dataesr/scientific-paragraphs-categorization huggingface dataset:
All quantitative explanatory variables were transformed using Box-Cox transformation prior to analyses (the corresponding lambda parameters were estimated by maximum likelihood  [46] ). For each SR tx , significant explanatory variables, as well as PCNM vectors, were chosen by model selection (forward and backward) and by minimizing the Akaike Information Criterion (AIC). Statistical significance was assessed by 1999 permutations of the reduced model. The respective contribution of contextual variables (explanatory variables or combinations of explanatory variables) was assessed using redundancy analysis ordination  [47]. All these analyses were performed with R using the vegan package (functions pcnm, varpart and rda).
Results Identification of Soil Abiotic (Physico-chemical) Properties Impacting L. monocytogenes Survival in a Panel of 100 Soils Total cultivable bacteria were enumerated for each soil at day 0, day 7, day 14 and day 84. At day 0, total number of bacteria range from 4. 78610 6 to 1. 07610 8 CFU per gram of soil. We found that the total bacterial community counts remain stable (within the same order of magnitude) over the 2-month experiment period for all investigated soils (Student's t test, p = 0. 265). As soils were stored for varying periods, a regression analysis between L. monocytogenes survival rates and soil sampling date (year) was done. This analysis revealed no correlation between these two variables (data not shown). The age of soil samples doesn't not seem to influence L. monocytogenes survival.
Acknowledgments The RMQS network is supported by a  French Scientific Group of Interest  on soils: the '' GIS Sol '', involving the  French Ministries in charge of Ecology and Agriculture, ADEME ,  INRA , the  Institute for Research and Development (IRD)  and the  National Institute for Geographic and Forest Information (IGN). We thank all the soil surveyors and technical assistants involved in sampling the RMQS sites. Special thanks are addressed to the technical assistants from the  National French Soil Bank  for sample handling and preparation.
3 (Capella-Gutierrez et al. 2009) with a consistency cutoff of 0. 1667 and a gap score cutoff of 0. 1. Trees were then reconstructed by neighbor joining using BioNJ  (Gascuel 1997)  with different models (JTT, WAG, MtREV, VT, LG, Blosum62, CpREV, and DCMut). The two best models according to the Akaike information criterion  (Akaike 1973)  were then used to reconstruct two maximum likelihood trees using phyML  (Guindon et al. 2010). In all cases, a discrete gamma-distribution model with four rate categories plus invariant positions was used, and the gamma parameter and the fraction of invariant positions were estimated from the data. A total of 5,052 trees were reconstructed. Trees, alignments, orthology, and paralogy predictions can be found in phylomeDB (http://phylomedb. org, last accessed December 20, 2013) with the PhyID code 181.
Phylogenomic Analysis To gain insight into the evolutionary history of K. capsulata, we reconstructed the evolutionary histories of all of its proteincoding genes using the PhylomeDB pipeline (see Materials and Methods). The resulting 5,052 gene trees were automatically scanned to predict orthology and paralogy relationships (Gabaldon 2008) and to date duplication events (Huerta- In total, 531 gene families were identified, of which 300 are composed of only two members and 105 of three members (see supplementary table S10, Supplementary Material online). Among the few large families (!10 members), some correspond to species-specific expansions. The three largest families of 85, 54, and 48 members, respectively, distributed throughout the genome, encode proteins with a ser/ thr protein kinase domain, sugar transporters, and proteins with WD40 repeats.
Acknowledgments The authors thank  Antonia Doyen  and  Bart Theelen  for technical advices and assistance, and  Laurent Chatre  for scientific discussions and expert microphotographies of yeasts. Sequencing was supported by  CEA/Genoscope  and performed using HPC resources from  GENCI-TGCC  (Grants 2012076389 and 2013036389). Data analysis and experimental aspects of the work were supported in part 1) by contract  DYGEVO  from  ANR  ( 2011SVSE6 ) to B. D. , 2) by the  Spanish ministry of Economy and Competitiveness  ( BIO2012-37161 ) and the  European Research Council  (Grant Agreement n  ERC-2012-StG-310325 ) to T. G. , and 3) by the  Qatar National Research Fund  grant ( NPRP 5-298-3-086 ) to T. B. and T. G.
Size exclusion chromatography (SEC) of GRA24-containing complexes after Flag affinity selection. Fractions were analyzed by silver staining and immunoblotting to detect GRA24-HF (anti-HA), total p38, and Thr180/Tyr182 phosphorylated *P-p38. Data are representative of two experiments. (C) Mass spectrometry analysis of SEC fraction 22. Identity of the proteins with their respective number of peptides is indicated on the right. (D) GRA24-p38 association detected in HFF and J774 MØ infected by RHku80 GRA24-HF (18 h). Tagged proteins were Flag-immunoprecipitated from extracts and eluates were analyzed by immunoblotting. Data are representative of two experiments. (E) GRA24-associated polypeptides were purified from nuclear extracts of 293-TRex cells induced to express an N-terminal HAFlag-tagged GRA24.
05. (A) Heat map of the GRA24-regulated cytokinerelated genes identified by KEGG analysis. Mean log 2 gene expression values were median-centered and genes were clustered according to the biological pathways identified. The complete set of genes is listed in GEO dataset accession no. GSE38782. (B) Heat map representation of the GRA24-regulated genes associated with classical (M1) or alternative (M2) MØ phenotypes after infection of BMDM with type II strains. Gene symbols and the fold change (FC, at least twofold mean difference) in gene expression are listed for GRA24II-dependent genes for comparison with those reported by  Jensen et al. (2011)  as ROP16I-and GRA15II-regulated. (C) KEGG analysis of the differentially expressed genes when comparing WT versus gra24-infected cells. Genes listed in GEO dataset accession no.
GSE38782 (fold change cutoff twofold or more) were analyzed to determine pathways that were statistically overrepresented (p-value <0. 05). JEM Vol. 210, No. 10 Ar ticle 2079 mice whose survival was monitored over the acute phase of infection. Mice infected by either the WT Pruku80 or Pruku80 gra24 parasites succumb to infection within 10 to 20 d (un published data; n = 6 mice per strain). Therefore, GRA24 deficient parasites did not fully recapitulate the phenotype of MCP1 / and CCR2 / mice that were shown to be pro foundly susceptible to acute toxoplasmosis  (Robben et al. , 2005; Dunay et al. , 2008). This difference might be a result of the genetic background of the parasite strains, the type II Pruku80 strain  (Fox et al. , 2011)  being more virulent and less cysto genic than the type II ME49 strain used in most Toxoplasma infection including those using MCP1 / and CCR2 / mice  (Robben et al.
10  E and not depicted). Unexpectedly, Toxoplasma elicits a prolonged induction of Egr1 and cFos, which correlates with the nuclear persistence of the GRA24-p38 complex during infection but differs from the transient usual canonical activation of these genes. The induction of these transcription factors by GRA24 is most likely followed by a second wave of host cell transcriptome alteration because both Egr1 and cFos then operate in a hierarchical manner by inducing the expression of their downstream genes (e. g. , Egr1regulated genes, see GEO accession no. GSE38782).
, 2012). In this regard, the Toxoplasma tachyzoite, in a GRA24dependent fashion, positively affects the transcription of two CREB or thologs, i. e. , CREB2/ATF2 and CREBL2 (Fig. 10 A ), as well as CREBdependent downstream genes (GEO accession no. GSE38782). We first thought that GRA24 might activate p38, which in turn directly phosphorylates CREBrelated factors in macrophages hosting tachyzoites. But this is appar ently more complicated because Toxoplasma induces the phosphorylation of both CREBSer133 and ATF2Thr71 independently of GRA24 (Fig. 4 D ). Therefore, GRA24 might act upstream in the pathway. It was previously shown that the phosphorylation of Ser 133 facilitates the ability of CREB to interact with CBP (CREBbinding protein) and its paralog p300, therefore leading to the transcription of CREB responsive genes  (Johannessen et al.
Protein bands were ex cised from colloidal blue-stained gels (Invitrogen), treated with DTT and iodoacetamide to alkylate the cysteines, and then immediately subjected to ingel tryptic digestion. Peptides were extracted with 5% vol/vol formic acid solution and acetonitrile, and injected into an Ultimate 3000 (Dionex) nanoLC system that was directly coupled to a LTQOrbitrap mass spectrom eter (Thermo Fisher Scientific). MS and MS/MS data were acquired using Xcalibur (Thermo Fischer Scientific) and processed automatically using Mascot Daemon software (Matrix Science). Tandem mass spectra were searched against a compiled T. gondii database using the MASCOT program (Matrix Sciences). Cell fractionation. For cytosolic and nuclear fractionation analysis, cells were washed in icecold PBS and harvested using a cell scraper.
Structural studies. Based on sequence similarities, we have generated a GRA24KIM 16aa (GLLERRGVSELPPLYI) model using the HePTP phosphatase KIM domain (RLQERRGSNVALMLDV) from structure 2GPH  (Zhou et al. , 2006)  as a template, with appropriate residue mutations. The conformation of the sidechains and orientation of the mainchain for N and Cterminal regions of peptides were modeled based on the bound peptide conformations in crystal structures of ERK2pepHePTPm (2GPH) and p38pepMEF2 (1LEW) peptide complexes, respectively. Similar work has been done with GRA24KIM2(519-534), i. e. , with an R instead of Y, with no significant differences. A p38-GRA24KIM binary complex was then prepared using Coot modeling software  (Emsley et al. , 2010). The structure of p38, isolated from the p38-pepMEF2 complex (PDB accession no.
1LEW;  Chang et al. , 2002)  was used as template for p38-GRA24KIM docking and energy minimization calculations. The p38-GRA24KIM complex was then subjected to energy minimization cycles under the Mac roModel software (Schrödinger, LLC) using the OPLS2005 (Optimized Potential for Liquid Simulations) force field and water as a solvent with con stant dielectric value of 1. All the atoms in protein as well as peptide models were allowed to move freely. Energy minimization of structures were com pleted via the PRCG (PolakRibiere Conjugate Gradient) energy mini mization procedure at 10,000 iterations and performed to a convergence threshold of 0. 02 kJ mol 1 Å). Microarray hybridization and data analysis. BMDMs were plated at 8-10 × 10 6 cells per 55 cm 2 plate, infected (MOI = 3) for 18 h with RHku80, RHku80 gra24, Pruku80, and Pruku80 gra24.
100 µl of hybridization solution was dispensed into the gasket slide and as sembled to the gene expression microarray slide. The Mouse 4× 44K Gene Expression Array was manufactured by Agilent Technologies. The slides were incubated for 17 h at 65°C in a hybridization oven (Agilent Technologies). The hybridized arrays were washed, fixed, and scanned using the DNA Mi croarray Scanner (part number G2505B; Agilent Technologies). Microarray data analysis of acquired array images was performed using the Feature Ex traction software (version 10. 7. 3. 1; Agilent Technologies). Quantile normal ization and subsequent data processing were performed using the GeneSpring GX software package (version 11. 5. 1; Agilent Technologies). After quantile normalization of the raw data, genes in which at least six out of nine samples have flags in Detected or Not Detected (All Targets Value) were chosen for further data analysis.
Differentially expressed genes with statistical signifi cance were identified through Volcano Plot filtering between two groups (WT versus GRA24 mutant) of compared arrays. The thresholds are fold change ≥ 2. 0, pvalue ≤ 0. 05. GO analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis were performed using the DAVID Bioinformatics Resources. Microarray data has been uploaded to GEO Datasets under accession no. GSE38782. Online supplemental material. Fig. S1  shows a schematic representation of the strategy and analysis of GRA24 knockout. Table  S1  provides a list of the vectors and the oligonucleotides used in this study. Online supple mental material is available at http://www. jem. org/cgi/content/full/jem. 20130103/DC1.
Identity of the proteins with their respective number of peptides is indicated on the right. (D) GRA24-p38 association detected in HFF and J774 MØ infected by RHku80 GRA24-HF (18 h). Tagged proteins were Flag-immunoprecipitated from extracts and eluates were analyzed by immunoblotting. Data are representative of two experiments. (E) GRA24-associated polypeptides were purified from nuclear extracts of 293-TRex cells induced to express an N-terminal HAFlag-tagged GRA24. SEC Fractions were analyzed by immunoblotting to detect HAFlag-GRA24 (anti-HA), total p38, and Thr180/Tyr182 phosphorylated *P-p38. Data are representative of two experiments.
Rain-snow partitioning Can improvements to rain-snow partitioning be made? Partitioning between rainfall and snowfall plays a major role in determining both the timing and amount of runoff generation in high altitude basins  (Guan et al. , 2010; White et al. , 2010 White et al. , , 2002;; Gourley et al. , 2009; Lundquist et al. , 2008; Kienzle, 2008; McCabe et al. , 2007; Maurer and Mass, 2006; Westrick and Mass, 2001; Kim et al. , 1998). The question for the NOAA/NWS is: can distributed models provide improved representation of the spatial variability of rain/snow divisions? Traditionally, surface temperature observations have been used to determine the form of precipitation, although such data are not the most reliable indicators of surface precipitation type  (Minder and Kingsmill, 2013; Minder et al.
Benchmarks and performance evaluation Two benchmarks (e. g. ,  Seibert, 2001; Perrin et al. , 2006)  were used to assess model performance. Observed hourly streamflow data from the U. S. Geological Survey (USGS) were used as 'truth. ' Simulations from the NWS operational precipitation/runoff model (hereafter referred to as the lumped (LMP) benchmark) were used as the second benchmark. This model was selected to address the science question regarding the improvement of distributed models compared to lumped models. In addition, the LMP model was chosen for consistency with the DMIP 1 and DMIP 2 Oklahoma experiments  (Smith et al. , 2012b; Reed et al. , 2004). The LMP model actually consists of several NWS components linked together. The NWS Snow-17 model  (Anderson, 2006 (Anderson, , 1976 ) is used to model snow accumulation and melt.
The upper basin comprises 37% of the total basin area, while the lower basin comprises 65%. In the East Fork of the Carson River we used an elevation value of 2134 m to separate the upper and lower zones. These elevation values agree with the configuration of the NWS California-Nevada River Forecast Center (CNRFC) operational models. The LMP model also included the ability to compute the elevation of the rain/snow line using the MAT data and typical lapse rates. This rain/snow elevation is then used in conjunction with the area-elevation curve for the basin in Snow-17 to determine how much of the basin receives rain vs. snow  (Anderson, 2006). A major consideration in any model evaluation or intercomparison is the question: what constitutes a ''good'' simulation or an acceptable level of simulation accuracy  (Seibert, 2001).
Data Participants were required to use the hourly precipitation and temperature grids posted on the DMIP 2 West web site. This gridded data was derived from the same types of in situ measurements used by RFCs to construct MAP and MAT time series for model calibration and operational forecasting. As in the other phases of DMIP, basic forms of many other physical basin features and meteorological variables were provided to promote participation. 2. 9. 1. Precipitation 2. 9. 1. 1. Precipitation data sources. DMIP 2 West used precipitation data collected by the NWS Cooperative Observer Network (COOP; NRC, 1998) available from the National Climatic Data Center (NCDC). Also used were daily precipitation observations from the SNOTEL network. Problems with the COOP and SNOTEL data are well known and include difficulties in distributing multi-day accumulations  (Eischeid et al.
, 2000) , data-entry, receiving, and reformatting errors  (Reek et al. , 1992) , observer errors (e. g. ,  NRC, 1998) , and dealing with varying daily station observation times (e. g. ,  Hay et al. , 1998). Hourly and daily stations from the COOP and SNOTEL networks were selected inside and near the basins. After initial screening for period-of-record (at least 5-10 years) and windward-leeward effects, 41 stations for NFDC1 and 69 for GRDN2 were selected for further analysis. The location of the precipitation and temperature stations is shown in Fig. 2. 2. 9. 1. 2. Generation of gridded QPE. Considerable effort was expended to generate a multi-year, hourly, 4 km gridded QPE data set  (Smith et al. , 2010). One goal in the development of the QPE data was to generate spatially varying gridded precipitation data based on the same types of in situ point measurements currently used by NWS RFCs for lumped model calibration and real time operational forecasting.
In this way we could address the science question: can distributed models be operationally implemented with currently available precipitation and temperature data? An initial QPE data set for 1987-2002 was used to launch the DMIP 2 West experiments  (Moreda et al. , 2006) , but was found to contain a large inconsistency when extended from 2002 to 2006  (Mizukami and Smith, 2012; Smith et al. , 2009). Developing an alternative procedure and QPE data set delayed DMIP 2 West experiments by nearly two years. Finally, an approach was developed using modified NWS procedures as shown in Fig. 4. The method consists of three major steps: (1) data quality control and generation of hourly point precipitation time series, (2) spatial interpola- tion of the point time series to a 4 km grid, and (3) water balance analyses.
Details of these steps can be found on the DMIP 2 West web site: http://www. nws. noaa. gov/oh/hrl/dmip/2/wb_precip. html. Step 1: Data quality control and generation of point hourly data time series. The goal of this step was to generate quality-controlled, serially complete, hourly precipitation time series at all the gage locations, including daily stations. The term serially complete means that precipitation was estimated for each station over the entire analysis period of  1987 -2006 (no missing values; Eischeid et al. , 2000). The hourly and daily data were quality controlled (QC) using standard NWS procedures described in  Smith et al. (2003)  and  Anderson (2002). Double mass analysis was used to identify and correct for human influences. Missing precipitation data were estimated using weighted observations from the closest station in each of four quadrants.
The precipitation records from 1988 to 2006 were examined month by month to identify and correct such errors. Step 2. Generation of gridded QPE. A major task is the estimation of precipitation at ungaged gridded points  (Tsintikidis et al. , 2002). The multi-sensor precipitation estimation (MPE:  Seo, 1998 ) algorithm was used to spatially distribute the point hourly time series onto the DMIP 2 West $ 4 km Hydrologic Rainfall Analysis Project (HRAP;  Green and Hudlow, 1982; Reed and Maidment, 1999)  grid. Data selected from 27 stations in and around NFDC1 and 62 stations for GRDN2 were used to derive the final gridded QPE data sets. MPE uses PRISM data to adjust the interpolation of point precipitation to grids. The 800 m resolution PRISM monthly climatological precipitation data derived for 1971-2000  (Daly et al.
Potential evaporation Participants were allowed to determine the values of potential evaporation data for their models. As in the DMIP 2 experiments in Oklahoma, estimates of climatological monthly PE for both basins were provided. Koren et al. (1998)  used information from seasonal and annual Free Water Surface (FWS) evaporation maps in NOAA Technical Report 33  (Farnsworth et al. , 1982)  and mean monthly station data from NOAA Technical Report 34  (Farnsworth et al. , 1982)  to derive parameters for an equation that predicts the seasonal variability of mean daily free water surface (FWS) evaporation. These parameters were used to derive the mean monthly FWS evaporation estimates for DMIP 2 West basins. The data for NFDC1 are in the same range as the estimates derived by  Carpenter and Georgakakos (2001)  for the entire American River basin.
Analysis of simulated hydrographs As a final check of the DMIP 2 West forcings, the precipitation, temperature, and climatological PE forcing data were tested in hourly lumped and distributed simulations over the project period. Snow correction factors used in Snow-17 for these simulations to compensate for gage undercatch were derived during the calibration of the benchmark LMP model. All suspect simulated hydrograph 'spikes' that did not seem to be consistent with the observed data were investigated  (Smith et al. , 2010). Cumulative streamflow simulation error plots were also examined.
Digital elevation data Participants were not required to use any particular digital elevation model (DEM). 15 arc-s and 1 arc-s DEM data were provided via the DMIP 2 West website to encourage participation. The 15 arc-s national DEM was derived by resampling 3 arc-s DEMs (1:250,000 scale) distributed by the U. S. Geological Survey. 1 arcs DEM data were available from the USGS National Elevation Dataset (NED).
Flow direction data Flow direction grid files at a 30 m resolution were provided for the convenience of any participants who wished to use them. These 30 m grids were used to define the basin boundaries. The basis for these flow direction grids was the 30 m DEM data from the USGS NED data server. The DEM data were projected and filled, and commercial software was used to calculate flow directions using the D8 algorithms of  Jenson and Domingue (1988). Flow directions were also provided at a 400 m resolution. Moreover, we also defined flow directions for coarse resolution model cells that matched or aligned with the grid of available radar-based forcing data (the HRAP grid) using the algorithm described by  Reed (2003). 2. 9. 8. Vegetation and land use data DMIP 2 West provided a 1 km gridded vegetation/land use dataset covering both basins.
These data were originally developed by  Hansen et al. (2000). Thirteen classes of vegetation were defined in these data. 2. 9. 9. Hourly observed streamflow data Instantaneous hourly flow data were acquired from the USGS. The data included some corrections and shifts but were defined as provisional. Unlike the mean daily flow data available from the USGS National Water Information Service (NWIS) web site, the instantaneous observations had not undergone rigorous quality control. However, OHD performed some rudimentary quality control steps. These involved: (1) downloading the approved mean daily flow data from the NWIS web site for the same time periods, (2) computing mean daily flow from the hourly data, (3) visually comparing the derived and approved daily flow time series, hourly streamflow data, mean areal precipitation data, and basic simulations for each basin, and (4) setting any suspicious data in the hourly time series to missing values.
2. 9. 11. Areal extent of snow cover NWS RFCs use snow covered area (SCA) data in the calibration of the operational hydrologic models. For DMIP 2 West, SCA data from the NWS NOHRSC were extracted for the basins. The data consisted of gridded 'snap shots' of SCA on available days, with values in each cell indicating clouds, snow, or no snow.