Marion Foure

Camille Dugardin

Benoit Foligne

Philippe Hance

Thierry Cadalen

Abigaël Delcourt

Bernard Taminiau

Georges Daube

Rozenn Ravallec

Benoit Cudennec

Benoît Foligne

Abigael Delcourt

Jean-Louis Hilbert

Anca Lucau-Danila

Chicory roots for prebiotics and appetite regulation: A pilot study in mice

Keywords: Cichorium intybus, prebiotics, metabolomics, metagenetics, satiety hormones

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 chicory root represents the main industrial source of inulin [START_REF] Shoaib | Inulin: Properties, health benefits and food applications[END_REF] and is also used as a food ingredient in the form of flour or roasted products. It goes into our food under various forms entailing an implicit consumption of inulin. Inulin-type prebiotics include fructooligosaccharides (FOS), oligofructose, and inulin. [START_REF] Roberfroid | Inulin-type fructans: functional food ingredients[END_REF] They escape digestion in the digestive superior tract and arrive in the colon, where they constitute the ideal fermentative substrate for resident bacteria. These ingredients are legally classified as food or food ingredients and are included in the daily diet. [START_REF] Coussement | Inulin and oligofructose: Safe intakes and legal status[END_REF] Inulin is especially consumed and has received a notice from the European Food Safety Authority concerning positive health effects. Inulin is incorporated into numerous foodstuffs such as yogurts and cheeses, [START_REF] Karimi | Application of inulin in cheese as prebiotic, fat replacer and texturizer: A review[END_REF] cakes, [START_REF] Liu | Effects of inulin on the structure and emulsifying properties of protein components in dough[END_REF] and dough. [START_REF] Latoch | The effect of replacing pork fat of inulin on the physicochemical and sensory quality of guinea fowl pate[END_REF] Added to coffee, inulin showed an effect on satiety by mitigation of the sensation of hunger. [START_REF] Singer | Safety and efficacy of coffee enriched with inulin and dextrin on satiety and hunger in normal volunteers[END_REF] By reinforcing the gut microbiota, inulin participates in the increase of resistance to infections as observed for the anti-inflammatory effects on asthmatic patients. [START_REF] Halnes | Soluble Fibre Meal Challenge Reduces Airway Inflammation and Expression of GPR43 and GPR41 in Asthma[END_REF] Similarly, by stimulating the growth of lactobacilli, inulin is able to activate T cells and NK cells as well as the phagocytes. [START_REF] Kelly-Quagliana | Dietary oligofructose and inulin modulate immune functions in mice[END_REF] Functional food refers to food containing ingredients that aid specific bodily functions in addition to being nutritious, [START_REF] Hasler | A new look at an ancient concept[END_REF] and so inulin is currently used as functional food, particularly in a variety of dairy products, to enhance the intensification of the beneficial intestinal bacteria. [START_REF] Menne | Fn-type chicory inulin hydrolysate has a prebiotic effect in humans[END_REF] A prebiotic is defined as a substrate that is selectively utilized by host microorganisms conferring to this host a health benefit. [START_REF] Gibson | Expert consensus document: The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics[END_REF] This new definition expands the concept of prebiotics to include noncarbohydrate substances, applications to body sites other than the gastrointestinal tract, and diverse categories other than food. The community of microorganisms that makes up the gut microbiota has been the object of many studies. The analysis of genetic material recovered directly from environmental samples by metagenomic approaches reproduces the profile of microbial biodiversity in the initial sample. Sequence analysis of the 16S rRNA gene has been widely used to identify bacterial species from the digestive tract, and next-generation sequencing techniques that have emerged during the past decade have facilitated the rapid analysis of these samples. [START_REF] Bragg | Metagenomics using next-generation sequencing[END_REF] This technique facilitates the monitoring of different modifications of the gut microbiota under different treatments or diets.

Concerning the food-intake regulation, it is known that the short-term regulation implicates multiple actors including the brain and the intestine, which coordinate with nervous and endocrine signals. Intestinal hormones, secreted by enteroendocrine cells (EECs) scattered along the gastrointestinal tract, are primordial signals implicated in this regulation. Thus, in response to nutrient sensing at their apical side, EECs secrete a wide range of gut hormones acting as peripheral signals transmitted to the brain to regulate appetite, energy expenditure, and insulin secretion. [START_REF] Rønnestad | Duodenal luminal nutrient sensing[END_REF][START_REF] Gribble | Enteroendocrine cells: chemosensors in the intestinal epithelium[END_REF] Among the many hormones produced by the gut, cholecystokinin (CCK) and glucagon-like peptide 1 (GLP-1) are well-studied anorexigenic hormones. However, released GLP-1 is quickly inactivated by the dipeptidyl peptidase IV (DPP-IV). Consequently, DPP-IV represents a good target for type 2 diabetes mellitus (T2DM) management, and the search for natural DPP-IV inhibitors seems today to be a promising approach. [START_REF] Caron | Protein Digestion-Derived Peptides and the Peripheral Regulation of Food Intake[END_REF][START_REF] Nongonierma | Enhancing bioactive peptide release and identification using targeted enzymatic hydrolysis of milk proteins[END_REF] The chicory root contains other major compounds as chlorogenic acids and sesquiterpene lactones, [START_REF] Willeman | A Method for the Simultaneous Determination of Chlorogenic Acid and Sesquiterpene Lactone Content in Industrial Chicory Root Foodstuffs[END_REF] which may be of further interest. The potential synergies between inulin and these different specialized metabolites are not yet described. Consequently, we interrogated many different chicory genotypes for their metabolites composition by a proton nuclear magnetic resonance spectroscopy ( 1 H NMR) and liquid chromatography-UV (LC-UV) for absolute quantification. Chicory roasted roots from different genotypes were next used to feed mice. Gut microbiota variations were then analyzed by NGS sequencing of 16S rRNA gene and metagenetic analysis in order to be associated with roots metabolites composition. In addition, the effects of roasted chicory on intestinal hormone modulation were investigated in vitro with the combination of simulated gastrointestinal digestion, secretion study using the STC-1 enteroendocrine cell line, and a dipeptidyl peptidase-IV (DPP-IV) inhibition assay.

■ MATERIALS AND METHODS

Chicory Metabolomics. Nine industrial chicory (Cichorium intybus) genotypes selected from a 48 genotype diversity field trial, conducted by Florimond-Desprez Veuve et Fils SAS (Cappelle-en-Pevele, France) in year 2011 and located in Coutiches (France), were used to produce fresh chicory roots. Roots were dried and roasted by Leroux SAS (Orchies, France). Samples (roasted products) were freeze-dried (48 h), ground (Waring 7011G blender with glass container, Waring), and sieved to 355 μm. Extraction of metabolites was performed by the decoction procedure. Warm water (100 mL, 85 °C) was added to a 150 mL glass tube containing 3 g of powder. The mixture was incubated for 20 min at 80 °C under weak agitation. The solution was filtered through a coffee filter, filtrates were centrifuged at 30 000g for 10 min at 4 °C, and the supernatants were store at -80 °C until feeding. The concentration of the decoctions was normalized on the basis of dry matter to 15.5 ± 0.5 mg/mL. 1 H NMR absolute quantification was carried out according to Moing et al. [START_REF] Moing | Quantitative metabolic profiling by 1-dimensional 1H-NMR analyses: application to plant genetics and functional genomics[END_REF] by the ERETIC method using calibration curves of C1H-(α+β) glucose. [START_REF] Akoka | Concentration Measurement by Proton NMR Using the ERETIC Method[END_REF] For chlorogenic acids and sesquiterpene lactones determination, LC-UV analyses were performed according to Willeman et al. [START_REF] Willeman | A Method for the Simultaneous Determination of Chlorogenic Acid and Sesquiterpene Lactone Content in Industrial Chicory Root Foodstuffs[END_REF] All experiments were conducted in triplicate. Statistical analysis was performed using R 2.15.1 for Windows with FactoMineR and agricolae libraries to perform principal component analysis (PCA) and analysis of variance (ANOVA), respectively. Normality and homogeneity of the variance were checked by Shapiro-Wilk's test and Bartlett's test, respectively, followed by Tukey's test to assess significant differences between means of groups. Differences were considered significant for a p-value of <0.05.

In Vitro Simulated Gastrointestinal Digestion. In vitro simulated gastrointestinal digestion (SGID) was performed as previously described, [START_REF] Caron | Simulated GI digestion of dietary protein: Release of new bioactive peptides involved in gut hormone secretion[END_REF] with some modifications. The first three steps of digestion were simulated (mouth, stomach, and duodenum), and three fluids were prepared to mimic the physiological conditions of each step. The composition of each fluid is described in Table 1, and the pH of the solutions was adjusted to physiologically relevant values using NaOH (5 M) and HCl (5 M) solutions. The whole digestion process was performed in a 200 mL reactor controlled at 37 °C under constant stirring with a magnetic stirrer over 240 min. Two g of chicory roasted roots (dry weight) was added to the reactor and solubilized in 16 mL of salivary fluids at pH 6.8 (125 g•L -1 dry matter) containing amylase at an enzyme/substrate (E/S) ratio of 1:430 (w/ w). Sampling of 4 mL was performed at the end of the salivary step. Twenty-four mL of gastric fluids containing pepsin at an (E/S) ratio of 1:40 (w/w) were added after saliva sampling, and the pH solution was adjusted (2.5-3.0). After 2 h, sampling of 16 mL was performed at the end of the gastric step (41.67 g•L -1 dry matter). Thirty-six mL of intestinal fluids (24 mL of duodenal juice and 12 mL of bile juice) containing pancreatin at an E/S ratio of 1:50 (w/w) and 4 mL of 1 M NaHCO 3 were then added to the batch, and the pH solution was adjusted to 7. Intestinal digestion was carried out again over 2 h, and sampling of 60 mL was performed at the end of the intestinal step. Final digest concentration reaches 13.89 g•L -1 dry matter. Once heated at 95 °C for 10 min to ensure enzyme denaturation, all samples were centrifuged at 13 400g for 10 min at room temperature. Supernatants were collected and frozen for further analysis.

CCK and GLP-1 Secretion Study. The murine enteroendocrine STC-1 cell line was a gift gratefully received from Dr. C. Roche Grangette (INSERM U865, Lyon, France). The STC-1 cells were grown at 37 °C, 5% CO 2 atmosphere, in Dulbecco's modified Eagle's medium (DMEM, 4.5 g•L -1 glucose) supplemented with 10% fetal bovine serum (FBS), 100 U•mL -1 penicillin, 100 μg•mL -1 streptomycin, and 2 mM glutamine. STC-1 cells were seeded to reach 60-80% confluence. Cells were washed twice with fresh media (without FBS) and incubated with 3 different concentrations (0.2, 0.5, and 1% (w/v)) of chicory roasted roots compared to their in vitro simulated gastrointestinal digestion (SGID) products diluted in incubation buffer (4.5 mM KCl, 1.2 mM CaCl 2 , 1.2 mM MgCl 2 , 140 mM NaCl, and 20 mM Hepes-Tris, pH 7.4) for 2 h at 37 °C in 5% CO 2 atmosphere. Cell supernatants were collected on ice and centrifuged (2 000g for 7 min). The supernatants were frozen and stored at -20 °C for further hormone-concentration determinations. Secreted CCK and active GLP-1 concentrations (pM) in culture media of STC-1 cells were assayed using commercial RIA kits (Active Glucagon-Like Peptide RIA kit from EMD Millipore from Merck KGaA, Darmstadt, Germany, and Gastrin kit from CisBio, France).

DPP-IV Inhibition Assay. The DPP-IV inhibitory activity of the native chicory roasted roots compared to their SGID products was assayed according to the method described by Lacroix and Li-Chan 22 with some modifications. Briefly, 25 μL of chicory samples and their corresponding SGID products at concentrations ranging from 0.87 to 13.89 mg/mL (dry matter) were preincubated with 75 μL of Tris/HCl Animal Experiments. Six-week-old female BALB/c BYj were used to evaluate the prebiotic effects of chicory in agreement with the directive 2010/63/EEC for the protection of animals used for scientific experiments and according to the law 2012-10 (2012) and 2013-118 (2013). The protocol was approved by the Ethics Committee for the experiment on animals (APAFIS 6539). The mice were randomly divided into groups (n = 5/group) and housed in a controlled environment (with a temperature of 22 °C, a 12 h/12 h light/dark cycle and ad libitum access to standardized food and water). Mice were fed with an aqueous decoction concoction (produced as described before) of the roasted powders from two selected genotypes (G12 and G35). This solution corresponded to 7 mg of chicory roasted roots/ mouse/day, which was considered to be the human equivalent weight/ body mass for a moderated alimentary dose. The mice gavages consisted in a daily force-feeding of 500 μL of stemming solution decoctions of roasted roots besides the standardized diet. Controls will undergo an equivalent force-feeding with water. Five mice by condition were used for 30 days followed by a week of resilience. Feces were individually harvested just prior to the starting of the treatment (D0), after 30 days of treatment (D30), and next to the washout period (D36). Samples were immediately frozen and preserved at -80 °C.

16S rRNA Gene Library Construction and Sequencing. Genomic DNA extraction from each fecal sample was performed with the QIAamp DNA Stool Mini Kit (kit QA) according to manufacturer's instructions. Quality and quantity of DNA extracted from fecal sample were determined using Agilent 2100 Bioanalyzer system (Agilent). Polymerase chain reaction (PCR) amplification of the V1-V3 region of the 16S rDNA and library preparation were performed with the following primers (with Illumina overhand adapters): forward (5′-GAGAGTTTGATYMTGGCTCAG-3′) and reverse (5′-ACCGCGGCTGCTGGCAC-3′). Each PCR product was purified with the Agencourt AMPure XP beads kit (Beckman Coulter, Pasadena, U.S.A.) and submitted to a second PCR round for indexing, using the Nextera XT index primers 1 and 2. After purification, PCR products were quantified using the Quant-IT PicoGreen (Thermo-Fisher Scientific, Waltham, U.S.A.) and diluted to 10 ng μL -1 . A final quantification, by qPCR, of each sample in the library was performed using the KAPA SYBR FAST qPCR kit (KapaBiosystems, Wilmington, U.S.A.) before normalization, pooling, and sequencing on a MiSeq sequencer using v3 reagents (ILLUMINA, U.S.A.). Positive control using DNA from 20 defined bacterial species and a negative control (from the PCR step) were included in the sequencing run. Sequence read processing was used as previously described using, respectively, MOTHUR software package v1.35 [START_REF] Schloss | Introducing mothur: open-source, platformindependent, community-supported software for describing and comparing microbial communities[END_REF] and UCHIME algorithm [START_REF] Edgar | UCHIME improves sensitivity and speed of chimera detection[END_REF] for alignment and clustering and chimera detection. Clustering distance of Roasted roots of G12 genotype present higher proportion of citrate, fumarate, lactate, formate, acetate, and inositol than those of G35 genotype. Inversely, roasted roots of G35 genotype were richer in inulin, sucrose, and fructose as well as in 3-mono-O-caffeoylquinic acid and dihydrodeoxylactucin. 0.03 was used for OTU generation. 16S Reference alignment and taxonomical assignation were based upon the SILVA database (v1.28) of full-length 16S rDNA sequences. [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF] Species assignment of representative sequence for each OTU was based upon a BlastN algorithm search using SILVA v1.28 database with an identity cutoff of 0.01. From 1 938 786 raw reads, we obtained 1 515 443 reads after cleaning (length and sequence quality). We retained 8 000 reads per sample as a subsampling process, which were further screened for chimeric contaminants. Finally, 217 790 reads (median: 7 632 per sample) were used for OTU clustering and taxonomic assignment. Good's coverage estimator was used as a measure of sampling effort for each sample, with a mean value of 98.44%. Subsample data sets were obtained and used to evaluate ecological indicators, richness estimation (Chao 1 estimator), microbial biodiversity (reciprocal Simpson index), and population evenness (derived from Simpson index) using MOTHUR. Population structure and community membership were assessed with MOTHUR using distance matrix based on Bray-Curtis dissimilarity index as a measure of community structure, which considers shared OTUs and their relative abundances. [START_REF] Bray | An ordination of upland forest communities of southern Wisconsin[END_REF] Ordination analysis and 3D plots were performed with Vegan, Vegan3d, and rgl packages in R. Nonmetric dimensional scaling, based upon the Bray-Curtis dissimilarity matrix, was applied to visualize the biodiversity between the groups. AMOVA test was performed to assess the diversity clustering of treatment groups with Bray-Curtis matrix using MOTHUR. [START_REF] Martin | Phylogenetic approaches for describing and comparing the diversity of microbial communities[END_REF] Statistical differences between bacterial biodiversity, richness, and evenness were assessed with twoway ANOVA corrected for multitesting (Benjamini, Krieger, and Yekutieli) using PRISM 7 (Graphpad Software); differences were considered significant for a p-value of <0.05. Statistical differences of population abundance between groups were assessed with ANOVA, using STAMP software. [START_REF] Parks | Identifying biologically relevant differences between metagenomic communities[END_REF] Statistical paired differences between treatment groups of specific bacterial populations were assessed by two-way ANOVA and Tukey-Kramerposthoc test using PRISM 7 (Graphpad Software); differences were considered significant for a pvalue of <0.05. All the biosample raw reads have been deposited at the National Center for Biotechnology Information (NCBI) and are available under ID PRJNA433087. Microbial profiling was validated for the most important bacterial phyla, using quantitative PCR (Supporting Information).

■ RESULTS

Metabolomic Analyses in Chicory Plants. Nine industrial chicory genotypes were selected from a 48 genotype diversity field trial. This selection was performed through a genetic diversity characterization using the combination of 47 metabolomic signals and 170 alleles issued from 19 monoloci, codominant, well-dispersed on the genome as informative chicory-specific simple sequence repeat (SSR) markers (unpublished results). From 9 interrogated chicory genotypes, metabolomic analyses revealed two very distinct genotypes: G12 and G35 (Figure 1). The G12 genotype presented significantly more citrate, fumarate, lactate, formate, acetate, and inositol than G35. Inversely, roasted roots of G35 genotype where richer in inulin, sucrose, and fructose as well as in 3mono-O-caffeoylquinic acid and dihydrodeoxylactucin. G12 and G35 genotypes were selected and used for mice feeding in order to observe modifications at the gut microbiota level.

In Vitro Gut Hormone Regulation. The roasted roots were predigested using SGID to mimic the first three compartments of the gastrointestinal tract, in order to compare the effects of the roots from two different genotypes. The samples for which SGID was not undertaken were incubated alongside the duodenal digests for 2 h with the STC-1 cells. Results showed that each genotype exerted a significant dosedependent stimulation of CCK release before and after SGID, and interestingly, the SGID treatment led to an increase in the CCK release for the G12 and the G35 genotypes (Figure 2A). These increases were significant for the highest dose assayed (1% w/v) and represented 20-and 25-fold the value of the control (buffer), respectively. In comparison, the hemoglobin digest, which was previously described as a strong stimulator of CCK release [START_REF] Caron | Simulated GI digestion of dietary protein: Release of new bioactive peptides involved in gut hormone secretion[END_REF] and used here as positive control, led to an increase of 30-fold of control.

The impact of SGID on the stimulation potential of GLP-1 secretion observed was significant (Figure 2B). Thus, the effects of the roasted roots before SGID on GLP-1 secretion were relatively weak and similar for the G12 and the G35 genotypes but significant compared to control (6-and 8.5-fold of control for the two highest doses). After the SGID treatment, a dosedependent increase on GLP-1 release, significant for 0.5 and 1.0% w/v, was observed for the G12 and G35 genotypes (28and 33-fold of control, respectively). At the same dose, the positive control, i.e., the hemoglobin digest, led to a 50-fold increase in GLP-1 release. Moreover, the DPP-IV inhibition capacities of the different roasted roots samples before and after SGID were assayed. No significant inhibition of the DPP-IV activity could be observed for any of the samples (data not shown). Roasted roots of G12 and G35 chicory genotypes were used for 30 days of daily supplementary dietary feeding of mice at 350 mg•kg -1 , a concentration estimated to be proportional to human daily consumption. No significant difference on the body weights of mice was found after treatments and after the washout period (Supporting Information). Fecal samples from mice before and after 30 days of chicory feeding and after the 1week washout were used for DNA extraction and 16S rRNA gene sequencing in order to perform a metagenetic analysis.

The microbial α-diversity within each group (G12, G35, and control) was quantified for each treatment range. Composed of scores between 0 and 5, the Shannon index (SI) represents a synthetic numerical value of the ecological α-diversity of the microbiota in each fecal sample, a higher SI indicating a greater diversity. A significant decrease in SI was observed after 30 days of treatment with G35 (Figure 3A), suggesting that the effect on microbiota could be dependent on the dietary chicory genotype. After the washout period (D36), both chicory genotypes seemed to maintain a decreased level of α-diversity compared to control (Figure 3B).

Chicory-dependent microbiota profile was next explored at the phylum level (Table 2). Four weeks of chicory diet induced several changes in the gut microbial community at the phylum level compared with the control (no-treated mice). The relative abundance of Firmicutes was less changed in G12-treated mice (15.6% vs 14.1%) but greatly reduced in G35-treated mice (3.1% vs 14.1%). The Bacteroidetes phylum was also less modified in the G12 group (83.6% vs 85.5%) but increased in the G35 group (96.5% vs 85.5%). Other phyla showed low variations during chicory treatment, <1%. In addition, the ratio of Firmicutes (F) to Bacteroidetes (B) was higher in G12treated mice and significantly lower in G35-treated mice compared to the control (0.187 to 0.032 vs 0.165) (Figure 4). qPCR analyses on three different feces samples confirmed this result (Supporting Information).

At the genus level, all changes observed in the relative abundance after 30 days of chicory treatment are represented in Figure 5 and the Supporting Information. Beta diversity among different conditions as a measure of genera turnover was considered by factorial correspondence analysis (FCA) (Figure 6) and showed a specific distribution of the significant bacterial genera abundances around each chicory treatment. Two taxa seemed to be more associated with the G35 treatment: Bacteroidales_unclassified and Alloprevotella, as they present a significantly increased abundance in G35 treatment. Six other taxa, Ruminococcaceae_unclassified, Alistipes, Oscillibacter, Lach-nospiraceae_unclassified, Blautia, and Clostridiales_unclassified, showed an important decrease in the G35 group (Supporting Information). The significant variations in classified taxa were compared to controls after 30 days (Figure 7). Alloprevotella relative abundance increased while abundance of Alistipes, Blautia, and Oscillibacter decreased during G35 chicory feeding.

■ DISCUSSION

Many studies have already been done on the inulin of chicory and its prebiotic effect. Our work was oriented toward a new analytical dimension. The composition of roasted roots of chicory was considered for the first time by all major metabolic compounds and also by the variability of these compounds. We tested the effects of the roasted products of chicory on the intestinal microbiota of mice, and these tests were performed for the first time on murine model. There are no safe or effective doses established for chicory consumption in humans as yet, but common doses that have been traditionally used range from 3 to 6 g of chicory root per day. [START_REF] Ulbricht | Natural Standard Herb & Supplement Guide: An Evidence-Based Reference[END_REF][START_REF] Ulbricht | The Authority on Integrative Medicine[END_REF] Taking this into account, 7 mg of roasted roots/mouse/day was used, which was considered to be the equivalent of a moderated human alimentary dose. We then analyzed the daily feeding consequences on mice gut microbiota composition and, in parallel, the in vitro effects on two satiety hormones. All results were then converged to be finally associated with the metabolite composition of each chicory genotype in order to estimate their effect as prebiotic and on appetite control. Chicory Genotypes Contain Different Metabolites Proportions. G12 and G35 chicory genotypes contain carboxylic compounds such as citrate, fumarate, lactate, Figure 6. Factorial correspondence analysis (FCA) of the significant abundant bacterial genera. FCA was used to describe variability among genera abundance (8 must abundant genera were used as variables, black letters) in the two analyzed chicory genotypes G12 and G35 (used as factors, red letters). The commonality created around the chicory G12 seems richer than those of G35. A genotype-dependent effect could be observed (the two rings). formate, or acetate, but a significant increasing amount of these was observed for G12 (Figure 1). Lactate and citrate could contribute to acetate production, [START_REF] Duncan | Lactate-Utilizing Bacteria, Isolated from Human Feces, That Produce Butyrate as a Major Fermentation Product[END_REF][START_REF] Flint | Links between diet, gut microbiota composition and gut metabolism[END_REF][START_REF] Hugenholtz | Citrate metabolism in lactic acid bacteria[END_REF] known as a short-chain fatty acid (SCFA). [START_REF] Rios-Coviań | Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health[END_REF] An important source of SCFAs is also inulin. This fermentable carbohydrate is a soluble dietary fiber that is present in noticeable amounts in chicory roots. In our experiments, inulin content of both chicory genotypes are elevated, with a significant increased amount for G35, representing 236.6% more inulin than G12. Similarly, fructose and sucrose contents were found to be significantly increased in G35 (Figure 1). It was demonstrated that supplementation of inulin resulted further in significantly elevated concentrations of total SCFAs in the cecum with positive effects on lipid metabolism and obesity development. [START_REF] Weitkunat | Effects of dietary inulin on bacterial growth, short-chain fatty acid production and hepatic lipid metabolism in gnotobiotic mice[END_REF] Fructose indirectly contributed to the formation of SCFAs because of its presence in fructans and fructooligosaccharides (FOS) that generate SCFAs under microbiota fermentation. [START_REF] Peshev | Prebiotics and immunomodulators[END_REF] Either ingested with food or produced by gut microbiota metabolism, SCFAs are then absorbed and used in different biosynthetic routes by the host, entering diverse carbohydrate and lipid metabolic pathways. The involvement of SCFAs in energy and lipid metabolism has largely been analyzed, and the potential role of SCFAs in the control of metabolic syndrome has also been observed and discussed in the literature. Butyrate and propionate have been reported to induce the production of the glucagon-like peptide-1 (GLP-1) gut hormone, thus reducing food intake. [START_REF] Lin | Butyrate and propionate protect against diet-induced obesity and regulate gut hormones via free fatty acid receptor 3independent mechanisms[END_REF] Acetate has also been found to reduce the appetite, but mainly through the interaction with the central nervous system, by a hypothalamic mechanism involved in the control of body weight. [START_REF] Frost | The short-chain fatty acid acetate reduces appetite via a central homeostatic mechanism[END_REF] Chlorogenic acids were detected in roasted chicory roots with a significant increased concentration for 3-mono-Ocaffeoylquinic acid (cqa) in G35 (Figure 1). Chlorogenic acids refer to a related polyphenol family of esters that can play a role in blood-pressure reduction [START_REF] Zhao | Antihypertensive effects and mechanisms of chlorogenic acids[END_REF][START_REF] Onakpoya | The effect of chlorogenic acid on blood pressure: a systematic review and meta-analysis of randomized clinical trials[END_REF] and have potential antiinflammatory effects. [START_REF] Tajik | The potential effects of chlorogenic acid, the main phenolic components in coffee, on health: A comprehensive review of the literature[END_REF] Another group of biologically active compounds in chicory are sesquiterpene lactones (SLs). SLs possess anti-inflammatory and antitumor activity and are the main determinants of bitterness. Control of their concentrations is a key point for the market value of different chicory varieties. [START_REF] Orofino Kreuger | Sesquiterpene lactones as drugs with multiple targets in cancer treatment. Focus on parthenolide[END_REF] Among SLs we noticed that the amount of 11β,13-dihydro-8-deoxylactucin (DHdLc) varied significantly between chicory genotypes (Figure 1), as G35 is richer in DHdLc than G12.

To conclude, we focused on the most significant differences between G12 and G35 chicory genotypes. The G35 roasted roots contained a higher amount of fermentable carbohydrates (inulin and fructose) that are considered as SCFA generators during colon digestion and present a potential satiety control by different digestive mechanisms. G12 roasted roots showed less fermentable carbohydrate content than G35 but an increased amount of acetate, which is involved also in the control of body weight. Moreover, G35 showed an increased content of anti-inflammatory compounds as cqa and DHdLc. Taking into account these significant differences, we then addressed one further question, whether the feeding with G12 and G35 chicory roots has different effects on the production of satiety hormones and on gut microbiota composition.

Satiety Hormones Increase Is Dependent on Chicory Genotype. To the best of our knowledge, this is the first work to describe the effect of chicory on the regulation of gut hormones in vitro. Thus, the results obtained highlight for the first time the capability of roasted chicory roots to enhance both CCK and GLP-1 secretion in EECs. Interestingly, SGID enhances the ability to induce both CCK and GLP-1 secretion for the G12 and G35 genotypes. Considering the small intestine compartment of the SGID, the effects obtained on CCK and GLP-1 secretion with G35 and G12 are very significant when compared to those obtained for the hemoglobin intestinal digest (used as positive control), which was previously reported to be a strong stimulator of gut hormone secretion. [START_REF] Caron | Simulated GI digestion of dietary protein: Release of new bioactive peptides involved in gut hormone secretion[END_REF] Interestingly, the bioactivities were impacted by the genotype; the G12 was more efficient to induce CCK secretion, whereas G35 was more potent for GLP-1 secretion. This is presumably due to the difference observed in the metabolite composition of the two genotypes as commented on earlier and shown in Figure 1. Thus, it is well-known that carbohydrates such as glucose and fructose are strong stimulators of gut hormone secretion. [START_REF] Sternini | Enteroendocrine cells: a site of "taste" in gastrointestinal chemosensing[END_REF] Moreover, recently an increasing number of works have shown the implication of polyphenols in energy homeostasis as potent stimulators of gut hormone secretion and/or inhibitors of DPP-IV activity. [START_REF] Domínguez Avila | The Antidiabetic Mechanisms of Polyphenols Related to Increased Glucagon-Like Peptide-1 (GLP1) and Insulin Signaling[END_REF][START_REF] Kim | Aster pseudoglehni extract stimulates cholecystokinin and serotonin secretion in vitro and reduces gastric emptying in vivo[END_REF] These properties together with the previously described effect of inulin and oligofructose on satiety, via its action on gut microbiota, [START_REF] Delzenne | Modulation of glucagon-like peptide 1 and energy metabolism by inulin and oligofructose: experimental data[END_REF] make chicory a very promising ingredient for testing for effects on obesity and T2DM in different experimental assays.

Diversity of the Gut Microbiota Changes with Chicory Genotype. Daily feeding with G12 and G35 genotypes of roasted chicory equivalent to a human moderated alimentary dose triggered changes in α-diversity, as indicated in Figure 3. At this level of analysis, the microbial community diversity alone does not necessarily provide understanding of community function, as was observed for other studies, [START_REF] Lozupone | Diversity, stability and resilience of the human gut microbiota[END_REF] but indicates a genotype-specific effect of the chicory feeding. The fact that the SI sustainably decreased in mice microbiota during G35 diet suggests that the composition of different specific metabolites of the G35 chicory genotype could probably more rapidly alter the gut microbiota.

Composition of the Gut Microbiota Changes with Chicory Genotype. The ratio between the Firmicutes and Bacterioidetes phyla was calculated for different chicory treatments, and a genotype-dependent effect was observed (Figure 4). The G35 genotype, which is richer in inulin and fructose, led to a decrease in the abundance of Firmicutes and a significant lower F/B ratio. A change of F/B ratio may be important in itself because this can influence the efficiency of processing of otherwise indigestible complex polysaccharides in the diet. In our pilot study, roasted chicory roots were used for feeding mice for a short period (30 days), and body weight was not significantly altered (Supporting Information), while a small decrease in body weight was observed for G35 feed mice. It was observed that diet-inducing obesity triggered changes in the F/B ratio as the abundance of Firmicutes dramatically increased within the distal gut microbiota. [START_REF] Turnbaugh | Dietinduced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome[END_REF] This ratio was found also to be significantly increased in patients developing type 1 diabetes. [START_REF] Wen | Innate immunity and intestinal microbiota in the development of Type 1 diabetes[END_REF] Parnell and Reimer 50 demonstrated a significant shift in the Bacteroidetes and Firmicutes groups with increasing doses of prebiotic fiber intake; F/B was thus reduced with beneficial effects on obesity physiopathology. Presently, changes in intestinal microbial composition are thought to be an important causal factor in the development of obesity. [START_REF] Sonnenburg | Diet-microbiota interactions as moderators of human metabolism[END_REF] Animal data and human studies have reported that the overall F/B proportion is increased in obese people compared to lean people. [START_REF] Koliada | Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population[END_REF][START_REF] Barlow | Role of the gut microbiome in obesity and diabetes mellitus[END_REF] However, this criterion is not consistent to our study design. In this context, we observed an effect of the G35 chicory genotype, which must be further investigated in different analogous models or in human subjects in order to specify the metabolic action related to diabetes and obesity.

Daily feeding with roasted chicory triggered changes in the relative abundance of several bacterial genera also (Figure 5). FCA was used to select the genotype-specific bacterial genera (Figure 6). Thus, the abundance of Alloprevotella was found to be increased in all chicory-treated groups and significantly increased in G35 treatment. The abundance of Blautia, Alistipes, and Oscillibacter was found to be significantly decreased in G35 treatments (Figure 7).

Alloprevotella belongs to Prevotelaceae family that was recently associated with an important role in gut-brain interactions as well as in metabolic disorders. Furthermore, low levels of Prevotellaceae have been reported in patients with autism [START_REF] Kang | Reduced incidence of prevotella and other fermenters in intestinal microflora of autistic children[END_REF] but also in patients with type 1 diabetes. [START_REF] Brown | Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes[END_REF] The mean abundance of Prevotellaceae in the feces of patients with Parkinson disease was reduced by 77.6% in comparison with control subjects. [START_REF] Scheperjans | Gut microbiota are related to Parkinson's disease and clinical phenotype[END_REF] On the basis of these observations, high fecal abundance of Prevotellaceae was proposed as a useful biomarker to exclude Parkinson disease. [START_REF] Scheperjans | Gut microbiota are related to Parkinson's disease and clinical phenotype[END_REF] Blautia is a genus in the bacterial family Lachnospiraceae that participates in nutrient assimilation. [START_REF] Biddle | Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities[END_REF] An increased abundance of Blaudia has been observed during antibiotic administration, [START_REF] Banerjee | Increased productivity in poultry birds by sub-lethal dose of antibiotics is arbitrated by selective enrichment of gut microbiota, particularly short-chain fatty acid producers[END_REF] giving reason to believe that this bacterial genus could be involved in antagonistic relationships at the gut microbial ecosystem level. The change in abundance must be correlated to other imbalanced bacterial groups and be specifically associated with a particular diet [START_REF] Jiao | Rhubarb Supplementation Promotes Intestinal Mucosal Innate Immune Homeostasis through Modulating Intestinal Epithelial Microbiota in Goat Kids[END_REF] or pathology [START_REF] Shen | Analysis of gut microbiota diversity and auxiliary diagnosis as a biomarker in patients with schizophrenia: A crosssectional study[END_REF] on a caseby-case basis.

Alistipes is a genus belonging to the Bacteroidetes phylum and was observed to be significantly correlated with autism disorders. Thus, decreased levels of ileal serotonin in the intestinal epithelial cell layer were associated with an increased abundance of the Alistipes genus. [START_REF] De Theije | Altered gut microbiota and activity in a murine model of autism spectrum disorders[END_REF] Oscillibacter belongs to the Clostridia class of Firmicutes, and in human gut microbiota it grows fermentatively, producing predominantly valerate when grown using glucose as a carbon source. [START_REF] Iino | sp. nov., a valerate-producing anaerobic bacterium isolated from the alimentary canal of a Japanese corbicula clam[END_REF] Valeric acid is similar in structure to GBH (γhydroxybutyric acid) and GABA (γ-aminobutyric acid), which are involved in neurotransmission in the mammalian central nervous system. Increasing abundance of Alloprevotella and decreasing Blautia, Alistipes, and Oscillibacter levels during chicory feeding could be further more closely correlated to these putative neurological effects and/or metabolic disorders.

In conclusion, we investigated many different chicory genotypes for their content of metabolites, and two genotypes, G12 and G35, were retained for their different compositions. The most important differences concerned amounts of fermentable carbohydrates (inulin and fructose), carboxylic compounds such as acetate, chlorogenic acids such as cqa, and sesquiterpene lactones (for example, DHdLc). In the literature, the majority of chicory analyses use the different compounds that are isolated and employed for targeted investigations. We did not extract isolated compounds but rather used chicory roasted roots from G12 and G35 genotypes as part of the food matrix to feed mice in order to analyze satiety hormones as well as variations in the gut microbiota. Because chicory treatments were performed with moderate quantities of roasted roots and because the two investigated chicory genotypes belong to the same species, no qualitative differences were observed in microbiome bacterial abundance. However, a genotype-dependent effect was observed. The G35 chicory genotype, which is richer in fermentable carbohydrates and also in antiinflammatory compounds such as cqa and DHdLc, could more rapidly alter the gut microbiota α-diversity. Also, a significant change in F/B ratio was generated by the G35 intake. CCK and GPL-1 secretion were found to be increased by the samples obtained after in vitro digestion of chicory for both genotypes, with G35 inducing the most significant increase of GLP-1. These observations explain for the first time the potential of chicory products in appetite control by inducing increased levels of satiety hormones. With the aim of defining the benefits of chicory for functional food, further studies must therefore be targeted through investigating microbiota composition and intestinal hormone levels in association with metabolomic composition of chicory, for a more detailed understanding of appetite-regulation mechanisms conferred by this plant. The abundance of Alloprevotella, Blautia, Alistipes, and Oscillibacter in the gut microbiota was also found to be modified during G35 chicory feeding. As these bacterial genera were described as neuroprotective or associated with gut-brain axis, there could be an interesting perspective to open for testing the psychobiotic effect of different chicory genotypes.

■ ASSOCIATED CONTENT * S Supporting Information

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.8b01055.

Microbial profiling using quantitative PCR; body weight of mice during chicory feeding; changes in the relative abundance of bacterial genera after daily feeding with roasted chicory roots; and changes in the relative abundance of significant and genotype-specific bacterial genera after daily feeding with roasted chicory roots (PDF)

■ AUTHOR INFORMATION

Corresponding Author *E-mail: Anca.Lucau@univ-lille1.fr.

ORCID

Anca Lucau-Danila: 0000-0002-9379-6241

  buffer (100 mM, pH 8.0) and 25 μL of DPP-IV (25 μL at 0.018 U• mL -1 ) at 37 °C for 5 min in a 96-well plate. For control purposes, the sample was replaced with Tris/HCl buffer (100 mM, pH 8.0). The reaction was initiated by the addition of 50 μL of Gly-Pro-pnitroanilide (1 mM). All the samples and reagents were diluted in Tris/HCl buffer. The plate was incubated at 37 °C for 30 min, and the absorbance of the released p-nitroaniline at 405 nm at 2 min intervals was read using a microplate reader (ELx808, Biotek, U.S.A.). The concentration of the sample required to cause 50% inhibition of the DPP-IV activity (IC 50 ) was determined by plotting the % DPP-IV inhibition as a function of sample final concentration natural logarithmic.

Figure 1 .

 1 Figure 1. Chicory metabolites content. Principal component analysis (PCA) with individuals factor map (A) and variables factor map (B) of the LC-UV and 1 H NMR metabolite profile differentiating roasted chicory root product of nine genotypes. Plots are based on the two first components, both explaining 53.85% of total variance. (C) Average levels of major metabolites detected in the two selected genotypes G12 and G35. Statistically significant differences measured by ANOVA (p < 0.05) according to genotype are shown with different letters in the same column (Tukey's test).Roasted roots of G12 genotype present higher proportion of citrate, fumarate, lactate, formate, acetate, and inositol than those of G35 genotype. Inversely, roasted roots of G35 genotype were richer in inulin, sucrose, and fructose as well as in 3-mono-O-caffeoylquinic acid and dihydrodeoxylactucin.

Figure 2 .

 2 Figure 2. Effect of chicory roasted roots on intestinal hormone secretion by STC-1 cells. STC-1 cells were incubated with different final concentrations (0.2, 0.5, and 1% dry matter) of chicory roasted roots (G12 and G35 genotype), before and after SGID, in Hepes buffer, pH 7.4. Bovine hemoglobin intestinal hydrolysate (Hb, I4, 1% dry matter final concentration) was used as positive control. Supernatants were collected after 2 h of incubation. (A) Cholecystokinin (CCK) and (B) glucagon-like peptide-1 (GLP-1) levels were determined by RIA and expressed as % of control (buffer) levels. Data are expressed in mean (n = 3) ± SD. Means without a common letter are different (p < 0.05) using one-way ANOVA with Tukey's test for pairwise comparisons.

Figure 3 .

 3 Figure 3. Shannon index of bacterial diversity during different chicory diets. G35 chicory triggered the most reduced bacterial diversity in fecal samples after 30 days of diet (D30). After the washout period (D36), both chicory genotypes indicate a decreased level of α-diversity compared to control. Significant differences (p < 0.05) are indicated with letters (a, b); * indicates p < 0.05.

Figure 4 .

 4 Figure 4. Relative abundance (%) of Firmicutes and Bacteroidetes in mice microbiota after chicory diet. Relative abundance of phyla Firmicutes and Bacteroidetes detected by NGS is expressed as a mean (Tukey's test, n = 5/group, ** indicates p < 0.01).

Figure 5 .

 5 Figure 5. Relative abundance (%) of main genera in mice fecal microbiota after chicory diet. Relative abundance of genera are indicated when their values are >0.1%. Genera with a low relative abundance were assigned as "other".

Figure 7 .

 7 Figure 7. Significant variations in four genera of mice microbiota after chicory diet. Bar graphs compare relative abundance of each condition between D0 and D30 for the most significant genera variations analyzed by NGS and metagenetics. Relative abundance is expressed as mean ± SD (* indicates P < 0.05, Student's t test, n = 5/group).

Table 1 .

 1 Chemical Composition, Protease Concentration, and pH Used for the Different Steps of the In Vitro Simulated Gastrointestinal Digestion

		saliva	gastric juice	duodenal juice	bile juice
	chemical composition	KCl (12 mM)	KCl (11 mM)	KCl (7.6 mM)	KCl (5 mM)
		KSCN (2 mM)	NaH 2 PO 4 (2.2 mM)	KH 2 PO 4 (0.6 mM)	NaCl (90 mM)
		NaH 2 PO 4 (7.4 mM)	NH 4 Cl (5.7 mM)	NaCl (120 mM)	NaHCO 3 (69 mM)
		Na 2 SO4 (4 mM)	NaCl (47 mM)	NaHCO 3 (40 mM)	HCl (1.5 mM)
		NaCl (5 mM)	HCl (6.5 mM)	HCl (1.8 mM)	CO(NH 2 ) 2 (4 mM)
		NaHCO3 (20 mM)	CaCl 2 (2.7 mM)	MgCl 2 (0.5 mM)
		CO(NH 2 ) 2 (3.3 mM)	CO(NH 2 ) 2 (1.4 mM)	CO(NH 2 ) 2 (1.7 mM)
	enzymes	amylase 1/430 (w/w)	pepsin 1/40 (w/w)	pancreatin 1/50 (w/w)
	pH	6.8 ± 0.2	1.3 ± 0.2	8.1 ± 0.2	8.2 ± 0.2

Table 2 .

 2 Mean Relative Abundances (%) of Bacterial Phyla in Mice Microbiota During Different Chicory Diets (D30) a

		control		G12		G35
	phylum	mean (%)	SD	mean (%)	SD	mean (%)	SD
	Bacteroidetes	85.593 a	5.114	83.646 a	3.960	96.500 b	1.256
	Firmicutes	14.131 a	5.155	15.670 a	3.760	3.122 b	1.209
	Verrucomicrobia	0.018	0.022	0.066	0.099	0.032	0.053
	Proteobacteria	0.042	0.032	0.023	0.029	0.169	0.032
	Cyanobacteria	0.150	0.140	0.464	0.225	0.141	0.064
	Tenericutes	0.046	0.032	0.089	0.091	0.023	0.035

DOI: 10.1021/acs.jafc.8b01055 J. Agric. Food Chem. XXXX, XXX, XXX-XXX

DOI: 10.1021/acs.jafc.8b01055 J. Agric. Food Chem. XXXX, XXX, XXX-XXX E

DOI: 10.1021/acs.jafc.8b01055 J. Agric. Food Chem. XXXX, XXX, XXX-XXX G

Funding

The work was funded by the University of Lille and has been carried out in the framework of Alibiotech project, which is financed by European Union, French State, and the French Region of Hauts-de-France.

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

The authors wish to thank Florimond Desprez SA and Leroux for providing genetic material and roasted products. Metabolomic characterization of the genotypes was part of the Qualichic program (2013-2017). The 1 H NMR analyses were performed at the Metabolome Facility-MetaboHUB of Bordeaux Functional Genomic Center.


Théo Mariotte
email: theo.mariotte@univ-lemans.fr

Antonio Almudévar

Marie Tahon

Alfonso Ortega

AN EXPLAINABLE PROXY MODEL FOR MULTILABEL AUDIO SEGMENTATION

Keywords: multilabel audio segmentation, explainability, non-negative matrix factorization, music detection, speech detection

Audio signal segmentation is a key task for automatic audio indexing. It consists of detecting the boundaries of class-homogeneous segments in the signal. In many applications, explainable AI is a vital process for transparency of decision-making with machine learning. In this paper, we propose an explainable multilabel segmentation model that solves speech activity (SAD), music (MD), noise (ND), and overlapped speech detection (OSD) simultaneously. This proxy uses the non-negative matrix factorization (NMF) to map the embeddings used for the segmentation to the frequency domain. Experiments conducted on two datasets show similar performances as the pre-trained black box model while strong explainable features arise. Specifically, the frequency bins used for the decision can be easily identified at both the segment level (local explanations) and global level (class prototypes).

INTRODUCTION

Audio segmentation is a key task for many speech technologies such as automatic speech recognition, speaker identification, and dialog monitoring in different multi-speaker scenarios, including TV/radio, meetings, and medical conversations. More precisely, these technologies must be aware of the presence of noisy environments (brouhaha, external noise), and how many speakers are active at each time. In many domains, such as health or human-machine interactions, the prediction of segment timestamps is not enough and it is necessary to include some explanations. Indeed, the current trend for explainable AI is a vital process for transparency of decision-making with machine learning: the user (a doctor, a judge, or a human scientist) has to justify the choice made based on the system output. Explainability for AI can be addressed at different stages of the process. Pre-hoc explainability intends to understand and describe data with explainable features and statistics. Another stage is to develop explainable-by-design models. The last stage consists of the extraction of explanations from a pre-trained model by the use of proxy models or perturbation mechanisms. Our work comes within the scope of such post-hoc explanations.

The proposed approach aims to train an explainable proxy model from a black-box segmentation system. While many works have This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101007666. This work was performed using HPC resources from GENCI-IDRIS (Grant 2022-AD011012565). The research reported here was conducted at the 2023 Frederick Jelinek Memorial Summer Workshop on Speech and Language Technologies, hosted at Le Mans University (France) and sponsored by Johns Hopkins University. been conducted in explaining neural networks in vision [START_REF] Ribeiro | Why Should I Trust You?": Explaining the Predictions of Any Classifier[END_REF] and NLP [START_REF] Murphy | Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding[END_REF], the literature is limited in the audio domain. The first intents are focused on saliency map extraction to reveal what information is used in the output [START_REF] Ribeiro | Why Should I Trust You?": Explaining the Predictions of Any Classifier[END_REF][START_REF] Qi | Visualizing deep networks by optimizing with integrated gradients[END_REF]. Audio classifier decisions can be interpreted by post-hoc analysis with approaches such as SoundLime [START_REF] Mishra | Local interpretable model-agnostic explanations for music content analysis[END_REF]. Recently, a few explainable models have been developed like APNet [START_REF] Zinemanas | An interpretable deep learning model for automatic sound classification[END_REF], which extends the training of prototypes to the audio domain, and post-hoc visualization of explanations obtained from Shapley values [START_REF] Ge | Explaining deep learning models for spoofing and deepfake detection with shapley additive explanations[END_REF]. In the architecture proposed in [START_REF] Parekh | Tackling interpretability in audio classification networks with non-negative matrix factorization[END_REF], the authors explain a black box audio classifier with a proxy model which is optimized to classify audio scenes while reconstructing the audio with the nonnegative matrix factorization framework [START_REF] Lee | Algorithms for non-negative matrix factorization[END_REF].

This paper focuses on automatic audio segmentation in the context of large-scale multimedia data from archivists, i.e. the detection of homogeneous segments containing speech (SAD), music (MD), noise (ND), and overlapped speech (OSD) with a unified model. Early studies on speech [START_REF] Sohn | A statistical model-based voice activity detection[END_REF], overlapped speech [START_REF] Charlet | Impact of overlapping speech detection on speaker diarization for broadcast news and debates[END_REF], and music [START_REF] Lavner | A decision-tree-based algorithm for speech/music classification and segmentation[END_REF] segmentation are focused on the statistical modeling of handcrafted acoustic features. Currently, the segmentation is mainly performed with neural networks and supervised learning. While each task has been generally solved independently as a binary frame-wise classification task (SAD [START_REF] Lavechin | End-to-End Domain-Adversarial Voice Activity Detection[END_REF], OSD [START_REF] Bullock | Overlap-Aware Diarization: Resegmentation Using Neural End-to-End Overlapped Speech Detection[END_REF][START_REF] Lebourdais | Overlapped speech and gender detection with WavLM pre-trained features[END_REF], MD [START_REF] Jang | Music detection from broadcast contents using convolutional neural networks with a mel-scale kernel[END_REF][START_REF] De Benito-Gorron | Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset[END_REF]), more recent approaches propose to solve multiple tasks simultaneously. The multiclass model predicts a single class and class intersection is empty. For example, in [START_REF] Gimeno | Multiclass audio segmentation based on recurrent neural networks for broadcast domain data[END_REF], authors propose to segment speech, music, and noise with a single multiclass model. A few works also report joint SAD and OSD [START_REF] Jung | Three-Class Overlapped Speech Detection Using a Convolutional Recurrent Neural Network[END_REF][START_REF] Bredin | End-To-End Speaker Segmentation for Overlap-Aware Resegmentation[END_REF][START_REF] Lebourdais | Joint speech and overlap detection: a benchmark over multiple audio setup and speech domains[END_REF]. In this paper, SAD, OSD, MD, and ND are simultaneously solved as a multilabel frame classification task. Thus, multiple classes can be predicted simultaneously, and the intersection between classes is not empty.

We propose to train an explainable proxy model from a pretrained multilabel segmentation model (designated as the teacher). The architecture is inspired by [START_REF] Parekh | Tackling interpretability in audio classification networks with non-negative matrix factorization[END_REF]. The proxy is trained following a teacher-student approach, commonly used in knowledge distillation [START_REF] Hinton | Distilling the knowledge in a neural network[END_REF]. The teacher inputs Wavlm pre-trained features [START_REF] Chen | Wavlm: Large-scale self-supervised pretraining for full stack speech processing[END_REF] and outputs the pseudo probability of each class. Two types of proxy models are investigated. The former inputs a spectrogram and the latter uses the teacher's Wavlm outputs. Contrary to [START_REF] Parekh | Tackling interpretability in audio classification networks with non-negative matrix factorization[END_REF], we consider frame-level, i.e. time segmentation, instead of utterance-level classification. We demonstrate that proxy models provide similar or even better performance as the teacher while being smaller and explainable. These models provide local (segment-level) explanations by highlighting the salient frequencies for a given audio input and global explanations that can be seen as class prototypes. Contrary to state-of-the-art methods such as Shapley [START_REF] Ge | Explaining deep learning models for spoofing and deepfake detection with shapley additive explanations[END_REF] or SoundLime [START_REF] Mishra | Local interpretable model-agnostic explanations for music content analysis[END_REF], our contribution not only brings post-hoc explanations but also provides decisions. Finally, we show that the relevant information for classification can be mapped to the spectral domain. A classification score confirms the selection of relevant components. To the best of our knowledge, this is the first intent to design an explainable neural multilabel segmentation model. This paper is arranged as follows: sec. 2 presents the system along with the proxy model training strategy. Sec. 3 introduces the experimental protocol and sec. 4 the segmentation results. Sec. 5 demonstrates the explainability capacities of the proposed system.

NMF-BASED EXPLAINABLE MODEL

The architectures and training framework of the proxy models are described in this section.

Wavlm Log Spec. Ψ Θ X ∈ R F ×T x ∈ R L ỹ ∈ R C×T ŷ ∈ R C×T X ∈ R F ×T L NMF (X, X) Teacher model H W L KD (ŷ, ỹ) A B
Fig. 1: Diagram of the proposed architecture for NMF-based multilabel segmentation explanation. The A and B branches represent the spectrogram and Wavlm-based proxy models respectively.

Multilabel audio segmentation formulation

Audio segmentation is solved as a multilabel framewise classification task. Let {S, y} be a training set composed of sequences of acoustic features S ∈ R D×T , where D is the feature vector dimension and T the number of frames, and the aligned annotations y ∈ R C×T , with C being the number of classes. Let f : R D×T → R C×T be a parametric function that estimates the logits for each class from the sequence of features such as ŷ = f (S). The parameters of f are optimized to minimize a loss function L(ŷ, y) (here we used a cross-entropy), with an iterative algorithm. The sequence of feature S is extracted from the audio signal by an additional function S = g(x).

Non-negative matrix factorization (NMF)

NMF has been extensively used for audio signal processing [START_REF] Richard | Audio signal processing in the 21st century[END_REF]. This approach factorizes a given non-negative matrix X ∈ R F ×T + into two non-negative matrices: W ∈ R F ×K + , usually denoted as dictionary, and H ∈ R K×T + , denoted as activations. K represents the rank of the factorization. Both matrices are jointly learned by solving W, H = arg min

W,H ∥X -WH∥ 2 2 , (1)
with a two-step optimization process [START_REF] Lee | Algorithms for non-negative matrix factorization[END_REF]. In this work, we consider the sparse NMF implementation [START_REF] Le Roux | Sparse nmfhalf-baked or well done?[END_REF] as suggested in [START_REF] Parekh | Tackling interpretability in audio classification networks with non-negative matrix factorization[END_REF]. In our segmentation proxy model, the dictionary W is pre-learned while the activation H is extracted by a neural model Ψ and referred to as an embedding. The proxy model architecture and the way NMF is integrated is described in the following subsection.

Proxy model framework

In this work, the f model is pre-trained with frozen weights and serves as a teacher for the proxy model. We use a similar approach as [START_REF] Parekh | Tackling interpretability in audio classification networks with non-negative matrix factorization[END_REF] where the proxy model is composed of two functions. Let Ψ be a function that maps a sequence of D-dimension feature vectors S ∈ R D×T to the embedding H ∈ R K×T

+

. The proxy model logits ỹ ∈ R C×T are obtained with an additional Θ function such as ỹ = Θ(H). The log-spectrogram of the input audio signal X is also reconstructed from H with the W dictionary: X = WH. Nonnegativity is ensured by a ReLU activation function σ, e.g. σ(H). Note that W can also be trained simultaneously with the model.

The training objective of such a model is composed of 3 loss terms. The first trains the proxy model to mimic the teacher's decisions. Considering multilabel segmentation, and following the common approaches in knowledge distillation [START_REF] Hinton | Distilling the knowledge in a neural network[END_REF], we use the binary Kullback-Leiber (KL) divergence between the teacher and the proxy model output distributions, denoted as LKD(ŷ, ỹ).

The second loss term constrains the H embedding to minimize the NMF-based spectrogram reconstruction and is implemented as the squared L2-norm between the target spectrogram X and the reconstruction X:

LNMF (X, X) = ∥X -WH∥ 2 2 . (2)
The last loss term minimizes the L1-norm of the H embedding to enforce its sparsity. Having a sparse embedding reduces the number of active components, and makes the explanation step easier (Sec. 5). Finally, the global training objective is the weighted sum of the 3 terms given in eq. 3 where (α, β, γ) is a triplet of hyperparameters to weight each term of the loss.

L = αLKD(ŷ, ỹ) + βLNMF (X, X) + γ∥H∥1. (3)

EXPERIMENTAL PROTOCOL

Datasets

In the current literature, there is no audio data annotated to perform SAD, OSD, MD, and ND simultaneously. Therefore, models are trained on 4 datasets listed in the table 1 to perform multilabel audio segmentation so that all the classes are represented. The teacher is optimized on the 4 training subsets described in the papers associated with each dataset. This represents about 300 hours of annotated audio for training. Due to the large amount of data, the teacher pretraining requires a lot of resources and a lengthy training process to converge. We propose to train the proxy model on a subset of the training set. Only Albayzin (AragonRadio train and 324TV) and Di-Hard train data are used for the knowledge distillation step, which represents 87 hours of audio.

To evaluate the segmentation performance, both teacher and proxy models are evaluated following the same protocol. The models are evaluated on Aragon Radio and DiHard III test sets. The test partitions follow the split proposed for the original challenges [START_REF] Ortega | The Albayzín 2012 audio segmentation evaluation[END_REF][START_REF] Ryant | The third dihard diarization challenge[END_REF].

Model architectures

The teacher model is similar to [START_REF] Lebourdais | Overlapped speech and gender detection with WavLM pre-trained features[END_REF] and is composed of two main parts: feature extraction and sequence modeling. The former is performed using pre-trained Wavlm Large [START_REF] Chen | Wavlm: Large-scale self-supervised pretraining for full stack speech processing[END_REF] that outputs a sequence of 1024-dimension vectors. The latter transforms the sequence of features to predict the segmentation. It is composed of a 64-channel bottleneck layer followed by 3 TCN blocks [START_REF] Bai | An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling[END_REF] composed of 5 1-D convolutional layers with exponentially increasing dilatation. A 1-D convolution layer processes the output of the TCN blocks to project Table 1: Datasets used to train both teacher (T) and proxy (P) models with the available labels in each of them. Missing annotations for a given class are removed from the classification loss.

Model

Available label Dataset T P SAD MD ND OSD Albayzin [START_REF] Ortega | The Albayzín 2012 audio segmentation evaluation[END_REF][START_REF] Butko | Audio segmentation of broadcast news in the Albayzín-2010 evaluation: overview, results, and discussion[END_REF]

✓ ✓ ✓ ✓ ✓ OpenBMAT [28] ✓ ✓ ALLIES [29] ✓ ✓ ✓ DiHard III [26] ✓ ✓ ✓ ✓
the hidden representations to the logits space. A sigmoid activation function is then applied to get normalized scores for each class.

As described in section 2, two types of proxy models are investigated. In the first approach, the proxy model (denoted as Spec. in Table 2) inputs a log-spectrogram extracted on 64ms sliding windows with 20ms step. In that case, S = X. The Ψ function is implemented with the same TCN-based architecture as the teacher but with 4 TCN blocks of 4 convolutional layers. The bottleneck and hidden layers are composed of 128 and 256 channels respectively. The second approach uses Wavlm features (denoted as Wavlm) from the teacher as input. In this case, the Ψ model is smaller to reduce the number of trainable parameters w.r.t. the teacher. The architecture is the same as the teacher's but with only 2 TCN blocks of 3 convolutional layers. The Θ function is implemented as a linear layer with no bias of K input dimensions and C outputs, followed by a sigmoid.

NMF pre-training and hyperparameters

As previously introduced, the W dictionary is pre-trained to map the embedding to the frequency domain. The pre-training is performed on a subset of Aragon Radio using NMF [START_REF] Le Roux | Sparse nmfhalf-baked or well done?[END_REF]. We select segments between 1s and 4s representing each type of class. In practice, we found that building a subset with 1200 segments containing 16% of speech and 42% of music and noise offers the best reconstruction quality for each type of signal. This approach however showed limited reconstruction quality with the Wavlm-based model. To tackle this, we explored training the W dictionary along with the proxy model (denoted as Wavlm (W trained)). In this case, the dictionary is implemented as a linear layer with no bias followed by a ReLU activation. For each approach, the NMF rank is fixed at K = 256 since it offers the best performance.

The proxy model is optimized with the ADAM optimizer with a learning rate of 10 -3 and batches of 64 segments. We set (α, β, γ) = (10, 5, 0.1) to scale the classification and reconstruction losses while lowering the impact of the sparsity term.

SEGMENTATION PERFORMANCE

This section compares the performances in terms of the F1-score of each proxy model to the teacher model which is considered as our baseline. The SAD, ND, MD, and OSD performances are presented in the table 2. The proxy models are expected to reach similar, eventually slightly lower, performances as the teacher. The score obtained on OSD by the teacher model compares with the state of the art which is 63.4% on DiHard III [START_REF] Lebourdais | Overlapped speech and gender detection with WavLM pre-trained features[END_REF].

The spectrogram-based system (spec.) shows 96.3% and 95.5% F1-score on the SAD task on DiHard and AragonRadio respectively. This represents a little degradation concerning the teacher. The ND (73.0%) and MD (87.6%) are also degraded by an absolute -5,6% Using Wavlm features as input of the proxy model drastically improves the segmentation performance. When the NMF dictionary is trained simultaneously as the segmentation task, the proxy model reaches 96.1% and 96.7% F1-score on SAD. The ND performance remains lower than the teachers with a 72.6% F1-score, which represents an absolute -6% degradation. The MD and OSD performances are similar to the teacher with 93.0% and 60.5% F1-score respectively. While showing similar performance as the teacher, training the NMF dictionary degrades noise detection. When the NMF dictionary is pre-learned, the proxy model delivers similar or even better segmentation performance as the teacher. The proxy model reaches a 79.5% F1-score on ND (+0.9% absolute improvement w.r.t. the teacher), and 61.4% F1-score on OSD. This system shows the best segmentation performance among all the models.

The Wavlm-based proxy models deliver the best performance when the dictionary is pre-learned. This model is kept for explanation extraction in the next section.

DECISION EXPLANATION

This section describes the explanation extraction process to identify the relevant frequency bins for the segmentation.

Explanation extraction

The NMF framework allows to map the

H = [h1, • • • , ht, • • • , hT ]
where ht ∈ R K embedding to the frequency domain. Furthermore, the segmentation prediction is obtained from this embedding with the Θ linear transformation. The first step in the explanation process is to identify the k ∈ [1, • • • , K] NMF components that are the most relevant for each classification task. We first apply a pooling operation to the embedding by averaging it over the time dimension:

z k = 1 T T t=1 h k,t .
To identify the relevant components to detect the class c, we define a relevance vector rc

= [r1,c, • • • , r k,c , • • • rK,c]
in which each element is computed following (4), where θ k,c is the kth weight of the linear layer associated to class c. The highest values in r correspond to the most relevant component. Therefore, the most relevant components are selected by applying a threshold τ . A filtered relevance vector Rc,τ is obtained, in which the k-th element is defined as:

R k,c (τ ) = r k,c = z k × θ k,c if r k,c > τ 0 otherwise. ( 4
)

Segment-level (local) explanation

The filtered relevance vector Rc(τ ) belongs to the same space as the H embedding. Hence, it can be projected to the frequency domain by the following NMF linear transformation:

Xc(τ ) = WRc(τ ), (5)
where Xc(τ ) ∈ R K is the projection of the relevant components in the frequency domain given class c and threshold τ . This representation highlights the relevant frequency bins to detect class c.

After identifying the relevant components, it is necessary to measure how confident the model is regarding this selection. Model confidence is evaluated with the classification scores. The score is obtained by only keeping the relevant components in the H embedding and forwarding this filtered embedding Hc(τ ) through the Θ layer such that ỹc(τ ) = Θ (Hc(τ )). Thus, the frequency-domain explanation Xc(τ ) can be compared to the model output for a given threshold τ .

Figure 2 shows the relevant components projected in the frequency domain with [START_REF] Zinemanas | An interpretable deep learning model for automatic sound classification[END_REF] for two types of segments: speech and music. The relevant components are presented as a function of the relevance threshold: the higher τ the fewer components are selected. The classification score obtained from the selected components is also presented. The figure shows that the relevant components for speech are located between 100Hz and 1kHz. While τ > 0.40, the most relevant components for speech classification remain, e.g., around 200Hz. As we are in a multilabel framework, the presence of speech can also imply the presence of overlap, therefore overlap prediction is also affected by these components. This can be explained by the presence of common relevant frequency bins between SAD and OSD (Fig. 3) as described in the next section.

In the case of MD, the relevant frequency bins are located between 50Hz and 200Hz. The components located in the band [60,80]Hz are highly relevant for music detection. When those are removed (τ =0.9), the music prediction drops. Speech and overlap scores remain similar for each τ since the frequency bins are different between music and these classes. The quality of the reconstruction in the frequency domain limits the explanation extraction. In the case of the Wavlm-based proxy model, the reconstruction is of low quality in high frequencies. Thus, no explanation can be extracted in this part of the spectrum.

Global explanation

The previous explanation is obtained at the segment level, meaning that the relevant components are identified locally. However, there is no guarantee that the components used to detect a class c in a segment are the same across an entire dataset. Thus, we propose a global explanation, rc vector, which highlights the global relevant components to detect the class c. To compute the average relevance rc, N = 100 segments containing exclusively each class of interest are selected to build each subset Dc. To better balance the data towards the overlap class, twospeaker artificial speech mixtures are generated by summing two randomly selected speech segments. The noise class is not represented since too few segments contain exclusively noise.

Figure 3 presents the average relevance rc for each k component, computed for SAD, OSD, and MD. It shows that some components are typical for SAD and OSD. For example, the component of index 50 is activated for both classes. The figure also demonstrates that the components related to music are different from speech and overlap. This confirms the behavior observed at the segment level in Fig. 2. Finally, Fig. 3 shows negative values that are due to negative weights in the Θ layer. This may highlight some components that are very discriminant between classes, e.g. around k = 70 where the relevance is negative for speech and positive for overlap.

CONCLUSIONS

This paper proposes a set of explainable proxy models for multilabel audio segmentation. The proxy is trained to fit the logit distribution of a pre-trained black-box teacher model. Non-negative Matrix Factorization (NMF) is the core of this approach. It maps the embedding used for the decision-making process to the frequency domain. Hence, one can easily identify the relevant frequency bins used to predict the segmentation. Experiments conducted on AragonRadio and DiHard III datasets show that the proxy model offers the same performance as the teacher while providing strong explainability capacities. In the future, we plan to work on the evaluation of the extracted explanations

Fig. 2 :

 2 Fig. 2: (Top) segment-level (local) relevant frequency bins for speech and music segmentation according to the relevance threshold τ . (Bottom) Classification scores for (-) speech, (--) music, and (• • • ) overlapped speech with each selected components. Two audio samples from AragonRadio eval set with speech only (left) and music only (right)

Fig. 3 :

 3 Fig. 3: Global relevant components for speech (sp), music (mu), and overlapped speech (ov).

1 N

 1 It averages the relevance vectors rc for a set of N segments Dc = {x1,c, • • • , xn,c, • • • xN,c} containing the class of interest c: rc = xc∈Dc rc.

Table 2 :

 2 F1-score (%) obtained for each segmentation task on both AragonRadio and DiHard III evaluation sets. Bold values indicate the significantly best scores. compared to the teacher. On OSD, the performance is strongly degraded with a 40.1% F1-score. The spectrogram-based model degrades the segmentation performance. However, this result is expected since the teacher is trained on Wavlm pre-trained features which perform better than the spectrogram. Considering the cost of Wavlm pre-training and the inference time, the spectrogram-based model offers a convincing performance.

		Aragon Radio	DiHard III
	Model	SAD ND	MD SAD OSD
	Teacher	96.8 78.6 93.2 96.9	60.7
	Spec.	96.3 73.0 87.6 95.5	40.1
	Wavlm (W trained) 96.7 78.5 93.0 96.8	60.8
	Wavlm	96.8 79.5 93.1 96.9	61.4