dataesr/scientific-paragraphs-categorization huggingface dataset:
The GTR+G+I model of nucleotide substitutions was selected by MEGA 5  [34]  according to the BIC and AIC criteria. The phylogenetic inference by Maximum Likelihood (ML) was then performed with PhyML v3. 0  [35] , and resampled 500 times by non-parametric bootstrapping. Bayesian inference was conducted on the concatenated dataset using Beast v. 1. 6. 1 and companion software  [36]  under the GTR model taking into account 4-class gamma and invariant sites. Two Markov Chain Monte Carlo (MCMC) chains were run for 30 million generations, sampling every 1000 generations. The two runs were combined with LogCombiner v1. 6. 1, and convergence of log-likelihoods and parameter values were assessed in Tracer v1. 4. 1. 10% of the total trees were discarded as burn-in, and the remaining trees were used by TreeAnnotator 1.
5. 4 to build the consensus tree and to calculate the posterior probabilities (PP) of each node. were retrieved from GenBank version 190 in March 2012. Any associated information for each sequence, such as location and depth of sampling was recorded. We used pplacer to compare the phylogenetic relationships between the environmental sequences and cyst sequences  [37]  with the 18S rRNA gene reference alignment. The advantage of using this program is that all the 18S  rRNA environmental sequences can be comprehensively analyzed whatever their length and region. The 260 environmental sequences (query sequences) were aligned with a Hidden Markov Model built from the reference alignment using tools from the HMMER v3. 0 suite (http://hmmer. org/), with default parameters. Using this alignment and the inferred reference tree, pplacer determined the most probable location for each query sequence and represented them as additional branches in the tree.
All filters were subsequently preserved in liquid nitrogen. DNA extraction was performed using the NucleospinH DNA II kit. The V9 hypervariable region of the 18S rRNA gene was PCR amplified with general-eukaryote primers (1389F/1510R from  [39] ) with the PhusionH High-Fidelity DNA Polymerase (Finnzymes), purified using the NucleoSpinH Extract II kit (Macherey-Nagel, Hoerdt, France), and sequenced from both sides using the Illumina technology. The quality of the V9 tag sequences was screened following different steps. Only sequences that have exact forward and reverse primer match were considered. In addition, based on the list of quality values at each position, sequences with more than 1% of error in any 50 bp-section were discarded. Chimeras were then checked on the filtered sequences using the Usearch program  [40].
The V9 reads were assigned based on a reference database of V9 sequences of Acantharia  [15]  and eukaryotes  [41]. Each environmental sequence was compared to all reference sequences using an exact global pairwise alignment algorithm  [42] , and received the taxonomic assignation of its nearest-neighbor in the reference database (or of the last common ancestor in case of a tie). Pairwise alignments were computed with ggsearch, a tool from the version 36 of the FASTA program package  [43] , using default parameters, and results were stored in a sqlite database. Alignments containing both reference sequences and the assigned environmental V9 sequences were visually checked. Only reads assigned with more than 85% of identity with a reference sequence were considered in this study since below this value the V9 reads exhibit numerous errors randomly distributed along the sequence, including in well-conserved regions.
Biogeography and vertical distribution of cyst-forming Acantharia Clone libraries. We retrieved all the 18S rRNA gene sequences from GenBank related to Acantharia that were found in environmental clone libraries, and placed them with pplacer onto a reference phylogeny of Acantharia using the pplacer program (Figure  S1 ). These environmental sequences were sampled between the surface and 3000 m in the Mediterranean Sea, and the Atlantic, Indian and Pacific Oceans, from different size fractions (total to ,3 mm). We found 88 sequences between 0-100 m representing a wide genetic diversity of Acantharia (Figure  3A ). Clades E and F accounted for about 50% of all 18S rRNA sequences, while the cyst forming ones accounted for 10%, 6% and 16% for A, B and C, respectively. Between 100-1000 m, which is essentially below the photic zone in most oceanic regions, clades B and C clearly dominated (more than 75% of the 114 acantharian sequences found at this depth), whereas the symbiotic clades E and F represented only 3% of sequences.
g. station 68). V9 sequences of Pec 9 and Vil 20 were either more abundant in the mesopelagic zone or in the photic layer depending on the station. shown together with the vegetative stage (column 2) that shares the highest genetic similarity. PhyML bootstrap percentages based on 500 pseudoreplicates and Bayesian posterior probabilities (PP) are indicated at each node (above and below, respectively) when support values were higher than 60% and 0. 8, respectively. Species name of each taxon and accession numbers are provided in Table  S1. doi:10. 1371/journal. pone. 0053598. g002.
Acknowledgments We thank  S. Romac  for technical assistance and  J. Dolan  from the LOV in Villefranche-sur -Mer  for providing sampling facilities. From the  Station Biologique de Roscoff , we thank  Morgan Perennou  and  Gwen Tanguy  from the  GENOMER platform. We are grateful to the TaraOceans expedition (doi:10. 1371/journal. pbio. 1001177),and to the  North Atlantic Bloom Experiment 2008 aboard R/V Knorr , which supported the collection of some samples. We also thank  Matthew Cooper  for help with Sr measurements and  Corinne Pebody  for help with sediment trap deployments. We are grateful to three anonymous reviewers for constructive criticism that improved this paper.
Introduction Laser pump, X-ray probe time-resolved wide-angle X-ray scattering (TRWAXS) experiments are one of the few techniques that allow for the direct study of transient structural changes of species in solution. In the earliest TRWAXS experiments, systems showing structural changes on the nanosecond timescale were investigated using synchrotron radiation. 1, 2 6] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46]  The data obtained in a TRWAXS experiment-difference scattering images generated by subtracting an image recorded before the pump has arrived at the sample from an image recorded in a given time after the pump has arrived at the sample-arise from structural changes, which can be divided into three terms:  35, 47, 48  (i) structural changes of the solute (solute term), (ii) structural changes of the bulk solvent (solvent term), (iii) structural changes of the solvent-shell surrounding the solute (solute-solvent cross-term).
The noise level was estimated from a second-order polynomial fit to a running 20 point interval in the data set as described previously. 35 The difference scattering curves presented in this work are plotted against the wave vector transfer ∆S(Q), Q = 4π/λ × sin(2θ). In order to generate these plots, the X-ray energy has been approximated by a monochromatic 18 keV spectrum. 61 Theory.
Ship emissions are based on  Eyring et al. (2010) , and a source-specific seasonality developed for the REanalysis of the TROpospheric chemical composition (RETRO) project (http://retro. enes. org/) was applied to the emissions. Monthly average emissions were derived using the seasonal patterns developed within the RETRO project. Biomass burning emission for the MACC reanalysis for the years 2003-2008 were generated from a preliminary version (v3. 0) of the Global Fire Emissions Database  (GFED, van der Werf et al. , 2010)  and fire radiative power (FRP) observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments  (Justice et al. , 2002). The Global Fire Assimilation System (GFAS) of MACC was used to create daily gridded FRP maps, with which the monthly GFED emissions were redistributed amongst the days of each month  (Kaiser et al.
The biogenic sources are in particular sensitive to temperature, and there might be inconsistencies between those of the MACC model and of the Goddard Earth Observing System used to generate the biogenic emissions. However, this effect is expected to be small. MOZART-3 also included several other natural emissions like NO x from soils and oceanic emissions from various sources, including the Present and future surface emissions of atmospheric compounds (POET) inventory  (Granier et al. , 2005 , available from the Emissions of Atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database, http://eccad. sedoo. fr/), and accounted for in situ production of NO x by lightning. After the MACC reanalysis had been started, it became apparent that using the MACCity emissions led to an underestimation of CO concentrations in the Northern Hemisphere compared to independent observations  (Granier et al.
The importance of the emissions for correct CO values in the lower troposphere in the MACC system was also found in a study of the 2010 Russian wildfires  (Huijnen et al. , 2012). Furthermore, some of the bias in the lower troposphere is likely to be a    Figure  8  shows a time series of monthly mean CO bias profiles at Frankfurt Airport. Frankfurt is the most frequented airport in the MOZAIC database and the most reliable in terms of data availability. A total of 7182 MOZAIC profiles were available over Frankfurt between January 2003 and December 2010. Concentrations are larger during the winter months and larger concentrations extend higher up in the troposphere. The plot confirms that CO is underestimated in the surface layer as seen in Fig. 7. This was also found for other MOZAIC urban sites affected by air pollution such as Beijing, Tokyo and Cairo (not shown).
Further details regarding the retrieval can be found, for example, in  Richter and Burrows (2002)  and  Richter et al. (2005). NO 2 tropospheric columns are only determined for clear sky pixels, i. e. for     cloud fractions smaller than 20 % according to the cloud cover data from the FRESCO database  (Koelemeijer et al. , 2001; Wang et al. , 2008). For the comparison with the IUP satellite retrievals, the model was sampled at the time and location of the SCIAMACHY overpass and the satellite data were averaged to the model spatial resolution. Figure  24  shows the seasonal mean differences between tropospheric NO 2 columns from the MACC reanalysis and IUP-SCIAMACHY. As the satellite data are relative to the clean Pacific reference sector, the same correction has been applied to the model data.
HCHO analysis HCHO data are not assimilated in the MACC reanalysis because the data quality of individual satellite retrievals is not sufficient. Monthly mean observations generally have a total error of 20-40 %, but individual observations can have much larger errors  (De Smedt et al. , 2008). Over Europe, for example, the mean HCHO column is smaller than the random error of SCIAMACHY observations, which does not favour assimilating individual observations. Hence, the HCHO reanalysis      To validate the tropospheric HCHO columns from the reanalysis, they are compared with SCIAMACHY HCHO data retrieved by IUP-Bremen. The IUP retrieval of HCHO columns follows a similar approach as the NO 2 method described in the previous section, using also the approach of reference sector to correct for instrumental drift and appropriate airmass factors to convert the slant to vertical columns.
Assimilating MOPITT and IASI CO retrievals in the MACC reanalysis leads to an improved CO field compared to a MOZART-3 stand-alone run carried out with the MACC configuration. The reanalysis CO field has a realistic seasonal cycle and interhemispheric differences. Total column values in 2008 to 2010 are low compared to the satellite retrievals at high northern latitudes, but in other areas the agreement with MOPITT and IASI is good. Using MACCity emissions to provide boundary conditions for the MOZART-3 CTM leads to tropospheric CO values that are 10-20 % too low compared to MOZAIC data through most of the troposphere. In the boundary layer at urban sites affected by air pollution, the negative biases are larger, suggesting an underestimation of CO or precursor emissions. Surface CO from the MACC reanalysis agrees well with NOAA/GMD observations, which indicates that, despite uncertainties of satellite measurements in the lower troposphere, assimilation of CO data from such products can lead to a good representation of surface CO concentrations for unpolluted regions.
Comparison with independent data has shown ozone from the MACC reanalysis to be considerably better than a freerunning MOZART-3 CTM. Stratospheric ozone fields from the MACC reanalysis agree with ozonesondes and ACE-FTS data to within ±10 % in most situations. In the troposphere the reanalysis shows biases of -5 % to +10 % with respect to ozonesondes and MOZAIC aircraft observations in the extratropics, but has larger negative biases in the tropics (up to -40 % around 100 hPa). These biases are partly due to biases in the underlying MOZART-3 CTM, but a time varying bias in the troposphere is the result of using a variational bias correction scheme without MLS as an anchor before 2008 (see also Sect. 2. 3. 2). Area averaged total column ozone agrees with data from KNMI's multi-sensor reanalysis product to within a few percent.
The MACC model and assimilation system is also run in NRT to produce daily analyses and 5-day forecasts of reactive gases and aerosols. Data from the MACC reanalysis and the NRT analysis are available from the MACC data server http:// www. gmes-atmosphere. eu. Further validation results from the MACC reanalysis can be found on the MACC verification web page http://www. gmes-atmosphere. eu/services/gac/ global verification/. Since November 2011, the MACC-II project has begun as a successor to MACC. This project will continue to deliver the daily analyses and forecasts of atmospheric composition. There are no plans for a new reanalysis in MACC-II, but the MACC reanalysis will be extended to more recent years, and selected periods will be rerun to test changes to the assimilation system and new input data sets.
Summary of known issues with the reactive gas fields There are some issues with the reactive gas analysis fields that a user should be aware of because they cause discontinuities in the data set: -The biomass burning emissions were changed on 1 January 2009 from a preliminary version of GFED3 to GFAS version 1. 0. The GFAS version 1. 0 emissions budget is about 18 % higher than those used during the first 6 yr. -Using MACCity emissions to provide lower boundary conditions for the MOZART-3 CTM led to too-low CO analysis values, especially in the boundary layer. -After 23 March 2010 NRT MOPITT CO data were used in the reanalysis instead of the offline product. This change did not have a noticeable impact on the reanalysis fields. -Assimilation of IASI CO after 1 April 2008 led to some changes in the CO field.
-Using variational bias correction for MLS ozone profiles led to increased tropospheric ozone and changes to ozone above 15 hPa. However, it did not affect the total column ozone field. These drifts stopped on 1 January 2008 when the bias correction was switched off for MLS, and afterwards agreement with independent ozonesondes and MOZAIC data was improved. -Using NRT MLS data instead of the offline product after 16 March 2009 resulted in larger departures in the upper troposphere and lower stratosphere, because the lowest 3 layers (68-100, 100-146, and 146-215 hPa) of the MLS data could not be used. -NRT OMI ozone columns were assimilated instead of the offline product between 21 March 2007 and 31 December 2007. This did not have a noticeable impact on the ozone analysis. -A model upgrade to MOZART 3.
Acknowledgements. MACC was funded by the  European Commission  under the  EU Seventh Research Framework Programme , contract number  218793. The CO products from IASI are available from the Ether French atmospheric database (http://ether. ipsl. jussieu. fr). We thank  Doug Kinnison  at  NCAR, Boulder , for support with the MOZART model. We acknowledge the free use of tropospheric NO 2 column data from the SCIAMACHY sensor from www. temis. nl. MOPITT data are from  NASA. Mihalis Vrekoussis  acknowledges  European Union  research fellowship ( FP7-PEOPLE-2009-RG Grant ,  PERG-GA-2009-256391 ). Edited by:  W. Lahoz.
SNPs included 93 synonymous base substitutions and 211 non-synonymous substitutions (Table  S3 ). Based on these polymorphisms, our 154 isolates were assigned to 121 haplotypes (Figure  1 ). Ninety-two SNPs were parsimony-informative (i. e. , the respective derived alleles were found in more than one haplotype) and the level of homoplasy was very low (homoplasy index, 0. 03). The nucleotide diversity = was 0. 0002660. 00001, which is nearly three times higher than the nucleotide diversity for S. aureus ST5  [23]. A SNP-based, maximum-likelihood phylogenetic analysis revealed at least six major clades, A to F, within S. aureus CC121 (Figure  1 ). We applied a Bayesian coalescent method of phylogenetic inference implemented in the BEAST program  [24]  to estimate evolutionary rates and divergence times for CC121.
Based on our set of DNA sequences from isolates that had been sampled between 1953 and 2009 (Table  S2 ), we estimated the nucleotide substitution rate at 2610 26 substitutions per nucleotide site per year (95% confidence intervals, 1. 2610 26 to 2. 8610 26 ). This short-term evolutionary rate is similar to rates previously reported for S. aureus  [25, 26]. Based on this rate and the sequence variation observed in our dataset, the most recent common ancestor of CC121 was dated to 1884 (95% confidence interval, 1827 to 1925). This age estimate should be interpreted with caution because it is based on a relatively long extrapolation from available sequence data (note the large confidence interval). However, it is several fold longer than estimates available for MRSA  [25] [26] [27] , which might reflect the widespread endemic nature of S.
aureus CC121 that precedes the antibiotic era. The 154 isolates of our study displayed 38 different spa types, with six spa types covering 67% of all isolates (Figure  S1 ). Some spa types were restricted to particular phylogenetic clades (t314 to clade B, t940 to clade C, t435 to clade D). However, the association of several other spa types (t284, t170, t272, t2155) with SNP-based phylogeny was imperfect, as each of them was found in several, unrelated haplotypes (Figure  S2 ). The maximum monophyletic clade (MC) statistics calculated for these spa types by using BaTS software  [28]  confirmed the absence of significant associations with phylogeny, because they were not significantly different from MC values for a randomized, null distribution (Table  S4 ). These results suggested spa homoplasies, similar to previous reports on other S.
Phylogeography The isolates' phylogenetic relationships reflect their geographic origin to a considerable extent, suggesting that specific subclones of CC121 are endemic in the regions sampled (Figure  1 ). Accordingly, for several geographic regions, Bayesian analysis with BaTS software indicated associations of geographic origins with phylogeny (Table  S4 ). For example, clade A encompasses all 12 isolates from Oceania, including strains from New Zealand, Samoa, Tonga and Fiji. Similarly, clade C encompasses six out of seven isolates from South America, including Peru, Panama, Paraguay and the Bahamas. Further, all 20 isolates in clade E are from Southeast-Asia, including Thailand and Cambodia, clade B contains most of the isolates from Africa (Nigeria, Uganda, Togo, South Africa and Kenya), and clades D and F contain isolates mostly from Europe.
Comparative Genomics We pyro-sequenced the genomes from ten CC121 isolates, including the oldest isolate in the collection, which had been collected in 1953, and one to three isolates from each of the phylogenetic clades A to F (Figure  1 ). Genome-wide SNPs from these selected isolates were ascertained by mapping sequencing reads onto an ST5 reference sequence by using SSAHA2 software. These SNPs largely confirmed the phylogenetic relationships that had been revealed from 47-kb sequences from the larger set of isolates, including the relatedness of MRSA and MSSA from Cambodia (Figure  1 , Figure  2 ). However, genome sequences provided more precise estimates of internodal distances and indicated that clades A, B, and C are related and clades D and E are related (Figure  2 ), which had not been resolved on the basis of more limited data (Figure  1 ).
Genome sequences provided no evidence of recombination (homoplasy index, 0. 05; no recombination detected with Recombination Detection Program RDP 3. 44). In addition, sequencing reads were assembled de-novo by applying Newbler software, revealing the gene content for each of the sequenced genomes (Table  S6 ). All CC121 isolates harboured a 41-kb prophage that was dissimilar from S. aureus phages sequenced previously with respect to DNA sequence, gene content, integrase group, and capsid-gene based phage type (Table  S5 ). The prophage encoded 55 open-reading frames, most of which could not be assigned any specific functions based on sequence similarity (Figure  S3 ). CC121 genomes contained one to three additional prophages, including an integrase group Sa1 phage encoding the eta gene or a group Sa2 phage encoding the PVL gene (Table  S6 ).
Each genome had three pathogenicity islands (SaPI), with strain-specific variation (Table  S6 ). MRSA carried a methicillin-resistance island that shared. 99% sequence similarity with previously reported SCCmecV (sequence accession number GQ902038  [33] ). Because methicillin-resistant CC121 had not been found until recently, we investigated whether CC121 genomes may lack the DNA sequences necessary for integration of the SCCmec element (the genetic determinant of methicillin resistance in staphylococci). SCCmec integrates into a specific attachment site (attB) at the 3'end of a gene named orfX in the staphylococcal chromosome, and it has been proposed that the core of the attachment site itself and the 100 basepairs left from it need to be highly conserved to enable efficient insertion or excision of the SCCmec element  [34].
Correlation of Clinical Phenotype and Toxin Gene Complement with Phylogeny Bayesian analysis by using BaTS software indicated that the isolate toxin gene complement was strongly associated with their SNP-based phylogeny, because the MC values calculated for the presence of the toxin genes, the association index (AI) and the parsimony score (PS) all were significantly different from statistics estimated for a null distribution; p#0. 01; Table  S4 ). Clade F encompassed 35 isolates, 21 of which carried the exfoliative toxin genes eta and etb, ten carried either one of the two, and four tested Figure  1. Phylogenetic relationships of 154 CC121 isolates. Maximum-likelihood phylogenetic tree based on 304 SNPs ascertained in 115 genetic loci (47 kb total) and annotated with the country of origin and year of isolation.
The continent of origin is indicated by the color of the isolate's name: blue, Europe; orange, Asia; green, Oceania; red, North America; yellow, South America; black, Africa. Bootstrap values are shown where $85%. The scale bar represents substitutions per SNP site. Asterisks indicate isolates selected for whole-genome sequencing. The European epidemic fusidic acid-resistant impetigo clone (EEFIC) was identified on the basis of fusidic acid resistance, PCR detection of the fusB gene, spa typing (t171), and MLST (ST123). doi:10. 1371/journal. pone. 0058155. g001 negative for both, eta and etb (Figure  3 ). We noted that three clade F isolates (97-01548, 97-01848-1, 00-01004-1) were etb-negative now, even though they had been tested etb-positive several years ago according to records in our strain collection database, presumably reflecting the instability of the etb carrying plasmid during long-term storage or sub-cultivation.