Maroua Ben Abdelkader

Nedra Azizi

Ayda Baffoun

Claire Bordes

Yves Chevalier

Mustapha Majdoub

Application of an experimental design methodology for the optimization of cosmetotextile impregnation process by β-cyclodextrin based microcapsules

Keywords: Microcapsules, impregnation process, cosmetotextile, optimization, experimental design, response surface methodology. Tunisia, Laboratory

Neroline-loaded microcapsules based on β-cyclodextrin polyurethane material were fixed on cotton fabric using an impregnation technique. The process parameters were optimized so as to achieve a maximum microcapsules deposition and binding. Simultaneous evaluation and optimization of the effects of interdependent parameters was achieved using an experimental design based on response surface methodology (RSM). The selection of the mathematical model and its use for statistical analysis were done by the least squares method using the Minitab 19 software. The optimal impregnation conditions yielding a fabric mass gain of 8.56 % are as follow: 21 g•L -1 for the microcapsules concentration, 31 g•L -1 for the polyurethane cross-linker concentration and 0.72 g•L -1 for the cationic surfactant concentration. The morphological and mechanical characterizations, and air permeability evaluations of the resulting cosmetotextile fabric show the suitability of the process in terms of applicability and end-use properties.

Introduction

The modern lifestyle is currently characterized by the production of performance clothing, which is one of the most dynamic and active branches in the textile sector. This type of clothing has a very high added value which makes it possible to offer consumers more functional performance (comfort, safety, aesthetics, ...) [START_REF] Jocić | Smart coatings for comfort in clothing[END_REF]. Cosmetotextile products provide various cosmetic activities like insect-repelling, correction of body odors, feeling of well-being, protection, changing appearance, UV protection, anti-ageing, refreshing, moisturizing, slimming, perfuming, … [START_REF] Petrusic | Controlled release of active agents from microcapsules embedded in textile structures[END_REF][START_REF] Bonaldi | Functional finishes for high-performance apparel[END_REF]. Microencapsulation is the preferred technology for the manufacture of cosmetotextiles [START_REF] Jyothi | Microencapsulation techniques, factors influencing encapsulation efficiency[END_REF][START_REF] Snehal | Microencapsulation: A review[END_REF]. This technique involves the production of microcapsules containing active ingredients inside their core protected by a wall material. By this way, this technology enhances not only the protection of the encapsulated ingredients from hazardous environments and from interactions with other compounds in the system, but also allows sustained release and/or stimuli-triggered release of them, providing long-lasting effects and durability [START_REF] Buschmann | Medical, cosmetic and odour resistant finishes for textiles[END_REF]. Cosmetotextiles articles can be prepared using microcapsules through different processes such as incorporation into textile fibers, adsorption on fiber surface or binding microcapsules to fiber surface especially by an impregnation process. The design of cosmetotextile is facing issues related to the many parameters that influence the functionalization process of textile fabric i.e. the temperature, the treatment duration, the choice of reagents and their concentrations, … To find optimal conditions for the multi-parameter process, Design of Experiment (DoE) statistical methodology provides a rigorous methodology allowing the determination of the most suitable ingredients, their concentrations and the process parameters [START_REF] Rezić | Design of experiment approach to optimize hydrophobic fabric treatments[END_REF]. Among the various multivariate DoE-based methods, the Box-Behnken Response Surface Methodology (RSM) developed by [START_REF] Box | On the experimental attainment of optimum conditions[END_REF] is especially used in the design of experiments for optimization purpose [START_REF] Javidi | Investigating the interfacial synthesis of polyurethane microcapsules and optimization of the process using response surface method[END_REF][START_REF] Karimifard | Application of response surface methodology in physicochemical removal of dyes from wastewater: a critical review[END_REF][START_REF] Jaswir | Optimization and formulation of fucoxanthin-loaded microsphere (F-LM) using response surface methodology (RSM) and analysis of its fucoxanthin release profile[END_REF]. The main benefit of such methodology is the reduction in the number of experimental tests necessary to evaluate the influence of several variables [START_REF] Zhong | Optimization of ultrasonic extraction of polysaccharides from dried longan pulp using response surface methodology[END_REF]. RSM has been successfully applied in optimizing different processes such as extraction [START_REF] Minjares-Fuentes | Ultrasoundassisted extraction of pectins from grape pomace using citric acid: A response surface methodology approach[END_REF], nanocomposite and self-healing coating [START_REF] Ebrahiminiya | Modeling and optimization of the parameters affecting the in-situ microencapsulation process for producing epoxy-based self-healing anti-corrosion coatings[END_REF][START_REF] Khoee | Size and core content optimization of epoxy nanocapsules by response surface methodology for use in self-healing coatings[END_REF], microencapsulation [START_REF] Jaswir | Optimization and formulation of fucoxanthin-loaded microsphere (F-LM) using response surface methodology (RSM) and analysis of its fucoxanthin release profile[END_REF][START_REF] Ning | Optimization of preparation conditions of eucalyptus essential oil microcapsules by response surface methodology[END_REF][START_REF] Zhao | Preparation of high encapsulation efficiency fragrance microcapsules and their application in textiles[END_REF][START_REF] Yingngam | Response surface optimization of spray-dried citronella oil microcapsules with reduced volatility and irritation for cosmetic textile uses[END_REF], textile pretreatment [START_REF] Abdelileh | Pretreatment optimization process of cotton to overcome the limits of its dyeability with indigo carmine[END_REF], dyeing [START_REF] Haji | Improved natural dyeing of cotton by plasma treatment and chitosan coating. Optimization by response surface methodology[END_REF][START_REF] Ticha | A promising route to dye cotton by indigo with an ecological exhaustion process: a dyeing process optimization based on a response surface methodology[END_REF], textile effluent (KweinorTetteh & Rathilal, 2021;[START_REF] Jadhav | Application of response surface methodology for the optimization of textile effluent biodecolorization and its toxicity perspectives using plant toxicity, plasmid nicking assays[END_REF], textile functionalization [START_REF] Rezić | Design of experiment approach to optimize hydrophobic fabric treatments[END_REF][START_REF] Salehi | Optimization of chemical activation of cotton fabrics for activated carbon fabrics production using response surface methodology[END_REF], … To the authors' best knowledge, there are no published studies on the application of RSM in microcapsules deposition and binding on textile fabric using impregnation process. Therefore, the aim of the present work is to show a demonstrative instance of such an optimization aiming at enhancing the functionalization of pure cotton fabric by impregnation process using nerolineloaded microcapsules based on β-cyclodextrin. The synthesis and properties of these microcapsules have been described in a previous work (Ben [START_REF] Abdelkader | Fragrant microcapsules based on βcyclodextrin for cosmetotextile application[END_REF]. The effect of the composition of the impregnation bath (concentrations of microcapsules, polyurethane cross-linking agent and cationic surfactant) on the fabric mass gain are presently studied.

Modelling and optimization of these experimental conditions are then investigated in order to improve the performances of the impregnation process. Finally, several textile properties are assessed, namely morphology, tensile strength and air permeability.

Experimental part

Materials

Microcapsules based on β-cyclodextrin were synthesized via an interfacial polycondensation method according to our previous published procedure [START_REF] Abdelkader | Fragrant microcapsules based on βcyclodextrin for cosmetotextile application[END_REF]. For textile application, a knitted fabric (jersey, 100 % cotton) bleached and mercerized, with a mass of 138 g•m -2 , was used. It was supplied by Esprit Maille (Bouhjar, Tunisia). A polyurethane cross-linking agent (Politex SW/N) was supplied by Prochimica Novarese SPA (Italy). Dodecylamino Diglycidyl Ether of Isosorbide (DoDGEDAS) was synthesized as previously reported [START_REF] Abdelkader | Synthesis and emulsifier properties of a new bio-sourced surfactant based on isosorbide[END_REF] and used as a cationic surfactant. The application of microcapsules was carried out using a Continuous Hot Air Dryer (Mathis KTF, Switzerland). Polyurethane and surfactant molecules are shown in Figure 1.

General methods and instrumentation

Textile application

Fragrant microcapsules were mixed with a polyurethane cross-linking solution in distilled water containing DoDGEDAS as a cationic surfactant. The cotton fabric was impregnated into this solution under ambient conditions during 20 min. Then the fabric was dried without squeezing during 6 min at 140 °C in air at atmospheric pressure to allow the cross-linking agent to harden the deposit.

Mass gain of the fabric

The fabric mass gain G was the ratio of the mass difference of dry fabric after and before the treatment to the initial mass of dry fabric. It was expressed as a percentage using Eq. 1 as follows:

G(%)=
the mass difference of the dry fabric after and before impregnation the mass of the dry fabric before impregnation × 100

(1)

Design of experiment (DOE)

Surface Response Methodology (SRM) was used to optimize the impregnation process [START_REF] Box | Statistics for Experimenters: Design, Innovation, and Discovery[END_REF][START_REF] D'agostino | Goodness-of-fit Techniques[END_REF][START_REF] Khuri | Response Surfaces: Designs and Analyses[END_REF]. The experimental factors studied were the concentrations of the microcapsules (X1), the polyurethane cross-linker (X2) and the surfactant (X3). The investigated experimental domain and the corresponding coded levels (-1, 0 and +1) are defined for each factor in Table 1 [START_REF] Kumar | Full factorial design for optimization, development and validation of HPLC method to determine valsartan in nanoparticles[END_REF][START_REF] Singh | Optimization and characterization of controlled release pellets coated with an experimental latex: I. Anionic drugs[END_REF][START_REF] Singh | Optimization studies on design and evaluation of orodispersible pediatric formulation of indomethacin[END_REF]. A Box-Behnken design was built allowing fitting a second-order quadratic model for the prediction of the fabric mass gain G [START_REF] Box | Statistics for Experimenters: Design, Innovation, and Discovery[END_REF]. Fifteen experiments were thus carried out including three center points (Table 2). All experiments were done in triplicate. Multiple linear regression, analysis of variance (ANOVA) and statistical analysis of data were done using the Minitab software (Version 19, State College, PA, USA).

Table 1. Experimental range and levels of independent process variables.

Coded variable Natural variable Unit

Levels -1 0 +1 X1 U1: microcapsules concentration (g•L -1 ) 10 20 30 X2 U2: polyurethane cross-linker concentration (g•L -1 ) 20 30 40 X3 U3: surfactant concentration (g•L -1 ) 0.4 0.7 1

Scanning electron microscopy

The residual bath after impregnation, the shape and surface features of untreated and treated textile were observed by Scanning Electron Microscopy (SEM) after drying using a Jeol JCM 5000 (Jeol, Japan) microscope. The samples were examined at room temperature without surface metallization and operating at 5 kV and 10 kV acceleration under moderate vacuum.

Tensile strength

Tensile test of knitted fabrics was carried out based on the ISO 13934-1 standard by using a LLOYD dynamometer under standard conditions (20 ± 2 °C and 65 ± 2 % of relative humidity) after preconditioning all samples in a standardized conditioning room during 24 hours. Measurements were performed in both directions of knitting (column direction and row direction). The specimen dimensions were 200 mm in length and 50 mm in width. Distance between clamps was 50 mm and the movement speed was 10 mm•s -1 . Three samples were tested for each type of fabric.

Air permeability

Air permeability of textile fabrics was measured based on the EN ISO 9237 standard by using a FX3300-III instrument (TEXTEST AG) under standard conditions (20 ± 2 °C and 65 ± 2 % of relative humidity). All samples were preconditioned in a standardized conditioning room during 24 hours. Measurements were carried out under an air pressure of 100 Pa per 100 cm 2 fabric surface. Tests were repeated three times and the measurements were performed by scanning the entire textile surface on both sides in order to examine the reproducibility of the results. Outcomes were rates of air flow per unit area and time expressed in "mm•s -1 ".

Results and discussion

Factors affecting impregnation process

Functionalization of mercerized cotton jersey fabric with neroline-loaded microcapsules based on β-cyclodextrin was studied. The variables were the main operating conditions influencing impregnation process, the microcapsules concentration, the polyurethane cross-linking agent concentration and the surfactant concentration. The effect was the mass gain of the treated textile (G). Throughout this study, each experiment was performed three times and each experimental point is an average of the obtained results. As a preliminary study, the influences of the variables were firstly investigated independently while keeping the other variable constant. Thereafter, a full optimization was performed using a multivariate statistical analysis.

Effect of the microcapsules concentration

The microcapsules concentration was varied between 10 and 40 g•L -1 for a cross-linking agent concentration of 30 g•L -1 and a surfactant concentration of 1 g•L -1 . The variation of G (Figure 2) indicates that the mass gain increased with respect to the amount of introduced microcapsules up to 25 g•L -1 . The mass gain declined above this concentration. To understand the origin of such loss of mass gain, residual bath initially containing the highest concentration of microcapsules (40 g.L -1 ) was analyzed by SEM as shown in Figure 3. It is clear that microcapsules are agglomerated which cause their sedimentation in the bath. The practical consequence is that there is an optimum. The concentration at optimum may depend of the concentrations of cross-linking agent and surfactant. This is farther addressed in the multivariate analysis.

Effect of the concentration of polyurethane cross-linking agent

For a microcapsules concentration of 20 g•L -1 and surfactant concentration of 1 g•L -1 , the effect of the polyurethane cross-linker concentration on the mass gain percentage (G) was studied at different concentrations (20-50 g•L -1 ). As shown in Figure 4, the mass gain increased by varying the concentration from 20 to 30 g•L -1 , reached a maximum and decreased above 30 g•L -1 . The first increase is a trivial result. The decrease above 30 g•L -1 can be explained by the fact that an excess of polyurethane resin prevents the capsules from penetrating deeply into the knit (in the inter-yarns and inter-fibers) because it forms a film coating the top surface of the fabric.

Effect of the surfactant concentration

For a microcapsules concentration of 20 g•L -1 and a polyurethane cross-linker concentration of 30 g•L -1 , the mass gain percentage (G) was assessed for DoDGEDAS concentrations varying between 0 and 1 g•L -1 (Figure 5). The use of a cationic surfactant contributes at improving the rate of attachment of microcapsules to the textile support. Indeed, the mass gain continuously increased with respect to increasing surfactant concentration. A larger concentration might improve again the mass gain: however, the concentration of surfactant should be kept moderate in order to avoid accumulation of it in the fabric that possibly will cause skin irritation during wearing the fabric. Optimization of the impregnation process by RSM

Model validation

Because the preliminary experiments showed that the variations of mass gain with respect to variables was not linear and there is a maximum, a quadratic model has then been postulated to explain the mass gain. For this purpose, the three experimental factors were simultaneously varied according to a Box-Behnken design. Results were investigated using the software Minitab 19 and led to the following equation:

G(%) = 8.48 + 0.73 X1 + 0.74 X2 + 0.23 X3 -3.74 X1² -3.46 X2² -2.63 X3² + 0.32 X1X3 + 0.66 X2X3 (2)
where Xi are the coded factors. All the regression coefficients are significant at the 5 % level (Student t-test). For the regression equation of the gain mass percentage (G) (Eq. 2), it was found that the determination coefficient R 2 is equal to 0.977, which proved the satisfactory adequacy of the developed model. The mass gain ranges from 0.64 % up to 8.50 % depending on the applied operating conditions (Table 2). Each experiment being carried out in triplicate, the 95 % confidence interval of G has been estimated about ± 0.1 % (standard deviation ~ 0.04 %) indicating a high reproducibility of the G measurements. Analysis of variance was first used to assess the fitting quality of the developed model [START_REF] Winer | Statistical Principles in Experimental Design[END_REF]. The evaluation is based on a F-test. Results of ANOVA in Table 3 proves the high significance of the regression model for the mass gain prediction (Eq. 2) (p-value < 0.01 %). To finally assess the validity of the developed model, the residual errors were also determined by calculating the difference between the experimental G and the G predicted by the model (Eq. 2). These residuals errors < 0.8 % indicated that the mass gain G is satisfactorily explained by the regression model.

Surface response

Once validated, the model was used to visualize the surface responses as a bi-dimensional plot of two factors (X1 and X2), while keeping the third one (X3) constant. Figure 6 shows the contour plot of the response surface for G at surfactant concentration of 0.4, 0.7 and 1 g•L -1 . According to Figure 6, maximum mass gain appears at the center of the three plots and reaches a maximal value of 5.63, 8.55 and 6.30 % for surfactant concentration of 0.4, 0.7 and 1 g•L -1 , respectively. The highest mass gain is obtained when all the concentrations of microcapsules, cross-linking agent and surfactant are fixed at the center of the investigated experimental domain. The shape of the contour plot being quite circular indicates similar effect of X1 and X2 on G.

Response optimization

The optimal conditions of the cotton impregnation process using fragrant microcapsules were predicted by the response optimizer tool of Minitab 19 software for maximum response.

Response optimization described in Figure 7 shows that optimal experimental conditions are a microcapsules concentration of 21 g•L -1 , a cross-linker concentration of 31 g•L -1 and a surfactant concentration of 0.72 g•L -1 , yielding a mass gain of 8.56 %. To check the validity of the model, experiments were performed to compare the experimental results with those predicted responses using the model equation. The theoretical optimal value of mass gain under these optimized conditions is 8.56 % while the experimental one is 8.61 %.

The difference between these two values is well included in the experimental uncertainty thus demonstrating the good predictive performance of the developed model.

Effect of the impregnation process on textile properties

After functionalization of cotton knitted fabric by impregnation process under optimized conditions, several textile properties were studied namely morphology, tensile strength and air permeability.

Morphology

Treated textile was analyzed by SEM after being impregnated in a bath containing fragrant microcapsules, the DoDGEDAS cationic surfactant and the polyurethane cross-linking agent, under optimized conditions. Figure 8 shows SEM micrographs of the untreated cotton knitted fabric compared to the treated one.

It is clear that the β-CDHDI microcapsules are well distributed on the textile surface: between the textile yarns, the fibers and in the cotton cavity. This is due to the use of DoDGEDAS which promotes the dispersion of the microcapsules in the impregnation bath. Indeed, fragrant microcapsules were well fixed and not altered by the heat treatment carried out (temperature of 140 °C for 6 min) since they kept their spherical shape intact. A higher density of microcapsules was also noticed in this case compared to previous works [START_REF] Azizi | Isosorbide-based microcapsules for cosmeto-textiles[END_REF][START_REF] Abdelkader | New microcapsules based on isosorbide for cosmetotextile: preparation and characterization[END_REF][START_REF] Azizi | New β-cyclodextrin-based microcapsules for textiles uses[END_REF][START_REF] Abdelkader | Microencapsulated neroline with new epoxy resin shell based on isosorbide: Preparation, characterization and application to cosmetotextile[END_REF]. This is the consequence of optimization. The binder is also found in an adequate quantity as no excess of it could be seen. Thus, two scenarios are possible, depending on the size of the particles. As for the small size microcapsules, they are stuck and completely covered in the polyurethane resin. The latter phenomenon promotes a longer life of the cosmetotextile.

Tensile strength

The evaluation of the effect of the optimized functionalization treatment on the tensile properties was carried out by a comparison between the tensile curves of the untreated and the treated samples in both knitting directions (row direction and column direction), in particular the maximum force, the elongation at maximum force as well as the rigidity (Figure 9). The general tensile behaviors of the different knitted fabrics are similar in the two solicitation directions and whether before or after impregnation. Thus, we distinguish several stages as follows [START_REF] Szabo | The study of the traction behaviour of knitted structures with controlled dimensional stability[END_REF]:  The start of the curve corresponds to the resistance to friction between the threads and the fibers.  The second part of the curve is linear. Significant deformation takes place under low loads. This is mainly due to a change in the configuration of the mesh which elongates in the direction of stress and tapers in the transverse direction while the yarns line up. Meshes slip and friction at the crossings takes place. The shape of the section of the yarn changes and flattens with a reorganization of the fibers under the effect of local compression. The mesh is mainly subjected to bending forces in the yarn. As the load increases, mesh legs continue to line up. The mesh lengthens in the tensile direction but no longer narrows in the transverse direction. Meshes continue their slip and the cross section of the yarn decreases until it reaches a critical mesh extension point at which the stress is transmitted to the fibers.  The transmission of stress forces to the fibers corresponds to the third step. Thus, the fibers are stressed in traction at the level of the legs of the meshes and in shear at the level of the crossings.  The last step corresponds to the rupture which starts by shearing at the intersection of the adjacent meshes causing unraveling throughout the column. A comparison was carried out between the maximum force, the elongation at maximum force as well as the rigidity of the untreated and the treated samples (Figure 10). The treatment of fixing the microcapsules to the cotton knitwear by the impregnation process increased the maximum force in both directions. Thus, the polyurethane resin coated the cotton threads and fibers while partially blocking their movements. As a result, the tensile strength was improved [START_REF] Potocic-Matkovic | Mechanical properties of polyurethane coated knitted fabrics[END_REF]. Regarding the elongation at maximum force, a slight decrease in both directions of stress of the impregnated knit was noticed. This is probably due to the fact that the polyurethane resin prevented slippage between yarns and fibers during traction [START_REF] Potocic-Matkovic | Mechanical properties of polyurethane coated knitted fabrics[END_REF]. Figure 10 shows that the impregnation treatment increased the rigidity in the elastic zone. This increase was more accentuated in the column direction than in the row direction. This was probably due to the establishment of new bonds, inter-fibers and inter-yarns, of physical or covalent types by impregnation. According to these results, it appears that the studied impregnation treatment slightly modifies the mechanical properties of the textile without altering them.

Air permeability

The assessment of air permeability was carried out by measuring the speed of passage of air flowing through the fabric under a pressure differential. The evaluation of the influence of the functionalization treatment carried out on the air permeability was carried out by comparing the value of the treated textile with that of the untreated textile.

The air permeability properties of untreated and treated knitted cotton fabrics are displayed in Figure 11. Results shows that the treatment for fixing the microcapsules by the impregnation process decreased the air permeability of the functionalized textile with a percentage of 42 % for a mass gain of 8.61 %. The textile support is not yet saturated, since the porosity is not negligible and there is no excess of the impregnation bath that can form a layer right side up and on the other side of the textile substrate. In fact, microcapsules and cross-linker are distributed inside textile sample in the sites between the thread and in statements between the fibers [START_REF] Romdhani | Drying morphologies and emport rate effect on wetting and spreading behaviours[END_REF].

According to Misra et al (2019), textile fabric become more permeable to air with decreasing GSM. That is why low GSM fabric would be more suitable for summer applications and vice versa. In addition, by the decrease of the air permeability from 992 mm•s -1 to 567 mm•s -1 , there is not a fall of the wearing comfort because the textile before treatment has a very high level of openness of the structure and air permeability [START_REF] Misra | Multicriteria decision-making in complex quality evaluation of ladies dress material[END_REF]. The uniformity of microcapsules coating was confirmed by this impregnation process as air permeability was performed at different points of the fabric. All of them presented similar air permeability behavior. The present functionalization technique seems to be efficient in not only promoting a high level of material attachment but also ensuring the coating uniformity [START_REF] Ghaheh | Antioxidant cosmetotextiles: Cotton coating with nanoparticles containing vitamin E[END_REF].

Conclusions

The effect of the composition of the impregnation bath (concentrations of microcapsules, crosslinker and surfactant) on the efficiency of the microcapsules deposition definitely shows sharp optima. This deserves a rigorous methodology of optimization. The use of the response surface methodology (RSM) was aiming at optimization of the fragrant functionalization of pure knitted cotton. Treated fabric was prepared using neroline-loaded microcapsules based on βcyclodextrin by impregnation process. The concentrations of microcapsules, polyurethane cross-linker and cationic surfactant have been investigated to determine the optimal conditions. Using the developed model, the maximum possible mass gain reached 8.56 %. The optimal impregnation conditions are as follow: 21 g•L -1 for the microcapsules concentration, 31 g•L -1 for the polyurethane cross-linker concentration and 0.72 g•L -1 for the cationic surfactant concentration. Optimization using an experimental design (RMS) definitely makes sense because there is a pronounced maximum of fabric mass gain with respect to the studied variables.

A study of some properties of the cosmetotextiles produced via the optimized impregnation protocol was carried out. SEM micrographs revealed an effective adhesion between microcapsules and cotton fibers. According to tensile strength tests, it appears that the impregnation treatment slightly modified the mechanical properties of the textile without altering them. Air permeability decreased by 42 % after functionalization. Overall, the studied impregnation treatment appears adequate for the development of a wide range of other cosmetotextile articles since it allows the addition of a specific functionality to the textile support without blatantly altering its intrinsic properties.

Figure 1 .

 1 Figure 1. Molecular representation structure of: (a) the polyurethane polymer based on βcyclodextrin forming the microcapsules shell based on β-cyclodextrin and (b) DoDGEDAS.

Figure 2 .

 2 Figure 2. Effect of the microcapsules concentration on the mass gain.

Figure 3 .

 3 Figure 3. SEM micrograph residual bath initially containing 40 g.L -1 concentration of microcapsules.

Figure 4 .

 4 Figure 4. Effect of the polyurethane cross-linker concentration on the fabric mass gain.

Figure 5 .

 5 Figure 5. Effect of the surfactant concentration on the mass gain.

Figure 6 .

 6 Figure 6. Contour plots showing the combined effect of microcapsules and cross-linker concentrations (b) on the mass gain (G) using different hold values of surfactant concentrations: (a) 0.4 g•L -1 , (b) 0.7 g•L -1 and (c) 1 g•L -1 .

Figure 7 .

 7 Figure 7. Response optimization of the impregnation process.

Figure 8 .

 8 Figure 8. SEM micrographs of: (a) untreated (b) treated cotton knitted fabric.

Figure 9 .

 9 Figure 9. Tensile behavior of untreated, impregnated and treated knitted fabric in both directions: (a) row direction and (b) column direction.

Figure 10 .

 10 Figure 10. Variation of maximum force, elongation at maximum force and stiffness depending on the impregnation treatment in row and column directions.

Figure 11 .

 11 Figure 11. Air permeability of untreated and treated knitted cotton fabrics.

Table 2 .

 2 Coded actual levels of studied variables and results obtained for a surface design.

Experiment N° Coded values Real values Mean response X1 X2 X3 U1 U2 U3 G (%) (n=3

					)
	1	-1 -1 0 10 20 0.7	0.64
	2	+1 -1 0 30 20 0.7	1.19
	3	-1 +1 0 10 40 0.7	1.33
	4	+1 +1 0 30 40 0.7	1.95
	5	-1 0 -1 10 30 0.4	1.15
	6	+1 0 -1 30 30 0.4	2.82
	7	-1 0 +1 10 30 1.0	0.74
	8	+1 0 +1 30 30 1.0	3.70
	9	0 -1 -1 20 20 0.4	1.58
	10	0 +1 -1 20 40 0.4	2.51
	11	0 -1 +1 20 20 1.0	0.96
	12	0 +1 +1 20 40 1.0	4.51
	13	0	0	0 20 30 0.7	8.46
	14	0	0	0 20 30 0.7	8.50
	15	0	0	0 20 30 0.7	8.49

Table 3 .

 3 Analysis of variance (ANOVA).

	Source	Degree of freedom Sum of squares Variance	F	p-value
	Regression	8	117.1	14.6	33781 < 0.01%
	Residuals	6	2.8	0.5	-	-
	Total	14	119.9	-	-	-

Acknowledgments. This study was funded by the "Ministry of Higher Education and Scientific Research" of


Dr Charles Chemel
email: charles.chemel@hmg.inpg.fr

Chantal Staquet

Jean-Pierre Chollet

ON THE ENTRAINMENT LAW AT THE TOP OF THE CONVECTIVELY-DRIVEN ATMOSPHERIC BOUNDARY LAYER *

We discuss the entrainment processes at the top of the convectively-driven boundary layer. The discussion is based on results from a high-resolution (256 3 grid points) large-eddy simulation initialized by a commonly used sounding of Day 33 of the Wangara experiment. We find that the mixed-layer turbulence precisely follows the Kolmogorov spectral law for the velocity field, whether two-dimensional (in horizontal planes) or one-dimensional spectra are computed. This behavior also holds for the frequency spectrum, when the Taylor's frozen turbulence hypothesis is used. As well, the fluctuating virtual potential temperature field follows the Corrsin-Oboukhov spectral law. The multiplicative constants of the power laws, inferred from the compensated spectra, are found to be in good agreement with previous measurements in the atmosphere. We next revisit the entrainment law at the mixed-layer top from two complementary Eulerian and Lagrangian approaches. The Lagrangian approach yields similar results to the eulerian one: the normalized entrainement velocity we/w⋆, where we and w⋆ are the entrainment and convective velocities, is found to vary as a function of the bulk Froude number squared at the interface with a multiplicative constant close to 1.2.

INTRODUCTION

The interfacial layer between the convectivelydriven atmospheric boundary layer (CBL) and the free atmosphere is subjected to intense turbulent mixing, due to entrainment of air by convective motions. As a result of the entrainment processes, the interfacial layer raises (or the boundary layer deepens) at the entrainment velocity w e = dz i /dt, with z i being the horizontally-averaged mixed-layer depth. Fig. 1 from Chemel et al. (2006) is a visualization of the instantaneous interfacial layer using virtual potential temperature contours from a large-eddy simulation of the Day 33 of the Wangara experiment [START_REF] Clarke | The Wangara experiment: Boundary layer data[END_REF]. It shows that the interfacial layer is strongly turbulent and has a rich structure. Thus, the thickness of the interface has a high variability. Updrafts originating from the warm underlying ground surface strike the interfacial layer sharply (that leads to a folding of the interface) or erode the interface by a 'scouring' mechanism. The free atmosphere air above is then entrained into the mixed layer. These entrainment events are localized and θv contours in a (x, z) from a large-eddy simulation of the Day 33 of the Wangara experiment in a vertical plane (x, z) located in the vicinity of an updraft at 1500 EST. The distances along x and z are normalized by the domain length and the mixed-layer depth, respectively. Grayscale color table indicates e θv variations at the interface (lower and higher e θv appear white). Both e θv profiles measured during the Wangara experiment (•) and computed from the LES outputs as a horizontally-averaged profile over the computational domain (-) are also included for comparison purpose. From Chemel et al. (2006).

As pointed out f.i. by [START_REF] Otte | Stably stratified interfacial-layer turbulence from large-eddy simulation[END_REF], the modeling of the entrainment process is an essential issue for both oceanic and atmospheric applications. Thus, several entrainment law formulations have been derived out to date, starting from the 'zero-order' jump model of turbulent entrainment by [START_REF] Ball | Control of inversion height by surface heating[END_REF] and [START_REF] Lilly | Models of cloud-topped mixed layers under a strong inversion[END_REF]. A thorough review of more general formulations in the shear-free CBL can be found in [START_REF] Fedorovich | Convective entrainment into a shearfree, linearly stratified atmosphere: bulk models reevaluated through large eddy simulations[END_REF].

From an experimental point of view, the entrainment processes across a density interface has extensively been studied. [START_REF] Hopfinger | Turbulence in stratified fluids: a review[END_REF] and [START_REF] Fernando | Turbulent mixing in stratified fluids[END_REF] gave a review for an interfacial layer that is destabilized by grid turbulence. The entrainment process is classically discussed as a function of a bulk Richardson number at the interface, defined as

Ri B = g β ∆θ v z i /w 2 ⋆ , (1)
where ∆θ v is the horizontally-averaged virtual potential temperature jump across the interface and w ⋆ is the convective velocity, g the gravitational acceleration and β the coefficient of thermal expansion. Hereafter, the overbar stands for a spatial average in a horizontal plane. The convective velocity within the mixed layer is expressed as

w ⋆ = g β w ′ θ ′ v s z i 1/3 , (2)
where w ′ θ ′ v s is the average heat flux just above the ground surface. When entrainment results from grid generated turbulence, the dimensionless entrainment rate w e /w ⋆ was found to scale like Ri -n B , where n is in the range 1 -2 depending upon the values of the Prandtl and Richardson numbers. In the convection tank experiment of [START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF], the dimensionless entrainment velocity was found to vary as

w e /w ⋆ = A Ri -1 B , (3)
where A = 0.25. As noted by [START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF], the entrainement law (3) immediately follows from the 'zero-order model' with an entrainement ratio equal to 0.25. [START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF] claimed that their data could also be fitted by the law w e /w ⋆ = 1.1 Ri

-3/2 B
, so that there seemed to be no clear dependence on Ri B .

The entrainment processes have also been investigated in several large-eddy simulations (LESs). The entrainment law (3) is generally retrieved for the convectively-driven boundary layer (e.g. [START_REF] Sullivan | Structure of the entrainment zone capping the convective atmospheric boundary layer[END_REF]Chemel et al. 2006). Nevertheless different LES investigations show a scatter of about 30 % in the value of the dimensionless parameter A [START_REF] Stevens | Observations, experiments, and large eddy simulation[END_REF]. In this study, we present our results from a high-resolution (256 3 grid points) large-eddy simulation of the convectivelydriven (shear-free) boundary layer initialized by a commonly used sounding of Day 33 of the Wangara experiment [START_REF] Clarke | The Wangara experiment: Boundary layer data[END_REF]). This 'realistic' initialization enables us to validate further our numerical results. The objectives of the paper are (i) to investigate the mixed-layer dynamics, which forces the interfacial layer, from a statistical point of view, and (ii) to revisit and discuss the Ri -1 B entrainment law (3), using an Eulerian approach (i.e. the LES) and a Lagrangian approach (i.e. a Lagrangian stochastic model coupled with the LES).

The numerical experiments presented in this paper were conducted with the Advanced Regional Prediction System (ARPS), a non-hydrostatic, compressible LES code devoted to meso-scale as well as small-scale atmospheric flows. [START_REF] Xue | The Advanced Regional Prediction System (ARPS) -a multi-scale non hydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification[END_REF]2001) gave an extensive description of the model formulation and applications. A lagrangian particle dispersion model has also been implemented in the ARPS code to track a large number of particle positions. A detailed description of the model and setup of the LES can be found in Chemel et al. (2006).

The outline of the short paper is then as follows. In § 2.1, we investigate the mixed layer to characterize the mechanisms responsible for the destabilization of the interfacial layer. We analyze the mixedlayer dynamics both from a statistical point of view, by computing 1D and 2D spatial as well as temporal spectra. In § 2.2, the entrainment rate formulation is discussed based on LES and particle dispersion outputs, and expressed as a function of a Froude number at the interface. Finally, concluding remarks are given in § 3.

RESULTS & DISCUSSION

Mixed-layer statistics

Since the mixed layer is homogeneous (in a statistical sense) in a horizontal plane, it is natural to consider horizontal 2D Fourier transforms. The expression of the 2D kinetic energy spectrum in the inertial range (between the integral scale and the dissipative scale) is given by (Chemel et al. 2006)

E ⊥ (k ⊥ , t) = C 2 ǫ 2/3 k -5/3 ⊥ , ( 4
)
where ǫ is the turbulent kinetic energy dissipation rate and

k 2 ⊥ = k 2 x + k 2 y . The constant C 2 is ex- pressed as a function of the Kolmogorov constant C K , namely C 2 = C K √ π/2 F Γ (4/3) /F Γ (11/6
), with F Γ being the Gamma function. Measurements in the atmosphere suggested that C K is in the order of 1.5 [START_REF] Champagne | Flux measurements, flux estimation techniques, and fine-scale turbulence measurements in the unstable surface layer over land[END_REF]. Hence, for C K = 1.5, this yields C 2 ≈ 1.26. The integral scale was computed as ℓ i = 2π/k i = u rms 3 /ǫ, where u rms is the root-mean-square of the horizontal velocity components. As for the dissipative scale, it is calculated as

ℓ v = 2π/k v = ν t 3 /ǫ 1/4
, where ν t is the turbulent viscosity.

In Fig. 2a, E ⊥ (k ⊥ ) averaged over the range 0.4 < z/z i < 0.6 at 1500 EST is displayed versus k ⊥ , for two different resolutions. The computed integral scale is close to 1900 m, which roughly corresponds to the typical size of the convective cells (e.g. [START_REF] Schmidt | Coherent structure of the convective boundary layer derived from large-eddy simulations[END_REF]Chemel et al. 2006). In the present LES the dissipative scale ℓ v is in the order of 5 m. The Kolmogorov law is observed over almost a decade in the inertial range for the 256 3 resolution run. As a comparison, the kinetic energy spectrum computed with a 128 3 resolution is superimposed in Fig. 2a. It exhibits only a half decade in the inertial range. To compare rigorously the computed spectrum with inertial-range theory, the compensated spectrum If the CBL is well-mixed, the virtual potential temperature θ v should behave as a passive scalar. We assume thereafter that the wavenumbers characteristic of the peaks of both kinetic energy and virtual potential temperature spectra are identical. This is a reasonable hypothesis since these wavenumbers are both imposed by the large convective motions. In the inertial-convective range (namely

E ⊥ ǫ -2/3 k 5/3 ⊥ = C 2 is displayed as a function of k ⊥ in the subplot of
k i ≪ k ⊥ ≪ k d , where k d = 2π/ℓ d with ℓ d = κ t 3 /ǫ 1/4
for Pr t < 1, which is the case here), the 2D spectrum of virtual potential temperature variance follows the Corrsin-Oboukhov law (Chemel et al. 2006)

E θ,⊥ (k ⊥ , t) = C CO,2 ǫ θ ǫ -1/3 k -5/3 ⊥ , ( 5
)
with ǫ θ being the dissipation rate of one half the average virtual potential temperature variance, which is given by ǫ

θ = κ s ∂ i θ v 2
, where κ s is the subgridscale thermal diffusivity. A derivation analogous to that leading to the expression of

C 2 gives C CO,2 = C CO √ π/2 F Γ (4/3) /F Γ (11/6)
, where C CO is the Corrsin-Oboukhov constant. Using measurements in the atmosphere, [START_REF] Champagne | Flux measurements, flux estimation techniques, and fine-scale turbulence measurements in the unstable surface layer over land[END_REF] found C CO ≈ 0.64. For C CO = 0.64, this yields C 2 ≈ 0.54.

In Fig. 2b we compare the virtual potential temperature variance spectrum E θ,⊥ (k ⊥ ) derived from our LES outputs with the theoretical prediction in From Chemel et al. (2006). relation (5). Our results agree well with the Corrsin-Oboukhov spectrum over almost a decade. Dividing the spectra by ǫ θ ǫ -1/3 k -5/3 ⊥ gives the value of C CO,2 , which is displayed versus k ⊥ in the subplot of Fig. 2b. The agreement of C CO,2 with the theoretical prediction of 0.54 is correct over the range of wavenumbers for which E θ,⊥ closely follows a k -5/3 ⊥ power law, that is for the smallest scales of the inertial range. In overall, we may say that the passive scalar assumption for θ v is quite justified.

Let us examine the assumption of local isotropy in the horizontal plane. Fig. 3a displays the 1D longitudinal spectra of u and v as a function of k x and k y , respectively, for two different resolutions, averaged over the range 0.4 < z/z i < 0.6 at 1500 EST. The spectra along k x and k y are nearly the same, which is consistent with local isotropy. From a theoretical point of view, if turbulence is homogeneous and isotropic, these spectra should behave as

E ii (k i , t) = C 1 ǫ 2/3 k -5/3 i , ( 6
)
with k i being k x or k y , and C 1 = (18/55) C K ≈ 0.49 for C K = 1.5 (e.g. [START_REF] Champagne | Flux measurements, flux estimation techniques, and fine-scale turbulence measurements in the unstable surface layer over land[END_REF][START_REF] Moeng | Spectral analysis of large-eddy simulations of the convective boundary layer[END_REF]. The numerically computed spectrum of u normalized by ǫ -2/3 k 5/3 x is displayed versus k x in the subplot of Fig. 3a. The value of C 1 thus obtained agrees well with the theoretical prediction of 0.49 for the smallest scales of the inertial range.

Since the large-scale flow within the mixed layer consists of convective cells, the scales contributing to the inertial range may be assumed to be advected by those cells. Thence, the [START_REF] Taylor | Diffusion by continuous movements[END_REF] frozen turbulence hypothesis may be assumed to hold. This reasoning also requires that the magnitude of the velocity fluctuations be much smaller than the convective velocity. Under these assumptions, a f -5/3 power law, with f being the frequency, is expected in the inertial range for scalar quantities as well as for velocity components. The frequency spectrum of u, S u (f ), computed from 1200 EST to 1500 EST at z = 500 m in the center of the (x, y) plane, is displayed versus f in Fig. 3b: a -5/3 inertial range is obtained over almost a decade. The overturning time scale associated with the integral scale may be estimated by τ i = τ eddy (ℓ i ) = ℓ i / u rms , and is in the order of 15 min. This is indeed the typical time period for air to circulate between the ground surface and the mixed-layer top, i.e. roughly z i /w ⋆ , as we checked it.

Since the Taylor's hypothesis is assumed to hold, temporal spectra gathered in this way can be converted to 1D spatial spectra by substituting the frequency f for k x | u|. The 1D spatial spectrum thus obtained is superimposed upon E ii in Fig. 3a: both spectra remarkably coincide over the inertial range, which demonstrates the reliability of the Taylor's hypothesis within the mixed layer.

Entrainment rate formulation

In this section, the focus is directed onto the interfacial layer, where turbulence is damped by buoyancy forces. As stressed by Chemel and Staquet (2006), the Froude number Fr B of the interfacial layer is the most appropriate dynamical parameter to characterize the interface in the convectivelydriven boundary layer. This parameter is defined as the ratio of the characteristic frequency w ⋆ /z i of the turbulent motions, which destabilize the interface, to From Chemel et al. (2006).

the buoyancy frequency N = g β ∂ 3 θ v at the interface, which characterizes it stability. Hence,

Fr B = w ⋆ / (N z i ) . ( 7
)
One may note that Fr 2 B is the inverse of the Ri N number introduced by [START_REF] Fedorovich | Convective entrainment into a shearfree, linearly stratified atmosphere: bulk models reevaluated through large eddy simulations[END_REF]. With N aproximated by g β ∆θ v /∆h, the parameters Fr B and Ri B are related by Ri B = (∆h/z i ) Fr -2 B , where ∆h is the horizontally-averaged mixed-layer thickness. The entrainment law (3) then becomes

w e /w ⋆ = B Fr 2 B , (8)
where B = A (z i /∆h). The value of B appears to be a constant of about 1.2 in the regime of equilibrium entrainment [START_REF] Fedorovich | Convective entrainment into a shearfree, linearly stratified atmosphere: bulk models reevaluated through large eddy simulations[END_REF]Chemel and Staquet 2006).

In the following, we check upon the reliability of the latter entrainment law from our LES results using both Eulerian and Lagrangian approaches.

Characteristics of the simulated interfacial layer are given in Chemel et al. (2006) (their Table 1). In respect to the Eulerian approach, the horizontallyaveraged entrainment velocity, namely

w e = d t z i , ( 9
)
is directly computed from the time derivative of z i using a centered difference scheme. A different approach using particle dispersion is proposed to retrieve the entrainment velocity w e . Using first-order closure the heat flux is classically expressed as a function of the mean vertical gradient of virtual potential temperature, namely

-w ′ θ ′ v = κ t ∂ 3 θ v . (10)
Using the 'first-order' jump model framework proposed by [START_REF] Betts | Reply to comment on the paper 'non-precipitating cumulus convection and its parameterization[END_REF], the heat flux across the interfacial layer w ′ θ ′ v i is related to the entrainment velocity by

-w ′ θ ′ v i = w e ∆θ v -δh ∂ t θ v 1,2 , (11)
where δh = z 2 -z 1 is the difference between the vertical position z 2 where the heat flux first goes to zero above z i and the vertical position z 1 of the minimum heat flux, and θ v 1,2 = θ v (z 1 ) + θ v (z 2 ) /2. In the limit of infinitely small thickness of the interfacial layer, i.e. δh = 0, relation (11) simply reduces to the 'zero-order' jump condition derived by [START_REF] Lilly | Models of cloud-topped mixed layers under a strong inversion[END_REF]. Assuming again that ∆θ v /∆h is a good approximation of ∂ 3 θ v within the interfacial layer, relations ( 10) and ( 11) together yield

w e = κ t /∆h + δh ∂ t θ v 1,2 /∆θ v . ( 12
)
The turbulent thermal diffusivity κ t can be evaluated directly from the dispersion of fluid particles. By 'fluid particles', we mean non buoyant particles simply advected by the velocity field. Let (δz) ms (t) be the mean square vertical displacement of fluid particles at time t for a given release. It is written as

(δz) ms (t) = (1/N p ) Np n=1 [z n (t) -z G (t)] 2 , ( 13
)
where N p is the number of particles of the release, z n (t) is the vertical position of the particle n and z G (t) the vertical position of the center of gravity of the particle cloud at time t. If the turbulence is locally homogeneous and stationary, and for t ≥ 2 T L , with T L being the Lagrangian time scale of the turbulence, κ t can be inferred from the growth rate of (δz) ms [START_REF] Taylor | Diffusion by continuous movements[END_REF][START_REF] Hunt | Diffusion in the stably stratified atmospheric boundary layer[END_REF], for a review), namely

d t (δz) ms = 2 κ t . (14)
Since the interfacial layer is continuously forced by the quasi-stationary convective cells, the turbulence within this layer may be assumed stationary as well.

For stationary and homogeneous turbulence, the Lagrangian time scale is in the same order of magnitude as the Eulerian time scale T E [for isotropic turbulence, the ratio T L /T E is close to 0.8 [START_REF] Yeung | Lagrangian investigations of turbulence[END_REF]]. This result also holds in the presence of a stable stratification [START_REF] Hunt | Diffusion in the stably stratified atmospheric boundary layer[END_REF] . In the present case T E = ℓ b /σ w , where σ w is the standard deviation of the fluctuating vertical velocity (e.g. Hopfinger 1987). From [START_REF] Otte | Stably stratified interfacial-layer turbulence from large-eddy simulation[END_REF], ℓ b ≈ 24 m and σ w ≈ 0.5 m s -1 for conditions analogous to our LES (their cases 19 to 22), so that T E ≈ 48 s implying that 2 T L is in the order of 1 min. Particles were carefully released within the bulk of the interfacial layer (approximately between zz i = ±100 m) in a region centered approximately on the horizontal plane. Nevertheless some of the particles were released below and above the interfacial layer since its thickness varies over a wide range within the simulated domain. The releases were made at 4 equally-spaced times from 1155 EST to 1325 EST over 10-min periods and resulted in a total of 57500 particles per release. These release times were chosen because they correspond to a nearly constant ground surface heat flux. A quasilinear growth occurs after about 1 min (not shown) whose growth rate may be interpreted as 2 κ t . Values for κ t between 2.94 and 3.83 m 2 s -1 were obtained depending upon the time of the release. κ t varies with time since the interfacial-layer properties also vary with time.

The resulting Ri -1 B and Fr 2 B dependences of the parameters A and B in relations (3) and ( 8), respectively, with w e values computed by relation ( 12), are shown in Fig. 4. The results are compared with those obtained when w e is computed by relation ( 9), as well as with the convection tank measurements of [START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF] over the region of common Ri -1 B or equivalently Fr 2 B . A good agreement is found, with relative differences being lower than 10 % on average. This emphasizes that the particle dispersion approach is well adapted to derive 8). In each frame, the entrainment velocity we is computed by three different methods: the Eulerian approach as triangles [△, relation (9)], the Lagrangian approach as squares [ , relation (12)], and a simplified model in the Lagrangian approach, with we being computed as κt/∆h, as circles (•). The convection tank measurements of [START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF] are also indicated as black dots (•). The filled area represents: (a) A in the range 0.18 -0.27 and (b) B in the range 1.0 -1.4. From Chemel et al. (2006).

the properties of the interfacial layer. As a comparison, the values computed from a simplified model in the Lagrangian approach (namely, w e computed as κ t /∆h) are superimposed in Fig. 4. The magnitude of the term δh ∂ t θ v 1,2 /∆θ v in relation ( 12) is found to be not negligible compared with κ t /∆h in the range of Ri B values considered in our LES. The former term indeed contribute to about 30 -35 % of the total entrainment velocity when Ri B is strong enough (approximately larger than 15 according to our data). At low Ri B , this term counts for more than 45 %. Our finding are consistent with the LES results of [START_REF] Sullivan | Structure of the entrainment zone capping the convective atmospheric boundary layer[END_REF].

CONCLUDING REMARKS

The entrainment processes at the top of the CBL were reexamined using data from high-resolution (256 3 grid points) LES. A detailed statistical analysis of the mixed-layer fields was performed. We found that the turbulence precisely follows the Kolmogorov spectral law for the velocity field, with a multiplicative constant in good agreement with previous measurements in the atmosphere, whether 2D (in horizontal planes) or 1D spectra are computed. This behavior also holds for the frequency spectrum, when the Taylor's frozen turbulence hypothesis is used. Hence, the turbulence within the mixed layer may be assumed to be locally homogeneous and isotropic. As well, the fluctuating virtual potential temperature field was found to follow the Corrsin-Oboukhov spectral law, both in power law and level. This is consistent with this field behaving as a passive scalar in the mixed layer.

The normalized entrainment velocity was found to vary as w e /w ⋆ = B Fr 2 B , with a multiplicative constant B of about 1.2. The scatter of our data points out the difficulties to estimate the boundaries of the interfacial-layer and consequently the values of ∆θ v and ∆h. Nonetheless, the ratio of the interfaciallayer thickness to the convective mixed-layer depth in the limit of strong stratification was found to be in the order of 0.2, consistently with the convection tank measurements of [START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF] when Ri B is high enough.

A particle dispersion approach using LES coupled with a Lagrangian stochastic model was used to compute the entrainment velocity from relation (12). The derived values were in rather good agreement with those computed directly from d t z i . Relative differences were found to be lower than 10 % on average. The 'first-order' jump model was found to work well. We find that the first order correction contributes to at least 30 % to the value of w e . Still, the Lagrangian approach provides a very simple and useful expression for the entrainment velocity (namely, w e = κ t /∆h), from which a preliminary good estimate can be obtained in a straightforward manner. These results may have applications for remote sensing of the mixed-layer top.

Figure 1 :

 1 Figure1: Visualization of the interfacial layer using resolved virtual potential temperature e θv contours in a (x, z) from a large-eddy simulation of the Day 33 of the Wangara experiment in a vertical plane (x, z) located in the vicinity of an updraft at 1500 EST. The distances along x and z are normalized by the domain length and the mixed-layer depth, respectively. Grayscale color table indicates e θv variations at the interface (lower and higher e θv appear white). Both e θv profiles measured during the Wangara experiment (•) and computed from the LES outputs as a horizontally-averaged profile over the computational domain (-) are also included for comparison purpose. FromChemel et al. (2006).

  Fig. 2a. In the inertial range, C 2 remarkably averages about the predicted value of 1.26.

Figure 2 :

 2 Figure 2: (a) 2D kinetic energy spectrum E ⊥ (k ⊥ ) at 1500 EST computed for the 256 3 resolution run (-) and averaged over the range 0.4 < z/zi < 0.6. The subplot displays computed constant C2 in relation (4) as a function of k ⊥ . The spectrum computed for a 128 3 resolution run (---) is superimposed as a comparison. (b) As in (a), but for 2D virtual potential temperature variance spectrum E θ,⊥ (k ⊥ ) and computed constant CCO,2 in relation (5).FromChemel et al. (2006).

Figure 3 :

 3 Figure 3: (a) 1D longitudinal velocity spectra Eii (ki) of e u and e v at t = 1500 EST computed for the 256 3 resolution run (-) and averaged over the range 0.4 < z/zi < 0.6. The subplot displays computed constant C1 in relation (6) averaged over both eu and e v spectra as a function of ki. The spectra computed for a 128 3 resolution run (---) are superimposed as a comparison. The dotted line (• • •) represents the spectrum of u deduced from the frequency velocity spectrum Su (f ) of e u, displayed in plot (b) and computed for the 256 3 resolution run, from 1200 EST to 1500 EST at z = 500 m in the center of the (x, y) plane. FromChemel et al. (2006).

Figure 4 :

 4 Figure 4: Dimensionless parameters: (a) A = (we/w⋆) RiB in relation (3) and (b) B = (we/w⋆) Fr -2 B in relation (8).In each frame, the entrainment velocity we is computed by three different methods: the Eulerian approach as triangles [△, relation (9)], the Lagrangian approach as squares[ , relation (12)], and a simplified model in the Lagrangian approach, with we being computed as κt/∆h, as circles (•). The convection tank measurements of[START_REF] Deardorff | Laboratory studies of the entrainment zone of a convectively mixed layer[END_REF] are also indicated as black dots (•). The filled area represents: (a) A in the range 0.18 -0.27 and (b) B in the range 1.0 -1.4. FromChemel et al. (2006).

Acknowledgements. All the major computations were realized with the Institut du D éveloppement et des Ressources en Informatique Scientifique (IDRIS) computing resources. The authors thank I. Vinkovic, C. Aguirre and S. Simo ëns for providing us with a part of the Lagrangian stochastic model and for helpful discussions.


Loren Billet
email: loren.billet@gmail.com

Stéphane Pesce

Fabrice Martin-Laurent

Marion Devers-Lamrani

Experimental Evidence for Manure-Borne Bacteria Invasion in Soil During a Coalescent Event: Influence of the Antibiotic Sulfamethazine

Keywords: Community coalescence, Manure amendment, Sulfonamide, 16S rDNA sequencing, Firmicutes, Soil Colonization

The fertilization of agricultural soil by organic amendment that may contain antibiotics, like manure, can transfer bacterial pathogens and antibiotic-resistant bacteria to soil communities. However, the invasion by manure-borne bacteria in amended soil remains poorly understood. We hypothesized that this kind of process is both influenced by the soil properties (and those of its microbial communities) and by the presence of contaminants such as antibiotics used in veterinary care. To test that, we performed a microcosm experiment in which four different soils were amended or not with manure at an agronomical dose and exposed or not to the antibiotic sulfamethazine (SMZ). After 1 month of incubation, the diversity, structure, and composition of bacterial communities of the soils were assessed by 16S rDNA sequencing. The invasion of manure-borne bacteria was still perceptible 1 month after the soil amendment. The results obtained with the soil already amended in situ with manure 6 months prior to the experiment suggest that some of the bacterial invaders were established in the community over the long term. Even if differences were observed between soils, the invasion was mainly attributable to some of the most abundant OTUs of manure (mainly Firmicutes). SMZ exposure had a limited influence on soil microorganisms but our results suggest that this kind of contaminant can enhance the invasion ability of some manure-borne invaders.

Introduction

Organic amendment enables fertilization of agricultural soils and allows the recycling of a substantial amount of waste produced by livestock such as manure [START_REF] Pandey | 16S rRNA analysis of diversity of manure microbial community in dairy farm environment[END_REF]. This environmentally sound practice enhances crop yields by increasing the soil organic matter, phosphate, and nitrogen contents [START_REF] Liu | Impact of manure on soil biochemical properties: a global synthesis[END_REF]. Besides these nutrient inputs, manure is also a source of exogenous biological material, mainly microorganisms, for amended soils [START_REF] Unc | Transport of bacteria from manure and protection of water resources[END_REF]. This biological input may be helpful in carrying beneficial microorganisms for the functioning of agroecosystems [START_REF] Wongsaroj | First reported quantitative microbiota in different livestock manures used as organic fertilizers in the Northeast of Thailand[END_REF] or even in suppressing plant and animal pathogens by antagonism [START_REF] Pugliese | Efficacy of microorganisms selected from compost to control soil-borne pathogens[END_REF][START_REF] Corato | Disease-suppressive compost enhances natural soil suppressiveness against soil-borne plant pathogens: a critical review[END_REF]. This biological input may be beneficial in carrying functionally interesting microorganisms [START_REF] Wongsaroj | First reported quantitative microbiota in different livestock manures used as organic fertilizers in the Northeast of Thailand[END_REF] or even in suppressing plant and animal pathogens by antagonism [START_REF] Pugliese | Efficacy of microorganisms selected from compost to control soil-borne pathogens[END_REF]. However, it can introduce biological contaminants in soils and the rhizosphere of plants, such as pathogenic bacteria [START_REF] Franz | Manure-amended soil characteristics affecting the survival of E. coli O157:H7 in 36 Dutch soils[END_REF][START_REF] Cook | Detection of pathogens, indicators, and antibiotic resistance genes after land application of poultry litter[END_REF][START_REF] Semenov | Transfer of enteric pathogens to successive habitats as part of microbial cycles[END_REF] or hazardous microbial functions such as antibiotic resistances [START_REF] Heuer | Antibiotic resistance gene spread due to manure application on agricultural fields[END_REF][START_REF] Hu | Temporal changes of antibiotic-resistance genes and bacterial communities in two contrasting soils treated with cattle manure[END_REF]. Therefore, understanding microbial outcomes of manure amendment in soils is of interest.

In this context, it is important to determine what enables or prevents a microbial invasion from manure to soils. In particular, knowledge is needed to better understand what make manure-borne microorganisms more likely to invade a soil, taking into consideration the influence of both the biotic and abiotic components of soil and manure (as well as their interactions). This question relates to the concept of microbial coalescence, which focuses on the outcome of mixing different environments, each presenting different biotic and abiotic characteristics [START_REF] Rillig | Interchange of entire communities: microbial community coalescence[END_REF][START_REF] Castledine | Community coalescence: an eco-evolutionary perspective[END_REF]. Amendment of agricultural soils with manure is an environmentally relevant example of coalescence event where the mix of two distinct environments results in a new one, namely an amended soil, with its own characteristics.

Previous studies showed that amended soils durably differ from manure-free soils when considering the abiotic part of coalescence [START_REF] Chang | Soil chemistry after eleven annual applications of cattle feedlot manure[END_REF][START_REF] Lungu | Acidification from long-term use of urea and its effect on selected soil properties[END_REF]. However, although the novel soil environment differs from the unamended one, the microbial community resulting from coalescence is generally relatively similar to that of the original one [START_REF] Suleiman | Temporal variability of soil microbial communities after application of dicyandiamide-treated swine slurry and mineral fertilizers[END_REF][START_REF] Semenov | Does fresh farmyard manure introduce surviving microbes into soil or activate soil-borne microbiota?[END_REF]. The rather imbalanced ratio between soil and manure matrices may partially explain this. Moreover, it is also hypothesized that autochthonous soil microorganisms constitute a natural barrier preventing the establishment of allochthonous microorganisms originating from manure by outcompeting them [START_REF] Van Elsas | Microbial diversity determines the invasion of soil by a bacterial pathogen[END_REF][START_REF] Chen | Do manure-borne or indigenous soil microorganisms influence the spread of antibiotic resistance genes in manured soil[END_REF]. For instance, it has been shown that manure-borne bacteria that are adapted to, among other things, high nutrient content and anoxic conditions, tend not to be competitive with soil autochthonous bacteria [START_REF] Semenov | Influence of aerobic and anaerobic conditions on survival of Escherichia coli O157:H7 and Salmonella enterica serovar Typhimurium in Luria-Bertani broth, farm-yard manure and slurry[END_REF].

Several environmental factors might influence the invasion success of manure-borne bacteria in soil. Firstly, based on the concept of coalescence, the invasion capacity is probably soil-dependent since it may vary according to the biotic (e.g., community structure and diversity) and abiotic (e.g., organic matter content) characteristics of the amended soil, which determine the potential presence of available ecological niches [START_REF] Mallon | The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader's niche[END_REF]. It can also be modulated by the repeated amendments, with two possible contradictory scenarios. On the one hand, transient and even unsuccessful invasion events can favor subsequent invasion [START_REF] Mallon | The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader's niche[END_REF]. On the other hand, the resistance of soil communities to invasion can be enhanced after a first invasion [START_REF] Gravuer | Invader-resident relatedness and soil management history shape patterns of invasion of compost microbial populations into agricultural soils[END_REF]. Another factor that can modulate invasion events in autochthonous microbiota refers to the possible presence of antibiotics in manure, which is frequently reported in the literature [START_REF] Haller | Quantification of veterinary antibiotics (sulfonamides and trimethoprim) in animal manure by liquid chromatography-mass spectrometry[END_REF][START_REF] Huygens | Presence of antibiotic residues and antibiotic resistant bacteria in cattle manure intended for fertilization of agricultural fields: a One Health perspective[END_REF]. These contaminants may disturb interaction patterns of microbial communities according to the antibiotic sensitivity profile of their respective members [START_REF] Adamowicz | Cross-feeding modulates antibiotic tolerance in bacterial communities[END_REF][START_REF] Jackson | Species interaction and selective carbon addition during antibiotic exposure enhances bacterial survival[END_REF], thus favoring the emergence of communities that would differ from those obtained under antibiotic-free environments. Since antibiotics are used on livestock [START_REF] Teillant | Economics of antibiotic growth promoters in livestock[END_REF], bacteria from animal tracts, which compose the manure community, may be more antibiotic-tolerant than non-antibiotic-exposed soil bacteria [START_REF] Munir | Levels of antibiotic resistance genes in manure, biosolids, and fertilized soil[END_REF]. Indeed manure contains more antibiotic-resistant bacteria than soils [START_REF] Chen | Do manure-borne or indigenous soil microorganisms influence the spread of antibiotic resistance genes in manured soil[END_REF][START_REF] Xu | Antibiotic contamination amplifies the impact of foreign antibiotic-resistant bacteria on soil bacterial community[END_REF]. Consequently, contamination of soil with antibiotics originating from manure amendment is likely to promote the invading potential of antibiotic-resistant manure-borne bacteria in soils. In that sense, antibiotics could increase the invading potential of isolated antibiotic-resistant strains in soil [START_REF] Xu | Antibiotic contamination amplifies the impact of foreign antibiotic-resistant bacteria on soil bacterial community[END_REF][START_REF] Billet | Antibiotrophy: key function for antibiotic-resistant bacteria to colonize soils-case of sulfamethazine-degrading Microbacterium sp. C448[END_REF] and manure amendment can result in an increase of the diversity of antibiotic-resistant bacteria in soil [START_REF] Chen | Do manure-borne or indigenous soil microorganisms influence the spread of antibiotic resistance genes in manured soil[END_REF].

In this context, the purpose of our microcosm study was to assess soil invasion by manure-borne bacterial OTUs during an agronomical coalescence event of soil fertilization with manure according to the presence or not of antibiotic pressure. We firstly hypothesized that the capacity of manure-borne bacteria to invade the amended soil would be variable according to the intrinsic abiotic and biotic properties of soils. In particular, among the four soils used, one was amended (in situ) 6 months earlier by the same manure to test whether previous amendment had an impact on invasion outcomes. In addition, we also hypothesized that some manure-origin OTUs would be distinguished from the emerging rare soil OTUs in response to the repetition of their invasive pattern in several of the tested soils. Finally, to verify whether antibiotics can interfere with the coalescence process by promoting the invasion of soils by manure-borne OTUs, a modality made of the antibiotic sulfamethazine (SMZ), which is commonly used in veterinary care, was considered [START_REF] Kobland | Comparative efficacy of sulfamethazine and sulfathiazole in feed for control of Bordetella bronchiseptica infection in swine[END_REF][START_REF] Fenwick | Experimental infection of weanling pigs with Salmonella typhisuis: effect of feeding low concentrations of chlortetracycline, penicillin, and sulfamethazine[END_REF]. After 1 month of incubation, the diversity, structure, and composition of bacterial communities of the manure-amended soils exposed or not with SMZ were compared to those of their respective non-amended control soil and of the native manure by sequencing of 16S rDNA.

Material and Method

Collection and Analyses of Soils and Manure

The four soils, named A, B, C, and D, are anthrosols selected for their different textures and physicochemical characteristics (see Supplementary table 1 for details). They were sampled from the upper 20 cm of different agricultural fields the Bourgogne-Franche-Comté region of France in November 2018. They originated respectively from an agricultural field frequently flooded by the nearby Saône River, the same plot but at a higher altitude, the experimental farm of Epoisses, and a field near a hog nursery that had received manure 6 months before the sampling. Each soil was air-dried to 60% of their water content at pF 2.7, sieved (mesh size 5 mm), and stored about 2 months in airtight plastic bags at 4°C until use. The manure was collected from the pig nursery whose manure had been previously used to fertilize soil D.

Microcosm Experiment Setup

The experiment consisted of four different soils amended or not with manure and treated or not with SMZ. Considering four conditions and five replicates per condition, 20 microcosms for each soil were prepared as described below. Fifty grams dry weight (DW) of soils were placed in glass bottles closed with air-permeable lids made of gauze and cotton. In order to gently activate the native soil microorganisms, soil microcosms humidified up to 80% of their water content at pF 2.7, were incubated for 1 week at 20°C (±1°C). SMZ sodium salt [SMZ, CAS number: 1981-58-4, 4-amino-N-(4,6dimethyl-2-pyrimidinyl)benzenesulfonamide, Sigma-Aldrich, France] was diluted to 100 mg ml-1 in water and filtered (0.2 μm). Before applying treatments, the manure was incubated overnight at room temperature (20 ± 1°C). Treatments were prepared a few minutes before their application on soils to reach nominal concentrations of 100 mg of SMZ kg-1 dry soil and 13 ml of manure kg-1 dry soil. Then, the microcosms were incubated for 1 month in the dark at 20°C (±1°C). Their humidity has been kept stable by regular watering.

Soil DNA Extraction and 16S rDNA Sequencing

DNA was extracted from subsamples of 250 mg of soil for each microcosm using the DNeasy PowerSoilhtp 96-well DNA isolation kit (Qiagen, France) following the manufacturer's instructions and stored at -20°C. As previously described by [START_REF] Billet | Antibiotrophy: key function for antibiotic-resistant bacteria to colonize soils-case of sulfamethazine-degrading Microbacterium sp. C448[END_REF], the V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified using a two-step PCR and sequenced on MiSeq (Illumina, 2 × 250 bp) and a Jupyter Notebook developed in-house was used to analyze the sequence data [START_REF] Billet | Antibiotrophy: key function for antibiotic-resistant bacteria to colonize soils-case of sulfamethazine-degrading Microbacterium sp. C448[END_REF]. Briefly, sequences were assembled using PEAR [START_REF] Zhang | PEAR: a fast and accurate Illumina Paired-End reAd mergeR[END_REF] with default settings. Further quality checks were conducted using the QIIME pipeline [START_REF] Caporaso | QIIME allows analysis of high-throughput community sequencing data[END_REF] and short sequences were removed (<400 bp). Reference-based and de novo chimera detection together with clustering in OTUs were performed using VSEARCH [START_REF] Rognes | VSEARCH: a versatile open source tool for metagenomics[END_REF] and Greengenes' representative set of 16S rRNA sequences as the reference database. The identity thresholds were set at 94%. Representative sequences for each OTU were aligned using Infernal [START_REF] Nawrocki | Infernal 1.1: 100-fold faster RNA homology searches[END_REF] and a 16S rRNA phylogenetic tree was constructed using FastTree [START_REF] Price | FastTree 2 -approximately maximum-likelihood trees for large alignments[END_REF]. Taxonomy was assigned using RDP Classifier [START_REF] Wang | Naïve Bayesian classifier for rapid assignment of rRNA Sequences into the new bacterial taxonomy[END_REF] and the latest released Greengenes database (v.05/2013; [START_REF] Mcdonald | An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea[END_REF]). α-Diversity metrics (PD whole tree) and UniFrac distance matrices [START_REF] Lozupone | UniFrac: an effective distance metric for microbial community comparison[END_REF] were determined from rarefied OTU tables of 25,700, 24,500, 28,300, and 23,100 sequences per sample for soils A, B, C, and D, respectively. Sequences were deposited in the SRA at NCBI under the accession number SUB8712901.

Statistical Analyses

Statistical analyses were carried out using RStudio statistical software (version 1.2.5033). For each soil, we used analysis of variance (ANOVA) model to determine the effects of treatments on the alpha diversity indices of bacterial communities. Normality and homogeneity of the residual distribution were inspected and log10-transformations were performed when necessary.

Permutational multivariate analysis of variance (PermANOVA) was used to test significant differences in communities' structure using adonis function implemented in the vegan package (permutations = 999).

For each soil, the OTU table was filtered (relative abundance >0.05%) and to detect the OTUs significantly impacted by treatments (antibiotic treatment and manure amendment) a generalized linear mixed model was developed. Considering that, the OTU abundance Y follows a Poisson law of parameter Λ as Y∼P(Λ), we used the following model :

log(Λijk)=oij+μ+αi+βj+(aβ)ij+Ik+Zijk, iid∼N(0,σ 2 )
oik is the offset calculated as the log of the sample read sum. μ is the intercept. αi is the fixed effect of manure amendment (i = 1,2). βj is the effect of SMZ treatment (j = 1,2). (aβ)ij is the interaction effect between the manure amendment and SMZ treatment. Ik is the random effect of the individuals. Zijk is the residual error.

The analysis was performed using the glmer function of the lme4 package (version 1.1-27). Chi-squared test was performed to detect significant pairwise differences for each treatment (significance threshold set at p < 0.05). Subsequently, we performed a post hoc Tukey test with the emmeans function of the emmeans package (version 1.6.1) to detect significant differences caused by manure amendment depending of treatment with SMZ (significance threshold was set at p < 0.05). A Bonferroni correction was used to decrease substantially the probability of detecting false positives (significance threshold was set at 0.05).

For the specific case where OTUs had a null abundance in soil non-amended with manure, we added a specific filter: for each OTU, systematic detection in every replicate of condition with manure was considered significant. Furthermore, only OTUs with a size effect implying the doubling of their abundance between treatments were retained.

Results

Manure's Effect on Diversity in the Soil Communities

Principal component analysis (PCoA) performed on unweighted unifrac matrices showed that after 1 month, the composition of the bacterial community in soils A, B, and C amended with manure was distinct from those not amended (Fig. 1, PERMANOVA, p<0.001). Only the community structure of soil D, which received in situ manure from the same pig nursery 6 months before the experiment, was not impacted by manure amending. Therefore, after the 1-month incubation period, the structure of bacterial communities in three of the four soils differed depending on manure amendment modality. Regarding alpha diversity, manure amendment significantly increased the PD whole tree index in all the soils, including soil D (Fig. 2, ANOVA, p<0.001 for soils A and C, p<0.01 for soils B and D). This indicates that phylogenetic diversity of bacterial community was higher in soils amended with manure than in those not amended.

Manure's Effect on the Relative Abundance of OTUs

The effect of manure on the structure of communities observed by PCoA analysis relied on few OTUs (Fig. 3). Indeed, among the 1646 OTUs with relative abundance > 0.05% in at least one soil, only 25 OTUs were significantly impacted by amendment with manure (Fig. 3a, ANOVA, p<0.05): 10 OTUs related to Proteobacteria, 11 to Firmicutes, 2 to Actinobacteria, 1 to Synergitetes, and 1 to Bacteroidetes (Fig. 3b). For each soil, significant effects of amendment with manure were observed on 20 OTUs (among 642) in soil A, 9 OTUs (among 639) in soils B, and 14 OTUs (among 671) in soil C. In soil D, only 1 of the 826 OTU was significantly affected by manure. representing all the studied OTUs (relative abundance > 0.05% in at least one soil). Among them the ones associated with a colored line were the ones that were significantly impacted by manure amendment.

(b) For each of the four soils impact of amendment with manure on the relative abundance of significantly impacted OTUs. Colors of leaves indicate the phylum of the OTU. The strength of the impact is indicated by the size of green circle which corresponds to the ratio of the relative abundance of the OTU between condition with and without manure. The symbol "+" indicates that the manure does not have a significant impact on the corresponding OTU in this soil. For each OTU, its relative abundance in the manure is indicated as a rank. (c) Abundance relative of OTUs impacted by manure amendment in soils amended (+) or not (-) with manure. Colors are phylum indication. Each segment corresponds to an OTU

Soil amendment with manure positively influenced the relative abundance of all these OTUs (Fig. 3b). Some of them were only detected in conditions with manure (11 in soil A, 5 in soil B, and 10 in soil C, see red stars in Fig. 3b), while others increased by a factor of 2.6 to 262 in amended soils (see size of green circles in Fig. 3b). Altogether, the manure-induced increase in the relative abundance of each impacted OTUs resulted in a noticeable increase in the sum of these relative abundances at the community level going up from 0.5% to 5.4%, 0.1% to 1.3%, and 0.1% to 1.6% in soil A, B, and C, respectively (Fig. 3c). In contrast, the effect of manure on only 1 OTU in soil D had almost no effect at community level (Fig. 3c), as observed previously with the PCoA analysis (Fig. 1).

Among the 25 different OTUs impacted by amendment with manure in at least one soils, 21 were abundantly detected in manure, with 15 of them even being among the 35 most abundant OTUs in manure where their relative abundance was greater than 0.5% (manure diversity = 654 OTUs). Interestingly, the comparison of the four manure-amended soils showed that 10 of these 15 OTUs had a redundant pattern in at least two of them. Among them, 1 Pseudomonodales was significantly impacted by manure in all four soils without exception. It was the most abundant in manure explaining by itself 11.4% of its composition while it was only detected in soil D and not in soil A, B, and C under manure-free conditions. Seven other OTUs (4 Clostridiales, 1 Turibacterales, 1 Campylobacterales, 1 Pseudomonodales) were significantly impacted in all four soils except in soil D. It is noteworthy that these OTUs were always more abundant in soil D under manure-free conditions than in others (Supplementary Tab 2). In addition, 1 Clostridiales and 1 Campylobacter were not detected under manure-free conditions in both soils A and C.

Influence of SMZ on Manure's Effect

SMZ treatment had a significant impact on bacterial community structure only for the soil A when considering the weighted unifrac matrix (Supplementary Fig 1, PermANOVA, p<0.001). This indicates that in the soil A SMZ did not significantly affect the presence and absence of OTUs but only the relative abundance of some of them. Eleven OTUs responded to manure spreading depending on SMZ (Fig. 4, p<0.05). They all were related to the 25 OTUs whose abundance was increased by manure. the OTU in absence of manure whether or not exposed to SMZ, in presence of manure and in presence of manure and SMZ are indicated above respectively the "Strikethrough pig", "pig" and "pill and pig" symbols. Bars indicated the standard deviation of the means (n = 5). Each OTU is entitled by its rank in the manure Two response patterns were observed. For 4 OTUs not detected in manure, SMZ exposure inhibited the stimulating effect of manure as compared to soil not exposed to SMZ. For the other 7 OTUs, SMZ exposure enhanced the increasing effect of manure on their abundance in a non-negligible way. Indeed, the sum of their relative abundance increased from 2.4% to 4.5% with SMZ addition (data not shown). For this latter pattern, it corresponded to OTUs always detected in manure, one being even only detected in soil amended with manure.

Discussion

The influence of manure amendment on the soil bacterial communities was still noticeable after 1 month, although the magnitude of changes varied between soils. The bacterial community of soil A showed, compared to others, a stronger shift following the manure amendment, while that of soil D was the least affected. The most abundant OTUs in the soils remained stable but the rare ones were affected (i.e., the rarest one among those with relative abundance > 0.05%) and possibly undetected ones (i.e., those with relative abundance < 0.05% and/or those that were not present in the soil). Changes in the composition of microbial communities due to rare OTUs during coalescence processes are in line with the observation of the emergence of the rare-biosphere microorganisms following environmental disturbances [START_REF] Rocca | Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental seawater intrusion[END_REF]. The least abundant OTUs under stable conditions are often those favored by a disruption of the environmental conditions [START_REF] Rocca | Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental seawater intrusion[END_REF][START_REF] Fuentes | From rare to dominant: a fine-tuned soil bacterial bloom during petroleum hydrocarbon bioremediation[END_REF]. They may benefit from the equilibrium disruption of the environment where they are normally outcompeted by the betteradapted to multiply and colonize newly available ecological niches [START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF].

The communities of soils amended with manure exhibited OTUs with a wider distribution along the phylogenetic tree. This increase in diversity within the soil bacterial community could either correspond to the emergence of endogenous rare OTUs with functional abilities better adapted to this new environment than the original one or to the invasion of soil community. While previous studies provided evidence suggesting soil invasion by manure-borne bacteria, they could not distinguish surely allochthonous invaders from autochthonous OTUs that emerge because of environmental disturbance caused by the adjunction of exogenous matter [START_REF] Semenov | Does fresh farmyard manure introduce surviving microbes into soil or activate soil-borne microbiota?[END_REF][START_REF] Gravuer | Invader-resident relatedness and soil management history shape patterns of invasion of compost microbial populations into agricultural soils[END_REF]. In the same line, we showed that some OTUs favored in response to manure amendment were not detected in the manure-free soils although they were almost all detected in the manure. However, even if these OTUs were not detected in control soils but detected in the manure, it is not possible to conclude definitively that there was an effective invasion of the soil with OTUs of manure origin since the lack of detection in the soil could be due to sequencing biases. An absence of detection by 16S rDNA sequencing does not necessarily mean its effective absence in the community because of detection thresholds that ignore the endogenous rarest OTUs and make it impossible to perceive the entire bacterial community [START_REF] Lynch | Ecology and exploration of the rare biosphere[END_REF].

However, through the use of different soils, including one already amended in situ with pig manure 6 months before its sampling (i.e., soil D), we provided additional evidences supporting the hypothesis of invasion by manure-borne OTUs. First, in response to manure amendment, some of these OTUs presented the same increase pattern in different soils. The extreme case involved the most abundant OTU in the manure, a Pseudomonodales, which was favored in each of the four soils and was not detected in control soils A, B, and C not amended with manure. This response pattern suggests a common manure origin since all the soils presented different biotic and abiotic properties, limiting the chance that they hosted the same rarest OTUs. Secondly, all these OTUs were detected in the nonexperimentally amended soil D, suggesting that they were successful in colonizing this soil following previous in situ manure amendment events (the last amendment having taken place 6 months before the experiment).

Considering this set of arguments, we assumed that all or almost all the OTUs detected in both manure and soil D and which increased in several soils were from manure origin. All of them were among the most abundant OTUs in manure. Their high abundance in manure bacterial community composition may explain the success of their invasion, since a high inoculation rate is more likely to be successful [START_REF] Gravuer | Invader-resident relatedness and soil management history shape patterns of invasion of compost microbial populations into agricultural soils[END_REF][START_REF] Recorbet | Survival in soil of genetically engineered Escherichia coli as related to inoculum density, predation and competition[END_REF]. One could argue that the detection of the most abundant bacteria of the manure in soil may be a bias due to DNA from dead cells that did not establish in the soil [START_REF] Kunadiya | Persistence and degradation of Phytophthora cinnamomi DNA and RNA in different soil types[END_REF][START_REF] Pathan | The extracellular DNA can baffle the assessment of soil bacterial community, but the effect varies with microscale spatial distribution[END_REF]. However, this argument can be rejected since not all the most abundant OTUs from manure were detected in the amended soils.

Interestingly, when examining the abundance of the OTUs that are suspected to be invaders established in soil D during previous in situ amendment of manure (see above), two kinds of establishment patterns can be differentiated, depending on their taxonomic affiliation: the first one relates to the Firmicutes that are quite abundant in soil D, while the other relates to the Proteobacteria that are rare OTUs (Supplementary Tab.2). The ability of OTUs related to Firmicutes to have become well represented in the soil following previous manure amendment may be because of their physiological attributes. Firmicutes, contrary to Proteobacteria, have sporulation capacity that may confer upon them an ability to persist for a long period in a less hospitable environment until favorable conditions return [START_REF] Filippidou | A combination of extreme environmental conditions favor the prevalence of endospore-forming Firmicutes[END_REF]. The long-term survival of Firmicutes in soil after manure amendment has been already reported, with detection even after the winter season [START_REF] Semenov | Does fresh farmyard manure introduce surviving microbes into soil or activate soil-borne microbiota?[END_REF].

It is noticeable that the community of soil D was much less impacted by manure amendment than the other soils, with only one OTU being significantly increased. This is in contrast to the hypothesis formulated by [START_REF] Mallon | The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader's niche[END_REF], who suggested that previous invasions generate legacy effects in soil communities facilitating future invasion attempts [START_REF] Mallon | The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader's niche[END_REF]. However, it is consistent with the study of Gravuer and Scow, (2021), who observed that soils' communities after a first disturbance by manure amendment were less impacted by the following ones [START_REF] Gravuer | Invader-resident relatedness and soil management history shape patterns of invasion of compost microbial populations into agricultural soils[END_REF]. They hypothesized that the endogenous soil bacteria may adapt to the occasional input of nutrients carried by manure, outcompeting manureborne bacteria during subsequent amendments. Here, we rather hypothesized that it is the manureborne OTUs already established in the soil that prevent the new ones from establishing since they are functionally similar and already adapted to soil.

Finally, the impact of the antibiotic SMZ on the structure of autochthonous soil bacterial communities was relatively low as it was significant in only one of the four soils (soil A). Previous results obtained from the same microcosms may explain this fact [START_REF] Billet | Antibiotrophy: key function for antibiotic-resistant bacteria to colonize soils-case of sulfamethazine-degrading Microbacterium sp. C448[END_REF]. After 1 month of incubation, the bioavailable fraction of SMZ in the SMZ-treated soils represented a low level of exposure for the community. Concordantly, the soil A in which the free-fraction of SMZ was the highest [START_REF] Billet | Antibiotrophy: key function for antibiotic-resistant bacteria to colonize soils-case of sulfamethazine-degrading Microbacterium sp. C448[END_REF] is the one for which its bacterial community was differently impacted by manure amendment according the presence or not of SMZ. A first group of seven OTUs, most of them suspected of being manure-borne OTUs, had their abundance even more increased by manure amendment in the presence of SMZ. The observed effect of SMZ could be explained by the selection of antibiotic-resistant bacteria. Persistence of resistant bacteria and contamination by antibiotics could be positively linked [START_REF] Xu | Antibiotic contamination amplifies the impact of foreign antibiotic-resistant bacteria on soil bacterial community[END_REF]. For instance, we showed in a previous study that the invasion of SMZ-resistant bacteria in soil can be favored in the presence of the antibiotic [START_REF] Billet | Antibiotrophy: key function for antibiotic-resistant bacteria to colonize soils-case of sulfamethazine-degrading Microbacterium sp. C448[END_REF]. The second group consisted of OTUs that were favored by the manure amendment in the absence of SMZ but that lost this advantage in the presence of the antibiotic. All were autochthonous soil bacteria. The lack of negative effects of SMZ on these OTUs in manure-free conditions (Supplementary Tab 2) suggests that they were not especially sensitive to SMZ.

Conclusion

Manure-borne OTUs can colonize soils during the coalescence event of manure amendment. Changes in the composition of bacterial community were mainly attributable to the most abundant OTUs of manure (mainly Firmicutes) with some variations due to soil type. At times, their establishment in bacterial community of soils was still perceptible 1 month after manure amendment. The idea of their long-term establishment was reinforced by their detection in the soil that was amended in situ 6 months prior its sampling for this experiment. SMZ treatment had a limited influence on soil bacterial composition, but in one of the four soils, it enhanced the invasion potential of some manure-borne invaders. It would be interesting to clarify their antibiotic resistant ability.

Figure 1 .Figure 2 .

 12 Figure 1. Comparison of the bacterial diversity in the four soils (A, B, C, and D) amended or not with manure and exposed of not to SMZ. Principal coordinates analysis (PCoA) of the unweighted Unifrac distance matrices of 16S rDNA amplicon sequences showing changes in bacterial community structure. The first two axes and the percent of variation explained by each are indicated. Significant effects of manure amendment are represented by ellipse (p<0.001)

Figure 3 .

 3 Figure 3. Impact of soil amendment with manure on the relative abundance of bacterial OTUs. (a) Phylogenetic tree

Figure 4 .

 4 Figure 4. Abundance of OTUs impacted differently by manure amendment depending on SMZ application. The abundance of

Acknowledgements

The authors thank Arnaud Mounier who built the Jupyter Notebook used in this study to analyze the sequencing data.

Funding

This work was supported by the French National Research Agency (ANTIBIOTOX project, ref: ANR-17-CE34-0003).

Data Availability

The datasets generated during the current study are available from the corresponding author on reasonable request

Contribution

Conceptualization: Loren Billet, Marion Devers-Lamrani, and Stéphane Pesce; formal analysis and investigation: Loren Billet and Marion Devers-Lamrani; writing-original draft preparation: Loren Billet, Marion Devers-Lamrani, and Stéphane Pesce; writing-review and editing: all authors; funding acquisition: Stéphane Pesce and Fabrice Laurent-Martin

Ethics declarations

The authors declare no competing interests. Supplementary Tab 2 -Phylogeny and relative abundance of the 10 OTUs that were significantly increased in several soils amended with manure (expressed per 10 000 OTUs counted).

Supplementary Information