dataesr/scientific-paragraphs-categorization huggingface dataset:
First, Hapmap-imputed GWAS data were meta-analysed together, as were Metabochip studies. This step was followed by a combined GWAS þ Metabochip meta-analysis. For primary analyses, we conducted meta-analyses across ancestries and sexes. For secondary meta-analyses, we conducted meta-analyses in European-descent studies alone, and sex-specific meta-analyses. There were two reasons for conducting secondary meta-analyses. First, both WCadjBMI and WHRadjBMI have been shown to display sex-specific genetic effects  2, 59, 60. Second, by including populations from multiple ancestries in our primary meta-analyses, we may be introducing heterogeneity due to differences in effect sizes, allele frequencies, and patterns of linkage disequilibrium across ancestries, potentially decreasing power to detect genetic effects.
See Supplementary Fig. 1  for a summary of the primary metaanalysis study design. The obtained SMK-stratified summary statistics were later used to calculate summary SNPjoint and SNPint statistics using EasyStrata  61. Briefly, this software implements a two-sample, large sample test of equal regression parameters between smokers and nonsmokers  59  for SNPint and the two degree of freedom test of main and interaction effects for SNPjoint  14. Lead SNP selection. Before selecting a lead SNP for each locus, SNPs with high heterogeneity I 2 Z0. 75 or a minimum sample size below 50% of the maximum N for each strata (for example, N4 max(N women smokers)/2) were excluded. Lead SNPs that met significance criteria were selected based on distance (±500 kb), and we defined the SNP with the lowest P value as the top SNP for a locus.
In Approaches 1-3 variants significant at Po5 Â 10 À 8 were considered GWS. In Approach 4 (SNPscreen) variants for which the P value of the test of interaction is less than 0. 05 divided by the number of variants carried forward were considered significant for interaction. We performed analytical power computations to demonstrate the usefulness and characteristic of the two interaction Approaches. Locuszoom plots. Regional association plots were generated for novel loci using the program Locuszoom (http://locuszoom. sph. umich. edu/). For each plot, LD was calculated using a multiethnic sample of the 1000 Genomes Phase I reference panels  62  , including EUR, AFR, EAS and AMR. Previous SNP-trait associations highlighted within the plots include traits of interest (for example, cardiometabolic, addiction, behaviour and anthropometrics) found in the NHGRI-EMI GWAS Catalog and supplemented with recent GWAS studies from the literature 1,2,51,60.
Conditional analyses. To determine if multiple association signals were present within a single locus, we used GCTA  15  to perform approximate joint conditional analyses on the SNPadjSMK and SMK-stratified data. The following criteria were used to select candidate loci for conditional analyses: nearby SNP ( ± 500kb) with an R 2 40. 4 and an association Po1E À 5 for any of our primary analyses. GCTA uses associations from our meta-analyses and LD estimates from reference data sets containing individual-level genotypic data to perform the conditional analyses. To calculate the LD structure, we used two U. S. cohorts, the Atherosclerosis Risk in Communities (ARIC) study consisting of 9,713 individuals of European descent and 580 individuals of African American descent, and the Framingham Heart Study (FramHS) consisting of 8,481 individuals of European ancestry, both studies imputed to HapMap r22.
edu/boehnke/snipper/) was used to identify any genes and cis-or trans-eQTLs within 500 kb of our lead SNPs. All genes identified by Snipper were manually curated and examined for evidence of relationship with smoking and/ or adiposity. To explore any potential regulatory or function role of the association regions, loci were also examined using several online bioinformatic tools/databases, including HaploReg v4. 1 (ref. 63), UCSC Genome Browser (http://genome. ucsc. edu/), GTeX Portal (http://www. gtexportal. org), and RegulomeDB 64. eQTL analyses. We used two approaches to systematically explore the role of novel loci in regulating gene expression. First, to gain a general overview of the regulatory role of newly identified GWAS regions, we conducted an eQTL lookup using 450 eQTL studies  65  , with specific citations for 4100 data sets included in the current query for blood cell related eQTL studies and relevant non-blood cell tissue eQTLs (for example, adipose and brain tissues).
Additional eQTL data was integrated from online sources including ScanDB, the Broad Institute GTEx Portal, and the Pritchard Lab (eqtl. uchicago. edu). Additional details on the methods, including study references can be found in Supplementary Note 3. Only significant cis-eQTLS in high LD with our novel lead SNPs (r 2 40. 9, calculated in the CEU þ YRI þ CHB þ JPT 1000 Genomes reference panel), or proxy SNPs, were retained for consideration. Second, since public databases with eQTL data do not have information available on current smoking status, we also conducted a cis-eQTL association analysis using expression results derived from fasting peripheral whole blood using the Human Exon 1. 0 ST Array (Affymetrix, Inc. , Santa Clara, CA). The raw expression data were quantile-normalized, log2 transformed, followed by summarization using Robust Multi-array Average 66 and further adjusted for technical covariates, including the first principal component of the expression data, batch effect, the all-probeset-mean residual, blood cell counts, and cohort membership.
To ensure that all SNPs of interest were available in the smoking GWAS, the program SHAPEIT2 (ref. 69) was used to phase a region 500Kb either side of each lead SNP, and imputation was carried out using IMPUTE2 (ref. 70) with the 1000 Genomes Phase 3 data set as a reference panel. Each region was analysed for three smoking related phenotypes: (i) Ever vs Never smokers, (ii) Current vs Non-current smokers and (iii) a categorical measure of smoking quantity  48. The smoking quantity levels were 0 (defined as 1-10 cigarettes per day [CPD]), 1 (11-20 CPD), 2 (21-30 CPD) and 3 (31 or more CPD). Each increment represents an increase in smoking quantity of 10 cigarettes per day. There were 10,058 Never smokers, 13,418 Ever smokers, 11,796 Non-current smokers, 6,966 Current smokers and 11,436 samples with the SQ phenotypes.
Acknowledgements A full list of acknowledgments appears in the Supplementary Note 4. Co-author A. J. M. d. C. recently passed away while this work was in process. This work was performed under the auspices of the  Genetic Investigation of ANthropometric Traits (GIANT) consortium. We acknowledge the  Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium  for encouraging CHARGE studies to participate in this effort and for the contributions of CHARGE members to the analyses conducted for this research. Funding for this study was provided by the  Aase and Ejner Danielsens Foundation ;  Academy of Finland  ( 41071 ,  77299 ,  102318 ,  110413 ,  117787 ,  121584 ,  123885 ,  124243 ,  124282 ,  126925 ,  129378 ,  134309 ,  286284 );  Accare Center for Child and Adolescent Psychiatry ;  Action on Hearing Loss  ( G51 );  Agence Nationale de la 359 Recherche; Agency for Health Care Policy Research  ( HS06516 ); ALF/LUA research grant in Gothenburg; ALFEDIAM;  ALK-AbelloA/S; Althingi ;  American Heart Association  ( 13POST16500011 );  Amgen ;  Andrea and Charles Bronfman Philanthropies ;  Ardix Medical ;  Arthritis Research UK ; Association Diabete  Risque Vasculaire ;  Australian National Health and Medical Research Council  ( 241944 ,  339462 ,  389875 ,  389891 ,  389892 ,  389927 ,  389938 ,  442915 ,  442981 ,  496739 ,  552485 ,  552498 );  Avera Institute ;  Bayer Diagnostics ;  Becton Dickinson ;  BHF (RG/14/5/30893 );  Boston Obesity Nutrition Research Center  ( DK46200 ),  Bristol-Myers Squibb ;  British Heart Foundation  ( RG/10/12/ 28456 ,  RG2008/08 ,  RG2008/014 ,  SP/04/002 );  Medical Research Council of Canada ;  Canadian Institutes for Health Research  ( FRCN-CCT-83028 );  Cancer Research UK ; Cardionics; Cavadis B.
Results and discussion We collected clinical and gene expression data of PDAC samples from 15 public data sets (Additional file 1) selected according to the following criteria: availability of data in the GEO, Array-Express, EGA, or TCGA databases, and presence of at least 20 samples. Three data sets  [4, 6, 9]  had been previously used across the three earlier studies. A column indicating the sets previously used in these earlier studies The final set contained 846 primary cancer samples, including 819 non-microdissected samples and 27 Collisson's epithelium microdissected samples (3% of cases). Before analysis, expression data were normalized as described  [10]. Briefly, we first normalized each DNA microarray-based data set separately, by using quantile normalization for the available processed data from non-Affymetrix-based sets (Agilent, Illumina), and Robust Multichip Average (RMA) with the non-parametric quantile algorithm for the raw Affymetrix data sets.
Then, we mapped hybridization probes across the different technological platforms present. We used SOURCE (http:// smd. stanford. edu/cgi-bin/source/sourceSearch) and Entrez-Gene (Homo sapiens gene information db, release from 09/ 12/2008, ftp://ftp. ncbi. nlm. nih. gov/gene/) to retrieve and update the non-Affymetrix gene chips annotations, and NetAffx Annotation files (www. affymetrix. com; release from 01/12/2008) for the Affymetrix annotations. The probes were then mapped according to their EntrezGen-eID. For the TCGA data, we used the available normalized RNASeq data that we log 2 -transformed. Finally, we defined the molecular subtype of each sample in each data set separately as defined in the original publications: the three Collisson's subtypes  [4] , the two Moffitt's "tumor-specific subtypes"  [5] , the two Moffitt's "stroma subtypes"  [5] , and the four Bailey's subtypes  [6].
We first searched for correlations between the Moffitt's classifications ("tumor" and "stroma") and clinical data (Additional file 2). Regarding the "tumor" classification, the "basal-like" subtype was associated with age ≤ 60 years, ductal type, pathological grade 3. Regarding the "stroma" classification, the "activated" subtype was associated with ductal type, grade 2, tumor size. Overall survival (OS), calculated from diagnosis to death, was available for 601 patients. With a median follow-up of 16 months, the 2year OS was 40%. The 2-year OS rates for the Moffitt's classifications were (Fig. 1a-b ) 49% in the "classical" "tumor subtype" vs. 28% in the "basal-like" (p = 8. 83E-07), and 49% in the "normal" "stroma subtype" vs. 34% in the "activated" (p = 7. 56E-05). In univariate analysis (Table  1 ), the Moffitt's classifications ("tumor": p = 1.
We compared the correlations between all classifications and clinicopathological data (Additional file 2). Some classifications were associated with age (Moffitt "tumor"), pathological type (Moffitt "tumor"), pathological tumor size (Moffitt "stroma", Collisson, Bailey), whereas all were associated with pathological grade, an important prognostic feature of PDACs. Based on the glandular cell differentiation, mitotic index, and nuclear atypia  [13] , the grade directly reflects important molecular characteristics of tumors, likely explaining its association with all these molecular classifications. Next, we assessed the concordance of the four classifications in term of assignment to the poor-prognosis and goodprognosis groups in the 601-sample series. We combined the Bailey's pancreatic progenitor, immunogenic and ADEX subtypes into a single good-prognosis group because their survival curves were not different.
DNA Sequencing Genomic DNA was enriched for selected RNA-binding proteins and known ALS genes using a custom designed Agilent SureSelect in solution kit. Sequencing was performed using an Illumina HiScan platform according to manufacturers instructions. Rare deleterious mutations were defined by frequency within the Exome Aggregation Consortium data set of <1/10,000 control alleles  (Lek et al. , 2016) , and a Phred-scaled Combined Annotation Dependent Depletion (CADD) score >10  (Kircher et al. , 2014). Comparison of various pathogenicity prediction tools recently supported the sensitivity and specificity of CADD  (Salgado et al. , 2016). Given that we were focused on exonic changes with an effect on protein function, synonymous changes were excluded. We excluded any changes with a read depth <10 and validated by Sanger sequencing any changes with read depth 10-15 or a novel allele frequency less than one third the reference allele frequency (Supplementary Figure  1 ).
The number of variants per patient correlated with rate of disease progression (Figure  1A , t-test, p = 0. 033) but not age of onset. Including C9ORF72 expansions in this analysis did not appear to be synergistic. The Project MinE browser (http://databrowser. projectmine. com/) was utilized to search for additional evidence of similar variants in these proteins. The Project MinE Consortium has to date reported whole genome sequencing of 1169 ALS cases and 608 controls from the Netherlands. For RBM4B, RBM45, RBMS2, RAVER1, PPARGC1B, and TRNAU1AP, the project Project MinE data identified additional variant(s) within the same exon which were present either exclusively in ALS patients or were more frequent in ALS patients than controls. RBM12, RBM12B, RBM15, RBM15B, and RBM45 are single exon genes, but Project MinE identified ALS cases with disease-associated variant(s) within <25 amino acids in each of these genes.
ACKNOWLEDGMENTS The authors would like to thank the  ALS Variant Server  (als. umassmed. edu)which is supported by funds from  NIH/NINDS  ( 1R01NS065847 ),  AriSLA (EXOMEFALS, NOVALS) , the  ALS Association , and the  MND Association. We acknowledge grants from  EU Framework 7 (Euro-Motor) , and the  JPND/MRC SOPHIA, STRENGTH  and  ALS-CarE  projects. JC-K holds a  NIHR Clinical Lectureship  and PS is supported as an  NIHR Senior Investigator. This work was also supported by the  NIHR Sheffield Biomedical Research Centre  and the.
Background Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. Our research was focused on the patient trajectory based on disease management and care, while also considering medico-economic aspects of the associated management. We approached patient care trajectories based on an example; the occurrence of a myocardial infarction (MI). As MI treatment is performed in a health facility, we were able to trace the patient trajectories through the national hospital financing system, using comprehensive hospital databases or registers, regularly collected for billing purposes. The first prospective payment system (PPS), based on diagnosis-related groups (DRG), was established in the United States in 1983. The objective of this system was to control the expenditures of health care institutions and streamline the costs  [1].
Thereafter, similar medical information systems were adopted in many other industrialised countries. Others, like France, also adopted an anonymised database with unique patient identifiers (for instance, through cryptographic hash functions) to facilitate chaining hospital stays  [2] [3] [4]. In addition, the gradual increase in fees-for-services enhanced the coding quality  [5]. The introduction of these systems enabled new epidemiological and/or economic studies  [6] [7] [8] [9]  using these databases, with temporal follow-up of patients allowing tracing of their trajectory of care. This review investigated how the trajectory concept is defined, studied and what it achieves. We carried out a literature search on PubMed using keywords related to trajectory, PPS and MI concepts. We then proceeded in two steps: (1) a non-a priori search with text mining techniques; and (2) a more standard analysis of a sub-selection of documents.
Step 1: document retrieval In PubMed, we searched for documents examining patient healthcare trajectories, as well as PPS and MI. The review selection process is summarised in Fig. 1. The trajectory concept can be expressed with different words such as "trajectory", "pathway" or "path". For the PPS theme, keywords used are "Prospective Payment System" and "PMSI" (Programme de médicalisation du système d'information, the French PPS equivalent), in addition to "DRG" (Diagnosis-Related Group) databases. This theme also arises in the International Classification of Diseases (ICD), pricing for the activity via the "fee-for-service" or "activity-based payment" expressions, but also in the national health registry or hospital registry concepts. We conducted searches according to the themes and constraints summarised in Table  2.
Step 2: first text mining approach The strategy was as follows: (i) From the selection of articles gathered in step 1, we created a corpus of texts, divided into three parts, T1 to T3 (corresponding to Table  2  topics), consisting of the title and abstract, in which we removed the keywords (see Table  2 ) in order to only keep the other terms; (ii) The three parts of the corpus were analysed separately with IRaMuteQ 1 software. This is an R interface for multidimensional analysis of texts and questionnaires  [12] , allowing statistical analysis of the text corpus  [13] ; (iii) We applied the following pre-processing techniques: (a) Lemmatization of texts, (b) Dictionary enrichment: we lemmatized unrecognised terms by TreeTagger 2 and added specific medical terms and well-known acronyms such as acute myocardial infarction (AMI).
Topics and constraints Keywords C1: Medical context "health", "patient(s)" T1: Trajectory "trajectories", "trajectory", "path", "pathway(s)" T2: PPS "prospective payment system", "PMSI", "DRG", "ICD", "regional information system", "fee for service system", "registry", "Activity-based Payment" T3: MI "myocardial infarction" in the title C2: Dates January 1, 2000 to October 31, 2015 C3: Languages English, French, Spanish and Italian studied, databases used and definition of the trajectory concept.
5 and IQ = 933. 25 for T1∩T3), with missing data for three articles (vs five for T1∩T3). We also focused on the observation duration, measured in months, available in more than 85% articles. Observation duration ranged from 5 to 180 months in T1∩T2 (vs 3 to 240 months in T1∩T3) with a median of 36 months and an IQ of 54 months (vs 12 and 99 months in T1∩T3).
In response to Q9, we found that interest in patient trajectory studies have increased in the last 5 years. The resurgence of studies in 2013 could be explained by the improvement in the quality of databases as of 2009 (ref ), particularly in France, and the possibility of chaining hospital stays and reconstructing patient care trajectories throughout the country. This interest in trajectories mainly stems from Europe and the Americas with 47 and 29% of studies, respectively. The PPS concept necessarily led to only including countries with a similar health system database organisation. This is a weakness of our study, since countries with a different information health system to the American model were not selected through this filter. Thus we have answered Q10. Then we sought to determine the rationale for why these studies were conducted and provide a response to Q11.
) are listed in the second part of Fig. 6  which solves question Q12. We pursued this investigation by assessing the study characteristics, and answered Q13. In the studies, the number of recruited patients was estimated a priori for statistical analyses in good conditions with sufficient power. However, we identified a few studies that were conducted on the entire population, without sampling. Overall, the study time was short, not more than a few years, which could be explained by economic considerations or a lack of data. For retrospective studies, for example, it was sometimes hard to trace back several years because the information is deleted after a certain period of time. Next, we investigated the origin of the data used. For T1∩T3, registry data were mostly used. For T1∩T2, hospital databases and hospitalisation billing databases were used, so the studies were mostly hospital-based.
Moreover, apart from hospital databases, some studies took patients' feelings into account via interviews and questionnaires. Some studies required additional information on, for instance, medication  [31, 58]  through pharmacy databases or non-hospital care  [59, 60]  with social security databases for complete patient care monitoring. To supplement previous findings concerning the list of diseases studied, for T1∩T2, the patient trajectories were closely focused on different cancers. Note that this brings us back to the results that were highlighted in step 2. We thus resolved question Q14.
Some authors used text mining for other purposes. For example, in  [70]  the authors created correspondence databases linking authors with the name abbreviations and processed a co-authorship analysis. In  [71]  the authors annotated abstracts in two ways, first the gene or protein of interest, then the protein interactions and/or gene functions. Ultimately, they categorised documents according to these annotations. Thus, combining text mining methods for systematic review is a hot topic  [72] [73] [74] [75]. While there is no consensus for a method in conducting a review with a huge number of documents, there are several techniques in text mining already used in various fields to explore text data  [76] [77] [78]. Here, we wished to gain an overview of the document content in a recent developing field of inquiry, in order to provide general information and to respond to research questions.
We illustrated our search using a frequent cause of hospital stay, the occurrence of a MI. We chose to trace the follow-up of these MI patients through the PPS. We addressed open-ended questions by determining the topics covered in each area, to explore areas transversely, Pinaire et al. Health Inf Sci Syst (2017) 5:1 while highlighting studies dealing with patient trajectories with regard to MI, based on PPS data. This semantic approach was demonstrated to be well-tailored for addressing our issues. Document retrieval on the patient trajectories was combined with two major themes, i. e. PPS databases and MI. The findings showed that this type of study is of interest in the biomedical community; for comparative trajectories of drug prescriptions and costs Sundberg et al. concluded  [58]  that: "Drug prescriptions and costs of analgesics increased following conventional care and decreased following integrative care, indicating potentially fewer adverse drug events and beneficial societal cost savings with integrative care".
Databases and methods used in trajectories studies Pinaire et al. Health Inf Sci Syst (2017) 5:1articles concerning patient trajectories. The first topic that we covered is disease with, for example, metabolic disorders such as diabetes and cardiovascular complications. Certain articles addressed patients' feelings, anxieties and disease experience. In the patient trajectory, there was support from the patient's immediate relations and family, but also health services, such as home nurses.
Using retrieved archival data from nine individuals that returned to the colony, we compare and validate abstracted transmissions against outputs from established accelerometer processing procedures. 3. Abstracted transmissions included estimates, across five segments of a dive profile, of time spent in prey catch attempt (PrCA) behaviours, swimming effort and pitch. These were then summarised and compared to archival outputs across three dive phases: descent, bottom and ascent. Correlations between the two datasets were variable but generally good (dependent on dive phase, marginal R 2 values of between. 45 and. 6 to >. 9) and consistent between individuals. Transmitted estimates of PrCA behaviours and swimming effort were positively biased to those from archival processing. 4. Data from this study represent some of the first remotely transmitted quantifications from accelerometers.