dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods Patients and samples. We studied 66 transplanted patients retrospectively selected patients from different hospitals in France: (i) 23 PTLD subjects were selected from a database of patients (DIVAT clinical prospective cohort (www. divat. fr, CNIL 891735) having received kidney transplants at the University Hospital of Nantes between 2003 and 2013; (ii) 22 subjects were additionally selected from a registry of patients (including six PTLD) having received solid-organ transplants (lung and kidney) at the University Hospital of Grenoble-Alpes between 2010 and 2013. In addition, 21 HSCT patients (including six PTLD) were enrolled at Saint-Louis Hospital (APHP, Paris) between 2010 and 2012. All patients were monitored by means of routine viral testing, resulting in 322 serum specimens.
Statistical analysis. Statistical analysis was performed using GraphPad Instat software, Version 3. 05 (Graphpad, San Diego, United states). For univariate analysis, the Chi-squared test was used to assess the association between categorical variables. For correlation Pearson's test, the R 2 coefficient was calculated. All tests were two-sided, with a p-value <0. 05 considered statistically significant. Ethics statement. An informed consent was obtained from all transplant patients. Donor and recipient data were extracted from the DIVAT clinical prospective cohort (www. divat. fr, CNIL Nr 891735 version 2, August 2004). Codes were used to ensure donor and recipient anonymity and blinded testing. The data were computerized in real time as well as at each transplant anniversary. The quality of the DIVAT data bank was validated by an annual cross-center audit, systematic verification during data entry and a weekly automatic report on the identification of incoherencies between parameters.
It should be noted that authors have underlined the complex and heterogeneous relationship between PTLDs and EBV, as well as the lack of a close correlation between viral load and PTLD  [56] [57] [58]  occurrence. Concerning patient follow-up, it is interesting to note that sZEBRA protein could be detected during periods in which EBV DNA-emia was not detectable when using qPCR. This discrepancy may be accounted for by the precocity of the ZEBRA signal measured over the course of EBV infection in this patient population. This precocity of sZEBRA detection when using qPCR is not dependent of the PCR format, since we observed the same phenomenon in PTLD patients investigated by means of EBV load measurement (expressed in copies/150,000 cells)(data not shown). In this retrospective study, we reported for the first time the detection of soluble ZEBRA in serum of PTLD patients.
Acknowledgements We thank  Charlene Thedevuide ,  Laurence Grossi , and  Romy Rouzeau  for their technical expertise and assistance. We also thank  Nicolas Tarbouriech  for his helpful support. This work was financially supported by the  SATT Grenoble Gate1  and by the  FUI DiagEBV. This work used facilities at the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
Statistical analyses To evaluate whether an SIE exists in amphibian assemblages in our system, we compared the fit of a simple linearized power model without an SIE (Equation  1 ) with the following two most widely used SIE models that were proposed by  Lomolino and Weiser (2001)  (the left-horizontal SIE model; Equation  2 ) and Dengler (2010) (a continuous two-slope SIE model; Equation  3 ). If Equation  2 or Equation 3 is supported, the evidence of an SIE is found  (Lomolino and Weiser 2001; Dengler 2010 ). However, datasets fit best by Equation 2 provided stronger evidence for an SIE, while those fit best by Equation 3 provided weaker evidence  (Morrison 2014; Wang et al. 2016). log S ¼ c þ z 1 log A (1) log S ¼ c þ ðlog A > TÞz 1 ðlog A À TÞ (2) log S ¼ c þ ðlog A TÞz 1 log A þ ðlog A > TÞ ½z 1 T þ z 2 ðlog A À TÞ (3) In these equations, S stands for species richness, A for island area, while c i (intercept), z i (slopes), and T (breakpoint) are fitted parameters.
We provided a case study to test for the existence of the SIE in a subtropical land-bridge archipelago. However, the more interesting and important question is to determine the overall prevalence of the SIE. Wang et al. (2016)  found that SIEs were quite prevalent for both all the islands (104 cases, 49. 3%) and the datasets excluding empty islands (73 cases, 34. 6%) in 211 global island datasets. Nevertheless, these datasets are focused on islands without species and are not gathered specifically to directly evaluate the prevalence of SIEs  (Wang et al. 2016). Thus, future study should collect all island datasets worldwide across a broad diversity of taxa and archipelago types and conducts a meta-analysis with the appropriate methods to determine the overall prevalence of the SIE. Model performance is assessed using AIC-based model selection among a set of candidate models.
Acetic acid and Ethylene glycol molecular frequencies We used the spectroscopic data parameters from  Ilyushin et al. (2008)  and  Ilyushin et al. (2013)  for acetic acid, with the following line selection criteria: Einstein spontaneous emission coefficient A ij ≥ 5×10 -5 s -1 and upper level energy E up ≤ 400 K. For the partition function we adopt the complete rotational-torsionalvibrational partition function given by Calcutt, Woods, Carvajal et al. (to be submitted to MNRAS). 1 http://www. iram. fr/IRAMFR/GILDAS/ For both ethylene glycol conformers we used the spectroscopic data parameters from  Christen & Müller (2003)  and  Müller & Christen (2004)  that are available from the Cologne Database for Molecular Spectroscopy catalog (CDMS,  Müller et al. 2005). More specifically, we searched for transitions up to E up 400 K, and A ij ≥ 1×10 -4 s -1.
Data analysis All the ODISEA data were calibrated using the Common Astronomy Software Applications package  (CASA v4. 2. 1; McMullin et al. 2007 ) by the ALMA observatory, including the the standard bandpass, phase, and amplitude calibrations, the offline Water Vapor Radiometer (WVR) calibration, and system temperature corrections. Online and nominal flagging, such as shadowed antennas and band edges, were applied for calibration. The observations from all 3 execution blocks were concatenated and processed together to increase the sig-nal to noise and uv-coverage. We also used CASA to image all ODISEA data using the standard clean algorithm, with uniform weightings. The cleaning resulted in a synthesized beam of 0. 20 × 0. 25 and a continuum rms of 0. 26 mJy beam -1. After visual inspection, Elias 2-24 immediately stood out as one of the brightest and largest disks of the 147 objects in the survey, and showed a clear ring pattern, with gaps at ∼0.
We estimate the widths of the gaps by assuming that they are fully evacuated and measuring their equivalent widths in the brightness profile along the major-axis (blue line in Figure  2 ). Gaussian fits to the gaps using the splot routine within the IRAF package noao. onedspec indicate that the gaps are located at 20±3 au, 52±2 au, 87±3 au, and have widths of 6±2 au, 28±3 au, and 11±4 au. Integrating the area under the curve between the edges of each gap, and/or using the deprojected radial average (gray line in Figure  2 , in the case of the the two outer gaps), produce consistent equivalent widths. From these gap widths, we derive "missing masses" of ∼4 M Jup , 20 M Jup , and 10 M Jup for the gaps at 20, 52 and 87 au, respectively.
This paper uses the following ALMA data: ADS/JAO. ALMA #2016. 1. 00545. S. ALMA is a partnership of ESO, NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The NRAO is a facility of the NSF operated under cooperative agreement by Associated Universities, Inc. L. A. C. , S. C. , G. H. M. B, A. O, and A. Z were supported by CONICYT-FONDECYT grant numbers 1171246, 1171624, 3170204, 1151512, and 3170657. L. A. C. , S. C. , S. P. , G. H. M. B. , and A. Z. acknowledge support from the Millennium Science Initiative (Chile) through grant RC130007. A. B. , J. O. , and M. R. S. acknowledge financial support from ICM Núcleo Milenio de Formación Planetaria, NPF. This paper use the Brelka cluster, financed by Fondequip project EQM140101.
Available P in the soil was determined using an anion exchange resin (resin-P) (QUAGGIO; RAIJ, 2001) and Mehlich-1 extractant (Mehlich-P) (EMBRAPA, 1999), and the results were expressed based on the dry weight of the samples. The soil P was analyzed within ten days after sampling. A non-parametric analysis using the Kruskal-Wallis test (α = 0. 05) was made after checking the absence of normality of data through Kolmogorov-Smirnov test (α = 0. 05). Multiple comparisons were made by the Student-Newman-Keuls test (SNK) with 5% probability level. The Bioestat 4. 0 software  (AYRES et al. , 2005)  was used for the statistical analysis. ( †) Phosphorus fertilization in pasture (P0, 0 kg ha -1 P 2 O 5 year -1 , P20, 20 kg ha -1 P 2 O 5 year -1 , P40, 40 kg ha -1 P 2 O 5 year -1 ) for 2003 to 2006, S, Phosphorus fertilization on soya (S0, 0 kg ha -1 , S50, 50 kg ha -1 , S100, 100 kg ha -1 ).
For each time point, a parallel set of samples was fixed and permeabilized prior to incubation with the primary antibody to ensure complete staining of intracellular bacterial, as described above. For quantification of bacterial infection, image acquisition was performed using an Ima-geXpress Micro (Molecular Devices) or an Operetta (PerkinElmer) automated high-content screening fluorescence microscope at a 20× magnification; a total of 9-16 images were acquired per coverslip/well, corresponding to approximately 2,500 cells analyzed per experimental condition and replicate. Image analysis was performed using the 'Multi-Wavelength Cell Scoring' application module implemented in MetaXpress software (Molecular Devices), or Columbus image analysis software (PerkinElmer). Briefly, nuclei and cells were segmented based on the Hoechst and CellMask stainings, respectively; cells were then classified as positive or negative for Shigella depending on the total area of bacterial staining.
Flow cytometry and cell sorting Analysis of cell viability following infection with Shigella was performed using 7-Amino-Actinomycin (7-AAD; BD Biosciences, 51-68981E), a viability dye excluded from cells with intact membranes (viable cells), as described previously  [20]. Flow cytometry was performed on a BD Accuri C6 flow cytometer (BD Biosciences); a minimum of 10,000 cells was analyzed for each subpopulation (Shigella -and Shigella +). FlowJo software (Tree Star Inc) was used for data analysis. Cell sorting to separate the subpopulation of cells infected with Shigella (Shigella +) from bystander cells (Shigella -) was performed as described previously  [20]. HeLa-229 cells infected with Shigella expressing GFP or DsRed at MOI 10, were collected at 6 hpi and sorted based on the intensity of the GFP or DsRed signal, using a FACSAria III flow cytometer (BD Biosciences).
RNA sequencing and computational RNA-seq analysis Library preparation and deep-sequencing was performed by Vertis Biotechnology AG, as described previously  [20]. RNA-seq analysis was performed using the READemption pipeline version 0. 3. 0 [57] , with Segemehl version 0. 1. 7 [58]. Reads were mapped against the human (build GRCh37) and Shigella flexneri (NCBI Reference Sequence NC_008258) genomes. Analysis of differential gene expression was performed with DESeq 1. 18. 0  [59]. The demultiplexed FASTQ files and gene-wise quantifications have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE75746 and GSE75747.
Statistical analysis No statistical methods were used to predetermine sample size. The investigators were not blinded to allocation during experiments and outcome assessment. The data are presented as mean ± standard error of the mean (s. e. m. ), of at least three independent experiments. The exact number of experiments performed for each panel is indicated in Figure legends. Statistical analysis was performed using Prism Software (GraphPad). For statistical comparison of datasets from two conditions, two-tailed Student's t-test was used; for data from three or more conditions, ANOVA with Tukey's or Dunnet's post-hoc test was used. A P-value of 0. 05 or lower was considered significant.
This work was supported by grants from the  Portuguese Foundation for Science and Technology  ( FCT Investigator Programme   IF/ 00694/2013  to MM),  Bavarian Ministry of Sciences, Research and the Arts in the framework of the Bavarian Molecular Biosystems Research Network (BioSysNet  to AE) and  German Research Foundation  ( DFG  project  BR 4837/1-1  to AE). This publication was funded by the  German Research Foundation (DFG)  and the  University of Wuerzburg  in the funding  programme Open Access Publishing. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The demultiplexed FASTQ files and gene-wise quantifications have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession numbers GSE75746 and GSE75747. http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE75746 http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE75747.
Identification of Family Members by BLAST For each gene family (RAYTs, IS200, IS100, def, tpiA) one arbitrarily chosen protein was used as a query for a BLAST search. The query sequence for the RAYT family was YafM from Pseudomonas fluorescens SBW25 (PFLU_RS20900). Query sequences for housekeeping genes were P. fluorescens SBW25 peptide deformylase, Def (PFLU_RS00090), and P. fluorescens SBW25 triosephosphate isomerase TpiA (PFLU_RS25840). Query sequences for the ISs were ECIAI1_4438 from Escherichia coli IAI1 (IS200 family) and ISEc32 from E. coli S88 plasmid pECOS88 (IS110 family, ECSMS35_RS25240). The sequence of each protein was used to interrogate 2,950 chromosomes and 2,106 plasmids using TBLASTN (protein search against nucleotide database). Search results were analysed as follows. First, all database matches with an e-value of less than 1e-2, 1e-5, 1e-15, and 1e-20 were recorded.
Second, for each of the recorded database matches all coding sequences (CDS) from Genbank annotations that overlapped with the match were determined. Third, for overlaps with a single gene, the DNA sequence of the gene and its encoded amino acid sequence were extracted; for multiple overlaps the longest overlapping open reading frame was used for sequence extraction; matches without overlapping genes were ignored. Fourth, in addition to recording the DNA and amino acid sequence of the overlapping gene, the DNA sequence of the 5 0 and 3 0 extragenic space immediately flanking the identified gene was stored.
Phylogenetic Congruence Analysis at the Subgenus Taxonomic Level We determined phylogenetic congruence at the subgenus taxonomic level by comparing the phylogenies of the two concatenated housekeeping genes (tpiA and def) with each of the five RAYT groups. We chose the RAYT subsets from those bacterial genera where RAYTs are most common. Hence we identified genera that are well represented in the genome database that also contain a large number of RAYTs. For Group 1 we chose Pseudomonas, for Group 2 we chose Escherichia, for Group 3 Pseudomonas, for Group 4 Haemophilus and for Group 5 we selected Pseudomonas. Nucleotide sequences were aligned with clustalo  (Sievers et al. 2011 ) and reconstructed trees with PhyML using a GTR substitution model  (Guindon and Gascuel 2003)  for both the concatenated housekeeping genes and the RAYT Groups.
The partitioning between transpiration and interception loss is done using a threshold based on relative humidity, and therefore conceptually quite different from the precipitation based calculation of GLEAM. There is no independent estimation of snow sublimation, and the same algorithms are applied for snow-covered areas. For this study, optimized vegetation products are used as inputs to the model. In WACMOS-ET, the Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) products, derived from the Joint Research Centre Two-Stream Inversion (JRC-TIP) package  (Pinty et al. , 2007 (Pinty et al. , , 2011a, b), b) , were converted by a simple biome-dependent calibration to a LAI/FAPAR product consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FAPAR before being used as inputs to the model  (Michel et al.
, 2006) , and corrected fluxes are typically preferred over the original uncorrected observations. However, correcting for the energy balance closure normally implies the need for surface radiation measurements, which are not routinely measured at all stations; e. g. , for the 84 stations selected here, 26 do not have corrected latent heat fluxes. For the 58 stations with corrected fluxes, the relative mean difference between raw and corrected fluxes averaged over all stations is 6. 1%, with a maximum value of 16. 5%. The uncorrected and corrected fluxes correlate very well in time, with a station-averaged square temporal correlation of 0. 96. As these numbers do not suggest strong differences between the corrected and uncorrected measurements at our selected stations, we use the original uncorrected latent heat fluxes to avoid mixing uncorrected and corrected fluxes in the analyses, and to keep a sufficiently large database of in situ measurements.
Ancillary data To help characterizing the spatial homogeneity of the grid cells where the stations are located, two data sets are considered: the MODIS Land Cover Type product MCD12Q1 at an original resolution of 500 meters, and the Terra MODIS Vegetation Continuous Fields product MOD44B at an original resolution of 250 meters. A homogeneity index (I h ) is constructed as: I h = 1 2 F gt IGBP + 1 2 (1-| F g bare -F t bare | -| F g herb -F t herb | -| F g f orest -F t f orest |) (1) where F gt IGBP is the fraction of MCD12Q1 500 meter cells included in the 25 km model grid cell containing the tower and having the same IGBP land cover than the model cell, F t bare , F t herb and F t f orest are, respectively, the bare, herbaceous, and forest fractions of the MOD44B 250 meter cell containing the tower, and F g bare , F g herb and F g f orest are the same fractions but calculated for the entire 25 km model grid cell where the tower is situated.
Acknowledgements. This study was funded by the  European Space Agency (ESA ) and conducted as part of the project  WACMOS-ET-Ensemble  ( ESRIN  Contract No. 4000117355/16/I-NB ). D. G. Miralles acknowledges support from the  European Research Council (ERC)  under grant agreement number  715254  ( DRY-2-DRY ). J. B. Fischer  contributed to this paper at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. Support to  J. B Fisher  was provided by  NASA 's SUSMAP, THP, and  INCA  programs, and the  ECOSTRESS mission. K. Tu , from the  Department of Ecosystem and Conservation Sciences, University of Montana , is acknowledged by providing guidance for the use of the vegetation products in this study.
Sequence Data Analysis The sequence files in FASTQ format were processed using mothur software v. 1. 33. 2  (Schloss et al. , 2009) , according to the standard operating procedure of  Kozich et al. (2013;  http://www. mothur. org/wiki/MiSeq SOP, accessed in March 2016). Amplicon sequencing for the 16S rDNA of bacteria and archaea generated 1,244,917 and 192,515 merged paired endreads, respectively. Approximately 95% of ruminal and 92% of cecal reads passed the initial quality trimming (mean phred score ≥ 25, length ≥ 350 nt, maximum three ambiguous basecalls, <8 homopolymers). Chimera search using chimera. uchime, implemented in mothur, removed 10% and 7% of the remaining bacterial and archaeal 16S rDNA sequences. For bacteria, on average 37 284 (±3,015) ruminal and 31,810 (±8,751) cecal sequences per sample were used for operational taxonomic units (OTUs) picking.
, 2014). Sequences were assigned to taxa in mothur with a curated mcrA reference database  (Yang et al. , 2014) , where recently published sequences of mcrA for Methanomassiliicoccales were added. Greengenes OTU table for bacterial 16S rDNA was used in PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states;  Langille et al. , 2013)  online version available in the online Galaxy platform (https:// huttenhower. sph. harvard. edu/galaxy/) to predict functional genes of classified members on ruminal and cecal bacterial populations.
Statistical Analysis Changes in methane production, dry matter intake, qPCR quantification and protozoa counts in ruminal digesta were analyzed using an independent 2-group Mann-Whitney test in R. Normal distribution in VFAs concentration in ruminal and cecal contents was confirmed by a Shapiro test and data were further analyzed by one-way ANOVA. mcrA sequencing libraries were prepared for ruminal contents, whereas 16S rDNA libraries were constructed for both ruminal and cecal contents. Raw counts per taxonomic level and OTU tables were used to compute relative abundance and then subjected to square root transformation before two-way ANOVA analysis in R. To minimize the variation created by different sample depths subsampling (resampling 100 times at a depth of 6,419 sequences for mcrA, 2,000 for archaeal and 17,000 for bacterial 16S rDNA) was used in our study before beta diversity analysis was performed.
Alpha diversity values for ruminal and cecal microbial communities in young bulls fed a CTL or LINNIT diet were obtained using various diversity indices (observed species, Chao estimate, and Shannon and Simpson diversity indices). A transformation-based principal coordinate analysis (PCoA) using the Bray-Curtis distance was used to ordinate 15 ruminal mcrA cDNA (one control animal was excluded from sequence analysis because of poor RNA extraction yield), 30 archaeal 16S rDNA (two ruminal content libraries, one from each experimental group, yielded low sequence numbers and were not included in data analysis) and 32 bacterial 16S rDNA animal libraries according to the corresponding OTU abundance data. For statistical analysis of beta diversity, we performed a nonparametric matrix-based ANOVA by using adonis implemented in the vegan package for R.
Ordination Analysis of Microbial Community Structure and Activity To analyze the distance between communities within mcrA cDNA, archaeal and bacterial 16S rDNA datasets, we used Values are means of eight individual measures; data were analyzed using two-way ANOVA in R. multivariate analysis of variance (adonis) of the braycurtis distance matrices. This analysis revealed a distinction between rumen and cecum samples, however, no change was observed due to dietary treatment (Figure  1D ). Similarly, PCoA of corresponding OTU tables showed that the samples clustered together according to fermentative compartment (Figures  1A-C ); some clustering by treatment was apparent for bacterial and archaeal 16S rDNA, though they overlapped resulting in homogenous group dispersions tests (mcrA P = 0. 871, archaeal 16S rDNA P = 0.