dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Mueller et al. : Evapotranspiration benchmark products In summary, we have presented here the first benchmark synthesis products for monthly, global land ET estimates. A reproduction of a negative trend in global ET during 1998-2005 with these benchmark synthesis products supports previous findings of a declining global ET trend over that period. However, caution is necessary when analyzing trends, because the considered time period is very short for trend analyses, the analyzed ET data sets are not totally independent from each other (e. g. same forcing data, similar methodologies), and agreement between them is not necessarily an indicator of their validity. Furthermore, spurious trends can be introduced through changes in the observing systems for.
Acknowledgements. The LandFlux-EVAL project was coordinated under the Global Energy and  Water Exchanges  ( GEWEX) LandFlux initiative. We also acknowledge the support of the  Integrated Land Ecosystem-Atmosphere Processes Study , iLEAPS, in the development of the synthesis products. CRU data were obtained from the  University of East Anglia Climate Research Unit (CRU) ,  British Atmospheric Data Centre , 2008, available from http://badc. nerc. ac. uk/data/cru. The GPCP combined precipitation data were developed and computed by the  NASA/Goddard Space Flight Center's Laboratory for Atmospheres  as a contribution to the  GEWEX Global Precipitation Climatology Project. GPCC precipitation data are available from the GPCC Homepage: http://gpcc. dwd. de. CPC merged analysis of precipitation data were provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their Web site at http://www.
Mueller et al. : Evapotranspiration benchmark products 3715. The reason for smaller uncertainties in ET than in precipitation could B. 300 250 250 200 800 1000 300 400 500 600 700 800 200 300 400 500 600 700 800 900 age 400mm/yr) Precipitation (average 487mm/yr) Precipitation (average 455mm/yr) Global land mean 750 12 700 10 Legend 650 8 GSWP Datasets ET schemes 600 WaterMIP GHM ET 6 WaterMIP LSM 550 Diagnostic 4 Reanalyses 500 All others 450 2 400 0 600 700 800 700 750 800 850 900 950 1000 0 2 4 6 8 10 age 432mm/yr) Precipitation (average 815mm/yr).
, 2011). Samples were collected during the first year of the expedition from the Strait of Gibraltar, through the Mediterranean and Red Sea, down to the middle of the Indian Ocean (Table  1 ). Some marine regions under-represented in previous metagenomic studies are included in this sample set, such as those from the Arabian Sea oxygen minimum zone (OMZ) and Indian Ocean lagoons. Most prokaryotic cells and many large virus particles are expected to be captured within the 0. 2-1. 6 mm size fraction used in the present metagenome study. Here we show that putative NCLDV sequences differ substantially from known reference genomes, suggesting a high diversity of giant marine viruses. The concentration of NCLDV genomes in the samples was estimated by factoring the metagenome data set with prokaryotic abundance determined by FC and microscopy on samples collected concurrently on Tara.
Sampling and DNA extraction At the end of March 2012, a 2. 5-year circum-global expedition was completed onboard Tara, an arctic exploration schooner modified for global marine research with innovative systems for multiscale sampling of planktonic communities. During the expedition, planktonic organisms ranging in size from viruses to fish larvae together with physico-chemical contextual data were collected from several depths at 153 stations across the world oceans. Plankton were collected from up to three depths: near the surface (SRF; B5 m), at the depth of maximum chlorophyll a fluorescence (deep chlorophyll maximum, DCM; 20-200 m) and in the mesopelagic layer (MESO; 200-1000 m) to capture deep oceanographic features, such as OMZs. As much as possible where sampling was shallower than 80 m, SRF and DCM samples were collected using a large peristaltic pump (A40, TECH-POMPES, Sens, France), whereas samples from deeper DCM and MESO were collected using 12-l Niskin bottles mounted on a rosette equipped with physico-chemical sensors.
Metagenomic sequence data All sequencing libraries were created using the Roche-454 Rapid Library kit (Roche Applied Science, Meylan, France). The input for nebulization used 500 ng of extracted DNA. Each library was indexed to avoid cross-contamination and sequenced on one-eighth to one-half of a GS-FLX Titanium plate (Meylan, France). Quality checking of the reads was performed using the 454 standard   et al. , 2007)  were downloaded from CAMERA  (Sun et al. , 2011). We used only the sequence data recovered from the samples corresponding to the size fraction between 0. 1 and 0. 8 mm (that is, 40 samples corresponding to GS001 to GS051). Proteincoding regions in the metagenomic sequences (TOP and GOS) were identified using the FragGeneScan software  (Rho et al. , 2010).
Small eukaryotic algae were identified in plots of side scatter vs FL3, and FL2 vs FL3  (Olson et al. , 1993) , and excluded in the enumeration of phototrophic prokaryotes. Data analysis was performed with the Paint-A-Gate software (Becton and Dickinson). The abundance of prokaryotic cells was based on the enumerations of heterotrophic and phototrophic prokaryotes.
Marker genes Sixteen NCLDV marker genes were selected from the 1445 clusters of NCLDV orthologs, represented in the NCVOG database  (Yutin et al. , 2009). These marker genes were selected based on their conservation in nearly all known NCLDV genomes (four markers) or in a majority of viruses from the two major marine NCLDV families (Megaviridae and Phycodnaviridae; 12 markers), as well as on the observation that these genes typically occur only once in their genomes if present (Supplementary Table  S1 ). For cellular organisms, we used 35 conserved genes normally encoded as a single copy in all the cellular organisms  (Raes et al. , 2007). Profile-hidden Markov models  (Eddy, 2008)  derived from the sequence alignments of these marker genes were used to identify their homologs (E-valuep10 À 3 ) in the translated amino-acid sequence sets derived from metagenomic data.
This Pplacer approach was used also for the phylogenetic analysis of the reads assigned to the Megaviridae and oomycetes taxonomic nodes. For the visualization of phylogenetic trees, we used Archaeopteryx  (Han and Zmasek, 2009) , FigTree (http://tree. bio. ed. ac. uk/software/figtree/) and MEGA version 5. 1  (Tamura et al. , 2011). 2bLCA taxonomic annotation Each 454 read 4100 bp in length was assigned a taxonomic classification using a dual BLAST  (Altschul et al. , 1997; Monier et al. , 2008b)  based last common ancestor (2bLCA) approach somewhat similar to the method applied by MEGAN  (Huson et al. , 2007)  but using an adaptive E-value threshold specific for each protein. For each 454 read, the best local alignment (high-scoring segment pair (HSP)) with known proteins was obtained by a first BLAST (B1; BLASTx) against the UniProt database release April 2011 (UniProt Consortium, 2012).
Reads without any HSPs at an E-valuep10 À 5 were classified as 'no hits'. For each read with at least one significant HSP, the subsequence of the UniProt subject fragment aligned in the best scoring B1 HSP was used as a second BLAST (B2; BLASTp) query against the same UniProt database. All the B2 database hits with an E-valuepB1 HSP were recorded and defined to constitute a set of close homologs for the read (denoted as set H). The taxonomic classifications  (Benson et al. , 2012)  of the set H were then reduced to their LCA, which was finally assigned to the read as its taxonomic annotation. Reads were annotated as 'ambiguous' if the set H contained representatives from several domains of life. This 2bLCA protocol was applied to the metagenomic reads as well as to the metagenomic marker gene homologs (predicted protein sequences).
Co-occurrence analysis The 712 taxa Â 17 samples matrix from above was first filtered to exclude taxa with o5 total reads, reducing the matrix to 609 taxa. To normalize the read counts with respect to varying sequencing depth across samples, the number of reads in each cell of the matrix was divided by the total number of reads for the corresponding column. In order to detect putative taxon co-occurrences across the 17 samples, rank-based Spearman correlation coefficients (r) were first computed between taxon pairs using the R 'stats' package 'cor' function (R Development Core Team, 2011). Significance of each r was tested by computing a two-sided P-value (asymptotic t approximation) using the R 'stats' package 'cor. test' function and controlled for multiple tests using false discovery rate (q-value) computed by the tail area-based method of the R 'fdrtool' package  (Strimmer, 2008).
Horizontal gene transfer (HGT) analysis To identify potential HGTs between Megaviridae and oomycetes, comprehensive proteome databases for each taxon were assembled as follows. The Megaviridae proteome database contained all 6678 publically available peptides for M. chilensis (1120 peptides), Megavirus courdo7 (1139 peptides), Acanthamoeba castellanii mamavirus (997 peptides), A. polyphaga mimivirus (972 peptides), A. polyphaga mimivirus isolate M4 (756 peptides), Moumouvirus Monve (1150 peptides) and CroV BV-PW1 (544 peptides). Because complete oomycete proteomes were poorly represented in the Uni-Ref100 database release December 2010  (Suzek et al. , 2007)  which we intended to use for HGT detection, we enriched UniRef100 with oomycete proteomes from the following publically available oomycete genome and transcriptome projects (Supplementary Table  S2 ): Aphanomyces euteiches ESTs (161 384 open reading frames (ORFs))  (Gaulin et al.
, 2008) , Hyaloperonospora arabidopsidis (14 937 ORFs)  (Baxter et al. , 2010) , Pythium ultimum (14 224 peptides)  (Levesque et al. , 2010) , as well as Hyaloperonospora parasitica (6452 peptides), Phytophthora infestans (14 580 peptides), Phytophthora ramorum (10 892 peptides), Phytophthora sojae (13 995 peptides) and Saprolegnia parasitica (17 437 peptides) available from the Broad Institute of Harvard and MIT 'Saprolegnia and Phytophthora Sequencing Project'. Where peptides were not made available, nucleotide sequences were translated into ORFs 450 amino acids. To these 265 433 non-redundant oomycete peptides, we added a none-oomycete stramenopile proteome from Thalassiosira pseudonana (11 532 peptides), absent from UniRef100 but publically available at the NCBI. The 386 000 additional stramenopile peptides were clustered (90% identity, 265 433 peptides) before concatenation with UniRef100 to form the 'UniRef100 þ stramenopiles' database.
Potential HGTs between Megaviridae and cellular proteins were first approximated by reciprocal best BLAST hits computed by a method similar to the one described by  Ogata et al. (2006). Briefly, the best cellular homolog in the UniRef100 þ stramenopiles database was first identified for each Megaviridae peptide (BLASTp, E-valuep10 À 5 ). If this best cellular homolog obtained a best hit against a Megaviridae peptide in a second BLASTp search against the UniRef100 þ stramenopiles þ Megaviridae database (excluding hits in the same cellular taxonomic group at the first three NCBI classification levels), they were considered a potential Megaviridae-cell HGT candidate. The six Megaviridae-oomycete HGT candidates revealed by reciprocal BLAST were then subjected to phylogenetic analysis. Homologs for the six Megaviridae peptides were collected by keeping representative sequences among all detected taxonomic groups using BLAST-EXPLORER  (Dereeper et al.
The hosts of the latter viruses are distributed widely in the classification of eukaryotes: C. roenbergensis (stramenopiles; Bicosoecida), P. orientalis (Viridiplantae; Chlorophyta; Prasinophyceae), P. pouchetii (Haptophyceae; Phaeocystales) and Haptolina ericina (formerly C. ericina; Haptophyceae; Prymnesiales). Interestingly, many metagenomic reads were assigned to relatively deep branches. For example, 17 PolB-like reads were assigned to the branch leading to the clade containing three prasinoviruses (OsV5, MpV1, BpV1), and    b ), three reads and one read assigned to Asfarviridae and Poxviridae, respectively, were omitted for presentation purpose. leading to four marine viruses (PpV, CeV, OLPV1 and OLPV2). To illustrate metagenome sequence divergence with known viral sequences, we arbitrary classified the metagenomic NCLDV marker sequences as 'known' if they showed X80% aminoacid sequence identity to their closest homolog in the databases and otherwise as 'novel' (or 'unseen').
A vast majority (73-99%) of the sequences turned out to be 'novel' when they were searched against the UniProt sequence database (Figure  5 ). Similarly, searches against the GOS sequence database revealed that large proportions (36-76%) of the TOP marker gene homologs were 'unseen' in this previous large-scale marine microbial survey. A fragment recruitment plot for the OLPV1 PolB protein sequence applied to PolB-like metagenomic reads that best matched OLPVs (OLPV1 or OLPV2) further showed a high level of richness among these sequences (even within a single sample) and their large divergence from the reference OLPV1 sequence (Supplementary Figure  S5 ). Overall, these results suggest that the majority of the NCLDVs represented in the TOP samples are highly diverse and only distantly related to known viruses, thus potentially corresponding to viruses infecting different marine eukaryotes.
Correlated abundance of MutS protein subfamilies with Megaviridae abundance Two recently identified subfamilies of DNA mismatch repair protein MutS are specific to a set of viruses with large genomes  (Ogata et al. , 2011). The MutS7 and/or MutS8 subfamilies are encoded in all the known members of the Megaviridae family and in HcDNAV (356 kb); the latter virus infects the bloom-forming dinoflagellate Heterocapsa circularisquama and appears to be related to the Asfarviridae family  (Ogata et al. , 2009). It has been suggested that these hallmark genes of giant viruses are required to maintain the integrity of viral genomes with large sizes (mostly 4500 kb;  Ogata et al. , 2011). These MutS genes are not included in our NCLDV marker gene set. Prompted by the observed high abundance of sequences of possible Megaviridae origin in the TOP data set, we screened our data for MutS7 and MutS8 homologs.
In total, we identified 78 reads similar to MutS (68 and 10 reads for MutS7 and MutS8, respectively) in 13 samples (Supplementary Figure  S6a ). If these MutS genes originate from putative Megaviridae viruses detected by our marker gene method, we expect to see a correlation in their abundance across samples. We tested this hypothesis and found a statistically significant correlation between the relative abundance of the Mut7/8 homologs and the Megaviridae marker gene density (R ¼ 0. 725, P ¼ 9. 90 Â 10 À 4 ; Supplementary Figure  S6b ). A similar level of correlation was also found in the GOS data set (R ¼ 0. 647; P ¼ 6. 55 Â 10 À 6 ; Supplementary Figure  S6c ). This result suggests that the TOP reads assigned to the Megaviridae family probably originate from viruses with a large genome as found in known viruses of this family.
Oomycetes or their stramenopile relatives co-occur with marine Megaviridae To test whether the present data set might serve to identify potential hosts of marine NCLDVs, we assessed association of taxon occurrences ('co-occurences' and 'co-exclusions') across samples using the whole set of the TOP metagenomic reads. We used two approaches for the detection of taxon associations: the first based on Spearman's correlation across all samples (3696 associations, qo0. 05), and the second combining Spearman's correlation with a Kullback-Leibler measure of dissimilarity on a reduced data set excluding two outlier OMZ samples (108 associations, q'o0. 05). This resulted in the identification of a total of 3703 potential taxon association pairs, of which 101 were supported by both methods (Supplementary Table  S3 ).
The discrepancy between the two lists was due to the higher intrinsic stringency of the second method, as well as to the specific photic-OMZ contrasts, which were only taken into account by the first method. Some of the inferred taxon associations simply reflected uncertainty in the taxonomic assignments, such as the associations between 'Archaea; environmental samples' and 'Archaea; Euryarchaeota; Marine Group II; environmental samples;' (q ¼ 1. 38 Â 10 À 8 , q'E0) or between environmental viruses and myoviruses (q ¼ 3. 8 Â 10 À 5 , q' ¼ 9. 4 Â 10 À 3 ). These could be explained by the taxonomic assignments of similar organisms into related but distinct taxonomic nodes in the NCBI taxonomy database. However, our analysis also revealed known biological associations of lineages. For instance, a correlated occurrence (q ¼ 1.
Despite their short sizes (B100 aa) and large evolutionary distances from database homologs, many of these reads appeared related to stramenopiles (12 out of 19 cases), including six cases showing distant yet specific relationships to known oomycete sequences (Supplementary Figures  S7-1 --S7-12). For the remaining seven reads, their phylogenetic positions were rather poorly resolved and showed no coherent relationship to specific taxonomic groups. A similar analysis of the 31 reads (D5 family proteins) assigned to the Megaviridae node confirmed in most cases their initial taxonomic annotation (Supplementary Figure  S8 ), with some of them assigned close to the root of the viral family. These reads are not closely related to the sequences from CroV (Megaviridae) and phaeoviruses (Phycodnaviridae), the only known NCLDVs parasitizing marine stramenopiles.
The tree was mid-point rooted for visualization purpose. The grouping of the Megaviridae and oomycete sequences suggests a gene exchange between the lineage leading to Megaviridae and the lineage leading to oomycetes. Phylogenetic trees for the remaining five putative cases of horizontal gene transfers between these lineages are provided in the Supplementary Figure  S9. in diameter) cannot be retained on the 0. 2-mm membrane (Grimsley and Clerissi, data not shown). Furthermore, underestimation was likely to be compounded by the fact that most NCLDV-infected cells are 41. 6 mm and thus were excluded from our size fraction. Filtration efficiency is another pitfall of quantitative estimates. Size of retained microbes may vary during pre-and retention filtration (progressively excluding smaller infected cells and retaining smaller NCLDVs than the filter's nominal pore sizes), though we rarely encountered filter clogging for the samples analyzed in this study.
3% of bacterial abundance  (Caron et al. , 1995). Therefore, the predicted NCLDV genome abundance by the present study suggests that NCLDVs equal or even outnumber eukaryotic organisms in the photic layer of the sea. In other words, our suggested NCLDV/ eukaryote ratio is not unlike the ratio of phage/ bacteria in seawater  (Suttle, 2007). Whole-genome amplification and sequencing of single microbial cells/viruses is becoming a powerful tool in revealing genomic contents of environmental uncultivated microorganisms  (Allen et al. , 2011; Yoon et al. , 2011). These studies reveal that a substantial fraction of the unicellular organisms in a population may be infected by viruses. The estimated relative genome abundance of NCLDVs (3% and 10% of bacteria in the TOP and GOS data sets, respectively) suggests that such single virus genomics approaches will be helpful in analyzing uncultivated marine NCLDVs from size-fractioned natural water samples.
Isolation of new viruses requires host cultures. Among known hosts of NCLDVs, amoebas of the Acanthamoeba genus have been the most efficient laboratory hosts to isolate new NCLDVs from aquatic samples  (Arslan et al. , 2011 , Boyer et al. , 2009 , La Scola et al. , 2010 , Thomas et al. , 2011). Taxon association analysis on the TOP data set hinted at an unexpected sympatric association between Megaviridae and stramenopiles possibly distantly related to oomycetes. The two sets of reads involved in this correlation showed a clear difference in their G þ C compositions. This rather suggests two distinct source organisms for these reads. Yet, an alternative scenario is that they originated from a single organism (a virus very recently acquiring cellular genes or a cellular organism with recently integrated viral genomes).
, 1987). Taken together, these observations lead us to hypothesize that there is a yet unrecognized close interaction between Megaviridae and stramenopiles (distantly related to oomycetes), either as a direct virus/host couple  (Monier et al. , 2009)  or through co-infection of a common third partner  (Ogata et al. , 2006; Boyer et al. , 2009). Limitations in the available genome data for marine stramenopiles and the scope of the present TOP data set, which targeted the girus/ prokaryote size fraction, make it difficult to obtain finer taxonomic resolutions for the potential eukaryotic counterpart. The present work provides a proof of principle that metagenomic sequence analyses promise to shed new light on the biodiversity of marine viruses and their interactions with potential hosts. Larger sets of environmental sequence data from diverse locations and different size fractions, such as those from remaining Tara Oceans samples, will be useful not only to test our 'Megaviridae-stramenopile' hypothesis but also to provide a larger picture of NCLDV-eukaryote interactions.
Acknowledgements We thank the coordinators and members of the  Tara Oceans consortium  (http://www. embl. de/tara-oceans/start/)for organizing sampling and data analysis. We thank the commitment of the following people and sponsors who made this singular expedition possible: CNRS, EMBL, Genoscope/CEA,  VIB ,  Stazione Zoologica Anton Dohrn, UNIMIB ,  ANR  ( projects POSEIDON/ANR-09-BLAN-0348 ,  BIOMARKS/ANR-08-BDVA-003 ,  PROMETHEUS/ANR-09-GENM-031 , and  TARA-GIRUS/ANR-09-PCS-GENM-218 ),  EU   FP7  (MicroB3/No. 287589 ), FWO ,  BIO5 , Biosphere 2, agnes b. , the  Veolia Environment Foundation ,  Region Bretagne ,  World Courier ,  Illumina, Cap L'Orient , the  EDF Foundation EDF Diversiterre, FRB , the  Prince Albert II de Monaco Foundation ,  Etienne Bourgois , the Tara schooner and its captain and crew.
Thirty of them had practiced sport at high level (>6 h per week) for more than six years before the symptoms had occurred and no history of severe infection was reported by their practitioners. In twenty five patients, who did not regularly practice any sport, severe infection (H1N1 influenza: 5; pneumonia: 7; encephalomyelitis: 6; sepsis: 4; unidentified: 3) was reported within the three to seven month period preceding the onset of CFS symptoms. The two groups of CFS patients were called NI (not infected) and I (infected), respectively. The data were compared to those obtained in a gender, age-, and weight-matched control group of forty Caucasian healthy volunteers who consulted for a medical check up and were coming from the same type of socioeconomic class. Additional biochemical analyses in venous blood samples were approved by the Ethics Committee of our institution (CCPPRB Marseille 1) and the protocol was performed in accordance with the Declaration of Helsinki.
Physiological Measurements The functional examinations at rest consisted in ECG recording, measurements of the arterial blood pressure measurements using a sphygmomanometer, the percutaneous measurement of oxygen saturation (SpO 2 ) using an infrared analyser (Nellcor model N3000, Tx, USA), and ventilation and respiratory gas exchange. A face mask was designed to form an air-tight seal over the patient's nose and mouth, with all the inspired and expired gas going into a turbine flowmeter (Triple V digital volume transducer, Jaeger, Germany) giving measurements of minute ventilation. Fast-response paramagnetic O 2 and infrared CO 2 analyzers measured the end-tidal partial pressures of O 2 and CO 2 , respectively. Exercise trials were performed on an electrically braked cycle ergometer (Ergometrics ER 800, Jaeger, Bunnik, The Netherlands) connected to a microcomputer software (Oxycon beta, Jaeger, Bunnik, The Netherlands).