dataesr/scientific-paragraphs-categorization huggingface dataset:
We propose that B. servocepacia Tatl-371 be used as type strain. Its phenotype has been extensively characterized and it was deposited in the BCCM/LMG database under accession number LMG30279  [26]. As a type strain should be representative of its species, it is relevant that Tatl-371 possesses a central position in the phylogeny of its clade (Fig. 1 ). Burkholderia sp. nov. was repeatedly isolated from soil environment (but also plants, water and aerosols) and possesses several genes improving its fitness in those contexts (green factors). While B. cenocepacia can also thrive in soils, it is often found as an opportunistic pathogen of humans and bears several genes improving its virulence (red factors). Burkholderia sp. nov. can use different plant derivatives (galacturonic acid, xylans, pectin) as carbon sources.
Three hundred one of the bacterial genomes studied here are publicly available on the NCBI database. One strain, CEIB S4-3, is publicly available on the JGI website. The respective strains are all registered as belonging to the B. cenocepacia species and are listed in Additional file 6: Table  S3  with additional genomic data. Strain ABIP444, was isolated during a survey of rice roots endophytes in Cameroon (E. Ngonkeu, unpublished). A single colony was grown in Luria low salt medium (Merck, Inc. , Darmstadt, Germany) for 24 h at 28 °C and its DNA was extracted using a modified JGI protocol for bacterial DNA isolation using CTAB. A genomic library with average insert size of 350 bp was prepared for sequencing using a TruSeq Nano DNA library preparation kit (Illumina, Inc. , San Diego, CA, USA).
Paired-end sequencing was performed by the MGX platform (CNRS, Montpellier, France) using a HiSeq 2500 (Illumina), generating 12,709,495 raw read pairs. Reads with overlapping sequence were assembled using CLC Genomics Workbench version 7. 04 resulting in 292 contigs ranging from 586 bp to 20. 2 kbp. The genome sequences can be found at the European Nucleotide Archive with accession number PRJEB31911.
Genomic comparisons Pairwise genome comparison using Average Nucleotide Identity (ANI) based on BLAST+ (ANIb) or MUMmer (ANIm) was performed using the PYANI software  [49]. Two strains were considered co-specific when they shared more than 95% nucleotide identity on at least 70% of their whole genome sequence  [99]. For the strains that did not fall within a clear ANI cluster, comparisons to the phylogenetically closest Burkholderia species were carried out  [1]. Digital DNA-DNA hybridization was carried out using the GGDC 2. 1 web platform  [100]. Briefly, this tool uses BLAST+ to align one genome against another and reciprocally and generate high-scoring segment pairs (HSPs). The intergenomic distance is then calculated by dividing the sum of all identities found in HSPs by overall HSP length.
Phylogenetic analysis The phylogenetic analyses were conducted using the MEGA v7. 0. 26 software  [101]. The core-genome output of 1057 genes (1,039,265 positions) was evaluated using a Maximum Likelihood phylogenetic reconstruction approach with a General Time Reversible model (Gamma distributed rates with invariant sites and 5 discrete gamma categories). We validated the resulting tree through an independent Bayesian analysis using BEAST v1. 10. 2  [102] , with the same substitution model, a strict clock model and the tree prior calculated using the Yule model. The MCMC length of chain was set to 1. 10 7 and the burn-in value was set to 1. 10 6 for analysis. The resulting consensus tree was constructed using a maximum clade credibility prediction.
Host-adaptive gene detection The specific core genomes of the two main clades, were screened for genes described as involved in host adaptation in the literature  [50-54, 70, 71]. The complete sequence of the 875 kbp long virulence plasmid from B. cenocepacia J2315 and the 24 genes of the afc pathogenic cluster were also searched in all 31 genomes by BLAST  [50, 51]. Gene clusters involved in anaerobic metabolism, showing potential adaptation to CF lung environments, were identified through similarity with their B. pseudomallei homologues  [68]. We also searched for the low-oxygenactivated locus (lxa) which was discovered in B. cenocepacia  [69]. The antiSMASH 4. 0 software  [103]  was used to screen for gene clusters allowing the production of secondary metabolites such as antibiotics and siderophores, involved in environmental resilience and competition with other microorganisms.
Acknowledgments The authors acknowledge the  IRD itrop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper. URL: https://bioinfo. ird. fr/-http://www. southgreen. fr. The LABGeM ( CEA/Genoscope & CNRS UMR8030 ), the  France Génomique and French Bioinformatics Institute  national infrastructures are acknowledged for support within the MicroScope annotation platform. The authors are grateful to  Marie Simonin  for her helpful comments on earlier versions of the manuscript. We also gratefully acknowledge the helpful insights of Annette Vergunst concerning B. cenocepacia's virulent behavior.
Funding The authors gratefully acknowledge financial support from the CGIAR research program (CRP) RICE. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materials 301 B. cenocepacia genomes analyzed during the current study are available in the NCBI repository (https://www. ncbi. nlm. nih. gov/genome/genomes/475 ). Strain CEIB S4-3, not currently annotated as B. cenocepacia, is available under RefSeq accession number JSBM00000000. 1. The novel sequenced strain ABIP444 is available at the European Nucleotide Archive under study accession number PRJEB31911, http://www. ebi. ac. uk/ena/data/view/PRJEB31 911. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
, 2012: p. 635). Its precise affinities with other titanosaurs could not be clarified, except for the fact that several aspects of its morphology set it apart from other European titanosaurian braincases  (Díez Díaz et al. , 2012: p. 635 ), all of which either Campanian or Maastrichtian in age. To produce a three-dimensional reconstruction of the endocast of the cranial cavity and endosseous labyrinth of the inner ear, the specimen was subjected to computed tomographic (CT) scanning at the Université de Poitiers (Poitiers, France), using a EasyTom XL duo (RX-Solutions, Chavanod, France) with a voltage of 130 kV and a current of 440 mA. The dataset consisted of 1165 slices (1089 × 1034 × 1165 pixels, 0. 116 mm voxel size) in tiff format. The CT data were imported into Avizo 7. 0 (Visualisation Science Group, Burlington, MA, USA) for image segmentation and digital reconstruction.
, 2004; Gopalswamy et al. , 2009). This is evidence of the drag forces from the ambient solar wind acting on CMEs and causing their speeds to tend toward its value of around 400 to 500 km s -1. Much recent analysis of the continuing stream of LASCO-C2 and -C3 data has focused on the differences in CME properties between the weak Solar Cycle 24 and the previous three stronger cycles. Interestingly, the analysis by  Gopalswamy et al. (2014a Gopalswamy et al. ( ,b, 2015) )  show that the CME rate did not drop in Cycle 24, despite a 40% decrease in SSN, and they also demonstrate a large increase in the abundance of both wide CMEs and high-latitude CMEs since the polar-field reversal of Cycle 23, in 2004. Those authors suggest that this is due to the weakened polar magnetic-field strength of Cycle 24, which serves to reduce both the ambient magnetic and dynamic solar-wind pressure and therefore allows CMEs to expand more easily.
The recent development of wide-angle imagers -firstly the Solar Mass Ejection Imager (SMEI:  Eyles et al. , 2003)  onboard Coriolis, followed by the STEREO/Heliospheric Imagers (HIs:  Eyles et al. , 2009)  -extends the range of observations of CMEs well beyond those of the aforementioned coronagraphs. Indeed, the HI instruments have permitted the tracking of CMEs to 1 AU and beyond (e. g. Byrne et al. , 2010; Davis, Kennedy, and Davies, 2010; Möstl et al. , 2010; Savani et al. , 2012; Harrison et al. , 2012; Rollett et al. , 2014; Temmer et al. , 2014). A substantial increase in the number of CMEs catalogued and analysed based on HI data was achieved during the three-year EU-funded Heliospheric Cataloguing Analysis and Techniques Service (HELCATS) project, which ran from May 2014. The core output of this project is the HICAT catalogue, consisting of detections of CMEs by the inner (HI-1) camera on the twin NASA STEREO spacecraft.
Other reasons include occasional data gaps in HI or because CMEs propagate at position angles too far from the Ecliptic to be tracked. Several different single-spacecraft geometrical models, based on assumptions of CME morphology and motion  (Davies et al. , 2012) , are then applied to the time-elongation profiles of the remaining subset in order to infer kinematic properties, more specifically speed, direction, and launch time. The results of this form a new catalogue, which we call HIGeoCAT. Following September 2014 both spacecraft entered a period of reduced communications as they approached superior conjunction with the Sun and contact with STEREO-B was lost entirely. Since November 2015, STEREO-A has resumed normal operations and so the total range of observations covered here (April 2007 -August 2017) spans the 11 years covering the decline of Solar Cycle 23 and the majority of Cycle 24.
The 2017 limit is chosen for consistency with Article 1, however, at the time of writing we are continuing to receive data from STEREO-A. In this article we begin by describing in detail the means by which the HIGeoCAT CME catalogue is generated from HI data. This is followed by a statistical analysis of the resulting kinematic properties of CMEs in the catalogue. The solar-cycle behaviour of CME speeds, propagation directions, and launch times are then compared to existing coronagraph catalogues from LASCO  (Yashiro et al. , 2004; Vourlidas et al. , 2017).
This is quite consistent with established CME speeds determined from coronagraphs;  Yurchyshyn et al. (2005) , for example, show a distribution of CDAW CME speeds that peaks at 350 km s -1 with a long tail extending to CMEs exceeding 1000 km s -1 during the ascending phase of Solar Cycle 23. Yashiro et al. (2004)  study CMEs from the same period as  Yurchyshyn et al. (2005)  using the LASCO CDAW CME list and find that average CME speeds range from 281 km s -1 at solar minimum (1996) to 502 km s -1 at maximum  (2001). As a means to study the solarcycle variation in speeds within our dataset we make a direct comparison of the annual distributions from CDAW, COR-2, and HIGeoCAT. A stacked annual histogram of the annual distribution of CME speeds from SSEF are shown in Figure  10 , in addition to the corresponding values from the COR-2 catalogue (Vourlidas et al.
The equations for each of the regression lines are shown explicitly in Table  1. There is a clear systematic difference where the HI values are greater than those from LASCO. This is again attributed to the sky-plane effect; the difference in speeds of CMEs in the CDAW and HIGeoCAT catalogues is predominantly due to the sky-plane assumption in the former. We recalculate the HIGeoCAT speeds using the same sky-plane assumption that is used to produce the CDAW catalogue. These results are presented in the bottom panels of Figure  11. The bottom two panels of Figure  11  shows the result of taking the same time-elongation data for each CME used in HIGeoCAT and assuming they are represented by a point source observed in the sky plane. That is, we determine CME speed using the same method as in the CDAW catalogue.
Acknowledgements We acknowledge support from the  European Union   FP7-SPACE-2013-1 programme  for the  HELCATS  project (# 606692 ). The HI instruments on STEREO were developed by a consortium that comprised the  Rutherford Appleton Laboratory (UK) , the  University of Birmingham (UK) ,  Centre Spatial de Liège  ( CSL, Belgium ) and the  Naval Research Laboratory (NRL, USA). The  STEREO / SECCHI  project, of which HI is a part, is an international consortium led by  NRL. We recognise the support of the  UK Space Agency for funding STEREO/HI  operations in the UK. We also acknowledge use of the CDAW SOHO/LASCO event catalogue (cdaw. gsfc. nasa. gov/CME_list/),which is generated and maintained at the  CDAW Data Centre , by  NASA  and the  Catholic University of America  in cooperation with  NRL , and the STEREO/SECCHI/COR2 CME catalogue (solar.
Rare diseases, an emerging global public health priority, require an evidence-based estimate of the global point prevalence to inform public policy. We used the publicly available epidemiological data in the Orphanet database to calculate such a prevalence estimate. Overall, Orphanet contains information on 6172 unique rare diseases; 71. 9% of which are genetic and 69. 9% which are exclusively pediatric onset. Global point prevalence was calculated using rare disease prevalence data for predefined geographic regions from the 'Orphanet Epidemiological file' (http://www. orphadata. org/cgi-bin/epidemio. html). Of the 5304 diseases defined by point prevalence, 84. 5% of those analysed have a point prevalence of <1/1 000 000. However 77. 3-80. 7% of the population burden of rare diseases is attributable to the 4.
It is also essential for current orphan drug legislation objectives to stimulate the development of RD treatments by incentivizing to compensate for the small market size. Definitions to-date however have not been based on robust evidence, as methodologically thorough analyses have not been possible due to insufficient epidemiological data, lack of scientific publications, and an absence of structured databases. Estimating the global point prevalence of RDs is challenging foremost for the diversity of the data, which are derived from a variety of disparate information sources that are not standardized or that are difficult to combine, including published case reports or systematic reviews, patient registries, expert opinions, and other anecdotal evidence. This is aggravated by differing methods employed during case ascertainment and a lack of firmly established, and/or specific diagnostic criteria or coding systems to capture this data  [15].
The database is a comprehensive, manually curated and expertreviewed knowledge-base specific for RDs, and is an IRDiRC Recognized Resource and Elixir Core Data Resource  [23, 24]. Orphanet catalogues RDs: encompassing diseases, malformation syndromes, morphological, and biological anomalies, as well as particular clinical situations considered as 'rare in Europe'. The catalogue is structured to provide a hierarchical representation classified by medical domain  [25, 26] , annotated with medical classifications, age of onset, inheritance, genes, and a directory of health and research resources. Since 2005 Orphanet has annotated RD information with epidemiological indicators via a systematic data collection procedure  [27] , with 81. 2% of RDs annotated by 2018. The methodology is an ongoing systematic literature survey of peer reviewed journals, specialized reports, registries, and international databases, with expert advice sought for epidemiological indicators not documented in the literature.
The type of epidemiological indicator is recorded (point prevalence, annual incidence, birth prevalence, lifetime prevalence, case report, or family report) for each disease and are annotated with epidemiological data as numerical estimates, preestablished ranges (<1/1 000 000, 1-9/1 000 000, 1-9/100 000, 1-5/10 000, 6-9/10 000, and >1/1 000), or "not yet documented" and "unknown" (where despite intensive bibliographical research, no data could be found) (see Fig. 1 ). All data are annotated with a geographical area (country, continent, or worldwide) and/or a particular population if relevant (e. g. , ethnic founder populations). As RDs become a global public health priority  [28]  and a global national policy priority, it is essential to provide an evidence-based estimate of the prevalence of RDs.
Methods The 'Orphanet Epidemiological file' was obtained on October 1, 2018  [29]. Data for unique clinical rare diseases (defined as 'disorders' in the Orphanet classification) were included for analysis, excluding 'groups of disorders' (e. g. , lysosomal diseases) and 'disorders subtypes' to avoid duplicate counts. Univariate analysis was performed on 'age of onset'. 'Genetic disorders' were found in the 'classification of rare genetic diseases' and as such are defined as 'known or suspected to be familial; inherited or de novo autosomal or x-linked single gene disorders; mitochondrial disorders; and chromosomal rearrangements'. The inheritance pattern was derived by analysis of disorders within the classifications 'rare genetic diseases' including 'rare chromosomal anomalies' and the annotations on 'mode of inheritance'.
'Point prevalence', defined as the number of all the existing cases in a population at a specific point in time, was chosen as the epidemiological indicator for analysis. Epidemiological data in Orphanet is recorded as numerical values or as predefined ranges when numerical values are not available. A subset of data was selected for homogeneity from the heterogeneous epidemiological data as follows, as described in Fig. 2:  (1) Data for groups of disorders and disorder subtypes were excluded. (2) Rare cancers, infectious diseases, and poisonings with an acute or subacute clinical course were excluded from analysis as they are described by 'Incidence' as the epidemiological indicator. Disorders were also excluded when point prevalence could not be calculated, such as those described by 'prevalence at birth', 'lifetime prevalence', or 'annual incidence'.
(3) Only one point prevalence per disorder was included; for disorders with more than one recorded geographic point prevalence, one value was selected in the order of preference: worldwide; or European (EU, Russia, Turkey, and Iceland). If no point prevalence was available from these regions then a USA point prevalence figure was used if it did not exceed the European threshold definition of 5/10 000. RDs that did not have a point prevalence reported from one of these geographic areas were excluded. (4) Disorders with a mean prevalence exceeding the threshold of 5/10 000 were excluded as they are not considered to be rare in Europe. Data were of three types and were treated separately depending on whether the prevalence was (1) a numerical value (2) a class (predefined range), or (3) based on case and family reports.
RDs with a prevalence range only Data were verified to ensure that the prevalence range assigned concurred with the prevalence categories listed on Orphanet for comparability. No numerical median or mean value could be assigned within the prevalence range as the distribution of point prevalence data within each class was not known. Accordingly, for each RD, data were recorded as (i) the minimum value within each class was assigned (e. g. , 1/100 000 for the class 1-9/100 000) and (ii) the maximum value within each class was assigned (e. g. , 5/10 000 for the class 1-5/10 000) for each disease. For the class <1/1 000 000, both the minimum and maximum values were assigned as 1/1 000 000.
Results Orphanet contains descriptions of 6172 clinically unique RDs excluding groups of disorders and disorders subtypes. Age of onset is described in 81. 3% (n = 5018): 3510 (69. 9%) are exclusively pediatric onset; 908 (18. 2%) have onset spanning both pediatric and adult groups and 600 (11. 9%) are exclusively adult onset. 4440 RDs (71. 9%) are classified as genetic. Of the genetic RDs, 79. 7% were annotated with one (72. 4%) or more (7. 3%) inheritance patterns, as described in Fig. 3  and Supplemental Table  1. Of the 6172 unique RDs (Fig. 2 ), 5304 RDs remained once disease described by incidence (cancers: 11. 1%, infectious diseases: 2. 6%, and poisonings: 0. 4%) were excluded (n = 868, 14. 1%). Data were excluded for 1719 RDs: those annotated with epidemiological figures other than point prevalence; those with 'unknown' point prevalence; or those not yet documented.
A total of 3585 RDs with point prevalence data were included in this analysis, representing 67. 6% of the RDs for which point prevalence is the pertinent epidemiological indicator: 745 RDs annotated with a point prevalence figure or class (20. 8%), 2496 RDs described by reports of single cases (69. 6%), and 344 described by reports of families (9. 6%). Figure  4  shows the distribution of the RDs included in the analysis according to their point prevalence class.
We have used a broader, more practical definition of 'genetic', including not only known single genes but diseases known or suspected to be familial with no underlying gene identified; mitochondrial diseases; and chromosomal rearrangements; as derived from the 'Orphanet classification of genetic disorders' by methods described in our supplemental files; and we have excluded double-counts by considering only the number of unique clinical entities. Although the 14. 1% of RDs described by incidence could not be considered in this analysis, the contribution of RDs measured by annual incidence, such as rare cancers, should be taken into account. For example, analyses of the RARECARE European population-based rare cancer registry data estimated that 4 300 000 people were living in the European Union with a diagnosis of a rare cancer  [39].
Our approach to determining overall RD point prevalence also has several potential limitations. The identification of the high-quality population-based studies is difficult in the context of RDs, despite existing guidelines  [40]. Collection of data is hampered by heterogeneity in the epidemiological approaches used to estimate the point prevalence, inconsistency in reporting 'incidence' and 'prevalence', and the use of anecdotal data. Orphanet, as a European based resource, registers RDs with a point prevalence of <5 per 10 000, and diseases defined as rare in most jurisdictions will be included in Orphanet's epidemiological review. Countries with a more restrictive 'rare disease' definition may not collect or publish data on diseases considered rare in Europe. Conversely, Orphanet excludes diseases considered to be rare in countries with more permissive definitions.
Orphanet's reliance on publications in peer-reviewed journals may exclude relevant data published elsewhere. The inclusion of only worldwide, European or American point prevalence values in our study could have a significant effect on our estimate of global point prevalence-either to underestimate the contribution of RDs that are not prevalent in Europe or the US but are prevalent elsewhere, or to overestimate the generalizability of European and American RDs to the rest of the world. The decision to select geographic regions was based on factors including the lack of RD epidemiological information from many areas of the world such as India, China, South America, and Africa; and the publication bias that results in mostly European and North American studies being published in the literature reviewed in Orphanet data collection.
Our research highlights disparities in even the definition of 'rare disease'. Although published national definitions of the point prevalence of RDs range from 5 to 80 per 100 000 population (Fig. 4 ), the distribution of the point prevalence of individual diseases in the Orphanet database shows that even the lowest point prevalence definition of 5 per 100 000 encompasses at least 90. 5 % of the RDs recorded on Orphanet, with the great majority (84. 5%) derived from very low point prevalence diseases (<1/1 000 000). However, an examination of the percentage of patients attributable to each prevalence category differs: the exclusion of the (1-5/10 000) category in the Korean, Australian, and Taiwanese RD definitions excludes ~80% of the patients that would be considered to have RDs in Europe.