dataesr/scientific-paragraphs-categorization huggingface dataset:
Multi-ancestry meta-analyses To account for the different reference panels used for imputation across GWAS, we restricted our analyses to autosomal bi-allelic SNVs from the 1000 Genomes Project reference panel (phase 3, October 2014 release)  (24)  that are also present in the Haplotype Reference Consortium reference panel  (25). We considered only those SNVs with MAF > 0. 5% in haplotypes in at least one of the five ancestry groups represented in the 1000 Genomes Project (phase 3, October 2014 release). Our primary multi-ancestry analysis utilized metaregression, implemented in the MR-MEGA software, which allows for allelic effect heterogeneity between GWAS that is correlated with ancestry  (27). We first constructed a distance matrix of mean effect allele frequency differences between each pair of GWAS across a subset of SNVs reported in all studies.
Investigating the source of heterogeneity in allelic effects on GDM We extended the meta-regression model implemented in the MR-MEGA software to investigate the impact of ancestry and the use of a universal blood-based test to define GDM status on heterogeneity in allelic effects on GDM at lead SNVs. We modelled allelic log-ORs across GWAS in a linear regression framework, weighted by the inverse of the variance of the effect estimates, incorporating a covariate indicating whether GDM status was defined by a universal blood-based test (Supplementary Material, Table  S1 ) in addition to the three axes of genetic variation.
For these metabolic traits, we used either the Wald ratio estimate (one SNV) or the inverse-variance weighted estimate (between two and five SNVs). All analyses were conducted in R version 3. 6 using the packages 'TwoSampleMR' (version 0. 5. 4) and 'MRInstrum ents' for the MR analyses and 'EpiCircos' (https://github. com/mattlee821/EpiCircos).
Downloaded from https://academic. oup. com/hmg/advance-article/doi/10. 1093/hmg/ddac050/6537590 by University of Exeter user on 12 April 2022 scientists, volunteers, managers, receptionists and nurses. AM. We thank the participants of the ANDIS-MDC study. The study is supported by Swedish Research Council (project grant 521-2010-3490 and infrastructure grants 2010-5983, 2012-5538 and 2014-6395). The study is supported by Crafoord foundation (project grant 20200891) and Hjärt-Lungfonden (project grant: 20180522). BIB. Born in Bradford (BiB) data used in this research was funded by Wellcome (WT101597MA), a joint grant from the UK Medical Research Council (MRC) and UK Economic and Social Science Research Council (ESRC) (MR/N024397/1), the British Heart Foundation (CS/16/4/32482) and the National Institute for Health Research (NIHR) under its Collaboration for Applied Health Research and Care (CLAHRC) for Yorkshire and Humber and the Clinical Research Network (CRN).
BiB is only possible because of the enthusiasm and commitment of the Children and Parents in BiB. We are grateful to all the participants, teachers, school staff, health professionals and researchers who have made Born in Bradford happen. BOTNIA. The Botnia Study (L. G. , T. T. ) have been financially supported by grants from Folkhälsan Research Foundation, the Sigrid Juselius Foundation, The Academy of Finland (grant nos 263401, 267882, 312063, 336822 to L. G. ; 312072 and 336826 to T. T. ), University of Helsinki, Nordic Center of Excellence in Disease Genetics, EU (EXGENESIS), MOSAIC FP7-600914, Ollqvist Foundation, Swedish Cultural Foundation in Finland, Finnish Diabetes Research Foundation, Foundation for Life and Health in Finland, Signe and Ane Gyllenberg Foundation, Finnish Medical Society, Paavo Nurmi Foundation, State Research Funding via the Helsinki University Hospital, Perklén Foundation, Närpes Health Care Foundation and Ahokas Foundation.
The study has also been supported by the Ministry of Education in Finland, Municipal Heath Care Center and Hospital in Jakobstad and Health Care Centers in Vasa, Närpes and Korsholm. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement (n • 269045). The study is also supported by Crafoord foundation (project grant 20200891) and Hjärt-Lungfonden (project grant: 20180522) (R. B. P. ). The skilful assistance of the Botnia Study Group is gratefully acknowledged.
Acknowledgements DECODE. We thank the women who have participated in the deCODE study. EstBB. This study was funded by the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 15-0012  and Project No. 2014-2020. 4. 01. 16-0125 ); by the  European Union  through  Horizon 2020 research and innovation programme  under grant agreement (No. 101017802  and  810645 ); and by the  Estonian Research Council  grants  PUT  ( PRG687 ,  PRG1291 ). Data analyses were carried out in part in the  High-Performance Computing Center of University of Tartu. We thank participants and support staff of  Estonian Biobank. FINNGEDI. The EPx-GDM study has been supported by the  French government , managed by the  National Research Agency (ANR)  under the  Future Investment Program (PIA)  with the references  ANR-10-LABX-0046  ( European Genomic Institute for Diabetes [E.
G. I. D])  and  ANR-10-EQPX-07-01  ( LIGAN-PM ). This work was also supported by the  ANR-16-CE17-0017-01 , by the  Fondation Francophone pour la Recherche sur le Diabète (FFRD)  that is sponsored by the  Fédération Française des Diabétiques (FFD) ,  Abbott ,  AstraZeneca ,  Eli Lilly ,  Merck Sharp & Dohme (MSD)  and  Novo Nordisk , and by the  National Center for Precision Diabetic Medicine-PreciDIAB , which is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), by the  European Union (FEDER) , by the  Hauts-de-France Regional Council  and by the  European Metropolis of Lille (MEL). The FinnGeDi study was supported by:  Academy of Finland ,  Diabetes Research Foundation, Foundation for Pediatric Research ,  Juho Vainio Foundation ,  Novo Nordisk Foundation ,  Signe and Ane Gyllenberg Foundation ,  Sigrid Jusélius Foundation ,  Yrjö Jahnsson Foundation for Pediatric Research ,  Finnish Diabetes Medical Foundation ,  Research Funds of Oulu University Hospital  (state grants),  Research Foundation ,  Novo Nordisk Foundation , funds of  Helsinki University Hospital  (state grants),  Medical Research Center Oulu and National Institute for Health and Welfare (Finland).
ERF. We are grateful to all participants and their relatives, to general practitioners and neurologists for their contributions, to  P. Veraart  for her help in genealogy, to  Jeannette Vergeer  for the supervision of the laboratory work and to  P. Snijders  for his help in data collection. The ERF study was supported by grants from the  Netherlands Organisation for Scientific Research (Pionier ,  047. 016. 009 , 047. 017. 043 ), Erasmus MC  and the  Centre for Medical Systems Biology  ( CMSB; National Genomics Initiative ). Ayse Demrkan was supported by  NWO  ( VENI-91616165 ),  WCRF-2017/1641  and  H2020-SC1-2019-874739. ESTGDM. This study was supported by the  Estonian Research Council  grants ( IUT20-43  and  PRG712 ). We are grateful to  Dr Anne Kirss  and midwife  Mrs Laura Lauren  from  Women's Clinic, Tartu University Hospital , for interviewing the participants and collecting the blood samples.
GEN3G. Gen3G has been supported by an American Diabetes Association accelerator award # 1-15-ACE-26  (M. F. H);  Fonds de Recherche du Québec en Santé  # 20697  (M. F. H. );  Canadian Institute of Health Research # MOP 115071  (M. F. H. ) and Diabète Québec grants (P. P. and L. B. ). Gen3G research team thanks all Gen3G participants. GIFTS. This study was supported by  Medical Research Council  [ Clinical Research Training Fellowship  ( G0800441 )], the  European Union  ( FP7 EU  grant:  83599025 ) and the  Diabetes Association of Bangladesh. The authors are indebted to all the participants from  Dhaka (Bangladesh)  and  London (UK). HONGKONG. The work for CUHK GDM cohort was supported by the  Research Grants Council General Research Fund  ( CUHK 473408 ,  CUHK 471713 ,  14118718 ). We also acknowledge support from the  RGC Themebased Research Scheme  ( T12-402/13  N) and  Research Impact Fund  ( R4012-18 ), the  Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Fund  and the  Croucher Foundation Senior Medical Research Fellowship.
NFBC Cohort 1966 31y follow-up study. We thank all cohort members and researchers who participated in the 31 years study. We also wish acknowledge the work of the  NFBC  project centre. NFBC1966 received financial support from  University of Oulu  (Grant nos 65354 and 24000692);  Oulu University Hospital  (Grant nos 2 /97 ,  8/97  and  24301140 );  Ministry of Health and Social Affairs  (Grant nos  23/251/97 ,  160/97 ,  190/97 ). National Institute for Health and Welfare, Helsinki  (Grant no. 54121 );  Regional Institute of Occupational Health, Oulu, Finland  (Grant nos 50621, 54231, 54121);  Regional Institute of Occupational Health, Oulu, Finland  (Grant no. 50621 ,  54231 ); and  ERDF European Regional Development Fund  (Grant no. 539/2010 A31592 ). The NFBC team is acknowledging the following Funding/Support: This work was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement (No.
633595 ) (DynaHEALTH) and grant agreement No. 733206  (LifeCycle)  H2020-824989 EUCANCONNECT ,  H2020-873749  LongITools,  H2020-848158 EarlyCause  and the  JPI HDHL ,  PREcisE  project  MR/S03658X/1 , ZonMw the  Netherlands  (no. P75416 ); the academy of Finland EGEAproject (285547). NFBC Cohort 1966 46y follow-up study. We thank all cohort members and researchers who participated in the 46 years study. We also wish acknowledge the work of the  NFBC  project centre. NFBC1966 received financial support from  University of Oulu  (Grant no. 24000692 );  Oulu University Hospital  (Grant no. 24301140 );  ERDF European Regional Development Fund  (Grant no. 539/2010 A31592 ). NFBC Cohort 1986. We thank all cohort members and researchers who have participated in the study. We also wish acknowledge the work of the  NFBC  project centre.
NFBC1986 received financial support:  EU QLG1-CT-2000-01643  (EUROBLCS) (Grant no. E51560 ); NorFA (Grant no. 731 ,  20056 ,  30167 );  USA/NIH 2000 G DF682  (Grant no. 50945 ). The NFBC team acknowledges the following funding/support: the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement (No. 633595 ) (DynaHEALTH) and grant agreement (No. 733206 ) (LifeCycle)  H2020-824989 EUCANCONNECT ,  H2020-873749  LongITools,  H2020-848158 EarlyCause  and the  JPI HDHL ,  PREcisE  project, ZonMw the  Netherlands  (no. P75416 ); the academy of  Finland EGEA-project  ( 285547 ). PREDO. The PREDO study was supported by  Academy of Finland, EVO  research funding (A special Finnish state subsidy for health science research),  Finnish Medical Foundation ,  Jane and Aatos Erkko Foundation ,  Päivikki and Sakari Sohlberg Foundation ,  University of Helsinki Research Funding, Jalmari ja Rauha Ahokas Foundation ,  Yrjö Jahnsson Foundation  and  Juho Vainio Foundation.
SNUH. This work was supported by the  Korea Health   21   R&D  Project,  Korean Ministry of Health and Welfare  (grant no. 00-PJ3-PG6-GN07-001 ). This study was supported by a grant from the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute , funded by the  Ministry of Health & Welfare, Republic of Korea  to S. H. K. (grant number  HI15C3131 ). STORK. The STORK study received additional funding from the  Norwegian Diabetes Association , the  Norwegian Odd Fellow Research Fund and Johan Selmer Kvanes' Endowment for Research in Diabetes. STORKG. Acknowledge Hormone Laboratory, Oslo University Hospital  for DNA-extraction. UKBB. UK Biobank analyses were conducted using the UK Biobank resource under application 11867. VIVA. Grants from the  US National Institutes of Health  ( R01 HD034568 ,  UH3 OD023286 ).
Project Viva is thankful to all Project Viva participants for their participation to research over many years. Individual author acknowledgements. D. A. L. is supported by the  European Research Council  ( 669545 );  US National Institute for Health  ( R01 DK10324 ) and  British Heart Foundation  ( CH/F/20/90003 ). D. A. L. and M. C. B. work in a Unit supported by the  University of Bristol  and the  UK Medical Research Council  ( MC_UU_00011/6 ) and are supported by a  British Heart Foundation   Accelerator Award  ( AA/18/7/34219 ). R. M. F. and R. N. B. were supported by  Sir Henry Dale Fellowship (Wellcome and Royal Society  grant:  WT104150 ). R. M. F. is funded by a  Wellcome   Senior Research Fellowship  ( WT220390 ). G. H. M. has received funding from the  South-Eastern Health Authority of Norway , the  Norwegian Diabetes Association and Nils Normans Minnegave.
G. H. M. is supported by the  Norwegian Research Council  (Post doctorial mobility research grant  287198 ). M. C. B. was funded by a  Medical Research Council (MRC)   Skills Development Fellowship  [ MR/P014054/1 ] and a  University of Bristol Vice-Chancellor's Fellowship. The authors acknowledge the use of the  University of Exeter High-Performance Computing (HPC)  facility in carrying out this work. Funders and others acknowledged here had no influence on the study design, data collection or interpretation of results. Views expressed are those of the authors and not necessarily any funder or other acknowledged here. This research was funded in part by  Wellcome  grants ( WT104150  and  WT220390 ). A CC BY or equivalent licence is applied to the author accepted manuscript arising from this submission, in accordance with the grant's open access conditions.
Additionally, she has shown that TEs promote various chromosomal rearrangements more efficiently and more precisely than other cellular processes. In 2007, Rangel et al. performed a comparative analysis study on the complete genomes of twelve Mollicutes by using a ''bidirectional best hit" approach to determine the number of genes shared by all genomes and to verify the synteny among shared genes  (Souza et al. , 2007). The authors determined the distribution of genes using COG classes  (Tatusov et al. , 2001) , while the comparisons of CDSs among the studied genomes accomplished using SABIA (System for Automated Bacterial genome Integrated Annotation) software  (Almeida et al. , 2004). Finally, in 2010, Plague et al. analyzed the neighboring gene orientations (NGOs) of all ISs in 326 fully sequenced bacterial genomes.
Their studied genomes are annotated according to the Comprehensive Microbial Resource database (release 1. 0-20. 0) at the Institute for Genomic Research 1. The approach used to extract IS elements were to consider that a coding sequence with the best BLASTX hit e-value lower than 10 À10 is an insertion sequence  (Plague, 2010). These articles illustrate the clear importance of the chosen detection method in the accuracy of the produced IS results. However, the genomes of these research works are based on NCBI annotations, which are of very relative and variable qualities. This is why ISsage was developed in 2011 by  Varani et al. (2011). They downloaded eight different bacterial genomes from NCBI and offer a web application pipeline that works with a semi-automated annotation genome based on BLAST against the ISFinder database.
Therefore, ISsage cannot automatically identify new insertion sequences not already located in the ISFinder database. A new computational tool for automated annotation of ISs has then been released in 2012 by  Robinson et al. (2012). This tool is called OASIS, that stands for ''Optimized Annotation System for Insertion Sequences". They worked with 1,737 bacterial and archaeal genomes downloaded from NCBI. OASIS in the other hand identifies ISs in each genome by finding conserved regions surrounding already-annotated transposase genes. It applies a maximum likelihood algorithm to show the edges of multicopy ISs based on the conservation between their neighboring regions. For defining inverted repeats, the same strategy as IScan  (Wagner et al. , 2007)  was used (Smith-Waterman alignment). The authors also used hierarchical agglomerative clustering to identify groups of IS lengths.
The ISs set is then classified according to the family and group names after a BLASTP best hit in the ISFinder database with an e-value less than 10 À12. When a cluster cannot match the database's entry, the IS set is recognized new. Thus, OASIS can discover new insertion sequences, that is, which are not located in ISFinder. In 2014, the NGOs analyzed all the IS elements within 155 fully sequenced Archaea genomes, which was presented by  Florek et al. (2014). To do so, they have launched BLASTP in the ISFinder database, with an e-value less than or equal to 10 À10 , for all proteincoding sequences downloaded from NCBI that are related to IS elements. In 2020, for gene order analysis and ancestral state estimation, three methods were proposed to estimate gene arrangement history in Thysanoptera: (1) mapping of ancestral and shared derived boundaries; (2) common interval analysis; and (3) maximum likelihood analysis of gene order  (Tyagi et al.
Preprocessing The dataset genomes for instance are considered as an input to the preprocessing stage. Three operations in this stage are operated: ORF identifications, ORF clustering, and phylogenetic tree inference. For ORF identification, we need to extract the so-called Open Read Frames (ORF) coding sequences, by using a bioinformatics annotation tool called Prodigal  (Hyatt et al. , 2010). Prodigal will annotate each genome that yield an annotated file contains a list of gene features. From a previous study  (Al-Nayyef et al. , 2015; Huda Al-Nayyef et al. , 2017) , a phylogenetic tree of 18 genomes of P. aeruginosa has been collected with a minimum bootstrap value of 76, as represented in Fig. 3. This phylogenetic tree has been obtained by computing the similarity distances among ORFs.
Check inversion boundary For checking the inversion boundary stage, two sub-processes are used, named RNA inspector and IS inspector. We need to investigate the boundary nature of each inversion between closed species in one clade in the tree represented in Fig. 3. Indeed, this study is concerned on investigate the insertion sequences and transposase gene types (e. g. , DDE, DEDD, or undetermined) too. Furthermore, the existence of RNA types (i. e. , tRNA and rRNA) in inversion boundaries. Each ORF is classified with ISFinder  (Siguier et al. , 2006)  database. Fig. 4  illustrates the detailed investigation methodology of checking inversion boundaries that have been applied in this research work to visualize the inversions between two genomes. As can be seen in Algorithm 1, we checked both left and right inversions boundaries by blocks of size 5,000 bp (two blocks per boundary, called ''InBoundary" and ''OutBoundary") between two close isolates.
IS Inspector: insertion sequence nature The distribution of families and subgroups within the input set of P. aeruginosa, according to the transposase gene type of genomes, is presented in Fig. 9. Fig. 9d  illustrates three types of transposases that were found, namely the DDE, DEDD, and NotDetermined, from the original five types recognized in the ISFinder database (DDE, DEDD, HuH, Serine, and NotDetermined)  (Siguier et al. , 2014). Fig. 9a  illustrates the insertion sequences families and counts of its subgroups. Fig. 9b  shows the total occurrences of the insertion sequence families, together with subgroup details according to the Fig. 4. Investigation methodology pipeline used to find a relation between inversions and their boundaries. We checked both left and right boundaries by blocks of size 5000 bp (2 blocks per boundary, called ''InBoundary" and ''OutBoundary") between two close isolates.
RNA inspector: impact of RNAs on genomics inversions In this section, we investigate the presence of RNA genes (i. e. , tRNA and rRNA) and their impact on genomics inversions. This process is performed by two stages. Firstly, all RNA gene types are extracted with their positions for all input sets of genomes sequences. Secondly, left and right boundaries of these genes (e. g. , in and out boundaries) are inspected, by comparing the RNA gene positions with the start and end of inversions boundaries according to Algorithm 1. The distribution of RNA types within all considered genomes depends on the NCBI database as follows: Four copies of rRNA types (from 5S, 16S, and 23S) are presented in all genomes except PAO1 (GI:110645304), in which additional one copy of type 4. 5S rRNA exists  (Winsor et al.
Transport sensitivity scenarios The setup of CLaMS 2. 0/MESSy follows the CLaMS 2. 0 configuration described in  Konopka et al. (2019) , and uses the Modular Earth Submodel System (MESSy) as a software framework  (Jöckel et al. , 2010)  to run the model on the supercomputer JUWELS (Jülich Supercomputing Centre, 2019). To simplify the notation, we omit the suffix MESSy in the following. ERA-Interim (EI) and ERA5 (E5) reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) provide the meteorology to drive the model  (Dee et al. , 2011; Hersbach et al. , 2020). Although the same horizontal resolution (1. 0 × 1. 0 • ) is used for both data sets, the vertical resolution is different, with 60 and 137 levels for EI and E5, respectively, corresponding to the maximal vertical resolution available for these products.
The vertical layer depths at 5 to 20 km altitude vary between 0. 3 to 0. 4 km for E5 and 0. 5 to 1. 4 km for EI. Hoffmann et al. (2019)  found that transition from EI to E5 improves the Lagrangian transport simulations mainly due to improved representation of convective updrafts. The baseline for the following comparisons is a CLaMS 1. 0 set-up described by  Pommrich et al. (2014) , which covers the period 1979-2017 and is available in two versions driven by EI and E5, respectively. All CLaMS 2. 0 simulations start on 1 January 2017, use CLaMS 1. 0 for initialization, and cover 1 yr. We choose 2017 because data of two major aircraft campaigns covering tropics and extra-tropics are available (StratoClim and WISE, see Sect. 3. 2), and the reanalyses products EI and E5 can be downloaded from the ECMWF server (EI only until mid of 2019).
e90 tracer The e90 tracer is an idealized tracer, uniformly distributed at the Earth's surface, with a lifetime of 90 d which is long relative to the timescale of vertical transport in the troposphere Table  2. List of performed runs. As a baseline, CLaMS 1. 0 runs are used. For pure advection runs (I0C0T0), 54 instead of 45 layers were used to get similar number of air parcels like in the runs with all transport modes switched on (I1C1T1). Suffix EI (ERA-Interim) or E5 (ERA5) is used in the text and figures to denote the respective reanalysis driving CLaMS. All runs are performed within the software framework MESSy and are also available as perpetuum runs (14-times 2017). less convection ( θ c = 30 instead 10 K) I1C1(-1. 0)T0 less convection (N 2 m = -1. 0 × 10 -4 instead 0 s -2 ) I1C1(-1.
In situ observations: CO profiles In the following, model results are compared to in situ data which were observed during the Geophysica campaign Stra-toClim (stratospheric and upper tropospheric processes for better climate predictions) in Nepal in July-August 2017 and during the HALO campaign WISE (wave-driven isentropic exchange) in Ireland in September-October 2017. While during StratoClim, tropical UTLS conditions within the Asian summer monsoon (ASM) anticyclone were sampled, WISE data represent much more extra-tropical and mid-latitude conditions in the vicinity of the subtropical jet. All CO, O 3 , N 2 O, and CO 2 time series measured during these two campaigns are included (all local flights) with details listed in Table  3. All time series are transported to the closest 12:00 UTC synoptic time using CLaMS trajectories calculated with the same reanalysis as the respective CLaMS run.