dataesr/scientific-paragraphs-categorization huggingface dataset:
volcanii. The three remaining MTases (Hvo 0019, Hvo 1715 and Hvo 2875) are found in some specific archaeal phyla such as halobacteriales (Hvo 0019, Hvo 1715 and Hvo 2875), thaumarchaeota (Hvo 0019) and thermococcales (Hvo 2875) but also in some bacteria. For instance, Hvo 0019 orthologues could be identified in Legionella shakespearei, Agrobacterium tumefaciens and Limnothrix rosea (Supplementary Figure  S9B ). Similarly, proteins sharing significant sequence identity with Hvo 1715 are present in Bacilli (such as Bacillus anthracis and Streptococcus pneumoniae) and in mycobacteria. This is also the case for Hvo 2875 present in nocardia. This suggests that in bacteria, Trm112 orthologues could also interact with MTases. This is further supported by several observations. First, the structures of bacterial Trm112 orthologues (PDB codes: 2KPI and 2JS4) are highly similar to the zincknuckle domains from H.
ACKNOWLEDGEMENTS We thank  J. -M. Strub  for protein identification by peptide mass fingerprinting. We thank  Dr A. Marchfelder  for sharing reagents with us. We are grateful to  Martin Savko  for assistance and to the  SOLEIL  staff for smoothly running the facility. Experiments were performed on the Proxima-2 beamline at SOLEIL Synchrotron, France (proposal number 20150742). Author Contributions: T. V. N. , L. M. , R. L. R. , R. L. , J. L. and N. U. performed the experiments. R. L. R. , R. L. , P. A. L. , S. C. and M. G. designed research; T. V. N. , R. L. R. , P. A. L. , V. d. C-L, S. C. and M. G. analyzed the data and wrote the paper. FUNDING Centre National pour la Recherche Scientifique (CNRS)  including a specific support by the  ATIP-AVENIR program  (to M. G. );  Agence Nationale pour la Recherche (ANR)  [ ANR-14-CE09-0016-02 ];  Ecole Polytechnique ; PhD fellowship from the  French Ministère de l'Enseignement Supérieur et de la Recherche (MESR)  (to T.
V. N. , L. M. , J. L. );  Ecole Polytechnique  (to T. V. N. );  CNRS  and the  University of Strasbourg  (to S. C. ); S. C. thanks the  GIS IBiSA ;  French Proteomic Infrastructure (ProFI)  [ ANR-10-INBS-08-03 ];  Région Alsace  for financial support;  National Institutes of Health  [ R01 GM70641  to V. d. C. -L. ]; Fondation de l'Ecole Polytechnique  (to V. d. C. -L. ); National Institutes of Health  [ R01 GM058843  to P. A. L. ]. Funding for open access charge:  Agence Nationale pour la Recherche (ANR)  [ ANR-14-CE09-0016-02 ].
(2008) , and were sequenced by the MiSeq Illumina (paired-end 2 x 300 bp) platform of the Molecular Research Laboratory (Texas, USA). Raw sequences were analyzed using QIIME 1. 9. 1 as described by  Caporaso et al. (2010). Briefly, the raw reads were checked for adapter, chimera and low-quality sequences. The trimmed reads were clustered into operational taxonomic units (OTU) using a 97% sequence identity threshold with UCLUST  (Edgar, 2010). The taxonomic assignment was performed by UCLUST taxonomy. Low abundance OTU (<0. 005%) were filtered as recommended by  Bokulich et al. (2013)  and the OTU table was normalized by random subsampling to the smallest number of sequences (i. e. , 18614). Similarity search by BLAST algorithm  (Altschul et al. , 1990)  against the NCBI non-redundant (NR) reference database was performed for OTU representative sequences.
Numerical and Statistical Analyses The alpha diversity was calculated in QIIME using the Shannon  (Shannon and Weaver, 1949)  and Simpson  (Simpson, 1949)  indices. The beta diversity (Bray-Curtis similarity) metrics were calculated and a dendrogram was generated with hclust function in R to group samples into clusters. The Good's coverage was calculated according to the equation: C = 1-(n/N) where n is the number of OTU and N is the total number of sequences  (Good, 1953). The relative OTU table was transformed by logarithm to down weight the influence of more abundant species masking shifts among less abundant species  (Cabrol et al. , 2012). The dynamics of microbial community structure along time were analyzed by Principal Coordinate Analysis (PCoA), using pcoa function of the ape package in R software (version 3.
ACKNOWLEDGMENTS This project was financially supported by the  French national program EC2CO-Biohefect/Ecodyn/Dril/MicrobiEn (DYNAMICA) CNRS/INSU , the  IRD French-Tunisian International Joint Laboratory LMI COSYS-Med , the  Ministry of Higher Education and Scientific Research of Tunisia  and the  CNRS/INSU MISTRALS-MERMEX-WP3-C3A program. The project leading to this publication has received funding from  European FEDER Fund  under project  1166-39417. We acknowledge the Isotopic & Organic platform from the  Institut des Sciences Analytiques  ( ISA, UMR 5280, Villeurbanne, France ) for performing the C/H/N/S analyses. We also thank  D. Delanghe  from the  sedimentology laboratory of the Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement (CEREGE, UMR 7330, Aix-en-Provence, France ) for particular grain size analyses.
Background: Increasing availability of therapeutic options for COPD may drive new treatment pathways. This study describes COPD treatment in France, focusing on identifying initial treatment modifications in patients with COPD who either initiated long-acting bronchodilator (LABD)-based therapy or escalated to triple therapy (long-acting muscarinic antagonist [LAMA] + long-acting β 2 -agonist [LABA] + inhaled corticosteroid [ICS]). Methods: This retrospective analysis of patients with COPD in a large general practitioner database (IQVIA Longitudinal Patient Database) in France included two cohorts: Cohort 1new initiators of LABD-based therapy (LAMA, LABA, LAMA + LABA, LAMA + ICS, LABA + ICS or LAMA + LABA + ICS); Cohort 2 -patients escalating to triple therapy from mono-or dual-bronchodilator-based maintenance treatment.
Background COPD is a respiratory disease characterized by chronic obstruction of lung airflow that interferes with normal breathing. 1 It is a leading cause of morbidity and mortality and is associated with a high clinical and economic burden. 1  COPD is currently listed as the fourth leading cause of death in countries with a middle sociodemographic index (ranked fourth to 20th depending on the sociodemographic index),  2  and the World Health Organization estimates that it will become the third leading cause of death worldwide in 2030. 3 This high epidemiological and economic burden was also observed in a study of medico-administrative databases in France. 4  Currently, the most commonly used maintenance medications for COPD include bronchodilators (eg, long-acting β 2 -agonist [LABA] and long-acting muscarinic antagonist [LAMA], alone or in combination) and inhaled corticosteroid (ICS; used in combination with a LABA, or a LAMA and a LABA [triple therapy]).
For the dual therapies (LAMA + LABA, LAMA + ICS, and LABA + ICS), both fixed-dose and multiple-inhaler combinations were considered. Singleinhaler triple therapy was not available in France at the time of the study, so only multiple-inhaler triple therapies were included in this analysis. The study included an 18-month pre-index baseline period and an 18-month post-index observational period (Figure  1 ). Because information on death and transfer out of the GP practice was unavailable, the earliest and latest recorded events of any type in the database (for a particular patient) were considered the start of availability of pre-index baseline data and end of availability of post-index observational data, respectively.
Methods study design This study was designed, implemented, and reported in accordance with the Guidelines for Good Pharmacoepidemiology Practices of the International Society for Pharmacoepidemiology,  25  the Strengthening the Reporting of Observational Studies in Epidemiology guidelines,  26  and with the ethical principles laid down in the Declaration of Helsinki. The study involved no interventions or treatment, and no identifiable patient data were collected; therefore, neither informed patient consent nor institutional review board/ethics committee approval was required. This was a retrospective database analysis using existing data. Personal identifiers were not required, and personally identifiable information was removed by the database provider prior to receipt by the study team.
Patient eligibility and subgroups Eligible patients were adults $40 years of age on the date of indexing, with evidence of a clinical diagnosis of COPD according to the International Classification of Disease, 10th edition (ICD-10) prior to or including the index date (ICD-10 codes J41. x, J42, J43. x, J44. x), with at least 18 months of pre-index and post-index data available. Patients with a diagnosis of asthma or a respiratory disease potentially incompatible with COPD diagnosis in the pre-or post-index period (eg, conditions that are related to lung or bronchial developmental anomalies, degenerative processes [cystic fibrosis, pulmonary fibrosis], bronchiectasis, pulmonary resection, or other significant respiratory disorders other than COPD; Table  S1 ), and who had a prescription of a LABD-based treatment (Cohort 1 [patients initiating maintenance treatment]) or triple therapy (Cohort 2 [patients escalating to triple therapy]) in the pre-index period were excluded from this analysis.
0 or J44. 1). Only primary care prescriptions issued by GPs and recorded in the database were used to identify exacerbations. Per-patient costs were derived for GP visits (all-cause and COPD-related) and primary care prescriptions for COPD treatments (Supplementary materials) for the 12 months prior to and including the index date. The first treatment modification (including no modification) occurring during the 18-month post-index observational period was identified for each patient, with the number and proportion of patients experiencing one of the prespecified treatment modifications reported by the cohort, and stratified by index class for patients initiating maintenance treatment (Cohort 1). For patients in both cohorts (initiating maintenance treatment or escalating to triple therapy) who had a switch or augmentation as their first treatment modification, the resulting treatment class after switching/augmenting was described.
The mean (SD) and median (interquartile range [IQR]) time to first treatment modification was assessed for each cohort and for each prespecified treatment modification. In an exploratory analysis of data for patients initiating maintenance treatment (Cohort 1), we used a segmentation analysis to assess whether specific demographics and clinical characteristics were potential drivers of the choice of initial maintenance therapy. For this segmentation analysis, multiple correspondence analysis (MCA) was performed, followed by hierarchical cluster analysis (HCA) using the Elbow method to identify the optimal number of clusters. 27 CA is a technique used to detect and represent underlying structures in a dataset containing nominal categorical data, whereas HCA is a method used to build the hierarchy of clusters.
Selection of covariates was prespecified and based on the literature, clinical knowledge, advice from a French respiratory clinician, and availability of variables with the database. For each covariate, the OR, P-value, and 95% CI were reported. All statistical analyses were performed using SAS ® 9. 2 software or a later version (SAS Institute Inc, Cary, NC, USA) via SAS Enterprise Guide version 6. 1.
Participants Of 61,986 individuals with an ICD-10 diagnosis code of COPD in the database, 5,065 patients initiated LABDbased therapy and were eligible for inclusion in Cohort 1. Cohort 2 included 501 individuals who escalated to triple therapy from an existing maintenance therapy (Figure  2 ). Patient demographics and baseline characteristics for both cohorts, as well as HCRU 12 months prior to and including the index date, are shown in Table  1. Overall, compared with patients initiating maintenance treatment (Cohort 1), patients escalating to triple therapy (Cohort 2) tended to be older, with a longer time between the first recorded ICD-10 code for COPD and index date and a higher number of both all-cause and COPD-related GP visits in the 12 months prior to and including the index date (Table  1 ).
4] [35]  Although we did not capture reasons for treatment discontinuation in this study, there may be some patients who would be at risk of their disease worsening because of discontinuation of prescription by their GP. However, it is possible that patients who were classified as permanent or temporary discontinuers received further treatment from a specialist, an event that would not be captured in our study using a GP database. 7] [38] [39]  For example, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) report recommends that treatment initiation and escalation should be driven by patients' exacerbation history and the presence of symptoms, such as dyspnea. 1  However, our exploratory analyses observed that these factors did not appear to consistently drive treatment choice; instead, other variables including age and comorbidities seemed to guide prescriptions for COPD medications more often.
This finding suggests that the assessment of dyspnea, a cardinal symptom of COPD, may not be well integrated into daily practice, perhaps owing to the difficulty for GPs to clinically appreciate this primary indicator. Other observational studies identified comorbid asthma, high exacerbation rates, airflow obstruction, and comorbidities as determinants of initial maintenance therapy,  40  whereas a retrospective database study in UK primary care reported only a minor role of exacerbations in treatment modifications (switching or augmenting maintenance therapy). 24 In addition, ~50% of patients who started an initial maintenance treatment with LABD therapy in our study did so in combination with ICS. This finding, in a population of patients with "pure" COPD (with patients identified by ICD-10 codes and exclusion of patients with asthma and other diagnoses incompatible with a COPD diagnosis), indicates a lack of physician adherence to the GOLD report and national guidelines, which recommend that use of ICS in combination with bronchodilators should be limited to patients with repeated exacerbations, or after failure of treatment with LABD monotherapy.
Limitations As with all observational studies, our study has limitations and should be interpreted with these in mind. First, the IQVIA Longitudinal Patient Database captures only prescriptions issued by a GP. Therefore, the analysis only partially captured treatment pathways and estimates of discontinuation may therefore be artificially high, as subsequent prescriptions issued by specialists outside of primary care would not be captured. Conversely, because the database contains no information as to whether a prescription was redeemed, our estimates of continuous prescribing may overestimate true use. Similarly, we may not have identified true initiation or true treatment escalation if these occurred outside of primary care. This is a particularly significant limitation as a recently published study conducted using a French claims database and with similar inclusion criteria to the study presented here found that 36.
7% of patients with COPD consulted with a specialist or were seen by a hospital practitioner during the year of follow-up. 45 Second, the database does not include spirometry information. FEV 1 was measured in only 1. 1% of patients, meaning that patients could not be classified by GOLD stage, which would have helped to understand disease severity of patients at baseline and perhaps would have provided additional insight to explore further the rationale for treatment choice. Had these data been available, it would be of interest to formally assess adherence to GOLD and national guidelines that were in place at the time of study (ie, pre-2017 GOLD when FEV 1 was recommended as a guide for treatment decision). 7] [48]  Third, no published studies have evaluated the extent to which the physicians included in the IQVIA Longitudinal Patient Database are representative of the French physician population; it is possible that physicians who willingly participate in the panel differ from the general physician population.
This was a retrospective analysis of patients with COPD in a large GP database (IQVIA Longitudinal Patient Database) in France from June 2006 to June 2015 (GlaxoSmithKline [GSK] study number: HO-15-16099). The database comprises electronic medical records collected routinely through a panel of GPs. It includes prescription data, medical history, and outpatient diagnosis information for ~1. 8 million active patients living in metropolitan France, with data going back to 1994. The overall study sample was divided into two cohorts. Cohort 1 comprised patients with COPD initiating maintenance treatment (ie, newly prescribed, by their GP, LAMA, LABA, LAMA + LABA, LAMA + ICS, LABA + ICS, or triple therapy [LAMA + LABA + ICS]) from January 1, 2008 to.
Acknowledgments Editorial support (in the form of writing assistance, including development of the initial draft based on author direction, assembling tables and figures, collating authors' comments, grammatical editing, and referencing) was provided by  Chrystelle Rasamison  of  Fishawack Indicia Ltd, UK. This study was funded by  GSK (GSK  study  HO-15-16099 ). The funders of the study had a role in study design, data analysis, data interpretation, and writing of the report. The corresponding author had the final responsibility to submit this work for publication. Study conduct and data analysis were performed by  Adelphi Real World and IQVIA , and it was funded by  GSK. IQVIA were holders of the data; Adelphi Real World and GSK authors had access to the aggregated outputs provided by  IQVIA.
Prevalent and incident cancer cases within the UK Biobank cohort were identified through linkage to national cancer registries. Complete follow-up was available through 30th November 2014 for England and Wales and 31st December 2014 for Scotland. Cancer incidence data were coded using the 10th Revision of the International Classification of Diseases (ICD-10). Proximal colon cancers included those found within the caecum, appendix, ascending colon, hepatic flexure, transverse colon, and splenic flexure (C18. 0-18. 5). Distal colon cancers included those found within the descending (C18. 6) and sigmoid (C18. 7) colon. Overlapping (C18. 8) and unspecified (C18. 9) lesions of the colon were included in colon cancers only. Cancer of the rectum included cancers occurring at the recto sigmoid junction (C19) and rectum (C20).
Similarly, the television watching time and colorectal cancer relationship was further assessed across subgroups of time spent using computers each day (none, anytime). To assess the possibility of reverse causality, cases diagnosed within the first two years of follow-up were excluded from all of the analyses. We repeated the analyses with multiple imputations to account for missing total physical activity information; however no differences were detected with the complete case analysis results presented (data not shown). Statistical tests were all two-sided and a P-value of o0. 05 was considered statistically significant. Analyses were conducted using Stata version 13. 1.
In addition, a recent large participant level pooled analysis reported similar magnitude inverse relationships between leisure-time physical activity and colorectal cancer when stratified by follow-up period (o5 years and X5 years)  (Moore et al, 2016) , which suggests our results observed in a relatively short follow-up time may not be substantially influenced by preclinical disease. Another limitation of the study is that physical activity and sedentary behaviour information was self-reported by participants at the baseline questionnaire. Recently, the UK Biobank has objectively measured activity levels using 7-day accelerometers, in over 100 000 participants  (Doherty et al, 2017). Currently, the follow-up time since these accelerometer measurements were collected is too short to study incident colorectal cancer, but these data will offer unique future insights into the physical activity and cancer relationship.
ACKNOWLEDGEMENTS This work has been conducted using the UK Biobank Resource under Application Number  25897  and we express our gratitude to the participants and those involved in building the resource. UK Biobank  is an open access resource. Bona fide researchers can apply to use the UK Biobank data set by registering and applying at http://www. ukbiobank. ac. uk/register-apply/. KEB was supported by a  Girdlers ' New Zealand Health Research Council Fellowship. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4. 0 Unported License.
A New Pipeline for the De Novo Assembly of Ascidian Transcriptomes We generated cDNA libraries from the 16 species we collected, from most of which 30-50 million reads were sequenced using the Illumina HiSeq 4000 platform (table 1). The number of sequence reads, accession numbers, and other basic statistics to assess the quality of the transcriptomes are shown in table 1. The initial assembly of these reads using Trinity software  (Grabherr et al. 2011 ) led to transcriptomes composed of between 76,825 and 410,479 contigs depending MBE on the species (supplementary table 1, Supplementary Material online). These values are far higher than expected from current knowledge of ascidian genome complexity  (Dehal et al. 2002; Voskoboynik et al. 2013; Velandia-Huerto et al. 2016). We therefore sought to eliminate redundant and low-quality contigs by developing a custom pipeline (detailed in fig.
The combination of high quality transcriptomes, fusion of fragmented sequences, and subsequent filtering (see Materials and Methods) led to the retention of 4,908 alignments devoid of apparent paralogy in tunicates (fig. 2D , TOTAL data set of 1,740,663 amino acid sites and 36. 5% missing data). A reduced version of this data set comprising 1,306 genes was selected, in which virtually all species were present (fig. 2D , REDUCED data set of 605,265 amino acid sites and 14. 4% missing data). Functional annotations showed that the genes belong to different families, functional groups, and have different kinds of protein domains (supplementary table 4, Supplementary Material online). Both TOTAL and REDUCED matrices are provided at https://github. com/ AlexAlie/styelidae, last accessed April 2018.
Cross-contaminations in RNA-seq experiments are known to arise when RNA molecules from one sample accidentally pass to another sample, leading to a misattribution of sequencing reads to the contaminated sample  (Ballenghien et al. 2017). Because of the Illumina sequencing depth, even extremely minor contamination events can be sequenced and result in unwanted assembled transcripts  (Ballenghien et al. 2017; Simion et al. 2018). The level of crosscontamination was important in our samples, ranging between 0. 07% and 24% of the contigs, depending on the species. Therefore removing cross-contaminants using recently developed CroCo software  (Simion et al. 2018 ) turned out to be a crucial step. Interestingly, the level of crosscontamination was of the same order of magnitude regardless of whether RNA was extracted in the same or different laboratories, but it was much higher between samples sequenced on the same Illumina lane than between samples sequenced on different lanes performed at different dates (supplementary fig.