dataesr/scientific-paragraphs-categorization huggingface dataset:
In the past decade, several studies have retrieved nifH sequences from the dark ocean, some also accompanied by low N 2 fixation rates  (Hamersley et al. , 2011; Bonnet et al. , 2013; Rahav et al. , 2013). Due to the immense volume of the dark ocean, aphotic N 2 fixation could influence the global nitrogen budget substantially. However, the number of published aphotic N 2 fixation rates is scant and our understanding of the metabolism and ecology of aphotic diazotrophs is still limited, hindering our ability to evaluate their impact on global fixed nitrogen inputs  (Moisander et al. , 2017). Non-cyanobacterial nifH sequences fall into four established nifH gene clusters  (Chien and Zinder, 1996) , are the most numerous in nifH gene databases  (Riemann et al. , 2010)  and are spread throughout the global ocean  (Farnelid et al.
, 2017   Moutin et al. , 2017). Temperature, salinity, chlorophyll fluorescence and oxygen data were obtained using an SBE 9 plus CTD mounted on a General Oceanics rosette frame fitted with 24-12 L Niskin bottles. Seawater samples were collected with Niskin bottles mounted on a rosette frame at 15 short-duration (SD, 8 h) and 3 long-duration (LD, 7 days) stations  (Moutin et al. , 2017). Samples for the determination of the inorganic nutrients nitrate (NO - 3 ), nitrite (NO - 2 ) and phosphate (PO 3- 4 ) were collected in 20 mL acid-washed polyethylene flasks, poisoned with 1 % mercury chloride and analyzed onshore using an AA3 Bran + Luebbe autoanalyzer. The detection limit for both NO - 3 and PO 3- 4 was 0. 05 µM. Samples for the determination of dissolved organic carbon (DOC) were collected in combusted glass bottles and immediately filtered through two mounted precombusted (4 h, 450 • C) 25 mm GF/F filters (0.
Particles were excited at 488 nm (plus 457 nm for unstained samples), and forward scatter (FSC; < 15 • ) , green fluorescence (530 / 40 nm), orange fluorescence (580 / 30 nm) and red fluorescence (> 650 nm) emissions were measured. Bacteria were discriminated based on their green fluorescence and FSC characteristics. Cytograms were analyzed using FCS Express 6 Flow Cytometry Software (De Novo Software, CA, US).
The resulting sequences were paired within mothur  (Schloss et al. , 2009) , and reads containing ambiguities or more than eight homopolymers were discarded. The sequences were assigned to OTUs (at 97 % cutoff) using the UCLUST denovo picking method (Edgar, 2010) implemented in MacQIIME v1. 9. 1  (Caporaso et al. , 2010). Low-abundance OTUs consisting of fewer than 15 sequences across all samples were discarded from further processing. A representative sequence of each OTU was extracted from the data and quality processed in ARB  (Ludwig et al. , 2004) , removing sequences that did not conceptually translate or were otherwise of poor quality. These OTUs were removed from further analysis. Remaining sequences were aligned based on protein alignment of the nifH fragments in a public nifH database (https://www.
jzehrlab. com/nifh, last access: 15 September 2017). Aligned protein sequences were assigned to nifH clusters using a decision tree statistical model, CART  (Frank et al. , 2016). The sequence data were normalized to the proportion of total reads in each sample. Total relative abundances of sequences that fell in major nifH clusters were used to create a heatmap within R Studio using the "vegan" statistical package  (Oksanen et al. , 2015). Sequences were further classified through a locally run blastp using the nifH database as a reference (April 2015 database update). A neighbor-joining tree was built in ARB with the 100 most abundant OTUs across all samples. All variables show a clear divide between the Melanesian Archipelago waters (MA; stations SD1 to SD12) and the South Pacific Gyre (GY; eastwards of SD12;  Moutin et al.
Cluster 3S had high relative abundances at station SD10 and in the 200 and 500 dbar depths of stations SD2 and SD12, as well as at 200 dbar at station SD13. The cyanobacterial subcluster 1B was observed at a very low relative abundance throughout all stations and depths (average 0. 5 % of total community) and included Trichodesmium, UCYN-A and Richelia. Consistent variations in community composition among depths and stations were not detected via cluster-based analysis; however, patterns emerged when observing data at the OTU level in abundant clusters (Fig. 3 ). In subcluster 1G, reads most closely matching an unclassified bacterium from the tropical North Atlantic (Unc12217, E value = 2. 82 × 10 -70 ; Table  1 , Fig. 4 ) in the nifH database dominated the communities in the 650 dbar depth, and this phylotype was found only at minor proportions in other depths.
This phylotype had an approximately 83 % amino acid identity with Agrobacterium tumefaciens (Table  1 , Fig. 4 ). This phylotype was present at high proportions across all other 650 dbar samples except at station SD2, the westernmost station. Although identified as "Other 1G" in Fig. 3 , this trend was also true for several other groups present only at the 650 dbar depth (best matches with database sequences Unc12243, Unc12270 and UncPr491; Table  1 ). An additional group was found only at 650 dbar in the easternmost stations (SD10-SD13), closely matching a sequence from the Amazon River (UncM2163, E value = 4. 22 × 10 -74 ; Table  1 ). Within the nifH cluster 3, subcluster 3S was the most widespread and abundant, with representation from three groups in the reference database: Unc12045, UncB2403 and UncMa132.
Sequences with the best matches with Unc12045 (Genbank ID: ADV51583;  Turk et al. , 2011)  and UncB2403 (AAP48957;  Steward et al. , 2004)  were present at stations SD2, SD4, SD10 and SD12, but had the highest relative abundance at station SD10,    The nifH amino acid neighbor-joining tree with the 100 most abundant OTUs from this study. Sequences beginning with "denovo", shown in red, are randomly chosen representative sequences from these OTUs (OTUs binned at 97 % identity). The clusters in the tree are grouped at an approximately > 95 % sequence identity. The tree includes reference sequences (if uncultivated, the names of these sequences start with "Unc"). The reference sequences are shown with accession numbers from the nifH database and with the cluster identifier shown in blue if indicated in the nifH database.
Figure4. The nifH amino acid neighbor-joining tree with the 100 most abundant OTUs from this study. Sequences beginning with "denovo", shown in red, are randomly chosen representative sequences from these OTUs (OTUs binned at 97 % identity). The clusters in the tree are grouped at an approximately > 95 % sequence identity. The tree includes reference sequences (if uncultivated, the names of these sequences start with "Unc"). The reference sequences are shown with accession numbers from the nifH database and with the cluster identifier shown in blue if indicated in the nifH database. Additional sequences are included from a previous study in the South Pacific mesopelagic layers (Benavides et al. , 2015) ; these clones are labeled with the original clone names M64XXAXX and depths.
Acknowledgements. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (https://outpace. mio. univ-amu. fr/,last access: May 2018) funded by the  French research national agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CYBER program  ( CNRS-INSU) , the  GOPS program (IRD)  and the  CNES  ( BC T23 ,  ZBC 4500048836 ). The OUTPACE cruise (https://doi. org/10. 17600/15000900) was managed by  MIO (OSU Institut Pythéas, AMU)  from  Marseilles (France). The authors thank the crew of the  R/V L'Atalante  for outstanding on-ship operations. Mar Benavides  was funded by the  People Programme  ( Marie Skłodowska-Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement number  625185. The  NSF   OCE-1733610  award to  Pia H.
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/ outpace/outpace. php (INSU/CNRS LEFE CYBER, 2017). The Supplement related to this article is available online at https://doi. org/10. 5194/bg-15-3107-2018-supplement.
A genome wide association (GWA) study was carried out to benefit from the advantages of the precise and multiple allele detection permitted by this approach  (Gupta et al. , 2014). A pea collection of 266 accessions, combining a wide range of phenotypes for RSA traits and resistance to A. euteiches, was used. The collection was genotyped using 14,157 recent SNP (Single Nucleotide Polymorphism) resources developed in pea  (Tayeh et al. , 2015; Boutet et al. , 2016). The collection was phenotyped for diverse plant architecture traits, especially in the root system, and for resistance to A. euteiches in young plants under controlled conditions. Correlation analysis between RSA and resistance data identified relationships between plant architecture and resistance traits. The GWA study confirmed the diversity of genomic regions associated with plant architecture, and identified common loci associated with RSA traits and resistance to A.
Images were then analyzed using Winrhizo R Software (Regent Instruments, Quebec, Canada) with a home-made color scale to take into account the maximum root area. Several traits were scored from image analyses: total root projected area (TProjArea), total root length (TRootL), and average root diameter (RootDia). Average lateral root length (LatRootL) was computed from measurements and image analyses as following: LatRootL = (TRootL -TapRootL)/NLatRoot. Roots and shoots were then dried at 80 • C for 48 h and weighed to obtain root biomass (RootB) and shoot biomass (ShootB). Total biomass (TB) and root to total biomass ratio (RootB:TB) were computed as TB = RootB + ShootB and RootB:TB = RootB/TB. Six of those traits, NLatRoot, TRootL, LatRootL, TProjArea, RootDia, and RootB, were used in this study as RSA traits, two others, ShootL and ShootB, as shoot architecture traits, and the last two, TB and RootB:TB, as overall plant architecture traits.
Among the five plants, two were chosen for a complementary description of the damage on roots from image analyzes using image analysis. Their roots were carefully spread on a transparent sheet and scanned as described for Exp#1 and Exp#2. Image analyses were then performed using the Winrhizo R Software with a home-made color scale which enabled to discriminate between healthy and diseased root projected area. A percentage of root system with browning symptoms (Br:TProjArea) was calculated as Br:TProjArea = BrProjArea × 100/TProjArea, with BrProjArea, the projected area detected as brown by Winrhizo R , and TProjArea, the total projected area (Figure  1 ).
Statistical Analysis of Phenotypic Data Phenotypic datasets for plant architecture traits and resistance to A. euteiches, obtained from the 266-pea collection were analyzed using the R 3. 1. 1 program (R Core Team, 2014). For each variable, a linear model (LM) [R function lm] was used, including G (genotype), R (replicate) and P (plant) as fixed factors. Normality of residuals and homogeneity of their variance were checked using Skewness, Kurtosis and Shapiro-Wilk statistics (p-value ≥ 0. 05), as well as Bartlett test (p-value ≥ 0. 05), as described in  Desgroux et al. (2016). Mean-based heritability (h²) was calculated for each variable from variance estimates using the formula h² = σ G ² / [σ G ² + (σ E ²/r)], where σ G ² is the genetic variance, σ E ² the residual variance and r the number of replicates per line.
Least Square Means (LSMeans) were calculated from each LM analysis (R function lsmeans of package lsmeans;  Lenth and Hervé, 2015). Histograms of LSMeans frequency distributions were drawn using the R function hist. Pearson correlation analysis was carried out between LSMeans of the different variables (R function corr. test of package psych;  Revelle, 2015). Significance of the Pearson correlations was tested with a false discovery rate correction for multiple testing (corrected p-value < 0. 05;  Benjamini and Hochberg, 1995). A regression curve was drawn between the Br:TProjArea and DS data using the linear model Br:TProjArea∼DS.
The genotyping dataset of the pea collection was reduced to 12,812 SNP markers based on genotyping quality, and was filtered using PLINK 1. 9 software  (Purcell et al. , 2007; Chang et al. , 2015). Six of the 266 pea lines, i. e. , the same lines as in  Desgroux et al. (2016) , with missing data for more than 10% of SNP markers were excluded from the GWA analysis. Markers with missing data that exceeded 10% or with a minor allele frequency (MAF) lower than 5% in the 260 remaining lines, were FIGURE 1 | Image acquisition of a root system (A) and image analysis with Winrhizo® (B). Root images were acquired with a color scanner at 300 dpi. The images were then analyzed with Winrhizo® software using a homemade color scale to discriminate brown and white parts of roots. also removed from the analysis.
A total of 11,789 SNP markers were thus retained for the genetic analysis. The genotyping raw data set of 11,789 SNP markers, containing 0. 7% missing values, was imputed using the R function knncatimputeLarge (package scrime;  Schwender and Fritsch, 2013). Imputation parameters were tested with 10 replicates, using a subset of 4,805 SNP markers of the dataset with no missing values. For each replicate, 0. 7% missing values were randomly simulated and imputation parameters were tested for one to 50 nearest neighbors and four different methods to determine distances between SNPs, as described in  Desgroux et al. (2016). Parameters with the lower error rate over the 10 replicates (eight nearest neighbors and Cohen's kappa method; error rate: 17. 06%) were applied to the 11,789-SNP-marker dataset to impute missing values.
The two marker sets used in this study, derived from the GenoPea Infinium SNP Array and the KASP TM assays, respectively, were previously mapped to different genetic maps  (Tayeh et al. , 2015; Boutet et al. , 2016; Desgroux et al. , 2016). In this study, a consensus marker map was obtained by projecting the 953 SNP positions from the 64,263 markers map of  Boutet et al. (2016)  (namely BP-WGGBS map), onto the consensus map established by  Desgroux et al. (2016) (namely THMap). For this the "iterative map projection" tool of Biomercator V4. 2 software was used  (Sosnowski et al. , 2012). The THMap contained the GenoPea Infinium array-SNPs, as well as individual-and meta-QTL previously identified for Aphanomyces resistance from linkage  (Hamon et al. , 2011 (Hamon et al. , , 2013) )  or association  (Desgroux et al.
, 2016)  mapping. Seventy-eight markers were removed from the new consensus marker map because they were located within inversions. The level of connectivity between the consensus marker map created and the THMap was estimated with the "InfoMap" tool of the software. Pairwise Linkage Disequilibrium (LD) between markers was explored within Linkage Groups (LGs) from imputed genotypic data using PLINK 1. 9 software. The square correlation coefficient (r²) values obtained were then plotted against genetic distances (cM), according to the consensus marker map obtained in this study, to estimate the LD decay. LD decay curve and rate for each LG were estimated as described in  Desgroux et al. (2016) , based on the consensus genetic map from  Tayeh et al. (2015).
Population Structure, Individual Relatedness and Genome-Wide Association To estimate the structure of the collection, a Principal Component Analysis (PCA) and a Kinship relatedness matrix were carried out, using the SNP marker dataset and the EMMA (efficient mixed-model association) method in the GAPIT (Genome Association and Prediction Integrated Tool) R package  (Lipka et al. , 2012)  as reported in  Desgroux et al. (2016). GWA analyses used (i) LSMean phenotypic data for six RSA traits (NLatRoot, TRootL, LatRootL, TProjArea, RootDia, RootB), two shoot architecture traits (ShootL, ShootB), two overall plant architecture traits (TB and RootB:TB) and three Aphanomyces root rot resistance variables (RRI, DS and Br:TProjArea) and (ii) genotyping data at 11,789 SNP markers over all the seven LGs.
GWA analyses were performed using a modified version of the multi-locus mixed model (MLMM) R package  (Segura et al. , 2012) , as described in  Desgroux et al. (2016). The PCA matrix of population structure and the Kinship matrix were defined as cofactors in the MLMM (see the mlmm_cof. r R script at https://sites. google. com/site/ vincentosegura/mlmm). Significant SNP markers were also used as cofactors in a forward/backward regression model. A multiple-Bonferroni (mBonf) threshold of 4. 58 (p-value of 2. 6E-5), taking into account 3,840 distinct genetic positions on the created consensus marker map, was used to declare significant SNPs. In each GWA analysis, the optimal MLMM step was determined as the largest stepwise mixed model regression in which all cofactors have -log (p-value) above the mBonf threshold defined.
Comparative Mapping The genetic map described in  Bourion et al. (2010)  was previously used to map QTL for root and aerial architecture traits in pea  (Bourion et al. , 2010)  and shares 141 common markers with the consensus marker map developed in this study. Seven RSA and aerial architecture traits analyzed in the previous study were common to the present one, including ShootL, ShootB, TB, TRootL, NLatRoot, RootB, and RootB:TB, this last trait being similar to the below ground to total biomass ratio (BGB:TB) measured on young plants without nodules in  Bourion et al. (2010). Plant architecture QTL detected in  Bourion et al. (2010)  were thus projected onto the present consensus marker map, using Biomercator V4. 2 software as described in  Desgroux et al. (2016). The loci detected by association and linkage mapping were visualized on the resulting comparative marker map using MapChart 2.
ACKNOWLEDGMENTS This work was supported by the pre-doctoral fellowship of AD, from  MAAF  ( Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt, Paris, France ). The  PeaMUST  project, which received funding from the  French Government  managed by the  Research National Agency (ANR)  under the  Investments for the Future call 2011  ( ANR-11-BTBR-0002 ), funded the phenotyping of the collection and the Master fellowship of VB. The  GENOPEA  ( ANR-09-GENM-026 ),  CropDL  ( INRA-Métaprogramme SELGEN ), and PEAPOL (FASO, managed by SOFIPROTEOL) French research programs funded the SNP genotyping of the collection. We greatly acknowledge  GSP  ( Groupement des Sélectionneurs de Protéagineux, France ) for having provided breeding lines for the establishment of the pea-Aphanomyces collection.
RNA extraction and gene expression analysis Total RNA was extracted using the NucleoSpin® RNA II kit (Macherey-Nagel Gmbh & Co. Kg, Germany). RNA was reverse transcribed using the Maxima H Minus First Strand cDNA Synthesis kit (Thermo Scientific, France) following the manufacturer's instructions. Real-time PCR was performed with a CFX96 real-time PCR detector system (Bio-Rad, France) with SYBR Select master Mix reagents (Life Technologies, France). Primer sequences are detailed in supplementary data 1.
Migration and invasion assays To evaluate migration ability of GFP-MNNG/HOS cells and isolated CTCs, cells were seeded in growth media without serum on the upper side of a Transwell chamber (Falcon) on a porous transparent polyethylene terephthalate membrane (8 µm pore size). The lower chamber of the transwell was filled with growth medium containing 10% FBS. After 24 hours of incubation, cells on the upper side of the filters were mechanically removed and cells migrated to the lower side were fixed (with glutaraldehyde 10%) and stained (with violet crystal 0. 1%). Five random fields were taken under light microscope and analysed for each chamber and cells were counted with the ImageJ software (NIH, USA). The same procedure was used for invasion assays seeding cells on the upper side of Matrigel® (Corning, France)coated transwells (50 ng Matrigel/well).
Statistical analysis Data analysis was performed using GraphPad Prism (GraphPad Software, Inc). SEM from replicate experiments was calculated as noted in the legends and is shown as error bars. All error bars show SEM for at least triplicate measurement from independent experiments. The mean ± SEM was calculated for all groups and compared by non-parametric Mann-Withney Wilconson test. SPSS statistics software (IBM) was used for all statistical analysis. Differences were considered significant at the 95% confidence level (p<0. 05).
CTCs are detectable before the tumour mass in a murine model of osteosarcoma To validate the methods used and to avoid any bias linked to the mode of cancer cell injection in mice, 2 x 10 7 GFP-MNNG/HOS cells resuspended in PBS or Matrigel® were inoculated intramuscularly in paraosseous site. After blood collection, the presence of CTCs was analysed by flow cytometry after 24 h and 48 h post inoculation. In all conditions used, no CTCs were observed into the blood demonstrating that CTCs further detected were not associated to a technical artefact such as a leak of cancer cells into the bloodstream immediately after cell inoculation (data not shown). We carried out a kinetic analysis of CTC release into the bloodstream. Thirty mice previously injected with 1. 5 x 10 6 GFP-MNNG/HOS cells, were randomly divided into 3 groups and sacrificed at days 10, 30 and 45 respectively after cell injection (Figure  2A ).
Using a method based on isolation by size of tumor cells (ISET),  Chinen et al [29]  detected CTCs in sarcoma patients. More recently,  Li et al. [30]  confirmed the detection of CTCs in cryopreserved human peripheral blood mononuclear cells and two studies analysing CTCs by FISH in small cohorts of osteosarcoma patients revealed a potential positive association between CTC count, disease progression and poor prognosis  [31, 32]. The present report is the first study investigating the regulation of CTC release after chemotherapy. CTCs were detectable in blood circulation at early stages of the disease (i. e. 10 days after cancer cell inoculation). This finding is in agreement with previous studies in breast  [33]  and prostate cancer  [34]. Overall, these data suggest that CTCs could be used as a non-invasive method to study the early biological events of osteosarcoma development and to monitor the recurrence of the disease in patients.
We present a sample of 329 low to intermediate redshift (0. 05 < z < 0. 3) brightest cluster galaxies (BCGs) in X-ray selected clusters from the SPectroscopic IDentification of eRosita Sources (SPIDERS) survey, a spectroscopic survey within Sloan Digital Sky Survey-IV (SDSS-IV). We define our BCGs by simultaneous consideration of legacy X-ray data from ROSAT, maximum likelihood outputs from an optical cluster-finder algorithm and visual inspection. Using SDSS imaging data, we fit Sérsic profiles to our BCGs in three bands (g, r, i ) with SIGMA, a GALFIT-based software wrapper. We examine the reliability of our fits by running our pipeline on ∼10 4 psf-convolved model profiles injected into 8 random cluster fields; we then use the results of this analysis to create a robust subsample of 198 BCGs.