dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods The data used in  Nelson-Sathi et al. (2015)  were kindly provided by the authors. Statistical analyses were performed in R (R Core Team, 2013) and ancestral gene repertoire reconstructions were performed with the Count program  (Csűrös 2010). Branch-specific rates of a birth/death model of gain and loss of gene clusters  (Csűrös and Miklós 2006)  were estimated using Maximum Likelihood from the 25,762 protein families, along the archaeal species tree reconstructed by  Nelson-Sathi et al. (2015). After optimization of the branch-specific parameters, ancestral reconstructions were carried out for each of the 2,264 families, by computing branchspecific posterior probabilities of evolutionary events. At a given branch, the expected number of gene acquisitions was computed by summing all family-specific gene gain posterior probabilities.
Wagner parsimony  (Farris 1970; Csűrös 2008 ) with a large range of gain and loss cost combinations was also tested. All parsimony and ML estimations gave very similar results (See Supp Fig. 2 ). A Count session file that will allow users to reproduce our results is available at ftp://pbil. univ-lyon1. fr/pub/datasets/DAUBIN/HGT_Archaea/.
EMEP results for total deposition of oxidised (NO x ) and reduced nitrogen (NH 3 ) were interpolated to the model grid. Second, we calculated the average annual deposition rates for the NO x and NH 3 for each grid cell, based on EMEP data for the period 1995-2012. The resulting spatially resolved arrays of average deposition rates were subsequently normalised by the spatial average of the whole domain to yield the spatially resolved anomaly fields. Finally, gridded deposition rates for individual years were obtained using (1) the gridded anomaly fields, (2) EMEP's spatially averaged (over our model domain) deposition rates for year 2005, and (3) long-term trends (normalised with respect to year 2005) for the temporal development of European emissions of NO x and NH 3  (Schöpp et al. , 2003, Fig.
Localised, temporally resolved data -Cefas-SmartBuoy and MARNET The problem of comparing sporadic O 2 measurements with simulated bottom O 2 concentrations was clearly shown by  Lenhart et al. (2010, Fig. 9a   (Greenwood et al. , 2010). At this site the water depth is 85 m and the measuring devices were located in 1, 31 and 85 m depth. T , S and O 2 concentrations were continuously recorded in the different depths. The O 2 concentrations were measured in a burst of between 5 to 10 min using an Aanderaa optode (AADI, Norway) with a frequency of 5 Hz (accuracy of 0. 5 %). A 0-100 % calibration was conducted annually on each optode and results were corrected for drift using O 2 concentrations determined from discrete water samples. These samples were collected and preserved in triplicate according to Winkler, and subsequently analysed on board using an automatic titration system (Sensoren Instrumente Systeme, Germany).
Spatially resolved "snapshot" data -the North Sea programme During the North Sea programme, carried out by the Royal Netherlands Institute for Sea Research (NIOZ) with support from the Dutch Science Foundation (NWO) and the European Union, the North Sea was sampled from 18 August to 13 September 2001, and from 17 August to 5 September 2005 and 2008. The North Sea was covered by an approximate 1 • × 1 • grid, sampling approximately 90 stations in each of the three years. The station grid was denser in the Southern Bight, which is strongly affected by the major continental freshwater run-offs and to a lesser extent by the Baltic outflow. The station grid was coarser in the more homogeneous central and northern North Sea  (Bozec et al. , 2005 (Bozec et al. , , 2006;; Salt et al. , 2013). During each cruise, a total of 750 water samples was collected for dissolved O 2.
In 2001, the O 2 concentration was determined by the Winkler titration using a potentiometric end-point determination with an accuracy of ± 2 µmol kg -1 (less than ± 0. 07 mg L -1 depending on T and S). In 2005 and 2008, the O 2 concentration was obtained applying the spectrophotometric Winkler approach with a precision of less than 0. 03 mg L -1. A detailed description of the measurement system used is given in  Reinthaler et al. (2006). The data available were gridded to the model grid (Fig. 2 ). In the case of multiple measurements for the same model grid cell and date, the average of these measure-12557 Figures.
Spatial distribution of late summer bottom O 2 The profile data collected within the North Sea programme during late summer 2001, 2005 and 2008 provide a highly valuable dataset as they reveal spatial patterns in the North Sea O 2 distribution as well as inter-annual variations within this region. This in turn implies that measurements taken around this period can be considered as representative for late summer conditions. In addition, the small standard deviations for the simulation confirm that using the average over a period of up to 4 weeks provides a reasonable measure for most areas. This implies that observations taken in late summer before the breakdown of stratification represent a synoptic picture of the late summer bottom O 2 conditions. However, the validation presented in Sect.
A quantitative assessment of the model performance The Taylor diagram (Fig. 6 ;  Taylor, 2001)  provides a quantitative assessment of the model performance with respect to simulated bottom O 2 concentrations in relation to the validation data used in Sect. 3. 2. 1 and Sect. 3. 2. 2: (a) station Cefas North Dogger (see Fig. 4a  and b ) station MARNET Ems (see Fig. 4c  and d ) the spatially resolved data (see Fig. 5a-c ). For this purpose, we merged the observations and corresponding simulation data of the different years into one continuous series of data for each of the three datasets. The Taylor diagram presents the correlation coefficients (COR), standard deviations (STD) and centred root-mean-square differences (RMSD) of the simulation relative to the observation. The two latter values are normalised by the STD of the corresponding observations.
The correlation coefficient COR between observation X and simulation Y is calculated as follows for each of the three datasets: Here, N equals the number of observation-simulation pairs. The index i indicates a single value of the considered dataset, while X and Y represent the average values. The unnormalised STD of a dataset Z (observation or simulation) is calculated as: COR(X , Y ) = 1 N • STD(X ) • STD(Y ) • N i =1 (X i -X )(Y i -Y ). ( 3 STD(Z) = 1 N • N i =1 Z i -Z 2. ( 4 ) The unnormalised RMSD between observation X and simulation Y is calculated as: RMSD(X , Y ) = 1 N • N i =1 (X i -X ) -(Y i -Y ) 2. ( 5 ) The statistics for the time series data confirm the good agreement between simulation and observation shown in Fig. 4. For both stations, COR is high with values of about 0. 95 and the normalised RMSD is less than 0.
(2010)  provide reasonable results. However, as NIT also accounts for about 8 % of the total biological consumption, this process should be considered to obtain more precise estimates for C remineralisation rates at Station North Dogger. Our analysis of the observed temporal development of bottom O 2 at station North Dogger using the HAMSOM-ECOHAM5 model system showed, that despite minor differences between simulation and observation, ecosystem models can help to in-Introduction terpret measurements with respect to the underlying dynamics. Meire et al. (2013)  undertook a similar study for the Oyster Grounds using a one-dimensional physicalbiogeochemical model. However, in our study we showed that three-dimensional models can provide the combined benefit of data interpretation and the identification of spatial and temporal patterns in the O 2 dynamics.
and provides 6 hourly information for air temperature, cloud coverage, relative humidity, wind speed and direction. The data were interpolated into the model grid and time step according to O'Driscoll et al. (2013) and Chen et al. (2014). Daily freshwater run-off data for 249 rivers were provided by Cefas and represent an updated dataset of that used by Lenhart et al. Discussion Paper | Discussion Paper | Discussion Paper | (2010) covering the whole simulation period. The same dataset encompassed nutrient loads used for the ECOHAM5.
BGD Acknowledgements. We like to thank  Sonja van Leeuwen  from  Cefas  for providing updated data on freshwater and nutrient loads for the major rivers across Europe. We further thank  Jerzy Bartnicki  for providing atmospheric nitrogen deposition data from the  European Monitoring and Evaluation Programme (EMEP). We also thank  Dilek Topcu  and  Uwe Brockmann  from the  University of Hamburg  for providing the map of observed North Sea hypoxia. The model simulation was conducted on Blizzard, the  IBM Power6 mainframe at the German Climate Computing Centre (DKRZ) in Hamburg. The North Sea sampling in 2001, 2005 and 2008 was supported by the  Dutch Science Foundation (NWO) ,  CARBOOCEAN  ( EU-FP6 ) and the  Royal Netherlands Institute for Sea Research (NIOZ). Markus Kreus  was partly financially supported by the  Cluster of Excellence "CliSAP"  ( EXC177 ),  University of Hamburg , funded by the  German Science Foundation (DFG).
Data availability The time series data from the Cefas SmartBuoy programme can be accessed via: http://cefasmapping. defra. gov. uk/Smartbuoy. The time series data of BSH MARNET programme can be accessed via: http://www. bsh. de/en/Marine_data/Observations/ MARNET_monitoring_network/. The spatially resolved data from the North Sea cruises 2001, 2005 and 2008 have been released in the framework of the EU-FP6 project CAR-BOOCEAN and can be accessed via: http://dataportal. carboocean. org/.
). They compared bottom O 2 concentrations simulated by three different models against in-situ observations at the Dutch monitoring station Terschelling 135. Their first approach to directly compare observations and simulations of a single year revealed significant differences between 12555 Figures simulated and observed bottom O 2 concentrations, with even lower simulated values falling below 6 mg L -1 in late summer. However, due to the sparse data, they could not determine whether these low simulated concentrations represented reasonable results for the corresponding period or not. Consequently, monthly averages and standard deviations of a 15 year dataset had to be used to unveil if O 2 concentrations below 6 mg L -1 were within a realistic range during the specific period at the location con-sidered.
PH was the average height (cm) of five plants in the plot. Five panicles were collected along a distance of one meter in the row, weighed and averaged to obtain the individual PW (g). Grains from these five panicles were added to the grains collected from plants along the remainder of the one-meter length to give YLD expressed in g/m. First, a diagnostic module was run using the "influence" option to detect outliers among the individual observations. It included the "iter =" sub-option that updates both fixed effects and covariance parameters by refitting the mixed model when an observation is deleted. (http:// support. sas. com/documentation/cdl/en/statug/63962/HTML/default/viewer. htm#statug_ mixed_sect027. htm). This diagnostic procedure led to the elimination of two elementary data. The selection of entries with both phenotypic data and genotypic data lead to a final number of 343 S 2:4 lines, which were used to develop the prediction model.
For each trait, experimental data were analyzed using the SAS 9. 3 MIXED procedure [23] to estimate the BLUP values and heritability. The ANOVA model was Y ijk = μ + g i + R j + b k(j) + e ijk in which Y ijk was the phenotype, μ the overall mean, g i was the genotype effect considered as random, R j was the replicate effect considered as fixed, b k(j) was the random effect of the block within a replicate, and e ijk was the residual considered as a random effect. Variance components were obtained, and a BLUP value was extracted for each line to be used in the GS models. The phenotypic BLUP x accession matrix is available for download at http://tropgenedb. cirad. fr/tropgene/JSP/interface. jsp?module=RICE as "SEPANG dataset". For each trait, narrow sense heritability was calculated using the ratio h Multi-dimensional analysis of phenotypic data by factorial discriminant analysis (FDA) was performed and pairwise Fisher distance between subpopulations was calculated using the XLSTAT package  [24].
r-project. org/web/ packages/synbreed/index. html). Beagle uses a hidden Markov model to infer haplotypes and imputes sporadic missing data on large scale phase known or unknown genotype data sets  [27]. The imputation procedure was performed on markers with less than 20% missing data and an MAF >5% and resulted in complete dataset for 8,336 SNP markers with imputed genotypes selected with a >0. 75 probability. The 8,336 SNP x accession matrix is available for download at http://tropgenedb. cirad. fr/tropgene/JSP/interface. jsp?module=RICE as "SEPANG dataset".
Methods for characterizing the population Population structure. The structuring of the 343 S 2:3 lines into four subpopulations corresponding to PCT4-C0, PCT4-C1, PCT4-C2 and PCT11-C1 was investigated by FDA  [24]. The analysis was performed with the coordinates of the S 2:3 lines on the 10 first axes of a principal components analysis, using the genotype on the 3,675 SNP loci with no missing data. Genetic differentiation. Genetic variation between subpopulations was estimated using a pairwise F ST test that estimates genetic differentiation based on allele frequency  [28]. The F ST statistics were calculated over the genome using 3,675 markers with no missing data with Arlequin software  [29]. Effective population size (Ne). Ne was estimated for each of the four subpopulations as well as for the whole population, from genotypes at 3,675 SNP loci.
Ne was calculated using the linkage disequilibrium method of Waples and Do  [30] , re-implemented in NeEstimator V2. 01  [31]. Kinship among the 343 S 2:3 lines. A marker-based kinship matrix was generated using the 8,336 SNP available after imputation. Kinship estimates were obtained by calculating a distance matrix (d ij = n/8,336; n being the number of SNPs that differ between line i and line j), which was converted into a similarity matrix by subtracting all values from 2, then scaled to rank values from 0 to 2, using the Synbreed package  [32]. Pairwise linkage disequilibrium. LD within the panel of 343 S 2:3 lines was evaluated for each chromosome by computing r 2 between all pairs of SNP markers of a given chromosome, as defined by Hill and Roberson  [33]. The r 2 were computed using the Synbreed package, which called for a R function under the option pairwiseLD use.
e. individual BLUP). RR-BLUP  [37]  is another early but still widely used method for genomic prediction  [9, 38]. The method consists in estimating simultaneously the effect of all markers listed in X, through an optimization function that minimizes both the sum of squared errors and the sum of squared marker effects multiplied by the λ parameter. This method gives similar individual predictions to those produced by G-BLUP  [39]. By shrinking all marker effects with the same degree and including all markers in the model, both RR-BLUP and G-BLUP methods imply that the trait is controlled by many loci with small effects. RR-BLUP was implemented using the rrBLUP package  [40]. The LASSO regression method drives some marker effects to exactly zero, selecting at most the same number of predictors as the number of observations  [41].
When predictors are highly collinear, LASSO retains only one random marker per group of correlated predictors, which could be non-physically linked to underlying genes controlling trait variation. LASSO retains or discards markers, which often results in high variance and high prediction error by the full model. A parameter λ that minimizes the prediction error was found by cross validation and used to predict GEBVs in the VP. We used the Glmnet package  [42]  to implement the LASSO method. Bayesian methods are used to select variables and to shrink estimates  [34]. Using this approach, a prior was assigned to all model unknowns, while the intercept and the vector of fixed effects were assigned flat priors. Under a Bayesian model, a separate variance was estimated for each marker, and the variance was expected to follow a specified prior distribution  [43].
Using data from four deep fields (COSMOS, AEGIS, ECDFS, and CDFN), we study the correlation between the position of galaxies in the star formation rate(SFR) versus stellar mass plane and local environment at z < 1. 1. To accurately estimate the galaxy SFR, we use the deepest available Spitzer/MIPS 24 and Herschel/PACS datasets. We distinguish group environments (M halo ∼10 12. 5-14. 2 M ) based on the available deep X-ray data and lower halo mass environments based on the local galaxy density. We confirm that the Main Sequence (MS) of star forming galaxies is not a linear relation and there is a flattening towards higher stellar masses (M * > 10 10. 4-10. 6 M ), across all environments. At high redshift (0. 5 < z < 1. 1), the MS varies little with environment. At low redshift (0. 15 < z < 0.
2. 2 and §3. 1 in E14). In addition, we use the latest and deepest available Spitzer MIPS and Herschel PACS (Photoconduct-ing Array Camera and Spectrometer,  Poglitsch et al. 2010 ) mid and far infrared surveys, respectively, conducted on the same blank fields to retrieve an accurate measure of the star-formation rate of individual group galaxies. The paper is organized as follows. In Sect. 2 we describe our dataset and how all relevant quantities are estimated. In Sect. 3 we describe our results and in Sect. 4 we discuss them and draw our conclusions. We adopt a  Chabrier (2003)  initial mass function (IMF), H0 = 71 km s -1 Mpc -1 , Ωm = 0. 3 and ΩΛ = 0. 7 throughout this paper.
DATA The aim of this work is to study the evolution of the location of galaxies in the SFR-stellar mass plane as a function of the environment. Three main ingredients are necessary for such analysis: an accurate estimate of the galaxy SFR and stellar mass and a proper definition of the environment. For this purpose, we use the best studied blank fields as the AEGIS field, COSMOS, the ECDFS and the CDFN, to build a multi-wavelength dataset which combines deep photometry from the UV to far infrared wavelength to estimate accurately the galaxy SFR and stellar mass, and deep X-rays observations and high spectroscopic coverage to study the galaxy environment. A full description of the data available in each field is provided in §2. 1 of E14. In this work we summarize how the galaxy SFR and stellar mass is estimated and how the environment is defined.
Star formation rate and stellar masses To accurately estimate the galaxy star formation rate, we use the deepest available Spitzer MIPS 24 µm and PACS 100 and 160 µm datasets for all considered fields (see §2. 3 of E14 for details). We fit the available infrared data with the SED templates from  Elbaz et al. (2011). The total galaxy IR luminosity is estimated by integrating the best fit SED from 8-1000 µm. The SFR is, then, derived from the IR luminosity by using the  Kennicutt (1998)  relation converted for a Chabrier IMF. However, due to the flux limits of the MIPS and PACS catalogs, the IR catalogs are sampling only the Main Sequence region and can not provide a SFR estimate for galaxies below the Main Sequence or in the region of quiescence. For a complete census of the star formation activity, we need an estimate of the SFR for all galaxies.
As reported in E14, the stellar masses estimated in the different catalogs are instead consistent with a scatter of ∼ 0. 2 dex after the correction to the same IMF. We, thus, use the stellar masses provided by the mentioned catalogs, converted to the Chabrier IMF. We note that  Ziparo et al. (2013) , using similar datasets, have already established the consistency of the SFRs and stellar masses derived from different methods (see §3. 3 of  Ziparo et al. 2013  for details).
Morphology We link the location of galaxies in the SFR-stellar mass plane also to the galaxy morphology. The galaxy morphological information are taken from the Advanced Camera for Surveys General Catalog (ACS-GC), which is a photometric and morphological database based on publicly available data obtained with the Advanced Camera for Surveys (ACS) instrument on board of the Hubble Space Telescope  (Griffith et al. 2012). This catalog contains ∼370,000 galaxies observed in the EGS, COSMOS, GEMS and GOODS surveys. Briefly,  Griffith et al. (2012)  use GALFIT to measure the structural parameters of each galaxy by modeling each source with a single Sérsic profile  (Sersic et al. 1986 ; see  Graham & Driver 2005  for the mathematical relationship) together with a model for the sky. By cross-matching our galaxy catalogs of EGS, COSMOS and the GOODS fields with the ACS-GC catalog, we find morphology information for 80% of our galaxy sample.
ACKNOWLEDGEMENTS The authors acknowledge  Alvio Renzini  and  Michael L. Balogh  for their useful comments and discussion on the early draft. PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KUL, CSL, IMEC (Belgium) ;  CEA, OAMP (France) ;  MPIA (Germany) ;  IFSI, OAP/AOT ,  OAA/CAISMI, LENS, SISSA (Italy) ;  IAC (Spain). This development has been supported by the funding agencies  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI (Italy) , and  CICYT/MCYT (Spain). This research has made use of  NASA 's Astrophysics Data System, of  NED , which is operated by  JPL/Caltech ,.
ACKNOWLEDGEMENTS We thank  Loïc Bocat  (excavation),  Renaud Roch  (preparation),  Olivier Noaillon  and  Bernard Migy  (photographs),  Pierre Widder  ( scientific drawings ),  Apolline Lefort  (discussion on stratigraphy) and the whole Paleontology A16 team. Further thanks go to  Silvan Thüring  of the  Naturmuseum Solothurn  for providing access to the lectotype of T. hugii, and to  Adán Pérez-García  for discussions on Thalassemys. Editor  Mathew Wedel  and the reviewers  Walter Joyce  and  Márton Rabi  provided very helpful comments.
After 24 h, D-luciferin (150 mg ml À 1 , Caliper) was added to the medium in each well and the plate was incubated for 5 min. Luciferase activity was measured with the IVIS Imaging System (Xenogen, Caliper Life Science, Hopkinton, MA, USA) and analysed with Living Image software, as previously described  (Abdelkarim et al, 2009). The clones generated were named V165-B, V189-B and cV-B, according to the construct used for transfection. V189-B31, V165-B45 and cV-B9 cells were selected on the basis of their having equivalent levels of bioluminescence (104, 97 and 81 photons second per cell, respectively) (Supplementary Data 1A). The sh-NRP-1 stable clones were performed as described before  (Vintonenko et al, 2011). Intracardiac experimental colonisation model. All animal experiments were carried out with the local and IUH Saint Louis ethics committees' approval.
(All these correlations were not shown and ex vivo images were in Supplementary data 2C. ) For each animal, two sections of lungs positive for tumour cell were immunostained with SM-alpha actin antibody. The quantification of positively stained cells was based on a grid-supported manual count. The results were expressed as the following percentage: (number of SM-alpha positive cells/total tumour cells in the lung) Â 100. Viability assay. Cells were plated on 96-well plates (5 Â 10 3 per well) and grown in DMEM-10% FBS for 3 days. The growth of bioluminescent cells was measured using the XTT assay (Roche Diagnostics), as previously described  (Herveet al, 2008). Absorbance was recorded at 450 nm, with a reference at 620 nm serving as a blank, with a microplate reader. The experiments were performed three times, each in triplicate.