have close MBV, around 4.90 g/(m 2 .%RH).

Thus, the MBV is impacted by the type of aggregate and binder, not only by bulk density.

%RH)) versus composites

To obtain additional information, the results are exploited through the design of experiment.

Following the F-test (analysis of variance), the model with 8 coefficients is significant and has the best adjusted determination coefficient (R 2 a = 0.9886) and determination coefficient (R 2 = 0.9909) which are close to 1. Thus, the equation to predict the moisture buffer value is the following:

M BV i = 2.38 + 0.86 × x 1 + 1.16 × x 2 + 1.20 × x 3 + 1.26 × x 4 + 0.23 × x 5 -0.30 × x 6 -0.48 × x 7 (12)
Figure 20 shows the interactions between the aggregates and the binders. The lines of the two extracts are confused. Their impact is the same on the MBV for these two aggregates. The slope of the lines for the black liquor and the BioChoice R lignin are the same. However, the interaction between the black liquor and the aggregates is better than the one of the BioChoice R lignin. The interaction interaction between the hemp shiv and the other binders is less important as the synergy is negative.

The interaction between the corn cob residues and the binders is not the same. Indeed, it is more important for the black liquor and less important for the BioChoice R lignin. Figure 21 shows that MBV increases when the hemp shiv are replaced by the corn cob residues 440 (coefficient B1) and when the bulk is converted into composites (coefficients B2, B3, B4 and B5). For the coefficients B6 and B7, when the bulk is converted into composites, the MBV decreases. These binders seal the pores of agro-resources because the shiv are coated with a thick film. The binder which has the most important impact on the increase of the MBV is the black liquor (coefficient B4). The binder with the least important positive impact is the BioChoice R lignin (coefficient B5). The corn cob MBV. This impact is somewhat less important than the one of black liquor. Compared with other bio-based composites, the hemp composites are in the average of the MBV.

For commercial hemp insulation materials in the UK, the MBV ranges from 1.5 to 2.7 g/(m 2 .%RH) [START_REF] Latif | Hygric properties of hemp[END_REF],

while for hemp-starch, the MBV ranges from 2.4 to 3.4 g/(m 2 .%RH) [START_REF] Maalouf | Study of the Hygric Behaviour and Moisture Buffering Performance of a Hemp-Starch Composite Panel for Buildings[END_REF].

Compared with other bio-based composites, the corn cob residues composites have the highest MBV.

For the composites made with residues of lavender and mineral pozzolanic binder, the MBV ranges from 3.5 to 3.9 g/(m 2 .%RH) [START_REF] Ratiarisoa | Assessment of distilled lavender stalks as bioaggregate for building materials: Hygrothermal properties, mechanical performance and chemical interactions with mineral pozzolanic binder[END_REF], while the composites made with bamboo fibers and bio-glues, the MBV ranges from 2.5 to 3.5 g/(m 2 .%RH) [START_REF] Nguyen | Hygrothermal properties of bio-insulation building materials based on bamboo fibers and bio-glues[END_REF].

Conclusion

This study shows that it is possible to produce fully bio-based composites. Indeed, the use of these combinations of binders and aggregates is very interesting from the environmental perspective because local agriculture is given priority, waste and by-products are used in the production of these binders where none additive is used.

Two green binders produced from alkaline extraction carried out on corn cobs and flax fines, have been developed throughout this work. This process allows to extract some cellulose, hemicellulose, lignin and pectin content, repolymerized or not, with a good reactivity under heating. So, these extracts are used as binder. During the production of composites, the composite curing step allows to initiate the repolymerization of components contained in the agro-resources extracts.

With a zero waste perspective, the corn cob residues that left after the alkali extraction are used as aggregates in this study. They have the advantage to have more important specific surface areas (for further polymerization) than the untreated corn cobs. Their cohesion with binder should be better and so, their mechanical properties improved. The performances of composites made with these aggregates have been compared with those of composites made with hemp shiv.

The density of developed composites ranges from 177 to 273 kg/m 3 with hemp shiv as aggregates, and ranges from 457 to 557 kg/m 3 with corn cob residues as aggregates. Their mechanical performances are sufficient to be used as self bearing materials. The thermal conductivity ranges from 67.5 to 147.9 mW/(m.K). It is mainly dependent on density but it is also slightly impacted by the type of binder.

Thus, the composites made with hemp shiv have a lower thermal conductivity than the ones made with the corn cob residues. The composites are all excellent hygric regulators (MBV > 2 g/(m 2 .%RH)) except the composite made with PLA, which is only a good hydric regulator. For a same binder, the composites made with corn cob residues have a better MBV than the composites made with hemp shiv. More, the use of the molasses and the PLA decreases the MBV because these binders seem to seal the pores of agro-resources.

Finally, the production of a two-layer thermal insulating panel would be ideal. Indeed, the composites made with hemp shiv can be used for distributed insulation because they have a low thermal conductivity.

Moreover, it may be even lower if the density of the composites is decreased. It is interesting to add a second layer made of corn cob residues and extracts from agro-resources, one centimeter thick, for its excellent ability to moderate the variations of the relative humidity in the surrounding air. Thus, further research is still required to qualify the hygrothermal properties of a such multi layer system in real condition.

Figure 1 :

 1 Figure 1: The extraction process on bio-aggregates

Figure 2 :

 2 Figure 2: The chemical composition of the corn cobs (up) and flax fines (down) before and after the alkali-treatment (C:

Figure 3 :

 3 Figure3: Schematic of alkali-treatment effects on agro-resources[START_REF] Mosier | Features of promising technologies for pretreatment of lignocellulosic biomass[END_REF]

Figure 4 :

 4 Figure 4: Aggregates used to test composite formulations

Figure 5 :

 5 Figure 5: The production of composites

Figure 6 :

 6 Figure 6: Developed composites

  190

Figure 7 :

 7 Figure 7: Skeleton density measurement protocol

(n • 5

 5 to 10) and the heating time is 120 seconds. The probe is placed perpendicular to the compression direction during production of composites.Before taking the measurements, the specimens are first dried at 60 • C in an oven. Then, the measurements are performed after weight stabilization at 23 • C at dry state in desiccator and after weight stabilization at 23 • C, 50 %RH in climate chamber. For each formulation, three pairs of specimens (A&B, A&C, and B&C) are measured. The thermal conductivity of a pair is the average of three values with a coefficient of variation lower than 5 %. The thermal conductivity of a composite is the average of the values obtained for the three pairs (Figure8).

Figure 8 :

 8 Figure 8: Experimental device for the measurement of thermal conductivity

Figure 9 :

 9 Figure 9: SEM micrographs at the interface between the aggregates and the binder: (a) and (b) composite n • 1, (c) composite n • 2, (d) composite n • 3, (e) composite n • 4, (f) composite n • 5, (g) composite n • 6, (h) and (i)composite n • 7, (j) composite n • 8, (k) composite n • 9 and (l) composite n • 10

Figure 10 :

 10 Figure 10: Strain-stress curve for composites 4.b (a) and 9.b (b)

Figure 11 :

 11 Figure 11: Stress at 10 % deformation or maximal stress versus apparent density at 23 • C and 50 %RH of composites

Figure 12 :

 12 Figure 12: Infrared thermography pictures of specimens 5 (on the left) and 8 (on the right) immediately after the

Figure 13 :

 13 Figure 13: Thermal conductivity of composites versus their apparent density : (a) Comparison between the composites and the aggregates [10] at (23 • C; dry) and (b) Comparison between the thermal conductivity values of the composites at (23 • C; dry) and at (23 • C; 50 %RH).

Figure 14 :

 14 Figure 14: Interaction graph for the thermal conductivity at dry state (mW/(m.K))

Figure 15 :

 15 Figure 15: Path diagram for the thermal conductivity at dry state (mW.(m.K))

Figure 16 :

 16 Figure 16: Monitored relative humidity and temperature in the climate chamber during MBV test

Figure 17 :

 17 Figure 17: Moisture uptake and release for sample n • 1-A

Figure 18 :

 18 Figure 18: Moisture buffer value (g/(m 2 .%RH)) versus composites

Figure 19 :

 19 Figure 19: Average moisture buffer value of composites (g/(m 2 .%RH)) versus apparent density

Figure 20 :

 20 Figure 20: Interaction graph for the moisture buffer value (g/(m 2 .%RH))

Figure 21 :

 21 Figure 21: Path diagram for the moisture buffer value (g/(m 2 .%RH))

Table 1 :

 1 Chemical composition of corn cobs and flax fines before and after the alkali-treatment (including weight loss)

	Agro-resources Cellulose (%) Hemicellulose	Lignin (%)	Solubles (%)	Ash (%)
			(%)
	Corn Cobs	36.78 ± 0.96	38.81 ± 0.72	3.30 ± 0.10	19.30 ± 1.74	0.46 ± 0.01
	Corn cob residues	27.10 ± 0.47	13.87 ± 0.86	0.64 ± 0.09	17.53 ± 1.23	0.43 ± 0.07
	Flax fines	28.51 ± 0.79	15.80 ± 0.26	18.14 ± 0.28	29.15 ± 0.35	4.20 ± 0.07
	Flax fine residues

16.21 ± 1.09 5.83 ± 0.71 7.74 ± 0.47 42.89 ± 3.60 2.92 ± 1.09

Table 2 :

 2 Standard analysis of variance table

	Source of variation	Degrees of	Sum of square	Mean square	Fisher
		freedom

Table 3 :

 3 Table of statistical analysis of the coefficients

	Coefficient	σ	t exp	Significance

Table 4 :

 4 Formulation of composites

	N • Aggregates	Binder (B)	B/A ratio
		(A)
	0A	Hemp shiv	Without	0 w%
	1	Hemp shiv	Corn cob	w%
			extract
	2	Hemp shiv	Flax fine	w%
			extract
	3	Hemp shiv	Black liquor	w%
	4	Hemp shiv	BioChoice R lignin w%
	5	Hemp shiv	Molasses	w%
	6	Hemp shiv	PLA	w%
	0B	Corn cob	Without	0 w%
		residues
	7	Corn cob	Corn cob	w%
		residues	extract
	8	Corn cob	Flax fine	w%
		residues	extract
	9	Corn cob	Black liquor	w%
		residues
	10	Corn cob	BioChoice R lignin w%
		residues
	11	Corn cob	Molasses	w%
		residues
	12	Corn cob	PLA	w%
		residues

  extracts and black liquor, have very close densities ranging from 520 to 557 kg/m 3 at dry state. The composite with the lignin has the lowest density (457 kg/m 3 at dry state) of the composites based on corn cob residues. The increase in apparent density between the dry state and the state at (23 • C; 50 %RH) ranges from 2.09 % (for the one made with hemp shiv and PLA) to 7.52 % (for the one made with corn cob residues and flax fine extract).

•

6, the composites based on hemp shiv have very close densities ranging from 177 to 191 kg.m 3 at dry state except the hemp shiv with PLA composite (n • 6) which has the highest density (273 kg/m 3 ). The composites based on corn cob residues have a density much higher than composites based on hemp shiv, due to a much higher aggregate density. The three composites, with the aggregate

Table 5 :

 5 Apparent density at (23 • C, 50%RH) and (23 • C, dry), skeleton density and total porosity of composites: average value and standard deviation

	Composites	1	2	3	4	5	6	7	8	9	10
	ρ 23 • C-50%RH	177.7	179.6	191.4	179.0	187.4	272.9	519.9	556.9	527.0	457.3
	(kg/m 3 )	± 2.4	± 5.7	± 0.9	± 1.3	± 1.4 ± 18.9 ± 9.8	± 9.7	± 5.4 ± 15.3
	ρ 23 • C-dry	167.0	168.8	180.7	170.9	177.4	267.2	481.4	515.0	488.4	427.0
	(kg/m 3 )	± 2.1	± 5.3	± 0.9	± 1.1	± 1.1 ± 19.0 ± 8.6	± 7.8	± 4.7 ± 14.0
	ρ s	1130.9 1178.0 1211.0 1150.1 1124.6 1186.9 1249.8 1286.7 1310.0 1238.8
	(kg/m 3 )	± 7.3 ± 12.5 ± 7.2	± 2.4 ± 16.9 ± 57.7 ± 1.4	± 9.8	± 1.4	± 6.4
	n tot	87.5%	85.7%	85.1%	85.1%	84.2%	77.5%	61.5%	60.0%	62.7%	65.5%

Table 6 :

 6 Stress at 10 % deformation or maximal stress for each composites

	Composites	1	2	3	4	5	6	7	8	9	10
	ρ 23 • C-50%RH	184.2	181.9	203.2	184.4	175.6	271.7	534.5	526.8	573.4	445.9
	(kg/m 3 )	± 4.1	± 3.1	± 1.1	± 0.7	± 2.1 ± 26.0 ± 3.5	± 4.8	± 6.7	± 6.5
	σ 10%	259.7	227.5	230.0	297.3	313.7	420.8	695.7	491.8	-	-
	(kPa)	± 34.2 ± 9.9	± 6.5	± 4.2	± 2.4 ± 25.7 ± 42.7 ± 47.8	-	-
	σ m	-	-	-	-	-	-	-	-	202.3	31.9
	(kPa)	-	-	-	-	-	-	-	-	± 32.7 ± 7.4
	m (%)	-	-	-	-	-	-	-	-	7.28	2.95
	h=3m (%)	0.20	0.22	0.22	0.16	0.14	0.13	0.36	0.47	0.47	2.95

Table 7 :

 7 Thermal conductivity of composites (mW/(m.K)) versus apparent density at (23 • C, 50%RH) and at (23 • C, dry)

	Composites	1	2	3	4	5	6	7	8	9	10
	ρ 23 • C-50%RH	176.4	178.5	190.4	178.1	186.1	272.7	513.0	547.5	521.5	455.7
	(kg/m 3 )	± 2.1	± 5.6	± 1.0	± 1.3	± 1.1 ± 19.3 ± 8.7	± 7.9	± 5.4 ± 17.0
	λ 23 • C-50%RH	78.5	78.1	78.2	75.6	77.8	81.2	156.9	171.6	157.7	143.5
	(mW/(m.K)) ± 1.3	± 1.8	± 3.3	± 1.8	± 1.6	± 4.0	± 5.3	± 4.5	± 5.1	± 2.8
	ρ 23 • C-dry	167.0	168.8	180.7	170.9	177.4	267.2	481.4	515.0	488.4	427.0
	(kg/m 3 )	± 2.1	± 5.3	± 0.9	± 1.2	± 1.1 ± 19.0 ± 8.6	± 7.9	± 4.7 ± 14.0
	λ 23 • C-dry	70.8	70.2	71.1	67.5	70.5	78.6	140.3	147.9	136.5	128.4
	(mW/(m.K)) ± 0.9	± 1.2	± 1.5	± 1.3	± 0.8	± 1.7	± 4.1	± 4.7	± 4.8	± 4.7

  i = 38.56 + 60.97 × x 1 + 36.35 × x 2 + 40.13 × x 3 + 34.75 × x 4 + 28.88 × x 5

• C; 50 %RH).

λ

Table 8 ,

 8

Figures 18 and 19 summarize the moisture buffer values obtained in absorption, desorption and in average. The standard deviations are low, leading to coefficients of variation lower than 4.5 %.

The average MBV ranges from 1.86 to 5.08 g/(m 2 .%RH). According the Nordtest classification

[START_REF] Rode | Moisture buffering of building materials[END_REF]

, only composite n • 6 is good hygric regulator (1 < MBV < 2 g/(m 2 .%RH)). The others composites are all excellent hygric regulators (MBV > 2 g/(m 2 .%RH)).

Table 8 :

 8 Moisture Buffer Value of composites in absorption, desorption and average: average value and standard deviation .%RH) ± 0.11 ± 0.11 ± 0.14 ± 0.06 ± 0.05 ± 0.07 ± 0.07 ± 0.06 ± 0.21 ± 0.04 M BV des. .%RH) ± 0.10 ± 0.13 ± 0.14 ± 0.06 ± 0.04 ± 0.09 ± 0.10 ± 0.07 ± 0.16 ± 0.07 M BV av. .%RH) ± 0.09 ± 0.11 ± 0.14 ± 0.06 ± 0.04 ± 0.07 ± 0.08 ± 0.06 ± 0.18 ± 0.05

	Composites	1	2	3	4	5	6	7	8	9	10
	M BV abs.	3.05	3.10	2.96	2.00	2.02	1.84	4.70	4.69	4.98	3.89
	g/(m 2 3.19	3.23	3.09	1.89	2.09	1.89	4.88	4.95	5.19	4.10
	g/(m 2 3.12	3.16	3.12	2.05	2.05	1.86	4.79	4.82	5.08	3.99
	g/(m 2

extract (coefficient B2) and the flax fine extract (coefficient B3) have nearly the same impact on the

Acknowledgments

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 636835 -The authors would like to thank them. CAVAC, industrial partner of the ISOBIO project, is gratefully acknowledged by the authors for providing raw materials. Thanks are due to Tony Hautecoeur for his participation in the completion of this work. Loïc Joanny and Francis Gouttefangeas are acknowledged for SEM images performed at CMEBA (ScanMAT, University of Rennes) which received a financial support from the Région Bretagne and European Union (CPER-FEDER 2007-2014). Thanks are due to Étienne Labussière (Van Soest analysis) and Yann Lecieux (Mechanical tests).

Thanks are also due to Céline Leutellier for having reviewed the English language.

Y (total sum of squares, SSTO) is divided into two components: the one is the regression equation between the hemp shiv and the binders are the same for the extract binders and the black liquor. The


Jimmy H Alarcan
email: jimmy.alarcan@anses.fr

Sabrina Barbé
email: sabrina.barbe.53@gmail.com

Benjamin Kopp
email: benjamin.kopp@anses.fr

Stefanie Hessel-Pras

Albert Braeuning
email: albert.braeuning@bfr.bund.de

Alfonso Lampen
email: alfonso.lampen@bfr.bund.de

Ludovic Le Hegarat
email: ludovic.lehegarat@anses.fr

Valérie Fessard
email: valerie.fessard@anses.fr

Ludovic Le Hégarat

Combined effects of okadaic acid and pectenotoxin-2, 13-desmethylspirolide C or yessotoxin in human intestinal Caco-2 cells

Keywords: phycotoxins, mixtures, antagonism, synergism, genotoxicity, inflammation ATP-binding cassette transporter, ADME: absorption, distribution, metabolism, excretion, CI: combination index, CYP: cytochrome P450, DSP: diarrhetic shellfish poisoning, EFSA: European Food Safety Authority, IL-8: interleukin 8, OA: okadaic acid, P-gp: Pglycoprotein, PP2A: protein phosphatase 2A, PTX-2: pectenotoxin-2, PXR: pregnane-X-receptor, ROS: reactive oxygen species, SM: shellfish meat, SPX-1: 13-desmethylspirolide C, TEF: toxicity equivalent factor

Introduction (750 words)

Marine biotoxins are secondary metabolites produced by specific phytoplanktonic species [START_REF] Visciano | Marine biotoxins: occurrence, toxicity, regulatory limits and reference methods[END_REF]. Among the lipophilic marine biotoxins, four main families have been described: okadaic acid (OA) and dinophysistoxins (DTXs), pectenotoxins (PTXs), yessotoxins (YTXs) and finally cyclic imines (including spirolides, pinnatoxins, pteriatoxins and gymnodimines). OA and DTXs are responsible for diarrhetic shellfish poisoning (DSP), characterized by diarrhea, nausea, abdominal pain and vomiting [START_REF] Valdiglesias | Okadaic acid: more than a diarrheic toxin[END_REF]. The OA group of toxins inhibits serine/threonine protein phosphatase 2A (PP2A) and, to a lesser extent, also PP1 [START_REF] Takai | Inhibitory effect of okadaic acid derivatives on protein phosphatases. A study on structure-affinity relationship[END_REF]. The group of PTXs, principally PTX-2, is not anymore considered a DSP toxin since its implication in gastro-intestinal symptoms is not clear [START_REF] Ito | Studies of diarrhetic activity on pectenotoxin-6 in the mouse and rat[END_REF]. At the cellular level, PTX-2 provokes actin depolarization leading to a disruption of the cytoskeleton (Alligham et al., 2007). The group of YTXs has no recorded effects in humans but has been frequently found together with OA in shellfish (Alarcan et al., 2018). YTX displays in vitro toxicity, for instance apoptosis induction and disturbance of the calcium flux (Tubaro et al., 2008). Its mechanism of action is still unknown but some studies suggest a possible connection with autophagy [START_REF] Fernández-Araujo | Key role of phosphodiesterase 4A (PDE4A) in autophagy triggered by yessotoxin[END_REF]. Finally, potent neurological effects in mice have been reported for the group of cyclic imines (Munday et al., 2012). SPX-1 is a selective inhibitor of nicotinic acetylcholine receptors (Araoz et al., 2015).

For some of them, regulatory limits were set to protect consumers from their potential harm as they can contaminate shellfish and be responsible for foodborne outbreaks (EFSA report 2009). OA and PTX-2 together share a limit of 160 µg /kg shellfish meat (SM), and for YTX and its analogues the limit is set to 3.75 mg YTX eq/kg SM. However, in the absence of reports on human intoxications, no regulatory limits have been established for the group of cyclic imines, even though they are frequently detected in shellfish and can be very toxic to mice following intra-periteonal or oral administration (Munday et al., 2012). Mixtures of marine biotoxins are only regarded by the abovementioned EFSA opinion in case of toxin analogs with established toxicity equivalent factors (TEFs). However, studies regarding deleterious effects generated by the mixtures of toxins from different chemical groups are lacking. Therefore the impact of toxin co-occurrence on toxicity needs to be more deeply investigated.

A high number of different approaches have been described to analyze the combined effects of mixtures [START_REF] Foucquier | Analysis of drug combinations: current methodological landscape[END_REF]. Even though antagonism and synergism are well-established concepts, the definition of additivity is broad [START_REF] Foucquier | Analysis of drug combinations: current methodological landscape[END_REF] resulting in several proposed models. Generally speaking, additivity models allow predicting the additive effects of a mixture from the toxicity of each component. They assume that each component exerts an effect without influencing the action of the other ones. Deviation from predicted additivity is considered as synergism or antagonism. The two main concepts used are concentration addition (CA) and independent action (IA). The CA concept relies on the assumption that the components in the mixture act on the same biological sites, by the same mechanisms of action and differ only in their potency [START_REF] Loewe | Ueber Kombinationswirkungen[END_REF]. The IA concept established by [START_REF] Bliss | The toxicity of poisons applied jointly[END_REF] relies on the hypothesis that a combined effect can be calculated from the responses of the individual mixture components, assuming that each one acts independently without interfering with the others (different sites of action). So far, only few studies have been undertaken with mixtures of phycotoxins. No combined effects were reported in rodents except in the distribution into internal organs where a reduced level was observed in case of mixtures [START_REF] Aasen | Combined oral toxicity of azaspiracid-1 and yessotoxin in female NMRI mice[END_REF][START_REF] Sosa | Repeated oral co-exposure to yessotoxin and okadaic acid: A short term toxicity study in mice[END_REF]. In human neuroblastoma cells, OA co-incubated with YTX or SPX-1 showed additive effect towards cell viability (Rodriguez et al., 2015). However, on proliferative human intestinal epithelial cells, binary combinations of azaspiracid-1 (AZA-1)/OA and YTX/OA resulted in an increasing antagonistic effect on viability when toxin concentrations raised, whereas AZA-1/YTX mixtures showed dose-dependent synergism (Ferron et al., 2016).

In this study we chose relevant mixtures based on our recently published review (Alarcan et al., 2018), highlighting that OA was predominantly found as the main toxin combined with PTX-2, YTX or SPX-1. Our objective was to complete the previous data by assessing a panel of different toxicity endpoints (cell viability, oxidative stress, inflammation, and DNA strand breaks) in the human Caco-2 cell line with binary mixtures of lipophilic toxins.

Materials and methods

Establishment of mixtures

Three binary mixtures were selected in the following molar ratios according to published literature on lipophilic phycotoxins co-exposure (Alarcan et al., 2018): 3:1 for OA/PTX-2 and OA/YTX and 9:1 for OA/SPX-1. Concentrations ranges were established based on preliminary cell viability experiments and published data. PTX-2, YTX and SPX-1 did not show any cytotoxicity (NRU, DAPI cell count) after 24h on differentiated Caco-2 cells up to 500, 200 and 510 nM, respectively (unpublished data). Based on the cytotoxicity results (MTT assay) for OA reported by [START_REF] Ehlers | Analysis of the passage of the marine biotoxin okadaic acid through an in vitro human gut barrier[END_REF], a maximum concentration of 600 nM was chosen in order to study biological responses in both cytotoxic and noncytotoxic conditions. Then, based on the ratios of toxins previously indicated, we deduced the concentrations for PTX-2, YTX and SPX-1 from those chosen for OA.

Chemicals

Toxin standards were purchased from the National Research Council Institute for Marine Biosciences (Halifax, NS Canada). DCFH-DA, potassium bromate (KBrO3), sulfuric acid (H2SO4) and TNFα were purchased from Sigma-Aldrich (St. Louis, MO, USA). Primary anti-interleukin 8 (IL-8) antibody (M801), biotin-conjugated human IL-8 (M802B), HRP-conjugated streptavidin (N100), and 3,3',5,5'tetramethylbenzidine (TMB) were obtained from Thermo Fisher Scientific (Waltham, MA, USA). Mouse monoclonal anti γH2AX S19 (ab26350) and goat pAb to Ms IgG Alexa Fluor 647 (ab150115) were purchased from Abcam (Cambridge, UK). All other chemicals including ethanol (EtOH), methanol (MeOH) and dimethyl sulfoxide (DMSO) were of analytical grade and purchased from Fisher Scientific (Leicestershire, England). Deionised water was prepared using a Milli-Q system (Millipore, Bedford, MA, USA).

Cell culture

Caco-2 cells were obtained from the American Type Culture Collection HTB-37 (LGC Standards, Molsheim, France). Cells (passages 30-38) were seeded at 10,000 cells/cm 2 in 96-well plates in culture medium (minimum essential medium, supplemented with 10% fetal calf serum, 1% nonessential amino acids, 50 IU/ml penicillin and 50 μg/ml streptomycin) and maintained at 37 °C under 5% CO2. Media, serum, amino acids solution and antibiotics were all from Invitrogen (Saint Aubin, France). For differentiation into an intestinal epithelial-like monolayer, cells were cultured for 3 weeks with renewal of medium every 2 to 3 days [START_REF] Sambuy | The Caco-2 cell line as a model of the intestinal barrier: influence of cell and culture-related factors on Caco-2 cell functional characteristics[END_REF]. All the assays were performed on differentiated Caco-2 cells.

Measurement of ROS production

ROS were measured using the probe DCFH-DA. Caco-2 cells were pre-treated with 25 µM DCFH-DA for 30 min. The culture medium was then removed and medium containing the toxins was added for 24 h of incubation at 37°C. Fluorescence was measured at λ ext = 485 nm and λ em = 520 nm using a Fluostar Omega microplate reader (BMG Labtek).

Cell viability and DNA strand breaks

After 24 h of treatment with the toxins, Caco-2 cells were fixed with 4% paraformaldehyde in phosphate-buffered saline (PBS) for 10 min and permeabilized with 0.2% Triton X-100 for 15 min. Plates were then incubated in blocking solution (PBS with 1% BSA and 0.05% Tween-20) for 30 min before the addition of primary antibody prepared in blocking solution and filtered with a 0.2 μm syringe filter. Mouse monoclonal anti γH2AX S19 (1:1000 dilution) was incubated with the cells for 1.5 h at room temperature. After washing with PBS + 0.05% Tween-20, goat pAb to Ms IgG Alexa Fluor 647 (1:1000) was added for 45 min incubation at room temperature. Nuclear DAPI (1 μg/mL) staining was used for automated cell identification by high content analysis. Plates were scanned with the Thermo Scientific ArrayScan VTI HCS Reader (Thermo Scientific, Waltham, USA) and analyzed using the Target Activation module of the BioApplication software. For each well, 10 fields (10X magnification) were scanned and analyzed for immunofluorescence quantification. Cell counts were determined by DAPI staining. γ-H2AX was quantified in the nuclei and expressed as fold increase compared to solvent controls. No γ-H2AX data are provided for toxin concentrations resulting in a cell viability < 40%, to ensure a reliable measurement of DNA damage.

IL-8 release by ELISA

After treatment with toxins, cell culture supernatants were collected and analyzed for IL-8 release. Samples were transferred to 96-well microplates coated overnight at 4°C with 1 µg/mL human recombinant anti-IL-8 primary antibody. Following addition of 0.1 µg/mL of the biotin-conjugated human IL-8 antibody for 1 h, streptavidin peroxidase (1:10000) was added for 45 min. After washing with PBS + 0.05% Tween-20, 3,3',5,5'-tetramethylbenzidine (TMB) solution was added to initiate the colorimetric reaction. The reaction was stopped by 1 M H2SO4 and absorbance was measured at 405 nm using a Fluostar Omega microplate reader (BMG Labtek).

Statistics/Data analysis

GraphPad Prism 5 (GraphPad Software, Inc) was used for statistical analyses. Two-way ANOVA followed by Bonferroni's post-hoc tests were used to compare the effects of toxins and solvent control All error bars denote standard error of the mean (SEM). Symbols *, **, ***, **** and #, ##, ###, #### indicate statistical significance between toxin and solvent control or OA and mixture respectively (p < 0.05, p < 0.01, p < 0.001, p < 0.0001 respectively).

Mixture effects predictions using additivity models

Since the tested phycotoxins do not share any known common mechanism of action, using the CA concept is therefore not relevant. Instead, the mathematical definition of additivity, also known as theoretical additivity, response additivity or linear interaction effect, was employed [START_REF] Foucquier | Analysis of drug combinations: current methodological landscape[END_REF]. The independent action concept was also employed.

Theoretical additivity method

The theoretical additivity (TA) method was used as described by [START_REF] Weber | A rapid screening method to test apoptotic synergisms of ochratoxin A with other nephrotoxic substances[END_REF]. This method compares theoretical predicted values calculated from the results of each individual compound to the measured values obtained with mixtures. In our study of binary phycotoxin combinations, the predicted mixture effect values were calculated as follows:

( + ) = ( + ) -For data expressed as fold change compared to solvent control, mean value solvent control = 1 while for cell viability analysis, the mean value for the solvent control was set to 100. Combination index (CI), a standard measure of combination effect, was calculated as indicated by [START_REF] Foucquier | Analysis of drug combinations: current methodological landscape[END_REF]:

= ( + ) ( + )
Threshold of the computed CI of CI< 0.9, 0.9 ≤ CI ≤ 1.1, and CI > 1.1 were set to indicate synergism, additive effects and antagonism, respectively. In order to get cell viability CI values that respect the combination effect range, ratios were calculated.

Independent action concept

The independent action (IA) concept was used as described by [START_REF] Foucquier | Analysis of drug combinations: current methodological landscape[END_REF]. This method established by [START_REF] Bliss | The toxicity of poisons applied jointly[END_REF] relies on the hypothesis that a combined effect can be calculated from the responses of the individual mixture components, assuming that each one acts independently without interfering with the others (different sites of action). In our study, for binary phycotoxin combinations, the predicted mixture effect values were calculated as follows:

( + ) = ( + ) -( × )
The CI were calculated as indicated by [START_REF] Foucquier | Analysis of drug combinations: current methodological landscape[END_REF]:

= ( + ) ( + )
To establish the predictions, the raw data of ROS production, IL-8 release and γ-H2AX phosphorylation, originally expressed as fold change compared to the respective solvent control, were processed to obtain data suitable for IA analysis, i.e. comprised between 0 and 1. For each endpoint, the condition among the phycotoxins tested individually and the binary mixture showing the highest effect was attributed the value of 1 (Amax). All other conditions were then normalized to the Amax. This data processing was described previously by Alassane-Kpembi et al. ( 2016) and allows assessing combined effects for endpoints other than cell viability. The raw data of cell viability were first divided by 100 to get data comprised between 0 and 1. Cell viability values superior to 100 were set to 1. Cell viability CI values were calculated as described for TA.

Results

Toxic effects following OA/PTX-2 mixtures treatment in Caco-2 cells

PTX-2 exacerbates the cytotoxicity of OA in Caco-2 cells

PTX-2 alone was not toxic for Caco-2 cells, whereas OA showed concentration-dependent toxicity (IC50 = 331 nM) (Fig. 1a). The binary OA/PTX-2 mixture induced strong cytotoxicity in Caco-2 cells, with a tremendous decrease of cells number (80% for the condition D) (Fig. 1a), indicating that the addition of PTX-2 increased the cytotoxicity of OA.

PTX-2 decreases ROS production induced by OA

After a 24 h treatment, OA not-statistically increased ROS production while PTX-2 had no effect (Fig. 1b). We observed that low and intermediate concentrations of OA/PTX-2 (conditions B and C) reduced the level of ROS production compared to that induced by OA alone (Fig. 1b).

PTX-2 decreases IL-8 release induced by OA only at low concentrations

Only OA induced IL-8 release in Caco-2 cells (conditions B to E) (Fig. 1c). For low concentrations the OA/PTX-2 mixture resulted in a reduction of IL-8 release compared to that induced by OA alone, which was statistically significant for 37.5/12.5 nM (condition B). PTX-2 did not decrease IL-8 production induced by OA at higher concentrations (Fig. 1c).

PTX-2 decreases γ-H2AX phosphorylation induced by OA at low concentrations

PTX-2 alone slightly induced the phosphorylation of γ-H2AX (1.8-fold, not statistically significant) whereas OA concentration-dependently induced the phosphorylation of γ-H2AX even at low concentration (37.5 nM condition B, or higher) (Fig. 1d). For 37.5/12.5 nM (condition B), the mixture resulted in a lower phosphorylation of γ-H2AX compared to that induced by OA alone (Fig. 1d). This mixture effect is also illustrated by the immunostaining in Fig. 2. No data are provided for the three highest concentrations due to the high cytotoxicity that prevents from a reliable measurement of DNA damage. Cells were incubated with toxins for 24 h and labeled with antibodies against γ-H2AX. The nuclei were stained with DAPI and images were captured with an Arrayscan VTi. White bar = 100 μm.

Toxic effects following OA/SPX-1 mixtures treatment in Caco-2 cells

SPX-1 reduces the cytotoxicity effect of OA in Caco-2 cells at low concentrations

SPX-1 was not toxic for Caco-2 cells in the tested range concentration. In mixture condition, a lower toxicity was depicted for low and intermediate concentrations (conditions B and C) even if not statistically significant (Fig. 3a).

SPX-1 decreases ROS production induced by OA

Whereas SPX-1 failed to induce ROS production, we observed that low and intermediate concentrations of OA/SPX-1 (conditions A, C and D) reduced the level of ROS production compared to that induced by OA alone (Fig. 3b).

SPX-1 decreases IL-8 release induced by OA only at low concentrations

SPX-1 did not induce the release of IL-8. For low concentrations the OA/SPX-1 mixture resulted in a reduction of IL-8 release compared to that induced by OA alone, which was statistically significant for 37.5/12.5 nM (condition B). SPX-1 did not decrease IL-8 production induced by OA at higher concentrations (Fig. 3c).

SPX-1 decreases γ-H2AX phosphorylation induced by OA at low concentrations

No induction of γ-H2AX phosphorylation was observed after treatment with SPX-1. For 37.5/12.5 and 600/200 nM (conditions B and F), the OA/SPX-1 mixture resulted in a reduced phosphorylation of γ-H2AX compared to that induced by OA alone (Fig. 1d). This mixture effect is also illustrated by the immunostaining in Fig. 4. Cells were incubated with toxins for 24 h and labeled with antibodies against γ-H2AX. The nuclei were stained with DAPI and images were captured with an Arrayscan VTi. White bar = 100 μm.

Toxic effects following OA/YTX mixtures treatment in Caco-2 cells

YTX reduces the cytotoxicity effect of OA in Caco-2 cells at low concentrations

YTX was not toxic for Caco-2 cells in the tested range concentration. In mixture condition, a lower toxicity was depicted for low and intermediate concentrations (conditions A and B) even if not statistically significant (Fig. 5a).

YTX decreases ROS production induced by OA

Whereas YTX failed to induce ROS production, we observed that low and intermediate concentrations of OA/YTX (conditions A and C) reduced the level of ROS production compared to that induced by OA alone.

YTX increases IL-8 release induced by OA at high concentrations

If YTX failed to induce IL-8 release in Caco-2 cells, the mixtures with a high concentration of YTX showed an increase of IL-8 release compared to that induced by OA alone (conditions D and F) (Fig. 5c).

YTX decreases γ-H2AX phosphorylation induced by OA at low concentrations

No induction of γ-H2AX phosphorylation was observed after treatment with YTX. Similarly to what was observed for the OA/SPX-1 mixture, for 37.5/12.5 and 600/200 nM (conditions B and F), the OA/YTX mixture resulted in a reduced phosphorylation of γ-H2AX compared to that induced by OA alone (Fig. 1d). This mixture effect is also illustrated by the immunostaining in Fig. 6. 3.4 Predictions of mixture effects using the theoretical additivity (TA) method and the independent action (IA) concept

OA/PTX-2 mixtures treatment on Caco-2 cells

Regarding cytotoxicity, TA model predicted additivity or antagonism for the two low concentrations while synergism occurred at higher concentrations except for the highest concentration where additivity was shown (Fig. 7a). IA model predicted additivity for the two low concentrations and synergism at higher concentrations. Using IA model, antagonism was observed for all concentrations towards ROS production while TA predicted additivity for the two highest concentrations. For γ-H2AX phosphorylation, no predictions at the three highest concentrations were made due to the high cytotoxicity that prevents from a reliable measurement of DNA damage. Both models gave similar predictions of antagonism for A and B conditions while C condition showed additivity. Antagonism towards IL-8 release was obtained for low concentrations, compared to slight synergism or additivity at higher concentrations with both models.

OA/SPX-1 mixtures treatment on Caco-2 cells

Regarding cytotoxicity, no clear tendency emerges but additivity seems to be the main effect occurring using TA model (Fig. 7b). IA model predicted additivity for the two low concentrations and synergism at higher concentrations. For IL-8 release, all kinds of effects were observed from antagonism at the lowest concentrations, to additivity or synergism at the intermediate and highest concentrations respectively. Using IA model, antagonism was observed for all concentrations towards ROS production while TA predicted additivity for the highest concentration. For γ-H2AX phosphorylation, both models predicted antagonism except for E condition where additivity was described.

OA/YTX mixtures treatment on Caco-2 cells

Regarding cytotoxicity, no clear tendency emerges but additivity seems to be the main effect occurring using TA model (Fig. 7c). IA model predicted additivity for the two low concentrations and synergism at higher concentrations. ROS measurement and γ-H2AX phosphorylation showed antagonism for almost all concentrations, additivity being sometimes described (e.g D condition for γ-H2AX phosphorylation or F condition for ROS with TA). Antagonism towards IL-8 release was obtained for low concentrations, compared to synergism at higher concentrations.

Fig. 7

Analysis of mixtures using the theoretical additivity (TA) method and the independent action (IA) concept on a panel of toxicity endpoints in differentiated Caco-2 cells. CI < 0.9, 0.9 ≤ CI ≤ 1.1 and CI > 1.1 indicate respectively synergism, additive effect and antagonism. Data represents means ± SEM from three independent experiments. Dashed lines indicate lower and upper limits of additivity.

Discussion (1500 words)

In this study, we investigated the effects of binary mixtures of lipophilic phycotoxins on a variety of toxicity endpoints in a human intestinal cell model. The mixture compositions and ratios were selected according to a review of the literature on phycotoxin mixtures (Alarcan et al., 2018). Three main mixtures were tested, all of them featuring OA as the major constituent and reflecting realistic exposure scenarios. Irrespective of the phycotoxin combined with OA, the effects observed were, according to the chosen mathematical approach, mainly antagonistic at low concentrations and, in a few cases, synergistic at higher concentrations.

When investigating mixture effects, the choice of endpoints is a main concern. A specific endpoint can be easily selected when the compounds share a common mechanism of action. In our case, the toxins tested have different mechanisms of action, and therefore a single relevant common endpoint can hardly be chosen. Therefore, we designed a multi-parametric approach for an integrative analysis of toxic responses. To this end, we selected different endpoints among widely studied toxicological pathways.

Our study revealed that OA was cytotoxic, induced DNA strand-breaks and triggered the release of IL-8 from differentiated Caco-2 cells, similar to the effects reported with proliferative Caco-2 cells by [START_REF] Ferron | Comparative Analysis of the Cytotoxic Effects of Okadaic Acid-Group Toxins on Human Intestinal Cell Lines[END_REF]. These effects correspond to observations from in vivo studies in rodents that reported erosion of the epithelium and release of inflammatory mediators following oral exposure to OA [START_REF] Aune | Study of possible combined toxic effects of azaspiracid-1 and okadaic acid in mice via the oral route[END_REF][START_REF] Tubaro | Oral and intraperitoneal acute toxicity studies of yessotoxin and homoyessotoxins in mice[END_REF][START_REF] Ito | Investigation of the distribution and excretion of okadaic acid in mice using immunostaining method[END_REF]. PTX-2 did not affect Caco-2 cell viability and only slightly induced DNA strand-breaks at the highest concentrations. SPX-1 and YTX did not induce any effect on all tested endpoints. The absence of toxic effects correlates well with in vivo studies in rodents that did not report particular effects on the gastro-intestinal tract following oral exposure to these toxins [START_REF] Aune | Comparison of oral and intraperitoneal toxicity of yessotoxin towards mice[END_REF][START_REF] Tubaro | Oral and intraperitoneal acute toxicity studies of yessotoxin and homoyessotoxins in mice[END_REF], Munday et al., 2012). Irrespective of the phycotoxin, no ROS production was detected. However we cannot exclude that ROS might have been produced earlier and were already neutralized after 24h due to protective cellular anti-oxidant systems Two different conceptual approaches were employed to assess the combined effects of phycotoxins in mixture. The TA method and IA concept offer the advantage to be easy to handle, fast and applicable to all kind of data. We showed that under the present settings and parameters additivity was rarely predicted irrespective of the mixture and the endpoint tested, meaning that the phycotoxins tested might be able to interact and modulate their toxic effects. Therefore, in absence of additivity, the two approaches are not appropriate to predict the toxicity of phycotoxin mixtures. In this case, the combined effects of phycotoxins must be determined from experimental data. Besides, caution needs to be taken with mixture simulations. For instance, in our study, IA analysis revealed strong synergism towards cell viability for OA/SPX-1 and OA/YTX (Figure 7) whereas raw data revealed only a slight or no increase of toxicity with mixtures (Figures 3a and5a). It is noteworthy that a general assumption of additivity may not be reliable since OA is believed to target different serine/threonine protein phosphatases (PP) depending on its concentration: the estimated IC50 for PP1 is 10-100 nM, while being 100-fold inferior for PP2A (0.1-1 nM) [START_REF] Cruz | Self-association of okadaic Acid: Structural and pharmacological significance[END_REF]. Indeed, [START_REF] Gehringer | Microcystin-LR and okadaic acid-induced cellular effects: A dualistic response[END_REF] suggested that the PP inhibitors microcystin-LR and OA showed a dualistic response by inducing different cellular effects depending on the concentration used, and therefore on the PP isoform inhibited.

Since few years, the Chou-Talalay method [START_REF] Chou | Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors[END_REF] has received considerable interest in the context of food contaminant mixtures (Alassane-Kpembi et al., 2016, Ferron et al., 2016[START_REF] Smith | Hepatotoxicity of fusariotoxins, alone and in combination, towards the HepaRG human hepatocyte cell line[END_REF][START_REF] Karri | An in vitro cytotoxic approach to assess the toxicity of heavy metals and their binary mixtures on hippocampal HT-22 cell line[END_REF]. This easy-to-use method is a powerful tool which offers a quantitative definition for additivity and the type of interaction (synergism or antagonism) in chemical mixtures.

Based on the median-effect equation, it requires that each compound alone and in mixtures follows the principle of the mass-action law (e.g., r > 0.95). Therefore the method is not suitable when one drug does not exert any effect [START_REF] Chou | Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies[END_REF]. In our study, most endpoints were not affected by at least one of the toxins, and consequently the Chou-Talalay method would not be appropriate.

Our study revealed that only OA provoked IL-8 release. However, we observed a greater effect at high concentrations with the OA/YTX mixture, meaning that YTX may potentiate the OA response. A similar hypothesis can be deduced for the cytotoxicity of OA/PTX-2 mixture. However, confirmation of such observations through a mathematical model was not possible since none exists for testing potentiation.

Irrespective of the toxin, mixtures with OA always resulted in a diminished toxic response at low concentrations (ROS production, IL-8 release and H2AX phosphorylation), whereas a higher toxic response was observed for some endpoints at high concentrations. A diminished ROS production might be linked to a diminished DNA damage response, as it is known that ROS can cause DNA breaks [START_REF] Moloney | ROS signalling in the biology of cancer[END_REF]. Moreover, many studies have shown that the inhibition of ROS by the use of a scavenger leads to a lowered DNA damage response [START_REF] Luo | Resveratrol induces premature senescence in lung cancer cells via ROS-mediated DNA damage[END_REF][START_REF] Zhang | G226, a new epipolythiodioxopiperazine derivative, triggers DNA damage and apoptosis in human cancer cells in vitro via ROS generation[END_REF][START_REF] Chen | Reactive oxygen species induced by icaritin promote DNA strand breaks and apoptosis in human cervical cancer cells[END_REF]. ROS have been shown to activate NF-κB, a transcription factor responsible for IL-8 regulation [START_REF] Kurata | Lipopolysaccharide activates transcription of the hemeoxygenase gene in mouse M1 cells through oxidative activation of nuclear factor kappa B[END_REF][START_REF] Pahl | Activators and target genes of Rel/NF-κB transcription factors[END_REF][START_REF] Oeckinghaus | The NF-kappaB family of transcription factors and its regulation[END_REF]. Since all these endpoints are interconnected, the systematic decrease of responses that was observed with mixtures at low concentrations in our experiments appears consistent. The protective mechanisms behind this might relate to changes in the absorption and/or the metabolism of toxins in the mixtures. For instance, OA was previously found to interact with regulatory nuclear receptors such as PXR [START_REF] Fidler | Activation of a tunicate (Ciona intestinalis) xenobiotic receptor orthologue by both natural toxins and synthetic toxicants[END_REF]Ferron et al., 2016), which regulate the expression of some cytochrome P450 enzymes [START_REF] Wang | Role of CAR and PXR in xenobiotic sensing and metabolism[END_REF]. PTX-2 is believed to interact with the AhR since it induces CYP1A protein in hepatic cells [START_REF] Alarcan | Metabolism of the Marine Phycotoxin PTX-2 and Its Effects on Hepatic Xenobiotic Metabolism: Activation of Nuclear Receptors and Modulation of the Phase I Cytochrome P450[END_REF]. Moreover, OA, PTX-2 and SPX-1 have been shown to be metabolized through cytochromes P450 [START_REF] Guo | The algal hepatoxoxin okadaic acid is a substrate for human cytochromes CYP3A4 and CYP3A5[END_REF][START_REF] Hui | Strategic identification of in vitro metabolites of 13-desmethyl spirolide C using liquid chromatography/high-resolution mass spectrometry: Identification of in vitro metabolites of 13-desmethyl spirolide C[END_REF][START_REF] Kittler | CYP3A4 activity reduces the cytotoxic effects of okadaic acid in HepaRG cells[END_REF][START_REF] Alarcan | Metabolism of the Marine Phycotoxin PTX-2 and Its Effects on Hepatic Xenobiotic Metabolism: Activation of Nuclear Receptors and Modulation of the Phase I Cytochrome P450[END_REF]. Therefore, at low concentrations, mixtures might induce CYP activity and efflux transporter expression, resulting in higher detoxification/excretion of toxins and thus decreased toxic effects. In favor of this hypothesis, the few oral in vivo studies conducted with phycotoxin mixtures showed a reduced uptake of toxins (Alarcan et al., 2018). As PTX-2, YTX and SPX-1 have different mechanisms of action, we assume that the potential protective effect, as suggested by the mathematical evaluation of our in vitro data, did not result from the disturbance of a single specific OA-dependent pathway. The strong synergistic effect observed for the cytotoxicity of the OA/PTX-2 mixture at higher concentrations may imply cytoskeleton homeostasis. Indeed, even if no cytotoxicity following treatment with PTX-2 has been observed, the toxin clearly disrupts the actin network (Alligham et al., 2007). OA has also been demonstrated to change the organization of the actin network [START_REF] Fiorentini | Okadaic acid induces changes in the organization of F-actin in intestinal cells[END_REF][START_REF] Vale | Marine toxins and the cytoskeleton: Okadaic acid and dinophysistoxins[END_REF]. Disruption of the cytoskeleton has been shown to be a starting event leading to an induction of pro-apoptotic effectors (e.g caspase-3 activation) [START_REF] Yamazaki | Cytoskeletal disruption accelerates caspase-3 activation and alters the intracellular membrane reorganization in DNA damage-induced apoptosis[END_REF][START_REF] White | Initiation of apoptosis by actin cytoskeletal derangement in human airway epithelial cells[END_REF][START_REF] Martin | Human MCF10A mammary epithelial cells undergo apoptosis following actin depolymerization that is independent of attachment and rescued by Bcl-2[END_REF][START_REF] Kulms | Apoptosis induced by disruption of the actin cytoskeleton is mediated via activation of CD95 (FAS/APO-1)[END_REF]. Therefore, the disruption of the actin cytoskeleton by both, PTX-2 and OA, may explain the possible synergism observed with the mixture.

Toxins of the OA group and of the PTX group share a common European regulatory limit but their behavior in mixtures is largely unknown. Results from this study suggest a possible synergistic cytotoxic effect at moderate and high concentrations in Caco-2 cells, a model mimicking the human intestinal barrier. Further research is needed to confirm the present findings and to judge on their relevance for humans exposed to phycotoxins via the diet.

Conclusions

Fig. 2

 2 Fig.2Representative images (10X magnification) of DNA damage induced by OA/PTX-2 mixtures. Cells were incubated with toxins for 24 h and labeled with antibodies against γ-H2AX. The nuclei were stained with DAPI and images were captured with an Arrayscan VTi. White bar = 100 μm.

Fig. 3

 3 Fig. 3 Effects of OA/SPX-1 mixtures on a panel of 322

  333 increased ROS production by 1.5-fold and induced 334 the phosphorylation of γ-H2AX by 2.7-fold. TNFα 335 failed to induce IL-8 release. Results were obtained 336 from three independent experiments. Data represents 337 means ± SEM of fold change compared to solvent 338 control. *, **, *** and #, ## indicate statistical 339 significance between toxin and solvent control or OA 340 and mixture respectively (p < 0.05, p < 0.01, p < 341 0.001 respectively) after two-way ANOVA followed 342 by Bonferroni's post-hoc test.

Fig. 4

 4 Fig.4Representative images (10X magnification) of DNA damage induced by OA/SPX-1 mixtures. Cells were incubated with toxins for 24 h and labeled with antibodies against γ-H2AX. The nuclei were stained with DAPI and images were captured with an Arrayscan VTi. White bar = 100 μm.

Fig. 5

 5 Fig. 5 Effects of OA/YTX mixtures on a panel of 397

Fig. 6

 6 Fig.6Representative images (10X magnification) of DNA damage induced by OA/YTX mixtures. Cells were incubated with toxins for 24 h and labeled with antibodies against γ-H2AX. The nuclei were stained with DAPI and images were captured with an Arrayscan VTi. White bar = 100 μm.

Abbreviations: ABC: ATP-binding cassette transporter; ADME: absorption, distribution, metabolism, excretion; CI: combination index; CYP: cytochrome P450; DSP: diarrhetic shellfish poisoning; EFSA: European Food Safety Authority; IL-8: interleukin 8; OA: okadaic acid; P-gp: P-glycoprotein; PP2A: protein phosphatase

2A; PTX-2: pectenotoxin-2; PXR: pregnane-X-receptor; ROS: reactive oxygen species; SPX-1: 13desmethylspirolide C; TEF: toxicity equivalent factor; YTX: yessotoxin.

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Funding information

This work was financed by the French Agency for Food, Environmental and Occupational Health & Safety (ANSES) and by the German Federal Institute for Risk Assessment (BfR).

In this study, we assessed the in vitro effects of binary mixtures of lipophilic phycotoxins on human intestinal cells towards multiple toxicity endpoints. Mainly antagonistic effects were depicted at low concentrations and, in few cases, a strong synergism was detected at higher concentrations. These findings need to be confirmed with additional experiments and completed with in vivo studies to bring further evidence on the combined effects of lipophilic phycotoxins.

Declarations of interest: none

Authorship