Leticia Peris
email: <leticia.peris@ujf-grenoble.fr>

Michael Wagenbach

Laurence Lafanechère

Jacques S Brocard

Ayana T Moore

Frank Kozielski

Didier Job

Linda Wordeman

Annie Andrieux
email: <annie.andrieux@ujf-grenoble.fr>

Peris Leticia

Wagenbach Michael

Brocard Jacques

Kozielski Frank

Job Didier

Wordeman Linda

Andrieux Annie

Motor-dependent microtubule

Keywords: Animals, Cell Shape, Cells, Cultured, Fibroblasts, cytology, metabolism, Hippocampus, cytology, Kinesin, genetics, metabolism, Mice, Mice, Knockout, Microtubules, metabolism, Mitotic Spindle Apparatus, metabolism, Molecular Motor Proteins, genetics, metabolism, Neurons, cytology, metabolism, Nocodazole, metabolism, Recombinant Fusion Proteins, genetics, metabolism, Tubulin, metabolism, Tubulin Modulators, metabolism, Tyrosine, metabolism

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Tubulin is subject to post translational modifications which principally affect the C-terminus of its -subunit. In one of these α modifications, the C-terminal tyrosine residue of -tubulin, is cyclically removed from the peptide chain by a carboxypeptidase and then α subsequently re-ligated to the chain by tubulin tyrosine ligase (TTL) (

). This cycle generates pools of tyrosinated and [START_REF] Hammond | Tubulin modifications and their cellular functions[END_REF] detyrosinated microtubules in cells. As a rule, dynamic microtubules are tyrosinated, whereas stable polymers are detyrosinated (Schulze ). Detyrosination does not stabilize microtubules ( ). However, microtubule stabilization in cells et al., 1987 per se [START_REF] Khawaja | Enhanced stability of microtubules enriched in detyrosinated tubulin is not a direct function of detyrosination level[END_REF] induces microtubule detyrosination which is hence considered as a consequence, not a cause, of microtubule stabilization (Webster et al., ). In recent years, important functions of tubulin tyrosination have been discovered. Thus, TTL loss and resulting tubulin 1987 detyrosination confers selective advantage to cancer cells during tumour growth (

). TTL suppression in mice, which [START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF] induces massive tubulin detyrosination, leads to lethal disorganization of neuronal circuits (

). Cells derived from TTL [START_REF] Erck | Microfilament-organizing centers in areas of cell contact: cytoskeletal interactions during cell attachment and locomotion[END_REF] deficient (TTL KO) mice display morphogenetic and polarity anomalies (

). Tyrosination has turned out to be crucial for [START_REF] Peris | Tubulin tyrosination is a major factor affecting the recruitment of CAP-Gly proteins at microtubule plus ends[END_REF] tubulin interaction with CAP-Gly end tracking proteins ( ; ; ;

+ [START_REF] Badin-Larcon | Suppression of nuclear oscillations in Saccharomyces cerevisiae expressing Glu tubulin[END_REF][START_REF] Bieling | CLIP-170 tracks growing microtubule ends by dynamically recognizing composite EB1/tubulin-binding sites[END_REF]Peris et al., 2006 Steinmetz and ) suggesting that the phenotypes observed following TTL suppression may arise from mislocalization of CAP-Gly Akhmanova, 2008 proteins at detyrosinated microtubule ends. However, a mechanistic explanation for the long-recognized correlation between microtubule + stability and tubulin tyrosination remains elusive. This prompted us to re-examine the relationship of tubulin tyrosination with microtubule dynamics, using TTL KO cells in which microtubules are extensively detyrosinated. We found that the tyrosination status of the microtubule had a profound effect on the microtubule-depolymerization activity of kinesin-13 family members.

RESULTS AND DISCUSSION

We initially observed decreased microtubule sensitivity to the depolymerizing drug nocodazole in TTL KO MEFs compared to WT ( ), suggesting that microtubules are stabilized in TTL KO MEFs. We have previously shown that the interaction of microtubules Fig. 1A with stabilizing factors such as structural MAPs or end binding proteins is either unaffected or inhibited by tubulin detyrosination ( + Peris ;

). Therefore, we hypothesized that in TTL KO cells, detyrosinated microtubules might be poor substrate for et al., 2006[START_REF] Saoudi | Stabilization and bundling of subtilisin-treated microtubules induced by microtubule associated proteins[END_REF] microtubule destabilizing factors, such as the kinesin-13 protein MCAK (mitotic centromere-associated kinesin) which is an important depolymerizing motor in cycling cells ( ; ; ; ; [START_REF] Hedrick | Effects of anti-microtubule agents on microtubule organization in cells lacking the kinesin-13 MCAK[END_REF][START_REF] Manning | The kinesin-13 proteins Kif2a, Kif2b, and Kif2c/MCAK have distinct roles during mitosis in human cells[END_REF][START_REF] Mennella | Functionally distinct kinesin-13 family members cooperate to regulate microtubule dynamics during interphase[END_REF]Newton et al., 2004 Ohi et al., ). Thereby, MCAK overexpression should rescue the loss of dynamic microtubules in TTL KO cells. Conversely, suppression of 2007 MCAK expression should prevent disassembly of normal tyrosinated microtubules in fibroblasts To test these possibilities, individual microtubule dynamics were monitored in WT or TTL KO MEFs. Cells were either untreated, transfected with MCAK cDNA, or with MCAK siRNAs ( shows MCAK depletion by siRNAs). Microtubule behavior was scored close to Supplementary Information, Fig. S1D the membrane in lamellipodial extensions.

In WT MEFs, most microtubules depolymerized upon contact with the membrane (

). In contrast, many microtubules continued Fig. 1B to grow tangential to the leading edge after touching the membrane in TTL KO MEFs ( and ). The time Fig. 1B Supplementary videos 1 2 spent by microtubules close to the membrane (persistence time) was markedly increased in TTL KO MEFs as compared to WT MEFs. In were incubated with a neck motor domain mutant of MCAK instead of full length MCAK ( ) but the extent of depolymerization + Fig. 2C was still less than that observed with WT microtubules ( ). Microtubule exposure to the motor domain alone, which is devoid of Fig. 2C sizeable depolymerizing activity ( ), did not induce any detectable depolymerization of any microtubule array ( ). [START_REF] Maney | Molecular dissection of the microtubule depolymerizing activity of mitotic centromere-associated kinesin[END_REF] Fig. 2C These results strongly indicate a direct relationship between microtubule detyrosination and MCAK inhibition which is evident even in the presence of the residual amounts of Tyr tubulin typically seen in TTL KO cells.

A possibility remained that tubulin detyrosination or CPA treatment inhibited MCAK by interfering with the interaction of the microtubules with other cellular effector proteins. To control for this possibility we assayed MCAK activity on microtubules assembled with purified tyrosinated or detyrosinated tubulin ( ). MCAK activity on detyrosinated microtubules was diminished compared to Fig. 3A tyrosinated microtubules, further supporting a direct relationship between microtubule tyrosination and MCAK activity (

). We Fig. 3B C then used microtubule binding assays to identify the point in the tubulin removal cycle that is affected by tubulin tyrosination. MCAK s ' apparent affinity for microtubules is influenced by its nucleotide state (

). Assays were run using the following [START_REF] Helenius | The depolymerizing kinesin MCAK uses lattice diffusion to rapidly target microtubule ends[END_REF] nucleotides or nucleotide analogs: the non hydrolysable nucleotide analog AMP-PNP, which mimics the ATP collision state; ADP-ALFx, which mimics the ATP transition state; ADP-vanadate, which mimics ADP-Pi; and ADP ( ). By fluorescence microscopy, MCAK Fig. 4 proteins yielded a punctuated decoration of microtubules, with some aggregates in the background ( ), as previously observed ( Fig. 4A ). In quantitative studies, the AMP-PNP-bound forms of GFP-MCAK or of the GFP-neck motor domain of [START_REF] Moore | C-terminus of mitotic centromere-associated kinesin (MCAK) inhibits its lattice-stimulated ATPase activity[END_REF] + MCAK associated to similar extents with tyrosinated or detyrosinated microtubules (

). In contrast, the ADP-AlFx-, Fig. 4B C -ADP-vanadate-and ADP-bound forms of full length MCAK exhibited a diminished association with detyrosinated microtubules compared to tyrosinated polymers (

). The difference was maximal with the ADP-vanadate ( ). Similar results were observed with the Fig. 4B Fig. 4B neck motor domain of MCAK, although differences were smaller ( ). Thus, detyrosination seems to diminish the affinity of the + Fig. 4C microtubule lattice for the ADP-Pi or ADP bound forms of MCAK.

To test whether the inhibition of depolymerizing motors was unique to TTL KO MEFs, we assayed TTL KO neurons for similar resistance to microtubule disassembly. When WT or TTL KO neurons were exposed to nocodazole, only residual tubulin staining was detectable in the axons of WT neurons whereas a persistent microtubule signal was evident in the axons of TTL deficient neurons (Fig 5A -). Axonal microtubule disassembly is largely dependent on the activity of KIF2A ( ), a neuronal kinesin-13 in the C [START_REF] Homma | Kinesin superfamily protein 2A (KIF2A) functions in suppression of collateral branch extension[END_REF] same family as MCAK. KIF2A KO neurons display morphogenetic anomalies affecting axonal length and branching [START_REF] Homma | Kinesin superfamily protein 2A (KIF2A) functions in suppression of collateral branch extension[END_REF]. In an analysis of TTL KO neurons morphology we observed an increase in both the axon length and of the total length of axon collaterals. The number of primary collaterals per unit of axonal length was unaffected whereas the total number of collaterals, including secondary or tertiary branches, was increased ( ). These anomalies resemble those observed in KIF2A KO neurons ( Fig. 5D G -

Homma et

). Collectively, our data strongly suggest that KIF2A activity is inhibited in TTL KO neurons and this is supported by experiments al., 2003 in lysed MEFs, showing inhibited KIF2A activity on WT CPA or TTL KO microtubules, compared to WT polymer (

). Fig. 5H The influence of the tubulin tyrosination status on the activity of depolymerizing motors provides an elegant mechanistic explanation for the observed stability of detyrosinated microtubules. Tubulin tyrosination affects the activity of the neck motor mutant of MCAK + which lacks both the N-terminal and the C-terminal domains of MCAK. This suggests that neither domain is centrally involved in MCAK interactions with the tubulin C-terminus. Both these domains are implicated in MCAK s ability to track on microtubule ends, which has ' been shown to be unaffected by tubulin detyrosination ( ;

). Based on our data, microtubule [START_REF] Moore | MCAK associates with the tips of polymerizing microtubules[END_REF][START_REF] Peris | Tubulin tyrosination is a major factor affecting the recruitment of CAP-Gly proteins at microtubule plus ends[END_REF] detyrosination negatively affects MCAK s tubulin removal activity directly. Tubulin detyrosination, and its negative effect on kinesin-13 ' family motors, is thus the most likely explanation for the impairment of microtubule disassembly observed in TTL KO cells. A contribution of other modifications of the tubulin C-terminus, such as polyglutamylation, which could be affected by detyrosination and resulting microtubule stabilization, cannot be completely ruled out but polyglutamylation is minimal in cycling cells.

Our results suggest a direct role for tubulin detyrosination in MCAK inhibition and invalidate an indirect effect through CAP-Gly + TIP de-localization in TTL KO cells. Indeed, while microtubule detyrosination impairs CAP-Gly TIP localization at ends (

+ + Peris et al.,
), this de-localization of CAP-Gly TIP from microtubule ends induces a decrease in the rescue frequency and premature microtubule 2006

+ catastrophes close to membranes (
). In contrast, we observed the opposite microtubule dynamic parameters in TTL [START_REF] Komarova | Cytoplasmic linker proteins promote microtubule rescue in vivo[END_REF] KO cells. Thus, we cannot attribute the increased microtubule stability to the absence of CAP-Gly TIP at microtubule ends.

+ +

The ADP-Pi-or ADP-bound forms of MCAK associate less efficiently with detyrosinated than with tyrosinated microtubules, suggesting that the lattice dwell time for MCAK between cycles of hydrolysis is lower on detyrosinated than on tyrosinated microtubules.

This would substantially decrease the number of tubulin dimers removed per motor prior to detaching from the microtubule (Helenius et ; ). It would also limit the ability of MCAK able to find microtubule ends by diffusive motility ( al., 2006Wagenbach et al., 2008 Helenius ). In current models, the ADP-Pi or ADP forms of MCAK are retained in the vicinity of the microtubule lattice through weak et al., 2006 electrostatic interaction with the charged E-hook of the -tubulin C-terminus ( ). Our data demonstrate an important ). Obviously, kinesin-13 inhibition provides a highly plausible explanation for the [START_REF] Mennella | Functionally distinct kinesin-13 family members cooperate to regulate microtubule dynamics during interphase[END_REF] impaired microtubule disassembly observed in different TTL KO cell types in this study. Additionally, MCAK appears to be important for microtubule disassembly upon encountering the cell edge, and may be crucial for end-on attachments to specific membrane complexes ( ). Geiger et al., [START_REF] Erck | Microfilament-organizing centers in areas of cell contact: cytoskeletal interactions during cell attachment and locomotion[END_REF][START_REF] Morrison | Action and interactions at microtubule ends[END_REF] Impaired activity of depolymerizing motors may be central to several anomalies observed in TTL KO cells or mice. There is extensive overlap between the phenotypes observed in TTL KO or KIF2A KO neurons or mice ( ; ). Anomalies [START_REF] Erck | Microfilament-organizing centers in areas of cell contact: cytoskeletal interactions during cell attachment and locomotion[END_REF][START_REF] Homma | Kinesin superfamily protein 2A (KIF2A) functions in suppression of collateral branch extension[END_REF] and adaptations in depolymerizing motor activity may also be important for the facilitating effect of TTL loss on tumor progression ( ). MCAK activity has been observed to be altered in cancer cells ( ; ). [START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF][START_REF] Hedrick | Effects of anti-microtubule agents on microtubule organization in cells lacking the kinesin-13 MCAK[END_REF][START_REF] Ishikawa | Mitotic centromere-associated kinesin is a novel marker for prognosis and lymph node metastasis in colorectal cancer[END_REF] Interestingly, both CAP-Gly end binding proteins and depolymerizing motors have been identified as factors whose alteration favors + genomic instability in aneuploid cells and thereby malignant cell invasiveness ( ; ). [START_REF] Pellman | Cell biology: aneuploidy and cancer[END_REF][START_REF] Storchova | Genome-wide genetic analysis of polyploidy in yeast[END_REF] Factors able to promote the differentiation of cellular microtubules into distinct stability subclasses have been subject of sustained interest in cell biology. Decades ago, tubulin tyrosination, which distinguished with striking clarity between stable and dynamic microtubules, seemed to be an ideal mechanism responsible for selective microtubule stabilization (

). This attractive [START_REF] Schulze | Posttranslational modification and microtubule stability[END_REF] view was discarded when it appeared that tyrosination does not alter microtubule dynamic properties. Based on our results, tubulin per se detyrosination could actually generate persistent stable microtubule subsets by rendering transiently stabilized microtubules resistant to motor-driven depolymerization.

MATERIALS AND METHODS

Antibodies

Detyr tubulin (L4), Tyr tubulin (clone YL1/2); -tubulin (clone 3a) (

), polyclonal anti GFP (Invitrogen), polyclonal α α [START_REF] Peris | Tubulin tyrosination is a major factor affecting the recruitment of CAP-Gly proteins at microtubule plus ends[END_REF] anti MCAK ( ). [START_REF] Andrews | Aurora B regulates MCAK at the mitotic centromere[END_REF]

Cell culture and transfection

Hippocampal neurons and MEFs (3 different embryos for each genotype) were prepared as in (

). MEF were [START_REF] Erck | Microfilament-organizing centers in areas of cell contact: cytoskeletal interactions during cell attachment and locomotion[END_REF] transfected using AMAXA Biosystem and GFP EB3 (N. Galjart, Rotterdam, The Netherlands); m-cherry -tubulin (F. Saudou, Institut siRNA 3 was used in microtubule dynamic analysis.

#

Recombinant proteins

The His -tagged HsKif2A neck motor domain (residues 188 to 537) was expressed in BL21 bacterial strain. His -His -tagged-full

Analysis of MT behaviour

Image capture was every 3 seconds. MT displaying a trajectory at straight angle (70 to 110 ) with the cell membrane were analysed °inside a region of 10 m from the cell edge. MT persistence time, measured for polymers whose growing end made contact with the μ membrane, was defined as the time during which at least a segment of MT kept running parallel to the membrane prior to disassembly. MT dynamic instability parameters were determined and analyzed as described in ( ). Kline-Smith and Walczak, 2002

Lysed cell experiments

Fibroblasts were lysed in PEM buffer (PIPES 80 mM, EGTA 1 mM, MgCl 1 mM, pH 6,7) with 0.5 Triton X-100 and 10 glycerol 2 % % and incubated PEM 2 M Taxol buffer in the presence or absence of 2 g/ml Carboxypeptidase A (CPA, Sigma C-9268). Lysed cells

+ μ μ

were then washed in CPA inactivating buffer (20 mM DDT, PEM-Taxol buffer) and extensively washed in PEM-Taxol buffer. Lysed cells were incubated with buffer alone (PEM buffer, 2 M taxol, 1 mM DTT, 75 mM KCl and 0.25 mM Mg-ATP) or with the same buffer μ containing purified motor protein (200 nM for MCAK proteins and 800 nM for KIF2A) for 30 minutes And then fixed in cold methanol.

MT network was quantified by the -tubulin fluorescence intensity measured inside the whole cell surface of lysed cells. were fixed in cold methanol and stained with anti tubulin antibody. Quantification of total amount of MTs was measured using ImageJ software.

Fig-

  Fig.2A

α

  [START_REF] Helenius | The depolymerizing kinesin MCAK uses lattice diffusion to rapidly target microtubule ends[END_REF] regulatory role for the C-terminal tyrosine of -tubulin in the autoregulation of microtubule stability. α Impaired microtubule disassembly has been previously observed in KIF2A deficient neurons ( ), or uponMCAK Homma et al., 2003 inhibition in cycling cells (

  ) and m-cherry-MCAK (Wordeman s laboratory). For inhibition of MCAK: Stealth siRNA Negative Control, Stealth Select ' siRNA 1 MSS232130; 2 MSS232131 and 3 MSS232132 from Invitrogen. MCAK inhibition was assayed by western blot analysis and# # #

  Cells were treated with carrier alone or with 20 M nocodazole for 60 min (neurons) or 15 min (MEFs), permeabilized in PHEM buffer μ (PIPES 60 mM, HEPES 25 mM, EGTA 10 mM, MgCl 2 mM, pH 6,9) with 0.02 saponine and 10 M Taxol and fixed in PHEM buffer 2 % μ with 2 paraformaldehyde and 0.05 glutaraldehyde. MT network was quantified by the -tubulin fluorescence intensity measured % % α inside the whole cell surface in fibroblasts (determined with F-actin staining) or in three fixed size regions (5 5 m) placed at the × μ proximal, medial and distal part of each axon. Immunofluorescence and video-microscopy Fluorescent images of living cells were captured with a charge-coupled device camera (CoolSNAP HQ; Roper Scienti c) using a 100/1.3 Plan-Neo fluar oil objective in an inverted motorized microscope (Axiovert 200M; Carl Zeiss MicroImaging, Inc.) controlled by MetaMorph software (Universal Imaging Corp.). Fixed images were captured with a charge-coupled device camera(CoolSNAP ES; Roper Scientific) in a straight microscope (Axioskop 50;Carl Zeiss MicroImaging, Inc.) controlled by MetaView software (Universal Imaging Corp.) using a 40 or 100/1.3 Plan-Neo fluar oil objectives.

α

  was assayed in different nucleotide conditions: AMP-PNP; ADP-Aluminium Fluoride, ADP-Vanadate and Motor binding to MTs ADP. Purified motors (1 M) were incubated with nucleotides (2mM) and then with 0.01 M Tyr or Detyr taxol-stabilized MTs ( μ μ Paturle et ). Reactions were stopped with PEM, 50 Glycerol and 1 Glutaraldehyde and centrifuged on coverslips. MT-motor complexes al., 1989 % % were fixed in cold methanol and double stained with -tubulin and GFP antibodies. For quantification, a line corresponding to each MT in α the -tubulin image was transferred to GFP images. The amount of motor bound per unit of MT length was calculated as the percentage of α positive GFP pixels versus total number of pixels in the MT line. taxol stabilized Tyr or Detyr MTs (10 nM) were incubated or not with MCAK-ATP (10 In vitro depolymerization experiments: nM). Reactions were stopped with PEM, 50 Glycerol and 1 Glutaraldehyde after 10 or 20 min and centrifuged on coverslips. Reactions % %

Figure 1

 1 Figure 1 Impaired MT dynamics in TTL KO MEFs (A) Analysis of nocodazole effects on WT or TTL KO MEFs. Data is expressed as the ratio of MT signals measured after nocodazole treatment versus control conditions (mean s.e.m). MT signals were estimated for a minimum of 39 MEFs from 3 independent experiments, ± for each genotype and treatment conditions. p<0.001 test (B) Video-microscopy examination of microtubules in WT or TTL KO MEFs ***

Figure 2 Figure 5

 25 Figure 2 Depolymerizing activity of MCAK on tyrosinated or detyrosinated microtubules in lysed cells (A) Microtubule tyrosination/detyrosination levels in lysed cells. WT or TTL KO MEFs were lysed in a triton based buffer, taxol-stabilized, fixed and labeled with tyrosinated and detyrosinated tubulin antibodies. WT CPA: WT lysed MEFs incubated with CPA prior to fixation. + Scale bar: 50 m. (B) MCAK effects on lysed cells. Images of MT arrays in control conditions or following exposure to recombinant full μ length MCAK. Scale bar: 50 m. (C) Quantitative analysis of MT depolymerization in the presence of full length MCAK or MCAK domains. μ MT signal: percentage s.e.m. of the area occupied by the MT network versus total cell area. A minimum of 78 lysed cells were analyzed in ± each condition. p<0.001, test.***

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

[START_REF] Mialhe | Tubulin detyrosination is a frequent occurrence in breast cancers of poor prognosis[END_REF]

Data processing and analysis

Data were analyzed blind to the genotype or the experimental conditions. Statistical analysis: t-tests with unequal variances for samples comprising more than 30 measures or on parametric Mann and Whitney U test for smaller samples.

Ackowledgements:

We thank AA team s members for comments on the manuscript; D. Proietto, A. Schweitzer, JP. Andrieu and B. Dublet for technical help.

'

This work was supported in part by grants: ANR Tyr-Tips to D.J., La Ligue contre le cancer to D.J.; ARC to D.J and A.A and NIH (GM069429) to LW.

Footnotes:

Online supplemental material illustrates endogenous MCAK expression in WT or TTL KO MEFs and its specific suppression with commercial Stealth siRNAs. --

Abbreviations

ADP AlF


Lena Barascou
email: lena.barascou@inrae.fr

Ugoline Godeau

Maryline Pioz

Olivier Martin

Deborah Sené

Didier Crauser

Yves Le Conte

Cedric Alaux

Didier Crauser

Real-time monitoring of honeybee colony daily activity and bee loss rates can highlight the risk posed by a pesticide

Keywords: pesticide risk assessment, specific protection goal, bee counter, mortality rates, foraging activity, Apis mellifera

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

Introduction

Plant protection products, also referred to as pesticides, have been developed to protect crops against harmful organisms (e.g. insects, fungi) or prevent the growth of undesirable plants (herbicides).

However, the yield and/or quality of around 75% of globally important crop types also depend on pollination mediated by animals, especially bees, which visit more than 90% of the leading 107 crop types [START_REF] Klein | Importance of pollinators in changing landscapes for world crops[END_REF]). As a consequence, bees, including the honeybee (Apis mellifera), can be exposed to several pesticides recovered from pollen and nectar [START_REF] Mullin | High levels of miticides and agrochemicals in North American apiaries: implications for honey bee health[END_REF][START_REF] Pohorecka | Residues of Neonicotinoid Insecticides in Bee Collected Plant Materials from Oilseed Rape Crops and their Effect on Bee Colonies[END_REF][START_REF] Sanchez-Bayo | Pesticide Residues and Bees -A Risk Assessment[END_REF][START_REF] Botías | Neonicotinoid residues in wildflowers, a potential route of chronic exposure for bees[END_REF][START_REF] Zioga | Plant protection product residues in plant pollen and nectar: a review of current knowledge[END_REF]. There is therefore a clear need to provide an assessment of the risk presented by pesticides and to determine to what extent they are safe for bees. For that purpose, OECD (Organization for Economic Co-operation and Development) and EPPO (European and Mediterranean Plant Protection Organization) test guidelines require toxicological data on honeybees (Apis mellifera). Indeed, the honeybee is relatively easy to rear, its biology has been well-studied, and it is one of the most important pollinators worldwide [START_REF] Klein | Importance of pollinators in changing landscapes for world crops[END_REF][START_REF] Hung | The worldwide importance of honey bees as pollinators in natural habitats[END_REF].

Within the current regulatory framework for pesticide risk assessment, the effects of pesticides on honeybees are assessed by standard tests in a stepwise approach. In Tier 1, active substances or formulated products are tested on honeybees at different life stages (larvae and adults) (OECD 1998a(OECD , 1998b(OECD , 2013(OECD , 2017)). Then, a deterministic approach to characterize risk quantitatively can be used by comparing the pesticide toxicity to environmental exposure (Hazard quotient -HQ). When the results of this first step identify a risk (for ex. HQ > 50 for foliar spray), semi-field and field tests on colonies (Tier 2 and 3) are required for marketing authorization (MA) (see also [START_REF] Thompson | The use of the Hazard Quotient approach to assess the potential risk to honeybees (Apis mellifera) posed by pesticide residues detected in bee-relevant matrices is not appropriate[END_REF] for risk identification upon oral exposure: Risk quotient > 0.4 and 1 in acute and chronic toxicity tests, respectively). Higher tier tests can also be performed in post-MA studies monitoring adverse effects linked to the use of pesticides.

Major issues are that the test guidance for the conduct of such semi-field and field studies [START_REF] Eppo | PP 3/10 (3) Environmental risk assessment scheme for plant protection products, Chapter 10: Honeybees[END_REF] relies on measurements of several key colony parameters that are very approximate [START_REF] Wang | Reduction of variability for the assessment of side effects of toxicants on honeybees and understanding drivers for colony development[END_REF] or do not overly account for chronic and/or sub-lethal effects. For instance, the estimation of flight activity is based on the count for a few seconds of bees foraging on flowers near the hive, which represents a small snapshot of what happens over a day that does not take into account the daily changes in foraging activity according to the weather and others environmental factors (e.g. discovery of new food sources) [START_REF] Winston | THe Biology of the Honey Bee[END_REF]). In addition, mortality estimates rely mainly on the number of bees in dead bee traps in front of the hive. It excludes bees that have died outside of the traps, but also suchtrap cannot be used for long time periods since its precision decline over time due for instance to predation. Therefore, such method does not provide robust information on the background mortality. Counting the number of bees entering and exiting the hive can also be used to assess forager activity and bee losses given that worker bees die in the field or are removed from the hive if found dead inside. To accurately determine these assessment endpoints, researchers in the past years have relied on individual tagging and detection of bees at the hive entrance. Notably, radiofrequency identification (RFID) tags coupled with detectors at the hive entrance and bee counters recording the activity of bees marked with datamatrix barcodes have been successfully used in several studies to automatically record individual bee activity and mortality in response to pesticide exposure [START_REF] Bortolotti | Effects of sub-lethal imidacloprid doses on the homing rate and foraging activity of honey bees[END_REF][START_REF] Schneider | RFID tracking of sublethal effects of two neonicotinoid insecticides on the foraging behavior of Apis mellifera[END_REF][START_REF] Henry | A common pesticide decreases foraging success and survival in honey bees[END_REF][START_REF] Prado | Exposure to pollen-bound pesticide mixtures induces longer-lived but less efficient honey bees[END_REF][START_REF] Monchanin | Hazard of a neonicotinoid insecticide on the homing flight of the honeybee depends on climatic conditions and Varroa infestation[END_REF][START_REF] Colin | Traces of a neonicotinoid induce precocious foraging and reduce foraging performance in honey bees[END_REF][START_REF] Hesselbach | Chronic exposure to the pesticide flupyradifurone can lead to premature onset of foraging in honeybees Apis mellifera[END_REF][START_REF] Shi | Sublethal acetamiprid doses negatively affect the lifespans and foraging behaviors of honey bee (Apis mellifera L.) workers[END_REF], Barascou et al. 2021a). While individual bee tracking is highly effective, it provides time-limited data on honeybee flight activity and mortality, based on a small portion of the bee population, or on a single age-cohort, ignoring the fact that pesticide sensitivity may depend on age with older bees being more sensitive than younger bees to certain pesticides, and less to others [START_REF] Mayland | Honey Bee Mortality As Related To Insecticide-Treated Surfaces and Bee Age12[END_REF][START_REF] Ladas | The influence of some internal and external factors upon the insecticide resistance of honeybee[END_REF][START_REF] Bendahou | Acute toxicity of cypermethrin and fenitrothion on honeybees (Apis mellifera mellifera) according to age, formulations and (chronic paralysis virus) insecticide interaction[END_REF][START_REF] Rinkevich | Genetics, synergists, and age affect insecticide sensitivity of the honey bee, Apis mellifera[END_REF][START_REF] Zhu | Effect of age on insecticide susceptibility and enzymatic activities of three detoxification enzymes and one invertase in honey bee workers (Apis mellifera)[END_REF][START_REF] Barascou | Pesticide risk assessment: honeybee workers are not all equal regarding the risk posed by exposure to pesticides[END_REF].

These methodological gaps of current semi-field and field regulatory tests combined with the lack of tools and methods for the field assessment of bee activity and mortality at the colony level represent a major gap for pesticide risk assessment, especially since the measurement of bee mortality has emerged as a benchmark for evaluating the magnitude of effects due to pesticide exposure [START_REF] Efsa | EFSA Guidance Document on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees)[END_REF][START_REF] Efsa | Review of the evidence on bee background mortality[END_REF]. Indeed, specific protection goals (SPGs) have been established as the maximum permitted levels of colony size reduction resulting from exposure to pesticides, and the levels and duration of bee losses that would cause this specific decline in colony size are defined as trigger values. For instance, a reduction in colony size that does not exceed 7 % has been considered by EFSA (European Food Safety Authority) as negligible and therefore allowable for proper honeybee colony strength and development [START_REF] Efsa | EFSA Guidance Document on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees)[END_REF]. Accordingly, it was determined by using a quantitative model of colony population dynamics [START_REF] Khoury | A quantitative model of honey bee colony population dynamics[END_REF], that for a healthy colony forager mortality should not increase, compared with controls, by more than a factor of 1.5 for 6 days, a factor of 2 for 3 days or a factor of 3 for 2 days [START_REF] Efsa | EFSA Guidance Document on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees)[END_REF].

In this context, tools, such as bee counters, that allow a colony-level and continuous monitoring of bee flight activity and mortality, should pave the way toward a more reliable pesticide risk assessment [START_REF] Odemer | Approaches, challenges and recent advances in automated bee counting devices: A review[END_REF]. First attempts to evaluate the potential of bee counters in pesticide risk assessment have been performed [START_REF] Struye | Microprocessor-controlled monitoring of honeybee flight activity at the hive entrance[END_REF][START_REF] Ngo | A real-time imaging system for multiple honey bee tracking and activity monitoring[END_REF]). However, a relatively low number of replicates was used and effects were not always statistically analyzed. We therefore aimed to further provide a proof-of-concept for the use of bee counters in pesticide risk assessment in honeybees by assessing i) the effect of a pesticide on the colony-level daily activity and bee loss rates and ii) the consecutive risk for colonies. For that purpose, we performed real-time monitoring of colony population flight activity with video-based bee counters. We first identified the background activity and mortality and then chronically exposed colonies either to a field realistic concentration of the insecticide sulfoxaflor or a higher concentration representing a worst-case exposure scenario. Finally, to assess the risk posed by sulfoxaflor to honeybee colonies, we compared the level and duration of bee losses to the theoretical trigger values associated with the SPG of 7% colony-size reduction. Sulfoxaflor is a sulfoximine-based insecticide that shares a mode of action with neonicotinoids as selective agonists of nicotinic acetyl choline receptors (nAChRs) [START_REF] Sparks | Sulfoxaflor and the sulfoximine insecticides: Chemistry, mode of action and basis for efficacy on resistant insects[END_REF]. It is increasingly used as an alternative to neonicotinoid insecticides for controlling sap-feeding insect pests since several neonicotinoids are now banned in the European Union [START_REF] Stokstad | European Union expands ban of three neonicotinoid pesticides[END_REF]) and there are numerous cases of resistance to this class of insecticides, contrary to sulfoxaflor so far [START_REF] Watson | Sulfoxaflor -A sulfoximine insecticide: Review and analysis of mode of action, resistance and cross-resistance[END_REF]. Sulfoxaflor was used for assessing the potential of bee counter in pesticide risk assessment under chronic exposure (even though its formulation product Closer is registered as a foliar spray) since we previously characterized its concentration-dependent toxicity in laboratory experiments (Barascou et al. 2021b[START_REF] Barascou | Pesticide risk assessment: honeybee workers are not all equal regarding the risk posed by exposure to pesticides[END_REF]. These laboratory tests and concentrations could then be used as landmarks for the toxicity tests performed with bee counters.

Materials and methods

Experimental setup

Experiments were performed in 2021 in a peri-urban area near Avignon (France, 43°540N-4°-520E) with honeybee colonies (Apis mellifera) from INRAE (Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement) livestock. All colonies were treated yearly in August with Apistan® (tau-fluvalinate) against the parasite Varroa destructor. Information on the landscape characteristics, within a 1.5 km radius area around the apiaries (mean foraging range; [START_REF] Steffan-Dewenter | Honeybee foraging in differentially structured landscapes[END_REF], was obtained from the French national remote sensing database on vegetation layers (Institut Géographique National) via BeeGIS (https://appli.itsap.asso.fr/app/01-beegis). The landscape was predominantly surrounded by urban areas (49%), cropping systems, including meadows, vegetables and cereal crops (32%), dunes and sand (10.5%), orchards (4.5%) and forests (4%).

Each colony, composed of five Dadant frames, was selected and then randomly assigned to one of the three treatments (see below 'Exposure to sulfoxaflor'). The day before exposure to sulfoxaflor, we assessed the demographic state and food storage (pollen and honey) of each colony. For that purpose, each side of each frame, was visually inspected and the area covered by adult bees, open and closed brood, as well as honey and bee bread, were reported in percentages (one full side = 100%). Percentages were then converted into numbers of adult bees, open and capped brood cells, and area in dm 2 of honey and pollen based on a previously determined coefficient for Dadant frames [START_REF] Hernandez | ColEval: Honeybee COLony Structure EVALuation for Field Surveys[END_REF].

Colonies were visually inspected again at the end of exposure and 10 days post-exposure to sulfoxaflor by following the same methodology [START_REF] Hernandez | ColEval: Honeybee COLony Structure EVALuation for Field Surveys[END_REF]. Finally, colony overwintering survival was checked the following year (March 2022).

Exposure to sulfoxaflor

Colonies were left untreated for four days and then exposed to one of the two sulfoxaflor concentrations, depending on their randomly assigned treatment groups. The sulfoxaflor concentrations were chosen based on pesticide residue data found in pollen and nectar. Depending on the application rates and the crops, field residue studies reported levels of sulfoxaflor ranging from 0.04 to 2.37 mg/kg (e.g. 0.047 to 2.80 µg/ml) in nectar collected by bees and from 0.00447 to 31.8 mg/kg (e.g. 0.005 to 37.52 µg/ml) in the nectar of flowers (EPA 2019). Thereby, colonies were provided with a solution of 50 % (w/v) sucrose, 0.1 % acetone and sulfoxaflor at a field-realistic concentration (0.5 µg/ml) or a higher concentration representing a worst-case exposure scenario (2 µg/ml). Similar concentrations were previously found to be non-toxic (0.02 µg/ml) or to significantly decrease bee survival (2.35 µg/m) in laboratory tests (Barascou et al. 2021b). Control groups were fed with pesticide-free sugar solutions (50 % w/v sucrose, 0.1 % acetone) and were used to determine whether differences in colony activity could be attributed to variation in field conditions (e.g. climate, resources). Stock solutions of sulfoxaflor (Techlab, France) were diluted in acetone, aliquoted and conserved at -20°C. The exact concentrations were checked with LC-MS/MS (Barascou et al. 2021b) and resulted in 0.59 µg/ml and 2.36 µg/ml for the prepared sulfoxaflor concentrations. Colonies were given 500 ml of pesticide-free or sulfoxaflorcontaminated sugar solutions every two days, over a 10-day exposure period (totaling 2,500 ml of syrup). The amount of sugar solution was chosen to optimize its full consumption by colonies. The sugar solutions were added in an internal feeder box placed above the hive frames. The experiment was repeated with new colonies, each month between April and September 2021 (except in August due to elevated environmental temperatures and therefore low colony activity). Each month was composed of one colony per experimental treatment, except in April (no colony was exposed to the 0.59 µg/ml sulfoxaflor treatment) and June and July (two colonies were exposed to the 0.59 µg/ml sulfoxaflor treatment); giving a total of n = 5 colonies for the control and 2.36 µg/ml sulfoxaflor treatments, and n = 6 colonies for the 0.59 µg/ml sulfoxaflor treatment.

Colony-level monitoring of daily flight activity and homing flight rates

To assess the effect of sulfoxaflor on the daily flight activity and homing flight rates, we equipped each colony with an video-based bee counter (patent IDDN.FR.001.130013.000.R.P.2010.000.31235) [START_REF] Crauser | Compteur d'entrées et sorties d'abeilles[END_REF]. For each replicate, bee counters were assigned to a different treatment group, so that each bee counter monitored the activity of colonies exposed, or not, to sulfoxaflor (0.59 and 2.36 µg/ml).

The bee counter device consists of a camera that monitors the hive entrance and image-analysis software that detects and registers in real time the activity of all bees by counting the number of out-going and in-coming bees (Fig. 1). The hive entrance was modified and composed of eight passages narrow enough so that only one bee can circulate in each tunnel and with only the back of the thorax facing the camera (see [START_REF] Dussaubat | Flight behavior and pheromone changes associated to Nosema ceranae infection of honey bee workers (Apis mellifera) in field conditions[END_REF] for more details). The software runs in the LabView environment for graphic programming and adjusts the frequency of images captured per second in order to reduce the chance of missing bees passing in front of the camera. Validation tests to assess the bee counter error rates were performed in 2007 and 2010 by comparing the numbers of bees recorded by the counter to the ones recorded by a human observer. Tests were done through 93 sessions spanning the bee season (May to October) and giving 27 855 bee counts (see the Excel supplementary file for raw data). The average error rate was of -2.26% and -1.3% in 2007 and 2010, meaning that the counter is slightly underestimating the flight activity by 1.3 to 2.26%. Most importantly, in 2007 we assessed the error rates for entries and exits separately, and found similar error rates for both type of flights (-2.23% for entries and -2.29 for exits). This indicates that bee loss rate (or gain) values are relatively little affected by the error rates.

The cumulated activity was recorded every 5 minutes and automatically saved with the time and date.

From these bee entry and exit data, we determined the daily: i) flight activity (sum of the number of bee exits during a day) and ii) homing flight rates, calculated as follows: (number of entries -number of exits) / number of exits for each day [START_REF] Odemer | Approaches, challenges and recent advances in automated bee counting devices: A review[END_REF]. When negative, the homing flight rate was assimilated to a loss rate. Positive values corresponded to a gain of bees, likely from drifting behavior because experimental colonies were located in the same apiary (two meters away from each other).

The colony-level monitoring with bee counters started 4 days before the exposure to sulfoxaflor and ended at 10 days post-exposure. We therefore had data on the daily flight activity and homing flight rates for three time periods: pre-exposure period (4 days), during exposure (10 days), and post-exposure (10 days).

Data analysis

Raw flight activity data are shown in the Excel supplementary file. Data were analyzed using the statistical software R v4.0.3 (R Core Team 2020). Statistical significance was set at α = 0.05 for all tests.

Since data were not normally distributed (Shapiro-Wilk test), variations among treatment groups in the number of honeybees, open and capped brood cells, and the amount of honey and pollen before exposure to sulfoxaflor were analyzed using Kruskal-Wallis tests. The influence of treatment groups on the cumulated syrup consumption was also analyzed using a Kruskal-Wallis test.

The effects of sulfoxaflor were determined on both the daily flight activity of bees and the homing flight rates, using a Linear Mixed Model (LMM) fitted by maximum likelihood using the lme4 package [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF]. P-values for the t-test of fixed terms of the LMM were obtained through the lmer function of the lmerTest package [START_REF] Kuznetsova | lmerTest Package: Tests in Linear Mixed Effects Models[END_REF]). For each model, we selected the best one based on the second-order Akaike's Information Criterion (AICc) [START_REF] Burnham | Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Page 655[END_REF]) by using backward model selection from the full model. Variations in the daily flight activity and homing flight rates were analyzed with treatment, pesticide exposure period and months as fixed factors (with a treatment x exposure period interaction), and colony replicates as a random factor. For homing flight rate analysis, due to a heavy tailed distribution of the response variable and non-normal residues of the LMM, we used a bootstrap procedure to assess the significance of variables. The 95% confidence intervals for model parameters were estimated using the lme4 R-package (bootMer function). We generated 10,000 bootstrapped samples from the model and refitted the model to these samples to obtain bootstrapped parameter estimates. If bootstrapped confidence intervals around the parameter estimate in that model did not overlap with zero, we concluded a significant effect.

In addition, we assessed for each treatment group the magnitude of sulfoxaflor effects on the daily flight activity and homing flight rates. For each pesticide treatment exposure periods (before vs during exposure; during vs after exposure; before vs after exposure), we calculated Hedges's g (g) or Glass's delta (Δ) as a measure of effect size (hedges_g or glass_delta functions of the effectsize package (Ben-Shachar et al. 2020)). Glass's delta was used when the standard deviations were significantly different between groups, as it uses only the second group's standard deviation. The interpretation values for both measures were as follows: 0.2 < g or Δ: very small effect, 0.2 ≤ g or Δ < 0.5: small effect, 0.5 ≤ g or Δ < 0.8: medium effect, and g or Δ ≥ 0.8: large effect [START_REF] Ben-Shachar | effectsize: Estimation of Effect Size Indices and Standardized Parameters[END_REF]). An effect size (g or Δ)

was considered significant if the 95% confidence intervals of effect size did not overlap 0.0, and as negative or positive when it was below or above 0.0, respectively [START_REF] Ben-Shachar | effectsize: Estimation of Effect Size Indices and Standardized Parameters[END_REF].

For each treatment group, variations in the number of honeybees, open and capped brood cells, and the amount of honey and pollen between the pre-exposure period and the post-exposure periods were analyzed using Wilcoxon rank test pairwise comparisons.

Finally, we determined whether or not an increase in mortality rates (only with negative homing flight rates) exceeded the theoretical trigger values associated with the SPG for honeybees (i.e. 7% colony size reduction) (EFSA 2013). For that purpose, we calculated the fold-changes in bee loss rates between the pre-exposure and exposure periods to sulfoxaflor. These values were determined for exposure lasting 2, 3 or 6 days and then compared to the theoretical trigger values (EFSA 2013).

Results

We did not find any difference between treatment groups in the colony demographic state and food storage before exposure to sulfoxaflor (number of bees: Kruskal-Wallis, p = 0.417, open brood cells: p = 0.309, capped brood cells: p = 0.284, honey: p = 0.803 and pollen: p = 0.564; Fig. S1). The cumulated syrup consumption also did not differ between treatment groups (control: 1 949 ± 874 ml, 0.59 µg/ml of sulfoxaflor: 2 235 ± 757 and 2.36 µg/ml of sulfoxaflor: 2 140 ± 492 ml, Kruskal-Wallis: p = 0.40; Fig. S2).

Colony-level effect of sulfoxaflor on the daily flight activity

The results of model selection regarding the effect of sulfoxaflor treatment on the daily flight activity are presented in Table S1 (LMM analysis). The best model selected based on the AICc criterion included the month and an interaction between treatment and exposure period as predictors.

The overall daily flight activity before exposure to sulfoxaflor did not differ between the three treatment modalities (Table 1). Similarly, no statistically significant difference in the daily flight activity was observed between months. The daily flight activity in control colonies was significantly higher after exposure than before exposure (Table 1, Fig. 2 and Fig. S3). In colonies exposed to 0.59 µg/ml, the daily activity during and after exposure did not differ from the activity before exposure (GLMM: p = 0.812 and p = 0.475, respectively). However, honeybee colonies exposed to 2.36 µg/ml of sulfoxaflor made consistently (each month) fewer flights per day during and after exposure than before exposure (GLMM: p < 0.001; Fig. 2

and Fig S3).

In fact, the daily flight activity was reduced by half during the period of exposure to 2.36 µg/ml of sulfoxaflor as compared to before exposure (DE vs BE: Δ = -1.76large effect; Fig. 3 and Table S2).

Additionally, colonies made around 5 000 more flights after exposure to sulfoxaflor than during the treatment period (AE vs DE: Δ = 0.59medium effect; Fig. 3 and Table S2).

Colony-level effect of sulfoxaflor on the daily homing flight rates

The results of model selection regarding the effect of sulfoxaflor treatment on the homing flight rates are presented in Table S2 (LMM analysis). The best model selected based on the AICc criterion included the month and an interaction between treatment and exposure period as predictors.

The overall homing flight rates before exposure to sulfoxaflor did not differ between the three treatment modalities, but were lower in May, June and July as compared to April (Table 2). No statistically significant difference in the homing flight rates of control colonies was observed between periods of exposure (Table 2, Fig. 4 and Fig. S3). In colonies exposed to 0.59 µg/ml, the daily homing flight rates during and after exposure did not differ from the activity before exposure (GLMM: p = 0.801 and p = 0.811, respectively). However, honeybee colonies exposed to 2.36 µg/ml of sulfoxaflor lost consistently (each month) more bees per day during exposure than before exposure (GLMM: p < 0.001; Fig. 4 and Fig. S3).

A 10-fold increase in the loss rate was found between the period of exposure to 2.36 µg/ml of sulfoxaflor (-5.97 ± 9.33 %) and the pre-exposure period (-0.54 ± 3.98 %; DE vs BE: Δ = -1.36large effect; Fig. 3 and Table S3). After exposure to this sulfoxaflor concentration, the bee loss rate decreased and was significantly lower (-1.14 ± 5.07 %) than during the treatment period (AE vs DE: Δ = 0.52medium effect), and was comparable to that observed before exposure to the insecticide (Fig. 3 and Table S3).

Finally, to determine whether this increase in bee losses caused by the exposure to 2.36 µg/ml of sulfoxaflor could be considered a risk for colonies, we compared the fold changes in bee losses to the theoretical trigger values considered to be safe for colonies, i.e. that cause a negligible reduction in colony size (EFSA 2013). We found that the fold changes in bee mortality were predominantly above these trigger values (Table 3). In some cases, homing flight rates before exposure to sulfoxaflor were positive or close to 0, but strongly negative afterwards, indicating a major effect of sulfoxaflor (i.e. above the theoretical trigger values).

Colony development and survival

The number of adult bees, open and capped brood cells, as well as the amount of honey and pollen storage, measured before and after exposure to sulfoxaflor are shown for each month in Fig. S2. We did not find any effect of treatment (control, 0.59 µg/ml and 2.36 µg/ml of sulfoxaflor) on the number of adult bees and open brood cells, and on the amount of honey and pollen storage between the different periods of exposure (before vs during and before vs post-exposure; Table S4 andTable S5). However, the number of capped brood cells in honeybee colonies exposed to 2.36 µg/ml of sulfoxaflor was significantly lower 10 days post-exposure than before exposure (Wilcox test: p = 0.032; Table S5).

All colonies provided with sugar syrup only (n = 5 colonies) or with sugar syrup laced with 0.59 µg/ml of sulfoxaflor (n = 6 colonies) survived throughout the winter. However, three out of the five colonies exposed to 2.36 µg/ml of sulfoxaflor were found dead at the end of winter; these three colonies were exposed to sulfoxaflor in April, May and June.

Discussion

Within the framework of pesticide risk assessment procedures for honeybees, data on effects on colonies are required if some risks have been identified on individual bees. Such Tier 2 and 3 studies are considered more environmentally realistic but demand greater resources and can be more difficult to interpret. In fact, the high uncertainty and variability of ecotoxicological data under semi-field and field tests may have contributed to some failures in detecting the risks associated with pesticides, such as neonicotinoids [START_REF] Franklin | Moving beyond honeybee-centric pesticide risk assessments to protect all pollinators[END_REF]Raine 2019, Sgolastra et al. 2020). However, such issues could be partly resolved thanks to the recent technological advances in the monitoring of honeybee behaviors [START_REF] Bromenshenk | Bees as Biosensors: Chemosensory Ability, Honey Bee Monitoring Systems, and Emergent Sensor Technologies Derived from the Pollinator Syndrome[END_REF][START_REF] Meikle | Application of continuous monitoring of honeybee colonies[END_REF][START_REF] Marchal | Automated monitoring of bee behaviour using connected hives: Towards a computational apidology[END_REF]. Indeed, by performing an in situ and real-time monitoring of colony daily flight activity, we were able to determine i) the background activity and consecutive homing rates, and then ii) to what extent they were affected by exposure to the neurotoxic insecticide sulfoxaflor for identifying risks to colonies.

We first assessed the effect of sulfoxaflor on the colony daily flight activity. Only control colonies, which were used as an environmental control, exhibited an increase in the daily flight activity between the pre-and post-exposure periods. This phenomenon might result from colony growth and/or a higher resource need over the course of the experiment. However, we did not observe any increase in the number of adult bees or brood production between these two periods. This increase in colony activity might rather be a direct consequence of sugar-syrup feeding, given that forager bees have high sugar requirements for performing foraging flights [START_REF] Rodney | Dietary requirements of individual nectar foragers, and colony-level pollen and nectar consumption: a review to support pesticide exposure assessment for honey bees[END_REF], and providing sugar syrup to a colony generally may affect its foraging efforts [START_REF] Free | The Behaviour of Honeybee Foragers when their Colonies are Fed Sugar Syrup[END_REF]. While some monthly tendencies in the increase of activity were observed for colonies exposed to the lower concentration of sulfoxaflor, the magnitude of effects was not significant. This suggests that at this concentration, sulfoxaflor prevented colonies from having the same dynamic as control colonies, and therefore triggered some minor but sublethal effects on colony activity. A stronger effect was however found at the higher sulfoxaflor concentration, with a reduction by half of the colony daily flight activity, likely due to the impairment of locomotion and foraging behaviors as previously described with neonicotinoids (Schneider et al. 2012, Alkassab and[START_REF] Alkassab | Assessment of acute sublethal effects of clothianidin on motor function of honeybee workers using video-tracking analysis[END_REF]. This confirms a recent study, which found lower activity in colonies fed for 21 days with sugar solutions contaminated by the formulated product Closer ® (active ingredient: sulfoxaflor, 0.0003 µg/ml) [START_REF] El-Din | Chronic exposure to a field-realistic concentration of Closer® SC (24% sulfoxaflor) insecticide impacted the growth and foraging activity of honey bee colonies[END_REF]. However, no change in flight activity was reported when colonies were not directly exposed to the pesticide- [START_REF] Tamburini | Fungicide and insecticide exposure adversely impacts bumblebees and pollination services under semi-field conditions[END_REF]. In this latter study, floral resources were treated with the formulated product at the recommended maximum rate for spray applications (i.e. 0.4 L/ha), and effects started to be monitored 6 days after the application. This lack of effect might therefore reflect a lower level of exposure and corresponds to our lower concentration treatment. Another difference in the experimental setup was that flight activity was estimated by counting the number of bees entering the hive within one minute each day, which likely gives a partial estimate of colony activity since flight activity is strongly governed by environmental factors and can exhibit daily changes [START_REF] Winston | THe Biology of the Honey Bee[END_REF].

The colony-level assessment of bee losses is a key parameter used for evaluating the magnitude of effects due to pesticide exposure and setting protection goals for honeybee colonies [START_REF] Efsa | EFSA Guidance Document on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees)[END_REF][START_REF] Efsa | Review of the evidence on bee background mortality[END_REF]. Indeed, colony demographics and dynamics, like for any population, are strongly governed by the death rate of individuals. By monitoring the colony homing flight rates of all worker bees, we identified a bee loss rate of 2.77 ± 3.18 % for control colonies and of 1.90 ± 2.99 % for colonies exposed to the lower concentration of sulfoxaflor. These values are lower but rather in accordance with a systematic review of the literature, which reported median values of daily background mortality rates of 3.5-4% for all honeybee workers (covering all behavioral roles; dataset based on visual and RFID records); foragers presenting the highest daily mortality rates, with median values at around 10-12% [START_REF] Efsa | Review of the evidence on bee background mortality[END_REF]). In addition, we observed a monthly change in the overall mortality rates, with higher losses in May, June, and July as compared to April. Despite minor changes in colony-level activity, this change may be a consequence of a higher individual foraging activity (e.g. longer foraging trips) during the spring since the more time bees spend foraging, the lower their survival probability is [START_REF] Prado | Honeybee lifespan: the critical role of pre-foraging stage[END_REF].

We then detected a significant decrease in the homing flight rates upon exposure to the higher concentration of sulfoxaflor, while no effect was found in control and colonies exposed to the lower pesticide concentration. Several studies have shown that the effects of pesticides could be modulated by others factors such as the quality of nutrition and co-exposure to pathogens or others pesticides [START_REF] Doublet | Bees under stress: sublethal doses of a neonicotinoid pesticide and pathogens interact to elevate honey bee mortality across the life cycle: Pesticide-pathogen interactions in honey bees[END_REF][START_REF] St | Interactions between pesticides and pathogen susceptibility in honey bees[END_REF][START_REF] Tosi | Lethal and sublethal synergistic effects of a new systemic pesticide, flupyradifurone (Sivanto ® ), on honeybees[END_REF], Barascou et al. 2021b[START_REF] Siviter | Agrochemicals interact synergistically to increase bee mortality[END_REF][START_REF] Castle | High nutritional status promotes vitality of honey bees and mitigates negative effects of pesticides[END_REF]. However, the decrease in the homing flight rates was highly consistent across each replicate (month, Fig . 4), indicating a robust effect of the higher concentration of sulfoxaflor. Interestingly, the effect on bee mortality, as for the flight activity, was restricted to the period of exposure, which might be explained by the relatively rapid elimination of sulfoxaflor in honeybees (Barascou et al. 2021b[START_REF] Barascou | Pesticide risk assessment: honeybee workers are not all equal regarding the risk posed by exposure to pesticides[END_REF]. Such an increase in bee mortality is consistent with previous semi-field studies, which showed adverse effects of sulfoxaflor on bee mortality after two spray applications of a crop (residues in flowers ranging from 5 mg/kg on the first day of application to 0.1 mg/kg 7 days after application) [START_REF] Cheng | A semi-field study to evaluate effects of sulfoxaflor on honey bee (Apis mellifera)[END_REF]). In addition, if no effect of sulfoxaflor on learning and memory has been reported at doses ranging from 0.024 to 2.5 ng [START_REF] Siviter | No evidence for negative impacts of acute sulfoxaflor exposure on bee olfactory conditioning or working memory[END_REF]), a recent study showed that honeybees fed with 26 ng of sulfoxaflor exhibited poor homing flight abilities: only 28% of them successfully returned to the colony compared to 75% for control bees [START_REF] Capela | Sub-lethal doses of Sulfoxaflor impair honey bee homing ability[END_REF]. We could therefore reasonably assume that the higher concentration of sulfoxaflor impaired the homing ability as well as their flight or foraging capacities as often observed with neonicotinoids [START_REF] Bortolotti | Effects of sub-lethal imidacloprid doses on the homing rate and foraging activity of honey bees[END_REF][START_REF] Matsumoto | Reduction in homing flights in the honey bee Apis mellifera after a sublethal dose of neonicotinoid insecticides[END_REF][START_REF] Ma | Impact of acute oral exposure to thiamethoxam on the homing, flight, learning acquisition and short-term retention of Apis cerana[END_REF][START_REF] Monchanin | Hazard of a neonicotinoid insecticide on the homing flight of the honeybee depends on climatic conditions and Varroa infestation[END_REF].

In a 10-day chronic toxicity experiment performed with honeybees reared in cages, we previously found no lethal effects of sulfoxaflor at a concentration of 0.02 µg/ml, while at 2.35 µg/ml bee survival significantly decreased (Barascou et al. 2021b). A preliminary experiment also showed that 0.1 µg/ml of sulfoxaflor was not toxic to bees (unpublished data). Altogether, these studies suggest a similar chronic toxicity in Tier 1 and Tier 2 toxicity tests. One difference might be the fold change in bee mortality given that in laboratory conditions the hazard ratio for bees exposed to the higher sulfoxaflor concentration was close to five (Barascou et al. 2021b), while in our current experiment the same concentration triggers a 10-fold increase in bee losses. However, if mortality rates (for all worker bees) upon exposure to the higher sulfoxaflor concentration rose to 21.8 % within a day, it rarely exceeded 13 % (8 times on 50 records). This is in contradiction with a recent study suggesting that daily loss rates less than 13 % could be classified as normal [START_REF] Ngo | Honey Bee Colony Population Daily Loss Rate Forecasting and an Early Warning Method Using Temporal Convolutional Networks[END_REF]. Besides a few exceptions, our data suggest that mortality rates below 5 % could rather be considered as normal, which is in line with the background mortality rates of 3.5-4% (for all honeybee workers) identified from a large dataset including studies carried out in different countries and environmental conditions [START_REF] Efsa | Review of the evidence on bee background mortality[END_REF]. Finally, it is interesting to note that in some cases (days), the homing flight rates were positive indicating a gain of bees for the colony. This was especially noticed during exposure to the higher concentration of sulfoxaflor in the month of April. Such rates were quite high but were associated with very low flight activity, which shows that the colony gained very few bees. This might be due to some drifting between colonies or to some bees that stayed outside overnight and entered the hive the next day.

Despite an increase in bee mortality, colony size was not impacted in the short-term. This could be explained by the lack of precision of the method used to evaluate the number of bees, since bees foraging in the field could not be included in the colony assessment. However, a significant decrease in the number of capped brood cells was found 10 days post-exposure in colonies exposed to 2.36 µg/ml of sulfoxaflor. Since the capped brood stage lasts between 11 and 12 days, most of these individuals were in the larval stage during exposure to sulfoxaflor. The impact of sulfoxaflor might therefore be an indirect consequence of reduced foraging efforts (lower flight activity combined with a higher bee loss) and therefore less food resources for larval nutrition. A reduction in the storage of pollen, which is used by nurse bees to provision larvae with jelly, was notably found after 21 days of exposure to sulfoxaflor [START_REF] El-Din | Chronic exposure to a field-realistic concentration of Closer® SC (24% sulfoxaflor) insecticide impacted the growth and foraging activity of honey bee colonies[END_REF]. Although not significant, we could observe a similar trend with some colonies having no pollen storage at the end of the treatment with the higher sulfoxaflor concentration. Finally, a non-mutually exclusive hypothesis would be a direct effect of sulfoxaflor on brood development due to the role of acetylcholine and its receptor on larval development [START_REF] Grünewald | Acetylcholine and Its Receptors in Honeybees: Involvement in Development and Impairments by Neonicotinoids[END_REF].

In order to have natural variations in colony state, we made the choice to not artificially homogenize their population size but rather to perform a random assignment of colonies to the different treatment groups. Although, there was no difference in colony state among these groups, colonies exposed to the higher sulfoxaflor concentration tended to be initially smaller than the others. This was not associated with differences in the colony flight activity and daily bee losses, but we cannot exclude a reinforcement of pesticide effects due to a potentially smaller population, which will need to be confirmed by experimentally testing pesticide effects according to colony size.

Lastly, to determine whether the specific protection goals (SPGs) for honeybees were satisfied for the higher sulfoxaflor concentration, we compared the observed fold changes in daily bee losses to the theoretical trigger values associated with the SPG of 7% colony-size reduction. Such fold-changes were often found to be above the SPG and therefore a risk for colonies. Some of them could not be determined due to positive homing flight rates before exposure to the pesticide, but the range of negative values during exposure to sulfoxaflor reflected even larger fold-changes. The risk posed by the bee loss rates was somewhat confirmed by the winter loss of three colonies out of the five. In fact, by using model simulations of colony dynamics, [START_REF] Rumkee | Predicting Honeybee Colony Failure: Using the BEEHAVE Model to Simulate Colony Responses to Pesticides[END_REF] found that an increase in adult bee loss rates had a much greater impact on colony survival than mortality of bee larvae or reduction in egg-laying rate.

In addition, bees might have stored some contaminated syrup, thereby prolonging the exposure and causing sublethal, rather than lethal, effects in the long-term, as evidenced by the absence of increased mortality once the exposure was over. For instance, a reduction in pollen foraging activity has often been described upon exposure to pesticides in honeybees and bumblebees [START_REF] Gill | Combined pesticide exposure severely affects individual-and colony-level traits in bees[END_REF][START_REF] Feltham | Field realistic doses of pesticide imidacloprid reduce bumblebee pollen foraging efficiency[END_REF][START_REF] Prado | Exposure to pollen-bound pesticide mixtures induces longer-lived but less efficient honey bees[END_REF], which might lead to carryover effects on colony survival [START_REF] Requier | The carry-over effects of pollen shortage decrease the survival of honeybee colonies in farmlands[END_REF]. The fact that only colonies exposed in the spring died over the winter, but not the ones exposed later in July and September, tends to support this hypothesis of carryover effects. Furthermore, a significant loss of foragers can lead to the early recruitment of nurse bees for foraging, and such precocious foragers were found to be less efficient than normal-age foragers; they have a lower lifespan, make fewer foraging trips and are much more likely to die during their first flights outside the hive [START_REF] Barron | Death of the bee hive: understanding the failure of an insect society[END_REF].

Although chronic exposure to sulfoxaflor might be an unrealistic scenario for honeybee colonies, our experiment with bee counters showed that, real-time and longitudinal data on bee mortality, combined with threshold values indicating which fold-changes in mortality rates represent a risk for colonies, can improve pesticide risk assessment under field conditions. Indeed, while we did not find any major effects on the colony demographic state, bee loss rates highlighted a risk for colonies. In addition, bee counters can significantly reduce the measurement complexity of mortality rates in the field and are relatively time-effective for use, which is essential for the regulatory assessments of pesticides. However, further efforts are needed to assess and improve bee counters precision for risk assessment (i.e. lower the error rates) and avoid erroneous interpretation [START_REF] Borlinghaus | Honey bee counter evaluation -Introducing a novel protocol for measuring daily loss accuracy[END_REF][START_REF] Odemer | Approaches, challenges and recent advances in automated bee counting devices: A review[END_REF]. Last but not least, since the in situ monitoring of daily bee losses can be connected to consequences for colonies (the ultimate protection goal), this measured endpoint has a strong ecological relevance in pesticide risk assessment. All of these criteria are important for test methods in chemical regulation. However, the trigger values for protecting bee colonies have been determined via in silico approaches (EFSA 2013), and therefore, future efforts should be made to empirically link mortality rates to effects on colonies and better specify the mortality rates that pose a risk for colonies.

Supporting Information

Parameters

Figure 1 .

 1 Figure 1. Video-based bee counters in the field measuring the activity of a honeybee colony. (A)The video-based bee counter with bee channels and the camera is at the entrance of the hive. The camera recording bee activity is directly connected to a computer and a program analyzing in real time bee traffic. (B) Number of bees exiting and entering the hive over a day during the experiment. During this day, the colony lost 849 bees, corresponding to a loss rate of 3.3 %.

Figure 2 .

 2 Figure 2. Daily flight activity in response to sulfoxaflor treatments. The experiment was repeated 5 times between April and September. Each plot represents the daily activity of a colony exposed to sulfoxaflor or not. The flight activity was recorded before, during and after exposure to sulfoxaflor. Each colored dot represents a day of flight activity measurement. Box plots show the 1st and 3rd interquartile range with a line denoting the median. Whiskers encompass 90% of the individuals.

Figure 3 .

 3 Figure 3. Effect size (hedges's g) and 95% confidence intervals of treatment effects on the daily (A) flight activity and (B) homing flight rates between the different periods of exposure. Asterisks indicate significant effect of treatment.

Figure 4 .

 4 Figure 4. Daily homing flight rates in response to sulfoxaflor treatments. The homing flight rate was calculated as (Number of entries -Number of exits) / Number of exits) x 100. Values below and above the dashed line represent a loss and gain of bees, respectively. The experiment was repeated 5 times between April and September. Each plot represents the daily homing flight rates in a colony exposed to sulfoxaflor or not. Data were recorded before, during and after exposure to sulfoxaflor. Each colored dot represents a day of homing flight rate measurement. Box plots show the 1st and 3rd interquartile range with a line denoting the median. Whiskers encompass 90% of the individuals.

Acknowledgments

This project received funding from the European Horizon 2020 research and innovation programs under grant agreements no. 773921 (POSHBEE) (LB, DS, YLC and CA). UG was supported by the European Horizon 2020 research and innovation program under grant agreement no. 817622 (B-GOOD).

Supplementary figures detailing colony monitoring upon pesticide exposure (Figure S1,S2 and S3).

Supplementary tables detailing statistical analyses of pesticide effects (Table S1, S4 and S5) and effect size of pesticide exposure (Table S2 andS3).

Author

Competing interests

The authors declare that they have no competing interests.


Christophe Reffay
email: christophe.reffay@univ-fcomte.fr

Marie-Laure Betbeder
email: marie-laure.betbeder@univ-fcomte.fr

Improving CSCL indicators by sharing multimodal teaching and learning Corpora

Keywords: e-research, sharing corpora, interaction analysis, CSCL indicators

We point out the need for CSCL community to reach large scale validation for its results by addressing the lack of sharing of interaction indicators and data. The main goal of the Mulce 1 project is a definition for teaching and learning corpora (especially for interaction tracks), a technical format to organize data and a platform for corpus sharing, providing analysis and visualization tools.

MOTIVATION

During the last decade a lot of technical propositions for indicators of social or cognitive process monitoring have been made. The very most part of these indicators (including ours) are designed in a given context, where they show some interesting properties and even promise usefulness for the various actors involved in CSCL real situations. Unfortunately, these indicators often stay in the researchers' hands and are rarely used by real actors of the situation. As far as we know, none of them have been validated or at least evaluated by real/concrete actors. The need for validation of these indicators, at least in a given context, becomes crucial if we want this domain to contribute to the distance learning area of the real world. These indicators are also rarely reused in other situations or contexts. With others [START_REF] Rourke | Methodological Issues in the Content Analysis of Computer Conference Transcripts[END_REF] we think that replicability, reliability and objectivity need to be improved in our work. A path on this direction would be sharing data so that they can be analyzed and compared. Therefore we propose a formalism to describe contextualized interaction data.

PROPOSAL

Our proposal consists in (1) a formalism to describe a learning and teaching corpus and (2) a platform for corpus sharing [START_REF] Reffay | Contribution à la structuration de corpus d'apprentissage pour un meilleur partage en recherche[END_REF]. The formalism defines the information which can be contained in a corpus and the structure of the data. Through the platform, researchers can share their corpora with the community and access the data shared by other members of the community.

Proposal 1: Learning and teaching corpus formalism

Building and recording interaction in an online training

We define a Learning & Teaching Corpus as a structured entity containing all the elements resulting from an online learning situation, whose context is described by an educational scenario and a research protocol. The core data collection includes all the interaction data, the training actors' production, and the tracks, resulting from the actors' actions in the learning environment and stored according to the research protocol. In order to be sharable, and to respect actor privacy, these data should be anonymised and a license for its use be provided in the corpus. A derived analysis can be linked to the set of data actually considered, used or computerized for this analysis. The definition of a Learning & Teaching Corpus as a whole entity comes from the need of explicit links, between interaction data, context and analyses. This explicit context is crucial for an external researcher to interpret the data and to perform its own analyses.

Corpus composition and structure

The main components of a learning corpus are:

-The Instantiation component, the heart of the corpus, which includes all the interaction data, production of the on-line training actors, completed by some system logs as well as information characterizing actors' profile.

-The Context concerns the educational scenario and the research protocol (optional element).

-The License component specifies both corpus publisher's (editor) and users' rights and the ethical elements toward the actors of the training.

-The Analysis component contains global or partial analysis of the corpus as well as possible transcriptions.

The Mulce structure aims at linking the components of the corpus. For example a researcher, while reading a chat session (which belongs to the instantiation component), may have to read the objectives of the activity (which belongs to the pedagogical context).

Instantiation formalism: Actors and environment description

If learning design is a general description of activities involving generic roles and environments, the instantiation phase is concerned by real actors and concrete platforms and tools. It consists in describing (1) the actors (identifier, learning profile, linguistical and cultural aspects, etc.), (2) the technological environments (name, version, URL, etc.), (3) the tools used during the learning activity (technical components actually used) and ( 4) the groups and their members.

Instantiation formalism: Workspace concept

The hierarchical structure of the learning stage (potentially spread in parallel groups) is captured in the Workspaces element, i.e.: a sequence of "workspace" elements (see figure 1). A workspace is generally linked to a learning activity (of the pedagogical scenario). It encompasses all the events observed during this activity, in the tool spaces provided for this activity, for a given (instantiated) group of actors. A workspace description includes its members (references to the actors registered in the learning activity), starting and ending dates, the provided tools and the traces of interaction that occurred in these tools. In order to fit the hierarchical structure of learning and support activities, a workspace can recursively contain one or more workspace elements. The lists of places, sessions, descriptors, contributors and sources defined in the workspaces element can be referenced by workspace, contribution, or act elements. For example, descriptors may list identified categories so that each act of the acts element list could refer to one or more of these categories. This principle enables to browse the interaction data in many different ways, independent to the concrete storage organization in the XML document. Our specification describes communication tools and their features with a great level of precision. The corpus builder can specialize/particularize the schema (i.e., restrict it) to fit the specific tools and features proposed to the learners in a specific learning environment. In the meantime, if a tool cannot be described with the specification, one can augment the schema by adding new elements, in order to take into account the tool's specificities. Both of these mechanisms offer two ways, the specification can be extended to fit the analysis needs. Moreover, recursive workspace description enables the corpus descriptor to choose the grain at which he needs to describe the environment. Thus, a workspace can be used to describe a complete curriculum, a semester, a module, a single activity or a work session (a concept generally related to synchronous learning activities). The workspace concept represents the space and time location where we can find interaction with specific tools.

Interaction traces are stored according to the act's structure. All actions, wherever they come from are described by an act element. Depending on its nature, an act is described by different elements, mandatory or optional like a reference to its author identifier, a beginning and an ending date, an act type and an actual content (or value). For example, a chat act can have the type in/out (actor entering/leaving), it may contain a message, can be addressed to all the workspace members or to a specific one (particularly if it is a private message). This XML Schema defines the storage structure for many act types, e.g.: forum message, chat act, transcribed voice act, and more. The complete Mulce schema2 for the instanciation component (structured information data) is available online.

Proposal 2: a Platform for corpus sharing

Sharing corpora

The deposit of a corpus consists in declaring it, describing it by means of general metadata, and uploading its components (described previously). Each component has a specific formalism. These can either be standard formalism such as Learning design (IMS-LD 2003) (used for the context components: educational scenario and research protocol), or the specific formalism described here above for structured interaction data. If these recommended formalisms are used to describe the various components of the uploaded corpus, the researchers will fully benefit from all the tools provided one the Mulce platform to navigate and analyse the entire corpus. Otherwise, the corpus will be downloadable as is by other researchers. Each component is described by its specific metadata. On the Mulce platform, these metadata can be used by a researcher to find corpora that fit particular constraints. For example the researcher can select the corpora concerning its own research interests, either in term of used tools, of targeted audience or learning domain.

Browsing and analyzing corpora

The second part of the platform proposes the visualization, the navigation and the analysis of structured interaction data. We distinguish two parts: the navigation (or visualization) aspect, and the analysis aspects of corpora. The interest of the navigation aspect is twofold. Firstly, the corpus becomes independent to the (evolving) software, where originally interaction took place. This is a major benefit for data longevity and reusability. Secondly, because of the main attention paid on the context of interaction in the Mulce project, the interaction navigator makes explicit links between interactions and their surrounded context. Finally, the researcher can select a part of a corpus by means of requests. He can, for example, select all the interactions of an actor using a specific communication tool. For each of the interactions he can access to the prescribed educational activity. The analysis aspect of corpora concerns the use of tools based on the instantiation component formalism. The XML format being defined, we hope that different analysis tools (including indicator synthesis), coming from various teams, will have a version that can operate on the Mulce structure. The tools proposed on the platform will originate from our research team [START_REF] Betbeder | Recherche de patterns dans un corpus d'actions multimodales[END_REF] or from partnership. For example we have two running collaborations: the Calico3 project, Tatiana [START_REF] Dyke | Analysing face to face computer-mediated interactions[END_REF]. The Calico project aims at proposing different visualization and analysis tools, specialized on discussion forums. Tatiana includes a navigator, a replayer and an annotator. The replayer functionality synchronizes the various data sources. We are currently adapting its XML schema to fit ours and extend its visualization functionalities to other communication tools. We are interested in other collaborations aiming at providing other analysis tools.

To conclude, this work suggests a way to access, share, analyze and visualize learning and teaching corpora. To make this technically possible, we propose (1) a formalism which defines, describes and organizes data provided by on-line training and (2) a platform to navigate through, visualize and analyze corpora (currently being integrated) through of a variety of tools.

Figure 1 .

 1 Figure 1. Extract of the XML Schema: workspaces and workspace elements.

The Mulce project, leaded by T. Chanier, is supported by the French National Research Agency. http://mulce.univ-fcomte.fr/axescient.htm#eng

http://mulce.univ-fcomte.fr/metadata/mce-schemas/mce_sid.xsd

Calico is a french research project described here: http://calico.inrp.fr/CALICO