dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the patients, their families and all of the investigators who participated in these studies. The conduct of this research, study design, data collection, and analysis was financially supported by  Boehringer Ingelheim. Medical writing assistance, supported financially by  Boehringer Ingelheim , was provided by  Lynn Pritchard , DPhil, of  GeoMed , an Ashfield company, part of  UDG Healthcare plc , during the preparation of this manuscript. The authors were fully responsible for all content and editorial decisions, were involved at all stages of manuscript development and have approved the final version.
10 , 11 n Hungary, finetyping, MLST and WGS (the latter during outbreak investigations) are used to further characterize strains, with phenotypic testing to determine antimicrobial susceptibility to rifampicin. Differences in surveillance systems were noted. In Georgia, sentinel surveillance of IMD is in place, with data submitted to the World Health Organization (WHO) on a monthly basis. Conversely, in Latvia and Romania, only passive surveillance of IMD is undertaken, while in Serbia, both passive and active surveillance of possible cases is performed. In most Eastern European countries, cases of IMD are reported to the European Centre for Disease Prevention and Control via the European Surveillance System (TESSy). 4 , 12.
4% of cases [serogroups W, X, Y] in 2017),  13  Russia (8% of cases in 2017),  17  Serbia (two cases from 2008 to 2018), Azerbaijan (11 cases from 2010 to 2018 among < 5 year olds)  13  and Turkey ( < 10% of cases in 2017). 14-16 , 21 Available data indicate that ST-3342 (cc865) has been recorded in the Czech Republic since 2010, as well as in Poland during 2013-2014. 13 , 26 ases of serogroup W disease (including ST-9316 and cc11) have recently increased in Poland (from 2. 2% in 2014 to 9. 8% in 2018),  18  Russia (8% of cases in 2017)  17  and Kazakhstan (from 2. 6% in 2015-2016 to 18. 4% in 2017 [serogroups W, X, Y]). 13  Conversely, the proportion of serogroup W cases in Turkey has declined following peaks in 2009-2010 and 2011-2012 (from > 50% to < 10% of cases in 2017),  21  together with cases in Hungary (from 9.
Thus, the RP is determined by a combination of antigenic expression and antigenic similarity to the vaccine antigen variant against which the ELISA antisera were raised. 66 ince the introduction of 4CMenB to the UK infant immunization program, MATS has formed an important part of enhanced surveillance. For example, up until the end of December 2017, surveillance data from England revealed that there were a total of 202 laboratory confirmed cases of IMD in infants born from May 1st 2015. Of these, 177 were eligible for vaccination, of which MenB accounted for 116 (66%) cases. Of the eleven vaccine-eligible MenB cases that had received three doses of vaccine, five were culture confirmed and two were MATS positive (unpublished data). To determine MenB-fHbp coverage via MEASURE, flow cytometry is used to determine fHbp expression, with mean fluorescence intensity calculated to assess strain coverage.
52 Protein expression with a mean fluorescence intensity of above 10 0 0 arbitrary units (corresponding to 30 pg of fHbp/ μg of cell extract) predicts with 91% accuracy that a MenB isolate would be killed by antibodies induced by the MenB-fHbp vaccine. 52 Currently, MenB-fHbp has not been used as part of a NIP. As such, population-based MenB-fHbp effectiveness data are not available. The use of MATS and MEASURE assays to estimate the strain coverage of MenB vaccines has a number of limitations, such as the inability to evaluate non-culture cases and limited availability for reference laboratories to be able to perform the assays. As a result, Genetic Meningococcal Antigen Typing System (gMATS)  67  has been developed to assess strain coverage of 4CMenB in culture and potentially non-culture cases.
gMATS was developed using a large international genotypic/MATS dataset ( n = 3481 isolates) to assign antigen peptides to covered, not covered or unpredictable (half of which are considered covered within national strain panel datasets). The Bexsero Antigen Sequence Type (BAST) scheme provides a reproducible method for collective typing of 4CMenB vaccine antigens whereby each unique combination of fHbp, NHBA, NadA, PorA-VR1 and PorA-VR2 is assigned a unique arbitrary identification number or 'BAST'. BASTs can be used in conjunction with predictors of antigenic coverage such as MATS data to aid prediction of coverage for, and comparisons between, genotypic datasets. When used in this way, it is important to define the basis on which a given BAST is considered covered/not covered. 68 hile gMATS and seven-locus MLST (used to demonstrate the utility of genotype-phenotype association) have previously exhibited 92%  67  and 80%  68  prediction accuracy versus MATS, respectively, both relied on correlation with MATS data for identifying cross-reactive variants.
Data suggest that gMATS can accurately complement MATS in predicting 4CMenB strain coverage in England and Wales in 20 07-20 08, with coverage estimated at 70% via MATS and 72-73% via gMATS, compared with 88% killing in the human complement serum bactericidal antibody assay. 67 , 69 Consequently, gMATS may constitute a reliable alternative to MATS; however, ongoing MATS analysis is likely to be necessary to maintain a relevant gMATS dataset (e. g. accounting for novel peptides or peptides expanding in their frequency among IMD cases).
Acknowledgments Authors would like to thank  Dr Olivier Ronveaux  ( Infectious Hazard Management, World Health Organization, Geneva, Switzerland ) for his contributions during this GMI  Roundtable Meeting  and for providing permission to use his presentation content in this manuscript. The authors were assisted in the preparation of the manuscript by  Hannah Birchby , a professional medical writer at CircleScience, an  Ashfield Company , part of  UDG Healthcare plc. Medical writing support was funded by  Sanofi Pasteur.
In order to do so, we first compared the MG-based approach with targeted diagnostic methods (microscopy for Entamoeba spp. detection and qPCR for Blastocystis sp. STs detection) in 68 Cameroonian adults. We then analyzed the metagenomes generated from these Cameroonian samples together with publicly available metagenomes from Tanzanian, Peruvian, USA and Italian individuals  [25, 26] , summing up to 127 worldwide fecal metagenomes of healthy individuals. We systematically looked for multiple pathogenic and non-pathogenic protozoa: Blastocystis sp. , Chilomastix mesnili, Cryptosporidium spp. , Cystoisospora belli, Dientamoeba fragilis, Endolimax nana, Entamoeba spp. , Enteromonas hominis, Giardia intestinalis and Iodamoeba bütschlii. We took advantage of this combined dataset to assess the prevalence of gut protozoa in populations with varying levels of industrialization (rural populations with traditional modes of subsistence, further referred to as non-industrialized populations, and urban industrialized populations).
Metagenomic data Metagenomic data were obtained for 57 of the 68 sampled individuals from Cameroon. The DNA Libraries were prepared using Nextera XT DNA Library Prep Kit (Illumina, San Diego, CA, USA) and sequenced as 100 bp paired-end reads on a HiSeq2000 sequencer (Illumina) at the University of Minnesota Genomics Center (Minneapolis, MN, USA). Raw data have been deposited in the European Nucleotide Archive (ENA) with the BioProject ID PRJEB27005, under the accession numbers ERS2539904-ERS2539960. We obtained an average of 25. 6 million read pairs per sample. The reads from low-quality areas of the flowcell were filtered out using FilterByTile tool from BBMap package  [27]. We further explored metagenomic datasets focusing on non-industrialized populations published by Obregon-Tito et al.
,  Rampelli et al. and Yatsunenko et al. [25, 26, 28] , which we downloaded from SRA NCBI and MG-RAST in June 2017. However, we did not pursue with the Yatsunenko dataset (rural individuals from Venezuela and Malawi, and urban individuals from USA) due to their low sequencing depth (average of 155,890 350bp reads per sample)  [28]. The Rampelli dataset (study accession: PRJNA278393; average of 10. 9 million 100bp read pairs per sample)  [26]   For all datasets, we used the bbduk tool  [27]  to trim the adapters, very low-quality bases (Phred Q<3) and to remove reads shorter than 36 bp. The quality of the metagenomic data was assessed by FastQC  [29].
Detection of various gut protozoa using metagenomic data We searched for multiple gut protozoa species, i. e. , Blastocystis sp. , Chilomastix mesnili, Cryptosporidium hominis, Cryptosporidium parvum, Cystoisospora belli, Dientamoeba fragilis, Endolimax nana, Entamoeba spp. , Enteromonas hominis, Giardia intestinalis and Iodamoeba bütschlii, by aligning quality-controlled data to publicly available data using BWA-MEM  [30]. The SSU rRNA gene sequences of Retortamonas and Pentatrichomonas available in public databases were either very short or not of human origin and therefore they were not included in the analysis. To detect Blastocystis sp. , Giardia intestinalis, Cryptosporidium hominis and C. parvum, we used whole-genome data, while, for the other species (including all Entamoeba spp. ), we used complete SSU rRNA gene sequences (S2 Table ).
For E. histolytica and E. dispar (for which both genomes and complete SSU sequences are available), we decided to use the SSU rRNA gene sequences, in order to obtain comparable data for a larger number of Entamoeba species. In order to retain only high-confidence alignments for the species with available genomes, we applied the following filtering procedure. We used SAMTools  [31]  to remove the reads with unmapped mates (-F8) and supplementary alignments (-F2048). We further used an in-house script to sequentially filter out the alignments with more than 0. 1 of the read length softclipped, with edit distance exceeding 0. 01 of the read length and finally with the alignment length shorter than 75 bp. As genomes from public databases can contain vectors and sequencing artifacts, we performed an additional step to remove the alignments to such regions.
In detail, we extracted the sequences of the hit genomic regions with BEDTools  [32]  and excluded those that mapped with high confidence (utilizing the similar procedure as above with adjusted cut-offs: 0. 02 of the read length for edit distance, and alignment length > 35 bp) to any of 120 bacterial reference genomes  [33] , UniVec database  [34]  or to the "sequencingartifacts" file provided by BBmap  [27]. To further decrease the rate of false positives due to similarity between the genomes, we removed the reads that mapped to multiple genomes from further analysis. However, this step had no effect on the results (results not shown). For the species with SSU rRNA gene sequences, we performed a similar procedure with the following adjustments: we did not require both reads to map and we applied a 0.
The sequences obtained were compared with all Blastocystis sp. homologous sequences available from the National Centre for Biotechnology Information (NCBI) using the nucleotide BLAST program. The STs were identified by finding the exact or closest match against all known mammalian and avian Blastocystis sp. STs according to the last classification by Alfellani et al. [36]. All sequences shared 99-100% identity with the publicly available sequences, allowing the direct subtyping of the corresponding isolates. The sequences were deposited in GenBank and are available under accession numbers MG907124-MG907188.
As logistic regressions do not allow for reliable estimation of coefficients and their confidence intervals if prevalence is 0%, we excluded the contrasts where all populations of one group had 0% prevalence. We tested if Entamoeba spp. and Blastocystis sp. preferentially infect the same individuals with a Chi-square test. We additionally calculated a Pearson's phi coefficient to provide an estimate of effect size where appropriate. We further looked for significant association patterns between each pair of protozoan species/STs by conducting co-occurrence analysis implemented in the R-package cooccur  [38]. Observed and expected frequencies of co-occurrence between each pair of species are calculated with the expected frequencies assuming random and independent distribution of each ST/species.
Prevalence of protoza in the human gut Out of ten genera we looked for, we detected all of them except Cryptosporidium spp. , but only three had appreciable (>10%) frequencies: Blastocystis sp. , Entamoeba spp. and Enteromonas hominis. As could be expected, we found few pathogenic species (absence of Cryptosporidium hominis, C. parvum and Entamoeba histolytica, one individual carrying Giardia intestinalis and three individuals carrying Cystoisospora belli). Furthermore, we found low prevalence of non-pathogenic species such as Iodamoeba bütschlii, Endolimax nana and Chilomastix mesnili. Similarly, Parfrey et al. 2014  [1] , in their SSU rRNA gene amplicon-based study of gut eukaryotic communities, found Blastocystis and Entamoeba, but no Chilomastix, Cryptosporidium or Dientamoeba. However, we must consider that the DNA extraction methods used for the generation of the analyzed metagenomic datasets had not been optimized for protozoa, and especially cystic forms of these parasites are known to be often difficult to break (e.
Conclusion and perspectives Our study is the first to use a metagenomic approach to determine the prevalence of gut protozoa other than Blastocystis sp. Specifically, we used MG data to examine the prevalence of ten protozoan genera in eight populations across the globe. The particular approach we applied enabled us to detect high prevalence of mixed colonization in healthy individuals from nonindustrialized populations, including both closely and distantly related protozoa. Our results support the hypothesis that protozoa could be common residents of the healthy human gut  [2] , but also highlight the pitfalls of MG-based diagnostics due to variety of strategies that can be applied to the analysis of MG data. The role of MG in diagnostics will likely become more important with the increasing number of good-quality genomes in the public databases and the development of well-defined diagnostic protocols.
consists of 27 healthy rural Hadza of Tanzania and 11 healthy urban Italians. The Obregon-Tito dataset (study accession: PRJNA268964, a mix of 75bp, 100 bp and 150 bp paired-end reads with an average of 22. 3 million read pairs per sample) [25] consists of 12 rural Tunapuco and 24 rural Matses healthy individuals from Peru and 22 urban healthy subjects from Norman, USA. As all Cameroonian subjects were adults, we removed the individuals younger than 18 years of age from the two downloaded datasets to increase comparability, resulting in 22 Hadza, 11 Italians, 8 Tunapuco, 10 Matses, and 19 adults from Norman. Details about the samples used, together with the number of reads per sample, can be found in S1 Table.
This work was supported by the  ANR MICROREGAL  ( ANR-15-CE02-0003 ), the  Centre National de la Recherche Scientifique (LS)  and the  Institut Pasteur of Lille. AC was supported by a  PhD fellowship  from the  University of Lille  2 and the  Institut Pasteur of Lille. The funding bodies had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
Definition of Cut-off Levels of Serologies For CCP2, we used commercially pre-defined levels, which corresponds to 98. 4% specificity in the Swedish EIRA. 14 Except for CCP2, we set cut-off levels for each serology in each cohort. We defined the cut-off levels of positivity for each serology as the 98 percentile of control levels based on the previous studies  12, 14, 15  and CCP2 specificity. The serology data was then compiled into a matrix S ij , where each entry is a binary indication of the presence of fine-specific ACPA serology i in individual j.
5 3 10 À15 ; Figure  S4 ). This association was mainly driven by aspartic acid residue (Table  S5 ), consistent with our previous reports. Then we analyzed subsets of CCP2(À) case subjects, subcatergorizing CCP2(À) case subjects by whether or not they were positive for canonical or non-canonical serologies and evaluated the associations of this position (Figure  2B ). Since subdividing CCP2(À) case subjects led to a small dataset, we had less power to detect associations. However, focusing on B pos-9 since this position is an established susceptibility position to RA, we found that nominal effect sizes of aspartic acid residue at B pos-9 were different between the subsets and that the residue is associated with presence of non-canonical serologies (Figure  2C ). CCP2(À) non-canonical(þ) subjects constitute less than one fourth of CCP2(À) RA, conferred the association between CCP2(À) RA and B pos-9 (omnibus_p ¼ 9.
Acknowledgments This study was supported by The  Swedish Research Council , the  EU/IMI  project  RTCure ,  Stockholm County Council , the  insurance company AFA , the  Swedish Rheumatism Association, King Gustaf V's 80-year Foundation , and the  Swedish Research Council for Health, Working Life and Welfare. We thank  Lena Israelsson  for handling serum samples and the  EIRA study group for help in individual characterization and capture of serum samples. S. R. is supported by funding from the  National Institute of Arthritis, Musculoskeletal , and  Skin Diseases  ( 1R01AR063759 ). A. B. is supported by funding from the  NIHR Manchester Biomedical Research Center.
Results Restriction-modification system analyses. Many restriction-modification systems (R-M systems) have been described in the genomes of mollicutes species (Supplementary Fig. S1 ), with several having been functionally characterized  8, [16] [17] [18]. These systems provide immunity against foreign DNA and may therefore constitute an important barrier to both natural and artificial bacterial transformations  14, 19. Thus, to facilitate the development of a transformation procedure for M. hominis, we sought a strain containing the fewest of these defense mechanisms. Twenty sequenced M. hominis genomes (13 from clinical strains described in this study, six already published  3, [20] [21] [22]  , and the M132 reference strain) were analyzed and compared using Molligen and Rebase databases  23, 24.
M. hominis strains a Type I Type II Type III Type IV Total EcoR124II EcoKI BcgIA Sau96I HaeIII FokI DpnII Eco57I BspRI VspI McrBC PG21 ♦○ b ○ ♦○ ♦ 6 ATCC 27545 ♦○ ♦ ♦ 4 ATCC 33131 ♦ ♦○ ♦ 4 AF1 ♦♦ ♦○ ♦ ♦ ♦ 7 AF3 ♦ ○ ♦○ ♦ ♦ 6 PL5 ♦ ♦ ○ ○○ ♦ ○ ♦ 8 M132 c ♦ ♦ ○ ○ ♦ ♦ ♦ 7 35 ♦ ♦♦○ ○ ♦ 6 331 ♦ ♦ ♦♦ ○ ○○○ ♦ 9 ♦ ○ ♦♦ ○ ♦♦ Table 1. In silico analyses of restriction-modification systems in the sequenced M. hominis. a Strains in italic correspond to strains with a genome sequence available in the NCBI database. b ♦ Corresponds to one complete copy. ○ Indicates a putative incomplete system. c Strains in bold are transformable strains. Development of the M. hominis PEG-mediated transformation protocol. The PEG-mediated transformation protocol for M. hominis was developed based on the previously developed transformation protocols for M.
Genotypic analyses of M. hominis transformants. Colonies generally appeared on selective plates after 3 to 14 days of incubation, with most colonies exhibiting a typical "fried-egg" morphology that is characteristic of the wild-type M. hominis strain. All of the transformants tested were positive by PCR for the tet(M) gene, suggesting that the transposon in the pMT85-Tet plasmid was present into the M. hominis cells. Moreover, the transformants were confirmed as M. hominis via sequencing of the 16S rDNA PCR products. Further analyses were performed to precisely identify the site of transposon insertion. Single-primer PCRs using the gDNA of 24 transformants was followed by Sanger sequencing of the PCR products, with results obtained for 16 of the 24 mutants. For these 16 mutants, the sequencing data showed that only one copy of the transposon was present in the bacterial genome and that its insertion seemed to occur at random position in the genome (Table  3 ).
Materials and Methods Mycoplasmas strains, culturing and numeration. The M. hominis reference strains PG21 (ATCC 23114) and M132 (ATCC 43521) and the clinical isolates 2674, 3299, 331, 3364, 35, 3631, 4016, 4235, 4788, 4796, 5012, 5060, and 5096 (SRA accession number: PRJNA493181) were grown at 37 °C in Hayflick modified medium supplemented with arginine for 24 to 48 hours  51. Transformants were cultured in the same medium containing 2 µg/mL of tetracycline. Bacterial titers were evaluated both by determining color changing units (CCU) and colony-forming units (CFUs) as previously described  51. MICs were determined according to the Clinical & Laboratory Standards Institute guidelines  52. plasmid and in vitro methylation. The vector used for M. hominis transformation was the plasmid pMT85-Tet, which was derived from the Tn4001-based mini transposon plasmid pMT85  25.
Acknowledgements The authors thank  Christophe Boury  and  Charlotte Mouden  for whole genome sequencing and assembling, performed at the  Genome Transcriptome Facility of Bordeaux. We thank  Brigitte Tauzin ,  Marie Gardette  and  Nadège Hénin  for technical assistance. We finally thank  Sébastien Rodrigue  and  Florence Tardy  for reading the manuscript.
Protein Analysis The amount of Sup35 in different strains was quantified using Western Blotting with rabbit polyclonal anti-Sup35 antibodies  (Chabelskaya et al. , 2004). Monoclonal anti-tubulin antibodies (T6074, Sigma) were used for tubulin detection. Densitometry measurements were performed in ImageJ software  (Schneider et al. , 2012). SDS-PAGE with additional boiling  (Kushnirov et al. , 2006)  was performed to detect Sup35NM-GFP and Sup35 in the aggregated and soluble fractions. For the analysis of Sup35 amyloid aggregates, SDD-AGE was used  (Kryndushkin et al. , 2003).
The potential effect of this mutation on the Sup35 aggregation was evaluated with ArchCandy program  (Ahmed et al. , 2015). Previously it was shown that this tool accurately predicts the impact of amino acid substitutions on aggregation properties of a protein  (Ahmed et al. , 2015; Bondarev et al. , 2015; Roche et al. , 2017). According to the analysis, sup35-M0 mutation could significantly decrease the amyloidogenic potential of Sup35 and have the highest effect on this parameter compared to known PNM2 mutation  (Doel et al. , 1994 ) and other sup35 KK , which were shown to eliminate the [PSI + ] prion (Figure  1 )  (Bondarev et al. , 2013).
This fact confirmed our assumption that the mutation eliminates the prion. We also compared the relative amount of the Sup35 protein for strains with the wild-type and FIGURE 1 | Substitutions Q33K/A34K within N-domain of Sup35 decrease the amyloidogenic potential of the protein. The ArchCandy program  (Ahmed et al. , 2015)  was used to predict amyloidogenic properties. Cumulative scores (sum of β-arch scores counted for each amino acid residue) are presented on the plot. WTwild-type protein; G58D, Q33K/A34K, Y46K/Q47K, and Q61K/Q62K substitutions that correspond to mutations PNM2, sup35-M0, -M1, and -M2, respectively. the mutant SUP35 allele and found no difference (Figures  2C, D ), suggesting that the prion loss was not caused by a decreased amount of Sup35. To compare the effects of the new mutation on the prion replication, we estimated the prion loss and transmission in the presence of sup35-M0 allele according to special protocols for each parameter [  (Afanasieva et al.