dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, inspired by this precursor work and following the guidelines of the Bio2RDF project, we developed new scripts producing a more complete RDF version of PharmGKB. These scripts transform the latest downloadable text files of PharmGKB, first, into a SQL database (with a script named pharmgkb2sql) and then into RDF triples (with a script named pharmgkb-sql2triples). Drug response phenotypes provided in clinical annotations by PharmGKB are non easy to translate as they are reported within plain-text sentences. Because PharmGKB also provides a broad type of drug responses (Efficacy, Toxicity/ADR, Metabolism/PK, Dosage, Other) in a structured manner, we decided, for simplicity, to use those directly instead of further text mining analysis on textual descriptions, and then considered only Efficacy and Toxicity/ADR, because they encompass the drug response phenotypes we want to capture.
Accordingly, PGx relationships in PGxLOD are associated with these two high level types of drug responses. Components of PGx relationships (i. e. drugs, genes and variants) are represented with URIs using both PharmGKB identifiers and Bio2RDF naming conventions. In addition, PharmGKB cross-references to external databases and ontologies are used to map PharmGKB URIs either to URIs already defined in our LOD, or to new ones. The type of relation used is bio2rdf:x-ref for every cross reference; doubled with either a owl:sameAs relation when the cross-reference points to an identical entity in an external database, or with a rdf:type relation when it points to an ontology class. Among the metadata associated with PharmGKB clinical annotations, we particularly keep the level of evidence. Levels of evidence have been defined by PharmGKB  [6]  as a six-level scale (1A, 1B, 2A, 2B, 3, 4), where higher levels (1A, 1B, 2A, 2B) indicate that a relationship has been significantly studied or is medically implemented; and lower levels  (3, 4)  indicate that the PGx relationship has only been reported in a single study or lacks clear evidence.
Trained models are applied to a test set of 176,704 sentences of PubMed abstracts, to extract automatically relations from text. After filtering out relationships that relate two GenomicVariations, two Phenotypes, or two Drugs, both the manually annotated relations and the automatically extracted ones are transformed into RDF. Types of entities are manually aligned to the corresponding classes of PGxO. Each annotated and extracted entity is associated with an URI that is constructed, depending on its type, either from an identifier of a reference database (such as NCBI Gene for genes) or from an identifier of an ontology (such as ATC for drugs). Distinct reference databases or ontologies may be used for each Entities annotated in multiple sentences are counted multiple times. Second-layer entities refer to entities which offset includes the annotation of a first-layer entity type of entities depending on their availability.
Accordingly, we defined an arbitrary order of choice for searching for references, presented in Table  4. This choice was motivated in part by the output of PubTator. For each type, the procedure is the following: given an entity, the first reference database or ontology is searched for the entry using string matching; if no entry matches, the next reference database or ontology is searched. Lastly, if no entry is found, we create a local URI within the PGxLOD namespace. Considering an extracted entity, when an entry is found in a reference database, its identifier is used to construct the corresponding URI. When an entry is found in an ontology (i. e. a class of an ontology), the extracted entity is given a local URI, and instantiates the ontology class.
Discussion Instantiating PGxO with knowledge extracted from various sources allows to answer the defined competency questions: we are able to represent PGx relationships extracted either from the state of the art (reference databases or the literature) as well as from EHR+biobank studies. The use of heterogeneous sources for instantiating our ontology improved in several ways the modeling of PGx relationships previously drafted in  [16]. Among other things, we enabled the representation of phenotypes as proxies for a specific genotype, such as an enzyme activity. The encoding of metadata has also been enriched to enable the encoding of the various metrics associated with the different kinds of knowledge extractions. By using Semantic Web technologies, our global framework for knowledge comparison in PGx can easily leverage knowledge defined elsewhere such as ontologies or other available LOD sets.
Conclusions In this article, we presented a simple ontology called PGxO to represent pharmacogenomic knowledge and its provenance. With the combined use of PROV-O and DUL, we demonstrated that PGxO can structure knowledge extracted from various sources such as reference databases (i. e. PharmGKB), the literature, clinical guidelines or EHR+biobank studies. We also defined and implemented a set of rules allowing to compare and reconcile PGx knowledge units from different sources. PGxO and the reconciliation rules constitute a first step to a semantic framework able to represent, trace, confront and reconcile PGx relationships from various origins. A first experiment with these rules highlights equivalent and comparable pieces of knowledge across various data sources, opening perspectives for fine grained comparison and interpretation of the content of PGx sources.
Number of skos:broadMatch links between PGx relationships from each sourceRows represent origins of the links and columns represent destinations as the reconciliation rules depend on existing mappings and subsumption relations. Moreover, the proposed encoding can easily evolve depending on one's needs. However, in a data warehousing perspective, Semantic Web technologies require high data maintenance to follow the evolution of associated databases, LOD sets and ontologies. Therefore, one challenge is to keep PGxLOD up-to-date with respect to the associated data sources. Several directions are considered to continue this work. Regarding the extraction from PharmGKB, more detailed drug response phenotypes could be extracted from the plain-text sentences describing the clinical annotations in the database.
Funding Publication charges are supported by  Inria DGDS-IES. This work is supported by the  PractiKPharma  project, founded by the  French National Research Agency (ANR)  under Grant No. ANR-15-CE23-0028 , by the  IDEX "Lorraine Université d'Excellence"  ( 15-IDEX-0004 ), by the  Snowball Inria Associate Team , and by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 701771.
Data analysis Univariate analysis was used to analyze data relevant with the pharmacopeia description. A medicinal use for a species was defined as a use-report which was recorded  [38]. Use-reports were divided into usage categories. Usage categories are groupings of medical conditions that affect a system of the body  [32]. Answers to semi-structured interviews on knowledge transmission were analyzed qualitatively. Pharmacopeia consistency was assessed by comparing the use of same species either collected in different places or as described in the literature (use convergence), and also through plant naming.
Determination of species All herbarium vouchers were deposited in the National Herbarium of Laos and the species were determined by specialists of the Lao flora: Dr. Vichith Lamxay, Dr. Somsanith Bouamanivong, Mr. Mouachan Xayvue, Dr. Sovanmoly Hul, and Dr. James F. Maxwell (deceased). Only plants determined by a specialist on the basis of herbarium numbers at genus level were included in this study. Synonyms were checked and confirmed through The Plant List data base  [39].
Acknowledgements We would like to thank  Chanthanom Manithip , vice-president of the  University of Health Sciences  for her ongoing support, all Hmong people interviewed for their willingness to share their knowledge with us. We would also like to thank our outstanding, highly motivated translators. We are grateful to  Marylin Magazin  for the English proof reading. Special thanks are due to The  Gibbon Experience Project  which manages the Neman National Park conservation activities and has organized the access to Ban Tup village and taken charge of the edition of a book on medicinal plants used in the Park area: Dubost J. M. 2013. Medicinal plants used in Nam Kan National Park: an ethnobotanic survey in Hmong,  Lue  and  Lamet communities-Vientiane, Ed. Animo 217 p.
Detection of molecular markers of antimalarial resistance We assessed polymorphisms in the PfKelch13 gene by nested PCR amplification covering the full length of the gene (total 2,181 bp) and sequenced the gene by ABI Sequencer (Macrogen, Seoul, Republic of Korea) as described previously. We monitored cross-contamination by adding negative control samples in every run. Sequencing results were aligned against PfKelch13 of reference strain 3D7 (putative 9PF13_0238 NCBI Reference Sequence [3D7]: XM_001350122. 1), using Bioedit software (Abbott, Santa Clara, CA, US). Two study technicians assessed polymorphic patterns blinded to the origin of the sample. We quantified Pfplasmepsin2/3 gene copy number using relative quantitative real-time PCR based on Taqman probe on a Corbett Rotor-Gene Q (Corbett Research, Mortlake, NSW, Australia).
The individual exposure time was defined as the number of days spent within the catchment area, i. e. , the village and the surrounding farms. The exposure time was estimated in 3-month intervals. For example, if a resident was present during 2 sequential surveys, the exposure time was 90 days. If a resident was missing during a survey, we assumed he/she stayed in the village for 45 days after the last participation in a survey. Similarly, we assumed a new arrival had arrived 45 days before the first participation in a survey. We assumed both losses to follow-up and intermittent missing data were missing at random. Seasons were defined as wet or dry by country as described above. The unit of randomisation and hence the unit of statistical inference was the village cluster. The primary approach to analysis was based on intention to treat (ITT).
The study teams collected survey data on case record forms and entered the data on smartphones before exporting them into OpenClinica (OpenClinica, Waltham, MA, US). Graphical summaries have been presented to show prevalence and incidence patterns over time. Treatment and AE data were recorded on registers and then entered in Excel (Microsoft, Redmond, Washington, US). Analyses were performed in STATA 15. 0 (StataCorp, College Station, Texas, US).
Tolerability and adverse events Following the administration of DP (59,375 individual doses), 121 (0. 2%) participants vomited. Of these, 104 vomited shortly after taking the drugs and were offered a repeat dose (Table  3 ). Data on dizziness and itching were not recorded in Vietnam, but in Cambodia, Lao PDR, and Myanmar, where a total of 26,898 doses of DP were administered, 586 (2. 2%) events of dizziness and 12 (0. 04%) events of itching were reported following drug administration on day 1. Within 1 month of the MDAs, 1,535 of 8,112 (19%) MDA participants recalled 2,577 AEs, of which 911 (35%) were considered related to the antimalarials; 592 (23%) of the 2,577 AEs were dizziness, 199 (8%) nausea, 96 (4%) vomiting, and 39 (2%) itching, and 1,653 (64%) participants reported a range of other minor complaints.
The studies were approved by the Cambodian National Ethics Committee for Health Research (0029 NECHR, dated 04 Mar 2013), the Institute of Malariology, Parasitology, and Entomology in Ho Chi Minh City (185/HDDD, dated 15 May 2013), the Institute of Malariology, Parasitology, and Entomology in Quy Nhon (dated 14 Oct 2013), the Lao National Ethics Committee for Health Research (Ref No 013-2015/NECHR), the Government of the Lao PDR, and the Oxford Tropical Research Ethics Committee (1015-13, dated 29 Apr 2013). Each participant, or parent/guardian in the case of minors, provided individual, signed, informed consent; illiterate participants provided a fingerprint countersigned by a literate witness (ClinicalTrials. gov Identifier: NCT01872702).
Acknowledgments We thank first and foremost the study communities that kindly agreed to participate in this project. We thank the members of the trials steering committee for their support:  Prof. David Lalloo  (Chair,  Liverpool School of Tropical Medicine, UK) ,  Prof. Decha Tangseefa  ( Thammasat University, Bangkok, Thailand ),  Prof. Mike Parker  ( Ethox Centre at the University of Oxford, UK ), and  Phan Kim Son  (lay member,  Ho Chi Minh City, Vietnam ). We thank the members of the data safety monitoring board- Prof. Ric Price  ( Menzies School of Health Research, Darwin, Australia ),  Prof. Sarah Walker  ( Clinical Trials Unit, Medical Research Council, UK ), and  Prof. Amir Hossain  ( Chittagong Medical College, Bangladesh )-and we thank the medical monitor,  Prof. Susanna J. Dunachie  ( University of Oxford, UK ).
The data are available upon request to the Mahidol Oxford Tropical Medicine Research Unit Data Access Committee (http://www. tropmedres. ac/data-sharing) for researchers and following the Mahidol Oxford Tropical Medicine Research Unit data access policy (http://www. tropmedres. ac/_asset/file/datasharing-policy-v1-1. pdf). Queries and applications for datasets should be directed to Rita Chanviriyavuth (rita@tropmedes. ac).
, 2007; Himmelberg et al. , 2018). The other microorganims of the TRIP culture include another member of the genus Desulfatiglans, a Paludibacter, as well as members of the family Spirochaetaceae and the phylum Zixibacteria (Table  1 ). In order to evaluate the metabolic potentials of the organisms in the TRIP culture, metabolic pathways were predicted from the genome sequences using the KEGG and MetaCyc databases  (Kanehisa and Goto, 2000; Caspi et al. , 2016) , followed by a manual curation to evaluate the completeness of each predicted pathway. Furthermore, expression of the predicted genes was evaluated through a combined metatranscriptomic and metaproteomic analysis of the TRIP culture during growth with phenanthrene. Differential transcriptomic and proteomic analyses were not possible because the TRIP culture is not pure and shifts of the bacterial community were observed upon growth with other substrates  (Himmelberg et al.
Only three of these genes were expressed during growth of the TRIP culture in phenanthrene, and putatively encode phenanthroate-CoA ligases  (Table 3(a) ). Noteworthy, one of the expressed genes is located in the vicinity of one of the putative carboxylase gene clusters. In Deltaproteobacterium NaphS2, the naphthoyl-CoA ligase gene is located in the vicinity of A B Fig. 1. A. Relative abundance of genomes reconstructed from the TRIP metagenome, calculated by re-mapping raw reads to the assembled scaffolds using the BWA software  (Li and Durbin, 2009). B. Operational taxonomic units (OTU) obtained by clustering 16S rRNA gene amplicon sequences (V3-V4 region) using MetaAmp Version 2. 0  (Dong et al. , 2017). the gene cluster coding for 2-naphthoyl-CoA reductase, the enzyme catalysing the first reduction step of 2-naphthoyl-CoA  (DiDonato et al.
Metagenome analysis The metagenome of the TRIP culture was sequenced (Illumina PE 250 on MiSeq) and genome-resolved analyses of the raw shotgun data were performed  (Himmelberg et al. , 2018). The preassembly protocol followed the steps described in  (Gkanogiannis et al. , 2016)  and the Spades engine  (Bankevich et al. , 2012)  was used for targeted assemblies. The resulting scaffolds were annotated using the MicroScope microbial genome annotation and analysis platform  (Médigue et al. , 2017). The genome sequence of candidate Desulfatiglans TRIP_1 has been submitted to the ENA databases and can be accessed under contig accession numbers: LT984854-LT984881. MicroScope's syntactic annotation pipeline was used to identify protein coding genes, transfer RNA (tRNA), ribosomal RNA (rRNA), noncoding RNA (ncRNA) and repeat elements.
Microorganisms relative abundance The relative abundance of the bacteria composing the TRIP culture was estimated by re-mapping the raw reads to the assembled scaffolds using the BWA software  (Li and Durbin, 2009) , and the estimates were confirmed by 16S rRNA gene amplicon sequencing. For the latter, the V3-V4 region of the 16S rRNA gene was amplified from the DNA of the TRIP culture and used for library construction. PCR products were converted into blunt ends using T4 DNA polymerase Klenow Fragment and T4 Polynucleotide Kinase previous to adaptor addition. After quality control, libraries were sequenced (Illumina PE 250 on MiSeq 2500). The raw data were filtered to eliminate adapters and low-quality calls to obtain clean reads. Overlapping paired-end reads were merged and clustered into operational taxonomic units (OTUs) using MetaAmp Version 2.
Metabolic reconstruction The metabolic capabilities of the different microorganisms were predicted from their genome using the KEGG  (Kanehisa and Goto, 2000)  and MetaCyc  (Caspi et al. , 2016)  databases; followed by a manual curation of the predicted pathways. Candidate genes relevant for PAH degradation were identified by homology to previously characterized enzymes involved in the anaerobic degradation of naphthalene using the blastp program  (Altschul et al. , 1990).
Metatranscriptome analyses The TRIP culture was grown as described above and cells were harvested in the mid-exponential phase by centrifugation (20 min, 6000 × g, 4 C). Total RNA was immediately extracted using the Trizol™ reagent (Invitrogen). Samples were treated with the DNA-free™ kit (Invitrogen) to remove DNA. After rRNA depletion, the mRNA was used for strand-specific library construction and sequencing (Illumina PE100 in Hiseq 4000). The raw RNA-seq reads were filtered to remove adapter sequences, contamination and low-quality reads. After quality control, the metatranscriptomic reads were mapped against the reconstructed genomes using bow-tie2  (Langmead and Salzberg, 2012). Gene expression levels were estimated from the abundance of mapped reads using the featureCounts software  (Liao et al.
Peptides were directly loaded onto the analytical column with a maximum flow rate that would not exceed the set pressure limit of 980 bar (usually around 0. 5--0. 8 μl min -1 ). Peptides were subsequently separated on the analytical column by running a 140 min gradient of solvent A and solvent B (start with 7% v/v B; gradient 7% to 35% v/v B for 120 min; gradient 35% to 100% v/v B for 10 min and 100% v/v B for 10 min) at a flow rate of 300 nl min -1. The mass spectrometer was operated using Xcalibur software (version 2. 2 SP1. 48). The mass spectrometer was set in the positive ion mode. Precursor ion scanning was performed in the Orbitrap analyser (Fourier Transform Mass Spectrometry, FTMS) in the scan range of m/z 300-1800 and at a resolution of 60 000 with the internal lock mass option turned on (lock mass was 445.
120025 m/z, polysiloxane,  Olsen et al. , 2005). Product ion spectra were recorded in a data dependent fashion in the ion trap (Ion Trap Mass Spectrometry, ITMS) in a variable scan range and at a rapid scan rate. The ionization potential (spray voltage) was set to 1. 8 kV. Peptides were analysed using a repeating cycle consisting of a full precursor ion scan (1. 0 × 10 6 ions or 30 ms) followed by 15 product ion scans (1. 0 × 10 4 ions or 50 ms) where peptides are isolated based on their intensity in the full survey scan (threshold of 500 counts) for tandem mass spectrum (MS2) generation that permits peptide sequencing and identification. Collision-induced dissociation (CID) collision energy was set to 35% for the generation of MS2 spectra. During MS2 data acquisition, dynamic ion exclusion was set to 60 s with a maximum list of excluded ions consisting of 500 members and a repeat count of one.
Peptide and protein identification Raw spectra were submitted to an Andromeda  (Cox et al. , 2011)  search in MaxQuant (version 1. 5. 3. 30) using the default settings  (Cox and Mann, 2008). Label-free quantification and match-between-runs was activated  (Cox et al. , 2014). MS/MS spectra data were searched against the in-house generated databases of predicted proteins from candidate Desulfatiglans TRIP_1 (4868 entries), candidate Desulfatiglans TRIP_2 (4891 entries), candidate Zixibacteria TRIP_5 (3045 entries), candidate Paludibacter TRIP_3 (2636 entries) and candidate Spirochaetaceae TRIP_4 (2930 entries). All searches included also a contaminants database (as implemented in MaxQuant, 267 sequences). The contaminants database contains known MS contaminants and was included to estimate the level of contamination.
Acknowledgements We acknowledge  Xiyang Dong  for his helpful advices on metabolic pathway reconstruction, and  Alexander Probst  ( University of Duisburg-Essen ) for advice during the analysis of transcriptomic data. We thank  Karine Labadie  and  Eric Mahieu (Genoscope)  for the shotgun sequencing of the TRIP metagenome, and Genoscope's LABGeM team for the creation and maintenance of the MicroScope annotation platform. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  705321  and from the  ERC  advanced grant EcOILogy no. 666952.
Data analysis Initial data analysis was performed using the PERSEUS computational platform version 1. 5. 5. 3  (Tyanova et al. , 2016). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Vizcaíno et al. , 2016)  partner repository (https:// www. ebi. ac. uk/pride/archive/) with the dataset identifier PXD010151.
AKI was defined by either a 50% decrease between two creatinine value within 7 days, either a difference of 0. 3 mg/dl within 48 hrs (if lowest serum creatinine < 1. 2 mg/dL). Examples of possible cases are provided in the Supporting Information (Figure  S1 ). Eligibility for the study required a minimum stay duration of 2 days and at least 2 creatinine values (including one at the time of admission). Only inpatients of 75 years and over, fitting these criteria were eligible for the study. All AKI events were automatically detected according to these criteria and then reviewed by two independent experts (LR, AS). AKI events were secondarily excluded in case of: 1) erroneous-automated detection; 2) missing data (in relation to the drugs prescribed, the diagnosis or the laboratory results). Cohen's Kappa evaluated the extent of agreement between the two experts for the validation of CA-AKI events.
Expert Review Of Each AKI Event All detected AKI events were analyzed by two independent reviewers (LR, AS) using the ADE-Scorecards ® software. This latter tool was developed as part of the European Patient Safety through Intelligent Procedure (PSIP) project. The ADE-Scorecards ® interface summarized the data available for each stay (administrative data, lab results, diagnostic data, drug prescriptions and freetext documents). 27, 28 This tool was used for data extraction, as detailed below. Only well-known risk factors for AKI were extracted, as the study did not intend to find new risk factors. Obstructive AKI was excluded from the drug analysis because the reviewers considered that drug involvement was secondary in the occurrence of the AKI, even if the Naranjo score was ≥1. The reviewers then evaluated the relationship between drugs and AKI, inappropriateness of the prescription, avoidability of the event.