domain: info spi

text:
Amel Raboudi
email: amel.raboudi@utc.fr

Marianne Allanic
email: mallanic@althenas.com

Daniel Balvay
email: daniel.balvay@inserm.fr

Pierre-Yves Hervé
email: pyherve@fealinx.com

Thomas Viel
email: thomas.viel@inserm.fr

Thulaciga Yoganathan
email: 2thulaciga.yoganathan@inserm.fr

Anais Certain
email: anais.certain@inserm.fr

Jacques Hilbey
email: jacques.hilbey@inserm.fr

Jean Charlet
email: jean.charlet@sorbonne-universite.fr

Alexandre Durupt
email: alexandre.durupt@utc.fr

Philippe Boutinaud
email: 1pboutinaud@fealinx.com

Benoît Eynard
email: benoit.eynard@utc.fr

Bertrand Tavitian
email: bertrand.tavitian@inserm.fr

The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology

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INTRODUCTION

Biomedical research studies often require extensive collaborations between various institutions and disciplines, with contributions of many experts from an increasingly diverse modalities for data acquisition: e.g. the multiple "omics", analytical and imaging techniques. The produced data need to be analyzed jointly. However, they are heterogeneous in their structure, semantics, formats (tables, images, proprietary formats) and have different levels of granularity (ranging from a checklist of physiological parameters to a thorough ECG follow up) [START_REF] Rance | Integrating Heterogeneous Biomedical Data for Cancer Research: the CARPEM infrastructure[END_REF]. As a consequence, biomedical researchers spend more and more time and efforts on data structuring and reporting [START_REF] Anderson | Issues in biomedical research data management and analysis: Needs and barriers[END_REF]. Therefore, data are increasingly difficult to share and reuse, even worse, the reproducibility of published results is a serious issue [START_REF] Borgman | The conundrum of sharing research data[END_REF], in spite of the worldwide open data initiatives.

Data reporting via rich metadata is critical to biomedical Research Data Management (RDM) and is encouraged in the open data context. Journals, research institutes, granting bodies and dataset repositories promote the respect of FAIR guidelines [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF] and the use of Data Management Plans (DMP) [START_REF] Miksa | Ten principles for machine-actionable data management plans[END_REF]. FAIR means that scientific data, with (partial or full) public funding, must be "Findable" through its public registration with a dedicated identifier, "Accessible" to peers, "Interoperable" with other domain data and metadata and sufficiently-well annotated to be "Reusable". DMPs ask researchers to identify relevant metadata standards before the beginning of a research study [START_REF] Miksa | Ten principles for machine-actionable data management plans[END_REF]. Both FAIR and DMP recommend the use of standard and published metadata sources to report and annotate data.

FAIRSharing [START_REF] Sansone | FAIRsharing as a community approach to standards, repositories and policies[END_REF], a web resource of guidelines, standards, databases and tips for data management was created to this aim. However, in daily practice, most researchers are not equipped with a proper knowledge of the tools to fulfill these demands [START_REF]Biocuration: Distilling data into knowledge[END_REF].

Several annotation templates and schemas, called Knowledge Organization Systems (KOSs), can be used for day-to-day data reporting with metadata. A KOS is defined as "any type of scheme for organizing information and promoting knowledge management" [START_REF] Hodge | Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files[END_REF]. In the present article, all schemes of metadata are considered as KOSs. BioPortal [START_REF] Whetzel | BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications[END_REF] is a public repository for biomedical KOSs that provides access to published KOSs, which can be classified according to different criteria [START_REF] Souza | Towards a taxonomy of KOS: Dimensions for classifying Knowledge Organization Systems[END_REF], e.g. HTML glossaries have lower semantic expressiveness than OWL ontologies [START_REF] Bergman | An Intrepid Guide to Ontologies[END_REF], and concept lists are less structured than data models or ontologies.

Because biomedical research studies are continuously evolving, and incorporate constantly new data types and formats, researchers often use local vernacular KOSs [START_REF] Bozec | The role of local terminologies in electronic health records. The HEGP experience[END_REF] in addition to the published KOSs for daily data annotation activities. Vernacular KOSs can take different forms: a standard modified to fit the needs of a study, a controlled vocabulary list shared among lab colleagues via excel files, terms from a domain ontology associated with other home-made and context-related terms, vernacular abbreviations, etc. In practice, a biomedical expert is living a dilemma. On the one hand, he is encouraged to use published KOSs in order to comply with the worldwide open data initiatives to share and reuse scientific data. On the other hand, local KOSs are more relevant to the researcher's own work. They can be enriched by terms from recent scientific advances, without the need to wait for updating of public KOSs, a slow process over which the researcher has no control. The use of abbreviated local terms is also easier for daily data annotation than the use of official terms. For instance, using "FDG" as a short term for "2'[18F]fluoro-2'-deoxy-D-glucose" is more convenient for oral and written communication. Therefore, sets of heterogeneous KOSs, both local and public, are generally used for data reporting. This further complicates biomedical Research Data Management (RDM) and generates difficulties in understanding shared data while hindering their reuse. In this context, biomedical researchers wonder how to make their data understandable by the scientific community, so that it can be reused with confidence long after the end of the study and without altering their research tasks?

The Biomedical Study Lifecycle Management BMS-LM data model made a step toward reuse with confidence by implementing a framework for traceability and data provenance annotation throughout the lifecycle of a research study [START_REF] Raboudi | Implementation of a Product Lifecycle Management framework for Biomedical Research[END_REF]. Initially proposed for neuro-imaging data [START_REF] Allanic | BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts[END_REF] it has been extended to the multi-modal data of preclinical research [START_REF] Raboudi | Implementation of a Product Lifecycle Management framework for Biomedical Research[END_REF] such as histology, in vivo imaging with Positron Emission Tomography coupled to computed X-ray Tomography (PET-CT), and proteomics data. Provenance information describes the data and its original context, and redraws data history from their creation to their sharing [START_REF] Simmhan | A survey of data provenance in e-science[END_REF]. As reported in [START_REF] Ciccarese | PAV ontology: provenance, authoring and versioning[END_REF], provenance is essential for trusting published scientific content. The more their sources are trusted, the more the data can be shared and the more reliable the research results are. The provenance of data acquisition and analysis includes the devices and their configuration, the algorithms used, the acquisition settings and processing parameters, the identification of the instrumentation, and others. The use of the BMS-LM data model prepares the data to be shared all the way to the end of the lifecycle, with all provenance metadata being collected all along the research study. Thus, confidence in data is privileged.

However, the BMS-LM data model do not include the biomedical researchers needs for flexible daily data annotation where the use of local terms and the non-conventional use of standard terms, a quasiconstant practice in any research team, is unavoidable. At the same time, it is an issue for data public sharing, because the local significance is not shared. How though to ensure the unbiased understanding of shared data that were annotated using local KOS? The proposition of this paper is to implement semantic interoperability between local and published KOSs, in the perspective to enable flexible annotation in the daily activities using local KOS as well as its public sharing using standard published KOSs. This paper details a methodology to implement the required semantic interoperability based on BMS-LM data model as a starting point to future industrialized software.

Semantic interoperability can be generally defined as "the ability of information systems to exchange information on the basis of shared, pre-established and negotiated meanings of terms and expressions." [START_REF] Garde | Towards Semantic Interoperability for Electronic Health Records[END_REF] The semantic interoperability issue is treated in the literature from different perspectives. In ontology engineering, OntoAnimal tools and eXtensible Ontology Development (XOD) principles [START_REF] He | The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability[END_REF] enable the partial reuse of published ontologies, as well as the integration of local KOSs.

In ontology matching, a compound alignment methodology was proposed [START_REF] Oliveira | Improving the interoperability of biomedical ontologies with compound alignments[END_REF], in order to boost intercommunication between ontologies. The Center for Extended Data Annotation and Retrieval (CEDAR) [START_REF] Martínez-Romero | Supporting Ontology-Based Standardization of Biomedical Metadata in the CEDAR Workbench[END_REF] has provided a researcher-friendly tool for metadata collaborative edition and standardization that is useful for terminologies management.

In this article, the semantic interoperability is handled from a knowledge organization point of view. It denotes the ability of different KOSs to be mapped to each other with preservation of the "intended meaning" according to the authors of [START_REF] Patel | Semantic interoperability in digital library systems[END_REF]. This intended meaning could only be conveyed using explicit terms and knowledge while reducing implicit ones. Ontologies, defined as "explicit specifications of a shared conceptualization", [START_REF] Studer | Knowledge engineering: Principles and methods[END_REF] are a good vector of explicit knowledge. Thus, the first objective of the present work was to convert the BMS-LM data model into an ontology. To design it, two recommendations from the state of the art were adopted: first, the use of a top foundational ontology encouraged by Gangemi et al. [START_REF] Gangemi | Sweetening Ontologies with DOLCE[END_REF] to design a "conceptually more rigorous, cognitively transparent and efficiently exploitable" KOS, i.e. more semantically interoperable KOS, and the use of a three-level ontology construction approach, incorporating top, core and domain ontologies, that are believed to enhance semantic interoperability by Patel et al. [START_REF] Patel | Semantic interoperability in digital library systems[END_REF].

A fourth level was added in our work to include local KOSs so that a maximum of local terms could be translated to their equivalent published terms when sharing the data. The paper presents methods and steps to build a four-level ontology that enables the semantic interoperability of local and published KOSs. To the best of the authors knowledge, this is the first initiative to integrate institutional recommendations into laboratory members daily data annotations using ontologies.

The paper is structured as follows: the implementation methods using the top/core/domain/local levels are described. Then, the resulting 4-level semantic framework and BMS-LM core ontology are explained and illustrated with examples from the application context (small animal preclinical research). Finally, the BMS-LM core ontology is discussed and compared with other ontologies for semantic interoperability of heterogeneous KOS for biomedical data annotation. Conclusion and future work end the paper.

MATERIALS AND METHODS

THE BMS-LM DATA MODEL AS THE STARTING POINT OF THE PAPER

This section summarizes the major features of the BMS-LM data model described in [START_REF] Raboudi | Implementation of a Product Lifecycle Management framework for Biomedical Research[END_REF] and converted later in the paper to an ontology. BMS-LM proposed two types of objects: Definition objects for provenance and Result objects for produced data (see 1-figures in the supplementary materials).

Mainly, the BMS-LM data model covers all phases of a study lifecycle previously proposed in [START_REF] Allanic | BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts[END_REF] as a set of four phases, inspired from [START_REF] Poline | Data sharing in neuroimaging research[END_REF]: (1) study specification [START_REF] Anderson | Issues in biomedical research data management and analysis: Needs and barriers[END_REF] The semantics of the BMS-LM data model were designed for machine use rather than for human understanding. For this reason, we decided to transform it into a core ontology (see the results section).

DEFINITION OF THE DIFFERENT KOS LEVELS REUSED IN THIS ARTICLE

In Patel et al.'s three-level approach, a top ontology is general and independent of a particular context or domain [START_REF] Roussey | An Introduction to Ontologies and Ontology Engineering[END_REF], and it reflects a consistent and formal view of the world, also called ontological commitment (for more on ontological commitment see [START_REF] Roussey | An Introduction to Ontologies and Ontology Engineering[END_REF] and [START_REF] Masolo | WonderWeb deliverable D18 ontology library (final)[END_REF]). In the biomedical research field, Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) [START_REF] Gangemi | Sweetening Ontologies with DOLCE[END_REF] [START_REF] Masolo | WonderWeb deliverable D18 ontology library (final)[END_REF] and Basic Formal Ontology (BFO) [START_REF] Smith | Basic Formal Ontology for bioinformatics[END_REF] [START_REF] Smith | The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration[END_REF] are the two most well-known ones. Top ontologies describe general concepts such as entities, concepts, perdurants (DOLCE), continuants (BFO), physical entities, immaterial entities, processes, etc. (Throughout the paper, the Courrier New 10px typeface will be used for concepts)

Core ontologies [START_REF] Roussey | An Introduction to Ontologies and Ontology Engineering[END_REF] provide the explicit semantics of a large domain; each can also be considered as a reference ontology for their large domain. Core Reference ontologies have been proposed in very different areas such as film production [START_REF] Chakravarthy | OntoFilm: A Core Ontology for Film Production[END_REF] and Business Process Analysis [START_REF] Pedrinaci | A core ontology for Business Process Analysis[END_REF]. A core ontology is essential to establish a minimal ontological commitment for a large domain [START_REF] Gruber | Toward principles for the design of ontologies used for knowledge sharing[END_REF], as it guarantees consistency between all specific metadata schemes (KOSs) used within this domain.

The third level is about domain ontologies [START_REF] Roussey | An Introduction to Ontologies and Ontology Engineering[END_REF]. All ontologies and KOSs that gather specific domain knowledge belong to this level. The DICOM standard [START_REF]DICOM Standard[END_REF], the RADLEX lexicon [START_REF] Langlotz | RadLex: A New Method for Indexing Online Educational Materials[END_REF], and the Quantitative Imaging Biomarkers Ontology (QIBO) [START_REF] Buckler | Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers[END_REF] are examples of domain KOSs from radiology. Some published domain ontologies reuse a top ontology for better semantic interoperability. For instance, the Ontology of Biomedical Investigations (OBI) [START_REF] Bandrowski | The Ontology for Biomedical Investigations[END_REF] and the Gene Ontology (GO) [START_REF] Ashburner | Gene Ontology: tool for the unification of biology[END_REF] specialize the BFO top ontology [START_REF] Smith | Basic Formal Ontology for bioinformatics[END_REF], whereas, the OntoNeuroLOG ontology [START_REF] Gibaud | OntoVIP: An ontology for the annotation of object models used for medical image simulation[END_REF] specializes the DOLCE [START_REF] Gangemi | Sweetening Ontologies with DOLCE[END_REF] top ontology.

Patel et al. [START_REF] Patel | Semantic interoperability in digital library systems[END_REF] also gives two examples from the multimedia domain, a core ontology [START_REF] Hunter | Enhancing the semantic interoperability of multimedia through a core ontology[END_REF] and a domain ontology [START_REF] Tsinaraki | Interoperability Support between MPEG-7/21 and OWL in DS-MIRF[END_REF] that were proposed according to the top/core/domain approach and which have been positively evaluated for their enhancing of semantic and syntactic interoperability. For the BMS-LM ontology, the top/core/domain method is used.

BUILDING THE FOUR-LEVEL SEMANTIC FRAMEWORK AND THE BMS-LM CORE ONTOLOGY

Having the BMS-LM data model as a starting point and the top/core/domain/local ontological levels as objective, several steps were followed to build the BMS-LM ontology. First, the top ontology was chosen. Then, the BMS-LM data model was converted to a core ontology. After that, we gathered local terms from the application domain to build both the domain and local levels. The domain level contains published KOS that can replace local terms and the local level is made of the remaining gathered local terms.

The BMS-LM core ontology was developed using the Protégé 5.1.0 and 5.5.0 software (http://protege.stanford.edu/) and the OWL-Lite (Web Ontology Language -Lite of the W3 Consortium). It was designed with a maximum reuse of published KOS and in collaboration with both data management experts and biomedical research experts.

The choice of a top ontology

To choose the BMS-LM top ontology from the most well-known top ontologies in the biomedical research field, DOLCE and BFO, a list of criteria was defined and applied, as indicated in Tab1. To ensure better semantic interoperability, the following three criteria were considered: metrics for community activity, citations number, and the number of reusing ontologies in the biomedical field. The number of reusing ontologies from BioPortal [START_REF] Whetzel | BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications[END_REF], the Pubmed number of articles, and the citations from Google Scholar were of particular interest. The comparison between these ontologies from an ontological point of view is a common practice, it reveals that BFO has a realism-based approach, while DOLCE has a cognitive bias [START_REF] Seyed | BFO/DOLCE Primitive Relation Comparison[END_REF]. However, this observation is irrelevant to the objective of this paper, concerned about the semantic interoperability maximization from a pragmatic point of view. Google scholar for the following articles: wonderweb [START_REF] Masolo | WonderWeb deliverable D18 ontology library (final)[END_REF], DOLCE [START_REF] Gangemi | Sweetening Ontologies with DOLCE[END_REF], BFO [START_REF] Smith | Basic Formal Ontology for bioinformatics[END_REF], OBO Foundry [START_REF] Smith | The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration[END_REF] on 20/08/2021

Tab1. The choice of a top ontology: results of the comparison between DOLCE and BFO top ontologies according to ontology reuse, citations and community criteria

Criterion

According to the comparison table results, BFO and its corresponding Open Biological and Biomedical Ontology (OBO) foundry consortium were chosen, for the following three reasons:

1. BFO is reused by more biomedical ontologies than DOLCE (see line 1 and 2 from Tab1.) 2. BFO is cited in more articles than DOLCE in the biomedical field and in general (see line 3 and 5 in Tab1.).

3. The BFO community is more active in the biomedical field than the DOLCE community (see line 4 in Tab1.).

From the BMS-LM data model to the BMS-LM core ontology

The BFO top ontology come with a set of principles from the OBO foundry consortium. These principles were used to review the BMS-LM data model. First, its objects were renamed and redefined according to the OBO Foundry principles. The reuse of already available definitions in other ontologies was privileged. Then, the top-core relations between the BFO top ontology and the BMS-LM core ontology were studied and added. An algorithm was implemented to assist the mapping decision. It had analyzed published ontologies in BioPortal and retrieved the top-core links for concepts that are synonyms to the BMS-LM core concepts. For example, a list of synonyms to "Exam Results" was established and used to search in BioPortal for equivalent terms. We selected specially ontologies that has BFO as a top ontology. The used algorithm is described in Fig2. It was implemented using the Knime Analytics Platform1 .

Algorithm 1 Script-Bioportal-Core Input: A list T of core Terms For all each t in T do

Send GET search request to Bioportal via: http://data.bioontology.org/search?q=t Get the list TS of available synonyms of t from Bioportal For all each ts in TS do Send GET request to Bioportal to retrieve the full ontology name source of ts Get all parents of ts till THING ADD the record to an Excel Spreadsheet E Delete redundancies from E Write the E file to the disc Fig2. The algorithm used to search BioPortal for core concepts to use them in the BMS-LM core ontology

The resulting Excel file, listing the identified concepts from BioPortal, indicates in this case all BFO parents linked to an "exam". Then, our choice was made manually by identifying the redundant BFO parent of a concept among the list provided by the Excel file. In our point of view, this redundancy reflects the community consensus about a top-level mapping that we tried to follow as much as possible. The list of input terms and a sample of output ones are provided as supplementary material (see 2-script1).

Next, the BMS-LM relations were redefined. On the one hand, they were made explicit using the Relation Ontology (RO) from the OBO foundry consortium. Relations such as : is about, has part, has member, is specified output of, were of great use to make the BMS-LM relations explicit. On the other hand, relations of provenance must be elucidated considering that the original BMS-LM data model relations were mainly used for traceability and provenance. The PROV-O [START_REF] Lebo | Prov-o: The prov ontology[END_REF] ontology was identified as a reference ontology in the field. It is proposed by the World Wide Web (W3) consortium where provenance is defined "as a record that describes people, institutions, entities, and activities involved in producing a piece of data or thing in the world" [START_REF] Belhajjame | Prov-dm: The prov data model[END_REF]. An entity can be physical, digital, or conceptual. An activity occurs within a time period and acts on one or many entities. An agent is responsible for the execution of an activity. Entities, activities, and agents are the three main concepts of PROV-O and are linked by a set of relations such as was derived from, was associated to, was attributed to, was started by, used. All were reused in the BMS-LM ontology to make provenance semantics explicit.

Building the local and domain levels of the application context

The first design rule of the BMS-LM semantic interoperability framework is the maximum reuse of, and matching with, existing ontologies. A script implementing the algorithm in Fig3. was used for this purpose. It explores BioPortal ontologies and assist ontologists to build the domain ontological level for the application context (in this work, small animal preclinical research). To use it, local terms were collected using interviews, mind maps, and data collection from the application context (see the results section for details). These were provided as input to the algorithm, which provides as output the list of the identified domain concepts together with their direct parent(s), children, and definitions. A list of input terms and a sample output file for the local term "souris" ("mouse" in French) are provided as supplementary material (see 3-script2). Once the domain concepts identified, the decision to include them in the domain ontological level and their matching to the BMS-LM core concepts has obeyed a criteria list. Mainly, the criteria of inclusion were (1) a resource's relevance to the BMS-LM ontological commitment and scope, (2) its reliance on a top ontology, (3) the consistency of the resource itself, and (4) the "redundancy" of the concept's definition and use. Redundancy is an indicator of consensus between different ontology providers. It raises the level of the domain-core-top matching confidence. Whenever inconclusive results were encountered, biomedical experts were consulted.

When no shared domain concept was found using Algorithm 2, local terminologies were used. They formed the local ontological level for the application context. Local terms are valuable because they represent the vocabulary of the application context experts. Their inclusion could engage more biomedical experts in BMS-LM-guided data reporting. As a result, the specific domain ontology for this specific laboratory case contains two levels: a domain level where a maximum reuse of already published KOS is processed, and a local level where specific terms from the lab were added. The domain-local matching enables links between published terms, that are explicit for the community, and local terms used in practice in daily research data reporting.

THE BMS-LM ONTOLOGY PROPERTIES ENCODING

A two-phase strategy for properties' encoding was adopted to enrich the BMS-LM domain and local ontological levels with relevant OWL properties. The first step was to form a collection of concept descriptors that answer the well-known Five Ws questions « Who ? do What ?, Where ?, When ? and Why ? » about each BMS-LM main concept. The Five Ws is also a reference tool for provenance information collection [START_REF] Sahoo | Semantic provenance for eScience -Managing the deluge of scientific data[END_REF] [START_REF] Ding | Reflections on Provenance Ontology Encodings[END_REF]. Therefore, provenance descriptors are also collected using the Five Ws in this first step. The resulting output was a list of terms that describe the main concepts of BMS-LM. For instance, if the BMS-LM concept is "exam", the list of terms would be: the operator doing the experiments, the examination protocol, the exam's place, date, and prescription. A single element from that list is called a descriptor δ associated to a BMS-LM concept C . The second step was to encode the list of collected descriptors. This encoding must follow the OWL encoding format used in OBO Foundry ontologies. An analysis of mainly the OWL encodings in the IAO (Information Artifact Ontology), and OBI (the Ontology for Biomedical Investigation) [START_REF] Brinkman | the OBI consortium: Modeling biomedical experimental processes with OBI[END_REF] ontologies was therefore performed. It provided a list of possibilities to encode descriptors in an OBO-compliant manner, summarized in Fig4. The list was called ADQIV for Annotation property, Data property, Quality, data Item, and Value specification.

I-

The specificity of the relation between the descriptor δ and the concept C : if the descriptor δ is generic and not dependent to the concept C , an existent OWL:AnnotationProperty could be used (option 1) or a new OWL:DataProperty could be added (option 2). For instance, the identifier, the name, a comment, etc.

II-

The nature of the first BFO parent of the conceptC (a BFO:continuant or a BFO:occurrent): if the descriptor δ is about a BFO:continuant and is inherent to C, the use of BFO:quality must be privileged (option 3) such as size, weight, height, otherwise the BFO:data item is directly used (option 4) for all BFO:occurrent and the remaining BFO:continuant.

For data items, three sub-options are proposed. Either to give a simple value for the descriptor using the BMSLM:has value data property, or to define a list of its possible values. If the list is local and non-standard, its elements must have the type of an OWL:individual, otherwise they can be represented as an OWL:class using the concept OBI:value specification.

THE BMS-LM ONTOLOGY MATCHING ENCODING

Following the OBO Foundry principles, each BMS-LM entity, from the core, domain, and local levels, was matched, when applicable, with external entities, reused from other KOSs. This was done through formal matching using the OWL:equivalentClass and OWL:equivalentProperty in the case of strict equivalence. In other cases, the skos:narrowMatch, skos:exactMatch and skos:broadMatch from the SKOS (Simple Knowledge Organization System) W3C standard [START_REF]SKOS Simple Knowledge Organization System Reference[END_REF] were adapted, changed and used. Indeed, SKOS annotations are more suitable to relations between instances of KOSs concepts. As a consequence, bmslm:broadMatch, bmslm:exactMatch and bmslm:narrowMatch annotation properties were added to the BMS-LM ontology to express the same meaning but with less restrictions. For example, the bmslm:data processing concept is bmslm:broadMatch the ncit:processing concept. This means that the NCIT concept has a broader meaning than the BMS-LM one. This annotation prepares for future alignment between the BMS-LM ontology and the NCIthesurus. When an external entity from a published KOS was really reused (mainly for the domain level), the MIREOT (Minimum Information to Reference an External Ontology Term) [START_REF] Courtot | MIREOT: the Minimum Information to Reference an External Ontology Term[END_REF] checklist was adopted as follows:

• the source ontology URI and the source term URI are reported using the IAO:imported from property; and

• the superclass is referenced using the IAO:in branch property.

In the case of an external definition reuse from published KOSs. The source was referenced using both annotations: IAO:definition source and RDFS:isDefinedby.

Regarding the interoperability of the BMS-LM ontology with previous developments (that might use the BMS-LM data model), some annotations were added to the core concepts to refer to the BM-LM data model old labels: OWL:backwardCompatibleWith was used for the previous data model names, and the IAO:alternative term was used to list the short and technical names of a data model concept.

RESULTS

Two main results are detailed in this section. First, the BMS-LM core ontology as an evolution of the BMS-LM data model. Second, the top/core/domain/local semantic interoperability framework made up with the BFO top level ontology and the BMS-LM core level ontology. It enabled the integration of local terms with published concepts using mapping relations at each level. As a preliminary test, examples from local terms of preclinical data were provided and discussed.

THE BMS-LM CORE ONTOLOGY AS AN EVOLUTION OF THE BMS-LM DATA MODEL

BMS-LM Concepts

A list of 29 concepts developed for BMS-LM is provided in Tab2. For each concept, its previous data model name and its new ontology name are specified as well as its parent in the BFO top ontology [62].

Possible matchings in other ontologies are indicated to inform about possible integrations for interoperability.

Tab2. Renaming and changes in generic concepts from the data model to the core ontology and their mapping to top-level concepts

During the conversion process of the BMS-LM data model to a core ontology, some concepts were split into two concepts to make their semantics more explicit, which explains why some previous names have become redundant (highlighted in bold in Tab2.). For instance, the Processing Unit Result(PUR) concept, defined as a process that analyzes input data and produces output results for another PUR, was expanded into the derived data unit concept to reference the produced data, and the data processing unit concept to describe the process executed. Reference Data reference data IAO: i-c-e* *:i-c-e = information content entity

BMS-LM Relations

The BMS-LM data model relations were extended in the ontology. Tab3. shows the new BMS-LM relations. Their OBO Foundry source ontologies are indicated and some use examples from the BMS-LM ontology are given. This table also gives information about the different relations categories in the BMS-LM core ontology.

Tab3. The list of BMS-LM relations, their source ontologies from the OBO Foundry, examples of their use from the BMS-LM core ontology, and their categories. min = minimum cardinality / exactly = exact cardinality In Tab3., relations are categorized to make their role explicit in the BMS-LM core ontology. To be specific, aggregation and composition correspond to the classical UML relations modeled respectively with and . contribution is when a BFO:continuant participates in a BFO:occurrent.

production, and its inverse category ingestion, designate the output and the input of a BFO:process respectively. transformation is when a BFO:continuant is a derived result of another BFO:continuant. qualification is a feature relating to an inherent property of a BFO:continuant.

A realization is when a BFO:realizable entity is realized in a BFO:process.

Provenance and lifecycle concepts and relations

This section contains an explanation of how the BMS-LM core ontology makes provenance and lifecycle semantics explicit. In fact, the information coverage of provenance and all lifecycle phases is the key to end-to-end traceability of biomedical data. It is an old feature of the BMS-LM data model that was preserved in the ontology as explained bellow.

Provenance

The BMS-LM ontology was designed to be interoperable with the general ontology PROV-O. This makes the BMS-LM provenance semantics explicit and allows semantic interoperability with PROV-O-compatible ontologies. In practice, PROV-O has, in its initial version, a list of three main concepts:

Agent(PROV-O), Entity, and Activity (see the Methods section). Tab4. provides a classification of the BMS-LM concepts according to these general concepts. 4) study results publishing concepts. However, we did not found relevant materials to help us identify its parent in the BFO ontology except in the UBERON [START_REF] Mungall | Uberon, an integrative multispecies anatomy ontology[END_REF] ontology. It defines a lifecycle as:

"UBERON:0000105life cycle stage -: A spatiotemporal region encompassing some part of the life cycle of an organism". The spatiotemporal dimension is relevant to an organism because it has a material and physical existence in space, whereas, for a study, only the time dimension seems to be relevant. The BMS-LM study lifecycle phase concept can thus be set as a subclass of the BFO:one-dimensional temporal region.

In terms of relations, each concept of BMS-LM ontology was associated with one or more study lifecycle phases to ensure traceability. All concepts can be related to study lifecycle phases, with the relation PROV-O:is associated with originally from the PROV-O ontology but adapted to BMS-LM requirements.

Accessibility

The OWL-Lite encoding of the BMS-LM ontology is available in the following URL (http://www.fealinxbiomedical.com/ontologies/bmslm/1.0/bmslm.owl) and as supplementary material of the present paper (see 4-BMSLM_ontology).

THE BMS-LM CORE ONTOLOGY AS PART OF THE FOUR LEVELS SEMANTIC INTEROPERABILITY FRAMEWORK

The BMS-LM semantic interoperability framework is composed of four levels: top, core, domain, local (see Fig5.). To comment on Fig. 5, the local level was added to the state-of-the-art three levels, to take into consideration the specificity of the laboratory and the research team that produced the data. After data collection, interviews and mindmapping with biomedical expert, local terms were collected and used as input to BioPortal exploration script for the domain concepts search (see the Methods section, Algorithm2). The BioPortal exploration help identifying related concepts from published KOSs and OBO foundry ontologies. Those concepts were reused and aggregated in the domain ontological level. Their reuse process respects the MIREOT principles [START_REF] Courtot | MIREOT: the Minimum Information to Reference an External Ontology Term[END_REF]. When local terms could not be replaced by domain ones, they were kept in the local ontological level. Thus, the local level specialized the domain one and the latter specialized the core level where the BMS-LM core ontology is used. The BMS-LM core ontology, mapped to the Prov-O ontology, provided a framework to enable biomedical data annotation with provenance information throughout the study lifecycle. The use of the BFO top ontology has made the resulting ontology more interoperable with published ontologies. In fact, BFO has an interoperability common ground with more than 200 biomedical ontologies via the OBO Foundry consortium.

Link with the top level

The BMS-LM core ontology was built and mapped to the BFO top ontology following the OBO Foundry guidelines in the Arp et al. book [START_REF] Arp | Building Ontologies with Basic Formal Ontology[END_REF], its compliance with the OBO 11 principles [START_REF]OBO principles[END_REF] is explained in Tab6. All elements of BMS-LM ontology are identified following a BMSLM_0000000 format.

Versioning

The versions of the BMS-LM ontology follow the M.m format. The follow up of the development process is done using SubVersioN (svn).

A clear scope

The BMSLM ontology scope is indicated in the OWL file, it states that it is a core ontology that covers biomedical studies lifecycles and provenance management with maximum reuse of existing ontologies. 6

Textual definition All core concepts are defined using the <IAO: textual definition> annotation. 7

Relations: reuse of the Relation Ontology (RO)

The RO ontology relations are all reused for BMS-LM ontology.

8 Documentation Documentation of the BMS-LM ontology is done through embedded comments in situ when necessary. 9 Documented Plurality of Users

The BMS-LM ontology is used in the DRIVE-SPC project (2015REMP9HE085 grant from Paris University) and will be used in the PACIFIC project (BPI n°2018-PSPC-07) and PsyCare project (ANR 18-RHUS-0014). All of them are in France. Information about reuse can be found in the list of OWL ontology annotations. 10

Commitment To Collaboration

The BMS-LM ontology construction is mainly based on maximum reuse and matching with existing ontologies, which reinforces collaboration between biomedical KOSs. 11

Locus of Authority

In the BMS-LM ontology annotations, the name and email address of the corresponding author are given. This author is the reference person for the ontology.

Link with the domain and local level of preclinical research

The BMS-LM core ontology was applied to the preclinical research studies conducted on animals, with a therapeutic or diagnostic objective of output to human beings. Small animal preclinical research data were produced by the IVIR (In-Vivo Imaging Research) laboratory of the Paris Cardiovascular Research Center (PARCC) in the context of the DRIVE-SPC project between the IVIR laboratory and the Fealinx company (2015REMP9HE085 grant from Paris University). We used mainly data from [START_REF]OBO principles[END_REF], together with data from ongoing biomedical studies. An exploration work was realized using data collection, recurrent data explanation meetings during the 6 first months of 2017, and structured interviews with identified key persons among the IVIR laboratory members (5 users, 5 structured interviews, 7 specification meetings, 9 follow-up meetings) during four months from February 2018 to June 2018.

In the supplementary material (FigSM2 in 1-figures), a detailed interview structure is given (collected answers are confidential, which is the reason why we could not provide all the interviews content). A mind map of the local terms collected from the PET-CT data is also given as supplementary material (see 5-local_terms). As a first run, 137 local input terms were used. They resulted in 3220 rows (concepts from BioPortal) and were analyzed to build the domain and local levels.

In the following figures, examples are given to show how the local and domain levels were constructed from sample data.

In Fig6., an image of histology from the pilot laboratory was annotated with local terms. "Staining", "DAPI", "Lectin", "FOV" are some of collected local terms from interviews. Using Algorithm2 described in the method section, domain concepts were identified from published KOSs in Bioportal. Concepts Each application-specific concept (local and domain levels) was linked to the generic core level of the BMS-LM semantic interoperability framework. For example, the DRIVE: Axioimager Apotome was linked to the core concept BMS-LM:device, which was linked in the more abstract level to an OBI:

processed material classified as a BFO: material entity. These four levels enabled the understanding of local terms by creating a structured tree of abstract (or generic) concepts around it.

This way, even a non-expert person will be able to understand the general meaning of the local term. By examining the four-level structure starting from local terms to top concepts, one can identify the radioactive substance used for the experiment. The four levels were constructed as follows: from the local term 'FDG', its full name 'QIBO:18-Fluoredeoxyglucose' was identified in the domain ontology QIBO [START_REF] Buckler | Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers[END_REF], which was linked to the core level as a 'BMSLM: agent product' which is classified as an 'object', a 'material entity' and an 'independent continuant' in the top level. By reading the structure from top to local, the "FDG" must be identified as the active product, or more precisely the radioactive tracer, used in this PET-CT experiment. This four level structure is not only understandable by domain experts working with FDG, who will concentrate on the domainspecific levels (domain + local), but it is also accessible to the non-specialist that will manage to understand using the remaining two generic levels (core + top).

Fig6. and Fig7. were shown to three users from the IVIR laboratory in order to evaluate the new ontology and its associated framework. The questionnaires used for the test are provided as supplementary material (see 6-questionnaires). Briefly, a first set of 11 questions was asked to assess their ease with the original BMS-LM data model. Then, both figures were explained, starting from local terms up to top terms. Naturally, the domain and local levels were more familiar to the users. After explanation, another set of 6 questions was asked to determine whether the core level (the BMS-LM

DISCUSSION

The present study describes the BMS-LM core ontology and its semantic interoperability framework applied to preclinical research data. It shows that the use of ontologies rather than data models offers a better solution towards interoperability and explicit semantics. As an example, the BMS-LM data model name for an examination protocol is Exam Definition which is less explicit than its equivalent in the BMS-LM ontology BMSLM: examination protocol. Likewise, the BMS-LM data model concept and psychology (The PsyCARE French consortium aiming to improve early intervention in psychosis 3 ).

Nevertheless, examples from the preclinical domain were provided and validated with a three-user test to show the relevance, and utility for data understanding, of local terms' inclusion in the ontology construction process.

In the literature, a list of works have been identified to deal with heterogeneous KOS and data. Brahaj et al. [START_REF] Brahaj | Ontological Formalization of Scientific Experiments Based on Core Scientific Metadata Model[END_REF] proposed the Core of Scientific Metadata Ontology (CSMO) to report contextual information about scientific data such as hardware, institutions, people, software, studies, investigations or experiments, resulting data, and related publications. However, no consideration was given to the local terms and the semantic interoperability between KOS.

The ISA software suite [START_REF] Rocca-Serra | ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level[END_REF] proposes a three-concept model (Investigation, Study, Assay) together with a variety of tools (ISACreator, ISAConfigurator, ISAValidator…) for the reporting of biological and molecular investigations by researchers. Recently, the ISA original format, ISA-Tab, was converted to RDF according to the LinkedISA approach using semantic web standards [START_REF] González-Beltrán | linkedISA: semantic representation of ISA-Tab experimental metadata[END_REF]. This promising set of tools for the annotation of biomedical data, built upon BFO to ensure more semantic interoperability, does not cover all the lifecycle of a research study.

The EXACT (EXperiment ACTions) ontology was proposed [START_REF] Soldatova | The EXACT description of biomedical protocols[END_REF], after a review of published protocols, to describe experimental protocols efficiently and unambiguously, and thereby reinforce their 2 ClinicalTrials.gov Identifier : NCT04189029 3 https://psy-care.fr/ reliability and reproducibility. A more recent work provides a framework for protocol conversion from a natural language format to a semantically-defined format [START_REF] Soldatova | EXACT2: the semantics of biomedical protocols[END_REF] to enable machine and human protocol reuse. A similar effort, focused on simplifying and making protocol semantics explicit, is the SMART Protocols (SP) ontology [START_REF] Giraldo | Using semantics for representing experimental protocols[END_REF] and its associated "minimal information" SIRO (Sample, Instrument, Reagent, and Objective) model. Both are promising initiatives but only cover the data acquisition part and do not integrate local terms in their models.

A simple domain-independent methodology and core ontology called SECO (Scientific Experiments Core Ontology) for LIMS (Laboratory Information Management System) data annotation was proposed recently [START_REF] Aloulen | A Domain-Independent Ontology for Capturing Scientific Experiments[END_REF]. SECO reuses domain ontologies and is based on BFO top ontology. SECO has the same objectives as the BMS-LM core ontology, create a domain-independent ontology, but it is limited to LIMS data and does not consider local terms and provenance annotations.

Thus, the advantage of using the 4-level framework to implement specific-domain ontologies is that it takes into account the local specific terms, the worldwide recommendation of published KOS reuse, the study lifecycle coverage, and the semantic clarity of the chosen labels. The 4-level framework thus ensures the semantic interoperability with published KOS, especially those reusing BFO and mapped to PROV-O. It helps the biomedical researcher manage KOS heterogeneity through the use of a fourlevel ontology construction method. In practice, a biomedical researcher annotates his/her data using his/her local terms to ensure smooth and easy intra-lab exchanges. The conversion of local annotations to published standard ones can be supported by a translation process that automatically converts local terms into published ones using the four levels of the BMS-LM based ontology. The data could then be shared with standard annotations that facilitates its understanding and reuse. It is to be stressed however that ideally, the collection of local terms and their mapping to other levels should be done by a referent person (ontologist, data manager) interacting with the research team.

Promoting reuse releases data providers from the burden of having to implement their guidelinecompliant procedures from scratch. Indeed, the BMS-LM core ontology plays a mediator role between the top-down incentives of institutions and governments to improve Research Data Management (RDM), and the bottom-up initiatives of data providers to structure their local terminologies. The FAIR guidelines compliance (top-down) and the local terms' inclusion (bottom-up) are two features of the 4-level semantic framework that tend to integrate both approaches (top-down and bottom-up), in an operational way.

CONCLUSION AND FUTURE WORK

The BMS-LM core ontology was built according to an OBO-compliant and FAIR-compliant approach. It was proposed together with its semantic interoperability framework in order to enable the semantic interoperability with heterogeneous KOS used for data annotation in a specific context. The objective was not to eradicate KOS heterogeneity, as it is inherent to scientific research. Instead, this work adopted an inclusive strategy, including published KOSs as much as possible via their partial reuse, and including different profiles of expertise with the use of local, domain, core, and top ontological levels, with the aim to foster understanding between biomedical experts, biomedical KOS experts, and data managers.

From a bottom-up perspective, each biomedical researcher uses his own vernacular terms. During data sharing or publishing, annotated data is re-annotated with more standard-compliant metadata (i.e. a set of MESH annotations, or RADLEX lexicon terms) through matchings from one level to another.

Future work will focus on the automation of reuse and matching of KOSs to enable instant interoperability, and on 'in situ' knowledge capture to collect more relevant local terms (also called interface terms in this context). The latter aspect will enable knowledge capture at the local level via ergonomic forms, GUIs (Graphical User Interfaces), and other HMIs (Human Machine Interfaces). The collected local terms will be mapped from level to level, up to the top ontological level. Naturally, this is fully dependent on further research in ontology matching, mapping, and evolution.

Two key lessons could be taken from this work. First, top-down recommendations must be instantiated in the bottom-up level to enable their day-to-day application. Second, data reporting with interoperable ontologies by biomedical experts will likely help the process.

  raw data acquisition, (3) derived data production, to (4) results publication. The data model is built with generic objects and traceability relations between them: a « Subject (SUB)» participates in a «Study (STU)» as a «Study Subject (SSU)». The latter undergoes «Exam Result (EXA)» defined by an «Exam Definition (EXD)» and composed of an «Acquisition Result (ACQ)». This is conducted using an «Acquisition Device (AQD)» that generates «Data Unit Result (DUR)». The DUR generated from the EXA is used in a «Processing Result (PCR)» composed of «Processing Unit Result (PUR)» and using a «Software Tool (STL)». All have their associated definition concepts (that not always end with the letter « D », «Processing Parameters (PCP)» and «workflow input (WFI)» are parts of the definition classes, for instance). At the end, the study results are published as a «Bibliography Reference (BBR)». The BMS-LM data model did not explicit all relations that exists between its classes (participates, undergoes, composed of, generated from, etc.). Only two types are used: ─ Traceability relation: a link of data provenance traceability between two result classes (R) or two definition classes (D). ─ Identification relation: a link between a result class and its corresponding definition class.

Fig3.

  Fig3. The algorithm used to search BioPortal for domain concepts to use them in the BMS-LM domain level

Fig4.

  Fig4. The BMS-LM ontology encoding rules: the list of possibilities for the encoding of concepts descriptors in an OBOcompliant manner, called the ADQIV -Annotation property, Data property, Quality, data Item, and Value specification With such a large choice, a strategy for a consistent encoding within the ADQIV list is required to simplify the process. The decision on how to choose whether a descriptor δ for a concept C should be encoded as an OWL:AnnotationProperty, an OWL:DataProperty, a BFO:quality, or an IAO:data item depends on the following criteria:

Fig 5 .

 5 Fig 5. A summary of the BMS-LM four levels semantic interoperability framework

  from PATIT (Placental Investigative Technique) ontology, OBI ontology, FBbi structured controlled vocabulary, NCIT (National Cancer Institute Thesaurus) were identified (see blue boxes in Fig6.). The reused domain level concepts are standard terminologies that are accepted by data publication repositories. When no standard domain term was found, a concept was added to the local level. For instance, the local term "Apotome" which references the microscope used to acquire the histology image was not found in Bioportal. Thus, a new concept DRIVE: Axioimager Apotome was added to the local level. This new concept enables the reporting of the microscope's different configurations used when acquiring the histology data.

Fig6.

  Fig6. Use of the BMS-LM core ontology with data from Histology domain.

Fig7.

  Fig7. Use of the BMS-LM core ontology with data from PET-CT imaging domain.

Fig7. presents the

  Fig7. presents the four levels structure for PET-CT imaging data. The PET-CT image shows a mouse in the scanner bed. It was injected with a radioactive substance (coded from red to yellow in the image).

Fig8.

  Fig8. Use of the BMS-LM core ontology with data from different specific domains: PET-CT, Histology, and Proteomics.

List of main abbreviations ADQIV:

 abbreviations Annotation property, Data property, Quality, data Item, and Value specification BMS-LM: BioMedical Study -Lifecycle Management DICOM: Digital Imaging and Communications in Medicine DMP: Data Management Plan FAIR: Findable Accessible Interoperable Reusable KOS: Knowledge Organization System NCBO Bioportal: National Center for Biomedical Ontology Bioportal (a repository for ontologies) OBO: Open Biological and Biomedical Ontology OWL: Ontology Web Language (W3C standard) PROV-DM: PROVenance Data Model PET-CT: Positron Emission Tomography -Computed Tomography RDF: Resource Description Framework RDM: Research Data Management SKOS: Simple Knowledge Organization System (W3C standard) UML: Unified Modeling Language Ontologies BFO: Basic Formal Ontology DCM: DICOM Controlled Terminology DOLCE: Descriptive Ontology for Linguistic and Cognitive Engineering FBbi: Biological Imaging Methods Ontology IAO: Information Artifact Ontology IOBC: Interlinking Ontology for Biological Concepts MESH: Medical Subject Heading MS: Mass Spectrometry Ontology

  LifecycleAlthough built to cover all lifecycle steps, the BMS-LM data model did not have explicit concepts for study lifecycle. In the BMS-LM core ontology, it has been decided to add the lifecycle steps of the BMSLM data model: a study's lifecycle phase concept and its four children: (1) study specification, (2) study data collection, (3) study data analysis, and (

	3.1.3.2
	Tab4. Classification of the BMS-LM concepts according to PROV-O main concepts
	Agent (PROV-O)	Entity	Activity	Not compatible
	software tool	expected data unit	examination	examination protocol
	agent product	derived data unit raw data unit	data acquisition data processing	data acquisition protocol data processing protocol
	study subject device principal investigator	reference data published content biological sample	data processing unit agent administration biomedical intervention biological sample preparation	data processing unit protocol agent administration protocol biomedical intervention protocol biological sample preparation protocol
	operator technical referent	study subject group studied organism	biomedical research study	workflow input profile processing parameters profile
	relation name	Domain/Range	example of use		source
	acts on behalf of	Agent→Agent	operator →min 1 operator		PROV-O
	has member	Entity→Entity	study subject group →min 1 study subject	PROV-O
		(extended to *→*)			RO
	is associated with	Activity→Agent	examination →min 1 study subject	PROV-O
		(extended to *→*)
	is attributed to	Entity→Agent	examination protocol →min 1 operator	PROV-O
	is derived from	Entity→Entity	biological sample →min 1 studied organism	PROV-O
					RO
	is generated by	Entity→Activity	biological sample → exactly 1 biological sample	PROV-O
			preparation
			raw data unit →min 1 data acquisition
	is informed by	Activity→Activity	data processing unit →min 1 data processing	PROV-O
	Uses	Activity→Entity	examination →min 1 device		PROV-O
	is influenced by	*→*	*→*		PROV-O
	has for protocol	process→protocol	examination →exactly 1 examination protocol	BMSLM
	has for process profile process→process	*→*		BMSLM
		profile
	has for parameters	process→processing	data processing unit →min 1 processing parameters	BMSLM
		parameters profile	profile

Some BMS-LM concepts do not fit the three-concept model of PROV-O. Indeed, the concepts whose parents are OBI:protocol and BFO:process profile (see Tab2.) are not compatible with the definitions of Agent(PROV-O), Entity, or Activity. However, they complete the provenance information about a BFO:process in the BMS-LM core ontology.

PROV-O relations use the past tense, which is not compliant with the OBO foundry Relation Ontology (RO) who uses the present tense. This motivated their renaming from "was" to "is". Tab5 details the BMS-LM core ontology relations for provenance. It includes a list of native BMS-LM relations as well as reused relations from the PROV-O ontology and from the OBO Foundry consortium.. Tab5. The list of the provenance relations in the BMS-LM core ontology. For each relation, its owl:domain and owl:range were indicated, an example of its use and its source ontology (PROV-O from the W3C, RO from OBO Foundry, or BMS-LM core ontology) are provided. min = minimum cardinality / exactly = exact cardinality

  Tab6. BMS-LM core ontology construction rules: their compliance with the OBO Foundry 11 principles

		OBO principle	application in BMS-LM
	1.1 Availability	Available at
			http://www.fealinx-biomedical.com/ontologies/bmslm/bmslm.owl
	1.2 Licensing	Available under CC-BY 3.0 license
	1.3 Other ontologies' reuse:	BMS-LM follows the MIREOT (Minimum Information to Reference an External Ontology
		credit and annotations	Term) checklist [47]
	2	Common format	The official release is in RDF/XML syntax.
	3	Identifier in
		<IDSPACE>_<NUMBER>
		format

  ontology) was better understood. Results are shown in the table below. It is noteworthy that all the users did progress and two managed to reach a score of 100%. Furthermore, two users confirmed just after the test that the ontology was clearer than the data model. Fig8. shows (from bottom to top) a list of local terms collected in the IVIR laboratory, their equivalent standard domain concept published in Bioportal, and their core and top corresponding concepts. Terms were collected from different domains: proteomics, histology, and in vivo imaging. This shows how the 4-layer semantic framework of the BMS-LM core ontology is inclusive and can easily integrate different modalities and heterogeneous data and KOS.

	Key users	user1	user2		user3
	(From the less "used to the
	data model" to the better
	used to it)
	Starting level	1.75/11 = 15.9% 6/11 =	54.55% 6.75/11 =	61.36%
	After test level	4/7	= 57.14% 7/7 =	100%	7/7	=	100%
	% of progress		41.24%	45.45%			38.64%
	In the same spirit,

  Sample Result could be understood with two different meanings: is Result the object output of the sampling process or the documentation of the sampling process? To make it clear, the Sample Result concept was split in two separate concepts in the BMS-LM ontology, the biological sample preparation describing the process and the biological sample describing the resulting object.The renaming of the BMS-LM data model concepts was of great use to explicit the core level semantics to biomedical experts. For instance, explaining that a STU (short name for a Study) has a list of SSU (short name of a Study Subject) which is linked to its EXA (short name for an Exam) and PCR (short name for a Processing) is far more complicated then explaining that a biomedical research study has a study subject which is linked to its examination and data processing. Concerning relations, the BMS-LM data model proposed two types of relation: identification and traceability. In Potential overlaps between OBI and BMS-LM were identified. OBI is directly based on the BFO, hence it has both core and domain concepts for biomedical investigations. OBI domain concepts may be reused when relevant, like any other biomedical KOS. However, some OBI core concepts are equivalent to BMS-LM core concepts, such as OBI:protocol and OBI:planned process (for more explicit semantics, the latter's preferred name in the BMS-LM ontology is "BMSLM:plan realization process"). Nevertheless, the scope of each is different. The OBI concepts do not cover the provenance or the study lifecycle. In addition, OBI is not a core ontology, even though it contains core concepts. A discussion with the BFO and OBI communities may help remove the overlaps identified in the next versions of both ontologies, given that BMS-LM submission to the OBO Foundry consortium and publication in BioPortal is in process.The BMS-LM core ontology was also mapped to the provenance ontology PROV-O from the W3C. Since the concepts in PROV-O do not cover all the complex provenance information of the BMS-LM, other provenance concepts were added. For the current BMS-LM ontology version (1.0), only the Agent, Activity, and Entity main concepts of PROV-O were found relevant. Other PROV-O concepts may be integrated in future versions.The BMS-LM core ontology was designed to incorporate the maximum reuse of, and matching with, heterogeneous KOSs. The use of the BMS-LM semantic interoperability framework and methods enables the semantic interoperability in the application context by facilitating data understanding and sharing between humans and by reusing published KOS. Four KOS were reused in the domain level shown in Fig6. and five for the domain level shown in Fig7. The semantic interoperability was then enabled by the conversion from the BMS-LM data model to the BMS-LM ontology. Further implementation of the top/core/domain/local ontology using data from a specific biomedical research study (including clinical study) is beyond the scope of this paper and is the focus of ongoing studies in cardiology (the PACIFIC Consortium to redefine heart failure with preserved ejection fraction (HFpEF) 2 )

practice, however, relations between BMS-LM concepts are richer and convey a more specific meaning, such as composition, aggregation, etc. Thus, to facilitate the understanding of their meaning, their semantics were made explicit with the use of the OBO Foundry ontology of relations (RO), BFO, and OBI.

Actually, the BMS-LM ontology is based on the BFO top ontology to ensure explicit and shared semantics. It reuses a set of OBO Foundry domain ontologies: RO, IAO, and OBI for concepts and relations.

https://www.knime.com/

ACKNOWLEDGMENT

The authors wish to express their sincere appreciation to the first key users of the BMS-LM early versions: Drs. Gwennhael Autret, Caterina Facchin, Anikitos Garofalakis, Mailyn Perez-Liva, and Joevin Sourdon for their feedback and time.

FUNDING

Amel Raboudi is supported by the ANRT CIFRE scholarship n°216/1649. The DRIVE-SPC project is supported by the COMUE Sorbonne Paris City University and the Fealinx Company. The work presented in this paper was partially supported by the PACIFIC (BPI n°2018-PSPC-07) and PsyCARE (ANR 18-RHUS-0014) projects.

CONFLICT OF INTEREST

The authors declare no conflict of interest. ARA, MAL, PYH, PBO are employees of Fealinx SAS.

AUTHOR'S CONTRIBUTION

ARA, PYH, JCA, JHI, and MAL contributed actively to the ontology encoding. BEY participated in the lifecycle semantics aspect. DBA, TVI, and BTA reviewed the application examples. ADU, DBA, BEY, and PBO reviewed the structure and content of the paper from its earlier incarnation. TVI, TYO, and ACE contributed to the interviews and data collection phase. ARA drafted the manuscript. ARA, DBA, MAL, PYH, BEY, TVI, ADU, PBO, BTA revised it. All authors read and approved the final version of this paper.


B Delbarre

A Rapin

F C Boyer

A Thierry

J M Perotin

S Dury

A Dumazet

J Hagenburg

D Perdu

G Deslée

Claire Launois
email: claunois@chu-reims.fr

Dyspnea assessment in Myotonic Dystrophy type 1 Authorship

Keywords: Myotonic Dystrophy type 1, dyspnea, mMRC, respiratory impairment, non-invasive ventilation, quality of life

Highlights

-Dyspnea scores are low in Myotonic Dystrophy type 1 (DM1) patients -A mMRC score ≥ 2 (dyspnea in daily living) is associated with lower Vital Capacity -A mMRC score ≥ 2 is associated with a lower six-minute walking test distance -A mMRC score ≥ 2 is associated with more severe motor handicap -The mMRC scale might be an useful tool to assess dyspnea in daily living in DM1

Abstract

In Myotonic Dystrophy type 1 (DM1), combining respiratory symptom screening and respiratory function testing, is crucial to identify the appropriate time for ventilatory support initiation. Dyspnea has been little investigated in DM1. To provide a multidimensional description of dyspnea, questionnaires assessing dyspnea were administered to 34 consecutive adult patients with DM1 (median(25 th -75 th centile) age of 36(28-49), Vital Capacity (VC) of 74(64-87)% of predicted value). Dyspnea scores were low whatever the questionnaire used: Multidimensional Dyspnea Profile score of 2(0-4.7)/50 for dyspnea sensory descriptor and of 0(0-4.7)/60 for the emotional descriptor, Visual Analogue Scale score of 0(0-0)/10 in sitting and supine position and Borg score after six-minute walk test (6MWT) of 2.2(1.8-4.2)/10. Eleven patients (32%) reported disabling dyspnea in daily living (modified Medical Research Council (mMRC) score ≥ 2). In comparison with patients with mMRC score < 2, patients with mMRC score ≥ 2 had a more severe motor handicap (Muscular Impairment Rating score

of 4.0(4.0-4.0) vs 3.0(2.0-3.5), p<0.01), a lower 6MWT distance (373(260-424) vs 436(346-499)m, p=0.03) and a lower VC (64(48-74)% vs 75(69-89)%, p=0.02). These data suggest that the mMRC scale might be an easy-to-use and useful tool to assess dyspnea in daily living in DM1 patients. However, the interest of integrating the mMRC dyspnea scale in clinical practice to guide therapeutic management of DM1 patients remains to be assessed in further studies.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethical Publication Statement

We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Disclosure of interest

None of the authors has any conflict of interest to disclose.

Abbreviations

1-Introduction

Myotonic dystrophy (dystrophia myotonica) type 1 (DM1) is one of the most common genetic muscular disease in populations of European descent [START_REF] Turner | Myotonic dystrophy: diagnosis, management and new therapies[END_REF]. It is caused by expansion of a CTG triplet repeat in the 3' non coding region of DMPK [START_REF] Mahadevan | Myotonic dystrophy mutation: unstable CTG repeat in the 3' untranslated region of the gene[END_REF], the gene encoding the DM protein kinase. DM1 is a multisystem disorder characterized by myotonia with dysfunction of skeletal and smooth muscles, as well as dysfunction of the cardiac, endocrine, nervous, digestive and respiratory systems [START_REF] Turner | Myotonic dystrophy: diagnosis, management and new therapies[END_REF].

The main cause of death is a respiratory failure (3) which can be caused by dysfunction in the central control of ventilation (4) (5) [START_REF] Zifko | Central and peripheral respiratory electrophysiological studies in myotonic dystrophy[END_REF], weakness of the respiratory muscles [START_REF] Mauro | Physiology of respiratory disturbances in muscular dystrophies[END_REF] [START_REF] Zifko | Central and peripheral respiratory electrophysiological studies in myotonic dystrophy[END_REF] [START_REF] Henke | Characteristics of respiratory muscle involvement in myotonic dystrophy type 1[END_REF], weakness of pharyngeal muscles associated with swallowing disorders [START_REF] Pilz | Oropharyngeal dysphagia in myotonic dystrophy type 1: a systematic review[END_REF] and aspiration pneumonia and concurrent obstructive apneas [START_REF] West | Sleepiness and Sleep-related Breathing Disorders in Myotonic Dystrophy and Responses to Treatment: A Prospective Cohort Study[END_REF]. Combining respiratory symptom screening and respiratory function testing allows to identify the appropriate time to initiate adequate ventilatory care including non-invasive ventilation (NIV) [START_REF] Boentert | Consensus-Based Care Recommendations for Pulmonologists Treating Adults with Myotonic Dystrophy Type 1[END_REF].

Dyspnea, a major respiratory symptom, has not been extensively investigated in patients with DM1. Dyspnea is a subjective experience of breathing discomfort with qualitatively distinct sensations that vary in intensity and emotional and behavioral significances [START_REF] Parshall | An official American Thoracic Society statement: update on the mechanisms, assessment, and management of dyspnea[END_REF]. Many tools exist to evaluate the dyspnea experienced including, the modified Medical Research Council (mMRC) [START_REF] Mahler | Evaluation of clinical methods for rating dyspnea[END_REF] [START_REF] Bestall | Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease[END_REF] to assess the functional impact of dyspnea, the Borg scale (15) (16), the Visual Analogic Scale (VAS) [START_REF] Gift | Validation of a vertical visual analogue scale as a measure of clinical dyspnea[END_REF] [START_REF] Attali | Mandibular advancement reveals long-term suppression of breathing discomfort in patients with obstructive sleep apnea syndrome[END_REF] to quantify its sensory intensity, and the Multidimensional Dyspnea Profile (MDP) [START_REF] Banzett | Multidimensional Dyspnea Profile: an instrument for clinical and laboratory research[END_REF] to analyze both sensory and affective dimensions.

The main objective of this pilot study was to provide a multidimensional assessment of dyspnea in patients with DM1, including sensory and affective dimensions of dyspnea and its functional impact. The secondary objectives were to analyze the relationships between dyspnea in daily living according to the mMRC scale and clinical history of DM1, number of CTG repeats, muscular impairment, respiratory assessment, emotional/cognitive disorders and quality of life. We also investigated respiratory symptoms according to NIV indication.

2-Methods

Adult

Patient demographic and clinical characteristics

Modifed Medical Research Council Dyspnea Scale

The mMRC scale (13) (14) consists of five statements that almost entirely describe the range of dyspnea from none (grade 0) to almost complete incapacity (grade 4). Significance thresholds ≥ 2 have been used to categorize patients according to their dyspnea.

Multidimensional Dyspnea Profile

The MDP [START_REF] Banzett | Multidimensional Dyspnea Profile: an instrument for clinical and laboratory research[END_REF] [START_REF] Williams | Multidimensional measurement of breathlessness: recent advances[END_REF] consists of eleven items, each evaluated on a 0-10 numerical rating scale, that describe the unpleasantness of dyspnea (one item [A1], maximum score of 10), its sensory qualities (5 items [SQ], maximum score of 50), and its emotional qualities (5 items [A2], maximum score of 50). MDP scores can be grouped in two dimensions (sensory dimension [SQ], maximum score of 50; affective dimension [A1+A2], maximum score of 60) or in two domains (immediate perception response [SQ+A1], maximum of 60; emotional response [A2], maximum score of 50), which is analogous to pain conceptual models.

Visual analogue scale

Patients were asked to rate their breathing discomfort on a non-graduated 100-mm visual analogue scale anchored by "no breathing discomfort" at the left end, and "intolerable breathing discomfort" at the right end [START_REF] Gift | Validation of a vertical visual analogue scale as a measure of clinical dyspnea[END_REF] [START_REF] Attali | Mandibular advancement reveals long-term suppression of breathing discomfort in patients with obstructive sleep apnea syndrome[END_REF]. VAS was first applied with the patients seated in a comfortable chair and then reapplied after they had assumed a fully supine position. A second non graduated 100-mm visual analogue scale was used to evaluate the changes in breathing comfort (in either direction) between the sitting and the supine positions ("transitional" scale; from "extreme deterioration" on the left end to "extreme improvement" on the right, with a middle marker to indicate "no change").

Epworth Sleepiness scale

The ES ( 21) is a questionnaire assessing the likelihood of falling asleep under various circumstances in daily life. The scale is composed of 8 items scored from 0 to 3 for a total score from 0 to 24. Excessive daytime sleepiness is defined by a score ≥11.

Pichot fatigue scale

The PF ( 22) is a questionnaire composed of 8 items scored from 0 to 4 for a total score from 0 to 32 distinguishing physical or psychic fatigue perception. A global score > 22 is in favor of excessive fatigue.

Pittsburgh Sleep Quality Index

The PSQI ( 23

Hospital Anxiety and Depression Scale

The HAD [START_REF] Zigmond | The hospital anxiety and depression scale[END_REF] scale is designed for patients with physical illnesses. It consists of 14 items: seven items relating to depression and seven items relating to anxiety with cut-off points for severity. For each domain (A or D), scores of 0-7 indicate no disorders, 8-10 indicate suspected disorders, and 11-21 indicate confirmed disorders.

Apathy Scale

Apathy was assessed by the AS scale [START_REF] Starkstein | Reliability, validity, and clinical correlates of apathy in Parkinson's disease[END_REF]. The AS consists of 14 items phrased as questions that are to be answered on a four-point Likert scale. Scores on the AS range from 0 to 42, with higher scores reflecting greater apathy symptoms, with a recommended cut-off score of 14.

Mini Mental State Examination

The items of the MMSE (26) [START_REF] Woodford | Cognitive assessment in the elderly: a review of clinical methods[END_REF] include tests of orientation, registration, recall, calculation and attention, naming, repetition, comprehension, reading, writing and drawing. If all items are answered correctly, the score is 30. A score greater than 25 is considered normal and a score between 20 and 25 indicates the presence of mild cognitive impairment.

Quality of life in genetic neuromuscular disease scale

The QOL-gNMDs (28) a specific quality of life (QoL) questionnaire structured in 3 domains (Impact of Physical Symptoms, Self-Perception, Activities and Social Participation) including 26 items with 2-4 response options: 2 general items, 7 items concerning impact of Physical Symptoms (sum 0 to 18), 8 items concerning self-perception (sum 0 to 24), and 9 items concerning activities and social participation (sum 0 to 27). Each domain has an ordinal scale, which has been transformed by an interval score (0 bad QoL, 100 best QoL) according to Rasch measurement theory. The Minimal Clinical Difference for these interval scores is between 10 to 12 points.

Muscular Impairment Rating scale

The MIRS ( 29) is a rating scale from 1 (asymptomatic) to 5 (severe proximal involvement), established in accordance with the clinically recognized distal to proximal progression of the muscular involvement in DM1, based partly on a manual muscle testing of 11 muscle groups.

Lung function tests

Arterial blood gases

Arterial Blood Gases were measured in a sitting position on room air.

Six-minute walk test

The six-minute walk test (6MWT) was performed in a 30-meter long, flat, covered corridor, marked meter-by-meter, according to the American Thoracic Society guidelines [START_REF]ATS statement: guidelines for the six-minute walk test[END_REF]. Oxygen saturation and modified Borg scale [START_REF] Wilson | A comparison of the visual scale and modified Borg scale for the measurement of dyspnoea during exercise[END_REF] [START_REF] Hareendran | Proposing a standardized method for evaluating patient report of the intensity of dyspnea during exercise testing in COPD[END_REF] subjectively assessing the degree of dyspnea graded from 0 to 10 were collected at the beginning and the end of the 6MWT. The distance covered was calculated at the end of the test.

Pulmonary function testing

PFTs were performed according to the American Thoracic Society/European Respiratory Society guidelines (31) (BodyBox 5500 Medisoft Sorinnes, Belgium). Vital capacity (VC), forced expiratory volume in one second (FEV 1 ), FEV 1 /FVC ratio, were measured during spirometry, which was performed both in upright seated and in supine positions. Results were expressed as the percentage of predicted values.

Evaluation of inspiratory and expiratory muscle strength

Respiratory muscle strength consisted of measuring Maximal Inspiratory Pressure (MIP) and Maximal Expiratory Pressure (MEP) [START_REF]ATS/ERS Statement on respiratory muscle testing[END_REF]. The maximum value of three available tests that varied by less than 20% was recorded. Results were expressed in cmH2O and percentage of predicted values.

Ventilatory response to carbon dioxide

Ventilatory response to carbon dioxide was measured by the Read rebreathing method (33) [START_REF] Read | REPRODUCIBILITY OF TRANSIENT RESPONSE TO CO2 INHALATION IN MAN[END_REF]. The patients were seated comfortably and attached to a mouth-piece with a nose clip.

They were instructed to breathe room air as quietly as possible until the end-tidal CO 2 tension (PetCO 2 ) stabilized. After this complete sitting rest of 5 minutes, the circuit was closed and they started rebreathing as quietly as possible the content of a bag with a gaseous mixture (approximately 7% of CO 2 and 93% of O 2 ) for 4 minutes. The gradual increase in PetCO 2 and ventilation (L/min) was measured by a fast response metabographe (Medisoft, Sorinnes, Belgium), with normal values between 1.5 and 3 L/min/mmHg [START_REF] Laza | Chronic respiratory failure and Steinert's myotonia[END_REF].

Overnight respiratory recording

Home or in-hospital overnight respiratory recording was performed including overnight polysomnography (Resmed Nox A1), overnight respiratory polygraphy (Resmed Nox T3) or nocturnal SpO 2 and PtcCO 2 (SenTec Inc., Terwil, Switzerland). These recordings were not systematically performed and were prescribed by clinicians according to patients' symptoms, results of lung function tests and results of previous overnight respiratory recordings.

Overnight polysomnography or respiratory polygraphy recordings were analyzed according to the 2015 update of the American Academy of Sleep Medicine rules for scoring respiratory events in sleep. Apnea was defined as the absence of airflow ≥ 10 s, hypopnea was defined as a reduction of airflow ≥ 30 % associated with a decrease in oxygen saturation ≥ 3% (or micro-awakening if polysomnography recording). The Apnea Hypopnea Index (AHI) was calculated as the number of apneas and hypopneas per hour (of total sleep time in polysomnography and night recording on respiratory polygraphy).

Criterions for Non Invasive Ventilation initiation

Criteria for initiating NIV were defined only by objective respiratory parameters: PaCO 2 > 45mmHg and/or VC < 50% and/or nocturnal desaturation defined by overnight time with SpO 2 < 90% > 10%.

Statistical analyses

Quantitative variables were described as median and interquartile range and qualitative variables as number and percentage. Patients were separated into two groups according to their mMRC dyspnea scale: mMRC < 2 (no disabling dyspnea in daily living) and mMRC ≥ 2 (disabling dyspnea in daily living). Comparison of clinical and genetic characteristics and quality of life and respiratory assessment between patients with mMRC < 2 and patients with mMRC ≥ 2 were studied using Student or Wilcoxon or Khi2 or Fisher exact tests according to application's conditions. Comparison of clinical and genetic characteristics and respiratory symptoms between patients with no NIV indication and patients with NIV indication were studied using Student or Wilcoxon or Khi2 or Fisher exact tests according to application's conditions. Comparison of MIR scale and 6MWT distance and vital capacity between patients with mMRC = 0, patients with mMRC = 1 and patients with mMRC ≥ 2 were studied using Kruskal Wallis tests.

A p-value < 0.05 was considered statistically significant. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

3-Results

3.1.Patient demographic and clinical characteristics

Ninety adult patients with DM1 were referred to our medical center. Twenty-three patients were not eligible for the study including legal protection measure (n=3), CPAP (n=5), NIV (n=20). Twenty-eight patients refused to participate in the study. One patient was excluded because of a MMSE score < 20. Finally, data of thirty-four patients were analyzed.

Patient characteristics are presented in Table 1. The median (25 th -75 th centile) age of the patients was 36 (28-49) years, including 21 men and 13 women. The median BMI was 24 Three patients (9%) didn't go to school or stopped in primary school, 20 patients (59%) went to high school and 11 patients went to university (32%). Mini-mental state examination was completed by all patients with a median score of 27 [START_REF] Zigmond | The hospital anxiety and depression scale[END_REF][START_REF] Starkstein | Reliability, validity, and clinical correlates of apathy in Parkinson's disease[END_REF][START_REF] Foreman | Assessing cognitive function[END_REF][START_REF] Woodford | Cognitive assessment in the elderly: a review of clinical methods[END_REF][START_REF] Dany | The quality of life in genetic neuromuscular disease questionnaire: Rasch validation of the French version[END_REF][START_REF] Mathieu | Assessment of a diseasespecific muscular impairment rating scale in myotonic dystrophy[END_REF]. Seven patients (21%) had an anxiety HAD score > 10, 5 patients (15%) a depression HAD score > 10, and 34 patients (100%) an apathy score ≥ 14.

3.2.Dyspnea assessment

Results of dyspnea assessment are presented in Table 2.

Eleven patients (32%) had a mMRC score ≥ 2, which is usually considered as disabling dyspnea in daily living and eight patients (25%) had a Borg scale score at the end of the 6MWT ≥ 5 which corresponds to severe dyspnea on exertion. The median score of dyspnea according to Borg scale after 6MWT was 2.2 (1.8-4.2), which corresponds to slight dyspnea on exertion.

At rest, the median score was 0 (0-0) in sitting position according to VAS, 0 (0-0) in supine position according to VAS, 0 (0-0) according to Borg scale before the 6MWT, 0 (0-0) according to A1 "immediate unpleasantness" of MDP. Twenty-nine patients (87%) according to VAS in sitting position, 27 patients (79%) according to VAS in supine position had a score of 0 at rest. Eight (23%) patients described worsening dyspnea when moving from sitting to lying (orthopnea).

The SQ and A2 scores of MDP (sensory and emotional descriptors, respectively, both with a maximal score of 50) were 2.0 (0.0-4.7) and 0.0 (0.0-4.7). For SQ, the most commonly chosen «best descriptor» was "breathing a lot" (n=11 (32%), median score 0 (0-3)), and for A2, "breathing sensations make feel angry" was the most chosen as «best emotional descriptor», (n=5 (15%), median score of 0 (0-0)).

3.3.Objective respiratory assessment parameters

Results of objective respiratory assessment parameters are presented in Table 3.

Eight patients presented criterion for initiating NIV, defined either by PaCO 2 > 45mmHg (n=5), or by VC < 50% (n=3), or by nocturnal desaturation defined by overnight time with SpO 2 < 90% > 10 % (n=4).

3.4.Comparison of clinical and genetic characteristics, quality of life and respiratory assessment between patients with mMRC < 2 and patients with mMRC ≥ 2

Compared with patients with mMRC < 2, patients who experienced disabling dyspnea in daily living (mMRC ≥ 2) had a higher BMI (25.9 (25.0-29.9) kg/m² vs 22.7 (19.6-25.2) kg/m², p<0.01), a clinically significant more severe motor handicap (MIRS score of 4.0 (4.0-4.0) vs 3.0 (2.0-3.5), p<0.01), 6MWT distance of 373 (260-424) m vs 436 (346-499) m, p=0.03) and a more impaired quality of life (Table 4a, Figure 1). 4b), patients with mMRC ≥ 2, experienced also more severe dyspnea according to VAS, MDP and Borg score at the end of the 6MWT than patients with mMRC < 2. They had also a lower VC (median 64 (48-74) % vs 75 (69-89) %, p=0.024)

Regarding respiratory assessment (Table

The mMRC scale was correlated with higher MIRS score (correlation coefficient + 0,55, p < 0.001), lower 6MWT distance (correlation coefficient -0,53, p < 0.01) and a lower CV (correlation coefficient -0,42, p= 0.016).

There was no difference between these 2 groups of patient regarding smoking status, medical history, MMSE score, HAD score and apathy score (data not shown).

3.5.Comparison of clinical and genetic characteristics, quality of life and respiratory symptoms between patients with no NIV indication and patients with NIV indication

Patients with no NIV indication and those with NIV indication were compared in terms of demographic and clinical characteristics, number of CTG and respiratory symptoms (Table 5). Compared with patients with no NIV indication, patients with NIV indication had a decreased sleep quality (PSQI median score of 7 (6.2-8.0) vs 3.5 (3.0-5.7), p=0.04).

4-Discussion

This study focused on dyspnea assessment in adult DM1 patients using several multidimensional questionnaires. Regarding sensory dimension of dyspnea, at rest, and whatever the scale used (VAS in siting position, VAS in supine position, Borg and SQ MDP), most of patients reported a dyspnea intensity score at zero. On exertion, Borg score after 6MWT was 2.2 (1.8-4.2) /10 which corresponds to slight dyspnea. Concerning affective dimension of dyspnea according to MDP, A1 + A2 intensity score was also very low 0.0 (0.0-4.7) /60. Onethird of patients reported disabling dyspnea in daily living (mMRC ≥ 2), which is closely to that a previous study in DM1 patients (presence of dyspnea during activities of daily living in 22% of patients) [START_REF] De Mattia | Screening for early symptoms of respiratory involvement in myotonic dystrophy type 1 using the Respicheck questionnaire[END_REF].

Several hypotheses can be formulated to explain the absence or the low intensity of dyspnea for most of DM1 patients.

First, it could have been hypothesized that cognitive deficits, emotional disturbances and avoidant or passive personalities traits may induce a lack of interest in answering dyspnea scales and/or cause difficulties in understanding the questionnaires [START_REF] Miller | Cognitive Deficits, Apathy, and Hypersomnolence Represent the Core Brain Symptoms of Adult-Onset Myotonic Dystrophy Type 1[END_REF] [START_REF] Leddy | Social cognition in type 1 myotonic dystrophy -A mini review[END_REF]. In our study, all patients had an apathy score ≥ 14, however voluntarily participated to the study and appeared to do their best to fill in scales, with the help of a junior doctor. Patients with moderate to severe cognitive deficit were excluded from the study and 90% of analysed patients went to high school. Regarding emotional disturbance, only 15% of patients had a depression HAD score > 10. Moreover, if MDP and mMRC may have introduced a difficulty regarding recall over a focal period, VAS and Borg scales explore immediate unpleasantness breathing sensation and are known to be easy to use in clinical practice. Third, it is possible that, notably because of muscular impairment, physical activity level is too low to induce dyspnea. Patients may also have adapted their gait speed to avoid dyspnea.

Finally, we propose that some DM1 patients could have an alteration of breathing sensations.

Breathing sensations arise from one or several stimuli, the transmission of these stimuli to the brain, and cognitive and affective processing of this interoceptive information arising from the respiratory system. In DM1 patients, several types of central nervous system lesions (brain lesions with notably limbic system lesions [START_REF] Weijs | Human brain pathology in myotonic dystrophy type 1: A systematic review[END_REF] and brainstem lesions [START_REF] Ono | Loss of catecholaminergic neurons in the medullary reticular formation in myotonic dystrophy[END_REF] with a reduced ventilatory response to CO 2 (5)) could interfere with this process and explain blunted breathing sensations. Baldanzi et al [START_REF] Baldanzi | Disease awareness in myotonic dystrophy type 1: an observational cross-sectional study[END_REF] showed that a high percentage of DM1 suffer from anasognosia (52%), ie were unaware of symptoms across different physical and life domains. Moreover, while patients with other neuromuscular disorders like ALS have rapidly progressing symptoms, the relative slowly progression of disease is likely to induce an adaptation to thoracic mechanical load induced by respiratory muscle impairment and/or an adaptation to daytime hypercapnia (respiratory gating mechanism or in other words an habituation process, that blunt the perception of dyspnea) [START_REF] Herzog | Reduced neural gating of respiratory sensations is associated with increased dyspnoea perception[END_REF]. It is also possible that the long duration of disease may cause less anxiety, a factor that is known to increase dyspnea level [START_REF] Von Leupoldt | Affective traits, states, and breathlessness[END_REF]. Only 21 % of DM1 patients had an anxiety HAD score > 10 in our study.

Our results provide evidence for the use of the mMRC scale, an easy-to-use and not timeconsuming scale, in the assessment of dyspnea in daily living in DM1 patients. First, our results demonstrated associations between mMRC and data that reflect disease severity, ie follow-up duration from diagnosis, muscle impairment severity (MIRS, 6MWT distance) and decrease quality of life (Figure 1, Table 4a). Second, in our between group comparisons, the mMRC scale was associated with other dyspnea scales (VAS, MDP and Borg after 6MWT) and with pulmonary function parameters which might be involved in dyspnea in DM1 (VC) (Figure 1,Table 4b). Of note, there was a lack of clinically significant respiratory symptomatic differences between patients with and without NIV indication, except for dyspnea according to mMRC scale (no statistically significant difference probably due to the low power of our study) and quality of sleep according to PSQI. It would be interesting to investigate the mMRC score modifications after NIV initiation. Nevertheless, it has already been shown that respiratory sleep-related symptoms did not worsen during one month NIV withdrawal period in DM1 patients and did not improve thereafter [START_REF] O'donoghue | Effects of 1month withdrawal of ventilatory support in hypercapnic myotonic dystrophy type 1[END_REF]. The lack of expected symptomatic benefit from NIV, might explain in part the difficulties with NIV treatment adherence in DM1 patients [START_REF] Boussaïd | Factors influencing compliance with non-invasive ventilation at long-term in patients with myotonic dystrophy type 1: A prospective cohort[END_REF] [START_REF] Buysse | The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research[END_REF]. Discriminating DM1 patients having a higher level of respiratory dysfunction from those having a lower risk of respiratory involvement remains a challenge. We believe that a mmRC score ≥ 2 could be an element that might alert clinicians and help them in identifying potential candidates for intervention. It would be interesting to investigate in further larger multicentric studies if the combination of different scales could improve respiratory involvement detection. For example, mMRC scale might be used in association with Respicheck [START_REF] De Mattia | Screening for early symptoms of respiratory involvement in myotonic dystrophy type 1 using the Respicheck questionnaire[END_REF], a new scale designed to quickly capture symptoms of respiratory involvement in DM1.

This study has several limitations. First, a large number of patients considered for inclusion refused to participate to the study, reflecting probably particular personality traits typically reported in DM1, and may represent a selecting bias. Second, the study was conducted in a single center, including a small number of patients, who, most often, had moderate respiratory function impairment, were ambulant and presented no severe cognitive disorders, which limit the generalizability of the results.

5-Conclusion

This first-in-kind analysis has yielded new data on the associations between dyspnea according to mMRC scale, muscular impairment severity, quality of life and VC in DM1 adult ambulant patients, without severe cognitive disorder. The mMRC scale might be a useful and easy-to-use tool to assess dyspnea in daily living in these DM1 patients. Additional studies are needed to determine the interest of integrating the mMRC dyspnea scale in clinical practice to guide therapeutic management of DM1 patients.

-DM1: Myotonic dystrophy type 1 -

 1 CTG: Cytosine-Thymine-Guanine repeat -DMPK: Dystrophia myotonica protein kinase -CPAP: Continuous Positive Airway Pressure -NIV: Non-Invasive Ventilation -MMSE: Mini Mental State Examination -BMI: Body Mass Index -mMRC: modified Medical Research Council -MDP: Multidimensional Dyspnea Profile -VAS: Visual Analogue Scale -ES: Epworth Sleepiness -PF: Pichot fatigue -PSQI: Pittsburgh Sleep Quality Index -HAD: Hospital Anxiety and Depression Scale -AS: Apathy Scale -QOL-gNMD: Quality Of Life in genetic Neuromuscular Disease -MIRS: Muscular Impairment rating scale -SQ: Sensory Qualities -6MWT: 6-minute walk test -PFTs: Pulmonary function tests -VC: Vital Capacity -FEV 1 : Forced Expiratory Volume in one second -MIP: Maximal Inspiratory Pressure -MEP: Maximal Expiratory Pressure -SNIP: Sniff nasal inspiratory pressure -AHI: Apnea Hypopnea Index -ALS: Amyotrophic Lateral Sclerosis

  DM1 patients were recruited from the Neuromuscular Diseases referral center of Reims. This study was performed in accordance with the declaration of Helsinki and approved by the Ethics Committee (Comité de Protection des Personnes -Ile de France 1, No. N2020-A01221-38) and was registered on clinicaltrials.gov (NCT04835298). DM1 patients referred to the Department of Pulmonary Medicine between July 2020 and July 2021 were considered for inclusion in this study. All patients received detailed information and gave their written consent. 2.1. Patient selection Inclusion criteria were age older than 18 years, a diagnosis of DM1 confirmed by molecular analysis, absence of the Continuous Positive Airway Pressure (CPAP) or NIV, and the absence of legal protection measures (guardianship, curatorship). Exclusion criteria were an ongoing or recent (i.e. within the last 4 weeks before study recruitment) unstable medical condition and a significant cognitive impairment defined by a Mini Mental State Examination (MMSE) score < 20.

  ) is a questionnaire, which assesses sleep quality and disturbances over a 1month time interval. Nineteen individual items generate seven component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication and daytime dysfunction. The sum of the component scores yields a global score from 0 to 21. A global PSQI score > 5 indicates impaired sleep quality.

  (21.6-26.3) kg/m². Regarding the DM1, first symptoms occurred at 19 (10-31) years. The median number of CTG triplets affecting DMPK gene was 550 (300-900). The median MIRS score was 3 (2-4).

  Second, the low dyspnea intensity could be explained by moderate respiratory function impairment of the DM1 patients included in this study. Nevertheless, patients with NIV indication in our study had also very low scores in dyspnea scales, except for mMRC, compared to other neuro-muscular patients with NIV indication. For example, Amyotrophic Lateral Sclerosis (ALS) patients with NIV indication (39) (median FVC of 46% predicted) had median SQ score of 28.0 (16.0-40.0) (sensory dimension of dyspnea) and A2 score of 15.0 (5.5-23.5) (affective dimension of dyspnea)[START_REF] Morélot-Panzini | The multidimensional nature of dyspnoea in amyotrophic lateral sclerosis patients with chronic respiratory failure: Air hunger, anxiety and fear[END_REF], while DM1 patients in our study had median SQ score of 4 (1.5-12.0) and A2 score of 9.5 (0.7-19.2) for a median FVC of 47% predicted.

Figure 1 :

 1 Figure 1 : Dyspnea in daily living assessed by the modified Medical research council scale according to Muscular Impairment rating scale, Six-Minute Walk Test and Vital Capacity. Data are expressed as minimum, maximum, median (25 th -75 th centile). Abbreviation mMRC: modified Medical Research Council