dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Susan Archer  for technical writing, and  Nick Downey  from  Integrated DNA Technologies  for support with probe/primer design. We acknowledge  CrB HCl-Neurobiotec  tumor bank ( Hospices Civils de lyon, lyon, France ). MDT is supported by a  CIHr Clinician Scientist Phase II award , funds from the garron Family Chair in Childhood Cancer research at  The Hospital for Sick Children  and the  University of Toronto , and operating funds from the  Canadian Institutes of Health  research, the  National Institutes of Health  (r01CA159859 and r01CA148699) and the  Pediatric Brain Tumor Foundation. Mr is supported by a fellowship from the  Dr. Mildred Scheel Foundation for Cancer research/german Cancer Aid  and funds from the  Baden-Wurttemberg Foundation. Vr is supported by a  CIHr fellowship  and an  Alberta Innovates-Health Solutions Clinical Fellowship.
Discussion The data presented here are, to our knowledge, the first direct evidence of intracellular vitrification in a cell suspension under ecologically relevant conditions, that is in a dilute aqueous medium and the absence of external cryoprotectants. Vitrification was observed in all cell types examined, and intracellular freezing was observed in none. The data show clearly that during slow cooling in the presence of extracellular ice, free-living microbial cells will be subject to dehydration as freeze-concentration of the external medium withdraws water from the cell, and at a temperature above , 226uC the cell contents will undergo a colloid glass transition. Following rapid rates of cooling (. 20 K min 21 ) intracellular ice formation can be detected by DSC in the yeast S. cerevisiae  [19] , but we did not detect any intracellular ice at the slow rates of cooling employed in this study.
Both Kir6. x and SUR subunits participate in the metabolic regulation of channel activity by nucleotides, with binding of ATP to Kir6. 2 closing the channel and binding of Mg-nucleotides (MgATP, MgADP) to SUR stimulating channel opening  [15] [16] [17]. Structural information on the K ATP channel is still limited. The 3D structure of a eukaryotic Kir channel (2. 2) has been solved  [18] , as have those of the cytosolic domains of two related eukaryotic Kir channels  [19, 20] , a bacterial Kir homologue  [21]  and eukaryoticprokaryotic chimeric channels  [22]. This has enabled construction of a molecular model of Kir6. 2  [23]. Information on the complete structure of ABC proteins is also sparse, and in the case of eukaryotic ABC proteins is limited to P-glycoprotein and ABCB10, both of which are not members of the ABCC subfamily and lack TMD0  [24]  (Dataset coordinates for ABCB10 can be found under accession number 4AYT).
Electron microscopy and image processing Images were recorded on Kodak SO 163 film using a Philips CM120 electron microscope operating at 120 kV under low dose conditions with a nominal magnification of 45 000 9 (actual magnification 41 000 9). Images were digitized with a step size of 12. 5 lm on a Nikon Super Coolscan 9000. The pixel size was 3. 04 A. Particles were picked by hand, masked and aligned with a reference-free method  [51]. The WEB and SPIDER software package  [52]  was used for all image processing. All particles (297) imaged were used. Resolution was assessed from the 0. 5 Fouriershell correlation cut-off  [52, 53].
Modelling of SUR2B A homology model of human SUR2B was built using MOD-ELLER 9v5  [54]  and the ADP-bound crystal structure of Sav1866 (PDB ID 2HYD) as template. A multiple sequence alignment of SUR2B, SUR1 and Sav1866 was created with CLUSTALX 2. 1  [53]  by aligning the half-transporters of SUR (TMD1-NBD1 and TMD2-NBD2) individually with Sav1866. The sequence alignments were combined to yield a complete alignment of SUR2B (residues 281-1549) with homodimeric Sav1866 (1156 residues). This alignment is shown in Fig. S1. The sequence identity is 21% overall, with higher identity in the NBDs (30-35%) and lower identity in the TMDs (10-14%) (Fig. S1 ). Loop insertions of SUR2B that are not present in Sav1866 were constructed using the loop modelling feature of MODELLER 9.
Fitting of structures and models into the projection map Crystal structures of Sav1866 (PDB ID 2HYD), P-glycoprotein (PDB ID 3G5U) and a homology model of SUR2B (based on the Sav1866 structure) were fitted to the EM maps using SPIDER  [52]. In this process, the 3D structures were first converted into 3D density maps using a Gaussian filter and centred on the origin. Next, the density was rotated in steps of 1°around the z-axis and projected along the z-axis to yield a 2D projection map of the crystal structure or model. For each rotation angle step, a cross-correlation and peak search was used to identify the best fit of this 2D projection into the SUR2B EM map, as described by Kuo et al. [55]. In order to fit three other protomers into the same EM map, the fitting was repeated at fixed rotation angles of +90°, +180°and +270°relative to the rotation angle of the optimal fit.
The resulting rotations and shifts were also applied to the PDB file itself, resulting in positions and orientations that correspond to the best fits. This enabled the best fit of the x and y orientations of the structures/ model to the SUR2B projection map to be obtained. For display purposes, in the figures the 2D SUR2B EM map is displayed in conjunction with the fitted 3D structure (or model). The EM map was positioned at the NBD-TMD interface and the 3D structure was then viewed from either the intracellular or extracellular side (Figs  4  and 5 ). The orientation of the projection map was not changed. The EM map and fitted structures were visualized in UCSF CHIMERA  [56]. The TM regions (residues 70-183) of the Kir2. 2 crystal structure (PDB ID 3JYC) were tetramerized using crystallographic symmetry transformations.
Acknowledgements We dedicate this paper to the memory of  Dr Michael Mikhailov , who passed away during the course of this work. We thank the  Wellcome Trust , the  Royal Society  and the  European Union  (EDICT:  201924 ) for support. JA held a  Wellcome Trust Structural Biology Studentship. CV-B held a  Wellcome Trust   University Award. FMA is a  Royal Society Research Professor. HdW is an  EFSD/Lilly  research fellow.
This finding is particularly important because it directs biological studies to understand the mechanisms that lead to this chronic life-long inflammatory disease. junction variants) with 56% of these already reported in dbSNP version 132, a non-synonymous/synonymous ratio of 1. 7 and a transition/transversion ratio of 2. 38 (Table  S1 ). These results are similar to those obtained from our recent re-sequencing study in CD, as well as those reported by the 1000 Genomes Project, and are indicative of a relatively high true-positive rate for our dataset. This was confirmed by genotyping the 350 discovery DNA samples for a random subset of 237 variants from the total of 1590 high quality variants (Table  S2 ).
Follow-up genotyping and association analyses After removal of variants that did not validate, variants observed only once in our sequencing dataset (singletons) and variants from the MHC region, 84 non-synonymous coding variants (missense, non-sense and splicing variants), were used for subsequent analyses. Following removal of common variants (frequency. 5%) and variants that did not design in our genotyping assays, we carried out follow-up genotyping for 42 of these variants. Genotyping was performed in 6 independent casecontrol cohorts totaling 7,292 UC cases and 8,018 HC (Table  S3 ), and additional data was obtained for 7,143 UC cases and 12,186 HC from the International IBD Genetics Consortium (IIBDGC) Immunochip project for 14 of these variants  [3]. Since our study focuses on infrequent and rare variants, we expect few non-reference alleles for these variants in each subcohort studied, which precludes the use of asymptotic statistics utilized in typical association studies of common variants.
Using GEO public microarray datasets, we pursued a systematic follow-up analysis of expression profiles of epithelial cells in response to bacterial products, PAMPs/pathogens. We found that RNF186 gene expression was significantly up-regulated in small intestine epithelium and induced by Shigella infection in mice (P = 4. 21610 28 ) (Figure  1 , Panel A)  [18, 19]. Both invasive (INV+) and non-invasive (INV2) strains of Shigella induced significant overexpression of RNF186 in intestinal tissues of 4-dayand 7-day-old mice infected for 2 or 4 hours. To further identify putative transcriptional regulators of RNF186 expression, we employed a text-mining and network-generating analysis of human protein-protein, protein-DNA, protein-RNA and proteincompound interactions. Specifically, from our analyses we   [39] [40] [41].
e. missense, nonsense or splice junction variants) in the NOD2, ATG16L1, IL23R, MST1, CARD9, IL18RAP and RNF186 genes, and at least suggestive evidence for causal variation in the CUL2, C1orf106, PTPN22, MUC19, CEP72, LAMB1, CCR6, JAK2, and STAC2 genes (Current study and references  [4, 5, 7, 11, 31] ). While only a small fraction of the recently identified 163 IBD loci have been sequenced  (  loci) in IBD patients and controls, this would suggest that from ,10% (15 of 163 total loci) to ,35% (15 of 42 loci sequenced) of IBD loci have causal variation affecting the protein-coding or splice junctions. There are an additional 5 loci (ITLN1, GSDMB, YDGL, SLC22A4, and FCGR2A) for which there are nonsynonymous coding or splice variants present in public databases (dbSNP, 1KG) that are correlated with the index SNP identified in the GWA studies that have yet been tested directly, thus potentially increasing the estimated number of IBD loci with causal variation within the coding and splice regions  [3, 32].
Moreover, should this observation not be limited to risk loci identified by GWA studies, this has implications with respect to future efforts for discovering risk loci. Specifically, if the occurrence of rare variants with large effects sizes is relatively infrequent, then this may favor the current paradigm of locus discovery by GWA followed by targeted sequencing rather than whole-exome or whole-genome sequencing for locus discovery as this would require even larger sample sizes. Alternatively, given the evergrowing size of public databases of common and rare variants, targeted genotyping of known variants within risk loci identified by GWA may prove to be an efficient approach. For example, all but two of the 22 candidate causal variants identified in the current study or that of Rivas and colleagues are now found in the Exome Sequencing Project database.
Target selection and design Target exonic sequences were selected based on the coding exons of 55 genes in 14 UC-associated regions and 7 regions identified in CD with nominal replication in our recent UC GWAS study, as well ECM1 identified from recent candidate-gene study in UC  [6, [8] [9] [10] 34]. Specifically, amplicons were designed from genome build Hg18 using a web-base automated pipeline (Optimus primer: Website (http://op. pgx. ca)) that uses the Primer 3 design software and user defined parameters  [35]. Design parameters included amplicon sizes between 400 and 600 base pairs, as well as the inclusion of Not1 tails for subsequent concatenation and shearing steps in library construction. PCR amplification reactions contained 40 ng of pooled genomic DNA, 16 HotStar buffer, 0. 8 mM dNTPs, 2 mM MgCl2, 0.
Sequencing and variant discovery The combined PCR products from each pooled DNA group were concatenated using the NotI adapters and sheared into fragments as previously described  [36]. Libraries were constructed according to Illumina single-end library protocol, with 150-200 bp gel size selection and PCR enrichment using 10 cycles of PCR, and then single-end sequenced with 36 cycles on an Illumina Genome Analyzer II. Each sample pool was sequenced using a single lane of Illumina GAII analyzer flowcell; 36-base pair reads were aligned to the genome using MAQ algorithm  [37]  and base qualities were recalibrated using GATK (Genome Analysis ToolKit)  [38]. Finally, variant discovery was performed using the previously described Syzygy software, designed to analyze sequencing data from pooled DNA sequencing  [7].
Genotyping, validation and follow-up genotyping We randomly selected 237 high quality variants for validation in our 350 discovery DNAs samples using Sequenom MassARRAY iPlex200 chemistry. Genotyping assay designs were obtained from the Assay Designer v. 3. 1 software, and genotyping oligonucleotides were synthesized at Integrated DNA Technologies. The correlation coefficient between observed minor allele frequencies and frequencies estimated from Syzygy for validated variants was calculated in order to evaluate the overall quality of our dataset (Figure  S2 ). Eighty-four high quality non-synonymous coding variants (missense, nonsense and splicing variants (within 2 bp of a splice site)) remained after the exclusion of singletons from our sequencing results, variants that did not validate and variants within the MHC region.
We then evaluated these variants in an independent cohort of North-American individual of European descent from the NIDDK IBD genetics consortium (754 cases and 1008 controls); only variants detected in this independent cohort were kept for follow-up genotyping. Following assay design, 42 SNPs were genotyped using Sequenom MassARRAY iPlex200 chemistry in 6 independent follow-up case-control cohorts (7292 cases and 8018 controls) (Table  S3 ). Because of design constraints and assay failures, not all markers were examined in all follow-up sample sets. For a subset of these variants, further genotyping data was obtained from the International IBD Genetics Consortium Immunochip data (7143 UC, 12186 controls) NanoDrop UV-VIS Spectrophotometer. Sample hybridization was performed according to the manufacturer's standard protocol and microarrays were scanned using the Sure Scan Microarray Scanner (Agilent technologies).
(A) Table of gene expression fold change statistics from comparison of PBMC transcriptional profiles in healthy subjects and patients with ulcerative colitis (UC) (GSE3365). Only top 10 genes and RNF186 are shown. The rank column refers to the rank of the gene for signal to noise ratio in the specific study (1061 significant genes ranked). (B) Plot of RNF186 gene expression in samples from normal individuals and patients with UC. The squares and crosses represent median and mean respectively. (TIFF) Figure S4 Comparative gene expression profiling in PBMC from healthy subjects and Crohn's disease patients. (A) Table of gene expression fold change statistics from comparison of PBMC transcriptional profiles in healthy subjects and Crohn's disease patients (GSE3365). Only top 10 genes and RNF186 are shown.
Summary of Pooled Sequencing Experiment. (XLSX)Table S4 Details of sequencing and follow-up genotyping results, as well as association analyses for each SNP tested in this study. (XLSX) Table S5 Power calculations for each SNP tested with observed minor allele frequency greater than 0. 0001. (XLSX) Table S6 Sequencing coverage per gene. (XLSX) Table S7 Table of datasets available in public domain that were processed and analyzed for RNF186 expression. (XLSX).
Acknowledgments The authors would like to acknowledge the important contributions of the  Quebec IBD Genetics Consortium , the  NIDDK North American IBD Genetics Consortium  and the  International IBD Genetics Consortium  for sample collection and for the Immunochip genotype data used in replication. data: PG MB GB KSL GG. Wrote the paper: MB PG GB MAR RJX MJD JDR. Functional characterization design and coordination: GG JB RJX. Study subject recruitment and phenotyping:  AB SRB FB RHD JHC DPBM GRS SS MSS JH VA RKW MD SV AF JDR RJX LT. Sequenom assay designs: PG MB AA CL. Pooled sequencing protocol was design at the  Broad Institute :  MAR CS MJD. Pooling  and sequencing protocol at the  Montreal Heart Institute : MB. Follow-up genotyping: AA CL CS DE MB. Supplied follow-up genotyping data: MD SV MJD AF RKW.
Research in Sweden was supported by the  Swedish Society of Medicine, Ihre Foundation, O ¨rebro University Hospital Research Foundation ,  Karolinska Institutet , the  Swedish National Program for IBD Genetics , the  Swedish Organization for IBD , and the  Swedish Medical Research Council. RKW is supported by a clinical fellowship grant ( 90. 700. 281 )from the  Netherlands Organization for Scientific Research (NWO). Genotyping of the German samples was supported by the  National Genome Research Network (NGFN)  of the BMBF. The German platform received infrastructure support from the  DFG Cluster of Excellence ''Inflammation at Interfaces '' and the PopGen biobank. The  NIDDK IBD Genetics Consortium  is funded by the following grants:  DK062431  (SRB),  DK062422 & DK062429  (JHC), DK062420 (RHD), DK062423 (MS), DK062413 (DPBM) and DK062432 (JDR).
A consortium newsletter was developed and circulated widely. An open access website (www. menafricar. org) was established as well as a separate, password protected site, accessible only to consortium members, which holds all operational documents and which hosts a discussion forum. Each centre established a crisis management committee to manage any unforeseen events, such as a death occurring during the course of a carriage study. The study was approved by the LSHTM Ethics Committee and by the ethics committees of each of the African partner institutions with the exception of Chad, which does not have a formal ethical committee, where approval was granted by a committee set-up to oversee MenAfriCar studies by the Ministry of Health. The study was registered with ClinicalTrials. gov (NCT01119482).
The 7. 5 mM MgCl 2 master mix was adjusted to 11 mM MgCl 2 to optimise the technique. Prior to the introduction of PCR testing as a routine procedure, each site underwent cross-validation of the technique, comparing results of approximately 50 samples with results obtained at the University of Oxford. On arrival in Oxford, heat-killed bacterial cell suspensions are archived at À20 °C and entered on a BIGSDB database  (Jolley & Maiden 2010)  for subsequent hierarchical analysis using a custom-built high-throughput Sanger sequencing pipeline. All sequence data are submitted directly into this database and the results interpreted automatically by querying with reference sequences. The first stage of characterisation of Neisseria species is done with an assay based on ribosomal multilocus sequence typing (rMLST)  (Bennett et al.
Data management Various systems for data management that required construction of local and a central database were considered, including manual double entry, the open-source electronic data capture system Open Clinica  (Paulsen et al. 2012)  and TeleForm, an automated form processing system for data management (TeleForm software: version 10. 4. 1 â 1991-2009, Autonomy). After preliminary tests at several African centres, was TeleForm chosen. This data management system allows study forms to be specially designed, scanned after completion, automatically read through a combination of optical/ intelligent character recognition, optical mark recognition and barcode recognition and then verified before being exported to a local database. It also allows rapid data entry and reduces opportunities for error, especially for optical mark recognition variables such as check boxes.
For harmonisation purposes and to facilitate technical support, centres were provided with similar scanning and computing equipment pre-loaded with TeleForm. To ensure that all centres used the same study forms, were developed centrally in both English and TeleForm French. A label form was designed, which could be populated automatically with study IDs and printed in self-adhesive label sheets for use in the laboratory. Local and central databases were designed to accommodate the study forms. A separate database module was developed centrally to automatically populate the main study database with genetic laboratory results from the Oxford BIGS database  (Jolley & Maiden 2010). Additional data validation software packages were developed and shared between centres to harmonise local data cleaning.
The TeleForm data management adopted for the study was new to most centres and because of lack of time for training and frequent changes in form design at the start of the project, completion errors and poor optical character recognition were initially common. However, this situation improved as form design became stable and field workers and local data managers gained experience and confidence in completing forms and using the system. Slow and intermittent Internet connectivity was initially a major challenge that led to delays in uploading local data and synchronising local and central databases, which, in turn, affected central monitoring. This challenge was partially addressed by the end of the project with some centres acquiring higher bandwidth or dedicated Internet connection, for example, through mobile broadband, and by others performing data upload operations in the evening or at night.
Acknowledgements The authors thank members of the  MenAfriCar Advisory Committee (Fred Binka ,  Mamoudou Djingarey ,  Robert Heyderman ,  Marie-Paule Kieny ,  Marie-Pierre Preziosi ,  David Stephens  and  Marcel Tanner  [chairman]) for their sound advice and support and the following who provided clinical monitoring:  Richard Nare  (Chad),  Frank Baiden  (Ghana),  Workeabeba Taye (Ethiopia) ,  Haoua Amadou  ( Niger and Mali ) and  Pierre Ndiaye (Senegal). The authors also thank the following who contributed to the establishment of the MenAfriCar consortium and to its activities in various ways:  Eric Bertherat  and  William Perea  ( WHO, Geneva, Switzerland ),  Dominique Caugant  ( NIPH, Oslo, Norway ),  Mamoudou Djingarey  ( WHO, Ouagadougou, Burkina Faso ),  Rana Hajjeh  and  Nancy Messonnier  ( CDC, Atlanta, Georgia ),  Marc LaForce  ( PATH, Seattle, USA ),  Judith Mueller  ( EHESP, Rennes, France ),  Pierre Nicolas  ( IMTSSA, Marseilles, France ),  Gerd Pluschke  ( STPI, Basle, Switzerland ) and  Muhamed-KheirTaha (Institut Pasteur, Paris, France ).
Acknowledgments We are grateful to  A. Barroso ,  M. Krawczyk  and  R. Santos  for useful discussions. S. D. acknowledges partial support from the  EU   FP7 ITN INVISIBLES (MC actions ,  PITN-GA-2011-289-442 ) and the  Lyon Institute of Origins. The work of R. G. F. and J. P. S. was partially supported by  FCT -Fundação para a Ciência e a Tecnologia , under the Projects PEst- OE/FIS/UI0777/2011  and  CERN/FP/123580/2011 , and by the  EU RTN Marie Curie  Project  PITN-GA-2009-237920. The work of H. S. is funded by the  European FEDER  and  Spanish MINECO , under the Grant  FPA2011-23596.
Data collection, laboratory measurements, and follow-up WC was measured in the standing position, using a tape placed midway between the lower ribs and iliac crests on the midaxillary line. BMI (kg/m 2 ) was calculated from the measured height and body weight. Supine blood pressure was measured in the right arm using a manual mercury sphygmomanometer, after a 10-minute rest. The first and the fifth Korotkoff phases were used to define the systolic blood pressure (BP) and diastolic BP. Three measurements were taken, and the mean of the last two was used. Standard biological parameters were measured by enzymatic methods using a Hitachi 917 automated analyzer (Hitachi Ltd, Tokyo, Japan); High-density lipoprotein (HDL) cholesterol was measured by direct enzymatic assay using cyclodextrin and lowdensity lipoprotein (LDL) cholesterol was calculated using the Friedwald formula.