dataesr/scientific-paragraphs-categorization huggingface dataset:
Other strengths are the longitudinal approach of the study, the large number of people with GDF-15 measurement and the adjustments for many confounders including inflammatory biomarkers. The limitations of this work rely to the use of data from a clinical trial that may have affected the investigated associations, the utilization of self-reported rather than objectively measured PA and the fact that GDF-15 levels were measured at one time point only. In addition, we could not include any measure of appetite, which would have been of relevance given its relationship with GDF-15. Lastly, the measure of body composition in addition to bodyweight would have further highlighted the present findings because bodyweight changes may occur through both fat mass and lean mass variations. Such analyses could not be performed due to the small number of subjects that had data for both GDF-15 and longitudinal assessment of body composition with dual-energy X-ray absorptiometry (DEXA) in MAPT (n = 127).
Acknowledgements The present work was performed in the context of the  Inspire Program , a research platform supported by grants from the  Region Occitanie/Pyrénées-Méditerranée  (Reference Number 1901175) and the  European Regional Development Fund (ERDF)  (Project Number  MP0022856 ). This study received funds from the  Alzheimer Prevention in Occitania and Catalonia (APOC Chair of Excellence-Inspire Program)  and the  Saint Louis University. The MAPT study was supported by grants from the  Gérontopôle of Toulouse , the  French Ministry of Health  ( PHRC 2008 (PHRC , 2009)),  Pierre Fabre Research Institute  (manufacturer of the omega-3 supplement),  ExonHit Therapeutics SA and Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the  University Hospital Center of Toulouse.
The dramatic increase in the number of microbe descriptions in databases, reports, and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis. Recent text mining methods offer powerful ways to extract textual information and generate ontology-based representation. This paper describes the design of the Omnicrobe application that gathers comprehensive information on habitats, phenotypes, and usages of microbes from scientific sources of high interest to the microbiology community. The Omnicrobe database contains around 1 million descriptions of microbe properties. These descriptions are created by analyzing and combining six information sources of various kinds, i.
e. biological resource catalogs, sequence databases and scientific literature. The microbe properties are indexed by the Ontobiotope ontology and their taxa are indexed by an extended version of the taxonomy maintained by the National Center for Biotechnology Information. The Omnicrobe application covers all domains of microbiology. With simple or rich ontology-based queries, it provides easy-touse support in the resolution of scientific questions related to the habitats, phenotypes, and uses of microbes. We illustrate the potential of Omnicrobe with a use case from the food innovation domain.
Introduction This paper describes the Omnicrobe database. It gathers comprehensive information on habitats, phenotypes, and usages of microbes. This information is critical for the development of a large range of research, economic and social activities among which microbial ecosystem services and healthcare are two significant topics. Microbial communities render important functions to their ecosystem  [1]. These functions are a result of biotic and abiotic interactions dependent on the habitat where microbes live. These last decades, communities have been inventoried by high-throughput culturomics and metagenomic analyses revealing the numerous ecosystems' microbial richness and their ecological importance  [2]. Notable examples include the ocean (e. g. , the Tara Ocean project  [3] ) and the human gut (Human Microbiome projects  [4] ).
Deciphering microbial traits and phenotypes enables researchers to identify possible beneficial uses for humans in several domains ranging from healthcare or pollution remediation to food  [5]. The quantity and the diversity of the information publicly available on focused microbial habitats and phenotypes exponentially increase as a consequence of the growing diversity of microbial ecology research and the parallel evolution of high-throughput sequencing technologies and novel modeling approaches. However, methods to assess how microbes are distributed across environments remain limited  [6]. The information about microbe habitats and phenotypes is indeed scattered among many different sources, ranging from structured databases (e. g. genetic banks, biological resources, biodiversity databases) to document collections (e.
g. scientific literature, reports). Despite the increase in volume and openness of these sources, this wealth of information remains largely underexploited for many reasons: it is distributed in a wide range of sources; the information is described by a broad variety of features and metadata that prevents semantic interoperability (e. g. habitat is also called source, isolation, location or host); a large part of the information is expressed in free texts even in databases, which makes it difficult to find. Centralization of microbe habitat and phenotype information by information providers and aggregators (e. g. PubMed Central; https://www. ncbi. nlm. nih. gov/pmc/, GOLD; https:// gold. jgi. doe. gov/), standardization of database metadata and increasing use of controlled vocabulary and nomenclatures (e.
g. NCBI species taxonomy; https://www. ncbi. nlm. nih. gov/ taxonomy) are heading in the right direction for better access, interoperability and reuse following FAIR principles  [7]. The Omnicrobe database project falls fully in line with this effort. Its purpose is to offer a powerful way to retrieve information on microbe taxa, phenotypes, habitats, and uses (we define a "use" as any microbial property that can be targeted for human purposes, such as food aromatization) across environments and usages through a single application. To achieve this goal, the Omnicrobe application automatically aggregates and structures the information that it collects from various public sources (section Information sources). Our work on Omnicrobe focuses on a neglected yet critical aspect: to be easily searched and processed at a large scale, the habitat, phenotype, and use information gathered on microbes should be organized along standard classifications  [8].
Information in Omnicrobe is indexed by a comprehensive controlled and hierarchically organized vocabulary so that the information can be searched in a concise and fast way, regardless of the diversity of terms in source texts. We use text mining methods for parsing and indexing taxon, habitat, phenotype and use mentions and their relationships  [9]. The taxa are indexed by the Omnicrobe taxonomic reference, an extended version of the NCBI taxonomy  [10, 11] , and the habitat, phenotype, and use information is indexed by the OntoBiotope ontology  [12]  (section Ontologies and taxonomies). The Omnicrobe database is publicly available at https://omnicrobe. migale. inrae. fr/.
Background and state of the art Microbiology covers a wide range of research domains, e. g. , molecular biology, ecology, systems biology, evolution, and epidemiology, which all deal with microbe environments and phenotypes. Advances in high-throughput technologies generate a tremendous quantity of open access data thanks to the historical investment in shared information systems since the nineties. As a result, data on microbe environments and phenotypes can be found in a diverse range of sources. The most prominent information sources are metagenomic experiment datasets, although the lack of species identification remains an obstacle. The Genome online database (GOLD) of the Joint Genome Institute aggregates isolation information from metagenomics experiments on thousands of identified strains  [13].
Genetic sequences are also often published with information on isolation samples, as in the GenBank or Biosample databases; this is notably true for complete genome sequences and sequences used for taxon identification (e. g. , 16S rRNA gene)  [14]. Numerous Biological Resource Centers (BRC) also publish catalogs of their microbial resources with detailed and curated information on the isolation places and phenotypes. The WFCC Global Catalogue of Microorganisms aggregates information from 133 collections on almost 500,000 strains with their isolation sources  [15]. The Bacterial Diversity Metadatabase (BacDive)  [16]  provides information on more than 80,000 strains of the DSMZ collection, one of the largest microbe collections in the world. Biodiversity inventories such as the Global Biodiversity Information Facility (GBIF) and the Encyclopedia of Life (EOL) include microorganisms with geographical information but lack information on observation places.
FoodMicrobionet gathered metagenomic food sample metadata from public databases and linked them with the FoodEx2 vocabulary  [17]. Though limited to food, FoodMicrobionet was one of the first attempts to link genomic and environmental data; it is no longer available, and only its archive can be accessed. These information sources can all be searched through open access web pages. The most advanced of them offer Application Programming Interface (API) access (e. g. BacDive). Among these sources, we selected a first core set of databases to populate the Omnicrobe knowledge base, to be extended in the future. These are GenBank, BacDive, and INRAE Biological Resource catalogs from the International Center for Microbial Resources (CIRM). These sources have been selected for either the quality of their data, their easy access, or their large coverage (section Information sources).
The standardization effort invested in these databases concentrates on taxa information rather than on habitats or phenotypes. The main microorganism references used are The List of Prokaryotic names with Standing in Nomenclature (LPSN; https://www. bacterio. net/  [18]  (e. g. in BacDive)), the NCBI taxonomy (e. g. in all NCBI databases and GOLD), and the Integrated Taxonomic Information System (ITIS) in EOL and GBIF. The taxonomic reference of Omnicrobe has a backbone based on the NCBI Taxonomy, with additional strains from BacDive and CIRM. We selected the NCBI taxonomy as a microbial taxonomic reference because it offers decent coverage of the living, taxa of a wide range of ranks including subspecific ranks and strains, and links to many sequence databases, which opens the way to genomic studies  [19].
The information on the microbe isolation sites in databases and articles is expressed in free text fields in various languages. Habitat classification is rarely used to index them. Most habitat classifications used to index microbe isolation sites are non-aligned in-house classifications, notably those used by GOLD and BacDive. Isolation sources can be searched in BacDive with the Microbial Isolation Source Ontology (MISO), a three-level-controlled vocabulary of 376 terms that is employed to manually index BacDive isolation sources. GOLD classifies the ecosystems of organisms and samples using a five-level controlled vocabulary of 800 terms  [13]. The Earth Microbiome Project Ontology, EMPO, defines 27 general classes that are relevant for studying correlation patterns between microbial sequences, environment, and environmental gradients at a very large scale  [6] , but are not sufficient to record the diversity of the microbial habitats for finer-grained studies.
For phenotype indexing, we also preferred the OntoBiotope classification of microbial phenotypes over other controlled vocabularies, namely the Ontology of Microbial Phenotypes, OMP  [23]. OMP focuses on phenotype change (presence, absence, alteration) but lacks some major phenotypes such as morphology phenotypes (e. g. colony color), energy sources beyond carbon and oxygen, or other environmental factors (e. g. response to various temperature scales). In the next section, we describe the organization of the database, the controlled vocabulary, the information sources, and the text mining process. In the following sections, we present the architecture of the Omnicrobe application and the analysis workflow. We also present the user interface, the content of the current version, and the use of Omnicrobe information in the food innovation domain.
Omnicrobe database schema The Omnicrobe schema is composed of entities of biological interest linked by specific relationships. Four types of entities are defined: microorganisms, habitats, phenotypes, and uses. They are linked by three types of relations: (i) the lives_in relation links a microorganism to its habitat; (ii) the exhibits relation links a microorganism to its phenotype; and (iii) the studied_for relation links a microorganism to its use. This formal schema structures the information in the Omnicrobe database, defines the relevant types of information to be extracted from the data sources, and guides the extraction process. The addition of a supplementary structured source of information requires not only textual information processing of the content but also the alignment of the source schemata with the Omnicrobe data schema.
Compared to free-text documents, the relationships between microorganism identifiers and their habitat, phenotype, and use descriptions can be more easily extracted from the semi-structured information of the databases. However, the level of information notably varies depending on the databases despite existing efforts on standard schemata for compiling biodiversity data such as the Darwin Core Standard  [24]  or RDA initiatives (https://rd-alliance. org). Some databases separate the habitats and the geographical location fields (e. g. , Source and Country in GenBank), they also separate the microbe host and the host part as distinct habitats when appropriate (e. g. , CIRM CFBP); some databases differentiate geographical information, between country and location (e. g. , BacDive). It also happens that all this information is mixed in a single field and that the actual content of the database fields does not fully comply with the database schemata, mixing languages for instance.
Ontologies and taxonomies The purpose of the Omnicrobe database is data linking and sharing. We have chosen standard controlled reference vocabularies to link microbial data collected from various sources and to make it more findable, accessible, interoperable, and reusable, following the FAIR principles. The OntoBiotope ontology and the NCBI taxonomy have been selected for their relevance to the microbial domain, their lexical richness that makes text parsing more efficient, and their deep structure. Indeed, the deep hierarchical organization of Omnicrobe data enables both smoother browsing by non-specialist users and microbial distribution analysis at various scales. Habitats, phenotypes, and uses in OntoBiotope. The OntoBiotope ontology (http:// agroportal. lirmm. fr/ontologies/ONTOBIOTOPE)  [12]  contains 4,219 classes split into three branches, Habitat, Phenotype, and Use (Table  1 ).
These products are the subject of a new and very strong interest in research and innovation  [26]. Microbial taxa. In order to detect and index microbial "Taxon" entities, we selected the NCBI Taxonomy (https://www. ncbi. nlm. nih. gov/taxonomy) as a reference. The NCBI Taxonomy organizes taxa according to state-of-the-art phylogeny. The NCBI Taxonomy consists of a classification of taxa in a taxonomic tree, and a nomenclature including valid scientific names, synonyms, and vernacular names, as needed to standardize taxa in publications even if their name has changed. The NCBI Taxonomy is regularly updated according to new requirements of NCBI databases. The NCBI Taxonomy encompasses all taxa of rank species or above, however, it only partially covers strains. Our ambition is to gather information about microorganisms, which is a purely phenotypic notion covering organisms that require a microscope to see them.
Information sources We designed Omnicrobe database to aggregate information extracted from various sources. The current version includes information from the bibliographic database PubMed (https:// pubmed. ncbi. nlm. nih. gov), from the nucleotide database GenBank (https://www. ncbi. nlm. nih. gov/genbank/), and from four microbial resource center catalogs: BacDive (https:// bacdive. dsmz. de) and three catalogs of CIRM (International Center for Microbial Resources) on food bacteria, plant pathogens, and yeast. CIRM BIA (for food bacteria) is available at https://collection-cirmbia. fr, CIRM-CFBP (for plant pathogens) at https://cirm-cfbp. fr, and CIRM-Levures (for yeasts) at https://cirm-levures. bio-aware. com. As stressed above, we have chosen these sources according to their richness, quality, popularity, open access license, and accessibility.
Omnicrobe also records the Qualified Presumption of Safety (QPS) of biological agents added to food as maintained by the EFSA (https://www. efsa. europa. eu/en/topics/topic/ qualified-presumption-safety-qps). The information on microorganisms and habitats is extracted by the Omnicrobe application from all the sources, while the information on phenotypes and uses is extracted from Pubmed only. PubMed. The open access bibliographic database PubMed contains more than 33 million citations and abstracts from the biomedical literature maintained by the NCBI and the NLM. Omnicrobe uses a thematic subcorpus of the PubMed references that mention at least one microbe taxon from the Omnicrobe taxonomic reference (section Ontologies and taxonomies). To identify those references, we use an alignment of the Omnicrobe taxonomic reference and the Organisms [B] subtree of the MeSH (Medical Subject Headings) thesaurus that indexes PubMed references.
MeSH is available at https://meshb-prev. nlm. nih. gov/search. At the time of publication of this paper, the Omnicrobe corpus comprises around 2,870,000 PubMed references. GenBank. The GenBank sequence database maintained by the NCBI is an open access, annotated collection of all publicly available nucleotide sequences and their protein translations. We feed the Omnicrobe taxon field with the parsed content of the "organism" and "strain" fields and the Omnicrobe habitat field with the "isolation_source" field from GenBank releases. The current release (release 249) contains around 5,100 sequence records in the Gen-Bank flat file format. Among these sequences, we select only 16S gene rRNA sequences because they are generally used to identify bacterial species  [27]. The isolation source of the sample is often described by the optional GenBank isolation_source field  [14].
We set the minimum sequence size to 800 base pairs to ensure the quality of species identification and we use the NCBI Taxonomy part of the Omnicrobe taxonomic reference to filter the relevant GenBank taxa. BacDive DSMZ. BacDive is a web service that gives access to the DSMZ catalog. In 2022, the DSMZ collection maintains more than 80,000 bacterial and archeal strains, including type strains, and is growing fast. The BacDive API returns detailed metadata for each entry such as taxonomy, morphology, physiology, environment, and molecular biology. Most of the BacDive data is manually curated. The current version of Omnicrobe covers BacDive taxon and habitat information. We feed the Omnicrobe taxon field with the parsed content of the BacDive fields "Full Scientific Name", "Strain Designation" and "Culture col.
no. ", and the habitat field with "Sample type/isolated from" information. BacDive also offers rich information on phenotypes that we plan to process in future work. CIRM. The International Center for Microbial Resources (CIRM), managed by INRAE, preserves more than 15,000 strains of bacteria and yeasts among which a subpart is publicly available. Omnicrobe integrates data from three catalogs: CIRM-BIA, dedicated to bacteria of food interest, CIRM-Levures, dedicated to traditional French ferments and yeasts involved in biotechnologies, and CIRM-CFBP on plant-associated bacteria. Alignments of CIRM database fields to Omnicrobe fields are given in the S2 Table. The upcoming availability of CIRM information through the Microbial Resource Research Infrastructure (MIRRI; https://catalog. mirri.
org/page/Strains_catalog_query) will provide a singlepoint and unified access in the near future. Volume of Omnicrobe sources. Table  2  gives the figures of the current version of Omnicrobe sources, with the last revision dates in brackets. S1 Text gives the queries that are used to gather data from PubMed and GenBank sources. These seven sources are the first to be processed and integrated into Omnicrobe but the architecture allows for the inclusion of new sources.
Text mining process A significant part of the information contained in the above-mentioned sources is unstructured and expressed in natural language, either in scientific abstracts (PubMed) or in free-text fields of the databases (GenBank, BacDive DSMZ, CIRM). The text mining process aims to automatically extract this information and structure it according to the Omnicrobe data schema to populate the Omnicrobe database. The text-mining process relies on Natural Language Processing (NLP) techniques and combines rules, lexical resources, and linguistic analysis to detect relevant information in texts  [28]. The general process consists of three main steps, namely entity recognition, entity normalization, and relation extraction. Entity recognition aims to detect textual expressions (or terms) that are of interest for a specific application domain (here, these expressions denote microorganisms, habitats, phenotypes, and uses).
Then, these entities are normalized with reference knowledge resources (taxonomies, ontologies). That is, each textual entity is linked to a specific entry in a given resource, with a unique identifier and a corresponding label. Here, entities are normalized according to two knowledge resources: microorganism mentions are mapped to taxa from the Omnicrobe taxonomic reference, while habitat, phenotype and use mentions are mapped to concepts from the OntoBiotope ontology. Finally, the third step links together entities that are in a relation, according to the predefined set of relations of the Omnicrobe data schema (i. e. lives_in, exhibits, and studied_for). Note that in the case of database fields, relations between entities are already known and this third step is skipped. Entity recognition and normalization methods.
These steps are materialized into the following workflows that process and integrate data from each source (https://forgemia. inrae. fr/omnicrobe/text-mining-workflow): • Data collection and preprocessing workflow: abstracts from PubMed and data from the Genetic and Biological resources centers (GenBank, BacDive, and CIRM) are collected and pre-processed by separate workflows. The ontologies and taxonomies are also preprocessed by selecting and filtering the useful parts. • Text mining workflow: this workflowhttps://forgemia. inra. fr/omnicrobe/text-miningworkflow implements the text mining process described in the Materials and Methods section (section Text mining process). It ensures the automatic extraction of information from the data sources (PubMed, GenBank, BacDive, and CIRMs). The main text mining steps are implemented using the AlvisNLP text-mining pipeline (https://github.