dataesr/scientific-paragraphs-categorization huggingface dataset:
These women were not recruited as part of thrombophilia screening and are unrelated to thrombotic patients. Nonetheless some of them have described a family history at the time of collection  (19/128). The PILGRIM study controls presented a selection bias due to having been recruited as part of thrombophilia screening due to family history  [11]  and several variables (family history, F5 and F2) were not used as such, as described below. All control women are taking CC but have not developed VTE by the time of the genotyping investigation. This study involved human subjects and was carried out in accordance with the tenets of the Declaration of Helsinki; all participants signed an informed consent and data were anonymised. The procedures regarding the collect of PILGRIM samples were reviewed and approved by the Assistance Publique des Hopitaux de Marseille insitutional review committee.
Genotyping 46 SNPs were selected according to their association with VTE development or hormone metabolism (principaly estrogens) as described in the literature. These 46 SNPs were genotyped using Illumina GoldenGate technology and assessed using Illumina BeadXpress and GenomeStudio V2011. 1 software. Clusters for each SNP were curated manually and undetermined samples were further genotyped using Sanger sequencing. SNP rs1053878 was genotyped using RFLP-PCR; in more details, the DNA region was amplified with the following primers (Forward: 5'-GCCACCGTGTCCACTACTATG-3' and Reverse: 5'-GTCCACGCA CACCAGGTAAT-3') and the amplicons were digested with PvuII restriction enzyme. Controls from the CoLaus cohort were previously genotyped as described  [13]. For the CoLaus controls, proxys (r 2 > 85%) were used for 9 SNPs (rs4572916 for rs10029715, rs8176704 for rs1053878, rs3736455 for rs13146272, rs6018 for rs1800595, rs4253417 for rs2289252, rs110 38993 for rs3136516, rs2169682 for rs7082872, rs687621 for rs8176719 and rs2069952 for rs9574).
46 SNPs selected according to their association with VTE development or hormone metabolism (principaly estrogens), as described in the literature, were successfully genotyped in the 1622 women involved in this study. Familial history and genotyping data of rs6025 and rs1799963 of 523 control women were treated as missing in order to avoid an ascertainment bias. To make sure that the frequency of each SNP in our control population corresponds to the frequencies expected in a general Caucasian population, we compared the allele frequencies (AF) observed in our control population to the ones reported in 1000 genomes project  [14]. The frequencies were similar (S1 Table, Fisher p-values > 0. 01) for all but two SNPs (rs1593812 and rs429358, which were discarded from further analysis) suggesting that our.
Applying this 5-SNP model to our population yielded an AUC of 0. 64 (0. 62-0. 68) (Table  3  and Fig 2) , which is less than the described AUC on MEGA and LETS cohorts (0. 69 and 0. 67 respectively) due to winners curse. The genetic score described by Bruzelius et al. [15]  is based on 7 SNPs (rs6025, rs1799963, rs514659, rs2289252, rs1799810, rs710446, rs2066865) and 4 interactions. Among the 7 SNPs, 4 are present and one (rs514659) has a good proxy (rs8176719) in our Pill Protect1 model, and one (rs2066865) is not part of our model but is present in our dataset. The last SNP (rs1799810) is absent from our dataset and was, therefore, not used in the comparison. However, given the small (and least significant) coefficient reported by Bruzelius, it would probably not affect significantly the performance.
Discussion In this study, we determined a new combination of parameters that predicts the risk of VTE in women using CC. This combination outperformed significantly previously published models as well as the clinical evaluation currently used by medical doctors. We also identified two new genetic markers associated with the development of VTE in our population. The risk of VTE development upon CC use is presently assessed by an oral anamnesis and based on physician's experience. In the presence of clinical risk factors and/or family history, some medical doctors may request a thrombophilia status that tests for the well-established markers FV-Leiden and Prothrombin. The current incidence of 1‰ of VTE per year in CC users indicates that the risk assessement needs to be improved. Analysing genetic and clinical data for a population of women using CC, we were able to calculate a risk score that outperfoms a model that simulates the current empirical approach even when combined with additional information on FV-Leiden and Prothrombin.
Acknowledgments The authors also express their gratitude to the participants in the Lausanne CoLaus study and to the investigators who have contributed to the recruitment, in particular the co-PIs,  Peter Vollenweider ,  Gerard Waeber ,  Martin Preisig  and research nurses of CoLaus:  Yolande Barreau ,  Anne-Lise Bastian ,  Binasa Ramic ,  Martine Moranville ,  Martine Baumer ,  Marcy Sagette ,  Jeanne Ecoffey ,  Sylvie Mermoud. The authors also thank  Professor P. Morange  for giving us access to the PILGRIM study samples.
Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa.
The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1. 1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere.
Purpose of data acquisition Sponges (phylum Porifera) are an ancient metazoan clade  [1] , with more than 8500 formally described species  [2]. Sponges are benthic organisms that have important ecological functions in aquatic habitats  [3, 4]. Marine sponges are often found in symbiotic association with microorganisms, and these microbial communities can be very diverse and complex  [5, 6]. Sponge symbionts perform a wide range of functional roles, including vitamin synthesis, production of bioactive compounds, and biochemical transformations of nutrients or waste products  [7] [8] [9]. The diversity of microorganisms associated with sponges has been the subject of intense study (the search of "sponge microbial diversity" returned 348 publications in the Scopus database)  [10]. Most of these studies were performed on individual species from restricted geographic regions [e.
g. ,  11, 12]. A comparative assessment of these studies is often hindered by differences in sample processing and 16S rRNA gene sequenc-ing. However, 2 recent studies incorporating a large number of sponge microbiomes (>30)  [5, 13]  revealed the potential of largescale, standardised, high-throughput sequencing for gaining insights into the diversity and structure of sponge-associated microbial communities. The purpose of this global dataset is to provide a comprehensive 16S rRNA gene-based resource for investigating and comparing microbiomes more generally across the phylum Porifera.
Of the samples included in this current dataset, 973 samples had been analysed previously  [13]. Samples were collected from a wide range of geographical locations (Fig. 1 ; Supplementary Table  S1 ). Total DNA was extracted as previously described  [13]  and used as templates to amplify and sequence the V4 region of the 16S rRNA gene using the standard procedures of the Earth Microbiome Project (EMP)  [14, 15].
Processing of sequencing data Clustering using the EMP standard protocols in QIIME Raw sequences were demultiplexed and quality controlled following the recommendations of Bokulich et al. [16]. Qualityfiltered, demultiplexed fastq files were processed using the default closed-reference pipeline from QIIME v. 1. 9. 1 (QIIME, RRID:SCR 008249). Briefly, sequences were matched against the GreenGenes reference database (v. 13 8 clustered at 97% similarity). Sequences that failed to align (e. g. , chimeras) were discarded, which resulted in a final number of 300 140 110 sequences. Taxonomy assignments and the phylogenetic tree information were taken from the centroids of the reference sequence clusters contained in the GreenGenes reference database (Greengenes, RRID:SCR 002830). This closed-reference analysis allows for cross-dataset comparisons and direct comparison with the tens of thousands of other samples processed in the EMP and available via the Qiita database  [17].
Clustering using Mothur Quality-filtered, demultiplexed fastq files were also processed using Mothur v. 1. 37. 6 (Mothur, RRID:SCR 011947)  [18]  and Python v. 2. 7 (Python Programming Language, RRID:SCR 008394)  [19]  custom scripts with modifications from previously established protocols  [13]. Detailed descriptions and command outputs are available at the project notebook (see Availability of supporting data). Briefly, sequences were quality-trimmed to a maximum length of 100 bp. To minimize computational effort, the dataset was reduced to unique sequences, retaining total sequence counts. Sequences were aligned to the V4 region of the 16S rRNA gene sequences from the SILVA v. 123 database (SILVA, RRID:SCR 006423)  [20]. Sequences that aligned at the expected positions were kept, and this dataset was again reduced to unique sequences.
Further, singletons were removed from the dataset, and the remaining sequences were preclustered if they differed by 1 nucleotide position. Sequences classified as eukaryote, chloroplast, mitochondria, or unknown according to the Greengenes (v. 13 8 clustered at 99% similarity)  [21]  and SILVA taxonomies  [22]  were removed. Chimeras were identified with UCHIME (UCHIME, RRID:SCR 008057)  [23]  and removed. Finally, sequences were de novo clustered into operational taxonomic units (OTUs) using the furthest neighbour method at 97% similarity. Representative sequences of OTUs were retrieved based on the mean distance among the clustered sequences. Consensus taxonomies based on the SILVA, Greengenes, and RDP (v. 14 03 2015; Ribosomal Database Project, RRID:SCR 006633)  [24]  databases were obtained based on the classification of sequences clustered within each OTU.
De-noising using Deblur Recently, sub-OTU methods that allow views of the data at single-nucleotide resolution have become available. One such method is Deblur  [27] , which is a de-noising algorithm for identification of the actual bacterial sequences present in a sample. Using an upper bound on the polymerase chain reaction and read-error rates, Deblur processes each sample independently and outputs the list of sequences and their frequencies in each sample, enabling single nucleotide resolution. For creating the deblurred biom table, quality-filtered, demultiplexed fasta files were used as input to Deblur using a trim length of 100 and minreads of 25 (removing sOTUs with <25 reads total in all samples combined). Taxonomy was added to the resulting biom table using QIIME  [28] , RDP classifier  [29] , and Greengenes v.
The ranksum P-value is then calculated using the Kruskal-Wallis test for F v (s) and F v (s) and shown only if significantly enriched in samples containing v (i. e. , rank difference of F v (s)-F v (s) > 0). We have set up a webserver [30] that performs this enrichment analysis for user-defined sequence submissions. The code for the webserver is also available in Github for a local installation.
Data description The dataset covers 4033 samples with a total of 1 167 226 701 raw sequence reads. These sequence reads clustered into 39 543 OTUs using QIIME's closed-reference processing, 518 246 OTUs from de novo clustering using Mothur (not filtered for OTU abundances), and 83 908 sOTUs using Deblur (with a filtering of at least 25 reads total per sOTU). We recommend that data users consider the differences in sequencing depths per sample and abundance filtering for certain downstream analyses, such as when calculating diversity estimates  [16]  and comparing OTU abundances across samples  [31]. In terms of taxonomic diversity, most Mothur OTUs were assigned to the phylum Proteobacteria, although more than 60 different microbial phyla were recovered from the marine sponge samples according to SILVA (n = 63) and Greengenes classifications (n = 72) (Fig.
Potential uses This dataset can be utilised to assess a broad range of ecological questions pertaining to host-associated microbial communities generally or to sponge microbiology specifically. These include: (i) the degree of host specificity, (ii) the existence of biogeographic or environmental patterns, (iii) the relation of microbiomes to host phylogeny, (iv) the variability of microbiomes within or between host species, (v) symbiont cooccurrence patterns, and (vi) assessing the existence of a core sponge microbiome. An example of this type of analysis is shown in Fig. 3 , where samples were clustered using unweighted UniFrac data  [10]  with a Principal Coordinates Analysis and visualization in Emperor  [15]  based on their origins from sponges, seawater, or kelps  [17].
Availability of supporting data Raw sequence data were deposited in the European Nucleotide Archive (accession number: ERP020690). Quality-filtered, demultiplexed fastq files, QIIME resulting OTU tables are available at the Qiita database (Study ID: 10 793)  [17]. The additional datasets that support the results of this article are available in the Giga-Science repository, GigaDB  [32]  and include an OTU abundance matrix (the output ". shared" file from Mothur, which is tab delimited), an OTU taxonomic classification table (tab delimited text file), an OTU representative sequence FASTA file, a table of samples' metadata, the biom files from QIIME and Deblur analyses, and the QIIME-generated tree file. The project workflow, Mothur commands, and additional scripts are available as HTML in GigaDB  [32].
Funding T. T. and N. S. W were funded by  Australian Research Council Future Fellowships   FT140100197  and  FT120100480 , respectively. T. T. received funds from the  Gordon and Betty Moore Foundation. This work was also supported in part by the  W. M. Keck Foundation  and the  John Templeton Foundation. R. K. received funding as a  Howard Hughes Medical Institute Early Career Scientist.
Light interferometric microscope. The second measurement of wafer flatness was performed with a full-field white light interferometric microscope Photomap 3D (Fogale company, Nimes, France). The measurement was realized in topography mode with maximum intensity of fringe envelope. Data visualization and RMS calculation were realized with FOGALE 3D Viewer 2006-06 software (Fogale, Nimes, France). Intra-oral intra-scanners. Two intra-oral scanners were evaluated and compared: CS3500 intra-oral scanner CS3500 intra-oral scanner from Carestream (Rochester, New York State, USA) is a powder-free system. It is a confocal parallel system with a green laser light and a pinhole to select a focal plan. The light projected on the tooth is collected on a CMOS (complementary metal oxide semiconductor) 1.
The DCM files from the Trios were converted into STL files with CrossManager 2014 software v2014. 4. 0 (Datakit, Lyon, France). Data analysis. Mesh visualization Mesh visualization was performed using Meshlab Software (ISTI, Italian National Research Council, Italia)  [19]. The meshes were systematically cropped to eliminate wafer edges. RMS calculation RMS, representing the noise of the intra-oral scanner, is calculated using mesh. Calculations of meshes were realized with CloudCompare v2. 6. 0 software (Research and Development Institute of ElectriciteDe France EDF, Paris, France). To visualize the noise recorded by the intra-oral scanner, the roughness function was used. A kernel size is selected, corresponding to the ball included near vertices neighbours. From these vertices, a mean plane is fitted to all the vertices using a least-square minimization, and the distances vertex-plan are calculated.
a TSPO Ki values (mean ± SD, duplicate) were determined using membrane homogenates of rat heart and screened against [ 3 H]PK11195 (Kd = 1. 8 nM, c = 0. 2 nM). b CBR binding affinity was evaluated using homogenates of rat cerebral cortex and expressed as % of inhibition at 1 µM against [ 3 H]flunitrazepam (Kd = 2. 1 nM, c = 0. 4 nM). c LogD7. 4 were determined by converting the retention time recorded for the tested compounds using a validated, standardized HPLC method. d cLogP and TPSA were both determined using ChemDraw 15. 0 software. As shown in Table  2 , and with the exception of the piperazinamide 17 (260 nM), all synthesized compounds displayed nanomolar to subnanomolar binding affinity for the TSPO, with K i values ranging from 0. 25 nM to 32 nM. Within this series, the N-methyl-N- with a lipophilic binding pocket of the protein  [81].
Acknowledgments This work was supported by  CEA -I 2 BM intramural programs, as well as partly by the  European Union 's  Seventh Framework Programme  [FP7/2007-2013 ] INMiND  (Grant agreement n°  HEALTH-F2-2011-278850 ) and  France Life Imaging  (Grant agreement n°  ANR-11-INBS-0006 ). The authors wish to thank  LGCR Analytical Sciences (Sanofi, Chilly Mazarin)  for logD 7. 4 measurements,  DMPK (Sanofi, Montpellier)  and  DSAR Drug Disposition  ( Sanofi, Vitry ) for preliminary metabolism studies. HRMS determination has benefited from the facilities and expertise of scientific service, HPLC-Mass spectrometry of  ICSN, CNRS, Gif sur Yvette, France  (www. icsn. cnrs-gif. fr).
Summary The first measurement of the differential cross sections for a W boson produced in association with jets in proton-proton collisions at a center-of-mass energy of 13 TeV was presented. The collision data correspond to an integrated luminosity of 2. 2 fb -1 and were collected with the CMS detector during 2015 at the LHC. The differential cross sections are measured using the muon decay mode of the W boson as functions of the exclusive and inclusive jet multiplicities up to a multiplicity of six, the jet transverse momentum p T and absolute value of rapidity |y| for the four leading jets, and the scalar       p T sum of the jets H T for an inclusive jet multiplicity up to four. The differential cross sections are also measured as a function of the azimuthal separation between the muon direction from the W boson decay and the direction of the leading jet for up to four inclusive jets, and of the angular distance between the muon and the closest jet in events with at least one jet.
Acknowledgments We extend our thanks to  Radja Boughezal  for providing the NNLO predictions for this analysis [9,31]. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR  and  RAEP (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI  and  FEDER (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Our approach (1) satisfies differential privacy by building on the well-known randomized response technique, applied by each worker to perturb locally her profile before sending it to the platform, and (2) copes with the resulting perturbation by benefiting from a taxonomy defined on workers profiles. We describe the lightweight upgrades to be brought to the workers, to the platform, and to the requesters. We show formally that our approach satisfies differential privacy, and empirically, through experiments performed on various synthetic datasets, that it is a promising research track for coping with realistic cost and quality requirements.
The variance reduction is mathematically sound and does not jeopardize privacy. 4. An experimental study, over a synthetic taxonomy and various synthetic datasets, that shows promising preliminary results about the practical adequacy of our approach from the sides of performance and quality. The rest of the paper is organized as follows. Section 2 introduces the notions used in our approach and defines more precisely the problem we tackle. We describe our algorithms in Section 3 and analyze them empirically in Section 4. Section 5 discusses related work. Finally, we conclude in Section 6, outlining interesting future works.
Security and Quality Security We say that our approach is secure against honest-but-curious participants if and only if no participant learns information about the set of nonperturbed profiles P that has not been perturbed by a differentially-private mechanism, where differential privacy is defined below. Differential privacy  [5]  is the current de facto standard model for disclosing personal information while satisfying sound privacy guarantees. Informally speaking, the differential privacy model requires that the outputs of a differentiallyprivate algorithm be almost insensitive to the participation of any possible individual in the dataset input. Differential privacy is composable and secure under post-processing.