dataesr/scientific-paragraphs-categorization huggingface dataset:
Cells stained only with labeling solution were used as a negative control as suggested by the manufacturer. The number of TUNEL-positive cells was measured using a BD FACSCanto II (BD Biosciences, USA). Data were analyzed using FlowJo 10 software (Ashland, OR, USA). All experiments were performed three times (biological replicates), and each experiment included duplicate (technical replicate) measurements from which the average was calculated and used for further analysis. Flow cytometry assay. Cells were infected the same way as described for the TUNEL assay. At time points 24, 48, and 72 h, cells were collected, fixed, and permeabilized using BD Cytofix/Cytoperm solution (BD Biosciences, USA) according to the manufacturer's instructions. Cells were blocked using 10% normal goat serum (NGS [Dako, Denmark]) for 10 min on ice.
Subsequently, Zika virus was detected using mouse monoclonal antibody against anti-flavivirus group antigen (MAB10216, clone D1-4G2-4-15; Millipore, Germany) at a 1:200 dilution or mouse IgG2a isotype control (MAB003; Dako, Denmark) at a 1:50 dilution in BD Perm/Wash containing 2% NGS and incubated for 1 h on ice and in the dark. Cells were washed twice, and goat anti-mouse IgG2a conjugated with Alexa Fluor 488 (Life Technologies, Inc. , The Netherlands) at a 1:250 dilution was incubated for 1 h in the dark and on ice. After incubation of the secondary antibody, cells were washed twice and resuspended in fluorescence-activated cell sorter (FACS) buffer. The percentage of infected cells was measured using a BD FACSCanto II (BD Biosciences, USA). Data were analyzed using FlowJo 10 software (Ashland, OR, USA).
All experiments were performed three times (biological replicates), and each experiment included duplicate (technical replicate) measurements from which the average was calculated and used for further analysis. Statistical analysis. The statistical analyses were performed using GraphPad Prism 6. 0h software (La Jolla, CA) for Mac. Student's t test was used for comparison between two groups. For comparison between multiple groups, one-way analysis of variance (ANOVA) with Tukey's multiple-comparison test was used. P values of Յ0. 05 were considered significant. Accession number(s). Sequences of the E protein from African ZIKV strain ArB41644 have been submitted to GenBank under accession no. KY576904.
ACKNOWLEDGMENTS We acknowledge  Claudia Schapendonk  for excellent technical assistance and  Thijs Kuiken  and  Barry Rockx  for critical reading of the manuscript. D. V. R. and this study were supported by a fellowship from the  Erasmus MC Foundation. F. A. was supported by a  Directorate of Higher Education (DIKTI)  PhD grant from the  Ministry of Research, Technology and Higher Education of the Republic of Indonesia. Part of this work was further supported by the  European Union program ZIKAlliance  (contract no. 734548 ). Work at UMR1058 was supported by  Reacting and La Région Languedoc-Roussillon.
Lineage Strain Source host Yr of isolation Location Passage history GenBank accession no. EVAg no. Asian ZIKVNL00013 Human 2016 Suriname 4ϫ on Vero cells KU937936 011V-01621 H/PF/2013 Human 2013 French Polynesia 4ϫ on Vero cells KJ776791. 2 001V-EVA1545 African MR766 Monkey 1947 Uganda Unknown (multiple times on SMB and 1ϫ KU955594 NA on Vero cells) Uganda 976 Monkey 1961 Uganda 2ϫ on SMB, 3ϫ on Vero E6 cells, 1ϫ on NA 007V-EVAg1585 Vero cells.
The Vanuatu Ambrym volcano is ranked as one of the top emitters, producing between 8% and 17% of global volcanic fluxes of SO 2 , HCl, HF, HBr, Hg and 210 Po  (Allard et al. , 2015). Data sets on cloud chemistry and pH in wet deposition are very limited in the region. Nagamoto et al. (1990)  provided the first survey of cloud chemistry across the Western Pacific and recorded rainwater pH values in the range of 5 to 6 in the equatorial and remote island region north of Solomon Islands. They suggested a role of the biogenic source (dimethyl sulfide) in rain composition. But over Vanuatu, Vetch and Haefeli (1997)  measured a pH of 2 in rainwater 4 km away from Ambrym's crater (Figure  1 ). Lefevre et al. (2015)  showed that Ambrym is the main source of sulfur species in the Coral and Solomon Seas, with a similar contribution to the regional sulfur budget as dimethyl sulfide (DMS) emitted from marine organisms.
Data and methods Most data analyzed in this paper originate from the two research cruises: Pandora carried out aboard the French research vessel l'Atalante (also recorded as GEOTRACES cruise GP12), and the MoorSPICE cruise aboard US research vessel Thomas G. Thompson. As part of the Tropical Ocean Climate Study (TOCS) project, a cruise took place aboard the R/V Mirai (MR14-02) in February 2014, around the New Ireland (Figure  2b ). This cruise was conducted by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) in order to explore the oceanic leakage between the Solomon Sea and the western equatorial Pacific through the NICU pathways (Figure  2b ). Data acquired during this cruise are not discussed here.
MoorSPICE Cruise, February-March 2014 The MoorSPICE cruise aboard the R/V Thomas G. Thompson was carried out from 28 February 2014 to 31 March 2014. Scientific equipment included a dual set of temperature-conductivity-oxygen SBE-911+ CTD, a Wetlab ECO fluorometer, a Wetlab C-star transmissometer and a pair of RDI L-ADCPs attached to the CTD frame (300 kHz down-and up-looking). The rosette was fitted with 24 12-L Niskin bottles. The ship directly sailed from Nouméa to the Solomon Strait and St. Georges Channel to recover the moorings previously deployed during the Pandora cruise; and a number of CTD stations were reoccupied. A saw-tooth pattern across the Bismarck Sea was then performed, in an area with poor in situ data coverage. The cruise continued through Vitiaz Strait, where mooring work was done.
Physical parameters For both cruises, data processing followed the GO-SHIP guidelines (online at http://www. go-ship. org/HydroMan. html). Temperature and conductivity sensors were calibrated by the manufacturer soon after the cruises. Calibration parameters were applied after checking agreement with salinity bottle samples. Every CTD cast was checked for spikes that might be due to particulate matter in the sensor circuits or electrical problems, and flagged accordingly. Dissolved oxygen sensor data were adjusted by comparison with a Winkler titration determination of water samples  (Langdon, 2010; Uchida et al. , 2010; Saout-Grit et al. , 2015). Shipboard ADCP velocity data were processed and calibrated using the CODAS software (online at http:// currents. soest. hawaii. edu). LADCP velocity data was processed using the GEOMAR/LDEO software v10.
, 2016). The NBCU had a similar speed and direction to that found during Pandora. Surface flow (0-100 m) in both St. Georges Channel and Solomon Strait was entirely southward into the Solomon Sea, then to the southwest along the coast of New Britain. In addition, strong variations in spatial mixing patterns were also detected between both cruises. Observations from the Pandora and MoorSPICE cruises and the Argo database were used to estimate the dissipation of kinetic energy and mixing in the Solomon Sea using the Thorpe scale method and finescale parametrization  (Alberty et al. , 2017). The mean profile of dissipation in the Solomon Sea provided in that work shows that the maximum is near the surface and decreases with depth. Depth-mean dissipation in the Solomon Sea was elevated by a factor of 8 relative to the rest of the equatorial Pacific and spatial patterns of mixing vary by up to two orders of magnitude along isopycnal layers.
As expected, the pH ant profiles mirror the C ant penetration (Figure  10b ). A large pH ant value of about -0. 1 units is estimated in upper layers (200 m) and is also significant at depth (-0. 05 units at 500 m). The DIC and TA observations obtained during the Pandora cruise enable the first evaluation of both anthropogenic carbon and ocean acidification in the Solomon Sea. Added to historical observations conducted in the Western Tropical Pacific ocean  (Suzuki et al. , 2013) , these data can be used to investigate in more detail the oceanic inorganic carbon system and its variability in a broader context.
Conclusions The Pandora and MoorSPICE multidisciplinary cruises documented the Solomon Sea and part of the Coral and Bismarck Seas during two contrasted seasons, in austral winter (July-August 2012) and austral summer (March 2014). This paper presents the climatic context of the cruises, explains the scientific rationales behind all measurements made and describes the data acquisition. It presents an ensemble view of available observations and some initial results on physics, biogeochemistry and geochemistry so that those can be used as a reference for further, refined physical, geochemical and biogeochemical studies and stimulate multidisciplinary analyses. Both cruises took place during neutral or weak ENSO conditions, and during weak active phases of MJO, precluding a strong influence of these phenomena on observed conditions.
Therefore, we collected information about 3D protein structures and drug-like molecules shown to bind them. These large data acquisition procedure resulted in 1480 3D protein structures with a bound drug-like ligand. The structure-ligand complexes represent 743 different drug-like compounds fulfilling our data requirement criteria binding to 247 different aging-associated target proteins. Next, we proceeded to rank the ligands using properties of the compounds and their target proteins in the model organism of interest. We developed an empirical scoring function, with the relevant factors multiplicatively linked. In this way, each of the factors is important and cannot be compensated by another factor, as would be the case in an additive scoring function. These factors include relevance to aging, the conservation of the protein domain or domains containing the binding site, the conservation of the binding site itself, the binding affinity and bioavailability.
Modelling and docking In the above high-throughput approach, binding of the compounds to the model organism proteins is estimated indirectly, through conservation of sequence. Here, we tested this assumption further by modelling the orthologues in D. melanogaster and C. elegans for one hit (compound P37 to human MK14) and docking the compound directly. P37 is a compound classified by DrugBank as experimental and does not violate any of the Lipinski rules. Its structure was determined by X-ray crystallography in complex with human MK14 at 2. 10 A (PDB:3GFE,  Wurz et al. , 2009). The closest homologues in D. melanogaster and C. elegans include p38a and pmk-1, respectively, which are 67% and 61% identical. We successfully modelled the protein structures of the homologues having removed the P37 ligand before modelling, not to bias the modelling procedure.
Literature mining for the top ranked compounds We conducted a thorough literature search for articles examining any of the top 25 ranked compounds with respect to lifespan effects. Searching the PubMed database with the compound names and their synonyms in combination with aging keywords and terms for C. elegans and D. melanogaster resulted in 38 hits. Querying the PubMed Central database of full-text articles resulted in 473 relevant hits of which only four publications describe relevant lifespan experiments, with variable results. Liu et al. (2011)  tested Sorafenib (BAX), for effects in Parkinson's models in C. elegans and D. melanogaster. They reported that sorafenib at 1 and 10 lM increased survival and reduced locomotor impairment in ddc-GAL4; UAS-G2019S-LRRK2 flies, while none of the concentrations tested was reported to alter lifespan of controls.
Data sources and mapping Genes and proteins associated with aging were obtained from the GENAGE database  (Tacutu et al. , 2013)  as well as from general databases (UniProt (UniProt Consortium 2014) and Ensembl  (Flicek et al. , 2014) ) and organism-specific databases (RGD  (Dwinell et al. , 2009) , MGI  (Eppig et al. , 2012 ), FlyBase (St Pierre et al. , 2014)  and WormBase  (Harris et al. , 2014) ) using the Gene Ontology term for aging (GO:0007568). We considered five selected species of interest: C. elegans, D. melanogaster, M. musculus, R. norvegicus and H. sapiens. All results were mapped to the corresponding Ensembl and UniProt entries, and all entries were cross-mapped between Ensembl and UniProt. For each gene/protein associated with aging, all orthologues in the five species were identified using Ensembl Compara.
PDB codes corresponding to these proteins were retrieved from UniProt, Ensembl and the PDBsum database  (de Beer et al. , 2014). DrugBank IDs  (Law et al. , 2014)  and ChEMBL IDs  (Gaulton et al. , 2012; release 15)  of compounds binding agingassociated proteins were collected from UniProt, PDBsum and ChEMBL. Compound IDs were cross-referenced with UniChem  (Chambers et al. , 2013)  and, where possible, mapped to the ZINC  (Irwin et al. , 2012)  and eMolecules databases (http://www. emolecules. com/) of commercially available compounds, ChEBI  (Hastings et al. , 2013)  and to PDB HET codes, the compound identifiers used in PDB structures. PDB HET codes were used to identify PDB files of aging-associated proteins with bound drug-like ligands of the DrugBank and ChEMBL set and at least one orthologue in C.
215 , resulting in a 0 to 1 scaled similarity measure. Values for amino acid codes B, Z and X were calculated as average of the D and N, Q and E and all amino acids, respectively (for Grantham-based similarity matrix see Data S2, Supporting information). We used protein domain assignments from Gene3D  (Lees et al. , 2014)  for the UniProt of the 3D structure. All domains in contact with the bound ligand where jointly considered. For amino acids contacts, which lay outside annotated domains, we used a window of AE50 amino acids around the contact instead. Domain conservation ¼ logistic (Grantham-based similarity of domain sequence, 0. 6, 0. 1). The logistic transformation, logisticðx; l; sÞ ¼ 1 1þe Àxþl=s , was applied, differences medium more important than equally large differences amongst very low or very high percentage similarities.
Acknowledgments We like to thank  Roman Laskowski  for computational access to PDBsum,  Rita Santos  for help with calculating the bioavailability predictions,  John Tyzack  for help with 47X-GEN comparison,  Thomas Holder  ( Schr€ odinger Inc ) for help with the automated creation of the 3D binding-site representations, CHEBI team for curation of highly ranked compounds not previously included. The whole Thornton group and Institute of  Healthy Ageing  for helpful discussion and advice.
: Finally, approved human drugs and drug candidates (as defined in DrugBank Law et al. , 2014) receive a bonus. These compounds would be especially attractive if found to extend lifespan in model organisms because they have already been shown to be tolerated by humans at least under certain circumstances. Thus, they might offer shorter development routes to beneficial interventions against human aging by drug repurposing. Drug repurposing for aging research, M. Ziehm et al. 1013 ª 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd. Availability to purchase bonus ¼ 0:1 if contained in ZINC or eMolecules database 0 else Drug approval status Approved drug bonus 0:1 if designated as known drug in ChEMBL 8 < ¼ 0:075 if not designated drug in ChEMBL, but contained in DrugBank 0 else.
X-ray structure determination Suitable X-ray quality crystals of 2-5 were grown by slow diffusion of a hexane-CH 2 Cl 2 solution. The crystal data for 2 and 3 were collected and integrated using D8 VENTURE Bruker AXS and for 4 and 5 were using Bruker Kappa apexII CCD single crystal diffractometer, equipped with graphite monochromated MoKα (λ = 0. 71078 Å) radiation. Data collection for 2 and 3 were carried out at 150 K and for compound 4 and 5 at 296 K, respectively, using ω-ϕ scan modes. Multi-scan absorption correction has been employed for the data using SADABS  [94]  program. The structures were solved by heavy atom methods using SHELXS-97 or SIR92  [95]  and refined using SHELXL-2014  [96]. Crystallographic data and structure refinement information has been shown in Table  2. Crystallographic data have been deposited with the Cambridge Crystallographic Data Center as supplementary publication no.
Computational details All the calculations (DFT) were carried out with the program package Gaussian 09 (Rev. C. 01)  [97]  and were performed on a parallel cluster system. The ground-state geometries were optimized without symmetry constraints using the B3LYP functional  [98] [99] [100]  in combination with mixed basis set: SDD with effective core potential for Ru and W and 6-31g* for C, B, H, O  [101] [102] [103]. To reduce the computational cost all the calculations were carried out with the Cp analog model compounds, instead of Cp*. The model geometries were fully optimized in gaseous state (without solvent effect) without any symmetry constraints. The optimized geometries were confirmed to be local minima by performing frequency calculations and obtaining only positive (real) frequencies.
DNA was previously extracted by EZ1 DNA Tissue Kit using BioRobot EZ1 Advanced XL (Qiagen, Courtaboeuf, France). The amplification and purification of the 16S rRNA gene was done as previously described by means of the universal primer pair fD1 and rP2 (Eurogentec, Angers, France). Sequencing was then done using the Big Dye Terminator v1. 1 Cycle Sequencing kit and ABI Prism 3230xl Genetic Analyzer capillary sequencer (Applied Biosystems; Thermo Fisher Scientific Life Sciences, Waltham, MA, USA), as previously described  [10]. The 16S rRNA nucleotide sequences were assembled and corrected using CodonCode Aligner software (http://www. codoncode. com), and the BLASTn searches were performed against the GenBank National Center for Biotechnology Information (NCBI) database (http://blast. ncbi. nlm.
Phylogenetic analysis A custom Python script was used to automatically retrieve all species from the same family of the new species and download 16S sequences from NCBI by parsing NCBI eUtils results and the NCBI taxonomy page. It only keeps sequences from type strains. In case of multiple sequences for one type strain, it selects the sequence obtaining the best identity rate from the BLASTn alignment with our sequence. The script then separates 16S sequences into two groups: one containing the sequences of strains from the same genus (group A) and one containing the others (group B). It finally considers all Bacillus strains with valid names from group A and the closest one from group B.
The predicted bacterial protein sequences were searched against the GenBank database  [16]  and the Clusters of Orthologous Groups database (COGs) database using BLASTP (E value 1e-03, coverage 0. 7 and identity percentage of 30%). If no hit was found, it was searched against the NR database using BLASTP with an E value of 1e-03, coverage of 0. 7 and an identity percentage of 30%, and if the sequence length was smaller than 80 amino acids (aa), we used an E value of 1e-05. The tRNAScanSE tool  [17]  was used to find tRNA genes, while ribosomal RNAs were found using RNAmmer  [18]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [19]. Mobile genetic elements were predicted using PHAST  [20]  and RAST  [21]. ORFans were identified if all the BLASTP performed did not give positive results (E value smaller than 1e-03 for ORFs with sequence size larger than 80 aa or E value smaller than 1e-05 for ORFs with sequence length smaller than 80 aa).
Such parameter thresholds have already been used in previous studies to define ORFans. Artemis  [22]  and DNAPlotter  [23]  were used for data management and the visualization of genomic features respectively. The Mauve alignment tool (version 2. 3. 1) was used for multiple genomic sequence alignment  [24]. Closest species for genomic comparisons were identified in the 16S RNA tree using the PhyloPattern software  [25]. For each selected genome, the complete genome sequence, proteome genome sequence and Orfeome genome sequence were retrieved from the NCBI FTP site. An annotation of the entire proteome was performed to define the distribution of functional classes of predicted genes according to the COGs of proteins (using the same method as for the genome annotation). Annotation and comparison processes were performed by the multiagent software system DAGOBAH  [26] , which includes Figenix  [27]  libraries which provide pipeline analysis.
We therefore suggested that our strain is a representative strain of a new species within the genus Bacillus for which we suggest the name 'Bacillus tuaregi' strain Marseille-P2489 T (= CSUR P2489 = DSM 103460). Strain SIT16 T (accession no. LN881595) revealed 96% sequence similarity with the 16S rRNA of Bacillus acidicola strain 105-2, the closest species with a validly published name. Strains Marseille-P2366 T and SIT16 T present 97% 16S rRNA sequence similarity. We therefore suggested that our strain is a representative strain of a new species within the genus Bacillus for which we suggest the name 'Bacillus phocaeensis' strain SIT16 T (= CSUR P2184 = CCUG 69739). All the spectra (Fig. 3A ) of these new species have been integrated into the URMITE database (http://www. NMNI mediterranee-infection.
02% G + C content. The 16S rRNA and genome sequences are available in the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) database under accession numbers LT223699 and FMSO00000000 respectively. The type strain Marseille-P2600 T (= CSUR P2600 = DSM 102861) was isolated from the Siberian permafrost. Its habitat is ice. Description of Bacillus massilinigeriensis sp. nov. Bacillus massilinigeriensis (massilinigeriensis is composed of mas. si. li, L. masc. adj. massili, 'of Massilia,' the old Roman name for Marseille, where the strain was isolated, and ni. ge. rien 0 sis, L. gen. adj. nigeriensis, 'of Niger,' referring to the nationality of the people who provided the stool samples). Cells are Gram-positive bacilli and are fusiform shaped with a length ranging from 3.
5 μg/mL), minocycline (0. 5 μg/mL), penicillin G (0. 47 μg/mL), imipenem (0. 001 μg/mL) and vancomycin (0. 293 μg/mL). The major fatty acid is 13-methyl-tetradecanoic acid. The genome of strain Marseille-P2366 T is 4 216 915 bp long with 36. 7% G + C content. The 16S rRNA and genome sequences are available in the EMBL-EBI database under accession numbers LT161887 and FOJL00000000 respectively. The type strain Marseille-P2366 T (= CSUR P2366 = DSM 102112) was isolated from the stool of a healthy Nigerien girl. Its habitat is the human digestive tract. Description of Bacillus mediterraneensis sp. nov. Bacillus mediterraneensis (me. di. ter. ra. neen 0 sis, L. masc. adj. mediterraneensis, from the Mediterranean Sea, which borders Marseille, where the strain was found). Cells are Gram-positive bacilli and are fusiform shaped with a length ranging from 2.
Strain Marseille-P2384 T was susceptible to amoxicillin (<0. 16 μg/mL), minocycline (0. 213 μg/mL), penicillin G (0. 003 μg/mL), imipenem (0. 006 μg/mL) and vancomycin (0. 158 μg/mL). The major fatty acid is 13-methyl-tetradecanoic acid. The genome of strain Marseille-P2384 T is 3 340 955 bp long with 42. 3% G + C content. The 16S rRNA and genome sequences are available in the EMBL-EBI database under accession numbers LT161888 and FPDX00000000 respectively. The type strain Marseille-P2384 T (= CSUR P2384 = DSM 102091) was isolated from the stool of a healthy Senegalese boy. Its habitat is the human digestive tract. Description of Bacillus tuaregi sp. nov. Bacillus tuaregi (tua. reg 0 i, L. masc. adj. tuaregi, from the Tuareg people, the ethnicity of the donor whose stool was used to isolate the strain).