dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction Data extraction was determined after discussion by all the authors. The information extracted from each article included the statistical model, model parameters required for its implementation, consideration of seasonal effects, factors relating to multicentre trials such as recruitment rates variation, centers initiation rates, and whether the models had been implemented and evaluated using real data and/or simulated data. Google Form was used for data extraction, with data extracted by E. G. and then checked and discussed with C. G. This is a systematic review of statistical models. Methods to assess risk of bias are not applicable. We did not publish a protocol for this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist  [13]  is provided as supplementary material.
TP, scales the response such that the expected waiting time is approximately T/n. This model assumes a constant accrual rate averaged across all centers. R code to implement the model is available on request from the authors of the article  [23]. Zhang et al. [24]  also used a Bayesian framework with a nonhomogenous Poisson process that allows recruitment rates to vary over time. Although they described how their approach could be amended to predict accrual in the planning stage of a clinical trial utilizing prior information only, they do so by assuming a constant accrual rate. The prior specification requires the anticipated average accrual rate after it stabilizes to be specified, along with the level of confidence in that prior belief. Therefore, the key difference between Gajewski et al.
and Zhang et al. is in the questions asked to elicit the priors. Jiang et al. [30]  provided a userfriendly interface program developed in R, based on the method of Gajewski et al. [23]  for the total number of patients that can be recruited within a fixed time in a clinical trial, with an updated version of the R package available  [31]. Bakhshi et al. [25]  used data from a meta-analysis of previous trials to estimate the parameters in the Poissongamma model. This method is described as not being fully Bayesian as there are no hyperdistributions set on the parameters and the modeling of parameters has been conducted independently. They provided guidance on how to produce initial estimates at the design stage, with one parameter accounting for the trial-specific and another one reflecting the different center-specific recruitment rate.
The complexity of some models may be a barrier to their implementation. However, the simplest stochastic models use the Poisson process, which is familiar to many statisticians and yet anecdotal experience would suggest they are not widely implemented. This may be because statisticians are not inherently involved in recruitment prediction. The approaches used to predict recruitment, including sources of information utilized, remains frequently hidden and unreported. Information may be available from other relevant clinical trials, from national disease registries and databases, from audit data from select centers that may be extrapolated across centers or a ''best guess'' approach based on clinician experience albeit without supporting data being readily available. Reliability of each needs to be assessed; however, not all factors that can impact recruitment may be known at the design stage.
This requires consideration before developing further academic models that may never be implemented either because of their complexity or because of the wide variation depicted for the required recruitment period. This review is the first to our knowledge that focuses on methods to predict recruitment at the design stage of a trial. The search strategy for the review utilized two existing resources: the review of Barnard et al. [10]  and the ORRCA database  [11] , meaning that there are differences in the search strategies implemented. However, no additional methods were identified via Google search or screening reference lists of eligible articles. The use of ORRCA meant that article inclusion was restricted to those published in 2016 at the latest. We will continue to monitor future ORRCA updates to determine when this review should be updated.
Acknowledgments The authors would like to thank  Ms Van Nguyen Thu , PhD candidate, on the  MiRoR  project, and  Professor Isabelle Boutron ,  Paris Descartes University , for their help in reviewing the manuscript before submission. This project is part of the  MiRoR (Methods in Research on Research )  Joint Doctoral Training Programme. The authors would like to acknowledge the  MiRoR network  for their support and guidance. The European Union's  Horizon 2020 Marie Sklodowska Curie Innovative Training NetworkseEuropean Joint Doctorate (ITN-EJD)  under grant agreement No. 676207 , fund MiRoR.
Background: In patients with chronic obstructive pulmonary disease, all efforts should be made to prevent exacerbations because each event modifies the trajectory of the disease. Treatment recommendations are mostly built on results from randomized controlled trials (RCTs) whose methodology ensure internal validity. However, their relevance may be compromised by the lack of generalizability, due to poor representability of study populations compared to real-life patients. In order to delimit to whom the results of studies on current and future treatments apply, we sought to identify and characterize the fraction of COPD population that would be eligible for inclusion into RCTs aiming at decreasing exacerbation risk. Methods: We used the Initiatives-BPCO database, a French cohort of 1309 real-life COPD patients monitored in academic centers.
Patients We used the Initiatives-BPCO French cohort, which contained 1309 patients at the time of the present analysis. Patients in this cohort were recruited under stable condition in 18 academic centers in France. Patients recruited in this cohort were aged ≥40 years with spirometry-confirmed diagnosis of COPD, as defined by post-bronchodilator FEV1/FVC < 0. 7. The exclusion criteria included a predominant diagnosis of asthma, bronchiectasis, or any other significant respiratory disease. Data collection has been extensively described previously  [20]. Briefly, a case-report form was used to collect in patient's medical file demographic data, smoking history, physician-diagnosed comorbidities, symptoms, dyspnea (modified Medical Research Council scale), results of pulmonary function tests, and the number of exacerbations and hospitalizations in the previous year.
The cohort was approved by the Ethics Committee of Versailles, France (ref 04-479). All patients have provided informed consent for the anonymized recording and analysis of their data. Selection of RCTs and eligibility criteria. Using clinicaltrials. com and Pubmed websites, we selected between January 2000 and December 2016 all industry-sponsored phase III and IV RCTs that enrolled more than 500 COPD patients, lasted at least 1 year and aimed at decreasing exacerbations risk. For twin studies, we kept only one study in the analysis. All the eligibility criteria used in these trials were compiled.
Several data were not collected or missing in the Initiatives-BPCO database. When this occurred, participants were deemed to remain eligible by the criterion we could not assess. This decision was made in order to avoid underestimating the number of patients that would have been eligible. All analyses were performed using GraphPad Prism version 7. 03 for windows (GraphPad Software, LaJolla California USA, www. graphpad. com). Phases, pharmacological agents and starting years' comparisons were performed using Mann-Whitney test, one-way ANOVA followed by Tukey's multiple comparison and Kruskal-Wallis tests, respectively.
Thus, our eligibility rates may be overestimated but this will not bias our conclusions. Our findings of eligibility rates ranging from 2. 3 to 46. 7% with a mean of 16. 5% are consistent with previously published results: we identified 6 studies dealing with external validity of COPD randomized controlled trials over the last two decades  [11] [12] [13] [14] [15] [16]. None of them focused specifically on trials aiming at decreasing exacerbation rates but our results are clearly complementary with theirs. Herland applied a limited set of criteria from a fictive "typical RCT" to 366 patients  [14]. They found a share of 17. 2% of potentially eligible patients. These results are consistent with those obtained by Kruis (17 to 42% out of 3508 patients from 7 European primary care databases would have been eligible to enter 6 large industrial RCTs  [15] ) and our own results.
To illustrate this point, three groups of criteria require particular attention: First, a selective FEV1-related criterion is set in 94% of RCTs and would be responsible for the exclusion of 39. 9% of patients from our database. Only one trial allowed the enrollment of patients with mild airflow limitation. To assess medications aiming at decreasing exacerbation risk, most RCTs only allowed the enrollment of patients with severe airflow obstruction, which is not necessarily correlated to exacerbation risk. Another interesting finding of Kruis is the proportion of frequent exacerbators among GOLD 1 patients: 12% are frequent exacerbators with ≥ 2 events a year. 34% have at least 1 event a year. Woodruff has similar findings pointing that COPD exacerbations and symptoms may occur in patient with preserved pulmonary function  [22].
If allergic status and diabetes rates are consistent with previous epidemiologic studies, we found lower cardiovascular comorbidities proportions in our cohort than previously reported  [28, 29]  while these where not exclusion criteria to enter the initiatives-BPCO cohort. Only arterial hypertension, heart failure, myocardial infarction and arrhythmias were collected in our database. Several comorbidities have been shown to be associated with the "frequent exacerbator" phenotype and to increase the exacerbation risk  [30]. This is particularly the case for heart failure, asthma and diabetes, which are all frequent exclusion criteria in RCTs in our study. Excluding patients with higher exacerbation risk is then questionable in studies aiming at decreasing exacerbation risk. All these restrictions in RCTs lead to poor external validity.
Placebo as Add-on Treatment for Frequently Exacerbating COPD Patients Characterized by Eosinophil Level A Randomised, Double-blind, Active-controlled Parallel Group Study to Evaluate the Effect of 52 Weeks of Once Daily Treatment of Orally Inhaled Tiotropium + Olodaterol Fixed Dose Combination Compared With Tiotropium on Chronic Obstructive Pulmonary Disease (COPD) Exacerbation in Patients With Severe to Very Severe COPD. [DYNAGITO] Clinicaltrials number NCT00563381 NCT00115492 NCT02138916 NCT02579850 NCT02465567 NCT02105961 NCT02296138.
Stratification scheme Clusters will be stratified according to historical dengue incidence and population density. In the Philippines, a barangay is the smallest administrative division in the Philippines (urban or rural), but which can be subdivided into puroks (zones) consisting of 20 to 50 households depending on the location and density of the households in an area. The previous 5 years of dengue incidence data at the purok administrative unit level will be acquired from the Philippines Integrated Disease Surveillance and Response (PIDSR) system to identify relative defined 'hotspots' and 'coldspots' of dengue activity during that period. This information will be entered into the Disease Data Management System (DDMS+) (see Data management below) along with information on geography, population density, and infrastructure at the barangay level.
Data confidentiality During field investigations, a file with a name and address and corresponding barcode assigned to the subject identity will be used. This file will only be accessible to the field investigations team members who have direct contact with the participants. To ensure confidentiality, the file will be kept in an access-limited and secured (locked) cabinet. Upon completion of the field work, the address of the individual will be removed from the database, with irreversible anonymisation. Fieldwork will involve initial paper files that will then be input into an electronic file which will be accessible only to the field investigation team. Study compliance will strictly adhere to the Republic Act No. 10173 (Philippines Data Privacy Act) stipulating full protection of an individual's personal information by coding and secure data storage, with no dissemination of this information outside the study context.
Specific data collection For each participating child, information on their name, age, sex, address, barangay, cluster number, dengue vaccination status, school, class, and subsequent dengue ELISA titres will be recorded using a paper case report form that will be subsequently input into a study electronic database. Unique bar codes will be used as identifiers. Data will be entered directly into DDMS (See Data management below) with 10% cross-checking. Data will remain secured at the Entomology unit in RITM, Alabang, The Philippines. Statistical analysis will be performed in RITM. After the final saliva sample has been taken and the data cross-checked for errors, the file linking the individual's name and code will be destroyed.
Data management Emerging innovative information technologies, such as data management system software packages, can assist disease control programmes to better manage and analyse data, and thus make it easier to carry out routine surveillance, monitor interventions, and evaluate control programme performance. This will lead to better informed decision making and actions on the part of health authorities. The Liverpool School of Tropical Medicine have developed a multi-disease data management system (DDMS+) platform that facilitates the real-time monitoring and evaluation of interventions that are essential for understanding the progress, success, and challenges facing disease control operations  [39]. DDMS+ is an open source data management software program that works as a modular, integrated system.
All parents/guardians (legal tutors) will be informed of the study and consent will be obtained from at least one of the parents or guardians for children enrolled. In addition, verbal assent will be obtained from children 7 years and older. For individuals (parents, guardians, children > 7 years old) unable to read sufficiently, BHWs will act as an independent witness and countersign the consent form. Participants and their immediate co-residing family members will be informed of any new information pertinent to the study arising during the study period. Regulatory framework of the study: Typology: interventional research with minimal risks on human volunteers (health-related study involving humans) with collection of personal data on a population of 4600 children aged 6 to 16 years in Lipa City.
ChIP-seq analysis Raw read quality was first assessed with fastqc (version 0. 11. 3, Babraham Bioinformatics). FastQC: a quality control tool for high-throughput sequence data (available online at: http://www. bioinformatics. babraham. ac. uk/ projects/fastqc), followed by adapter clipping and quality trimming with Trimmomatic version 0. 36  (57). Reads were then aligned to the mouse (UCSC mm9) or human (UCSC hg19) reference genomes with Bowtie with options -n 2 -k 10 (58), and converted to BAM format with Samtools  (59). ChIP and alignment quality was evaluated computing library complexity (PBC), FRiP, insert size and strand crosscorrelation analysis. All samples passed the ENCODE quality standards for ChIP-seq studies. Peak calling was performed using MACS version1. 4. 2  (60) , and RPKMnormalized signal files were generated from BAM data using DeepTools version 2.
4. 2  (61). Repetitive element annotations for the mm9 and hg19 reference genomes were obtained from repeatmasker (RepeatMasker Open-4. 0. 2013-2015 <http://www. repeatmasker. org>), and were processed to defragment elements based on their ID field. Intersection of ChIP peaks with repetitive elements was performed with the Bedtools package version 2. 17. 0  (62). Classification of ChIP peak genomic context was performed with CEAS version 0. 9. 9. 7  (63). Heat map and average profile plots were generated using in-house scripts utilizing metaseq version 0. 5. 6  (64) , pybedtools version 0. 6. 9 (65)  and Matplotlib version 2. 0. 2 (Zenodo. http://doi. org/10. 5281/zenodo. 573577). Individual profile plots were generated using IGV version 2. 3. 98  (66). PhastCon scores (30-way vertebrate) against the mm9 reference genome were obtained from UCSC  (67, 68) , and average profiles were generated with in-house scripts.
Retrotransposition assays WT and SirT7 -/-primary MEFs cells were transfected with pWA125 or pWA126 retrotransposition vectors (see Supplementary Figure  S9A  for more details), and with the pMSCV-GFP transfection efficiency control vector using the Amaxa P4 Primary Cell 96-well Nucleofector Kit (Lonza) according to the manufacturer's instructions. Briefly, 10 5 cells were resuspended in 20 l of the Nucleofector Solution together with 500 ng of plasmid DNA and were electroporated in a 96-well Shuttle Nucleofector with the program setting 96-CZ-167. Transfected cells were then transferred to cell culture dishes with pre-warmed complete DMEM and treated 24 h post-transfection with puromycin (Sigma-Aldrich) at a final concentration of 1 g/ml. Cells were harvested 7 days post-transfection. The number of EGFP positive cells and the mean fluorescence intensity of GFP + cells was quantified by flow cytometry using FAC-Scalibur (Beckman Coulter) and analyzed with Flow Jo 7/8 (Tree Star).
Finally, slides were counterstained with DAPI and mounted with slowfade gold mounting media (Invitrogen). For all FISH experiments, images were acquired using a LSM510 META confocal microscope (Carl Zeiss) equipped with a Plan-Apochromat 100×/1. 46 NA oil immersion lens. 3D reconstruction and image analysis were performed using Imaris software (Bitplane, A. G. ). Nuclei were segmented based on DAPI staining intensity using the standard Surfaces function of Imaris software. For single locus FISH, hybridization signals were located and modeled using the Spots module, which detects the hybridization signal center of mass. These coordinates were used to compute the radial position of each hybridization signal relative to the nucleus. For L1 probe FISH (see Supplementary Figure  S5 ), the relative radial position of each voxel within each nucleus was computed and the average L1 probe intensity along the nuclear radius, which was divided into 20 bins, was measured.
To compute the relative radial position of an arbitrary point within a given nucleus, we used the library geometry3Sharp (https://github. com/gradientspace/ geometry3Sharp) to generate 3D AABBTrees representing the isosurface mesh used to segment each nucleus. We then query this tree to find a point on the surface that lies on the ray originating at the center of the nucleus and passing through the point of interest. We define the radial position for this point as the ratio of distances: distance(center, point) / distance(center, surface). All images among all samples within a given experiment were acquired and quantified using the same microscope and analysis parameters. Correlation metrics were computed as previously described  (56). Additional reagents details can be found on Supplementary Tables  S1  and S2.
SIRT7 promotes the association of L1 elements to Lamin A/C Previous reports have shown that L1 elements are enriched at Lamin-associated domains (LADs)  (44, 51) , a transcriptional repressive nuclear compartment  (46). Strikingly, analysis based on our own and previously published data (GSE36132 (44)) showed an enrichment of lamin proteins to all L1 elements as indicated by a positive Lamin-DamID score, which was dramatically increased in the subset of L1 elements bound by SIRT7 (Figure  2A ). Consis-   tently, endogenous SIRT7 and Lamin A/C are able to be copurified by immunoprecipitation in MEFs cells (Figure  2B  and Supplementary Figure  S3 ). Importantly, we observed that Lamin A/C association with L1 elements is reduced in SIRT7-depleted cells (Figure  2C ), despite similar levels of Lamin A/C between SirT7 -/-and WT cells (Figure  2D ).
DATA AVAILABILITY ChIP-sequence data used in this study is available at GEO Accession Number GSE106964. Lamin B1 DamID performed in MEFs was obtained from GEO Series GSE36132. Called peaks for ChIP of H3K27Ac, H3K4me1, H3K4me3 and POLR2A were obtained from the ENCODE project under accession numbers ENCSR000CDI, ENCSR000CAZ, ENCSR000CBA and ENCSR000CBX.
ACKNOWLEDGMENTS This work was supported by the  French national program EC2CO-Microbien  (project  MICROMAR ) and the  MaCuMBA  project funded by the  European Union's Seventh Framework Programme  (grant agreement no. 311975 ). Klervi Crenn received a doctoral grant funded by  Région Bretagne  and  CNRS. We thank  Simone Severitt  and  Nicole Heyer  for excellent technical assistance.
Data availability. The complete genome sequence of S. algicola strain KC90 T (ϭ DSM 103371T ϭ RCC 4681 T ) has been deposited in the NCBI GenBank under the accession no. CP034588 (BioProject no. PRJNA504651, BioSample no. SAMN10396653). The version described in this paper is the first version, CP034588. 1. Raw sequence reads have been submitted to the NCBI SRA under the accession no. SRR8529664 (PacBio) and SRR8529665 (Illumina).
Curated databases of scientific literature play an important role in helping researchers find relevant literature, but populating such databases is a labour intensive and timeconsuming process. One such database is the freely accessible Comet Core Outcome Set database, which was originally populated using manual screening in an annually updated systematic review. In order to reduce the workload and facilitate more timely updates we are evaluating machine learning methods to reduce the number of references needed to screen. In this study we have evaluated a machine learning approach based on logistic regression to automatically rank the candidate articles. Data from the original systematic review and its four first review updates were used to train the model and evaluate performance. We estimated that using automatic screening would yield a workload reduction of at least 75% while keeping the number of missed references around 2%.
Introduction A wealth of biomedical information is buried in the free text of scientific publications. Curated databases play a major role in helping researchers and clinicians access this data, by selecting articles and specific facts of interest in the subfield of biomedicine they address  (12, 17). One such database is maintained by the Core Outcome Measures in Effectiveness Trials (Comet) Initiative, which aims to improve the usefulness of outcomes in research and help tackle problems such as outcome reporting bias, inconsistency and lack of importance or relevance of outcomes to patients. These problems are being addressed through the development and use of core outcome sets (COS). A COS is an agreed standardized set of outcomes that should be measured and reported, as a minimum, in all trials for a specific clinical area  (28).
Comet facilitates the development and application of COS, by bringing relevant material together and thus making it more accessible. Since 2011, Comet has maintained a public repository of studies relevant to the development of COS (The Comet database, http://www. comet-initiative. org/studies/search). The database was originally populated through completion of a systematic review  (8) , which is annually updated to include all published COS, currently up to and including December 2017  (5, 7, 10, 11). The database is an integral resource not only to the development of COS, but also to the uptake of COS in research and in the avoidance of unnecessary duplication and waste of scarce resources  (7). A survey demonstrated that the database is also used by a variety of other users in addition to COS developers, including clinical trialists, systematic reviewers, auditors, guideline developers and funders  (11).