dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). Considering the integration of biochemical data for DS, several sources, such as UMLS, DrugBank, CTD, and UniProt were integrated in one large RDF graph for ADR detection  (Abdelaziz et al. , 2017). Several heterogeneous data sources were also integrated to interrelate biochemical and phenotypic information for predicting ADEs through an SP approach  (Huang et al. , 2011; Cheng and Zhao, 2014). Furthermore, the Adverse Drug Reaction Classification System (ADReCS) combines a hierarchical structure of concepts (similar to the MedDRA structure) and integrates information from a large number of ADE and biochemical data sources, explorable through a Web interface for signal assessment  (Cai et al. , 2015). Similarly, DisGeNet is a comprehensive centralized repository created by integrating data from curated databases and two datasets obtained by mining the scientific literature  (Piñero et al.
, 2014). The D3 (Drug-drug interactions Discovery and Demystification) system aimed to infer MoAs for DDIs based on an integrated RDF schema of 12 biomedical resources and 15 DDI databases  (Noor et al. , 2016). Some data sources included data in RDF format obtained from Bio2RDF, which were semantically aligned through the use of UMLS and a set of specific relationships (e. g. , "has indication"). Non-UMLS compatible data sources were also integrated via explicit database cross-references. The LAERTES knowledge base which was built in the context of the Observational Health Data Sciences and Informatics (OHDSI) collaborative (Knowledge Base Workgroup of the Observational Health Data Sciences and Informatics (OHDSI) Collaborative, 2017), integrated multiple data sources into a common knowledge schema for signal investigation, in compliance with the OMOP Common Data Model (CDM)  (Boyce et al.
, 2016). Alternatively, relational databases were used as a knowledge base storage formalism, since they provide a mature data storage paradigm, able to support vast data storage in a computationally effective manner that is widely used in realworld enterprise systems. Compared to ontologies, relational databases are not specifically designed to support KE activities (e. g. , automatic reasoning). On the other hand, while ontologies can support formal semantics and automatic reasoning given their underlying robust mathematical background, i. e. , DL, the respective data storage systems are not yet mature enough and the automatic reasoning process is computationally expensive for large knowledge graphs, making relational databases a competing alternative for large knowledge bases. To this end, MEDLINE abstracts were used to extract knowledge on drug metabolism and interactions (storing the corresponding structured representation into a database in the form of a treestructure representation) and queried to identify DDI mentions  (Tari et al.
, 2010). SemMedDB contains statements in the form of triples (subject-predicate-object) extracted from MEDLINE and stored in a relational format  (Zhang et al. , 2014). MetaADEDB relies on a relational schema to integrate several heterogeneous data sources for DS  (Cheng et al. , 2013). Hybrid data storage approaches have been also proposed, using both relational and RDF formalisms. For example, LAERTES used relational databases as its basic data storage paradigm  (Boyce et al. , 2014 ; Knowledge Base Workgroup of the Observational Health Data Sciences and Informatics (OHDSI) Collaborative, 2017). However, it also employs the Web Annotation Data Model (WADM), to enable "drill-down" into evidence supporting a statistic measure of association between a drug and a Health Outcome of Interest (HOI) (e.
ADE assessment ADE assessment mostly refers to the analysis of the underlying MoA as well as the comparative analysis of drugs. These activities typically require the integration of heterogeneous data sources, including biochemical and genetic information databases. Dynamic reconstruction of drug metabolic pathways from primitive molecular events using information modeled in DIO was conducted, showing that unknown potential pathways can be inferred through the combination of ontologies and rule-based inference  (Arikuma et al. , 2008). Similarly, drug target information was used to identify clusters of similar DDI cases reported in FAERS and provide explanations for their MoA  (Lin et al. , 2010). The ability to interpret the MoA of the respective DDIs was demonstrated by exploiting drug metabolism knowledge encoded in the form of rules linking proteins and drugs via four types of relationships (i.
e. , metabolizes, induces, inhibits, regulates)  (Tari et al. , 2010). For each DDI identified, the respective triggered rules could be considered as a description of the respective MoA. Alternatively, a gene interaction graph regarding vaccines was built based on bibliographic data, and provided a method to identify genes potentially related with the ADE of fever  (Hur et al. , 2012). In DIO, drug-enzyme relationships were used to model the mechanism of drug metabolism for DDI detection in biomedical literature, achieving an F-measure of 84. 97% for drug-enzyme relationships recognition and 83. 19% for DDI recognition against the "in vivo" dataset used for evaluation  (Zhang et al. , 2016). Finally, in the context of the eTOX project a Web application was presented, aiming to facilitate the exploration of a knowledge base regarding drugs, genes and compounds' toxicity associations for investigating liver toxicity  (Cañada et al.
Another significant contribution of the reviewed KE approaches regarding ADE assessment concerned the prioritization of ADE signals according to their importance. A normalized AERS dataset and the Common Terminology Criteria for Adverse Events (CTCAE) were used to prioritize DDI-induced ADEs identified in FAERS (according to their severity), as well as occurrences of medications and problems extracted from clinical notes from Mayo Clinic's EHR  (Jiang et al. , 2015). This ontology-based approach facilitated automatic prioritization of DDIs related to Warfarin, Clopidogrel, and Simvastatin, three frequently prescribed cardiovascular drugs. Finally, regarding the investigation of ADE MoAs, the D3 system uses a rule-base with nine rules corresponding to nine different interaction mechanisms divided into four levels  (Noor et al.
e. , SRSs and bibliography, to observational healthcare databases and even social media platforms, while linkage with biochemical and genetic information would be desirable to provide MoA and may allow to identify more unexpected AEs. In order to achieve this advancement and take into account these requirements, DS monitoring organizations have to face new challenges, both scientific and technical, given that the above sources are not designed to serve DS aspects per se. In particular, there is an emerging need for high-throughput computational methods that will enable, from the one hand, efficient data analysis and interpretation and, on the other hand, knowledge extraction, representation, exploitation and management  (Koutkias and Jaulent, 2015). Up to now, the emphasis in computational DS surveillance was mostly given on data-driven and statistics-based approaches.
Despite the inherent noise in social media content and the complexity in analyzing it, this data source cannot be neglected due to its wide penetration in everyday life and its capacity to provide insights especially for rare healthrelated events  (Klein et al. , 2018). To a great extent, the reviewed studies relied on publicly available data, provided, for example, via PubMed/MEDLINE and FAERS. Nevertheless, important, systematically curated and rather new Linked Data infrastructures, such as the EBI-RDF platform 23  and OpenPHACTS 24  are available, which were not adequately considered in the reviewed studies. In terms of knowledge sources, UMLS, MedDRA, ATC, SNOMED-CT, and ICD were the most widely used terminologies, as these constitute reference and well-curated resources with varying granularity.
For example, in order to avoid over-alerting, alert generation was based on thresholds considering the statistical significance of each ADE rule in each particular clinical site. In addition, clinicians (both hospital pharmacists and medical doctors) were engaged in the system design and evaluation. The electronic service for DDI and ADE prevention during medication prescription of the Panacea CDSS was evaluated using patient data from a public hospital in Thessaloniki, Greece  (Doulaverakis et al. , 2014). Similarly, the E-pharmacovigilance system was deployed through a Web interface to present DS data to treating physicians (pediatric and internal medicine inpatient clinics from the University Hospital of Erlangen-Nurnberg) within the local HIS  (Neubert et al. , 2013). The SALUS project focused on a practical implementation to automatically produce ADE reports based on real-world clinical data in two pilot sites (a regional clinical data warehouse maintained in Lombardy Region, Italy and the commercial EHR system in Uniklinikum Dresden, Germany)  (Declerck et al.
The practical challenges of these processes in each context have already been highlighted. Notably, clinicians do not feel confident about the currently applied DS procedures  (Vallano et al. , 2015) , while the need for better and more active DS surveillance has been identified by both drug regulatory organizations  (Weaver et al. , 2008)  and the industry  (Lu, 2009). The use of KE technologies like the Semantic Web and Linked Data paradigms could significantly facilitate information and knowledge extraction, integration, elicitation and dissemination and, therefore, accommodate the imposed challenges on routine DS tasks. The main advantages of using KE approaches in real-world DS applications could be summarized as following: (a) Information linking could be significantly improved and automated, reducing the need for manual data exploration through automatic processing.
(b) Semantic enhancement of already established information processing workflows (typically based on statistical measures like disproportionality analysis). The already established statistical processing could be combined with well-defined knowledge sources and their semantics to improve outcomes (e. g. , regarding causality assessment). (c) Error prevention could be facilitated by integrating different data sources to be used as control sources, in order to prevent false positives, over-alerting etc. (d) Process acceleration as KE approaches could save a lot of time via (semi)-automatic data retrieval and interlinking. The above advantages advocate for more intense research in the domain, in order to increase the level of maturity, which is necessary to employ KE approaches in routine DS practice.
In addition, we referred to a number of knowledge-based tools and platforms that have been employed to reinforce DS and support ADR detection, contextualized ADE prevention, and large-scale, semantically-enriched combinatorial signal detection. Through the conducted analysis, our study illustrated the contribution, the complementarity as well as the advances that KE-based approaches may bring to traditional data analytics applied in DS. Despite the increasing number of studies exploiting KE for DS, the lack of a major "success-story"-beyond proof-ofconcept-is apparent. This constitutes a significant challenge for researchers in the domain, that have to respond to, in order to foster the value of KE approaches in the context of DS and, therefore, facilitate their wider adoption by DS stakeholders.
FUNDING This research has been co-financed by the  European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation , under the call RESEARCH- CREATE-INNOVATE  (project code:  T1EDK-03789 ). The study relied also on material and research outcomes supported by a  Marie Curie Intra European Fellowship  project awarded to the corresponding author within the seventh European Community Framework Programme FP7/2007-2013 under REA grant agreement  330422 -the  SAFER  project.
KE core activities Knowledge dissemination, Knowledge elicitation, Knowledge extraction, Knowledge integration, and Knowledge representation Computational Data mining, Disproportionality analysis, Graph-based inferencing, Information extraction (e. g. , Natural Language Processing), method(s) Machine Learning, Ontology reasoning, Rule-based inferencing, Simulation, Terminological reasoning, Vector-based similarity identification Challenges/weaknesses Commercial tools, Competing interests, Evaluation against small dataset, Evaluation restricted on a narrow scope, Evaluation with simulated data, Knowledge model not available, Knowledge model not validated for completeness, No evaluation regarding knowledge modeling quality criteria, No statement regarding competing interests, Not applying formal DL semantics, Not using a knowledge representation standard, Proprietary datasets, Significant dependence on manual work Reference terminologies/ontologies Adverse Event Reporting Ontology (AERO), Anatomical Therapeutic Chemical (ATC) classification system, Basic Formal Ontology (BFO), British National Formulary (BNF) Dictionary, ChEBI, etc.
Solubilization of ferroquine may therefore have reached completion within this time. Although the fat content of the full-cream milk used for the Raman studies was slightly lower than that for the equivalent X-ray scattering studies (3. 3% vs 3. 8%, due to differences in the fat content of commercially available milk in New Zealand and Australia, respectively), the results obtained from the analysis of the low-frequency Raman spectra were in good agreement with the SAXS data where diffraction peaks associated with crystalline ferroquine also disappeared after about 15-20 min of digestion (Figure  3d ). This signifies that 0. 58 g of milk fat (17. 5 mL of 3. 3% milk fat) is sufficient to provide complete solubilization of 93 mg of ferroquine API (equivalent to 186 mg of ferroquine granules), as verified by the SAXS measurements.
Materials and Methods A program using Python was developed to generate cell coordinates from a set of initial parameters i. e. cell size, cell density, bounding volume size. A cubic cell arrangement was assumed and the calculation approach was based on an analytical formula using predetermined S factors (absorbed dose per unit time-integrated activity). These S values were calculated by MCNP6 modelling considering radioactivity uniformly distributed in a source cell placed at variable distances from another cell with the nucleus as the target. The mean absorbed dose to the cell nucleus was determined as a function of the cell density and the intra-to-extra cellular activity distribution considering both the contribution of radioactivity located within the cells (self-and cross-dose) and in the culture medium.
Chromatography Apparatus: A high-performance liquid chromatograph Waters Alliance 1695 (Waters, Milford, MA), equipped with a Waters DAD 2996 photodiode array detector, Hewlett-Packard computer with a Waters Empower 3 software and a Waters autosampler with a 50 µL loop was used, with simultaneous detection at 220 nm and 270 nm. The spectra (detection wavelengths from 200-600 nm) were recorded for all peaks. The HPLC system was coupled on line with a Waters TOF mass spectrometer.
Small-Angle X-ray Scattering SAXS experiments were performed on two different set-ups. Lab SQAd was studied at the SWAXSLab in CEA Saclay with a Xeuss 2. 0 apparatus (Xenocs) equipped with a GeniX3D X-ray generator (λ = 1. 542Å) and a PILATUS3 1M 2D detector. The detector to sample distance was chosen to attain the 0. 01-0. 5 Å -1 q-range. Averaging and data treatment were performed with PYSAXS software (https://pypi. org/project/pySAXS/). The sample Ind SQAd was studied at the synchrotron SOLEIL (Saint-Aubin, France) on the SWING beamline. Data were recorded at 12keV with a 4M bi-dimensional Eiger detector in the chosen 0. 014-1. 9 Å -1 q-range. The data treatments were performed using FOXTROT software. For each set-up, the samples were inserted in 1. 5 mm diameter quartz capillaries sealed with paraffin wax and analyzed with a counting time of 3600s and 250 ms for respectively the SWAXSLab and SWING beamlines.
Protein Separation by 1D-PAGE. Proteins were separated by 1D-PAGE according to Laemmli  [22]  with some modifications. Proteins were desorbed from SQAd NPs by adding SDS-PAGE sample buffer to the pellet and heating the solution to 95 °C for 5 min. A Biorad Mini-PROTEAN® TGX Stain-Free™ 4%-15% Bis-Tris polyacrylamide gel was used as separating gel. Electrophoresis was carried out at a constant voltage of 300 V in electrophoresis buffer, until the dye front reached the lower end of the gel (20 min). Separated proteins were imaged using a Biorad ChemiDoc Imaging system and analyzed with Biorad Image Lab software.
a) Cytotoxicity of hydroxylated SQAd impurities To elucidate the impact of Ph1 impurities on the observed batch-to-batch discrepancies in NPs properties, a preparative HPLC separation (HPLC-Prep) was performed to isolate Ph1 from SQAd. Practically, two series of experiments were performed (Figure  7 ). First, Ph1 only NPs were prepared and tested as such for their cytotoxicity and physicochemical properties. When prepared by nanoprecipitation, Ph1 yielded stable NPs and DLS data showed a strongly negative zeta potential of -35 mV and sizes of ca. 100 nm. Further studies focused on the biological effects of Ph1 NPs which were found to be dramatically cytotoxic (IC 50 values around 5 µM) comparatively to purified SQAd which barely induced cytotoxicity in the tested concentration ranges. Later, a series of additional experiments were performed in which pure SQAd was spiked with increasing concentrations of Ph1 (from 0.
The authors wish to thank Karine Leblanc from the BioCIS platform for her expert technical advice on running and performing chromatography experiments as well as Audrey Solgadi from UMS-IPSIT SAMM platform for her help in designing and performing the MS n experiments as well as helpful corrections in the manuscript. Jéril Degrouard is thanked for his technical help and discussions relating to cryo-TEM images. : SQAd, Squalene-Adenosine; NBS, N-Bromosuccinimide; HOBt, 1-hydroxybenzotriazole hydrate; HATU, 2-(1H-7-azabenzotriazol-1-yl)-1,1,3,3-tetramethyluronium hexafluorophosphate; DIIPEA, Diisopropylethylamine; MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; DMEM, Dulbecco's Modifidied Eagle's Medium; DXT, Dextrose; THF, Tetrahydrofurane; NMR, Nuclear Magnetic Resonance; MTBE, Methyl tert-butyl ether; NPs, Nanoparticles; DLS, Dynamic Light Scattering; FBS, Fetal Bovine Serum; PBS, Phosphate-buffered saline; ES-MS, Electrospray Mass Spectrometry; TEM, Transmission Electron Microscopy; ATP, Adenosine tri-phosphate; SDS, Sodium dodecyl phospage; PAGE, Polyacrylamide gel electrophoresis Funding: The authors gratefully acknowledge the financial support from the 7th EuroNanoMed-II call for proposals, project NanoHeart n°ANR-16-ENM2-0005-01.
DLS: Measuring the size distribution of Janus nanoparticles The hydrodynamic diameter and the polydispersity index were measured with a Zetasizer Nano ZS90 (Malvern Instruments, Orsay, France), piloted by Zetasizer Software. The Zetasizer Nano ZS90 detector was positioned at 90° to the sample and the attenuation was determined automatically by the Nanosizer during the measurement sequence. The measurement cell contained 1. 5 mL of water to which was added 5 µL of the dispersion of the nanoparticles (a 1/300 dilution) and the cell contents were homogenized. Each sample was analyzed in triplicate at controlled temperature (25°C). The mean hydrodynamic diameter (DH) of the produced batch was 222 ± 66 nm and the polydispersity index (Pdi) was 0. 069 ± 0. 025.
Infrared vibrational spectroscopy: ATR-FTIR spectra recording The infrared spectrophotometer used for the study was ATR-FTIR Spectrum two from PerkinElmer (Waltham, Massachusetts) piloted by PerkinElmer Spectrum software (version 10. 4. 2. 279). The spectral range used was 450-4000 cm -1 with resolution of 4 cm -1 and 16 scans were acquired by run. The background of the ATR-FTIR spectrophotometer was air. Each skin biopsy was deposited on the crystal ATR and a spectrum was recorded on the surface of the sample. The only pre-treatment which was performed for the spectra was baseline correction.
Ex vivo skin penetration study: untreated skin, Skin+JNP and Skin+L The kinetics performed on untreated skin showed that the spectral signature of skin did not vary over time (Data not shown). The kinetics of skin+JNP are shown in figure  2a. At t0h, after cutaneous application of JNP, the spectral signature of the skin disappeared, consequently the infrared spectrum of JNP was observed. As function of time, the spectra exhibited variations of the intensity of vibration bands. The ν(O-H) from 3150 to 3500 cm -1 and δ(H-O-H) at 1639 cm -1 of water decreased, giving a direct reflection of water evaporation. The ν (CH2) at 2926 cm -1 , ν (CH2) at 2853 cm -1 , ν(C=O) at 1739 cm -1 and ν(C-O) from 1110 to 1180 cm -1 of Labrafil (major compound after water) increased as result of the increasing concentration of the oil.
19, 20 Walker et al. 19  determined the UV spectrum of pyrazole in the spectral range 110-250 nm at ambient temperature. However, the main objective of this study  19  was the determination of the bands positions. Palmer et al. 20  measured the vacuum ultraviolet photoabsorption spectrum of 1H-1,2,3-triazole in the spectral range of 115-245 nm at 35 C. The obtained measurements were analyzed using the comparison of the UV valence photoelectron ionizations and the results of ab initio conguration interaction (CI) calculations. However, the reported cross sections were very noisy beyond 220 nm. Thus, more accurate UV spectra of these species are required. To understand the atmospheric reactivity and to enrich kinetic and spectroscopic databases regarding these species further laboratory measurements are needed.
Given the low vapor pressure of these compounds, temperature studies were carried out between 298 and 357 K, using benzaldehyde as a reference. The rate coefficients obtained at different temperatures by FTIR (1H-1,2,3-triazole and pyrazole) and GC/ MS (pyrazole) are summarized in Table  2. Each experiment was performed three times under the same experimental conditions. The relative error on the slope corresponds to one standard deviation. The Arrhenius diagram was then determined by plotting ln(k OH ) as a function of 1/T (Fig. 6 ). The Arrhenius equations for the OH oxidation of 1H-1,2,3-triazole and pyrazole were as follows (in cm 3 per molecule per s): The obtained kinetic data show that the OH-oxidation of 1H-1,2,3-triazole and that of pyrazole exhibit a low negative temperature dependence.
Conclusions In this work, the UV absorption spectra and temperature dependent kinetic data of the reaction of two ve-membered nitrogen heterocycles (1H-1,2,3-triazole and pyrazole) with respect to OH radicals were determined. Spectroscopic studies were performed in the spectral domain 200-300 nm. The absorption spectra of 1H-1,2,3-triazole and pyrazole showed a very low absorption capacity beyond 240 nm, so their atmospheric photodissociation is a negligible degradation process. The cross-section values obtained in the spectral range 210-230 nm have thus been determined with a good accuracy and can be used for the quantication of these compounds in both the laboratory and the atmosphere. Moreover the reactivity of these species with OH radicals was performed in an atmospheric simulation chamber coupled to an FTIR spectrometer and to a GC/MS using the relative mode.