Oak Species Quercus robur L. and Quercus petraea Liebl. Identification Based on UHPLC-HRMS/MS Molecular Networks

Two species of oak are dominant in French forests: pedunculate oak (Quercus robur L.) and sessile oak (Quercus petraea Liebl.). Their differentiation is not straightforward but is essential to better understand their respective molecular content in order to better valorize them. Thus, to improve oak species identification, an untargeted UHPLC-HRMS/MS method associated with a two-step data treatment was developed to analyze a wide range of specialized metabolites enabling the comparison of both species of oak extracts. Pooled extracts from sessile and pedunculate oaks, composed of extracts from several trees of pure species from various origins, were compared using first the Venn diagram, as a quick way to get an initial idea of how close the extracts are, and then using a molecular network to visualize, on the one hand, the ions shared between the two species and, on the other hand, the compounds specific to one species. The molecular network showed that the two species shared common clusters mainly representative of tannins derivatives and that each species has specific molecules with similar fragmentation patterns, associated in specific clusters. This methodology was then applied to compare these two pooled extracts to unknown individuals in order to determine the species. The Venn diagram allowed for the quick presumption of the species of the individual and then the species could be assigned more precisely with the molecular network, at the level of specific clusters. This method, developed for the first time, has several interests. First, it makes it possible to discriminate the species and to correctly assign the species of unknown samples. Moreover, it gave an overview of the metabolite composition of each sample to better target oak tree utilization and valorization

Chêne sessile et chêne pédonculé, une nouvelle approche pour les reconnaître ?

Le bois de Chêne jusqu’au XIXème siècle, était la principale essence utilisée en Europe dans la construction navale et la construction des charpentes. C'est aujourd'hui encore une essence très prisée, notamment pour la fabrication des tonneaux dans lesquels les vins et spiritueux sont vieillis.Le fût de chêne n’est pas un simple contenant, il transmet de nombreux composés aux vins qu’il contient et en modifie les propriétés sensorielles. Aujourd’hui, il n’est plus employé pour le transport des boissons mais est devenu un outil essentiel pour l’élevage des vins et spiritueux qui bénéficient d’une complexité et d’une originalité qui leur sont propres, grâce à l’élevage sous-bois

Flexible querying of fuzzy RDF annotations using fuzzy CGs

absen

Oak Species Quercus robur L. and Quercus petraea Liebl. Identification Based on UHPLC-HRMS/MS Molecular Networks

International audienceTwo species of oak are dominant in French forests: pedunculate oak (Quercus robur L.) and sessile oak (Quercus petraea Liebl.). Their differentiation is not straightforward but is essential to better understand their respective molecular content in order to better valorize them. Thus, to improve oak species identification, an untargeted UHPLC-HRMS/MS method associated with a two-step data treatment was developed to analyze a wide range of specialized metabolites enabling the comparison of both species of oak extracts. Pooled extracts from sessile and pedunculate oaks, composed of extracts from several trees of pure species from various origins, were compared using first the Venn diagram, as a quick way to get an initial idea of how close the extracts are, and then using a molecular network to visualize, on the one hand, the ions shared between the two species and, on the other hand, the compounds specific to one species. The molecular network showed that the two species shared common clusters mainly representative of tannins derivatives and that each species has specific molecules with similar fragmentation patterns, associated in specific clusters. This methodology was then applied to compare these two pooled extracts to unknown individuals in order to determine the species. The Venn diagram allowed for the quick presumption of the species of the individual and then the species could be assigned more precisely with the molecular network, at the level of specific clusters. This method, developed for the first time, has several interests. First, it makes it possible to discriminate the species and to correctly assign the species of unknown samples. Moreover, it gave an overview of the metabolite composition of each sample to better target oak tree utilization and valorization

Fuzzy semantic annotation of XML documents

The e.dot project consists in the semi-automatic construction of an XML data warehouse in the field of food safety. The construction and the querying of the data warehouse are guided by an ontology. We are working on the annotation of tables extracted from scientific articles in microbiology with terms of the ontology. Terms from those articles may be connected to several terms of the ontology, but each connection is uncertain. We represent the annotations as possibility distributions: we associate each term of the ontology with the degree of possibility that this term represents the original term from the article. In this paper, we present two ways of computing this degree of possibility. One is based on a syntactic comparison, each word of an ontology term being weighted according to its “semantic power” in the term. The second way of computing the degree of possibility is based on the first one, but it also uses the hierarchy defined in the ontolog

An ontology driven annotation of data tables

This paper deals with the integration of data extracted from the web into an existing data warehouse indexed by a domain ontology. We are specially interested in data tables extracted from scientific publications found on the web. We propose a way to annotate data tables from the web according to a given domain ontology. In this paper we present the different steps of our annotation process. The columns of a web data table are first segregated according to whether they represent numeric or symbolic data. Then, we annotate the numeric (resp.symbolic) columns with their corresponding numeric (resp. symbolic) type found in the ontology. Our approach combines different evidences from the column contents and from the column title to find the best corresponding type in the ontology. The relations represented by the web data table are recognized using both the table title and the types of the columns that were previously annotated. We give experimental results of our annotation process, our application domain being food microbiology

Semantic annotation of data tables using a domain ontology

In this paper, we show the different steps of an annotation process that allows one to annotate data tables with the relations of a domain ontology. The columns of a table are first segregated according to whether they represent numeric or symbolic data. Then, we annotate the numeric columns with their corresponding numeric type, and the symbolic columns with their corresponding symbolic type, combining different evidences from the ontology. The relations represented by a table are recognized using both the table title and the types of the columns. We give experimental results for our annotation method

Fuzzy annotation of web data tables driven by a domain ontology

We propose an automatic system for annotating accurately data tables extracted from the web. This system is designed to provide additional data to an existing querying system called MIEL, which relies on a common vocabulary used to query local relational databases. We will use the same vocabulary, translated into an OWL ontology, to annotate the tables. Our annotation system is unsupervised. It uses only the knowledge defined in the ontology to automatically annotate the entire content of tables, using an aggregation approach: first annotate cells, then columns, then relations between those columns. The annotations are fuzzy: instead of linking an element of the table with a precise concept of the ontology, the elements of the table are annotated with several concepts, associated with their relevance degree. Our annotation process has been validated experimentally on scientific domains (microbial risk in food, chemical risk in food) and a technical domain (aeronautics

Annotation sémantique floue de tableaux guidée par une ontologie

Nous présentons dans cet article différentes étapes de l'annotation de tableaux de données à l'aide d'une ontologie. Tout d'abord, nous distinguons les colonnes de données numériques et symboliques. Les données symboliques sont ensuite annotées de manière floue à l'aide des termes de l'ontologie. Cette annotation nous permet de déduire le type des colonnes de données symboliques. Pour trouver le type des colonnes de données numériques, nous utilisons à la fois le titre de la colonne et les valeurs numériques et unités présentes dans la colonne. Chaque étape de notre annotation est validée expérimentalement