dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Linda van der Graaf-van Bloois  for valuable advice and  Arjen Timmerman  for technical support. We thank  Mary Chapman  and  Nathaniel Simon  for the generation of Illumina MiSeq reads and assistance in the final assembly and genome closure. This work was partially supported by  USDA-ARS   CRIS  project  5325-42000-047-00D.
iguaniorum strain 1485E genome was compared with the genome of strain 2463D and the completed genomes of the closely related taxa C. fetus subsp. testudinum (strains 03-427 and SP3), C. fetus subsp. fetus (strain 82-40), C. fetus subsp. venerealis (strain 97/608), C. hyointestinalis subsp. hyointestinalis (strain LMG 9260), C. hyointestinalis subsp. lawsonii (strain CCUG 27631), and C. lanienae (NCTC 13004). For Campylobacter species outside the C. fetus clade, BLASTP comparisons to proteins in the NCBI non-redundant (nr) database were performed. A local BLAST was performed based on the predicted proteomes of all genomes and the results were screened for features specific for one or both Cig strains and the other members of the C. fetus clade. Using JSpecies v. 1. 2. 1 (Richter and Rossello-Mora 2009) and BLAST v.
2. 2. 26, average nucleotide identity (ANI) Species Strain Source Source Location Sequence Sequence Reference Accession number Cig, C. iguaniorum; Comparative Genomics of Campylobacter iguaniorum organism type data method Cig 1485E Lizard (Pogona vitticeps) Feces NL WGS 454, Illumina, PacBio Gilbert et al. 2014b CP009043-CP009044 Cig 2463D Lizard (Iguana iguana) Feces NL WGS 454, Illumina, PacBio Gilbert et al. 2015 CP010995 Cig 11S02571-1 Chelonian (Chelodina mccordi) Feces NL MLST Sanger Gilbert et al. 2014a KU697811, KU697824, KU697837, KU697850, KU697863, KU697876, KU697889 Cig 11S02571-4 Chelonian (Chelodina mccordi) Feces NL MLST Sanger Gilbert et al. 2014a KU697812, KU697825, KU697838, KU697851, KU697864, KU697877, KU697890 Cig 11S02590-1 Lizard (Pogona henrilawsonii) Feces NL MLST Sanger Gilbert et al.
Sampling and Oceanographic Parameters The MALINA cruise took place onboard the Canadian research icebreaker CCGS Amundsen during summer 2009 from Victoria (BC, Canada) to the Beaufort Sea (Leg 1b) and then throughout the Beaufort Sea (Leg 2b) (Supplementary Figure  S1 ). Most of the stations sampled on the west-east transect in the Beaufort Sea (Leg 2b) were ice-free. However, surface waters of eastward offshore waters were still ice-covered. Surface seawater samples were collected with an acid-cleaned bucket during Leg 1b and in the Mackenzie plume  (stations 395, 398, 694, and 697)  during Leg 2b. In the Beaufort Sea, seawater was collected from six depths (up to 150 m) using Niskin bottles mounted on a CTD (conductivity temperature depth probe) rosette. The methods used to collect the ancillary data of temperature, salinity, pH, dissolved oxygen, colored dissolved organic matter (cDOM), photosynthetically active radiation (PAR), inorganic and organic nutrients, and chlorophyll a data are provided in the Supplementary Material.
PR genes and transcript amplicons were cloned directly or after gel extraction using the TOPO4-TA cloning kit (Invitrogen) according to the manufacturer's instructions. Recombinant clones were sequenced as in  Boeuf et al. (2013). The sequences were trimmed to remove any vector and primer sequences. DNA sequences were translated into amino acid sequences and aligned according to the tertiary structure of proteins using the E-INS-I strategy and BLOSUM62 substitution matrix from MAFFT  (Katoh et al. , 2009). The resulting protein alignment was back translated to nucleotide acid sequences using Geneious software  (Drummond et al. , 2012). A conservative value of 82% amino acid sequence similarity  (Campbell et al. , 2008)  was chosen for clustering sequences into operational taxonomic units (OTUs) using MOTHUR  (Schloss et al.
, 2009). Representative sequences (defined as the sequence with the minimum distance to all other sequences in the OTU) were obtained using MOTHUR. The coverage value  (Mullins et al. , 1995) , the Shannon index, H Shannon  (Shannon, 2001)  and the bias-corrected richness-estimator, S Chao1  (Chao et al. , 2005)  were calculated for each library (Supplementary Table  S2 ). A PR gene database containing about 2,000 aligned sequences of cultured species and environmental sequences retrieved from GenBank was constructed using Geneious  (Drummond et al. , 2012). A phylogenetic tree was constructed with entire protein sequences by Bayesian inference using MrBayes 3. 0  (Ronquist and Huelsenbeck, 2003)  with mixed models of amino acid substitution estimated by Markov chain Monte Carlo (MCMC) procedure.
Four parallel MCMC chains of 1. 5 million generations were run and trees were sampled every 100 generations. A consensus tree was constructed after the exclusion of 13,000 'burn-in' trees. The PR gene database and the consensus tree were imported into ARB software  (Ludwig et al. , 2004). Representative sequences of each OTU and short PR gene environmental sequences were aligned as above and added to the consensus Bayesian tree using the ADD_BY_PARSIMONY algorithm implemented in ARB. Non-informative taxa were removed from the final tree.
The 16S rRNA genes for all bacteria and for SAR11 clade were quantified using primers (Supplementary Table  S1 ) and conditions described by  Suzuki et al. (2000 Suzuki et al. ( , 2001)). Plasmid clones from our Beaufort Sea PR and 16S rRNA libraries were used as positive controls and in standard curves (10-fold dilution series) in the qPCR assays. Plasmid DNA was linearized with HindIII and quantified using the Picogreen assay (Invitrogen). QPCR was performed in triplicate with 1 µl of environmental DNA normalized to 100 pg. µl -1 or plasmid DNA solution in a total reaction volume of 10 µl using the ABsolute QPCR SYBR Green/ROX Master Mix kit (Thermo Scientific). QPCR assays were performed using a Chromo4 Real-Time PCR Detection System and the Opticon Monitor software package (Bio-Rad) with the following thermal cycling conditions: 95 • C for 15 min, 45 cycles of denaturation at 95 • C for 45 s, primer annealing at the primer-specific temperature (Supplementary Table  S1 ) for 45 s with fluorescence measurements after this step, and polymerase extension at 72 • C for 15 s.
Community Composition and Diversity of PR Sequences Of a total of 617 PR gene sequences retrieved along the transect covering the North Pacific Ocean to the Beaufort Sea, 306 were obtained from DNA and 311 from cDNA libraries. 72 distinct OTUs were identified after clustering sequences at 82% amino acid sequence similarity  (Campbell et al. , 2008). Coverage estimates (>80%) and rarefaction curves (data not shown) indicated that most of the diversity was sampled at both offshore and shelf stations (Supplementary Table  S2 ). This observation was confirmed by S Chao1 values (estimated numbers of expected OTUs), which agreed with the numbers of retrieved OTUs. The diversity of PR types, assessed with Shannon index, was generally higher in surface than in deeper waters with the highest diversity observed in the Mackenzie River mouth while the coverage index was low (Supplementary Table  S2 ).
Abundance and Distribution of Main PR Types Using the dataset of PR sequences retrieved from the Beaufort Sea, we designed qPCR primers to target six major groups of PR types namely the alphaproteobacterial OTU3b and OTU1, the SAR92-related OTU9 and -11, the Glaciecola-related OTU10 and a cluster of 4 isolates related to Flavobacterium frigoris  (Boeuf, 2013). Primers sets designed by  Campbell et al. (2008)  and  Cottrell and Kirchman (2009)  were also used to investigate the abundance of SAR11 PR and OTU3a clades, respectively. The design of universally conserved qPCR primers able to capture most PR types is impossible. Therefore, to estimate total abundances of PR-containing bacteria, we added together the abundance estimates of all eight tested microbial groups containing PR genes (Table  1 ).
PR-Based Phototrophy Is the Main Prokaryotic Phototrophic Process in the Arctic Ocean We observed a substantial overlap between DNA-and cDNAderived libraries, indicating that a large fraction of the summer PR community actively expressed proteorhodopsin in the Beaufort Sea euphotic waters at the time of sampling. We chose to quantify PR types representing the largest active OTUs by qPCR. Although, the targeted OTUs covered more than half of the recovered sequences, our total numbers are inevitably incomplete. Therefore, they have to be viewed as lower estimates of the actual contribution of PR bacteria to the total bacterial community. These estimates remain lower than those reported from the analysis of the GOS metagenome datasets (about 65%)  (Venter et al. , 2004; Rusch et al. , 2007). The mean abundance values were also lower than previous qPCR analyses that found a PR gene in half of the bacteria in the Sargasso Sea, 23% in the North Atlantic  (Campbell et al.
Proteorhodopsin in the Arctic SAR11 Community To evaluate PR gene contribution within the SAR11 community, we assessed the relative abundance of SAR11 16S rRNA gene copies by qPCR. Our estimates of SAR11 contributions (about 30% of the total prokaryotes) were in agreement with CARD-FISH experiments  (Malmström et al. , 2007; Alonso-Sáez et al. , 2008)  and 454 sequencing analysis  (Comeau et al. , 2011)  conducted in Arctic waters. Based on our average estimates obtained by qPCR, only about a third of SAR11 bacteria contained a PR gene. However, proportions of SAR11 containing the PR gene in the SAR11 community were highly heterogeneous, ranging from 5 to 90% according locations. This result contrasts with previous data from field experiments that found proteorhodopsin in about 80% of the SAR11 community  (Campbell et al.
ACKNOWLEDGMENTS This study was conducted as part of the  Malina Scientific Program  funded by  ANR  ( Agence Nationale de la Recherche ),  INSU-CNRS  ( Institut National des Sciences de l'Univers -Centre National de la Recherche Scientifique ),  CNES  ( Centre National d'Etudes Spatiales)  and  ESA  ( European Space Agency). We thank all participants to the MALINA cruises for their help, especially  M. Babin  who coordinated the project,  K. Lévesque  for the logistics, and all CCGS Amundsen crew members. We warmly thank  D. Doxaran ,  P. Raimbault ,  N. Garcia , and  J. Ras  for providing ancillary data. We are grateful to  M. Perennou  and  G. Tanguy  ( Biogenouest Sequencing Platform-FR2424-Station Biologique ) for help with sequencing. This work was supported by the  ANR RHOMEO  ( ANR 11 BSV7 021 02 ) and the  European Union program MicroB3  ( UE-contract-287589 ).
Patients will undergo a comprehensive baseline assessment and will be followed up annually for up to 5 years with the goal of providing high-quality longitudinal data on outcomes, treatment patterns and quality of life. Data from the registry will be available in the form of annual reports. and will be disseminated in conference presentations and peer-reviewed publications. The European Bronchiectasis Registry aims to make a major contribution to understanding the natural history of the disease, as well as guiding evidence-based decision making and facilitating large randomised controlled trials.
Study objectives The objectives of the study are: • to develop a pan-European, multicentre bronchiectasis registry incorporating baseline data collection with annual follow-up data for at least 5 years; • to describe the demographics, comorbidities, aetiology, medication usage, resource consumption, exacerbations, microbiology, severity and prognosis of bronchiectasis across Europe (in summary, a comprehensive description of characteristics and burden of this disease across the continent); • to facilitate multinational cooperation, within and outwith Europe; and • to facilitate the creation of national registries in European countries that currently do not have a bronchiectasis research infrastructure. The project as a whole also aims to ensure the needs and experiences of people with bronchiectasis across Europe inform all EMBARC activities.
Study design The registry is a multicentre, prospective, observational cohort study enrolling consecutive adult patients with bronchiectasis across Europe. The study is noninterventional. Patients are managed according to their local procedures and policies with no interference from the study team. Patients give informed consent to participate and data are collected at baseline (recruitment) as described below. This includes comprehensive data across the following domains: demographics; comorbidities; aetiological testing; lung function; exacerbations; disease impact; quality of life (QoL); microbiology; radiology; and treatment and physiotherapy. The study is set in primary and secondary care settings reflecting differences in how bronchiectasis is managed in different European countries.
Data collection, variables and definitions Data will be collected in three distinct fields. 1) Baseline study entry: the basic dataset for recruitment of patients into the study. 2) Annual review form: follow-up data completed for each year of the study, indicating yearly exacerbation frequency, hospitalisations and survival status. 3) There is also an exacerbation substudy being conducted in a small number of sites collecting detailed information at the time of an acute exacerbation. A complete listing of registry fields is contained in the supplementary material. The data fields have been developed and agreed following an exhaustive consultation process with stakeholders throughout Europe and in collaboration with the US and Australian registries  [27]. The dataset will allow a number of ancillary studies to be proposed by investigators.
Exacerbations The definition of exacerbation is debated and therefore, for pragmatic reasons, the registry defines exacerbation as an acute deterioration in the patient's condition resulting in a physician diagnosis of exacerbation and the prescription of systemic antibiotics  [6]. Severe exacerbations will be defined by British Thoracic Society criteria as an exacerbation requiring hospital admission  [6]. The database will also capture emergency department visits for exacerbations that do not result in hospital admission  [6].
Chronic obstructive pulmonary disease There is a reported association between COPD and bronchiectasis, but whether COPD is a cause of bronchiectasis and how to differentiate the two conditions is not clearly defined  [31] [32] [33]. The dataset will capture both the physician diagnosis of COPD, and also the smoking history (current, ex-or never-smoker plus pack-years smoked) and the presence of airflow obstruction by spirometry, allowing a careful dissection of the relationships between these parameters.
Microbiology Data are collected on any lower respiratory tract specimen (spontaneous, induced sputum or bronchoalveolar lavage) when clinically stable and during exacerbation  [34]. A recent publication has highlighted different definitions of chronic colonisation but for the purposes of the study, colonisation is defined as two positive samples at least 3 months apart while clinically stable  [34]. The database will also record antibiotic susceptibility testing. Pseudomonas aeruginosa has a strong association with morbidity and mortality that is consistent across multiple datasets  [35] [36] [37]. Therefore, the database will collect specific data on the management of P. aeruginosa, which will include any historical isolation of P. aeruginosa, phenotype (mucoid versus nonmucoid) and the use of Pseudomonas eradication treatment.
Governance, oversight and data sharing The registry is held securely in the University of Dundee Health Informatics Centre and anonymised data will be accessible to researchers throughout Europe, using the Safe Haven model (www. scot-ship-toolkit. org. uk/information-page/ship-safe-havens). This is a virtual desktop that allows secure data access and data analysis but prevents copying or downloading of data, thereby ensuring complete data security. Active investigators and other stakeholders will have unrestricted access to their own data. Requests to analyse the database as a whole will be managed by submission of a study protocol to the EMBARC scientific committee. The EMBARC registry is committed to open data sharing and the scientific committee operates on the basis that no reasonable request for data should be rejected.
Full details regarding data access are reported on the EMBARC website (https://www. bronchiectasis. eu/dataaccess). The study is conducted in accordance with the principles of Good Clinical Practice. A favourable ethical opinion is obtained by each partner site from the appropriate research ethics committee or institutional review board, where required. Additionally, any other necessary approvals required by partner sites are obtained prior to commencement of the study at each site. All patients must provide written informed consent to participate. Study results will be disseminated in the form of annual reports, conference abstracts and peer-reviewed publications. The EMBARC study group will follow the International Committee of Medical Journal Editors recommendations regarding authorship.
Future studies The EMBARC network has been developed to provide a platform for future international multicentre clinical trials and the first such trials are expected to begin in early 2016. In addition, the EMBARC platform has been developed to allow investigator-initiated ancillary studies and to support patient registries in countries outside Europe. EMBARC will make the registry platform available in Asia, Australasia and other continents to produce a harmonised dataset for international comparisons. The burden of bronchiectasis is not limited to adults, and future developments of the registry may seek to address bronchiectasis in children and/or to collect additional data in specific subgroups of patients such as those with nontuberculous mycobacterial (NTM) disease  [39, 40]. The development of the EMBARC registry has benefitted greatly from collaborations with the US COPD Foundation Bronchiectasis and NTM registry and with the emerging registry in Australia.
Discussion Disease registries have the potential to provide important insights into the natural history of disease, and to provide information required for health service planning, quality improvement and enrolment of patients into clinical trials  [41]. Registries are essential in cases where diseases are sufficiently rare that epidemiological data cannot be provided by single-centre or even single-nation studies  [42]. In developing the European Bronchiectasis Registry, we have followed guidance proposed in the EU and the extensive experience of the European Cystic Fibrosis Society Patient Register (ECFSPR), which has enrolled 30 000 patients across the continent  [42]. Recommendations from the ECFSPR have been published and have informed the development of the present project  [42]. These recommendations include defining a minimum required dataset, limiting as far as possible the burden on participants entering the data.
Standardising definitions across participating sites is essential to ensure that data are consistent and has been a challenge for the ECFSPR. This has been resolved in the EMBARC registry through the use of a shared data collection platform and standardised definitions across all studies sites. National registries have fully collaborated with the European registry, sharing the data collection platform and protocols to ensure fully compatible data for collaborative analysis. The data collection tool has been made freely available to national registry groups to facilitate this. Data quality is being rigorously ensured. Bronchiectasis can no longer be regarded as a rare disease but remains neglected in the sense that there is a still a paucity of research data and there are no therapies licensed by regulatory authorities in Europe or the USA  [41].
An improved understanding of the pathophysiology of bronchiectasis is needed in order to develop improved therapies, and registries can greatly enhance this kind of research by improving disease phenotyping and by linking to patient biobanks/bioresources  [43, 44]. Epidemiological studies are challenging because bronchiectasis is both a radiological disorder and a clinical disease. Administrative databases can provide important information on disease burden but may greatly underestimate the true incidence where bronchiectasis is frequently not recognised or misdiagnosed as COPD, but might also overestimate the prevalence of the disease if the radiological disorder is recorded without also requiring a clinical syndrome of cough, sputum production and respiratory infections  [9] [10] [11] [12] [13] [14].
Registries therefore provide an important adjunct to such data by providing higher quality phenotyping while also providing data in countries where administrative data is not available  [45, 46]. Late-phase clinical trials in bronchiectasis have struggled to meet their primary end-points in recent years for a variety of reasons  [18] [19] [20] [21]. In some cases, this may reflect differences in the pathophysiology of the disease but may also reflect a lack of data on patient stratification, the natural history of the condition or simply a lack of patients available for recruitment. It is hoped that the registry can facilitate clinical trials by aiding identification of patients, and by providing high-quality data on patient stratification and prognosis. The ultimate objective of the European Bronchiectasis Registry should be to provide high-quality research that can contribute to evidence-based guidelines and have an impact on patient care, both in Europe and internationally.
Acknowledgements EMBARC acknowledgements partnership, collaboration and support from:  ELF  and the  ELF PAG, BRONCH-UK , the UK bronchiectasis registry and biobank, PROGNOSIS, the  German Bronchiectasis Network , the  Italian Society of Respiratory Medicine (SiMER) , the  US COPD Foundation , the  Lung Foundation of Australia , the  National Institute for Health Research UK , and the ECFSR.
Acknowledgements I thank  Guillaume Huguet  for drawing the figures. This work was funded by the  Institut Pasteur , the  Bettencourt-Schueller foundation, the ''Centre national de la recherche scientifique'', Paris Diderot University, the ''Agence nationale de la recherche'' (SynDiv-ASD) , the  GenMed labex, the BioPsy labex , the  Conny-Maeva Charitable Foundation , the  Cognacq-Jay Foundation , the  Orange Foundation , and the  Fondamental Foundation.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions.