dataesr/scientific-paragraphs-categorization huggingface dataset:
5 and 94. 7% 16S rDNA sequence identity, GMD21A06 and GMD21D06, were isolated from the Sargasso Sea and cultivated in microdroplets  (Zengler et al. , 2002). BLAST searches carried out against the NCBI 16S rDNA sequence database yielded [Rhodopseudomonas] julia KR-11-67 T (DSM 11549; AB087720) as best hit. The 16S rDNA sequence of the strain KR-11-67 T indicates that this strain belongs to the genus Rhodobium. Using the EzTaxon database, the first hit for the 16S rDNA sequence of "Ca. P. ectocarpi" Ec32 was the unclassified Rhizobiales Parvibaculum indicum P31 T (FJ182044;    1 ) did not reveal "Ca. Phaeomarinobacter"-like sequences in datasets for large kelp species, but in one data set for Ectocarpus and one for Fucus, respectively. The Ectocarpus data set corresponds to Illumina 16S rDNA metabarcoding experiments of bacteria present in 20 different algal cultures, and amplicons with 100% identity to that of "Ca.
P. ectocarpi" were detected in five cultures from different locations. The Fucus data set corresponds to a metabarcoding experiment based on 454-sequencing. Here samples were collected from the Kiel bight (Baltic Sea), and "Ca. Phaeomarinobacter"-like sequences were detected in 8 of 78 samples. Interestingly, in the Fucus data set, two different sequences were present: a more abundant sequence with 99% identity to the 16S rDNA sequence of "Ca P. ectocarpi," and a second sequence with only 97% identity, suggesting the existence of different species associated with algae within this candidate genus. Altogether, these BLAST analyses indicate that "Ca. P. ectocarpi" belongs to the class Alphaproteobacteria. To determine the exact taxonomic position of "Ca. P. ectocarpi" within the Alphaproteobacteria, two phylogenetic analyses were performed: one with a representative sample of 236 full-length 16S rDNA sequences comprising all orders of the class, and a second, extended analysis, comprising all available families.
e. , enzymes featuring a biotin-binding site (IPR001882), have been annotated in the E. siliculosus genome. Again the algal genome encodes two enzymes likely to catalyze the three enzymatic reactions necessary to synthesize biotin from 8-amino-7-oxononanoate (Esi0392_0016, a bifunctional dethiobiotin synthetase/7,8-diamino-pelargonic acid aminotransferase; Esi0019_0088, a biotin synthase) (PWY0-1507). Ascorbate is an essential vitamin in plants where it serves as antioxidant in chloroplasts and as a cofactor for some hydroxylase enzymes  (Smirnoff, 1996) , and we found an L-galactose (plant-type) pathway for ascorbate synthesis in E. siliculosus (PWY-882). Lastly, the E. siliculosus genome encodes several methyltransferases potentially involved in the last step of vitamin K2 synthesis, in particular for menaquinol-6, -7 and -8 (Esi0009_0155, Esi0182_0017, and Esi0626_0001).
ACKNOWLEDGMENTS We would like to thank  Christophe Caron  and  Erwan Corre  from the  CNRS-UPMC ABIMS  bioinformatics platform (http:// abims. sb-roscoff. fr)for setting up the public Pathway Tools server at the  Station Biologique de Roscoff  and for providing access to a local version of the GOS database. We are also grateful to  Mark Cock  for granting us access to the "Ca. P. ectocarpi" genome sequence and to preliminary genome annotations performed using the MAGE platform, as well as to  Philippe Potin  for enriching discussions. This work benefited from the support of the  French government via the National Research Agency  in the context of the  investment expenditure program  IDEALG  ANR-10-BTBR-02-04-11.
Data analysis and statistical methods Data processing and analysis were conducted using tools from Microsoft Excel and Prism GraphPad V5. 0d software (GraphPad Software, CA). Comparisons of quantity and quality data were performed using Mann Whitney or Kruskal-Wallis test depending on the distribution of the data. Only miRNAs with a cut-off of cycle threshold (Ct) < 32 were considered in PBMCs while no cut-off was applied in the case of serum since in this fluid, the quantities of starting materials are much lower. Correlations were calculated using the Spearman method. For the Bland-Altman analysis, the association between the difference and the average was evaluated by the coefficient of correlation and tested by the non-zero correlation test. Wilcoxon test was used to assess whether the median difference (bias) between the two conditions was significantly different from 0 and the limits of agreement were defined as median difference +/-1.
Acknowledgements This work was supported by Theradiag compagny, as part of a collaborative research project between Prestizia (Theradiag's affiliate), University of Montpellier 2 and CNRS (# CNRS-069146/UM2-2110797 ). SB, MM, TG, VC and CHL are supported by the  Centre National de la Recherche Scientifique (CNRS). DB is supported by  Theradiag. We thank  O. Prigneau  (Theradiag), for critical reading of the manuscript and providing constructive comments. We are grateful to the  EFS  and the anonymous blood donors for their contribution.
Each sample was loaded on a C 18 precolumn (300 m inner diameter ϫ 5 mm; Dionex) at 30 l/min in 2% acetonitrile, 0. 1% formic acid. After 3 min desalting, the precolumn was switched online with the 15 cm capillary column (75 m diameter, PepMap C 18 3 m) equilibrated in 96% solvent A (2% acetonitrile, 0. 1% formic acid) and 4% solvent B (80% acetonitrile, 0. 08% formic acid). Peptides were eluted using a 4% to 60% gradient of solvent B for 90 min at 300 nL/min flow rate. The LTQ-Orbitrap Velos was operated in datadependent acquisition mode with the Xcalibur software (Thermo Fisher Scientific, Bremen). Survey scan MS were acquired in the Orbitrap in the 300 -2000 m/z range with the resolution set to a value of 60,000 at m/z ϭ 400. The 20 most intense ions per survey scan were selected for CID fragmentation, and the resulting fragments were analyzed in the linear trap (LTQ).
Dynamic exclusion was employed within 90 s and repeated during 30 s to prevent repetitive selection of the same peptide. Control samples (0 to 24 h induction) and flagella-enriched fractions (48 to 72 h induction) were analyzed in 3 and 5 biological replicates respectively. Data Analysis-Raw data were analyzed using MaxQuant (MQ) software (V 1. 3. 0. 5) with Andromeda as the search engine  (42). A MudPit analysis was done for each replicate (pellet and supernatant). The search was done against a database containing the Trypanosoma brucei proteins (TbruceiTreu927AnnotatedProteins_TriTrypDB-4. 1. fasta -9826 sequences). Andromeda searches were performed choosing trypsin as specific enzyme with a maximum number of two missed cleavages. Possible modifications included carbamidomethylation (Cys, fixed), pyroglutamylation (Nterminus/Gln/Glu, variable), and oxidation (Met, variable).
Slides were either stored at Ϫ20 °C or immediately analyzed, either with a DMR microscope (Leica) and images captured with a CoolSnap HQ camera (Roper Scientific, Trenton, NJ) or with a DMI4000 microscope (Leica) and images acquired with a Retiga-SRV camera (Q-Imaging, Surrey, BC, Canada). Pictures were analyzed using the IPLab Spectrum 3. 9 software (Scanalytics and BD Biosciences) and were merged and superimposed using Adobe Photoshop (CS2). Measurements of cell parameters were performed with the ImageJ 1. 38ϫ software (NIH). Quantification of the FLAM8 Signals-FLAM8::YFP expressing cells were fixed in methanol and stained by IFA with the anti-GFP (green) and mAb25 (red), as well as DAPI as described above. Zstacks were acquired for each channel (100 planes in Z-steps of 100 nm). Stacks were deconvolved using the Huygens Essential software with the theoretical point spread function calculated for the used objective.
To make sure that weak signals were not erased by this procedure, the non-deconvolved raw data stacks were analyzed in parallel. Using the ImageJ software, the stack corresponding to the Mab25 image was assembled to one plane using the Z-project tool. Cells with 2 flagella were selected and length of both flagella was measured. The DAPI content was exploited to define cells as 1Kd1N (K for kinetoplast, N for nucleus, d for dividing, recognizable as a single large kinetoplast), 1Kb1N (b for bone-shaped kinetoplast clearly incised in the middle), 2K1N, and 2K2N. On the green channel (FLAM8::YFP detected by the anti-GFP), a Z-projection was made without averaging, so that the pixel with the maximum intensity remains as such. In order to determine the range of fluorescence intensity for each cell, the tool HiLo (in the lookup tables) was used to code the dimmest pixel blue and the brightest red.
The measurements made with ImageJ were exported to Excel to calculate the ratio of the new flagellar length versus the old one, the difference of maximum to minimum signal for old and new flagellar tip providing the ratio of new versus old, and similarly the ratio of the integrated density of new versus old. Fluorescent Recovery After Photobleaching (FRAP) Analysis-The expression of FLAM8::YFP was first observed directly with a DMI4000 Leica microscope using a mercury bulb for excitation to verify correct protein expression and localization. For FRAP analysis, a Zeiss inverted microscope (Axiovert 200) equipped with an oil immersion objective (magnification x63 with a 1. 4 numerical aperture) and a spinning disk confocal head (CSU22, Yokogawa) was used  (51). Images were acquired using Volocity software with an EMCCD camera (C-9100, Hamamatsu) operating in streaming mode.
A sample (a 100 l drop) was taken directly from the culture grown at 6 to 8. 10 6 cells/ml and trapped between slide and coverslip. The samples were kept at 27 °C using a fast response mini-stage temperature controller allowing rapid changes in temperatures. Time-lapse sequences were acquired to analyze GFP signal recovery after photobleaching. A total of 12 image sequences were acquired in three independent FRAP experiments. Movies were taken using a time lapse of 30 s. Exposure time was 0. 8 s per frame (binning was 1 ϫ 1 pixels). Fluorescence intensity was measured in the region of interest using the ImageJ software. To normalize data, background was subtracted and overall photo-bleaching over time was normalized using another equivalent source of GFP (the fluorescent spot at the distal tip of a flagellum in a cell that had not been bleached).
Acknowledgments-We thank  Pascal Lenormand  for expert assistance with initial mass spectrometry analysis,  Claudio Pereira  for providing the anti-AK antibody, as well as  Linda Kohl  and  Cher Pheng Ooi  for critical reading of the manuscript. I. S. thanks  Markus Engstler  for his continuous support and  Tim Kru  ¨ger for sharing his expertise in microscopy.
* This work in the Trypanosome Cell Biology Unit was supported by the  Institut Pasteur , the  CNRS  and  ANR  grants ( ANR-06-MRAR-014  and  ANR-08-MIE-027 ). Funding for the Orbitrap acquisition was secured through a  DIM Malinf grant  from the  region Ile-de-France. I. S. was funded by a  FNR  ( Fonds National de la Recherche du Luxembourg ) fellowship; D. J. and L. V. were funded by  Roux  post-doctoral fellowships; J. B. was funded by  MNRT, FRM  and  Pasteur-Weizmann  doctoral fellowships; D. H. was funded by  MNRT  and FRM doctoral fellowships and J. S. -R. is funded by  FRM  and  Institut Carnot  postdoctoral fellowships. □ S This article contains supplemental Figs  S1  to S7, Tables  S1  to S7 and Movies  S1  and  S2.
Acknowledgements We thank  Harinder Singh  ( Genentech and University of California at San Francisco ) for the DEMD-GFP-lacI plasmid,  Naoko Imamoto  for the POM121 plasmid ( RIKEN Advanced Science Institute ),  Tom Misteli  ( National Cancer Institute, National Institutes of Health ) for the cherry-lacI-BRG1 plasmid,  Val erie Schreiber  (  Ecole Sup erieure de Biotechnologie de Strasbourg ) for the XRCC1 and PARP1 antibodies, and  Jop Kind  ( Hubrecht Institute ) for the Dam-laminB/m6a-Tracer cell line. We thank  Jiri  and  Claudia Lukas  for critical reading of the manuscript, and the  Soutoglou laboratory  for helpful discussions. C. L. was supported by the  R egion Alsace and Fondation Association pour la Recherche sur le Cancer (ARC) , K. T. received support from  Marie Curie network address , and T.
P. received support from  Fondation pour la Recherche M edicale (FRM). Research in E. S. 's laboratory is supported by  Agence Nationale de la Recherche (ANR) , the  Fondation Schlumberger pour l'Education et la Recherche (FSER) ,  Fondation ARC  ( ARC fix ), and  La Ligue Contre le Cancer (R egion). G. D. 's laboratory's contributions to this study were funded by a  Discovery Grant  ( RGPIN 386049 ) from the  Natural Sciences and Engineering Research Council (NSERC). K. M. A. is a recipient of a  NSERC   PGSD3  studentship and is a  Killam Foundation Scholar. B. K. 's laboratory 's contributions to this study were supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)  ( ANR-10-INSB-05-01 ) and Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI).
Data are represented as rate of wound closure. Tube formation assay A Matrigel-based capillary-genesis assay was performed on E4+EC to assess the ability of these cells to form an organized tubular network as previously described  [77]. E4+EC were starved in M199 for 6 h then 100,000 cells were cultured on 250 μl of Matrigel (BD bioscience) in tube assay medium (Medium 199, 0. 2 % (v/v) fetal calf serum (FCS), 10 ng ml-1 FGF2 and 20 U ml-1 heparin). The degree of tube formation was quantified at different time-points by measuring the intersection of tubes in three randomly chosen fields from each well using Image J. Proteins were then transferred to nitrocellulose membrane at 70 V in transfer buffer (25 mM Tris-HCl pH 8. 3, 150 mM glycine and 5 % v/v methanol) for 90 min. The nitrocellulose membrane was blocked with 5 % BSA in Tris-buffered saline (TBS) (20 mM Tris pH 7.
6, 137 mM NaCl) for 1 h. Immunostaining was carried out using a goat monoclonal ARF6 and PhosphoAKT antibody (1/1000, Cells signalling) and a secondary polyclonal mouse anti-goat antibody HRP conjugated (1/2000, cell signalling). Blots were developed using HRP and chemiluminescent peroxidase substrate (#CPS1120, Sigma). Data were collected using Geliance CCD camera (Perkin Elmer), and analyzed using ImageJ software (NIH).
During debulking surgery metastatic nodules were removed and processed as follows. Upon serial washing with PBS and red blood cell Lysis buffer (eBiosciences, San Diego, USA) nodules were minced and cultured with or without MPs extracted from E4+ECs in DMEM high glucose (Hyclone, Thermo Scientific) supplemented with 20 % FBS (Hyclone, Thermo Scientific), 1 % Penicillin-Streptomycin-Amphotericyn B solution (Sigma), 2 mM L-glutamine (Sigma), 1X Non-Essential Amino-Acid (Hyclone, Thermo Scientific). Statistical analysis All quantitative data were expressed as mean±standard error of the mean (SEM). Statistical analysis was performed by using SigmaPlot 11 (Systat Software Inc. , Chicago, IL). A Shapiro-Wilk normality test, with a p =0. 05 rejection value, was used to test normal distribution of data prior further analysis.
Acknowledgments We would like to acknowledge  Oscar Meca Cortes  for his critical view of the paper. We would like to thanks  Damien Rioult  for his constant help in several informatics issues. We would like to appreciate greatly the help of  Mariam El Bakry  for the order and all her administrative work. Funding This publication was made possible by grants from the  Qatar National Research Fund  under its  National Priorities Research Program  award number  NPRP 09-1174-3-291  and  NPRP 4-640-1-096. Its contents are solely the responsibility of the authors and do not necessarily represent the views of the  Qatar National Research Fund. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
2008). GGE biplot analysis has been used to evaluate test locations in addition to mega-environment analysis and genotype evaluation, but often for a limited number of varieties and locations, and for a small number of years of multi-environment trials. Fan et al. (2007)  applied GGE biplot analysis to multi-environment trials at provincial level with ten locations in two years, with emphasis on the genotype effect for grain yield. Yan et al. (2000)  applied GGE biplot analysis to the datasets of the Ontario winter wheat trials composed of 10 yr and 7-14 locations for cultivar evaluation and mega-environment investigation, but it was less focused on test-location evaluation. Blanche and Myers (2006)  applied the analysis to cotton in the variety registration scheme of the State of Louisiana, but only six locations were involved.
In Spain, for similar purposes, data for only four test locations were processed  (Baxevanos et al. 2008). In Nigeria, GGE biplot analysis was used to evaluate maize cultivars, but test location evaluation was not touched, and only four locations and 2 yr METs were involved (Badu-Apraku and Akinwale 2011). The objective of this study was to assess the suitability of GGE biplot analysis for evaluating test locations and investigating possible mega-environment differentiation in the case of a large number of locations over many years.
The winning cultivars and the corresponding locations in Fig. 1  were summarized in the format of "<1> AQ, CD, CX, HG, JJ, JZ, NJ, NY, NT, WH, XF, YC, YY / Ckz3; <2> SH/GM9618; <3> JY/XK5" in Table  2. The results shown in Fig. 1  suggested that there might be distinct mega-environments (three in this case) in compliance with the large magnitude of GEI and the The F criteria of environment (E), genotype (G) and genotype by environment interaction (GEI) effects were significant at the 0. 01 level in all datasets, and are omitted from the Table.
Fig. 3  shows that locations AQ, XF, NT, and NY had strong discrimination ability, in contrast  1) The figures in the lower triangular matrix stand for the frequencies (as %) of two locations belonging to the same sector out of 27 datasets, calculated from data in Table  2 ; the figures in the upper triangular matrix stand for the similar frequencies calculated from the results of the cluster analysis based on the first two principal component scores (PCs) of GGE biplot. PC 1 PC 2 PC 1 =50. 8%, PC 2 =17. 5%, Sum=68. 3% Transform=0, Scaling=2, Centering=2, SVP=2 -1. 2 -0. 8 -0. 4 0. 0 0. 4 0. 8 1. 2 1. 6 2. 0 -1. 6 -1. 2 -0. 8 -0. 4 0. 0 0. 4 0. 8 1. 2 1. 6 2. 0 SH AQ CD CX NT JZ YY WH JY HG NJ YC XF NY JJ GM9618 Hz302 J55173 J96-1 XK-5 Sm3 X244 SK103 CKZ3 GM96-1 Fig. 2  "Location relationship" view of GGE biplot depicting the frequencies of locations in the same mega-environment in the 27 METs for cotton lint yield, regional cotton trials in YaRV across 2000-2010.
Varietal test scheme Our study used data from the regional cotton variety trials in the Yangtze River Valley (YaRV) which covers eight provinces, namely, Sichuan, Hunan, Hubei, Henan, Jiangxi, Anhui, Jiangsu, and Zhejiang provinces in China. The trials are supervised by the Ministry of Agriculture in charge of identifying varieties suitable for commercial release on a trans-provincial scale after the candidate varieties have been recommended at the provincial level. Through the regional tests varieties are chosen mainly on the basis of their broad adaptation and their average breeding values across the test locations. The effectiveness and fairness of such a scheme have become of greater importance since the variety market was liberalized and became highly competitive  (Fok and Xu 2011). Given the purpose of the regional schemes, varieties tested varied from one year to another; only varieties showing good performance were tested a second time for confirmation.
Dataset source In YaRV, the regional cotton trial scheme has consisted of about 18 test locations each year since it was launched in 1956, but 15 locations were more regularly involved over the 2000-2010 period to which this paper refers. Details on the latitude, longitude, altitude, and soil class of the 15 test locations are presented in Table  9 , in which four locations (NY, XF, SH and JY) are distinguished as their relative remoteness from the Yangtze River valley. Over the 2000-2010 period, the number of varieties submitted to the regional scheme has increased greatly, as the variety market becoming increasingly competitive. Consequently, the test in each location was divided into one to four independent sets, each comprising around 10 varieties plus a control. So there were 27 multi-environment tests (METs) over the period, hereafter called the "27 METs".
Each dataset is a multi-environment test in a single year. Each dataset was named as the "year" plus an ordinal small letter to indicate a multi-environment test within a year, e. g. , "2007b" stands for the second set in 2007. In almost all locations and years, cotton was transplanted at the three-true-leaf stage, except in the AQ and YY locations, where cotton was directly sown under plastic mulching from 2000 to 2005. In each trial, varieties were tested in randomized complete blocks with three replicates. Plot size was 20 m 2 with 3 to 6 rows. Depending on the year, the space between and within rows varied from 80 to 110 cm, and from 30 to 40 cm, respectively. Plots were transplanted at the target density of 32 000 plants ha -1. Pre-planting fertilizers were applied at 165 kg N ha -1 , 79 kg P ha -1 and 113 kg K ha -1.
Analysis of variance The cotton lint yield data with three replicates in each set of the 27 datasets were subjected to ANOVA using the PROC GLM of the SAS System ver. 9. 0  (Cody and Smith 1997; SAS-Institute 2002)  to partition lint yield variation into cultivars or genotypes (G), environments (E), and genotype× environment interactions (GEI). The G and GE were further partitioned into the respective PC 1 and PC 2 components using GGE biplot software  (Yan 2001). Cultivars were treated as fixed effect factors and test locations as random effect factors in ANOVA model for multi-environment cultivar trials  (Anothai et al. 2009). Heritability values (h 2 ) were estimated for each dataset as the overall effectiveness of the regional trials to discriminate between cultivars  (Guillen-Portal et al.
2004; Baxevanos et al. 2008). for combinations of multiple traits. In a GGE biplot, the test location vector length, the cosine value of the angle between the location and the average location, and the distance between the positions of a location and the "ideal" test location were used as measures for the location discrimination ability, representativeness, and overall desirability. The 27 datasets for each parameter were treated as 27 replicates in an F-protected Duncan's multiple range test  (SAS-Institute 2002). The standard deviations were adopted as uncertainty measure to declare the differences between test locations.
Acknowledgements This work was funded by the  Jiangsu Agriculture Science and Technology Innovation Fund, China  ( CX(12)5035 ), the  National Natural Science Foundation of China  (  30971735 ), the  China Agriculture Research System  ( CARS-18-20 ), and the  Special Fund for Agro-Scientific Research in the Public Interest of China  ( Impact of Climate Change on Agriculture Production of China ,  200903003 ).
Nucleotide sequence analysis Sequence data were obtained from the Artemis-mediated  [39]  genome annotations of the B. animalis subsp. lactis CNCM I-2494  [10]. Database searches were performed using non-redundant sequences accessible at the National Centre for Biotechnology Information internet site (http://www. ncbi. nlm. nih. gov) using BLAST  [40, 41]  and the REBASE  [42]  site accessible at the New England Biolabs internet site (www. neb. com/rebase). Sequence assembly, verification and analysis were performed using the Seqman and Seqbuilder programs of the DNASTAR software package (DNASTAR, Madison, WI, USA).