dataesr/scientific-paragraphs-categorization huggingface dataset:
eligibility Criteria and Literature Search Strategy A systematic and comprehensive search of electronic databases, including MEDLINE, Scopus, http://ClinicalTrials. gov, the PROSPERO International Prospective Register of Systematic Reviews, Science Direct, Springer Link, and EMBASE was done from March 2016 to September 2016. The search process was completed using the keywords: "hygiene hypothesis, " "gut microbiota, " "microbiota heritability, " "vitamin D, " "VDR, " "autoimmune disease, " and "immune tolerance. " The search was not restricted to the type of study (i. e. , species, meta-analysis, case-control, prospective cohort studies, and reviews), sample size, year of publication, publication status, or follow-up. However, we only consulted articles published in English. Bibliographies of the identified reviews and original research publications were hand selected for additional studies that may have been missed by the database searches.
Data extraction and Synthesis Full copies of citations coded as potentially relevant were obtained, and those meeting the inclusion criteria were read in detail and data were extracted. One reviewer (Allison Clark) extracted information about the study aim, population and sample size, experimental design and duration of follow-up, species, individual characteristics, changes in the gut microbiota composition, and immune response and association or not with an autoimmune disease. The primary outcome was the gut microbiota profile, aberrant changes in the immune response, vitamin D status, VDR functions, or other clinically relevant outcomes related to autoimmune and immune-related conditions. Details were then checked by a second reviewer (Núria Mach). If eligibility could be determined, the full article was retrieved.
Data Synthesis A search conducted in March 2016 resulted in the following list of key terms combinations (hygiene hypothesis, the gut microbiota, and autoimmune disease = 5; vitamin D and autoimmune disease = 18; vitamin D3, VDR function, intestinal microbiota, and autoimmune diseases = 16). A total of 47 experimental studies and 54 reviews met the inclusion criteria and were included in the review. Most of the articles were reviews or randomized controlled trials. Periods of data collection spanned from 1989 to 2016, proving data from humans and animals models (i. e. , mice and rats).
VDR Polymorphisms and Improper VDR Function Given the potential role VDRs have on immune responses and intestinal homeostasis, VDR genetic variants have also been studied as potential factor of autoimmune diseases since they may influence VDR activity. VDR is encoded by a large gene (>100 kb) mapped to chromosome 12q12-14. Its 14 exons spanning ~75 kb (105) exhibit a high number of polymorphisms, with at least 4,710 reported variants, most of which are either undetectable or occur at a low frequency in the general population and appear to have no potential functional significance according to the dbSNP database 2016. Among the known VDR polymorphisms, the most common single-nucleotide polymorphisms (SNPs) that influence VDR expression within the immune system include Bmsl (rs1544410), ApaI (rs7975232), TaqI (rs731236), and FokI (rs10735810)  (106).
B A Fig. 1  (See legend on next page. ) performed as described by Thibault et al. [24]  and the profile number assigned by INRA and published in an online database  [25]. The presence of LSP A 20 (for Type C strains) and LSP A 4-II (for Type S strains) was determined by PCR using primers reported by Semret et al. [26]  and conditions described by Biet et al. [23]. Type I and III strains were assigned according to the SNP analysis of the gyrA and gyrB genes as described by Castellanos et al. [19]  or according to their PFGE profile  [15, 17]. SNP analysis of IS1311 by PCR restriction enzyme analysis to determine types S, C and B was undertaken as described by Whittington et al. [20]. Two other isolates representing members of the Mycobacterium avium complex (Mycobacterium avium subspecies avium [Maa] and Mycobacterium avium subspecies silvaticum [Mas]) were included in the panel for WGS to place the Map strains in a wider mycobacterial context.
SNP analyses and phylogenomics The sequencing data were mapped against the Map K10 reference  [27]  using SMALT (http://sourceforge. net/projects/smalt/) with default parameters. Consensus variants were called using SAMtools and bcftools  [28]  using filters designed to keep false positives to a minimum, which include a minimum base quality of 50, a minimum mapping quality of 30, support from at least 4 reads (2 forward and 2 reverse) and an absence of heterozygosity. Previously these filters have been shown to keep the false positive rate to lower than 1 SNP per genome  [29]. Variants that passed these filters were used to build a maximum likelihood tree using RAxML v. 7. 0. 4 [30]  with 100 bootstrap replicates. The BEAST package (v1. 7. 5), a program for Bayesian Markov chain Monte Carlo (MCMC) analysis of genetic sequences, was used to estimate substitution rates  [31].
These were then mapped to the Map K10 reference  [27]  using SMALT with repeat mapping switched off. The specific position of these reads indicates the position of the IS1311 element. Publically available Camelid genome sequences  [6]  were incorporated into the phylogenetic tree by simulating short reads from the de novo assembly, and following the variant calling procedure described above. To do this an in-house script was used to create 75 bp paired reads, assuming a 200 bp insert size in a moving window procedure for every 3 bp, resulting in an approximate depth of 25×. These simulated reads will not have sequencing errors like normal short-reads, however this approach relies on the accuracy of the original de novo assembly so results should be treated with caution.
Results and discussion Phylogenetic classification of Map isolates WGS data were obtained for 141 isolates. The sequences were mapped to the corrected and annotated reference Map strain K10  [27]  to identify SNPs. A maximum likelihood phylogenetic tree based on SNPs detected in non-repetitive regions of the genome is depicted in Fig. 1  and Additional file 2: Figure  S1. Maa and Mas were included to place the Map strains in a wider mycobacterial context as shown in the inset in the figure. There were more than 40,000 SNPs between the Map isolates and these other members of the Mycobacterium avium complex. SNP distances between the major strain groups are shown in Table  1. The phylogenetic analysis confirmed the division of Map isolates into two major lineages, concordant with the previous Type S and Type C designations.
Type C strains are isolated from a broad range of hosts and do not appear to have a host preference. The evidence for interspecies transmission is compelling, but the relative risk of transmission of the different strain types between host species cannot be determined with our data. It has been reported that the risk of natural transmission of Type S strains from sheep and goats to cattle is low and occurs only when susceptible animals are exposed to high doses  [33]. The phylogenetic analysis clarified the relationship between the previously described Type I and Type III A B C D Fig. 3  Investigating the presence of a clock-like signal in the data. The presence of a molecular clock in the dataset was assessed by plotting the root to tip distance of isolates in the phylogeny against isolation date  [53]  for both the dataset as a whole (a + b) and only Type II isolates (c + d).
Both had evidence of a very weak positive signal, as indicated by a low linear regression correlation coefficient (a + c). In order to test the significance of these observations, 99 comparison datasets were produced, in which the isolation dates were permuted on the phylogeny. b and d show histograms of correlation coefficient values from the permuted datasets, with the red line indicating the value from the real data. In both cases the correlation coefficient of the real data is significantly greater than the permuted data at the 0. 05 level. Passing this test is a minimal requirement for the application of BEAST analysis (Fig. 4 ), which assumes the presence of a molecular clock strains. These strain groups are distinct sublineages of Type S, and Type III is not intermediate between the Type S and Type C strains as previously suggested.
Since this work was initiated, further Map isolates have been sequenced and their genome sequences made available in the public databases. These include the Camelid strains JQ5 and JQ6  [6] , S5 Indian bison-type  [36] , USA Type S isolate S397  [37] , Australian ovine isolate CLIJ361  [34] , two Australian bovine isolates CLIJ623 and CLIJ644  [34] , Australian human isolates Pt139, Pt144, Pt145, Pt146, Pt154, Pt155 and Pt164  [34] , USA caprine isolate JTC1285 and an isolate from an oryx JTC1281  [35]. To identify the phylogenetic relationships between these strains and those in our panel, we included these strains in our analysis and the resulting phylogenetic tree is depicted in Additional file 3: Figure  S2. It is clear from the analysis that the available camelid strains represent another subtype of Type S.
The S5 Indian bison-type strain clustered with the other non-USA Type B strains confirming the existence of an Indian bison-type group. The USA S397 isolate was most Fig. 4  Estimates of substitution rate. Coalescent analyses were implemented using the BEAST package (v1. 7. 5)  [31]  as described in the text. This was carried out both on the entire data and the Type C isolates alone, using a variety of population models as shown. The mean value represents the mean estimated substitution rate from three independent runs. The confidence intervals represent the maximum and minimum higher posterior densities obtained. As a comparison, the estimated rates are shown alongside a predicted substitution rate of M. tuberculosis in the context of transmission  [49]  closely related to the Type III isolates originating from Spain whereas the Australian ovine isolate CLIJ361 was most closely related to a New Zealand Type I isolate.
Relationship between MIRU-VNTR genotype and the whole genome phylogeny MIRU-VNTR genotyping is based on detecting the number of copies of mycobacterial interspersed repetitive units (MIRUs) and variable number tandem repeats (VNTRs) in the genome, which differ between strains. This is probably the most commonly used genotyping method for Map as it is easy to perform, requires only a small amount of DNA and the digital sequence profiles can be stored in databases and compared between laboratories  [12]. For this study we used the eight MIRU and VNTR loci described by Thibault and coworkers  [24] ; MIRUs 292 and X3 and VNTRs 25, 47, 3, 7, 10 and 32. Profile numbers (designated INMV numbers) were assigned by INRA and collated in a publically available database  [25]. MIRU-VNTR data available for 105 sequenced isolates were mapped onto the phylogenetic SNP-based tree as shown in Fig.
Genomic changes following continuous passaging in vitro WGS was performed on three passages (P1, P26 and P32 or 37) of strains M21/02, JD143 and MapK10 and the sequences compared to determine any changes in the genome between passages. The field strains M21/02 and JD143 were passaged from primary isolation on 7H11+, P1 representing the first subculture. The MapK10 strain was obtained from the American Type Culture Collection (ATCC BAA 968) and there are no data regarding how many times this strain had been passaged. Therefore, for this strain P1 represents the first passage in our laboratory. For strain M21/02 a SNP (A → C) was detected at position 2041004 at P26 causing a non synonymous mutation in the MAPK_1803 (glnE) gene. This SNP was also detected at P37 but no further changes were observed.
Estimation of the substitution rate in Map We investigated the presence of a molecular clock in the dataset by plotting the root to tip distance of every isolate in the phylogeny against the date of their isolation. This was carried out on the dataset as a whole (where the date of isolation was available) as well as on only Type-C isolates, using a variety of different evolutionary and population models. A weak positive signal was detected (Fig. 3a, c ), providing weak evidence for the presence of a molecular clock. However, when this signal was compared to those obtained from trees where isolation date was randomized, this relationship was found to be statistically significant for the entire dataset but not Type Cs in isolation (Fig. 3b, d ), which provides support for the presence of a molecular clock in this dataset.
A central public repository of Map genome sequences and corresponding metadata (particularly epidemiological, pathological and virulence data) would greatly facilitate future studies. With the dataset employed in this study, there was no evidence for strong geographical clustering. The data supports the perception that Type S strains have a preference for sheep and goats suggesting host adaptation may have occurred but this could be biased by the selection of isolates available for study. The human isolates from Crohn's patients did not comprise a distinct single group but isolates from the same country were often closely related to one another and other Map isolates from sheep and cattle from the same country, suggesting livestock as a possible source of transmission. Overall, the Map isolates exhibited restricted genetic diversity and estimates of the substitution rate were less than half a SNP per genome per year, which presents a significant challenge for genotyping and epidemiological tracing.
Acknowledgements The authors would like to thank the following colleagues/organizations for contributing strains to this project:  F Saxegaard ,  S Nilsen ,  TB Johansen  ( National Veterinary Institute, Oslo, Norway ),  D Bakker  ( Central Veterinary Institute, Lelystad, Netherlands ),  P Overduin  ( Netherlands National Institute for Public Health and the Environment [RIVM], Netherlands ),  Z Dimareli-Malli  ( Veterinary Research Institute of Thessaloniki, National Agricultural Research Foundation, Thessaloniki, Greece ),  P Kostoulas  ( University of Thessaly, Greece ),  I Pavlik  ( National Veterinary Institute, Brno, Czech Republic ),  N Arrigoni  ( Istituto Zooprofilattico Sperimentale della Lombardia e del l'Emilia Romagna -Sezione di Piacenza, Italy ),  N Pozzato  ( Istituto Zooprofilattico Sperimentale delle Venezie, Verona, Italy ),  P Möbius  ( Friedrich-Loeffler-Institut [FLI], Jena, Germany ,  BN Tripathi  ( Indian Veterinary Research Institute, Izatnagar, India ),  DM Collins  ( AgResearch, Limited, Upper Hutt, New Zealand ),  G Thomas  ( University of Wisconsin School of Veterinary Medicine, Wisconsin, USA ),  J O'Mahony  ( Cork Institute of Technology, Cork, Republic of Ireland ),  I Grant  ( Queen's University, Belfast, Northern Ireland, UK ),  R Pickup  ( Lancaster University, Lancaster, UK ),  G Caldow ,  D Henderson  ( Scotlands Rural College [SRUC], Scotland, UK ),  M Cranwell ,  A Holliman  ( Animal Health Veterinary Laboratories Agency, England, UK ).
Multi-Gauge software (FUJI film) was used for image protein quantification. All densitometric quantifications were normalized using actin as loading control. RNA extraction and Quantitative Real-time PCR. Total RNA extractions were performed with RNA easy extraction kit (Qiagen) on Qiacube device following manufacturer's material and methods. RNA quantities were normalized at 2000 ng and reverse-transcripted with GoScript reverse-transcriptase (Promega) on a Biometra thermocycler. Real time quantitative PCR were performed on Rotor-gene6000 (Qiagen) using SYBR Green protocol according to manufacturer's recommendation. Gene expression of BACE1, XBP-1s and HRD1 were normalized using human or mouse ribosomal protein 69 (Rpl 69) gene expressions depending on RNA species. RNA relative expression was calculated following Livak K.
Statistical analysis. Statistical analyses were performed with the Excel software. For statistical comparison between two groups, the parametric Student T-test (two tailed) was used. For statistical comparison between three groups, a one-way ANOVA test was performed, followed by the Bonferroni-Holme post hoc correction for multiple comparisons. Graphs represents the mean of the values obtained for each group of samples (biological replicates consisting of independent cells cultures, technical replicates were used in quantitative PCR as internal control for accuracy but are excluded from statistical analysis), error bars represent the standard error of the calculated mean (s. e. m). Criteria for data exclusion were: high background noise and low or heterogeneous specific signal, important loading control variation (Western blot), important modulation of housekeeping gene expression or low accuracy between technical replicates (quantitative PCR), unequal β -galactosidase activity between samples (Luciferase dosage), flat specific enzymatic curve with no exponential phase (β -secretase enzymatic activity assay).
Acknowledgements Drs L. Glimcher  and  R. Vassar  are warmly thanked for providing us with the XBP-1 constructs and 3D5 antibody, respectively. Dr D. K. Lahiri  is acknowledged for the kind gift of human Bace1 promoter cloned in pGL3-basic vector. This work has been developed and supported through the  LABEX (excellence laboratory, program  investment for the future) DistALZ ( Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease ) and by the  Hospital University federation (FHU) OncoAge. YG has been supported by  DistALZ. The present work was supported by  CNRS ,  INSERM  and by the  "Conseil Départemental des Alpes Maritimes" and the "Fondation Claude Pompidou ".
Expression and purification of P. aeruginosa PA14 TrmJ The gene encoding the complete TrmJ (Genbank accession: KFL12361. 1) from Pseudomonas aeruginosa PA14, with codons optimized for bacterial expression, was purchased from Genscript. The DNA corresponding to the complete TrmJ protein spanning residues 1-257 ('TrmJ') and 1-167 (comprising the N-terminal SAM binding domain, referred to as 'TrmJ-NTD') were cloned into the pNIC28-Bsa4 vector that contains six histidine residues and a TEV cleavage site at the N-terminal end. E. coli BL21 (DE3) Rosetta T1R cells were grown at 37 • C to an OD 600 of 1. The proteins were then overexpressed by the addition of 0. 5 mM IPTG at 18 • C for 18 h. Cells were pelleted and stored at -80 • C. All proteins were purified at 4 • C. The thawed pellets were resuspended in 30 ml of lysis buffer (20 mM Na-HEPES, pH 7.
Template cDNA (1 ng) and 200 nM primers for either oxyR, katA, katB, katE ankB, recG or 16S rRNA (listed in Supplementary Table  S2 ) were used in 20 L reaction of KAPA SYBR FAST qPCR kit Master Mix (ABI Prism). The reaction mixtures were incubated for 3 min at 95 • C followed by 40 cycles of denaturation at 95 • C for 3 s, annealing at 57 • C (16S, oxyR, recG, katE and ankB) and 64 • C (katA and katB) for 20 s and extension at 72 • C for 45 s. Data were obtained using STEPONE software v2. 1 (Applied Biosystems). The level of 16S rRNA expression was used to normalize target gene expression in the corresponding strain. The relative gene expression was determined using the 2 -C T method  (27). Expression of a gene was quantified as fold-change relative to the level in the PA14 wild-type strain without H 2 O 2 treatment.
Data collection and structure determination Prior to data collection, crystals were briefly soaked in their respective precipitating solution supplemented with 20% (v/v) glycerol and rapidly frozen in liquid nitrogen. X-ray diffraction data were collected at the PXIII beamline at SLS (Villigen, Switzerland) for the free TrmJ-NTD crystal (form I). Free TrmJ-NTD crystal (form II) and the complex with sinefungin were collected at Taiwan Light source NSRRC, beamline 13B1. Diffraction intensities were integrated with XDS (28), scaled, merged and truncated with SCALA/TRUNCATE  (29). The structures of TrmJ-NTD was determined by molecular replacement using the CCP4 software (  29 ) with the structure from E. coli (PDB code: 4CND) as search probe. The TrmJ protein from P. aeruginosa and E. coli share 53% overall amino acid sequence identity.
The structure of TrmJ-NTD from P. aeruginosa was built iteratively at the computer graphics using COOT  (30)  and refined using Autobuster  (31). The geometrical parameters for sinefungin were generated using coordinates 4R8S from the PDB (www. rcsb. org). Buried solvent accessible surface areas upon dimer formation were calculated using the PDBePISA server (www. ebi. ac. uk/pdbe/pisa/) and structure comparison was performed with the DALI server (http: //ekhidna. biocenter. helsinki. fi/dali server/start). The quality of the structures was assessed using the MOLPRO-BITY server (molprobity. biochem. duke. edu/) and figures were generated using the Pymol software (Schrodinger). Data collection and structure refinement parameters are summarized in Table  1. The atomic coordinates and structure factors are deposited with the Protein Data Bank under accession codes 5GMC and 5GMB for the free TrmJ-NTD protein, and 5GM8 for TrmJ-NTD bound to sinefungin.
A summary of diffraction data collection and structure refinement statistics for TrmJ-NTD is given in Table  1. The TrmJ-NTD monomer adopts the 'SPOUT' fold  (35)  -a variant of the classical Rossmann nucleotide-binding fold with the N-and C-termini in close proximity and a central ␤-sheet composed of seven parallel ␤-strands surrounded by eight ␣-helices (Figure  7A  and B ). The additional four residues derived from cloning at the N-terminal end of the polypeptide participate in the formation of helix ␣1. A peculiar topological feature also found in other SPOUT methyltransferases is the presence of a knot in the C-terminal region of the protein (Figure  7B ). An automatic superimposition of TrmJ-NTD from P. aeruginosa with structures deposited in the PDB returned TrmJ from E. coli (residues 1-164, PDB code: 4CND) as the closest homologue (59% amino-acid sequence identity) with r.
m. s. d. of 1. 3 Å (34). Both proteins are tRNA cytidine/uridine 2 -Omethyltransferase. The TrmJ-NTD structure is also closely related to various putative ribosomal RNA methyltransferases including HI038 from Haemophilus influenzae (PDB code: 3ILK), SpoU from Rhodobacter sphaeroides (PDB code: 3ONP) and the RlmB 23S rRNA methylransferase from E. coli (PDB code: 1GZ0, (  36 )) that founded this fam-ily of methyltransferases  (36). A structure-based sequence alignment is displayed in Figure  7. Both the free and sinefungin bound TrmJ-NTD crystals contain two dimers per asymmetric unit consistent with the observation that the protein eluted as a dimer in gel filtration. The buried surface area at the interface of the TrmJ-NTD dimer is 1210 Å2 , which represents approximately 14% of the total accessible surface area per monomer (8430 Å2 ) and is comparable to the interface between the N-Terminal Domain (NTD) of TrmJ from E.
ACKNOWLEDGEMENTS The authors thank  Mr. Ee Pin Koon  for technical assistance with synthesis and purification of tRNA,  Ms. Maggie Cai  for technical assistance with QQQ operation,  Ms. Thanyaporn Srimahaeak  for handling of bacterial strains,  Dr. Amnart Khongmanee  and  Mr. Bhawat Wongkhamprai  for technical assistance with QTOF analysis, and  NISB  for sharing the  Swiss Light Source and Taiwan Light Source synchrotron beamtime.