dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteins were transferred onto nitrocellulose membranes. Membranes were blocked with TBS/0. 1% Tween 20 containing 3% milk for 1 h and then incubated overnight with the primary antibodies (mouse anti-ROCK I at 1:400 mouse anti-ROCK II at 1:2500, BD Transduction Laboratories, USA; anti-myc at 1:500 9E10, Thermofisher Scientific, USA) diluted in TBS/ 0. 1% Tween 20 containing 3% milk. After several washes in TBS/Tween, membranes were incubated with anti-mouse Ig antibodies linked to HRP. Membranes were developed with ECL according to the manufacturer's instructions. Scanned autoradiographs were quantified using AIDA/2D densitometry software.
Enzyme sources The amylosucrase gene from Deinoccocus geothermalis DSM11300 (DgAS) (accession number ABF44874) was amplified by means of PCR from a pGST-DgAS plasmid template  42  using the two following primers: 5′-CACCATGCTGAAAGACGTGC TCACTTCTG-3′ as the forward primer and 5′-TGCTGGAGC CTCCCCGGCGGTCAGC-3′ as the reverse primer. The addition of a CACC sequence to the 5′-forward primer allowed the gene to be correctly inserted into the pENTR/D-TOPO® vector (Life Technologies). The recombinant gene was then transferred to the pET-53-DEST (Novagen) destination vector using the Gateway® LR Clonase® II enzyme mix (Life Technologies). Recombinant clones were selected on Luria-Bertani (LB) agar plates supplemented with 100 μg mL -1 of ampicillin. Plasmids were extracted using the Sigma-Aldrich GenElute HP Plasmid Miniprep kit, and verified using restriction analyses and sequencing (GATC Biotech).
α-Glucan structure analysis High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Aliquots of the reaction media were collected and diluted with water to a final concentration of 1 g L -1. HPAEC-PAD analysis was performed on a 2 × 250 mm Dionex Carbopac PA100 column. A gradient of sodium acetate in 150 mM NaOH was applied at a flow rate of 0. 25 mL min -1 as follows: 0-10 min, 0-140 mM; 10-26 min, 140-400 mM; 26-29 min, 400-500 mM. Detection was per-formed using a Dionex ED40 module with a gold working electrode and an Ag/AgCl pH reference. 1 H NMR. Freeze-dried polymer samples were re-suspended in deuterated water at a final concentration of 20 mg mL -1. 1 H NMR spectra were recorded on a Bruker Avance 500 MHz spectrometer and the data were processed with TOPSPIN 3.
SAXS data were collected online for the duration of the elution time, with a time frame of 1 s, and a dead time between frames of 0. 5 s. For each sample, the frames corresponding to the main elution peak were checked for the stability of the associated radius of gyration and global curve shape, and the resulting selection of curves was averaged, as described elsewhere. 49 Selected frames corresponding to the main elution peak were averaged using FOXTROT, a custom homemade application. A large number of frames were collected during the first minutes of elution, and these were averaged to account for buffer scattering, which was subsequently subtracted from the signal during the elution of the polymer. Data reduction to absolute units, frame averaging, and subtraction were carried out using FOXTROT.
According to the 1 H NMR analysis, the number of α(1 → 3) linkages (including the α(1 → 3,6) branching points) decreased from 37% in the alternan acceptor to 9% in the diblock, indicating that the alternan and dextran blocks represented 24% and 76% of the diblock respectively. Methylation data confirmed the increased proportion of α(1 → 6) linkages in the diblock and the decrease of α(1 → 3) linkages and branching points. Finally, from the HPSEC-MALLS analysis of the diblock, a weight-average molar mass of 27 700 g mol -1 (DP171) was determined. Since the molar mass of the alternan block was 5388 g mol -1 (DP of 33), it was calculated that the alternan and dextran blocks made up 19% and 81% of the diblock respectively. These values do not significantly differ from those obtained from the 1 H NMR analysis (24% and 76% respectively).
The contour length (R L ) of the fitted worm-like chain ranged from 85. 5 to 271. 0 Å (Table  4 ). This model was also used to estimate the polymers' DPs using a contour length (R L )/monomer size (radius) ratio. The calculated DPs were 20, 30 and 59 for amylose, alternan-b-amylose and dextran-b-alternan-b-amylose, respectively. These values are very close to those deduced from the HPAEC-PAD and 1 H NMR analyses. The flexibility of the products in solution was assessed based on the persistence length (R LL ) factor values. The addition of the alternan block to the amylose block increased the flexibility, as revealed by the R RL shift from 20. 7 to 15. 4 Å. With regard to dextran-b-alternan-b-amylose, the fitted values obtained from the worm-like model differed slightly from the experimental data for q values higher than 0.
Acknowledgements The HPLC analyses were performed using the equipment at the  ICEO facility , which is dedicated to the screening, discovery and characterization of new enzymes. ICEO, part of the  Integrated Screening Platform of Toulouse (PICT, IBiSA ), is supported by grants from the  Région Midi-Pyrénées , the  European Regional Development Fund  and the  Institut National de la Recherche Agronomique (INRA). The permethylation analyses were performed with the equipments of the  BIBS platform of Nantes  ( UR1268   BIA ,  IBiSA ,  Phenome-Emphasis-FR  (grant number  ANR-11-INBS-0012 )). We are grateful to MetaSys, the  Metabolomics and Fluxomics Center at the Laboratory for the Engineering of Biological Systems and Processes (Toulouse, France ) for the NMR experiments. We would like to cordially thank  Marion Claverie ,  Nelly Monties ,  Gianluca Cioci ,  Karine Cahier  and  Brigitte Laillet  for their technical support.
E. 3. 5. Genotoxicity Studies on the photo-breakdown product 3a (S8900) S8900 was tested in a very limited test in S. Typhimurium strains TA98 and TA100, using a micromethod in multiwells plates. Results showed that S8900, tested up to 250 lg/ well (considered by the authors as equivalent to 5,000 lg/plate), with or without metabolic activation, was not mutagenic in both strains (Midwest BioResearch, 2009). E. 3. 5. 1. Conclusions on genotoxicity studies for S8900 S8900 (photo-breakdown product 3a) was not mutagenic in a very limited test in bacteria. The Panel considered that these data are too limited to assess the genotoxic potential of S8900. The Panel considered that a complete battery of assays performed according to EFSA scientific committee opinion on genotoxicity testing strategies (EFSA Scientific Committee, 2011) should be provided.
E. 3. 6. Conclusions on genotoxicity of the photo-breakdown products Photo-breakdown products S7859, S4462, S7863 and S4465 are positive in at least one genotoxicity assay in vitro. S8900 has been tested in a limited microAmes study. For S9665 and photo-breakdown product 3b, no data are available. S7859 and S4462 have been tested in rats, in a combined bone marrow micronucleus assay and comet assay in liver and duodenum that gave negative results. However, in this study, there is no evidence of systemic exposure to the test substance. Therefore, it is only possible to conclude that S7859 and S4462 do not induce primary DNA damage locally in the duodenum. Based on the available data, the concern for genotoxicity cannot be ruled out for the photo-breakdown products. Bioreliance, 2013b Each strain was tested with two different metabolic activation systems: aroclor-induced rat liver S9 or phenobarbital-benzoflavone-induced rat liver S9.
Only a single malformation of conjoined twinning was noted at 1,000 mg/kg bw per day and was considered spontaneous in origin. There were no test article-related soft tissue or skeletal malformations noted for fetuses at any dose level. Skeletal developmental variations that were noted in all groups, consisted of 14th rudimentary rib(s), vertebral centra not fully ossified, 7th cervical rib(s) and reduced ossification of the 13th rib(s). These findings were noted similarly in the concurrent control group and/or the mean litter proportions were not statistically significantly different from the concurrent control group and were within the historical control data ranges of the laboratory. Based on the lack of adverse maternal toxicity or effects on intrauterine growth, survival and fetal morphology at any dosage level, 1,000 mg/kg bw per day (the highest dosage level evaluated) was considered to be the NOAEL for maternal toxicity and embryo/fetal development when [FL-no: 16.
e. , interoperable and well-structured format. The introduction of information technology (IT) tools and the use of semantically-enriched metadata can reinforce data expressiveness, exchange, linkage, and verification (through provenance information), as well as processing capabilities. The need for using metadata to annotate publicly available datasets has been pinpointed both by research and industry, and such technologies are currently used in order to facilitate data discovery and interlinking  (Weaver and Tarjan, 2013; Noy, 2017). Such an improvement in PV signal dissemination could have significant impact for (a) PV experts exploiting such information to investigate candidate PV signals, (b) regulatory authorities which typically use such information to decide for further action on the specific drugs, and (c) healthcare professionals (HCPs) who may consult such reports during their clinical practice.
g. , a Web page, a person, a data item, a process, a concept, etc. ). URIs make data uniquely identifiable and, thus, findable and accessible through the Internet. • RDF, RDF Schema 7  , and the Web Ontology Language (OWL) 8  -the main "languages" used to define knowledge in the Semantic Web paradigm-enable both syntactic and semantic interoperability by defining the rules for communicating data, the semantic structures to represent knowledge, and the interlinking of data with third-party datasets or ontologies. • The use of existing semantic models facilitates reusability of the published data, as these are accompanied by well-defined metadata (e. g. , about data provenance, time-related information, etc. ). The adoption of these models facilitates their integration in already established processing pipelines based on the semantics of the referenced models.
Biomedical ontologies, thesauri, and vocabularies, such as RxNorm 13  , NDF-RT 14  , and the Unified Medical Language System (UMLS)  15  , are used to specify concepts and normalize the interlinked data. The ADEpedia ontology consists of a rather lean concept schema, including two main concepts, namely, "Medication" and "ADE, " and does not include provenance information or statistical information on ADEs  (Jiang et al. , 2011). OntoADR  (Souvignet et al. , 2016 ) is an OWL ontology, aiming to address the difficulties in expressing the inherent semantics of MedDRA in an OWL format, in order to support automatic reasoning via well-defined OWL semantics upon MedDRA terms. Similar to ADEpedia, OntoADR does not include statistical or provenance information regarding PV signals  (Bousquet et al.
EXEMPLAR APPLICATION OF OpenPVSignal In this section, we present an exemplar application of OpenPVSignal, aiming to illustrate its applicability and validate its effectiveness by highlighting the added value of interlinkage and automatic reasoning. In particular, we describe the instantiation of OpenPVSignal based on three PV signal reports published by different organizations and also present two example use cases to demonstrate the value of data interlinking and automatic reasoning. Finally, we provide a validation of the model's compliance with the FAIR principles.
Elaborated PV Signal Reports The use of OpenPVSignal is demonstrated by elaborating on three specific types of PV signal reports: (a) signal information contained in the WHO Pharmaceuticals Newsletter, (b) reports from the Netherlands Pharmacovigilance Centre Lareb, and (c) announcements contained in the FDA Drug Safety 21 https://github. com/inab-certh/OpenPVSignal/ 22 http://purl. org/OpenPVSignal/OpenPVSignal. owl Communication. The WHO Pharmaceuticals Newsletter disseminates "information on the safety and efficacy of pharmaceutical products, based on information received from a network of "drug information officers" and other sources. " 23. Its "Signals" section presents specific PV signal information in a free-text format, also referring to relevant ICSRs contained in VigiBase 24  , publications, as well as other data sources.
OpenPVSignal Instantiation Currently, there is no automatic tool for instantiating OpenPVSignal from the original data sources. Therefore, the instantiation of the elaborated PV signal reports has been performed manually, using Protégé 5. 2. As shown in Figure  3 , apart from the main signal information (i. e. , the drug and the adverse effect), complementary information can also be modeled through OpenPVSignal. For example, the similar effects of other drugs that belong to the PPIs class are also elaborated in the Lareb report and specific drugs with the same or similar effects are mentioned, e. g. , pantoprazole highlighted in Figure  3  with red color. The freetext snippet of the report from which this specific information is inferred is also depicted in the bottom-right corner of Figure  3.
Added Value of Data Interlinking and Reasoning via OpenPVSignal In order to highlight the value of OpenPVSignal, we present two example use cases according to which a user exploits the OpenPVSignal instantiations presented above. In the first use case, we assume that the user investigates new, possible adverse effects of drugs belonging to the PPI class. Typically, she/he would search the free-text PV signal information sources, in order to find signals referring to PPIs. While the considered Lareb PV signal report and the FDA Drug Safety Communication explicitly refer to PPIs as the class of drug pantoprazole, the WHO Pharmaceuticals Newsletter does not refer to PPIs, since ibrutinib does not belong to the PPI class. Therefore, although the expert would have identified the two PV signal reports (by LAREB and FDA) through manual search, probably she/he would not notice ICSR 12 (depicted in Figure  5 ) mentioned in the WHO Pharmaceuticals Newsletter.
While the FDA Drug Safety Communication indicates a relationship between PPIs and magnesium deficiency and the WHO Pharmaceuticals Newsletter refers to the use of pantoprazole as a concomitant drug in an ICSR regarding pneumonia, this information could only be retrieved if the expert expanded her/his free-text search to include all drugs belonging to PPIs too, thus, including pantoprazole as a search keyword. However, using a knowledge graph based on the OpenPVSignal model enables the retrieval of ICSR 12 as relevant with hypomagnesemia and pneumonia. As depicted in Figure  7 , the parts highlighted in red can provide the interlinking between the concept of magnesium deficiency, the PPI drug class, and the specific ICSR mentioned in the WHO Pharmaceuticals Newsletter. While the data interlinking presented in these two exemplar use cases can be important for a drug safety investigation, it is difficult to be identified manually, as it would require a significant expansion of the search space across distinct, multiple data sources.
This expansion would complicate the investigation process and proliferate the chance of missing useful information. Therefore, the use of the Linked Data paradigm as employed in OpenPVSignal can be particularly helpful to avoid missing information that may be useful when searching for PV signal information across multiple report sources. It should be noted that the logical paths depicted using the blue dashed line are automatically inferred by reasoners, despite the fact that these relationships are not explicitly declared in the respective instantiations.
OpenPVSignal Evaluation: Compliance With the FAIR Principles The four main guiding principles of the FAIR model, namely, Findable, Accessible, Interoperable, and Reusable, are further analyzed in a set of 15 more detailed guidelines presented in Table 2  (Wilkinson et al. , 2016). Applying FAIR guiding principles to PV data would inherently enhance their value and, therefore, enhancing the "FAIRness" of such data is one of main goals of OpenPVSignal. Therefore, in Table  2  we present a qualitative evaluation of OpenPVSignal against the FAIR principles. It should be noted that in the context of the analysis presented in Table  2 , we consider that the term data refers to the original free-text PV signal reports and the term metadata refers to the respective OpenPVSignal instantiations. Furthermore, we have evaluated our proposed model against the respective emerging FAIR metrics framework 28 proposed by the FAIR Metrics Group 29.
As the current version of the proposed metrics refers to rather low-level technical details, we consider this evaluation process out of scope for the journal audience, and 28 https://github. com/FAIRMetrics/Metrics/blob/master/ALL. pdf 29 http://fairmetrics. org/ we consider our qualitative analysis presented in Table  2  more suitable in order to illustrate the "FAIRness" of OpenPVSignal. Based on the presented analysis, we can conclude that OpenPVSignal complies fully with the FAIR principles.
DISCUSSION The value of Linked Data and Semantic Web technologies for pharmacological research has been illustrated in various studies and projects. Beyond research on ADR representation, which was extensively presented in section "Related Work: ADR Representation Formalisms and Frameworks", of note is the Linked Open Drug Data (LODD) initiative  (Samwald et al. , 2011) , a project conducted by the W3C Semantic Web for Health Care and Life Sciences Interest Group (HCLS IG), exploiting semantic discovery techniques to automatically interlink diverse datasets. The Bio2RDF project transforms a variety of life science data sources to RDF [among which DrugBank  (Law et al. , 2014) , SIDER  (Kuhn et al. , 2016)  and FDA Structured Product Labels  (Hassanzadeh et al. , 2013) ], through a well-defined transformation process.
Furthermore, OpenPHACTS is an ongoing European initiative, building the so-called "Open Pharmacological Data Space" by collecting and integrating biochemical data from several heterogeneous sources  (Hu and Bajorath, 2014) , aiming to facilitate the discovery of new drugs. Interlinking heterogeneous datasets to facilitate drug research using Linked Data has been presented in  Boyce et al. (2014) , in the scope of the OHDSI initiative, elaborating also on PV use cases [Knowledge Base workgroup of the Observational Health Data Sciences Informatics (OHDSI) collaborative, 2017], while an approach for combining the results of diverse computational PV signal detection methods applied in diverse data sources using Semantic Web technologies was elaborated in the SAFER project  (Koutkias and Jaulent, 2015).
The PredicTox project aimed to foster ADR prediction through the combination of various data sources  (Zaman et al. , 2017). Interestingly, biomedical knowledge sources have also been integrated and used for drug repurposing  (Himmelstein et al. , 2017). A Linked Data model targeting PV signal investigation was presented in  Natsiavas et al. (2017). This model was partially based on RDF resources available via Bio2RDF  (Callahan et al. , 2013) , namely, DrugBank, SIDER, Linked SPL, PharmGKB, and ClinicalTrials. gov, and it was evaluated using three reference datasets containing both positive and negative PV signal controls. The evaluation process confirms or rejects each candidate PV signal based on the information provided by the model. The result was compared for the three reference datasets, aiming to highlight the value of interlinking various data sources for PV signal investigation.
In the current work, we aimed to address the shortcoming arising from the free-text format based on which PV signal reports are made publicly available from organizations which monitor and investigate PV signals. This practice does not facilitate systematic search and automatic interlinking of information. To this end, we presented OpenPVSignal, a novel ontology which provides the knowledge model and the semantics upon which PV signal information contained in the current reports could be annotated and enriched. Through the adoption of the Linked Data paradigm and Semantic Web standards, OpenPVSignal enables overcoming the diversity in the provided free-text report's syntactic structure and the provided information granularity level. Based on common practices in ontology modeling, OpenPVSignal reuses several existing semantic models, namely, Micropublications, PROV-O, WADM, OAE, and TO.
For illustration purposes, three PV signal reports originated from different sources were instantiated and two exemplar use cases exploiting these instantiations highlighted the value of OpenPVSignal. Data interlinking, retrieval, and automatic reasoning are crucial in the PV domain, where the currently applied typical workflow for signal generation and verification relies on complex manual exploration of multiple (mostly free-text) data sources  (Koutkias et al. , 2017). The advantages of using OpenPVSignal could be summarized as follows: (1) Facilitates the reusability of valuable information, which can be currently lost due to its unstructured nature. (2) Saves time and effort, as it significantly facilitates the automation of manually conducted work. (3) Provides the basis for an advanced computational framework, aiming to facilitate PV signal assessment OpenPVSignal is based on OWL and RDF which identify each resource (e.
F2 Data are described with rich metadata. This is a subjective requirement as it heavily depends on the definition of "richness". OpenPVSignal defines the concepts required for the intended use cases, namely, the interlinking and the further processing of PV signal reports. The instantiations (metadata) presented in section "Added Value of Data Interlinking and Reasoning" depict the richness of the model in the context of the elaborated PV use cases. However, the development of such a model is a continuous and iterative work. Therefore, we expect that the OpenPVSignal conceptual model will be further developed and refined, contributing to a richer and more accurate metadata model. F3 (meta)data are registered or indexed in a searchable resource. OpenPVSignal aims at facilitating and enhancing the process of publishing PV signal report data, which are available online.
Therefore, we consider that the OpenPVSignal instantiations would also be published online and could be easily retrieved through typical Internet search. However, it should be clarified that this issue is not related with the presented model per se, but with the applications that will use it. Therefore, indexing the produced OpenPVSignal instantiations is considered an application-level issue. F4 metadata specify the data identifier. OpenPVSignal instantiations are directly linked to original data, i. e. , the free-text PV signal reports. The original free-text report public URL is considered as its public identifier (reference to the original report is done using the rdf:isDefinedBy annotation property). TO BE ACCESSIBLE: A1 (meta)data are retrievable by their identifier using a standardized communications protocol.
A2 metadata are accessible, even when the data are no longer available. In the context of OpenPVSignal intended use, even if an original PV report becomes unavailable, the respective OpenPVSignal instantiation could remain online as it is a completely independent OWL file. TO BE INTEROPERABLE: I1 (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. The structure of the OpenPVSignal model is defined based on OWL and RDF which are open and widely accepted standards, independent of specific platforms (e. g. , operating systems, programming languages etc. ) and, thus, universally implementable. OpenPVSignal instantiations are compiled in OWL, therefore satisfying the specific requirement.