dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluating the Effectiveness of Cloud Situation Reasoning using Smart Fog Situation Reasoning The second performance was used to compare cloud situation reasoning and smart fog situation reasoning. In our experimentation, we measured the data transfer from mobile devices and then evaluate the reasoning time from the cloud. We also measured the reasoning time between the smart-health fog and the cloud, as shown in Figure  10. The results show that the smart-health fog generated results earlier than the cloud. This means the proposed method uses intelligent situation reasoning to identify early urgent situations. However, the smart-health fog was more effective on new situations update at run-time, making the context monitor send relevant context information more frequently.
This is due to a high rundendent constraints elimination. This method can be applied with near real-time e-Health smart domain where the time required is computed with fractions of a second. We apply fog-based distributed situation reasoning to manage all aspects of situation identification in all connected smart environments. These new solutions include intelligent situations management (sensors and smart devices) coupled with distributed intelligent decision support tools and communication tools. The presented approach offers the users the possibility to identify early urgent situations dedicated to assist users in their everyday needs and to manage all services at once. To prove the efficiency of smart fog, we measured the data transfer from mobile devices and evaluated the reasoning time from the cloud.
TE content and distribution along the 21 bread wheat chromosomes Building from a decade-long effort from the wheat genomics community, we used the accumulated knowledge about TEs to precisely delineate the TE repertoire of the 21 chromosomes based on a similarity search with a high-quality TE databank: ClariTeRep  [17]  which includes TREP  [19]. This represents 3050 manually annotated and curated TEs carried by the three subgenomes and mainly identified on bacterial artificial chromosome (BAC) sequences obtained during map-based cloning or survey sequencing projects, especially on chromosome 3B  [20]. CLARITE was used to model TEs in the sequence and their nested insertions when possible  [17]. This led to the identification of 3,968,974 TE copies, belonging to 505 families, and representing 85% of RefSeq_v1.
0 (Additional file 1: Table  S1 , Figure  S13 ), which was in line with the expectations and confirmed the linear relationship between flLTR-RTs and genome size within the Poaceae. This is two times higher than the number of flLTR-RTs assembled in TGAC_v1  [32] , while almost no flLTR-RTs were assembled in the 2014 gene-centric draft assembly  [33]. We exploited this unique dataset to gain insights into the evolutionary history of hexaploid wheat from a transposon perspective. flLTR-RTs are evenly distributed among the subgenomes, with on average 8 elements per Mb (Additional file 1: Table  S1 ). Among them, there were two times more Copia (RLC) than Gypsy (RLG) elements, although Gypsy elements account for 2. 8× more DNA. This means that the proportion of young intact elements is higher for the Copia superfamily than for the Gypsy superfamily.
This dataset of well-defined transposon lineages now provides the basis to further explore the factors controlling transposon dynamics. Founder elements may help us obtain better insights into common patterns which could explain how and why amplification starts. A stable genome structure despite the near-complete TE turnover in the intergenic sequences As described above, intergenic sequences show almost no conservation between homeologous loci. That means they contain practically no TEs that have inserted already in the common ancestor of the subgenomes. Instead, ancestral sequences were removed over time and replaced by TEs that have inserted more recently. Despite this near-complete turnover of the TE space (Fig. 2a ), the gene order along the homeologous chromosomes is well conserved between the subgenomes and is even conserved with the related grass genomes (sharing a common ancestor 60 Mya  [40] ).
TE modeling using CLARITE The Triticum aestivum cv. Chinese Spring genome sequence was annotated as described in  [26]. Briefly, two gene prediction pipelines were used (TriAnnot: developed at GDEC Institute [INRA-UCA Clermont-Ferrand] and the pipeline developed at Helmholtz Center Munich [PGSB]), and the two annotations were integrated (pipeline established at Earlham Institute  [47] ) to achieve a single high-quality gene set. TE modeling was achieved through a similarity search approach based on the ClariTeRep curated databank of repeated elements [48], developed specifically for the wheat genome, and with the CLARITE program that was developed to model TEs and reconstruct their nested structure  [17]. ClariTeRep contains sequences present in TREP, i. e. , a curated library of Triticeae TEs from all three subgenomes (originating from BACs sequenced during map-based cloning or survey sequencing projects) and TEs manually annotated in a previous pilot study of chromosome 3B  [20].
Thus, if a gene on the B or D subgenome was oriented in the opposite direction compared to its homeologous copy in the A subgenome, it was assumed that that gene is part of a local inversion. Therefore, the distance to the preceding gene on the chromosome was calculated. The second approach was more stringent, based only on triplets for which all three homeologs are in the same orientation in the three subgenomes. The results obtained from the two approaches were extremely similar, and we presented only the results from the second, more stringent, approach. For the control dataset, we picked a number of random positions along the chromosomes that is equal to the number of homeologs for that chromosome group. Then, homeologous gene identifiers were assigned to these positions from top to bottom (to preserve the order of genes but randomize the distances between them).
Analyses of TEs in the vicinity of genes and enrichment analyses We developed a Perl script (gffGetClosestTe. pl [54] ) to retrieve gene-flanking TEs from the feature coordinates in the GFF file. It was used to extract the closest TE on each side of every predicted gene (considering "gene" features that include untranslated regions). It was also used to extract all predicted TE copies entirely or partially present within 2 kb upstream of the "gene" start position, i. e. , the TSS. Enrichment analyses were then automated using R scripts.
Availability of data and materials In terms of data availability, IWGSC RefSeq_v1. 0 assembly  [55] , gene and TE annotation  [56] , as well as related data are available on the IWGSC Data Repository hosted at URGI  [57]  and the source code of perl scripts developed for this study [54]. The raw sequencing data is in the Sequence Read Archive under accession number SRP114784  [58].
Funding The research leading to these results has received funding from the French Government managed by the Research National Agency (ANR) under the Investment for the Future programme (BreedWheat project ANR-10-BTBR-Authors' contributions TW, HG, and FC conceived and designed the study. FC coordinated the study. TW performed analyses of TE distribution along chromosomes, comparisons of intergenic distances between subgenomes, centromeric TE analyses, and also analyzed the TE landscape around genes. HG carried out the k-mer analyses and the flLTR-RT identification, dating, clustering, and phylogenetic analyses. FC carried out the A-B-D comparisons of TE families, enrichment analyses in gene promoters, comparisons between homeologs, and association with expression profiles. CU, RHRG, and PB built the RNAseq-based gene expression network.
2 RLG_Erika 4. 9 2. 0 0. 4 Enriched A RLG_famc7. 3 RLG_Sumaya 2. 9 4. 1 1. 4 Enriched A RLG_famc7. 4 RLG_Sumana 0. 2 0. 9 4. 1 Enriched B RLG_famc9. 2 RLG_Daniela 0. 6 7. 1 11. 0 Enriched B RLX_famc4 Unnamed 0. 5 0. 2 0. 5 Enriched D a Name under which the family was previously described and/or stored in the TREP database bcd Ratio of abundance between subgenomes e Subgenome in which abundance or distribution of the respective TE families differs.
II. ANALYTICAL MODEL OF THE ENERGY HARVESTING A. Forces field applied to the ring When using our sensor in a flowing fluid, the sphere is deformed under the action of the stresses produced by the fluid; the deformation of the structure creates stresses on the piezoelectric ring. Therefore to calculate the force field applied to the ring due of the fluid flow, a finite element model was built under COMSOL Multiphysics (fig. 7 ). This model makes it easy to combine three approaches: acoustic, mechanical and piezoelectric conversion using three modules "Pressure Acoustics", "Solid Mechanics" and "Electrostatics". So from this model we determined the sound pressure per unit area applied in the plane of the piezoelectric ring which varies depending on the frequency. The integration of this pressure on the entire ring surface gives the force applied on the faces of the piezoelectric ring placed at the junction of the two half spheres (fig.
Background: Despite a wide adoption of English in science, a significant amount of biomedical data are produced in other languages, such as French. Yet a majority of natural language processing or semantic tools as well as domain terminologies or ontologies are only available in English, and cannot be readily applied to other languages, due to fundamental linguistic differences. However, semantic resources are required to design semantic indexes and transform biomedical (text)data into knowledge for better information mining and retrieval. Results: We present the SIFR Annotator (http://bioportal. lirmm. fr/annotator), a publicly accessible ontology-based annotation web service to process biomedical text data in French. The service, developed during the Semantic Indexing of French Biomedical Data Resources (2013-2019) project is included in the SIFR BioPortal, an open platform to host French biomedical ontologies and terminologies based on the technology developed by the US National Center for Biomedical Ontology.
Despite a large adoption of English in science, a significant quantity of biomedical data uses other languages, e. g. , French. For instance, clinicians often use the local official administrative language or languages of the countries they operate in to write clinical notes. Besides the existence of various English tools, there are considerably less terminologies and ontologies available in French  [7, 8]  and there is a strong lack of related tools and services to exploit them. The same is true of languages other than English generally speaking  [8]. This lack does not match the huge amount of biomedical data produced in French, especially in the clinical world (e. g. , electronic health records). In the context of the Semantic Indexing of French Biomedical Data Resources (SIFR) project (www.
g. , ontologies, mappings, metadata, notes, and projects are stored in an RDF 4 triple store). NCBO technology  [11]  is domain-independent and open source. A BioPortal virtual appliance 5 embedding the complete code and deployment environment is available, allowing anyone to set up a local ontology repository and customize it. The NCBO virtual appliance is quite regularly requested by organizations that need to use services like the NCBO Annotator but have to process sensitive data in house e. g. , hospitals. NCBO technology has already been adopted for different ontology repositories such as the MMI Ontology Registry and Repository  [25] , the Earth Sciences Information Partnership earth and environmental semantic portal (see http://commons. esipfed. org/node/ 1038). We are also working on AgroPortal  [26] , an ontology repository for agronomy.
The Health Multiple Terminology Portal (HMTP)  [31]  developed by the CISMeF group, which later became HeTOP (Health Terminology / Ontology Portalwww. hetop. eu) [32]. HeTOP is a multiterminological and multilingual portal that integrates more than 50 terminologies or ontologies with French content (but only offers public access to 28 of them 6 ). HeTOP supports searching for terms, accessing their translations, to identifying the links between ontologies and especially querying the data indexed by CISMeF in platforms such as Doc-CISMeF  [33]. The added value of the portal clearly comes from the medical expertise of its developers, who integrate ontologies methodically one by one, produce translations of the terms and index (semimanually) the data resources of the domain. The philosophies of HeTOP and NCBO BioPortal are different even if they occupy the same niche.
HeTOP's vision, similar to that of UMLS, is to build a "metathesaurus" so that each source ontology is integrated into a specific (and proprietary) model and is manually inspected and translated. Of course, this tedious work has the added value of a great wealth and confidence in the data integrated, but comes at the cost of a complex and long human process that does not scale to the number of health or biomedical ontologies produced today (similarly, the US National Library of Medicine can hardly keep pace with the production of biomedical ontologies for integration into UMLS). In addition, this content is difficult to export from the proprietary HeTOP information system, which does not offer publicly API or standard and interoperable format for easy retrieval (although, in the context of this work, several ontologies were exported by CISMeF in OWL format thanks to a wrapper developed during the SIFR project).
Hereafter, we describe each of the sources: Our first source of semantic resources is the UMLS Metathesaurus, which contains six French terminologies, translations of their English counterparts. For instance, the MeSH thesaurus is translated and maintained in French by INSERM (http://mesh. inserm. fr) and new releases are systematically integrated within the UMLS Metathesaurus. We used the NCBO-developed umls2rdf tool (https://github. com/ncbo/umls2rdf ) to extract three of these sources in RDF format and load them in our portal. 9 These sources are regularly updated when they change in the UMLS. Our second source of French terminologies is the CISMeF group, which in France is the most important actor to import and translate medical terminologies. During the SIFR project, the group developed an OWL extractor for the HeTOP platform which can be used to produce an OWL version of any resource integrated by CISMeF within HeTOP.
The SIFR Annotator mainly relies on Mgrep  [57]  as concept recognizer. Although experiments have been carried out -both by NCBO and us-to swap the underlying concept recognizer with another (MetaMap, Alvis, Mallet, UniTex), Mgrep is still the default recognizer. It uses a simple label matching approach but offers a fast and reliable (precision) matching that enables its use in real-time high load web services. Mgrep and/or the NCBO Annotator have been evaluated  [58] [59] [60] [61]  on different English-language datasets and usually perform very well in terms of precision e. g. , 95% in recognizing disease names  [62]. A comparative evaluation of MetaMap  [39]  and Mgrep within NCBO Annotator was made in 2009  [12]  when the NCBO Annotator was first released. There are, however, no evaluations of Mgrep on French text.
Experimental Protocol & Parameters Tuning We now present the experimental protocol used for the evaluation of SIFR Annotator on the EMEA and MED-LINE sub corpora of Quaero (original and adapted) on both the Plain Entity Recognition [PER] and the Normalized Entity Recognition [NER] annotations tasks, along with a description of the parameter tuning process. We present the baseline annotation setting along with two post-annotation disambiguation heuristics. In the baseline setting, we used the "quaero" output format of the SIFR Annotator which produces a BRAT output format compliant with the evaluation scripts for the task. The parameters of SIFR Annotator used for the baseline annotation were the following: match_longest_only = false as the gold dataset annotates both multi-word terms and their constituents.
Mapping algorithm between concept URIs and ICD-10 codes Given that we used the SIFR Annotator, besides manually curating the created SKOS dictionaries, the final step to obtaining a working system for the task was to write a complete workflow to 23:   1. Read the corpus in the raw or aligned formats; 2. Send the text to the SIFR Annotator REST API with the right ontologies and annotation parameters and retrieve the annotations produced; 3. Apply post-annotation heuristics to reduce ambiguity; 4. Produce the output in the right raw or aligned format. We have used only the "RawText" information of both the aligned and raw datasets. We did not use any other information/features such as age or gender contained in the files. The evaluation run performed the annotation with the longest_only parameter activated on a local instance of the SIFR Annotator with CIM-10 and the SKOS dictionary we produced as target ontologies.
Results 13 runs have been submitted by 9 teams to the French raw evaluation. Seven runs have been submitted by five teams to the French aligned evaluation. Table  5  presents the results obtained by our SIFR Annotator against the average and median results of the runs submitted to the evaluation task. The SIFR Annotator results are exactly the median value of all the results with the raw dataset, but slightly under the median value for the aligned datasets (all causes). Indeed, teams that have used other information from the aligned dataset probably performed better than the SIFR Annotator here. Regarding the external causes, we obtain better precision and F1 than the average and median results submitted to the challenge. The other systems that participated on the French Raw task can be divided in three categories: supervised machine learning (TUC, LIMSI), information retrieval models (IMS-UNIPD, LITL) and annotation approaches (SIFR Annotator, SIBM, Mondeca).
Death certificate coding errors Similarly, we sampled 200 false positives and false negatives from the best runs of the SIFR Annotator on the French aligned development dataset and proceeded to manually determine the causes of the errors (Table  7 ). The most frequent types of error are the following (see examples in Table  7 ): (79%) Errors because of missing synonyms that cannot be matched at a string-match level. (16%) Morphosyntactic or lexical variation (e. g. , accent, dash, comma, spelling). The errors due to morphosyntactic variation (and more general concept annotation due to a partial match) have the same cause that similar errors in the PER and NER evaluations and their possible solutions are the same: an alternative concept recognizer. The mapping expansion mechanism in SIFR Annotator could tackle such an issue, but there are very few mappings to and from CIM-10 at the moment.
Acknowledgements We would like to thank the  US National Center for Biomedical Ontology at Stanford University , for their assistance with the NCBO Annotator, the CépiDC and CLEF eHealth organizers ( Aurélie Névéol  in particular) for their authorization to use the corpora (Quaero and  2017 Task 1 ), the  European Hospital Georges Pompidou  for the evaluation of ConText on a clinical corpus, as well as all users of the SIFR Annotator that guide us in its development. We would also like the thank  CISMeF , an early partner of the  SIFR  project, for providing numerous health terminologies.
Funding This work was funded by the  Semantic Indexing of French biomedical Resources  (SIFRwww. lirmm. fr/sifr)and PractiKPharma projects (http:// practikpharma. loria. fr)that received funding from the  French National Research Agency  (grant  ANR-12-JS02-01001  and  ANR-15-CE23-0028 ) as well as by the European  H2020   Marie Skłodowska-Curie action  (agreement No  701771 ), the  University of Montpellier  and the  CNRS.
Availability and requirements Project name: SIFR Annotator Web application: http://bioportal. lirmm. fr/annotator Project home page: http://www. lirmm. fr/sifr Code repository: http://github. com/sifrproject NCBO codebase: https://github. com/sifrproject/ ncbo_annotator Proxy: https://github. com/sifrproject/annotators Operating system(s): The Web application is platform independent. An easy local deployment procedure is available using Docker to process sensitive (e. g. , clinical) data in-house (https://github. com/sifrproject/docker-compose-bioportal). This works on Linux.
Availability of data and materials Data for NER Evaluation: Quaero corpus available at https:// quaerofrenchmed. limsi. fr under the GNU Free Documentation License. Evaluation programs and sub-corpus generators available at https:// github. com/twktheainur/bpannotatoreval. Data for CLEF eHealth evaluation: CépiDC data not publicly available due to privacy constraints (clinical text), access to data possible upon signature of a data use agreement as per the official instructions for the task https://sites. google. com/site/clefehealth2017/task-1. Data for Clinical Context evaluation: Clinical corpus not publicly available as it is based on actual electronic health records from the European Hospital Georges Pompidou. Our experiments were produced in-house with partnership with an hospital team.
Characteristics of the Putative sec epi Gene Encoded by Clinical S. epidermidis Strains To examine the superantigenic capacity of the clinical S. epidermidis strains, we previously screened 22 S. epidermidis isolates from various clinical samples for enterotoxins genes (sea, seb, sec, seg and seh) by polymerase chain reaction (PCR) using specific primers. Two isolates (9%) from blood were found to carry and to express sec  [24]. When using radial immunoprecipitation method and a standard range of purified toxin concentrations (data not shown), sec epi is expressed at 60-90 µg/mL by both SE90 and SE95 isolates culture supernatants after 24 h of growth at 37 • C. The sec gene was sequenced from these two S. epidermidis isolates. Homology analysis of the deduced amino acids sequences of the amplified sec genes revealed that SEC epi were 100% conserved among both two isolates, while analysis of the generated dendrogram issued from the MALDI-TOF spectrum of the 22 strains shows that both the two sec positive isolates strains do not aggregate in a single cluster (Figure  1 ).
Structural Features of SEC epi and Structural Homology with SEC aureus Using I-Tasser™ and WebLab ViewerPro™ software, we generated a predicted SEC epi 3D structure. The predicted conformation of SEC epi is characterized by unequal sized domains, as found too, in other bacterial superantigens. The larger domain top left is the β-grasp fold consisting of four to a five strand β-sheet over, which is in front of an α-helix. The small domain bottom right is the fold of a Greek-key β-barrel capped by an α-helix at its end and contains the disulfide loop (Figure  3A ). A structural alignment of the predicted form of SEC epi with the previously determined structure of S. aureus SEC type 3 in both I-TASSER™ software and in TM-align structural alignment program revealed that the amino acid residues involved in TCR and MHC bindings are structurally identical despite a M216I polymorphism (Figure  3B ).
A zinc coordination site is found in the structure of SEC epi and is coordinated by T36, D83, H118, H122 residues (Figure  3C ). The zinc-binding site residues observed in the SEC3 aureus crystal structure (deposited in the Protein Data Bank with the accession code 1ck1) vary with the amino acid D9 replaced by T36. This clearly shows a high structural homology between the two isoforms. The predicted conformation was determined as "superantigen" in reference to its remarkable sequence. The resulting conformation is close to that of other superantigens 3D structures. The mutated amino acids are in ball and stick; (B) Structural alignment of SEC epi. Superimposition of the structure of SEC aureus (in line ribbon) with the predicted structure of SEC epi (in solid ribbon) shows that both toxins significantly matched together.