dataesr/scientific-paragraphs-categorization huggingface dataset:
Leon-Rot, Germany) according to the manufacturer's instructions. Spot detection and subsequent analysis was performed using the software Delta2D (Decodon, Greifswald, Germany) with default settings. Images were first aligned using the "group warping strategy". Within both AF sample and total leaf sample, a master gel was selected to which all other gels in that sample were aligned. Following this, the master gels from the four replicate were aligned. All gel images were then fused to produce a single synthetic image containing all spots detected across the entire experiment. Protein spot volumes were normalized by dividing the individual spot volume by the sum of all spot volumes in that gel. After gel scanning, spots were visually selected and robotically punched from Coomassie stained gels for subsequent analysis using the Proteineer spII Spot picking robot (Bruker Daltonics, Bremen, Germany).
Only the spots present in each replicate were further analysed. The reproducibility of the 2-D protein profiles was confirmed by carrying out 4 independent biological experiments. In-gel digestion of proteins and sample preparation for mass spectrometry analysis. MALDI data acquisition and database searching Coomassie-stained protein spots were visually selected and then robotically excised and digested using the Proteineer spII and dp systems (Bruker Daltonics, Bremen). The resulting peptide mixtures were spotted as an HCCA suspension on AnchorChip TM targets by Proteineer dp robot for subsequent MALDI-TOF/TOF MS analysis. PMF data were collected on an UltraflexIII MALDI TOF/TOF mass spectrometer (Bruker Daltonics). Following a first round of database searching and ontarget recrystallization of the sample spots, MS/MS spectra were collected on selected precursors (see Additional file 3 for detailed information).
Bioinformatic analysis Description of non-described amino acid sequences was taken up by Blast-p  [22]  (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Peptide pI and M r values were theoretically determined using the Compute pI/M r tool on the ExPASY Molecular Biology Server (http://expasy. org/tools/pi_tool. html) [70]. Gene ontology (GO) annotations (www. geneontology. org) were obtained by mapping GI numbers of the NCBI non-redundant protein database to the existing annotations of characterized proteins with GORetriever, provided by the AgBase web server (www. agbase. msstate. edu). Plant GO-slims identification for protein molecular functions were obtained from all GO terms associated with the protein annotation list by using the GOSlimViewer from the AgBase web server  [27]. The secretion of apoplastic proteins was predicted using TargetP (www.
Acknowledgements The authors thank the  Max-Planck-Institute for Plant Breeding Research (MPIPZ, Cologne, Germany)  for welcome, scientific support and facilities. We are grateful to  Pr. Laurent Martiny  and to  Pr. Manuel Dauchez  for access to the  'Plateforme de Modélisation Moléculaire'  ( URCA, Reims, France ), to  Ursula Wieneke  ( MPIZ, cologne, Germany  for technical assistance, to  Jarod Rollins  ( MPIPZ, Cologne, Germany ) and  Jessy Robbins  ( ISU, Terre haute, Indiana US ) for careful reading and English correction; to  Dr. Marc Boutry  ( University of Louvain, Belgium ) for the generous gift of the polyclonal anti-H + -ATPase antibody and to  Vranken Pommery  ( Reims, France ) for access to their vineyard. This study was supported by the ' Region Champagne Ardennes ' for the PhD grant and through the project  VINEAL2 , by  'Comité Champagne' (Epernay, France)  for the PhD grant and by  AROCU  ( Association Recherche Oenologique Champagne et Université ).
b the protein group. c the number that identifies protein spots on 2-D apoplastic map. d the accession number from GenBank assigned to the peptide after MS/MS analysis. e the predicted localisation of the peptide according to the presence of a N-terminal signal peptide (S), non-classical signal peptide (NC), transmembrane helices (TM), chloroplast (C), mitochondria (M) and unclassified (UC) location. f the peptide identification based on homology to proteins characterized in other species by Blast-p search on NCBI database. g the relative homology percentage of protein obtained by Blast-p. Shared proteins identified in both AF sample and total leaf sample are highlighted in bold.
parva that the 'Subcommittee on the Taxonomy of Leptospira'  [5]  decided to exclude L. parva from the genus Leptospira and assign it as the type strain of a new genus: 'Turneria' as 'Turneria parva'. The genus was named in honor of Leslie Turner, an English microbiologist who made definitive contributions to the knowledge of leptospirosis  [1]. However, as the generic name is also in use in botany and zoology, this name was rendered illegitimate and invalidate, but was used in the literature  [6, 7]. The first 16S rRNA genebased study (Genbank accession number Z21636), performed on Leptospira parva incertae sedis, confirmed the isolated position of L. parva among Leptonema and Leptospira species  [8] , a finding later supported by Morey et al. [9]. The reclassification of L. parva as Turneriella parva com.
nov. was published by Levett et al. [1] , reconfirming the separate position of the type strain  [10]  and an additional strain (S-308-81, ATCC BAA-1112) from the uterus of a sow from all other leptospiras on the basis of DNA-DNA hybridization and 16S rRNA gene sequence analysis (Genbank accession number AY293856). The strain was selected for genome sequencing because of its deep branching point within the Leptospiraceae lineage. Here we present a summary classification and a set of features for T. parva H T together with the description of the complete genomic sequencing and annotation.
Classification and features 16S rRNA gene sequence analysis A representative genomic 16S rDNA sequence of T. parva H T was compared using NCBI BLAST  [11, 12]  under default settings (e. g. , considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database  [13]  and the relative frequencies of taxa and keywords (reduced to their stem  [14] ) were determined, weighted by BLAST scores. The most frequently occurring genera were Geobacter (48. 7%), Leptospira (19. 2%), Pelobacter (13. 4%), Spirochaeta (8. 1%) and Turneriella (6. 4%) (56 hits in total). Regarding the single hit to sequences from members of the species, the average identity within HSPs was 95. 8%, whereas the average coverage by HSPs was 89. 8%. Among all other species, the one yielding the highest score was Leptonema illini (AY714984), which corresponded to an identity of 85.
7% and an HSP coverage of 62. 6%. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification. ) The highest-scoring environmental sequence was DQ017943 (Greengenes short name 'Cntrl Erpn Rnnng Wtrs Exmnd TGGE and uplnd strm cln S-BQ2 83'), which showed an identity of 95. 6% and an HSP coverage of 97. 8%. The most frequently occurring keywords within the labels of all environmental samples which yielded hits were 'microbi' (5. 5%), 'sediment' (2. 6%), 'soil' (2. 5%), 'industri' (2. 1%) and 'anaerob' (1. 9%) (194 hits in total). Environmental samples which yielded hits of a higher score than the highest scoring species were not found. Figure  1  shows the phylogenetic neighborhood of T. parva H T in a 16S rRNA based tree.
Genome project history This organism was selected for sequencing on the basis of its phylogenetic position  [40] , and is part of the Genomic Encyclopedia of Bacteria and Archaea project  [41]. The genome project is deposited in the Genomes On Line Database  [21]  and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI) using state of the art sequencing technology  [42]. A summary of the project information is shown in Table  2.
Genome sequencing and assembly The genome was sequenced using a combination of Illumina and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website  [45]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler assembly consisting of 217 contigs in 1 scaffold was converted into a phrap  [46]  assembly by making fake reads from the consensus, to collect the read pairs in the 454 paired end library. Illumina GAii sequencing data (8,018. 4 Mb) was assembled with Velvet  [47]  and the consensus sequences were shredded into 1. 5 kb overlapped fake reads (shreds) and assembled together with the 454 data. The 454 draft assembly was based on 200. 6 Mb 454 draft data and all of the 454 paired end data.
Newbler parameters are -consed -a 50 -l 350 -g -m -ml 21. The Phred/Phrap/Consed software package  [46]  was used for sequence assembly and quality assessment in the subsequent finishing process. After the shotgun stage, reads were assembled with parallel phrap (High Performance Software, LLC). Possible mis-assemblies were corrected with gapResolution [45], Dupfinisher  [48] , or sequencing cloned bridging PCR fragments with subcloning. Gaps between contigs were closed by editing in Consed, by PCR and by Bubble PCR primer walks (J. -F. Chang, unpublished). A total of 361 additional reactions and 11 shatter library were necessary to close some gaps and to raise the quality of the final contigs. Illumina reads were also used to correct potential base errors and increase consensus quality using a software Polisher developed at JGI  [49].
Genome annotation Genes were identified using Prodigal  [50]  as part of the DOE-JGI annotation pipeline  [51] , followed by a round of manual curation using the JGI GenePRIMP pipeline  [52]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes -Expert Review (IMG-ER) platform  [53].
Acknowledgements We would like to gratefully acknowledge the help of  Sabine Welnitz  for growing T. parva cultures, and  Evelyne-Marie Brambilla  for DNA extraction and quality control (both at DSMZ). This work was performed under the auspices of the  US Department of Energy Office of Science, Biological and Environmental Research Program , and by the  University of California ,  Lawrence Berkeley National Laboratory  under contract No. DE-AC02-05CH11231 ,  Lawrence Livermore National Laboratory  under Contract No. DE-AC52-07NA27344 , and  Los Alamos National Laboratory  under contract No. DE-AC02-06NA25396 ,  UT-Battelle  and  Oak Ridge National Laboratory  under contract  DE-AC05-00OR22725.
In the present study the outputs of a RCM from the Med-CORDEX project at different spatial resolutions are considered, together with different methods to evaluate the future changes on surface water resources. Due to limited data availability, a conceptual lumped hydrological model is chosen. Different downscaling approaches are tested, including the direct application of RCM outputs, a quantile-mapping bias correction method and a quantile-perturbation approach. The purpose of this study is not to give a complete diagnostic of the future projections but to review different approaches relevant to conduct hydrological impact studies with high resolution RCMs in the semi-arid catchments of North Africa. Several methodological issues that have never been investigated for this region are addressed in the present study for a medium-size catchment in Morocco, in particular: 1.
2. 11 stations with daily precipitation (P ) located within and outside the catchment were considered (Fig. 1 ). The study period is 1984-2010 (27 hydrological years from September to August). The rainfall depth at the catchment scale was obtained through spatial interpolation with the Thiessen approach. No trends were detected in precipitation or temperature data aggregated over annual or monthly time steps between 1984 and 2010. However there is a significant trend in annual temperatures from 1975 to 1982 (Fig. 2 ), according to the Mann-Kendall trend test at the 5 % significance level. The individual precipitation records at each station have been carefully checked to remove unrealistic values and identify the missing days/periods.
Quantile perturbation A quantile perturbation method does not consider the differences between observed and simulated datasets but only the relative changes between past and future time periods as simulated by climate models. Therefore it belongs to the family of the so called "Delta change" methods  (Willems and Vrac, 2011). Several authors have implemented this method for hydrological impact studies  (Chiew et al. , 2009; Liu et al. , 2011; Willems and Vrac, 2011) : we introduced a variant that is producing random ensembles of perturbed series, to prevent that a single random generation may contain outliers. The computation was done for each month separately, between an observed period (CTRL), a simulation during the observation period (SIM) and a projected future period (PROJ). The approach followed these steps: 1.
, 2012; Milano et al. , 2012 Milano et al. , , 2013)). Further work should now test other approaches to evaluate the climate change signal and compare the results with those obtained in the present study. In particular other types of bias-correction methods and hydrological models should be considered but also the use of RCM with an improved description of the present-climate hydrological cycle (improved mean behavior and extreme events) We are however aware that individual RCM simulations do not allow to take into account two of the main sources of uncertainty in regional climate change, that is to say the choice of the RCM and the choice of the forcing GCM  (Déqué et al. , 2012). This will be possible in the future with the extension of the number of Med-CORDEX simulations and database (www.
Acknowledgements. The observed data was provided by the  department of Water Research and Planning (DRPE) of Morocco  and the  Hydraulic Basin Agency of Loukkos-Tétouan. We thank  A. Alias  ( CNRM ) for running the Mediterranean ALADIN simulations. This work is a contribution to the  HyMeX program  (HYdrological cycle in the Mediterranean Experiment) through INSU-MISTRALS support and to the  Med-CORDEX initiative (COordinated Regional climate Downscaling EXperiment -Mediterranean region ). The third author has received funding from the  French National Research Agency (ANR)  project  REMEMBER  (contract  ANR-12-SENV-001 ) and from the  European Commission  through the  FP7   CLIM-RUN  project. The ALADIN simulations used in the current work can be downloaded from the Med-CORDEX database (www.
4b ), thereby slowing Muller's ratchet (that is, the irreversible accumulation of detrimental mutations in asexual populations of finite sizes, Supplementary Note E2 and Supplementary Fig. 11 ). Over 8% of the genes of A. vaga are much more similar to nonmetazoan sequences in GenBank than to metazoan ones (AI log score. 45 (ref. 16), Supplementary Note E4) and were therefore probably acquired through horizontal gene transfer (HGT). This class of genes has significantly fewer introns per kilobase of coding sequence compared with probable core metazoan genes (AI # 245, Supplementary Table  2 ). More than 20% of genes with AI. 45 are found in quartets (groups of four homologous copies in conserved syntenic regions) and were therefore probably incorporated into the rotifer genome before the establishment of tetraploidy, which itself pre-dates the divergence of extant bdelloid families  8.
METHODS SUMMARY Genomic DNA was extracted from laboratory cultures of a clonal A. vaga lineage and shotgun-sequenced using 454 and Illumina platforms at respective coverage of 25 and 440 times (using both single reads and mate reads from inserts up to 20 kb). The 454 reads were assembled into contigs using MIRA  27  ; the contigs obtained were corrected using single Illumina reads and linked into scaffolds using paired Illumina reads  28  (Supplementary Table  1 ). We annotated protein-coding genes by integrating evidence from RNA sequencing, ab initio predictions and comparison with UniProt. Most synteny and Ka/Ks (non-synonymous divergence/synonymous divergence) analyses were performed using the package MCScanX  29  and synteny plots were drawn using Circos  30. with letters, and dashed lines connect allelic gene pairs.
Acknowledgements The authors would like to thank  M. Meselson  for his support during the initiation phase of this project and for inspiring us with his seminal works on bdelloid genetics. The authors are also grateful to  M. Radman  for useful discussions,  M. Knapen  and  N. Debortoli  for participating in laboratory work,  M. Lliros  for helping with Fig. 1,  S. Henrissat  for participating in CAZyme analyses, and  S. Oztas ,  B. Vacherie ,  P. Lenoble  and  S. Mangenot  for performing PCR validations of the assembly. This work was supported by  Genoscope-CES  (where most of the sequencing was performed), by  US National Science Foundation  grants  MCB-0821956  and  MCB-1121334  to I. A. , by  German Research Foundation  grant  HA 5163/2-1  to O. H. , by grant  11. G34. 31. 0008  from the  Ministry of Education and Science of the Russian Federation  to A.
0 (SF-1 overexpression experiments) or Human Gene 1. 0 ST (SF-1 and NRSF/REST knockdown experiments) arrays (Affymetrix) using the manufacturer's protocols. Three biological replicates were analyzed for both the knockdown and the overexpression experiments. Data were analyzed using the Affymetrix Command Console and Expression Console softwares. Transcripts were considered as differentially expressed when there was a 2-fold or greater difference in expression levels, with a P < 0. 05. Differentially expressed genes were assigned to different Gene Ontology categories using the DAVID software (http://david. abcc. ncifcrf. gov). The microarray data from this study have been submitted to the NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo) under accession number GSE43035.
High-throughput sequencing and data analysis Immunoprecipitated DNA were sequenced on an Illumina platform, and reads passing quality control criteria were mapped to the human genome (version hg19) using ELAND v2 software. SF-1-binding sites were identified using the CisGenome v2 SeqPeak algorithm (18) from triplicate samples for each SF-1 dosage (basal or increased) condition and input DNA as control, using the following parameters: read extension length = 150, local rate window = 10 kb, local rate cutoff = 1e-005. The Sole-Search web tool  (19)  was used to annotate the position of SF-1binding peaks in relationship to gene localization and to import their sequence for further motif searching using the MEME suite  (20). The within-gene mode in the Sole-Search Location Analysis Tool was used to assign binding sites to genes.
Overlaps between gene groups were identified using the GeneVenn software (http:// genevenn. sourceforge. net). To calculate the significance of overlap between gene sets, the chi-square test with Yates' correction was used, considering that 21 679 genes were present in total on the HG-U133 Plus 2. 0 arrays and 19 889 on the Human Gene 1. 0 ST arrays. Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) sequencing was performed as described  (21). Highthroughput sequencing data are deposited in Gene Expression Omnibus under accession number GSE44224.
FUNDING  Institut National du Cancer  [ 2008-045 ];  Agence Nationale de la Recherche  [ BSV1 005 01 -BeyondTASKs ]; Conseil Ge´ne´ral 06; CNRS;  Association pour le Recherche sur le Cancer  [Projets ARC SFI20111203563];  European Union Seventh Framework Programme  [FP7/2007-2013] under grant agreement [n 259735 ( ENS@T-CANCER)  to E. L. ]. Funding for open access charge:  Institut National du Cancer.
Data and Methods [5] To display the water mass structure and the baroclinic component of the flow associated with the MC, we use highresolution quasi continuous conductivity-temperature-depth and water sample data collected on 9-11 September 1997. Upper ocean velocities from a hull mounted acoustic Doppler current profiler (ADCP) and hydrographic data collected in late December 1992  [Saunders and King, 1995]  are also analyzed. The ADCP data are available through NOAA/ NCDDC/University of Hawaii (http://ilikai. soest. hawaii. edu/sadcp/). To illustrate the regional flow patterns in the upper layer, we use satellite tracked surface drifters. The drifters are WOCE-Surface Velocity Program type, drogued at 15 m depth, designed to follow the water to within AE. 013 m. s À1 in 10 m. s À1 winds  [Niiler et al.
, 1995]. The original position data, collected at irregular time intervals, have been quality controlled and optimally interpolated to uniform 6-h interval trajectories  [Hansen and Poulain, 1996]. The data are available at NOAA's Atlantic Oceanographic and Meteorological Laboratory (AOML, http://www. aoml. noaa. gov/phod/index. php). Only drogued drifters are used in this study. To analyze the regional characteristics of the mean flow, we estimated the surface geostrophic velocity based on satellite altimetry-derived mean dynamic topography (MDT). We use the Danish National Space Center mean dynamic topography model (DNSC08 MDT). DNSC08 MDT is a high-resolution (1 min) global data set constructed from the combination of 12 years  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004)  of sea surface height derived from nine satellite missions and a geoid model  [Andersen and Knudsen, 2009].
The data are available at http://www. space. dtu. dk/. Prior to estimating the mean geostrophic velocity, the MDT data were spatially smoothed with a 20 km low-pass filter. In addition, nighttime MODIS Aqua and Pathfinder version 5 sea surface temperature (SST) data are used to display the cross-slope structure of ocean fronts. These 4 km resolution SST data are available through NASA/Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (http://podaac. jpl. nasa. gov/dataaccess).
Given that the baroclinic shear is much weaker than in cores A and B, no definitive conclusion on the existence of core C can be drawn from the hydrographic section. Summer hydrographic data from a section collected at 43 S also present a main MC core and a secondary onshore core, both being displaced ~50 km onshore from the winter location (not shown). [8] There are various shortcomings associated with the geostrophic velocity estimates in the MC. First, based on the analysis of hydrographic data combined with mass conservation arguments and direct current measurements, studies suggest that the barotropic transport of the MC is three to four times larger than its baroclinic counterpart  [Peterson, 1992; Saunders and King, 1995; Spadone and Provost, 2009]. Second, the steep bottom slope creates large "bottom triangles" within which we lack information on the density structure and therefore also velocity estimates.
MDT [13] To further explore the cross-shore velocity structure of the MC, we analyzed the geostrophic velocity derived from a high-resolution (1 min) gridded MDT field. The MDT was constructed by combining satellite altimeter data collected by various platforms from 1993 to 2004, and a global geoid model  [Andersen and Knudsen, 2009]. The DNSC08 MDT provides the high spatial resolution required to resolve the rather thin high-velocity cores (~10-20 km) apparent in the hydrographic, ADCP, and drifter data. Figure  5b  displays the zonal distribution of the meridional velocity component at 45ºS. The mean meridional velocity increases from ~0. 1 m. s À1 in the mid shelf (west of 62ºW, not shown in Figure  5 ) to 0. 22 m. s À1 in the outer shelf (61ºW) and presents two sharp peaks that exceed 0.