dataesr/scientific-paragraphs-categorization huggingface dataset:
3d ). Overlap between significant genes detected by both platforms increased with the increase of the FDR threshold. Interestingly, HTA was more similar to TCGA data than RNAseq for FDR thresholds within the range 10 -5 -10 -2. Unlike protein-coding genes, lncRNAs showed very low similarity between our data and the TCGA dataset as only 442 lncRNAs were quantified in TCGA. Therefore, the top 111 were used (FDR < 10 -8 ) as a reference list. The confirmation level was only 5% for lncRNAs commonly detected by both platforms. In summary, the differential expressed analysis showed a higher number of significant genes in HTA compared to RNA-seq (for FDR < 0. 01). The genes commonly identified by both platforms exhibited a higher confirmation rate by comparison to the results from the TCGA dataset.
Predictive capacity of expression data The next step was to investigate which platform provided more genes with predictive capacity in discriminating cancer from normal samples. Calculations were performed as described in Methods. Distributions of AUC values for protein coding mRNAs and lncRNAs are shown in Fig. 4. A higher number of genes with AUC values close to 1, and therefore higher diagnostic capacity, were seen for the HTA platform, for both types of RNA. An AUC > 0. 95 was shown by 4287 proteincoding mRNAs in microarray data, while only 3012 genes showed the same predictive capacity in RNA-seq (Fig. 4a ). Both platforms were able to detect 2344 mRNAs as markers (Jaccard index of 0. 473). As for the DEA, these results were verified with the TCGA dataset, where 2016 genes with AUC > 0.
We also excluded the effect of the background gene list. As suggested in the work of Timmons et al. , we repeated the analysis leaving out not-or low expressed genes from the enrichment analysis (both from query and background gene lists)  [11]. Only genes with an expression level above the median in at least 50% of the samples were kept. This correction decreased the number of enriched ontology terms in general, but did not remove the bias in the cellular components. Finally, in order to exclude database or tool-specific biases, we repeated the enrichment with the Reacto-mePA tool. The categories found enriched supported the general trend observed for GO: more enriched pathways were observed in the microarray-derived list of significant genes and the similarity between the pathways was 0.
Regarding differences between tumour and normal samples, over 50% of significantly differentially expressed genes were equally found by both platforms. The genes found in common were better confirmed by the large reference TCGA dataset than the genes uniquely identified by one platform. Finally, both platforms identified the key differences between tumour and normal samples at functional level. Increased cell division and loss of normal tissue functions, such as cilium activity, were only observed in tumour samples. Despite the general similarity of the data sets, we repeatedly detected higher variability in sequencing data than in microarray data. Indeed, the fraction of unexplained variability was much higher for RNA-seq, especially for lncRNAs. Biological replicates also showed more variability in sequencing data, notably for the genes that were characterized by a low number of mapped reads.
We selected the second approach and aimed at the detection of exons and exon junctions that are differentially used in transcripts produced by tumour and normal cells. Estimating the expression of exons with RNA-seq data, we first tried to use the same counting method for exon expression that we used for genes -HTSeq. However, a very low correlation between the platforms for exon expression was observed around 0. 2 (the correspondent correlation at gene level was 0. 76). This discrepancy was caused by two facts: first, the human genome contains many overlapping exons which are considered as different entities in the Ensembl annotation; second, the size of many exons is comparable to the size of a read (77 bases in our case), and thus the same read is shared between two exons via exon-exon junctions.
These facts make a large portion of sequencing reads ambiguousbelonging to several exons. Such reads are omitted by HTSeq counting, as this tool is not designed for exon quantification. Therefore, in order to count exons in a reasonable way we used the recently presented method of Rsubread package of R/Bioconductor, which resulted in an increase of the average Spearman correlation to 0. 66. Despite the high correlation for exon expression, the results of differential exon/junction usage analysis were very discordant. Even within one platform the similarity index between the lists of differentially used exons (originated from exon and junction analyses) was low: the best concordance was observed for HTA (Jaccard index of 0. 16). Similarity between the two platforms varied between 0. 03-0. 08.
Acknowledgments The authors would like to thank  Christelle Ghoneim , who was strongly involved in sample preparation and run of the  C08/BM/05   FNR  project. The authors wish to thank the  Translational Genomics Institute (Phoenix, AZ, USA)  for RNA sequencing, as well as  Vasily V. Grinev ,  Belarusian State University , for fruitful discussions and exchange of ideas related to the analysis of sequencing data. The authors are grateful to  Francisco Azuaje ,  Luxembourg Institute of Health , for thorough reading of the manuscript and sharing his comments.
Study population We searched the Individual-Participant Data Metaanalysis in Working Populations (IPD-Work) consortium  [20, 21] , the Inter-University Consortium for Political and Social Research (www. icpsr. umich. edu/icpsrweb/ICPSR), and the UK Data Service (http://ukdataservice. ac. uk) to identify eligible large-scale prospective cohort studies for which data on BMI and dementia were available. We included 39 prospective cohort studies from Europe, the United States, and Asia (Appendix 1), which comprised a total of 1,349,857 participants with no history of dementia; were population based with BMI assessed from all participants before the ascertainment of dementia; recorded hospital-treated dementia or dementia deaths; and had accrued a minimum of 3 years of follow-up.
Statistical analysis We used a two-step individual-participant data metaanalysis including study-specific analyses in the first step and pooling the study-specific estimates in the second. In each study, we performed Cox regression to generate hazard ratios and accompanying 95% confidence intervals (CIs) for the association between BMI and dementia. In the National Health Interview Survey (NHIS), the National Health and Nutrition Survey (NHANES), and the Health and Retirement Study (HRS)  [24] [25] [26] [27] , appropriate sampling weights were used. As one study was based on twins  [28] , individual observations were not independent, and we therefore used Cox regression with cluster information to get robust standard error estimates applying Breslow's method for handling ties. In all studies, each participant was followed up from the date of BMI assessment to the first record of dementia, death, or the end of follow-up.
For example, participants who died before the age of late-onset dementia (65 years) have less opportunity to receive a diagnosis of dementia than those who live longer. In contrast, participants dying around the median age of dementia recording (85 years in this study), have increased opportunities to be diagnosed with dementia. To study this bias, we examined whether BMI was associated with mortality before the age of 65 years and mortality after the age of 85 years  [33]. In addition, we repeated the main analyses excluding dementia cases ascertained from death registries only. We used SAS (version 9. 4) or Stata (MP version 13. 1) to analyze the study-specific data. In the second step of the analysis, we used meta-analysis to combine study-specific estimates. To provide conservative estimates and take into account that the associations are not necessarily similar across cohort studies, we present the summary hazard ratios from the random-effect analysis.
Sensitivity analyses We performed a series of sensitivity analyses with mortality as the outcome to examine potential ascertainment bias (Appendix 5). In most data sets included in this study, dementia cases included those ascertained using death certificates; thus, dementia could be ascertained only in participants who died. Higher baseline BMI was associated with higher overall mortality before the age of 65 years (when dementia is still rare) and lower mortality after 85 years (the median age of dementia diagnosis in this study). These findings suggest that survival bias, if anything, underestimates the status of high BMI as a risk factor. To further examine the robustness of our findings, we repeated the main analyses after excluding 34 studies where dementia was ascertained only using death certificates.
In total, 437 dementia cases in five studies (total N 5 95,851) were ascertained using data other than death certificates. The pattern of results in the main analyses was replicated. In fact, the hazard ratios per 5-unit increase in BMI were slightly higher: 0. 92, 0. 99, 1. 11, 1. 17 and 1. 33 after excluding the first 0, 5, 10, 15 and 20 years of follow-up (Fig. 2B ; study-specific findings in Appendix 6). Higher effect estimates in Fig. 2B , corresponding to approximately one 5-year exclusion, are expected given that dementia is recorded first in morbidity data and only later in mortality data (Fig. 2A ). In analysis stratified by duration of follow-up, the hazard ratio per 5-unit increase in BMI was 0. 72 during the first 10 years of follow-up, 0. 98 between 10 and 20 years, and 1. 33 more than 20 years after baseline, again replicating findings in the main analysis (Fig.
Interpretation: We explored the BMI-dementia association using raw, unpublished data from 1. 3 million adults from Europe, the United States, and Asia. Our findings from analyses stratified by duration of followup lend support for a direct association between BMI and dementia and also provide some support for reverse causation. Higher BMI was associated with increased dementia risk when BMI was assessed more than 20 years before dementia diagnosis, but lower BMI predicted dementia when BMI was assessed less than 10 years before diagnosis. 3. Future directions: To avoid bias, the etiologic phase should be taken into account in studies examining dementia risk factors.
Modelling Assumptions The numerical simulations were conducted using the finite element analysis software SAP 2000. The beams that were designed for gravity loads only were considered as pinned at both ends, whereas full-strength rigid connections were considered at the ends of the MRF beams  [25, 33]. The beams of MRF spans are all-steel members without any composite interaction with the slab. The geometric nonlinearities were considered through the P-Delta formulation under large displacements. Material nonlinearity was modelled through lumped plasticity with discrete plastic hinges distributed along the elements. The modelling parameters and acceptance criteria for the plastic hinges were adopted in accordance with the FEMA Report 356  [29]. The pushdown analyses were carried out under displacement control until global structural failure was reached.
Data on TNM stage and histological grade were collected from each centre, where possible. Grade was stratified as low-intermediate (Gleason score of < 8 or grade coded as well, moderately or poorly differentiated; n = 3749) or high grade (Gleason score of ≥ 8, or grade coded as undifferentiated; n = 726) (Additional file 1: Table  S2 ). Advanced stage cases were tumours that had spread beyond the prostate at diagnosis (T 3 -T 4 and/or N 1 -N 3 and/or M 1 , and/or stage coded in the recruitment centre as metastatic, n = 1388). Localised stage cases were those confined within the prostate and with no metastases at diagnosis (TNM staging score of ≤ T 2 and N 0 /N x and M 0 , or stage coded in the recruitment centre as localised, n = 2634). Fatal cases were men who died of prostate cancer (n = 934).
Assessment of anthropometrics and other predictor variables The anthropometry protocols in the EPIC study centres have been previously described in detail  [17]. Anthropometric data were directly measured by trained study personnel in most of the participants, but it was self-reported in the majority of participants from EPIC-Oxford, although the accuracy of these self-reported data has been validated  [18]. Briefly, weight and height were measured with participants wearing no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in metres squared (kg/m 2 ). Waist circumference was measured either at the narrowest torso circumference or at the midpoint between the lower ribs and iliac crest or a combination of these methods. Hip circumference was measured at the level of the largest lateral extension of the hips or over the buttocks.
To compute the waist to hip ratio (WHR), waist circumference was divided by hip circumference. Each participant's anthropometric data were corrected for the clothing worn during measurement in order to reduce heterogeneity due to protocol differences between centres  [17]. Weight, height, waist circumference and hip circumference were missing for 640 (0. 4%), 391 (0. 3%), 13,285 (9. 3%) and 15,657 (11. 0%) participants, respectively, and these individuals were not included in analyses that include these variables. Baseline data on lifestyle, health status and sociodemographic characteristics were collected via standardised questionnaires, including diet, medical history, lifetime history of tobacco smoking and alcoholic beverage consumption, physical activity  [19] , marital status, occupational history and level of education  [15].
Statistical analysis Analyses of the association of anthropometric factors and covariates with prostate cancer risk were conducted by using Cox proportional hazards regression, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The date of last follow-up ranged from January 2011 in Germany to October 2013 in Spain. Age was used as the underlying time variable and data were stratified by centre and age at recruitment (<50, 50-54. 9, 55-59. 9, 60-64. 9, 65-69. 9, and ≥ 70 years) in all models. Entry time was defined as age at recruitment, while exit time was age at censoring (i. e. age at last follow-up, first diagnosis of incident cancer, loss to follow-up or death, whichever came first). Exit time for the analysis of prostate cancer death was age when participants died due to the prostate tumour or censoring (other cause of death, lost to follow-up or end of followup period for each centre, whichever was first).
Sensitivity analyses was also performed by excluding extreme values (percentiles outside 1-99); additionally adjusting for total intake of energy, alcohol, fruit and vegetables, red meat, processed meat, protein from dairy sources or height; excluding men with missing values for the main covariates; and using the waist circumferenceadjusted residuals of BMI and the BMI-adjusted residuals of waist circumference by regressing these variables in a linear regression model and using the residuals (that are statistically independent of waist circumference or BMI, respectively), as the exposures of interest  [24]. Statistical analyses were performed with the Stata 14. 0 statistical software package 16. All tests of statistical significance were two-sided and P values below 0. 05 were considered significant.
Acknowledgements We thank all participants in the EPIC cohort for their contribution to the study. 4  Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. 5Danish  Cancer Society Research Center, Copenhagen, Denmark. 6Department of  Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark. 7Division of  Cancer Epidemiology ,  German Cancer Research Center (DKFZ) ,  Heidelberg, Germany. 8Department of  Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany. 9Hellenic Health Foundation, Athens, Greece. 10WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece.
Characteristics of the study sample The DCE survey was completed by 101 patients (enrolled by 14 physicians). Most of the patients were male (76%), with a mean Thirty-one patients were interviewed for the qualitative part (enrolled by ten physicians). No new themes were introduced in the final ten interviews (considered to have reached data saturation). This suggests that the sample size is adequate and additional interviews would have been unlikely to introduce additional concepts. Most of the patients were male (77%), with a mean age of 50. Patients had been diagnosed with HIV for a mean of 15 years, and 94% had an undetectable viral load. Twenty-one patients participated in both studies. Patient characteristics are presented in Table  3.
Conclusion This study provides a new light on preferences of PLWHs regarding different attributes associated with ARV therapies and on the main factors associated with PLWH satisfaction. One of the key results is the importance placed by PLWHs on characteristics of ARVs related to quality of life, in particular for ARV therapy with no increased risk of long-term health problems and side-effects. This should be taken into consideration when prescribing ARV therapies, in order to optimize adherence and satisfaction of PLWHs. These data will be compared to those from the four other participating European countries in order to identify similarities and differences in the factors influencing patient satisfaction and preferences between countries.
Acknowledgments We would like to thank the patients who agreed to take part in this study, the investigators,  Miranda Murray ,  Nathalie Dang  ( ViiV Healthcare, London, UK ),  Paul Swinburn ,  Katy Gallop ,  Hayley de Freitas ,  Andrew Lloyd  ( Icon plc, Oxford, UK ),  Julie Salia  (patient reported outcomes, Lyon),  Céline Aubin  and  Camille Correia Da Silva  ( GlaxoSmithKline, Paris, France ). Study sponsored and financed by  ViiV Healthcare.
Background. Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. Methods. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.
Background During the last two decades, the biomedical community has grown progressively more interested in ontologies. Ontologies provide the common terminology necessary for biomedical researchers to describe their datasets, enabling better data integration and interoperability, and therefore facilitating translational discoveries  [1, 2]. BioPortal  [3, 4] , developed by the National Center for Biomedical Ontology (NCBO)  [5] , is a highly used platform  1  for hosting and sharing biomedical ontologies. BioPortal users can publish their ontologies as well as submit new versions. They can browse, search, review, and comment on ontologies, both interactively through a Web interface, and programmatically via Web services. In 2008, BioPortal 2 contained 72 ontologies and 300. 000 ontology classes.
As of 2017, the number of ontologies exceeds 500, with more than 7. 8 million classes, making it one of the largest public repositories of biomedical ontologies. The great number, complexity, and variety of ontologies in the biomedical field present a challenge for researchers: how to identify those ontologies that are most relevant for annotating, mining or indexing particular datasets. To address this problem, in 2010 the NCBO released the first version of its Ontology Recommender (henceforth 'Ontology Recommender 1. 0' or 'original Ontology Recommender')  [6] , which informed the user of the most appropriate ontologies in BioPortal to annotate textual data. It was, to the best of our knowledge, the first biomedical ontology recommendation service, and it became widely known and used by the community 3.
Some years later, with the birth of the Semantic Web  [13] , the need for reusing ontologies across the Web motivated the development of the first ontology search engines  [14] [15] [16] , which made it possible to retrieve all ontologies satisfying some basic requirements. These engines usually returned only the ontologies that had the query term itself in their class or property names  [17]. However, the process of recommending ontologies involves more than that. It is a complex process that comprises evaluating all candidate ontologies according to a variety of criteria, such as coverage, richness of the ontology structure  [18] [19] [20] , correctness, frequency of use  [21] , connectivity  [18] , formality, user ratings  [22] , and their suitability for the task at hand. In biomedicine, the great number, size, and complexity of ontologies have motivated strategies to help researchers find the best ontologies to describe their datasets.
Widely used examples of these systems in biomedicine include BioPortal, the Unified Medical Language System (UMLS)  [31] , the OBO Foundry  [32] , Ontobee  [33] , the Ontology Lookup Service (OLS)  [34] , and Aber-OWL  [35]. The calculation of ontology acceptance is based on two factors: (1) The presence or absence of the ontology in ontology repositories; and (2) the number of visits (pageviews) to the ontology in ontology repositories in a recent period of time (e. g. , the last 6 months). This method takes into account changes in ontology acceptance over time. The acceptance score for each ontology is calculated as follows: )""%B')*"% # = K YZ[P[\L[ * B$%!%*"%,"#$% # + K ]^P^_P * 3+!+'!,"#$% # where: • presenceScore(o) is a value in the interval [0,1] that represents the presence of the ontology in a predefined list of ontology repositories.
Because of the system's modular design, new ontology evaluation modules can be easily plugged in. NCBO provides a Virtual Appliance for communities that want to use the Ontology Recommender locally. This appliance is a pre-installed copy of the NCBO software that users can run and maintain. More information about obtaining and installing the NCBO Virtual Appliance is available at the NCBO Wiki 9. The system uses a set of predefined parameters to control how the different evaluation scores are calculated, weighted and aggregated. Given that high input coverage is the main requirement for ontology recommendation systems, the weight assigned by default to ontology coverage (0. 55) is considerably higher than the weight assigned to ontology acceptance, detail and specialization (0. 15). Our system uses the same coverage weight than the BiOSS system  [21].
Evaluation To evaluate our approach, we compared the performance of Ontology Recommender 2. 0 to Ontology Recommender 1. 0 using data from a variety of well-known public biomedical databases. Examples of these databases are PubMed, which contains bibliographic information for the fields of biomedicine and health; the Gene Expression Omnibus (GEO), which is a repository of gene expression data; and ClinicalTrials. gov, which is a registry of clinical trials. We used the API provided by the NCBO Resource Index 12   [39]  to programmatically extract data from those databases.