dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene set enrichment analysis To statistically evaluate the enrichment of previously reported gene signatures (Gene Sets)  (Hombrink et al. , 2016; Kumar et al. , 2017; Savas et al. , 2018; Villani et al. , 2017)  in our dataset, we used pairwise comparisons using the gene set enrichment analysis (GSEA)  (Subramanian et al. , 2005)  method from the Massachussets Institute of Technology (https://www. broadinstitute. org/gsea). GSEA tests the relative position of a collection of genes (Gene Set) within an independent, ranked dataset (GeneList). Statistical analysis was performed by evaluation of nominal p value and false discovery rate (q value) based on 1,000 random permutations. Results were considered significant when the p value was below 0. 05 and when the q value was below 0. 25 (false discovery rate below 25%) accordingly to the recommendation from the software developers.
For each pairwise comparison, the GSEA output can be represented as a bar code where each bar corresponds to the projection of one Probe Sets of the Gene Sets on the list of all the Probe Sets of the gene chips ranked from those having a high signal in one cell population (represented in red) to those having a high signal in the other cell population (represented in blue). Each bar code can be characterized by two parameters. The normalized enrichment score (NES) represents the number and differential expression intensity of the genes enriched in the corresponding cell subset. The NES is positive if the Gene Set is enriched in the first cell population and negative if it is enriched in the second cell population. The false discovery rate (FDR) statistical value (q) represents the likelihood that the enrichment of the Gene Set represents a false positive finding (for example if q = 0.
Culturing supernatants were collected and stored at À20 C until the LEGENDplex TM assay execution day. LEGENDplex Human Macrophage/Microglia Panel (13-plex) with V-bottom Plate (Biolegend Cat# 740503) and Human Proinflammatory Chemokine Panel (13-plex) with V-bottom Plate (Biolegend Cat# 740003) was used according to manufacturer's instructions. In short, samples and standards were thawed and plated with capture beads and incubated for 2 h. Plate was then washed, and Detection Antibodies were added. After 1 h incubation SA-PE was added and incubated for 30 min. Samples were acquired on BD FACSCanto II. Samples were analyzed using LEGENDplex TM Data Analysis Software.
ACKNOWLEDGMENTS P. G. is a CNRS investigator. J. H. is an INSERM investigator. We thank the  Anthony Nolan Cord Blood Bank and Cell Therapy Centre  for providing the cord blood units used in this study, the flow core and NGS platform from  Institut Curie , and  Daniel Chung  for technical help. This research was supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London  and/or the  NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , or the  Department of Health. The research was supported by the  BBSRC ,  NC3RS ,  CRUK ,  Rosetrees Trust, CRUK King's Health Partners Cancer Centre in United Kingdom. The research was supported by  IDEX  ( ANR-18-IDEX-0001 ) and  INCA  ( PL-BIO-ICR1 ),  Institut Curie ,  Institut National de la Santeet de la Recherche Medicale ,  Labex DCBIOL  ( ANR-10-IDEX-0001-02   PSL ),  ANR-11-LABX0043 ,  SIRIC INCa-DGOS-INSERM_12554 , in France and the  Programa de Apoyo a Centros con Financiamiento Basal  ( AFB 170004 ), and  FONDECYT  1171703 in Chile.
, 2009)  as well as greenhouse gas concentration budgets  (Gerbig et al. , 2008). As far at it reflects in temporally consistent aerosol gradients, it can be inferred from lidar/ceilometer profiles  (Münkel et al. , 2007; Haeffelin et al. , 2012). In this study, we primarily use ceilometer profile data from the German ceilometer network to 135 evaluate CAMS aerosol forecasts. Section 2 describes the data and the methods to compare particle information in the CAMS global model with respect to observations. The results are presented in section 3 together with some auxilliary data sets for interpretation and are discussed in the context of possible model improvement in chapter 4. Key findings are summarized with an outlook to upcoming activities in chapter 5. 140 2 Data sets and methodology.
Data availability The source code of the ECMWF IFS model is not available for public as it is intellectual property of the ECMWF and its member states. ECMWF IFS model simulation results are available to the meteorological offices of the ECMWF member states. The ceilometer raw data are available on request from the data originator DWD (datenservice@dwd. de). GAW in-situ data are 935 available from the EBAS data centre at http://ebas. nilu. no/. The database of aerosol optical properties used in this study is available on request from the corresponding author (harald. flentje@dwd. de).
Filamentous nanomachines composed of type IV pilins are virtually ubiquitous in Bacteria and Archaea  (1) , to which they confer a variety of unrelated functions including adhesion, motility, protein secretion, and DNA uptake. These type IV filaments (T4F) 2   are assembled by conserved multiprotein machineries, which further underlines their phylogenetic relationship  (2). Much of our current understanding of this superfamily of nanomachines comes from the study of type IV pili (T4P), the best characterized T4F  (1). In brief, T4P are micrometer-long and thin surface-exposed filaments, which are polymers of type IV pilins. Type IV pilins (simply named pilins hereafter) are defined by an N-terminal sequence motif known as class III signal peptide  (3). This motif, IPR012902 entry in the InterPro database  (4) , consists of a hydrophilic leader peptide ending with a tiny residue (Gly or Ala), followed by a tract of 21 mostly hydrophobic residues, except for a negatively charged Glu 5.
This hydrophobic tract represents the N-terminal portion (␣1N) of an extended ␣-helix of ϳ50 residues (␣1), which is the universally conserved structural feature of type IV pilins  (3). Although some small pilins consist solely of this extended ␣-helix  (5) , most pilins have a globular head consisting of the C-terminal half of ␣1 (␣1C) packed against a ␤-sheet composed of several antiparallel ␤-strands, which gives them their typical "lollipop" 3D architecture  (3). Upon translocation of prepilins across the cytoplasmic membrane (CM), where they remain embedded via their protruding hydrophobic ␣1N, the leader peptide is processed by an integral membrane aspartic acid protease named prepilin peptidase (IPR000045)  (6). Processing primes pilins for polymerization into filaments. Filament assembly, which remains incompletely understood, is mediated by a multiprotein machinery in the CM, centered on an integral membrane platform protein (IPR003004) and a cytoplasmic extension ATPase (IPR007831)  (1).
DNA is subsequently bound by the DNA receptor ComEA and further translocated across the CM through the ComEC channel  (9). In diderm-competent species, the T4F involved in DNA uptake is a subtype of T4P, known as T4aP  (11) , which rapid depolymerization is powered by the retraction ATPase PilT (IPR006321), generating exceptionally large tensile forces  (12). In brief, T4aP bind DNA directly, via one of their major or minor (low abundance) pilin subunits  (13) , and then are retracted by PilT, bringing DNA to the ComEA receptor  (14). In monoderm-competent species, DNA uptake is mediated by a distinct T4F named competence (Com) pilus  (9) , much less well-characterized than T4P. Com pili are composed mainly of the major pilin (ComGC)  (15, 16) , and are assembled by a simple machinery composed of four minor pilins (ComGD, ComGE, ComGF, and ComGG), a prepilin peptidase (ComC), an extension ATPase (ComGA) and a platform protein (ComGB)  (17, 18).
The mature proteins start with a tract of 21 predominantly hydrophobic residues (shaded in black), which invariably form the protruding N-terminal portion of an extended ␣-helix that is the main assembly interface within filaments. ComGC pilin exhibits a novel type IV pilin fold and 10. 4 kDa). Moreover, unlike most other pilins, in which the only detectable sequence homology is usually in the ␣1N portion of the class III signal peptide (3), ComGC orthologs show extensive sequence identity. For example, processed ComGC SS and ComGC SP display 65. 6% overall sequence identity (Fig. 2 ). Similarly, processed ComGC SS and ComGC BS (from B. subtilis) show 33. 3% sequence identity overall (Fig. S1 ). This is consistent with the existence of a ComGC signature in the InterPro database (IPR016940) (4), which lists 2,809 ComGC entries.
Bioinformatic analyses Protein sequences were routinely analyzed using the DNA Strider program. Protein sequence alignments were done using the Clustal Omega server at EMBL-EBI. Pretty-printing of alignment files was done using the BoxShade server at ExPASy. Reformatting of large multiple alignment files was done using the MView server at EMBL-EBI. Prediction of functional domains was done using the InterProScan server at EMBL-EBI, which was also used to download all the ComGC protein entries with an IPR016940 domain. Protein secondary structure prediction was done using the JPred server at the University of Dundee. Protein 3D structures were downloaded from the RCSB PDB server. Molecular visualization of protein 3D structures was done using PyMOL (Schrödinger). The GETAREA server, at UTMB, was used for calculating the solvent accessible surface area of ComGC proteins.
Detection of the Com systems in genomes available in the NCBI RefSeq database (last accessed in April 2019, 13,512 genomes of Bacteria and Archaea) was done as described previously (2), using MacSyFinder  (25)  and the relevant HMM Com model  (2). Phylogenetic analysis based on protein sequences of major pilins of different T4F involved an initial alignment of the sequences using MAFFT version 7. 273  (40) , specifically the linsi algorithm. Multiple alignments were analyzed using Noisy version 1. 5. 12 (41)  with default parameters, to select the informative sites. Next, we inferred maximum likelihood trees from the curated alignments using IQ-TREE version 1. 6. 7. 2  (26) , with option -allnni. We evaluated the node supports using the options -bb 1,000 for ultra-fast bootstraps, and -alrt 1,000 for SH-aLRT  (27).
This work was supported by  Medical Research Council  Grant  MR/P022197/1  (to V. P. ), Doctoral School Complexité du vivant-ED515 contract number  2449/2016  (to R. D. ), and  INCEPTION  project  PIA/ANR-16-CONV-0005  (to E. P. C. R. ). The authors declare that they have no conflicts of interest with the contents of this article.
Data availability The NMR solution structures of ComGC SS and ComGC SP have been deposited in the Protein Data Bank under accession numbers 6TXT and 6Y1H, respectively. Chemical shift assignments and NOE-based restraints used in structure calculations are available from the Biological Magnetic Resonance Data Bank under accession numbers 34477 and 34490, respectively. All the other data described in the manuscript are either contained within the manuscript, or are to be shared upon request to corresponding author.
Sampling and Storage The samples were divided into two categories: (1) from patients with acute hepatitis and (2) from patients without liver disease. The samples collected from SPIDHI originated from patients (n = 48) with acute hepatitis of unknown etiology (patients with clinical symptoms of hepatitis in which the most frequent causes were excluded: hepatic viruses, alcohol and drug use, as well as the uncommon causes: other viral infections and leptospirosis). Out of these, only one originated from a person who had traveled abroad during the sampling year. The collected data revealed that the patients included in the study are pork meat consumers. The patients were divided according to age category and the sampling unit. The study also included serum samples (n = 16) from the hospital's medical staff, aged between 27 and 46 years.
Nucleotide Sequencing and Phylogenetic Analysis Sequencing of positive amplicons was performed by BASECLEAR (Netherlands) using the Sanger's method. Sequencing of PCR products was performed for each strand using the inner primers: 3158 and 3159. Nucleotide sequences were analyzed and individually edited using Bioedit software. The sequences were loaded to the alignment explorer in MEGA 10 (15) and aligned with ClustalW  (16). Datasets added for alignment included also the reference sequences  (17). A model test was run in MEGA X prior to the construction of the tree for the selection of the suitable model. The phylogenetic tree was built using the Maximum Likelihood method based on the Kimura 2-parameter model (  18 ) with 1,000 replicates. A human HEV-1 strain (EF530670. 1/) was used as out group.
HEV Sequence Analysis The two RT-nested PCR products positive for HEV RNA were sequenced. Each sequence was manually edited and, by overlapping the complementary ones, a 333 nt sequence for the HuRO-3. 30 isolate (GenBank: accession number MN229279) and a 332 nt sequence for HuRO-11. 29 isolate (GenBank: accession number MN229278) were obtained. The bioinformatic analysis revealed that these isolates belong to HEV genotype 3. Based on the alignment of our sequences with the best hits in GenBank, the obtained isolates were assigned to the HEV-3c subtype (3chi monophyletic group) (Figure  2 ). BLAST analysis showed that the percentages of nucleotide identity between our human HEV isolates and the homologous isolates from the GenBank database, range from 95 to 98%. The percentage of nucleotide identity between the two isolates is 92%.
Moreover, BLAST analysis has shown that our isolates are related to both humans and animals HEV strains circulating in Europe. The HuRO-3. 30 isolate had a maximum 98% nucleotide identity with a Dutch human strain isolated in 2013 (GenBank accession no. KR362784). The maximum nucleotide identity (97%) of HuRO-3. 30 isolate and GenBank HEV3c animal isolates was observed for an Italian wild boar strain (GenBank accession no. KU508285) and a swine isolate from the Netherlands (GenBank accession no. KY781997). Also, a 96% nucleotide identity was observed with a goat HEV strain from Italy (GenBank accession no. KX470598) and with two human strains isolated in Netherlands (GenBank accession nos. KY775030 and KY775001). The HuRO-11. 29 isolate had 98% maximum nucleotide identity with two Dutch human isolates (GenBank accession nos.
KR362823 and vKY775005). This Romanian HEV isolate showed a 97% nucleotide identity with the manure HEV strain from Netherlands (GenBank accession no. MF185108) and a 96% with two wild boar isolates: one from Italy (GenBank access no. KX549306) and one from Germany (GenBank accession no. FJ705359). The latter isolate represents the reference sequence for subtype 3c  (17). The nucleotide identity between the human isolates of this study and those previously obtained from Romanian pig and wild boar range between 83 and 87%.
2% (14/48) of the analyzed patients. Subsequently, the serological screening of HEV IgM antibodies in some of these patients (44/48) and those with non-A, non-B acute hepatitis (n = 46), point out that 10% (9/90) of the analyzed subjects had an acute HEV infection at the time of hospitalization. In the context of this study, analysis and interpretation of HEV testing results on patients with hepatitis of unknown etiology FIGURE 2 | Phylogenetic tree based on the alignment of the generated sequences with the best hits to our sequences in GenBank and with the reference strains of each HEV3 subtype. The tree was constructed on the basis of a 348 bp fragment of the capsid protein-encoding region (ORF-2). The phylogenetic tree was built using the Maximum Likelihood method based on the Kimura 2-parameter model with 1,000 replicates.
Regarding pattern of viremia and fecal shedding in HEV infection, similar results were described by Goel et al. (34). The overlap between the serological results and the molecular analysis demonstrate a recent infection at the time of hospitalization. The HEV RNA positive sample from the hospitalized ECHSPF patient, allowed us to collect data on potential risk factors associated to HEV infection. The patient had consumed pork meat from a local grocery store, which raises concern about the potential for transmission through consumption of contaminated food. In Europe symptomatic cases of hepatitis E are associated with consumption of pork meat and pork products. However, the number of cases directly related to the consumption of contaminated food remains limited, zoonotic transmission being indicated by genetic relatedness between human viral strains and pig, wild boar and deer viral isolates, or with those isolated from meat products, available in GenBank  (8).
Characterization of fluid and mass transfer in the system In order to characterize solute transport in the device, a chip featuring 1 mm long channels was filled with distilled water, then perfused with a 50 mg l -1 fluorescein solution at 9 μl min -1 through the distal chamber using a peristaltic pump (Ismatec). The intensity of fluorescence in the chambers was monitored by fluorescence microscopy over 3 h using the fluorescein isothiocyanate (FITC) filter. Average fluorescence intensity at five different y positions in the perfusion chamber was measured for each time point. The mean grey value was measured using ImageJ software for each image over a 130 × 600 μm rectangular area as close as possible to the channel openings (but not including them).
Imaging and data analysis In order to measure colony growth rates on a solid medium, N. crassa and T. helicus were each inoculated in three Petri dishes on MYEA and incubated at 22°C. Pictures of the N. crassa colonies were taken every 6 h for 48 h. For T. helicus, pictures were taken every day for 4 days, and then every 2 or 3 days until colonies reached the edge of the dish. All image analysis was performed in ImageJ. Colony size on each image was measured manually due to the low contrast. For each image, the maximum and minimum diameters of the colony were measured, and the mean of both values was calculated. Microscopic observations were made using an inverted microscope (Leica DMI-8) equipped with a motorized stage, and images of five fields for each chip were taken at a 10 × magnification (or 4 × for the fluorescein perfusion) with a camera (Leica DFC 3000G), every 10 min for T.
Data accessibility. Our data are accessible at the Dryad Digital Repository: https://doi. org/10. 5061/dryad. 76hdr7ss1 [39]. Authors' contributions. C. B. , A. F. and A. L. G. designed the research. C. B. performed the research. C. B. , A. F. and A. L. G. analysed the data and wrote the paper. All authors gave final approval for publication.
We prepared a 100 nm-thick section from the bone sample for transmission electron microscopy studies using the FIB lift-out technique. The region of interest for TEM lamella was determined after visualizing the surface of bone pieces by SEM. A thin section was cut parallel to the orientation of the long axis of the bone meaning that collagen fibrils would have in-plane view upon visualizing the thin section by TEM. The thin section (10 × 10 × 0. 1 µm) was transferred to a three post lift-out TEM half-grid (Agar Scientific AGJ420) in order to use it for further ET characterization. Analysis of collagen structure. The electron density map of 8 × 8 × 1 collagen unit cells was built using the UCSF Chimera package  76  , based on the structure obtained by a previous X-ray study  32. The map was generated with a resolution of 0.
3 nm and voxel size of 0. 1 nm. From this electron density map we segmented the intermolecular channels within the collagen structure by applying in-house Matlab scripts. The edges of the channels were defined by considering the diameter of each collagen triple-helix molecule as 1. 5 nm  77. Channels with diameters <8 pixels (0. 8 nm) were removed from the segmentation by eroding and then dilating the segmented channels for 4 pixels for an enhanced separation of the main channels. The channels on the model surface were also removed for improved visualization of the channels inside the collagen structure. The remaining channels were then labeled with different colors according to their connectivity, and visualized in 3D using Amira-Avizo software (Thermo Scientific TM ). Mineralization with HAp.
For bone samples and collagen fibrils mineralized with lepidocrocite, the tomography tilt series were taken by tilting the specimen from approximately -65°to 65°, 2°per step. For collagen fibril mineralized in vitro with HAp, the tomography tilt series was taken by tilting the specimen from approximately -60°to 60°, with a low-angle tilting step of 2°(from -45°to 45°) and a high-angle tilting step of 1°(<-45°or >45°). The electron dose of all the tomography tilt series is 1. 0 e -Å -2 per frame. The alignment and 3dimensional reconstructions of the data sets were performed in IMOD. Image analysis was carried out using Gatan Digital Micrograph and Matlab.
Acknowledgements We thank  Dr. Koen Pieterse  ( Institute for Complex Molecular Systems ,  Eindhoven University of Technology , the  Netherlands (TU/e))  for his help with data analysis,  Zaf Khalil  ( Department of Chemical Engineering and Chemistry, TU/e ) for his help with bone sample preparation and  Andreas J. Fijneman  ( Department of Chemical Engineering and Chemistry, TU/e ) for helpful discussions. We thank  Dr. Elena Macias  ( Radboud Institute for Molecular Life Sciences ) for her critical review of the manuscript. This work was supported by the  Netherlands Organization for Scientific (NWO)  through VICI (F. N. , M. J. M. W. ) and Toppunt (Y. X. ) grants to N. S. E. D. E. and N. S. were supported by the  European Research Council  ( ERC) Advanced Investigator grant  ( H2020-ERC-2017-ADV-788982-COLMIN ).
Y. X. was supported by the  Marie Curie Individual Fellowship  ( H2020-MSAC-IF-2019-885795-PolyTEM ), and A. A. was supported by the  Marie Curie Individual Fellowship  ( H2020-MSCA-IF-2017-794296-SUPERMIN ). E. D. E. and A. A. were also partially supported by an  NWO   Echo Grant. W. B. 's contribution is partially based upon work supported by  Oak Ridge National Laboratory , managed by  UT-Battelle, LLC , for the  U. S. Department of Energy. The work was further supported by an  Engineering and Physical Sciences (EPSRC)   Platform Grant  to F. C. M. ( EP/N002423/1 ), an  EPSRC Program Grant  ( EP/R018820/1 ) which funds the Crystallization in the Real World Consortium (F. C. M. ), and  a Leverhulme  research project grant (F. C. M. , N. S. , and Y. X. ). NWO is also gratefully acknowledged for making access to the DUBBLE ESRF beamline possible.
Declaration of interests. We declare no competing interests Disclaimer: JH is staff of the World Health Organization. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the World Health Organization Data sharing. Data collected for the study, including deidentified participant data, data dictionary and additional related documents such as study protocol and statistical analysis plan, will be made available to others upon request to dpco@epicentre. msf. org, following Epicentre's data sharing policy and in accordance with WHO statement on public disclosure of clinical trial results. We wish to thank study participants and the Scientific Committee, the DSMB and the National Institute for Biological Standards and Control (United Kingdom) for critical support to this project.
Acknowledgements. The study was funded by  Médecins Sans Frontières Foundation ,  Wellcome Trust  (grant no. 092654 ) and the  UK Department for International Development. The vaccines were donated in kind by  Bio-Manguinhos/Fiocruz (Brazil), Federal State Unitary Enterprise of Chumakov Institut of Poliomyelitis and Viral Encephalitides (Russia) ,  Institut Pasteur de Dakar (Senegal) , and  Sanofi Pasteur (France). Epicentre receives core funding from  Médecins Sans Frontières.