dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources Medline, Elsevier databases (i. e. EMBASE until October 2015, SCOPUS thereafter), Cochrane Central Register of Controlled Trials, PEDro, Pascal, and Francis databases were searched from inception until January 14, 2019 (S1 Table and S1 Protocol for search strategy  [44] ). Scopus was replaced by Embase because we had no longer access to it. These two databases are both produced by Elsevier and the recall by Scopus of references found by Embase was optimal or suboptimal that is considered as acceptable  [48]. Unpublished studies, conferences, and presentations were searched without language restriction.
Data extraction Two authors (AH and JDM) independently extracted data; potential conflicts were resolved with the help of three other authors (IB, FG, GR). In case of unclear or missing data, we contacted the authors of the respective studies. Extracted data included: study design, participant characteristics, risk of bias, PT characteristics, and outcomes (S1 Protocol  [44] ). All outcomes were statistically treated as continuous measures. We extracted the mean value, the standard deviation (SD), and the number of participants to the outcome measurements in each intervention group. The change-from-baseline was used to determine the outcome. Due to poor, variable or incomplete reporting of change score, different methods were used to obtain the mean and SD of changes when necessary. The most parsimonious statistical treatment was preferred.
Data synthesis and analysis Statistical analyses were performed using R (R Foundation for Statistical Computing, Vienna, Austria; available in http://www. R-project. org/; version 3. 5. 2). Concordance between authors for the selection of studies was estimated using the Cohen's Kappa coefficient and the recommendations of Landis and Kock  [49]. Post-intervention effects were investigated by calculating the change from baseline to the immediate post-intervention assessment, and persisting effects by computing the change from baseline to the last follow-up assessment. These changes were compared between groups. The inverse-variance method was applied to summarize effects across studies. The summary effect estimate for all scales was calculated as the mean difference and its 95% confidence interval (95%CI).
Acknowledgments The authors thank  Caroline Giroudon  ( DRCI, Hospices Civils de Lyon ) for help in collection of records, and  Dr Philip Robinson  ( DRCI, Hospices Civils de Lyon ) for help in manuscript preparation. The full dataset used for this meta-analysis is available from the dataverse1 project (https://dataverse. org/). The access to the full dataset will also be possible by contact the corresponding author.
Procedure The study was conducted in eight European countries involved in the HOME_EU project (France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, Sweden). A research protocol was shared among partners and approved by the European Ethics Committee (Ref. Ares (2017)535021-31/01/2017) and the Ethics Committee of each University/Research partner of the consortium. The data were collected through focus group (FGs) discussions during May-June 2017. Participants were recruited through convenience sampling within HF and TS programs. To be eligible, service providers must have had at least six months of experience in their role with the service. At minimum, four service providers were recruited per FG. Before the FG session, consent forms were provided to each participant, adapted on the basis of the laws of their country.
Data Analysis We used thematic analysis  [30]  to interpret responses to questions following two steps: Step 1-Country analysis: all the focus groups were transcribed verbatim in the local language. Two independent coders conducted the qualitative coding in each country. Each coder independently read the data for familiarization. Next, each coder created a first set of codes based on the empowering community settings model  [28] , selecting all complete responses (by eliminating potential sentences not completed or without a meaning). Each partner created a Microsoft Word document (translated into English) that contained listed excerpts from transcripts that related to service goals and the category 'belief system' of the empowering community settings model. Step 2-Cross-national analysis: The second step involved coding the data (the answers referred to goals and principles) through a thematic analysis  [30].
Funding: This research was funded by the European Commission through a grant, as part of H2020 research project HOME_EU: Reversing Homelessness in Europe H2020-SC6-REVINEQUAL-2016/GA726997. The grant agreement between the European Commission and each research unit involved allows complete liberty and autonomy of researchers in the design of the study; the European Commission will take no part in data collection procedures, analyses, interpretation of the data, or decision to submit results.
Data sources We did an international comparative study of risk factors for shorter life expectancy in Europe. We collected and harmonised register-based mortality data from 15 European countries between 2010 and 2016: Finland, Sweden, Norway, Denmark, England and Wales, Netherlands, Belgium, Austria, Switzerland, France, Spain, Hungary, Poland, Lithuania, and Estonia. These data covered 2010-14, with the exceptions of Sweden (2005-08), Norway (2006-09), and France (2004-07). Most data covered complete national populations, with the exceptions of England and Wales and France, for which nationally representative 1% samples of the population were available, and the Netherlands, where available data covered 65% of the population. Most data were from a longitudinal mortality follow-up after a census, with the exceptions of the Netherlands (follow-up of a mix of registry data and labour force surveys) and Hungary and Poland (cross-sectional unlinked studies).
More details on the data sources for mortality are shown in the appendix (p 2). We aimed to collect survey data on as many risk factors for mortality as possible. Risk factors were selected if a reliable estimate of their relative risk of mortality is available in the literature and estimates of their prevalence by level of education in the 15 countries are available from internationally harmonised surveys. Eight risk factors that are differentially distributed between people with low and high levels of education met these criteria. Many other candidates, such as unemployment and housing conditions, were excluded, because we could not find reliable estimates of relative risks for the exposure categories measured in the surveys. Additionally, because the definition of the risk factor had to be identical for relative risks and prevalence data, risk factor categories often had to be merged into two or three levels only.
11 Further more, father with a manual occupation partly deter mines a person's educational achievement  12  and, in contrast to the other risk factors, should not be seen as a possible mediator of the effect of education on mortality, but as a factor capturing the persistent effect of childhood conditions on the risks of mortality in later life. We extracted data on the prevalence of all risk factors (February, 2017), except low income, from the European Social Survey (ESS), which was designed to collect harmonised data on risk factors for morbidity and mortality in its seventh round fielded in 2014 and 2015. 13 e applied restricted cubic spline models to smooth the gender-specific and education-specific prevalence of each risk factor across age groups. For low income, we used the EU Statistics on Income and Living Conditions Survey, which has more detailed income questions than ESS.
More details on the survey data used in our analyses can be found in the appendix (p 3). Socioeconomic position was indicated by highest level of completed education: low, mid, high corresponding to International Standard Classification of Education 1997 categories 0-2, 3-4, and 5-6. We focused on educational inequalities (instead of occupational inequalities, for example), primarily because comparable mortality and survey data by educational attainment were available for many more European countries than data by other indicators of socioeconomic position. Education is also the most stable measure of socioeconomic position, because it is normally completed early in adulthood, avoiding most of the problems of reverse causation. 14 The analyses were restricted to ages 35-79 years because education becomes less reliable as an indicator of socioeconomic position at greater ages.
Research in context Evidence before this study Socioeconomic inequalities in mortality are a major challenge for public health policy in all European countries. Although many studies have determined the contribution of specific risk factors to inequalities in mortality within specific countries, comparative studies that have quantified the role of different risk factors in a range of countries are almost non-existent. Our consortium has created a harmonised database of inequalities in mortality in a range of countries in Europe, and we have previously published a study on the contribution of six risk factors to educational inequalities in mortality in 21 European countries and a study on the contribution of three risk factors to educational inequalities in life expectancy in five European countries, both in the early 2000s.
Acknowledgments This study was done as part of the  LIFEPATH  project, which has received financial support from the  European Commission (Horizon 2020  grant number  633666 ), and as part of the project "Longer life, longer in good health, working longer?Implications of educational differences for the pension system", which has received financial support from  Network for Studies on Pensions, Aging and Retirement. Data were partly collected as part of the  DEMETRIQ  project, which also received support from the  European Commission  (grant numbers  FP7-CP-FP  and  278511 ). The permission of the  Office for National Statistics (ONS)  to use the Longitudinal Study is gratefully acknowledged, as is the help provided by staff of the  Centre for Longitudinal Study Information & User Support (CeLSIUS).
CeLSIUS is supported by the  ESRC Census of Population Programme  (award reference  ES/K000365/1 ). The authors alone are responsible for the interpretation of the data. This work contains statistical data from  ONS , which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets, which might not exactly reproduce National Statistics aggregates. The mortality data for Switzerland were obtained from the  Swiss National Cohort , which is based on mortality and census data provided by the  Federal Statistical Office  and supported by the  Swiss National Science Foundation  (grant numbers  3347CO-108806 ,  33CS30_134273 , and  33CS30_148415 ).
| Bronchial epithelial brush sample RNAseq in EvA Samples were extracted using the AllPrep DNA/RNA Mini Kit (Qiagen) on a Qiacube robot. RNA quality was tested by running the samples on a Bioanalyzer 2100 from Agilent, using the RNA6000 Nano LabChip kit (Agilent Technologies, Inc), and cDNA libraries were prepared on Sciclone robot (PerkinElmer Inc) using the RNATRUSEQ protocol (Illumina Inc). RNAseq was performed using Hiseq 2000 with 100 bp paired-end reads. Samples with low sequencing throughput (<10 M reads) were removed from the analysis. The selected RNAseq samples were aligned with the GEMTools RNAseq pipeline v1. 7 (http://gemto ols. github. io). 15 The transcriptome was generated from version 15 of the Gencode annotation. After mapping, all alignments were filtered to increase the number of uniquely mapping reads.
| Replication cohorts Genes that were associated with the blood eosinophil count and met the 1% false discovery rate (FDR) (adjusted P <. 01) for both asthma and COPD were tested in available replication cohorts. Bronchial brush gene array (Affymetrix HG-U133 or Human Gene 1. 0 ST) and matching blood eosinophil data were available from a pooled analysis of asthma from four studies BOBCAT, Leicester, HINKS and SARP (n = 213)  [17] [18] [19]  and from GLUCOLD (n = 79)  20  for COPD.
TA B L E 1 Clinical characteristics of eosinophilic versus noneosinophilic COPD subjects using blood eosinophil cut-off >200 eosinophils/μL in the whole EvA cohort (n = 458) archive: EGAD00001002003 and EGAD00001002004). The clinical characteristics were similar to the entire dataset (Table  S2 ). The clinical characteristics of the U-BIOPRED subjects are shown in Table  S3. Total IgE and bronchodilator reversibility were higher in those with versus those without a blood eosinophil count >200 cells/μL. All of the asthma subjects were treated with inhaled corticosteroids. There were assessable gene array data from bronchial brushes in all 85 U-BIOPRED subjects (gene array uploaded to gene expression omnibus: GSE76226). RNA quality was not different between the EvA and U-BIOPRED cohorts. After removing lowly expressed genes, 20 143 genes were left for RNAseq differential expression analysis for EvA and 17 175/54 675 (31%) probe sets were detected in the U-BIOPRED samples.
Funding information This work was funded in part by  Airway Disease Predicting Outcomes through Patient Specific Computational Modelling (AirPROM)  project (funded through  FP7 EU  grant),  U-BIOPRED (IMI)  (No. 115010 ),  eTRIKS (IMI)  (No. 115446 ),  Emphysema versus Airways Disease  (EvA FP7; 200506),  Wellcome Trust  and  National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Centre. This paper presents independent research funded by the  NIHR. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the Department of Health.
Nevertheless, great care must be taken in the treatment of TPA test variables which are not absolute values, they are often misinterpreted, and they can only be used in a comparative way  (Nishinari & Fang, 2018). All the in vivo data used to validate or to program the masticator apparatus were obtained after collecting the informed consent of each subject and experiments were performed in accordance with different local and national ethical agreements (CE-CIC-GREN-10/06-#5044; DREAM-2013-A00096-39).
Funding: This work was supported by the  European Community  [ FP7-222 654-DREAM ]; the  "Qualiment" French Carnot Institute [Qualigras-Phy ,  AAP 2012 ]; and the  French National Research Agency  [ AlimaSSenS -ANR-14-CE20-0003-01 ]. The AM 2 has been developped with the support of the  Auvergne Region (Bourse Innovation) , the  Université d'Auvergne  and the  French National Institute for Agricultural Research (INRA) , and is a platform of the  Université Clermont Auvergne  ( UCA-PARTNER ).
As these were the mice that had a more marked alteration in the microbiota composition in response to co-amoxiclav, we next investigated whether correlation between the transcriptome and microbiota was evident. Host transcriptome-microbiota correlation reveals marked covariance between the microbiota and GO pathways in donor A mice but not in donor B mice Due to the large number of differentially expressed genes across the different contrasts, we used Gene Set Variation Analysis (GSVA) to identify gene pathways associated with antibiotic treatment. To perform correlation with microbial taxa, we used Hierarchical All-against-All significance testing  [30] , a recently developed technique for determining relationships between multi-omics datasets (Fig. 5 , Additional files 6 and 7). This method identifies significant correlations between individual features of both datasets, as well as identifying correlations between clusters of features.
Differential gene expression analysis Principal component analysis was performed using the ade4 (v1. 7-13)  [50]  and factoextra (v1. 0. 5) packages in R. Differential gene expression was performed using the limma package (v3. 36. 5) and the eBayes test  [51]. Multiple testing corrections were made using the Benjamini-Hochberg method (adjusted P values < 0. 05). Differentially enriched pathways in the Gene Ontology (GO) databases were identified using the goana function in the limma package. To remove redundancy, GO terms that were significantly enriched (adjusted P value < 0. 01) were submitted to REVIGO  [52]  and the output presented by way of -log10 (P value).
Microbiome analysis Demultiplexing of pyrosequencing fasta and quality files was performed with QIIME (v1. 9. 1)  [53]  and FASTQ files were created which were loaded into R and analyzed using the DADA2 package (v1. 10. 0), optimized for pyrosequencing output  [54]  (see Additional file 8) to give a table of amplicon sequence variants (referred to as operational taxonomic units (OTUs) throughout). Taxonomic assignments were performed using the RDP v16 (Ribosomal Database Project) training set specially formatted for the DADA2 package  [55]. Phylogenetic tree construction utilized the DECIPHER  [56]  and Phangorn  [57]  packages. All subsequent microbiome analyses were performed through the phyloseq (v1. 24. 2) and vegan (v2. 5-5) packages in R  [22, 58, 59]. Alpha diversity was presented using the Simpson diversity index.
Differential abundance between donor groups and between different time points within donor groups was assessed using DESeq2 (v1. 24. 0) (FDR P values < 0. 01) and significance was confirmed by Mann-Whitney testing  [60]. Beta diversity was assessed with principle coordinate analysis (PCoA) of weighted unifrac distances  [61]  on proportional abundance data (proportion normalized). A two-way PERMANOVA was performed, using donor and day as main effects and the interaction between donor and day as the interaction term. Post hoc pairwise testing was performed using the "pairwise. perm. manova" function from the RVAideMemoire package (v 0. 9-73)  [62]. Spearman's correlation of genus-level microbial taxa was also performed against the first two principal coordinate axes for each donor group individually to illustrate bacterial genera associated with major variation.
Correlation of microbiota and transcriptome To determine if there was evidence of the correlation between antibiotic-induced changes in the microbiota and the transcriptome, we firstly used Gene Set Variation Analysis (GSVA) to identify enriched GO pathways across the entire transcriptome data set, using the R package GSVA (v1. 32. 0)  [64]. GO pathways were derived from a murine version of the Molecular Signatures Database (MSigDB)  [65]. GSVA output was then submitted to limma to determine differentially expressed pathways. This allowed for dimensionality reduction, reducing the total number of transcripts  (18, 217)  and differentially expressed genes (3630) to 182 differentially expressed pathways across all contrasts. Due to the extreme differences in microbiota composition between the two donor groups, pathway scores were correlated with genus-level bacterial abundance for each group individually.
Funding Eric Guédon and Philipe Langella also thank Syndifrais-CNIEL (Paris, France) for their financial support with the project Exibam. Aonghus Lavelle has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n. PCOFUND-GA-2013-609102, through the PRESTIGE programme coordinated by Campus France.
A few cohorts also have access to patient-level claim data and hospitalization records through record linkage with a health administrative database, thus combining the benefit of well-defined patient phenotypes with so-called big data. Whether and how such data can be used for international comparisons would be worth testing. Such initiatives require funding to help build European nephrology research networks and infrastructures where data from various countries can be analysed according to current European data protection rules. This may provide the opportunity to train nephrologists in clinical epidemiology and include the support of PhD students and post-doctoral researchers analysing the collaborative databases. Based on these principles and with the advantages of existing cohorts, the selection process for research projects to be supported by the ERA-EDTA may thus strongly contribute to improve the scientific quality and scope of clinical research in Europe and evidence-based nephrology worldwide.
Roadmap for ongoing nephrology research for screening, diagnosing, preventing and treating CKD. Although valuable registry data and cohort studies exist for defining the current burden of CKD, novel studies are certainly needed for screening, diagnosing, preventing and treating this public health problem. Information that may be gained from future research may be useful for establishing novel predictive biomarkers for screening and diagnosing CKD, and thus be useful for creating/implementing guidelines and finally performing interventions for prevention and treatment of CKD. National societies, kidney foundations, ERA-EDTA and EKHA may collaborate and support all parties involved in managing the problems encountered during the planning and the support of these projects. AKI and related metabolic disorders are increasingly recognized to represent significant causes of morbidity and mortality.
On the other hand, the decentralized data collection has led to heterogeneity in the data sets between registries. What data do the national registries provide and what should they provide to the ERA-EDTA Registry? Many of the national registries provide the ERA-EDTA Registry with a core dataset with individual patient data, which enables international comparisons on essential epidemiological measures such as incidence and prevalence of kidney replacement therapy (KRT) including kidney transplantation rates and patient outcomes (ERA-EDTA Annual Report, please consult www. era-edta-reg. org). Some registries provide aggregate data rather than individual patient data, which gives basic information about KRT in the country. In recent years, some registries have provided the ERA-EDTA Registry with an extended data set on clinical performance indicators, such as blood pressure and various laboratory variables.
So far these data have been used in a few research projects  [26, 27] , but have not been presented in annual benchmarking reports. An aim for the future is that more countries could provide similar data on clinical performance indicators because this would enable comparison of achievements of treatment targets between countries and initially. This could be done in a representative sample of patients in order to enhance feasibility. The collaboration of the ERA-EDTA Registry and the national registries is clearly a necessary imperative to define the requested common parameters and explore the feasibility of such collection. How can the ERA-EDTA Registry support the national registries? The ERA-EDTA Registry is dependent on well-functioning national and regional registries, which the ERA-EDTA Registry has supported, mainly by providing know-how.
Since 2004, the Registry staff has organized almost 30 courses on clinical epidemiology and statistics in various parts of Europe. In addition, educational articles on epidemiological methods have been published in Nephrology Dialysis Transplantation, Kidney International and Nephron Clinical Practice (please consult www. era-edta-reg. org), and young researchers have been able to visit the Registry office in Amsterdam to work on research projects. A central aim must be to continue and develop this support to national registries as it helps improve the quality and enhance the conformity of the registries. A large amount of data is available in the national registries and it is important that this are analysed and reported by knowledgeable persons. How to get the most out of the ERA-EDTA Registry data in order to improve nephrology in Europe? The data provided to and held by the Registry are needed to produce information to guide decisions about KRT in Europe.