dataesr/scientific-paragraphs-categorization huggingface dataset:
SNPs were removed if HWE P < 1. 00 × 10 -4. After QC, the Chinese replication consisted of 3,043 cases and 5,074 controls with genotyping on 64 SNPs. The European replication data comprised 1,478 cases and 6,925 controls genotyped for 18 SNPs with an FDR of 1% in the Chinese replication study. The cases were of European ancestry and were a subset of those used in the replication study in the European GWAS3; in the current study we carried out new genotyping on these 18 SNPs, and the controls were the same as used in that study (these samples were checked for European ancestry using a principal component analysis spiked with HapMap samples; see the original paper3). One of the 18 SNPs typed in the European replication cohort for this study (rs2297550) failed genotyping, and the remaining 17 SNPs passed QC (<3% missing data, HWE P > 1.
Gene expression data Gene expression data came from two sources. We obtained data from Fairfax et al. 17 and unpublished data from B. Fairfax and J. Knight for NK cells, naive monocytes, monocytes stimulated by lipopolysaccharide (harvested after 2 h and 24 h), monocytes stimulated by interferon, and B cells. We obtained CD4 (CD4 + T cells) and CD14 (CD14/16 + monocytes) data from a previous study of gene expression in immune-related cells16. We made an adjustment for multiple testing using FDR = 0. 01. To test whether observed associations between SNPs and expression levels of cis-acting genes were due to chance, we calculated the RTC score18.
Fine-mapping Bayesian credibility sets For each of the associated loci in Supplementary Table  1  and Table  1 , we calculated a Bayes factor for each SNP within the 2-Mb window. We used the approximate Bayes factor of Wakefield32. We then calculated the posterior probability that each SNP was driving the association, using the Bayes factors, and created credibility sets as recently described32. We created credibility sets using the European data and the Chinese data separately and overlaid the sets (Supplementary Fig. 5 ). We focused on the intersection of these two sets and determined the SNPs with highest posterior probability within this intersection, along with allele frequencies. We focused on the intersection of the two populations' sets, as credibility sets calculated from the overall meta-analysis were driven by the European data.
We tested for differences in GRS using a t-test. A Q-Q plot for each data set satisfied assumptions of normality, and given the large sample sizes, the central limit theorem would satisfy normality for the distribution of sample means. As there was evidence of differences in variances of the GRSs between some pairs of populations (EUR versus AMR, P = 9. 97 × 10 -5 ; AMR versus SAS, P = 5. 37 × 10 -5 ; SAS versus EAS, P = 4. 50 × 10 -3 ), we used a Welch two-sample t-test that does not assume equal variances. The variances in each group were as follows: Chinese controls, 0. 75; European controls, 0. 69; 1KG EAS, 0. 86; 1KG EUR, 0. 67; 1KG SAS, 0. 66; 1KG AMR, 0. 99; 1KG AFR, 0. 77. We used the SNPs in Supplementary Table  1a  to calculate the GRS for each population. We used the estimated OR from the EUR GWAS for the calculation of the GRS in Europeans (EUR and GWAS controls) and the OR from the Chinese GWAS for the calculation of the GRS in the EAS and Chinese GWAS controls.
Acknowledgments P. T. is employed by the  Biomedical Research Centre. L. C. was funded by the  China Scholarship Council  ( 201406380127 ). The research was funded/supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. T. J. V. was awarded funding to carry out genotyping and analysis from  G. Koukis , an  Arthritis Research UK Special Strategic Award , and the  Wellcome Trust  (grant  085492 ). T. J. V. was awarded funding by the  MRC  ( L002604/1 , "Functional genomics of SLE: a transancestral approach"). Y. C. ,  X. Zhang , S. Y. and Y. S. acknowledge support from the  Key Basic Research Program of China  ( 2014CB541901 ,  2012CB722404  and  2011CB512103 ), the  National Natural Science Foundation of China  ( 81402590 ,  81371722 ,  81320108016  and  81171505 ), the  Research Project of the Chinese Ministry of Education  ( 213018A ), the  Program for New Century Excellent Talents in University  ( NCET-12-0600 ) and the  Natural Science Fund of Anhui Province  ( 1408085MKL27 ).
W. Y. and Y. L. L. acknowledge support from the  Research Grant Council of the Hong Kong Government  ( GRF HKU783813M ,  HKU 784611M ,  17125114  and  HKU 770411M ). Y. Z. thanks the  Health and Medical Research Fund  ( 12133701 ) from the  Food and Health Bureau ,  Hong Kong. We thank  T. Raj  and  P. De Jager  for contributing gene expression data (CD4 + T cells and  CD14/16  + monocytes in Asian and European populations; available in the  NCBI Gene Expression Omnibus  under accession number  GSE56035 ). We thank  B. Fairfax  and  J. Knight  for contributing gene expression data on NK cells, naive monocytes, LPS-stimulated monocytes (harvested after 2 h and 24 h), interferon and B cells. We thank  S. Daffern  for downloading the ChIP-seq data in contribution to the epigenetic analysis.
Acknowledgments: Moderation of working group activities was managed by an independent medical education and communications company, Meducom B. V. , Spierdijk, The Netherlands (financially supported by  Sanofi-Genzyme Corp. / Genzyme Europe B. V. , Naarden , The Netherlands). We would also like to acknowledge the contribution of a medical writer,  Sandy Field , who substantially supported with the generation of the first draft of the manuscript based on notes from the working group meeting and helped with language, style, and formatting of the manuscript at later stages.
Biological/clinical validation -domain 2 The terms 'biological validation', 'clinical validation' and 'clinical utility' describe the stepwise linking of biomarkers to tumour biology, outcome variables and value in guiding decision-making, respectively. Clinical validation requires demonstration that the biomarker merely relates to a clinical variable and, therefore, is less difficult to establish than clinical utility (but also less meaningful)86. Clinical utility is achieved when the biomarker leads to net improvement of health outcomes or provides information useful for diagnosis, treatment, management, or prevention of a disease10,88. The REMARK guidelines89 provide a framework for the assessment of clinical utility and validation. Typically, prospective testing of IBs in clinical populations is required in adequately powered studies with follow-up times of 3-5 years to provide outcome data associated with the IB.
Double baseline studies Biomarker precision can be assessed by measuring on multiple occasions and calculating the repeatability or reproducibility of the parameter. Typically, this precision is achieved by measuring the biomarker twice at baseline in the absence of any treatment effects. IBs that have demonstrated clinical validity can have important roles in drug development. For example, RECIST 1. 1-defined objective response is a widely used phase II clinical trial end point that has proved useful for preliminary screening of new therapeutic agents45. More-rigorous criteria must be met to validate an IB as a trial-level surrogate end point to completely replace a well-established clinical trial end point. The Prentice criteria92 describe idealized conditions for demonstration of surrogacy, which unfortunately are rarely achieved and would typically require extremely large datasets93.
Approaches for surrogate validation more pragmatic than the Prentice criteria involve the application of meta-analysis methods to a collection of large or moderate-sized datasets that measure both the putative surrogate biomarker and the definitive clinical end point94. These meta-analytic approaches often require some degree of IB standardization in order to combine datasets in a meaningful manner. Some IBs have been well-standardized (such as the RECIST 1. 1 objective response), whereas others (for example, DCE-MRI-derived K trans 53; BOX 6) have not. Large prospective multicentre studies relating IBs to an outcome can only be initiated when exhaustive technical validation has established multicentre IB precision and accuracy. Clinical utility (and sometimes, validation) necessarily happens late in the IB roadmap; therefore, alternative validation strategies must be sought for many novel IBs.
Recommendations 8-11 -biological validation and clinical validation- Clinical validation occurs relatively late in the development process for most IBs compared with biospecimen-derived biomarkers71. Extensive preclinical studies can provide wellcontrolled data to examine the relationship of the IB to pathology, and the IB to the effects of interventions, and therefore are strongly encouraged (recommendation 8). The choice of experimental model is an important consideration. Tumour xenograft models in immunodeficient mice are well-studied and have reproducible growth characteristics115, and can be ideal for initial IB development, but tumour models that better portray the relevant biological characteristics found in human cancers are also needed116,117. Firstly, in situ tumours, or those implanted into their orthotopic site often better recapitulate the local microenvironment of human tumours117,118.
IB studies often generate rich datasets that can be collected and banked for future re-use. These data include raw images, processed images and parameter maps, ancillary files (such as regions of interest, mask files, and stored header information detailing scan parameter settings), and essential patient metadata covering demographics, treatment history and clinical outcome (such as response, progression-free survival and/or overall survival). Useful data-archiving systems require financial support; collaboration between multiple academic, industry and funding partners; and ongoing curation and active management -as exemplified by the NCI Informatics Technology for Cancer Research platform131. Technological advances (such as cloud-based solutions) must be accompanied by suitable information governance arrangements, potentially crossing international legal and regulatory frameworks102, and by commitment from funders to resource these initiatives.
Creation of animal and human cancer image repositories, following the lead of organizations including the NCI and the ESR132, is strongly encouraged (recommendation 10) to enable rapid testing of new analyses by researchers from different institutions. In some cases, this approach can even lead to a reduction in the number of animal experiments (in line with the 3Rs of animal welfare in cancer research116) or in the number of new patients recruited. Standardized data collection, analysis and archiving are required to support multisite clinical trials102. Complete and transparent study reporting is essential to avoid selective reporting bias and publication bias (recommendation 11). Results of all prespecified analyses (secondary analyses as well as primary analyses) should be reported, regardless of whether they are consistent with expectations, or whether they achieve statistical significance133,134.
Highlighted exploratory analyses (either prespecified or post hoc) should be accompanied by a description of all other exploratory analyses performed. This strategy will more-accurately reflect the potential for false-positive and false-negative findings. Adherence to these principles of reporting will help to eliminate the distortion of research findings resulting from selective reporting and failure to publish negative studies. These biases can also be reduced by registering studies and detailing their hypotheses on publically available websites (such as ClinicalTrials. gov) before the study is initiated or before outcome data are unblinded89,135. Recommendation 12 -qualification-Robust study design and predefined statistical analysis plans are vital to ensuring that the highest quality evidence is available to qualify IBs.
Acknowledgements Development of this roadmap received support from  Cancer Research UK  and the  Engineering and Physical Sciences Research Council  (grant references  A/15267 ,  A/16463 ,  A/16464 ,  A/16465 ,  A/16466  and  A/18097 ), the  EORTC Cancer Research Fund , and the  Innovative Medicines Initiative Joint Undertaking  (grant agreement number  115151 ), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  European Federation of Pharmaceutical Industries and Associations (EFPIA)  companies' in kind contribution.
Acknowledgments We thank the staff of the  Pediatric Endocrine Society  for their help in sponsoring and organizing this practice guideline, the patients from around the world whose contributions to research allowed the development of these guidelines, and  Elaine Cochran  for providing the guideline for metreleptin dosing. Address all correspondence and requests for reprints to:  Rebecca J. Brown, MD, MHSc ,  National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Building  10-CRC, Room 6-5942 , 10  Center Drive , Bethesda, MD 20892. E-mail: brownrebecca@niddk. nih. gov. This practice guideline was sponsored and organized by the  Pediatric Endocrine Society  via an unrestricted education grant from  AstraZeneca. Individual authors were supported by the intramural research program of the  National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health  Grants  RO1-DK088114 ,  RO1-DK105448 , and  RO1-DK101941 ; the  Sopha Fund for Lipodystrophy Research at the University of Michigan , and Grant  14-35-00026  of the  Russian Science Foundation.
Detailed information should be recorded in a disease-specific pregnancy registry. Ongoing registries collecting data on pregnancy and lactation Provide ongoing education to patients about their disease, genetics, and inheritance Start discussions about genetics and inheritance during teens. Explore relationship status and fertility desires from the time of transition from pediatric to adult health-care onward as part of an ''annual review. '' All patients of reproductive age, also those who are not in relationships, should be aware that pregnancy is possible with MPS. Further discuss genetics, fertility, and pregnancy with patients after expressing interest (at least before pregnancy) Give contraceptive advice to those who are not planning a pregnancy, with special focus on those in vulnerable situations Discuss the importance of multidisciplinary prepregnancy review and counseling and optimization of maternal health before pregnancy (including review of medications, evaluation of spinal problems, treatment of underlying cardiac issues, etc).
Fluorescence imaging-Immunofluorescence and short time-lapse acquisitions were performed using an inverted confocal microscope (LSM 710, Zeiss, Jena, Germany) with a 40× oil-immersion lens (NA 1. 3 with an optical resolution of 176 nm) with an optical resolution of X nm. DAPI, Alexa 488/GFP, mCherry and Alexa 647 were imaged using UV, Argon 488 nm, DPSS 561 nm and Helium/Neon lasers 633 nm. Images were processed with ZEN software. Long time-lapse acquisitions were performed using an inverted Yokogawa Spinning Disk confocal microscope with a 63× oil-immersion lens (NA 1. 4 with with an optical resolution of 164 nm) and epifluorescence microscope (Eclipse Ti-E, Nikon, Tokyo, Japan) with a 40× air-immersion lens (NA 0. 9 with an optical resolution of 338 nm). Images were processed with ZEN software and NIS software.
Images for (a), (b) and (c) were acquired via laser-scanning confocal microscopy using a 40× oil-immersion lens (NA 1. 3) and processed with ZEN and ImageJ software. A focal plane was collected for each specimen. TNTs are shown in enlargements. For (a), (b) and (c), to observe TNTs in primary neurons, 1 μM hTau1N4R fibrils were added in extracellular medium. TNTs were never observed in primary neurons without addition of exogenous Tau fibrils. Scale bars: 10 μm   5  Exogenous Tau fibrils are found within TNTs. CAD neuronal cells (a) or primary neurons (b) were plated in Lab-Tek chamber slides and infected with LVs encoding mCherry-Actin (red). At 48 h post-infection, cells were incubated for six hours with extracellular ATTO 488-hTau1N4R fibrils/liposomes preparation (green). Cells were processed for immunostaining analysis using an anti-acetylated tubulin visualized with an Alexa 647-labeled secondary antibody (white).
Nuclei were labeled with DAPI (blue). TNTs containing Tau are shown in enlargements (white arrows). For acquisition, a focal plane was collected for specimen. Images were acquired with an inverted laser-scanning confocal microscope using a 40× oilimmersion lens (NA 1. 3) and processed using the programs ZEN and ImageJ. Scale bars: 10 μm transfer of Tau assemblies between neurons. While these assemblies are fibrillar in our setup, the Tau species involved in inter-neuronal propagation in vivo remain unclear and subject to debate  [69]. Both oligomeric and fibrillar forms are found during the course of Tau pathology. Thus, one can speculate that both forms are involved in the process, albeit with different kinetics and functions. Nonetheless, our data clearly demonstrate that fibrillar Tau transfer from neuron to neuron through TNTs.
a Snapshots selected from a time-lapse video and placed in a gallery to visualize neuron-to-neuron transfer of extracellular Tau fibrils by TNTs in neuronal CAD cells. Time-lapse videos were acquired during 5 h and 33 min with an inter-image interval of 6 s. b Images selected from a video to visualize transfer of extracellular hTau1N4R fibrils by TNTs in primary neurons. Neurons were imaged every 6 s for 51 min. For (a) and (b), cells were filmed with an inverted spinning disk microscope using a 63× oil-immersion lens (NA 1. 4) and processed with ZEN blue and ImageJ software. For time-lapse acquisition, a focal plane was collected for each specimen. Scale bars: 10 μm Fig. 7  Characteristics of Tau fibrils trafficking in TNTs. For (a) and (b), cells were infected with LVs encoding GFP-Actin (red).
At 48 h post-infection, cells were incubated for six hours with 1 μM ATTO 568-hTau1N4R fibrils (green). a The speed of Tau fibrils in TNTs in CAD neuronal cells was calculated from 17 independent videos. b Co-localization analysis between Tau fibrils and actin in TNTs. Significant positive correlation was found between Tau fibrils and actin (Pearson correlation coefficient: 0. 727). For (a) and (b), cells were filmed with an inverted spinning disk microscope using a 63× oilimmersion lens (NA 1. 4) and processed with ZEN blue and ImageJ software. A focal plane was collected for each specimen Scale bar: 10 μm in the adult mouse cornea  [28, 72] ). Nevertheless, even if in different in vitro and in vivo experimental models  [6, 7, 13, 14, 72] , there is now a strong evidence for the role of TNTs in different stress-induced pathological processes including the transport of many pathological proteins and pathogens, it has never been addressed in vivo in the brain and remains to be investigated.
Acknowledgments We thank  Dr. Isabelle Arnal  for her critical reading of the manuscript and BiCel and IMAGIF facilities for access to microscopes. This work was supported by grants from  ANR (SPREADTAU) ,  Association France Alzheimer ,  Region Hauts de France StartAIRR  grant (TiNTs) and from the  program Investissement d'avenir LabEx (laboratory excellence) DISTALZ (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease). Our laboratories are also supported by  LiCEND  ( Lille Centre of Excellence in Neurodegenerative Disorders ),  CNRS ,  Inserm ,  Métropole Européenne de Lille ,  Univ. Lille 2,  FEDER ,  DN2M  and the  Fondation Bettencourt Schueller. The authors declare no conflict of interest regarding the present work.
In-vivo Porcine Experiments Experimental data were obtained from anesthetized, closed-chest, pigs (n=5, 30-40 kg)  [6]. Epicardial and torso potentials were recorded simultaneously using an elastic "sock" (239 unipolar electrodes, Auckland Uniservices Ltd, New Zealand) and flexible strips attached to the body surface (184 electrodes, BioSemi, the Netherlands). For each pig, recordings were made during sinus rhythm, and pacing left and right endo-, and epicardium sites. Overall, 70 records were obtained. Upon completion, the heart was arrested and MRI performed. The heart was excised and perfusion-fixed. Epicardial electrode locations were captured with a multiaxis digitizing arm (FARO Technologies, FL). MRI contrast markers placed on the "sock" and body surface strips were localized in the MR images and used to register electrode locations.
ATs were also evaluated using the error in localization of earliest activation (LE), and the difference in total activation duration (ADd). The data were analyzed using the General Linear Model facility of SAS (Statistical Analysis System). For each metric, the significance of differences among the means of the inverse methods were examined using a Nested Factorial ANOVA. The factors were "animal" and "sequence type" ('sinus rhythm', 'endocardial pacing' and 'epicardial pacing'). "Pacing locations" were nested within both factors. The resulting 8 different variance terms completely accounted for the total variance of the data. Error terms (denominators of the F-ratio) were given by higher-order interactions. When an ANOVA returned a value of p < 0. 05, then the source of the difference among means was sought using a matrix of mutuallyorthogonal contrast vectors.
Analysis of polyfunctionality was performed using Pestle and SPICE (version 5. 3), downloaded from http://exon. niaid. nih. gov. All ICS data are corrected for background. The limit of detection by ICS equates to an IFN-c ELI-Spot response of 150 spot-forming units (SFU)/10 6 PBMCs; therefore, to allow HEV-specific T-cell response to be detectable, pools were combined if individual pool responses by ELISpot were less than 150 SFU/10 6 PBMCs.
GENERATION OF PHYLOGENETIC TREE AND PAIR-WISE DISTANCE ANALYSIS HEV complete genome sequences were downloaded from GenBank and aligned using SSE (v1. 2). (48)  Human-derived HEV genotype 3 isolates that differed from one another at >2% of nucleotide positions were assigned to a subtype and used to produce a neighbor joining tree using MEGA6. (49) The distribution of pair-wise AA distances among these sequences was computed using SSE.
STATISTICAL ANALYSES Nonparametric tests were used, throughout, paired for within-individual comparisons (Wilcoxon) and unpaired for group comparisons (Mann-Whitney). Prism (v6. 0 for Mac) was used throughout. A Pearson partial correlation was run using IBM SPSS (version  22) to determine the relationship between the total magnitude of the HEV-specific T-cell response by IFN-c ELISpot assay and the interval between the time of this assay and diagnosis, while controlling for age, sex, and viremic status. Data with a non-Gaussian distribution (ELISpot response in SFU/10 6 PBMCs and time of blood draw in months) were transformed to the logarithm of base 10.
FINE MAPPING OF ANTI-HEV T-CELL RESPONSES We identified the individual peptide targets of anti-HEV T-cell responses using IFN-c ELISpot assays. Fine mapping was performed by repeat testing of positive IFN-c ELISpot pools using smaller pools of peptides (minipools) and individual peptides contained within these. Using this approach, we identified 40 individual 15-AA-long peptides targeted by HEV- specific T-cell responses (Table  2A ). Assuming that positive overlapping peptides contained a single T-cell target, we identified 23 T-cell targets in total, nine of which were further mapped to 11-AA-long peptides (Table  2A ,B). Responses to 14 of the identified targets were detected in more than 1 HEV exposed subject. In addition, using knowledge of the responding patient(s) HLA type and the experimentally defined 15-AA-long peptide T-cell targets, in silico epitope prediction was performed using program NetMHC and resulted in a further 12 T-cell epitopes (Supporting Table  S4 ).