dataesr/scientific-paragraphs-categorization huggingface dataset:
To study whether the flavodoxin/ferredoxin switch can be detected using our dataset, we carried out an analysis of the ferredoxin and flavodoxin families using the Pfam domains PF00111 and PF00258. These families not only include the photosynthetic versions but also other isoforms and domains, and there is an overlap of redox properties between different members of these two families, being potential isofunctional proteins in many reactions  29. Thus, we studied the relative levels of the two families of genes in the five major phytoplankton groups by calculating the ratio of their relative abundances and expression (Fig. 6 ). With the exception of diatoms, gene abundances show little variations and only weak correlations with iron concentrations (Fig. 6a ; "Metagenome" column and Supplementary Data 5).
Discussion The global ocean transcript catalog reported here represents a first resource to study extensively and uniformly the gene content of eukaryotes and the dynamics of their expression in the environment, and notably adds to previous DNA-based resources that describe the viral and prokaryotic components of the ocean  [9] [10] [11]. The gene repertoire of planktonic eukaryotes is massive and diverse, much more so than the prokaryotic gene space  9. The impressive number of genes without functionallycharacterized homologs in databases points to the large numbers of understudied yet widely distributed genera inhabiting marine ecosystems, for which even widely conserved GFs have yet to be investigated. The restricted distribution of totally new GFs highlights the need to develop methods for revealing their roles without the support of homology-based hypotheses.
Water was filtered immediately after sampling. Wholeplankton communities were subsequently filtered on polycarbonate membranes, rapidly flash-frozen and preserved in liquid nitrogen on board Tara. Physicochemical parameters measured during the expedition are available in the Pangaea database (https://www. pangaea. de/ and Supplementary Data 5) and described in Pesant et al  20. Due to the sparse availability of direct observations of iron in the surface ocean, concentrations were derived from a global ocean simulation using the MITgcm ocean model configured with 18 km horizontal resolution and a biogeochemical simulation which resolves the cycles of nitrogen, phosphorus, iron and silicon  34. The biogeochemical parameterizations, including iron, are detailed in Follows et al  35. Atmospheric deposition of iron was imposed using monthly fluxes from the model of Mahowald et al  36.
NPP values were derived from satellite measurements from 8-day composites of the vertically generalized production model  37. Physicochemical parameters of each station analyzed in this article are indicated in Supplementary Data 5. Nucleic acid extraction, library construction, and sequencing. DNA and RNA were extracted simultaneously by cryogenic grinding of cryopreserved membrane filters using a 6770 Freezer/Mill or 6870 Freezer/Mill instrument (SPEX Sample-Prep, Metuchen, NJ) followed by nucleic acid extraction with NucleoSpin RNA Midi kits (Macherey-Nagel, Düren, Germany) combined with DNA Elution buffer kit (Macherey-Nagel). DNA and RNA were quantified by a fluorometric method using Qubit 2. 0 Fluorometer (ThermoFisher Scientific, Waltham, MA). DNase treatments were applied to all RNA extractions.
A detailed description of nucleic acid extractions and library construction protocols is available in Alberti et al  38. After library profile analysis by Agilent 2100 Bioanalyzer (Agilent Technologies, USA) and qPCR quantification (MxPro, Agilent Technologies, USA), libraries were sequenced on HiSeq2000 instruments (Illumina) with a read length of 101 bp in a paired-end mode. In average, 160 million reads per sample were obtained. Reads, assembly and gene catalog construction. An Illumina filter was applied to remove the least reliable data from the analysis. The raw data were filtered to remove any clusters with too much intensity corresponding to bases other than the called base. Adapters and primers were removed on the whole read and low quality nucleotides were trimmed from both ends (while quality value is lower than 20).
6. 1, with the following parameters: -id 95 -aS 90 (95% of nucleic identity over 90% of the length of the smallest sequence). For each cluster of contigs, the longest sequence was kept as reference for the gene catalog. Ribosomal, chloroplastic, and mitochondrial sequences were removed from the resource after blast comparisons and Pfam domains identification. Prokaryote 16S-like unigenes were mega-BLAST scanned for removal. Mitochondrial or chloroplastic sequences were removed based either on the basis of a positive BLAST hit against dedicated reference databases manually curated, and having matches with at least 70% identity over at least 80% of the unigene length or at least 300 bp long, or based on the presence of specific protein domains identified by CDD search. Domains COX1, COX2, COX3, COX2_TM, Cytochrom_B_N_2, Cytochrom_B_C, Cyto-chrom_B_N, Oxidored_q1, Oxidored_q2, Oxidored_q3, Oxidored_q4, Oxidor-ed_q5_N, Oxidored_q1_N, NADHdh, NDH_I_M, NDH_I_L, and ATP_synt_6_or_A were used as signature for mitochondrial based genes, domains and Photo_RC, PsaA_PsaB, PSII, RuBisCO_large, and RuBisCO_large_N for the chloroplastic ones, while unigenes also bearing domains Peptidase_M41, Gp_dh_N, or Gp_dh_C, GAPDH-I were kept in the resource, being considered as nuclear genes.
In summary a unigene as defined here is a complete or partial transcript assemble from metatranscriptomic reads of at least one Tara Oceans station. The gene catalog is accessible at http://www. genoscope. cns. fr/tara/. Taxonomic assignment. To assign a taxonomic group to each unigene, a reference database was built from UniRef90 (release of 2014-09-04)  42  , from the MMETSP project (release of 2014-07-30)  14  manually curated to remove sequence redundancy, from Tara Oceans Single-cell Amplified Genomes (PRJEB6603)). The database was supplemented with three Rhizaria transcriptomes (Collozoum, Phaeodaea and Eucyrtidium, available through the European Nucleotide Archive under the reference PRJEB21821 (https://www. ebi. ac. uk/ena/data/view/ PRJEB21821) and transcriptomes of Oithona nana  33.
Sequence similarities between the gene catalog and the reference database were computed in protein space using Diamond (version 0. 7. 9)  43  with the following parameters: -e 1e-5 -k 500 -a 8. Taxonomic affiliation was performed using a weighted Lowest Common Ancestor approach. For each unigene, all protein matches with a bitscore value ≥90% of the best match bitscore were kept. For each taxon, only matches with the highest bitscores were retained, and total LCA and weighted LCA (covering at least 67% of all bitscores), were further computed. In order to limit the number of false taxonomic assignments explained by the lack of reference genomes, the LCA result was corrected according to the percentage of identity of selected matches. The maximal taxonomic precision allowed was corrected as follows: >95% of identity = species, <95% of identity = genus, <80% of identity = family, <65% of identity = order, <50% of identity = class.
1b2)  52  against the Pfam-A database (release 28). Only matches exceeding the internal gathering threshold (-cut_ga) were retained. Pfams often detected on the same unigenes were grouped together in a single name (i. e. , Arrestin_C;Arrestin_N). These associations of Pfams followed two criteria: (1) The number of unigenes carrying the two pfams is higher or equal to 30% of the average number of unigenes carrying each Pfam. (2) The number of unigenes carrying the two pfams was higher than 30. The list of associated Pfams is given in Supplementary Data 7. The functional characterization of unigenes is accessible at http://www. genoscope. cns. fr/tara/. Unigenes without Pfam domains are excluded from analyses presented in Figs. 3, 6  and Supplementary Figs. 3, 4 , 5, 9. The Pfam domain PF01036 was searched in unigenes and the MMETSP collection using hmmscan (from HMMer 3.
1b2)  52. NCBI sequences carrying the Pfam motif were retrieved through the PFAM portal (http://pfam. xfam. org/, May 2017). All-vs. -all BLAST comparisons were run at the protein level using BLAST + 2. 6. 0 and sequences were clustered with the MCL algorithm 53 using the -log(e-value) as edge weights and an inflation parameter of 1. 4. For each of the three largest clusters, protein sequences were aligned using MAFFT 7. 31  54  and positions with more than 50% of gaps were discarded. Logo consensus sequences were created using weblogo 3 program  55. Transmembrane helices were predicted using TMHMM Server 2. 05 on the consensus sequences  56. Global phylogenetic tree was constructed from a global alignment using MAFFT 7. 310. The phylogenetic inference was made using approximate maximum likelihood with FastTree  57  , under the gamma model of heterogeneity.
Peptide signal sequences and cleavage sites were detected with signalP  60  and added to the alignment. the sequence logo representations were made with weblogo program  55. All statistical analyses and graphical representations were conducted in R (v 3. 1. 2) with R packages ggplot2 (v 2. 1. 0). The PCA results shown in Supplementary Fig. 3  were obtained using the R package FactoMineR v 1. 32, world maps with maps (v 3. 1), phylogenetic trees with ggtree (v 1. 6. 11), and graph representation Fig. 5a  and Supplementary Fig. 8a ,e with igraph (v 1. 0. 1) and ggnetwork (v 0. 5. 1).
taxonomic groups: Haptophyceae, Pelagophyceae, Bacillariophyta, Dictyochophyceae, O/U Stramenopiles, Chlorophyta, Dinophyceae, Ciliophora, O/U Alveolata, Rhizaria, Copepoda, O/U Protostomia, Tunicata, O/U Deuterostomia, O/U Metazoa, O/U Eukaryota, Bacteria, root (unigenes with matches in at least two of the Eukaryota, Archaea, Bacteria, and Virus superkingdom), unknown (unigenes that have no similarities in amino acid databases)), O/U = unigenes for which taxonomic affiliation ended at the indicated level or belonged to minor classes of the affiliation.
Acknowledgements We thank the commitment of the following people and sponsors who made this singular expedition possible: CNRS (in particular Groupement de Recherche  GDR3280 ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , the  French Governement 'Investissement d'Avenir' programs Oceanomics  ( ANR-11-BTBR-0008 ),  MEMO LIFE  ( ANR-10-LABX-54 ),  PSL* Research University  ( ANR-11-IDEX-0001-02 ), and  FRANCE GENOMIQUE  ( ANR-10-INBS-09 ),  Fund for Scientific Research-Flanders ,  VIB ,  Stazione Zoologica Anton Dohrn, UNIMIB ,  ANR  (projects ' PHYTBACK/ANR-2010-1709-01', POSEIDON/ANR-09-BLAN-0348 ,  PROMETHEUS/ANR-09-PCS-GENM-217 ,  TARA-GIRUS/ANR-09-PCS-GENM-218 ),  EU   FP7  (MicroB3/No. 287589 ), ERC   Advanced Grant Award  (Diatomite:  294823 ), the  LouisD foundation of the Institut de France , a  Radcliffe Institute Fellowship  from  Harvard University  to CB,  JSPS/MEXT KAKENHI  (Nos.
Code availability. Computer codes are available from the corresponding authors upon request. Data availability. Sequencing data are archived at ENA under the accession number PRJEB4352 for the metagenomics data and PRJEB6609 for the metatranscriptomics data (see Supplementary Data 8 for details). Unigene catalog is available at ENA under accession number ERZ480625. Environmental data are available at PANGAEA (URLs for each sample are indicated in Supplementary Data 5). The Marine Atlas of Tara Oceans Unigenes (MATOU) along with functional and taxonomic annotations, unigenes abundances, expression levels and GFs are accessible at http://www. genoscope. cns. fr/tara/. Other relevant data are available in this article and its Supplementary Information files, or from the corresponding authors upon request.
9 million for TEX-treated and 16. 8 million for the untreated libraries). The data obtained by the two approaches were combined to quantify the transcripts abundance and to determine the primary transcriptome architecture (BioProject ID: PRJNA430365). All raw and processed data are compiled in an Assembly Hub (Fig. 1 ) and can be publicly accessed and incorporated into any UCSC Genome Browser instance by the following address: http://bit. ly/2Euo5HZ.
Among these 614 genes, 99 also display at least one secondary TSS. We validated the TSS of several transcripts by 5 0 RACE. The TSS of several coding transcripts (ptxA, bipA, fim2) and of candidate transcripts (see below) were confirmed or determined using this approach (Fig. S1 ). The TSSs were then used to determine putative promoter sequence motifs in their upstream region using MEME  [35]. Beside a convincing TATA box motif (sequence TANAAT) in the ¡10 regions, no significant motif was shared by more than 50 TSS (Fig. S2 ). Among the 614 primary TSS, 35 were localized at the position of the translational start codon of the annotated gene (Fig. S2 ). More than a third of them are of unknown function or are annotated to code for hypothetical proteins. However, within the functionally annotated genes a large fraction (»26%) consists of genes encoding regulators, including ompR (BP3222) and basR (BP3534).
Positions with an enrichment in the TEX treated library in comparison to the untreated library based on statistical significance level of 0. 1 were denominated as transcription start sites (TSS). Finally, consecutive TSS positions separated by less than 10 nt were joined and represented by the most prominent peak. 5 0 race using PGM Ion Torrent sequencing technology Starting sites were determined by 5 0 RACE for NGS sequencing as described by Beauregard et al  [66]. PCR was performed with a first primer specific for the adaptor sequence and a second primer specific for the target cDNA (see Table in supplementary data part 1). A and P1 sequences for PGM sequencing were ligated using the Ion Plus Fragment Library Kit, following manufacturer's recommendations (Life Technology). PCR products were purified with 1.
Acknowledgment Funding: FA was supported by the  Austrian Science Fund (FWF)  project  SFB F43. This work was supported by the  Czech Science Foundation  (www. gacr. cz)(grant  16-34825L  to B. V. ), by the  Czech Health Research Council  (www. azvcr. cz/)(grant  16-30782A  to B. V. ) and by funding from  RVO61388971. This work was also supported by the  Ministry of Education, Youth and Sports of the Czech Republic projects  ( CZ. 1. 07/2. 3. 00/20. 0055  to B. V. and I. B. ,  CZ. 1. 07/2. 3. 00/30. 0003  to B. V. and K. K. ).
Study setting GPs from four French regions will participate: Brittany, Aquitaine, Massif Central, and Languedoc-Roussillon-Midi--Pyrénées. In each of the four regions, a leading investigator will be identified, to be responsible for recruitment of GPs, identification, data collection and completion of CRFs, along with following up study patients and adherence to study protocol. The list of study sites can be obtained from the Direction de la Recherche Clinique et de l'Innovation at Brest. The GPs will not be incentivised to take part.
Data collection Outcomes and measurements To measure the incidence of patients suffering from acquired and lifelong PE, natural variable PE and premature-like ejaculatory dysfunction, the final diagnosis of PE will be made by referring to the answers given to the Premature Ejaculation Diagnostic Tool (PEDT) sent back by the patient 4 weeks after the consultation (please refer to the 'Measurements' section). The patients suffering from PE will be identified if their score is higher than 9 on the PEDT. The PEDT questionnaire is an extensively validated, self-report measure that can systematically assess DSM-IV-TR criteria to provide accurate diagnoses of PE/no-PE  [26]. It is a screening tool with cut-off scores, brief and easy to administer, and recommended by the International Society of Sexual Medicine  [8, 26, 27].
Assignment of interventions (for controlled trials) Allocation The randomisation is stratified by region, and by physician's gender, using a random block randomisation sequence generated in Stata software (version 13, StataCorp, College Station, TX, USA). The GP's surgery is the unit of randomisation. A GP investigator and their patients will be assigned to the same group. The patients will not know to which group their GP has been randomised. Investigators from the same GP's surgery are randomised within the same group in order to avoid contamination bias. The participating GPs will be randomised using Stata version 13 (StataCorp, College Station, TX, USA) which is an online, central randomisation service. Allocation concealment will be ensured as the service will not release the randomisation code until the GP has been recruited into the trial in each region.
Statistical methods Analyses will be performed using Stata software, version 13 (StataCorp, College Station, TX, USA). All data will be analysed on intention to treat. The tests will be two-sided, with a type I error set at α = 0. 05. Baseline characteristics (GPs and their patients) will be presented for each randomisation group as the mean ± standard deviation (SD) or the median (interquartile range) according to statistical distribution for continuous data and according to the number of patients and associated percentages for categorical parameters. The comparisons between the randomised groups will be carried out using the Student t test or the Mann-Whitney test where appropriate, (1) normality verified by the Shapiro-Wilk test and (2) homoscedasticity by the Fisher-Snedecor test for quantitative parameters and using chi-squared or Fisher's exact tests for categorical variables.
Monitoring In each of the four regions, a Steering Committee member will have participated in the design of GET UP and will review the progress of the study and, if necessary, will agree changes to the protocol. A data monitoring committee is not needed here as it is a low-risk trial, not involving any pharmaceutical substances. An interim analysis is performed on the primary endpoint when 50% of patients have been randomised and have completed the SF-12 and the PEDT 1 month after the inclusions. The interim analysis will be performed by the statistician, blinded to the treatment allocation. The statistician will report to the data manager. The data manager will have unblinded access to all data and will discuss the results of the interim analysis with the Steering Committee in a joint meeting.
Auditing A clinical research associate commissioned by the promoter will ensure the successful completion of the study, the collection of data generated in writing and documentation, and will record and report in accordance with the standard operating procedures of the DRCI Brest and in accordance with good clinical practice and the laws and regulations in force. The investigator and his team members agree to make the data available during the quality control visits at regular intervals by the clinical research associate. During these visits, the following items will be reviewed:.
We wanted to connect research and practice in order to make the conclusions helpful for patients. All study-related information will be stored securely at the study site. All participant information will be stored in locked filing cabinets in areas with limited access. All records that contain names or other personal identifiers, such as locator forms and informed consent forms, will be stored separately from study records identified by code number. All local databases will be secured with password-protected access systems. Forms, lists, logbooks, appointment books, and any other listings that link participant ID numbers to other identifying information will be stored in a separate, locked file in an area with limited access.
Acknowledgements We thank  Alex Gillman  who provided medical writing services on behalf of the  Collège Brestois des Généralistes Enseignants ;  Florence Morvan  from the  Délégation à la Recherche Clinique et à l'Innovation, CHRU de Brest ,  Sébastien de Villèle ,  Jean Yves Le Disez , and  Helen McCombie-Boudry  from the  Bureau de Traduction Universitaire, Université de Bretagne Occidentale , and  Jean Sébastien Cadwallader  who provided the translations for the linguistic validation procedure of the PEDT.
Resolution of the structures is indicated after the PDB ID. Only the variable chains of the antibodies were simulated (see Materials and methods). We generated five 100ns trajectories for each structure, starting from different initial conditions (velocities; see Materials and methods). The root mean square distances (RMSD) from the initial structures are shown in Figure  1 -figure supplement 1. The RMSD time series for each of the five trajectories are concatenated to show that trajectory differences are significant, thus indicating that the trajectories are not correlated and each is meaningful. With the exception of the heavy chain (HC) of the mature 3BNC60, the RMSD are under about 1. 5 A ˚, indicating that the structures are stable in the simulation. Examination of the 3BNC60 trajectory showed local b-sheet instability near residue P61 which leads to partial unfolding of a beta hairpin in simulation 5 (see Figure  1 -figure supplement 1A, and also Figures  1A  and 2A , discussed below).
Molecular dynamics simulations Antibody structures listed in Table  1  were downloaded from the PDB databank, and include both the variable regions and the constant regions, which are connected by a single b-strand for each HC and LC (Figure  2 -figure supplement 2). To save simulation cost, the structures were truncated to retain only the variable regions, since the constant regions do not contact the antigens. To truncate all of the antibodies in equivalent locations, a multiple alignment of each chain was performed. The truncation locations correspond to res. 120 and res. 98 for the heavy and light chains of 3BNC60, respectively. Histidine protonation was performed by optimizing visually the hydrogen bonding patterns for each histidine. The CHARMM36 energy function was used for the MD simulations.
Each structure was energy-minimized in vacuum using CHARMM  (Brooks et al. , 2009)  with restraints on the protein heavy atoms, and immersed in a cubic box of TIP3P water of sufficient size that the smallest distance between the antibody and the box boundary was 11. 5 A ˚. Na À and Cl + ions were added to maintain system neutrality, while achieving an approximate salt concentration of 100 mM. The final system sizes were between 40,000 and 51,000 atoms; variations were mainly due to differences in the CDR loop lengths (e. g. , the PGT bnAb has the longest CDR3 region, and had about 51,000 atoms). MD simulations were performed using GPU hardware with the program ACEMD  (Harvey et al. , 2009). Each system was equilibrated for 2ns with restraints on the heavy atoms that were resolved in the ray structure.
The shapes of the VSFs we obtained from these selected parameterisations were similar to existing measurements (Supplementary Fig. 5 ). The coated-sphere model is validated with independent data. The selected parameterisations could also reproduce field c p and b bp coefficients for an independent dataset (Fig. 4 ). These surface data were also collected across the Atlantic Ocean (Supplementary ), b bp predictions were less accurate (Fig. 4b ). This suggests that the particulate optical backscattering may be more sensitive than c p to changes in particle composition and structural characteristics. c p and b bp detect particles within similar size ranges. The selected coated-sphere parameterisations predict that both the beam attenuation and backscattering coefficients measured across the Atlantic Ocean are generated by particles in approximately the same size range (Fig.