dataesr/scientific-paragraphs-categorization huggingface dataset:
I2 (meta)data use vocabularies that follow FAIR principles. The underlying semantic models of OpenPVSignal, namely PROV-O, Micropublications, WADM, OAE and TO, are compliant with the FAIR principles and their overall rationale. Furthermore, the vocabularies/terminologies that are referenced in OpenPVSignal (i. e. , ICD-10, ATC, MeSH and MedDRA) are compliant with the specific requirement. The use of licensed vocabularies (e. g. MedDRA) does not affect the "FAIRness" of the overall conceptual model, as FAIR principles do not require a "free-of-charge" model. More specifically, principle R1. 1 requires that "(meta) data are released with a clear and accessible data usage license" without requiring this license to be free of charge. I3 (meta)data include qualified references to other (meta)data.
OpenPVSignal references to a set of underlying semantic models, which are fully qualified as they are based on the RDF and OWL referencing mechanisms and refer to their respective RDF implementations. TO BE RE-USABLE: R1 meta(data) have a plurality of accurate and relevant attributes. The OpenPVSignal model incorporates several concepts and attributes from the domain of PV signal communication. The ones presented in the manuscript are only indicative, while full details are provided in our GitHub repository dedicated to OpenPVSignal. R1. 1 (meta)data are released with a clear and accessible data usage license. The data currently investigated are publicly available online and the OpenPVSignal model is provided as an open source implementation, under GNU license, including its exemplar instantiations.
However, the license of the OpenPVSignal instantiations produced by a specific application are left to be decided on the application level. R1. 2 (meta)data are associated with their provenance. OpenPVSignal utilizes Micropublications and PROV-O to enhance provenance tracking capabilities of the PV signal reports' data. As demonstrated in Figure  3  and Figure  5 , the produced RDF statements can be directly associated with the respective free-text snippet in the original free-text PV signal report by using WADM. R1. 3 (meta)data meet domain-relevant community standards. There are currently no domain-relevant community standards regarding the publication of PV signal reports. However, in this paper we demonstrate the usage of OpenPVSignal upon three different PV signal report formats (WHO Pharmaceuticals Newsletter, Lareb reports and FDA Drug Safety Communications), which could be considered indicative in publishing PV signal reports.
Furthermore, we semantically related OpenPVSignal concepts with concepts defined in OAE  (He et al. , 2014  by interlinking the respective information with other data sources and by applying semantic reasoning. (4) Given that the RDF representation uses URIs to uniquely identify each information resource (e. g. , an ICSR), the use of Linked Data and OpenPVSignal could facilitate the detection of duplicate information and, therefore, allow for their better processing. The limitations of the presented work include: (a) the need for a more thorough validation of OpenPVSignal by elaborating on PV signal reports published by additional sources and, consequently, (b) the need for potential extensions of our ontology model and (c) the size of the model, which can be a significant barrier for automatic reasoning 30.
Removing the OAE import allows a reasoner to build the inferred class hierarchy in seconds. In the presented example instantiations, we used the Hermit reasoner (Hermit, SCR_016006) incorporated in Protégé, in a computer with 16GB RAM and Intel i7 processor. appropriate for use by PV signal monitoring organizations and drug regulatory authorities, and (c) the development of a knowledge-based, computational framework for assessing candidate PV signals by exploiting the semantic reasoning capabilities that OpenPVSignal offers. The development of such tools can be facilitated by frameworks that can automatically extract information from free-text data sources. For example, BioKB  (Biryukov et al. , 2017)  provides a paradigm for semantically annotating free-text content and interlinking it with reference vocabularies, while PoeM  (Gaignard et al.
ACKNOWLEDGMENTS The authors would like to thank Mr. Magnus Wallberg, Technology Evangelist at Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden, for his contribution in formulating the initial idea about a common, semantic model for representing pharmacovigilance signal reports, and Dr. Linda Härmark, Head of Innovation and Projects in the Netherlands Pharmacovigilance Centre Lareb, Netherlands, for her constructive comments and suggestions in the manuscript.
clinicaltrials. gov). These trials are preparing treatment possibilities, for instance by studying treatment at the human cellular level. Somatic gene editing may prove to be a game changer not only in the treatment of a whole range of serious hereditary, particularly Mendelian, disorders, but also in the treatment of cancer and infectious diseases. Gene editing is a medicinal product for human use, where regulations apply. The European Medicines Authority (EMA) that oversees access of medicinal products to the market, including advanced therapy medicinal products, has identified scientific and regulatory guidance on gene editing as an issue for its future agenda  (EMA, 2017). Ethical, legal and societal issues for somatic gene editing include safety and accessibility, and meaningful stakeholder engagement, education, and dialogue must be organized  (Howard et al.
Acknowledgements We thank the persons who commented on earlier versions of the Recommendations or the Background document to the Recommendations, especially the members of the  Public and Professional Policy Committee of ESHG  and the  Ethics Committee of ESHRE  and  Erika Kleiderman ,  Bartha Maria Knoppers ,  Raul Piña-Aguilar ,  Alessandra Renieri ,  Gijs Santen  and  Kirmo Wartiovaara.
There is evidence of contamination in the lokiarchaeal genomes On top of the very high heterogeneity detected in Loki's genome (78. 21%), our quality analyses with CheckM (Parks, Skennerton, Imelfort, http://ecogenomics. github. io/CheckM/)) and Anvi'o (Meren, http://merenlab. org/software/anvio/) also revealed a high contamination index (between 45% and 57%), necessarily underestimated because quality analyses are limited to defined sets of markers  [5]. Considering a good part of this contamination index as the result of the heterogeneity still leaves room for actual contamination from other sources. There is no need for important contamination to bias phylogenetic reconstruction if it is located in strong markers, as we have shown with the elongation factor 2 (EF2)  [1]. We could identify such a likely contamination in EF2 by detecting long insertions matching to eukaryotic paralogs, essentially in Heimdall LC3 (formerly Loki 3)  [1].
This could explain why not only Asgards belonging to different phyla (Lokiarchaea, and Heimdallarchaea [represented by the formerly named Loki2 and Loki3]) but also to the same phylum (Heimdallarchaeota) were not always monophyletic in our single-protein analyses  [1]. We suspected that patches of contamination could also be present in Heimdall LC3 RpoA since this subunit is encoded by a single gene in LC3 (like in Thaumarchaeota and the related Bathyarchaea and Aigarchaea), whereas all other Asgards have a dimeric version (like all Crenarchaeota, all Euryarchaeota, and most DPANN). Unlike Spang et al. [4] , we never observed best hits to Lokiarchaea nor to the two other Heimdallarchaeotes using the complete LC3 sequence (best hits to Bathyarchaea only) or various portions of N-and C-terminal sequences (Loki3/Heimdall LC3 RpoA access numbers: AKC94880/OLS19521).
The presence of FES in dataset favours 2D topologies Spang et al. [4]  argued that their 2D trees are not affected by Long-Branch Attraction (LBA), because LBA would have attracted eukaryotes outside the Archaea. However, the branching of Archaea in the well-known fast-evolving Methanopyrus kandleri in the 2D tree of Spang et al. [2]  clearly reveals an LBA artefact. The Euryarchaea could then be possibly attracted outside Archaea because of their evolutionary relationship with DPANN  [6] [7] [8]. This probably explains why removing FES dramatically increased (from 1 to 11) the number of trees recovering the 3D topology  [1].
There is no strong LBA affecting the position of Eukarya in the 3D trees Spang et al. [4]  argued that LBAs were affecting the 3D trees we obtained (i. e. , with the concatenated 6 AU-relevant Woese and the 11 Woese proteins, and the RNA polymerase  [1] ), resulting in the misplacement of Eukaryotes between the bacterial outgroup and the Archaea. This seems unlikely for the RNA polymerase, since we have shown that Asgards remained a sister group to Euryarchaeota (Archaea rooted in Thaumarchaea) in the absence of bacteria (Figure  S35  in  [1] ). We have now performed similar analyses with the two concatenated Woese protein datasets after inclusion of all available Asgard sequences and obtained trees again displaying the grouping of Asgards with Euryarchaeota (Fig  2 ). These results strongly support the absence of strong LBA affecting the position of Eukarya in the 3D trees.
Phylogenetic analyses excluding EF2 are compatible with the presence of conflicting signals within the universal markers We never claimed that the Asgard/Eukarya affiliation could not be obtained without EF2. We ourselves obtained it when we removed EF2 from the concatenation of the eocyte proteins (Figures S26, S27 in  [1] ). Zaremba-Niedzwiedzka et al. indeed obtained significant support in a maximum-likelihood framework for a 2D universal proteins tree based on 48 universal markers without EF2  [3]. However, their species dataset still contained many FES that could introduce a bias favouring 2D trees  [1]. Furthermore, they could not obtain any "good" (maxdiff <0. 1) nor "acceptable" (maxdiff <0. 3) convergence in Bayesian framework with the CAT-GTR model (Supplementary Table  4  in  [3] ; see Phylobayes manual) and hence could not corroborate their results with this approach.
VDC, MG, and PF are supported by a  European Research Council (ERC)  grant from the  European Union 's  Seventh Framework Program  (FP/2007-2013)/Project  EVOMOBIL-ERC Grant  Agreement no. 340440. AN is supported by the  Higher Education Commission, Pakistan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This overestimation of the vocal tract length from the acoustic data indicates that the animal's vocal tract produces more/lower formants than expected from its anatomical length. Combined with the observation that formants are stereotypically unevenly spaced in groans  (McElligott et al. , 2006) , this suggests the vocal tract configuration during groan production is more complex than previously assumed. Here we investigate the hypothesis that male fallow deer simultaneously use the oral and nasal vocal tracts as resonating systems during call production, allowing this species to produce a larger number of formants and a more complex formant pattern than predicted by a simple cylindrical tube model. To this end, we performed computed tomography (CT) scans of head-and-neck specimens of male fallow deer to achieve a more detailed description of the complex anatomical structure of the male fallow deer supra-laryngeal vocal tract.
DISCUSSION In this study, the artificially extended vocal apparatuses of two adult male fallow deer were CT-scanned, and the cross-sectional areas of the complete supra-laryngeal vocal tract (common laryngopharyngeal tract, oropharynx, oral cavity, nasopharynx and nasal cavities) of both specimens were measured along the oral and nasal vocal tracts. We then used this data to model resonance patterns produced by these supra-laryngeal cavities including the oral vocal tract only, the nasal vocal tract only or the combined oral and nasal vocal tracts. We found that the configuration combining the oral and nasal vocal tract geometries produced a resonance pattern which more closely matches the formants observed in fallow deer groans, both in term of formant frequency pattern and in term of formant frequency scaling.
With this approach, we identified 229 and 289 unique tyrosine-phosphorylated peptides in two independent biological experiments and found 105 common tyrosine-phosphorylated peptides from 83 unique proteins (Dataset EV1). DDR1, BCR and PEAK1 were the only proteins that were reproducibly and significantly phosphorylated by a DDR1 kinase-dependent mechanism (Fig  4C ). DDR1 was phosphorylated on Tyr484 of the juxtamembrane domain and on Tyr792 and Tyr796 of the activation loop. This confirms that DDR1 is the major TK activated by collagen I stimulation. The pseudokinase PEAK1, previously reported to be involved in CRC  (Wang et al, 2010) , was phosphorylated on Tyr462 by DDR1, and BCR on Tyr177. BCR  function has been essentially described in the context of BCR-ABL-transformed leukaemia  (Deininger et al, 2000).
Western blot analysis revealed that nilotinib treatment did not affect MAPK and AKT activities, consistent with the RAS-independent nature of DDR1 kinase signalling in these metastatic tumours  (Fig EV2D). The phosphoproteomic analysis to determine the phosphotyrosine profiles of metastatic nodules from animals treated or not with nilotinib (Fig  4D ) allowed us to quantify 430, 482 and 482 unique tyrosinephosphorylated peptides from three biological replicates and to identify 18 tyrosine-phosphorylated peptides from 14 unique proteins common to all experiments (Dataset EV2). The levels of 12 tyrosine-phosphorylated peptides from 10 unique proteins were decreased upon nilotinib treatment in all three experiments  (Fig 4E). We confirmed that DDR1 and BCR were major DDR1 substrates and also identified SHB, PKCd and FRK (signalling), ITSN2 (trafficking), CRK and Tensin-3 (cytoskeletal-associated proteins), MAGI1 and DLG3 (cell adhesion and polarity proteins, respectively), and the orphan receptor DCBLD2.
PEAK1 phosphorylation level on Tyr635 and Tyr531 was decreased in two of the three experiments, suggesting that it may be a DDR1 substrate also in vivo (Dataset EV2). We did not detect any of the other known nilotinib targets, suggesting that DDR1 is the main target of this drug in liver metastatic nodules. Overall, these findings indicate that BCR and PEAK1 are important DDR1 signalling substrates in CRC cells. Source data are available online for this figure.
Immunocytochemistry Cells were grown in Nunc Lab-Tek II chamber slide and fixed 5 min with 4% paraformaldehyde and 10 min with ice-cold methanol. Permeabilization and blocking were done using 0. 3% Triton X-100-0. 3% BSA in PBS for 2 h at room temperature. Cells were incubated with primary antibodies overnight at 4°C (dilution 1:1,000). Fluorescent secondary antibodies (dilution 1:3,000) were used for 1 h at room temperature, and the slides were mounted with DAPI Fluoromount-G (SouthernBiotech, Birmingham, USA). Images were acquired on a Zeiss Axioimager Z2 microscope, processed and analysed using ImageJ software.
Invasion and 3D cell migration assays Cell invasion assays were performed in Fluoroblok invasion chambers (BD Bioscience) using 70,000-100,000 cells in the presence of 100 ll of 1-1. 2 mg/ml Matrigel (BD Bioscience). After 24 h, cells were labelled with calcein AM (Sigma-Aldrich) and invasive cells were photographed using the EVOS FL Cell Imaging System. Quantification of the number of invasive cells per well was done with the ImageJ software. For 3D cell migration assays, 1,000 cells were resuspended in medium containing 2. 4 mg/ml methylcellulose (Sigma-Aldrich) and plated in a U-bottom 96-well plate to allow the formation of one spheroid per well. Two days after, single spheroids were embedded in a 1:1 mix of 2. 4 mg/ml neutralized bovine type I collagen (Purecol; Advanced BioMatrix) and medium supplemented with 12 mg/ml methylcellulose, according to the manufacturer's protocol.
Each sample was loaded onto a 15-cm column packed in-house with 3 lM ReproSil-Pur C18 (75 lm inner ª 2018 The Authors EMBO Molecular Medicine 10: e7918 | 2018 diameter). Buffer A consisted of H 2 O with 0. 1% formic acid and buffer B of 100% acetonitrile with 0. 1% formic acid. Peptides were separated using a gradient from 0 to 24% buffer B for 65 min, from 24 to 40% buffer B for 15 min and from 40 to 80% buffer B for 15 min (a total of 95 min at 250 nl/min). Data were acquired with the "Top 15 method", where every full MS scan was followed by 15 data-dependent scans on the 15 most intense ions from the parent scan. Full scans were performed in the Orbitrap at 120,000 resolution with target values of 1E6 ions and 500 ms injection time, while MS/MS ion trap scanning parameters were 1E4 ions as target value and 200 ms as maximum accumulation time.
Database searches were performed with Mascot Server using the human Uniprot database (version 3. 87). Mass tolerances were set at 10 ppm for the full MS scans and at 0. 8 Da for MS/MS. Label-free quantification was performed on duplicate LC-MS runs for each sample using Progenesis LC-MS (Nonlinear Dynamics Software). Peptide intensities were normalized across all LC-MS runs, and normalized peptide intensities were summed for each unique phosphorylated peptide with a Mascot score exceeding 25. These intensities were then used to calculate the log2 fold change ratios of each unique phosphopeptide or each unique protein. In case of ambiguous phosphorylation site assignments, spectra were manually interpreted to confirm the localization of the phosphorylation site using Scaffold (Proteome software).
Acknowledgements We gratefully thank  C. Laschinger ,  N. Maurin  and  P. Mangeat  for various reagents and plasmids;  I. Ait Arsa  and  J. Vitre  for technical assistance; and our colleagues for helpful discussions. We also thank  M. Boyer  ( IGMM, Montpellier ) for flow cytometry experiments, the  Montpellier RIO Imaging  and the RHEM histology platforms;  P. Balaguer  ( Screening Platform in Cancer PCC, IRCM Montpellier ) and  M. Del Rio  ( IRCM, Montpellier ) for the HCT116 LUC+ and SW620 LUC+ cell lines respectively, the  Montpellier Genomic Collection (MGC) , the  Montpellier Platform of CRC preclinical models (MPCC) , the  "Centre de.
Phenotypic analysis of control and inhibitor-treated larvae To assess the effect of inhibitors on the embryos after five days of exposure to the inhibitors, we analysed seven observable phenotypic parameters: (1) movement pattern, (2) yolk sack utilization, (3) hatching, (4) heartbeat, (5) body shape, (6) swim bladder development and (7) oedema using a stereo microscope and recorded the observations for each group. The images of the developing larvae were taken using a Lumar V1. 12 fluorescence microscope attached to a camera (Carl Zeiss MicroImaging GmbH, G€ ottingen, Germany). The images were analysed with AxioVision software versions 4. 7 and 4. 8.
Determination of CA IX activity in Xenopus oocytes Catalytic activity of CA IX in Xenopus oocytes was determined by monitoring the  18  O depletion of doubly labelled 13C  18  O 2 through several hydration and dehydration steps of CO 2 and HCO 3 À at 23 C as previously described  35, 36. The reaction sequence of  18  O loss from 13C   18  O degradation was obtained from the linear slope of the log enrichment over the time, using the spreadsheet analysing software OriginPro 8. 6 (OriginLab Corporation, Northampton, USA). The rate was compared with the corresponding rate of the non-catalysed reaction. Enzyme activity in units was calculated from these two values as defined by  37. From this definition, one unit corresponds to 100% stimulation of the non-catalysed  18  O depletion of doubly labelled 13C  18  O 2.
Korte et al. (2009)  also included archeomagnetic data in order to infer information going back to AD 1400. Such an interest has also been present in Eastern Europe  (Bucha, 1959 , for the Czech and Slovak territories;  Valach et al. , 2004, for Slovakia; Atanasiu, 1968; Constantinescu, 1979; Soare et al. , 1998, for Romania) ; reconstructions go back to 1850 and include intensity elements of the geomagnetic field. Also note the collection of declination and inclination data from sea voyages over the 15-18th centuries by  Jonkers et al. (2003)  and the spherical harmonics (SHs) model by  Jackson et al. (2000). This model includes these data and describes the geomagnetic field evolution since 1590 for all geomagnetic elements (the intensity values are based on an assumed uniform dipole decay rate before 1850).
, 2009)  have pointed to periodicities at several timescales, such as ∼ 11, 13-30, 50-60, and 60-90 years. Buffett (2014)  explores the MAC (magnetic, Archimedes, and Coriolis forces) waves and their role in generating longperiod variations (60 years) of the field. Periods shorter than the solar cycle timescale have also been detected, both in some geomagnetic field models  (Silva et al. , 2012)  and in observatory data (e. g. ,  Ou et al. , 2017). These studies revealed periodicities in the time domain of about 4-7 years and 2-3 years. The latter was considered of external origin, while the former has been used as an argument in favor of a 6-year internal variation. Recent studies by  Gillet et al. (2010 Gillet et al. ( , 2015) )  and  Holme and de Viron (2013)  point to a ∼ 6-year variation originating in the core that is also observable in length-ofday data.
Observatory data Annual means of declination as given by http://www. geomag. bgs. ac. uk/data_service/data/annual_means. shtml have been used. The locations of the 24 observatories with 100-150-year-long time series, labeled with their IAGA codes, are shown in Fig. S1  (Supplement) superimposed on the WMM2010 declination map at the geomagnetic epoch 2010. 0 (http://www. ngdc. noaa. gov/geomag/WMM/data/ WMM2010/WMM2010_D_MERC. pdf). In Fig. 1  we show, as an example, time series of declination and of its first time derivative at Niemegk (Germany) Observatory (IAGA code NGK).
Historical data The five long time series referenced above for Edinburgh, London, Paris, Munich, and Rome have been considered in the present study. Data used, in the order of decreasing latitude of the location, are as follows. -Edinburgh: raw data published by  Barraclough (1995) , adjusted to Eskdalemuir Observatory (ESK); -London: raw data published by  Malin and Bullard (1981) , adjusted to Hartland Observatory (HAD); -Paris: raw data published by  Alexandrescu et al. (1996 Alexandrescu et al. ( , 1997) )  and reviewed recently by  Mandea and Le Mouël (2016) , adjusted to Chambon-la-Forêt Observatory (CLF); -Munich: 11-year filtered smoothing spline fitted to raw data, as published by  Korte et al. (2009) , adjusted to Fürstenfeldbruck Observatory (FUR); -Rome: assembled time series using data published by  Cafarella et al.
) are retained almost entirely in this time series and on the other hand as a result of the timederivative operator that enhances noise and brings forward the harmonics of the 11-year constituent that are not significant in the data (compare also Fig. 2a  and b ). The solar cycle length variability (between 8 and 14 years over the past 10 cycles) also contributes to the noise in the high-passed 11-year time series. By superimposing spectra of the cyclic component for the 24 declination time series (Fig. S4 ), the noisiness is evident. However, in spite of that, some specific lines can be distinguished: lines in the 15-25-year interval corresponding to the ∼ 22-year constituent detected in the last ∼ 40 years of the cyclic component time series of Fig. 3 ; lines in the 8-14-year domain corresponding to the 11year constituent; and lines in the 4-7-and 2-3-year domains corresponding to the first two harmonics of the 11-year constituent (we note that the 4-7-year signal covers the 6-year signal detected in variations of length of day (Holme and de Viron, 2013) and in wave processes discussed by  Gillet et al.