Delphine Parrot

Laurent Intertaglia

Philippe Jehan

Martin Grube

Marcelino T Suzuki

Sophie Tomasi
email: sophie.tomasi@univ-rennes1.fr

Chemical analysis of the Alphaproteobacterium strain MOLA1416 associated with the marine lichen Lichina pygmaea

Keywords: Lichen-associated bacteria, Lichina pygmaea, Alphaproteobacteria, LC/MS n, Dereplication workflow, Molecular networking

The length of time necessary for the discovery of novel bioactive compounds in complex matrices remains a challenge for natural products research. To facilitate and accelerate analysis and identification using mass spectrometry data, several computational approaches were recently developed (Allen et al.,

molecular networking), is an open-source advanced data analysis platform that includes tools for dereplication, comparative dereplication, molecular networking and automated data reanalysis (http://gnps.ucsd.edu (Wang et al., 2016)). Here, we applied this recently-developed approach to study the chemical profiles of bacterial communities associated with a marine lichen.

Lichens are symbiotic communities of a shape-providing ascomycete that shelters extracellular photosynthesizing partners (a green alga and/or a cyanobacterium). This self-sustaining association provides an ecological niche for additional microorganisms that have been identified by culture independent gene-sequencing studies as lichenicolous fungi, as well as a plethora of non-photosynthetic bacteria (eg. [START_REF] Bates | Bacterial communities associated with the lichen symbiosis[END_REF][START_REF] Cardinale | In situ analysis of the bacterial community associated with the reindeer lichen Cladonia arbuscula reveals predominance of Alphaproteobacteria[END_REF][START_REF] Cardinale | Molecular analysis of lichen-associated bacterial communities[END_REF][START_REF] Grube | Exploring functional contexts of symbiotic sustain within lichen-associated bacteria by comparative omics[END_REF][START_REF] Grube | Species-specific structural and functional diversity of bacterial communities in lichen symbioses[END_REF][START_REF] Hodkinson | A microbiotic survey of lichen-associated bacteria reveals a new lineage from the Rhizobiales[END_REF][START_REF] Selbmann | Culturable bacteria associated with antarctic lichens: affiliation and psychrotolerance[END_REF][START_REF] Spribille | Basidiomycete yeasts in the cortex of ascomycete macrolichens[END_REF]. Several studies concerning bacteria were also conducted by culture-dependent approaches (recently reviewed by [START_REF] Suzuki | Lichens as natural sources of biotechnologically relevant bacteria[END_REF][START_REF] Aschenbrenner | Understanding microbial multi-species symbioses[END_REF]. Among bacterial metabolites retrieved from these lichen-associated communities, some showed bioactivity (Parrot et al., 2016a). Those include angucycline and butenolide [START_REF] Motohashi | A new angucycline and a new butenolide isolated from lichen-derived Streptomyces spp[END_REF], coumabiocins A-F [START_REF] Cheenpracha | Coumabiocins A-F, aminocoumarins from an organic extract of Streptomyces sp. L-4-4[END_REF], cladoniamides A-G [START_REF] Williams | Cladoniamides A-G, tryptophan-derived alkaloids produced in culture by Streptomyces uncialis[END_REF], uncialamycin [START_REF] Davies | Uncialamycin, a new enediyne antibiotic[END_REF] and the more recently described unciaphenol [START_REF] Williams | Unciaphenol, an oxygenated analogue of the bergman cyclization product of uncialamycin exhibits anti-HIV activity[END_REF]. Finally, we have recently isolated the two new acrylate compounds, cyaneodimycin and its derivative cyaneomycin, from isolates of Streptomyces cyaneofuscatus MOLA1488 isolated from Lichina confinis (Parrot et al., 2016b).

In the current study, we have focused on the intertidal lichen Lichina pygmaea from which we isolated 24 different strains [START_REF] Parrot | Littoral lichens as a novel source of potentially bioactive Actinobacteria[END_REF], and in particular, Alphaproteobacterium strain MOLA1416 likely corresponding, based on 16S rRNA gene sequence analysis [START_REF] Parrot | Littoral lichens as a novel source of potentially bioactive Actinobacteria[END_REF] to a putative new bacterial species if not a new genus in the order Rhizobiales. We selected this strain based on the cytotoxic activity of its raw organic extracts (IC 50 7.2 ± 0.8 μg/mL against B16 melanoma cell lines). Furthermore, no secondary metabolite had ever been reported for the two bacterial species (Mycoplana ramosa and Chelativorans intermedius) most closely related to MOLA1416.

Strain MOLA1416 was subject of a dereplication workflow and molecular networking analysis using GNPS along with seven Actinobacteria isolated from either a subtidal lichen (L. confinis) or a supralitoral lichen (Roccella fuciformis;

Table 1) [START_REF] Parrot | Littoral lichens as a novel source of potentially bioactive Actinobacteria[END_REF] occurring in the same geographic region as MOLA1416. Our main goal was to study the chemical diversity of MOLA1416 by classifying its metabolites in chemical structural families and by structurally identifying some of these metabolites.

Table 1 Characteristics of bacteria isolated from Lichina pygmaea, L. confinis and Roccella fuciformis [START_REF] Parrot | Littoral lichens as a novel source of potentially bioactive Actinobacteria[END_REF] used in the molecular networking by GNPS (http://gnps.ucsd.edu).

alt-text: [START_REF] Matsuyama | Microbacterium kitamiense sp. nov., a new polysaccharide-producing bacterium isolated from the wastewater of a sugar-beet factory[END_REF] a Putative new species based on 16S rRNA gene sequence analysis (identity ≤ at 97%); NF: not found.

Results and discussion

Molecular networking and dereplication approaches

A LC-UV/visible low-resolution MS/MS approach was performed to describe the qualitative chemical diversity of strain MOLA1416. The combined of detection by UV:Vis photodiode array (PDA) and electrospray ionization mass spectrometry (ESI-MS), both coupled to a C 18 reversed phase HPLC, provided an accurate method for determining the chemical profile of the strain (Fig. 1). A PDA chromatogram (between 200 and 600 nm; Fig. 1A) and base peak chromatograms in both positive and negative ion modes (Fig. 1B-C) were obtained. MS parameters were optimized using the small generic and synthetic compounds NH 3 + -Phe-Phe-OMeHCl -for the positive mode and N-Boc-Phe-Phe-COO -Na + for the negative mode, both dissolved in MeOH/acetonitrile (50:50) and injected onto the mass spectrometer using a syringe pump. Optimized parameters permitted the visualization of several compounds. Nevertheless, only the positive mode was considered subsequently as it allowed the detection of a larger number of compounds.

Mass spectrometry data were used to facilitate the chemical analysis of the MOLA1416 strain. Dereplication and molecular networking workflows were performed using GNPS based on similar fragmentation spectra. The dereplication workflow analysis revealed up to twenty-five putatively different known compounds (Table 2, Figs. 2 and3). Among them, several chemical families including isocoumarins (1), isoflavones (2), macrolactones (3), erythrinan alkaloids (4), glucocorticoids (5 to 7), cyclohexane-diones (8) sterols (secondary metabolites; 9) as well as amino acids (primary metabolites; 10) and diketopiperazines (11 to 25) were highlighted (Table 2 and Fig. 3). Diketopiperazines are already known for their biological properties [START_REF] Huang | Diketopiperazines from marine organisms[END_REF][START_REF] Martínez-Luis | Antitrypanosomal alkaloids from the marine bacterium Bacillus pumilus[END_REF][START_REF] Martins | Diketopiperazines: biological activity and synthesis[END_REF][START_REF] Rui | Cyclopeptides from metagenomic library of a Japanese marine sponge[END_REF][START_REF] Soliev | Pigmented Bioactive Compounds from Marine Bacteria and Their Mechanisms of Action Involved in Cytotoxicity[END_REF]. Surprisingly, some metabolites revealed structural similarities with synthetic compounds [betapar (5), budesonide (6), prednisone (7) and trinexapac (8)] analogous to vertebrate steroid hormones. Sterol biosynthesis is relatively rare among bacteria but some species are know to synthesize sterol-like compounds such as hopanoids [START_REF] Rohmer | Distribution of hopanoid triterpenes in prokaryotes[END_REF], sterols [START_REF] Wei | Sterol synthesis in diverse bacteria[END_REF] and even glucocorticoid-like 3-keto st erols [START_REF] Simmons | bioactive sterols from the marine sponge-derived Actinomadura sp. SBMs009[END_REF]. Interesting among alphaproteobacteria, strains in the order Rhizobiales to which MOLA 1416 belongs have been shown to produce hopanoids (Agrobacterium tumefaciens; [START_REF] Rohmer | Distribution of hopanoid triterpenes in prokaryotes[END_REF] and cycloartenol (Methyloceanibacter caenitepidi; [START_REF] Simmons | bioactive sterols from the marine sponge-derived Actinomadura sp. SBMs009[END_REF]. Overall our results point to several possibilities: 1) MOLA 1416 produced glucocorticoid-like compounds 2) they were misidentified due to the limitation of low resolution mass spectrometry and/or due to the absence of more relevant metabolites in spectral databases available in GNPS and 3) one cannot entirely rule out contamination arising possibly from the culture medium, but the fact that these compounds were only found in extracts of this bacterium and not others prepared and analyzed under the same conditions alt-text: Fig. 1 r wichih somewhat weakens this possibility. Among all putative chemical families revealed, only diketopiperazines [START_REF] Cho | Isolation and structural determination of the antifouling diketopiperazines from marine-derived Streptomyces praecox 291-11[END_REF][START_REF] Li | A bisamide and four diketopiperazines from a marine-derived Streptomyces sp[END_REF] and genistein (2) [START_REF] Hawas | Mansouramycins A -D, cytotoxic isoquinolinequinones from a marine Streptomycete[END_REF][START_REF] Huang | A new isoflavone derivative from Streptomyces sp. YIM 3536[END_REF][START_REF] Tchize | Isoflavones with unusually modified B-rings and their evaluation as antiproliferative agents[END_REF][START_REF] Wu | Amorphane sesquiterpenes from a marine Streptomyces sp[END_REF] were previously reported as bacterial secondary metabolites.

Table 2 Identification of compounds from MOLA1416 using the dereplication workflow in GNPS (http://gnps.ucsd.edu) in positive mode with a cosine ≥ 0.5; t R in MOLA1416 raw extract; m/z of the library compounds; Δ m/z: difference between the m/z of compounds on MOLA1416 extract and m/z of library compounds and shared peaks: number of ions fragments shared between the spectrum from MOLA1416 and the library spectra.

alt-text: [START_REF] Iga | Uptake of bile acids by isolated rat hepatocytes[END_REF] 10 L-arginine 2.64 176.12 1.340 0.75 6 amino acid Many sources (plants, fungi, bacteria, Ascidia, etc.) [START_REF] Diak | The study of some compounds biosynthesized by Naematoloma fasciculare (Huds ex Fr.) P. Karst. Part I. Analysis in vitro[END_REF][START_REF] Shan | Amino-acid and mineral composition of Stellaria media[END_REF][START_REF] Work | Chromatographic investigations of amino acids from micro-organisms. The amino acids of Corynebacterium diphteriae[END_REF] 11 cyclo-(D-Trp, L-Pro) 20.74 284.14 0.059 0.91 8 diketopiperazine Many sources (sponges, fungi, etc.) [START_REF] Cho | Isolation and structural determination of the antifouling diketopiperazines from marine-derived Streptomyces praecox 291-11[END_REF][START_REF] Huang | Diketopiperazines from marine organisms[END_REF][START_REF] Kelecom | Secondary metabolites from marine microorganisms[END_REF] 2).

alt-text: Fig. 2 s

The results of the molecular networking analysis performed with MOLA1416 along with seven Actinobacteria strains associated with littoral lichens is shown in Table 1. Among the Actinobacteria strains were members of the Streptomycetaceae (MOLA1488), Nocardioidaceae (MOLA1448 and MOLA1522), Promicromonosporaceae (MOLA1450), Microbacteriaceae (MOLA1545) and Cellulomonadaceae (MOLA1528 and MOLA1554) strains (Table 1). Among these Actinobacteria strains, three (MOLA1488, MOLA1448 and MOLA1450) associated with L. confinis show bioactivity (IC 50 of raw extract against B16 melanoma cell lines below 100 μg/mL, Table 1). Among the four remaining strains associated with R. fuciformis, raw extracts of two (MOLA1522 and MOLA1554) showed a cytotoxic activity against B16 melanoma cell lines below 300 μg/mL (Table 1). Metabolites that were already isolated from these selected strains are listed in Table 1.

GNPS allowed clustering of MS/MS data and to compare the chemical diversity of MOLA1416 relative to the seven Actinobacteria strains (Fig. 4). The results revealed the presence of several clusters (with a mass range between m/z [M+H] + 131 and m/z [M+H] + 1295) where metabolites of MOLA1416 were also observed in the other bacterial strains. Among these clusters, three contained known compounds and consequently indicated the presence of several putative derivatives (Figs. 4 and 5). As an example, diketopiperazines, relative close chemical structures, or compounds possessing similar fragmentation patterns were associated to Cluster A and B (Fig. 5). The frequent 2).

alt-text: Fig. 3 retrieval of diketopiperazines in all eight bacterial strains studied confirmed an absence of specificity of this chemical family (Fig. 5) which is already known to be widely distributed in fungi, bacteria, bacteria associated with sponges and many others (e.g. [START_REF] Cho | Isolation and structural determination of the antifouling diketopiperazines from marine-derived Streptomyces praecox 291-11[END_REF][START_REF] Huang | Diketopiperazines from marine organisms[END_REF][START_REF] Kelecom | Secondary metabolites from marine microorganisms[END_REF]. Moreover, diketopiperazines belong to a family of signaling metabolites and play roles in regulating different bacterial functions that are widespread, such as antibiotic biosynthesis, production of virulence factors, exopolysaccharide biosynthesis and bacterial swarming among others [START_REF] Brelles-Mario | Detection, purification and characterisation of quorum-sensing signal molecules in plant-associated bacteria[END_REF]. The third cluster (Cluster C) seems to be related to compounds with fragmentation spectra similar to glycocholic acid and by extension to sterols (Fig. 5). Interestingly, a compound specific to MOLA1416 (red node in Fig. 5 group) (Fig. 5). However, without any additional information, we hypothesize this compound from MOLA1416 belongs to a family putatively identified as sterols.

In addition to these three clusters, some other clusters were identified but, without known representative metabolites, no chemical family has been proposed for them. Overall, these results demonstrate the possible ability of strain MOLA1416 either to produce these metabolites from these families, or to produce metabolites with a close structural similarity and/or other compounds with very similar fragmentation patterns. Overall the results show that metabolomic approaches using molecular networks based on MS/MS similarities can not only identify known compounds, but also cluster similar chemical compounds and putatively identify the production of compounds in specific chemical families accelerating dereplication of molecules produced by different biological sources.

Following dereplication by molecular networking, we focused on "unknown" compounds, in an attempt to characterize and discover putative new, bioactive and specific metabolites associated with MOLA1416. Unfortunately, the compound specific to MOLA1416 (m/z [M+H] + 328.917) found in cluster C was one of its minor metabolites and could not be isolated in sufficient amount for structural characterization. To broaden the chemical characterization of the strain we then focused on the more "apolar" compounds identified in the PDA chromatogram (Fig. 1A) and that were not ionized under our analytical conditions.

Isolation process and chemical identification

The EtOAc organic extract (360 mg) was subjected to flash chromatography (CH 2 Cl 2 /EtOAc/MeOH gradient) and was partitioned using semi-preparative silica TLC leading to the isolation of two compounds: one "polar" (13)

[using CH 2 Cl 2 /EtOAc/MeOH (15/15/3; v/v/v)] and one "apolar" (26) [using CH 2 Cl 2 /EtOAc (95/5; v/v)] (Fig. 6). Those metabolites, Cyclo-(L-Leu, L-Pro) (13) (2.79 mg; t R 18.15 min) [START_REF] Begum | Isolation and characterization of antimicrobial cyclic dipeptides from Pseudomonas fluorescens and their efficacy on sorghum grain mold fungi[END_REF][START_REF] Bernart | 3-(Hydroxyacetyl)indole, a plant growth regulator from the Oregon red alga Prionitis lanceolata[END_REF][START_REF] Martínez-Luis | Antitrypanosomal alkaloids from the marine bacterium Bacillus pumilus[END_REF][START_REF] Rui | Cyclopeptides from metagenomic library of a Japanese marine sponge[END_REF][START_REF] Yang | Metabolism studies of indole derivatives using a staurosporine producer, Streptomyces staurosporeus[END_REF]) and 6-methoxy-2-methyl-3-hept ylprodiginin (26) (25 mg; t R 41.72 min) [START_REF] Miao | Optimization of the culture condition for an antitumor bacterium Serratia proteamacula 657 and identification of the active compounds[END_REF][START_REF] Soliev | Pigmented Bioactive Compounds from Marine Bacteria and Their Mechanisms of Action Involved in Cytotoxicity[END_REF] had been already isolated from several biological sources such as bacteria, sponges and fungi among others. Compound 13, also highlighted during the GNPS dereplication (Table 2) and molecular networking with a t R at 18.15 min in the raw extract confirms the prediction by the GNPS dereplication workflow analysis, and increases information of a chemical family of metabolites present in complex biological samples. Moreover, a synthetic cyclo-(L-Leu, L-Pro) was also analyzed by LC-MS/MS analysis with a t R 18.15 min. This last information inferred the L,L configuration of compound 13. Structural characteristics (NMR data) for these two compounds (13 and 26) are available as supplementary data files (Figs. S2-S3).

Metabolite ( 26) had previously been reported to show cytotoxic activities against L5178Y mouse lymphocytes (IC 50 0.677 μM) [START_REF] Soliev | Pigmented Bioactive Compounds from Marine Bacteria and Their Mechanisms of Action Involved in Cytotoxicity[END_REF] and against S-Jurkat T cells (IC 50 2.27 μM) [START_REF] Ranches | Heptylprodigiosin induces apoptosis in jurkat leukemia T cells via CD95 death receptor and evades anti-apoptotic Bcl-2 and Bcl-xL proteins[END_REF]. In the present study, cytotoxic activity of compound 26 was shown against HaCaT (IC 50 = 2.6. 10 -2 ± 1.3. 10 -2 μg/mL) and B16 (IC 50 = 0.6 ± 0.07 μg/mL) cell lines confirming its biological interest. This metabolite, which constituted 7% of the total mass of an unfractionated sample, was sufficient to account for the observed cytotoxicity of the crude extract.This metabolite was the main constituent responsible for the cytotoxicity of the raw extract since it represented 7% of the total mass of the crude extract.

Conclusion

A chemical profiling of the Alphaproteobacterium strain MOLA1416 was performed by LC/MS n after optimization of ionization parameters. A dereplication workflow and molecular networking analysis using GNPS, and structural analysis led to 1) the identification of two known compounds (26 and 13) and 2) the putative identification of twenty-five known compounds (1 to 25) or compounds with relative close chemical structures and/or chemical compounds with similar fragmentation patterns. In addition, we have shown that a combination of these different approaches facilitated the analysis of mass spectrometry data, decreased the time of analysis and gave a more global vision of the chemical diversity associated with the organisms, prior to more time-consuming procedures for separation and structural identification. Furthermore, due to the presence of a scalable database on the GNPS platform, analysis and chemical identification of metabolites could be repeated and chemical profiles easily updated. Further, the approach presented here enabled efficient analysis of the biosynthetic potential of bacterial strains associated to lichens. Lichens host a large number of different bacteria [START_REF] Aschenbrenner | Understanding microbial multi-species symbioses[END_REF], but only a few of these have been characterized. These few strains already show a promising capacity for production of specialized bioactive compounds. We argue that lichens are a treasure chest for bacterial potential which has to be exploited. In the future, we plan to extend these studies to lichens from another environments.

Experimental

Chemicals and reagents

All commercial reagents were purchased from Carlo Erba Reactifs and/or from Sigma Aldrich (Val de Reuil, France and St Quentin Fallavier, France). For chromatographic analysis, HPLC and LC/MS grade water was obtained using an EasyPure (Barnstead, USA) water purification system. Deuterated solvents were purchased from Euriso-top (Gif-sur-Yvette, France).

Microorganism

MOLA1416 was isolated from the lichen Lichina pygmaea collected in Erquy (Northwest of Rennes, France, 48°37′45″ N, 02°28′30″ W) in April 2012 [START_REF] Parrot | Littoral lichens as a novel source of potentially bioactive Actinobacteria[END_REF]. To identify the strain, its 16S rRNA gene was sequenced using the dideoxy termination Sanger sequencing as previously described [START_REF] Parrot | Littoral lichens as a novel source of potentially bioactive Actinobacteria[END_REF]. Comparison with sequences in the EZtaxon server type strain database [START_REF] Kim | Introducing EzTaxon-e: a prokaryotic 16S rRNA gene sequence database with phylotypes that represent uncultured species[END_REF] revealed that the closest phylogenetic neighbor of the strain was Mycoplana ramosa DSM 7292 or Chelativorans intermedius CC-MHSW-5, both with 93.6% sequence identity. The strain was cryopreserved at -80 °C in 35% v/v glycerol or 5% v/v DMSO after growth in marine broth medium (MB) (Difco ® ) (Banyuls/Mer collection, reference: MOLA1416). Strain MOLA1416 was cultivated in 50 mL test tubes containing 30 mL of Marine Broth (MB, Difco) medium. Test tubes were shaken on an orbital shaker (110 rpm) at 25 °C for 72 h. Nine flasks of 300 mL of liquid MB medium were each inoculated with 3 mL of the test tubes cultures. Flasks were incubated for 14 days at 25 °C with 110 rpm shaking. Prior to harvesting of cultures, 5 g of sterile resin XAD7HP (Sigma-Aldrich, St Louis, Missouri), were added to each flask and incubated under growth conditions (25 °C, 110 rpm) for 4 h. Cultures were centrifuged at 5000 rpm for 15 min at 4 °C. Supernatants were removed and solid residues (resin and bacteria cells) were lyophilized before further experimentation.

Production of raw extracts of strain MOLA1416

The solid residue of the combined 2.7 L fermentation cultures was extracted at least three times with acetone/MeOH (50/50, v/v) successively. Acetone/MeOH extracts were dried under vacuum and extracted at least three times with EtOAc/H 2 O

(3/1, v/v). The organic phase (EtOAc extract) was collected and filtered through a 0.45 μm Nylon filter membrane (F54N45, AIT, France) Residual H 2 O was removed by the addition of anhydrous sodium sulfate power (5 g) and the organic phase was dried under vacuum yielding ca. 360 mg of crude extract. The crude extract was used for LC-MS/MS analysis and was also partitioned using various preparative methods (SPOT Flash liquid chromatography (Armen Instrument ® ) and preparative TLC.

4.3.2.1 Isolation using C-18 flash chromatography C-18 flash chromatography was performed using a SPOT ® Flash liquid chromatography (Armen Instruments). The stationary phase consisted of a silica (SiO 2 ) column pre-packed normal phase (FSHP-1207-0025, 25 g, Biotage) and the mobile phase a gradient: CH 2 Cl 2 (A)/EtOAc (B) fractions (100:0 to 0:100 in 75 min). The following gradient was applied at a flow rate of 10 ml/min: initial, 100% B; 0-10 min, 100% B linear; 10-40 min, 50% B linear; 40-45 min 50% B linear; 45-75 min, 0% B linear followed by washing the column with 100% MeOH for 30 min. 105 fractions (f1-f105) of 10 mL each were collected.

Preparative TLC

Fermentation and extraction in small scale (other strains)

Other bacterial strains used were cultivated in 50 mL test tubes containing either 30 mL of a fermentation broth: [5 g yeast extract (Sigma-Aldrich, St Louis, Missouri), 10 g malt extract (Sigma-Aldrich, St Louis, Missouri) and 5 g NaCl (Sigma-Aldrich, St Louis, Missouri) in 1L for MOLA1488)] or Marine Broth (MB) for MOLA1450, MOLA1522, MOLA1528, MOLA1545, MOLA1554 and MOLA1528. Tubes were incubated at 25 °C for 14 days with 110 rpm shaking. The same extraction procedure was applied (cf. 4.3.2) and EtOAc extracts were obtained for all seven strains. These crude EtOAc extracts were used for LC-MS/MS analysis.

LC-ESI-MS/MS analysis

Mass spectrometry analyses were carried out on a LC-ESI and ESI-MS n mass spectrometer as already described [START_REF] Parrot | Comparative metabolite profiling and chemical study of Ramalina siliquosa complex using LC-ESI-MS/MS approach[END_REF]. A Prevail C18 column (5 μ, 250 × 4.6 mm, GRACE ® ) kept at 30 °C was used. For HPLC a gradient system was applied: A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile). The following gradient was applied at a flow rate of 1 mL/min in the HPLC system: initial, 99% A; 0.01-5 min, 90% A linear; 5-7 min, 90% A linear; 7-15 min 75% A linear; 15-17 min 75% A linear; 17-25 min 50% A linear; 25-27 min 50% A linear; 27-40 min 0% A linear; followed by washing and reconditioning of the column. A split to 0.2 mL/min was applied before the mass spectrometry system and 20 μg were injected. The MS n spectra were recorded during the HPLC run using the following conditions: MS/MS analysis with starting collision induced dissociation energy of 35 eV and data analyses were performed using Xcalibur v1.0.

Dereplication workflow and molecular networking

All MS data were converted to mzXML format using msconvert, a part of the ProteoWizard package [START_REF] Chambers | A cross-platform toolkit for mass spectrometry and proteomics[END_REF] and imported into the GNPS platform (http://gnps.ucsd.edu, Wang et al., 2016). Using this procedure, all spectra were converted into unit vectors in n-dimensional space, and a pair of vectors was compared with a dot product calculation, which by definition includes the cosine of the angle between the two vectors, herein referred to as the cosine similarity score.

All MS data were also subjected to a dereplication workflow analysis against databases available in the GNPS platform (17 databases including NIH, MassBank spectral library, ReSpect spectral library among others) using the following parameters: score threshold 0.5, minimum intensity peak 50, parent mass tolerance 2.0 Da, ion tolerance 0.5 Da, minimum matched peaks 6 and filter standard deviation intensity of 2.0.

A molecular networking was created using the online workflow at GNPS. The data was filtered by removing all MS/MS peaks within ± 17 Da of the precursor m/z. MS/MS spectra were filtered by choosing only the top 6 peaks in the ± 50 Da window throughout the spectrum. The data was then clustered with MS-Cluster with a parent mass tolerance of 2.0 Da and a MS/MS fragment ion tolerance of 0.5 Da to create consensus spectra. Further, consensus spectra that contained less than 2 spectra were discarded. A network was then created where the edges were filtered to have a cosine score above 0.5 and more than 6 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top-10 most similar nodes. The spectra in the network were then searched against the GNPS's spectral libraries. The spectra databases were filtered in the same manner as the input data. All matches kept between network spectra Luria-Bertani (LB) broth and library spectra were required to have a score above 0.5 and at least 6 matched peaks.

Mass spectrometry data of all EtOAc bacterial extracts used in this study were added to the Public Massive datasets in GNPS (Massive ID MSV000078947).

NMR analysis

All spectra were recorded on a Bruker DMX 300 spectrometer [at 300 MHz ( 1 H) and 75 MHz ( 13 C)] and 500 cryo-spectrometer [at 500 MHz ( 1 H) and 125 MHz ( 13 C)] using adequate deuterated solvents. Chemical shift values were referenced to residual solvent signals for CDCl 3 (δ H /δ C , 7.21/77). HSQC, HMBC, COSY or TOCSY data were recorded using a Bruker DMX 500 cryo-spectrometer instrument. NMR data were processed using the MestReNova version 1.3 software.

HRMS measurements

HRMS measurements for exact mass determination were performed on a MICROMASS Zabspec TOF spectrometer for electrospray ionization at the CRMPO (Centre Régional de Mesures Physiques de l'Ouest), University of Rennes 1.

Characteristics of isolated compounds

Cyclo-(L-Leu, L-Pro) ( 13

Biological assays

Cytotoxic properties of extracts and pure compounds were determined with a standard tetrazolium assay [START_REF] Millot | Cytotoxic constituents of the lichen Diploicia canescens[END_REF]. For the total EtOAc organic extracts of MOLA1416 and compound 26, B16 cells were seeded at 15 000 cells/mL and HaCaT cells were seeded at 17 000 cells/mL in RPMI1640 medium with 5% fetal calf serum (FCS) at day 0 in well-plate. Cell lines were incubated at 37 °C in an atmosphere of 5% CO 2 during 24 h. After this time, pure compounds or crude extracts were added and after 48 h of incubation, cell growth and viability were measured at 540 nm, using a MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) assay. Each experiment was repeated at least three times.

Fig. 1

 1 Fig. 1 PDA Chromatogram (at 280 nm) (A) and base peak chromatograms in positive mode (B) and negative mode (C) of MOLA1416 (13 and 26 correspond to compounds isolated).

Fig.

  Fig. Annotated mass spectrum in positive mode of MOLA1416 raw extract . Numbers correspond to compounds identified by the dereplication workflow using GNPS (Table2).

Fig. 3

 3 Fig. 3 Chemical structure of MOLA 1416 metabolites identified by the dereplication workflow in the positive mode using GNPS (Number relative to those of Table2).

Fig. 4

 4 Fig. 4 Molecular networking of MS spectra in positive mode using Cytoscape ® visualization highlighting three clusters (A-C) containing known compounds (details in Fig. 5) from MOLA1416 and seven other cultivable bacteria associated with marine/maritime lichens (red: Alphaproteobacterium (MOLA1416); green: Streptomyces sp. (MOLA1488); magenta: Cellulomonadaceae (MOLA1554, MOLA1528); blue: Promicromonosporaceae (MOLA1450); yellow: Nocardiaceae (MOLA1448, MOLA1522); cyan: Micrococcaceae (MOLA1545). The coloring of each node is a pie, and the proportion colored of that pie is the proportion of the spectral counts coming from each respective group. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) alt-text: Fig. 4

  ) was identified in cluster C. This unknown metabolite with a m/z [M+H] + 328.917 (t R 17.48 min) could be structurally similar to glycocholic acid (9) or could have yielded a similar fragmentation pattern. It presents a mass difference of 44 (potentially corresponding to the loss of a CO 2 group) to another unidentified compound (m/z [M+H] + 373.108) itself correlated to glycocholic acid with a mass difference of 93 (potentially corresponding to a loss of C 6 H 5 O or C 6 H 7 N

Fig. 5

 5 Fig. 5 Molecular networking of MS spectra in positive of cluster A to C visualized by Cytoscape ® including known compounds. MOLA1416 and seven other cultivable bacteria associated with marine/maritime lichens (red: Alphaproteobacterium (MOLA1416); green: Streptomyces sp. (MOLA1488); magenta: Cellulomonadaceae (MOLA1554, MOLA1528); blue: Promicromonosporaceae (MOLA1450); yellow: Nocardiaceae (MOLA1448, MOLA1522); cyan: Micrococcaceae (MOLA1545). The coloring of each node is a pie, and the proportion colored of that pie is the proportion of the spectral counts coming from each respective group. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) alt-text: Fig. 5

Fig. 6

 6 Fig. 6 Chemical structure of compounds isolated (13 and 26): 13. Cyclo-(L-Leu,L-Pro) and 26. 6-methoxy-2-methyl-3-heptylprodiginin .

Table 1

 1

	Lichens	Bacterial strains	Genus	16S rRNA similarity (%)	Family	Production of metabolites	IC 50 on B16 cell lines of (μg/mL) EtOAc extract AcOEt	References
	Lichina pygmaea (marine)	MOLA1416	Mycoplana intermedius a ramosa a Chelativorans	93.56 93.56	Brucellaceae Phyllobacteriaceae	--	7.2 ± 0.8	(Kämpfer et al., 2015; Urakami et al., 1990)
						daunomycin, cosmomycin B, galtamycin B, maltophilin,
		MOLA1488	Streptomyces cyaneofuscatus	100.00	Streptomycetaceae	cyaneodimycin, cyaneomycin, 3-(hydroxyacetyl) indole, cyclo-(Phe, Pro), cyclo-(L-Leu, L-Pro), N-	0.33 ± 0.16	(Braña et al., 2015; Parrot et al., 2016b)
	Lichina					acetyl-β-oxotryptamine, N-methyldactinomycin, usnic acid
	confinis (marine)	MOLA1448	Marmoricola aequoreus a	95.04	Nocardioidaceae	NF	83 ± 10	-
		MOLA1450	Micrococcus luteus	97.93	Micrococcaceae	lutoside, [3H]-indole acetic acid	36 ± 4	(Barazani and Friedman, 1999; Bultel-Poncé et al., 1997)
		MOLA1522	Nocardioides mesophilus a	94.91	Nocardioidaceae	teichoic acid, leucylblaticidin, rodaplutin (Nocardioides sp.)	250 ± 10	(Dellweg et al., 1988; Shashkov et al., 1999)
	Roccella fuciformis	MOLA1528	Sediminihabitans luteus	98.43	Cellulomonadaceae NF	>400	-
	(maritime)	MOLA1554	Sediminihabitans luteus	98.07	Cellulomonadaceae NF	215 ± 35	-
			Microbacterium

Table 2

 2

		Identification	(min) t R	m/z	Δ m/z Cosine	peaks Shared	Chemical family	Sources	References
	1	3-(3,4-Dihydroxyphenyl)-8-	29.23 271.06 1.849 0.60	6	isocoumarin	Botrichium lanuginosum, Hydrangea macrophylla	(Zhang et al., 2009)
		hydroxyisochromen-1-one						var. thunbergii
	2	Genistein	12.47 271.20 0.420 0.52	6	isoflavone	Cytisus sp.	(Pereira et al., 2012; Raja et al., 2007)
	3	10,11-Dehydrocurvularin	33.71 291.13 1.905 0.54	6	fungal	Aspergillus terreus	(Xu et al., 2013)
							phytotoxin
	4	8-Oxoerythraline epoxide	4.43	296.10 0.950 0.55	7	alkaloïd	Erytrhina verna	(Callejon et al., 2014)
	5	Betapar	32.17 373.16 1.924 0.61	6	glucocorticoïd	Synthesis	(Fernández et al., 2003)
	6	Budesonide	32.60 431.24 0.917 0.52	6	glucocorticoïd	Synthesis	(Thalén and Brattsand, 1979)
	7	Prednisone	37.99 359.19 1.946 0.54	9	glucocorticoïd	Synthesis	(Li et al., 2009)
	8	Trinexapac	2.55	225.08 1.764 0.69	6	cyclohexane-	Synthesis	(Cooper and Storey, 2006)
							dione
	9	Glycocholic acid	28.30 466.00 0.040 0.84	7	sterol	Rat hepatocytes

  After a first separation by flash chromatography(cf. 4.3.4.1), two pools, F1 (f12-f38; 44 mg) and F2 (f55-f59; 6 mg) were purified by preparative TLC (10-12 μm, Kieselgel 60 F 254 , Merck 5554) using a model III automatic TLC (Camag ® ). Two systems solvents were applied: CH 2 Cl 2 /EtOAc (95/5, v/v) for F1 and CH 2 Cl 2 /EtOAc/MeOH (15/15/3, v/v/v) for F2 and led to the isolation of two compounds (13 and 26).

  ): white powder; Rf 0.38 in CH 2 Cl 2 /EtOAc/MeOH (15/15/3); t R 18.15 min in H 2 O/acetonitrile with 0.1% of HCOOH in the both solvents (cf. 4.4 in Experimental section); 1 H-NMR and 13 C-NMR data (CDCl 3 , 125 MHz) as described in the literature (Rui et al., 2013); HRESIMS m/z 233.126057 [M + Na] + (calcd for C 11 H 18 N 2 O 2 Na, 233.124952). 6-methoxy 2-methyl-3-heptylprodiginin (26): pink powder; Rf 0.73 in CH 2 Cl 2 /EtOAc (95/5); t R 41.72 min in H 2 O/acetonitrile with 0.1% of HCOOH in the both solvents (cf. 4.4 in Experimental section); 1 H-NMR and 13 C-NMR data (CDCl 3 , 125 MHz) as described in the literature (Soliev, 2012) HRESIMS m/z 352.2384 [M + H] + (calcd for C 22 H 30 N 3 O, 352.23889).

Acknowledgments

We gratefully acknowledge the INSA Rennes for the ministerial PhD fellowship of Delphine Parrot. This work was partly supported by the EMR, a partnership between the UPMC, the Laboratoires Pierre Fabre and CNRS and by the project MALICA (10-INBS-02-01) of the Agence Nationale de la Recherche, France. We thank the Bio2Mar platform for access to its instrumentation and facilities. We also thank Aurelie Sauvager, Isabelle Rouaud (University of Rennes 1, PNSCM team) and Clémence Rohée, Cécile Vilette (Banyuls-sur-Mer) and Eric Hitti (University of Rennes 1) for their technical help. Dr. Nicolas Gouault (University of Rennes 1, PNSCM team) for the synthesis of the two generic compounds used for the mass parameters. Dr Sylvain Guyot, Hélène Sotin for P2M2 platform access. We thank the CRMPO center (Centre Rennais de Mesures Physiques de l'Ouest) for HRMS analysis and Dr Arnaud Bondon and Sandrine Pottier (PRISM-BioGenOuest, Plate-forme rennaise d'imagerie et spectroscopie multi-modales) for NMR material access.

Appendix A. Supplementary data

Supplementary data related to this article can be found at https://doi.org/10.1016/j.phytochem.2017.10.005. [START_REF] Schneider | Structure and function of the symbiosis partners of the lung lichen (Lobaria pulmonaria L. Hoffm.) analyzed by metaproteomics[END_REF].

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Appendix A. Supplementary data

The following is the supplementary data related to this article:

Graphical abstract

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Highlights

• Chemical investigation of a Rhizobiales strain associated with Lichina pygmaea.

• Dereplication workflow based on MS/MS signatures and molecular networks were used.

• 25 putative known compounds were revealed.

• 2 metabolites were isolated and structurally identified.

alt-text: Image 1


Claudia S Bianchini

Léa Chèvrefils
email: leachevrefils@gmail.com

Patrick Doan

Claire Danet
email: claire.danet@gmail.com

Morgane Rébulard
email: morgane.rebulard@gmail.com

Adrien Contesse
email: adriencontesse@gmail.com

Dominique Boutet
email: dominique_jean.boutet@orange.fr

Coding movement in sign languages: the Typannot approach

Keywords: Sign Language, Transcription System, Handshape; Movement, Initial Localization, Intrinsic Frame of Reference, Motion Capture, User Interface

come

INTRODUCTION

The analysis of any language requires the implementation of a system of graphical representation to set the characteristics that make them analyzable and comparable. Linguistics started its development through written materials [START_REF] Goody | La Raison graphique: la domestication de la pensée sauvage. Collection "Le sens commun[END_REF][START_REF] Auroux | La révolution technologique de la grammatisation: introduction à l'histoire des sciences du langage[END_REF], and only in the last few decades it has been involved with spoken languages. The development of new multimedia technologies has not changed the situation: even if we can record sound and gestures associated with any language without problems, organizing and storing data on the basis of the elements deemed relevant requires a transcription system (TranSys) to pass from a simple collection to a usable corpus of data: i.e., to record is not to categorize.

To make possible accurate analyses and their reading, a TranSys must meet 5 main criteria [START_REF] Boutet | Italy écritures des langues signées[END_REF]: readability, writability, searchability, genericity and modularity. A TranSys must be able to be written and read by human transcribers but also by the machines that manage the information (readability and writability). On one hand, the system shall allow a detailed and structured representation of languages using a systematic graphematic formula (genericity) and, on the other hand, it shall propose an integration of these minimal components within larger formal structures, allowing synthetic representations (modularity). The data thus transcribed should allow computer queries for fine and distinct characteristics (searchability) while displaying different levels of visual synthesis in order to facilitate their understanding (readability).

For the vocal languages (VLs), these criteria are satisfied by more or less detailed adaptations of existing writing systems, in particular by the Latin alphabet [START_REF] Bigi | Influence de la transcription sur la phonétisation automatique de corpus oraux[END_REF] and by its derivation, the International Phonetic Alphabet (IPA) [START_REF]Handbook of the International Phonetic Association: a guide to the use of the International Phonetic Alphabet[END_REF]. But what about Sign Languages (SLs)?

SLs are languages used by most deaf 1 people to communicate within their community. SLs are produced through the simultaneous movement of several articulators located on the upper limbs (hands, forearms and arms) and the face (eyes and mouth, mainly) of the signer. Despite various attempts over the centuries [START_REF] Bébian | Mimographie ou essai d'écriture mimique, propre à régulariser le langage des sourds muets[END_REF][START_REF] Déléage | Le geste et l'écriture: langue des signes, amérindiens, logographies[END_REF], SLs have not yet developed a notation system of their own, so there is a lack of a graphic base that can be adapted to create a TranSys. The absence of a TranSys was felt as a problem since the early researches on SLs done by Stokoe [START_REF] William | Sign Language structure: an outline of the visual communication systems of the American deaf[END_REF], who for this reason invented a rudimentary TranSys focused on the shapes and movements of the hands, known as the "Stokoe notation".

Given the difficulty of taking into account the formal characteristics of SLs, some researchers have opted for TransSys that encode only the signified of VLs (for example Id-Gloss [START_REF] Johnston | From archive to corpus: transcription and anoatation in the creation of signed language corpora[END_REF]). In contrast, a small number of researchers tried in the '80s to find solutions to this problem, generating various systems, e.g. Signfont [START_REF] Newkirk | SignFont handbook[END_REF], D'Sign [START_REF] Jouison | Ecrits sur la Langue des Signes Française[END_REF], SignWriting (SW) [START_REF] Sutton | Lessons in SignWriting: textbook & workbook. Deaf Action Committee for Sign Writing[END_REF][START_REF] Bianchini | Analyse métalinguistique de l'émergence d'un système d'écriture des Langues des Signes: SignWriting et son application à la Langue des Signes Italienne (LIS)[END_REF], Hamburg Notation System (HamNoSys) [START_REF] Prillwitz | Hamburg notation system for sign languages: an introductory guide[END_REF]: today, only the last two are still in use, SW being aimed more to deaf people and educators, HamNoSys to linguists. SW (Figure 1) is a system based on a set of characters detailing all the components of SLs (manual and non-manual). The characters are arranged in a two-dimensional space that analogically represents the signing space. SW is easily readable by humans, but this does not go together with its very laborious writability, by both hand and computer. Moreover, born outside of the concerns of the world of linguistic research, SW is not easy to search. Finally, its characters are a global representation of a sign parameter, and therefore they are neither generic nor modular.

HamNoSys (Figure 1) is a more modern and detailed version of the Stokoe Notation. It is a linear graphic system that accurately represents the manual components of the SLs. It has been conceived to promote computer writability and searchability, perhaps to the detriment of human readability. The system is partially modular but not generic. This article starts with the description of the HandShapes (HS) parameter to explain the concept of Typannot and its user interface (UI), followed by a presentation of initial localization (LOCini) and movement (MOV) parameters, which obey multiple (one per segment of the upper limb), intrinsic Frames of Reference (iFoR) according to Levinson [START_REF] Stephen | Frames of reference and Molyneux's question: cross-linguistic evidence[END_REF], allowing to understand the relationship between the position of the limb articulators and the movement. Finally, the article shows how data transcribed using Typannot and its iFoR make it possible to give an answer to these two questions: -how is it possible that MOV in SLs is standardized and simple, while its very principle brings it to continual changes? -how may we determine MOV from its preparation phase?

Coding Movement in Sign Languages: the Typannot Approach MOCO'18, June, 2018, Genoa, Italy

PRESENTATION OF TYPANNOT

Typannot is a typographic system that offers a description of the SLs forms using both symbolic and explicit visual analogies, thus enabling the advancement of linguistic knowledge [START_REF] Boutet | Italy écritures des langues signées[END_REF]. It allows coding different parameters, both manual (HS, LOCini and MOV) and non-manual (mouth action and eye expressions). It is designed to meet the 5 criteria of a TranSys: readability, writability, searchability, genericity and modularity.

The analysis of the HS parameter allows understanding the general framework of the approach, since for all the other components the creation process follows the same steps and generates the same levels of representation (Figure 2). The issue for us is not to animate an avatar, but rather to transcribe SLs corpora. Therefore, the synchronization between several parameters is not a sought-after outcome, being each one sufficient separately. In the first place, it has been necessary to establish a graphematic formula [START_REF] Bianchini | La notation des corpus de Langue des Signes: un défi encore ouvert mais parfois ignoré[END_REF], i.e. an ordered list of features relevant for the description (Figure 2).

The HS parameter is the most studied by the SLs linguists [START_REF] William | Sign Language structure: an outline of the visual communication systems of the American deaf[END_REF][START_REF] Boyes-Braem | Preliminaries to a distinctive feature analysis of handshapes in American Sign Language[END_REF][START_REF] Scott | Structures for representing handshape and local movement at the phonemic level[END_REF][START_REF] Ann | On the relation between ease of articulation and frequency of occurrence of handshapes in two sign languages[END_REF]. After evaluating various studies on this subject, it has been chosen to refer to the phonological study of 9 SLs by Eccarius and Brentari [START_REF] Eccarius | Handshape coding made easier: a theoretically based notation for phonological transcription[END_REF], which seems to be quite complete to serve as a descriptive basis for all the HS of the 142 SLs of the world [START_REF] Lewis | Ethnologue: languages of the world[END_REF]. For HS, the graphematic formula is composed by 22 features (Figure 3).

It was not the same for the other parameters, where the GestualScript team had to establish the list of the relevant traits by itself.

2.1.2 Generic characters . On a second step, the 22 features of the graphematic formula are translated into 22 generic characters (Figure 3), that are written in a linear way following strict syntactic rules (Figure 2).

In order to ensure data queries with every operating system and software, the GestualScript team aims to have Typannot recognized by the Unicode Consortium. The set of possible HS being of several millions (although those actually recorded in 9 SL [START_REF] Eccarius | Handshape coding made easier: a theoretically based notation for phonological transcription[END_REF] do not exceed half a thousand), only the 22 generic characters will be registered in Unicode. Coding Movement in Sign Languages: the Typannot Approach MOCO'18, June, 2018, Genoa, Italy single unit (oppose to the linear vector of the generic forms) to build a logographic form. Similarly to the generic characters, composed forms are displayed automatically using the OpenType ligature functionality, replacing an entire line of generic characters by a single composed form (Figure 2). Thus, like in the Latin alphabet "a+e→ae", typing several generic characters may create alternative level of representations (composed forms) that are back-decomposable (and therefore searchable).

For the HS, the three levels of representation have been fully realized. A font allowing to visualize both generic and composed characters of each HS is at advanced stage. Moreover, Typannot allows to represent mouth actions, eye expressions and LOCini: for these components, development reached the second level. Conversely, MOV representation is still at the early level, i.e. the availability of a sketch of the graphematic formulas without its development into character sets.

Typannot User Interface (UI)

To enable the use of Typannot in every platform and software, a UI is under development, allowing to write it according to different modalities, according to the users' requirements and skills, thanks to the presence of three complementary subinterfaces (sUI): It is possible to switch from one sUI to another to refine or correct the description, and a signing avatar should allow, at any time, to verify the accuracy of the transcript.

Whatever the sUI used, the UI restores both the generic formula and the specific "morphological" character. Using the Typannot font allows viewing the result in all OpenType supporting programs.

Along with the HS, Typannot proposes a new system to transcribe three interdependent parameters: the position and the orientation of the hand, and the posture of the arm. These data are translated through a single notion, i.e. LOCini, a principle to characterize MOV in a novel way.

INITIAL LOCALIZATION (LOCini) AND MOVEMENT (MOV)

The movements of the upper limbs (MOV) are the result of a concatenation of actions of different articulators that act simultaneously: for this reason, MOV is considered the most difficult parameter to describe and, therefore, to represent [START_REF] Goldin | Gesture, sign and language: the coming of age of sign language and gesture studies[END_REF].

In order to successfully overcome the drawbacks of MOV representation, most SLs TranSys focus on describing the trajectory of the hands, annotating trajectory and/or position of the other segments only if it cannot be easily deduced by the information about the hand itself [START_REF] Johnson | A segmental framework for representing signs phonetically[END_REF][START_REF] Wilbur | Contributions of sign language research to gesture understanding: what can multimodal computational systems learn from sign language research[END_REF]. Conversely, the GestualScript team starts on a completely different basis.

The LOCini is classically seen according to an extrinsic, egocentric Frame of Reference (eFoR) in which only the position of the hand is considered, according to a frame centered on the speaker (front, back, left, right, top and bottom).

The approach chosen by GestualScript considers that LOCini and MOV are both accountable for the relative positions and movements of each segment (SEG: hand, forearm and arm) and therefore novel iFoR, intrinsic to each SEG and multiplied by all the SEGs, shall constitute the structuring level of the signs [START_REF] Boutet | Structuration physiologique de la gestuelle: modèle et tests[END_REF]. Typannot therefore proposes a novel approach based on taking into account the three SEGs of the upper limbs: arm, forearm and hand. For each of them, the different Degrees of Freedom (DoF) [START_REF] Adalbert | Anatomie fonctionnelle -Tome 1: membre supérieur[END_REF] (Figure 5) are underlined, together with their grades, going from one pole extremity to the other:

Novel approach for the movement (MOV)

Coding Movement in Sign Languages: the Typannot Approach MOCO'18, June, 2018, Genoa, Italy arm: flexion/extension (with grades Flex2, Flex1, Flex0, Ext1, Ext2); abduction/adduction (Abd1, Abd2, Abd0, Add1, Add2); internal/external rotation (RInt2, RInt1, RInt0, RExt1, RExt2); -forearm: flexion/extension (Flex2, Flex1, Flex0, Ext1, Ext2); pronation/supination (Pron2, Pron1, Pron0, Supi1, Supi2); -hand: flexion/extension (Flex2, Flex1, Flex0, Ext1, Ext2); abduction/adduction (Abd1, Abd0, Add1, with just 3 grades). To allow the description of DoF, the usual eFoR is not suitable. Therefore, Typannot is based on an iFoR for each SEG:

the shoulder iFoR allows describing MOV and LOCini of the arm; -the arm iFoR allows describing those of the forearm; -the forearm iFoR allows describing those of the hand. The three iFoR make it possible to precisely locate the position of all the SEGs participating in the MOV. If at first this approach seems more complicated than the eFoR, the iFoR allow many savings on the transcription of the MOV, as shown below.

Initial Location (LOCini).

The second basic premise of Typannot for MOV is that the LOCini, i.e. the stable position assumed by the SEGs of the signer's upper limb(s) before the deployment of MOV, is the key towards a simple description of MOV itself.

In SLs, MOV is a nearly continuous and complex flow that oscillates between a preparation moment (which culminates in a LOCini) and a deployment phase (that is, the MOV itself). However, these displacements are not the result of chance and one can find simple and standardized patterns. This paradox leads to two questions:

how can MOV be standardized and simple, as it changes continuously? -can we, and how, predict MOV from the observation of LOCini? Before MOV takes place, the signer stops his/her action for a while: he/she blocks the LOCini, and his/her limbs are positioned to allow deploying MOV whilst minimizing the motor control. It is therefore possible to say that the preparatory phase for LOCini also consists in setting up the SEGs in a balanced and sufficiently stable LOCini to serve as anchor point and as basis for a simple MOV flow (from the point of view of the motion scheme). In other words, in order to reach the LOCini, the signer performs a series of potentially complex actions implementing each SEG, so that the sign MOV can be deployed through a simple motor scheme. One of the issues with motor schemes, especially with a large gestural lexicon, is avoiding to imply a massive storage of schemes, all specific [START_REF] Schmidt | Motor schema theory after 27 years: reflections and implications for a new theory[END_REF]. Naturally, the conception of the motor scheme takes into account the motions laws, like the Fitts law [START_REF] Paul | The information capacity of the human motor system in controlling the amplitude of movement[END_REF], the 2/3 power law [START_REF] Viviani | Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning[END_REF] and the diadochal movements [START_REF] Michael | The movements of bones and joints[END_REF].

Movement (MOV).

The representation of LOCini renders superfluous the description of the preparation phase, thus strongly reducing the elements to be described in MOV. After registering the LOCini, it is sufficient to consider the DoF on which MOV has repercussions and the type of impulse.

Although it often seems visually that several SEGs are in action, it is possible to deduce which is the SEG that "carries" the MOV impulse and how MOV propagates.

An example allows to better understand these last affirmations. The two signs on Table 1

means [THANK YOU] in

French SL (LSF). Arm Ext However, looking at their transcript it is possible to note that: their LOCini differs slightly in terms of grades and not in terms of poles (-2 grades for the forearm, which remains in RInt, and +1 grade for the hand which stays in Flex); the SEGs affected by MOV are not the same, and if the pulses are different, they affect in any case two aligned poles, i.e. the extension of the arm and the extension of the forearm.

They are thus two realizations of the same sign, which go visually in different directions but which are physiologically quite similar: the difference of LOCini being only a question of grades. Only an approach based on iFoR and a description of the SEGs makes it possible to note this difference: the egocentric observation of the only trajectory of the hand would probably not have made it possible to reconcile these two realizations of which one sees a MOV directed inward and downward, while for the other MOV is directed forward and downward. The analysis in terms of separate DoFs we develop here is not the only one we would like to investigate: vectors of quaternions (angles) or vectors of positions are some other potential solutions.

How Typannot generates new knowledge

As stated in the presentation of Typannot ( §2), the goal of the GestualScript team is not only to create a TranSys, but also to Coding Movement in Sign Languages: the Typannot Approach MOCO'18, June, 2018, Genoa, Italy provide an instrument capable of advancing knowledge on SLs and gestures: the following section shows how Typannot makes it possible to investigate SLs.

Hypotheses . The first assumption looks at the complexity of the motor control. By observing the sign [NEVER]

2 in LSF, we see that it can be done with the palm in a horizontal plane and downwards or in a vertical plane, but in both cases MOV will be a simple abduction of the hand. The motion scheme is simple and identical for this sign, regardless of the achievements. This example, and many others, allows us to formulate a first hypothesis:

-Hypothesis A: MOV has a simple motor scheme aimed at minimizing motor control during MOV. Generally, the great fixity of the HS during signing seems to show that the flow is mainly a FLOW Dist→Prox . However, working on MOV in general (and not on the SLs), Dumas [START_REF] Dumas | Joint and segment coordinate systems revisited[END_REF] states that the inertia of the hand compared to the forearm is only 24% and that of the forearm to the arm is 65%: there is an inertial tendency towards a FLOW Prox→Dist which should also affect the fingers (change of HS). The difference between what Dumas asserts and the observations on HS changes leads to a second hypothesis:

-Hypothesis B: the inertial tendency towards a FLOW Prox→Dist of MOV is predominant also in SLs.

Materials and methods .

To test these hypotheses, Typannot was used to transcribe three short extracts (about 1 2 [NEVER] in LSF, with vertical palm: see Elix-LSF.fr https://goo.gl/aPGC7u [NEVER] in LSF, with horizontal palm: see Sematos.eu https://goo.gl/svStQH min each) of narrations, each told in a different SL 3 . These are "L'histoire du cheval" [START_REF] Huillier | L'histoire du cheval, extract from Corpus LS-Colin. Collection[END_REF] in French SL (LSF) (duration: 60 sec), "Pinocchio in LIS" [START_REF] Bertone | Pinocchio in LIS (chapter 1). DVD[END_REF] (duration: 113 sec) in Italian SL (LIS) and "Holiday in Lanzarote (BF21n)" [START_REF] Cormier | Holidays in Lanzarote (extract #BF21n) from the British Sign Language (BSL) Corpus. Economic and Social Research Council[END_REF] (duration: 50 sec) in English SL (BSL).

The extracts have been transcribed with the ELAN software [START_REF] Lausberg | Coding gestural behavior with the NEUROGES-ELAN system[END_REF], using the Typannot generic characters to encode MOV.

It must be remembered that only the graphematic formula has been developed for LOCini and MOV (see §1) and that the UI is still under realization. The transcription work could therefore only be based on the graphical aspect of Typannot: in fact, the corpus has been annotated with an alphanumeric keyboard, without resorting to the generic and composed characters and without recourse to the UI. In these conditions, the transcription of a single minute may require up to 5 hours of annotation (ratio: 1/300), which more or less corresponds to the time required using other TranSys [START_REF] Onno | Research methods in sign language studies: a practical guide[END_REF]. This length depends not only on the complexity of the parameters but also on the ergonomics of the tools used, both in terms of graphic signs and input interfaces.

A collateral hypothesis emerges from these considerations:

-Collateral hypothesis: using iFoR common to the IMU MoCap device and to Typannot, together with a suitable interface (such as the Typannot UI), drastically reduces MOV transcription times. 2).

The predominance of MOVs concerning only one DoF (initiator of the rest of the MOV) on a single SEG is highly significant (p<0.001): whatever the SLs considered, nearly 80% of MOVs are directed by a single DoF and the other SEGs reposition accordingly with the MOV-initiating DoF. Coding Movement in Sign Languages: the Typannot Approach MOCO'18, June, 2018, Genoa, Italy Therefore, the preliminary results seem to validate Hypothesis A: despite the apparent complexity of MOVs, these are, for the most part, governed by a simple motor scheme.

Test 2. MOVs were also classified on their potentiality for being tracked by a MoCap system, i.e. MOVs being initiated by a single DoF and those whose form matches an algorithm like the circle; Table 3 reports these numbers and their sum. These data make it possible to show the interest of a UI integrating a system of IMU-like MoCap. This is based on an iFoR system, so the relative data that such equipment provides are easily convertible into Typannot categorical data, based on an iFoR system too. If the IMU and the development implemented in the UI allow to recognize 90% of MOVs, it will be sufficient for the transcriber to redo the movements that he/she visualizes in the video to automatically transcribe them in Typannot, thus minimizing the transcription duration.

Test 3. MOVs were classified based on their flow direction (FLOW Prox→Dist or FLOW Dist→Prox ) (Table 4). Results are less marked here, but it appears that FLOW Dist→Prox is dominant in more than 60% of MOVs (p<0.001), while the remaining 40% is subdivided between FLOW Prox→Dist (less than 10%), absence of a well-defined flow, MOV on a single DoF (without transfer to another SEG), and total absence of MOV (static). These preliminary results do not corroborate Hypothesis B: in spite of the inertial tendency towards a FLOW Prox→Dist established by Dumas [START_REF] Dumas | Joint and segment coordinate systems revisited[END_REF] for the movements in general, it is the FLOW Dist→Prox to be predominant in SLs. As for Test 1, a classical approach, based on the trajectories of the hands and an eFoR, would not have made it possible to unfold this phenomenon, which on the contrary appears when using Typannot.

CONCLUSIONS

Typannot is a SLs representation system that wants to satisfy 5 basic criteria for transcription: readability, writability, searchability, genericity and modularity. To meet these criteria, it is based on a rigorous protocol for creating characters which makes it possible to obtain a system having at the same time a robust graphematic and a coherent typographic formula. The use of the novel TranSys Typannot makes it possible to understand the SLs signifier form in a new light that emphasizes features of the SLs which remained hidden with the previous TranSys. This is shown by the MOV analysis, based not on the trajectory of the hands in an eFoR, but on the observation of the movement propagation flow on the different SEGs and DoF, in an iFoR and from a steadied LOCini.

Two hypotheses have been tested with Typannot on a short corpus allowing to obtain some preliminary but promising results.

In the first place, it has been shown that the motor control necessary for the realization of the signs can be reduced to very simple motor schemes (1 DoF on 1 SEG) for the parameter which appears as the most unstable, i.e. MOV. This observation is only detectable if we adopt iFoR.

Subsequently, it was highlighted how the LOCini participates in the conditions of realization of the sign standardization. This standardization goes through a simple motor scheme, requiring only minimal motor control. This is the case here for more than 90% of signs.

These data, obtained thanks to Typannot, open the way to other hypotheses, for example that the preparation phase of LOCini is probably more unstable, more complex and with a greater occurrence of FLOW Prox→Dist compared to the MOV deployment phase. These hypotheses need to be verified through recordings made with MoCap.

Figure 1 :

 1 Figure 1: [GOLDILOCKS] in American Sign Language, written with three different transcription systems. Source: www.signwriting.org Since 2013, a third TranSys of SLs is emerging: it is Typannot [15], realized within the l'École supérieure d'art et design (ESAD, Advanced School for Art and Design) of Amiens (France) by the GestualScript team, a multidisciplinary group of linguists, graphic designers, calligraphers, designers and computer scientists.This article starts with the description of the HandShapes (HS) parameter to explain the concept of Typannot and its user interface (UI), followed by a presentation of initial localization (LOCini) and movement (MOV) parameters, which obey multiple (one per segment of the upper limb), intrinsic Frames of Reference (iFoR) according to Levinson[START_REF] Stephen | Frames of reference and Molyneux's question: cross-linguistic evidence[END_REF], allowing to understand the relationship between the position of the limb articulators and the movement. Finally, the article shows how data transcribed using Typannot and its iFoR make it possible to give an answer to these two questions: -how is it possible that MOV in SLs is standardized and simple, while its very principle brings it to continual changes? -how may we determine MOV from its preparation phase?

Figure 2 : 2 . 1 . 1

 2211 Figure 2: The three levels of representation of Typannot: example of a HandShape

Figure 3 : 2 . 1 . 3

 3213 Figure 3: Typannot primary generics characters for HandShapes. 2.1.3 Composed forms . Although using just generic characters is sufficient to identify a HS, Typannot allows a composed typographic form for each HS generic transcription. This final level of representation aims at providing a better readability and writability. It integrates a set of typographic components into a

-

  generic sUI (selection of generic characters); -module sUI (selection of "morphological" modules of the specific typographic formula); -gestural sUI (selection through the physical reproduction of the component to be described, thanks to the use of capture devices, i.e. LeapMotion for HS [Figure 4] and Inertial Measurement Unit [IMU] for LOCini).

Figure 4 :

 4 Figure 4: Typannot User Interface: gestural sub-interface for handshapes (back-end version).

3. 1 . 1

 11 Degrees of Freedom (DoF) and intrinsic Frames of Reference (iFoR).

Figure 5 :

 5 Figure 5: The degrees of freedom (DoF) of the upper limbs.

Table 1 :

 1 Two signs to say [THANK YOU] in French Sign Language with transcription of initial location (LOCini), movement (MOV) and impulse (differences shown in bold).

Figure 6 :

 6 Figure 6: The flows in French Sign Language signs [LOCATION] and [TO GRASP]. The second supposition concerns the MOV propagation flow (FLOW) along the upper limb. If we compare the signs [LOCATION] and [TO GRASP] in LSF (Figure 6), we notice that MOV follows a proximal-distal flow (hand→fingers, FLOW Prox→Dist ) in the first case, and a distal-proximal flow (hand→arm; FLOW Dist→Prox ) in the second case. The FLOW Prox→Dist only affects the hand and the fingers, and only appears in the few realizations showing a change of HS during signing. Generally, the great fixity of the HS during signing seems to show that the flow is mainly a FLOW

3 . 2 . 3

 323 Tests and preliminary results . In the corpus, 333 MOVs have been counted: 92 in LSF, 153 in LIS and 88 in BSL. These MOVs were then categorized differently to test the hypotheses: Test 1 pertains to Hypothesis A, Test 2 to Hypothesis A and the Collateral hypothesis, and Test 3 to Hypothesis B. Test 1. MOVs were ranked on the basis of the number of MOV-deploying SEGs and within each SEG the number of DoF at the MOV origin, if this is the case (Table

Table 2 : Distribution of movement complexity.

 2

		⊖ ←---Movement complexity --→ ⊕
	Ø SEG	1 SEG	1 SEG		2+ SEG	Proba-
	Ø DoF	1 DoF	2+ DoF	2+ DoF	bility
	LSF 7 LIS 15 BSL 9 TOT 31	% 7.6% 72 9.8% 119 77.8% 11 % 78.3% 5 10.2% 70 79.5% 2 9.3% 261 78.4% 18	% 5.4% 8 7.2% 8 2.3% 7 5.4% 23	% 8.7% 10 5.2% 10 6.9% 10 6.9% 10	-7 -12 -8 -26

Table 3 : Frequency of trackable movements (MOV).

 3

	1 SEG 1 DoF	Circles	Trackable MOV	Non-trackable MOV
		%	%	%		%
	LSF 72	78.3% 11	12.0% 83	90.2% 9	9.8%
	LIS 119	77.8% 27	17.6% 146	95.4% 7	4.6%
	BSL 70	79.5% 14	15.9% 84	95.5% 4	4.5%
	TOT 261	78.4% 52	15.6% 313	94.0% 20	6.0%
	It appears that for more than 90% of the signs it is possible to
	determine the MOV origin, irrespective of MOV performed.

Table 4 : Distribution of flow.

 4

		FLOW	FLOW	Static	Un-	1 DoF	Proba
		Dist→Pro	Prox→Dis			define	-bility
		x	t				d
		%		%		%	%	%
	LSF 54 58.7% 8	8.7% 7 7.6% 0 0.0% 2	25.0	0.117
							3	%	4
	LIS 98 64.1%	15 9.8% 1	9.8% 2 1.3% 2	15.0	0.000
		.			5		3	%	6
	BSL 61 69.3% 0	0.0% 9 10.2	2 2.3% 1	18.2	0.000
	TO	213 63.7% 23 6.9% 3	% 9.3% 4 1.2% 6 6	% 18.6	4 10	-8
	T				1		2	%

We decided to use the term "deaf" because of a cultural issue: even if, for hearing people, this term might seems "politically incorrect", most deaf people, especially those who use SLs, do prefer to be called "deaf" (that underlines a difference) than "hearing impaired" (that underlines a handicap).

These three extract were transcribed by one single person, thus making consistent the comparisons presented in the following tables; still, for more stringent analyses, it is envisaged to have more people interacting and cooperating, with the aim of standardizing the transcriptions.

ACKNOWLEDGMENTS

This work was supported by: École supérieure d'art et de design (ESAD) d'Amiens; Amiens Métropole; Conseil regional des Hauts-de-France; Délégation générale à la langue française et aux langues de France (DGLFLF); Ministère de la Culture; Robofont; Crédit Agricole; Dynamique du langage in situ (DyLIS) de l'Université de Rouen-Normandie.


Laure Ngabirano
email: laure.ngabirano@inserm.fr

Marc Fadel

Annette Leclerc

Bradley A Evanoff

Ann Marie Dale

Yves Roquelaure

Alexis Descatha

Comparison Between a Job-Exposure Matrix (JEM) Score and Self-Reported Exposures for Carrying Heavy Loads Over the Working Lifetime in the CONSTANCES Cohort

Keywords: job-exposure matrix, self-report, exposure assessment, questionnaire, retrospective, biomechanical, carrying heavy loads, musculoskeletal, epidemiology

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

A c c e p t e d m a n u s c r i p t and specificity were calculated. For both methods, associations with low back pain and knee pain were computed using multinomial logistic models. Additional analyses compared older (>10 years) to more recent (≤10 years) exposures.

Results. AUCs ranged from 0.795 (0.789-0.800) when all periods were considered, to 0.826 (0.820-0.833) for more recent biomechanical exposure (≤10 years). Associations between carrying heavy loads and low back pain or knee pain were less strong using JEM assessment than individually SR exposure: for low back pain OR SR =3.02 (2.79-3.26) vs OR JEM =1.70 (1.59-1.82) and for knee pain OR SR =2.27 (2.10-2.46) vs OR JEM =1.64 (1.53-1.77).

Conclusion.

JEM Constances' assessment of cumulative exposure of carrying heavy loads seems to be a useful method compared to a self-administrated questionnaire for large population-based studies where other methods are not available.

A c c e p t e d m a n u s c r i p t INTRODUCTION Work-related biomechanical exposures are a major determinant of musculoskeletal disorders (MSDs), the most common work-related disorders in terms of frequency and disability. [START_REF] Bevan | Economic impact of musculoskeletal disorders (MSDs) on work in Europe[END_REF]Descatha et al., 2019a;[START_REF] Hulshof | WHO/ILO work-related burden of disease and injury: Protocol for systematic reviews of exposure to occupational ergonomic risk factors and of the effect of exposure to occupational ergonomic risk factors on osteoarthritis of hip or knee and selected other musculoskeletal diseases[END_REF][START_REF] Roquelaure | Promoting a Shared Representation of Workers' Activities to Improve Integrated Prevention of Work-Related Musculoskeletal Disorders[END_REF]. However, assessment of biomechanical exposures remains difficult [START_REF] Leclerc | Exposure assessment in ergonomic epidemiology: is there something specific to the assessment of biomechanical exposures?[END_REF]. Direct measurements or observations obtained prospectively are considered as the reference, but are unrealistic in large-scale population studies for cost and feasibility reasons (van der [START_REF] Van Der Beek | Assessment of mechanical exposure in ergonomic epidemiology[END_REF]. In such studies, self-reported (SR) assessment is the most commonly used exposure assessment method [START_REF] Leclerc | Exposure assessment in ergonomic epidemiology: is there something specific to the assessment of biomechanical exposures?[END_REF]. In this context, job-exposure matrices (JEMs) for biomechanical exposures were created [START_REF] Solovieva | Development and validation of a job exposure matrix for physical risk factors in low back pain[END_REF][START_REF] Descatha | Development of "MADE", a French Job exposure matrix for evaluation of biomechanical exposure[END_REF]Evanoff et al., 2019b) for use where job title is available and not individual exposure data [START_REF] Jakobsen | Long-term prognosis for neck-shoulder pain and disorders: a 14-year follow-up study[END_REF][START_REF] Sirén | Physical and psychosocial work exposures as risk factors for disability retirement due to a shoulder lesion[END_REF].

We compared a cumulative exposure score based on JEM Constances, an existing biomechanical JEM, to SR exposures of carrying heavy loads in a large French cohort.

METHODS

Population

CONSTANCES is a population based multicenter cohort which included, from 2012 to 2018, 162,115 volunteers between 18 to 69 years of age who were randomly selected among people covered by the French National Health Insurance [START_REF] Goldberg | CONSTANCES: a general prospective population-based cohort for occupational and environmental epidemiology: cohort profile[END_REF]. Data collection included self-administered questionnaires conducted at home and in health screening centers (HSC).

A c c e p t e d m a n u s c r i p t

Variables

From the baseline questionnaire at the HSC, participants were asked to report whether they carried heavy loads during their working life as 'ever/never'. If 'ever', up to three periods of exposure to heavy loads could be reported with calendar years. "JEM Constances" was previously developed among 35,526 currently employed workers in the CONSTANCES cohort (Evanoff et al., 2019b). Self-reported exposures from each workers' current job were averaged at the level of 407 job titles (French 4-digit PCS2003 Profession et Catégorie Sociale). To avoid reporting biases due to current symptoms, only data from workers without musculoskeletal symptoms were used. One JEM question addressed carrying heavy loads: "During a typical working day, are you required to regularly handle, move or carry a load, part or object weighing between 10 and 25 kg?" Responses were 0= No; if yes 1= [Never or nearly never] 2= [Rarely (< 2 hours per day)] 3= [Often (2 to 4 hours per day)] 4= [Always or nearly always]. There was some overlap between the 26,929 subjects who provided SR data on carrying heavy loads (ever/never) and the 26,821 asymptomatic workers whose SR exposures were used in the creation of the JEM; due to sampling procedures of these separate studies we cannot quantify the degree of overlap.

In a questionnaire completed at home, each CONSTANCES participant also completed a job history, listing all jobs and the dates held during their working life. For the current study, the JEM Constances exposure rating (0 to 4) for each job held was multiplied by the number of years worked in that job. The summation of these 'lifting-years' products was the cumulative JEM exposure score during each participants' working life, which was subsequently Low back pain and knee pain were the two pain outcomes considered for analysis. Each outcome was measured by three questions: "Pain over the last 12 months(ever/never)"; "Pain duration over the last 12 months(<24 hours, 1-7 days, 8-30 days, >30 days, permanent)"; and "Intensity of pain at the time of the questionnaire(0 to 10 scale)". We defined pain in 3 levels:

(i)No pain over the last 12 months; (ii)Moderate pain (non-permanent pain, with intensity of pain ≤5); and (iii)"Important" pain (permanent pain or pain intensity >5).

Statistical analysis

We used Spearman's rank and intraclass correlations to assess the relationship of exposure duration between the two assessment methods. Area Under Curve (AUCs) provided by Receiver Operating Characteristic (ROC) curves were calculated for JEM exposures with the SR exposure (ever/never) as reference. Then the optimal score cut-off was identified as the point on the curve with the highest sensitivity and specificity, and used to dichotomize exposures as 'Not exposed' and 'Exposed', allowing calculation of sensitivity and specificity with their 95% confidence intervals (95%CI). Multinomial logistic models computed the odds ratios (ORs) and 95%CI for associations between each exposure, SR and JEM exposure (Exposed/Not exposed), and each of two outcomes: low back pain and knee pain ('No pain' as reference).

Additional analyses compared potential recall effects for SR and JEM exposures from the recent and more distant past. ROC statistics (AUCs, sensitivity, specificity) and multinomial

RESULTS

We analyzed 26,929 subjects (53.4% women) for whom both SR exposure and cumulative lifting-years score based on the JEM were available. The mean age was 48.6 years. Twentysix percent of the sample reported exposure to carrying heavy loads in their working lives; three exposure periods were reported by 0.6% of the sample (n=149). The computed score of cumulative exposure based on the JEM lifting-years ranged from 0 to 200, with an average of 16.71. Half of the sample suffered from knee pain (37.2% moderate and 13.6% "important" pain) while two-thirds complained of low back pain (47.8% moderate and 19.3% "important" pain).

Among the exposed subjects, the mean duration of exposure based on the job history and JEM lifting-years score was longer than the duration of SR lifting exposure (24.9(±11.0) vs 14.6(±11.6) years respectively). Exposure durations between the two methods were moderately correlated (rho Spearman =0.49, rho intraclass =0.43).

ROC curves and AUCs (Figure 1) showed overall good agreement between the SR and JEM lifting-years score (AUC=0.795). A higher AUC was found for more recent exposures (≤10 years) compared to older exposures (>10 years) (respectively 0.826 and 0.797). Using the optimal cut-off points of 8.07 (all periods), 6.32 (older exposure) and 3.53 (more recent exposure) produced the following sensitivity and specificity values: sensitivity all-periods , sensitivity old-exposure and sensitivity recent-exposure were 76% (95%CI 75-77), 78% (95%CI 77-79) and 81% (95%CI 80-82) respectively; specificity all-periods , specificity old-exposure and A c c e p t e d m a n u s c r i p t specificity recent-exposure were of 71% (95%CI 70-71), 68% (95%CI 68-69) and 73% (95%CI 72-73) respectively.

The multinomial logistic regression models of low back pain and knee pain associated with exposures assessed by SR and JEM Constances are presented in Table 1. For both 'all periods' and 'subgroup periods', SR exposure to carrying heavy loads showed statistically significant associations with low back pain and knee pain. Associations were stronger for "important" pain than with moderate pain. Models using JEM-based exposures to pain outcomes showed similar findings but smaller effect sizes.

DISCUSSION

Exposure to heavy lifting and carrying obtained from JEM Constances showed general agreement and produced similar exposure-disease associations when compared with individually self-reported exposures to carrying heavy loads. This agreement with individual SR exposures suggests that JEMs for biomechanical exposures are a useful tool for assigning cumulative work exposures when job titles and job histories are available. Our statistical approach compared JEM values to SR values using AUC, sensitivity and specificity in a similar fashion to previous studies examining the usefulness of general population JEMs for biomechanical exposures [START_REF] Solovieva | Development and validation of a job exposure matrix for physical risk factors in low back pain[END_REF].

Our study has some limitations. JEM Constances was developed using pooled self-reported exposures within a different sample of the CONSTANCES cohort participants (Evanoff et al., 2019b) than that used in this study, though it is likely that there is overlap between the two A c c e p t e d m a n u s c r i p t samples. The SR and JEM exposure questions were different, as were the methods of assessing years of exposure (time period based vs. job based), likely resulting in some of the differences between the two methods. The moderate correlation of time of exposure illustrated that the methods are different, as expected [START_REF] Barrero | Work pattern causes bias in self-reported activity duration: a randomised study of mechanisms and implications for exposure assessment and epidemiology[END_REF]. The fact that the SR data were limited to three jobs periods while the JEM included all periods, including those with relatively low exposure levels, could also explain differences. While SR exposures have shown low validity when compared to direct measurements in some studies [START_REF] Balogh | Self-assessed and directly measured occupational physical activities--influence of musculoskeletal complaints, age and gender[END_REF][START_REF] Hansson | Questionnaire versus direct technical measurements in assessing postures and movements of the head, upper back, arms and hands[END_REF], they may represent an alternative to more resource-intensive and invasive exposure biomechanical assessment methods [START_REF] Buchholz | Subjective ratings of upper extremity exposures: inter-method agreement with direct measurement of exposures[END_REF] in large epidemiological studies, where average SR at the group level appear reasonably correct [START_REF] Pulido | Correctness of Self-Reported Task Durations: A Systematic Review[END_REF], especially for handling loads over 5 kg [START_REF] Wiktorin | Validity of self-reported exposures to work postures and manual materials handling. Stockholm MUSIC I Study Group[END_REF]. Moderate to high correlations were previously observed between JEM Constances and a different JEM based on expert opinion [START_REF] Descatha | Development of "MADE", a French Job exposure matrix for evaluation of biomechanical exposure[END_REF](Descatha et al., , 2019b;;Evanoff et al., 2019a). JEM Constances was developed from current work exposures reported at study inception; the cumulative work exposure does not account for potential temporal variations in exposures since the JEM assigned the same value to all years in the same job title.

These results showing associations between Constances JEM exposures and "important" pain confirmed previous studies showing the usefulness of JEMs based partially or entirely on SR for studying musculoskeletal disorders [START_REF] Dale | Incident CTS in a large pooled cohort study: associations obtained by a Job Exposure Matrix versus associations obtained from observed exposures[END_REF][START_REF] Jakobsen | Long-term prognosis for neck-shoulder pain and disorders: a 14-year follow-up study[END_REF][START_REF] Sirén | Physical and psychosocial work exposures as risk factors for disability retirement due to a shoulder lesion[END_REF]. The difference in strength of association seen in our study between the two exposure methods can be interpreted differently. Unlike SR, JEM exposures do not account for individual variation of exposures within the same job [START_REF] Bouyer | Retrospective evaluation of occupational exposures in populationbased case-control studies: general overview with special attention to job exposure matrices[END_REF] and so may underestimate the true relationship between exposure and symptomatic disorders.

Alternatively, by providing an estimate of past exposure unbiased by disease status, JEM A c c e p t e d m a n u s c r i p t exposures may avoid overestimation of exposure associated with recall bias when using individual self-reported exposures, and thus provide a better estimate of true associations [START_REF] Viikari-Juntura | Validity of self-reported physical work load in epidemiologic studies on musculoskeletal disorders[END_REF].

In conclusion, JEM Constances' assessment of cumulative exposure of carrying heavy loads found similar results to those obtained using a self-administrated questionnaire in a large population-based survey. Use of JEM for estimating biomechanical exposures in large population studies seems to be a useful epidemiological tool when other exposure data are not feasible, or when control for biased recall of exposures is desired. The utility and limitations of such JEMs are an active area of current research.

  A c c e p t e d m a n u s c r i p t compared to the SR data. This JEM exposure score was then compared to each individuals' SR data.

  s c r i p t logistic models were performed comparing jobs from ≤10 and >10 years ago. All analyses were done on R3.5.1.

Figure 1 :

 1 Figure 1: ROC curves for carrying heavy loads (10-25kg) A c c e p t e d m a n u s c r i p t

Funding This study was supported by IReSP, CapaciT project (Institute for research in public health, in Frenchhttp://www.iresp.net/). The Constances Cohort Study was supported and funded by the Caisse nationale d'assurance maladie (CNAM). The Constances Cohort Study is an "Infrastructure nationale en Biologie et Santé" and benefits from a grant from ANR (ANR-11-INBS-0002) and from the Ministry of Research. Constances is also partly funded by MSD, AstraZeneca and Lundbeck by Inserm-Transfert.

Conflict of interest

x: Number of subjects exposed after dichotomization using the cut-off chosen by the maximum point on the ROCs.

A c c e p t e d m a n u s c r i p t


Daniel Jacob -Umr1332

Modeling the growth of tomato fruits based on enzyme activity profiles

FRIM data analysis interfaced by ODAM

To assess the influence of the environment on fruit metabolism, tomato (Solanum lycopersicum 'Moneymaker') plants were grown under contrasting conditions (optimal for commercial, water limited, or shaded production) and locations. Samples were harvested at nine stages of development, and 36 enzyme activities of central metabolism were measured as well as protein, starch, and major metabolites, such as hexoses, sucrose, organic acids, and amino acids.

Initialisation [2]

Init ODAM object : retrieve the dataset structure along with their metadata [3] levelName

Modelisation of the plant growth

Analysing and modelling plant growth is an important interdisciplinary field of plant science. The use of relative growth rates, involving the analysis of plant growth relative to plant size, has more or less independently emerged in different research groups and at different times and has provided powerful tools for assessing the growth performance and growth efficiency of plants and plant populations.

To measure the bioproductivity of a plant, the component of immediate interest is the net primary production or total yield. The plant weight -usually the dry weight -is a needed measurements for growth analysis. First we have to model the plant growth. The optimization parameters are based on a Levenberg-Marquardt algorithm.

NLS problems with the Levenberg-Marquardt algorithm

The purpose nlsLM is to minimize the sum square of the vector returned by the function fn, by a modification of the Levenberg-Marquardt algorithm https://www.rdocumentation.org/packages/minpack.lm/versions/

Explanation of the Relative Growth Rate (RGR)

Relative growth rate is a standardised measure of growth with the benefit of avoiding, as far as possible, the inherent differences in scale between contrasting organisms so that their performances can be compared on an equitable basis

The Relative Growth Rate (RGR) of a plant at an instant in time (t) is defined as the increase of plant material per unit of material present per unit of time.

The equation is written as: RGR = -W stands for Weight

To calculate the RGR curve, we will leverage on the previous modelisation of the Weight curves (i.e. fitObj object obtained by the fitSigmoid function)

List of 'samples' quantitative variables

		¦ Lipids AG Subset	¦--lipids_AG 57 Attribute	11 AliquotID aliquot Description Type CV_Term_ID
		9	<chr>	¦	°--AminoAcid <chr>	<chr>	12 AliquotID aliquot <chr> <chr>
		Amino Acids	69
		10		°--pools			Fruit	7	PoolID	pool
		Pools of remaining pools samples FruitDiameter	195 diameter	numeric	http://aims.fao.org/aos/ag
		11			¦--qMS_metabo	(mm)	8	PoolID	pool	MS
		Compounds quantification	25
		12 Compounds quantification °--qNMR_metabo samples FruitHeight	64 Fruit height (mm)	9	PoolID numeric	pool http://aims.fao.org/aos/ag NMR
			samples	FruitFW		Fruit Fresh Weight(g)	numeric	http://aims.fao.org/aos/ag
			List of 'samples' factors samples FruitDW	Fruit Dry Weight(g)	numeric	http://aims.fao.org/aos/ag
	[4]	Percentage of dh$getWS('(samples)/factor') dry matter (%
			samples	DW			DW), measured	A data.frame: 3 × 6 numeric
		Subset source("Functions.R") Attribute	Description a er lyophilisation	Type	CV_Term_ID
			<chr>		<chr>		<chr>	<chr>	<chr>
								Treatment
			plants		Treatment	applied on	string	http://www.ebi.ac.uk/efo/EFO_0
								plants
								fruit
		# FRIM1 dataset managed by ODAM samples DevStage development	string	http://purl.obolibrary.org/obo/P
		dh <-new('odamws', 'https://pmb-bordeaux.fr/getdata/', 'frim1') stage
		# Show dataset structure options(width=128) samples FruitAge fruit age (dpa)	string	http://purl.obolibrary.org/obo/P
		show(dh)
								SetID Identifier WSEntry
		Description Count
		1 plants				1	PlantID	plant
		Plant features	552
		2	°--samples			2	SampleID sample
		Sample features 1288
		3		¦--aliquots		3 AliquotID aliquot
	[5]	Aliquots features dh$getWS('(samples)/quantitative') 530
		4		¦	¦--cellwall_metabo	4 AliquotID aliquot	Cell wall
		Compound quantifications	75	A data.frame: 5 × 6
		5 Compound quantifications (FW) ¦ ¦--cellwall_metaboFW Subset Attribute Description 5 AliquotID aliquot Cell Wall 75 Type CV_Term_ID
		6 Activome Features ¦ ¦--activome 266 <chr> <chr>	<chr>	6 AliquotID aliquot <chr> <chr>
		7		¦	¦--plato_hexosesP	10 AliquotID aliquot
		Hexoses Phosphate	266

determine which variable(s) could explain the fruit growth. References Arne

  Then we will apply linear modeling using the 'cv.glmnet' in order to Pommerening & Anders Muszta (2015) Methods of modelling relative growth rate, Forest Ecosystems -doi:org/10.1186/s40663-015-0029-4 Beauvoit et al (2014) Model-Assisted Analysis of Sugar Metabolism throughout Tomato Fruit Development Reveals Enzyme and Carrier Properties in Relation to Vacuole Expansion, Plant Cell, American Society of Plant Biologists, epub ahead of print. 10.1105/tpc.114.127761 hal-01058814

					1
		(Intercept)	5.730e-01
		PGM			-1.040e-01
		cFBPase		2.321e-03
		PyrK			.
		CitS			.
		PFP			.
		Aconitase		.
		PFK options(repr.plot.width=12, repr.plot.height=8) -6.313e-03 FruK . par(mfrow=c(1,2)) Linear Modeling with cv.glmnet pFBPase . plot_fittedCurve(fitObj) GluK 1.687e-01 plot_RGRCurve(fitObj) run_cvglmnet :Application of linear modeling using the 'cv.glmnet' function of the R NAD_ISODH .
		glmnet package Enolase	2.971e-01
		NADP_ISODH		-9.027e-05
		PEPC	for the Relative Growth Rate (RGR) .
		using the models previously fitted by the fitSigmoid function (fitObj) Aldolase -3.589e-02
		by selecting some one or more data subsets (setNameList -the data subsets will be Succ_CoA_ligase 2.045e-01
		merged if severals) NAD_MalDH 1.836e-01
		alpha=1 => lasso penalty, alpha=0 => ridge penalty, 0 < alpha < 1 => elastic-net AlaAT 1.827e-03
		penalty Fumarase		.
		AspAT GLMNET NADP_GluDH		. .
		NAD_GAPDH https://cran.r-project.org/web/packages/glmnet/glmnet.pdf . NADP_GAPDH -5.692e-03 https://cran.r-project.org/web/packages/glmnet/vignettes/glmnet.pdf NAD_GluDH -1.480e-01
		TPI			.
		PGK			.
	[9]	Neutral_Inv # List of data subset names for explaining the RGR -1.623e-02 Acid_Inv 3.795e-02 setNameList <-c('activome') # qNMR_metabo G6PDH -2.069e-01
		UGPase system.time( fitRGR <-run_cvglmnet(fitObj, dh, Tname, . SuSy . setNameList, alpha=1, info=TRUE) ) NAD_ME 1.495e-02
		ShiDH Getting & Merging ... NADP_ME Fitting ... R2 = 0.9994468 OK . . PGI -9.186e-02
		user system elapsed StarchS 6.404e-03
		0.39 AGPase	0.01	2.13	.
		SPS			-8.475e-02
			CV Glmnet : RGR = F('activome') 'RGR' fitted curve -activome
					1	dW
	[10] [11]	W print.table( as.matrix(fitRGR$cvfitList), digits=4, dt
		zero.print=".", na.print='-' )

options(repr.plot.width=12, repr.plot.height=8) plot_fittedRGRCurve(fitRGR$cvfitOut)

Plot variables [12] options(repr.plot.width=16, repr.plot.height=18) plot_vars(dh, Tname, setNameList [1], smoothtype='lowess', Gmax=20, ncol=4, cex.axis=2, cex.lab=2, cex.main=3)

Session Information