Emnachichti A1

Matthieu George
email: mgeorge@univ-montp2.fr

Jean-Yves Delenne
email: delenne@supagro.inra.fr

Valérie Lullien-Pellerin

Changes in the starch-protein interface depending on common wheat grain hardness revealed using Atomic Force Microscopy

Keywords: AFM), atomic force microscopy, (A T ),projected frontal abrasion area, (ESEM), Environmental Scanning Electron Microscopy, (F N) , normal force, endosperm, nanomechanics, microstructure, puroindoline, T. aestivum

suggested to act at the starch-protein interface through their association with polar lipids.

This study revealed, for the first time, in situdifferences in the nano-mechanical properties at the starch-protein interfacein the endosperm of wheat grainsdepending on the puroindoline allelic status.

HIGHLIGHTS

-Exploresthe starch surface by nano-scratch AFM in the wheat grain endosperm -Shows differences in mechanical properties related withgrain hardness -Relates the differences to distinctpuroindoline b allele with near-isogenic lines -Demonstrates for the first time an effect of puroindolines in grain mechanics -Identifies a potential lubricant interface between starchand gluten in soft grains

ABSTRACT

TheAtomic Force Microscopetip was used to progressivelyabradethe surface of noncutstarch granules embedded in the endosperm protein matrix in grain sections from wheat near-isogenic lines differing in the puroindoline b geneand thus hardness. In the hard near-isogenic wheat lines, starch granulesexhibitedtwo distinctprofilescorresponding either toabrasion in the surrounding protein layer or the starch granule. An additional profile,only identified in soft lines, revealed a marked stop in the abrasionat the protein-starch transitionsimilar to a lipid interface playing a lubricant role. Itwas relatedto the presence of both wild-type puroindolines,already

INTRODUCTION

The major part of common wheat (TriticumaestivumL.) grains corresponds to the starchy endosperm (80-85 % of the dry grain mass) and is composed of cells filled with at least two distinct size populations of starch granules, between 8 and 30 m, embedded in a protein matrix, mainly composed of storage proteins able to form the so-called gluten network [START_REF] Evers | Cereal structure and composition[END_REF]. Changes in the interface between the endosperm composing polymers are now recognized as the major factor to explain distinct hardness wheat classes [START_REF] Barlow | The nature of starch-protein interface in wheat endosperm[END_REF][START_REF] Simmonds | The biochemical basis of grain hardness in wheat[END_REF]. It was found to be related to differences in the amount and allelic form, at the starch granule surface [START_REF] Day | Characterization of wheat puroindoline proteins[END_REF], of basic low-molecular weight cystein rich proteins carrying a unique tryptophan domain [START_REF] Gautier | Triticumaestivumpuroindolines, two basic cystein-rich seed proteins: cDNA sequence analysis and developmental gene expression[END_REF], called Puroindolinea(PINA) and Puroindolineb(PINB), whose corresponding genes were found to be located within the main genetic locus (Ha) controlling hardness in the short arm of chromosome 5D [START_REF] Chantret | Molecular basis of evolutionary events that shaped the hardness locus in diploid and polyploid wheat species (Triticum and Aegilops)[END_REF][START_REF] Morris | Puroindolines: the molecular genetic basis of wheat grain hardness[END_REF][START_REF] Sourdille | Linkage between RFLP markers and genes affecting kernel hardness in wheat[END_REF]. Expression of the wild-type alleles Pina-D1aandPinb-D1a, encoding PINA and PINB respectively,was found to lead to a soft mechanical behavior whereas mutation or deletion in one or both of the PINencoding genes results in a harder grain texture [START_REF] Bhave | Molecular genetics of puroindolines and related genes: regulation of expression, membrane binding properties and applications[END_REF]. In Europe and North America, the most common mutation in PINB is encoded by the gene allele Pinb-D1band corresponds to a single amino acid change (Gly46Ser),which leads to a hard grain phenotype [START_REF] Giroux | Wheat grain hardness results from highly conserved mutations in the friabilin components puroindolinea andb[END_REF][START_REF] Huang | Development of SNP assays for genotyping the puroindoline b gene for grain hardness in wheat using pyrosequencing[END_REF][START_REF] Morris | Prevalence of puroindoline grain hardness genotypes among historically significant North American spring and winter wheats[END_REF].

These differences in grain mechanical resistance has a main impact on the wheat grain end-use properties as it plays a role in the energy required for starchy endosperm isolation duringprocessing, in the obtained particle sizes and final flour yield, as well as in starch granule dissociation from the protein matrix or starch granule damage [START_REF] Feiz | Relationship between wheat (Triticumaestivum L.) grains hardness and wet milling quality[END_REF][START_REF] Igrejas | Genetical, biochemical and technological parameters associated with biscuit quality. I. Prediction using grain hardness and water extractable arabinoxylans[END_REF][START_REF] Morris | Cereal Grain Quality[END_REF][START_REF] Pasha | Grain hardness: a major determinant of wheat quality[END_REF][START_REF] Pomeranz | Wheat hardness: its genetic, structural and biochemical background, measurement and significance[END_REF]. More recently, construction of a numerical model mimicking the endosperm granular structure showed that lowering the adhesion strength between starch granules and the protein network leads to a decrease of the starch damage as observed in soft grains [START_REF] Topin | Strength and failure of cemented granular material[END_REF][START_REF] Topin | Mechanical modelling of wheat hardness and fragmentation[END_REF].

If differences in a soft and a hard grain phenotype have been clearly linked to the presence of both of the wild-type PIN genes thanks to the use of near-isogenic lines (NIL) [START_REF] Morris | Identification and characterization of near-isogenic hard and soft hexaploid wheats[END_REF][START_REF] Rogers | Milling and cookie baking quality of near-isogenic lines of wheat differing in kernel hardness[END_REF]and complementation of null [START_REF] Wanjugi | The role of puroindoline A and B individually and in combination on grain hardness and starch association[END_REF]or mutated alleles [START_REF] Beecher | Expression of wild-type pinB sequence in transgenic wheat complements a hard phenotype[END_REF], introduction of PIN genesin other cereals, such as durum wheat [START_REF] Morris | Transfer of soft kernel texture from Triticumaestivum to durum wheat, Triticumturgidum ssp[END_REF], rice [START_REF] Krishnamurthy | Expression of wheat puroindoline genes in transgenic rice enhances grain softness[END_REF]or corn [START_REF] Zhang | Seed-specific expression of the wheat puroindoline genes improves maize wet milling yields[END_REF] lacking the corresponding genes, was moreover found to reduce grain hardness. In addition, RNAi-mediated silencing of one of the PIN genes was shown to decrease the expression of the others and led to a significant increase of grain hardness [START_REF] Gasparis | The RNA-mediated silencing of one of the Pin genes in allohexaploid wheat simultaneously decreases the expression of the other, and increases grain hardness[END_REF].

However, the mechanismsby which PINs impact on the grain texture and have a role in the starch protein interface remain unclear.

The main hypothesissuggeststhat interactionof PINs with the starch granule surface is mediated by polar lipids from the remnant amyloplasts [START_REF] Pauly | durum) kernel hardness: I. Current view on the role of puroindolines and polar lipids[END_REF]. This hypothesis is supported by the differences in polar (phospho-and glyco-) lipid amount found associated with starch depending on PIN composition [START_REF] Feiz | Puroindolines co-localize to the starch granule surface and increase seed bound polar lipid content[END_REF][START_REF] Finnie | Variation in polar lipid composition among near-isogenic wheat lines possessing different puroindoline haplotypes[END_REF][START_REF] Kim | Puroindolines are associated with decreased polar lipid breakdown during wheat seed development[END_REF]and by in vitrodemonstration of the tight bonding of PINs withpolar lipids [START_REF] Dubreil | Interaction of puroindolines with wheat flour polar lipids determines their foaming properties[END_REF], as well as possibleinsertion into the lipid bilayer [START_REF] Biswas | Interaction between puroindolines and the major polar lipids of wheat seed endosperm at the air-water interface[END_REF][START_REF] Bottier | Structure and orientation into wheat galactolipid monolayers[END_REF]. PINs are thus believed to impair starch-protein adhesion via their lipid interaction and thus affect grain hardness, but evidence of mechanical changes at the starch granule interface was still missing.Probing the starch granule interface in the wheat endosperm from nearisogenic lines differing in hardness thus appears interesting to study to better understand the differences in the interactions between the main endosperm polymers.

As a method to investigate both the local structure and mechanical properties [START_REF] Kasas | Mechanical properties of biological specimens explored by atomic force microscopy[END_REF][START_REF] Kurland | Measurement of nanomechanical properties of biomolecules using atomic force microscopy[END_REF],

Atomic Force Microscopy (AFM) appears appropriate to investigate the endosperm structure and hardness properties. Indeed it was used to reveal imagingdifferences between hard and soft endosperm structure [START_REF] Scudiero | Filed emission scanning electron and atomic force microscopy, and Raman and X-ray photoelectron spectroscopy characterization of near-isogenic soft and hard wheat kernels and corresponding flours[END_REF]but no mechanical studies were pursued. Recently, using an AFM tip as an abrasive tool to abrade the surface of tablets made of starch or the storage protein matrix isolated from wheat grains, distinct mechanical properties of the two main endosperm components were determined and revealed relatively high respective hardness values of around 2.4 and 0.6 GPa [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF].Therefore,this original AFM method which proceeds by scratching of the surface layer-by-layer appeared the most appropriate to both explore the wheat endosperm polymer properties in situandinvestigate the starch granule interface with the surrounding protein matrix.

MATERIALS AND METHODS

2.1.Plant material and growth conditions

NIL of Triticumaestivum L. displaying either a or b allelic forms of Pinb-D1

encoding the wild-type or mutated (Gly46Ser) PINB protein,respectively,and thus differing in grain hardness were described previously [START_REF] Greffeuille | Grain characterization and milling behavior of near-isogenic lines differing by hardness[END_REF]. Genetic similarity (> 97 %) between the NILs was confirmed with the use of Diversity Array Technology markers [START_REF] Akbari | Diversity arrays technology (DART) for high-throughput profiling of the hexaploid wheat genome[END_REF]generated by Triticarte Pty.Ltd (www.triticarte.com.au). Grains were harvested in summer 2008, cleaned to remove broken kernels andimpurities, and then stored at 4°C before analysis. Grain hardness was evaluated using a Percon NIRS apparatus (method 39-70A [START_REF]AACC Method 39-70A[END_REF])on randomly chosen grain sub-samples of each near-isogenic line and results wererespectively equal to 17 for soft NIL (Pina-D1a/PinbD1a genotype) and 63 for hard NIL (Pina-D1a/PinbD1b genotype).

2.2.Sample preparation for microscopy

Wheat grain sections were obtained with a cryotome (Microm HM 520 Cryostat, Thermo Scientific, USA). Grains were picked up randomly in the grain population of each near-isogenic line and cut at both ends to eliminate the germ and the brush then glued on a sample holder using a Frozen Section Medium (Neg-50, Richard-Allan Scientific, Thermo Scientific, USA). The grains were fixed from the widest part corresponding to the germ and placed into the cryotome at -28°C before sectioning, until the glue was frozen (2 min). Sections of 50 μm were removed progressively to reach the middle of the grain. Then continuous cutting of the sample surface was performed by removing thinner sections of 5 μm. The remaining 500 μm grain sections were finally fixed on glass slides, protected with parafilm and kept under controlled temperature and humidity conditions (20°C, 30% relative humidity) before microscopy analysis.

Environmental Scanning Electron Microscopy (ESEM) and AFM

Microscopy

ESEM (Fei Quanta 200 FEG, FEI Co, Hillsboro, OR, USA) without sputter coating was used for grain section imaging.

AFM assays were performed as previously described [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF]with a Nanoscope V atomic force microscope (Bruker instruments, Santa-Barbara, CAUSA), operating in the contact mode under controlled conditions of temperature and humidity (20°C, 30% relative humidity). Commercial silicontips (Ref. FMV, 2.8N/m, Bruker, Camarillo, CA, USA) mounted on a rectangular cantilever with stiffness ranging between 1 and 5 N/m were chosen to preserve reasonable measurement sensitivity and to exert sufficiently large forces to abrade the samples. Before each measurement, the normal (F N ) and tangential forces (F T ) were calibrated and the contacting areas of the tips werecharacterized before and after calibration and at the end of AFM measurements, as described previously [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF]. The contacting areas of the tips were characterized through reverse imaging obtained with AFM on a calibrating grid of equally-spaced sharp points of apex radius around 10 nm (TGT01, Mikromasch, Inc., Estonia).

These measurements clearly showed that the AFM tip apex can be well fitted after calibration by a sphere from the extremity to 20 nm high, with an average radius for the set of tips R = 82 ± 32 nm. This value is significantly above the initial tip radius of 10 nm due to wear off occurring during the calibration process and was checked to remain stable during abrasion assays.

AFM assays were performed on wheat grain sections after selection of a working area i.e at the surface of a small starch granule. A first topographic image (5 x 5 μm)

was acquired at a low applied normal force (F N =100nN) and a scan tip velocity V T = 10μm/s (512×512 pixels) and was used as the reference of the undamaged surface.

The abrasion process was then initiated in the central area (L×L=1×1μm) with an increase of the applied normal force (F N >200nN) and a decrease of the scan velocity V T = 2μm/s (256×256 pixels). Both the trace and retrace F T force maps were acquired (respectively scanning from left side to right side of the image and from right side to left side) to determine the average force sustained by the sample in the direction of displacement. Then, the normal force was lowered back to its initial value (100nN) and a second topographic image (5 x 5 μm) was recorded at V T =10μm/s before again increasing the normal force to further abrade the material.

A progressive and controlled abrasion of the sample was ensured by repeating the abrasion step at least twenty times with a mean number of abrasion scans N=50, depending on the abraded area, interrupted by regular acquisitions of larger topographic images. Large topographic images (5x 5 μm) were acquired after each scan until five scans were reached and then after each group of five scans.

Repetitions of the abrasion process were made on different starch granules (at least twelve) chosen in the middle of the grain cheek section from randomly selected soft or hard NIL samples. The acquired AFM images were visualized and analyzed using the Gwyddion 2.26 software (Department of Nanometrology, Czech Metrology Institute, Brno, CZ) in order to evaluate the abrasion depth and the friction force F T .The depth increment between two successive abrasion steps was obtained by subtracting the two corresponding topographic images after careful lateral positioning through correlation techniques and sensible leveling of the height to account for instrumental drift. The evolution of abrasion depth was then determined by summing all depth increments. More details on the measurement protocol and the error estimations can be found in Chichti et al [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF].

2.4.Statistical analysis

Analysis of variance was performed with the XLSTAT software (2008 version, Addinsoft, Paris) using the Tukey test at 5 % probability for significance between differences.

RESULTS

3.1.Microstructure analysis of the grain endosperm depending on hardness

Microstructure of the starchy endosperm from cryo-cut near-isogenic wheat grains differing inhardness was either observed using ESEM or AFM (Fig. 1). Both types of observation highlighted clear differences in grain endosperm microstructure according to wheat hardness. Endosperm from soft grains (Pina-D1a-Pinb-D1a genotype) was shown to display a loose structure (Fig. 1A) in comparison with hard grains (Pina-D1a-Pinb-D1b),which presented amore tightly compacted endosperm with microcracks bypassing the starch granules (Fig. 1B). Differences were more pronounced using AFM, and at a lower scale (10 versus 50 m), where the endosperm from soft grains was found to present more uncut starch granules (Fig. 1C). Starch granules also appeared less embedded in the protein matrix and even dislodged from the protein matrix in comparison with the endosperm structure from hard grains where the sectioningentailedcutting of the major part of the large starch granules and some of the small ones, depending on their position in the endosperm(Fig. 1D).

Figure 1

Therefore, only small starch granuleswhich were observed as undoubtedly uncut in AFM topographic images (mean diameter around 5μm),wereselected, as shown in Fig. 1E,to explore the starch-protein interface. As revealed in Fig. 1F, the AFM topographic image did not allow the identification ofthe polymer nature at the surface of the selected starch granule.

Probing of the polymer present at the starch granule surface duringendosperm abrasion

A square of L×L=1x1μm(Fig. 1 F) was selected in the middle of each small starch granule in order to avoid the curvature and border effect. Its surface was abraded using an AFM tip according to the conditions recently described [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF]. According to these conditions, the abrasion process could be interpreted as a linear scratching test to evaluate the resistance of the analysed material. As already described in Chichti et al. [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF], atribological model was developed, to assess the material hardness which is related to the normal applied force F N and the depth of the abraded area z. The projected frontal area in contact with the AFM tip, A T , corresponds to the surface of the removed material obtained for each scratching line andcan be determined from the equation of removed volume, taking into account the total depth z, the length of the abraded scan L, the number of abrasion scans N (20<N<70) and n l the number of scan lines in the acquired image (256 lines):

= = Eq. 1
Considering the definition of hardness (ratio between the applied normal force and the area under the tip) and assuminga spherical approximation of the tip apex, we get that A T is also a function of the applied normal force F N , the tip radius R and the hardness H of the abraded material:

= / ( ) / Eq. 2
According to equations 1 and 2, the depth of the scratched area z should increase linearly with the scan number N when abrasion occursin a homogeneous material, at a given applied force F N and for a determined tip radius R. As AFM assays were performed at different applied forces F N and with different AFM tips to explore potential differences in polymer hardness and to avoid tip wear, the depth z was normalized (z norm ) according to F N and R for comparison between distinct abraded areas:

= / = Eq. 3

Changes in the normalized depth z norm as a function of the abrasive scan numbers N was thus relevant to characterize the different polymer layers which can be encountered during the progressive abrasion of the heterogeneous endosperm microstructure.

In a homogeneous material, the slope α describing the linear variation between the normalized z norm and the number of abrasive scan N could be considered as a direct indication of the material hardness H since the dimension of the abraded area L and the number of scan line n l were kept constant in all of themeasurements:

= ( ) / Eq. 4
This slope will be used to identify the nature of the polymers encountered duringprogressive abrasion at the starch surface. According to the previously determined hardness of the endosperm constitutive polymers [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF], reported in Table 1, values for the curve slope were expected to be equal to 0.012± 0.005nm 2 /nN 3/2 and 0.09 ± 0.04 nm 2 /nN 3/2 forstarch and protein network, respectively, in our working conditions. In one out of three AFM scratching assays, a constant linear progression of the normalized depth according to N was observed consistent with the removal of a homogeneous material (Fig. 2A). Considering the curve slope α(α =0.013 nm 2 /nN 3/2 as an example in Fig. 2A),the abraded material canbe considered as starch polymerin accordancewithits previously determined hardness value [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF].The considered abraded area thus appeared to correspond tothe naked starch granule.

Figure 2

Normalized depth according to the number of AFM tip scans at the surface of other analysedstarch granules in the endosperm of hard grainswasfound to present nonlinear curvescharacterized by a progressive transition from a linear regime with a high slope α (α = 0.05 nm 2 /nN 3/2 as forexample in Fig. 2B), consistent with that calculated with protein hardness, to another linear regime with a lower slope (α = 0.009 nm 2 /nN 3/2 ) corresponding to the expected slope for starch polymer (Fig. 2B).

Indeed as the mechanical resistance of protein polymer is lower than starch it was more easielyremoved with a lower number of AFM tip scans in comparison with the starch polymer. This material heterogeneityrevealed that AFM abrasion in these cases occuredat a starch granule covered with a protein layer. Mean thickness of this protein layer was estimated to be in the order of a hundred nanometers, ranging from 48 to 188 nm, depending on the selected starch granule.

The hardness H was determined with equation 4 using the slopes α of each linear part of the curve for each abraded area and compared,in Fig. 3,to hardness values previously determined in similar AFM conditions on tablets made of isolated starch and protein polymers [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF]. Taking into account the uncertainty of our measurements (calculated and represented by bar errors in Fig. 3), hardness values obtained for starch and protein from assays with hard wheat grain sections were in good agreement with hardness values obtained on isolated biopolymers whatever the applied normal force used in the AFM assays. However, in afewcases, determined hardness values appeared outside the range of the data corresponding to the extracted polymers and were found between those of theprotein matrix and starch.

Figure 3

Comparison of protein and starch hardness mean values and relative errors calculated with data obtained fromendosperm samples of hard wheat grains were summarized in Table 1 and showed no significantdifferences with the values determined with isolated polymers.

AFM abrasion at the starch surface in the endosperm of soft wheat grain sections

Distinct starch granules were abraded with the AFM scratching method in endosperm from soft near-isogenic grains carrying bothwild-typePIN genes Pina-D1a and Pinb-D1a. Results are expressed as described before and compared with those obtained in endosperm from NIL carrying the mutated allele encoding PINB.

In half of the AFM assays, the obtained curves of normalized depth as a function of the number of abrasion scan presented a linear curve corresponding to purestarch as pointed out in Fig. 2A, whereas around a quarter displayed an abrasion profile, similar to thatobserved inFig. 2B, consistent with a starch granule covered with a protein layer.

Interestingly, a distinct abrasion profile, not previously observed in the endosperm from hard wheat grains,was revealed in four of the abraded areas and reported in Fig.

4A

. In this case, abrasion started withthe removal of a proteinpolymer and ended by digging into the starch granule as determined by the corresponding values of respective curve slopes . But, a marked transition between the two parts of the profile, characterized by a "stop" of the AFM tip abrasion,was observed. Indeed, for up to twenty abrasion scans, normalized depth remained fairly constant and thus no material removal could bemeasured.

Figure 4

At this non-abrasive step, a significant drop of the lateral force F T was observed, as reported in Fig. 4Bby the study of the F T /F N ratio as a function of the abrasive scan number N. Indeed, at this interface step, the F T /F N ratio was found to be close to the friction coefficient while imagingthe sample topography and therefore confirmed the absence of abrasion. After a number of AFM tip scans without detectable abrasion, an increase in the F T /F N ratio was observed, when abrasion was recovered and the starch polymer was beginning to be removed. No significant depth variation and thus no measurable volume of removed material were therefore possible at this interface step in comparison with those corresponding to protein or starch abrasion (inserts, Fig. 4A). However superficial damage of the scratched surface must have occured as further abrasion of the endosperm material could be continued.Depending on the selected scratching area, the mean thickness of the protein layer covering the starch surface was found to range from 7 to 40 nm.

Hardness values of protein and starch obtained from the distinct scratched endosperm surface in soft NIL were also reported in Figure 3 and Table 1 and compared to values obtained from hard NIL and for isolated polymers. Results confirmed the higher mechanical resistance of wheat starch polymer in comparison with the endosperm protein network.Moreover, no significant differences were notedfor the polymers in wheat grains whatever their hardness, nor with the extracted polymers.

DISCUSSION

The origin of the mechanical differences between soft and hard wheat grains was generally recognized to be linked to changes in the interactions between starch granules and the surrounding protein matrix in the endosperm and to be mediated by either the presence of both wild-typePINs or the mutation or deletion of one of them [START_REF] Pauly | durum) kernel hardness: I. Current view on the role of puroindolines and polar lipids[END_REF]. In this study, NILs carrying either the wild-type alleles Pina-D1a/Pinb-D1a or the mutated version of the gene encoding PINB (Pina-D1a/Pinb-D1b)were intentionally chosen to investigate the local differences between hard and soft endosperm.

ESEM, as well as AFM, observation of the starchy endosperm microstructure after grain cutting clearly confirmed the looser structure of the soft NIL in comparison with the hard, which displayed a more compact microstructure. Indeed, cutting in the endosperm from soft grains revealed local spaces in the protein matrix and possible sites of dislodged starch granules whereas starch granules in the endosperm from hard grains were firmly bound to the matrix and were more often cut, especially the large ones. Differences in cohesionbetween starch and the protein matrix were already pointed out in cultivated common wheat cultivars [START_REF] Moss | The relationship between wheat microstructure and flour milling[END_REF]andfurther clearly related to differences in PIN genotype by others [START_REF] Beecher | Expression of wild-type pinB sequence in transgenic wheat complements a hard phenotype[END_REF][START_REF] Scudiero | Filed emission scanning electron and atomic force microscopy, and Raman and X-ray photoelectron spectroscopy characterization of near-isogenic soft and hard wheat kernels and corresponding flours[END_REF][START_REF] Turnbull | Early expression of grain hardness in the developing wheat endosperm[END_REF][START_REF] Xia | Silencing of puroindolinea alters the kernel texture in transgenic bread wheat[END_REF]. It was also in accordance withthe attributed role of wild-type PINs as non sticking agents and correlates well with endosperm modelling which results in more starch damage when adhesion between starch granules and the protein matrix increased [START_REF] Topin | Strength and failure of cemented granular material[END_REF][START_REF] Topin | Mechanical modelling of wheat hardness and fragmentation[END_REF]. As a consequence only the interface between the non-cut small starch granules and the surrounding protein matrix could be explored,in soft and hard NIL grains, using an original AFM scratching method,previouslydescribed [START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF], and found to differenciate between the respective mechanical properties of wheat starch and the protein matrix (extracted as gluten).

Even if the number of AFM experiments on each type of NIL grains was necessarily low due to the time of data acquisition, between 12 and 20 independent starch granule surfaces were explored from each near-isogenic line, at different locations withinthe wheat endosperm and with distinct grains randomly selected. No differences between the mechanical properties ofthe two main endosperm polymers were measured in situwhatever the grain hardness,in accordance withprevious studies [START_REF] Barlow | The nature of starch-protein interface in wheat endosperm[END_REF][START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF][START_REF] Glenn | Mechanical properties of starch, protein and endosperm and their relationship to hardness in wheat[END_REF], clearly highlighting the importance oflocal investigationsofthe starch-protein interface. Indeed,distinct behaviorsat the starch surface were revealed between hard and soft NILs using an AFM tip as an abrasion tool.

In hard wheat grain sections, probed starch granule surfaces were mainly found covered with a protein layer of significant thickness (48-188 nm) whereas, in fewer cases,the AFM tip directly abraded the naked starch granule. When the starch granules were coated with the protein matrix, the abrasionprofile presented a gradual transition from the mechanical strength of proteins toward that of starch as abraded depth increased. This transition fits well with the traditionalarea-law-of-mixtures or volume-law-of-mixture models used to describe the mechanical hardness of a coated substrate [START_REF] Burnett | The relationship between hardness and scratch adhesion[END_REF][START_REF] Bull | New developments in the modelling of the hardness and scratch adhesion of thin films[END_REF][START_REF] Jonsson | Hardness measurements of thin films[END_REF]. When the adhesion between the coating and the substrate is strong enough, stresses aretherefore transferred from the coating to the substrate and the apparent hardness of the coated material is the average hardness of both materials weighted by the respective areas/volumes loaded in each of them. The mechanical reinforcement of the protein layer as the underneath starch sustains increasing strain by the AFM tip seems to fall well inside this frame and appears thus to indicate a sufficientadhesion at the protein/starch interface to allow for the stress/strain transfer.This law of mixtures could also explain the relative out of range values of a few protein or starch hardness data if obtained too close to the interface.In the endosperm from soft wheat grain sections, the surface of starch granules was mainly found naked revealing a non-cohesive interface with the protein network.

Additionally,if in some positions, the starch granule was found to be covered witha protein layer,this layerwasthinner (7-40 nm) than that observed in the endosperm from hard NIL grains.Moreover, an additional abrasion profilenever observed in hard grainsanddisplaying a marked 'stop' of the AFM tip abrasion, along with a significant drop of the lateral forces, at the interface between starch and the surrounding protein matrix was observed. In this last case, the mechanical properties of both protein and starch were well defined within the expected hardness values and were not affected by the presence of the interface. This behaviorthus appears coherent with a lack of adhesion between the protein matrix and the starch granule at the interface and thus suggests that the protein network in this casewas not adhesively coupledwith the starch granule. Furthermore, the observed stronger resistance tothe tip abrasion concomitant with a drop of the lateral friction forces at the interface between the endosperm polymers appears to correlate well with the lubricating properties observed when exploring a non-puncturedpolar lipid covered surfaceusing AFM [START_REF] Grant | Normal and lateral forces between lipid covered solids in solution: correlation with layer packing and structure[END_REF][START_REF] Trunfio-Sfarghiu | Role of nanomechanical properties in the tribological performance of phospholipid biomimetic surfaces[END_REF].Taking into account the genetic difference between soft and hard NILs, theseobserved distinct abrasion patterns at the surface of the starch granulecan be related to the PIN nature (wild-type versus mutant of PINB). The higher frequency for the observation of a naked starch surface in soft endosperm alsoconfirms the lower interaction between starch and the surrounding protein network andhigher decohesion of the starch granule with the protein matrix, already pointed out by ESEM or AFM imaging. Furthermore, a specific sliding interface was only found in soft grains in one out of four analyzed samples. Taking into account the number of samples analyzed with AFM in each near-isogenic lineand considering a similar frequencyto encounter such a sliding interface in both types of wheat grain, it is possible to estimate that the probability to find a specific mechanical behavior in one quarter of the samples from the soft line, but never for the samples from the hard line,is in the order of a few per cent. Therefore, even if the sampling appears small to perform a complete statistical analysis of the encountered abrasion behavior at the starch/protein interface, the observed rheological differences between starch and protein interface depending on hardness appear relevant and robust with a level of confidence higher than 95 %.Thus, it is the first time that significant mechanical differences at the protein-starch granule interface are experimentally evidenced.

As the peculiar sliding behaviorat the protein-starch interface was not observed in each of the explored positions in soft grains, it thus implies that changes in the PIN nature only impact in some places and not around the overall granule surface. This appears coherent with previous PIN detection at thestarch granule surface by immunolabelling [START_REF] Darlington | Starch granule associated proteins in barley and wheat[END_REF],which clearly showed non-continuous location at the starch granule surface even if precise location and sub-cellular distribution in grains was not consensual [START_REF] Pauly | durum) kernel hardness: I. Current view on the role of puroindolines and polar lipids[END_REF].Unfortunatelly, AFM abrasion could not be coupled with immunolocalisation.

Most importantly, the peculiar abrasion profileonly observed at certain positions at the starch granule surface in the endosperm from soft NIL grains revealed a lipid interface between the protein matrix and the starch surface displaying a mechanical rolelikea lubricant. Taking into account the strong interactions with polar lipids pointed out for the wild-type PINs [START_REF] Dubreil | Interaction of puroindolines with wheat flour polar lipids determines their foaming properties[END_REF][START_REF] Biswas | Interaction between puroindolines and the major polar lipids of wheat seed endosperm at the air-water interface[END_REF][START_REF] Bottier | Structure and orientation into wheat galactolipid monolayers[END_REF]and their suggestedmode of action at the starch surfacevia polar lipids [START_REF] Bhave | Molecular genetics of puroindolines and related genes: regulation of expression, membrane binding properties and applications[END_REF][START_REF] Pauly | durum) kernel hardness: I. Current view on the role of puroindolines and polar lipids[END_REF], this observation appears particularly relevant.

Indeed it is believed that wild-type PINs bind to remnant amyloplast polar lipids at the starch surface via their tryptophan-rich domain resulting in a soft mechanical behavior of the endosperm. However, the mechanismswerestill not identified.Our AFM observation unambiguously revealed possible mechanical differences at the starch granule surface mediated by lipids in the soft endosperm. As shown previously, a Glycine to Serine mutation at position 46 in PINB, next to the tryptophan rich domain,reducesits lipid-binding capacity [START_REF] Clifton | Single amino acid substitutions in puroindoline-b mutants influence lipid binding properties[END_REF], leadingto a lower amount of the deposited PINsat the starch granule surfaceand to a hardness increase in comparison with wheat grains containing the wild-type allele [START_REF] Day | Characterization of wheat puroindoline proteins[END_REF][START_REF] Giroux | A glycine to serine change in puroindoline b is associated with wheat grain hardness and low levels of starch-surface friabilin[END_REF][START_REF] Hogg | Wheat puroindolines interact to form friabilin and control grain hardness[END_REF][START_REF] Gazza | Genetic and environmental factors affecting grain texture in common wheat[END_REF]. A higher association and stabilization of polar lipids around the starch granule with wild-type PINswas previously demonstrated [START_REF] Kim | Puroindolines are associated with decreased polar lipid breakdown during wheat seed development[END_REF] which could explainthe unique AFM abrasion profile found in soft NIL grains. However, it is difficult to determine if this unique profile observed in soft endosperm results from a higher amount of PINs linked to the starch granule in grains via a lipid stabilization or to the higher amount of polar lipids found at the starch surface when both wild-type PINs are present [START_REF] Finnie | Variation in polar lipid composition among near-isogenic wheat lines possessing different puroindoline haplotypes[END_REF]. Indeed, recent experiments suggested that PINs need lipids for binding to the starch granule surface [START_REF] Pauly | Incubation of isolated starch with proteolytic or lipolytic enzymes and different extraction media reveals a tight[END_REF].

Thus, our AFM experiments highlighted for the first time, differences in mechanical properties at the starch granule surface depending on the PIN nature and consequently grain hardness. Observed abrasion profiles at the surface of starch granules in hard endosperm were in accordance with the suggested close association with the surrounding matrix as in the major part of the assays, starch granules were found to be covered with a thick protein layer even if cutting grain before AFM analysis could also lead to naked starch granules. By contrast, when wild-type PINs were present, a naked granule was preferentially found, as a loose interaction with the protein matrix. In some cases, identification of a lubricant interface highlights the possible mechanisms underneath the lower adhesion between the main wheat endosperm polymers found in soft NIL. A few assays also pointed out starch granule surface covered with a thinner protein layer in accordance with the fact that PINs were not uniformly distributed and more probably form patches at the starch surface [START_REF] Bottier | Structure and orientation into wheat galactolipid monolayers[END_REF][START_REF] Darlington | Starch granule associated proteins in barley and wheat[END_REF]. However, if our AFM scratching method was found efficient to probe in situ polymer interfaces and explore the local mechanical properties, complementary studies will be necessary to better define the nature of the interactions and to more precisely identify the molecular partners. was reported to point out non-abrasive conditions.

Table 1 3. 3 .

 13 AFM abrasion at the starch surface in the endosperm of hard wheat grain sectionsDistinct areas selected in apparent uncut starch granulesurfaces were scratched using the AFM tip in the endosperm of grains from thehard NILdisplaying the Pina-D1a/Pinb-D1b genotype. Evolution of the normalized depth z norm as a function of the number of abrasive scans N revealed two distinct types of abrasion profiles summarized in Fig.2depending on the analysed starch granule.

Figure 1 :

 1 Figure 1: ESEM images of soft endosperm from Pina-D1a/Pinb-D1a NIL (A) or

Figure 2 :

 2 Figure 2: Example of normalized depth z norm progression as a function of AFM

Figure 3 :

 3 Figure 3: Hardness values obtained for the easily abraded protein material (circles)

Figure 4 :

 4 Figure 4:A. Changes in the normalized depth z norm as a function of the abrasive scan

Fig. 1

 1 Fig. 1

Fig. 2

 2 Fig. 2

Fig. 3

 3 Fig. 3

Fig. 4

 4 Fig. 4

Table 1 .

 1 Hardness values for protein and starch obtained from soft and hard wheat grain sections and comparison with data obtainedfrom tablets made of pure polymers using similar AFM abrasion scratching assays[START_REF] Chichti | Nanomechanical properties of starch and gluten biopolymers from atomic force microscopy[END_REF]. Different letters indicate statistically distinct values.

ACKNOWLEDGMENTS

We would like to thank F.-X Oury(Institut National de la RechercheAgronomique, UMR GénétiqueDiversitéetEcophysiologie des Céréales", Clermont-Ferrand) for providing the hard and soft NIL and for grain NIRS hardness measurements. We would also like to thank Jacques Escoute (Plate-formed'Histocytologie et d'ImagerieCellulairevégétale, CIRAD Montpellier) and the Montpellier RIO imaging platform for grain cryo-sectioning. We are also grateful to Montpellier 2 University and INRA (department Caractérisation et Elaboration des ProduitsIssus de l'Agriculture) for the PhD grant forE. Chichti and to A. Duri (UMR Ingénierie des Agropolymères et Technologies Emergentes, Montpellier) for her help to establish contact with the Institut Charles Coulomb. interaction between puroindolines and lipids at its granule surface, Cereal Chem. 91(2014)240-246.


Amina Ben Hamida
email: amina.ben-hamida@univ-smb.fr

Alexandre Benoit
email: alexandre.benoit@univ-smb.fr

Patrick Lambert
email: patrick.lambert@univ-smb.fr

Ben Chokri
email: chokri.benamar@ieee.org

Amar

Could Multimedia approaches help Remote Sensing Analysis?

Keywords: Bag of visual words, Deep Learning, Remote sensing, classification

The paper explores how multimedia approaches used in image understanding tasks could be adapted and used in remote sensing image analysis. Two approaches are investigated: the classical Bag of Visual Words (BoVW) approach and the Deep Learning approach. Tests are performed for the classification of the UC Merced Land Use Dataset which provide better results than the state of the art.

I. INTRODUCTION

Remote sensing is a rapidly growing field that has been witnessing various breakthroughs over the last decades. In fact, the advent of satellites such as the recently launched Sentinels is allowing a high acquisition rate coupled with a very rich images' content which resulted in the data deluge era. Given the overwhelming masses of available remote sensing data, a review of the currently used approaches is needed. One solution is to resort to the multimedia field and select the most suitable candidates among the existing methods. The inspiration can be derived from the fact of not tackling the image as a unity which can be achieved whether by the use of more locally focused descriptors or by the increase of its semantic level. Migrating from the color, texture, shape… descriptors [START_REF] Campbell | Introduction to Remote Sensing[END_REF] to richer ones such as SIFT, SURF, FREAKS LBP… is one effective solution. Therefrom, comes the opportunity to resort to approaches with higher semantic level such as the Bag of Visual Words (BoVW), or to step into the data mining, machine learning or deep learning fields.

In the remote sensing literature, we can find a few recent attempts which transpose these approaches, mostly dedicated to the multimedia domain, for remote sensing image analysis. As it was first successfully introduced by Yang [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF] who defines a BoVW-based approach for the classification of land areas that are reshaped by humans (parking, golf courses, etc.) or later introduced by Dos Santos to extract features independently from their shape through the use of BoVW for cultivated areas [START_REF] Santos | Improving Texture Description in Remote Sensing Image Multi-Scale Classification Tasks By Using Visual Words[END_REF]. In [START_REF] Vaduva | Deep learning in very high resolution remote sensing image information mining communication concept[END_REF], Vaduva proposes a deep learning algorithm for semantic annotation of very high resolution remote sensing images.

In this paper, we use the "UC Merced Land Use Dataset" and Yang [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF] results as a baseline to explore the interest of the Bag of Visual Words and the Deep Learning approaches in the classification of remote sensing images. This dataset is a small one with low resolution images, but it works as a first step to demonstrate the potential of such approaches in the RS context. The outline of this paper is as follows. In Section II, we carry out and test several improvements of the BoVW approach proposed by Yang [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF]. In Section III, we adapt the deep learning architecture proposed in [START_REF] Krizhevsky | Hinton ImageNet Classification with Deep Convolutional Neural Networks[END_REF] and test it on the same dataset. In Section IV, we conclude with a short summary.

II. IMPROVEMENT OF BOVW APPROACH

A. The Bag of visual words

The "bag of visual words" approach [START_REF] Csurka | Visual categorization with bags of keypoints[END_REF] basically consists of: (1) extracting a large collection of local invariant features from a large set of images, (2) quantizing the features, often using k means clustering, in order to create a k sized vocabulary. Once the visual words are selected as the centers of the k clusters, (3) features of a new image can be translated into visual words by deciding which clusters they are nearest to and the new image is then mapped over an empirical space, captured into an histogram that counts how many times each word occurs within it. Supervised classification algorithms are applied afterwards, generally using SVM classifier, to find the link between different BoVW histograms and different semantic concepts.

For the sake of a good image representation, the features must be extracted from interesting locations or key points that are characterized by invariance toward geometric transformations including rotations, translations….

The literature encompasses many feature extraction techniques. However, we choose to work with both the Scale Invariant Feature Transform (SIFT) [START_REF] Lowe | Distinctive Image Features from Scale-Invariant Keypoints[END_REF] and the Speed Up Robust Features (SURF) [START_REF] Bay | Speeded-Up Robust Features (SURF)[END_REF] as a baseline for further image analysis since they have been proven to be the best performers in many image retrieval approaches.

B. The Extentions of BoVW

We investigate and compare BoVW state of the art methods. Starting from the usual sparse, gray level SIFT detector, we experimented different options in terms of density, color space and spatial structure.

Dense feature extraction

Contrarily to the basic sparse feature extraction, where the descriptors are only dedicated to the points of interest, the dense sampling consists in breaking the image into a number of patches and lately creates a descriptor for each selected patch center. Such strategy was first initialized by [START_REF] Tuytelaars | Dense interest points[END_REF]. It allows a full coverage of the entire scene thanks to the equal contribution of all regions drawing the image. Thus, we end up with a constant number of extracted features allowing an accurate, yet costly representation of the objects. As it is deeply tackled in [START_REF] Chatfield | The devil is in the details: an evaluation of recent feature encoding methods[END_REF], although the dense sampling does not consider keypoint orientation and imposes scale, it succeeded to improve over sparse keypoint detection for BoVW classification.

BoVW applied to RGB images

Usually the BoVW is applied to gray level images, in total absence of the color components. The idea is then to examine the contribution of the Red, Green and Blue channels in the process of the "visual vocabulary" creation.

In order to procure a BoVW for an RGB image, we need to first separate the channels. The features are then computed for each and every one of the three components independently. In that case, we acquire three 128 dimensioned SIFT descriptors for each keypoint that are concatenated into a 128×3= 384 long vector that feeds the BoVW toolchain. Note that color space change can be applied before descriptors extraction. For example, opponent color space and HSV are often used in the multimedia context.

C. Experiments

We have resorted to the "UC Merced Land Use Dataset1 "

[2] that was manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The pixel resolution of this public domain imagery is 1 foot. 100 images measuring 256×256 pixels were manually selected for each of the following 21 classes: agricultural, airplane, baseball diamond, beach, buildings... Figure 1 shows a few images.

This dataset is of moderate size but has the advantage to allow us to compare to a previous study for a fair comparison. It also allows various techniques to be experimented and directions to be drawn for further analysis on much larger datasets. The process was made with 1000 sized vocabulary and a classification phase was performed using the Support Vector Machine (SVM) with and 'RBF' kernel.

The evaluation process consists in training all the methods on a specific subset of the dataset. It is made of 95% of the images with similar class proportions. Performances evaluation is carried on the remaining validation subset. 6 fold cross validation is used and we present average performance values. Each classification rate was calculated based on the number of correctly labeled images among all the collection of testing, and the presented results are an average rate over all classes.

As shown in table 1, the use of SIFT as a feature extraction tool ensures relatively high classification rates. Our implementation of the sparse features approach is equivalent to the one of Yang [START_REF] Yang | Bag-of-visual-words and spatial extensions for land-use classification[END_REF] and presents a similar performance baseline. However, the interference of the dense sampling instead of the classical sparse SIFT keypoints detection, significantly enhances the results (9% above the Yang baseline 78%). Some classes witness a better classification rate than others. For example, Chaparral, harbor, and parking perform much better than storage tanks and tennis classes.

Taking the color components into account, in the BoVW process, doesn't seem to increase the overall classification rate at first glance. But, going through all classes one by one and comparing their correctly labeled rate each one aside proves that such approach is highly dependent from the images scenes nature. Therefore, it's obvious that for some sort of classes such as 'mobile home park', 'golf course' and 'overpass', the RGB channels enhances the classification rate while decreases it for some other classes: 'airplane' and 'storage tanks'. Here again the combination of the color space and the dense sampling allows an improvement of performances.

A. CNN networks

The fast paste of technologies development and the continuous enhancement of images resolution stimulate an urgent need for images deeper analysis. More and more details and concepts are included in a single image; therefore, especially in the satellite imaging domain, the analysis at different scales and regions is now mandatory for a comprehensive, accurate description of the Data. For each concept to detect, a model should be provided according to the type of the targeted information, namely, pixels, objects, structures…. However, current BOW approaches are difficult to design in this aim and relying on a single hand crafted feature such as SIFT or SURF cannot be the perfect method for all the cases. As an illustration, SIFT is built to describe detailed areas and it won't be efficient to describe untextured areas such as lakes, deserts, etc.

A very different approach recently emerged and tries to answer such challenge by learning representations from the data. In this family of methods, Deep Learning with convolutional neural networks recently proved to progressively outperform all the other approaches in the multimedia community starting from the ILSVRC2012 challenge with the work proposed by Hinton&al [START_REF] Krizhevsky | Hinton ImageNet Classification with Deep Convolutional Neural Networks[END_REF]. Such methods inherited from the work of Yann LeCun [START_REF] Lecun | Gradient-Based Learning Applied to Document Recognition[END_REF] rely on neural layers that can be trained in a supervised manner using error back-propagation. It is now adaptable to a wide variety of context thanks to new operators, the availability of very large annotated training databases and massive computing machines such as Graphical Processing Units (GPU).

B. CNN components

We briefly describe the main components of a deep neural network and more details can be obtained from the following reference paper [START_REF] Krizhevsky | Hinton ImageNet Classification with Deep Convolutional Neural Networks[END_REF]. Basically, there's no unique architecture for the neural networks. Although it involves the same fundamental elements:

Convolution layers (Conv) take as input only limited areas of the input space thus preserving locality. They correspond to a set of convolutionnal operations applied all over the input. Such layers, applied successively allow the semantic description of the image to increase along layers from edges, corners, to parts and objects.

Fully Connected (FC) layers are similar to CL but are the classical neural net layer that takes as input all the available outputs of the previous layer thus ignoring locality information. It generally handles the majority of the parameters to train and is generally used at the end of the architecture. It finalizes the representation learning process by modeling the target concepts from a composition of their already high semantic level input features.

Non linearities (NL) are a key property of neurons. They are thus systematically applied at the output of Conv and FC layers. They are historically Sigmoids but the recently introduced Rectified Linear Units (ReLU, f(x)=max(0,x)) proved more efficient network training.

Pooling operators (Pool) which are used to subsample input data by applying fusion operators over local neighborhood such as max or average. It enforces robustness against translations.

Local Contrast Normalization operators (LCN) that enhance local contrast and help luminance and contrast invariance.

Depending on the task, a final layer may be used to format output to the required type. In a classification task, a Softmax layer is generally used to generate a probability distribution and predict a single class over a set of mutually exclusive classes. SoftMax can also be used as a loss in the training stage. Such loss measures the error made by the net when misclassifying samples. It is used to compute layer weights corrections (gradients) in a chain-rule manner (back-propagation) from net top to bottom.

C. CNN architecture

Several network designs have already been proposed and a critical point concerns its depth that should be enough to get high semantic level and transferable enough learned representations. The counterpart is the computational cost. A compact yet efficient architecture is AlexNet [START_REF] Krizhevsky | Hinton ImageNet Classification with Deep Convolutional Neural Networks[END_REF] but deeper ones generally improve as discussed and compared in [START_REF] Chatfield | Return of the Devil in the Details: Delving Deep into Convolutional Networks[END_REF]. In this paper, we want to study the learned multimedia features representation of CNN to our context. To do so, we propose to use a AlexNet like network with weights learned from the multimedia dataset of ILSVRC2012 and we show that it can adapt to remote sensing images with few changes. Chosen network implementation is CaffeNet from the caffe library [START_REF] Jia | Caffe: Convolutional Architecture for Fast Feature Embedding[END_REF] with the available pretrained weights.

As illustrated in figure 3, CaffeNet is composed of 5 Conv layers, all using ReLU non linearities. Some of them are followed by Pooling and Contrast normalization steps. The net ends with 3 fully connected layers each being followed by a ReLU non linearity. The last layer has an output dimension of size 1000 which corresponds to the 1000 imagenet multimedia classes on which the net has been trained for and is followed by a Softmax layer that generates class probability distributions. We briefly present the considerations taken into account when choosing the mostly performing model. Therefore, first, we have selected 5 conv layers based on the complexity level and the lack of the amount of the available data. Secondly, since the pooling layers allow a significant decrease on the spatial level, the introduction of 3 layers is a decision made based on the hardware capacities. Finally, the fully connected layers number is fixed to 3 in order to overcome the over fitting problems resulting from the increased model capacity. CaffeNet_FineTune: we follow the classical fine tuning procedure: we keep the original net with its initial weights but replace the last layer by a new one having uninitialized weights and a number of outputs corresponding to the new number of classes of the new classification task. In a training stage, only this last layer is retrained using a softmax loss.

CaffeNet_ fc7_features: we use the net as a feature extractor. To do so, we keep the entire original net but avoid the last specialized layer (FC8+SoftMax) and only use the net to extract the feature vectors from FC7 layer that feed a linear SVM classifier.

D. Experiments

Using the same training and testing datasets design described with the BoVW evaluation, we report the obtained results in table 3. All images are resized to 227*227 pixels to comply with the neural net configuration.

First, the impressive performance of both approaches are surprising since all the layers of the net except the new FC8 have been trained on multimedia concepts recognition (animals, persons, sports, etc.) that are totally different from the ones that we try to classify here. Second, it appears that the linear SVM classification of features extracted from FC7 layer is slightly more efficient than training a new specialized neural layer. However, both approaches significantly outperform the hand crafted feature methods that we evaluated. A per class analysis of the CaffeNet_ fc7_features approach shows that the few misclassifications made concern some resembling classes such as "dense residential" and "mobile homepark", or "forest" and "agricultural".

IV.CONCLUSION

  Fig. 1. examples of the UC Merced Land Use Dataset

Fig. 2 .

 2 Fig. 2. CaffeNet deep neural network architecture Then, we use this network in the two following configurations.

TABLE 1 .

 1 CLASSIFICATION RATE FOR BOVW AND ITS EXTENSIONS USING SIFTThe results for the use of SURF as a feature extraction tool in this context are summarized in table 2. In fact, SURF here has proven to be outperforming the SIFT on different levels: whether it's handled with sparse or dense sampling or even with the RGB channels concept adoption. It is important to note, that although SURF has better results, it still follows the same pattern as the SIFT performances toward the different BoVW extensions.

		BoVW using SIFT
	Yang sparse features baseline [2]	Our sparse feature detection	Sparse feature sampling detection Dense + RGB	RGB Dense sampling + RGB
	Classification
	78%	79%	87%	78%	86%

rate

TABLE 2 .

 2 CLASSIFICATION RATE FOR BOVW AND ITS EXTENSIONS USING SURF

			BoVW using SURF
		Sparse feature	Dense	BoVW	RGB
		detection	sampling	+ RGB	+ Dense
	Classification
	rate	86%	91%	85%	90%
		III. DEEP LEARNING APPROACH

TABLE 3 .

 3 Classification rate for Deep learning approaches

		Deep learning using original CaffeNet trained
			architecture
		CaffeNet_FineTune	CaffeNet_fc7_features +Linear SVM
	Classification rate	94%	95%

http://vision.ucmerced.edu/datasets/landuse.html

We describe and compare the BoVW and its extensions to the deep Learning approaches in the use of remote sensing images classification field. Although the BoVW seems to be a robust tool for such purposes, deep learning has managed to take the lead. This work catalyzes more efforts toward the exploration of new enhancement paths and basically the validation of this work using a larger and more delicate database. Another direction would be to train a deep architecture from scratch on a remote sensing database instead of using weights pretrained in other contexts but the availability of a large collection of annotated regions will be part of the challenge.


If basal ganglia are widely accepted to participate in the high-level cognitive function of decision-making, their role is less clear regarding the formation of habits. One of the hardest problem is to understand how goal-directed actions are transformed into habitual responses, or, said differently, how an animal can shift from an actionoutcome (A-O) system to a stimulus-response (S-R) one while keeping a consistent behaviour?

Experiments Two monkeys were tested on a two-armed bandit task using 20 sessions in control conditions and 20 in muscimol conditions (10 for each monkey in each condition). We defined as success rate, the number of trials in which the animals chose the optimal target.

In saline conditions, the animals maximize their choice in the habitual condition and learn progressively the difference between the two cues in the novelty condition. They choose randomly at the beginning of training to finally display a clear preference for the target associated to the best reward.

In muscimol conditions (inhibition of the internal globus pallidus), the animals are still able to make the optimal choice in habitual conditions (with slower reaction time) but are unable to learn in novelty condition and make random choices from start to end of the session.

THE FORMATION OF HABITS

The implicit supervision of the basal ganglia The model is based on the model introduced in [1,2]. This former model introduces an action selection mechanism that is based upon the competition between a positive feedback through the direct pathway and a negative feedback through the hyperdirect pathway. The model has been further extended and exploits the parallel organization of circuits between the basal ganglia and the cortex [3] using segregated loops: one for making the selection between the two presented cues, and the other for making the selection between the two possible movement directions. To solve the task, it is necessary for the model to choose the cue shape and to select the right movement direction which depends upon the chosen cue. The model has been further refined such as to have a competition mechanisms within each cortical group. Using short range excitation and long range inhibitions, this competition ensures that a unique cognitive and motor decision eventually emerges, even if these decisions might be unrelated at this stage.

Learning occurs between the cognitive cortex and the cognitive striatum using a simple reinforcement learning where the value of the different cues are updated after each decision (see [2] for details). We added Hebbian learning (LTP) at the cortical level between the cognitive/motor cortical groups and the associative cortical group. This learning is enforced once per trial, at the time a move is made and independently of the actual reward. In habitual (resp. novelty) condition, the model is trained using cues 1 (resp. 3) & 2 (resp. 4) which are presented simultaneously at random positions. Cue 1 (resp. 3) is associated with a reward probability of 75% while cue 2 (resp. 4) is associated with a reward probability of 25%. In habitual condition, the model is trained until it achieves a mean performance of 0.95. This takes between 40 and 50 trials depending on the initial conditions (noise) and whether first cues are rewarded or not. This training impacts significantly Hebbian learning at the cortical level because cue 1 is chosen most of the time and consequently, the associative link relative to cue 1 is strengthened compared to associative link relative to cue 2.

[1] M. Guthrie, A. Leblois, A. Garenne, and T. Boraud. Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study. Journal of Neurophysiology, 109:3025-3040, 2013.

[2] Leblois A., Boraud T., Meissner W., Bergmann H., Hansel D. Competition between feedback loops underlies normal and pathological dynamics in the basal ganglia. J Neurosci 26: 3567-3583, 2006.

[3] Piron C., Daisuke K., Topalidou M., Goillandeau M., N'guyen T., Orignac H., Rougier N.P., Boraud T. The role of the basal ganglia in the formation of habits in monkeys, submitted. Results are in accordance with the experiments in monkeys. In the habitual condition, performances are optimal with or without lesion, indicating the cortex is able to make the optimal decision without the help of the basal ganglia if it has been learned previously. In novel condition, performances of the intact model are initially at chance level but after a few trials, it reaches a near-optimal performance, indicating the model has learned the respective reward probability associated with each novel cues. However, for the lesioned model, performances stay at chance level, indicating the cortex is unable to learn the new task without the help of the basal ganglia.

Habits are better expressed following extensive training on the two armed-bandit task, when the reward probability is reversed. If behavior were goal directed, then the model should react to the devaluation of reward. However, there is period (B) during which model persists in old behavior and seems to be inflexible to such devaluation. But, if extensive training is not too strong, model can recover and switch back to a goal-directed behavior that ultimately overcome habits.

  Neurodégénératives -INRIA -CNRS -Université de Bordeaux


Fayrouz Soualah-Alila
email: fayrouz.soualah-alila@univ-lr.fr

Mickaël Coustaty
email: mickael.coustaty@univ-lr.fr

Nicolas Rempulski
email: nicolas.rempulski@gmail.com

Antoine Doucet
email: antoine.doucet@univ-lr.fr

DataTourism: designing an architecture to process tourism data

Keywords: DataTourism, Tourinflux, TIFSem, TimeML, SentiML

With the rapid diffusion of new technologies in tourism, professionals face new challenges to use efficiently the vast amount of data created by tourists. Nowadays, these information come from multiple and varied sources, as cellular or social networks, touristic location attendance or dematerialized satisfaction surveys, and in huge amount. They are an important resource for the tourism industry, but their heterogeneity makes it difficult to aggregate and analyse them. The key issue for tourism actors, professionals or politics, is to manage and operate tourism information about their territory effectively. The purpose of this paper is to describe synthetically how tourism information are managed under the Tourinflux project. We describe in this paper an architecture named DataTourism for tourism data management which solve multiple technical locks encountered when working with tourism data: heterogeneity, quality, interoperability, reusability and standardisation.

Introduction

In today's rapidly changing world, many data related to tourism area are produced. This is primarily the result of increasing possibilities to digitize growing volumes of data, and the development of open-sources and open-data policies. Likewise, more and more data are being generated by sensors, mobile telephones, and connected devices on the one hand, and the democratization of comparative services dedicated to tourism on the other hand, as Kayak or Yelp for instance. Most of these data could be collected and used by politics to efficiently assign public funds to increase tourists' attendance and satisfaction and thus making their territory attractive. But today, they are mostly unused due to a lack of suitable tools.

However, the business sector is already using these data. They are analysed for marketing strategies, predicting trends and also for producing detailed statistics. Tourism professionals are using multiple sources, and fully use the recent development of the World Wide Web and its social services. The web has changed people's daily life, this is also true for tourism. It has significantly influenced the way information about users are gathered and exchanged in the tourism sector. With the intensive use of social networks and web sites specialized in e-tourism (TripAdvisor, Booking.com, etc.) web users are no longer passive recipients of contents; they absorb information from the web and in return produce their own new content. But when users collect these information, from professional sources or from other users, they also create their own set of information: tourism goods they are looking for, future date of their vacation, etc. Professional tourism services collect these information while providing information or services to users. We could make the same example with cellular carrier, which are tracking movements of their user. These Information, wherever they are from, are then used to improve the service quality by enhancing employee's knowledge about customer's preferences and opinions.

Two main problems occur with tourism data management: their heterogeneity and their volume. As mentioned before, tourism information are continuously enhanced and updated using dedicated websites. These data are contained on web pages that are originally designed to be human-readable, and so, most of information currently available on the web are kept in large collections of textual documents. As the web grows in size and complexity, there is an increasing need for automating time consuming tasks, such as information extraction and interpretation. Some automatic process to annotate and enrich textual information knowledge is needed.

Tourism domain is characterized by a significant information heterogeneity and by a high volume of online data. Data related to tourism are produced by different experts (travel agents, tourist offices, etc.) and by visitors, thus creating an heterogeneous data set from a semantic and typology point of view. Moreover, this set is often incomplete and inconsistent. For instance, these data could contain information related to tourism objects (hotels, concert, restaurant, etc.) with raw information, service description for instance, temporal information, about opening hours or days in the week, and opinions, as users' average satisfaction. There are already multiple taxonomies and catalogues which are designed and used internally by tourism actors to allow them to manage heterogeneous tourism data efficiently. Efforts are now made to generate standards to facilitate inter and intra tourism data exchange.

The Tourinflux1 project falls within this context where it meets a basic need: helping professional and political tourism actors to develop their territory. One way to valorise territories is to generate reports, called dashboards, based on enriched data collected from Tourist Information Systems (TIS) and the web. The emergence dashboards was a consequence of managers needs to monitor a complex subject with indicators clearly showing how a territory's tourism activities are perceived and evaluated. Experts from tourism industry use and need these dashboards to improve their knowledge about the tourist attractiveness of their territory.

The technical architecture we created in Tourinflux is aimed at providing the tourism industry with a set of tools (1) allowing them to handle both their internal data, and the information available on the web, and (2) allowing to improve the display information available about their territory on the web. In this paper we describe synthetically how tourism data, composed of information related to tourism objects (hotels, concert, restaurant, etc.), temporal information and opinions, are managed under the Tourinflux project. We present an architecture named DataTourism for the management of tourism data that allows solving different locks: heterogeneity of tourism data sources, quality of these data, interoperability, reusability and standardisation.

Designing touristic dashboards

A touristic dashboard is a set of management indicators, built periodically, for a tourist actor or a group of tourist actors, in order to guide their decisions and actions to achieve performance goals. A touristic dashboard is considered as:  An instrument of control and comparison: It allows tracking the evolution of tourist offers;  A tool for taking decision: It communicates key information about the situation of a touristic activity;  A communication tool: It provides a permanent communication between the various tourism actors and between different hierarchical levels;  A standby tool: It allows to identify emerging opportunities and risks. Figure 1 shows an example of dashboard.

Fig. 1. Example of dashboard

In France, we distinguish five main institutional publishers of tourist dashboards: Tourist Offices (TO), Departmental Tourist Committees (DTC), Enterprises General Direction (EGD), National Institute for Statistics and Economic Studies (Insee) and Atout-France2 . Today, each of these publishers has independently developed various techniques to assess a territory (a city, a department, a region, the whole country) and despite all the efforts made so far in developing their own dashboards, these dashboards remain insufficient to fulfil goals described above:  They are not sufficiently representative of tourism territory. They focus more on

accommodations. An objective is to integrate other sources such as content about opinions and intentions of visitors.  They are limited to the territory scale they are developed for. It is impossible to generate dashboards at all hierarchical levels (department, region, country) or make a comparison between territories. This is mainly due to the heterogeneity of TIS.

In order to generate rich dashboards, it is necessary to exploit optimally all information available. The data set used has to be the most complete and varied as possible to reflect faithfully the touristic activity of a territory.

In the next section we describe our DataTourism architecture for the aggregation of tourism information from different data sources. The goal of the architecture is to provide a framework that allows the aggregation of tourism information following an ontological based approach. The framework must access tourism data sources, extract their information combine them, as different as they are, and present them to the tourist professionals in the form of dashboards.

Sources and types of tourism data

Tourism in its nature is an industry strongly dependent on data exchange. In the last decade, more and more data have been becoming available for research and development. These data are from different sources. The main sources of tourism data which we consider as part of this project are: the data available in the different TIS, the data available on the web and Open-Data. This data could be composed of (1) information related to tourism objects (hotels, concert, restaurant, etc.), ( 2) temporal information and (3) events and opinions. Our architecture to generate dynamic dashboards has four major phases: integration of information related to tourism objects in the system, annotation of temporal information and opinions in web pages, enhancement of the tourism objects from the annotated information (temporal and opinions), and finally dynamic generation of dashboards. All the components that are used in each phase are illustrated in Figure 2. We describe briefly in the following sections how we modelled tourism objects in our architecture and how we proceed to annotate temporal information and opinions in web pages to complete the description of the tourism objects.

Tourism objects modelling

The interoperability of TIS is a major challenge for the development of tourism domain. Several national, European and international institutional initiatives have proposed different standards to meet the specific needs of tourism professionals. In France, the major TIS such as Raccourci Interactive3 , TourinSoft4 and Sitra5 , have adopted the TourInFrance standard (TIF). Since its creation in 1999 by the tourism ministry, TIF is used today by more than 3000 tourist offices in France, by DTC and by different tour operators, to facilitate data exchange between these different actors.

In 2004, the TourInFrance Technical Group (TIFTG) approved the new version of the standard, TIF V3. In this version, the standard has evolved towards XML technologies to facilitate publishing information on the web and information exchange between systems, and is accompanied by several thesaurus. Since 2005, this standard stopped evolving. As a result, tourism professionals have adapted the standard to their own needs (different tags syntaxes, new tags added, etc.) and proposed their own evolution in an unorganized way, producing TIS that are not really interoperable among themselves and that cannot directly be shared using international standard. Accordingly, the exploitation of tourism information is trapped in its own territory, and so it is impossible to aggregate these information [START_REF] Bittner | Ontology and Semantic Interoperability[END_REF].

To overcome these limitations we propose to evolve the TIF standard to share the knowledge it contains and to ensure data interoperability, by applying the concept of ontology to represent the standard terminology. An ontology is considered as the specification of a conceptualization, in other words, a specific artifact designed with the purpose of expressing the intended meaning of a shared vocabulary [START_REF] Hirst | Ontology and the lexicon[END_REF].

Having a common semantic base compensates the interoperability problem [START_REF] Fodor | Harmonise -a Step Towards an Interoperable e-Tourism Marketplace[END_REF] that comes along with the integration of heterogeneous data sources by converting existing heterogeneous unstructured tourism data into structured ontological data. In the tourism area, some researches have already tackled the design of ontologies. Several available tourism ontologies show the current status of the efforts: the OTA (Open Travel Alliance) (OTA, 2000), the Harmonise ontology (Dell' Erba and al., 2002), the Hi-Touch ontology [START_REF] Legrand | Semantic Web Methodologies and Tools for Intra-European Sustainable Tourism[END_REF], the QALL-ME ontology [START_REF] Ou | Development and Alignment of a Domain-Specic Ontology for Question Answering[END_REF], the Tourpedia catalogue [START_REF] Cresci | Towards a dbpedia of Tourism: the Case of tourpedia[END_REF], etc. These models focus on different areas of tourism domain but there is not exist one single ontology which matches all the needs of different tourism related applications. We then propose in (Soualah-Alila and al., 2015) an ontology called TIFSem (Semantic TourInFrance) to globally describe tourism objects (TO) mixing heterogeneous content. Concepts included in the defined ontology will allow us to describe information sources on tourism. This enriched information can be used: (1) from the user side, to match tailored package holidays to client preferences for instance, and (2) from tourism experts' point of view, to analyse and better manage online data about their territory.

In order to elaborate the TIFSem ontology, we have consulted different kinds of sources to enable the understanding and the creation of concepts related to the specialized domain of tourism, and the corresponding vocabulary. Sources coming from Departmental Tourism Committee of the Charente Maritime 6 (CDT17) and Departmental Tourism Committee of the Aube7 (CDT10) were consulted. We are also in the process of extending the TIFSem ontology by collecting contents about more touristic service providers.

In a second step we are interested on facilitating the process of researching and publishing tourism data on the Web. Since TIF standard is unable to easily share and interoperate with global Web standards, we propose to enrich the TIFSem ontology with the Schema.org8 model. This initiative started a few years ago, led by Bing, Google and Yahoo to standardize structured data format in the Semantic Web. Schema.org tends to become a de-facto norm to easily share semantic content. Launched in 2011, Schema.org aims to create and to support a common set of schemas for structured data mark-up Web pages. The purpose of Schema.org is to provide a collection of schemas (HTML tags) that webmasters can use to mark-up HTML pages in a way recognized by major search providers, and that can also be used for structured data interoperability (RDFa, JSON-LD, etc.). When these tags are used in a website, search engines can better understand the meaning of embedded resources(text, image, video) of that website; which in turn would allow them to return better search results when a user is looking for a specific resource through a search engine (Toma, 2014). Schema.org is a very large vocabulary counting hundreds of terms from multiple domains. In our approach we want to spread enriched semantic tourist data that can be easily indexed by search engines. We propose to match terms of TIFSem with terms of Schema.org by using semantic relations, and work with Schema.org community to extend the schema, either formally by adding new terms or informally by defining how Schema.org can be combined with some additional vocabulary terms. This method is complex to implement because it requires to find matching between the terms of TIF and those of Schema.org and seeking what is lacking in each model.

After collecting the concepts and corresponding lexical items from our sources, we started structuring the first version of the ontology, selecting classes and subclasses.

The mapping with Schema.org enhanced the ontology to make it more complete, up to date and coherent. We now want to expand the TIFSem model with other types of data, about time and opinions, to provide semantic and contextual answers to queries.

In the following sections we describe how in our architecture we integrate information about time and opinions to complete the TIFSem model.

Tourism temporal data

In the Tourinflux project, we analysed how temporal data could be collected from the web and integrated into DataTourism. Temporal data are pieces of information frequently encountered in tourism web pages. Most tourism objects (events, hotels, restaurants, etc.) on the web are associated to periods and events and are characterized by different timestamps like date, duration, opening hours, opening conditions, frequency, etc. Textual tourism data on the web is a rich body of phenomena for linguistic analysis. The automatic recognition of temporal and event expressions in natural language text has recently become an active area of research in computational linguistics and semantics.

In our proposed model, events will be annotated according to the TimeML language, a robust specification language for the challenging task of annotation of temporal information over natural language text [START_REF] Pustejovsky | The Specification Language TimeML[END_REF]. TimeML has been developed in the context of AQUAINT workshops and projects. The 2002 Time and Event Recognition for Question Answering Systems (TERQAS) workshop set out to enhance natural language question answering systems to answer temporally-based questions about the events and entities over free text on the Web. The first version of TimeML was defined and the TimeBank corpus was created as an illustration. In 2003, TimeML was further developed in the context of the TimeML Annotation Graphical Organizer (TANGO) workshop. In 2009 TimeML has been developed into an ISO standard (ISO WD 24617-1:2007).

TimeML includes four major data structures: EVENT, TIMEX3, SIGNAL, and LINK. In TimeML, events are situations that occur or happen, or predicates that describe states or circumstances in which something obtains or holds the truth [START_REF] Pustejovsky | TimeML: Robust Specification of Event and Temporal Expressions in Text[END_REF]. Events in TimeML are annotated with the tag EVENT.

TIMEX3 is used to tag explicit temporal expressions, such as time, dates, and durations. SIGNAL is used to annotate sections of text, typically function words that indicate how temporal objects are related to each other (when, during, before, etc.). Finally, LINK encode various relations that exist between the temporal elements of a content. Each of these tags are associated to attributes to integrate temporal expressions. As the description of TimeML is not the aim of this paper, a complete description of the language is given in the manual [START_REF] Sauri | Annotating Events in English, TimeML Annotation Guidelines, Version TempEval[END_REF].

Within the Tourinflux project, in order to facilitate the extraction of temporal data, a corpus of tourism Web pages to be analysed is first constituted. This corpus is constituted of:

 A free text corpus containing festivals and events description, provided by the Local Action Group Othe Armance9 . This corpus is available under LGPL/LR license (Lesser General Public License For Linguistic Resources).  A corpus provided by the CDT10. This corpus contains descriptions of Places of Interest (POI): hotels, restaurants, etc. containing in particular information about opening and closing dates, opening and closing times, etc.  Open data, including data concerning national museums.

In our case, annotation of temporal expressions of our corpus is performed by a set of finite state transducers, developed with the Gramlab Unitex 10 corpus processor. Unitex is a corpus processing system for processing natural language texts using electronic resources such as electronic dictionaries and grammars, and Gramlab is an integrated development environment, based on Unitex software components, designed for industrial project management purpose. Before applying the transducers, Unitex performs some pre-processing that consist on cleaning the text. It (1) normalizes apostrophes, quotes etc., (2) segments the text into sentences and tokenizes it and (3) applies a number of built-in lexical resources, such as dictionaries to identify, for instance, compound word forms, proper names, etc. (Paumier, 2008). Once the text is cleaned, temporal expressions are tagged according to their TimeML type. The tagger performs identification of events. Then Unitex detects and annotates temporal expressions and calculates the value attribute for each of the tags as specified by the TimeML guidelines. The tagger also detects certain relation markers, such as temporal prepositions like before, after, etc. The last spot of the tagger is to determine the links between the different annotations. The resulting output of Gramlab is the original corpus annotated with EVENT, TIMEX3, SIGNAL and LINK tags, whose values can be after integrated in the TIFSem model.

Tourism opinion data

An important part of our information-gathering behaviour has always been to find out what people think about their touristic experience. Opinions help to analyse a situation from different aspects and take an appropriate decision. The opinion of one individual may influence another individual opinion and hence the concept of public opinion is generated. Public opinion is considered important in tourism area.

The amount of opinionated data on tourism websites has exponentially increased especially after the rapid growth of online social networks. With the availability and popularity of rich opinion resources, we need to have reliable mechanisms which help identifying all aspects of opinions in a text and to extract useful information from it. Thus, we introduce the concept of opinion mining.

Opinion Mining is the process of automatic extracting opinions from textual segments [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF]. In the literature, it has commonly been referred as sentiment analysis or sentiment classification and sometimes as subjectivity analysis [START_REF] Cambria | New Avenues in Opinion Mining and Sentiment Analysis[END_REF]. There are many related sub-tasks of opinion mining and semantic annotation of opinions is one of them. Semantic annotations are very important for preparing data for machine learning and evaluation of opinion mining approaches. Besides this, it helps the automatic extraction of opinions. Some annotation schemas have been proposed by the research community such as SentiML, OpinionMining-ML and EmotionML. A detailed comparison of SentiML with other existing annotation schemas is also presented in [START_REF] Malik | SentiML ++: An Extension of the SentiML Sentiment Annotation Scheme[END_REF].

In our case, we used SentiML for annotating opinion data. In SentiML we talk about sentiments rather than opinions. The goal of SentiML is to identify and classify sentiment groups (positive and negative) at the sentence level. In order to do this, the schema focus on three categories: target (expression the sentiment refers to) and modifier (expression conveying sentiment). A target is any entity (object, person or concept) that is implicitly or explicitly regarded as positive or negative by the author of the text. A modifier is what modifies the target. It can be an adjective, a verb, an adverb or a noun. However, SentiML also adds in its vocabulary a much needed appraisal type tag. An appraisal group represents an opinion on a specific target. For this reason, it is defined as the link between the target and the modifier (example, link a noun with an adjective, a verb with an adverb, etc.). Besides this, SentiML is based on Appraisal Framework (AF) which is a strong linguistically-grounded theory. AF helps to define appraisal types (affect, judgments and appreciation) within the modifier tag.

Fig. 1 .

 1 Fig. 1. DataTourism general architecture

http://tourinflux.univ-lr.fr/index.php/component/content/?view=featured

http://atout-france.fr/

http://www.raccourci.fr/

http://www.tourinsoft.com/

http://www.sitra-tourisme.com/

http://www.charente-maritime.org/

http://www.aube-champagne.com/

https://schema.org

http://www.tourisme-othe-armance.com/

http://www-igm.univ-mlv.fr/~unitex/


Keywords: freezing, Parkinson, Stepping in place, timing

Discussion/conclusion: SIP is of limited interest in the assessment of freezing in patients with partial "on state". Analysis of correlations with neurological status, kinematics, perceptual and motor timing tasks will aid in clarifying the interest of this new task in the assessment of freezing in PD patients.

Material and methods: 24 PD patients have been included: 8 without freezing, 8 with severe freezing and 8 mild "freezers". PD subjects were assessed using the MDS-UPDRS, MoCA, FAST, MiniBest test, questionnaires, and were submitted to the BAASTA battery, which consists of a series of perceptual timing and sensory timing tasks. Single wireless inertial sensors were placed on the patient's lower limbs. A computerized algorithm providing automatic detection of FEs during SIP, Timed Up and Go test and 20 m walk has been used.

Results: Preliminary results show that there is some discrepancy between patients' selfassessment, SIP and walking task measurements. The occurrence of freezing episodes is highly variable in mild freezers and depends on the emotional context and the repetition of the tasks. This could be explained by the fact that the timing of our assessment has been performed in less severe off state than in Nantel's study(12 hours off dopa) and which is closer to the patients' daily life.

  Interest of the task of "Stepping in place" in the assessment of freezing in Parkinson's disease patients Benoît Sijobert, Cindy Lebrun, Valentin Begel, Claudia Verna, Giovanni Castelnovo, Simone Dalla Bella, Christine Azevedo Coste, Christian Geny To cite this version: Benoît Sijobert, Cindy Lebrun, Valentin Begel, Claudia Verna, Giovanni Castelnovo, et al.. Interest of the task of "Stepping in place" in the assessment of freezing in Parkinson's disease patients. Annals of Physical and Rehabilitation Medicine, 2015, 58, pp.e71-e72. 10.1016/j.rehab.2015.07.176. lirmm-Interest of the task of "Stepping in place" in the assessment of freezing in Parkinson's disease patients Mr. Benoit SIJOBERT a , Mrs. Cindy LEBRUN b , Mr. Valentin BEGEL b , Ms. Claudia VERNA c , Dr Giovanni CASTELNOVO d , Prof Simone DALLA BELLA d , Mrs. Christine COSTE a , Dr Christian GENY e a INRIA-LIRMM, Montpellier, b M2H Euromov, Montpellier, c Centre Expert Parkinson, Montpellier, d Service de neurologie, CHU Caremeau Nimes, e Centre Expert Parkinson , Service de Neurologie, M2H-Euromov, Montpellier Objectives: Freezing of gait (FOG) in Parkinson's disease (PD) is challenging to measure.Repetitive stepping in place (SIP) task on force plates has been proposed by Nantel and al in 2011 for identifying freezing episodes (FEs) in PD patients. These authors have correlated this task with the validated FOG questionnaire (FOG-Q). The inability to maintain a walking rhythm could be alleviated by external cues involving a sensorimotor network underlying the processing of rhythm(Dalla Bella et al., 2014)