dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA In this paper we analyse a stellar mass selected sample. Our parent sample is the GOODS-North catalog from Rainbow (PG08), with IRAC 3. 6 µm as the selection band. The IRAC catalog is estimated to be 75% (90%) complete at S 3. 6 = 1. 6 (5. 0) µJy. We restrict the sample to galaxies within the 141 arcmin 2 area surveyed by SHARDS, and also apply stellar mass and redshift constraints to ensure a highly complete sample with reliable measurements of the 4000 Å break in the SHARDS SED (see §2. 4 for the details on the sample selection). The SHARDS filterset consists of 24 contiguous mediumband optical filters. All of them have FWHM = 170 Å except the two reddest ones, which have FWHM = 350 Å. As of May 2013 SHARDS observations are still ongoing, and the current spectral coverage is limited to 16 filters spanning the 619-883 nm range.
Redshifts The GOOODS-N field has been thoroughly explored by redshift surveys, resulting in a large fraction of sources having spectroscopic redshifts down to very faint magnitudes. We use the spectroscopic redshift catalog from the Rainbow database, which contains reliable spectroscopic redshifts for 80% of the sources brighter than R=24. However, since we aim at a mass-complete sample, photometric redshifts are required to avoid underepresentation of the faintest sources within our mass and redshift limits. The photometric redshifts of Rainbow sources are described in PG08. They use standard template-fitting through χ 2 minimisation with a large set of SPS models covering a range of star formation histories and extinction. A preliminary catalog of photometric redshifts using both broadband and SHARDS photometry is also available for sources brighter than K=24  (Ferreras et al.
, in preparation). It uses a template-matching technique, comparing both broadband photometry (from U to Spitzer/IRAC 3. 6µm) and the medium band SHARDS data with a grid of 2000 synthetic templates built from the models of  Bruzual & Charlot (2003). The models explore a wide range of star formation histories, including dust and emission lines. A comparison with the available spectroscopic redshifts in GOODS-N gives an accuracy ∆(z)/(1+z)=0. 02 (median) for the whole SHARDS dataset down to K<24 in the redshift interval of our working sample (with a 1-σ scatter about the median of just 0. 03), in contrast to ∼0. 05 for the photometric redshifts from broadband data alone.
Stellar masses The procedure used to estimate stellar masses is described in great detail by PG08. Very briefly, they used a maximum likelihood estimator to find the SPS model that best fits all the available photometric data points for wavelengths <4µm (rest-frame). The stellar emission in the models was taken from the PEGASE code (Fioc& Rocca-Volmerange 1997) assuming a Salpeter (1955) IMF with 0. 1<M * /M ⊙ <100, and star formation histories (SFH) described by a declining exponential law with timescale τ and age t [i. e, SFR(t) ∝ e -t/τ ]. Stellar masses calculated in this way are considered to be accurate to within a factor of 2-3. The 75% completeness limit of Rainbow for a passively evolving population is at log(M * /M ⊙ )=9. 0 at z=0. 65 and log(M * /M ⊙ )=9. 5 at z=1. 07 (PG08, see also Figure  2 ).
A1 Simulation of SHARDS photometry We simulated SHARDS photometry by convolving a library of optical spectra with the transmission profiles of the SHARDS filters. The library was selected from the zCOSMOS  (Lilly et al. 2007 ) Data Release 2, and includes the 1377 sources in the 0. 5 < z < 1. 2 range with reliable redshift determinations that are not classified as AGN and are brighter than magnitude 22. 5 (AB) in the i band. The cut in optical magnitude ensures the spectrum has a well detected continuum and the 4000 Å break can be properly measured in the full resolution spectrum. Photometric errors are simulated by adding to each magnitude an error term m err = 0. 1σ, where σ is a random variable with normal distribution. This roughly corresponds to the 1-σ uncertainty of the SHARDS photometry at AB magnitude ∼25.
A3 Dependency with the photometric errors The calibration used to obtain corrected D n (4000) values assumes photometric uncertainties around 0. 1 magnitudes, but in some sources (particularly the more massive galaxies) these can be an order of magnitude lower. Repeating the above calibration steps on simulated SHARDS data with 0. 01 magnitudes uncertainties yields nearly identical calibration coefficients. Furthermore, the dispersion of residuals is only slightly lower (Figure  A4 ), indicating that it is the interpolation error, and not the photometric errors, the main cause of dispersion in the Dn4 M versus Dn4 C relationship.
ACKNOWLEDGEMENTS A. H. -C. and A. A. -H. acknowledge funding by the  Universidad de Cantabria Augusto González Linares program. We acknowledge support from the  Spanish Programa Nacional de Astronomía y Astrofísica  under grants  AYA2009-07723-E  and  AYA2009-10368. SHARDS has been funded by the  Spanish MICINN/MINECO  under the  Consolider-Ingenio 2010 Program  grant  CSD2006-00070 : First Science with the GTC. This work has made use of the Rainbow Cosmological Surveys Database, which is operated by the  Universidad Complutense de Madrid (UCM). Based on observations made with the  Gran Telescopio Canarias (GTC) , installed at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias, in the island of La Palma. We thank all the  GTC Staff  for their support and enthusiasm with the  SHARDS  project, and we would like to especially acknowledge the help from  Antonio Cabrera  and  René Rutten.
RESULTS Figure  1  shows the genome-wide association results obtained with two complementary approaches, case/control analysis in PLINK and family-based analysis in estimation of maternal, imprinting and interaction effects software (EMIM) (see Materials and Methods). With PLINK, the strongest signal of association occurred at a group of SNPs on chromosome 12q24 (top SNP rs11065987, P ¼ 4. 6 × 10 28 ), with several other regions, including SNPs on chromosomes 3, 10, 13 and 16 reaching more modest, but suggestive levels of significance (P ≤ 1 × 10 25 ). Quantile -quantile (QQ) plots (Supplementary Material, Fig. S1A ) indicated a slight inflation in the genome-wide distribution of test statistics (genomic control inflation factor l ¼ 1. 076)  (9) , possibly due to unmodelled population substructure.
All samples were screened for the known 22q11. 2 deletion associated with TOF  (33, 34)  using multiplex ligation-dependent probe amplification (MRC-Holland) and excluded, if the deletion was present. SNP genotyping was carried out at the Centre National de Genotypage (Evry Cedex, France) using the Illumina 660wQUAD array, and the genotypes were compared with genotype data for UK population-based controls (5667 individuals genotyped on the Illumina 1. 2M chip) obtained from the Wellcome Trust Case Control Consortium 2 (WTCCC2) (https://ccc. wtccc. org. uk/ccc2). For the replication phase, individuals with TOF of selfreported Caucasian ancestry were recruited from Oxford, Nottingham, Newcastle, The Netherlands and Canada. Five hundred and fifteen Dutch TOF cases were identified from the Netherlands national registry and DNA bank of patients with CHD (CONCOR), the design of which has been previously described  (35).
One hundred and forty-four Canadian TOF cases, together with Canadian controls of self-reported Caucasian ancestry, were obtained from the SickKids Heart Centre Biobank, an Ontario province-wide biorepository for CHD. These replication samples were genotyped at the Centre National de Genotypage using Sequenom matrix-assisted laser desorption/ionization-time of flight. Additional UK population-based control data for the replication were obtained from the TwinsUK resource (http://www. twinsuk. a c. uk), an adult twin registry comprising 12 000 (predominantly female) twins. Genotype data for 3512 twin individuals (genotyped using the Illumina 610K array) were obtained from the Department of Twin Research and Genetic Epidemiology at King's College, London. Only the first twin from each pair of genotyped twins (2603 unrelated individuals) was used in the current study.
Statistical analysis Following stringent quality control (QC) procedures (see below), the final discovery dataset for analysis consisted of 835 unrelated cases and 5159 controls (plus 717 additional family members, including both parents for 293 of the cases), genotyped at 516 131 autosomal and X-chromosomal SNPs. The primary analysis performed was a case/control analysis of the 835 unrelated cases and 5159 controls using either the Cochran -Armitage trend test (for autosomal SNPs) or logistic regression, allowing for gender as a covariate (for Xchromosomal SNPs), using the software PLINK  (36). We also used two alternative approaches designed to correct for possible population stratification: Firstly, we used the 'smartpca' routine of the EIGENSOFT package  (37)  to calculate the top 10 principal components that were entered as covariates into a logistic regression analysis of each SNP across the genome.
Secondly, we analysed each SNP using a score test from a linear mixed model  (38) , allowing for possible relatedness between individuals via consideration of their genome-wide estimated kinship coefficients, using the 'mmscore' function in the R package GenABEL  (39). This approach has been previously proposed as a means of correcting for unknown population structure between apparently unrelated individuals in a genome-wide association study  (40). In addition to the case/control analyses, we also performed a family-based association analysis (of autosomal SNPs only) of all cases, their relatives and controls using the software package EMIM  (41). EMIM is primarily designed for the investigation of complex genetic effects, including maternal genotype effects, maternal -fetal interactions and imprinting  (41).
In this instance, we used the 'child trend' model in EMIM to model only an effect of the child's (case's) own genotype. The resulting analysis was, thus, conceptually similar to the case/control analysis we had performed in PLINK, but with the advantage of allowing additional information from parental genotypes to be incorporated, where available. The replication cohort (after QC) consisted of 798 cases and 2931 controls. Genotypes were analysed using logistic regression in PLINK, allowing for two levels of nationality (British/ Dutch or Canadian) as a covariate. P-values for association in the combined (discovery and replication) datasets were calculated using Fisher's trend approach for combining P-values as implemented in the online software MetaP (http://www. svap roject. org/metap. php).
To further refine the association signal at chromosome 12q24, we carried out imputation in the discovery cohort within the 5 Mb region centred around rs11065987. We used the program IMPUTE version 2  (42)  with data from the 1000 Genomes Project (Phase I interim data, released June 2011) as a reference panel. Data at 11 208 SNPs passing postimputation QC (from an original 56 637 imputed SNPs) were analysed using the program SNPTEST to test for association with disease status.
Discovery cohort We used stringent QC checks to ensure that only high-quality data were included in the final analysis. QC procedures were carried out in PLINK version 1. 07  (36)  with visualization performed in R (http://www. r-project. org/). For the current study, genotype data were generated at 557 124 SNPs across the genome for 1733 individuals (comprising 913 TOF cases plus a number of unaffected relatives). We excluded individuals with genotype call rates ,98. 78% and average heterozygosities outside the range (0. 310, 0. 336) (based on consideration of 540 241 autosomal SNPs passing loose QC, namely successfully genotyped in. 95% of individuals and with a Hardy -Weinberg equilibrium test P-value. 10 28 ). These exclusion thresholds were chosen based on visual inspection of the call rates and heterozygosities (Supplementary Material, Fig.
S6 ). These individuals were excluded. We used the '-check-sex' option in PLINK to check (based on the average X chromosomal heterozygosity) that the gender of our samples matched its expected value and excluded samples for which we were unable to resolve inconsistencies. Following QC, we were left with 835 unrelated TOF cases (plus 717 additional family members), whose genotypes were compared with genotype data from 5159 UK population-based controls obtained from the WTCCC2 (https://ccc. wtccc. org. uk/ccc2). These controls comprised 2673 samples from the 1958 British Birth Cohort study (58C) and 2486 National Blood Service (NBS) samples (selected from an initially genotyped set of 2930 58C samples and 2737 NBS samples). We excluded the same controls as had been excluded in the WTCCC2 (44) and WTCCC3 (45) studies, plus an additional four controls that we found to be outliers, following a principal components analysis using the 'smartpca' routine of the EIGENSOFT package  (37).
Replication cohort QC was also performed on the genotype data from the TwinsUK replication sample. From the 2603 first twins considered, we excluded 43 showing genotype call rates ,99% and average heterozygosities outside the range (0. 312, 0. 331) (based on the consideration of 576 610 autosomal SNPs passing loose QC, namely: successfully genotyped in. 95% of individuals and with a Hardy -Weinberg equilibrium test P-value. 10 28 ). These exclusion thresholds were chosen based on visual inspection of the call rates and heterozygosities. We carried out testing of relationships/sample duplications and ethnicity using the same approach as described above for the TOF cohort and excluded first twins who did not cluster with the CEU HapMap samples and one of each pair of first twins who showed high IBD sharing (mean proportion of alleles IBD.
Post-imputation QC We used the program IMPUTE version 2  (42)  to carry out imputation in the discovery cohort within the 5 Mb region centred around rs11065987. Data from the 1000 Genomes Project (47) (Phase I interim data, released June 2011) were used as a reference panel. Post-imputation QC involved excluding any SNPs likely to be poorly imputed (specifically those with an 'info' score ,0. 5 or with minor allele frequency in controls ,0. 01). Data at 11 208 SNPs passing postimputation QC (from an original 56 637 imputed SNPs) were analysed in the program SNPTEST version 2. 1. 1  (46) , using the '-method ml' option (a Newton -Raphson algorithm that maximizes the missing data likelihood) to account for genotype uncertainty.
ACKNOWLEDGEMENTS This study made use of data generated by the  Wellcome Trust   Case-Control Consortium 2  ( WTCCC2 ). A full list of the investigators who contributed to the generation of the data is available from http://www. wtccc. org. uk. Access to genotype data from the TwinsUK cohort was kindly provided by the  Department of Twin Research (DTR) and Genetic Epidemiology at King's College, London. We thank the staff of the  Twin Research Unit  for their help and support in undertaking this project. We would also like to thank all patients, their families and control subjects for their voluntary contribution to this research project. B. D. K. and J. A. G. acknowledge the support of the  National Institute for Health Research , through the  Northumberland, Tyne and Wear CLRN. Wellcome Trust  (grant number  087436 ), the  Heart and Stroke Foundation of Ontario ,  Ontario Ministry of Research and Innovation ,  McLaughlin Centre, Labatt Family Heart Centre and Canadian Foundation for Innovation  (to S.
These sequence data have supported development of faster and more reliable, virus-species/serogroup, and virus-serotype specific diagnostic assays, using both conventional and real-time RT-PCRs  [27] [28] [29] [30] [31]. Comparisons of nucleotide (nt) and amino acid (aa) sequence data also provide a basis for grouping of orbivirus isolates into topotypes and for molecular epidemiology studies  [23, 25, 32, 33]. However, full-genome sequence data are currently available for representatives of only 11 of the recognized Orbivirus species  [24]  (accession numbers given in Table  S1 ). Additional partial-sequences are available for the highly conserved genome-segment encoding the subcore 'T2' protein (VP3 of BTV) of some Orbivirus species, including Warrego virus (WARV), Wallal virus (WALV), Wongorr virus (WGRV) and CORV  [32]  (Table  S1 ).
'California mosquito pool virus' (CMPV) was isolated in 1974 from pooled Culex tarsalis mosquitoes collected as part of an infectious agent surveillance program conducted by The California Department of Public Health  [5]. Partial sequences for genome segments 2, 4, 6, 7 and 9 from CMPV (accession numbers EU789391 to EU789395) were compared to available data for other orbiviruses, suggesting that CMPV might represent a novel virus species  [5]. However, the lack of reference sequences for representatives of all Orbivirus species, made it impossible to confirm the taxonomic status and species identity of CMPV at that time. We report the full genome sequence of CORV-MRM1 (AUS1960/01). Comparisons of nucleotide (nt) and deduced amino acid (aa) sequences for the conserved polymerase 'VP1(Pol)', subcore-shell 'T2-protein', and outer-core 'T13protein', to data published for other orbiviruses, indicate that CORV and CMPV belong to the same species -Corriparta virus.
Characterisation and coding assignments of CORV genome segments Sequences for Seg-1 to Seg-10 CORV-MRM1 (AUS1960/ 01) have been deposited in the GenBank with accession numbers KC853042 to KC853051, respectively. They range from 3,925 bp to 790 bp (encoding proteins of 1,290 aa to 108 aa) with a total length of 19,093 bp, (Table  1 ). The different genome segments of CORV-MRM1 all share six fully conserved nucleotides at their 59 ends, and 10 at their 39 ends (+ve: 59-GUAUAG………. CAAAGGAUAC-39). Two terminal nucleotides at the 59 end and three nucleotides at the 39 end (59-GU……UAC-39) are characteristic of the genus Orbivirus and the first and last two nucleotides represent inverted complements (http://www. reoviridae. org/dsRNA_virus_proteins/ CPV-RNA-Termin. htm). Collectively the terminal non-coding regions (NCR) represent 4.
99% of the CORV-MRM1 genome (Table  2 ). Comparisons with data from GenBank for other mosquito-borne-orbiviruses (MBOs) showed similar or higher percentages, while the tick-borne orbiviruses (TBOs) and Culicoides-borne orbiviruses (CBOs) show similar or lower values (Table  2 ). Like other orbiviruses, most genome segments of CORV-MRM1 (AUS1960/01), have shorter 59 than 39 NCRs, except for Seg-6 (encoding the smaller outer capsid protein VP5(OC2)) which has a longer 59 NCR and Seg-9 (encoding VP6) which has 39 and 59 NCRs of equal length (Table  1 ). Exceptions also occur in Umatilla virus (UMAV) and Great Island virus (GIV) although the significance of these variations is unclear. Seg-5 (1,936 bp) of CORV-MRM1 which encodes the 'tubule' protein NS1(TuP) (one of the most abundantly expressed orbivirus proteins) is longer than the homologous Seg-5 of the CBOs or TBOs (Table  2 ) with a long (150 bp) 39 NCR, but is smaller than that of some other MBOs  [1].
Full names of virus isolates and accession numbers of T2 protein sequences used for comparative analysis are listed in Table  S1  (supplementary data). 'e' and 'w' after serotype number indicate eastern and western topotype strains, respectively. doi:10. 1371/journal. pone. 0070779. g002 the incomplete sequence data available for CMPV (Table  4 ). CMPV again grouped closely with CORV-MRM1 (AUS1960/ 01), sharing aa/nt identities of 75/67. 4% and 86. 2/74. 3% in VP4(Cap) and VP5(OC2) respectively, further supporting the identification of CMPV as a member of Corriparta virus species. Seg-9 of CORV-MRM1 (coding for NS4 and VP6(Hel)) shares only 42. 6/61. 6% aa/nt identities with CMPV. However, no closer matches were found in either of these proteins with members of other Orbivirus species. Further analysis of CMPV Seg-9 confirmed the presence of an alternate ORF, encoding a 153 aa NS4 protein, which shares 55.
Sequence and phylogenetic analysis 'Raw' ABI sequence data was assembled into 'contigs' using the SeqManII sequence analysis package (DNAstar version 5. 0). The ORFs of CORV-MRM1 genome segments were identified and translated to aa sequences for further analysis using EditSeq (DNAstar version 5. 0). The putative function of each protein was identified by Blast X comparisons to homologous orbivirus (BTV) proteins in GenBank (http://blast. ncbi. nlm. nih. gov/Blast. cgi?CMD = Web&PAGE_TYPE = BlastHome). Multiple alignments of consensus sequences were performed using Clustal X (Version 2. 0)  [62] , Clustal Omega (http://www. ebi. ac. uk/Tools/ msa/clustalo/) and MAFFT  [63]  to ensure proper alignment. Aligned protein sequences were back translated to nucleotide sequences using DAMBE  [64] ) or RevTrans 1.
4 server available online (http://www. cbs. dtu. dk/services/RevTrans/) for further nucleotide analysis. Pairwise distance (aa and nt) calculations and phylogenetic trees constructions were done using MEGA 5 software  [65]  with the p-distance parameter and neighbourjoining method  [66]. GenBank nucleotide accession numbers of polymerase (VP1), T2 and T13 protein sequences that were used in phylogenetic analyses are provided in Table  S1  (supplementary data).
1 -*Figure 3. Figure 4. 134 Figure 3. Unrooted neighbour-joining for orbivirus polymerase VP1(Pol) proteins. An unrooted NJ phylogenetic tree for orbivirus VP1(Pol) proteins was constructed using a p-distance algorithm and pairwise deletion parameters, as indicated in Figure 1. The CORV-MRM1 isolate characterised in this study is indicated in red font in amber coloured circle. Full names of virus isolates and accession numbers of polymerase sequences used for comparative analysis are listed in Table S1 (supplementary data). 'e' and 'w' after serotype number indicate eastern and western topotype strains, respectively. doi:10. 1371/journal. pone. 0070779. g003.
Images were captured with a microscope (ZEISS Axiophot) coupled with a CCD camera connected to a computer. MHC analysis was realized with Image-JH software. Muscle fiber Cross-Sectional Area (CSA) was obtained in 610 magnification images from 1500 fibers per group (250 fibers per muscle from 6 rats). Western Blotting 20 mg FDP samples were homogenized in 10 volumes of lysis buffer (Tris 20 mM pH 6,8, NaCl 100 mM, EGTA 1 mM, NaF 100 mM, Triton X-100 0,5%, Na 3 VO 4 5 mM) with inhibitory protease cocktails (P8340, Sigma Aldrich, St Louis, MO). The homogenate was rotated 10 min at 4uC and the supernatant collected. Protein samples (50 mg) were denatured and separated on 10% SDS-PAGE. The proteins were transferred onto a nitrocellulose membrane and blocked in 5% wt/vol BSA for phosphoSTAT1 (Tyr 701), phosphoSTAT3 (Tyr 705), phos-phoErk1/2 (Tyr 202/204) and 5% wt/vol dry milk for STAT1, STAT3, Erk1/2 and a-tubulin antibodies in Tris-buffered saline with 0.
1% vol/vol Tween 20 (TBST) for one hour at room temperature. Primary antibodies pSTAT1 (1/1000), pSTAT3 (1/ 1000), pErk1/2 (1/1000), STAT1 (1/1000), STAT3 (1/1000), Erk1/2 (1/1000) and a-tubulin (1/2500) diluted in blocking buffer were applied and incubated overnight at 4uC. Membranes were subsequently washed three times with TBST and incubated one hour at room temperature with a secondary antibody conjugated to horseradish peroxidase, donkey anti-rabbit IgG (1/4000) for pSTAT1, pSTAT3, Erk1/2, STAT1 and STAT3 and rabbit antimouse IgG (1/4000) for pErk1/2 and a-tubulin. Proteins were visualized by enhanced chemiluminescence (32106, PierceH, Rockford, IL) and quantified with ImageJH software. Levels of pSTAT1, pSTAT3 and pErk1/2 proteins were expressed relative to total STAT1, STAT3, Erk1/2 respectively.