dataesr/scientific-paragraphs-categorization huggingface dataset:
To assess the mean prestimulus BOLD signal in V1, the functional images were averaged over the 500 most visually responsive V1 voxels. Differences in prestimulus overall activity levels between trial types were assessed by a two-way repeated-measures ANOVA with factors "trial type" (levels: CRs and FAs) and "time" (levels: t ϭ Ϫ2 s and t ϭ 0 s). To probe the orientation specificity of these signals, the functional images were averaged separately in both 100-voxel orientation-selective subpopulations. We aggregated the data according to voxel preference and block orientation: activity in 45°voxels during 45°blocks and activity in 135°voxels during 135°blocks were averaged together as "voxels tuned to expected orientation" and activity in 45°voxels during 135°blocks and activity in 135°voxels during 45°blocks were averaged together as "voxels tuned to nonexpected orientation"  (Kok et al.
10, p ϭ 0. 76). Next, we investigated whether FAs were associated with larger prestimulus activity in the voxels representing the expected percept, that is, the specific oriented stimulus that could be presented during a particular block. For this, we estimated neural activity in V1 voxels preferring 45°and 135°based on an independent localizer dataset (see Materials and Methods) during 45°a nd 135°blocks for both prestimulus time points. We aggregated these data into "voxels tuned to the expected orientation" and "voxels tuned to the nonexpected orientation" and again compared prestimulus neural activity patterns for FAs and CRs, two conditions in which stimulus input was identical (stimulusabsent) but perceptual choice diverged. The 3-way (voxel tuning ϫ trial type ϫ time) ANOVA yielded a significant 2-way interaction between voxel tuning and trial type (F (1,18) ϭ 5.
This work was supported by the  James S. McDonnell Foundation  ( JSMF Scholar Award for Understanding Human Cognition  to F. PGrant DynaMind Project to  SNationale Recherche  (Grants  ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL  to the  Institut d'E ´tudes Cognitives). We thank  Valentin Wyart  and  Tobias Donner  for helpful discussions.
Protein identifications were obtained using the the Phenyx software platform (GeneBio, Geneva) to perform database searching against the human UniProt (SwissProt, Trembl downloaded 11 May 2010) database. A concatenated target-decoy database search strategy was also employed to estimate the rate of false discovery rate,  30  calculated to be 1% and as such were well within the 5% recommended for reporting proteomic data. 31 thematical, bioinformatic and statistical analysis Hierarchical clustering and principal component analysis were performed to group the data based on the degree of similarity between samples. Agglomerative clustering using the squared Euclidean distance between log 10 iTRAQ ratios and smallest inter-cluster dissimilarity linkage procedure was performed (Mathematica 7. 0.
Recruitment and participant demographics The inclusion, exclusion criteria, justification and study design have previously been reported. 36 Of 81 male participants attending for diagnostic colonoscopy were recruited, 19 were excluded from pooling by butyrate owing to a diagnosis of cancer (n=11), failure to provide a stool sample (n=3), or bowel preparation other than Kleanprep (n=5). Biopsies from participants prepared for endoscopy with Picolax (n=5) were pooled separately as bowel preparation influences proliferation of colon epithelial cells. 37 Colonoscopy was completed to the caecal pole in all participants. Adenomatous polyps (confirmed histologically) were present in 28 participants and macroscopically normal colonic mucosa in the remaining 34. The morphometric and AD positional data are shown in the SOI, section 4.
Workflow The workflow for this study is set out in figure  1. The insoluble fraction was processed to yield intermediate filament (IF)-enriched material. 26 The soluble fractions were pooled in fours (empirically determined to yield analysable acetylproteins by IP, data not shown). Samples from the highest and lowest butyrate pools from normal participants, and AD participants at each of the three biopsy sites (a total of eight pools) were immunoprecipitated to yield a pooled IP eluate (acetyl proteins), and a pooled flow-through (other soluble proteins). Participants recruited had biopsies taken and provided stool (for faecal butyrate) and diet information. The frozen biopsies were lysed to yield soluble and insoluble fractions. The former was pooled according to butyrate and subject to immunoprecipitation for acetyl proteins, to yield an acetyl-enriched and a soluble fraction.
The data were further analysed using a Euclidian agglomerative clustering approach, the data suggested that the high-butyrate group were more alike than the low-butyrate group. Figure  2  iTRAQ analysis of the insoluble proteome shows effects of lesional proximity and butyrate level. Samples were separated and relatively quantified by 8-plex iTRAQ. Global analysis of the data was undertaken by principal component analysis (PCA) ( panel A) and hierarchical clustering analysis (HCA) (panel B). PCA showed clustering by butyrate level with low butyrate samples in the blue oval and high butyrate in the orange oval. HCA showed the high-butyrate samples were more alike than the other samples. A protein interaction network was generated from the whole dataset (orphan nodes not shown) indicating proteins interlinked with clusters around extracellular matrix (solid line), keratins (dashed line) and metabolism (dotted line).
Protein interaction networks were built as described above. A large network accounted for many of the proteins, with several smaller clusters (SOI section 11). Significant changes were analysed either by controlling for the butyrate status and assessing the impact of lesion on the proteome (figure  3C ) or by controlling for the lesional status and assessing the impact of butyrate on the proteome (figure  3D ). For clarity on cytoskeletal and related proteins are shown, versions with complete listings appear in the SOI (SOI, section 11). Venns show proteins significantly altered at each site by high or low butyrate. Seven proteins were subject to orthogonal validation by western immunoblot: keratin 8, keratin 19, α-tubulin, ApoA1, M2PK, GAPDH and cofilin (SOI section 12). The data show different responses to butyrate with progressive lesional proximity.
). Analysis in figure  4Eiii , iv and v shows each end point against lesional proximity in samples from MS (normal) MS (AD present) and CL to AD. All outcomes were tested statistically and revealed significant differences between each measure and lesional proximity ( p=0. 019, p=0. 014, p=0. 045, respectively, Jonkheere-Terpstra test). As the normal group was a mixture of individuals from the AD surveillance and index colonoscopies, retrospective data on history of AD was obtained (where available) and a subgroup analysis undertaken. There were no significant differences between any of the keratin end points between subgroups (figure 5Fi-iii and SOI, section 14). Taken together these data reveal significant differences in expression of multiple keratins and their products as a function of butyrate status and lesional proximity.
The similarity between the sporadic and surveillance subgroups suggests that the changes observed associated with presence of a lesion may be reversible by lesional resection. Figure  3  iTRAQ analysis of the global soluble proteome shows effects of butyrate and presence of an adenoma. Samples were separated and relatively quantified by 8-plex iTRAQ. Global analysis of the data was undertaken by principal component analysis (PCA) ( panel Ai) and hierarchical clustering analysis (HCA) ( panel Aii). PCA showed macroscopically normal samples could be separated by a single factor to distinguish high from low butyrate (dashed axis) and a straight line (function of two factors) could separate normal from lesion-associated samples. Panel B hierarchical clustering was used to group the data based on the degree of similarity between the samples analysed using the complete iTRAQ data set.
Quantitative data were used to identify significant differences between the soluble proteome samples according to lesional proximity and controlling for butyrate. Panel C shows comparisons of lesion proximity in high and low butyrate samples and panel D shows changes associated with butyrate level, controlling for lesional proximity. Proteins listed in red are significantly down-regulated, while those in green are up-regulated. iTRAQ, isobaric tags for relative and absolute quantification. Box-and-whiskers plots show distributions of each data for each end point between three different sample sets-mid-sigmoid (normal and adenoma) and contralateral to adenoma. All end points the data showed significant differences ( Jonkheere-Terpstra). When the normal group were separated into new cases (sporadic-spor) or surveillance cases free from pathology but with a history of adenoma (surveillance) there were no significant differences between end points.
11 12 Our data also indicate that keratin forms may be altered as a consequence of the lesion and butyrate status: in lesional tissue the insoluble keratins appeared to be degraded, an observation paralleled in low-butyrate conditions in lesion-free participants. This suggests a contributing chemoprevention action of butyrate. Critically, keratin changes may remain modifiable risk factors: the fibre intervention increased keratin level and retrospective subgroup analysis implied restoration of keratin postpolypectomy. Changes in metabolic proteins were also noted. The colonocyte utilises butyrate as a fuel source,  43  proximity to a lesion impacts on the expression profile of metabolic enzymes, possibly indicating local areas of Warburg metabolism in a primarily β-oxidative tissue. Alterations in pyruvate handling may change the functional threshold for butyrate's histone deacetylase (HDAC) inhibitory activity.
versailles. inra. fr/). Eugene relies on combination of ab initio gene predictions (Eugene_IMM, SpliceMachine  100  and Fgenesh http://www. softberry. com/berry. phtml) and similarity (BlastX against Swissprot and Trembl) evidences. All the gene models were then manually curated with the help of RNAseq data previously assembled with SOAP on the ORCAE platform (http://bioinformatics. psb. ugent. be/orcae/  101 ) and visualized on GenomeView (http://genomeview. org  102 ) and Artemis (http://www. sanger. ac. uk/resources/software/artemis/). All regions potentially coding for peptides of over 100 amino acids (aa) were annotated. CDS of less than 100 aa were only annotated when they presented sequence similarity with known proteins and/or associated with spliceosomal introns and were represented in the RNAseq library.
The genes encoding tRNA were predicted using tRNAscan-SE (http://lowelab. ucsc. edu/ tRNAscan-SE/) using default parameters. The protein coding genes were first functionally annotated by comparison with the S. cerevisiae genome. Genes that failed to show sufficient sequence similarity with S. cerevisiae genes were annotated by comparison against other available yeast genomes, filamentous fungal genomes and Swissprot; they received the annotation "conserved hypothetical protein" when their sequence showed similarity with that of proteins from several species. When a functional annotation was available in the databanks, it was associated to the "conserved hypothetical protein" annotation. Nomenclature for naming genes is the following: species name GECA, scaffold number from 1 to 27 and 32, s for scaffold, gene number with an incrementing step of 11, g for protein coding gene (for example, GECA01s00065g encodes a protein similar to Saccharomyces cerevisiae YNR018W), r for RNA coding gene (for example, GECA01s00238r encodes tRNA-Asp).
Phylome reconstruction. A phylome comprises the collection of phylogenetic trees for each gene encoded in a genome. We reconstructed the G. candidum phylome in the context of 21 additional fungal species ranging across the main dikarya groups, i. e. 10 Saccharomycotina, 8 Pezizomycotina, one Taphrinomycotina and two Basidiomycota (Supplementary Table  S9 ). An automatic pipeline described previously was used to reconstruct the phylome  109. This pipeline includes the standard tree reconstruction steps: homology search, multiple sequence alignment and finally reconstructing the maximum likelihood tree. The homology search was performed using a Smith-Waterman search for each gene (seed gene) in the G. candidum genome (seed genome) against the protein database that contained the proteomes of interest.
Gene expression analysis. Available RNAseq reads were mapped against the produced reference genome using the GSNAP software  121  with default parameters. The resulting alignment files were transformed into raw read counts for each gene making use of htseq-count  122  and the predicted G. candidum gene-models. To obtain the final expression values the raw read counts were normalized for CDS length. Afterwards subset of genes (and expression values) were created based on whether the gene has an ortholog in other Saccharomycotina (141 genes) or not (122 genes). The expression of the genes in these two subsets was then compared to the expression of all other genes in the genome. To investigate the potential difference in expression between the gene sets a Wilcoxon rank-sum test was applied.
Genome characteristics comparison. Data from S. cerevisiae were taken from SGD (http:// www. yeastgenome. org/); Z. rouxii, K. lactis, D. hansenii and Y. lipolytica from Genolevures (http://www. genolevures. org and H. polymorpha from Ravin et al. (2013). Annotations for G. candidum are part of this work and are available from the ORCAE online database (http://bioinformatics. psb. ugent. be/orcae/). nd, not determined. *obtained from cytological analysis (Gente et al. 2002).
Acknowledgements We are grateful to the  Genolevures consortium  for letting us use the proteome of Blatobotrys adeninivorans prior publication. We thank  Jonathan Kreplak  for help with the automatic annotation. Christelle Louis-Mondésir  and  Jan Van de Velde  are gratefully acknowledged for expert technical and statistical assistance respectively. GM received financial support via a joint  CIFRE fellowship  from  ANRT  and the  Centre National Interprofessionnel de l'Economie Laitière  (http://www. maison-du-lait. com/fr/les-organisations/cniel). This work received financial support via an  ANR  [French national research agency] grant under the  ALIA  " Food Microbiomes" project  ( ANR-08-ALIA-007-02 ) and an  INRA  grant under the  AIP Bioressources "CRB CIRM " project.
75, P<0. 001) and Olig2 (fold change=0. 78, P<0. 001) from the monosomic interval were found to be underexpressed (Fig. 5B ). The second group contained 96 upregulated genes that were dispersed throughout the genome, including Cbs (fold change=1. 34, P<0. 001), which is a strong candidate gene for Down syndrome cognitive traits. In our analysis, a majority of deregulated genes showed a fold change that varied from 0. 4 to 1. 6, except for two genes, Klk6 (fold change=1. 70, P<0. 001) and Myoc (fold change=2. 49, P<0. 001). We analysed the enrichment of functional annotation using the DAVID software  (Huang et al. , 2009b).
We did not estimate lung function in adults; nevertheless, our data reinforce the hypothesis of lethality owing to full monosomy 21 in humans  (Schinzel, 1976; Toral-Lopez et al. , 2012). Adult behavioural phenotyping confirmed dramatic changes in locomotor coordination and spatial learning, as well as memory defects of Ms5Yah mice. During the open-field and elevated plus maze tests, Ms5Yah mice showed normal exploratory behaviour and did not demonstrate anxiety. In the rotarod test, mutant mice showed severe deficits in motor skills. Ms5Yah animals were unable to improve their performances during the training phase and failed to stay on the rod, even at speeds lower than those used in the test  phase. Coordination deficits were confirmed by using the notched bar test. The grip-strength test did not reveal any muscular weakness, and no gross anatomical changes were observed upon histological analysis of Ms5Yah cerebellum (supplementary material Fig.
Whole-genome expression arrays In conjunction with the Affymetrix GeneChip WT Terminal Labeling Kit, the Ambion WT Expression Kits were designed to generate amplified and biotinylated sense-strand DNA targets from the entire expressed genome and hybridized onto GeneChip Mouse Exon 1. 0ST arrays (Affymetrix). Chips were washed and scanned using the GeneChip Scanner 3000. Digitized images were generated with AGCC v3. 2 (Affymetrix) software, and data files were generated with the Expression Console v1. 1 (Affymetrix) software. The raw data were processed using the Robust Multiarray Average (RMA) algorithm (developed by  Irizarry et al. , 2003)  and values that were log transformed using the Partek (Partek Inc. ) and GeneSpring (Agilent Technologies) software. Statistical analysis was performed using GeneSpring (one-way ANOVA), and the 192 genes with a fold change above 1.
2 or below 0. 8 and a P-value below 0. 05 were selected for clustering analysis. Hierarchical clustering was conducted using the Cluster3. 0 software  (de Hoon et al. , 2004) , implementing Euclidian distances to calculate the distances between the genes and between the samples. Calculated distances were then clustered by complete linkage clustering. Post-hoc analysis using GeneSpring gave a list of statistically deregulated genes. Known mammalian phenotype database (Mouse Genome informatics, The Jackson Laboratory) and functional annotation clustering using Database for Annotation, Visualization and Integrated Discovery (DAVv) bioinformatics were performed to estimate the potential impact of deregulated genes in transgenic mice. This latter tool primarily provides typical batch annotation and gene-gene-ontology (gene-GO)-term enrichment analysis to highlight the most relevant GO terms that are associated with a given gene list  (Huang et al.
Statistical analysis The results were processed for statistical analysis using the Sigma Plot software (Sigma). All acquired behavioural data were analysed using a one way ANOVA test when applicable or the non-parametric Kruskal-Wallis test. Post-hoc analysis was performed using Tukey's test. Pearson's Chi-squared test was used to evaluate mutant allele transmission. The data are represented as the mean±s. e. m. , and the significant threshold was P<0. 05 unless otherwise indicated.
Funding This work was supported by the  National Centre for Scientific Research (CNRS) ; the  French National Institute of Health and Medical Research (INSERM) ; the  University of Strasbourg (UDS) ; the  'Centre Europeén de Recherche en Biologie et en Medecine '; the  European commission (AnEUploidy project ,  LSHG-CT-2006-037627  to Y. H. ); and a fellowship from the  'Fondation pour la Recherche Medicale ' to T. A. ( FDT20130928080 ). This study was supported by  French  state funds through the  'Agence Nationale de la Recherche ' under the frame programme  Investissements d'Avenir labelled   ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07 PHENOMIN.
Pharmacokinetic Analysis Pharmacokinetic analysis was performed using Win-Nonlin (version 5. 2. 1, Pharsight Corporation, Mountain View, CA, USA). The data obtained after plasma concentration determination were analyzed by compartmental methods. The compartmental analysis was evaluated based on Akaike Information Criteria estimates and coefficient of determination application for the best fit model  (Liu et al. , 2012). The data were analyzed by non-compartment modeling. To find the values of the area under the concentration-time curve (AUC) and area under the first moment curve (AUMC), we used the linear trapezoidal route. The apparent volume of distribution at steady state (VSS) and volume of distribution (VDarea) were calculated according the following equation. VD area = Dose AUC × β VSS = Dose/ AUC AUMC/ AUC The pharmacokinetic parameters are presented as mean ± SD.
The tubes containing bacterial culture and serum were than incubated at 37 • C, and viable counts were determined at 0, 2, 5, 8, and 24 h  (Shan et al. , 2014). The time-kill curves obtained with serum were analyzed with a pharmacodynamic model described by the following equation dB dt = k net × 1 - B B max × B - E max + C γ EC γ 50 + C γ × B Where B, is the number of bacterial cell expressed as cfu/mL, k net the net growth rate, B max the maximum number of bacteria, E max the maximum killing rate, EC 50 , the concentration to reach half of maximal killing rate and γ, the steepness. The ex-vivo time kill curve was fitted with this model with the hypothesis of a decrease in cefquinome concentration according the incubation time using the proc lsqnonlin (Matlab).
Pharmacodynamic Analysis, PK-PD Integration and PK-PD Modeling Analysis For cefquinome, the surrogate markers of antimicrobial activity, AUC 24 h /MIC, C max /MIC, and T>MIC, were determined by linking in vitro data and in vivo pharmacokinetic parameters for serum. dB dt = k net × 1 - B B max × B -k death × B - E max + C γ EC γ 50 + C γ × B The relationship between the ex-vivo AUC 24 h /MIC ratio and thevariation between the initial bacterial count and the bacterial count after 24 h of incubation (cfu/mL) in serum content was established by using the inhibitory sigmoid E max model  (FS and Lees, 2001 ). This model is described by the following equation E = E max - (E max -E 0 ) • C N C N + EC N 50 where E is the effect of antibacterial agent measured as the change in log 10 difference of bacterial count after 24 h incubation compared with the initial log cfu/mL in the serum sample; E 0 is the change in log 10 difference in bacterial count of control sample between 0 and 24 h; E max is the change in log 10 difference in bacterial count between 0 and 24 h in the cefquinome containing samples; EC 50 is the AUC 24h /MIC value producing 50% of the E max ; C is the AUC 24 h /MIC ratio being examined; and N is the Hill coefficient that describes the steepness of the (AUC 24 h /MIC) effect curve.
These PD parameters were calculated using the GraphPad Prism software (version 5. 01, USA). The ex-vivo antibacterial effect of cefquinome after intravenous administration were quantified MIC from the sigmoid E max equation by determining AUC 24 h /MIC for four levels of effect: for bacteriostatic action (no change in bacterial count, that is E = 0), for 50% reduction in the bacterial count, for bactericidal action (a 99. 9% reduction in bacterial count) and for bacterial elimination (a 99. 99% reduction)  (FS and Lees, 2001). The dose was calculated by the using the following formula. Dose = AUC 24 MIC • MIC • CL fu • F Cefquinome is time dependent drug and the PK/PD index responsible for the efficacy is T > MIC. To find T > MIC% we used the following formula T ≻ MIC = In D Vd • MIC • T1/2β In2 • 100 t Where T > MIC (in percent) is the time interval during which the drug plasma concentration is above or equal to the minimal inhibitory concentration (MIC) values; D is the planned dose; t 1/2 the terminal elimination half-life; and t is the dose interval  (Smiet et al.
Acknowledgments This article is financially supported by  National Natural Science Foundation of China  ( 31101856/31302143 ), Grants from  National Basic Research program of China  ( 2013CB127200 ),  National Key Technology R&D Program  ( 2012BAK01B00 ),  Fundamental Research Funds for the Central Universities  ( 2662015PY035 ) and project supported by the morning program of Wuhan in China (2015070404010191).