dataesr/scientific-paragraphs-categorization huggingface dataset:
The observed northward transport rates west of Sardinia were about half of the estimated 1 Sv LIW entering the WMED. Data availability. All REP14-MED experimental data are available on the data server at the Centre for Maritime Research and Experimentation (CMRE). The data are NATO Unclassified and are available only for the partners of the experiment. However, interested institutions can sign up for partnership at any time. Requests may be directed to registry@cmre. nato. int. Author contributions. MK was responsible for the collection and processing of CTD, CTD chain, UCTD, and ADCP data on Planet. IB and AR contributed to the analysis of the CTD, ScanFish, and ADCP data of Alliance and the CMRE Slocum gliders. AF processed the data of the WTD71 glider. The Seaglider data were processed by MH and BQ, supervised by JK.
Structure calculation The backbone 1 H N , 1 H ␣ , 13 C ␣ , 13 C ␤ , 13 C' and 15 N H chemical shift values of Pcf11 (530-626) provided and angular restraints for 51 residues by using TALOS-N  (34). In addition, 30 1 angular restraints were obtained. Two addition angle restraints for Val552 and Ile588 were based on analysis of 13 C side chain resonances and the program Sider  (35, 36). Distance restraints were obtained from 2D 1 H, 1 H-NOESY (150 ms mixing time), 3D 1 H, 1 H-NOESY- 13 C-HMQC (150 ms mixing time) and 3D 1 H, 15 N-HSQC-1 H-NOESY (150 ms mixing time) with an initial set of 976, 701 and 338 crosspeaks, respectively. The final list of unambiguous restraints included 353 intraresidue, 160 sequential, 54 medium and 332 long range distances, with an additional list of 271 ambiguous distance restraints.
The four distance and 13 dihedral zinc-binding geometry constraints were excluded from the initial structure calculation, and were only incorporated in the final annealing step performed in explicit water. The final ensemble contains the 15 lowest energy structures calculated by using CNS1. 1  (37)  implemented within ARIA1. 2  (38). The structure ensemble has been deposited in the Protein Data Bank (http: //www. ebi. ac. uk/pdbe) with PDB ID 5M9Z.
Intact masses of individual Pcf11 constructs were first determined in denaturing conditions by diluting proteins to 2 M in a 1:1 (v/v) water/acetonitrile mixture acidified with 1% (v/v) formic acid. Non-covalent complexes analysis in non-denaturing conditions was then performed by diluting samples to 5 M in 250 mM NH 4 Ac buffer at pH 7. 5. Fine-tuning of instrumental parameters was notably applied to preserve noncovalently bound species in the gas phase and ensure efficient ion transmission. Particularly, LCT and Synapt G2 source back pressures were respectively raised to 6. 7 and 7. 7 mbar using a throttling valve, while acceleration voltages applied on the sample cone were set to 120 and 80 V. Data processing was finally carried out on MassLynx 4. 0 (Waters, Manchester, UK).
Recombinant expression of CF IA Prompted by the cell growth defects in the mutations described above and in order to circumvent native CF IA purification from these alleles, we first set-up a procedure for reconstituting S. cerevisiae CF IA from bacterially expressed proteins as previously described  (22). This approach allows for the study of CF IA containing deletions of essential domains (e. g. Pcf11 CID) or point mutations that cannot be studied in vivo due to their lethal phenotype, and thus enable studies on CF IA architecture and pre-mRNA 3 -end processing. Briefly, each subunit was cloned into a plasmid carrying a resistance to a different antibiotic allowing for the introduction of up to four plasmids at a time into a single bacterial cell  (30, 31, 45). Due to limited levels of protein expression of full-length Pcf11 (data not shown), we opted for a slightly altered version of Pcf11 in which a 20-aminoacid stretch of glutamine residues has been deleted (residues 234-253).
Pcf11 contains two Zn 2+ atoms rCF IA provides an ideal source to quantify the Zn 2+ content in the complex by atomic absorption, as well as recombinant sub-complexes of Rna14-Rna15 and Pcf11-Clp1 heterodimers. Samples were analysed at 213. 9 nm using the dialysis buffer as the baseline, and a titration curve ranging from 1. 25 to 20 M of ZnS0 4 solution was also prepared in the dialysis buffer. According to these measurements, only trace amounts of Zn 2+ were detected in the Rna14-Rna15, whereas the rCF IA and Pcf11-Clp1 samples displayed significant Zn 2+ content (Table  1 ). These data demonstrate the  presence of natively bound Zn 2+ atoms within rCF IA and most specifically within Pcf11, since the structure of Clp1 did not reveal any bound Zn 2+ atoms  (18, 19). To further identify an exact stoichiometry and to better define the zinc-binding regions in Pcf11, we used mass spectrometry analysis in non-denaturing conditions.
The overall structure is composed of seven ␤-strand elements and a single C-terminal ␣-helix (Figure  4C ). The relatively large zinc-binding domain and high content of ␤-strands suggested an unusual zinc-binding fold. A recent structure of a similar region of Pcf11 from residues 538 to 608 (PDB ID 2NAX; (29)) displays the same overall fold, with a global RMSD between the two ensembles of 0. 7 and 1. 3 Å for the backbone and all heavy atoms, respectively, calculated by using SuperPose  (48). A search of the available structures by using DALI (49) failed to find a close structural homologue, but instead iden- tified local homology (Z-score > 2) to one of the two zincbinding sites within the RING finger fold (Figure  4D ). For example, comparison to the recent structure from RING finger protein 165 (RFP165; PDB ID 5D0I; (50)) reveals a common ␤-turn placement of the first two cysteine residues in a C-x-I-C motif (C564 and C567 in Pcf11; C294 and C297 in RFP165), as well as a helical location of the remaining H-x-x-C motif (H596 and C599 in Pcf11; H317 and C320 in RFP165).
The rest of the Pcf11 fold has no structural similarity to the RING finger. As a result, it appears that the second zinc-binding region at the C-terminus of Pcf11 is a new structural class of the CCHC zinc-binding family  (51). Other members of the CCHC family are mainly found in the nucleocapsid protein of retroviruses and are generally involved in packaging and early infection process. In eukaryotes, it is found in proteins binding RNA or singlestranded DNA. A sequence alignment was then used to identify putative functional residues within the second zinc-binding domain (Figure  5A ). Outside of the CCHC residues, only W582 is identical in all 13 species. This residue is found to be >40% solvent exposed in the structural ensemble by using the NACCESS program  (52)  and is thus able to contribute to biomolecular interactions.
FUNDING French National Research Agency  [ ANR-12-BSV8-0016  to S. F. , C. D. M. , L. M. -S. ];Aquitaine regional government (to 10130 Nucleic Acids Research, 2017, Vol. 45, No. 17 A. D. and S. F. ); INSERM and the  University of Bordeaux; Ministère de la Recherche Scientifique  (to J. G. );  Centre National de la Recherche Scientifique ;  Université de Strasbourg; Région Alsace  (to J. S. , S. C. );  Centre National de la Recherche Scientifique  [IR-RMN-THC Fr3050]. Funding for open access charge: INSERM, CNRS.
Human and monkey parasite reference sequences from the database are labelled by black squares and circles, respectively. Brackets indicate non-Laverania species, including P. malariae, P. vivax, P. ovale curtisi and P. ovale wallikeri (available sequences are too short to differentiate ape-and human-specific lineages) as well as the monkey parasites P. inui and P. hylobati. Newly identified bonobo parasite sequences are indicated by arrows, all of which are from the TL2 site. One TL2 cytB sequence clusters with a previously reported parasite sequence from a chimpanzee sample (DGptt540), forming a well-supported lineage that is only distantly related to human and ape P. malariae, and thus likely represents a new P. malariae-related species. The trees were constructed using PhyML  58  with GTR+G (a), TRN+I (b, d) and TIM2+I (c) as evolutionary models.
These comprised a 500 bp fragment of the rbcL gene and a 750 bp fragment of the matK gene, both of which have been used as barcodes to identify land plants  [30] [31] [32]  , including in stool samples from endangered species  33. Laverania-positive (n = 14) and -negative (n = 15) chimpanzee faecal samples were analysed for control (Supplementary Table  6 ). Samples were sequenced to a mean depth of 16,054 matK and 21,995 rbcL paired-end reads, which were clustered into operational taxonomic units (OTUs) and assigned to taxonomic groups using a custom matK and rbcL reference database (Supplementary Fig. 4 ). Using a permutational multivariate analysis of variance (PERMANOVA) to compare unweighted UniFrac distances  34  as a measure of large-scale differences in plant composition, we found small differences between faecal samples from bonobos and chimpanzees (matK: 2.
Therefore, we used numerical optimisation to find the maximum likelihood estimate of sensitivity for these data and determined 95% CIs using likelihood ratios. Using intensified PCR data from both bonobo and chimpanzee samples (Supplementary Table  1 ), we estimated the sensitivity of a single PCR replicate to be 16. 7% (95% CI: 12. 9-20. 8), and thus the sensitivity of 8 and 10 independent PCR reactions to be 76. 8% and 83. 9%, respectively. Separate analyses of chimpanzee and bonobo samples yielded similar sensitivity estimates for single PCR replicates: 17. 5% (95% CI: 13. 0-22. 5) for eastern chimpanzees and 14. 6% (95% CI: 8. 47-22. 3) for bonobos. Intensified PCR was also used to screen 199 bonobo faecal samples from the TL2, KR and LA field sites for non-Laverania infections using parasite-specific primer sets.
Representative sequences of each OTU were aligned using MAFFT  66  and a phylogenetic tree was inferred using FastTree 67. To create a database for taxonomic assignment, all reads matching the search terms 'matK' or 'rbcL' were downloaded from the European Nucleotide Archive and indexed in a BLAST database. This database was searched using a representative sequence from each OTU, and taxonomy was assigned as the most specific taxonomic rank shared by all BLAST hits with a total bit score within 98% of the best hit. Samples with <5000 reads were removed from the analysis. Characterisation of faecal bacterial constituents. The same faecal DNA samples used for plant analyses were also subjected to bacterial 16S rRNA gene sequencing (Supplementary Table  6 ). The 16S rRNA gene amplification was performed as previously described  68  , using 5 μl of faecal DNA, the AccuPrime Taq DNA Polymerase High Fidelity System (Thermo Fisher), and V1V2 region primers containing Illumina adapters, barcode and linker regions.
Each faecal sample was amplified in four independent reactions, with the products pooled and purified using AMPure XP beads (Beckman Coulter) before sequencing using Illumina MiSeq v2 (500 cycle). Sequences were separated by barcode, and paired reads were merged using bbmerge  69. Reads were clustered into OTUs using a cutoff of 97% identity and taxonomically assigned using QIIME v1. 9. 1 and the Greengenes database  70, 71. OTUs formed from single reads were discarded. Samples with <15,000 sequences per sample were removed from the analysis. Statistical analyses. All analyses were performed in R v3. 3. 3  72. Within-sample (alpha) diversity was calculated using the Shannon diversity index  73. Betweensample (beta) diversity of matK and rbcL data was calculated using unweighted UniFrac distances after rarefaction to 5000 reads per sample  34.
Between-sample diversity of 16S rRNA data was also calculated using unweighted UniFrac, but after rarefaction to 15,000 reads per sample  34. We opted to use unweighted distance values because they permit the examination of rare taxa that might be related to the phenotype examined; however, weighted UniFrac as well as weighted and unweighted Bray-Curtis dissimilarity values gave comparable results (matk: all Mantel tests r > 0. 34, p < 10 -6 ; rbcl: all Mantel tests r > 0. 68, p < 10 -6 ; 16S: all Mantel tests r > 0. 77, p < 10 -6 ). Unweighted UniFrac distances were also used for principal coordinates analysis  74  , t-distributed stochastic neighbour embedding  75  and permutational analysis of variance (http://CRAN. R-project. org/ package=vegan)  76. Analyses of 16S rRNA data revealed that TL2 bonobo samples formed three distinct clusters (Supplementary Fig.
Acknowledgements We thank the staff of the TL2 site, the  Institut National de Recherches Biomédicales (INRB, Kinshasa, DRC)  and the  Bonobo Conservation Initiative  for field work in the DRC;  Richard Carter  for helpful discussions; the  Ministry of Scientific Research and Technology , the  Department of Ecology and Management of Plant and Animal Resources of the University of Kisangani , the  Ministries of Health and Environment  and the  National Ethics Committee  for permission to collect samples in the DRC. This work was supported by grants from the  National Institutes of Health  ( R01 AI 091595 ,  R01 AI 058715 ,  R01 AI 120810 ,  R37 AI 050529 ,  T32 AI 007532 ,  T32 AI 007632 ,  P30 AI 045008 ), the  Agence Nationale de Recherche sur le Sida  ( ANRS 12125/12182/12255 ), the  Agence Nationale de Recherche  ( Programme Blanc ,  Sciences de la Vie, de la Santé et des Ecosystémes  and  ANR 11 BSV3 021 01 , Projet PRIMAL),  Harvard University  and the  Arthur L.
DGptt540 SYptt72 SYptt64 BQptt479 ONpts1318 LBptt208 BFpts1532 SYpte75 LBptt208 SYptt117 GTptt901 BFpts1534 BMptt1016 SYptt82 SYpte70 KApts1703 MEptt2520 BApts1280 SYptt117 TCptt18 KApts1698 SYptt5 KApts1682 BFpts1534 KApts1680 KApts1680 TCptt18 TL2pp3915 SYptt117 BFpts1532 BFpts1535 KApts1682 KApts1681 KApts1680 SYpte93 SYptt5 KApts1674 SYpte75 GTptt901 SYptt13 LBggg314 LBggg314 SAggg3066 NDggg3203 SAggg3066 NDggg3203 LBggg177 LBggg314 BQggg61 LBggg1222 NDggg3203 BQggg60 DSggg60 SAggg3066 GTggg8 GTggg509 BQggg60 SAggg3066 GTggg118 SAggg3066 Code availability. Analysis code is archived on Zenodo (https://zenodo. org/ record/886174) at DOI: (http://doi. org/10. 5281/zenodo. 886174). Data availability. Newly derived Laverania and non-Laverania parasite sequences as well as bonobo mtDNA haplotypes have been deposited in GenBank under accession numbers KY790455-KY790593 (also see Supplementary Data 1 and 2).
For all RT-PCR, mRNA is expressed as 2 ÀDCt , as normalized to 18 seconds. Data are expressed as means AE SD. n Z 2 healthy CTRL donors (A and C); n Z 6 patients with PMF (A and C); n Z 3 independent experiments for Western blot analysis (E and G). *P < 0. 05, **P < 0. 01. Scale bars: 50 mm (A); 30 mm (C). a-SMA, a-smooth muscle actin; Bmp6, bone morphogenetic protein 6; CTRL, control; Fsp1, fibroblast-specific protein 1; JAK2, Janus kinase 2; p-, phospho-; SEC, spleen endothelial cell;VE, vascular endothelial cadherin; wt, wild-type. from the JAK2 V617F/WT KI mice were analyzed at the initial stage of the disease (ie, 2 months), to determine whether there were mesenchymal markers in endothelial cells before the apparent fibrosis. Hematoxylin and eosin staining revealed increased numbers of megakaryocytes in bone marrow sections of the mutated mice (Figure  6A ), which resembled the PMF pathologic process.
Acknowledgments B. G. E. , C. G. , and M. C. performed the experiments and analyzed the data; B. G. E. and E. D. wrote the manuscript; F. P. performed the immunohistochemical analysis of the human and mouse samples; V. R. and G. B. isolated and cultured the endothelial colony-forming cells from human samples and provided human bone marrow and spleen from patients with PMF; J. -L. V. developed the transgenic mice; N. B. and A. M. V. provided transgenic mice and bone marrow and spleen samples; C. G. and A. B. designed and contributed to the experiments on megakaryocytes and to the overall strategy of the manuscript; E. D. designed the project and the experiments, discussed the data, and wrote the manuscript.
Importantly, major clusters did not always correspond to the targeted loci. Nine specimens exhibited two divergent sequences (up to 10%). In 95% of the species, MiSeq and Sanger sequences obtained from the same sampling were similar. For the remaining 5%, species were paraphyletic or the sequences clustered into divergent groups on the Sanger + MiSeq trees (>7%). These problematic cases may represent coding NUMTS or heteroplasms. Our results illustrate that Illumina approaches are not artefact-free and confirm that Sanger databases can contain non-target genes. This highlights the importance of quality controls, working with taxonomists and using multiple markers for DNA-taxonomy or species diversity assessment. While next-generation sequencing (NGS) is commonly used to analyse bulk environmental samples (metabarcoding)  [1] [2] [3]  , Sanger sequencing remains the standard approach in generating DNA barcode libraries  4.
This is unfortunate as the cost-effective acquisition of barcode sequences from hundreds of specimens identified to species by expert taxonomists could accelerate the construction of accurate reference libraries and increase their completeness  2, 5. As it generates up to 25 million paired-end reads (2 * 300 bp), the Illumina MiSeq platform makes possible the sequencing of several hundreds of individuals on a set of informative barcodes. This allows for the increase not only in the number of species but also in the number of specimens included in reference databases, which is crucial, as a better coverage of the geographical range of the species and a better characterisation of the intraspecific variability lead to more accurate identification  6, 7. Two-step polymerase chain reactions (PCR) are convenient methods to generate amplicon libraries for Illumina sequencing  [8] [9] [10] [11] [12] [13] [14].
Finally, adding one or a few nuclear genes aside the standard mitochondrial fragment (COI) may facilitate the identification of mtDNA introgression  25. Recently, genome skimming, the low-coverage shotgun sequencing of total genomic DNA  26  has been proposed as a next generation barcoding tool  27. However, a switch to databases including the complete genome sequence of each organism on Earth is still unrealistic due to unaffordable costs. Furthermore high consumable costs, increased demands on data storage, analytical issues, as well as potential difficulties in obtaining material transfer agreements  27  , challenge the implementation of this method. In any case, identification of random scaffolds is not possible with current databases. Thus, when genome skimming was used to capture the genomic diversity of bulk arthropod samples  28  , ca 70% of the recovered scaffolds could not be identified to species with existing databases.
Therefore, there should be a gradual and step-wise implementation of genome skimming. In this light, taking advantage of current databases seems more realistic, especially to make use of the huge effort undertaken over the past 15 years in compiling millions of COI sequences for hundreds of thousands species (e. g. the International Barcode of Life project iBOL). Finally, in many groups of living organisms, COI or a couple of genetic markers provide an accurate identification, even if problems do exist in some groups  [29] [30] [31]. Here, we want to go a step further than Shokralla et al. 2, 4  , who were the first to show the potential of NGS technology in generating COI barcode sequences from over a thousand of arthropod specimens from multiple taxa. We propose to test the feasibility and reliability of high-throughput sequencing of multiple amplicons for barcoding and integrative taxonomy.
To do so, we focused on a group of chalcid wasps (Hymenoptera, Agaonidae, Ceratosolen) for which we have accumulated Sanger sequences on two mitochondrial [COI and cytochrome b (Cytb)], and one nuclear markers [elongation factor-1a (EF1a)], over the past 20 years and on which we have a strong taxonomic expertise that is essential to detect mismatches between morphological and molecular identification. Using a two-step PCR approach (Fig. 1 ) and Illumina MiSeq sequencing, we amplified and sequenced the same three markers (Table  1 ) on 115 species of Ceratosolen (369 specimens). We processed raw data using a custom workflow including quality control steps (Figs  2  and 3 ) and compared our results to the Sanger data set. The first objective of this study was to test the feasibility of a two-step PCR approach on our target group and using our usual primers.
Then, we wanted to determine the best strategy to analyse MiSeq raw data from multiple amplicons for reference database construction, DNA-based identification and integrative taxonomy. Indeed, with the thousands of sequences per sample produced by the MiSeq platform, sequence correction is not a burden anymore, but other issues may appear that need to be considered. On the one hand, thank to sequencing depth, chances of actually getting sequences of the target locus are higher compared to Sanger sequencing. On the other hand, non-target loci (i. e. pseudogenes, heteroplasmic sequences) are also sequenced and target DNA region must be sorted out from the rest of the sequences. More specifically, one may wonder whether the cluster that contains the largest proportion of reads always corresponds to the targeted loci.
Results MiSeq library construction and sequencing. Amplification success for each gene region (bands on the gel at the expected size after the first PCR step) is summarized in Tables  2  and 3. The success of PCR was higher for the mitochondrial genes. A PCR amplification product was observed for 80. 9% of the species for COI-long, 86. 1% for COI-short, 85. 2% for Cytb, and 77. 4% for EF. As might be expected, we found a negative correlation between amplification success and time elapsed since specimen collection (Fig. 4 ). Interestingly, the overall amplification success between Sanger and MiSeq data sets were similar, though longer primers were used in the two-step PCR approach. DNA extraction seemed to have failed for 47 specimens (no PCR amplification product visible on gel, DNA concentrations below the detection limit of ethidium bromide).
Therefore, the cluster that contained the largest proportion of reads did not necessarily represent a valid sequence. After completion of the workflow, only 15 COI sequences (7 species) presented internal gaps after the merging of COI-long and COI-short sequences. Sanger sequences were available in all cases. The position of these sequences in the tree topology was correct (Fig. S1 ). A few specimens (2. 4%) were represented by two consensus sequences in the final MiSeq data set: one specimen for COI, for which sequences of COI-long and COI-short were different (JRAS03502_0153, Fig. S1 ) and eight specimens for Cytb, for which the major and the second major clusters had different consensus sequences (JRAS02196_0155, 56; JRAS01683_0151, 55, 56; JRAS02370_0151, 55, 56; Fig. S2 ). Phylogenetic inference revealed that one of the copies was (almost) identical to Sanger sequences while the other clustered apart with an average pairwise sequence divergence ranging from 7.
3% to 10. 3% (Figs 4, S1, S2, S4, Table  4 ). These cases are problematic as no objective criteria allow the removal of one of the sequences from the final data set. Lastly, when combining Sanger and MiSeq datasets, 9 species formed paraphyletic assemblages or clustered into two divergent (> 7%) groups of sequences: 3 on the COI tree (Fig. S1 ), 4 on the Cytb tree (Fig. 5 , S2), 2 on both COI and Cytb trees, Table  4 ). Although two copies of EF have been reported in Hymenoptera  32  , no problematic case was detected on the tree obtained from the analysis of the EF data set (Fig. S3 , Table  4 ).