dataesr/scientific-paragraphs-categorization huggingface dataset:
5% SDS. (Hydroxy)methylated DNA was recovered by phenol-chloroform extraction followed by ethanol precipitation. Chromatin Immunoprecipitation-ChIP assays were performed as in Ref. 22 except that magnetic beads (Dynabeads) were used. The antibody against H3K4me2 was from Millipore (antibody 07-030). Real Time PCR-Quantitative real time PCR (qPCR) were performed using SyberGreen-based chemistry. DNA (hydroxy)methylation and H3K4me2 levels were analyzed by qPCR using 1 l of input DNA and 5 l of immunoprecipitated DNA. All primers used are listed in Table  1. High Throughput Sequencing-Library preparation and high throughput sequencing (single-read, 36 base pairs) for MeDIPseq was performed on Genome Analyzer II using reagents and standard protocols from the manufacturer (Illumina). MeDIP-seq Data Processing-The Casava 1.
MeDIP-seq data have been deposited into the Gene Expression Omnibus database under accession number GSE50992. Bioinformatical Analyses-Public functional genomic data  (10, 11, 14, 21, 24 -26)  used in this study were downloaded from Gene Expression Omnibus or the UCSC Genome Browser  (27)  and are listed in Table  2. RefSeq gene coordinates as well as vertebrate PhyloP  (28)  and PhasCons (29) conservation scores were downloaded from the UCSC Genome Browser  (27). H3K4me1, H3K27ac, PPAR␥, and C/EBP␤ significantly enriched regions identified in 3T3-L1 cells in Refs. 10 and 15 and myogenin (Myog) binding sites identified in C2C12 cells by the ENCODE consortium  (26)  were used. To specifically analyze PPAR␥-recruiting potential enhancers, PPAR␥ binding sites that lie within 2. 5 kb of a RefSeq gene transcriptional start site were discarded.
Peak overlaps were determined using the Cistrome analysis platform  (30). Wig files used to define signal intensity or conservation were generated at 25-bp resolution from raw data (and were normalized to the total number of uniquely mapped sequenced reads extended to 200 bp for ChIP-seq data). Average signal plots as well as CpG density plots were generated as described in Ref. 22. First, the center of enhancers was determined, and regions of interest were defined as spanning 2. 5 kb in each direction around the center. These regions were then split into 80 windows where the average signal from all enhancers was determined and plotted. ChIP-seq data were visualized using wig files and the Integrated Genome Browser  (31). De novo motif discovery was performed using Sampling with Expectation maximization for Motif Elicitation  (32) , and logos were generated using enoLOGOS  (33).
Enrichment for known transcription factor DNA-binding motifs was determined using the SeqPos tool of the cistrome analysis platform  (30). Genomic Regions Enrichment of Annotations Tool was used to associate distal regulatory regions to genes (default parameters) and subsequently identify gene set over-representation  (34). Gene sets that were more than 90% identical were merged into a single class for analyses of their expression. Transcriptomic Data Analyses-Raw transcriptomic data from 3T3-L1 cells  (15, 35)  were analyzed using the GeneSpring GX software using the gene level analysis (Agilent). Background-corrected raw data were Log2-transformed and quantile-normalized following the robust multichip average procedure giving rise to gene expression values centered on 0  (36). Transcriptomic data for additional mouse tissues or cells were obtained from BioGPS  (37).
Normalized expression values    (24). Statistically significant differences between preadipocytes and adipocytes (*, p Ͻ 0. 05) as well as between constitutive and activated PPAR␥-recruiting enhancers in preadipocytes ( §, p Ͻ 0. 05) are indicated. F, average C/EBP␤ recruitment levels at constitutive and activated PPAR␥-recruiting enhancers in 3T3-L1 preadipocytes analyzed as in Fig. 1B. The percentage of constitutive and activated PPAR␥-recruiting enhancers overlapping with a C/EBP␤ recruitment site is also indicated (% occupied enhancers). for the genes of interest were extracted from the Mouse GNF1M Gene Atlas (Gene Expression Omnibus accession number GSE1133)  (38). Statistical Analyses-Statistical analyses were performed using Prism software (GraphPad, San Diego, CA. ). Statistical significance was determined using a Student's t test comparison for unpaired or paired data and was indicated as follows: *, p Ͻ 0.
e. , insulin/IGF signaling). Moreover, the DNA sequence of constitutive enhancers showed a greater evolutionary conservation compared with activated enhancers as judged by their stronger average PhyloP and PhasCons vertebrate conservation scores (Fig. 6A ). Therefore, we reasoned that constitutive enhancers might exhibit a more widespread activity. To test that hypothesis, we used DHS data obtained by the ENCODE consortium in various mouse tissues and cells as an indicator of enhancer activity  (26). Interestingly, we found that the pattern of overlap with DHS was significantly different for activated and constitutive enhancers (p Ͻ 2. 10 Ϫ16 ). Indeed, ϳ50 -60% of activated PPAR␥-recruiting enhancers overlapped with DHS from fat tissues, which was significantly higher than the overlap obtained with DHS from other cells or tissues (Fig.
Datasets A series of nine satellite images with high/very high spatial resolution acquired in 2010 during the vegetation period were used for this study. Four cloud-free optical images acquired with SPOT-5 and Landsat sensors were used (Table  1 ). In addition, fully polarimetric RADARSAT-2 images were acquired in ascending orbit and in fine quad-polarization mode (single look complex (SLC) products), thus presenting a nominal swath width of 25 km with a spatial resolution of 12 m (range) × 8 m (azimuth). The FQ18 (Fine Quad-Pol) beam that corresponds to a radar incidence angle of 37. 56 • was used. Five C-Band images (5405 GHz) were programmed from February 2010, to August 2010, over the study site (Table  2 ). All acquired images were used to identify grasslands from crops, since grassland temporal profiles of biophysical or polarimetric variables are very different according to grassland management practices and, thus, can be very similar with crop temporal profiles of biophysical or polarimetric variables.
Optical and SAR Data Preprocessing In order to reduce errors resulting from instrumental variations in data acquisition, image noise and misregistration, the optical images were corrected from radiometric and atmospheric effects. These corrections were performed applying the 6S model (Second Simulation of a Satellite Signal in the Solar Spectrum vector code) developed by  [25]. Then, geometric corrections were undertaken using ArcGIS 10. All data were georeferenced based on the Lambert 2 conformal conic system, the root mean square error being less than one pixel. Finally, the NDVI was calculated  [26] , and biophysical variables (LAI and fCOVER) were retrieved from satellite images using the PROSAIL (coupling of PROSPECT and SAIL models) radiative transfer model proposed by  [27, 28]. A 3 × 3 coherency matrix (T3) was firstly extracted from the raw Radarsat-2 images using PCI Geomatics c software.
In total, fifteen types of variables were extracted from the satellite remote sensing images: three from optical images (NDVI, LAI and fCOVER) and twelve from SAR images (σ 0 HH, σ 0 VV, σ 0 HV, HH/VV, HH/HV, VV/HV, Freeman-Durden decomposition with double-bounce, volume and roughness and Cloude-Pottier decomposition with H, A, α). Since the remote sensing data used in this study have different spatial resolutions, variables derived from optical and SAR images that were calculated at pixel scale were computed at the field scale by applying the mean variable value within each field to the entire field. Field boundaries were delineated each year by photo-interpretation using the orthophotoplan and the satellite images. For each of these variables, temporal profiles were created: profiles derived from optical images include four points (four dates), while profiles derived from SAR images comprise five points (five dates).
Acknowledgments This work was supported by the  National Agency for Research (ANR)   SYSTERRA-ACASSYA (Supporting the agro ecological evolution of breeding systems in coastal watersheds) program  ( ANR-08-STRA-01 ). We would also like to thank the  National Centre for Space Studies (CNES)  for providing us with the optical satellite images,  Vigisat-CLS (Collecte Localisation Satellites)S  for providing us with the SAR data,  Sally Ferguson  for revising the manuscript and the anonymous referees for their valuable comments.
Specimens examined For morphologic phylogenetic analysis 85 species of Candidae were included (Text S1). To prevent incompleteness of data and mislead of the characters/character states in the matrix, we did not include in the phylogenetic analysis: fossil species, species with bad preserved type material, and poorly known species whose type specimens have not been found or examined using scanning electron microscopy. In the absence of any previous phylogenetic hypothesis related to the family Candidae, we selected 8 species from four genera to serve as outgroups for the analysis, viz. Notoplites  Harmer, 1923 , Tricellaria Fleming, 1828, Canda Lamouroux, 1816 and Caberea Lamouroux, 1816. Incorporating these taxa was also important in order to test the monophyly of Scrupocellaria. We chose Notoplites marsupiatus  (Jullien, 1882)  to root the unrooted cladogram of the analysis because of its distinct scutum shape (when compared with other Candidae species) and the presence of abfrontal avicularia rather than abfrontal vibracula (a vibracular chamber is considered to be a defining characteristic of Scrupocellaria species  [5] ,  [11] ,  [26] ,  [31] ).
Morphology and phylogenetic analysis All specimens were examined under the stereomicroscope. Selected specimens were mounted for examination in a scanning electron microscope (SEM) (Zeiss EVO-60, Zeiss LEO 1455-VP and Zeiss DSM 940) for description and selection of the characters. We scored 35 characters for all terminal taxa; some of these characters show homoplasies among the family members (e. g. absence of scutum and rhizoids with hooks). All characters were treated as unordered and equally weighted. Autapomorphies of terminal taxa, which do not provide evidence to support monophyly at supraspecific levels, were excluded from the analysis. Unknown states were indicated as ''?'' in the data matrix, inapplicable states were indicated as (''-''). Whenever necessary, polymorphisms were explicitly considered in the coding and are marked in the data matrix.
The list and discussion of characters used are given below. The character matrix (Table  S1 ) was edited using Mesquite v2. 75  [32]. Maximum parsimony analyses were carried out using TNT v1. 1  [33] , adopting ''New Technology'' search algorithms (sectorial search, ratchet, tree drifting, and tree fusion) for 3,000 random addition sequences, 10 random number seeds, opting for collapsing trees after the search. The resulting forest of trees was summarized in a semi-strict consensus  [34]  topology. Bremer support  [35]  was calculated using the script Bremer. run in TNT, with configuration ''search for trees 10 times longer'', ''do 10 ratchet iterations in constrained searches'', and other settings following the standard script. The length (L), consistency index (CI) and retention index (RI) for both tree and characters were calculated in TNT.
Acknowledgments Logistical support was provided for L. M. Vieira by the  American Museum of Natural History (USA) ,  Musee oceanographique de Monaco (Monaco) ,  Museu de Zoologia da Universidade de Sa ˜o Paulo (Brazil) ,  Museum of Comparative Zoology, Harvard University (USA) ,  Museum national d'Histoire naturelle (France) ,  Natural History Museum (UK) , the  Nationaal Natuurhistorisch Museum -Naturalis (Netherlands) , The  Smithsonian's National Museum of Natural History (USA) ,  Universidade Federal da Bahia (Brazil)  and the  Virginia Museum of Natural History (USA). We are grateful to several people who helped during Museum visits:  Adam Baldinger  (MCZ),  Robert Woollacott  (MCZ),  Christine LeBeau  (AMNH),  Estefania Rodriguez  (AMNH),  Elly J. Beglinger  (Naturalis),  Koss Egmond (Naturalis), Facelucia Barros Cortes Souza (UFBA) ,  JoAnn Sanner  (USNM),  Linda Cole  (USNM),  Marcos D.
Discussion Observations of patients carrying germline mutations in the EGLN1 gene showed a relatively homogeneous phenotype that can be presumed to be erythrocytosis. However, at the present time, the total number of patients is still too small to perform genotype/phenotype correlation studies to explain exceptional clinical manifestations, such as the tumor occurrence described in only one case. The international database on erythrocytosis, the European Congenital Erythrocytosis Consortium (http://www. erythrocytosis. org) should help solve this limitation. Mapping erythrocytosis-associated mutations onto the structure of the PHD2 protein has indicated they are typically present in conserved regions. Several frameshift and nonsense mutations are predicted to result in loss of the C-terminal portion of PHD2 and, in some cases, the full catalytic domain, thereby resulting in a nonactive enzyme.
Acknowledgments The authors are grateful to the European networks that made collaboration possible: HypoxiaNet,  Cooperation in Science and Technology (COST)  Action  TD0901 , and  Molecular Diagnosis  of  Myeloproliferative Neoplasms  and  MPN-Related Congenital Diseases-EuroNet, COST Action BM0902. Research in Dr Gardie 's lab has been supported by grants from the  Ligue Nationale contre le Cancer  ( Comités du Cher, de l'Indre, de la Loire Atlantique et des Côtes d'Armor ) We thank  Dr Aguado-Diaz  and  Dr Francesca Pierdomenico  for their precious help in recruiting patients and sharing data.
Each laboratory received seven samples grouped into sets of three. So a minimum of 21 X-ray diffraction measurements need to be performed. As said previously, residual stress calculation method has been left free for each partner. However all the X-ray patterns were saved and sent to be treated by a program called StressDiff. StressDiff software is dedicated to the acquisition and processing of the X-ray data for the evaluation of the constraints. This package was developed as a result of Stress AT program. Four calculation methods (centroid, middle point, maximum of peak, fitting…) were used to analyse the results. Following the ISO5725 standard on accuracy (trueness and precision) of measurement methods and results  [7] , the aim was to obtain for each reference sample: -R σ : reproducibility of residual stress value (global standard deviation) -r σ : repeatability of the residual stress value (standard deviation of repeated measurements in one laboratory under repeatability conditions) -σ mean : mean stress value of the results of a given laboratory -σ general : general mean stress of a given sample -σ material : material mean stress (on all the samples and all the laboratories) These parameters were calculated for each reference sample, for each material and for each residual stress calculation method.
Patients and methods This study was conducted at 22 investigational sites from United States of America (USA) (n = 8), France (n = 5), United Kingdom (UK) (n = 4), Germany (n = 2), Italy (n = 2) and Spain (n = 1) according to the Declaration of Helsinki, Good Clinical Practice guidelines and local regulations on clinical trials, and was approved by respective independent ethics committees. Signed informed consent was obtained from all patients. An Independent Data Monitoring Committee (IDMC) reviewed the study conduct. Trial codes were Eudra CT: 2008-002326-11; Clinical-Trials. gov Identifier: NCT00796120.
After interim analysis, sample size could be increased if the desired power was not achieved with the observed HR but was still clinically meaningful. Response rates were compared by Fisher's exact test. Unstratified log rank test and Cox regression were also used to evaluate secondary time-to-event end-points, such as overall survival (OS). EAST v. 4 and SAS v. 9. 2 were used to calculate sample size and for all statistical analysis outputs, respectively. Patients will be followed for overall survival until August 2014.
Acknowledgements This study was supported by funding from  Pharma-Mar S. A. The design of this study was presented at the  American Society of Clinical Oncology (ASCO) 2012 Meeting. 'Blay JY, Leahy MG, Nguyen BB, Patel S, Santoro A, Hohenberger P, Demetri GD, Lardelli P, Perez I, Chawla SP. Randomised multicenter phase III trial of trabectedin (T) versus doxorubicin-based chemotherapy as first-line therapy in patients with translocationrelated sarcoma (TRS). J Clin Oncol 30, 2012 (suppl. ; abstr. TPS10101). 'Preliminary results of this study were presented at the  American Society of Clinical Oncology (ASCO) 2013 Meeting. 'Hendifar AE ,  Chawla SP ,  Leahy MG ,  Italiano A, Patel S, Santoro A, Staddon AP, Penel N, Piperno-Neumann S, Demetri GD, Hayward L, White J, Gouw LG, De Miguel B, Lardelli P, Soto A, Nieto A, Blay JY.
, 2008)  and drought monitoring  (Vidal et al. , 2010b). LSMs also proved their usefulness for agronomy application such as irrigation monitoring  (Olioso et al. , 2005). This work focuses on the evaluation of evapotranspiration (ET) simulated from a land surface model over a crop site for a long period of time. ET has been recognized as one of the most uncertain term in the surface water balance  (Dolman and de Jeu, 2010; Mueller and Seneviratne, 2014). Uncertainties in simulated ET may propagate large errors in both LSM-atmosphere and LSM-hydrological coupled models. ET uncertainties can arise from (1) errors in the large-scale datasets used to force LSMs, (2) shortcomings in the model structure and (3) errors in the parameter values. Since LSMs were originally designed to be coupled with atmospheric or hydrological models over large areas, their parametrization is generally parsimonious and their spatial integration is generally based on coarse resolution (∼ 1-10 km) maps of parameters.
Back Close Full The latter relies on the use of the ECOCLIMAP-II dataset for the surface parameters  (Faroux et al. , 2013)  and the ISBA PTFs for the soil properties  (Noilhan and Laccarère, 1995). No local calibration of the parameters is achieved to test the portability of the model parameters over a typical Mediterranean crop site  (Olioso et al. , 2002). The goal is to assess the capability of ISBA-A-gs to represent ET over a 12 yr Mediterranean crop succession and to identify the shortcomings in the physical process representation. The main originality of this work consists in representing the succession of crop cycles and inter-crop bare soil periods in the simulations and assessing its impact on the dynamic of simulated and measured evapotranspiration over a long period of time. The model performances are thoroughly quantified for a large range of surface and atmospheric states.
Micrometeorological measurements Half-hourly observations of precipitation, air temperature and humidity, wind speed, atmospheric pressure, radiations, energy fluxes, were continuously performed over the 12 year period. The net radiation (RN) was computed from the measured shortwave and longwave upwelling and downwelling radiations. Sensible (H) and latent (LE) heat fluxes were computed from an eddy-covariance system. The latter was composed of a 3-D sonic anemometer set up in 2001 and of an open-path gas (H 2 O, CO 2 ) analyzer set up in November 2003. The system was monitored following the state of the art guidelines for cropland sites  (Rebmann et al. , 2012; Moureaux et al. , 2012). Fluxes were computed on 30 min intervals using the EDIRE software 2  The flux data processing included spike detection on raw data and standard eddy-covariance corrections (coordinate rotation, density fluctuations, frequency-loss).
The ECPP 3  software  (Beziat et al. , 2009)  was used to discard spurious flux (e. g. friction velocity and footprint controls) and to apply the  Foken et al. (2004)  quality control tests on the temporal stationarity and the development of turbulence conditions. In this work, only the best quality class of data  (Mauder et al. , 2013).
Experiment design The first analysis consists in an overall evaluation of ET over the 12 year crop succession. The model is used in its standard implementation (simulation Sa). The vegetation parameters and the soil depths are provided by the ECOCLIMAP-II database  (Gibelin et al. , 2006; Faroux et al. , 2013). ECOCLIMAP-II gives a d 2 value of 1. 5 m which is equal to the mean d 2 estimate derived from the soil moisture vertical profiles (Table  3 ). The soil properties are derived from the local soil texture using the ISBA pedotransfer functions. The second analysis aims at evaluating the impact of using in situ w sat , w fc , w wilt and d 2 on ET simulated over a long period of time. The simulations Sb, Sc, Sd and Se were achieved using distinct in situ estimates of these soil parameters (Table  4 ).
Back Close Full of soil property variability. The ISBA pedotransfer functions were established upon the  Clapp and Hornberger (1978)  database. These functions were calibrated using mean values of soil properties over few classes of soil texture and do not represent the variability within each soil class. The second source of uncertainty is related to the estimation method. PTFs were designed to be applied over readily available variables such as soil texture. Improvements of the prediction equations may require the use of additional predictors related to soil structure  (Vereecken et al. , 1989). Most PTFs are based on simple statistical regressions such as the ISBA ones  (Noilhan and Laccarère, 1995). The more advanced ROSETTA PTF  (Schaap et al. , 2001)  addresses the uncertainty in the predicted soil parameters through the use of an ensemble of functions calibrated over distinct soil datasets.
Uncertainties in eddy covariance measurements Random errors in eddy covariance measurements arise from turbulence sampling errors, instrument errors and flux footprint uncertainties  (Richardson et al. , 2006). While random errors are likely to cancel out when the measurements are cumulated over long period of time, their magnitude can be substantially large at half-hourly time scale. An overall random error of 15 W m -2 is obtained for LE measurements in our dataset following the  Richardson et al. (2006)  method (Appendix B). The non closure of the measured energy balance (EB) is a critical source of uncertainties when these measurements are compared to LSM estimates. Reasons for EB non-closure include footprint mismatch between the eddy fluxes and the other energy fluxes, horizontal and vertical advection, unresolved low frequency turbulence, inaccuracies in the eddy covariance processing  (Foken, 2008; Leuning et al.
The random error is estimated as the SD of the paired flux differences. The overall random error obtained for the LE measurements in our dataset is σ R = 15 W m -2. The variance of the error (σ        3 ). MawAWC is the the maximum root-zone water stock available for the crop. (h = -150 m) w fc (h = -3. 3 m) w fc (K = 0. 1 mm day -1 ) (%) (%) (g cm -3 ) (m 3 m -3 ) (m 3 m -3 ) (m 3 m -3 ) (m 3 m -.
-2 minimized the magnitude of the EB non-closure of our dataset. The mean and the SD of the absolute value of the EB non-closure are 28 and 22 W m -2 , respectively. This is comparable to the non-closure reported for cropland in Wilson et al. (2002), Hendricks et al. (2010) and Ingwersen et al. (2010). The EB non-closure is further assessed comparing the direct measurement of LE with two other estimates. The first estimate is computed as the residue of the energy balance assuming that H is error-free. The second estimate is Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |.