dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, intermediate forms were analysed for the first time to assist the identification of transcripts that showed progressive increase or decrease in abundance between slender and stumpy forms, or transient increase at the intermediate stage. These profiles were expected to highlight programmed developmental changes. Total RNA from each life cycle stage of these two strains was polyA-enriched, converted to cDNA, and sequenced using Digital SAGE transcriptome techniques. Although this approach has been largely superseded by RNAseq methods since the experiments were carried out, Digital SAGE provides information of similar sensitivity with respect to relative mRNA abundance and at lower cost, enabling more biological samples to be analysed. RNA samples were prepared as previously described  [29] , fragmented using NlaIII and Solexa sequenced at MWG Eurofins Operon and 'The Genepool', University of Edinburgh (library coverage data for each sample is provided in Table  S1 ).
Adapter sequences and short or poor quality reads were trimmed from the data using Trimmomatic and aligned using SMALT v0. 6. 4 with a maximum of 2 mismatches per read and multiple mappings given a score of 0. The alignment index for SMALT was created with a word length of 11 from the most recent GeneDB trypanosome contigs (version: 13/03/2012) (http://www. genedb. org/Homepage/ Tbruceibrucei927) with ORFs extended using the predicted UTR data from the Cross Laboratory  [30]. Including the predicted UTRs increased the ability to map alignment reads to whole transcripts through its inclusion of more possible NlaIII fragments  [31]. Pairwise comparisons of the relative expression of transcripts between life cycle stages were performed using the DESeq 1. 11. 2 package within R 2. 15. 1 using a binomial model and a blind variance stabilising transformation, this normalising for variation in raw read counts from individual samples.
8. 760), Fibrillarin (Tb927. 10. 14750), Gu (Tb927. 5. 4420), cyclophilin NCP1(Tb927. 8. 2000)  and Puf7 (Tb11. 01. 6600), the latter two of which have been shown to interact with each other  [33]. Among the stumpy-enriched transcripts were several that affect transmission to the tsetse stage of the infection. These include MSP-B (Tb927. 8. 1640), required for VSG release upon differentiation, PSSA-2 (Tb927. 10. 11220) and TbPIP39 (Tb09. 160. 4460), required for recognition of the CCA differentiation signal  [34]. In common with intermediate cells, there are nucleolar-associated transcripts that are enriched that may be involved in quiescence (NOG1 Tb11. 02. 0620). Reassuringly, several transcripts previously identified as being elevated in stumpy forms are present in our dataset. These include the purine nucleoside transporter NT10 (Tb09.
As false discovery rate (FDR) could not be robustly estimated for the dataset, five genes predicted to be enriched in stumpy cells were selected for verification by qRT PCR (Tb927. 6. 4570; Tb927. 2. 2540; Tb11. 01. 3790; Tb927. 8. 6380; Tb927. 8. 6990; Tb927. 7. 3150) (Figure  3 , Figure  4B ). All of these transcripts showed significantly increased abundance in stumpy form RNA relative to slender form RNA (Tb927. 6. 4570, p = 0. 036; Tb927. 2. 2540, p = 0. 035; Tb11. 01. 3790, p = 0. 036; Tb927. 8. 6380, p = 0. 019; Tb927. 7. 3150, p,0. 001), except Tb927. 8. 6990 (p = 0. 284). Tb927. 7. 3150, encoding a protein with a predicted CRAL-TRIO domain (see later) was also analysed by Northern blotting (Figure  4A ) and this again showed the expected progressive increase in expression from slender to intermediate and stumpy forms.
Analysis of genes that are Enriched in the Polysomal Fraction of Stumpy Forms To investigate transcripts that escaped the general translational repression observed in stumpy forms, total mRNA from slender and stumpy forms was subjected to polysome gradient fractionation in order to enrich actively translated mRNAs from each life cycle stage. Insufficient material was available to derive polysomal A. Realtime qRT PCR data for the relative expression of five transcripts predicted to be enriched in stumpy forms. mRNAs from slender and stumpy forms were subject to real time qRT-PCR using a constitutive transcript, GPI8, as a normalisation control. Although the scale of relative expression in each case differed from the global transcriptome study the direction of change, and the enrichment of each transcript in stumpy forms, was consistent, with the exception of Tb927.
Further analysis of this dataset involved comparison with the surface phylome analysis of slender and stumpy forms, to identify transcripts that were predicted to be membrane associated and polysomally enriched in stumpy forms, hence revealing molecules potentially important in transmission capacity (Data File S8). This subset included GRESAG4 family members, amino acid transporter family members, purine nucleoside transporters, ESAG3 pseudogenes and trans-sialidases.
Both components add value to the dataset, allowing genes whose expression fluctuates dramatically under different conditions (potentially unrelated to development) to be identified as well as transcripts elevated early in the development to transmission stages. Matching earlier studies  [44, 49] , mitochondrial transcripts and proteins were among those most elevated during development to stumpy forms. However, Gene Ontology (GO) terms associated with translation, ribonuclear complexes and macromolecular biosynthetic processes were also enriched, emphasising the preadaptation of stumpy forms to onward development in the tsetse fly. This was further supported by analyses of the enriched metabolic pathways in stumpy forms, where there was the expected up regulation of TCA cycle components, oxidative phosphorylation, amino acid metabolism as well as components associated with ribosomal components.
Glycolysis was down regulated. The dataset of enriched transcripts was also analysed with respect to their surface phylome categories  [38]  in order to identify membrane-associated molecules potentially up-regulated in stumpy forms, and of importance for transmission or stumpy Western blot for the expression of PAD 2 in stumpy forms freshly harvested from a rodent infection (ST) or incubated in HMI9 medium at either 20uC or 37uC. PAD2 expression is clear at 20uC but little is expressed at 37uC. This matches the analysis previously reported by Dean et al 2009  [10]. Analysis of the same samples using an antibody to trypanothione synthetase (TS) provided a loading control. doi:10. 1371/journal. pone. 0067069. g007 Figure  8. Validation of the polysomal enrichment of test mRNAs predicted from Digital SAGE analysis to exhibit elevated abundance in stumpy polysomal fractions.
001 (Data File S9)). When analysed for regulatory motifs by the Regulatory Sequences Analysis Tool (RSAT)  [52] , the 39UTRs of transcripts that were polysome-enriched in either slender or stumpy forms revealed that the motif TTATT was overrepresented in polysomal stumpy transcripts (Data File S10). A similar motif was also identified when analysing procyclic enriched transcripts in a previous study by the same approach  [53].
Conclusions The comparison between different strains of trypanosomes revealed that developmental responses at the mRNA level were sufficiently similar to be considered biological replicates of the same material. Moreover, comparison of the transcriptome datasets revealed good agreement for those transcripts previously highlighted as being up-regulated in either slender or stumpy forms based on Nimblegen analysis in another trypanosome strain, T. brucei TREU927/4  [17]. Thus the biological events that trypanosomes undergo as they prepare for transmission are quite predictable and consistent regardless of strain, reflective of an underlying developmental pathway. By identifying the genes that are controlled in slender, intermediate and stumpy forms in the blood, genes that are coincidently, or differentially regulated can be identified.
Data analysis All data were analysed using the open-source statistical programming language R (version 2. 13. 1). Quantile normalization of the array was carried out using the lumi package and posterior analysis was done with the genefilter package (Bioconductor). For general analysis, all probes with detection values of P 5 0. 01 in 415% of the samples (25 probes) and probes located on chromosome X (17 probes) were excluded. Poor-quality samples (410% failed probes) were also excluded. For unsupervised clustering analysis, probes with a general standard deviation 50. 05 were excluded as non-informative. Subsequently, heatmaps were analysed using the gplots package. Spectral map analysis was carried out using the mpm package  (Wouters et al. , 2003). Statistical analysis was performed using the lumi package (Mann-Whitney and Kruskal-Wallis tests for two-and multiple-group comparisons, respectively, adjusting P-values with the FDR algorithm).
Protein loading was monitored using a mouse monoclonal antibody against b-actin (1:30 000, Sigma-Aldrich). Membranes were then incubated for 1 h in the appropriate horseradish peroxidase-conjugated secondary antibodies (1:2000, Dako) and immunocomplexes were revealed by an enhanced chemiluminescence reagent (ECL AdvanceTM, Amersham Biosciences). Densitometric quantification was carried out with TotalLab v2. 01 software (Pharmacia). Protein bands were normalized to b-actin levels and expressed as a percentage of the control group level.
Herein, we obtained similar ratios between both techniques and high overall correlation (r 2 = 0. 721, Supplementary Fig. 3B  and C ). In addition, we interrogated the biological relevance of such DNA methylation correlating these data with the RNA expression profiles available in Allen Brain Atlas database (Supplementary Fig. 4 ). In this regard, we obtained an excellent correlation between both sets of values (Fisher's exact test P 5 0. 001) underlying its biological significance. Using this approach, we identified 72 genes (Supplementary Table  5 ) that were significantly differentially methylated (FDR 5 0. 05) across the described 12 brain regions of C57BL/6J mice, enabling them to be distinguished by the hierarchical clustering approach (Fig. 1 ). The observed differences were not associated with cellular heterogeneity at the level of glia versus neuron lineage.
Thus, consistent with the previously reported RNA expression profiles  (Johnson et al. , 2009; Lein et al. , 2007) , our results suggest that major brain divisions are not only structural bisections but also have distinct functional significance. These DNA methylation patterns also reflect the cell type composition of these regions: diencephalon-basal ganglia, which are mainly enriched in GABAergic neurons, and cerebral cortex, which is enriched in glutamate neurons, cluster according to their cell characteristics (Fig. 1 ). DNA methylation microarray data for 384 genes in the 12 studied mouse brain regions are available to download from NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/ geo): GSE47038. The DNA methylation clustering analyses that recognize cerebral cortex as a distinct branch are relevant to our understanding of Alzheimer's disease because this is the region most strongly affected in the disorder.
, 1997; Oddo et al. , 2003)  to the DNA methylation microarray. Strikingly, we observed that thromboxane A2 receptor (Tbxa2r), coagulation factor II (thrombin) receptor-like 2 (F2rl2), sorbin and SH3 domain containing 3 (Sorbs3) and spectrin beta 4 (Spnb4) were hypermethylated in the frontal cortex of APP/PSEN1 mice observing a similar trend in 3xTg-AD mice (Fig. 2C ). DNA methylation microarray data for the 384 genes in the frontal cortex samples from control animals and the two transgenic models APP/PSEN1 and 3xTg-AD are available to download from NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo): GSE47036. To achieve biological replication of the data obtained with the first group of samples, we confirmed the hypermethylation of the four genes identified above using an additional set of samples from the frontal cortex of APP/PSEN1 and 3xTg-AD mice using DNA methylation in Alzheimer's disease Brain 2013: 136; 3018-3027 | 3021 Figure  1  Unsupervised heatmap clustering of a genome-wide promoter DNA methylation microarray across 12 brain regions in the C57BL/6 mouse.
This work was supported by the Human Frontiers Science Program (HFSP) Ref. RGP0018/2007-C, the European Community's Sixth Framework Programme (FP7/2007-2013) under grant agreement PITN-GA-2009-238242-DISCHROM project and grant agreement 278486-DEVELAGE, NIH Grants NIA P50-AG16573 and HD065160-01, the Instituto de Salud Carlos III-Ministerio de Sanidad y Consumo Proyecto FIS (Fondo Investigacion DNA methylation in Alzheimer's disease Brain 2013: 136; 3018-3027 | 3025.
The E148 residue is conserved among centrins. Because there is only one centrin in unicellular green algae, the same centrin isoform interacts with all cellular targets regardless of the centrin-binding motif orientation. We used the Scherffelia dubia centrin to investigate the role of the E144 residue (which corresponds to E148 residue in human centrin) in the interaction with XPC and Sfi1. We also retrieved sequences for Sfi1 and XPC homologues protein from databases and analyzed their centrin-binding motifs.
The centrin targets Sfi1 and XPC are found in green algae A single centrin is found in S. dubia that must interact with all cellular targets expressed in this unicellular green alga. Sequences of centrins have been found in several other green algae genomes using human centrins as query sequence. Several of the green algae genomes that were investigated contain only one centrin gene (data not shown), and these algal centrins are conserved ( Fig. S1  ). The unique centrin sequences from S. dubia (SdCen) and Ostreococcus lucimarinus (OlCen) were aligned with each other and with human centrins using the ClustalW program ( Fig. 1  ). The C-terminal domain of SdCen (M93-F168) has 100% identity with OlCen. Residues involved in the interactions with XPC, Sfi1, and Sac3 (E105, A109, and F113 in human centrin) are conserved among the centrins from green algae as is the E148 residue specific for XPC binding ( Fig.
S1  ). S. dubia is a unicellular green alga of the order Chlorodendrales that belongs to the clade of Prasinophyceae. Unfortunately, the genome of S. dubia has not been sequenced yet. However, the genomes of two species of Mamiellales, O. lucimarinus (OstLu) and Ostreococcus tauri (OstTa), which belong to the same clade as S. dubia , have been sequenced and analyzed. A BLAST search using Sfi1 from both human and S. cerevisiae as query sequences identified Sfi1 homologues in the genomes of two other green algae, namely O. lucimarinus (Sfi1 containing 1315 residues) and O. tauri (Sfi1 containing 1370 residues). We identified centrin-binding motifs using a sequence alignment of human and algae Sfi1 ( Table  1  ) as well as the hydrophobic cluster analysis program HCA. Fig. S2  shows that the hydrophobic clusters identified by HCA are composed of the same residues identified by the sequence alignment ( Table  1  ).
In green algae, a single centrin must bind several targets regardless of the orientation of their centrin-binding motif Although the genome of S. dubia has not been sequenced yet, several other genomes from green algae have been sequenced, including O. lucimarinus and O. tauri. The sequences of the centrin found in these green algae were retrieved from genomes databases using the psiBlast program with the SdCen sequence as the query and aligned with SdCen using the ClustalW program ( Fig. 1  ). Full-length SdCen has 90% identity with OstLu centrin, and the C-terminal domain of SdCen (M93-F168) is identical to the OstLu C-terminal domain (M90-F165). The SdCen sequence was also aligned with the human centrins, HsCen1 and HsCen2. In terms of the C-terminal domain, SdCen has the corresponding K131 residue of HsCen2 but lacks the S158 and N160 residues found in HsCen2.
Isothermal Titration Calorimetry (ITC) The thermodynamic parameters of the molecular interactions between SdCen (C-terminal domain of wild type and variants) and the target peptides (P17-XPC, R18-Sfi1) were investigated via ITC using a MicroCal MCS instrument (MicroCal Inc. , Northampton, MA). The protein and peptide were equilibrated in the same buffer (50 mM MOPS, 100 mM NaCl pH 7. 5) with 1 mM CaCl 2 or 2 mM EDTA. The centrins (10 μM) in the calorimeter cell were titrated with the target peptide (100 μM) by automatic injections of 8-10 μL at 30 • C or 37 • C. The first injection of 2 μl was ignored in the final data analysis. Integration of the peaks corresponding to each injection and baseline correction were performed using the Origin-based software provided by the manufacturer. The data were fit to an interaction model to generate the stoichiometry (n), equilibrium binding constant (Ka), and enthalpy of complex formation ( H ).
Determination of the Sfi1 repeats and sequence analysis of Sfi1 protein The Sfi1 sequences were aligned with HsSfi1 and ScSfi1 sequences using the ClustalW program  [ 36 ]. Centrin-binding motifs are known for both HsSfi1 and ScSfi1, making it possible to determine the centrin-binding motifs of the new Sfi1 sequences from the alignments. Hydrophobic cluster analysis (HCA)  [ 37 , 38 ]  was performed for the new Sfi1 proteins, allowing a better determination of their respective repeats. The program for detection of hydrophobic amino acids clusters (HCA) is accessible on the Ressource Parisienne en Bioinformatique (RPBS) server. The programs used to perform protein sequence analyses are accessible on the SWISS-PROT web site  [ 39 ]. We used GOR4  [ 40 ] , SOPMA  [ 41 ]  for protein secondary structure predictions.
1 was met by all athletes. The highest HR attained at exhaustion was recorded and was considered as maximal HR (HRmax). Cardiorespiratory variables were determined using a breath-by-breath system (ZAN 680; Messgerate, Oberthulba, Germany). HR was recorded using a Polar Team System HR monitor (Polar, Kempele, Finland). Data collection: All participants competed in a seniorlevel international competition (International Karate Dutch Open, Rotterdam 2010). Twelve international teams took part on this tournament. The competition was held in a gymnasium on standard mats, and all qualifying, semifinal and final phases of the event occurred between 9:00 and 19:00 h of the same day. Only athletes who have won three matches during the competition were selected. Thus, each athlete performed a total of three matches.
HR was recorded using Polar Team System HR monitors (Polar, Kempele, Finland) with HR recorded every 5 s for each combat. Competitors were familiarized with the HR monitors during a number of karate training activities (e. g. contact sparring) and sessions before their use during competition. They indicated that these HR monitors did not impede their movements or action patterns in any manner during the karate activities. Each HR polar transmitter/receiver belt was attached across the participants' chest under the body armor before each individual combat. Data were subsequently downloaded using Polar Precision Software (Version 4. 0, Polar Electro Ltd. ). The HR was expressed as a percentage of competitors' HRmax and classified into five intensity zones as identified by Edward  [12]. The average percentage of time spent in each HR intensity zone was also calculated for each combat.
Statistical Analysis: All data were assessed for normality using the Shapiro-Wilks test before analysis. Pearson Correlation coefficients (r) were adopted for relationship analysis. Matches' comparison was conducted using ANOVA one-way with repeated measurements. Mauchly test was used to test Sphericity or Compound Symmetry. Friedman's repeated-measures rank test was used to identify differences in the time spent in specific HR intensity zones. Post-hoc analysis included Wilcoxon's matched-pairs signed-ranks tests, with Bonferroni adjustment for multiple comparisons. All statistical procedures were performed using SPSS for Windows (Version 14. 0, SPSS Ltd, Surrey, U. K. ). Descriptive data are expressed as means±SD unless stated otherwise.
SDH activity analyses Semi-quantitative analysis of SDH activity in the entire striatum was performed using densitometry analysis (using MCID software) as described previously  (69, 70). In the case of the analysis of the entire striatum, 8 -10 animals per group were examined. For subregional analysis of SDH activity in the striatum based on the visualization of the expression of GFP, acquired image of SDH-stained section was aligned with the adjacent section imaged for GFP fluorescence using a Zeiss Axioplan2 Imaging microscope, a ×1. 25 objective and cooled CCD camera and an image acquisition and analysis system (Mercator, Explora Nova). For each animal, region of interest was delineated on the GFP images (5 -12 sections, 200 mm interspace), pasted and translated to be located at the same anatomical level on the SDH image for the determination of optical density in the region.
3D reconstruction of SDH activity Images of serial sections revealed for SDH histochemistry were digitized using a flatbed scanner (ImageScanner; GE Healthcare Europe, Orsay, France) with 600 dpi resolution (pixel size 42 × 42 mm 2 ). 3D post-mortem reconstruction was performed using the in-house BrainRAT software freely available on internet (http://brainvisa. info) based on a propagative co-registration strategy. On all coronal sections (n ¼ 18 serial sections/brain) separated by 200 mm, the loss of staining seen as a palor in the striatum infected with lenti-Htt171-82Q was manually segmented in 3D, and corresponding lateralized areas were symmetrically computed in the control (lenti-Htt171-18Q) striatum. Image processing techniques used were described in previous work  (72).
Statistical analysis All data were expressed as the means + SEM. Unpaired Student's t-test was used for the comparison between two groups. When more than two groups were compared, a one-way ANOVA with multiple comparisons using the post hoc Bonferroni test was carried out using commercially available software (StatVieww software, SAS Institute Inc. , USA). A two-way ANOVA followed by a post hoc Bonferroni/Dunn test was used for the analysis of SDH activity throughout the anteroposterior anatomical extension of the striatum in rats infected with lentiviral vectors. For time-dependent changes in mtDNA levels, data were analyzed by two-way ANOVA (ages/genotypes) and pairwise multiple comparison procedures (Holm-Sidak method). For all statistical tests performed, a probability level of 5% was considered significant.
Genotyping Total genomic DNA was either extracted from mycelial cultures (T 1 ) or from young lesions (T 2 ). Genotyping was carried out using 15 microsatellites loci  (Neu et al. 1999; Zapater et al. 2008; Robert et al. 2010). Protocols and polymerase chain reaction conditions are detailed in  Robert et al. (2010). Among the 15 markers, two are trinucleotide and four are di-nucleotide microsatellite loci already used in the 2011 study. The nine others are tetra-nucleotide markers developed between the two studies (Appendix S3). Amplified fragments were separated on a 16-capillary Sequencer (ABI Prism 3130XL (Applied Biosystems, Foster City, CA, USA) and analysed for length variation using GeneMapper â Software. In total, 144 individuals (T 1 ) and 244 individuals (T 2 ) were genotyped. Delineating the genetic discontinuity using spatial genetic clustering Spatial population genetic structure was assessed using the clustering algorithms implemented in GENELAND v3.