PhD Didier Morin
email: didier.morin@inserm.fr

Romain Long

Mathieu Panel

Lydie Laure

Adela Taranu

Cindy Gueguen

Sandrine Pons

Valerio Leoni

Claudio Caccia

Stephen F Vatner

Dorothy E Vatner

Hongyu Qiu

Christophe Depre

Alain Berdeaux

Bijan Ghaleh

Hsp22 overexpression induces myocardial hypertrophy, senescence and reduced life span through enhanced oxidative stress

Keywords: Hsp22 overexpression, oxidative stress, myocardial hypertrophy, senescence, life GSSG, Oxidized glutathione, GSH, reduced glutathione, H2O2, hydrogen peroxide, LV, left ventricular, ROS, reactive oxygen species, TG, transgenic, TSPO, translocator protein, VDAC, voltage-dependent anion channel, WT, wild type Hsp22 overexpression, oxidative stress, myocardial hypertrophy, senescence, life span

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

The stress-responsive Heat shock protein 22 (Hsp22) is highly expressed in the heart and skeletal muscle [START_REF] Acunzo | Small heat shock proteins HSP27 (HspB1), αBcrystallin (HspB5) and HSP22 (HspB8) as regulators of cell death[END_REF][START_REF] Depre | H11 Kinase is a novel mediator of myocardial hypertrophy in vivo[END_REF] and is involved in the response to the two most common forms of cardiac cell stress, i.e. pressure overload and hypoxia. Its cardiac expression is increased in various models of cardiac hypertrophy and ischemia, including human patients, underlying a possible participation in the mechanisms of cardiac cell growth and cell survival [START_REF] Depre | Program of cell survival underlying human and experimental hibernating myocardium[END_REF]. It was previously shown that a transgenic (TG) mouse model with cardiac-specific overexpression of Hsp22, reproducing the increased expression found in patients, is characterized by a pattern of chronic and stable myocardial hypertrophy [START_REF] Acunzo | Small heat shock proteins HSP27 (HspB1), αBcrystallin (HspB5) and HSP22 (HspB8) as regulators of cell death[END_REF][START_REF] Hedhli | Proteasome activation during cardiac hypertrophy by the chaperone H11 Kinase/Hsp22[END_REF]. Mitochondrial Hsp22 was also shown to regulate lifespan in Drosophila [START_REF] Morrow | Decreased lifespan in the absence of expression of the mitochondrial small heat shock protein Hsp22 in Drosophila[END_REF].

We also previously showed that Hsp22 increases oxidative phosphorylation [START_REF] Qiu | H11 kinase/heat shock protein 22 deletion impairs both nuclear and mitochondrial functions of STAT3 and accelerates the transition into heart failure on cardiac overload[END_REF]. This was associated with an enhancement of the production of mitochondrial reactive oxygen species (ROS; [START_REF] Laure | Cardiac H11 kinase/Hsp22 stimulates oxidative phosphorylation and modulates mitochondrial reactive oxygen species production: Involvement of a nitric oxide-dependent mechanism[END_REF]). ROS have emerged as key mediators in cardiac pathophysiology, playing a key role stimulating pathways leading to cardiac hypertrophy [START_REF] Maulik | Oxidative stress and cardiac hypertrophy: a review[END_REF], senescence and reduced lifespan [START_REF] Gilca | The oxidative hypothesis of senescence[END_REF].

Accordingly, the goal of this investigation was to determine whether Hsp22 overexpression reduced longevity and age-related myocardial dysfunction, as well as myocardial hypertrophy through ROS production and oxidative stress. To accomplish these goals, we first compared the life span of wild type (WT) and TG mice and characterized their ROS production and oxidative stress. Then we compared these responses after chronic treatment with the whole cell antioxidant, Tempol, which is a superoxide dismutase mimetic agent that has been shown to reduce oxidative stress in mice [START_REF] Ritchie | The antioxidant tempol inhibits cardiac hypertrophy in the insulin-resistant GLUT4-deficient mouse in vivo[END_REF].

Methods

All animal procedures used in this study were in strict accordance with the European Community Council Directive 2010/63/EU and recommendations of the French Ministère de l'Agriculture. All experimental procedures were approved by the Animal Ethics Committee Afssa/ENVA/Universite Paris Est Creteil (approval number 09/10/12-5).

Animal model

We used TG mice (male FVB/NRj) expressing the coding sequence of human Hsp22 and a C-terminal hemagglutinin tag as previously described [START_REF] Depre | H11 Kinase is a novel mediator of myocardial hypertrophy in vivo[END_REF] and their WT littermates. This overexpression of Hsp22 was cardiac-specific. When indicated, mice were treated with the antioxidant 4-hydroxy-2,2,6,6-tetramethylpiperidin-1-oxyl (Tempol, 100 mg/kg, po) administered daily for 7-9 weeks from the weaning to the day of sacrifice.

Additional WT and TG mice were bred in the same conditions until their death and survival curves were plotted. The mean life span was calculated using Kaplan-Meier analysis. To establish the survival curve with Tempol treatment, mice were fed with Tempol (100 mg/kg/day) or with vehicle (water) during 27 weeks (or until the natural death).

Isolation of cardiac mitochondria, mitoplasts and outer mitochondrial membranes

Mitochondria were isolated by differential centrifugation as described previously [START_REF] Laure | Cardiac H11 kinase/Hsp22 stimulates oxidative phosphorylation and modulates mitochondrial reactive oxygen species production: Involvement of a nitric oxide-dependent mechanism[END_REF]. Left ventricular (LV) tissue was homogenized in a buffer (220 mM mannitol, 70 mM sucrose, 10 mM HEPES, 2 mM ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), pH 7.4 at 4°C) supplemented with 0.25% bovine serum albumin, using a Potter-Elvehjem glass homogenizer in a final volume of 10 ml. The homogenate was filtered through cheese cloth and centrifuged at 1000g for 5 min at 4°C. The pellet was discarded and the supernatant was centrifuged at 10000g for 10 min at 4°C. The mitochondrial pellet was resuspended in 50 µl of homogenization buffer without EGTA and bovine serum albumin. The final supernatant was designated as the "cytosolic/membranes fraction" and was used to measure NADPH and xanthine oxidase activities. "Cytosolic/membranes fractions" from livers and kidneys were prepared according to the same protocol.

For western blot experiments, mitochondria were purified on a Percoll® gradient [START_REF] Laure | Cardiac H11 kinase/Hsp22 stimulates oxidative phosphorylation and modulates mitochondrial reactive oxygen species production: Involvement of a nitric oxide-dependent mechanism[END_REF][START_REF] Townsend | Urocortin prevents mitochondrial permeability transition in response to reperfusion injury indirectly by reducing oxidative stress[END_REF]. The submitochondrial localization of the translocator protein (TSPO) was determined by use of a digitonin solubilization method to separate the outer mitochondrial membranes from the mitoplats as described previously [START_REF] Anholt | The peripheral-type benzodiazepine receptor. Localization to the mitochondrial outer membrane[END_REF]. Freshly prepared purified mitochondria were incubated in the presence of digitonin (0.8 mg/mg protein) for 15 minutes. At the end of this incubation period, the suspension was centrifuged for 10 minutes at 15000 g. The pellet was resuspended in the following buffer: 220 mM mannitol, 70 mM sucrose, 10 mM Hepes, 0.1mM EGTA, pH 7.4 at 4 °C and designated the "mitoplasts". The supernatant contained the outer mitochondrial membranes released by the digitonin treatment.

Determination of mitochondrial reactive oxygen species production

Mitochondrial ROS generation was assessed by measuring the rate of hydrogen peroxide (H2O2) production through oxidation of Amplex red to fluorescent resorufin, coupled to the enzymatic reduction of H2O2 by horseradish peroxidase as described previously [START_REF] Lo Iacono | A carbon monoxide-releasing molecule (CORM-3) uncouples mitochondrial respiration and modulates the production of reactive oxygen species[END_REF].

Superoxide anion generated in mitochondria was converted endogenously to H2O2 and then measured by the assay. Briefly, amplex red (10 µM) and horseradish peroxidase (1 U/ml) were added to isolated mitochondria (0.2 mg protein) in a respiration buffer containing 100 mM KCl, 50 mM sucrose, 10 mM HEPES, 5 mM KH2PO4, pH 7.4 at 30°C. The reaction was initiated by addition of the respiratory substrates (pyruvate/malate 5/5 mM or succinate 5 mM + rotenone 2 µM). The subsequent increase in fluorescence was monitored over time using a fluorescence spectrometer (Perkin-Elmer SA LS 50B, excitation wavelength 563 nm; emission wavelength 587 nm).

Western blot analysis

Proteins were denatured by boiling, resolved on SDS-PAGE 10% polyacrylamide gels, and transferred to polyvinylidenedifluoride membranes. Membranes were blocked with 5% nonfat dry milk in a Tris buffer (Tris 10 mM, NaCl 100 mM, pH 7.5) containing 0.1%Tween 20 for 1 h at room temperature. Subsequently, membranes were exposed for 12 h at 4°C to either anti-Nox2/gp91phox (1:2000; Abcam), anti-Akt (1:2000; Cell Signaling), anti-pAkt (Ser 473, 1:2000; Cell Signaling), anti-Hsp22 (1:500 Abcam), anti-TSPO (1:2000; Abcam), anti-Voltage Dependent Anion Channel (VDAC, 1:2000; Cell Signaling) or anti-actin (1:2000; Cell Signaling). After incubation with goat anti-mouse (Santa Cruz Biotechnology) or goat antirabbit (Cell Signaling) as a secondary antibody at 1:5000, blots were revealed by Pierce ECL Western Blotting Substrate (Thermo Scientific) in a G:BOX chemi XT4 (Syngene). Bands were analyzed by densitometry with Genesys software (Syngene).

Quantitative RT-PCR

LV tissues were sampled from TG and WT mice. Total RNA was isolated from mouse LV tissues using the RNeasy Fibrous Tissue Mini Kit (Qiagen, Courtaboeuf, France). cDNA was synthesized from 1 µg of total RNA using the SuperScript first-strand synthesis system for the RT-PCR kit (Invitrogen, CergyPontoise, France) and random oligonucleotides. Expression of genes encoding p53, p16, p21, p19 was monitored by a real-time qRT-PCR method using an Applied Biosystems 7000 real-time PCR system. The ubiquitous 18S RNA was used to normalize the data across samples. Its expression was monitored by SYBRGreen incorporation.

Telomere length

After genomic DNA extraction using DNA Isolation Kit for Cells and Tissues (Roche), the measurement of telomere length was carried out with the TeloTAGGG Telomere Lenght Assay kit (Roche, Meylan, France) in accordance with the manufacturer's instructions. Briefly, DNA was digested with specific enzymes and then DNA fragments were separated by electrophoresis and transferred by Southern blot. DNA was detected by hybridizing a specific digoxigenincoupled probe and anti-digoxigenin antibody coupled with alcalin phosphatase that had allowed chemiluminescent detection. Densitometry measure was used to determined telomere length.

Telomerase activity

Telomerase activity was analyzed with a TeloTAGGG Telomerase PCR ELISA PLUS kit (Roche diagnostic, Manheim, Germany), in accordance with the manufacturer's instructions. This was assessed in 3 successive steps: 1) protein extraction and dosage, 2) elongation/amplification and 3) detection with an enzyme-linked immunosorbent assay. The absorbance was measured at 450 nm and telomerase activity calculated.

β-galactosidase staining

Left ventricle slices were fixed with 2% formaldehyde and 0.2% glutaraldehyde for 15 min at room temperature. Then, slices were washed with phosphate buffer saline and stained in a titrated pH 6 solution containing 40 mM citric acid, 150 mM NaCl, 2 mM MgCl2, 5 mM potassium ferrocyanide, and 1 mg/mL 5-bromo-4-chloro-3-indolyl-beta-D-galactoside (X-Gal; QIAGEN, courtabeuf, France). The slices were then immersed in a 4% formaldehyde solution which fixes the staining obtained.

GSH/GSSG ratio

Samples (10 mg) of left cardiac ventricle, liver or kidney were homogenized in 1 ml of sulfosalicylic acid (2 %) by means of an Ultra Turrax homogenizer. The homogenate was centrifuged at 10000g for 5 minutes and the supernatant was separated in two parts to measure total and oxidized (GSSG) glutathione levels. GSSG and reduced glutathione (GSH) levels were determined using the enzymatic recycling method [START_REF] Rahman | Assay for quantitative determination of glutathione and glutathione disulfide levels using enzymatic recycling method[END_REF].

Enzymatic assays

NADPH oxidase activity was measured in cytosolic/membrane fractions from WT and TG mice as the apocynin inhibitable superoxide anion production induced by NADPH. Superoxide anion production was evaluated with nitrobluetetrazolium which is reduced by superoxide anion in formazan. Samples (1 mg/mL) were introduced in a phosphate buffer saline maintained at 30°C in the presence of NADPH (20 mM) and the appearance of formazan was followed spectrophotometrically (560 nm). Apocynin (1 mM) was added to evaluate specific NADPH oxidase formation of superoxide anion.

Xanthine oxidase activity was measured in the same cardiac fractions by monitoring the increase in absorbance resulting from the appearance of uric acid produced by xanthine oxidase.

Samples (2 mg/mL) were introduced in a phosphate buffer saline maintained at 30°C. The reaction was initiated by addition of xanthine (100 µM) and uric acid production was followed spectrophotometrically (290 nm). A blank was obtained in the same conditions but in absence of xanthine.

Determination of mitochondrial cholesterol and oxysterol levels

Mitochondria (5 mg protein/ml) were added to a chloroform-Triton X-100 1% mixture (v:v) and submitted to strong agitation for 1 minute. The samples were centrifuged at 8,000 g for 5 minutes at room temperature. The upper phases were discarded and the lower phases containing cholesterol and sterols in the presence of deuterium labeled internal standards underwent an alkaline hydrolysis with ethanolic KOH, separation by liquid to liquid extraction, evaporation under nitrogen stream and derivatisation with BSTFA-TMS 1% for 1 h at 60°C. Cholesterol and oxysterols were assayed in mitochondria extracts by isotope dilution-mass spectrometry as described previously [START_REF] Leoni | Metabolic consequences of mitochondrial coenzyme A deficiency in patients with PANK2 mutations[END_REF].

Statistical analysis

Results are presented as the mean ± sem for the number of samples indicated in the legends.

Statistical comparison was performed using the Student t-test. Two-way analysis of variance followed by Scheffe's post-test was used for multi-group comparison. The statistics for survival was determined by the Log-Rank Test. Statistical significance was set at p<0.05.

Results

Myocardial Hsp22 overexpression reduced life span and was associated with senescence markers in mice.

To assess the effect of myocardial Hsp22 overexpression on longevity, we examined the life span of WT and TG mice (Fig. 1A). Life span of TG mice was shortened as compared to WT with TG mice dying at a faster rate (Fig. 1A). The life span of TG mice did not exceed 60 weeks whereas 30 % of WT mice stayed alive at this age. The mean life span was 34.3±1.2 and 63.3±3.3 weeks for TG and WT mice, respectively. This reduction of life span in TG mice was associated with a reduction of left ventricular (LV) ejection fraction in one year old TG mice (49% vs 74% in WT, p<0.01;Fig. 1B) and significant LV hypertrophy which was already observed in 10-12 week old mice, as shown by the significant increase in LV /tibial length ratio (4.0±0.1 vs 6.3±0.2mg/mm in WT vs TG mice, respectively (n=13); Fig 1C).

At the cellular level, hearts from TG mice displayed ß-galactosidase positive cells, a wellknown marker of senescence. This marker was already present in the myocardium of young adult TG mice (10-12 week old) whereas it was not detectable in WT mice of the same age (Fig 2A). At the molecular level, senescent cells were characterized by an overexpression of cell cycle inhibiting proteins [START_REF] Bringold | Tumor suppressors and oncogenes in cellular senescence[END_REF].We observed that the cell cycle inhibitors p16 and p19 increased in the myocardium of young adult TG mice compared to WT mice, whereas expression of the transcription factor p53 and cyclin-dependent kinase inhibitor p21 were not modified(Fig. 2B).

The enhancement of p16 and p19 was rather specific to the myocardium as it was not found in the kidney (Fig. S1), confirming that the effect was related to Hsp22 overexpression. A typical feature of senescent cells is a decrease in telomere length. To confirm the senescent profile of the myocardium of TG mice, we compared the telomere length of the genomic DNA extracted from WT and TG mice. As shown in Figure 2C, we did not observe any significant variation in the length of telomeres between WT and TG mice. This could be due to the fact that the telomeres of the murine DNA are large, which makes it very difficult to detect the reduction of their size. Figure 2C also shows that myocardium from TG mice displayed a very high activity of telomerase as compared to WT mice.

Myocardial Hsp22 overexpression induced cardiac oxidative stress

To evaluate oxidative stress, we measured the concentrations of reduced GSH and GSSG in whole myocardial homogenates of 10-12 week old mice. While concentrations of GSH were unchanged in TG compared to WT mice (804±46 and 894±89 nmol/g tissue in WT and TG mice, respectively), the amount of GSSG increased in TG mice (489±17nmol/g tissue) compared to WT mice (307±17 nmol/g tissue; p<0.05). This resulted in a lower GSH/GSSG ratio in TG mice (Fig. 3A). Several sources of ROS are responsible for the enhancement of oxidative stress in TG mice. The first one is NADPH oxidase which is a major generator of ROS production in the cardiovascular system [START_REF] Griendling | NAD(P)H oxidase: role in cardiovascular biology and disease[END_REF]. The activity of the enzyme was strongly stimulated in TG mice (Fig. 3B) and this can be ascribed to an increase in the expression of the protein as demonstrated by immunoblotting of Nox2 (Fig. 3B). Similarly, the activity of xanthine oxidase, which produces hydrogen peroxide in the cell, was more important in TG than in WT mice (Fig. 3C).Such changes were selective to the myocardium as the significant increases in activities of NADPH and xanthine oxidase as well as GSSG/GSH ratio were not observed in other organs of TG mice (Fig. S2).The mitochondrial respiratory chain also contributes to the oxidative stress. Indeed, Fig. 3D confirms our results demonstrating that Hsp22 overexpression increases the net production of H2O2 from mitochondria in the basal state. This effect was related to the activation of complex I [START_REF] Laure | Cardiac H11 kinase/Hsp22 stimulates oxidative phosphorylation and modulates mitochondrial reactive oxygen species production: Involvement of a nitric oxide-dependent mechanism[END_REF] as ROS production increased when the respiratory chain was fed with pyruvate/malate but not with succinate in the presence of rotenone.

The translocator protein TSPO, another mitochondrial component emerging as a modulator of oxidative stress [START_REF] Batoko | Enigmatic Translocator protein (TSPO) and cellular stress regulation[END_REF][START_REF] Morin | Mitochondrial translocator protein (TSPO): From physiology to cardioprotection[END_REF], has been shown to interact with the voltage-dependent anion channel (VDAC) and to promote ROS production [START_REF] Gatliff | TSPO interacts with VDAC1 and triggers a ROS-mediated inhibition of mitochondrial quality control[END_REF]. In an attempt to better understand the mechanisms of the increased ROS production, we therefore measured the expression of TSPO in mitochondria issued from WT and TG mice. First, the expression was evaluated on purified mitochondria, mitoplasts and mitochondrial outer membranes. Immunoblotting analysis confirmed the presence of TSPO in the outer membrane of cardiac mitochondria and showed that TSPO expression was higher in 8 week old TG mice compared to WT mice (Fig. 4A). As shown in Fig. 4B, the ratio TSPO/VDAC remained similar in mitochondria issued from young mice (3 weeks) and then increased with age. As TSPO is considered as one of the proteins that facilitates the transport of cholesterol into mitochondria [START_REF] Papadopoulos | Translocator protein-mediated pharmacology of cholesterol transport and steroidogenesis[END_REF], we determined whether the overexpression of TSPO was associated with an accumulation of cholesterol and a subsequent increase in its oxidative derivatives oxysterols in cardiac TG mitochondria. As shown in Fig. 4C, the concentration of cholesterol and oxysterols remained similar in cardiac mitochondria of TG and WT mice.

Tempol reduced oxidative stress in TG mice

To clarify the potential role of the observed oxidative stress in the phenotype of TG mice, mice were treated from the weaning to the adulthood with Tempol (100 mg/kg/day), a membrane permeable and general antioxidant, that was shown to reduce the markers of oxidative stress in membrane, cytosol and mitochondrial compartments [START_REF] Kimura | Role of NAD(P)H oxidase-and mitochondria-derived reactive oxygen species in cardioprotection of ischemic reperfusion injury by angiotensin II[END_REF][START_REF] Mariappan | TNF-alpha-induced mitochondrial oxidative stress and cardiac dysfunction: restoration by superoxide dismutase mimetic Tempol[END_REF]. First, we verified that Tempol did not modify the expression of Hsp22 in 10-12 week old TG mice (Fig. S3). Then, we observed that Tempol had no effect on GSH and GSSG contents in WT mice but in TG mice, GSH was increased (from 895±89 in control to 1124±52 in treated TG mice; p<0.05) and GSSG was decreased (from 489±17 in control to 427±6 in treated TG mice; p<0.05). In these conditions, Tempol abolished the difference in GSH/GSSG ratio previously observed between WT and TG mice (Fig. 3A). This effect can be accounted for by the significant decrease in ROS production induced by NADPH (Fig. 3B), xanthine oxidase (Fig. 3C) and by the mitochondrial respiratory chain (Fig. 3D). As shown in Fig. 4D, this effect was not related to a decrease in TSPO/VDAC ratio which was not modified by Tempol.

Tempol treatment reduced myocardial hypertrophy and senescence markers and improved lifespan in mice.

As oxidative stress and ROS generated by NADPH oxidase play a key role in myocardial hypertrophy [START_REF] Maulik | Oxidative stress and cardiac hypertrophy: a review[END_REF], we investigated the effect of Tempol treatment on this parameter in 10-12 week old TG mice. Myocardial hypertrophy was significantly reduced in TG mice receiving Tempol, underlying for the first time the role of oxidative stress in this model (Fig. 5A) and no effect was observed in WT mice. Previous studies highlighted an increase in Akt phosphorylation in the hypertrophic effect mediated by Hsp22 [START_REF] Acunzo | Small heat shock proteins HSP27 (HspB1), αBcrystallin (HspB5) and HSP22 (HspB8) as regulators of cell death[END_REF][START_REF] Depre | H11 Kinase is a novel mediator of myocardial hypertrophy in vivo[END_REF]. Here, Tempol significantly blunted the increase in pAkt/Akt ratio (Fig. 5B).

Concomitantly, Tempol also lowered the aging markers that were shown to be increased in TG mice. Figure 5C shows that both p16 and p19 mRNA levels were reduced in TG mice after Tempol treatment, reaching the levels found in WT mice. This was associated with an abolition of the increased mortality caused by Hsp22 overexpression at 30 weeks, the life span of treated TG mice being similar to that of WT mice (Fig. 5D). This result demonstrates that oxidative stress is the mediator by which Hsp22 over-expression induces over-mortality in mice and importantly that this effect can be reversed by an antioxidant treatment.

Discussion

The present study demonstrates that Hsp22 overexpression leads to senescence, reduced lifespan and cardiac hypertrophy due to a significant augmentation of oxidative stress mediated by enhanced activities of major sources of ROS production, i.e., the mitochondrial respiratory chain, NADPH and xanthine oxidase. TSPO may also contribute to the oxidative stress. Indeed, Hsp22 overexpression enhanced TSPO expression, a property that has been shown to increase the accumulation of ROS, to limit the efficiency of mitochondrial autophagy [START_REF] Gatliff | TSPO interacts with VDAC1 and triggers a ROS-mediated inhibition of mitochondrial quality control[END_REF] suggesting that it participates in the hypertrophic response observed in TG mice [START_REF] Nishida | Autophagy during cardiac remodeling[END_REF]. The overexpression of TSPO could be linked to the Hsp22-dependent activation of the PKCε and STAT3 pathways [START_REF] Qiu | H11 kinase/heat shock protein 22 deletion impairs both nuclear and mitochondrial functions of STAT3 and accelerates the transition into heart failure on cardiac overload[END_REF][START_REF] Danan | Therapeutic potential of H11 kinase for the ischemic heart[END_REF] which have been described to drive tspo transcription and protein expression [START_REF] Batarseh | Protein kinase C epsilon regulation of translocator protein (18 kDa) Tspo gene expression is mediated through a MAPK pathway targeting STAT3 and c-Jun transcription factors[END_REF].This increase in TSPO/VDAC ratio does not seem to be a consequence, but rather a contributor to the oxidative stress, as it was not modified by the Tempol treatment. Finally, the contribution of TSPO on oxidative stress is not related to its well-known role in cholesterol transport [START_REF] Papadopoulos | Translocator protein-mediated pharmacology of cholesterol transport and steroidogenesis[END_REF] as mitochondrial cholesterol and oxysterol accumulation was not altered in TG mice.

Our data demonstrate that this oxidative stress participates in the activation of signaling pathways involved in growth. Indeed, the treatment of TG mice with the general antioxidant Tempol strongly inhibited ROS production and limited the development of myocardial hypertrophy underlying the role of oxidative stress in this model. As Tempol treatment only began just after weaning (3-4 weeks) when hypertrophy was already established [START_REF] Hedhli | Proteasome activation during cardiac hypertrophy by the chaperone H11 Kinase/Hsp22[END_REF], it should be emphasized that ROS inhibition prevented the progression of LV hypertrophy but did not reverse a process which was already in place. A growing body of evidence reveals the causative role of redox-sensitive pathways in the processes underlying cardiac hypertrophy [START_REF] Maulik | Oxidative stress and cardiac hypertrophy: a review[END_REF] and the antihypertrophic effect of Tempol was previously described in various models of myocardial hypertrophy. For example, Tempol was shown to reduce myocardial hypertrophy in insulin resistant GLUT4-knockout mice [START_REF] Ritchie | The antioxidant tempol inhibits cardiac hypertrophy in the insulin-resistant GLUT4-deficient mouse in vivo[END_REF] and to prevent this process in a model of aortic banding [START_REF] Chess | The antioxidant tempol attenuates pressure overload-induced cardiac hypertrophy and contractile dysfunction in mice fed a high-fructose diet[END_REF]. Numerous studies have suggested a key role of ROS generated by NADPH oxidase in cardiac hypertrophy. Interestingly, the expression and activity of this enzyme were increased in TG mice and these effects were also reduced by Tempol. One of the signaling pathways involved in this hypertrophic effect related to NADPH oxidase is the Akt pathway, which is also one of the main pathways implicated in Hsp22-induced hypertrophy [START_REF] Acunzo | Small heat shock proteins HSP27 (HspB1), αBcrystallin (HspB5) and HSP22 (HspB8) as regulators of cell death[END_REF]. The present study shows that the activation of Akt by Hsp22 is inhibited by Tempol and therefore is the consequence of ROS overproduction. Taken together these data support the concept that Hsp22 overexpression is responsible for the activation of a global ROS production which stimulates the Akt pathway and consequently hypertrophy. However, it should be acknowledged that other mechanisms could participate to the development of LV hypertrophy. For example, proteasome activation has been shown to be involved in Hsp22-induced hypertrophy [START_REF] Hedhli | Proteasome activation during cardiac hypertrophy by the chaperone H11 Kinase/Hsp22[END_REF].

Our study further demonstrates that, the oxidative stress promoted by Hsp22 overexpression induces senescence and reduced lifespan. Multiple mechanisms such as suppression of the growth hormone/insulin-like growth factor axis [START_REF] Holzenberger | IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice[END_REF][START_REF] Chen | Longevity and lifespan control in mammals: lessons from the mouse[END_REF], deletion of p66shc adaptator protein [START_REF] Migliaccio | The p66shc adaptor protein controls oxidative stress response and life span in mammals[END_REF] or disruption of type 5 adenylyl cyclase [START_REF] Yan | Type 5 adenylyl cyclase disruption increases longevity and protects against stress[END_REF] have been involved in the regulation of lifespan. They are associated to increasing resistance to oxidative stress which plays a determinant role to extend life span [START_REF] Sohal | Oxidative stress, caloric restriction, and aging[END_REF][START_REF] Murakami | Multiplex stress resistance in cells from long-lived dwarf mice[END_REF]. Indeed, it is well established that aging and lifespan are correlated with oxidative stress [START_REF] Harman | Aging: a theory based on free radical and radiation chemistry[END_REF][START_REF] Barja | The mitochondrial free radical theory of aging[END_REF][START_REF] Liguori | Oxidative stress, aging, and diseases[END_REF]. Excessive ROS production and oxidative stress damages the structure of DNA, phospholipids, and proteins, resulting in cellular and tissue injury. This is a critical mechanism for ROS-induced aging [START_REF] Sohal | The redox stress hypothesis of aging[END_REF]. Hearts from TG mice produced high levels of ROS and showed a significant increase in β-galactosidase positive cells as well as p16 and p19 mRNA levels, which are key characteristics of senescence and have been shown to be increased in several cardiac cell lines during aging [START_REF] Chimenti | Senescence and death of primitive cells and myocytes lead to premature cardiac aging and heart failure[END_REF][START_REF] Torella | Cardiac stem cell and myocyte aging, heart failure, and insulin-like growth factor-1 overexpression[END_REF][START_REF] Wong | Aging, telomeres and heart failure[END_REF][START_REF] De Jong | Paradoxial down-regulation of p16INK4a mRNA with advancing age in acute myeloid leukemia[END_REF]. These findings characterize a premature aging rather than a replicative senescence process induced by telomere erosion. This supports the hypothesis that the elevated oxidative stress is also involved in these pathophysiological responses observed in TG mice. This is confirmed by the fact that Tempol treatment prevents the increase in p16 and p19 mRNA levels. Interestingly, Hsp22overexpression was also associated with a high telomerase activity which is usually very low in quiescent tissues such as the heart. This can also participate in the premature senescence as high telomerase activity can promote a senescence phenotype [START_REF] Gorbunova | Evidence that high telomerase activity may induce a senescent-like growth arrest in human fibroblasts[END_REF]. Cellular senescence is often accompagnied by a senescence-associated secretory phenotype (SASP) which is characterized by the secretion of bioactive molecules such as cytokines, proteases or regulatory factors [START_REF] Kumar | Senescence-associated secretory phenotype and its possible role in chronic obstructive pulmonary disease[END_REF].

SASP can lead to tissue dysfunction and has been involved in the development of cardiac hypertrophy [START_REF] Anderson | Length-independent telomere damage drives post-mitotic cardiomyocyte senescence[END_REF][START_REF] Shinmura | Cardiac Senescence, Heart Failure, and Frailty: A Triangle in Elderly People[END_REF] and it would be interesting to assess the potential role of SASP in the development of left ventricle hypertrophy by Hsp22 overexpression.

Paradoxically, Hsp22 overexpression in Drosophila has been associated with increased resistance against oxidative stress and prolonged life span [START_REF] Morrow | Decreased lifespan in the absence of expression of the mitochondrial small heat shock protein Hsp22 in Drosophila[END_REF]. This opposite behavior may likely be explained by the location of Hsp22 which is only in the mitochondria in Drosophila.

It is conceivable that a cytosolic source of ROS may be necessary to develop a senescent phenotype in Hsp22 overexpressing mice. However, it is still unknown how expression of Hsp22 increases the activity of the protein producing ROS and this is a limitation of the study.

Further investigations are needed to determine this mechanism.

In conclusion, Hsp22 overexpression increases oxidative stress, which results in the induction of hypertrophy and senescence and ultimately reduction in life span.

Sources of Funding

R. Long was supported by a doctoral grant from the Ministère de la Recherche et de la Technologie.

Disclosures

Nothing to declare

Legends for Figures

Figure 1 :

 1 Figure 1: Hsp22 overexpression reduces lifespan, left ventricular ejection and increases cardiac hypertrophy.

Figure 2 :

 2 Figure 2:Myocardial Hsp22 overexpression induces myocardial cellular senescence A: Senescence determined by measurement of β-galactosidase positive cells (blue coloration) in the left ventricle from WT and TG mice. Left: Pictures from the subendocardial regions showed a positive β-galactosidase staining in TG mice which is absent in WT mice. Right: Percentage of β-galactosidase positive cells in hearts from WT and TG mice. Each value represents the mean ± sem of at least six hearts. *p<0.05 vs WT. B: Myocardial mRNA levels of p53, p21, p16 and p19 were determined using quantitative RT-PCR in WT and TG mice. *p<0.05 vs WT. A.U.:arbitrary unit. Each experiment was performed in duplicate on at least four animals. C: Telomere length was measured by southern blot after DNA digestion in hearts from WT and TG mice and telomerase activity was determined in cardiac homogenates from WT and TG mice. Each value represents the mean ± sem of at least six independent preparations. *p<0.05 vs WT. A.U. : arbitrary unit.

Figure 3 :

 3 Figure 3: Hsp22 overexpression enhances ROS production and oxidative stress in transgenic mice and these effects are abolished by Tempol treatment.

Figure 4 :

 4 Figure 4: Hsp22 overexpression enhances TSPO expression in cardiac mitochondria. A: Western Blot experiments of the expression of TSPO in mitochondria (M), mitoplasts (mp) and outer mitochondrial membranes (OMM) in WT and TG mice of 8 week old. B: Western blot experiments and band density analysis showing the increase in the mitochondrial TSPO/VDAC ratio during ageing in TG mice. C: Cholesterol and oxysterol levels were not altered in cardiac mitochondria isolated from TG mice. Each value represents the mean ± sem of 8 independent preparations. Chl: cholesterol; 7αOHC: 7α-hydroxycholesterol; 7βOHC: 7β-hydroxycholesterol; 7KC: 7-ketocholesterol; αEpoxy: cholesterol-5α,6α-epoxide; βEpoxy: cholesterol-5β,6β-epoxide. D: Western blot experiments and band density analysis showing the increase in the mitochondrial TSPO/VDAC ratio in cardiac mitochondria from control and Tempol treated WT and TG mice of 8 week old. Each value represents the mean ± sem of at least three independent preparations. *p<0.05 vs WT.

Figure 5 :

 5 Figure 5:Tempol treatment reduces myocardial hypertrophy and senescence markers and improves lifespan in Hsp22 overexpressing mice.

  Figure 2

Figure 5

 5 Figure 5


Clément Lalanne

Maxence Rateaux

Laurent Oudre

Matthieu P Robert

Thomas Moreau

Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding

come   L'archive ouverte pluridisciplinaire

I. INTRODUCTION

Nystagmus is a variety of involuntary eye movement, acquired in infancy or later in life, that may be associated with reduced or limited vision. It is characterized by a periodic movement of one or both eyes. The many types of Nystagmus can be roughly classified in two large classes of motions. The first ones, referred to as pendular Nystagmus, manifest as slow periodic oscillations. The second ones, referred to as jerk Nystagmus, are characterized by the alternation of slow phases and quick phases corresponding to eye saccades. In total, Hertle et al. [START_REF] Hertle | Differential diagnosis of nystagmus in infancy and childhooh[END_REF] reported 49 differents types of Nystagmus and 17 types of saccadic intrusions and oscillations; classified in 8 main categories according to the Classification of Eye Movement Abnormalities and Strabismus (CEMAS) [START_REF] Hertle | National Eye Institute Sponsored Classification of Eye Movement Abnormalities and Strabismus Working Group[END_REF]. Several etiological conditions may explain the presence of Nystagmus, such as the presence of an infantile strabismus, a developemental disturbance of the afferent visual pathways or of the oculomotor sytem. However, there is currently no medical consensus on the diseases that are linked to a specific type of Nystagmus. It is therefore an active research question: the automatic classification and characterization of this phenomenon could improve the diagnosis, prevention and follow-up of patients suffering from this condition.

Traditionally, the detection and monitoring of Nystagmus on young infants was performed by specialized ophthalmologists who study the eye movements by visual inspection. Recently, some dedicated sensors were developed to track the position of both eyes with respect to time (see Figure 1). Figure 2 displays a typical eye-trajectory in one axis. This record comes from one eye of a patient with a pendular Nystagmus. It shows the horizontal projection of the angle that makes the left eye with a normal vector to the face plan. Three simultaneous phenomena are actually recorded by the eye tracker: the low-frequency component is due to the natural eye movement, the abrupt changes correspond to saccades and eye blinks artefacts (in green and orange) and the small oscillations are due to the Nystagmus (here periodic). The aim of this article is to automatically extract from such signals the components linked to the Nystagmus as patterns associated with activations. This task is especially tricky since it requires to be robust to the other phenomena, i.e. saccades, artefacts and eye movements. To that aim, we propose to use a machine learning tool known as convolutional sparse coding [START_REF] Grosse | Shift-Invariant Sparse Coding for Audio Classification[END_REF] and to adapt it to deal with signals corrupted by a trend (eye movements) and ruptures (saccades and artefacts).

II. BACKGROUND

Convolutional sparse coding aims at representing an input signal as the convolution between patterns (that capture the phenomenon present in the signals) and activations (that specify when these patterns appear in the signal).

More specifically, given a input signal x of length T , the aim is to learn a library of K patterns D = (d k ) 1≤k≤K of length W (with W << T ) and their associated activations

Z = (z k ) 1≤k≤K of length L = T -W + 1. The general model writes as x = K k=1 d k * z k + e = D * Z + e , (1)
where * is the discrete convolution product and e a noise term [START_REF] Grosse | Shift-Invariant Sparse Coding for Audio Classification[END_REF]. With matrix notations, the Convolutional Sparse Coding (CSC) problem rewrites as min

D,Z ∀k,||D k || 2 2 ≤1 1 2 ||D * Z -x|| 2 2 + λ||Z|| 1 , (2)
where λ allows to control the level of sparsity put on the activations. This problem is usually solved through alternate minimization, where matrix Z is updated while D is fixed (SPARSECODING), and reciprocally (DICTUPDATE). Popular solvers for this problem include AlphaCSC [START_REF] La Tour | Multivariate convolutional sparse coding for electromagnetic brain signals[END_REF] and Sporco [START_REF] Wohlberg | Efficient Algorithms for Convolutional Sparse Representations[END_REF]. It has been used in several contexts related to healthcare such as blood cell detection in holographic imaging [START_REF] Yellin | Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding[END_REF] or to measure respiration and heartbeat rates in EEG signals [START_REF] Wang | Respiration and Heartbeat Rates Measurement Based on Convolutional Sparse Coding[END_REF].

III. PROPOSED METHOD

In our context, the input signal x corresponds to an eye trajectory in horizontal or vertical direction, and we aim to learn from this signal a series of recurring patterns stored in D that would correspond to Nystagmus waveforms and their temporal activations stored in Z. However, as seen on Figure 2, two types of eye movements limit the use of CSC in this context. Indeed, the input signal x does not only include the Nystagmus phenomenon, but also the voluntary eye movements, that manifests as a smooth motion and a few eye saccades. Hence, the input signal presents abrupt ruptures that are not taken into account in the model [START_REF] Hertle | Differential diagnosis of nystagmus in infancy and childhooh[END_REF]. The aim of the proposed model is to modify the standard CSC problem (1) so as to deal with smooth trends and abrupt ruptures.

Algorithm 1 Locally Greedy Coordinate Descent

Require: x, D D D (0) , parameter λ > 0, λ T V > 0, and > 0.

1:

y (0) = PROXTV(x, λ T V ) 2: repeat 3:
Z (q+1) = SPARSECODING(X -y (q) , D (q) , λ)

4:

y (q+1) = PROXTV(x -Z (q+1) * D (q) , λ T V ) 5: D (q+1) = DICTUPDATE(x -y (q) , Z (q) , λ) 6: until Z (q+1) -Z (q) ∞ <

A. Convolutional Sparse Coding with Piecewise Constant Trend

The Convolutional Sparse Coding with Piecewise Constant Trend (CSC-PCT) model introduced in this article consists in adding an additional term into the standard CSC model:

x = D * Z + y + e, (3)
where y corresponds to a piecewise constant trend term. The associated optimization problem writes as min

D,Z,y ∀k,||D k || 2 2 ≤1 1 2 ||D * Z+y-x|| 2 2 +λ||Z|| 1 +λ T V ||∇y|| 1 , (4)
where ||∇y|| 1 is a Total Variation (TVL1) constraint defined as

||∇y|| 1 = t |y t+1 -y t |. (5)
The additional constraint imposes a piecewise constant structure to y, which allows the modelling of the abrupt ruptures as well as the changes in baseline values due to lowfrequency phenomenons.

B. Resolution

In order to solve (4) with respect to D, Z, y, the problem is divided into 3 sub-problems that each updates one term while keeping the remaining terms fixed. These steps are summarized in Algorithm 1.

Note that the Dand Z-updates are actually the same than in classical CSC, by replacing x in (2) by xy (q) , where y (q) is the trend estimation at the q th iteration. Therefore, classical solvers can be used. We relied on AlphaCSC solver [START_REF] La Tour | Multivariate convolutional sparse coding for electromagnetic brain signals[END_REF] which uses a coordinate descent approach for SPARSECODING and FISTA for DICTUPDATE.

For the y-update, the aim is to compute a piecewise estimate y (q) of the current residual x -D (q) * Z (q) . This corresponds to applying the proximal operator of TVL1 to the residual, for which several off-the-shelf solvers are available. In this article, we used PROXTV [START_REF] Barbero | Modular proximal optimization for multidimensional total-variation regularization[END_REF] which computes the update through a dual framework.

C. Scalability to other setups

The problem (4) uses a classical method in optimization called penalization. Namely, it includes the constraints (smooth trend with saccades) in the function to optimize by measuring "how far" the result is from the constraints set. Here, this is done with the term ||∇y|| 1 . As a consequence, our algorithm can be extended to any setup where the motion to get rid of can be expressed by a proximable penalization term.

IV. DATA

A. Synthetic dataset

To evaluate our framework, we used the generative model Equation 3to simulate look-a-like signals for our data. The voluntary gaze movements are created as the sum of a low frequency pattern and saccades that are sigmoidal ruptures and hence allow abrupt yet smooth mode shifting. The localisation and amplitude of the saccades are chosen randomly with a Poisson law of parameter 1s and a mean amplitude of 20 • . The Nystagmus waveforms are generated according to the rough existing Nystagmus classification. The pendular Nystagmuses are modelled by a sinusoidal signal and the jerks are modelled by quadratic slow phases and linear quick phases. The frequency and amplitude of the Nystagmus are also chosen randomly with mean 5Hz and 3 • . The resulting signal x is obtained by summing these two components. The generated signals have a sampling rate of 1000Hz and length of 10s and a white noise of 0.5 • is added to match visually the signals obtained with our recording device.

B. Real dataset

Our clinical evaluation was perfomed on two different eye movements recordings from two different patients. The first one was realized on an 6-years-old girl with a Down syndrome. The patient exhibited a Spasmus Nutans type-Nystagmus (SNTN). There was no ocular deviation and the refraction was: -6.25 (+1.75 80 • ) for the right eye (RE) and -5.00 (+1.25 110 • ) for the left eye (LE). The Nystagmus was monocular (only in the RE) and purely pendular. There was no head nodding and no head turn. The second eye movement recording was performed on a 36-years-old women with an oculocutaneous albinism (AOC) and an infantile Nystagmus syndrome (INS). The best corrected visual acuity was +0.7 logMAR (RE and LE) and the refraction was: -7.25 (+4.00 80 • )/ -8.75 (+3.25 100 • ). Ophthalmological examination revealed an iris transillumination, a peripheral hypopigmentation of the fundus and a foveal hypoplasia. Consents were collected in both cases, according to the French regulation about personal data.

V. RESULTS

All experiments are performed in Python and the code is available online 2

A. Pattern recovery on synthetic signals

To evaluate the benefit of joint detrending with CSC on pattern discovery, we used 160 signals generated according to the procedure described in subsection IV-A with random parameters. For each signal x i , we learn K = 1 atom d i using either our method (JOINT) or the vanilla CSC method with either no pre-processing (NONE) or an initial detrending step (INIT). We then evaluate the recovery of the pattern d * i used to generate the Nystagmus part. Due to the periodicity of the Nystagmus signal, the learned atoms might have a different phase compared to the original pattern and capture multiple periods. To make sure we correctly capture the pattern with the learned atoms, we used the following metric

ρ(d * , d) = max t=1..L l=1..W -L < d * [t : t + L], d[l : l + L] > d * 2 d 2
where d is the periodized version of d and d[t 1 : t 2 ] denotes the coefficient from t 1 to t 2 in d. This metric extends the Pearson coefficient to be invariant to the phase and the repetition of d * in the learned atom d. Figure 3 summarizes the results for different values of λ T V and λ = 0.5λ max . Here, λ max is the smallest value of λ for which 0 is solution of (2). NONE (green) is able to recover the generating patterns with a median score of 0.64 in this configuration. When adding the detrending as a preprocessing step, INIT (orange) is able to better recover the patterns, with a median score of 0.76 if λ T V = 0.9 is large enough. However, the variance of the recovery is largely increased, indicating that it can either recover well the patterns or fail badly. The proposed method JOINT (blue) has a median score above 0.95 for λ T V < 0.9, with a low variance. Note that the behavior of JOINT and INIT are reverse with the variation of λ T V . When λ T V is small, the initial guess for the trend is very close to the full signal and thus vanilla CSC fails to learn the generating atoms as it is only working on the residual. The JOINT method is able to modify the trend estimate to increase the residual where it matters for pattern recovery. As λ T V increases, the residual contains more and more of the original signal and the performances of INIT are improved toward the ones of NONE. It becomes better when the trend of a signal is well estimated to not deform the original patterns. But this depends critically on the choice of λ T V , which cannot be set globally to reach good results. Also, the benefit of JOINT decreases, as the trend is better estimated initially. Thus, the joint estimation of the trend and the patterns allows for an aggressive initial estimate of the trend that will be refined by the joint estimation. We repeated this experiment for different values of λ but it did not changed the finding, except by degrading the performances of INIT.

B. Results on real data

We applied Algorithm 1 to learn 3 patterns from data collected on the two patients presented in subsection IV-B. As suggested by Figure 3 we tuned the values λ T V = 0.05 and λ = 0.9λ max . The patterns learned with JOINT and NONE are presented in Figure 4. For both patients, the result with vanilla CSC are not highlighting the specific waveform of the Nystagmus while our method is able to capture it. In the first case, the algorithm extracts a single symmetric pattern, and confirms the pendular nature of the Nystagmus identifyed in the clinical examination. For the second patient, two similar waveforms are extracted. They are both composed of a slow phase followed by a fast movement, which are closed to text book Nystagmus description [START_REF] Hertle | Differential diagnosis of nystagmus in infancy and childhooh[END_REF]. The presence of foveation periods in the blue pattern -stabilization of the movement in the slow phaseis specific of infantile Nystagmus syndrome. Their presence allows to confirm the INS diagnosis in doubious cases.

VI. CONCLUSION

Analysing the waveforms of Nystagmus is crucial to the clinician, since the causes -and therefore the workup to be performed -depend on the type of Nystagmus. In many cases, the sole clinical analysis does not allow for a robust classification of the Nystagmus. Sometimes, parents report a transient Nystagmus, which is not visible during the clinic, while infraclinical oscillations are identifyable through oculomotor recording. This can lead to overlooking potentially serious underlying diagnoses, such as retinal dystrophies or brain tumours.

Fig. 1 :Fig. 2 :

 12 Fig. 1: EyeFant Tracker

2Fig. 3 :

 3 Fig.3: Pearson correlation coefficient between D and the patterns used to generate the signals for different value of λ T V and λ = 0.5λ max . The atoms learned without detrending (green) are better than the one learned with the addition of a initial detrending (orange) while the ones learned with our proposed methods (blue) are closer to the ground truth.

( 3 )Fig. 4 :

 34 Fig. 4: Results for recordings on two patients presenting (a) a SN syndrome with a pendular pattern and (b) a INS syndrome with characteristic jerky pattern. The patterns learned with JOINT (1) are better at capturing the characteristic waveform of the Nystagmus than the one learned with NONE (2). The bottom part (3) displays a selected part of the original signals (blue) as well as the estimated nystagmic component (orange). The signal parts are selected to exclude artefacts and saccades, as it is easier to see the Nystagmus patterns in this configuration.

  Clément Lalanne is with Centre Borelli, CNRS, ENS Paris Saclay, Université de Paris, SSA.

a b Maxence Rateaux and Matthieu P. Robert are with Hôpital Necker-Enfants Malades, AP-HP, and Centre Borelli, CNRS, ENS Paris Saclay, Université de Paris, SSA. c Laurent Oudre is with Université Sorbonne Paris Nord, L2TI, UR 4443, F-93430, Villetaneuse, France d Thomas Moreau is with University Paris-Saclay, Inria, CEA

thomas.moreau@inria.fr.

http://www.ober-consulting.com/


Thanh Hung Pham

Sanda Lefteriu

Cécile Labarre

Eric Duviella

Stéphane Lecoeuche

Auto-adaptive and Dynamical Clustering for Open-Circuit Fault Diagnosis of Power Inverters

published or not. The documents may come

L'archive ouverte

I. INTRODUCTION

An inverter is a power converter that changes Direct Current (DC) to Alternative Current (AC). It is usually used to connect DC sources (e.g., battery) and AC electrical devices (e.g., AC electric motor). Fault diagnosis of the 3-phase inverter associated to a 3-phase motor represents an active area of research [START_REF] Trabelsi | Experimental investigation of inverter open-circuit fault diagnosis for bi-harmonic five-phase permanent magnet drive[END_REF]. Because of aging or abnormal operating conditions, semiconductor switches, such as Insulated-Gate Bipolar Transistors (IGBTs), are the most vulnerable components in the inverter. Their failure is due to the Short-Circuit (SC) and Open-Circuit (OC) faults. The SC fault leads to a high current which is destructive and makes the system shut down immediately thanks to standard protection systems [START_REF] Cai | A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems[END_REF]. However, when the OC fault happens, the system can still run with degraded performance causing secondary faults on other system components. Thus, this paper focuses on OC fault diagnosis of 3-phase power inverters.

In terms of methodology, most researchers focus on one of the following approaches: model-based, signal-based, datadriven and hybrid [START_REF] Gao | A survey of fault diagnosis and fault-tolerant techniques-part II: Fault diagnosis with knowledgebased and hybrid/active approaches[END_REF]. The model-based approach uses the system model, obtained by physical principles or system identification techniques, to predict the output, which is compared to the measured output for diagnosing the fault [START_REF] Poon | Model-based fault detection and identification for switching power converters[END_REF]. It should be noted that this model is generally difficult to derive for a complex system, e.g., an asynchronous motor associated with an inverter. Instead of system models, the signal-based approach considers the measured signals for extracting features which are used, together with prior knowledge on the symptoms of healthy systems, to make diagnosis decisions [START_REF] Trabelsi | Experimental investigation of inverter open-circuit fault diagnosis for bi-harmonic five-phase permanent magnet drive[END_REF], [START_REF] Ahmad | Open and short circuit switches fault detection of voltage source inverter using spectrogram[END_REF]. However, this approach does not take into consideration the dynamics of the input signal which is usually impacted by unknown disturbances or unbalanced Thanh Hung Pham, Sanda Lefteriu, Cécile Labarre, Eric Duviella, Stéphane Lecoeuche are with Institut Mines Telecom Lille Douai, IA, F-59508 Douai, France {thanh-hung.pham, sanda.lefteriu, cecile.labarre, eric.duviella, stephane.lecoeuche} @imt-lille-douai.fr conditions. In contrast to the two previous approaches, datadriven approaches consider a large volume of data and prior knowledge of considered systems to make fault diagnosis decisions thanks to data classification or clustering. Nevertheless, this approach suffers from a high computational cost, and may not identify unknown fault types. To combine the strengths of the previous approaches, our paper proposes a hybrid approach where signal-based and data-driven methods are combined.

For the 3-phase inverter, such a hybrid approach may be based on measurements of voltages and/or currents. Voltagebased methods use measurements of the three voltages at the inverter output [START_REF] Cai | A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems[END_REF]. This method is independent of the load, but requires additional sensors. The latter disadvantage motivates us to develop a current-based method, the load independence being ensured by simply normalizing the current data [START_REF] Trabelsi | Experimental investigation of inverter open-circuit fault diagnosis for bi-harmonic five-phase permanent magnet drive[END_REF], [START_REF] Estima | A new algorithm for real-time multiple open-circuit fault diagnosis in voltage-fed PWM motor drives by the reference current errors[END_REF].

The data-driven approach is used to determine the feature classes, which are feature vector sets characterizing system operation modes. In the context of OC fault diagnosis for inverters, previous research proposed well-established statistical methods such as Principle Component Analysis (PCA) [START_REF] Cai | A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems[END_REF] and Support Vector Machines (SVM) [START_REF] Kim | Fault diagnosis of three-phase PWM inverters using wavelet and SVM[END_REF], [START_REF] Delpha | Multiple incipient fault diagnosis in three-phase electrical systems using multivariate statistical signal processing[END_REF]. For these previous methods, reference classes are already available from the training data, and one needs to determine to which class the new feature vector belongs to. For this reason, they are called supervised methods, and unknown operation modes may not be detected during the actual system operation. To avoid this, Neural Networks (NN) can be applied to learn and diagnose the OC faults of inverters thanks to their powerful ability in nonlinear approximation and adaptive learning [START_REF] Martins | Unsupervised neuralnetwork-based algorithm for an on-line diagnosis of three-phase induction motor stator fault[END_REF]- [START_REF] Talha | A Matlab and Simulink based three-phase inverter fault diagnosis method using three-dimensional features[END_REF]. However, in these papers, the number of classes is fixed, while the characteristics of the class is adapted to include the new feature vector. This issue may be tackled using adaptive unsupervised methods, where reference classes (called clusters) are not available a priori, but are created online. To the author's best knowledge, such a method has yet to be applied to diagnose the OC fault of inverters. Moreover, most of existing clustering methods do not predict the time evolution of modelled data classes and their fusion.

In this paper, we employ the normalized DC current for the feature extraction, and Auto-adaptive and Dynamical Clustering (AUDyC) algorithm [START_REF] Lecoeuche | New supervision architecture based on on-line modelling of non-stationary data[END_REF], [START_REF] Boubacar | Audyc neural network using a new gaussian densities merge mechanism[END_REF] for feature clustering. AUDyC does online, adaptive and unsupervised feature learning, hence the unknown modes of operation can be learnt from the measurement data. The main contribution of our work resides in the application of AUDyC to perform single OC fault diagnosis of inverters by identifying representative data classes. Furthermore, appropriate conditions for labelling classes and for diagnosing faults are presented.

The paper is organized as follows. Section II formulates the fault diagnosis problem. Section III describes the proposed method, and section IV validates it on two test cases: the first considers simulation data from a 3-phase inverter, while the second is measurement data from a 5phase inverter. Section V concludes the paper and presents future work directions.

II. THE INVERTER IN NORMAL AND FAULTY OPERATION

MODES

This section presents the 3-phase inverter associated with a motor in normal and faulty operation modes. A. Inverter operation Fig. 1: 3-phase inverter driving a motor [START_REF] Cai | A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems[END_REF].

The considered system is a combination of a 3-phase inverter and a motor with the following specifications: star coupling for the stator with the isolated neutral, symmetric motor electrical circuit operating under open-loop control based on pulse width modulation (PWM). We assume that no fault occurs during the motor starting period. Each inverter leg is composed of two IGBTs and two inverse diodes as in Fig. 1.

ia < 0 ia > 0 T 1 open T 1 closed T 1 open T 1 closed normal or normal or OC fault OC fault or OC fault OC fault or SC fault SC fault T 2 open D 1 closed D 1 closed D 1 open D 1 open D 2 open D 2 open D 2 closed D 2 open va = V dc va = V dc va = 0 va = V dc normal or normal or OC fault or OC fault or SC fault SC fault SC fault SC fault T 2 closed D 1 open D 1 open D 2 open D 2 closed va = 0 va = 0
Since the three legs of the inverter are symmetric, only the operation states of leg a are described in Table I. An inverter operation mode is defined as a time sequence of IGBT operation states. In the normal operation mode, T 1 and T 2 are alternatively open or closed with the PWM switching frequency. Note that the dead time is not considered here, i.e., it is assumed to be zero. When IGBT T 1 or T 2 is closed all the time, the inverter is in SC fault mode, which is not considered in this work. When IGBT T 1 or T 2 is open all the time, the inverter is in OC fault mode, which will be investigated in the next subsection. B. Open-circuit fault analysis Fig. 2 illustrates the profiles of the three stator AC currents in steady-state where the system is in the normal mode before 0.40 s, and in the T 1 OC fault mode after 0.40 s. The switching frequency of IGBTs is assumed to be much higher than the sinusoidal signal frequency. In the normal operation mode, when we generate the PWM signal to control the IGBTs based on three sinusoidal signals with frequency f and shifted by 2π/3 between their phases, the three profiles of the stator currents are also sinusoidal with the same frequency and phase shifts at the steady state. Indeed, there is still the small oscillation in the current profiles because of IGBT switching (see Fig. 2).

The inverter is in T 1 (respectively T 2 ) OC fault mode if T 1 (respectively T 2 ) is open all the time while other IGBTs are still alternatively closed and open. In this case, if the current i a (t) is negative, the current profiles are still sinusoidal. It can be seen in Fig. 2 where the fault happens at 0.4 s. This is due to the fact that the electric potential profile of phase a, v a (t), does not change with respect to the normal mode according to Table I. However, if the current i a (t) is positive, the electric potential of phase a, v a (t), is always equal to zero, and the phase-a current, i a (t), reduces to zero quickly and stays like this for about half a period [START_REF] Kim | Fault diagnosis of three-phase PWM inverters using wavelet and SVM[END_REF].

Thus, we can deduce an important characteristic of the stator currents in the faulty mode: the positive part of the phase-a current, i a (t), is "cut-off" when T 1 is faulty. Consequently, only the phase-a current is always negative during the T 1 fault mode. This results in the fact that the ratio between its average and the average of its absolute value on a fundamental signal period is equal to -1. Moreover, these ratios for the other phase currents are theoretically constant. Similarly, this observation can be easily extended to the faulty cases of the other IGBTs. Hence, each mode can be represented by a vector constructed from these ratios. These presented fault characteristics will be exploited to formulate the feature variable in the next section.

III. HYBRID FAULT DIAGNOSIS METHOD

In this section, a hybrid diagnosis method is presented which includes the following steps (as illustrated in Fig. 3): feature extraction, data clustering, labelling and fault detection and isolation.

A. Feature extraction

In this step, we focus on finding the feature vector which gathers all the feature variables. The linear space representing this vector is called feature space where feature vectors corresponding to different operation modes are located in different regions in this space.

Let x(t), β(t) be, respectively, the observation and feature vectors at time instant t = kt s , where t s is the sampling time, and k ∈ N is the sampling time index. In our case, the measured variables are the 3-phase currents denoted by i a (t), i b (t), i c (t) ∈ R. Therefore, the observation vector is defined as

x(t) = i a (t) i b (t) i c (t) T ∈ R 3 .
According to the faulty mode analysis in Section II, the feature vector is chosen as:

β(t) = β a (t) β b (t) β c (t) T ∈ R 3 , with (1a)
β l (t) = i l (t) |i l (t)| , l = a, b, c, (1b)
where f (t) =

N -1 k=0 f (t -kt s )/N is the average of function f (t), N ∈ N is the number of time samples on a fundamental signal period. Notice that -1 ≤ β l (t) ≤ 1. When the lower transistor of phase l is faulty, i l (t) > 0 ∀t, and thus, β l (t) = 1. Similarly, when the upper transistor of phase l is faulty, β l (t) = -1. These remarks illustrate the relevance of (1b) for the feature extraction. The feature vector will be used as the input to the following clustering procedure. B. Data clustering Fig. 4: AUDyC algorithm [START_REF] Boubacar | Audyc neural network using a new gaussian densities merge mechanism[END_REF].

Here, we describe AUDyC, the algorithm chosen for data clustering, thanks to its online, adaptive and unsupervised learning capabilities [START_REF] Lecoeuche | New supervision architecture based on on-line modelling of non-stationary data[END_REF], [START_REF] Boubacar | Audyc neural network using a new gaussian densities merge mechanism[END_REF]. AUDyC automatically creates data classes in the feature space from the feature data. In our case, these classes represent the normal and single OC fault (i.e., a single transistor is faulty at a time) modes. Since each operation mode is ideally represented by a unique nominal feature vector, we consider a simplified formulation of AUDyC in this paper. This simplification is obtained by assuming that the data of each class follows a gaussian distribution around the nominal feature vector. Thus, a class is characterized by the mean vector, the covariance matrix and a set of feature vectors. Class parameters are determined in the following 5 main procedures: the similarity verification, the creation, the adaptation, the fusion and the evaluation of classes, as illustrated in Fig. 4. At each time instant, a loop over these steps is performed on a vector set composed of the new feature vector and the rejected vectors from the previous loop. After each iteration, a vector of this set is clustered, and class parameters are modified. Similarity verification: This procedure finds the class to which the new feature vector, β(t), belongs, by quantifying similarities between this vector and existing classes based on membership functions defined as:

µ j (β(t)) = exp - 1 2 β(t) T Σ -1 j (t -t s ) β(t) , (2)
where β j (t) and Σ j (t) are, respectively, the mean vector and the covariance matrix of class C j , and β(t) = β(t) -β j (tt s ). Then, all classes j satisfying µ j (β(t)) ≥ µ min with the given threshold µ min are gathered in a set denoted by C win .

Based on the cardinality of this set, we determine the next steps.

Creation: If card (C win ) = 0, a new class C j is created with the following parameters:

β j (t) = β(t), Σ j (t) = σ 2 I 3 , (3)
where I 3 is the identity matrix of size 3, and σ ∈ R + is a chosen parameter which determines the learning capacity of the created class at its first moment. Adaptation: If card (C win ) = 1, the similarity class C j ∈ C win is adapted. Let n j (t) ∈ N be the number of feature vectors belonging to C j at time instant t. If n j (t -t s ) < N min , where N min is a given threshold, the feature vector is stored in the memory of class C j . Moreover, the parameters of C j are modified using the new feature vector:

       n j (t) = n j (t -t s ) + 1, β j (t) = β j (t -t s ) + β(t) n j (t) , Σ j (t) = Σ j (t -t s ). (4)
If N min ≤ n j (t -t s ) < N max , where N max is a given threshold, the feature vector is stored. n j (t) and β j (t) are modified according to the same formulas in (4), and Σ j (t)

is given as:

Σ j (t) = n j (t) -2 n j (t) -1 Σ j (t -t s ) + β(t) β(t) T n j (t) . ( 5
)
If n j (t -t s ) = N max , the new feature vector is stored, and the oldest one is forgotten. Therefore, the class parameters are adapted by the following rules:

       n j (t) = N max , β j (t) = β j (t -t s ) + ∆β(t) N max 1 -1 , Σ j (t) = Σ j (t -t s ) + ∆β(t)B∆β(t) T , (6)
with

                 ∆β(t) = β(t) -β j (t -t s ) β(t -N max t s + t s ) -β j (t -t s ) T , B = 1 N max     1 1 N max -1 1 N max -1 - N max + 1 N max -1     . (7) Fusion: When card (C win ) ≥ 2, if each two classes C j , C m ∈ C win satisfy the condition: tr Σ j (t)Σ -1 m (t) + Σ m (t)Σ -1 j (t) -6 < f , (8)
all the similarity classes are fused, where tr(X) is the trace of matrix X, f is a given threshold. The classes are organized in the appearance order. The new class obtained by fusing two first classes is fused with the third class. The procedure is repeated until all the similarity classes are fused.

Then, the parameters of the final fusion class is adapted to include the new feature vector. If [START_REF] Delpha | Multiple incipient fault diagnosis in three-phase electrical systems using multivariate statistical signal processing[END_REF] is not satisfied, only the parameters of the class with the highest value of the membership function in ( 2) is adapted with the new feature vector.

Evaluation: After every N min time samples, a noisy cluster C j is eliminated if card(C j ) < N min . C j is removed from the set of the reference classes, and all its feature vectors are stored in a rejection set for reuse in the next learning iteration (see Fig. 4). AUDyC clusters feature data to different classes representing different system operation modes. These classes have no physical meaning, hence, it is necessary to determine their corresponding names in the following labelling procedure.

C. Labelling

Based on the physical knowledge available, conditions are formulated to determine the class names. In our case, we assume that there are 7 operation modes: normal mode and 6 single OC fault modes corresponding to the 6 IGBTs in the 3-phase inverter.

Let β a,j , β b,j , β c,j ∈ R be, respectively, the coordinates of the mean vector β j of class C j . From the analysis in the last paragraph of Section II, we remind that the feature variables defined by (1b) are close to zero in the normal mode and converge to 1 or -1 in the faulty modes. Thus, a labelling threshold l is given to verify if the mean vectors

< √ 3 l Fault T 1 -1 -l < β a,j < -1 + l Fault T 2 1 -l < β a,j < 1 + l Fault T 3 -1 -l < β b,j < -1 + l Fault T 4 1 -l < β b,j < 1 + l Fault T 5 -1 -l < β c,j < -1 + l Fault T 6 1 -l < β c,j < 1 + l
of the modelled classes are close to these reference values as in Table II. If there is no satisfied condition, the class represents an unknown mode.

Based on these labels, we can determine the mode names of the new feature vectors in the following section.

D. Fault detection and isolation

This procedure concentrates on finding fault indicator. Using only the new feature vector for diagnosing the OC fault may cause false alarms due to two reasons: perturbations or temporary passage. The temporary passage implies that the feature vector may rapidly pass through a class which does not represent the actual operation mode. To deal with these false alarm causes, we consider a sequence of consecutive feature vectors, B(t) = {β(t -N f t s + t s ), . . . , β(t)}, where N f ∈ N is a chosen parameter defining the width of the sequence. We find the class to which most of vectors of B(t) belong. Then, the number of vectors of B(t) that belong to this class is used as the fault indicator.

Let n p (t) ∈ N be the number of existing classes. We define the indication matrix M(t) ∈ {0, 1} N f ×np(t) such that:

m kj (t) =      1, if µ j (β(t -kt s + t s )) = max p µ p (β(t -kt s + t s )) ≥ µ f , 0, if else, (9)
where m kj (t) is the element on the k th row and j th column of matrix M(t), µ j (β(t)) is the membership function of class C j defined in (2), µ f is a given indication threshold. The fault indicator is the maximum absolute column sum norm of the matrix M(t), that is

I(t) = M(t) 1 .
Let j max be the index of the column whose absolute column sum is highest. The system operation is in the mode corresponding to class C jmax if I(t) ≥ I f , where I f ∈ N is a given fault threshold. Otherwise, the system is in an unknown faulty mode. The fault detection strongly depends on the parameters chosen for the AUDyC algorithm. Therefore, the detection time is not easy to be mathematically estimated.

The presented diagnosis procedure is validated using simulation and experimental data in the next section.

IV. DIAGNOSIS RESULTS

Using the proposed method, this section presents the diagnosis results for the inverter single OC fault based on simulation and experiment data. In Scenario 1, simulation data are obtained using Simscape Electrical toolbox in Matlab. In Scenario 2, experimental data on a 5-phase inverter is provided by the L2EP laboratory in Lille, France. The properties of the data and the parameters of the diagnosis algorithm are presented in Table III. In the following result analysis, the mode flag is described by the natural variable f ault. It is set to 0 if the operation mode is not detected. Generally, there is rule for choosing the algorithm parameters. Their values given in Table III were obtained by trying different combinations for Scenario 1. The profiles of phase currents, of feature variables and of fault flag are described in Fig. 5. This figure illustrates the remark in Section II.B: when an OC fault happens, the positive (or negative) part of the current in the corresponding leg is cut-off. It results in the -1 (or 1) value of the corresponding feature variable.

Fig. 6 describes the feature classes obtained using AUDyC corresponding to the operation modes. To verify if these classes characterize the operation modes in Scenario 1, we recall the conditions in Table II.

The last subfigure in Fig. 5 and Table IV describe the diagnosed operation modes and the corresponding detection times. We see that all the operation modes are identified. The time duration of the mode diagnosis includes two periods. For example, we consider the time period from 0.2000s to 0.2132s. First, from 0.2000s, the system is still in normal operation mode even though a fault happens. From 0.2132s, an unknown operation mode is declared since the feature set, B(t), is not close to any class. Until 0.2226s, Mode 2 is declared, and thus, the detection time is 0.0226s. The obtained detection times (in Table IV) are short enough for Even though the paper focused on the 3-phase inverter, the procedure can be applied to 5-phase inverters with minimal changes as shown in this test case. The normal mode and 4 alternative single OC fault modes are implemented as given in Table V. In particular, the Auto-adaptive and Dynamical Clustering algorithm was employed for data clustering thanks to its online, adaptive and unsupervised learning capacities. Based on the obtained classes, we formulate the conditions for the labelling and fault detection and isolation. The approach was validated on simulation and experimental data. The short term future work concentrates on investigating the influence of the algorithm parameters on the detection time and making suggestions for choosing the ones yielding the smallest possible detection times. The algorithm robustness may be also evaluated with the load switching and/or intermittent fault. Moreover, the proposed approach will be compared with state of the art signal-based methods in the power electronics community.

Fig. 2 :

 2 Fig. 2: Phase AC currents in the normal and T 1 faulty modes.

Fig. 3 :

 3 Fig. 3: Fault diagnosis algorithm.

Fig. 5 :

 5 Fig. 5: Profiles of currents, of feature variables and of fault.

Fig. 6 :

 6 Fig. 6: Clustered classes in the feature space in Scenario 1.

Fig. 7 :

 7 Fig. 7: Schematic diagram of the 5-phase inverter [1].

Fig. 8 :

 8 Fig. 8: Current profiles in Scenario 2.

TABLE I :

 I Operation modes, diode states, transistor states and electric voltage of leg a.

TABLE II :

 II Labelling conditions.

	Mode	Condition
	Normal	β j

TABLE III :

 III Parameters for the data and the diagnosis algorithm.

	Parameter	Unit	Scen. 1 Scen. 2
	Data
	Signal frequency	[Hz]	50	50
	Sample time ts	[µs]	80	80
	PWM switching frequency	[Hz]	5000	10000
	AUDyC
	Class membership threshold µ min	-	0.7	0.7
	Initial covariance σ	-	0.8	0.8
	Minimum class cardinality N min	-	150	150
	Maximum class cardinality Nmax	-	250	250
	Fusion threshold f	-	3.2	3.2
	Labelling
	Labelling threshold l	-	0.1	0.1
	Fault detection and isolation
	Indication threshold µ f	-	0.7	0.7
	Sliding window width N f	-	150	150
	Fault threshold I f	-	130	130

A. Scenario 1: Simulation data

The normal mode and 6 alternative single OC fault modes are implemented as described in Table

IV

. The first row indicates the time durations of each mode.

TABLE IV :

 IV Settings and detection times in Scenario 1.

	Time [s]	0.2	0.1	0.1	0.1	0.1	0.1	0.1
	Fault	None	T 1	T 2	T 3	T 4	T 5	T 6
	f ault ref	1	2	3	4	5	6	7
	Det. time [ms]		22.6	30.6	30.9 28.5	17.8	28.4

TABLE V :

 V Settings and detection times in Scenario 2.

	Time [s]	0.25	0.25 0.25	0.25	0.25
	Fault	None	T 4	T 3	T 2	T 1
	f ault ref	1	5	4	3	2
	Det. time [ms]		22.9 36.6	27.2	30.3

ACKNOWLEDGEMENT

This work has been achieved within the framework of the CE2I project (Convertisseur d'Energie Intégré Intelligent). CE2I is co-financed by European Union with the financial support of European Regional Development Fund (ERDF), the French State and the French Region of Hauts-de-France. The authors would like also to thank Tiago Dos Santos Moraes, Eric Semail and Ngac Ky Nguyen from the L2EP laboratory in Lille (France) for providing the experimental data used in the second scenario.


R Fara

N Bel-Haj-Maati

D.-T Phan-Huy

N Malhouroux

M Di Renzo

First experimental evaluation of ambient backscatter communications with massive MIMO reader

Keywords: Ambient backscatter, 5G, internet of things, Massive MIMO, LSE

Ambient backscatter communications have been introduced as low-power communications for green networking. This technology is very promising as it recycles ambient radio frequency waves, however such systems have limitations and suffer from poor performance due to their low-power. In this paper, we present and evaluate the performance of an ambient backscatter system equipped with a massive multiple input multiple output (MIMO) antenna at the reader side. A mobile device transmits a signal that is backscattered by a tag and received by the reader. Thanks to the spatial diversity of the massive MIMO antenna, the reader is able to identify, for each received signal, the state of the tag (backscattering or transparent) that corresponds to a bit of the tag message. First, we experimentally determine the channel between the device and the reader for two states of the tag. Then using a minimum mean square error algorithm we evaluate the performance of the ambient backscatter communication. We demonstrate that the performance of the ambient backscatter system is significantly improved by the massive MIMO reader.

I. INTRODUCTION

Internet of Things (IoT) has the potential to provide wireless connectivity between a large number of devices. This important growth in the number of devices is continually increasing energy consumption of wireless networks [START_REF] Gati | Key technologies to accelerate the ict green evolution: An operators point of view[END_REF].

In 2013, ambient backscatter (AmB) communications have been introduced as low-power technology [START_REF] Liu | Ambient backscatter: Wireless communication out of thin air[END_REF]. In AmB system, a Radio-Frequency (RF) tag transmits a binary message to a RF reader without battery and without generating additional wave. The tag is illuminated by an RF ambient source of the environment such as: a base station, a TV tower or a Wi-Fi hotspot. The tag transmits "1" and "0" bits by backscattering or not the ambient waves, each bit corresponds to one state of the tag (backscattering and transparent respectively). A tag based on a dipole antenna changes its state by switching the load impedance connected to its antenna. An close to 0 impedance corresponds to the backscattering state: the antenna is shortcircuited. An infinite impedance corresponds to a transparent state: the antenna is open-circuited. The state of the tag impacts the ambient waves and can be detected by a simple energy detector reader. As AmB systems do not generate additional wave they are ultra-low power and can be powered by energyharvesting. Indeed, ambient backscatter communications have been identified as a promising technology for future networks and green communications [START_REF] Zhang | A green paradigm for Internet of Things: Ambient backscatter communications[END_REF].

However, such technology has limitations, in [START_REF] Rachedi | Demo Abstract : Real-Time Ambient Backscatter Demonstration[END_REF] it was observed that a simple tag and receiver made of one dipole antenna suffers from interference and fading. The tag may not be well illuminated by the source and not able to backscatter signal to the reader. The tag may also interfere with the legacy signal and the reader may not be able to detect the backscattered signal.

Multiple-input multiple-output antennas (MIMO) have largely been studied in the past years, especially for 5G networks. MIMO antennas exploit the spatial diversity of the propagation channel to increase the robustness or the throughput of the wireless communication link. Additionally, it was shown in [START_REF] Phan-Huy | Single-Carrier Spatial Modulation for the Internetof Things: Design and Performance Evaluation byUsing Real Compact and Reconfigurable Antennas[END_REF] that 5G MIMO antenna can provide large benefits for low power Internet of Things (IoT) devices. Indeed, the space-time digital processing capability of the massive MIMO 5G base station can be used to detect low power connected objects.

A 5G massive MIMO antenna has been proposed as a smart source for AmB communications in [START_REF] Fara | Ambient backscattersfriendly 5G networks: creating hot spots for tags and good spots for readers[END_REF]. In downlink configuration, the source beamforms the signal to create a hot spot on the tag and to avoid interference at the reader side. These promising results were obtained through simulations with simple models. In this system the reader was composed of one antenna and was not exploiting the receive diversity to detect the tag.

In [START_REF] Zhao | Spatial Stream Backscatter Using Commodity WiFi[END_REF] and [START_REF] Liu | VMscatter: A Versatile MIMO Backscatter[END_REF], an AmB WiFi system is proposed where source, tag and reader are equipped with MIMO antennas. Such system exploits channel diversity thanks to multiple radiating elements per antenna. However in experiments, the number of receiving antenna elements was limited to a maximum of 4 elements for the source, tag and reader.

MIMO reader is studied in [START_REF] Mishra | Multi-Tag Backscattering to MIMO Reader: Channel Estimation and Throughput Fairness[END_REF] to overcome the limitations of AmB systems. A low complexity algorithm is proposed for the multi-antenna reader to optimize the throughput. However, tag used is not entirely passive and this system has not been experimented and tested in real environment.

In this paper, for the first time, we propose to evaluate experimentally an AmB system using a massive MIMO base station as reader. As illustrated in Fig. 1, we consider an uplink transmission scenario in an anechoic chamber. First, a device, considered as source in the AmB system, sends pilots to the reader. The AmB tag, in the environment, impacts the propagation channel between the device and the reader. The considered tag has two states: a backscattering state in which the dipole antenna is short-circuited (null impedance) and a transparent state in which the antenna is open-circuited (infinite impedance). The propagation channel between the device and the reader depends on the tag state. The reader receives the signal and estimates the propagation channel for the backscattering and the transparent state of the tag. As each state codes a bit of the tag message, the reader is able to decode the tag message.

We propose to exploit the channel spatial diversity using an antenna with 64 elements as reader. The considered massive MIMO antenna is optimized for a carrier frequency between 3.6-3.8 GHz. This antenna has been designed for space-time channel sounding and presented in [START_REF] Pajusco | Massive antenna array for space time channel sounding[END_REF]. The antenna is a Slant Uniform Planar Array (SUPA), composed of 32 elements in vertical polarization and 32 in horizontal polarization with a uniform spacing of half-wavelength. Based on the experimental channel estimation campaign, we propose to evaluate the performance of the system using a least square error (LSE) estimator to detect the state of the tag. The performance is evaluated by adding additive white gaussian noise (AWGN) to the received signal based on the real channel estimation. Hence, the performance can be evaluated as function of the signal to noise ratio (SNR).

The paper is organized as follows: section II presents our system model, section III presents experimental setup, section IV presents our results and the section IV concludes this paper.

II. SYSTEM MODEL

A. Channel estimation

As illustrated in Fig. 1, we consider an AmB system, in an uplink configuration, with a mobile device as a source and a massive MIMO antenna as a reader. The reader is equipped with a SUPA of 𝐾 elements with uniform spacing of half-wavelength.

We consider a single-antenna tag with two states: a transparent state (OFF) and a backscattering state (ON), depending on the tag state, the channel between the device and the reader is slightly different as shown in Fig. 1.

In order to use a realistic model of the massive MIMO antenna and the propagation channel, the channel impulse response (CIR) is measured for each antenna element of the SUPA. The measured CIR is a discrete channel acquired for a maximum excess delay 𝑇 𝑚𝑎𝑥 , with symbol duration of 𝑇 𝑠 . The channel coefficients are determined for a bandwidth 𝐵 = 1/(𝑇 𝑠 ). The CIR is measured 𝑀 times in order to average the measurement noise. The 𝑚 𝑡ℎ experimental measurement of the CIR 𝐡 (𝒎) is defined as:

𝐡 (𝒎) (𝑡) = ∑ ℎ (𝑚,𝑞) 𝛿(𝑡 -𝑞𝑇 𝑠 ) 𝑄-1 𝑞=0 , (1)
where ℎ (𝑚,𝑞) ∈ ℂ is a complex gain, 𝑞 the time index and 𝑄 the number of time index. We have (𝑄 -1)𝑇 𝑠 , the maximum excess delay.

The estimated channel coefficient vector 𝐡 is determined by averaging the 𝑀 experimental measurement. Therefore 𝐡 is defined as:

𝐡(𝑞𝑇 𝑠 ) = 1 𝑀 ∑ 𝐡 (𝒎) (𝑞𝑇 𝑠 ) 𝑀 𝑚=1 , (2)
The reader stores 𝐡 for the two states of the tag. We denote 𝐡 (𝑶𝑭𝑭) and 𝐡 (𝑶𝑵) , as the stored 𝐡 for the transparent and backscattering states, respectively.

The signal received for the 𝑞 𝑡ℎ time index, over the 𝐾 antenna ports of the reader is 𝐲(𝑞𝑇 𝑠 ) ∈ ℂ 𝐾×1 , it is defined for the antenna 𝑘 ∈ [1 … 𝐾] as:

𝐲 𝒌 (𝑞𝑇 𝑠 ) = 𝐡 𝒌 (𝑞𝑇 𝑠 )√𝑃 𝑢 + 𝐯 𝒌 (𝑞𝑇 𝑠 ), (3)
where and 𝐡 𝒌 (𝑞𝑇 𝑠 ) the normalized channel coefficient between the device D and the antenna element 𝑘 for the bandwidth 𝐵, 𝐯 𝒌 (𝑞𝑇 𝑠 ) is the noise for the element 𝑘 of the reader, and 𝑃 𝑢 is the transmit power spectral density. We denote

𝑃 𝑛𝑜𝑖𝑠𝑒 = 𝐸[‖𝐯‖ 2 ]

𝐾

, as the received noise power spectral density, where ‖. ‖ 2 is the Frobenius norm. We define a signal to noise ratio metric SNR:

𝑆𝑁𝑅 = 𝑃 𝑢 𝑃 𝑛𝑜𝑖𝑠𝑒 . (4)

B. LSE Estimator

The reader compares 𝐲 and the stored estimation 𝐡 and calculated 𝑦 ̂ the mean square error:

𝑦 ̂= arg min {∑‖𝐳(𝑞𝑇 𝑠 ) -𝐲(𝑞𝑇 𝑠 )‖ 2 𝑄-1 𝑞=0 } 𝑤𝑖𝑡ℎ 𝐳(𝑞𝑇 𝑠 ) ∈ {𝐡 (𝑶𝑵) (𝑞𝑇 𝑠 ), 𝐡 (𝑶𝑭𝑭) (𝑞𝑇 𝑠 )}.
(5)

Based on the Equation ( 5) we obtain two values of 𝑦 ̂, for 𝐳 = 𝐡 (𝑶𝑵) and for 𝐳 = 𝐡 (𝑶𝑭𝑭) , the estimator keeps the minimum value between the two. It associates an estimated bit equals to 1 if the backscattering state is detected (ON) and 0 if the transparent state is detected (OFF).

Fig. 2. Overview of system detector

To evaluate the general performance of the system, we consider a random sequence of 𝑅 sent bits 𝐛 = 𝐛 1 … 𝐛 𝑟 … 𝐛 𝑅 , if 𝐛 𝑟 = 1 (or 𝟎 respectively) we use 𝐡 = 𝐡 (𝑶𝑵) (or 𝐡 (𝑶𝑭𝑭) respectively) in Equation (3). Then, we deduce a sequence of 𝑅 bits from the LSE detector 𝐛 ̂= 𝐛 ̂1 … 𝐛 ̂𝑟 … 𝐛 ̂𝑅, we compare the detected bits 𝐛 ̂ with the sent bits 𝐛 and we calculate the 𝐵𝐸𝑅 as follows:

𝐵𝐸𝑅 = ∑ |𝐛 ̂𝑟 -𝐛 𝑟 | 𝑅 𝑟=1 2 𝑅 . (6)
III. EXPERIMENTAL SETUP CIR measurements were conducted in an anechoic chamber. The experimental system consisted of a 64 elements massive MIMO antenna, a device to illuminate a two states tag. These elements are illustrated in the complete system in Fig. 3 and in Fig. 4 as separated elements.

For a sample acquisition, the device, illustrated in Fig. 4-c), transmits a pulse signal at the carrier frequency of 3.7 GHz. The massive MIMO antenna, illustrated in Fig. 4-a), receives the device signal through the propagation channel acquires the signal on each of the 64 elements. The system response is measured for a maximum excess delay of 4. 10 -8 seconds for different antenna and bandwidth configurations. The channel has been measured for a distance separating the tag and the device equal to 2λ, and for a fixed position of the device, the tag and the MIMO antenna. The measurement has been done for the two states of the tag (transparent and backscattering). Each channel acquisition was repeated between 𝑀=5 to 𝑀=6 times. The gains and the phases for each elements of the MIMO antenna are obtained with the measurement campaign. Based on these acquisitions, the 64 channels are evaluated for the two states of the tag.

In this simulation we evaluate the performance of the tag state detection using a LSE algorithm for a SNR between -10 dB and 20 dB.

A. Massive MIMO Antenna

We evaluate the performance as function of the number of receiving antenna elements. The different configurations are illustrated in Fig. 5. The number K of antenna elements depends on the number of active elements on the reader antenna. The configurations are chosen in such a way that the total spatial occupancy of the active elements increases with K. This enables us to emulate a large variety of readers: from small devices, laptops, tablets, small cells, MIMO base stations, up to massive MIMO base stations.

B. Bandwidth

The performance of the system is also evaluated as a function of the bandwidth. We consider 3 different configurations for the bandwidth of the measured signal, detailed in Table II. For each configuration, we keep the same central frequency, as illustrated in Fig. 6. In order to allow fair comparison between the three configurations, the power spectral densities of the signal and the noise are kept identical when changing the bandwidth. As a consequence, when we consider a large bandwidth, the number of symbols increases but the SNR (per symbol) is the same.

TABLE II. BANDWIDTH CONFIGURATIONS

Bandwidth

IV. RESULTS

This section presents experimental results based on the model depicted in II, and the setup given in Section III. We first observe in Fig. 7 the amplitude of the experimental CIR of the system for the first element of the reader antenna as function of the time. The channel has several echoes corresponding to the tag and the support table. The support creates a non-negligible reflection even if the material of the support is not very sensitive to electromagnetic waves.

In Fig. 8, the 64 elements of the massive MIMO antenna are positioned on a grid of 8 lines and 8 columns. The maps illustrate the spatial signature in line as function of the time for the two states. The top maps (a) illustrate the amplitude of the CIR coefficients for 𝑞𝑇 𝑠 = 0 to 40 ns. The bottom maps (b) illustrate the phase between -180° and 180° of the CIR. We observe the spatial diversity of the massive MIMO antenna. One can note that between the two states of the tag the differences are visible in the phase domain. The amplitude of the signal received for each element of the reader is not significantly impacted by the state of the tag. However, the impact is more significant for the phase domain. Fig. 9 illustrates the BER as function of the SNR for different configurations of 𝐾 antenna elements. Each antenna configuration is evaluated for the bandwidth configuration B1 (100 MHz). We observe that as the number of antennas increases the perfomance is improved. The MIMO antenna exploits the channel diversity and this improves the reliability and the robustness of the detection. Fig. 10 illustrates the BER as function of the SNR for different bandwidth configurations. Each bandwidth configuration is evaluated for 𝐾 = 64 antenna elements. We observe that the performance is improved if the bandwidth increases. This is due to the fact that the frequency diversity of the channel increases with the bandwidth. Indeed, the impact of the echoes, which create frequency diversity, increases with the bandwidth. Diversity improves the performance of the LSE detector. The performance of the bandwidth 𝐵 = 20 MHz is poor because the bandwidth is too reduced to have the impact of the echoes. Finally, we evaluate the performance depending on the distance between the device and the tag. Device location is fixed and the tag is located at 1.5 or 2 lambda of the device. In the Fig. 11 we observe that the performance is impacted by the distance. This effect has been observed in SISO with ellipses in [START_REF] Rachedi | Demo Abstract : Real-Time Ambient Backscatter Demonstration[END_REF] and was due to the phase of the signal. As we observe in Fig. 12 the phase difference is more significant for the configuration 𝑑 𝐷-𝑇 = 1.5𝜆 and may result in the performance improvement. For further studies, remains the performance evaluation with respect to the distance between the tag and the device in a MIMO reader configuration.

V. CONCLUSION

In this paper, for the first time, we have evaluated the performance of an ambient backscatter system with a massive multiple input multiple output antenna as a reader and a mobile device as a source, experimentally. A device sends a signal to the reader that is backscattered or not, by a tag, depending on the tag state. The reader estimates the channel for the two states of the tag (transparent and backscattering) and applies a least square error algorithm to detect the bit sent by the tag corresponding to the state of the tag. This experimental evaluation has been done for multiple values of signal to noise ratio. We show that the spatial diversity of the reader antenna highly improves the performance of the detection. We have also shown the impact of the number of receiving antenna and the bandwidth. Future studies will assess the performance on more complex propagation channels, outside anechoic chamber and multi-state tags.

Fig. 1 .

 1 Fig. 1. Backscattering system using MIMO reader in uplink configuration

Fig. 3 .

 3 Fig. 3. Photo of the complete experimental system The two states tag is illustrated in Fig. 4-b), we observe the half wavelength dipole antennas and a switch that allows us to change the state of the tag. The switch can open-circuit the antenna branch (transparent) and short-circuit the branch of the antenna (backscattering).

Fig. 4 .

 4 Fig. 4. Photos of the 64 elements antenna (a), of the tag (b), of device and tag (c)

Fig. 5 .

 5 Fig. 5. Configurations of the MIMO antenna.

8 Fig. 6 .

 86 Fig. 6. Configurations of the bandwidth

Fig. 7 .

 7 Fig. 7. Exemple of CIR as function of the time for the first antenna element : a) real part b) imaginary part

Fig. 8 .

 8 Fig. 8. Exemple of CIRs for the 64 receive antenna elements configurations. a-1) amplitude of the CIR for the transparent state, a-2) amplitude of the CIR for the backscattering state, a-3) amplitude difference between the two states b-1) phase of the CIR for the transparent state, b-2) phase of the CIR for the backscattering state, b-3) difference of phase between the two states (on average equal to 16 degrees).

Fig. 9 .

 9 Fig. 9. Bit error rate function of SNR for different 𝐾 antenna elements configurations.

Fig. 10 .

 10 Fig. 10. Bit error rate as function of SNR for different bandwidth configurations.

Fig. 11 .

 11 Fig. 11. Bit error rate as function of SNR for different device to tag distance configurations.

Fig. 12 .

 12 Fig. 12. Phase difference of the CIR between the two states (transparent and backscattering) for different distance device to tag configurations. 64 elements of the massive MIMO antenna are positioned on a grid of 8 lines and 8 columns.

TABLE I .

 I EXPERIMENTAL PARAMETERS

	Parameters	Details	Value	Units
	𝐾	Number of Reader Antenna	{1, 2, 4, 8, 16, 32, 64}
	𝑅	Number of bits per sequence	10 6
	𝑑 𝐷-𝑇	Distance between the Device and the tag	2𝜆	m
	M 𝑂𝑁	Number of acquisition (ON state)	6
	M 𝑂𝐹𝐹	Number of acquisition (OFF state)	5

ACKNOWLEDGMENT

This work is partially supported by the French Project ANR Spatial Modulation under grant ANR-15-CE25-0016 (https://spatialmodulation.eurestools.eu/).