dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  F. Darbellay  and  R. Schep  for their help in some experiments, and  J. -M. Matter  as well as members of the  Duboule laboratories  and  M. Ros  for sharing material and discussions. We also thank  S. Gitto  and  T. H. Nguyen Huynh  for help with mutant stocks. We thank the  Geneva Genomics Platform (University of Geneva ) and the  Bioinformatics and Biostatistics Core Facility of the Ecole Polytechnique Fédérale in Lausanne  for their assistance. This work was supported by funds from the  Ecole Polytechnique Fédérale , the  University of Geneva , the  Swiss National Research Fund, European Research Council grants RegulHox , and the  Claraz Foundation  (to D. D). N. Y. -K. was supported by the  Naito Foundation. S. Y. and A. K were supported by the " Integrative Graduate Education and Research in Green Natural Sciences" program  ( Ministry of Education, Culture, Sports, Science and Technology, Japan)  and a  Grant-in-Aid for Scientific Research on Innovative Areas  ( 25124703 ;  Ministry of Education, Culture, Sports, Science and Technology, Japan ), respectively.
org, last accessed May 18, 2016) and the A. lyrata MN47 reference genome (Araly1,  [Nordberg et al. 2014 ], using tophat2 with the built-in Bowtie2 mapping program  [Trapnell et al. 2010] ) as the following parameter: -p 5 (number of threads), -N 5 (read mismatches), -read-edit-dist 5 (Final read alignments having more than 5 edit distance were discarded), -read-gaplength 5 (Final read alignments having more than 5 total length of gaps were discarded). Uniquely and high-quality mapping reads were selected by "samtools view -q 10" (-q 10, selecting genome fractions were 90% possibility of the mapping is correct). We focused our analysis on orthologues present in each of the three species, A. thaliana, A. lyrata, and A. halleri. The Araly1 genome annotation detects 17,846 genes orthologous between A.
halleri for calling the parental origin of each read in both hybrids. For this, we used data collected by whole-genome sequencing of 22 and 72 representative genotypes of A. halleri and A. lyrata, respectively (Novikova, Nordborg, GMI, Vienna, unpublished). The reads of these 92 genomes were mapped on the A. lyrata reference genome  (Hu et al. 2011 ) using the BWA-MEM algorithm from BWA (version 0. 7. 4) with an increased penalty for unpaired read pairs of 15. Duplicated reads were removed with Samtools (version 0. 1. 18) rmdup function. Local realignment were performed with Genome Analysis ToolKit (GATK, version 2. 5. 2), IndelRealigner  (McKenna et al. 2010; De Pristo et al. 2011) , and filtered for primary alignment of the reads. To reduce erroneous variant callings caused by undetected gene duplications, we calculated coverage distribution using GATK Pileup and genomic regions with coverage lower or higher than the 3rd and 97th percentiles were removed.
doi:10. 1093/molbev/msw096 MBE orthologous CDS sequences were aligned using MAFFT  (Katoh and Standley 2013). The number of nonsynonymous mutations per nonsynonymous site (dN), the number of synonymous mutations per synonymous site (dS) and their ratio (dN/dS) were calculated with the formulae described in  Li (1993)  implemented in the "kaks" function of R package seqinr  (Charif and Lobry 2007). We used the glm procedure implemented in R to test for the effect of dN/dS ratio and expression level (measured as standardized read number FPKM) on the following dependent variables: the probability to belong or not to an ASE enriched GO category in A. lyrata or A. halleri, or the probability to be ASE derived in A. lyrata or A. halleri. Because the dependent variables took the values 0 or 1, we tested the effect of dN/dS ratio and expression level on these dependent variables with a quasibinomial error distribution.
Acknowledgments The authors thank  M. Lercher  and  U. Kr€  amer for valuable advice and  Christa Lanz  from the  Max Planck Institute of Developmental Biology in Tu ¨bingen  for sequencing of hybrid DNA. This research was supported by the  Deutsche Forschungsgemeinschaft (DFG) , through programs  SPP1529 "Adaptomics " (J. d. M. ) and  SFB 680 "Molecular Basis of Evolutionary Innovations " (to A. B. ) and by the  European Research Council (ERC-Co Grant "Adaptoscope")  to J. d. M. Data are accessible on Gene Expression Omnibus  GSE80462.
Data availability The data sets are available from the Aviso website (http: //aviso. altimetry. fr/) and the CMEMS website (http://marine. copernicus. eu/). Level 2 (GDR) input data are provided by CNES, ESA, and NASA. The altimeter standards used in DT2014 were selected, taking advantage of the work performed during the first phase of the Sea Level Climate Change Initiative (SL_cci) led by ESA in 2011-2013 (details are provided at http://www. esa-sealevel-cci. org).
S1  and Fig. S2 ). To better assess the diversity of those genomic fragments predicted to represent head-tailed viruses, we constructed a phylogenetic tree using the large-subunit terminase domain (Fig. 1 ). It is apparent that the deep terminases correspond to a number of novel lineages. However, only terminase_6 and the related termi-nase_3 type domains were found in the uvDeep data set, suggesting a somewhat limited diversity of phage terminase types in samples from bathypelagic waters in comparison to those from the DCM. Of the remaining non-caudovirales contigs, 13 were related to viruses infecting eukaryotes. Of these, 12 had a majority of hits corresponding to the giant virus of Phaeocystis globosa from the Phycodnaviridae family  (25). Viruses from this family are characterized by their replication within the host nucleus (25-27) and usually have large genomes of between 200 and 500 kb.
The viral genome uvDeepFos-CGR0-KM22-C158 recruited the greatest number of reads from deeper waters. The recruitment plot in Fig. 3b  shows that these genomes recruit along their entire lengths with nucleotide identities in the 80% to 95% range, suggesting that highly related viral genomes are also found in the POV data set. Overall, these results indicate that uvDeep Mediterranean viruses are widely distributed, being present also in the Pacific Ocean. The lack of viromes from deep waters limited the assessment of the distribution of these viruses on a worldwide scale. However, we also searched for the presence and abundance of uvDeep viral sequences in the available metaviromic datasets from the Tara Oceans expedition  (31, 32). Again, the uvDeep viruses do not appear to have been abundant in the Tara's photic zone and DCM viromes, reinforcing their specificity with respect to deep waters (see Fig.
Fosmid sequencing, assembly, and annotation. For this work, a total of 6,432 fosmid clones (3,168 from AD1000 and 3,264 from KM3) were chosen. DNA was extracted from pools of 48 clones using a QIAprep Spin Miniprep kit (Qiagen, Valencia, CA, USA). DNAs from ~250 fosmids were subsequently pooled to generate a total of 24 batches. These 24 pools were tagged and sequenced (Beckman Coulter Genomics, Fort Collins, CO, USA) using paired-end Illumina HiSeq 2000 (2 by 100 bp) in a single lane (~68 Gb data with ~450 million reads, ~180ϫ coverage for each fosmid, assuming each fosmid length to be ~50 kb). After demultiplexing, sequences from each of the 24 pools were independently assembled using Velvet (45) (version 1. 2. 03; k ϭ 71). All genes were predicted using prodigal  (46)  and annotated using BLAST against the NR database (E value, Ͻ1eϪ5; Ͼ70% hit and query coverage), Pfam (47) (using trusted score cutoffs), COGs (Ͼ70% hit and query coverage; E value, Ͻ1eϪ5) (48), TIGRfams (using trusted score cutoffs)  (49) , and phage orthologous groups (POGs) (Ͼ70% hit and query coverage; E value, Ͻ1eϪ5)  (50).
All viral proteins were also annotated using the HHPred server  (51). Identification of viral contigs. Several criteria were used to identify viral genomic fragments. In addition to the preclassification based on the contig annotation, the presence of phage orthologous groups (POGs) was used to identify putative viral contigs. All contigs were manually inspected to confirm their viral origin. Complete viral genomes were identified by the presence of end redundancy in a single contig. These methods have been previously described  (10). Terminase tree phylogeny. To collect reference sequences to create the tree of terminase domains shown in Fig. 1 , the NCBI NR database was searched for phage large-subunit-terminase-domain-containing proteins (terminase_1, terminase_3, terminase_6, and terminase_GpA) using Pfam HMMs and the HMMER3 package  (52).
These were aligned using an iterative refinement strategy implemented in MAFFT  (54). The maximum likelihood phylogenetic tree was computed using FastTree2 (55) with a JTT ϩ CAT model and an estimation of the gamma parameter. Bootstrapping was performed using the Seqboot program in the PHYLIP package  (56). Genomic comparisons and classification. The heat map shown in Fig. S1  in the supplemental material was created by comparing the predicted viral proteins from all viral genomes to each other using BLASTP  (57)  and the BLOSUM45 matrix. A hit was considered conserved if the protein sequence was Ͼ30 amino acids (aa) in length, had an E value of 0. 01, and showed Ͼ30% identity. Only one hit per protein was counted, providing a percentage of conserved hits between two genomes (normalized by self-comparisons).
Genomes with Ͼ20% conserved hits were considered part of the same group. The complete viral genomes were also compared to reference genomes as described previously  (10) , and a neighbor-joining tree reflecting the relationship between reference genomes and those obtained here was constructed. Additional comparisons among related viral genomes, selected genomic fragments, and reference genomes were performed using tBLASTx and BLASTN  (57). Comparative fragment recruitment. To estimate the abundance and distribution of these novel viruses, we performed fragment recruitment using different marine metagenomes  (5, 7)  and viromes  (10, (20) (21) (22) 58). The Pacific Ocean virome (POV) data set was concatenated according to the different depths (for surface data, 16 samples from depths of 5 to 105 m; for mesopelagic data, 4 samples from a depth of 500 m; for bathypelagic data, 12 samples from depths of 1,000 to 4,300 m).
Metagenomic reads were compared to viral genomes using BLASTN, and only hits with Ͼ95% identity, an alignment length of Ͼ50 bp, and an E value of Ͻ1eϪ5 were considered for computing the number of reads recruited per kilobase of genome per gigabase of metagenome (RPKG). Host identification. We compared all viral genomes harboring an integrase against available microbial genomes from the marine environment, especially those from deep samplings, using BLASTN. Only hits with lengths greater than 30 bp and with 100% nucleotide identity were considered. A significant host-virus relationship was confirmed only if the hit in the host genome was coincident with a tRNA and the region in the viral genomes was close to the integrase gene. Accession numbers. All the viral genomic sequences described in this work were deposited in GenBank (accession numbers KT997784-KT997882).
FUNDING INFORMATION Work in FR-V laboratory was supported by projects MEDIMAX BFPU2013-48007-P from the  Spanish Ministerio de Economía y Competitividad ,  MaCuMBA  Project  311975  of the  European Commission   FP7  and  PROMETEO II/2014/012  project  AQUAMET  from the  Generalitat Valenciana. CMM was partially supported by an  EMBO  short-term fellowship. RG was partially supported by the  Grant Agency of the Czech Science Foundation  under the research grant  13-00243S. Work in PLG's lab was funded by the  French National Agency for Research  ( ANR-08-GENM-024-002 ) and the  European Research Council (ERC)  under the  European Commission   7th Framework Program  ( ERC Grant Agreement 322669 ).
Moreover, a study by  Gruber et al. (2015)  over the contiguous US found that there was no advantage of 2D Kalman filtering over 1D Kalman filtering when assimilating ASCAT SSM data into a soil moisture model. In large-scale land surface DA, it is common to assimilate satellite derived SSM observations and screen-level temperature and humidity observations into a LSM, in order to improve soil moisture and screen-level variables. Typically the root-zone soil moisture (WG2) (1-3 m deep) is of more interest than SSM as it has a much larger water capacity and a long memory (from weeks to months). Land surface DA is often performed using a 1D ensemble Kalman filter (EnKF) or a simplified extended Kalman filter (SEKF). The SEKF simplifies the EKF by assuming that the errors in the background state are fixed and uncorrelated between gridpoints.
Version 8. 0 of SURFEX was used in the experiments, which contains the "Interactions between Soil, Biosphere and Atmosphere" (ISBA) LSM  (Noilhan and Mahfouf, 1996). The model uses the same horizontal grid resolution as SAFRAN of 8 km. The ISBA-A-gs version was used, which allows for the influence of leaf-scale physiological proceseses, including photosynthesis  (Calvet et al. , 1998). Each grid cell is split into twelve land cover types (so called "patches"). Soil and vegetation paramaters are derived from the ECOCLIMAP database  (Faroux et al. , 2013). The nitrogen dilution version (referred to as "NIT" hereafter) of ISBA-A-gs was applied, which dynamically simulates the LAI evolution  (Gibelin et al. , 2006). The NIT version allows for the effects of atmospheric conditions on the LAI, including the carbon dioxide concentrations.
Assimilated observations The SSM observations were retrieved from ASCAT radar observations, which observe at 5. 255 GHz (C-band) and a resolution of approximately 25 km. The radar is on board EUMETSAT's Meteorological Operational (MetOP) satellite. The assimilation of ASCAT data was chosen because it was available throughout the analysis period. The original ASCAT values were converted into the surface degree of saturation (SDS, with values between 0 and 1) using a change detection technique, which was developed at the Vienna University of Technology (Tu-Wien) and is detailed in  Wagner et al. (1999) ;  Bartalis et al. (2007). The historically lowest and highest backscatter coefficients are assigned values for dry and saturated soils respectively. The Copernicus Global Land Service then calculates the surface water index (SWI) by applying a recursive exponential filter to these SDS values  (Albergel et al.
System validation A system validation was performed by comparing the LAI and WG1 states with the LAI and SSM observations respectively for all the simulations and data assimilation experiments. Note that this was not an independent validation of the performance of the system, for which we would have needed independent observations. The rationale was to check the effectiveness of the SEKF i. e. to see if it improved the fit between the model simulations and the observations. The fit to the observations was determined by the root mean square difference (RMSD), the correlation coefficient (CC) and the bias. These checks were important because the effectiveness of the SEKF had an important impact on the drainage and runoff fluxes.
The influence of human activity, such as dams and irrigation, is not accounted for by MODCOU. The simulated river discharge from SIM was compared with the observations from 546 river gauges that had data during the period of evaluation  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). These observations are available from the French hydrographical database (http://www. hydro. eaufrance. fr/, last accessed March 2016). We also analyzed the results for the subset of 67 stations with low anthropogenic influence from the original 546 stations. The fit of the average daily river discharge from MODCOU (measured in m 3 /m 3 s -1 ) to the observations was measured using the Nash efficiency score  (Nash and Sutcliffe, 1970). The Nash efficiency can range from -∞ to 1, with 1 corresponding to a perfect match of the model to the observed data and a negative value implying that the model performs worse than a constant model with a value equal to the average of all the observations.
Acknowledgements. This work is a contribution to the  IMAGINES  (grant agreement  311766 ) project, co-funded by the  European Commission  within the  Copernicus initiative in FP7. The work was also funded by the  EUMETSAT H-SAF  service. Discussions with  Patrick Le Moigne  were useful for understanding the SIM hydrological model. Useful feedback was also obtained through discussions with DA scientists at the  Met Office. We would like to thank the two anonymous reviewers for their constructive comments. We would also like to thank  Dr Jean-Philippe Vidal  from  IRSTEA  for his useful comments and suggestions regarding anthropogenic water management in the SIM.
The recent breakthrough in molecular techniques has allowed additional data such as the complete description of the genome, to be added to classical bacterial classification, which is based on phenotypic and genotypic characteristics and phylogeny based on the 16s rRNA gene [7e9]. Thanks to the decreasing cost of genome sequencing, a new concept of bacterial description  [10] , taxonogenomics, was created in our laboratory allying classical bacterial classification features to a proteomic description with the MALDI-TOF profile  [11]  and a description of the complete annotated genome of the described species  [12]. Here follows the description of a new member of the Abbreviations: AGIOS, average of genomic identity of orthologous gene sequences; bp, base pairs; COG, Clusters of Orthologous Groups; CSUR, Collection de souches de l'Unit e des Rickettsies; dDDH: digital, DNA-DNA hybridization; DSM, Deutsche Sammlung von Mikroorganismen; EI, electron impact; FAME, fatty acid methyl ester; FTP, file transfer protocol; GC/MS, gaz chromatography/mass spectrometry; INSDC, International Nucleotide Sequence Database Collaboration; kb, kilobases; MALDI-TOF MS, matrix-assisted laser-desorption/ionization time-offlight mass spectrometry; MIC, minimal inhibitory concentration; MS, mass spectrometry; NRPS, non-ribosomal peptide synthase; ORF, open reading frame; PKS, polyketide synthase; TE buffer, tris-EDTA buffer; SDS, sodium dodecyl sulfate; URMITE, Unit e de Recherche sur les Maladies Infectieuses et Tropicales Emergentes.
Helium flowing at 1. 2 mL/min was used as carrier gas. The MS inlet line was set at 250 C and EI source at 200 C. Full scan monitoring was performed from 45 to 500 m/z. All data were collected and processed using Turbomass 6. 1 (Perkin Elmer). FAMEs were identified by a spectral database search using MS Search 2. 0 operated with the Standard Reference Database 1A (National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK). Retention time correlations with estimated nonpolar retention indexes from the NIST database were obtained using a 37-component FAME mix (Supelco; Sigma-Aldrich, Saint-Quentin Fallavier, France); FAME identifications were confirmed using this index. Our results were compared to that of Anaerococcus strains readily available in our in house collection, CSUR, namely Anaerococcus vaginalis CSUR P727, Anaerococcus senegalensis CSUR P156, Anaerococcus murdochii CSUR P1063, Anaerococcus tetradius CSUR P1064 and Anaerococcus octavius CSUR P179.
Strain identification by MALDI-TOF MS Strain mt16 was identified using MALDI-TOF MS technology  [11, 19]. This proteomic analysis was conducted using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany) with a MTP 96 MALDI-TOF target plate (Bruker). The obtained spectra for a colony are imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the 7567 references contained in our database (Bruker database incremented with our data). An identification score was obtained: a score !1. 9 allowed an identification at the species level; a score between 1. 9 and 1. 7 allowed identification only at the genus level and finally a score <1. 7 gave no identification.
Genome annotation and analysis Open Reading Frames (ORFs) were predicted using Prodigal  [22]  with default parameters but the predicted ORFs were excluded if they were spanning a sequencing gap region (contain N). The predicted bacterial protein sequences were searched against the Clusters of Orthologous Groups (COG) using BLASTP (E-value 1e À03 , coverage 0. 7 and identity percent 30%). If no hit was found, it was searched against the NR database using BLASTP with E-value of 1e À03 coverage 0,7 and identity percent of 30%, and if the sequence length was smaller than 80 amino acids we used an E-value of 1e À05. The tRNAScanSE tool  [23]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [24]. Lipoprotein signal peptides and the number of transmembrane helices were predicted using Phobius  [25].
ORFans were identified if all the BLASTP performed did not give positive results (E-value smaller than 1e À03 for ORFs with sequence size larger than 80 aa or E-value smaller than 1e À05 for ORFs with sequence length smaller 80 aa). Genomes were automatically retrieved from the 16S RNA tree using XEGEN software (Phylopattern)  [26]. For each selected genome, the complete genome sequence, proteome genome sequence (all gene sequences encoding proteins in a genome) and Orfeome genome sequence (all gene sequences encoding orphan proteins in a genome) were retrieved from the FTP of NCBI. All proteomes were analyzed with proteinOrtho  [27]. Then for each couple of genomes, a similarity score was computed. This score is the mean value of nucleotide similarity between all couples of orthologues between the two genomes studied (AGIOS)  [28].
A n annotation of the entire proteome was performed to define the distribution of functional classes of predicted genes according to the clusters of orthologous groups of proteins (using the same method as for the genome annotation). To evaluate the genomic similarity among studied Anaerococcus strains, we determined two parameters; dDDH, which exhibits a high correlation with DDH  [29, 30] , and AGIOS  [28] , which was designed to be independent from DDH. dDDH was determined using the Genome-to-Genome Distance Calculator (GGDC) server (http://ggdc. dsmz. de/distcalc2. php). Genomes were aligned by the GGDC 2. 0 Blast þ method and results were interpreted with the formula 2 as recommended  [30]. Annotation and comparison processes were performed in the Multi-Agent software system DAGOBAH  [31]  that include Figenix  [32]  libraries to provide pipeline analysis.
Strain identification by MALDI-TOF MS Strain mt16 was first isolated in April 2015 after a 10 days' anaerobic pre-incubation in liquid Colombia broth supplemented with 5% sheep blood at 37 C. The liquid culture was then inoculated on 5% sheep blood Colombia agar (BioMerieux) and incubated in anaerobic condition at 37 C. According to the MALDI-TOF analysis, strain mt16's highest match was A. vaginalis with a score of 1. 575. This score under 1. 7 did not allow the identification of strain mt16 at the species nor at the genus level and suggested that the closest species might be A. vaginalis. Strain mt16 was deposited on a MTP 96 target plate twelve times and the reference spectra obtained (Fig. 3 ) were incremented into our database. The spectrum of strain mt16 was also compared to other Anaerococcus species as represented in Fig.
Strain identification by 16S rRNA sequencing In order to identify strain mt16, the 16S rRNA gene was sequenced and the obtained sequence (Accession number LN881592) showed a 97. 9% similarity level with A. vaginalis strain CCUG 31349 T (Accession number AF542229. 1), the phylogenetically closest validated species (Fig. 5 ). The general characteristics of strain mt16 T are presented in Table  2. According to the similarity level threshold defined by Stackebrandt and Ebers  [20] , we propose strain mt16 T as the type strain of a new species within the Anaerococcus genus for which we suggest the name Anaerococcus rubiinfantis sp. nov.