dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations Our study involved the use of an internationally standardised instrument for service assessment, online training materials, a brief face-to-face training course and monitoring of data collection by the coordinating group. This study provides multicountry, multi-site analysis of patterns and balance of mental health care provision across Europe. This is a major advance over previous studies that compared services availability between    (Dahl et al. , 2017). This methodology can be used in future studies for longitudinal monitoring of service change within an area, for monitoring mental health reform in different world regions, as well as being applied to other areas of integrated care, such as for older people. However, the large number of researchers needed to collect data across the eight partner countries, variable levels of information available in different databases in these countries, as well as the practicality of the assessment tool and the complexity of assessment of mental health systems and integrated care may have led to data inconsistencies.
Acknowledgements. The members of the REFINEMENT Group:  Florian Endel ,  Gisela Hagmair ,  Heinz Katschnig ,  Sonja Scheffel ,  Christa Straßmayr ,  Barbara Weibold  (Austria);  Tihana Matosevic ,  David McDaid ,  A-La Park  (England);  Taina Ala-Nikkola ,  Niklas Grönlund ,  Irja Hemmilä ,  Grigori Joffe ,  Jutta Järvelin ,  Raija Kontio ,  Maili Malin ,  Petri Näätänen ,  Sami Pirkola ,  Minna Sadeniemi ,  Eila Sailas ,  Marjut Vastamäki ,  Kristian Wahlbeck  (Finland);  Matthias Brunn ,  Karine Chevreul ,  Amélie Prigent (France) ;  Francesco Amaddeo ,  Gaia Cetrano ,  Valeria Donisi ,  Laura Grigoletti ,  Ilaria Montagni ,  Laura Rabbi ,  Damiano Salazzari ,  Federico Tedeschi  (Italy);  Øyvind Hope ,  Jorid Kalseth ,  Brigitte Kalseth ,  Jon Magnussen  (Norway);  Juan M Cabases ,  Jordi Cid ,  Ana Fernandez ,  Andrea Gabilondo ,  Carlos García ,  Juan Luis González-Caballero ,  Mencía R.
Bioinformatic analysis From raw data to alignment files. Raw data were first trimmed to remove the 3 adapter sequence with CutAdapt 1. 9. 1 (28) configured with -e 0. 12, -m 24, -M 35 anda options to select a read size in a range from 24 to 35 nt allowing 12% of error. The trimmed reads were then mapped against the rRNA sequences of A. thaliana (www. arabidopsis. org) with Bowtie 1. 1. 2 (29) set up with -all andun options. The -un option was used to select all unmapped reads. These filtered reads were finally mapped against the complete Col-0 mitochondrial genome (JF729201. 1) or only the coding sequences with Bowtie 1. 1. 2. The latter alignments were configured with two mismatches allowed (-n 2) and only uniquely mapped reads were selected (-m 1). The sam formatted files generated by the aligner were converted to sorted and indexed bam formatted files using the Samtools program  (30).
Differential expression. The number of reads for each gene was calculated with featureCounts 1. 5. 0-p2 program  (32)  and normalized with DESeq2 method (33) through SAR-Tools R Package  (34). Snakemake workflow engine was also used to automatize the steps of this analysis. For the analysis of editing, we generated new alignment files by mapping the filtered reads on the non-edited Col-0 mitochondrion genome and the edited one. These alignments were done with zero mismatch allowed and selecting only uniquely mapped reads. Then, with a homemade Python3 script, we parsed the alignment files and extracted the percentage of C and U for each mitochondrial editing position listed in  (14). Using this approach, editing efficiencies were calculated from both Ribo-seq and RNAseq sequencing data that were generated in the course of the present analysis.
FUNDING Institut National de la Recherche Agronomique (INRA)  [ UMR 1318 ];  French Ministère de l'Enseignement et de la Recherche and Fondation pour la Recherche Médicale  [ FDT20160736470  to N. P. ];  LabEx Saclay Plant Sciences-SPS  [ ANR-10-LABX-0040-SPS]; INCa [RiboTEM 2014-092 ];  ANR  [ Rescue Ribosome   ANR-17-CE12-0024-01  to O. N. ]. Funding for open access charge: IJPB;  INRA  [ UMR 1318 ].
1 + WT versus CD45. In hierarchical clustering analysis, Ifnar1-KO pDC isolated from infected Ifnar1-TST MBMC segregated with the pDC isolated from uninfected MBMC and C57BL/6 mice  (Fig 1C). In contrast, MyD88-KO pDC isolated from infected Myd88-TST MBMC regrouped with all the other pDC populations isolated from infected animals in the second major branch of the tree. Consistently, in principal component analysis (PCA), the first axis (PC1) accounting for most of the variability of the dataset (26%) separated pDC samples according to the infection status of the mice, except for Ifnar1-KO pDC from infected MBMC which regrouped with pDC from uninfected animals  (Fig 1D). The second axis (PC2, 17%) separated pDC populations according to each experiment, with a stronger difference between the Myd88-KO and Ifnar1-KO MBMC experiments than between the latter and non-irradiated C57BL/6 animals, consistent with hierarchical clustering.
mAbs recognizing mouse CD3e (2C11), CD19 (1D3), Ly6G (1A8), NK1. 1 (PK136), CD11b (M1/70), CD11c (HL3), CD86 (GL3), TNF (MP6-XT22), and IL-12 (C15-6), as well as their respective isotype controls, were purchased from BD Biosciences. Purified anti-mouse IFNa (RMMA1) and IFNb (RMMB1) were purchased from PBL Interferon Source (Tebu, France); purified rat IgG was used as isotype control (Jackson Immuno Research); these mAbs were coupled to Alexa647 by using a monoclonal labeling kit (Southern Biotech, France). Anti-mouse Bst2/CD317/PDCA1 (eBio927), Irf7 (MNGPKL), and its isotype control were purchased from Affymetrix eBiosciences, France. Irf7 intracellular staining was performed by using a FoxP3 staining kit (Affymetrix eBiosciences, France). Samples were acquired on a FACS LSR II (BD Biosciences, France) and analyzed with FlowJo software (FlowJo, LLC, USA).
Heatmaps were performed using Gene-E (http://www. broadinstitute. org/cancer/software/GENE-E/). The microarray data have been deposited in the GEO database (http://www. ncbi. nlm. nih. gov/geo/) under the series accession number GEO: GSE115450. qPCR Total mRNA was converted into cDNA by using QuantiTect Reverse Transcription kit (Qiagen). qPCR was performed by using a Sybr-Green kit (Takara) and run on a 7500 Real Time PCR System apparatus (Applied Biosystems). We performed two independent replicates for each indicated condition by using 2 ng of cDNA/ sample. Relative gene expression was calculated with the DDC t method using Hprt as housekeeping gene for normalization. Primers used were the following: Hprt Forward 5 0 -GGCCCTCTGTGTGCTC AAG-3 0 ; Hprt Reverse 5 0 -CTGATAAAATCTACAGTCATAGGAATGG A-3 0 ; Irf7 Forward 5 0 -TCCAGTTGATCCGCATAAGGT-3 0 ; Irf7 Reverse 5 0 -CTTCCCTATTTTCCGTGGCTG-3 0 ; Isg15 Forward 5 0 -GGTGTCCG TGACTAACTCCAT-3 0 ; Isg15 Reverse 5 0 -TGGAAAGGGTAAGACCG TCCT-3 0.
Statistical analyses Statistical analyses were performed using a nonparametric Mann-Whitney test for comparisons between two groups. For comparisons among three or more groups, we used a nonparametric Kruskal-Wallis test combined with Dunn's multiple correction test. All the analyses were performed with GraphPad Prism 6 software. ns, not significant (P > 0. 05); *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001. Mean values are indicated as horizontal black bars. Expanded View for this article is available online.
EVenn diagram showing the overlap between the lists of genes significantly upregulated in WT pDC of infected mice (n = 14) as compared to WT pDC of uninfected animals (n = 14) (gray circle), or downregulated in Ifnar1-KO (n = 2) (red circle) or Myd88-KO (n = 2) (green circle) pDC isolated from infected TST MBMC, as compared to WT pDC (n = 6) isolated from the same TST or from their matched CTR infected MBMC. F Enrichment of the gene modules identified in (E) for independent gene sets previously established from the analysis of other microarray datasets (blue font), belonging to specific signaling pathways (red font), associated with specific functions or diseases (violet font) or regulated by the indicated upstream regulators (black font), as assessed by using Ingenuity Pathway Analysis (IPA).
Acknowledgements We thank all the staff of the CIML and CIPHE mouse houses,  Atika Zouine ,  Marc Barad , and  Sylvain Bigot  of the CIML flow cytometry facility,  Mathieu Fallet  of the CIML microscopy facility, and the  France-BioImaging  infrastructure supported by the  Agence Nationale de la Recherche  ( ANR-10-INSB-04-01 , call " Investissements d'Avenir "). We thank  Violaine Alunni  and  Christelle Thibault-Carpentier  from  Plate-forme Genomeast (Strasbourg, France ) for microarray experiments (http://www. igbmc. fr/technologies/5/team/54/),and  Pr. Stipan Jonjic  for the anti-MCMV Croma antibody. We thank  Frédéric Fiore ,  Bernard Malissen , and all the staff of  Centre d'Immunophénomique [CIPHE] (UM2 Aix-Marseille Université ,  Institut National de la Santé et de la Recherche Médicale   US012 ,  Centre National de la Recherche Scientifique UMS3367, Marseille, France ) for generating mutant mice.
We acknowledge  Pr. Tadatsugu Taniguchi  ( University of Tokyo, Japan ),  Dr. Claude Leclerc , and  Dr. Molly Ingersoll  ( Institut Pasteur, Paris ) for generous gift of mutant mice and/or critical reading of the manuscript. This work benefited from data assembled by the  ImmGen consortium. This research was funded by grants from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 Grant Agreement 281225 , "SystemsDendritic"), the  Fondation pour la Recherche Médicale  (FRM, reference  DEQ20110421284 ), and the  Agence Nationale de la Recherche (ANR)  ( SCAPIN ,  ANR-15-CE15-0006-01 ). We also acknowledge support from the  DCBIOL Labex  ( ANR-11-LABEX-0043 , grant  ANR-10-IDEX-0001-02 PSL* ), the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  French Government 's  "Investissements d'Avenir" program  managed by the  ANR , and institutional support from  CNRS ,  Inserm ,  Aix Marseille Université , and  Marseille Immunopole.
A viral factory (VF) is then formed (7-12 h. p. i. )  15  where particle morphogenesis occurs (Fig. 2o-q ; Supplementary Movies 1 and 2). During this step, the virion tail is attached to the capsid after its formation and closure. Later in the process (16-24 h. p. i. ), the amoebal cytoplasm is filled with viral particles, followed by cell lysis and the release of viruses (Supplementary Fig. 1 ). This nucleus-like viral factory has also recently been reported in bacteria and fuels the concept of a virocell  16, 17. In that perspective, viral factories actively producing the progeny could be considered as the nuclei of virocells  16, 17. The genomes of tupanviruses. The tupanvirus soda lake and tupanvirus deep ocean genomes (GenBank accession number KY523104 and MF405918) are linear dsDNA molecules measuring 1,439,508 bp and 1,516,267 bp (GC% ~28%), respectively-the fourth largest viral genomes described to date  10, 18  -containing a total of 1276 and 1425 predicted ORFs, 375 and 378 of which are ORFans (ORFs with no matches in current databases), respectively.
Cells were then stained for 1 h at room temperature with an anti-tupanvirus antibody produced in mice (According Aix Marseille University ethics committee rules). After incubation with an anti-mouse secondary antibody, fluorescently labeled cells were viewed using a Leica DMI600b microscope. For Tupanvirus virion characterization, we also used scanning electron microscopy (SEM)  35. Chemical treatment with proteases and sonication was performed as described elsewhere  35  to investigate fiber composition and the attachment between capsid and tail. For tomography videos, tilt series were acquired on a Tecnai G2 transmission electron microscope (FEI) operated at 200 keV and equipped with a 4096 × 4096 pixel resolution Eagle camera (FEI) and Explore 3D (FEI) software. The tilt range was 100°, scanned in 1°increments.
The magnification ranged between 6,500 and 25,000, corresponding to pixel sizes between 1. 64 and 0. 45 nm, respectively. The image size was 4,096 × 4,096 pixels. The average thickness of the obtained tomograms was 298 ± 131 nm (n = 11). The tilt series were aligned using ETomo from the IMOD software package (University of Colorado, USA) by crosscorrelation (http://bio3d. colorado. edu/imod/). The tomograms were reconstructed using the weighted-back projection algorithm in ETomo from IMOD. ImageJ software was used for image processing. Genomes sequencing and analyses. The tupanviruses genomes were sequenced using an Illumina MiSeq instrument (Illumina Inc. , San Diego, CA, USA) with the paired end application. The sequence reads were assembled de novo using ABYSS software and SPADES, and the resulting contigs were ordered by the Python-based CONTIGuator.
py software. The obtained draft genomes were mapped back to verify the read assembly and close gaps. The best assembled genome was retained, and the few remaining gaps (three) were closed by Sanger sequencing. The gene predictions were performed using the RAST (Rapid Annotation using Subsystem Technology) and GeneMarkS tools. Transfer RNA (tRNA) sequences were identified using the ARAGORN tool. The functional annotations were inferred by BLAST searches against the GenBank NCBI non-redundant protein sequence database (nr) (e-value < 1 × 10 -3 ) and by searching specialized databases through the Blast2GO platform. Finally, the genome annotation was manually revised and curated. The predicted ORFs that were smaller than 50 amino acids and had no hits in any database were ruled out. Tupanvirus codon and aa usages were compared with those of A.
castellanii and other lineages of mimiviruses. Sequences were obtained from NCBI GenBank and subjected to CGUA (General Codon Usage Analysis). The global distribution of Tupanvirus tRNAs was analyzed and compared manually with viral aa usage considering the corresponding canonical codons related to each aa. Phylogenetic analyses were carried out based on the separate alignments of several genes, including family B DNA polymerase, 18 S rRNA intronic regions (copies 1/2) and 20 aminoacyl-tRNA synthetases (aaRS). The predicted aa sequences were obtained from NCBI GenBank and aligned using Clustal W in the Mega 7. 0 software program. Trees were constructed using the maximum likelihood evolution method and 1000 replicates. The analysis of aaRS domains was carried out using NCBI Conserved Domain Search (https://www.
ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi). A search for promoter sequences was performed in intergenic regions based on a search for the mimivirus canonical AAAATTGA promoter sequence, as previously described  20. Single-nucleotide polymorphisms (SNP) in the AAAATTGA promoter sequence were also considered for each base, considering all possibilities. Gene sets available for members of the family Mimiviridae and those of Tupanvirus soda lake and Tupanvirus deep ocean were used for analyses of the mimivirus pangenome. Groups of orthologues were determined using the Proteinortho tool V51 with 1e-3 and 50% as the e-value and coverage thresholds, respectively. Concurrently, BLAST searches were performed using ORFs of all mimivirus genomes available in the NCBI GenBank nucleotide sequence database against the set of clusters of orthologous groups previously delineated for mimiviruses (mimiCOGs) (n = 898), with 1e-3 and 50% as the evalue and coverage thresholds, respectively.
For rhizome preparations, all coding sequences were blasted against the NR database, and results were filtered to retain the best hits. Taxonomic affiliation was retrieved from NCBI. For the construction of a translation-associated elements network, the different classes of translation elements of each organism included in the analysis were obtained by searching for each component within their genome, according to protein function annotation using Blastp searches against the GenBank NCBI non-redundant protein sequence database. The tRNA components were obtained using the ARAGORN tool. Different tRNA molecules were included in the analysis, considering the anti-codon sequence. Repeated elements were eliminated to avoid analysis of duplicate events. The layout of the network was generated by a force-directed algorithm-followed by local rearrangement for visual clarity, leaving the network's overall layout unperturbed-using the program Gephi (https://gephi.
The resulting 100 best hits were tabulated, and the information was used to construct diagrams. For the phylogenetic analysis, the sequences of these best hits were also aligned, using Clustal W in the Mega 7. 0 software, and constructed using the maximum likelihood evolution method of 1,000 replicates. To analyze subjacent regions of the core sequence of 18 S rRNA intronic regions in the Mimiviridae family, one member of the lineages A (HQ336222. 2), B (JX962719. 1) and C (JX885207. 1) was chosen and analyzed. The expression of both copies was checked using fluorescence in situ hybridization (FISH) and qPCR. For this, A. castellanii cells were infected with Tupanvirus with a multiplicity of infection of 5 and collected at 30 min and at 6 and 12 h post-infection. As a control, A. castellanii cells were also incubated with PAS alone and collected.
Acknowledgements We thank our colleagues from  URMITE and Laboratório de Vírus of Universidade Federal de Minas Gerais  for their assistance, particularly  Julien Andreani ,  Jean-Pierre Baudoin ,  Gilles Audoly ,  Amina Cherif Louazani ,  Lina Barrassi ,  Priscilla Jardot ,  Eric Chabrières ,  Philippe Decloquement ,  Nicholas Armstrong ,  Said Azza ,  Emeline Baptiste ,  Claudio Bonjardim ,  Paulo Ferreira ,  Giliane Trindade  and  Betania Drumond. In addition, we thank the  Méditerranée Infection Foundation ,  Centro de Microscopia da UFMG ,  CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico) ,  CAPES  ( Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)  and  FAPEMIG (Fundação de Amparo à Pesquisa do estado de Minas Gerais)  for their financial support. We thank  Petrobras  for the collection of sediments from ocean.
This work was also supported by the  French Government  under the  « Investissements d'avenir » (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). J. A. , B. R. and E. K. are CNPq researchers. B. L. S. , J. A. , L. S. , P. C. , and E. G. K. are members of a  CAPES-COFECUB  project.
A database containing 10 min means of solar irradiance measured on a horizontal plane in several ultraviolet and visible bands from July 2014 to December 2016 at three stations in the area of the city of Ostrava (Czech Republic) is presented. The database contains time series of 10 min average irradiances or photosynthetic photon flux densities measured in the following spectral bands: 280-315 nm (UVB); 315-380 nm (UVA); and 400-700 nm (photosynthetically active radiation, PAR); 510-700 nm; 600-700 nm; 610-680 nm; 690-780 nm; 400-1100 nm. A series of meteorological variables including relative air humidity and air temperature at surface is also provided at the same 10 min time step at all three stations, and precipitation is provided for two stations. Air pressure, wind speed, wind direction, and concentrations of air pollutants PM 10 , SO 2 , NO x , NO, NO 2 were measured at the 1 h time step at the fourth station owned by the Public Health Institute of Ostrava.
The details of the experimental sites and instruments used for the measurements are given. Special attention is given to the data quality, and the original approach to the data quality which was established is described in detail. About 130 000 records for each of the three stations are available in the database. This database offers a unique ensemble of variables having a high temporal resolution and it is a reliable source for radiation in relation to environment and vegetation in highly polluted areas of industrial cities in the of northern mid-latitudes. The database has been placed on the PANGAEA repository (https://doi. org/10. 1594/PANGAEA. 879722) and contains individual data files for each station.
The detailed measurements spanning several vegetative seasons performed at Ostrava aim at supporting a comprehensive analysis of the changes in intensity and spectral composition of incoming irradiation in relation with air quality characteristics and other meteorological variables, and support further analysis of the impact of air pollution on the radiation regime in that region. The database discussed here is closely connected with studies of plant photosynthesis and metabolism because this is the main research topic of the group of Biophysics at the Department of Physics at the University of Ostrava. The original goal of performing measurements of solar radiation was to inform about solar irradiances in different spectral regions during different meteorological conditions and to use these data in experiments on plants in artificial conditions in growth chambers.
In addition, the database can be used for any environmental modelling, e. g. for atmospheric chemistry models or urbanistic studies. Because of the existence of systematic errors in radiation measurements (e. g. Muneer and Fairooz, 2002; Younes et al. , 2005) , particular attention was devoted to a careful quality check (QC) on the measurements (e. g. Journée and Bertrand, 2011; Roesch et al. , 2011)  before releasing the data for further analyses. The aim of this paper is to describe a database elaborated in cooperation between the University of Ostrava (Department of Physics, Faculty of Science) and the research centre Observation, Impacts, Energy of MINES ParisTech. The database covers the period 2014-2016 and includes qualitychecked 10 min averages of seven spectral regions within the UV and VIS, as well as the broadband irradiance at three measuring stations situated at two locations within the wider area of the city of Ostrava.
Meteorological variables, such as air temperature and relative air humidity, are also included in the database for all three measuring stations, and precipitation is included for two measuring stations. Other meteorological variables -such as air pressure, wind direction, wind speed, and concentrations of several air pollutants -are included for one location. This paper is organized as follows: the study area and details about sensors for radiation measurements are given in Sect. 2, the QC procedures applied to the measurements are described in Sect. 3, their results and the structure of the database are detailed in Sect. 4. Finally, a summary and perspectives are given in Sect. 5. The qualitychecked database is available at the PANGAEA repository (https://doi. org/10. 1594/PANGAEA. 879722) under Creative Commons Attribution 3.
, 2013)  and concentrations of air pollutants are greater at BGOU (stations S1, S2) than at CHMI (station S3), especially in the winter months (Table  2 ). Jančík et al. (2013)  reported that the main source for air pollution at BGOU is industry while it is the road traffic at CHMI. It was desirable to locate the stations in a very open space area but this was not possible in this project due to the necessity of a fenced and easy-to-visit area. There are some shrubs and trees in the surroundings of the measuring stations (S1, S2) at BGOU and other measuring devices in the surroundings of the measuring station (S3) at CHMI. Their shading effects were studied and included in the database. Shaded and non-shaded data were determined visually by coloured plots of the irradiance as a function of the solar azimuth and elevation angles (figures with details are included in the datasets and in the Supplement, Figs.