Guillaume Salle
email: guillaume.salle@inrae.fr

Véronique Deiss

Céline Marquis

Gwenola Tosser-Klopp

Jacques Cortet

Delphine Serreau

Christine Koch

Didier Marcon

Frédéric Bouvier

Philippe Jacquiet

Guillaume Sallé

| Véronique Deiss

| Céline Marquis

| Didier Marcon

| Frédéric Bouvier

Alexandra Blanchard

Marie-Madeleine Mialon

Carole Moreno-Romieux

Genetic x environment variation in sheep lines bred for divergent resistance to strongyle infection

Keywords: resistance, sheep, SNP, stress

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

genome-wide resolution in commercial sheep populations [START_REF] Kemper | The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections[END_REF] and evidence of multiple Quantitative Trait Loci (QTL) with mild effects were found across European breeds [START_REF] Riggio | A joint analysis to identify loci underlying variation in nematode resistance in three European sheep populations[END_REF][START_REF] Sallé | A genome scan for QTL affecting resistance to Haemonchus contortus in sheep[END_REF][START_REF] Sallé | Functional investigation of a QTL affecting resistance to Haemonchus contortus in sheep[END_REF] or in sheep lines bred for divergent susceptibility towards GIN infection [START_REF] Mcrae | Signatures of selection in sheep bred for resistance or susceptibility to gastrointestinal nematodes[END_REF]. This genetic network likely causes functional trade-offs between immune response and fitness as a result of pleiotropy, although weak positive [START_REF] Assenza | Genetic parameters for growth and faecal worm egg count following Haemonchus contortus experimental infestations using pedigree and molecular information[END_REF][START_REF] Bishop | Genetic parameters for faecal egg count following mixed, natural, predominantly Ostertagia circumcincta infection and relationships with live weight in young lambs[END_REF][START_REF] Bouix | Genetic resistance to | 11 SALLÉ et AL. gastrointestinal nematode parasites in Polish long-wool sheep[END_REF] or negative [START_REF] Douch | Genetic factors affecting antibody responses to four species of nematode parasite in Romney ewe lambs[END_REF][START_REF] Eady | Resistance to nematode parasites in Merino sheep: Correlation with production traits[END_REF] genetic correlations between resistance and growth traits were found in domestic populations.

Environmental perturbations allow different genotypes to express maximal fitness across conditions, as a result from host genotype × environment (G h × E) interactions [START_REF] Hoffmann | Heritable variation and evolution under favourable and unfavourable conditions[END_REF][START_REF] Lazzaro | Immunity in a variable world[END_REF][START_REF] Lynch | Genetic x Environment interaction[END_REF][START_REF] Seppälä | Do coinfections maintain genetic variation in parasites[END_REF] or by inflecting the strength and direction of selection [START_REF] Hayward | Evidence for selection-by-environment but not genotype-by-environment interactions for fitness-related traits in a wild mammal population[END_REF]. For instance, analyses of faecal egg count (FEC) data from commercial Merino sheep revealed increased heritability under the lowest and the highest parasite exposure, as a result from variation in sire estimated breeding values across environments [START_REF] Hollema | Sensitivity of the breeding values for growth rate and worm egg count to environmental worm burden in Australian Merino sheep[END_REF]Pollott & Greeff, 2004). In addition, exposure to unpredictable aversive events such as transportation, isolation or restraints is a common feature across farming systems [START_REF] Proudfoot | Social stress as a cause of diseases in farm animals: Current knowledge and future directions[END_REF]. This is known to induce chronic stress in sheep and significantly impact their behaviour [START_REF] Destrez | Long-term exposure to unpredictable and uncontrollable aversive events alters fearfulness in sheep[END_REF][START_REF] Proudfoot | Social stress as a cause of diseases in farm animals: Current knowledge and future directions[END_REF]. Because of the close links between chronic stress and the immune response in sheep [START_REF] Ciliberti | Peripheral blood mononuclear cell proliferation and cytokine production in sheep as affected by cortisol level and duration of stress[END_REF], unfavourable genetic correlations between the two traits may exist as found in birds [START_REF] Buitenhuis | Genetic and phenotypic correlations between feather pecking behavior, stress response, immune response, and egg quality traits in laying hens[END_REF]. This could impact on the expression of resistance potential across farming conditions.

Third, selection for more resistant hosts could promote the rewiring of GIN species assemblage by reducing resistance to other parasite species (G h × G p interactions). Limited evidence from domestic Merino [START_REF] Woolaston | Response to helminth infection of sheep selected for resistance to Haemonchus contortus[END_REF] or Romane sheep breeds [START_REF] Gruner | High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep[END_REF], however, suggest that selection for resistance to H. contortus confers significant but incomplete cross-resistance to Trichostrongylus colubriformis, an intestinal GIN.

Therefore, abrupt variation in environmental conditions could release host cryptic variation overlooked under controlled conditions, and directional selection for host resistance could disrupt host-parasite interactions. This is critical to the sustainability of the breeding option for the control of GIN populations. To investigate that matter, we created divergent sheep lines selected for resistance or susceptibility to H. contortus infection using three sires in each case. We monitored their resistance potential under chronic stress or against T. colubriformis infection following one and two rounds of divergence respectively. This was to model farming systems that would either differ in animal management (e.g. social isolation or animal restraint) or in the relative abundances of GIN species.

Observations made on these lines suggest that sheep genetic potential was almost fully expressed under chronic stress, but significant

G h × G p interactions occur.
Breeding for resistance is sustainable but allowance is to be made for environmental changes or worm species while breeding for resistance.

| MATERIAL S AND ME THODS

| Creation of two divergent lines of sheep

A full description of the selection scheme has been provided in the Appendix S1. Briefly, a QTL detection scan across European breeds identified eight regions (Table 1) significantly associated with resistance to GIN [START_REF] Riggio | A joint analysis to identify loci underlying variation in nematode resistance in three European sheep populations[END_REF]. SNP from the 800K SNP chip located within these QTL regions (approximately 110 SNPs per region) were subsequently selected and genotyped using the KASP™ assay (LGC Genomics Ltd, UK) that consists in a competitive allele-specific PCR [START_REF] He | SNP genotyping: The KASP assay[END_REF]. A nucleus Romane flock (ancestral generation G0, n = 274) was genotyped for these markers and phenotyped for resistance to H. contortus by two consecutive challenges with 10,000 larvae, as previously outlined [START_REF] Sallé | A genome scan for QTL affecting resistance to Haemonchus contortus in sheep[END_REF].

A marker-assisted selection approach was subsequently applied to retain the most resistant and most susceptible G0 sires and ewes for conditional mating using single-step GBLUP [START_REF] Aguilar | Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score[END_REF][START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF]. Instead of relying only on the genomic relationship matrix [START_REF] Terlouw | Relationship between plasma cortisol and stereotypic activities in pigs[END_REF]Yang et al., 2010), this approach models the phenotype as the sum of fixed effects and a random animal effect estimated from a blended relationship matrix H that accounts for differences between pedigree and genomic information [START_REF] Aguilar | Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score[END_REF][START_REF] Christensen | Genomic prediction when some animals are not genotyped[END_REF]. The relative weight given to the pedigree or genomic information is defined by a scaling parameter that ranges from 0 (pedigree only relationship matrix) to 1 (marker only relationship matrix) and was, in our case, set to 0.5 [START_REF] Aguilar | Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score[END_REF]. The genomic relationship matrix used here consisted of the raw genomic information scaled by the parameter

k = 2 ∑ n i p i (1 -p i )
, where p i refers to SNP i allele frequency. Matrix was then weighed to facilitate its inversion [START_REF] Terlouw | Relationship between plasma cortisol and stereotypic activities in pigs[END_REF]. As a summary, measured sheep phenotypes were hence modelled as the equal contribution of the selected 8 QTL and a polygenic effect accounting for the rest of the genome (estimated from pedigree information). Genomic estimated breeding values (geBVs) were computed for FEC at first and second infection and averaged to retain individuals with most extreme potentials.

Using these geBVs, the six most extreme G0 sires (three at each end of the geBV distribution) were mated with 55 and 63 resistant and susceptible G0 ewes respectively (among 118 females with breeding value estimations), to create 236 lambs (generation G1).

Out of these 236 G1 lambs, a subset of 180 lambs were selected for genotyping with the 1000-SNP chip, according to their expected breeding value (average of their parent breeding values, eBV). eBVs for first and second infection FEC were computed using a model including known fixed effects (litter size, sex) and an individual random effect estimated from the pedigree relationship matrix. Their geBVs were subsequently estimated using their genotype information and

| 3 SALLÉ et AL.
the SNP effect calculated in G0. 87 G1 lambs were retained for the experiment based on their geBVs (Figure 1).

A second generation of lambs was produced following the mating of six G1 rams (three within each line, selected on their breeding values, eBVs) with 82 ewes selected among G1 ewes (n = 23 and 19 selected on their eBVs for both R and S lines respectively) and G0 ewes (n = 19 and n = 20 selected on their eBVs for both R and S lines respectively). A total of 111 lambs were created in generation G2 (from 55 ewes), out of which 80 lambs were subsequently selected within each line according to their expected eBVs (average of their parent breeding values). Analyses were run with AIREMLF90 and BLUPF90 for eBVs and geBVs estimations respectively [START_REF] Misztal | BLUPF90 and related programs (BGF90)[END_REF]. S2). Plasmatic cortisol level was not considered because of inconsistencies in its variation under chronic stress exposure.

| Behavioural treatment to establish how sheep resistance potential holds in stressful environments

| Artificial infection with Haemonchus contortus and Trichostrongylus colubriformis

Lambs were kept indoors during the whole experiments to prevent natural GIN infection. The G1 lambs were challenged twice with 10,000 H. contortus infective larvae, given orally after 3 months of age. At the end of the first infection (30 days postinfection, dpi), lambs were drenched with ivermectin (2.5 ml/10 kg body weight of Oramec ® , Merial, France) and left for a resting period of 2 weeks before another infection took place with the same infection dose.

Lambs were weighed on the same day and Faecal Egg Counts were quantified at 24 and 30 dpi.

To evaluate whether genetic resistance was sustained in the face of an intestinal GIN, the second generation of sheep from both divergent lines were either infected by H. contortus, or by 10,000 infective T. colubriformis larvae. In the latter case, FEC were measured at 24, 30 dpi after the first infection and at 30 dpi after the

TA B L E 1

Metadata associated with considered QTL for selection of G1 lambs second infection. Lambs were weighed before and at 14 and 30 days postinfection.

| Statistical analyses

Statistical analyses were implemented with R v3.5 unless stated otherwise (R Core Team, 2016). For every analysis, raw FEC data were normalized by a fourth-root transformation that outperformed the logarithmic transformation (Shapiro-Wilk's test ranging between 0.96 and 0.90 for fourth-root transformed FEC but 0.90 and 0.84 for log-transformed FEC). Summary statistics of considered variables and detailed modelling outputs have been provided in Tables 1,2 and Table S2 respectively. Considered response variables were either average FEC at first or second infection (average between measures taken at 24 and 30 days postinfection) or across the two infections (average across infections).

| Association study in the G 0 population

A regression-based association study was performed between FEC measured at first and second infection in the G 0 population to evaluate the segregation of the targeted QTL and their effect in the G 0 population. At each polymorphic marker (n = 898, Table S1), phenotypic data were regressed upon fixed effects (sex, feeding lot and lamb suckling modality, i.e. artificial or not), a polygenic effect estimated from all SNPs (accounting for population structure) and the marker effect as already implemented elsewhere [START_REF] Legarra | A comparison of methods for wholegenome QTL mapping using dense markers in four livestock species[END_REF]. SNP substitution estimates and associated standard errors were used to derive P-values from a Student's t-test statistic. QTL heritability was estimated by aggregating the ratios of additive genetic variance to phenotypic variance estimated as part of the REML procedure run for each SNP. For the sake of comparison between FEC at first and second infection, we considered a cut-off corresponding to a Bonferroni correction for 8 independent tests (p-value = 6.25 × 10 -3 ).

| Response to selection

To estimate phenotypic divergence in FEC between sheep lines, individual H. contortus infection data were pre-corrected for fixed environmental effects, that is sex, milking mode (artificial or not) and generation (accounting for year effect), and an individual random effect, using the nlme package v3.1-140 [START_REF] Pinheiro | nlme: Linear and Nonlinear Mixed Effects Models (Version R package version 3[END_REF].

Individual random effects were standardized to unselected G0 mean and standard deviation.

Responses to selection for FEC at first and second infection or across infections were evaluated within each generation and each line by regressing individual random effects from H. contortus-infected offspring upon their respective midparent values, computed as the average value of each lamb's sire and dam [START_REF] Falconer | Introduction to quantitative genetics[END_REF].

This regression coefficient provides an estimate for realized heritability [START_REF] Falconer | Introduction to quantitative genetics[END_REF] and was used to establish the asymmetry of response between resistant and susceptible lines.

To estimate the expected genetic gain across infection, we computed the mean genetic gain between first and second infection. We considered pedigree-based breeding values (eBV) as they were available across generations (geBVs were only available for G0 and G1 individuals) and were strongly correlated with geBV. eBVs were estimated from recorded phenotypes in G0, G1 and G2 individuals using a mixed model including fixed environmental effects and a random individual effect estimated from the pedigree relationship matrix (encompassing 1559 individuals) as implemented in the AIReml software (http://nce.ads.uga.edu/wiki/doku.php?id=readme.aireml).

Genetic gain within each line (R line ) was expressed in genetic standard deviation (σ g ) and was estimated as the cross-product between the overall selection intensity i (average of selection intensities in males, i m , and in females, i f ) and the selection accuracy h [START_REF] Falconer | Introduction to quantitative genetics[END_REF] as:

R line = 0.5 × [h FEC1 × 0.5 × (i f + i m )] + 0.5 × [h FEC2 × 0.5 × (i f + i m )],
with h FEC1 and h FEC2 corresponding to the square-root of estimated heritabilities for FEC measured at first infection (h 2 FEC1 = 0.52 ± 0.11) and second infection (h 2 FEC2 = 0.33 ± 0.07).

To create the G 1 population, 2% of the G 0 males (i m = 2.421) were mated to 47% (i f = 0.846) and 53% (i f = 0.75) of the resistant and susceptible G 0 females respectively (Figure 1, Appendix S1). The creation of G2 individuals involved 5% of the G 1 males (i m = 2.063).

These were mated with 19 G 0 (i f = 1.059 and 1.259 for the resistant and susceptible lines respectively) and 63 G 1 females in both lines (Figure 1, Appendix S1). To account for this, the expected genetic gain was computed as the sum of the genetic gains from each type of mating (G 1 × G 1 or G 1 × G 0 ) following the aforementioned equation, weighing each i f coefficient by the respective proportion of females, that is 23% and 72% for G 0 and G 1 females.

For both G1 and G2 generations, total expected divergence was derived as the sum of expected responses from resistant and susceptible lines.

To estimate putative trade-offs between resistance to GIN and lamb weight, measured body weights were modelled using a mixed model with repeated measures, including fixed effects (litter size, sex, generation, day postinfection, and an effect aggregating line and corresponding generation), and a random effect accounting for interindividual variations. To account for differences in body weight at the beginning of the trial, weight data measured before any infection took place was fitted as a covariate.

| Sheep line × Environment (G h × E) and sheep line × Parasite (G h × G p ) interactions

To test for G h × E and G h × G p interactions, normalized FEC data collected at every time point (24-or 30-day postinfection) were scaled within each experimental block (infection rank, day postinfection and considered environment) to prevent spurious signals from heterogeneous variances between blocks (Pollott & Greeff, 2004). A mixed model for repeated measures was built, whereby scaled normalized FEC were regressed upon two interaction terms (between the lamb genetic line and the time postinfection, and between the lamb genetic line and the TA B L E 2 Measured raw and 4th root transformed FEC in G 0 and divergent lines environment), and an additional random effect accounting for inter-individual variation.

Behaviour data were considered as normally distributed (Shapiro-Wilk's test ranging between 0.91 and 0.98). Physical contact records were however skewed towards 0 and were thus binary encoded, that is 0 or 1 in absence or presence of contact with their mates or with the operator, and modelled using a logistic regression framework. To test for the effect of behaviour treatment, recorded behaviour data were regressed upon sheep line, sex and day and their interactions, and a random effect accounting for inter-individual variation. Regression models were built following stepwise variable selection procedure that aims to find the model with minimal Akaike Information Criterion (AIC). Bleating records in phase 1 of the test were also corrected for their initial value before chronic stress treatment took place to correct for the increased bleating in susceptible lambs from the stress group.

Pearson's correlations were estimated with the rcorr() function from the Hmisc package [START_REF] Harrell | Hmisc : Harrell Miscellaneous[END_REF].

| RE SULTS

| Overview of the selection process

An overview of the selection process is showcased in Figure 1 and the detailed breakdown of individuals retained for G1 and G2

creation can be found in the Appendix S1. The most striking features of this procedure are recalled herein. The G0 nucleus flock was genotyped for 8 QTL regions (Table 1) covered with approximately 100 SNPs each. SNPs were positioned between 40,595 and 53,895 bp apart for most regions but on OAR4 and OAR21 (mean distance of 635,222 and 291,979 bp respectively). Estimated QTL heritabilities ranged between 0.02 and 0.07 in the populations they were originally found to be segregating in (Table 1). An association study performed in G0 individuals found statistical associations (p < 6.3 × 10 -3 ) between FEC at first infection and most QTL (OAR4, 5, 7, 12 and 13; genomic inflation factor = 1.19; Figure 1, Figure S1).

The only QTL found on OAR3, 4 and 7 reached the same threshold for FEC at second infection (genomic inflation factor = 1.14, Figure 1, Figure S1) with lower associated explained variances (Table 1). From these estimates, the overall QTL heritability was 0.14 ± 0.04 and 0.02 ± 0.02 for FEC at first and second infection respectively. This was lower than that found using both molecular and pedigree information in that population (h 2 = 0.52 ± 0.11 and 0.33 ± 0.07 for the two traits respectively).

| Achieved divergence and response to selection

To evaluate the response to selection in both lines and our selection procedure accuracy, we compared the performance of both R and S sheep infected with H. contortus (Figure 2, Figure S2). G1

and G2 generations significantly diverged from the unselected G0 nucleus on the phenotypic scale: R lambs of respective generations showed FEC reduction of 0.62 σ p (t 70 = -3.8, p = 2 × 10 -3 ) and 0.67 σ p (t 20 = -2.8, p = 5 × 10 -3 ) relative to the G0 nucleus flock, and S lambs FEC increased by 0.72 σ p (t 55 = 4.99, p = 10 -6 ) and 0.61 σ p (t 31 = 3.11 p = 2 × 10 -3 ) relative to their G0 unselected relatives (Figure 2). This corresponded to phenotypic divergence between R and S lamb FEC across infection of 1.89 and 1.87 σ p for G1 and G2 generations respectively (Figure 2). Accordingly, 3.14 and 3.8 genetic standard deviations (σ g ) were found between R and S lines at G1 and G2 generations (Figure 2). This was slightly higher than the expected respective genetic gains of 2.08 and 2.87 σ g for G1 and G2 populations. This difference mirrors the truncation sampling performed within each population to create the experimental groups.

Despite similar selection intensity in R and S lines, observed response to selection for average FEC across infection was asymmetrical between lines in G2 lambs (Figure 2). This was accompanied by a reduced variance in FEC upon reinfection (Figure S2), with measured values at 30 dpi remaining high in a range between 1,650 and 14,250 eggs/g (median FEC = 5,625 eggs/g). On the contrary, the resistant line achieved half the response of their susceptible counterpart for FEC across infections (0.38 σ p ± 0.27 and 0.82 σ p ± 0.27 for the resistant and susceptible lines, respectively, Table S2). This was the result of more variable FEC in that line (Figure 2, Figure S2) that was also evident at first and second infection (Figure S2, Table S2). This lower response could not be related to inbreeding that was not significantly different between G2 R and S lines (t 35 = -2.01, p = 0.05).

Of note, estimated geBVs and eBVs showed strong correlation (r 241 = 91% and 71% for FEC at first and second infection, respectively, p < 10 -4 ). The lower correlation for FEC at reinfection corroborated the lower overall association between SNPs and this trait in the G0 population (Figure 1). This reduced association between

SNPs and FEC at 2nd infection may contribute to the lesser divergence found for that trait between R and S individuals in G1 lines (Figure S1).

| Trade-off between resistance to Haemonchus contortus and lamb growth

Lambs were weighted to establish putative trade-offs between FEC and lamb growth (Figure 3). Analysis of their weight trajectories showed that they were not statistically different between selected lines (average weight differences of 568 g, t 38 = -0.61, p = 0.54 in G1 and of 1.27 kg, t 34 = -1.31, p = 0.19 for G2 lambs), suggesting that higher resistance was not detrimental to production traits (Figure 3, Table S2). On the contrary, weight gains were reduced in susceptible G1 individuals (gain difference ranging between 2.3 and 2.5 kg, p < 0.05; Table S2) in the early phase of the second infection (Figure 3).

However, the G2 R lambs were significantly lighter than their susceptible counterparts before any infection took place (4.3 kg difference, F 1,35 = 8.99, p = 5 × 10 -3 ). This was not related to age

| 7 SALLÉ et AL.
difference between both groups (136 days on average in R and S groups, t 73 = 0, p = 0.99). It hence appears that selection for resistance did not impact on sheep growth curve although lighter lambs were obtained in the R line after two rounds of divergence.

| Limited effect of chronic stress on sheep resistance but significant interaction following

Trichostrongylus colubriformis infection

To identify putative G × E interactions, related individuals with divergent resistance to H. contortus infection were exposed to various environments, that is chronic stress or the intestinal parasite T. colubriformis. Half of the G1 selected lambs were exposed to chronic and unexpected stresses, while the other half were maintained under controlled environmental conditions.

Behaviour modifications are described in full under the Appendix S2 with statistical details listed in Table S2. Despite significant alterations in sheep behaviour following chronic stress exposure, limited interactions were found between genetic line and their environment (Figure 4; Figure S3). Indeed, the relationship between genetic line and transformed FEC did not significantly differ across conditions (F 1,82 = 0.03, p = 0.87). However, phenotypic divergence between lines exposed to chronic stress significantly decreased 24 days after the second infection (Figure 4). In that case, susceptible lambs excreted less eggs (-0.66 σ p , t 252 = -2.91, p = 4 × 10 -3 ) than their sibs maintained under controlled conditions (Figure 4). This would be compatible with limited genetic correlations between response to chronic stress exposure and FEC. Of note, the magnitude of G h × E interactions were statistically different between sire families (Figure S4): progenies of two susceptible sires displayed higher phenotypic variability following exposure to stressful conditions (Figure S4). In some sire families chronic stress appeared to be beneficial (interaction term equal to -1.16 σ p , p = 0.024 for sire S-132550) while in others was detrimental (interaction term equal to 0.65 σ p , p = 0.046 for sire S-132361; Figure S4).

The second trial aimed to investigate whether the genetic potential for resistance or susceptibility to H. contortus infection would be sustained in the face of another GIN species (Figure 4). Of note, T. colubriformis infection yielded fewer eggs (average FEC of 411 eggs/g across conditions, ranging between 0 and 1,100 eggs/g) as a result of the lower fecundity of this parasite species. The phenotypic divergence between R and S sheep remained significant across considered GIN species (1.52σ p difference, F 1,73 = 34.9, p = 3 × 10 -8 ) but was largely driven by the existing divergence between H. contortus infected individuals. Indeed, the phenotypic expression of lamb genetic potential was significantly reduced after infection by the intestinal T. colubriformis species. In that case, lambs genetically susceptible to H. contortus were less affected by T. colubriformis infection than their susceptible counterparts, as evidenced by the F I G U R E 2 Achieved phenotypic and genetic divergences between sheep lines across generations. Top panels show the corrected average faecal egg counts (mean ± SE) observed across two infections for parental (G0), first (G1) and second (G2) generations. Bottom panels represent the distribution of estimated breeding values (eBVs) using the pedigree information only. For the sake of comparison, FEC and eBVs data were scaled (mean centred and reduced to G0 standard deviation unit). Grey dashed line indicates G0 mean value

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 G0 G1 G2 Generation Standardized average FEC (σ p ) Line R S -2.2 -1.7 -1.2 -0.7 -0.2 0.3 0.8 1.3 1.8 G0 G1 G2 Generation Standardized Breeding Value (σ g ) Line R S
significant G h × G p effect (-0.9 σ p , F 1,73 = 9.77, p = 0.003; Figure 4).

Overall, mild difference in egg counts were measured between both lines (138 eggs/g difference, t 36 = 1.92, p = 0.06) with significant differences observed at 24 days postinfection at first infection (t 36 = -2.30, p = 0.027) and 30 days postinfection upon reinfection (t 36 = -2.38, p = 0.026).

On the other hand, we found no evidence that the magnitude of G h × G p interactions varied across families.

These two trials hence provide evidence that the phenotypic expression of H. contortus-resistant individuals holds in the face of chronic stress, but can vary across families. It also offers a significant advantage towards infection by a different intestinal GIN species although to a lower extent than for the species used during selective breeding.

| DISCUSS ION

Understanding how the host-parasite system behaves following changes in their respective environments is central to ensure sustainable control of GIN in livestock through animal breeding. Our work investigated how directional selection for contrasting levels of resistance to GIN infection would affect expression of sheep potential towards environmental change.

Our design relied on divergent sheep lines that provide a model system to quickly evaluate consequences of such environmental variation. Following two rounds of selection, we observed significant divergence on both phenotypic and genetic scales. We obtained aggregated estimates of the genetic variance across environments, either quantified by the interaction between lamb genetic groups and their environment, or by sire reaction norms across environments. This represents a first glimpse into the contribution of directional selection to genetic × environment interactions. Because of the infancy of these lines and the need to apply high selection intensity, observations were made on a limited number of generations and a few sire families. Despite the lack of replication and the contribution of drift to our design, our findings were in good agreement with past independent observations. In addition, pushing the divergence selection process further in this flock to increase divergence may be limited by the contribution of inbreeding depression to parasite resistance [START_REF] Coltman | Parasite-mediated selection against inbred Soay sheep in a free-living island populaton[END_REF]. Our trial did not evidence major effects of chronic stress on sheep resistance, but we found significant interactions between exposures to diverse parasite species. Trade-offs were limited although resistant lambs exhibited reduced body weight after the second round of selection before infection took place. This would corroborate previous observations from divergent selection lines in the Romney breed that indicated reduced growth rate in resistant individuals [START_REF] Bisset | Breeding sheep in New Zealand that are less reliant on anthelmintics to maintain health and productivity[END_REF]. A definitive conclusion on the impact of selection for enhanced resistance on growth traits is difficult as other genetic correlations estimated under natural infection conditions with different breeds were either positive or nonsignificant [START_REF] Bishop | Genetic parameters for faecal egg count following mixed, natural, predominantly Ostertagia circumcincta infection and relationships with live weight in young lambs[END_REF][START_REF] Bouix | Genetic resistance to | 11 SALLÉ et AL. gastrointestinal nematode parasites in Polish long-wool sheep[END_REF]Pollott et al., 2004;[START_REF] Robinson | The impact of environmental heterogeneity on genetic architecture in a wild population of Soay sheep[END_REF]. Of note, the considered QTL were mostly associated with FEC at first infection in the founder population while their heritability for FEC at 2nd infection was negligible. Because G1 selection was partly driven by these regions, it would corroborate the higher divergence observed for that trait despite accounting for the polygenic effect (through the simultaneous use of pedigree information) in our singlestep selection procedure. Altogether, these QTL would account for a quarter of available additive genetic variance for FEC in naive animals challenged with H. contortus.

The response to selection was asymmetrical in G2 lambs. It yielded an increased divergence towards susceptibility rather than resistance, despite similar selection intensity across sexes and lines within generations. In the absence of replicated lines, it remains difficult to disentangle this observation from the differential contribution of random drift within each line [START_REF] Falconer | Introduction to quantitative genetics[END_REF].

This may also indicate that the proportion of phenotypic variance of genetic origin is more difficult to estimate for resistant individuals than for susceptible lambs. Indeed, resistance is measured from FEC, whose distribution will be censored to 0 across a range of resistance levels, thereby hampering variance estimation. It is also possible that the selection applied to the pleiotropic gene networks underpinning the immune response to GIN infection [START_REF] Lazzaro | Immunity in a variable world[END_REF][START_REF] Sallé | A genome scan for QTL affecting resistance to Haemonchus contortus in sheep[END_REF][START_REF] Sparks | The genetic architecture of helminth-specific immune responses in a wild population of Soay sheep (Ovis aries)[END_REF] could yield asymmetric phenotypic expression upon selection. Additional rounds of divergence would be needed to support this hypothesis. Another aspect that could contribute to this pattern might come from maternal effects, for example carry-over effect linked to the transfer of antibodies through the milk [START_REF] Sparks | Maternally derived anti-helminth antibodies predict offspring survival in a wild mammal[END_REF]. However, estimation of this effect was beyond the scope of our study and would require many more observations by ewes, that is temporal replicates. Beside, maternal effect represents a limited fraction of FEC genetic variance [START_REF] Ciappesoni | Estimates of genetic parameters for worm resistance, wool and growth traits in Merino sheep of Uruguay[END_REF][START_REF] Ngere | Genetic parameters for fecal egg counts and their relationship with body weights in Katahdin lambs[END_REF] and it vanishes after the lamb reaches 3 months of age [START_REF] Bishop | Genetic parameters for faecal egg count following mixed, natural, predominantly Ostertagia circumcincta infection and relationships with live weight in young lambs[END_REF] as in our design. Characterizing milk composition and antibody transfer in ewes from both lines would contribute to a deeper understanding of these effects.

In line with previous reports [START_REF] Gruner | High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep[END_REF][START_REF] Woolaston | Response to helminth infection of sheep selected for resistance to Haemonchus contortus[END_REF], susceptibility levels towards H. contortus infection were well correlated to that measured upon T. colubriformis infection and we found no indication of between sire variations across environ-

ments. Yet, lambs selected for diverging susceptibility to H. contortus did not express the same divergence towards T. colubriformis infection. Previous estimated genetic correlations between FEC of both GIN species were positive but ranged between 0.9 and 0.76 at first and second infection [START_REF] Gruner | High genetic correlation between resistance to Haemonchus contortus and to Trichostrongylus colubriformis in INRA 401 sheep[END_REF]. This indicates that a large common genetic background is associated with resistance to both GIN species, but that a minor species-specific genetic component contributes to immune mechanisms associated with either the GIN species or with its niche (abomasum in one case or the small intestine in the other) or both. In contrast, only 38 genes were found differentially expressed across two divergent sheep lines bred for resistance to H. contortus or T. colubriformis and infected with H. contortus upon primary infection [START_REF] Zhang | Transcriptome analysis unraveled potential mechanisms of resistance to Haemonchus contortus infection in Merino sheep populations bred for parasite resistance[END_REF]. This intersecting set vanished following reinfection as differentially expressed genes were private to each sheep line [START_REF] Zhang | Transcriptome analysis unraveled potential mechanisms of resistance to Haemonchus contortus infection in Merino sheep populations bred for parasite resistance[END_REF]. These lines of evidence would suggest that despite a largely common genetic architecture between resistances to both GIN species, selection for resistance to one species may result in an efficient nonadaptive response at the transcriptomic level upon abrupt environmental modification [START_REF] Ghalambor | Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments[END_REF]. In our experiment, observed plasticity would also result from a similar maladaptive response originating from partially correlated traits in lambs selected for resistance to one species and exposed to another. This nonadaptive response may highlight cryptic mechanisms that have been maintained through time by selection, to provide selective advantage against seasonal variation in GIN community structures [START_REF] O'connor | Ecology of the free-living stages of major trichostrongylid parasites of sheep[END_REF]. Selection would hence have acted on plasticity itself [START_REF] Lynch | Genetic x Environment interaction[END_REF].

Of note, we did not evidence systematic difference in sheep phenotypes following chronic stress exposure but a reduced susceptibility of stressed lambs upon reinfection and within-family variation in their differential FEC response upon isolation. This lack of major interaction may reflect the moderate stress response induced by the applied behavioural treatment. However, social isolation is known to induce strong stress response [START_REF] Minton | Effect of acute stressors on endocrinological and immunological functions in lambs[END_REF] or pessimistic judgement bias [START_REF] Doyle | The effect of repeated testing on judgement biases in sheep[END_REF] in sheep and similar experimental setting as ours previously induced strong chronic stress response [START_REF] Guesdon | Behavioral and endocrine evaluation of the stressfulness of single-pen housing compared to group-housing and social isolation conditions[END_REF]. In addition, the stress response induced by social isolation depends on an individual's ability to develop a coping strategy. Variation of this ability could have obscured putative interactions between the behavioural treatment and measured FEC.

The mild interactions found (at the family level) may also reflect the limited effect of selection after a single generation of divergence.

Because we aimed to maximize the selection intensity (thereby reducing the number of lambs available) and because of technical limitations (behavioural treatment and phenotyping applied on more than 80 lambs), it was not possible to estimate genotype x environment interactions at every generation. Nonetheless, the estimated departure between genotypes across environments was limited. As such it would be compatible with tight genetic correlation between

FEC measured with or without exposure to chronic stress that are unlikely to be detected after a second round of divergence. The complex interactions between the immune response and chronic stress is primarily driven by the hypothalamic-pituitary-adrenal and the sympathetic-adrenal medullary axes that, respectively, control the release of glucocorticoids and catecholamines [START_REF] Khansari | Effects of stress on the immune system[END_REF][START_REF] Padgett | How stress influences the immune response[END_REF]. The intricacies of both neuronal and immune system have not been fully resolved, but evidence suggests that the glucocorticoid corticosterone dampens the immune response by promoting a shift from a pro-inflammatory Th-1/Th-17 response to a Th-2 response [START_REF] Elenkov | Glucocorticoids and the Th1/Th2 Balance[END_REF][START_REF] Harpaz | Chronic exposure to stress predisposes to higher autoimmune susceptibility in C57BL/6 mice: Glucocorticoids as a doubleedged sword: Immunomodulation[END_REF][START_REF] Padgett | How stress influences the immune response[END_REF]. This latter polarization is associated with beneficial outcome of GIN infection and enhanced in more resistant hosts, as reported in mice [START_REF] Filbey | Innate and adaptive type 2 immune cell responses in genetically controlled resistance to intestinal helminth infection[END_REF] or in sheep [START_REF] Terefe | Immune response to Haemonchus contortus infection in susceptible (INRA 401) and resistant (Barbados Black Belly) breeds of lambs[END_REF]. The decrease in FEC observed in lambs exposed to chronic stress 24 days after reinfection is compatible with a delayed worm development that could be underpinned by an enhanced Th-2 response in these individuals. However, the lack of any beneficial effect at primary infection and the transient nature of this observation upon reinfection warrant further validation. Because of the variations in the extent and polarization of glucocorticoid level changes following chronic stress [START_REF] Doyle | Measuring judgement bias and emotional reactivity in sheep following long-term exposure to unpredictable and aversive events[END_REF][START_REF] Terlouw | Relationship between plasma cortisol and stereotypic activities in pigs[END_REF], such validation would require technical developments including cortisol dosage from the wool as proposed for humans [START_REF] Russell | Hair cortisol as a biological marker of chronic stress: Current status, future directions and unanswered questions[END_REF].

Our experimental trial found limited genetic x environment interactions but significant host x parasite interactions. This suggests that the selection of more resistant sheep would be relatively insensitive to farming management practices, although a few individuals may have an increased environmental sensitivity. On the contrary, the composition of GIN communities could limit the benefits of the selection strategy. Additional environmental disruption like feeding restriction could be investigated to confirm that the resistant potential holds under resource-limited environments.

  Animal experiments and experimental procedures were approved by the French Ministry for Higher Education and Research and the Centre Val de Loire ethics committee under agreement numbers 2015010811379451_v4 and APAFIS#8973-2017022108587640_ v3. Behavioural treatment was applied to 84 enrolled G1 lambs allocated to four indoor pens, each housing 22 females or 20 males with equal proportion of lambs from both lines. Half of the sheep were submitted to a stress treatment or a control treatment 7 weeks prior to the first H. contortus infection. This behavioural treatment lasted throughout the experimental infection. The control treatment involved mild enrichment: sheep had access to a wool brush and were exposed to daily positive tactile contacts with humans. Twice a week, a familiar sheep keeper entered the pen, stayed passive and gave positive contacts to sheep that initiated contacts. The chronic stress treatment consisted in moving animals from their free-range pens to individual cages where they remained locked down and isolated from their mates once a week. Isolation was applied until the end of the second artificial challenge and lasted 20 min in the first week, 40 min the following month and 10 min afterwards. This chronic stress treatment significantly impacted on sheep behaviour as measured by behavioural reactivity and standing-lying behaviour (Appendix

F

  Overview of the selection process guided by 8 QTL. Left panels summarize the selection strategy to create divergent lines. At each selection step (to retain individuals enrolled in subsequent experimental infections or for matings), the number of sheep is given with corresponding selection intensities in brackets. The right panels show the association between 898 SNP within 8 QTL regions (materialized by colours) and faecal egg count at 1st (FEC1) and 2nd infection (FEC2) by H. contortus in G0 individuals. The dashed line corresponds to the retained statistical cut-off for association. Sheep pictures were downloaded from https://smart.servi er.com and used without any changes. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License

F

  Weight gain trajectories measured in resistant and susceptible lambs following Haemonchus contortus infection. Weight gain (mean ± SE) from day 0 are presented for resistant (orange or red) and susceptible (purple or blue) sheep lines. Phenotypes recorded on first-generation lambs (G1; n = 23 and 20 individuals for resistant and susceptible groups respectively) are given in top panels, and bottom panels present that measured on second-generation lambs (G2; n = 18 and 20 individuals for resistant and susceptible groups respectively). Asterisks indicate significant reduction in the susceptible individuals

OAR Region size SNP count Population Trait-Original h 2

	3	4,993,965	106 (47,113)	SBF	Strongyle FEC
					-0.06
	4	67,333,487	106 (635,222)	MBR, SBF,	Strongyle FEC
				SDL	-0.02
	5	5,928,484	110 (53,895)	MBR	H. contortus FECa
					-0.03
	7	5,004,258	115 (43,515)	MBR	H. contortus FECa
					-0.03
	12	12,858,699	250 (51,435)	MBR	H. contortus FECa
					-0.04
	13	3,897,157	96 (40,595)	MBR	H. contortus FEC2
					-0.05
	14	5,128,189	105 (48,840)	SBF	Nematodirus sp
					FECa -0.07
	21	2,919,789	10 (291,979)	MBR, SBF	Strongyle FEC
					-0.02 (MBR),
					0.07 (SBF)
	Note: QTL genomic (region size in bp, number of SNPs within region and average inter-SNP distance
	in bp in brackets) and genetic metadata are listed for 8 regions considered to guide the selection
	of G

0 individuals. Populations used for QTL detection are also listed (MBR, Martinik × Romane back-cross; SBF, Scottish Blackface; SDL, Sarde × Lacaune back-cross) with original trait and h 2 estimates provided. FECa, faecal egg count (FEC) animal solution; FEC2, FEC at reinfection.

  FEC, faecal egg count; G0, nucleus flock; G1 and G2 correspond to first and second round of divergent selection; R and S stand for resistant and susceptible respectively.

					Std.
		Generation	Line	Mean	deviation	Minimum	Maximum
	Raw FEC	G0		9569	5956	50	32900
		G1	R	424	594	0	2264
		G2	R	1000	1196	0	3900
		G1	S	3177	2213	199	8272
		G2	S	3999	1897	937	7750
	4th-root FEC	G0		9.44	1.82	2.66	13.47
		G1	R	3.60	1.76	0.00	6.90
		G2	R	4.49	2.32	0.00	7.90
		G1	S	7.03	1.65	3.76	9.54
		G2	S	7.79	0.98	5.53	9.38
	Note:

DATA AVA I L A B I L I T Y S TAT E M E N T

R scripts used for raw data analysis and associated data files are freely available at https://github.com/guiSa lle/GEMANEMA.

O RCI D

Guillaume Sallé

https://orcid.org/0000-0002-4032-139X

ACK N OWLED G EM ENTS

This project was funded by the ANR Institut Carnot Santé Animale programme (GEMANEMA project N° 11 CARN 016-01).

CO N FLI C T O F I NTE R E S T

The authors declare no conflicts of interest.

S U PP O RTI N G I N FO R M ATI O N

Additional supporting information may be found online in the Supporting Information section.


Léo Cances
email: leo.cances@irit.fr

Thomas Pellegrini
email: thomas.pellegrini@irit.fr

Patrice Guyot
email: patrice.guyot@irit.fr

SOUND EVENT DETECTION FROM WEAK ANNOTATIONS: WEIGHTED GRU VERSUS MULTI-INSTANCE LEARNING

Keywords: Sound event detection, weakly supervised learning, multi-instance learning, convolutional neural networks, weighted gate recurrent unit

In this paper, we address the detection of audio events in domestic environments in the case where a weakly annotated dataset is available for training. The weak annotations provide tags from audio events but do not provide temporal boundaries. We report experiments in the framework of the task four of the DCASE 2018 challenge. The objective is twofold: detect audio events (multicategorical classification at recording level), localize the events precisely within the recordings. We explored two approaches: 1) a "weighted-GRU" (WGRU), in which we train a Convolutional Recurrent Neural Network (CRNN) for classification and then exploit its frame-based predictions at the output of the time-distributed dense layer to perform localization. We propose to lower the influence of the hidden states to avoid predicting a same score throughout a recording. 2) An approach inspired by Multi-Instance Learning (MIL), in which we train a CRNN to give predictions at framelevel, using a custom loss function based on the weak label and statistics of the frame-based predictions. Both approaches outperform the baseline of 14.06% in F-measure by a large margin, with values of respectively 16.77% and 24.58% for combined WGRUs and MIL, on a test set comprised of 288 recordings.

INTRODUCTION

The coming of Deep Learning [START_REF] Lecun | Deep learning[END_REF] has opened a new era in the domain of artificial intelligence. Deep neural networks in particular became the state of the art in many application domains involving classification and detection tasks. Most often, these improvements rely on the availability of ever-growing annotated datasets to train the models. While many previous works that heavily rely on supervised training based on a precise manual annotation, new challenges arise from the use of large datasets without supervision.

Recently, different databases of Terabytes of data have been released by Google. The Audioset database provides a large set of audio data extracts from video [START_REF] Gemmeke | Audio set: An ontology and human-labeled dataset for audio events[END_REF]. Annotations for audio events are mainly based on tags by YouTube users and do not contain temporal information.

In that scope, the DCASE challenge includes a task on sound event detection in domestic environment [START_REF] Serizel | Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments[END_REF]. This task proposes a framework to build a system that aims at detecting audio events from a set of 10 classes of sound events forming a subset of Audioset. More precisely, the aim is to provide starting and ending boundaries of the audio events (strong labels) while the training set relies only on global tags (weak labels). As mentioned in [START_REF] Serizel | Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments[END_REF], the duration of the targeted sounds depends heavily on their class. For example, the class vacuum cleaner contains mostly audio events of 10 seconds, while the class dog is mainly composed of sounds shorter than half-second.

Sound event detection (SED) has been deeply investigated [START_REF] Virtanen | Computational analysis of sound scenes and events[END_REF]. In real life, sound events overlap to produce a mixture. In the same way, the current challenge aims at detecting overlapping sound events, referred as polyphonic SED. Polyphonic SED covers a wide set of applications including ecology [START_REF] Stowell | Bird detection in audio: a survey and a challenge[END_REF] and surveillance [START_REF] Crocco | Audio surveillance: A systematic review[END_REF]. The detection of domestic sounds provides interesting clues for health applications [START_REF] Guyot | Water sound recognition based on physical models[END_REF] and Intelligent Virtual Assistants such as Google Home or Amazon Echo.

Ongoing research works on SED are mostly based on a Deep Neural Networks (DNNs). They include fully-connected DNNs [START_REF] Cakir | Polyphonic sound event detection using multi label deep neural networks[END_REF], Convolutional Neural Networks (CNNs) [START_REF] Zhang | Robust sound event recognition using convolutional neural networks[END_REF] and Recurrent Neural Networks (RNNs) [START_REF] Parascandolo | Recurrent neural networks for polyphonic sound event detection in real life recordings[END_REF]. Most of recent approaches are based on a combination of layers including these different elements [START_REF] Cakir | Convolutional recurrent neural networks for polyphonic sound event detection[END_REF]. In particular the issue of audio event detection using weakly labeled data was addressed in [START_REF] Kumar | Audio event detection using weakly labeled data[END_REF][START_REF]Deep cnn framework for audio event recognition using weakly labeled web data[END_REF] and formulated into a Multi-Instance Learning (MIL) problem.

The baseline method provided by the challenge organizers relied on two Convolutional Recurrent Neural Networks (CRNNs): the first one for file-level audio classification (weak labels), the second one for the localization of the previously detected events within the recordings (strong labels). We explored two separate approaches that both outperformed the baseline.

Firstly, we modify the recurrent layer of a CRNN to be able to weight the influence of the hidden state of the recurrent cells. Secondly, we generalize to our multiclass classification problem a new loss function inspired by Multi-Instance Learning (MIL), very recently proposed for singing bird localization in [START_REF] Morfi | Data-efficient weakly supervised learning for low-resource audio event detection using deep learning[END_REF].

Section 2 describes the first approach, that we will refer to as "weighted Gated Recurrent Unit" (WGRU), followed by a section describing the second approach (MIL). We report the experimental setup in Section 4, and finally analyze the results and limitations of the two approaches.

WEIGHTED GATED RECURRENT UNIT (WGRU)

Temporal detection from an adapted baseline method

As mentioned above, the baseline system is based on two convolutional recurrent neural networks (CRNN). The first CRNN detects the presence/absence of the ten sound events of interest at file-level. Then, a second CRNN is used for localization. Still, we may assume that the temporal information required for localization is reachable from the first CRNN. In this way, after the classification training of the first CRNN on weak labels, we propose to simply remove its final global average pooling layer in order to get frame-based predictions used for detection. This modified model produces frame-level predictions thank to its time-distributed dense comprised of ten sigmoid units corresponding to the ten classes of interest.

In the following, we will focus on the recurrent layer of this CRNN, since modifications of its internal functioning had to be made to make localization possible.

Recurrent Neural Networks

Recurrent Neural Networks capture temporal behavior in sequential data [START_REF] Dey | Gate-variants of gated recurrent unit (GRU) neural networks[END_REF]. The hidden state of a RNN cell depends on xt, the incoming output of the previous layer at time t, and ht-1, its hidden state at time t -1, as defined in Equation 1:

ht = g(W xt + U ht-1 + b) ( 1
)
where g is a point-wise activation function (hyperbolic tangent function in our case); and W and U are weight matrices to be learned together with the bias b.

Weighted RNN

RNNs have proven to model sound events efficiently, since these often have an underlying sequential structure [START_REF] Parascandolo | Recurrent neural networks for polyphonic sound event detection in real life recordings[END_REF].However, some audio event classes have different typical durations, as described in [START_REF] Serizel | Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments[END_REF]. Long-duration sounds (vacuum cleaner, running water) are expected to be easier to model by an RNN than short sounds (dog, cat). In order to adjust our models to different kinds of sounds, we propose a new adaptation of RNNs. This approach aims at configuring different kinds of temporal behavior according to the class of a sound event. As a first attempt in that direction, we weight the influence of the hidden state of the recurrent cells by a factor ω ≤ 1, which we set globally for the classes on a development subset. This modifies the impact of sequentiality in the RNN, as formulated in equation 2. Figure 1 shows the impact of the weight on a cell at time t, colored in blue. The rational behind lowering the impact of the hidden states lies in the fact that the CRNN is trained to detect a sound event at file level. Thus, if an event is detected at the beginning of the file, the hidden states are expected to keep that information throughout the file even if the detected event is not present in the whole file, and the localization afterwards will fail. The baseline method is based on Gated Recurrent Units (GRU) [START_REF] Cho | Learning phrase representations using rnn encoder-decoder for statistical machine translation[END_REF]. In the following, we will use weighted RNN models using a GRU layer that we will be referred to as Weighted GRU (WGRU).

ht = g(W xt + U ωht-1 + b) (2)
Figure 1: Configuration of the recurrent links between RNN cells according to the weight ω.

The next question that arises is how to set ω. We decided to set a single value for all the sound types based on the localization performance measured on a held-out validation subset. A weight of ω = 1 corresponds to a standard GRU. A lower weight is expected to be more adapted for events of short duration.

The use of different weights does not require to retrain a model. We simply replace the GRU layer by a WGRU at inference time. Thus, we perform two forward passes with a single model: one with GRU for classification, one with WGRU for localization. Finally, we are able to produce temporal predictions with different weights, and eventually combine them to improve the localization.

MULTI-INSTANCE LEARNING

Another approach is related to the Multi-Instance Learning (MIL) paradigm [START_REF] Dietterich | Solving the multiple instance problem with axis-parallel rectangles[END_REF], which handles cases with weak labels. In our case, we need to make predictions at frame-level, whereas the reference tags are at file-level. When a recording is labeled with the Cat class, for instance, not all the acoustic frames are positive with respect to that class. Thus, we are in presence of both positive and negative instances at frame-level. A straightforward but suboptimal solution is the so-called "false strong labeling", in which we consider that all the frames are positive for a given class. MIL consists instead of considering that at least one instance is positive, i.e. the highest score should be equal to the weak label, as written in the following relation for the i th instance: maxj ŷikj = y ik , where ŷikj and y ik are the prediction for frame j and class k and the reference tag for class k, respectively.

There are drawbacks to this approach. For instance, the training of the model will focus only on the highest scored frame and ignore the other ones. To remedy to this issue, Morfi and Stowell [START_REF] Morfi | Data-efficient weakly supervised learning for low-resource audio event detection using deep learning[END_REF] proposed a loss function that takes into account frames with the lowest prediction scores, which should tend towards zero, and also a naïve assumption that in general a specific event will be present in half of the frames. They applied this idea successfully on a binary classification problem, namely the presence/absence of singing birds in audio recordings. For the need of the present challenge, we generalized their loss function to K > 2 classes, as written in equation 3, where binCE stands for the binary cross-entropy loss. In our experiments, a first network is used to identify which classes should be considered for localization by a second network trained to minimize the MIL loss function. In our work, we only used the labeled subset to train our models, and, in the case of GRU-WGRU only, we also tried to extend it by using pseudo-labels made on the unlabeled in domain subsets. The labeled training subset is comprised of 1578 clips (2244 class occurrences) for which weak annotations have been verified and cross-checked.

The test subset is comprised of 288 files for which we were provided with strong labels. We will thus report performance results on this subset. The results obtained on the evaluation subset, comprised of 880 files, are not available.

Audio features

For both approaches, only the labeled (weak labels) and the unlabeled in-domain subsets were used. We used the first one to train a classifier (for both WGRU & MIL), and also to add weak annotations to the unlabeled in domain subset. Finally, both subsets were used to retrain the model and perform localization. The use of the unlabeled in-domain subset proved useful for WGRU only, but not for MIL. More experiments are needed to draw conclusions for this semi-supervised setting.

As input to the networks, 64 log-Mel filter-bank (F-BANK) coefficients were extracted every 23 ms on 100 ms duration frames, with 20 Hz and 11025 Hz as minimum and maximum frequency values to compute the Mel bands, respectively. Hence, for each 10second file, a 431 × 64 matrix is extracted. This matrix is used as a single input image fed to the networks.

Different normalization and features scaling methods were tested as pre-processing stage such as global mean removal, mean and variance standardization, but no gains were observed compared to using raw F-BANK.

Neural networks

For both approaches, we use two recurrent CNNs: the first for audio event classification at file level, and the second for localization. Both networks are identical to the baseline ones [18] except for WGRU, we use 2-d spatial dropout instead of standard dropout and 2 × 4 max-pooling instead of 1 × 4 for the first convolution layer. Spatial dropout allows to decrease correlation between activation maps and eventually overfitting.

For the MIL approach, in the first classification network, the GRU layer is replaced by a 2-d average-and a 2-d max-global pooling layer followed by a dense one with 1024 units. For classification, this network was found to perform better than the recurrent one. It yielded 85.84% and 82.86% f1 scores on our training and validation subset (proportion: 80/20 % of the weakly labeled training set).

Regarding the second network, used for localization, its architecture is the same as the baseline localization CRNN. The output of this network for a single recording is of dimension 431 × 10, 431 being the number of time frames, and 10 the number of classes.

In all cases, we used the Adam optimizer and a simple learning rate decay policy: dividing it by two after 30 epochs and 60 epochs. All the networks were trained on 100 epochs except the MIL localization network trained on 10 epochs only.

Threshold optimization

The score curves are individually rescaled to [0, 1]. The final event segments are obtained by first smoothing the score curves with a moving-average filter of size 19 frames. Second, the curves are binarized with a 0.07 threshold for MIL only. Neighbor segments are merged when separated by less than 200 ms, the tolerance margin used for evaluation.

For classification (WGRU and MIL) and localization (WGRU only), we use an ad-hoc threshold optimization algorithm to set the ten thresholds. It consists of a genetic algorithm inspired by simulated annealing [START_REF] Kirkpatrick | Optimization by Simulated Annealing[END_REF]. This method reaches optimal values much faster than grid search.

RESULTS

Approach

Baseline Table 1 shows the performance results on the test subset in terms of F-measure (F-score) for the baseline and our approaches. Both WGRU and MIL outperform the 14.06% baseline F-score with 16.34% and 24.58% scores, respectively. MIL behaves better than GRU-WGRU for all the classes. We only reported our best results in this table, using classification thresholds optimized on our validation subset (20% of the training data). Classification performance decreases by about 1% in F-score if using the default 0.5 threshold for all the classes.

WGRU

Temporal dependency weakening improves localization

Figure 2 gives an example in which the standard CRNN (GRU) does not allow to localize the sound event Dog that was correctly detected for that particular recording. The curves are the output of the last time-distributed layer of the network, the Green one being the curve using GRU and the orange one using WGRU. The vertical rectangles denote the ground truth and represent the segments where the dog should be detected. For GRU, Dog is detected throughout the whole audio clip. For WGRU, with ω = 0.25, the dog segments are properly localized based on the curve peaks. This failure of the standard CRNN may be due to the fact that it is trained to detect a sound event in 10-s duration recordings, regardless of where in the file. Thus, the memory brought by the recurrent cell states keeps the information that Dog is present as soon as it is detected in the file, either at the beginning or at the end of the recording since bi-directional GRU layers are used.

Combination of WGRUs

Table 2 allows to compare the baseline performance of 14.06 in Fscore and 1.54 in Error Rate (ER) to WGRU with different values of the weighting scalar ω. As one can see, WGRUs alone are worse than the baseline. The best weighting factor value was found to be estimated to about 0.20, with a 11.35% F-score. Smaller weight values revealed less efficient, showing that keeping some information from the previous hidden cell state is important. The best results, also reported in Table 1, were obtained by combining two WGRUs with 1. and 0.20 weights.

The combination of WGRU predictions is subjective and made after observation using the test dataset. The classes have been divided into two categories: stationary sounds (Blender, Electric shaver toothbrush, Running water, Vacuum cleaner) and short sounds (Alarm bell ringing, Cat, Dog, Speech). The predictions of the WGRU weighted at 1 are kept for the stationary sounds and the predictions of the WGRU weighted at 0.20 for the short sounds.

MIL

Figure 3 shows a successful example of the MIL model for a test file that contains speech and dog barking in segments given below the spectrogram. The first classification CNN correctly identified these two classes at file level, and the second MIL-CNN provides the two peaky curves, blue for Speech, red for Dog.

As shown in Table 1, MIL outperformed the baseline by a large margin for about haf of the classes and especially for Cat with a 48.9% F-score. For the other classes, its performance is lower but close. It is remarkable that all approaches failed for Dishes.

By observing the localization predictions, it appears that the MIL model confuses Dishes and Frying. In the training subset, about 46% of the Dishes samples also contain Frying. There are even more files with Dishes and Speech, namely 52%, and 32% with the three classes together. Dishes is not confounded that much with Speech probably because there are many more Speech files than Dishes files: 550 versus 184 files.

CONCLUSION

In this paper, we reported experiments in the framework of the task four of the DCASE 2018 challenge. We had a two-fold objective of first, detecting sound events globally in audio recordings, second, localizing as precisely as possible where the detected event categories occur in time. We trained our models on weakly annotated dataset. The weak annotations provide tags from audio event but does not provide their temporal boundaries.

We explored two new approaches: 1) a "weighted-GRU" one (WGRU), in which we train a Convolutional Recurrent Neural Network for classification and then exploit its frame-based predictions at the output of the time-distributed dense layer to perform localization. We propose to lower the influence of the hidden states to avoid predicting a same score throughout a recording ; 2) An approach inspired by Multi-Instance Learning (MIL), in which we train a CNN to give predictions at frame-level, using a custom loss function based on the weak label and statistics of the frame-based predictions. Both approaches outperform the baseline of 14.06% in F-measure by a large margin, respectively 16.34% and 24.58% for combined WGRUs and MIL, on a test set comprised of 288 files.

Limitations were described. In particular, when two classes of sound events occur very often together, such as Dishes and Frying, MIL fails to learn how to distinguish between them. Our next step will be to modify the MIL loss function to penalize the fact that the prediction outputs for two different classes are too similar. Another improvement would be to achieve the same performance but using a single neural network rather than two.

ACKNOWLEDGMENT

This work is partially supported by the Labex ANR-10-LABX-0002-01 (ANR-5-IDEX-002, AMIES-UMS 3458), within the PEPS REP4SPEECH project, and the LUDAU ANR project.

We thank Benjamin Chamand for his valuable suggestions and technical support, and Julien Pinquier for his advises on acoustic features.

  material The DCASE 2018 Task 4 is related to discovering audio events from a set of 10 sound categories occurring in domestic environments, namely Speech, Dog, Cat, Alarm/Bell ringing, Dishes, Frying, Blender, Running water, Vacuum cleaner, and Electric shaver/toothbrush. All the files are 10-second clips extracted from Youtube user videos and are part of the Audioset corpus [2]. The recordings most often contain several overlapping event categories. The challenge corpus is divided into three subsets: the training, test and evaluation subsets. Three different splits of training data were provided: a labeled, an unlabeled in domain and an unlabeled out of domain training sets.

Figure 2 :

 2 Figure 2: GRU (green) and WGRU (orange) score curves for the correct class Dog. The vertical rectangles denote the groundtruth and represent the segments where Dog should be detected. Green Localization results of the CRNN with GRU. The class dog is detected but in the entire clip. Orange: The prediction of the CRNN with WGRU with a temporal weight of 0.25. The dog barking segments are detected and localized properly.

Figure 3 :

 3 Figure 3: Score curves by MIL for the two correctly detected classes 'Speech' (Blue) and 'Dog' (Red). Below the spectrogram is represented the groundtruth.

Table 1 :

 1 Global and class-wise F-measures (F-scores) on the test subset.

	WGRU	MIL

Table 2 :

 2 Performance comparison and impact of the weight used with WGRU.

		Weight(s)	F-score (%)	ER
	Baseline		14.06	1.54
		1.	6.68	2.55
	WGRU	0.50	4.69	2.92
		0.30	8.24	3.18
		0.20	11.35	3.37
	Combined WGRUs 1. and 0.20	16.77	1.60