dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to acknowledge  Dr A. Penna  for critical reading of this manuscript and exciting discussions. This work was funded by the  JCJC ANR « invadocontrol » program and LLNC as "Equipe labellisée Ligue 2014 ",  Spanish Ministry of Economy and Competitiveness  through grants  RTI2018-099718 , institutional " Maria de Maeztu "  Programme for Units of Excellence in R&D  and  FEDER  funds S. V. was funded by  ANR ,  ARC Foundation  and the  European Marie-Sklodowska-Curie funding program. We are thankful for the support of the discovery platform and informatics group at EDyP. Proteomic experiments were partly supported by the  Proteomics French Infrastructure  ( ANR-10-INBS-08-01  grant) and  Labex GRAL  ( ANR-10-LABX-49-01 ). The authors declare no competing financial interests.
Paper Selection Method We conducted the selection of KRM papers, based on the set of queries optimized during the last editions of the International Medical Informatics (IMIA) Yearbook  [1] [2] [3] [4]. In comparison with the latest editions of the IMIA Yearbook  [3, 4] , where both PubMed/ MELDINE and Web of Science™ were used to search for KRM articles, we did not expand the search to the Web of Science (WoS) database this year. We have observed limited additional contributions in WoS in 2018 (3. 4%, n=34/962) and 2019 (1. 5%, n=18/1189) and no candidate paper was selected among these additional contributions, mostly because they were more bioinformatics-related than actual KRM papers. We followed a generic method to select the best papers, commonly used in all sections of the Yearbook and have done so since 2013.
Of particular interest is how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies, and how ontologies can provide constraints that improve machine learning models. One use case is provided along with a novel benchmark dataset for the prediction of protein-protein interactions with ontologies. In another machine learning driven method, Sousa et al. [12]  use genetic programming over a set of semantic similarities derived from different semantic representations, to support supervised learning tasks. They combine symbolic and statistical methods of artificial intelligence with applications in bioinformatics. This approach is validated by a use case of protein-protein interaction prediction. Alag  [13]  exploits the Clin-icalTrials. gov dataset to build a corpus annotated with HPO and MeSH terms and to extract protein mutations and single nucleotide polymorphisms.
8K genome-wide association studies. MeSH and HPO support a precise identification of genetic variants associated with diseases, phenotypes and traits of interest. The Experimental Factor Ontology (EFO), the Disease Ontology Lite (DOLite) and the International Classification of Diseases (ICD-10) are also used to code the phenotype data in GWAS. Shefchek et al. [16]  describe the status of the Monarch initiative in 2019. Even if this is not a research paper per se, the importance of such initiative is obvious to support research and is a large-scale example of knowledge management across species. The Monarch platform integrates data and allows rich analytics for connecting phenotypes to genotypes. From a KRM perspective, they use a large collection of widely adopted ontologies but also develop (and maintain) additional ontologies and mappings.
Conclusions In the KRM selection for 2020, we observe significant contributions in research on knowledge representation addressing the challenge of ontology curation. As in previous years, bioinformatics, machine learning and EHRs are still of major interest in the KRM community. In this article, Le presents a unified tool to support semantic similarity-based research called UFO. Ontology-based similarity has become a routine approach in many applications (ontology curation, enrichment, decision support, gene association studies…). Similarity measurements in ontologies can be performed using many metrics and methods, and current solutions are scattered over different tools and platforms. UFO is implemented as an app for the Cytoscape platform (open-source software for visualizing complex networks and integrating data in molecular and systems biology, genomics, and proteomics) and supports the OBO format.
Phenotypic abnormalities are represented by Human Phenotype Ontology (HPO) terms and the LHR calculations are derived from the subsumption hierarchies in the HPO. In the methods section, the algorithm is entirely described. This work illustrates how the structure of a knowledge representation (HPO) can contribute to a bioinformatics workflow. The performances of LIRICAL are demonstrated to be state-of-the-art, on simulated data from 384 published case reports and data from 116 solved cases from the 100,000 Genomes Project. LIRICAL is available for academic use for free, and source code can be downloaded on a GitHub repository. Slater LT, Gkoutos GV, Hoehndorf R Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies BMC Med Inform Decis Mak 2020;20(Suppl 10):311 In this paper, the authors present a method to identify hidden unsatisfiabilities in an ontology that arise when combined with other ontologies.
The sequences were analysed using the bioinformatic pipeline from the ARTIC Network (https://artic. network/ncov-2019). The Pangolin software package (https:// github. com/cov-lineages/pangolin) was used to assign the lineage to each genomic sequence of studied samples. Mutation analysis was performed using the GISAID mutation analysis tool. All the consensus genome sequences have been deposited in the GISAID database (https://www. gisaid. org/). Eighty-three of 100 samples were sequenced successfully. There were 51 samples (61%) that belonged to the B.
Our genomic sequences were compared to those of the GISAID database, and we constructed a phylogenetic tree to highlight the relationship of Senegal's strains with other strains in the world (Fig. 2). In conclusion, variant B. 1. 1. 7, termed the 'UK variant', 'British variant' or 'English variant', was detected for the first time in Senegal in an Indian woman. The emergence of new SARS-CoV-2 variants throughout the world should lead health authorities to rely on next-generation sequencing tools to monitor and control the strains circulating in Senegal.
The conformational plasticity of ASR proteins is likely of physiological relevance  [17]. Previous works illustrate the broad impact of intrinsic disorder in plant protein families in many areas of plant biology. Among these are the LEA proteins, a plant protein family with the largest number of known IDPs  [21]. The importance of intrinsic disorder in LEA proteins is reflected in their striking functional versatility, a common characteristic of IDPs  [22]. The ASR family was firstly proposed to be group 7 within the LEA superfamily  [23, 24]. Later on, and according to Hunault and Jaspard, the ASR family was classified as class 12 within the LEA proteins database (LEAPdb)  [25]. In 2012 however, the same authors decided to exclude the ASR proteins from the LEA family  [26]. The ASR family classification is still very ambiguous.
In 2014, the group of Gonzalez supported the view that the ASR family should be classified as an important group of ABA-Water Deficit Stress Domain (ABA-WDS) proteins with distinct and unique functions and roles  [3]. The conservation of the ABA-WDS domain (Pfam 02496 and IPR063494) in members of the ASR family (as illustrated in the multiple sequence alignment of ASR proteins we previously reported  [9] ) argues for a functional significance. Noteworthy, in some cases, as for instance in the ASR protein from rice (Os ASR1), the domain represents more than 65% of the full-length protein  [27]. Domains represent one of the most useful levels to understand protein function, and domain family-based analysis has a profound impact on the functional characterization and annotation of recently released genome data.
Cloning of TtABA-WDS and HvABA-WDS cDNA and Disorder Prediction of the Encoded Protein Domains Using primers designed from a previously reported multiple sequence alignment of plant full-length ASR proteins and conceived to amplify ABA-WDS domains  [9] , two cDNAs were amplified, one from salt-stressed durum wheat (Tt) and one from barley (Hv) leaves. The amplified cDNAs were subsequently cloned and sequenced. Analysis of the two deduced amino acid sequences against InterPro and Conserved Domains Database (CDD) confirmed that the two sequences (hereafter referred to as TtABA-WDS and HvABA-WDS) correspond to ABA-WDS domains. TtABA-WDS and HvABA-WDS cDNA encode polypeptides of 66 amino acids in length with a predicted molecular mass of ~7. 2 kDa. The TtABA-WDS and HvABA-WDS sequences are very close to each other (94% of identity) and only differ in four amino acids (10H-> R; 17A-> V; 45V-> L; 63Q-> T, Figure  1A ).
The two domains' acidic isoelectric points are 6. 12 and 6. 24, respectively (Table  1 ). Their predicted grand average of hydropathy index (GRAVY) values (as obtained from http://www. gravy-calculator. de/ (accessed on 25 January 2021)) are negative, underscoring their hydrophilic nature (     The sequence composition of TtABA-WDS and HvABA-WDS domains were compared to that of proteins within the SWISS-PROT database (Figure  1B ). Both domains have a biased sequence composition, being enriched in the most disorder-promoting amino acids (A, G, R, D, H, Q, K, S, E and P) and depleted in order-promoting residues (W, F, Y, I, M, L, V, C and T)  [28]. Moreover, Ala (A), Glu (E) and His (H) are the most abundant amino acids in both domains. In line with these observations, TtABA-WDS and HvABA-WDS domains are predicted to be intrinsically disordered by the mean hydrophobicity/mean net charge ratio  [29] , as judged from their location in the left-hand side of the RH-plot (Supplementary Figure  S1A, B ).
The group of Pappu showed that sequence polarity is a determinant of IDP compaction, with polar IDPs having been found to favor collapsed ensembles in water de-  The sequence composition of TtABA-WDS and HvABA-WDS domains were compared to that of proteins within the SWISS-PROT database (Figure  1B ). Both domains have a biased sequence composition, being enriched in the most disorder-promoting amino acids (A, G, R, D, H, Q, K, S, E and P) and depleted in order-promoting residues (W, F, Y, I, M, L, V, C and T)  [28]. Moreover, Ala (A), Glu (E) and His (H) are the most abundant amino acids in both domains. In line with these observations, TtABA-WDS and HvABA-WDS domains are predicted to be intrinsically disordered by the mean hydrophobicity/mean net charge ratio  [29] , as judged from their location in the left-hand side of the RH-plot (Supplementary Figure  S1A, B ).
Hydrodynamiques Properties of HvABA-WDS and TtABA-WDS Domains from Size Exclusion Chromatography (SEC) The Stokes radii (R S of TtABA-WDS and HvABA-WDS domains were inferred using SEC. The elution profile of proteins in fact reflects their hydrodynamic properties. SEC was carried out using PBS pH 7. 3 as elution buffer. TtABA-WDS and HvABA-WDS domains were eluted from the gel filtration column as sharp peaks (data not shown). From their elution volume (75. 5 mL for HvABA-WDS and 72. 1 mL for TtABA-WDS), their R S were estimated to be 20. 3 Å for HvABA-WDS and 21. 5 Å for TtABA-WDS (Table  2 ). By comparing the measured R S (R s obs ) for HvABA-WDS and TtABA-WDS with the theoretical Stokes radii expected for various conformational states (R s NF : natively folded protein; RsU: fully unfolded form; R S IDP : expected for an IDP), the two domains were found to have R S values ~1.
Hydrodynamiques Properties of HvABA-WDS and TtABA-WDS Domains from Size Exclusion Chromatography (SEC) The Stokes radii (RS of TtABA-WDS and HvABA-WDS domains were inferred using SEC. The elution profile of proteins in fact reflects their hydrodynamic properties. SEC was carried out using PBS pH 7. 3 as elution buffer. TtABA-WDS and HvABA-WDS domains were eluted from the gel filtration column as sharp peaks (data not shown). From their elution volume (75. 5 mL for HvABA-WDS and 72. 1 mL for TtABA-WDS), their RS were estimated to be 20. 3 Å for HvABA-WDS and 21. 5 Å for TtABA-WDS (Table  2 ).
PMGs possess some transiently populated secondary structure and are thus more structured than RCs. According to their ellipticity values at 200 and 222 nm, TtABA-WDS and HvABA-WDS domains can be classified in the RC-like and PMG-like region, respectively (Figure  5B ). Thus, HvABA-WDS is more compact than TtABA-WDS, in agreement with SEC data that highlighted a larger compaction index (CI) for HvABA-WDS compared to TtABA-WDS (Table  2 ). We next assessed whether the two domains undergo temperature-induced folding. To this end, we monitored the ellipticity at 230 nm as a function of temperature (Figure  5C ). While TtABA-WDS undergoes temperature-induced folding, the profile of HvABA-WDS is roughly linear and devoid of a clear temperature-dependence (Figure  5C ). To rule out any possible temperature-induced aggregation of the proteins, we monitored the HT voltage associated with the transmission of light through the protein in the CD spectropolarimeter during the heating of the proteins.
Discussion Experimental information on the conserved ABA-WDS domain of the ABA-WDS family (Pfam 02496) in isolation is lacking. To fill this gap in knowledge, we herein isolated from stressed leaves the cDNAs encoding two ABA-WDS domains, one from durum wheat and one from barley. Using various bioinformatics approaches, we showed that these domains are predicted to be intrinsically disordered, and further confirmed their disordered nature experimentally. Analysis of their deduced amino acid sequence revealed a biased composition, i. e. , an enrichment in disorder-promoting amino acids (A, H, and E) as reported for most ASR  [17]  and LEA proteins  [21]. Many Pfam domains contain sequences with 100% predicted intrinsic disorder  [50]. Noteworthy, the Pfam database recently added "disordered" as a specific entry type, underscoring the im-.
Discussion Experimental information on the conserved ABA-WDS domain of the ABA-WDS family (Pfam 02496) in isolation is lacking. To fill this gap in knowledge, we herein isolated from stressed leaves the cDNAs encoding two ABA-WDS domains, one from durum wheat and one from barley. Using various bioinformatics approaches, we showed that these domains are predicted to be intrinsically disordered, and further confirmed their disordered nature experimentally. Analysis of their deduced amino acid sequence revealed a biased composition, i. e. , an enrichment in disorder-promoting amino acids (A, H, and E) as reported for most ASR  [17]  and LEA proteins  [21]. Many Pfam domains contain sequences with 100% predicted intrinsic disorder  [50]. Noteworthy, the Pfam database recently added "disordered" as a specific entry type, underscoring the importance of disorder in the definition of protein domains  [51].
Removal of the GST tag by thrombin results in a recombinant product bearing a vector-encoded N-terminal GSPEF and a C-terminal EFPGRLERPHRD amino acid extension. Disorder content analysis of the resulting recombinant products (Supplementary Figure  S1C, D ) indicates that the presence of the vector-encoded residues preserves (actually slightly increases) the predicted disordered nature of the domains, in line with the expectedly disordered nature of vector-encoded linkers. Protein concentrations were calculated using the theoretical absorption coefficients at 280 nm as obtained using the program ProtParam at the EXPASY server (https://web. expasy. org/protparam/ (accessed on 25 January 2021)).
Intact Protein Mass Analysis Protein masses were determined on purified solution samples. An amount of 1 µL of protein at ~20 µM was mixed with 1 µL of α-Cyano-4-hydroxycinnamic acid matrix (Bruker Daltonics, Wissembourg, France) solution in 0. 3% TFA/CH3CN (50:50 v/v). One µL of the mix was spotted on the target and analysed by MALDI-TOF on an Ultraflex III spectrometer (Bruker Daltonics, Wissembourg, France) controlled by the Flexcontrol 3. 0 package (Build 51) and operated in the linear mode, using a maximum accelerating potential of 25 kV and a 5000-15,000 m/z range (LP_Protmix_Method). The laser frequency was fixed to 100 Hz and ~1000 shots per sample were cumulated. Four external standards (Protein Calibration Standard I, Bruker Daltonics, Wissembourg, France) were used to calibrate each spectrum to a mass accuracy within 100 ppm.
MS spectra were acquired in the 300-1700 m/z range at full width at half maximum (FWHM) resolution of 30,000 measured at 400 m/z. For internal mass calibration, the 445. 12 m/z ion was used as lock mass. The 10 most abundant precursor ions were selected and collision-induced dissociation fragmentation was performed in the ion trap to insure both maximum sensitivity and a maximum amount of MS/MS data. The signal threshold for an MS/MS event was set to 500 counts. Charge state screening was enabled to exclude precursors with 0 and 1 charge states. Dynamic exclusion was enabled with a repeat count of 1, exclusion list size of 500 MS runs and exclusion duration of 30 s. The acquired raw data were processed using Proteome Discoverer (version 1. 4. 1. 14, Thermo Fisher Scientific). Spectra were searched using SEQUEST (Thermo Fisher Scientific) against a homemade database comprising 20,150 human and 4306 E.
coli sequences and the TtABA-WDS and the HvABA-WDS sequences. To detect contaminants, an additional search against the Swiss-Prot database (https://www. uniprot. org/uniprot (accessed on 25 January 2021); version 2014. 02; 20,284 entries) supplemented with a set of 245 frequently observed contaminants (e. g. , human keratin) was performed using MASCOT (http://www. matrixscience. com (accessed on 25 January 2021)). Search parameters were: (i) GluC; five miscleavage allowed; (ii) mass tolerance of 6 ppm for monoisotopic precursor ions and 0. 8 ppm for fragment ions from MS/MS; (iii) Cys carbamidomethylation (+57. 02146 Da) as a fixed modification; Met oxidation (+15. 99491 Da) and N-terminal acetylation (+42. 0106 Da) as variable modifications; (iv) minimum peptide length of four residues. Only high-score peptides were selected.
Circular Dichroism (CD) Measurements The CD spectra of TtABA-WDS and HvABA-WDS were recorded using a Jasco 810 dichrograph, flushed with N2 and equipped with a Peltier thermoregulation system  [17]. One-mm or 1-cm thick quartz cuvettes were used for far-and near-UV CD measurements, respectively. Proteins concentrations were 0. 12 mg mL -1 and 1. 2 mg mL -1 for far-and near-UV CD studies, respectively. Far-UV CD spectra were measured between 190 and 260 nm, while near-UV CD spectra were recorded between 250 and 350 nm. Unless differently specified, CD spectra were recorded in 10 mM sodium phosphate pH 7 at 20 • C. The scanning speed was 20 nm/min, with data pitch of 0. 2 nm. Each spectrum is the average of three acquisitions. The spectrum of buffer was subtracted from the protein spectrum. Spectra were smoothed using the "means-movement" smoothing procedure implemented in the Spectra Manager package.
Far-UV CD spectra were also recorded in the presence of increasing concentrations of TFE (from 10% to 50%), glycerol (from 10% to 50%) and sucrose (0% and 30%). Mean molar ellipticity values per residue (MRE) were calculated as (Θ) = 3300 M∆A/(lcn), where l is the pathlength in cm, n is the number of residues, M is the molecular mass in Daltons and c is the concentration of the protein in mg mL -1. Numbers of amino acid residues are 83 for both domains. The molecular mass is 9200 Da and 9234 Da for TtABA-WDS and HvABA-WDS, respectively. Temperature-induced protein folding events were assessed by measuring the ellipticity at a fixed wavelength of 230 nm in the temperature range of 20-80 • C, with data pitch 2 • C and a temperature slope of 2 • C/min and protein concentrations of 0. 12 mg mL -1.
Bioinformatics Analyses Identification of the conserved domain of TtABA-WDS and HvABA-WDS was carried out by query against the Conserved Domain Database (CDD) (http://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi (accessed on 25 January 2021)). The ExPASy server (http://web. expasy. org/protparam/ (accessed on 25 January 2021)) was used to derive the physicochemical properties of the domains. Sequence alignment of the TtABA-WDS and HvABA-WDS domains was performed using Clustal Omega (https://www. ebi. ac. uk/Tools/msa/clustalo/ (accessed on 25 January 2021)). The RH-plot was generated by the PONDR server (http://www. pondr. com (accessed on 25 January 2021)). The phase diagram plots of TtABA-WDS and HvABA-WDS were generated using the CIDER server (http://pappulab. wustl. edu/CIDER/analysis/ (accessed on 25 January 2021)).
Deviations in amino acid composition of TtABA-WDS and HvABA-WDS were computed as previously described  [17]  using the average amino acid frequencies of the SWISS-PROT database (as obtained from https://web. expasy. org/protscale/pscale/A. A. Swiss-Prot. html (accessed on 25 January 2021)) as the reference value. The average amino acid frequencies of the SWISS-PROT database roughly correspond to the mean composition of proteins in nature. If the average composition of an amino acid X in SWISS-PROT proteins is CSPX, and CPX is the composition of X within a protein P, deviation from the composition of X of SWISS-PROT proteins was defined for P as (CPX-CSPX)/CSPX. The HCA plots and the IUPRED disorder prediction were obtained using the MeDor server  [34]. Structure prediction was carried out using the Phyre2 protein fold recognition server (http://www.
Funding: This work was supported jointly by French and Tunisian funding/research agencies. On the French side, this work was carried out with the financial support of the  CNRS  to S. L. The Marseille Proteomics facility (marseille-proteomique. univ-amu. fr) is supported by ' Infrastructures Biologie Santé et Agronomie' (IBiSA) ; ' Plateformes Technologiques Aix-Marseille'; 'Cancéropôle PACA '; ' Région Sud Provence-Alpes-Côte d'Azur'; 'Institut Paoli-Calmettes'; 'Centre de Recherche en Cancérologie de Marseille' (CRCM) ; ' Fonds Européen de Développement Régional' (FEDER) ; and 'Plan Cancer'. On the Tunisian side, this work is supported by the  Tunisian Ministry of Higher Education.
Normalized libraries were pooled into a single library for sequencing on the MiSeq. The pooled single strand library was loaded onto the reagent cartridge and then onto the instrument along with the flow cell. Automated cluster generation and paired end sequencing with dual index reads were performed in a single 39-hour run at a 2 × 250 bp read length. Total information of 9. 5 Gb was obtained from a 1063K/mm 2 cluster density, with a cluster passing quality control filters of 89. 2%. Within this run, the index representation for Microvirga mediterraneensis was determined to index 3. 4%. The 20 050 916 paired end reads were filtered according to the read qualities. 16S RNA gene sequence was extracted, and a phylogenetic tree was obtained using the maximum likelihood method and Kimura two-parameter within MEGA 7 software  [11].
Genome annotation and genome comparison Genome annotation was obtained through the NCBI Prokaryotic Genome Annotation Pipeline  [23]. BlastP was used to predict the bacterial proteome (E value of 1e03, coverage of 0. 7 and percent identity of 30) according to the Clusters of Orthologous Groups (COGs) database. The Genome-to-Genome Distance Calculator (GGDC) web server (http:// ggdc. dsmz. de) was used to estimate the overall similarity among compared genomes and to replace the wet-lab DNA-DNA hybridization by a digital version. The degree of genomic similarity of Microvirga mediterraneensis strain Marseille-Q2068 with closely related species was estimated using the OrthoANI software  [24]. Antibiotic resistance genes (ARG) were searched using the Comprehensive Antibiotic Resistance Database (CARD)  [25].