dataesr/scientific-paragraphs-categorization huggingface dataset:
A putative homolog to the extracellular effector Cf_Ecp6 of Cladosporium fulvum  [67]  is overrepresented at 48 hai. The higher expression of this gene in planta compared to that in vitro was confirmed in our qRT-PCR analysis. Cf_Ecp6 has high affinity for short chitin oligosaccharides and competes efficiently with extracellular plant receptors for degraded chitin fragments preventing activation of PTI in plant hosts  [69]. In our transcriptome analysis at 48 hai we identified plant transcripts (data not shown) with very high homology to OsCERK1, the rice chitin elicitor receptor kinase 1. OsCERK1 forms a hetero-oligomer receptor complex with a glycoprotein, chitin-binding receptor CEBiP that upon recognition of chitin fragments triggers production of reactive oxygen species, diterpenoid phytoalexins, and expression of basal defense genes in rice  [111, 112].
1371/journal. pone. 0154122. g001 Table 4. Identification and structure of nonribosomal peptide synthetases in Cochliobolus miyabeanus TG12bL2. Cochliobolus miyabeanus TG12bL2 protein identification number. b Percent of protein coverage in pairwise alignment, e-value and percent of identity determined using BLAST. c Predicted domain structural organization based on similarity to the HMMER database (http://hmmer. janelia. org/) with a cut-off value = 0. 01 and hit = 0. 03. Domains within brackets were detected with cut off > 0. 01 and 1, parentheses indicate domains detected after manual inspection based on conserved amino acids. A = AMP-binding (pfam00501); T = PP (pfam00550), phosphopantetheine attachment site or thiolation domain (or peptidyl carrier protein); C = condensation domain (pfam00668), N = NAD_binding_4 (pfam07993), D = short chain dehydrogenase (pfam00106), M = methyltransferase domain (pfam13847), R = Ferric reductase-like membrane component (pfam01794), SC = spore coat protein U domain (pfam05229), dA = putative degenerated A2 domain, AAA = ATPases associated with diverse cellular activities (pfam13671.
Identification and structure of polyketide synthases in Cochliobolus miyabeanus CmTG12bL2. -DH-PP-PP-PP-ME-abH-Cochliobolus miyabeanus protein identification number. b Percent of protein coverage in the pairwise alignment. c Predicted domain structural organization based on similarity to the CDD (http://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml) with a cut-off evalue = 0. 01, and/or HMMER database (http://hmmer. janelia. org/) with a gathering cut-off value Model = 0. 01 and hit = 0. 03. d PKS18 (AAR90272) and PKS1 (BAD22832) from C. heterostrophus and C. miyabeanus, respectively are both homologs proteins of CM_5798. KS = beta-ketoacyl-synthase domain (pfam00109); AT = acyl transferase domain (pfam00698); PP = phosphopantetheine attachment site (pfam00550 or smart00823); DH = polyketide synthase dehydratase domain (pfam14765); ME = methyltransferase domain (pfam08242); ER = enoyl reductase domain (cd05195) or ADH_N, alcohol dehydrogenase (pfam08240) and ADH_Zinc_N, zinc-binding dehydrogenase (pfam00107), TE = thioesterase (pfam00975), N = NAD_binding_4 (pfam07993), abH = alpha/beta hydrolase (pfam07859), Pep = Peptidase (pfam00326); A = AMP-binding (pfam00501).
Acknowledgments We would like to thank  Bruce Elckloff  from the  Mayo Clinic, Rochester, MN  for sequencing the C. miyabeanus isolate, and  Dr. B. Gillian Turgeon  and  Bradford Condon  for NRPS sequence data from the C. miyabeanus WK-1C and C. heterostrophus C4 and C5 strains, and other ascomycetes used in the analyses, and to  Dr. B. Gillian Turgeon  and the  U. S. Department of Energy Joint Genome Institute  (http://genome. jgi. doe. gov) for sharing the C. miyabeanus WK-1C DNA sequences and proteins. The genome and transcriptome of C. miyabeanus work were sequenced and annotated by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231.
doi: 10. a 10 1371/journal. pone. 0154122. g001 Table 4. Identification and structure of nonribosomal peptide synthetases in Cochliobolus miyabeanus TG12bL2. Cochliobolus miyabeanus TG12bL2 protein identification number. b Percent of protein coverage in pairwise alignment, e-value and percent of identity determined using BLAST. c Predicted domain structural organization based on similarity to the HMMER database (http://hmmer. janelia. org/) with a cut-off value = 0. 01 and hit = 0. 03. Domains within brackets were detected with cut off > 0. 01 and 1, parentheses indicate domains detected after manual inspection based on conserved amino acids. A = AMP-binding (pfam00501); T = PP (pfam00550), phosphopantetheine attachment site or thiolation domain (or peptidyl carrier protein); C = condensation domain (pfam00668), N = NAD_binding_4 (pfam07993), D = short chain dehydrogenase (pfam00106), M = methyltransferase domain (pfam13847), R = Ferric reductase-like membrane component (pfam01794), SC = spore coat protein U domain (pfam05229), dA = putative degenerated A2 domain, AAA = ATPases associated with diverse cellular activities (pfam13671.
com) using the 27F/Gray519R primer set (S1 Table) on all gDNA and cDNA samples, followed by pyrosequencing using a Roche 454 GS-FLX Titanium platform. The sequences were checked for the following quality criteria: no ambiguous nucleotides, a minimum quality score of 27 according to Pangea trimming  [34] , a minimum length of 200 bp and no sequencing error in the forward primer. Chimeras were detected using the UCHIME program  [35]  and removed before further analysis (1. 3% of reads). The remaining reads were clustered in operational taxonomic units (OTUs) at a 97% similarity threshold. The process was automated by PANAM, which also computed the richness and diversity indexes (https://code. google. com/p/panam-phylogenetic-annotation/) [36]. Clone libraries and phylogenetic analyses. The 16S rRNA amplicons obtained with specific PCR primer sets for different OHRBs (S1 Table ) on the gDNA and cDNA samples were cloned using the TOPO TA cloning kit (Invitrogen Inc.
, Carlsbad, CA, USA) and sequenced using the Sanger sequencing technology by Eurofins Genomics sequencing services (https:// eurofinsgenomics. eu). Sequence similarities were identified using the online NCBI BLASTn  [37]. Phylogenies were constructed with MEGA6 software  [38]  using the maximum composite-likelihood model and neighbor-joining algorithm. All 16S rRNA sequences were submitted to GenBank (accession numbers KJ475586 to KJ475644 and KJ475666 to KJ475673).
Real-time PCR Real-time PCR assays (qPCR) were performed on the gDNA samples using a Mastercycler Epgradients realplex (Eppendorf, Hamburg, Germany) and data analysis was done with Realplex software (version 1. 5). The primer sets used in this study are listed in S1 Table. Briefly, the PCR reactions (each conducted in triplicate) contained 12. 5 μL of 2X MESA GREEN qPCR MasterMix Plus for SYBR Assay1 (Eurogentec S. A, Liege, Belgium), 1 μL of DNA template (pure or diluted 1:100), 1. 75 μl of each primer (0. 7 μM final concentration), 0. 125 μL of BSA (0. 25 μg μL -1 final concentration) and 7. 875 μL of ultra-pure sterile water. qPCR reactions consisted of an initial denaturation step at 94°C for 15 min, followed by 40 cycles of denaturation at 95°C for 15 s, annealing for 30 s (54°C for bacterial and Dehalogenimonas 16S rRNA genes and 59°C, for Dehalococcoides 16S rRNA gene, S1 Table ) and elongation at 72°C for 30 s.
Metagenome annotation The metagenomic dataset (NCBI accession number SRA049219) used in this study corresponded to a gDNA sample collected at 90 m depth in 2011 in Lake Pavin by Denonfoux et al. [39]. It included 116,365 high quality reads with a total of 54,740,219 bp. The length of sequence reads ranged from 335 to 598 bp (mean size ~471 bp). Read assembly was done with Roche's GS de novo Assembler ('Newbler') v2. 6 and required read overlaps of 60 bp at 95% nucleotide identity. After assembly, the contigs (maximum length 8,485 bp) and the 99,917 non-assembled reads were analyzed and annotated using the MG-RAST server (MG-RAST ID: 4487833. 3) [40]  to identify candidate genes corresponding to rdhA and insertion element (IS) sequences. Candidate genes identified in the metagenomic dataset were amplified by PCR from gDNA samples using specific primer sets (S1 Table ) and confirmed by cloning and sequencing as described above.
We then assessed the density of the two most abundant DEH OTUs along the water column by qPCR (Fig  4 ), i. e. OTU1, which was closely related to Dehalogenimonas alkenigignens SBP1 and most often retrieved by the Sanger sequencing approach, and OTU2, which was closely related to Dehalococcoides sequences and the most abundant DEH sequence in the pyrosequencing dataset (Table  1 ). OTU1 was detectable at only low abundance (approximately 10 2 copies mL -1 ) along the water column and total density decreased with water depth. In contrast, a 10 3 -fold increase in OTU2 copy numbers was observed between depths of 58 m (approximately 10 2 copies mL -1 ) and 68 m (10 5 copies mL -1 ), and slightly decline at 80 m (6 x 10 4 copies mL -1 ). The concentration of total bacterial 16S rRNA genes was >2 x 10 6 copies mL -1 at all analyzed depths.
Application of gene capture for enrichment of DEH genomic fragments In order to obtain more genomic information from DEH in Lake Pavin, we used an enrichment strategy for specific gDNA fragments using targeted hybridization probes and applied it to the enrichment of DEH gDNA fragments from the water sample collected at 68 m depth where most DEH OTUs were found by pyrosequencing. We used a two-step capturing approach: in the first step ("capture 1"), we used probes targeting DEH genes identified through the PCR product sequencing (i. e. , 16S rRNA amplicons) and the exploration of the metagenome dataset (i. e. , rdhA genes and insertion sequence (IS) elements because of their frequent link to rdhA genes in sequenced DEH genomes)  [46]. In a second step ("capture 2"), we used the information of genomic fragments captured during the first step to design fragment-specific probes and enriched more fragments from a targeted locus.
Overall, our results showed that 16S and 23S rRNA genes are physically distant in the genome of Lake Pavin phylotypes represented by OTU1 and OTU2. Hence, those strains seem to have rRNA gene features as outlined in previously sequenced DEH. Capturing DEH gDNA fragments using rdhA gene probes Chromosomes of cultivated and sequenced DEH strains carry 10 to 36 different rdhA genes  [46] [47] [48]. These rdhA genes are not strongly conserved making it difficult to define broad capture probes  [49]. In this context, one of the strategies adopted to isolate rdhA genes from Lake Pavin was based on the exploration of a previously described metagenomic dataset (MG-RAST ID: 4487833. 3) [39]  to detect rdhA signatures followed by specific sequence enrichment using our gene capture method. Only one rdhA homologous sequence (478 bp) was detected in the metagenomic dataset.
Capturing DEH gDNA fragments by using probes targeting IS elements The other capture strategy adopted to isolate supplemental rdhA genes from Lake Pavin was to target IS elements. Indeed, all DEH strains currently sequenced carry a large number of IS elements in their chromosome and most of the rdhA genes are located in the neighborhood of these elements. For example, the D. lykanthroporepellens genome contains 74 IS elements  [46]  and 19 rdhA genes, of which 75% are adjacent to an IS element  [7]. Assuming that the Dehalogenimonas-related phylotype (OTU1) detected in Lake Pavin has a similar gene organization, all D. lykanthroporepellens IS protein sequences were downloaded from the NCBI database and used to screen the metagenomic dataset using TBLASTn (minimum 30% protein identity over a stretch of 100 amino acids).
Such abundance and co-occurrence were also observed in Lake Pavin with similar methanogenic conditions  [31, 32]. Currently, two nearly complete genomes are available for this group, one obtained from an aquifer sediment using metagenomics (RBG-2,  [19] ) and the other from marine sediment by using single-cell genomics (DEH-J10  [16] ). On the basis of genomic analysis and the lack of rdhA genes in their chromosome, both strains are supposed to use other processes than OHR for energy conservation. OTU2 shares only 85. 5% 16S rRNA gene sequence identity with the 16S rRNA gene of RBG-2 and 86. 5% with that of DEH-J10, indicating that we detected a distinct phylotype among this group. The closest 16S rRNA gene sequence found in public databases was annotated as an uncultured bacterium derived from the anoxic area of Green Lake, a meromictic lake in New York (accession number FJ437868, 98% nucleotide identity).
The detection of DEH-related 16S rRNA genes in an ecosystem, especially when they are divergent from reference strains, is not sufficient to conclude that they are able to respire with organohalides. In contrast the detection of rdhA genes, the key genes in OHR, is more indicative for OHR potential. The rdhA gene family exhibits significant diversity across gene sequences. The capture approach applied in this study is a powerful PCR-independent tool to investigate previously uncharacterized rdhA genes. It enabled the identification of two novel rdhA genes, one using probes targeting a rdhA gene sequence identified in a metagenomic dataset from Lake Pavin (RdaseH8) and the second, using probes targeting adjacent IS elements (Contig_RdaseC5 and Contig_RdaseC5F9). The first RDase shows highest similarity with H.
Acknowledgments This study includes results from PhD researches supported by various instances:  Région Auvergne ,  CNRS, l'Agence De l'Environnement et de la Maitrise de l'Energie (ADEME) ,  Direction Générale de l'Armement (DGA) ,  Université Blaise Pascal and Ministère de la Recherche et de la Technologie. The study was also granted from different sources:  French National Programme INSU-EC2CO  ( EChlore Project ) and  ANR   Programme PRECODD (EVASOL Project ). The work of LA was supported by the  Deutsche Forschungsgemeinschaft   FOR1530. We are grateful to  Najwa Taib  for her technical support and to  Cécile Lepère  for her help in the English language correction. We thank  Didier Jézéquel  for his data communication.
Background: The potential for global collaborations to better inform public health policy regarding major non-communicable diseases has been successfully demonstrated by several large-scale international consortia. However, the true public health impact of familial 2 A. J. Vallejo-Vaz et al. / Atherosclerosis Supplements 22 (2016) 1e32 hypercholesterolaemia (FH), a common genetic disorder associated with premature cardiovascular disease, is yet to be reliably ascertained using similar approaches. The European Atherosclerosis Society FH Studies Collaboration (EAS FHSC) is a new initiative of international stakeholders which will help establish a global FH registry to generate large-scale, robust data on the burden of FH worldwide. Methods: The EAS FHSC will maximise the potential exploitation of currently available and future FH data (retrospective and prospective) by bringing together regional/national/international data sources with access to individuals with a clinical and/or genetic diagnosis of heterozygous or homozygous FH.
In this context, the European Atherosclerosis Society FH Studies Collaboration (EAS FHSC) is being developed as an ambitious international initiative to gather data derived from FH focused efforts from investigators worldwide  [21]. The development of this wide collaborative network of investigators from different regions and countries will support an international large-scale FH registry and improve reliability of data on current clinical practices and corresponding outcomes. A strategic data sharing and integration plan will be implemented to efficiently combine data residing in different sources and formats, bring disparate data silos together, and provide for the first time a unified view of these large combined datasets. Sharing and pooling the data in a structured systematic approach will allow us to overcome previous barriers and limitations to international comparisons across different cohorts [32e36], such as the heterogeneity, diversity and complexity of the data and standards used.
EAS FHSC methodology Data from multiple cohorts, registries, databases and collections with access to information on individuals with a clinical and/or genetic diagnosis of HoFH or HeFH will   be merged and harmonised into the global EAS FHSC registry. Investigators from the local sites will contribute to the FH registry through the NLI/CLs for the corresponding region or country. The latter will transfer and upload the data to the Coordinating Centre to be incorporated into the FH registry (Fig. 3A ). In order for the investigators to provide reliable data for inclusion into the registry, a set of minimum predefined quality and operational criteria have been laid out (Table  1 ), which must be satisfied by individual contributing cohorts. A minimum set of data (core data) are required for inclusion in the registry, comprising general and demographic information, familial and personal cardiovascular history, FH diagnostic criteria, lipid-lowering medication with doses, laboratory lipid profile, and clinical outcomes if follow-up data are available (retrospective or prospective).
Where genetic tests are available, these genetic data form part of the core data requested; in case of HoFH, data about LDL-aphaeresis are also collected. Additionally, where available, a number of other parameters of interest will be collected (Appendix 1). Where possible, standardised field definitions will be used and free entry text avoided. Our main long-term outcomes of interest will include all-cause and cardiovascular mortality and fatal/non-fatal cardiovascular events. Nevertheless, given the unique nature of the EAS FHSC database, information on factors such as statin intolerance, incident diabetes, aortic valve/supravalvular disease or cancer will be also sought and analysed. Updates of the shared datasets will be encouraged to be provided within a reasonable time-frame. For each cohort/dataset a two-stage approach is followed: the first step comprises the collation of the crosssectional and retrospective data readily available; the second stage involves the integration of longitudinal, prospective records and follow-up.
Data management Management of this large volume of data securely will require the development of an informatics platform, which requires various steps from designing data models, collection, validation, harmonisation, transformation and analysis and reporting. In the first instance, a secure web portal will be developed for the datasets to be transferred to the Coordinating Centre to provide an automated solution for data transfer, visualization and coordination. Data sharing plans involve instructions to the lead investigators for data to be transferred and data to be collected, updated with feedback for the individual investigators providing the data. The preliminary data shared will be checked for consistency and accuracy by at least 2 independent investigators of the Coordinating Centre (scientific coordinator, data manager) and any discrepancies will be resolved by raising queries with the individual investigators.
After ensuring satisfactory data quality, the information will be saved and harmonised into a bespoke central database (FH Data Warehouse [FHDW]) securely hosted on a university server by the Coordinating Centre, in strict adherence with all data safety protocols and regulatory requirements. The Coordinating Centre, supported by a technical team, will develop cutting edge and artificially intelligent routines incorporating machine learning for automated data cleaning, harmonising and make the data uniform and compatible. The data architecture and platform will support secure incorporation of data irrespective of source or format from multiple global repositories/databases/file systems allowing: data cleaning, validation, quality control and merging into the FHDW; retrospective and ongoing prospective data pooling, harmonisation and analysis.
(i) The data must be in electronic format. (ii) The amount of data shared should be considered enough to add relevant information to the registry. (iii) The data must have been collected rigorously following a standardised well-designed protocol; inclusion and exclusion criteria must be well defined; FH must be well defined according to accepted criteria and not rely only on self-reported history of FH; any measurements recorded must have been done with validated and standardised methods and devices; standardised definitions should have been used; the data source/participants included should be representative of the target population. (iv) The parameters recorded must include at least the minimum set of data (core information) as specified in the protocol. (v) Local ethical and security aspects must be in place; a data sharing agreement must be signed prior to any data transfer.
Analysis plan After ensuring consistency and compatibility of the information and description of the variables, the data from various providers will be merged and analysed at an individual level as a composite dataset. Where this is not possible, analyses will be undertaken at a country/provider level, by analysing each dataset individually and then pooling the results together. We will conduct different types of analyses, such as assessing the validity of previous published findings with greater power and precision, conducting alternative analyses of the same data, cross-cohort comparisons, analysing unpublished data (not previously conducted due to low sample size within each separate dataset), conducting exploratory analyses to generate new research hypotheses, etc. We will analyse the data to address a number of research questions as shown in Table 2  [37, 38].
and data available we will also try to roughly approximate the prevalence of FH by extrapolating the data to the general population. The different sources of incoming data will also help assess potential variations depending on different geographic settings, including factors such as population genetics, healthcare delivery systems and other patient-, socio-economic-or treatment-related factors. Following a "database-query" model  [34] , investigators involved in the registry will be able to submit research questions they would like to assess through the Coordinating Centre; if the request is deemed to be scientifically relevant by the Executive Committee and considered feasible by the Coordinating Centre, the latter will construct and run the analyses and return the results to the requester.
Ethics, security and regulatory considerations This project and related research will be conducted in accordance with the principles of the Declaration of Helsinki. Where required, approval from the corresponding ethical and/or research committees and informed consent from participants will be requested by the investigators to collect, transfer and share the data. Data sharing agreements will be executed between the NLI/CLs sharing the data and the Coordinating Centre as the recipient of the data (Appendix 4). The investigators and the Coordinating Centre will use all reasonable safeguards in connection with any transfer, communication or remote access connectivity involving the data. A secure web portal will be developed as a means for data entry/coordination and controlled environment. Only de-identified, pseudo-anonymised data will be collected, and removal of identifiers, replacement of dates with time intervals where possible, and categorization of several characteristics will be performed.