dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the following colleagues who kindly sent us specimens of Riccardia for molecular analyses: M. von Konrat (material from Chile and Ecuador),  M. Wigginton  ( Atlantic islands: St. Helena and Ascension ),  P. de Lange  ( New Zealand ),  L. Thouvenot  ( New Caledonia ),  C. Ah-Peng  ( Tahiti and La Réunion ),  J. Bardat  ( Comores and La Réunion ),  B. Goffinet  (Chile),  J. Cuvertino-Santoni  (Patagonia, Chile),  Yong Kien Thai  (Malaysia),  E. Lavocat-Bernard  ( French Antilles: Martinique and Guadeloupe ), V. Hugonnot (France) ,  A. Unterheimer  (France) and  Ö. Şimşek  (Turkey). We also thank the curators of the following institutional herbaria who send us material on loan and allowed us to sequence herbarium samples: AK, BORH, CONN, E, HSNU,  KLU  and PC. We also would like to thank  Agnès Dettai  and  Cyril Gallut  for their kind and helpful advice on primer design.
This study has been conducted with the support of the Genoscope thanks to the  BDV program  " Bibliothèque du vivant " (funding  CNRS ,  MNHN ,  INRA  and CEA) and LabEx  ANR-10-LABX-0003-BCDIV  with the support of the  ANR  involved in the program " Investissements d'avenir " with the reference  ANR-11-IDEX-0004-02. Sequences were acquired at the " Service de Systématique Moléculaire " of the MNHN (UMS 2700 OMSI) and we thank  Céline Bonillo  and  Josie Lambourdière  for their kind help.
TE Analyses: Read Mapping and Differential Expression All our sequencing data were treated and analyzed with the TEtools pipeline  (Lerat et al. 2017 ; https://github. com/l-mod olo/TEtools) as described thereafter. First, data were trimmed using UrQt  (Modolo and Lerat 2015)  in order to remove polyA tails (from RNA-seq reads) and low-quality nucleotides (from both RNA-seq and smallRNA-seq reads). The resulting trimmed reads were aligned to our TE library using Bowtie2 v2. 2. 4 for RNA-seq  (Langmead and Salzberg 2012 ) and Bowtie1 v1. 1. 1 for piRNAs and endo-siRNAs  (Langmead et al. 2009) , with the most sensitive option and keeping a single alignment for reads mapping to multiple positions (-very-sensitive for Bowtie2 and -S for Bowtie). The read count step was computed per TE family, adding all reads mapped on copies of the same family.
Finally, we performed the differential expression analyses between TE families using the R Bioconductor package DESeq2  (Love et al. 2014 ) on the raw read counts, using the Benjamini-Hochberg multiple test correction (FDR level of 0. 1,  Benjamini and Hochberg 1995). DESeq2 models the read counts using a negative binomial distribution. We use a likelihood ratio test that compares the full model (which accounts for the differences between samples) with the reduced model (which does not). If the reduced model is rejected in favor of the full model after multiple testing correction (at an FDR level of 0. 1), the TE family is considered differentially expressed between samples  (Love et al. 2014). Statistical summaries of these analyses are available in supplementary files S2 (RNA-seq) and S3 (smallRNA-seq), Supplementary Material online, including both raw and normalized read count tables.
We discarded genes with 10 aligned reads per sample and considered them to be unexpressed in the text. Trinity's tool TransDecoder  (Haas et al. 2013 ) was employed to predict ORFs within D. buzzatii and D. koepferae transcriptomes, using Pfam-A database v. 29  (Punta et al. 2012). Then, we performed a functional annotation of the resulting proteomes using Gene Ontology (GO) terms (Gene Ontology Consortium 2000) with the eggnog-mapper tool (https://github. com/jhcepas/eggnog-mapper). First, we mapped our sequences to eggNOG orthologous groups from eukaryotic, bacterial, and archaeal databases  (Huerta-Cepas et al. 2016)  using an e-value of 0. 001. Then, we transferred the GO terms of the best orthologous group hit for each gene. GO enrichments for deregulated genes in hybrids were analyzed using the Topology-Weighted method built in Ontologizer  (Bauer et al.
Divergence Time between Parental Species We aligned all sequences !2,000 bp of the D. buzzatii de novo transcriptome against D. koepferae's ones using BLAST  (McGinnis and Madden 2004)  in order to identify contig pairs between D. buzzatii and D. koepferae. We kept only the best hit for each query and subject, resulting in a total of 2,656 pairs of contigs, which were translated using EMBOSS getorf  (Rice et al. 2000). We used the most likely protein sequences of each contig (i. e. , the longest) to perform codon alignments with MUSCLE  (Edgar 2004). Finally, the dS rate of each pair was calculated using the codeml program in PAML version 4  (Yang 2007). PAML results are available in the supplementary file S6, Supplementary Material online. Finally, we estimated the divergence time between D.
Acknowledgments The authors wish to thank  Nuria Rius  and  Alfredo Ruiz  for providing advanced access to the  Drosophila buzzatii genome  and its TE list;  Xavier Grau-Bove  ´for his useful help with orthology methods;  Esteban Hasson  and  Diego Nicol as de Panis  for sharing sequencing data of Drosophila koepferae genome; Clement  Goubert  for his valuable help with dnaPipeTE;  Inma Moreno  for her technical assistance with RT-PCRs; and  INRA-UMR AGAP (France)  and  Fasteris SA (Switzerland)  for RNA sequencing services. This work was supported by research grant  CGL2013-42432-P  from the  Ministerio de Econom ıa y Competitividad (Spain)  and grant  2014 SGR 1346  from  Generalitat de Catalunya  to the  Grup de Gen omica, Bioinform atica i Biologia Evolutiva (GGBE). VRS was supported by a  PIF   PhD fellowship  from the  Universitat Autunoma de Barcelona (Spain).
Most of the abovementioned studies were performed in microcosms and mesocosms and further open-ocean studies combining the set of complementary approaches described above are needed to better assess the fate of N 2 fixation and its role in C export. The western tropical South Pacific (WTSP) is an ideal location to study the fate of N fixed by N 2 fixation, as it is considered a hot spot of N 2 fixation in the world ocean  (Bonnet et al. , 2017a). While average N 2 fixation rates range from 20 to 200 µmol N m -2 d -1 in the tropical North Atlantic (Benavides and Voss, 2015) and Pacific  (Böttjer et al. , 2017; Dore et al. , 2002) , they reach 30-5400 µmol N m -2 d -1 (average ∼ 800 µmol N m -2 d -1 ) in the Solomon Sea (western part of the WTSP)  (Bonnet et al. , 2009 (Bonnet et al. , , 2015)) , which is in the upper range of rates reported in the global N 2 fixation MAREDAT database and even surpassed its upper rates (100-1000 µmol N m -2 d -1 )  (Luo et al.
General characteristics of the upper water masses in the WTSP Water characteristics (temperature, salinity, density, chl a fluorescence) for the upper 700 m as measured by the C-R are presented in Fig. 3. The deep CTD casts from all SD and LD stations are presented subsequent to post-cruise processing using Sea-Bird Seasoft software, adopting the TEOS-10 standard. The upper surface layer (0-30 m) observed during the OUTPACE transect was characterized by warm waters, with temperatures between 26. 18 and 29. 93 • C (Fig. 3a ), and relatively low salinity, i. e. , absolute salinity between 35. 03 and 35. 81 g kg -1 (Fig. 3b ). Density anomalies of 21. 72-22. 91 kg m -3 between 0 and 30 m increased gradually between 30 and 200 m to reach 24. 89-25. 38 kg m -3 (Fig. 3c ). The salinity increase in subsurface waters (100-200 m) from 187 • W longitude (Fig.
cpc. ncep. noaa. gov/, downloaded 5 December 2016). The SOI metric uses differences in standardized sea level air pressure between Papeete (Tahiti) and Darwin (Australia) to represent the ENSO phase, as previously mentioned. The OUT-PACE region lies between these two locations (Fig. 2 ), highlighting again ENSO's possible influence on the cruise. WTSP conditions were estimated with SST and surface chl a concentration measured by the MODIS Aqua satellite mission and available at the NASA Ocean Color Data website (https://oceandata. sci. gsfc. nasa. gov/, downloaded 14 December 2016). Global annual and monthly (March) averages of both SST and chl a were provided by NASA at level 3 (i. e. , mapped) with 4 km satellite pixel resolution, resulting in four separate datasets from 2003 to present.
The data within the OUTPACE region, defined between 25 • S, 155 • E, and 15 • S, 149 • W, to envelope the cruise track as in Fig. 2 , were extracted. March was chosen as the month of study because it was the central month of the cruise. Plots of these four datasets can be found on the OUTPACE dataset website (http://www. obs-vlfr. fr/proof/php/outpace/outpace_ figures. php). Probability density distributions were generated for each dataset in its entirety. Additional probability distributions were also calculated on data subsets, for years 2003 and 2011 as chosen by SOI to represent opposite ENSO phases, along with 2015, the year of the cruise. In order to gauge significance between probability distributions of different years, the temporal standard deviation of each pixel was calculated for each of the four datasets.
In summary, the SOI metric of ENSO showed that 2015 was an El Niño event, and both the SST and chl a satellite data in the WTSP partially reflected this. The March 2003 and 2015 SST distributions resembled each other, but they also resembled the entire dataset more than the 2011 La Niña distribution. Looking at SST, perhaps La Niña events impact the region more than El Niño. Chl a distributions for the El Niño years also overlapped more than with La Niña, but the variability inherent in the time series precluded the declaration of significant differences. Overall, in the WTSP both SST and chl a were not atypical from what one would expect; thus, we determined that climatological effects upon the results of OUTPACE were minimized.
Special issue presentation The goal of this special issue is to present the knowledge obtained concerning the functioning of WTSP ecosystems and associated biogeochemical cycles based on the datasets acquired during the OUTPACE experiment. The cruise strategy was organized to promote collaboration between physicists, biologists, and biogeochemists with expertise including marine physics, chemistry, optics, biogeochemistry, microbiology, molecular ecology, genetics, and modeling. Most of the contributions to this volume have benefited from this collective effort and are presented according to the main objectives of the OUTPACE experiment. The hydrological and dynamical context of biogeochemical sampling is described for the entire cruise route  (Fumenia et al. , 2017 ) and specifically at the three long-duration stations, where low physical variability validated the quasi-Lagrangian sampling strategy employed  (de Verneil et al.
Acknowledgements. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (https://outpace. mio. univ-amu. fr/)funded by the  French research national agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CYBER program  ( CNRS-INSU) , the  GOPS program (IRD) , and the  CNES  ( BC T23 ,  ZBC 4500048836 ). The OUTPACE cruise (https://doi. org/10. 17600/15000900) was managed by  MIO (OSU Institut Pythéas, AMU)  from  Marseilles  The authors thank the crew of the  RV L'Atalante  for outstanding on-ship operations. Gilles Rougier  and  Marc Picheral  are warmly thanked for their efficient help in CTD rosette management and data processing, as well as  Catherine Schmechtig  for the LEFE-CYBER database management. The satellite-derived data of sea surface temperature, chl a concentrations, and currents have been provided by  CLS  in the framework of the CNES funding; we warmly thank  Marie Isabelle Pujol  and  Guillaume Taburet  for their support in providing these data.
Untouched AEC II were collected in a 15 ml PE tube and mRNA, messenger Ribonucleic Acid; N, Neutrophil granulocyte; PD, O-phenylenediamine; PFA, Paraformaldehyde; PhePyr, Phenylpyruvate; proSP-C, proSurfactant Protein-C; PVDF membrane, Polyvinylidene difluoride membrane; TdTomato, Tandem Tomato fluorescent protein; TFA, Trifluoroacetic Acid. centrifuged at 4 °C, 250 G for 10 min (Fig. 1 ). The pellets were kept at Ϫ80 °C. Proteomics Experimental Design and Statistical Rationale-Isolated primary AEC II from A. fumigatus infected and mock (sterile tap water) infected C57BL/6 mice were collected. In preliminary experiments using 2-D DIGE we had established that the maximum change in protein composition between AEC II from uninfected versus infected lungs occurred at 24 h after infection (data not shown).
1% FA, solvent B: 0. 1% FA, 84% acetonitrile; flow rate 400 nL/min; column oven temperature 60 °C). The mass spectrometer was operated in data-dependent mode and full scan mass spectra were acquired at a resolution of 70,000 at 200 m/z within a mass range of 350 -1400 m/z. The spray voltage was 1600 V (ϩ), and the capillary temperature was 250 °C. The ten most abundant precursors (charge state ϩ 2, ϩ 3, ϩ 4) were selected for MS/MS analysis, fragmented by higher-energy collisional dissociation (HCD) at a normalized collision energy of 27% and a fixed first mass at 130 m/z and measured in the orbitrap mass analyzer (m/z values initiating MS/MS were set on a dynamic exclusion list for 30 s,  35 ,000 resolution at 200 m/z). Peptide Identification-Peptide identification was performed using Proteome Discoverer Software (ver.
1. 4. 1. 14, Thermo Fisher Scientific, Rockford, IL). The mass spectra were searched against Uni-ProtKB/Swiss-Prot database restricted to mus musculus (Uniprot/ Swissprot-Release 2013_10; 541,561 entries, 16641 after taxonomy restriction) using the Mascot search engine (Matrix Sciences Ltd. , London, UK, version 2. 3). The mass tolerance was set to 5 ppm for precursor ions and 20 mmu for fragment ions. One tryptic missed cleavage was considered as well as chemical modifications of methionine (oxidation, dynamic) and cysteine (carbamidomethyl, static). The target decoy PSM validator implemented in proteome discoverer was used to estimate the peptide confidence and peptides that passed a false discovery rate Ͻ 1% (q-value Ͻ 0. 01) were considered for analysis. Protein Quantification-Ion intensity-based label-free quantification was performed using Progenesis LC-MS software.
To account for retention time shifts the LC-MS runs were aligned to one run automatically chosen by the software. A master list of features considering retention time and m/z was generated in which only peptide ions with minimum three isotopic peaks and charges states ϩ2, ϩ3, and ϩ4 were considered. The peptide identifications (peptides spectrum matches) from Proteome Discoverer were imported into the software and matched to the respective features. The protein abundances were calculated considering the normalized ion intensities of all nonconflicting peptides of a protein. An ANOVA p value Յ 0. 01, a ratio of means (A. fumigatus infected/Mock infected) Ն 1. 5 or Յ0. 67 and a minimum number of two quantified peptides per protein were set as significance threshold. Proteins passing the threshold were significantly differentially abundant among the experimental groups and considered for further analysis.
The significantly differentially abundant proteins were ranked according to the Euclidian distance which accounts for both the p value and the fold change: d eucl ϭ ͙ ͑Log 10 ͑ p value͒͒ 2 ϩ ͑Log 10 ͑ratio of means͒͒ 2 Confidence intervals were calculated using the normalized protein abundances derived from the Progenesis QI software. The mass spectrometry proteomics data have been deposited to the Pro-teomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD005834 and 10. 6019/PXD005834. Functional Annotation and Analysis-Proteins that passed the applied significance threshold were analyzed using PANTHER version 11  (25)  and STRING v10. 0  (26). In PANTHER the statistical overrepresentation test was performed while in STRING enrichment analysis was carried out.
3A ). In total, 20,750 peptides were identified that correspond to 2,703 protein groups (3,005 proteins) of which 2019 were identified with 2 unique peptides (supplemental Table  S1 ). 2256 proteins were quantified using the Progenesis QI software (supplemental Table  S2 ). 1668 proteins were quantified with minimum 2 peptides (as counted by the Progenesis software i. e. peptides measured with modifications or various charge states are counted multiple times). The median ratio of all quantified proteins was 1. 05 with a maximum of 45. 63 and a minimum of Ϫ29. 85 (Fig. 3B ). 394 proteins with a higher abundance in the A. fumigatus infected group showed a ratio greater than 1. 5. Only 34 proteins that were higher abundant in the mock infected group passed the threshold of 1. 5 thus illustrating a striking shift toward higher protein abundance in the A.
6E ). These data are further confirming an induction of IL4I1 activity in the lung within 24 h after A. fumigatus infection. Collectively these data showed proteome changes in AEC II exposed to an infection with A. fumigatus with IL4I1 showing the most prominent increase in abundance both, in terms of relative abundance as well as activity. The 95% confidence intervals were calculated using the normalized protein abundances. Displayed are the mean protein abundance and the difference to the upper and lower bound, respectively. c The protein list was ranked according to the Euclidian distance.
Here, we show for the first time that IL4I1 is present in primary murine AEC II during A. fumigatus infection. IL4I1 transcript expression and protein abundance were illustrated on the mRNA and protein level, respectively, in the purified AEC II 24 h post infection. Furthermore, this data was confirmed by demonstrating the presence of IL4I1 enzymatic activity in the infected lungs by measuring the main products of IL4I1 mediated L-amino acid metabolism using a novel HPLC approach in a pilot study (data not shown). Because AEC II were also reported to function as an antigen presenting cell type makes this data compatible with the previous findings  (53). IL4I1 abundance in monocytic phagocytes has been shown to have a direct bactericidal activity dependent on H 2 O 2 production amplified by basification because of the concomitant accumulation of ammonia  (54).
Growth curves showed that all cells supported replication of all four ZIKV strains included, but virus titers were significantly lower for both Asian strains compared to ZIKV AF-MR766 strains on cells of the Vero, SK-N-SH, and U87-MG lines and hNPCs (Fig. 2A  and B ). On hNPCs, ZIKV AF-MR766 grew faster to significantly higher titers than ZIKV AF-976  (Fig. 2A  and B ). There were no evident differences in the growth curves between ZIKV AS-FP13 and ZIKV AS-Sur16. One-step growth curves of Asian and African ZIKV strains on neuronal cells. One-step growth curves (OSGCs) were assessed in vitro by using a high MOI (MOI of 10). Data from OSGC experiments on the different cell lines were used to calculate the percentage of infection and burst size (progeny virus produced per cell). OSGCs showed that baseline virus titers were higher for the African ZIKV strains than for the Asian ZIKV strains, and both African ZIKV strains grew to higher titers on all cells (Fig.
Upon sequencing, we did not find any deletion in the E protein (GenBank accession no. KY576904) of this low-passage-number African lineage ZIKV strain. Therefore, these studies together suggest that Asian ZIKV strains infect hNPCs less efficiently than African ZIKV strains, regardless of the passage history of the ZIKV strains. Both Asian lineage ZIKV strains do not seem to induce cell death early after infection, whereas ZIKV AF-MR766 does. This fits with previous observations, where more apoptotic nuclei were observed after infection with an African ZIKV strain than with an Asian ZIKV strain  (12) , which suggests that there are intrinsic differences between Asian and African ZIKV strains in their ability to cause cell death in hNPCs. The observed phenotypic characteristics of Asian lineage ZIKV strains might contribute to their ability to cause chronic infection in tissues of the CNS  (17) (18) (19) (20) (21).
5 mg/ml sodium bicarbonate (Lonza). Both immortalized cell lines SK-N-SH and U87-MG were used below passage 25. Vero cells (ATCC, USA) were grown in Dulbecco's modified Eagle's medium (DMEM) containing 10% HI-FBS, 100 g/ml streptomycin, 100 U penicillin, 2 mM L-glutamine, 1% sodium bicarbonate, and 1% HEPES buffer (all from Gibco). Human NPCs are primary cells, while the other cells are from immortalized cell lines. All cells used in this study tested negative for Mycoplasma sp. Viruses. Zika virus strain Uganda 976 (ZIKV AF-976 ) was provided by Misa Korva (University of Ljubljana; European Virus Archive goes Global [EVAg] no. 007V-EVAg1585). Zika virus MR766 (ZIKV AF-MR766 ) was provided by Stephan Günther (Bernhard-Nocht-Istitute for Tropical Medicine). This strain has three nucleotides different (C6258T, G6273T, and G10671A) from the reference MR766 strain (GenBank accession no.
Next-generation sequencing. For genomic characterization of the virus strains, RNA was isolated from 140 l of the virus stocks with the QIAmp Viral Mini RNA kit (Qiagen, Germany). Subsequently, the product was eluted in 40 l double-distilled water. Viral metagenomic libraries were constructed with a Abbreviations: SMB, suckling mouse brain; Vero, African green monkey kidney cells; Vero E6, African green monkey kidney clone E6 cells; EVAg, European Virus Archive goes Global; NA, not available. machine (Roche, USA) according to the manufacturer's instructions. Phylogenetic analysis. Nearly full-length ZIKV genomes of 4 isolates (ZIKV AF-976 , ZIKV AF-MR766 , ZIKV AS-FP13 , and ZIKV AS-Sur16 ) and other reference sequences were obtained from the GenBank database. The sequences were aligned using ClustalW, and a phylogenetic tree was constructed by using the PhyML method in SeaView 4 (http://pbil.
univ-lyon1. fr/software/seaview) with the approximate likelihood ratio test based on a Shimodaira-Hasegawa-like procedure which used general time reversible as a substitution model. Nearest-neighbor interchange, subtree pruning, and regrafting-based tree search algorithms were used to estimate tree topologies  (28). The obtained tree was visualized by using FigTree version 1. 3. 1 (http://tree. bio. ed. ac. uk/software/figtree). Replication kinetics of Zika virus strains. Replication kinetics of ZIKV strains ZIKV AS-976 , ZIKV AS-MR766 , ZIKV AS-FP13 , and ZIKV AS-Sur16 were studied in vitro by means of one-step growth curve (OSGC) experiments with a multiplicity of infection (MOI) of 10 and focal experiments (growth curves) with MOI of 0. 1 and 0. 01. Human neural progenitor cells and SK-N-SH, U87MG, and Vero cells were seeded into 96-well plates (2 ϫ 10 4 cells) (Greiner, USA).
All images were processed using Zen 2010 software (Zeiss). Per sample, 5 high-power fields were photographed and scored blindly by three individuals to determine the percentages of infected and noninfected cells. Calculation of percentage of infection and burst size. The percentage of infection and burst size were calculated from the OSGC experiment. The burst size is defined as the number of progeny virus particles produced per infected cell and was calculated as follows. The time at which half the number of progeny virus were released into the supernatant (50% effective concentration [EC 50 ] for dose-response curve) was determined, which was calculated by using a nonlinear regression analysis (sigmoidal dose-response, variable slope) in GraphPad Prism 6. 0h using the infectious virus titer data from the OSGC measured over 24 h (2-h increments).