dataesr/scientific-paragraphs-categorization huggingface dataset:
The proteins present in the samples were identified from MS and MS/MS data by using MASCOT v. 2. 2 software for search into two concatenated databases: (i) a homemade database containing all the predicted proteins of the P. freudenreichii strain CIRM-BIA 129 used in this study and (ii) a portion of the UniProtKB database corresponding to P. freudenreichii. Search parameters were set as follows. A trypsin enzyme cleavage was used, the peptide mass tolerance was set to 0. 2 Da for both MS and MS/MS spectra, and two variable modifications (oxidation of methionine and deamidation of asparagine and glutamine residues) were selected. For each protein identified in NanoLC-ESI-MS/MS, a minimum of two peptides with MASCOT score corresponding to a P value below 0. 05 were necessary for validation with a high degree of confidence.
Gels were then fixed and Coomassie Blue-stained as described above. Visible images were acquired on an ImageScanner III (GE Healthcare). Images were further analyzed using Image-Master 2D software. Fluorescent profiles of 2-DE-separated proteins were reproducible in at least three individual experiments. Fluorescent and Coomassie-Blue visible images of the 2D electrophoresis gels were matched to detect surface-exposed proteins. Fluorescent spots corresponding to surface-exposed proteins were excised from 2-DE gels as previously described  [7]  when detectable by Coomassie-Blue staining. Proteins were identified by tandem mass spectrometry (MS/MS) after an in-gel trypsin digestion adapted from Shevchenko  [9]. Briefly, gel pieces were washed with acetonitrile and ammonium bicarbonate solution, and then dried under vacuum in a SpeedVac concentrator (SVC100H-200; Savant, Thermo Fisher Scientific, Waltham, MA, USA).
Motivation is important for learning and cognition. Although dopaminergic (D2) transmission in the ventral striatum (VS) is associated with motivation, learning, and cognition are more strongly associated with function of the dorsal striatum, including activation in the caudate nucleus. A recent study found an interaction between intrinsic motivation and the DRD2/ANKK1 polymorphism (rs1800497), suggesting that A-carriers of rs1800497 are significantly more sensitive to motivation in order to improve during working memory (WM) training. Using data from the two large-scale imaging genetic data sets, IMAGEN (n ¼ 1080, age 13-15 years) and BrainChild (nB300, age 6-27), we investigated whether rs1800497 is associated with WM. In the IMAGEN data set, we tested whether VS/caudate activation during reward anticipation was associated with WM performance and whether rs1800497 and VS/caudate activation interact to affect WM performance.
Furthermore, genetic variations in COMT and MAOA have been associated with VS BOLD  (Camara et al, 2010; Nymberg et al, 2013a) , but these studies did not make any connection with WM and it is unclear whether the effect of rewardrelated brain activity on WM is moderated by genetic variations in dopaminergic genes. Using two independent data sets, the international IMAGEN study and the Swedish BrainChild cohort, we investigate genetic and neural mechanisms that may affect WM. The IMAGEN study is composed of genetic, neuroimaging, and behavioral data from B2000 13-15-year-old adolescents, whereas the BrainChild data set is composed of genetic, neuroimaging, and cognitive data from B300 children, adolescents, and young adults, age 6-27 years. Whereas the caudate is activated during performance of WM tasks, VS BOLD, reflecting motivational drive has not been associated with WM performance.
Information regarding exclusion criteria and movement outliers is available in Supplementary Information. This study is based on results from So ¨derqvist et al (2013) who divided genotype groups into AA/AG versus GG-homozygotes; thus, we merged the AA and AG groups. Previous analyses of the IMAGEN data set have addressed questions regarding reinforcement-related behavior. These studies particularly target questions of how brain function affects normal and pathological behavior in adolescence. For reviews on the IMAGEN data set, please see  (Nymberg et al, 2013b; Schumann et al, 2010). Genetic data. Deoxyribonucleic acid was extracted from blood samples. The DRD2/ANKK1 Taq1a genotype is located on chromosome 11q23. 1, rs1800497. Genotyping procedures have been previously published  (Nymberg et al, 2013a).
MRI analysis. Preprocessing and statistical analysis of fMRI data were performed in SPM8 (Statistical Parametric Mapping, http://www. fil. ion. ucl. ac. uk/spm). Slice-time correction was conducted to adjust for time differences owing to multislice-imaging acquisition; all volumes were aligned to the first volume and non-linear warping was performed to an EPI template. Images were then smoothed with a Gaussian kernel of 5-mm full-width at half-maximum. At the first level of analysis, changes in the BOLD-response for each subject were assessed by linear combinations at the individual subject level; for each experimental condition, each trial was convolved with the hemodynamic response function to form regressors that account for potential noise variance associated with the processing of reward anticipation.
Acknowledgements This work was supported by the  Institut Universitaire de France (IUF) , the  CNRS  ( UMR 7509 ), the  University of Strasbourg , the  Agence Nationale de la Recherche  ( ANR , grant number  11-BS07-003-02 ), and a doctoral fellowship from the  French Department of Research  to  Mathieu L. Lepage. We also thank  Michel Schmitt  and  Lionel Allouche  for NMR measurements and  Hélène Nierengarten  for MS measurements. We thank the  University of Salerno (FARB)  and the  Italian MIUR  ( PRIN 20109Z2XRJ_006 ) for financial support. We thank  Dr. Patrizia.
The percentage of PMNs in BALs, defined as Gr-1 pos CD11b pos cells, was determined by flow cytometry analysis (BD Bioscience, LSR-II). Monoclonal antibodies used for flow cytometry were obtained from BD Biosciences (Le Pont de Claix, France): anti-CD11b (M1/70), and from eBiosciences (San Diego, CA, USA): anti-Gr-1 (RB6-8C5). The percentage of PMNs in BAL was also performed by using Ly6G  [24]. Data were analyzed using FlowJo software (Treestar, USA).
Fluorescence imaging Fluorescence images were first performed using a TCS SP5 AOBS (Leica Microsystems; implemented at the Centre de Photonique Biomedicale of Orsay). Cells were observed using a 63x, 1. 4 numerical aperture plan Apochromat oil immersion objective. An Argon ultraviolet laser with a maximum wavelength of 364 nm (1. 3 mW mesured at the back focal plan of the objective) was used as the excitation source for the cell endogenous NAD(P)H. Fluorescence was collected in the 400-550 nm range. The flavin fluorescence, which was tested with an Argon laser with maximum wavelength of 488 nm, appeared to be insignificant in these immune cells (data not shown). The resolution of the confocal images was 5126512 pixels (corresponding to an area of ,2506250 mm 2 ), recorded on 12 bits with a zoom value of one.
Each image corresponds to an average of four scans. The quantification of the fluorescence intensity was carried out with LASAF software (Leica Microsystems, Wetzlar, Germany) using Region of Interest (ROI) over isolated cells present in the field of each image (approximately 10 ROI per image). Each ROI was carefully outlined by avoiding any area presenting multi-layer superoposed cells. Data were collected in three to five independent experiments for each condition. For the mouse and human BAL study, fluorescence intensity analyses were carried out by two different trained operators blinded to mouse or patient status. Further fluorescence images were obtained using a conventional right epifluorescence microscope (Olympus BX 51, Tokyo, Japan) equipped with a continuous mercury-arc lamp (X-cite 120Q, EXFO) for excitation and a monochrome cooled CCD camera (CCD XM10 camera, Olympus) for fluorescence emission detection through a 40x objective.
Obtained nucleotide sequences were edited with BioEdit 7  [21] , translated to protein sequences, and identified with blastp using GenBank database. Protein sequences were aligned using ClustalW and phylogenetically compared with references obtained from fungal cultures, GenBank and PeroxiBase  [22]  using MEGA 5  [23]. Following parameter for a neighbor-joining analysis were used: substitution model: Poisson, site rates: uniform, gaps: pairwise deletion. For branch support 1,000 bootstrap replicates were performed; only values above 50% were shown. All 173 newly obtained sequences were submitted to GenBank and are available under accession numbers JQ654238-JQ654411 and FR875240-FR875262.
All new reference sequences were submitted to GenBank under accession numbers JQ654412-JQ654445. To test extracellular MnP activity, all strains were cultured on Kirk agar plates supplemented with 100 mg l 21 MnCl 2. Plates of MnP-positive fungi developed characteristic dark-brown to black rings or spots caused by precipitating manganese(IV) oxide (MnO 2 ) that, for its part, resulted from the disproportionation of Mn 3+ (2 Mn 3+ RMn 4+ +Mn 2+ ), formed through the oxidation of Mn 2+ by MnP  [24].
Phylogenetic analysis All new fungal peroxidase sequences were subject to phylogenetic analyses (Figs. S1, S2, S3 ). A phylogenetic analysis of 80 (76 unique when compared on nucleic acid sequence level, 71 on protein level, Table  2 ) partial class II peroxidase protein sequences from environmental samples together with 360 reference sequences obtained from fungal cultures, GenBank and PeroxiBase, yielded a distinct clustering structure (Fig. S1 ). Class II peroxidases were clustered following Lundell and coworkers  [4]  and Floudas Additional support for this finding was obtained when the amino acid residues of the partial protein sequences were analyzed in more detail. The majority (96%) of the environmental sequences displayed no putative catalytically active tryptophan residue (Trp-171 in Phanerochaete chrysosporium LIPH8, Fig.
S1 ). Altogether, 76 (72 unique) environmental partial UPO protein sequences were compared to 75 reference sequences from GenBank and data from this study (Fig. S2 ). The majority of environmental sequences clustered close to basidiomycete references in a larger clade (group I. 1, Fig. S2 ), apart from clades dominated by ascomycete reference sequences (group I. 2). However, none of the investigated environmental sequences were found to be closely related to the clade (group II) of A. aegerita UPO1 and UPO2 or C. cinerea UPO sequences, which contains well-studied UPO (previously assigned APO) enzymes  [28]. This finding is supported by large differences in length in the amplified partial protein sequences. Within the amplified region, environmental sequences of group I. 1 were generally 66-74 (mean 70) amino acids long, whereas homologous regions in the full sequence of A.
Some of the analyzed species, e. g. , C. nebularis, C. tuberosa and E. glandulosa (a wood degrader), exhibited transcripts of all three (super)families of secretory peroxidases (MnPs, DyPs, UPOs). A number of other litter-decomposing fungi were found to contain expressed MnP genes, which supports the hypothesis that Mn oxidizing peroxidases are key enzymes of litter and humus degradation in forest floor  [34] ,  [37]. Mn-agar plate tests complemented the finding of MnP transcripts by the formation of dark-brown MnO 2 rings and spots originating from MnPactivity  [24]. A current limitation, however, is the still scarce availability of peroxidase gene references in databases. For example, as recently evaluated (mid 2013), PeroxiBase and GenBank contained 311 entries of class II peroxidases from wood-decay fungi vs.
Supporting Information Figure S1 Phylogenetic analysis of class II peroxidases from basidiomycetes. The phylogeny was inferred using the Neighbor-Joining method. The optimal tree with the sum of branch length = 41. 32300055 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1,000 replicates) is shown next to the branches. The evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site. The analysis involved 440 amino acid sequences. There were a total of 148 positions in the final dataset. Classification and annotation of larger groups shown in green followed Lundell and coworkers  [4] , in red: single genome references followed Floudas and coworkers  [3] , and GenBank entries in blue.
There were a total of 98 positions in the final dataset. In orange: peroxidases from litter and soil saprotrophs, green: ectomycorrhizal fungi, black bold: environmental sequences. Transcriptional expressed own new reference sequences are underlined. (JPG) Figure  S3  Phylogenetic analysis of fungal dye-decolorizing peroxidases (DyPs). The evolutionary history was inferred using the Neighbor-Joining method. The optimal tree with the sum of branch length = 33. 91019168 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1,000 replicates) is shown next to the branches. The evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site. The analysis involved 160 amino acid sequences.
Acknowledgments We thank especially the management teams and the main PIs of diverse research sites and research programs. Further we like to thank  Britta Bittner ,  ReneUllrich ,  Ulrike Schneider ,  Kristiina Hilden ,  Angel T. Martı ´nez ,  Francisco J. Ruiz-Duen  ˜as,  Laurent Vallon ,  Richard Joffre ,  Jacques Ranger  and many more for help, advices or laboratory assistance.
The work has been funded by the  Deutsche Forschungsgemeinschaft -DFG   Priority Program 1374 ' ' Infrastructure-Biodiversity-Exploratories '' ( HO 1961/ 4-1 ,  KR 3587/1-1 ),  Gembloux Agro-Bio Tech and Fonds de la Recherche Scientific -FNRS  (Grant No  2. 4503. 08 ), French Research Foundation  ( EC2CO  program  MicrobiEn 2009  and project  ANR-06-BDIV-006  Fundiv),  US National Science Foundation 's  Long-Term Research in Environmental Biology program  and the  US Department of Energy's Biological and Environmental Research Division , and  European Union  integrated project '' Peroxicats '' ( KBBE-2010-4-265397 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Software such as Visual Gene Developer may help to optimize the study of pharmaceutical recombinant genes and proteins by analyzing synthetic genes and testing new algorithms in bioinformatics  (Jung & McDonald, 2011). The software can be found and downloaded from http://www. visualgenedeveloper. net. In silico analysis is a very good tool for the design of new drugs and for studying existing vaccines and antibodies, as Plant Science Today (2014) 1(  4  it is necessary to optimize drug design by the simulation of spatial structures and molecules  (Jiang & Zhou, 2005; Kamphausen et al. , 2002)  The design of suitable linkers to separate domains of biofunctional proteins is an indispensable tool in protein engineering  (Arai, Ueda, Kitayama, Kamiya, & Nagamune, 2001). A few scientists have suggested and created a web server and software for this purpose, such as ''Linker'', a program to generate linkers for protein fusions  (Crasto & Feng, 2000).
Data The SNP genotypes obtained using DNA samples extracted from blood were performed according to the French National Guidelines for the care and use of animals for research purposes. Animal genotypes used in this study were the same as in Carillier et al. [3]. After a quality check (MAF > 1%, call rate > 98%) that was done separately for the two breeds, 46 959 SNPs (out of 53 347 of the Illumina SNP50 BeadChip  [1] ) were validated. Missing SNP genotypes (0. 1%) were not imputed but the GBLUP method used took missing data into account when estimating GEBV. From the 825 genotyped bucks Five milk production traits that were derived from a total lactation were analyzed, i. e. milk yield (kg), fat and protein yields (kg), fat and protein contents (g/kg), along with somatic cell score (SCS: log-transformed somatic cell counts), and five udder type traits that were scored on a linear scale of 1 to 9, i.
e. udder floor position, udder shape, rear udder attachment, fore udder and teat angle. Udder type traits were recorded once for each female. Repeatability was not modeled for the udder type traits. Data originated from the official genetic evaluation of January 2013, using only records on purebred Alpine and Saanen goats. For milk production traits, 4 178 315 Alpine records (30. 2% first lactations, 24. 2% second lactations and 45. 6% third or more lactations) and 3 173 516 Saanen records (31. 1% first lactations, 24. 5% second lactations and 44. 4% third or more lactations) of females born between 1950 and 2012 were used. Recently, the number of records for SCS and type traits has been smaller than that for milk production traits. Weights for SCS and milk production records were as defined in the official genetic evaluation  [20, 21]  according to lactation number (from 1 to 10) and length of lactation (up to 180 days or not).
Estimation of genetic parameters The genetic parameters used in the multi-breed model were those used in official genetic evaluations (Table  1 ). For the multiple-trait and within-breed models, genetic parameters were estimated from the data (Table  2 ), except for repeatabilities, which were those in Table  1  and considered similar for both breeds. Genetic and residual variances σ 2 u s , σ 2 u a , σ 2 e s and σ 2 e a and the genetic covariance were estimated by restricted maximum likelihood (REML) with remlf90 software  [22] , using a multiple-trait model (see above) and the multibreed H matrix described earlier. Standard deviations of heritabilities and genetic correlations were estimated using the approximation of Klei  [24] , using airemlf90 software  [22]. Because of computational issues, we chose to estimate these parameters on a subset of first lactation records from the data, without considering repeatability.
For milk production traits, these parameters were estimated using 113 980 first lactation records (49 201 for the Saanen and 64 779 records for the Alpine breed) from the population of all females born between 2008 and 2012. Since the data included fewer records for SCS and udder type traits, all first lactation records on all females born between 2002 and 2012 were used for these traits (i. e. 130 230 records for SCS and 202 102 for udder type traits). These analyses included 1985 females genotyped with the Illumina SNP50 BeadChip  [1] , using the same quality control as for males. Genotyped females were groups of 100 half-sibs from 20 different sires  [3]  involved in a design for quantitative trait locus (QTL) detection  [25]. [26]  for the validation males, and regression coefficients of DYD on GEBV.
Acknowledgements The authors thank the  French Genovicap and Phenofinlait programs  ( ANR, Apis-Gène, CASDAR, FranceAgriMer, France Génétique Élevage, French Ministry for Agriculture ) and the European  3SR  project, which funded part of this work. The first author also received financial support from the  Midi-Pyrénées region  and the  French National Institute for Agricultural Research (INRA) SELGEN program. We also thank the  GenoToul bioinformatics facility in Toulouse  for providing computing and storage resources. This study would not have been possible without the goat SNP50 BeadChip developed by the  International Goat Genome Consortium (IGGC) : http://www. goatgenome. org. The authors thank  Ignacy Misztal  for the availability of the blup90iod2 program. Author details 1  INRA ,  UMR1388 Génétique, Physiologie et Systèmes d'Elevage, 31326 Castanet-Tolosan, France.
The UKGPCS (stage I) dataset has been previously described  (6). Briefly the cases (n=2017) were clinically detected and selected if the man had a diagnosis at ≤60 years of age or a first/ second degree family history of PrCa. Self-reported "non-white" men were excluded, as were those who were diagnosed through asymptomatic screening. UKGPCS controls (total n = 1893) were solely selected from men in the ProtecT study who had a PSA <0. 5ng/ml. In the current meta-analysis, we excluded from the UKGPCS results those men who were in the ProtecT GWAS control series described above (n=456) so that the populations in the pooled analysis were independent samples (Figure  1 ).
Imputation of autosomal genotypic data used Markov Chain Haplotyping software (MACH v. 1. 0. 16  (22) ) and phased haplotype data from CEU individuals (HapMap release 22, Phase II NCBI B36, dbSNP 126) on 514,432 autosomal SNPs. All SNPs with poor imputation quality (r 2 hat <0. 3) were removed. The final working dataset included 2950 individuals (cases n=1146, controls n=1804). A total of 1272 controls were included from the first round of "unrestricted" control selection (PSA <0. 5ng/ml n=238; PSA ≥0. 5 and PSA<3ng/ml n=941; PSA≥3ng/ml n=93) and a further 532 from the second round ("supernormal" control selection).
Statistical Analysis The statistical analysis was undertaken in four stages (see Figure  2 ). In the first stage, a genome-wide association analysis was performed on the ProtecT samples using the software package MACH2DAT  (22, 23) , employing a logistic regression model based on an expected allelic dosage model for SNPs. Associations between each SNP and disease were assessed by a Wald test and a p-value of < 5 × 10 -8 was considered genome-wide significant. All estimates are reported in the direction of the risk allele. In the second stage, we meta-analysed the top SNPs identified in ProtecT with the UKGPCS dataset. A total of 381 SNPs reaching a threshold for suggestive significance of 5 × 10 -5 in the ProtecT GWAS were included in the meta-analysis. The threshold level of 5 × 10 -5 was a pragmatic choice, based on similar thresholds for suggestive significance cited in the literature, because of the relatively small sample size in ProtecT.
genome. gov/gwastudies/: downloaded 21/11/2012). We supplemented this list with additional SNPs associated with PrCa in a recent meta-analysis (2), resulting in a total of 83 SNPs. Using this list we extracted the individual genotypic data for all participants in our ProtecT dataset, although two SNPs were neither genotyped nor imputed (rs7210100 and rs16902094). Using the genotypic data we generated effect estimates for each SNP using multinomial logistic regression, where each control group (low and high) was compared to cases separately (Figure  2 ). To test the null hypothesis, that there was no difference between the two control groups when used to estimate the odds ratios for the associations of SNPs with PrCa, we carried out a separate logistic regression model that tested the association of each SNP with control type (low vs.
Acknowledgments The authors thank the tremendous contribution of all members of the  ProtecT study research group , and especially the following who were involved in this research ( Athene Lane ,  Prasad Bollina ,  Sue Bonnington ,  Lynn Bradshaw ,  James Catto ,  Debbie Cooper ,  Michael Davis ,  Liz Down ,  Andrew Doble ,  Alan Doherty ,  Garrett Durkan ,  Emma Elliott ,  David Gillatt ,  Pippa Herbert ,  Peter Holding ,  Joanne Howson ,  Mandy Jones ,  Roger Kockelbergh ,  Howard Kynaston ,  Teresa Lennon ,  Norma Lyons ,  Hing Leung ,  Malcolm Mason ,  Hilary Moody ,  Philip Powell ,  Alan Paul ,  Stephen Prescott ,  Derek Rosario ,  Patricia O'Sullivan ,  Pauline Thompson ,  Sarah Tidball ). We thank  Gemma Marsden , who processed the blood samples at the biorepository and  Dr Chris Metcalfe  for providing statistical advice.