Pierre-Henri Paris
email: pierre-henri.paris@upmc.fr

Fayçal Hamdi
email: faycal.hamdi@cnam.fr

Nobal Niraula

Samira Si-Said Cherfi

Contextual Propagation of Properties for Knowledge Graphs A Sentence Embedding Based Approach

Keywords: RDF, contextual identity, property propagation, knowledge graph, linked data, sentence embedding

With the ever-increasing number of RDF-based knowledge graphs, the number of interconnections between these graphs using the owl:sameAs property has exploded. Moreover, as several works indicate, the identity as defined by the semantics of owl:sameAs could be too rigid, and this property is therefore often misused. Indeed, identity must be seen as context-dependent. These facts lead to poor quality data when using the owl:sameAs inference capabilities. Therefore, contextual identity could be a possible path to better quality knowledge. Unlike classical identity, with contextual identity, only certain properties can be propagated between contextually identical entities. Continuing this work on contextual identity, we propose an approach, based on sentence embedding, to find semi-automatically a set of properties, for a given identity context, that can be propagated between contextually identical entities. Quantitative experiments against a gold standard show that our approach achieved promising results. Besides, the use cases provided demonstrate that identifying the properties that can be propagated helps users achieve the desired results that meet their needs when querying a knowledge graph, i.e., more complete and accurate answers.

Introduction

Open and RDF-based knowledge graphs (KGs), like prominent Wikidata 3 or DBpedia 4 , are continuously growing in terms of size and usage. Consequently, the number of entities described in those KGs leads to a problem for both data publishers and data users: how to know if two entities are the same or not? According to Noy et al. [START_REF] Noy | Industryscale knowledge graphs: lessons and challenges[END_REF], this question remains one of the top challenges in knowledge graphs industry. To interlink KGs, the owl:sameAs property has been defined by the W3C5 in 2004 to link entities that are the same. Indeed, a (real world) object is described across several KGs, and those descriptions are linked thanks to the owl:sameAs property. However, the semantic definition of owl:sameAs is very strict. It is based on Leibniz's identity definition, i.e., the identity of indiscernibles: ∀x, ∀y(∀p, ∀o, ( x, p, o and y, p, o ) → x = y). And its converse, the indiscernibility of identicals: ∀x, ∀y(x = y → ∀p, ∀o, ( x, p, o → y, p, o )). Hence, two entities are considered identical if they share all their property,value pairs in all possible and imaginable contexts. In other words, two entities are identical if all their properties are indiscernibles for each value.

Once an identity link is stated between two entities, it is possible to use property, value pairs from one entity to another. However, it is a very strong assertion to state that two objects are the same whatever the context. From a philosophical point of view, there are multiple counterarguments to the definition of Leibniz's identity. For example, if we consider two glasses from the same set of glasses, they are indiscernible from each other and yet they are two different physical objects. Similarly, is a person the same as he or she was ten years ago?

It is also a technical problem because of the open-world assumption [START_REF] Drummond | The open world assumption[END_REF], on the one hand, and on the other hand, because of what a data publisher has in mind that could be different from what the user expects when using data. Besides, when data is published, it is "almost" impossible to know the consensus behind the decision of creating an owl:sameAs link. Several works such as [START_REF] Halpin | When owl: sameas isn't the same: An analysis of identity in linked data[END_REF] and [START_REF] Ding | owl: sameas and linked data: An empirical study[END_REF] have demonstrated that the use of owl:sameAs was inadequate. Indeed, established links might be considered as true only in specific contexts.

As a first intuition, a contextual identity between two entities might be seen as a subset of properties Π for which these entities share the same values for each p ∈ Π.

Example 1. Two different generic drugs Drug1 and Drug2 can be identical when considering the active ingredient. If a KG contains the triples Drug1 activeIngredient Molecule1 and Drug2 activeIngredient M olecule1 , then Drug1 ≡ activeIngredient Drug2 when the context is activeIngredient.

One of the core features of owl:sameAs is to be able to propagate all properties from an entity to other identical entities. Hence, owl:sameAs allows to discover more knowledge and to increase completeness. In the same way, contextual identity must help to discover more knowledge and to increase completeness, but only under specific circumstances. So, to be useful, a contextual identity must specify what is happening with properties that are not part of the context. In other words, an identity context must have propagating properties.

Example 2. Following the example 1, stating only Drug1 ≡ activeIngredient Drug2 has a limited interest, if we do not know what to do with other properties besides activeIngredient. Considering the context activeIngredient, the property targetDisease is propagating, and if the statement Drug1 targetDisease Dis-ease1 exists then we can state that Drug2 targetDisease Disease1 . But if we consider the property excipient as context, then the property targetDisease is not propagating.

Moreover, the ability to propagate a property between entities depends on the context, i.e., the same property might be propagating in a context C 1 and not propagating in a context C 2 as illustrated in Example 2.

Research questions: With a given identity context between two entities, how to find properties that can be propagated? Is it possible to find propagating properties (semi-)automatically?

In this paper, based on the context definition of Idrissou et al. [START_REF] Idrissou | Is my: sameas the same as your: sameas?: Lenticular lenses for context-specific identity[END_REF], we propose an approach to find propagating properties to facilitate knowledge discovery for users. Instead of manually listing the propagating properties as in Idrissou et al. [START_REF] Idrissou | Is my: sameas the same as your: sameas?: Lenticular lenses for context-specific identity[END_REF], we automatically identify the propagating properties for a given context using semantic textual similarity, significantly reducing burden to users. The semantic similarity is based on the sentence embeddings corresponding to the textual descriptions of the properties. We validated our approach through quantitative and qualitative experiments.

The rest of the paper is organized as follows. In following section, we present the related work. In Section 4, we present our approach. In Section 5, we present the experiments we have conducted. Finally, we conclude and define the next directions for our future work in Section 6.

Related work

In the first part of this section, we describe papers that pointed out the problems raised by the owl:sameAs usage. In the second part, we discuss the proposals that tackle these problems.

Identity Crisis

As early as 2002, Guarino and Welty [START_REF] Guarino | Evaluating ontological decisions with ontoclean[END_REF] raised the issue of identity for ontologies. Especially when time is involved, stating that two things are identical became a philosophical problem. The authors proposed to involve in identity only essential properties, i.e., a property that cannot change. As described in Horrocks et al. [START_REF] Horrocks | The even more irresistible sroiq[END_REF], the owl:sameAs property purpose is to link two entities that are strictly the same, i.e., both entities are identical in every possible context. owl:sameAs has a strict semantics allowing to infer new information. Many existing tools produce such owl:sameAs links [START_REF] Ferrara | Data linking for the semantic web[END_REF], and several surveys are available to this end [START_REF] Achichi | A survey on web data linking[END_REF][START_REF] Ferrara | Data linking for the semantic web[END_REF][START_REF] Nentwig | A survey of current link discovery frameworks[END_REF].

However, none of these approaches consider contextual identity links. Their purpose is to discover identity links that allegedly always hold. This is, from a philosophical point of view, hard to obtain as underlined by Leibnitz's identity definition. Indeed, as stated for example in Halpin et al. [START_REF] Halpin | When owl: sameas isn't the same: An analysis of identity in linked data[END_REF] or Ding et al. [START_REF] Ding | owl: sameas and linked data: An empirical study[END_REF], because of the strict semantic of owl:sameAs, the burden of data publishers might be too heavy. As a matter of fact, owl:sameAs links are not often adequately used. Some might be simply wrong, and, more insidiously, some might be context-dependent, i.e., the owl:sameAs link does not hold in every possible context because it is hard to obtain a consensus on the validity of a statement. What a data modeler means may not be what a data user expects. This misuse of owl:sameAs is often referred to as the "identity crisis" ( [START_REF] Halpin | When owl: sameas isn't the same: An analysis of identity in linked data[END_REF]).

Contextual Identity

Beek et al. [START_REF] Beek | A contextualised semantics for owl: sameas[END_REF] addressed this issue by constructing a lattice of identity contexts where contexts are defined as sets of properties. All entities belonging to a context share the same values for each property of this context. Hence, a context is a set of indiscernible properties for an entity. However, the authors do not give indications about the usage of properties not belonging to such contexts. Raad et al. [START_REF] Raad | Detection of contextual identity links in a knowledge base[END_REF] proposed an algorithm named DECIDE to compute contexts, where identity contexts are defined as sub-ontologies. Nevertheless, as in the first work, properties of entities that are not in the sub-ontology are ignored. So, in both previous works, there is a limitation of properties that do not belong to a context. This limitation cripples the interest of using such approaches. Indeed, one of the goals of an identity context is to define an identity relation between two entities to use information about one on the other. The solution by Idrissou et al. [START_REF] Idrissou | Is my: sameas the same as your: sameas?: Lenticular lenses for context-specific identity[END_REF] involves such propagation of properties, and thus, increases completeness of an entity according to a context. However, this proposal requires users to provide both the propagating and indiscernible properties as input. Hence, it leaves the burden to the user to identify and provide context and properties.

In this work, we propose to remove this burden partially from the user, i.e., to semi-automatically compute the propagation set of properties given an indiscernibility set of properties. For this, we will use sentence embedding (presented in Section 4.3) to compute the embeddings of properties using their descriptions to discover the propagating properties with respect to a given identity context (as defined in [START_REF] Idrissou | Is my: sameas the same as your: sameas?: Lenticular lenses for context-specific identity[END_REF]).

Motivation

Sometimes, real-world entities may be close regarding their properties but not the same. For example, the French capital, Paris, is both a city and a department (an administrative subdivision of the French territory). While considering that the city and the department are the same concerning their geography, they are two distinct entities administratively (or legally) speaking, i.e., they are not considered the same per owl:sameAs. Now, suppose both Paris are represented in a KG as distinct entities, and both are linked to (possibly distinct) movie theaters. If one wants to retrieve movie theaters located in the city of Paris, results will not be complete if some of them are linked to the department (see Figure 1). A French citizen might know this ground truth, but how to allow an automated agent to discover this fact? Contextual identity is a possible answer to this question, i.e., a set of properties for which values are the same for both entities. Considering the present example, both Paris (city and department) are geographically the same and some properties related to geography might be propagated. In Figure 1, the dotted red properties (geo and label ) are indiscernible (have the same values) and the located in properties are propagating. Although the two entities do not share the same values for the located in property, this one is related to the geographic context. Indeed, for a human agent, the located in property might be obviously propagated between the two entities. While we expected to have the four movie theaters located in Paris, a query on the City of Paris will only return movie theaters 1, 2 and 3 (see Figure 1).

Thus, discovering such contexts of identity between entities, might improve completeness of query results. Our intuition is inspired by Tobler's first law [START_REF] Tobler | A computer movie simulating urban growth in the detroit region[END_REF], that is: "Everything is related to everything else, but near things are more related than distant things." Therefore, we hypothesize that, from a semantic point of view, the closer a property is to the identity context, the more likely it could be a good candidate for propagation. In the previous example, located in clearly refers to a geographic fact, and the context of identity is about geography since it is composed of geographical coordinates. So, the idea is to compute a semantic distance between indiscernible properties and candidate properties for propagation. Consequently, numbers, and in our case numerical vectors, are best suited to compute this distance. A numerical representation of the textual description of each property through its rdfs:comment or schema:description can provide a basis to get this vector. In most KGs, properties are described with such sentences. For example, 99% of properties in Wikidata have descriptions. Sentence embeddings of property descriptions output numerical vectors such that semantically similar descriptions appear closer in the vector space.

Approach

Preliminaries

As mentioned in Section 2, several proposals have been made to define an identity context. We choose the one from Idrissou et al. [START_REF] Idrissou | Is my: sameas the same as your: sameas?: Lenticular lenses for context-specific identity[END_REF] since it is the only one that considers the propagation of properties. They give the following definition of the identity context: ≈) is defined by two sets of properties (Π and Ψ ) and an alignment procedure (≈). Π is the indiscernibility set of properties (equation 1) and Ψ is the propagation set of properties (equation 2). In the following, x and y are entities.

Definition 1. (Identity Context) An identity context C = (Π, Ψ,
x = (Π,Ψ,≈) y ↔ ∀(p 1 , p 2 ) ∈ Π 2 with p 1 ≈ p 2 and ∀v 1 , v 2 with v 1 ≈ v 2 : x, p 1 , v 1 ↔ y, p 2 , v 2 (1)
x = (Π,Ψ,≈) y → ∀(p 1 , p 2 ) ∈ Ψ 2 with p 1 ≈ p 2 and ∀v 1 , v 2 with v 1 ≈ v 2 : x, p 1 , v 1 ↔ y, p 2 , v 2 (2)
Moreover, we define the level of a context |Π C | as the number of its indiscernible properties.

In the case where similar entities according to an identity context belong to the same KG, it is not necessary to have an alignment procedure.

An entity can have several identity contexts, depending on properties in the indiscernibility set Π. Indeed, two different combinations of properties can give different sets of similar entities. The identity lattice of all identity contexts of an entity e is defined as follow: Definition 2. (Identity Lattice) An identity lattice L is a lattice, where each element is an identity context. The set inclusion between indiscernibility set of properties of each context is the binary relation responsible for the partial order.

The last notion is the seed of a lattice or a context that we define as follows: Definition 3. (Seed of a lattice or a context) Each context of a lattice is constructed from the same entity e. This entity e is called the seed of the lattice.

As per Definition 2, to build an identity lattice, we need to start from a seed, despite the fact that the lattice could potentially be valid with another seed (see Figure 2). Now that we have defined the necessary concepts, we will explain the core of our approach.

Computation of contexts

We present Algorithm 1 that computes an identity lattice. It takes as input the seed entity, the source KG to which the seed belongs, the target KG (possibly the same as the source KG) and an alignment procedure if the two KGs are distinct. The main idea is to start by computing level one identity contexts with each seed's property and finally combine those contexts to obtain upper-level identity contexts. When building a context, its first part is its indiscernibility set, from which we then get similar entities, to obtain candidate properties for propagation and, in the end, propagating properties.

The first step, line 3, is to compute all level 1 identity contexts (see Definition 1). Indeed, for each property p of the seed, there is exactly one identity context (its indiscernibility set is Π = {p}). Later, identity contexts with only one indiscernibility property will be merged to give identity contexts of higherlevels. Next, we retrieve similar entities entities p to the seed that have the same value(s) for the given property p. If p is multi-valuated, then entities in entities p are similar to the seed for all values o such that seed p o . It is worth noting that, when filling entities p , we search only entities that have the same type(s) Data: KG 1 : the source KG, KG 2 : the target KG, seed: an entity of KG 1 , ≈: an alignment procedure between KG 1 and KG 2 Result: L: a lattice of identity contexts between the seed and entities in the target KG with the seed. This is because we want to avoid absurd results, e.g., comparing a person with an airplane. It also has the advantage of lowering the number of possible identity contexts to compute. Finally, based on entities p , we compute the propagation set Ψ (line 8) as explained in the following section (Section 4.3).

The second step (see Algorithm 2) is to compute upper-level identity contexts based on those from level 1. The loop (line 2) of the algorithm calculates these upper-levels by combining contexts of the same level, and stops when it cannot construct new upper-level identity contexts. This calculation is based on an identity lattice operator, which is the set inclusion on indiscernibility sets. For example, a level 2 context is built on two contexts from level 1. Again, to lower the number of possible identity contexts to compute, if there is no similar entity to the seed for a given context C i , there is no need to compute higher-level contexts based on C i .

Data: L: the lattice with only level one contexts, KG 1 : the source KG, KG 2 : the target KG, seed: an entity of KG 1 , ≈: an alignment procedure between KG 1 and KG 2 Result: L: a lattice of identity contexts between the seed and entities in the target KG /* lvl is the current level in the lattice */ 1 lvl = 1;

2 while ∅ / ∈ L do 3 contexts = ∅; 4 for (C1, C2) ∈ {(Ci, Cj) ∈ L × L : |ΠC i | = |ΠC j | = lvl, i > j} do 5 Π = ΠC 1 ∪ ΠC 2 ; /*

Propagation set using sentence embedding

Our approach for computing propagation set (Line 8 in Algorithm 2) is elaborated in Algo. 3. It is based on sentence embedding which maps a sentence to a numerical vector. Ideally, semantically close sentences appear nearby in the numerical vector space.

Sentence embedding is a technique that maps a sentence to a numerical vector. Ideally, semantically close sentences are represented by close vectors in the numerical space considered. The reasons behind using sentence embedding instead of a more classical distance measures, e.g., the edit distance, RDF graph embedding like RDF2Vec [START_REF] Ristoski | Rdf2vec: Rdf graph embeddings for data mining[END_REF], or an ontological alignment technique are: (i) classical string distances ignore sentence semantics, (ii) RDF graph embedding techniques are not yet adapted to such task, and (iii) ontological alignment techniques align pairwise properties and not sets of properties. Sentence embedding is widely used in several tasks such as computing semantic similarities between two texts. An encoder derives sentence embeddings, to capture the semantics of Data: seed: the entity that generated Π, entities: set of entities similar to seed with respect to Π, Π: an indiscernibility set Result: Ψ : a propagation set /* computation of the embeddings of each property in Π by using one of the encoder */ a language, from a large text corpus. State-of-the-art encoders include Universal Sentence Encoder [START_REF] Cer | Universal sentence encoder[END_REF], GenSen [START_REF] Subramanian | Learning general purpose distributed sentence representations via large scale multi-task learning[END_REF] and InferSent [START_REF] Conneau | Supervised learning of universal sentence representations from natural language inference data[END_REF]. A lot of attention has been given to sentence embeddings lately.

As presented in Section 1, our intuition, based on Tobler's first law, is that a propagation set of properties can be found given an indiscernibility set, if vectors of descriptions of those two sets are close enough. In this work, we propose to use property descriptions (e.g., rdfs:comment or schema:description as "standard plug type for mains electricity in a country") to find properties that are semantically related and consequently good candidates for propagation for a given indiscernibility set Π. For example, in Wikidata, the property "director" has the follow description: "director(s) of film, TV-series, stageplay, video game or similar". Descriptions are mainly composed of one sentence. Most of the properties are described with such annotations, e.g., properties of Wikidata are annotated with an english schema:description at 98.9%. For the embedding computation, any of the previously described encoders can be used.

Algorithm 3 presents our proposal to compute Ψ given a Π. It takes as input three parameters: a seed (an entity), a set of property built from the seed (indiscernibility set Π), and a set of entities that are similar to the seed with respect to Π. The computation of Π is presented in the previous section (see Algorithm 1).

First, for each property in the indiscernibility set Π, we calculate its representational vector. Then, we compute the mean vector that represents the indiscernibility set. Similarly, we consider each property of the seed or its similar entities, and compute their representational vectors. Therefore, on the one hand, we have one vector that represents the set of indiscernibility and, on the other hand, we have n vectors for the n properties that are candidates for propagation. Properties of similar entities (with respect to the indiscernibility set Π) are also considered as candidates since possibly one of them can have a propagating property that the seed does not have.

Then we loop on each candidate property to compute a cosine similarity [START_REF] Singhal | Modern information retrieval: A brief overview[END_REF] between each candidate vector and the mean vector representing the indiscernibility set Π. If the cosine similarity is high enough (above a specified threshold as explained in the following section) the candidate property is considered as a propagating property.

Experimental Results

For evaluation, we first implemented our approach, and then we present several SPARQL queries that benefited from our approach.

Implementation and set-up

We implemented our approach in Python. For the sake of reproducibility, the code is made available on a GitHub repository 6 . As mentioned earlier, we used three sentence embedding approaches, namely InferSent7 , GenSen8 and Universal Sentence Encoder9 . We used an HDT file (see [START_REF] Martínez-Prieto | Exchange and consumption of huge rdf data[END_REF] and [START_REF] Fernández | Binary rdf representation for publication and exchange (hdt)[END_REF]) that contains a dump of the last version of Wikidata 10 . The computer we used had an i7 processor and 32 GB of RAM. As an indication, the complete calculation of the identity lattice for an entity such as the city of Paris, France takes about 1396 ms. It has more than 1000 property-object pairs and, in Wikidata, the mean number of property-object pairs is about 60. Thus, it is a rather large entity and this approach could scale well.

Quantitative Study

The goal of quantity study is to evaluate how well the proposed approach can retrieve the propagating properties specified in Ψ , given the indiscernibility set of properties Π for each identity context (Π, Ψ , ≡). Since there is not a prior work or dataset that we can leverage to evaluate our algorithm, we manually constructed a gold standard dataset from the Wikidata KG that is known for its high data quality [START_REF] Färber | Linked data quality of dbpedia, freebase, opencyc, wikidata, and YAGO[END_REF].

The dataset consists of 100 identity contexts where each context contains the indiscernibility set of properties Π and the propagation set of properties Ψ .

We do not need an alignment procedure (≡) specified for identity contexts since both source and target KGs are the same i.e., the Wikidata KG. To test the performance across different classes, identity contexts were constructed across five diverse class types: country, comics character, political party, literary work, and film. We randomly selected 20 entities from each class type and computed their identity lattices. One context was selected from each lattice. The propagation set of properties (Ψ ) for each context was manually identified by looking at its indiscernibility set of properties Π.

Evaluation: We compared our algorithm with a baseline system that computes, using Jaccard index (JI) [START_REF] Jaccard | Nouvelles recherches sur la distribution florale[END_REF], a similarity score (range 0-1) between a candidate property and each property in Π of a given identity context. If the mean similarity score is above a specified threshold, we considered the candidate property as a propagating property for this context.

We evaluated the performance of a model using standard P recision = tp tp+f p , Recall = tp tp+f n , and F measure = 2×P recision×Recall P recision+Recall where tp (true positive) is the number of predicted properties that are actually in Ψ , fp (false positive) is the number of predicted properties which are not in Ψ , and f n (false negative) is the number of predictive properties in Ψ not selected by the model.

We experimented with different sentence embeddings (namely InferSent, Universal Sentence Encoder, and GenSen) and with different thresholds (0 to 1). Due to space constraint, we only present the results in Table 1 corresponding to In-ferSent and thresholds of 0.1 and 0.9. The proposed approach outperformed the baseline for every threshold. This is expected because the baseline uses Jaccard Index, and thus it relies only on the exactly matching tokens between the property descriptions. Because our approach uses InferSent, it can obtain semantically similar descriptions even though the descriptions themselves do not contain the exact tokens. As we increased the similarity threshold, precision increased, but recall decreased. The threshold of 0.9 was a balance between precision and recall that yielded the F1 scores up to 0.69 (for film and literary work). In fact, F1 scores were above 0.60 for every class except for the country class, which had an F1 score of 0.36 due to overlapping descriptions among propagating and non-propagating properties. The overlapping descriptions for the country class appeared very close in the vector space, which reduced the precision and F1 scores. In addition, the performances were impacted by noisy descriptions, and thus, better preprocessing techniques can potentially improve these scores. In sum, we obtained an overall F1 score of 0.59 and validated our Tobler-inspired hypothesis, i.e., properties can be sorted using their descriptions obtaining the propagating properties at the top. Our result further provides a strong baseline for future research for this novel research problem.

Qualitative Study

In this section, we describe three different queries that could demonstrate the benefits of our approach by extending their results. To achieve our goal, we used InferSent and the threshold value equal to 0.9. All of these queries are simplified 1. Baseline and InferSent results with thresholds of 0.1 and 0.9.

queries tested on Wikidata (for ease of reading). The original queries can be found on the GitHub repository 11 . Task description: For each query, the goal is to find an identity context that will allow expanding the query with similar entities according to the user's objective. In this way, users can benefit from more complete results. The workflow is the following (see Figure 3): first, from the query, we extract the instantiated entity (or entities) that will be the seed(s) (step 1). Second, for each seed, we compute its identity lattice (step 2) that will contain in each of its nodes an indiscernible and a propagating set of properties (cf. Algorithms 1 and 3). Third, with the instantiated property (or set of properties) linked to the seed in the query, we select from the lattice, the node having this property in its propagation set (step 4). This node will be considered as the identity context of the query. Indeed, if multiple identity contexts are possible, the user must choose the best suited for its task purpose. Finally, based on the selected identity context, we can get similar entities (step 5) and rewrite the query with both the seed and similar entities (step 6).

Queries: We tested our approach with three queries. The first query (Listing 1.1) is to retrieve all clinical trials of the drug "Paracetamol". An interesting expansion of this query could be to find all trials of similar legal drugs in terms of medical conditions treated and physical interactions. The second query is to retrieve all persons who once lead France. However, France has a complicated history and has changed its political regime several times (for example, during World War II, or the Napoleonian period). Thus, even if the French territory was almost always the same during the past centuries, each political regime has its own entity in Wikidata. Finally, the third query is to retrieve French politicians from The Republican party that have been convicted. The peculiarity here is that this major political party changed its name several times because of either political scandal or humiliating defeats. We only give details about the first query because of space limitation, the other two are available on the GitHub repository.

SELECT DISTINCT ? c l i n i c a l T r i a l WHERE { ? c l i n i c a l T r i a l : r e s e a r c h I n t e r v e n t i o n : Paracetamol . } Listing 1.1. SPARQL query retrieving all studies about the painkiller named Paracetamol. Table 2 shows the additional results brought by our approach. Each column corresponds to a query. For the first query (and also for the next ones), there is only one seed "Paracetamol" ("France" and "the Republicans" in the second and the third columns respectively) as it is the only instantiated entity in the query. To fill this table, we first computed the lattice of the seed. Then, we selected a context containing the property "research intervention" in its Ψ since this property is instantiated in the query. Moreover, as explained, our goal is to retrieve trials of similar drugs in terms of the condition treated and legal status. Finally, the query is expanded with similar entities, as shown in Listing 1.2. The results show a 47% increase in the number of clinical trials for the considered context. For the second query, it should be noted that among the 99 results, 22 persons were not head of France. 14 were head of Paris City Council, and 8 were Grand Master of Masonic obedience in France. This is because the council and the obedience are misplaced in the Wikidata ontology. These errors cannot, therefore, be attributed to our approach. The results show a 542% increase in the number of France leaders for the considered context. The results of the third query show a 550% increase in the number of convicted politicians for the considered context. SELECT DISTINCT ? c l i n i c a l T r i a l WHERE { VALUES ( ? drug ) { ( : Paracetamol ) ( : I b u p r o f e n ) ( : A s p i r i n ) } ? c l i n i c a l T r i a l : r e s e a r c h I n t e r v e n t i o n ? drug . } Listing 1.2. Expanded SPARQL query retrieving all studies about Paracetamol similar entities.

Discussion

As we have seen, our approach allows for discovering propagating properties for a given indiscernibility set of properties Π. An identity context with its indiscernibility and propagation sets can provide more complete answers to queries through query expansion. The results are very promising but need to be confronted to more different kinds of KGs and to combination of distinct KGs. Also, our approach does not work when the property of an entity lacks property describing it (such as rdfs:comment or schema:description). Hence, the first step for future work is to circumvent this flaw with a multifaceted approach that can include other information than the descriptions alone. Moreover, sophisticated preprocessing and textual similarity techniques can be incorporated to further improve the results.

Conclusion and future work

In this paper, we demonstrated that propagating properties can be discovered semi-automatically. To this end, we presented an approach based on sentence embedding. Given an indiscernible set of properties, the proposed system discovers properties that could be propagated using semantic similarities between the properties. Our approach computes, for an entity, an identity lattice that represents all its possible identity contexts, i.e., both indiscernible and propagating properties. We validated using quantitative and qualitative evaluations that the proposed approach generates promising results for both discovering propagating properties and providing complete answers to the given queries.

Future work includes using other features to improve the results, like values of properties, number of property usage, or semantic features of the property should be tried. However, capturing ontological information of a property when embedding is still an open problem. Secondly, using only sentence embedding, combined with intuition from Tober's first law, might be naïve in some cases. Therefore, there is a need to challenge our work with a combination of distinct KGs. For the time being, we only considered in lattices the case where the entity is subject to a triple, and we should also consider cases where it is the value of a triple. Moreover, using SPARQL queries to help the user to select the best-suited identity context might be an interesting starting point for later work. Finally, to explore SPARQL queries expansion (presented in Section 5.3), a prototype should be implemented to allow users selecting the proper context according to an ordered list of contexts. Also, using RDF* and/or SPARQL* [START_REF] Hartig | Foundations of an alternative approach to reification in rdf[END_REF] to represent the context as defined in this paper should be investigated.

Fig. 1 .

 1 Fig. 1. Excerpt of a KG about Paris, France. The properties in red are indiscernible for both the city and the department. The properties in blue are propagating given the red properties are indiscernible.

Fig. 2 .

 2 Fig. 2. Simplified identity lattice from Figure 1: each node is an indiscernible set of properties. Only the red nodes have similar entities.

13 if Ψ = ∅ then 14 Π = {p}; 15 C 16 L

 13141516 = (Π, Ψ, ≈); = L ∪ C; 17 end 18 end /* Now we can combine contexts of the same level */ 19 return constructU pperLevels(L, KG 1 , KG 2 , seed, ≈) Algorithm 1: createLattice: calculate identity lattice of an entity.

Fig. 3 .

 3 Fig. 3. Qualitative experiment workflow: the elements in red are the inputs and the element in green is the output. To simplify the diagram, we consider only one instantiated entity linked to one instantiated property in the query.

  1 L = ∅; /* Get all explicit and implicit types of the seed */ 2 T seed = {t : seed rdf :type t ∈ KG 1 }; /* the following will create all contexts of the level 1 (with only one indiscernible property) */ 3 for each property p of seed do for each value o such as seed p o ∈ KG 1 do /* entitiesp,o is the set of indiscernible entities with seed with respect to the p, o pair */

	4	candidateEntities = ∅;

5 6 entitiesp,o = {e : (∃(p , o ), p ≈ p, o ≈ o, e p o ∈ KG 2 ) ∧ (∃t ∈ T seed , t ≈ t, e rdf :type t ∈ KG 2 )}; 7 if entitiesp,o = ∅ then 8 candidateEntities = candidateEntities ∪ {entitiesp,o}; 9 end 10 end /* entitiesp is the set of indiscernible entities with seed with respect to the property p */ 11 entitiesp = candidateEntities; 12 Ψ = getP ropagationSet(seed, entitiesp, {p});

  getEntities function gives the set of entities that are similar under the given identity context in the given KG */

	17 end
	18 return L
	Algorithm 2: constructUpperLevels: calculate upper-levels of the identity
	lattice of an entity.

6 entities = getEntities(C1, KG 2 ) ∩ getEntities(C2, KG 2 ); 7 if entities = ∅ and Π / ∈ L then 8 Ψ = getP ropagationSet(seed, entities, Π); /* see Algo. 3 */ 9 if Ψ = ∅ then 10 C = (Π, Ψ, ≈); 11 contexts = contexts ∪ C; 12 end 13 end 14 end 15 L = L ∪ contexts; 16 lvl = lvl + 1;

Table 2 .

 2 Identity context contribution to queries.

		Listing 1.1	See GitHub Repo	See GitHub Repo
	Seed	Paracetamol	France	The Republicans
	Ψ	research intervention	head of	member of political party
		condition treated,	capital,	country,
	Π	interacts with,	official language	political
		legal status		ideology
	Similar	Ibuprofen	French 2nd Republic,	UMP,
	entities	Aspirin	July Monarchy, . . .	RPR, . . .
	# of results
	w/o context	586	12	2
	# of results
	w/ context	860	99 (77)	13

https://www.wikidata.org

https://wiki.dbpedia.org/

https://www.w3.org/TR/owl-ref/

https://github.com/PHParis/ConProKnow

https://github.com/facebookresearch/InferSent

https://github.com/Maluuba/gensen

https://tfhub.dev/google/universal-sentence-encoder/2

http://gaia.infor.uva.es/hdt/wikidata/wikidata2018 09

11.hdt.gz

https://github.com/PHParis/ConProKnow


Binta Diémé

Sylvie Mavel

Hélène Blasco

Gabriele Tripi

Frédérique Bonnet-Brilhault

Joëlle Malvy

Cinzia Bocca

Christian R Andres

Lydie Nadal-Desbarats

Patrick Emond

Binta Dieme

Frederique Bonnet-Brilhault

Joelle Malvy

Frédérique Bonnet- Brilhault

Joëlle Malvy

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Keywords: metabolomics, Autism Spectrum Disorders, ASD, NMR, LC-HRMS, data fusion

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

Introduction

Autism spectrum disorder (ASD) refers to a group of complex neurodevelopmental disorders present since early childhood and persisting lifelong. [START_REF] Baio | Prevalence of Autism Spectrum Disorders: Autism and Developmental Disabilities Monitoring Network, 14 Sites[END_REF] The prevalence of ASD was recently estimated in France to 36.5/10 000 children with a sex ratio of 4.1 boys for 1 girl. [START_REF] Van Bakel | Low but Increasing Prevalence of Autism Spectrum Disorders in a French Area from Register-Based Data[END_REF] Autism is typically diagnosed before three years of age [START_REF] Kolvin | Studies in the childhood psychoses: I. Diagnostic criteria and classification[END_REF] and ASD children are characterized by deficits in social communication and social interaction, as well as restricted and repetitive behaviors and interests as listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). [START_REF]Diagnostic and statistical manual of mental disorders: DSM-5[END_REF] Diagnosis is made clinically by using different scale tests that evaluate the behavior of the patient [START_REF] Lord | Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders[END_REF] and to date, there is no reliable biochemical marker of this disorder.

Although etiologies of autism remain unknown, studies have found implication of genetic, environmental and metabolic factors. [START_REF] Goldani | Biomarkers in autism[END_REF] ASD has several suspected causes, including dysfunctions of the neurologic, immunologic and/or gastrointestinal systems with some markers showing ubiquitous distribution. Due to the social and communication impairments of patients with ASD, identifying gastrointestinal problems remains difficult. However, some studies have linked an imbalance of the gut microbiota with ASD. [START_REF] Wang | Gastrointestinal microbiota and metabolite biomarkers in children with autism spectrum disorders[END_REF] Some metabolic disorders have been found to be more frequent in the autistic population compared to the general population. These abnormalities include phenylketonuria, creatine deficiency syndromes, adenylosuccinate lyase deficiency, 5-nucleotidase and metabolism of purine pyrimidine disorders. The part of metabolic abnormalities that contribute to the etiology of autism is still unknown, but these findings suggest that ASD phenotypes may be associated to metabolic pathways imbalance. In order to explore this hypothesis, metabolomics studies which have already shown their potential in biomarker research in central nervous system disorders have been performed. [START_REF] Trushina | Recent advances in the application of metabolomics to Alzheimer's Disease[END_REF] Metabolomics is the study of the metabolome, which represents the whole content of lowmolecular weight compounds present in biological fluids, cells, or tissues. [START_REF] Nicholson | Metabonomics": understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data[END_REF] Analytical platforms most commonly used to identify and quantify metabolites 10 -12 are Mass Spectrometry coupled to separation techniques such as gas chromatography (GC-MS) or liquid chromatography (LC-MS) [START_REF] Żurawicz | Chromatographic methods in the study of autism[END_REF] and Nuclear Magnetic Resonance spectroscopy (NMR).

To date, few metabolomics studies have been described for biomarkers exploration in ASD. 14- 17 These studies have been primarily based on urine screenings, since urine can be obtained in large quantities by non-invasive sampling. Moreover, repeated sampling is easy to achieve, a major consideration in the case of ASD children. These studies have been performed using a single analytical platform, based on either NMR or MS technologies. Although they have made the proof of concept that exploration of the metabolome allows to classify ASD children compared to control, each analytical platform cannot cover the whole diversity of metabolites in body fluids (molecular diversity and expression levels). Using a single platform also results in partial information and difficulties to confirm identified metabolites as reliable biomarkers.

In this study, we would like to take the full advantage of NMR and MS complementarity to explore the urine metabolome, using the combination of 1 H-NMR, 1 H- [START_REF] Żurawicz | Chromatographic methods in the study of autism[END_REF] C-HSQC (Heteronuclear single quantum coherence)-NMR and LC-HRMS (Liquid Chromatography coupled to High Resolution Mass Spectrometry). One of the challenges of this approach is to associate data from different analytical platforms in order to generate a statistical model that better represents urinary metabolic differences between children with or without ASD. In order to achieve this, we first built independent supervised multivariate models for each analytical modality in order to select the most discriminant metabolites, which were then concatenated in a new single matrix for a block-scaling model analysis. This multi-platform approach, combined with data fusion, gave us the opportunity to better classify children with or without ASD compared to a unique analytical platform approach.

Experimental section

Patients and Controls

Patients who met ASD diagnostic criteria according to the International Classification of Diseases 10 th Edition [START_REF]International statistical classification of diseases and related health problems[END_REF] and the DSM-IV-TR Edition 4 th 19 were included in the study after medical consultations at the Regional Center for Autism (CRA) in Tours, between 2011 and 2012. All parents of participants and participants provided informed consent. Urine samples were collected from 30 children with ASD and 32 control children living in France.

The 62 patients urine samples (30 ASD and 32 controls) were split into two sets : (i) a training set of 46 samples (22 ASD and 24 controls), and (ii) an independent validation set of 16 samples (8 ASD and 8 controls). Informations including sex, diagnosis, gastrointestinal disturbances and age at sampling were collected for each participant. Urinary samples were collected in sterile polypropylene tubes untreated with preservatives. After centrifugation at high speed, each urine sample was aliquoted in 1.5 mL sterile Eppendorf tubes and stored at -80°C immediately after collection until analysis.

NMR study

Sample preparation

Urine samples were thawed out at room temperature, and centrifuged at 3000 g for 10 min as described previously. [START_REF] Mavel | 1H-13C NMR-based urine metabolic profiling in autism spectrum disorders[END_REF][START_REF] Nadal-Desbarats | Combined 1 H-NMR and 1 H-13 C HSQC-NMR to improve urinary screening in autism spectrum disorders[END_REF] The supernatant (500 µL) was then added with 100µL of phosphate buffer (pH = 7.4 ± 0.5) and 100 µL of D 2 O solution for 1D analysis or 100 µL of D 2 O with internal reference [3-trimethylsilylpropionic acid (TSP), 0.05 wt% in D 2 O] for 2D analysis.

Samples were then transferred into conventional 5-mm NMR tubes for NMR analysis.

NMR spectroscopy experiments

1 H-NMR spectra were obtained as previously described using a Bruker DRX-500 spectrometer (Bruker SADIS, Wissembourg, France) operating at 500 MHz, using a "cpmg" pulse program.

All sensitivity-enhanced 1 H- [START_REF] Żurawicz | Chromatographic methods in the study of autism[END_REF] C-HSQC spectra were adapted from a previously described method on a Bruker DPX Avance spectrometer operating at 300 MHz, using an "hsqcgpphpr" pulse program in the Bruker library.

Data preprocessing for NMR analysis

Spectra were processed using TopSpin version 2.1 software (Bruker Daltonik, Karlsruhe, Germany), then aligned using the work package "speaq" [START_REF] Vu | An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data[END_REF] in the R program. The aligned spectra were then displayed in R, and zones with no peaks were removed from file. Finally, intensities of points corresponding to the same peak or nearby peaks were added. These buckets corresponded to either single metabolites or a range of overlapped metabolites. The signal intensity in each bucket was normalized by the total sum of peak intensities, and gathered in the 1 H-NMR matrix for further statistical analysis.

2D spectra were processed using MestReNova version 7.1.0 software (Mestrelab Research, S.L., Santiago De Compostela, Spain) as previously described. Each urine spectra was normalized with an external reference TSP that served as a chemical shift reference set at 0 ppm and as a quantitative reference signal. The final 2D matrix contained 677 different 1 H-13 C cross-peaks between 10 and 150 ppm.

Peaks with low variability (relative standard deviation [RSD] lower than 15 %) were excluded from all data tables since they would not be good predictive biomarkers. [START_REF] Xia | Translational biomarker discovery in clinical metabolomics: an introductory tutorial[END_REF]

LC-HRMS study

Sample preparation

Samples were prepared from 20 µL of urine, diluted at 1/10 in water or acetonitrile depending on the type of analytical column used (for HILIC or C-18, respectively). After vortexing for 10 min and a centrifugation of 10 min at 10 000g, 150 µL of the supernatant was transferred into a 96 well plate. Quality controls samples (QCs) were obtained from a pooled mixture of equal volumes of all urine samples. QCs followed the same pre-analytic and analytical steps described above. Fifteen QC samples were injected to equilibrate the chromatographic system before each analytic batch. The running order of samples was randomized, and QCs were analyzed every 10 samples.

Liquid Chromatography-High Resolution Mass Spectrometry Analysis

MS1 analysis

LC-HRMS analysis was performed on a UPLC Ultimate 3000 system (Dionex), coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) and operated in positive (ESI+) and negative (ESI-) electrospray ionization modes (one run for each mode).

The system was controlled by Xcalibur 2.2 (Thermo Fisher Scientific). Four untargeted LC-HRMS methods were conducted for better metabolome coverage, including C18 and HILIC chromatography coupled to electrospray ionization in both positive and negative ion polarities. Each sample analysis resulted in four separate data acquisitions. Chromatography was carried out with a Phenomenex Kinetex 1.7µm XB -C18 (150 mm×2.10 mm) column with a Waters Cortecs 1.6 µm HILIC (150 mm×2.10 mm) column kept at 40°C. For C18 chromatography, a multi-step gradient (followed by a 2 min equilibration time) had a mobile phase A of 0.1% formic acid in water, and a mobile phase B of acetonitrile (ACN) acidified with 0.1% formic acid; the gradient operated at a flow rate of 0.2 mL/min over a run time of 30 min for both negative and positive modes.

The multi-steps gradient was programmed as follows: 0-3min: 0 % B; 3-8min: 0-15% B, 8-15min: 15-50 % B; 15-20min: 50-100 % B; 20-25min: 100 % B; 25-28 min: 100-0 % B. For HILIC chromatography, a multi-step gradient (followed by a 2 min equilibration time) had a mobile phase A of ammonium formate in water (10 mM) and a mobile phase B with ACN containing ammonium formate (10 mM); the flow rate was 0.4 mL/min over a run of 22 min for both positive and negative modes. The multi-steps gradient was programmed as follows: 0-5min; 0% A, 5-12min; 0-20% A, 12-18.5min; 20-60% A, 18.5-19.5min; 60% A, 19.5-20min; 60-0% A. The autosampler temperature (Ultimate WPS-3000 UHPLC system, Dionex, Germany) was set at 4°C and the injection volume for each sample was 5µL for C18 and 10µL for HILIC. HESI (heated electrospray ionization) source parameters were, for both modes, a spray voltage of 3 kV, capillary temperature of 380°C or 325°C, heater temperature of 350°C or 325°C, sheath gas flow of 40 arbitrary units (AU) or 35 AU, auxiliary gas flow of 20 AU or 10 AU, spare gas flow of 2 AU or 1 AU, and tube lens voltage of 50 V or 60 V for C18 or HILIC, respectively. During the full-scan acquisition, which ranged from 66.7 to 1000 m/z, the instrument operated at 70 000 resolution (m/z= 200), with an automatic gain control (AGC) target of 1×10 6 charges and a maximum injection time (IT) of 250 ms.

MS 2 analysis of VIPs

Firstly, a pool sample was injected for molecular ion mass determination at a resolution of 140 000. The most discriminant metabolites (VIPs) obtained from mass spectrometry were then further investigated for their identification. Targeted MS 2 experiments were conducted with an inclusion list of ion mass selected from HILIC and C18 analysis using an isolation window for the quadrupole of 0.5 m/z and a resolution of 35 000 (m/z=200) for the fragmentation spectrum with an automatic gain control (AGC) target of 2×10 4 charges, a maximum injection time (IT) of 100 ms and a normalized energy collision (Figure S-3).

Data preprocessing for LC-HRMS analysis

XCMS

software [START_REF] Smith | XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[END_REF] implanted in the galaxy platform (http://galaxy.workflow4metabolomics.org/) was used to process raw data for peaks alignment and framing. This step produced a table of detected features, characterized by sample retention time, m/z ratio, and intensity (i.e. peak area). We normalized each peak area to the total peak area of each chromatogram. For chromatograms obtained with HILIC column in negative ionization mode, intensities of signals were corrected with Loess 23 after we observed an analytical deviation of signals before normalization to total peak area.

CAMERA package [START_REF] Kuhl | CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets[END_REF] was used to group isotopes and adducts in order to annotate and to identify features.

The stability of signals intensities across batches was evaluated. Extracted ions chromatograms (EICs) were checked in order to review consistency of integration across samples, peak shapes, and to exclude background noise. Variability of features passing this EIC quality review process was then evaluated. QCs variability given by RSD of each feature was assessed. Features with RSD in QCs higher than in samples were excluded. We only kept features with RSD in QCs below 30 % for further multivariate analysis. Features greater than 30 % variance in QC samples were not considered, except if significant variance was observed between groups, meaning that biological variability may exceed analytical variability. [START_REF] Want | Global metabolic profiling procedures for urine using UPLC-MS[END_REF] Similarly to NMR experiments, we excluded peaks with RSD in samples lower than 15 %.

Data processing

Multivariate data analysis

Quality Control Analysis for LC-HRMS Clustering of QC samples was assessed by principal component analysis (PCA) according to total peak area data in order to compare analytical variability with biological variability.

Samples batch data analysis

The 62 patients samples (30 ASD and 32 controls) were split into two sets: (i) a training set of 46 samples (22 ASD and 24 controls) for the identification of the most discriminants metabolites between ASD and control urine samples, and (ii) an independent validation set of 16 samples (8 ASD and 8 Controls) to evaluate the performance of the classification models.

This was accomplished by randomizing samples. The training and independent validation sets were matched by age and sex.

The general workflow for the training set is shown in Figure 1.

Please insert figure 1 Each analytical method generated a data table with detected features presented in columns, and urine samples presented in lines. The preprocessed data sets were used as input for Simca P+ version 13.0 (Umetrics, Umeå, Sweden) and data analysis was preceded by log transformation and Unit variance (UV) scaling. The training set of these six data sets were tested individually in order to find the best orthogonal partial least square discriminant analysis (OPLS-DA) model. Model development was performed in order to: (i) select a minimum set of predictive metabolites (VIP, value > 1.0) that are the most implicated in the difference between ASD and control urine samples and (ii) test performance of the optimal model with Receiver Operating Characteristic (ROC) curve analysis and the validation set data. The SIMCA prediction score (Ypred) on the independent validation set was used to build the ROC curves. The main benefit of OPLS-DA compared to PLS-DA is its ability to separate the systemic variation in variables X into two parts: variation related to class membership to variation unrelated to class membership (orthogonal). This partitioning of the X-data will facilitate model interpretation and prediction of new samples. [START_REF] Eriksson | Multi-and megavariate data analysis basic principles and applications[END_REF] OPLS-DA models performed on training set were then evaluated by cross-validation by withholding 1/7 of the samples in seven successive simulations, so that each sample was omitted once in order to prevent against overfitting. The set of multiple models resulting from cross-validation was used to calculate Jackknife uncertainty measures. [START_REF] Blasco | Metabolomics in cerebrospinal fluid of patients with amyotrophic lateral sclerosis: an untargeted approach via high-resolution mass spectrometry[END_REF] We set a maximum number of iterations at 200 in order to ensure convergence of the OPLS algorithm. [START_REF] Westerhuis | Multivariate paired data analysis: multilevel PLSDA versus OPLSDA[END_REF] A Pearson correlation test was performed between discriminant variables for each analytical method to remove the redundancy of information due to VIPs that correspond to the same metabolites. Each of the six OPLS-DA models performances were assessed using a validation set data and ROC curves analysis.

A data fusion block-scaling model was then generated by combining data tables coming from the previous six OPLS-DA models. Block scaling, in the context of data fusion provides a way to balance influence of blocks of variables in relation to their size. [START_REF] Boccard | Harnessing the complexity of metabolomic data with chemometrics[END_REF] Block scaling allows each group of variables to be considered independently as an entity with a specific variance. [START_REF] Biais | GC-EI-TOFMS, and Data Set Correlation for Fruit Metabolomics: Application to Spatial Metabolite Analysis in Melon[END_REF] This final data fusion block-scaling OPLS-DA model was also evaluated using a ROC curve analysis.

ROC curves were performed using GraphPad Prism version 6.00 for Windows, La Jolla California USA, www.graphpad.com.

Univariate Data Analysis

Univariate analysis focused on the Variable Importance in Projection (VIP>1) obtained from the data fusion block-scaling model using Wilcoxon test. A statistical correction for multiple tests was applied in order to adjust the p-value for significance by accounting for the number of metabolites evaluated (Bonferonni adjustment). So differences were deemed significant when p<0.05/n (n= number of VIP). Statistical analyses were performed with JMP statistical software version 7.0.2 (SAS Institute, Cary, NC).

Variable Importance Parameter Annotation

Most discriminant VIPs in multivariate analyses were investigated to be annotated or identified. The VIP assignments for molecular formula elucidation were made with the help of seven golden rules in mass spectrometry. [START_REF] Kind | Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry[END_REF] From molecular formula, free access databases queries: Chemspider (http:/www.chemspider.com/), Human Metabolome Database fragmentation and rearrangement rules. Fragmentation spectra were then compared to ab initio fragmentation spectra of potential identified metabolites using Mass Frontier. [START_REF] Huang | Identification of Metabolites from Maropitant Using a Dual-Pressure Linear Ion Trap and Mass Frontier Software[END_REF] Experimental MS 2 mass spectra with fragment structure annotations are given in the supplementary data (Figure S-3) which illustrates this compound annotation workflow.

For VIPs obtained from 2D-NMR ( 1 H-13 C-HSQC), chemical shifts were submitted to Metabominer database (http://wishart.biology.ualberta.ca/metabominer/). In MetaboMiner, a compound is considered to be present if the requirements of minimal signatures are met. A minimal signature is defined as the minimum peak set of that can uniquely identify a compound from all others in a given spectral library [START_REF] Xia | MetaboMiner-semi-automated identification of metabolites from 2D NMR spectra of complex biofluids[END_REF] . A compound is considered potentially identified when a minimal signature is highlighted.

For VIPs obtained from 1 H-NMR, we used the Chenomx database (http:/ http://www.chenomx.com/) . These queries resulted in metabolites propositions that were considered for identification if all chemical shifts of theoretical spectra matched our experimental spectra. Chemical shifts of NMR VIPs are given in supplementary information ( 2). The sensitivity (percentage of ASD children correctly identified) obtained from the OPLS-DA model using the training set was 81.8% and specificity (percentage of healthy children correctly identified) was 91.7%. OPLS-DA model p-value was significant (p-value= 6.10 -4 ). This OPLS-DA model was then externally evaluated using the independent validation set and ROC curve analysis. Area under curve (AUC) was 0.83 and p-value was 0.03 (Supplementary material S-2). The sensitivity and specificity obtained from the ROC curve analysis using the independent validation set were lower, with respective values of 62.5% and 87.5% (table 2).

This result shows that the internal validation during OPLS-DA model construction overvalued prediction's capacity due to overfitting of data. Assessing classification performances of each model by an independent set appears to be necessary in order to minimize this overvaluation.

Please insert Table 2 1 H- [START_REF] Żurawicz | Chromatographic methods in the study of autism[END_REF] C-NMR

1 H-NMR signals can be disturbed by spectral overlap, resulting in a potential lack in spectral resolution and making difficult the identification step. To improve the resolution of urine components, a two-dimensional NMR ( 1 H-13 C-HSQC-NMR) acquisition may be valuable.

1 H-13 C-HSQC-based NMR avoids spectral overlap by dispatching the overall information in two dimensions. Comparatively to the data table of 138 buckets of 1 H-NMR, 1 H-13 C-HSQC-NMR a list contained 677 different 1 H- [START_REF] Żurawicz | Chromatographic methods in the study of autism[END_REF] C cross-peaks between 10 and 150 ppm was established as previously described due to less overlap of the signals.

The model obtained by 1 H- [START_REF] Żurawicz | Chromatographic methods in the study of autism[END_REF] C-HSQC-NMR gave a better information on prediction with a Q 2 =0.51 (Table 2) obtained from 7 cross peaks in the optimized OPLS-DA model compared to [START_REF] Baio | Prevalence of Autism Spectrum Disorders: Autism and Developmental Disabilities Monitoring Network, 14 Sites[END_REF] H-NMR OPLS-DA model (Q 2 =0.37). The OPLS-DA model sensitivity for the training set was found higher compared to the 1 H-NMR (86.36% vs 81.82%). However, specificity was found lower compared to the 1 H-NMR (75% vs 91.7%). p-value of the 1 H-13 C-HSQC-NMR OPLS-DA model was significant (p-value= 2.10 -7 ).

Regarding the validation set, AUC ROC curve gives similar results compared to 1 H-NMR (0.84 vs 0.83) and p-value of 1 H-13 C-HSQC-NMR ROC curve was 0.03.

The sensitivity of ROC curve analysis for independent validation was found better compared to the 1 H-NMR (75% vs 62.5%) with an equal specificity between them (87.5%). There again, these results show that overfitting of data in OPLS-DA models could be attenuated by the use of a validation set and a better spectral separation results in a better sensitivity in discriminant analysis.

LC-HRMS analysis

Since only a few untargeted metabolomics studies by LC-MS have been realized with urines of ASD patients, and since they have been done using a single chromatographic column type (C18), [START_REF] Fei | Comprehensive and simultaneous coverage of lipid and polar metabolites for endogenous cellular metabolomics using HILIC-TOF-MS[END_REF] we decided, in order to better cover the urine metabolome, to analyze each sample using LC-HRMS coupled to a HILIC or C18 column in both positive (ESI+) and negative (ESI-) ionization modes. Four independent models were built depending on column separation and ionization mode.

Quality control

Fifteen QC samples were injected to equilibrate the chromatographic system before each analytical batch.

PCA scatter plot of QCs and samples were analyzed in order to compare analytical and biological variabilities for each batch. As shown in supplementary data, figure S-1, the clustering of QCs compared to samples clearly shows that biological variability exceeds the analytical one. This QCs step validates all batches series.

HILIC analysis

HILIC provides a good separation for polar metabolites that are of major abundance in urine. This is the first study performed with HILIC chromatography in order to explore ASD urine metabolome. After data preprocessing (see section II. 2). Model's curves were not found to be significant (p>0.05), unlike p-values of OPLS-DA models that were significant (table 2). Reasons for these discrepancies could be an OPLS-DA models overfitting and/or a too small number of samples for the validation set.

C18 analysis

Reverse-phase separations are ideal for relatively non-polar metabolites. 1555 and 1226 features in ESI+ and ESI-, respectively, were selected as repeatable. From C18 ESI-data, an OPLS-DA model built with 11 discriminant variables, gave statistical parameters R 2 Y(cum)= 0.48 and Q 2 = 0.39 (table 2). The sensitivity and specificity of this OPLS-DA model were 86.36% and 75% respectively with a significant p-value of 2.10 -5 . Performances of this model were also evaluated using ROC curve and we found similar sensitivity and specificity compared to the OPLS-DA model (table 2). AUC was found to be 0.83 with a significant pvalue 0.03 (Supplementary material S-2). The significant p-value of ROC curve confirms here that the OPLS-DA model is able to classify ASD patients and controls with minimum overfitting.

The OPLS-DA model obtained with 9 discriminant variables from C18-ESI+ data gave better information on the class descriptor with a R 2 Y(cum)=0.64. Moreover, compared to other LC-HRMS modalities, ESI+ model gave highest predictive ability with a Q 2 =0.53 and a p-value of 3.10 -6 . OPLS-DA model sensitivity and specificity were 90.9% and 91.7% respectively.

Despite these models results, the ROC curve AUC value was not found to be significant (pvalue 0.06).

Multivariate Statistical Analysis of the combined 1 H-NMR, 1 H-13 C-HSQC-NMR,

ESI±C18 column and ESI±HILIC column

The fusion and extraction of information from multiple data tables has become a decisive issue. [START_REF] Boccard | A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion[END_REF] Several strategies can be used to associate data from different analytical platforms.

The simplest approach is to concatenate the different data sets in a low-level fusion approach where the data matrix results in a fused data table that is used for multivariate analysis. [START_REF] Roussel | Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry[END_REF] This approach is greatly affected by disparate signal intensities range and size of data matrix obtained from the different analytical platforms. In order to resolve this issue, the selection of the most relevant/predictive variables from each data tables may solve the problem of dimensionality. Such strategies are called intermediate or mid-level data fusion. [START_REF] Blanchet | Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis[END_REF] The model responses are combined in order to produce a final 'fused' response that provides a meaningful synthesis. Following a mid-level data fusion strategy, the most discriminant variables of each analytical platform ( 1 H-NMR, 1 H-13 C-HSQC, C18 ESI±, and HILIC ESI±)

were selected and combined in order to build a data fusion block-scaling model. Since a subset of the most valuable variables can be selected from each data sources, the prediction performance can be increased when compared with individual analysis. While some signals are expected to be common to different blocks, the remaining would be specific and should bring complementary information. Since a data fusion model could be dominated by the largest matrices for numerical reasons, the fairness between blocks was ensured by UV scaling normalization.

We first built six independent OPLS-DA models in order to select the most discriminant variable (VIP >1.0) for each analytical platforms: 8, 7, 7, 11, 9 and 4 variables from 1 H-NMR, The data fusion block-scaling model was obtained from X=46 features giving performances parameters R 2 Y(cum)=0.88 Q 2 =0.75, p-value=9.10 -12 and 100% of specificity and sensibility (table 2, figure 2).

When we performed the ROC curve analysis, an AUC of 0.91 was obtained with a significant p-value 0.006 (table 2, figure 2). In addition, sensitivity was found to be improved when compared to each separate analytical platform.

Please insert figure 2

Besides its classification capacities, OPLS-DA model gives the opportunity to highlight metabolites that are the most involved with this classification.

Table 3 shows the most discriminant metabolites (VIP>1), their variations in ASD group and univariate p-value associated.

Please insert table 3

Firstly, we found higher levels of N-acetylarginine in the ASD group compared to controls.

Arginine has been described to be higher in plasma samples of ASD children. [START_REF] Kuwabara | Altered metabolites in the plasma of autism spectrum disorder: a capillary electrophoresis time-of-flight mass spectroscopy study[END_REF] Excessive arginine is thought to induce oxidative stress via NO production. [START_REF] Delwing | Protective effect of nitric oxide synthase inhibition or antioxidants on brain oxidative damage caused by intracerebroventricular arginine administration[END_REF] We also found perturbations of guanidinosuccinic acid and methylguanidine which are produced from oxidation of argininosuccinic acid and creatine respectively by free radicals. [START_REF] De Jonge | Overexpression of arginase alters circulating and tissue amino acids and guanidino compounds and affects neuromotor behavior in mice[END_REF][START_REF] Tachikawa | Transport characteristics of guanidino compounds at the blood-brain barrier and blood-cerebrospinal fluid barrier: relevance to neural disorders[END_REF] The exact relationship between arginine pathway and oxidative stress in neuropsychiatric disorders remains unclear; however, a common susceptibility gene for ASD and schizophrenia, NOS1, has been suggested to be involved in the arginine-NO pathway. [START_REF] Kim | Family-based association study between NOS-I and -IIA polymorphisms and autism spectrum disorders in Korean trios[END_REF][START_REF] O'donovan | Identification of loci associated with schizophrenia by genome-wide association and follow-up[END_REF] This metabolic profile is consistent with impaired oxidative stress in children with autism. [START_REF] James | Metabolic biomarkers of increased oxidative stress and impaired methylation capacity in children with autism[END_REF] The exact relationship between guanidino compounds pathway and autism needs further investigation. Dihyroxy-1H-indole glucuronide I and desaminotyrosine, which are related to tyrosine metabolism, were also found in the most discriminant metabolites. These results, associated to perturbations of level of valine [START_REF] Evans | Altered amino acid excretion in children with autism[END_REF][START_REF] Tu | Application of LC-MS/MS analysis of plasma amino acids profiles in children with autism[END_REF] and N-acetylasparagine, are in agreement with perturbations of amino acids levels published by Tu et al [START_REF] Tu | Application of LC-MS/MS analysis of plasma amino acids profiles in children with autism[END_REF] and may be interpreted as abnormal amino acids metabolism affecting neurotransmitters levels such as dopamine, noradrenaline and epinephrine as observed in ASD children. [START_REF] Tu | Application of LC-MS/MS analysis of plasma amino acids profiles in children with autism[END_REF] Level of dihydrouracil, an intermediate breakdown product of uracil, was found altered in ASD children's metabotypes. Purines and pyrimidines disorders such as dihydropyrimidine dehydrogenase or dihydropyrimidinase deficiencies, adenylosuccinate lyase or adenosine deaminase deficiencies, have been linked to autistic features.

Deficiencies of dihydropyrimidine dehydrogenase, the enzyme which catalyzes the conversion of uracil to dihydrouracil have been linked to ASD. Indeed, symptoms of this pyrimidine disorder include psychomotor retardation, epileptic encephalopathy and autistic features.

Recent studies have shown that an antipurinergic therapy could reverse the behavioral and metabolic disturbances in the maternal immune activation mouse model [START_REF] Naviaux | Reversal of autism-like behaviors and metabolism in adult mice with single-dose antipurinergic therapy[END_REF][START_REF] Naviaux | Antipurinergic therapy corrects the autism-like features in the poly (IC) mouse model[END_REF] and in the Fragile X (Fmr1 knockout) mouse model. From these two mechanically distinct examples of ASD mouse models, the purinergic pathway is a neurochemical hypothesis that triggers the evolutionarily conserved cell danger to stress that may be associated with ASD.

Indoxyl and indoxyl sulfate, which are produced by tryptophan metabolism in gut bacteria, were found here as metabolite candidates (from different analytical modality: NMR 1D and 2D and HILIC ESI+) and confirmed our previous results. [START_REF] Mavel | 1H-13C NMR-based urine metabolic profiling in autism spectrum disorders[END_REF] Since indoxyl sulfate has been found as a VIP in more than one analytical platform, it may be assumed that this metabolite expression is modified between our two groups of children.

Desaminotyrosine, which can also be the result of deamination of tyrosine by intestinal microflora [START_REF] Holmes | Understanding the role of gut microbiome-host metabolic signal disruption in health and disease[END_REF][START_REF] Lambert | Production of p-hydroxyhydrocinnamic acid from tyrosine by Peptostreptococcus anaerobius[END_REF] , was found increased in the ASD group. Moreover, a recent study by Noto et al using GC/MS as an analytical platform points out a gut microbiota dysfunction, including tyrosine metabolism perturbation. [START_REF] Noto | The urinary metabolomics profile of an Italian autistic children population and their unaffected siblings[END_REF] Our study also points out perturbations of phenylacetylglutamine (PAG) and p-cresol sulfate concentrations, which are also produced by the microbiota respectively from tyrosine [START_REF] Patel | The production of p-cresol sulfate and indoxyl sulfate in vegetarians versus omnivores[END_REF] and phenylalanine. [START_REF] Gabriele | Urinary p-cresol is elevated in young French children with autism spectrum disorder: a replication study[END_REF] These results which confirmed those previously reported [START_REF] Nadal-Desbarats | Combined 1 H-NMR and 1 H-13 C HSQC-NMR to improve urinary screening in autism spectrum disorders[END_REF][START_REF] Gabriele | Urinary p-cresol is elevated in young French children with autism spectrum disorder: a replication study[END_REF] underline the importance of mammalian-microbial cometabolites in ASD [START_REF] Yap | Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and age-matched controls[END_REF][START_REF] Ming | Metabolic perturbance in autism spectrum disorders: a metabolomics study[END_REF][START_REF] Noto | The urinary metabolomics profile of an Italian autistic children population and their unaffected siblings[END_REF] , supporting emerging evidence for a gut-brain connection in autism, wherein gastrointestinal microbiota may contribute to the ASD symptoms. [START_REF] Hsiao | Gastrointestinal issues in autism spectrum disorder[END_REF] It has to be noticed that half of our autistic cohort is clinically diagnosed as suffering of gastrointestinal disturbances that include diarrhea, constipation and colitis.

Whether it is the cause or the consequence of autism's physiopathology, our results confirm that gut microbiome seems to be associated with this disorder.

It has been recently shown that there are Gender differences in emotional and sociability in children with autism spectrum disorders [START_REF] Head | Gender differences in emotionality and sociability in children with autism spectrum disorders[END_REF] . This suggests that, in addition to phenotypic differences, there are metabotype differences linked to gender. One should keep in mind that the statistical power of our study is related to the small size of our groups of patients (training and control sets), emphasized by the small number of patients enrolled. Since only a few females have been included in this study, this tends to decrease the biological significance of our conclusions. However, significant metabolic differences found in this work between ASD and control children may underline a typical general metabolic signature of ASD.

Conclusion:

Discovery of biomarkers for an early diagnosis of ASD, as well as a follow up of its evolution for an improved patient care, is still a challenge to achieve. Because of its non-invasive accessibility and since it has already shown its potential for containing discriminative metabolites, we decided to explore the children's urines metabolome as deep as possible, using six complementary analytical platforms.: 1 H-NMR, 1 H-13 C NMR, ESI+ ESI-LC-MS with C18 and HILIC chromatographic support.

Firstly, our results highlighted and confirmed that several metabolic pathways belonging to amino acids including tyrosine, asparagine, phenylalanine, tryptophan and arginine-seem to be involved in ASD. In addition, as other studies have previously shown, a gut dysbiosys is likely to be associated with ASD. It has to be noticed that all these metabolism perturbations could be observed in a single study, thanks to the use of a multiplatform strategy that enabled Our results need to be validated within a larger cohort of patients, firstly in order to confirm this metabolites panel as a potential clinical tool, but also in order to explore the metabotype variability associated with ASD phenotype's heterogeneity especially when considering the gender.

Associated content

Supporting information

Two figures showing PCA of QC samples, and ROC curve analysis for each analytical platform. This material is available free of charge via the Internet at http://pubs.acs.org.

Contents:

1 H

 1 -13 C-HSQC, C18 ESI+, C18 ESI-, HILIC ESI+ and HILIC ESI-, respectively. An equal weight was then assigned to each block corresponding to each analytical platform in the data table.

Page 18 of 34 ACS

 34

a

  deep exploration of the metabolome. Secondly, our results show that OPLS-DA model construction based on data fusion and block scaling by combining the most discriminant variables from multiple analytical methods, results in a valuable model of prediction. Using the complementarity of analytical methods associated to the selection of the most relevant variables, our OPLS-DA model has a raised the predictive power with an AUC of 91 % in the validation set, which confirms the promise of combining multiple analytical methods by multivariates analyzes.

Figure S- 1 :Figure S- 3 :

 13 Figure S-1: PCA scatter plot of quality controls (QCs) and controls injected during each batch analysis. ASD patients, controls and QCs are respectively colored in blue, green and grey.

* 31 Figure 1 :Figure 2 :

 3112 Figure 1: workflow of treatment of data tables from NMR and LC-HRMS analysis Figure 2: OPLS-DA model and ROC Curve analysis obtained from training set and independent validation set. a) data fusion block-scaling of OPLS-DA model on training set [R2Y(cum)=0.88 Q2=0.75, p =9.10 -12 ], b) ROC curve analysis of independent validation set (8 ASD and 8 controls).

Page 11 of 34 ACS Paragon Plus Environment Journal of Proteome Research (

  http:/www.hmdb.ca/), and MassBank (http:/www.massbank.jp/), were used to annotate compounds. Finally, each VIP was analyzed at high resolution and by MS 2 . From HRMS (ion mass and isotopic abundances) one or several parent's structures are generated. To determine the structure of these compounds, Mass Frontier software's fragment comparator gives the opportunity to compare fragments derived from different compounds. Firstly, Mass Frontier software predicted comprehensive pathways based on a set of general ionization,

12 of 34 ACS Paragon Plus Environment Journal of Proteome Research 13 RESULTS AND DISCUSSION The

  Table S-1). 62 patient urine samples (30 ASD and 32 controls) were split into two sets: (i) a training set of 46 samples (22 ASD [mean 8.64 years] and 24 controls [mean 8.08 years]) and an independent validation set of 16 samples (8 ASD [mean 9.24 years] and 8 controls [mean 9.37 years]). Informations collected for each participant, including age, sex, age at diagnosis, medication and age at sampling, are shown in table 1. Differences of age and sex between groups of training sets are not significant (p-value=0.52 for age and p-value=1 for sex) likewise for validation set (p-value=0.73 for age and p-value=1 for sex).

	Page Please insert Table 1
	NMR Experiments
	1 H-NMR

1 H-NMR spectroscopy, a rapid, robust and reliable analytical tool with high reproducibility, has shown its potential to explore the urine metabolome in ASD patients.

[START_REF] Ryan | Recent and potential developments in the analysis of urine: a review[END_REF][START_REF] Zhang | Evaluation of coupling reversed phase, aqueous normal phase, and hydrophilic interaction liquid chromatography with Orbitrap mass spectrometry for metabolomic studies of human urine[END_REF]

1 H-NMR spectra (0 to 9.5 ppm) were divided in 147 buckets. After removing buckets with low variability in patients (RSD <15 %), the data table of the training set (138 buckets) was analyzed by OPLS-DA. Performances of OPLS-DA internal cross-validated model obtained with 8 discriminant variables, were R 2 Y(cum)=0.52, Q 2 =0.37 (Table

  3.3), data tables contained 1067 and 845 features in ESI+ and ESI-. Multivariate analysis gave the similar ability to separate the two populations of urine samples with slightly better statistical values (R 2 Y(cum)=0.51 and Q 2 =0.47) for ESI+ compared to ESI-(R 2 Y(cum)=0.4 and Q 2 =0.34).

	OPLS-DA model's sensitivities (built with 9 and 4 variables from ESI+ and ESI-respectively)
	were 86.4% and 77.3% for ESI+ and ESI-respectively. Likewise, OPLS-DA model's
	specificity were 91.7% and 79.2% for of ESI+ and ESI-respectively. AUCs curves for the
	validation set (ESI+ and ESI-respectively) were 0.70 and 0.64 ((Supplementary material S-

Table S -

 S 1: putatively annotated metabolites detected by NMR (VIP>1).

Table 1 :

 1 ASD and controls groups characteristics

					Training set	Independent validation set
					ASD (n=22)	Controls (n=24)	ASD (n=8)	Controls (n=8)
	age, years	a			8.64 ± 3.62	8.08 ± 3.67	9.24±3.79	9.37±4.07
	no. of males (%)	a		19 (86.36%)	21 (87.50%)	7 (87.50%)	6 (75%)
	no. of females (%)	a	3 (13.64%)	3 (12.50%)	1 (12.50%)	2 (25%)
	Diagnostic
	Autism disorder		9	0	2	0
	Asperger Syndrome	2	0	0	0
	PDD-NOS			11	0	6	0
	Neurotypical			0	24	0	8
	Gastro-intestinal disturbance
	yes				9	NA	5	NA
	no				12	NA	3	NA
	NA				1	24	0	8

a Data are expressed as mean

Page 28 of 34 ACS Paragon Plus Environment Journal of Proteome Research

Table 3 :

 3 putatively annotated metabolites of data fusion OPLS-DA model (VIP>1)

	Analytical platform	Potential assignment	p-value a	Differentiation for ASD samples b
	C18 ESI-	Dihyroxy-1H-indole glucuronide I	0.005	↑
	C18 ESI+	Dihydrouracil	0.03	↓
	C18 ESI+	N-a-Acetyl-L-arginine	0.009	↑
	C18 ESI+	Unknown	0.009	↓
	C18 ESI+	Unknown	0.006	↑
	C18 ESI+	N-Acetylasparagine	0.03	↑
	C18 ESI+	Desaminotyrosine	0.006	↓
	C18 ESI+	Guanidinosuccinic acid	0.007	↓
	HILIC ESI+	Indoxyl	0.01	↑
	HILIC ESI+	Unknown	0.003*	↓
	HILIC ESI+	Valine, norvaline, 5-aminopentanoic acid	0.007	↑
	HILIC ESI-	Unknown	0.0004*	↑
	HILIC ESI-	Alpha-N-Phenylacetyl-L-glutamine	0.004	↑
	HILIC ESI-	p-cresol sulfate	0.02	↑
	RMN 1D	Indoxyl sulfate	0.01	↑
	RMN 1D	Methylguanidine	0.003*	↓
	RMN 2D	Valine	0.06	↑
	RMN 2D	Indoxyl sulfate	0.003*	↑
	RMN 2D	Glucuronic acid	0.009	↑

a p-value of univariate analysis (Wilcoxon test)

b ↑ denotes higher level in ASD urines, ↓ denotes lower level in ASD urines

ACS Paragon Plus EnvironmentJournal of Proteome Research

Acknowledgements

The authors thank Odette Viaud who has contributed to this work with drive and dedication.

The authors thank Anne Wick who has corrected the English language of this work. This work was supported by the "Institut National de la Santé et de la Recherche" INSERM, by the University François-Rabelais de Tours. We thank "La Région Centre" for a PhD graduate grant. We thank the "Département de métabolomique et d'Analyses Chimiques, " PPF-ASB, Tours, France website : http://ppf.med.univ-tours.fr for chemical analyses.

Author information

Corresponding Author * Corresponding author: Université François Rabelais, INSERM U930, 10 Bv Tonnellé, 37044 Tours, France Tel: + 33 2 47 36 61 53 ; e-mail: patrick.emond@univ-tours.fr

Notes

The authors declare no competing financial interest.