dataesr/scientific-paragraphs-categorization huggingface dataset:
The ratio between the number of simulations in which the average cluster size is larger than in the real data and the total number of Monte Carlo simulations (here: 10,000) yields the p-values, indicating the probability that an experimentally observed cluster formation may as well have been achieved by random cell distribution (Fig. S1 ). In addition to significance evaluation using Monte Carlo simulations for each dataset, we compared the cluster formation differences between the UbowCreERT2 at 1, 5, and 15 wk using standard t test statistics. Parabiosis. 10-wk-old WT and Ubow +/ mice were surgically attached as previously described  (Waskow, 2010). Parabionts were analyzed 3 mo later. BM chimeras. Mice were -irradiated (twice with 500 rads) from an x-ray source and were reconstituted with at least 5 million BM cells.
Epidermal sheets were separated from the dermis and fixed in Antigen Fix (Microm Microtech) for 20 min, washed in phosphate buffer, and stained for Langerin expression. Immunofluorescence confocal microscopy was performed with a LSM 780 confocal microscope (Carl Zeiss). Separate images were collected for each fluorochrome and overlaid to obtain a multicolor image. Final image processing was performed with Imaris software (Bitplane) and Photoshop (Adobe). Flow cytometry. Epidermal sheets were prepared according to the previous section and incubated in collagenase I (Life Technologies) for 30 min at 37°C. Cells were blocked with 2-4G2 for 15 min at 4°C and stained with the indicated antibodies in PBS 1% FCS 2 mM EDTA for 45 min at 4°C. Data were acquired on FACS-LSRII UV (BD) and were analyzed on DIVA software (BD).
(D) Ear epidermis and skin-draining LNs of Lang-Cre + Ubow +/ mice were stained for Langerin expression and analyzed by confocal microscopy. "F" indicates the location of monocolored foci of LCs. (E) All confocal pictures of epidermal LCs similar to the ones imaged in D were processed to generate Voronoi-tessellated pictures (see Materials and methods and Fig. S1) amenable to computational simulation. The numbers of CFP + LCs per cluster were then compared with Monte Carlo-simulated datasets in which the same LC populations were randomly distributed (1 simulation displayed out of 10. 000). ***, P < 10 9. Data are representative of five different experiments (two mice per experiment) in C-E. Bars show the medians.
9 e extracted nucleic acids and did all PCRs in duplicate on a Rotor-Gene 3000 or 6000 (Qiagen, Germany) for real-time PCR and a DNA Engine (MJ Research, Canada) for conventional PCR. We based detection of dengue virus on the single-step TaqMan real-time PCR assay. 18 or Plasmodium spp, we used a nested conventional PCR assay  19  targeting the ssrRNA gene, with distinguishing of P falciparum from P vivax. For Leptospira species, we used a TaqMan real-time PCR assay, detecting the Leptospira rrs gene. 20 2] [23] [24]  We regarded Rickettsia genus 17 kDa real-time PCR-positive samples, and R typhi ompB real-time PCR-negative samples as Rickettsia spp, which subsequently underwent a panel of nested conventional PCR assays targeting the 17 kDa, gltA, ompB, ompA, and sca4 genes. 22, 24 For positive amplicons, DNA sequencing was done by Macrogen (Seoul, South Korea), followed by Basic Local Alignment Search Tool (BLAST) searches of GenBank.
Statistical analysis We analysed data with SPSS and Stata (both version 10). We compared normally distributed data with Student's t test, and non-normally distributed data with the Mann-Whitney U test. We compared categorical data with χ² and Fisher's exact tests. To predict factors associated with cause, we included all signifi cant variables from the univariable analysis in a multivariable logistic regression model using a backward stepwise approach; we retained signifi cant variables in the fi nal models. We checked the fi t of models with the Hosmer-Lemeshow or Pearson's goodness-of-fi t tests.
Scrub typhus, dengue, Japanese encephalitis virus, and leptospirosis all showed seasonality, with much higher hospital presentations in the wettest and hottest season (May to September) than in the cooler months (fi gure 3, appendix). Com munity-acquired bacteraemia did not show obvious seasonality (data not shown). Only 6 months of data were obtained for infl uenza but, during this period, frequency of infection was highest in September to October. Six inpatients died in hospital (appendix). The appendix assesses data for peripheral white blood cell counts and CRP. Of 1095 (79%) patients at Luang Namtha with data for hospital antibiotic use, 560 (51%) received antibiotics, of whom 67 (12%) received more than one (appendix). Amoxicillin was most commonly used, in 294 (53%) of 560 patients, with doxycycline in 116 (21%).
Acknowledgments We thank the patients and their families, the staff of the  Microbiology Laboratory, Mahosot Hospital, National Centre for Laboratory and Epidemiology , and  Luang Namtha  and  Salavan Hospitals  for their assistance;  Service Fraternel  d'Entraide;  Sommay Keomany  for his help;  Institut Pasteur (Phnonh Penh)  for their collaboration and for undertaking the C-reactive protein assays, especially  Didier Menard  and  Siv Sovannaroth  of the  National Center for Parasitology, Entomology , and  Malaria Control  (Cambodia); the former and current Ministers of Health and Directors of the  Curative Department, Ministry of Health, and Mahosot Hospital Directors  for their support for this study, which was part of the  Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit ;  Ivo Elliott  and reviewers for helpful comments; and  Sue Lee  for statistical advice.
Gene expression microarray and next generation sequencing efforts on conventional, clear cell renal cell carcinoma (ccRCC) have been mostly performed in North American and Western European populations, while the highest incidence rates are found in Central/Eastern Europe. We conducted whole-genome expression profiling on 101 pairs of ccRCC tumours and adjacent non-tumour renal tissue from Czech patients recruited within the ''K2 Study'', using the Illumina HumanHT-12 v4 Expression BeadChips to explore the molecular variations underlying the biological and clinical heterogeneity of this cancer. Differential expression analysis identified 1650 significant probes (fold change $2 and false discovery rate ,0. 05) mapping to 630 up-and 720 down-regulated unique genes. We performed similar statistical analysis on the RNA sequencing data of 65 ccRCC cases from the Cancer Genome Atlas (TCGA) project and identified 60% (402) of the downregulated and 74% (469) of the upregulated genes found in the K2 series.
Unsupervised Hierarchical Clustering (K2 Series) To compare the gene expression profiles of all 101 tumour and adjacent non-tumour tissue sample pairs of the K2 series (described in Table  1 ), we first performed an unsupervised hierarchical cluster analysis of vst-transformed and quantile normalized gene expression data without background subtraction using all 47,231 probes present in the dataset (corresponding to 28,688 well-established coding transcripts). In unsupervised clustering of tumour and non-tumour tissue, all tumour samples clustered together: the dominant distinction was between tumour and non-tumour tissues rather than between individuals (Figure  1 ). Furthermore, we examined the expression profiles of tumour and adjacent non-tumour tissue samples separately (Figure  S1 ). We found that all tumour samples were tightly clustered together suggesting homogeneity of ccRCC samples used in this study.
GSEA (http://www. broadinstitute. org/gsea/) is a powerful bioinformatics tool that determines whether a priori defined sets of genes (''pathways'') show statistically significant, concordant differences between two biological states (e. g. phenotype). We found 84 pathways to be enriched in ccRCC phenotype at FDR cut-off values of 0. 25  [22]  using the Biocarta pathway database from the Molecular Signatures Database -MsigDB (http://www. broad. mit. edu/gsea/msigdb/index. jsp). These included cell cycle pathways (G1, G2, CELLCYCLE) characterized by the presence of cyclins (CCND1, CCND2, CCND4, CCNB1), cyclin-dependent kinases (CDK2, CDK4, CDK6, CDK7), cyclin dependent kinase inhibitors (CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CDKN2D) as well as retinoblastoma 1 (RB1), CHEK1 and CHEK2. Furthermore, GSEA identified several oncogenic signalling pathways, such as ARF, P53, ATM, MET, ATRBRCA, WNT and ETS pathways.
Replication of Differential Expression in an Independent TCGA Population To explore whether the ccRCC gene expression signatures identified in the Czech Republic population were reproducible in other populations and using a different experimental platform, we evaluated the gene expression status of 65 tumour/non-tumour renal tissue pairs from ccRCC patients recruited in the US for the TCGA project and sequenced through the RNA sequencing (RNA-seq) technology. Key clinical characteristics of the cases available are described in Table  2. The Level 3 processed RNA-Seq TCGA data, generated by an Illumina HiSeq2000 platform, were downloaded from https://tcga-data. nci. nih. gov/tcga/. The flowchart of the previous analysis performed by the TCGA data analysis centres and further data normalization and transforma-  tion are presented in Figure  S2A  and S2B, respectively.
Taking into consideration that significant genes found using the RPKM method were overlapping with count data analysis, this set of genes was used for all downstream analysis. As with the microarray analysis described above, genes with BH FDR-adjusted p-values ,0. 05 and FC $2 were considered. Using paired t-tests for the complete set of genes (20,532), we identified 2493 significant differentially expressed genes (Table  S4 ), of which 1299 genes were upregulated and 1194 genes were downregulated. To obtain a broader overview of the biological changes occurring at the gene expression level, we used Gene Ontology term enrichment (DAVID software) to identify annotated functions and processes that were overrepresented in genes that were significantly up -or down -regulated. Likewise in microarray analysis, downregulated genes were involved in metabolic and catabolic processes, excretion, ion transport, oxidation-reduction, localization and developmental processes (including kidney development).
Besides, the upregulated genes enriched mainly immune system responses and regulation, inflammation, response to stimulus, cell activation (primarily Tcell activation), antigen processing and presentation, lymphocyte differentiation, angiogenesis and signal transduction (Table  S5 ). Using GSEA on the TCGA RNA-Seq dataset we did not find significant overrepresented pathways (data not shown). We then investigated the correlation between the RNA-Seq data and the microarray K2 study. A Venn diagram was used to represent the overlap between significant differentially expressed genes obtained with both platforms (Figure  3 ). RNA-Seq results broadly agree with gene expression measurements obtained with previous microarray data, with approximately 60% (402) of downregulated and 74% (469) of upregulated genes from the discovery phase having been validated in the RNA-Seq analysis.
Gene Expression-based Survival Predictor Survival data were available for 89 out of 101 ccRCC cases from the K2 series (Czech Republic) and 464 out of 465 TCGA (US) cases. There were significant differences in age and grade distribution between both populations (Table  3 ). The TCGA series had a longer follow-up duration and higher number of events (deaths) than the K2 series. We conducted survival analysis to model overall survival (OS, all-cause mortality) against gene expression levels in tumour tissues in the microarray data from K2 series. Gene expression data of 89 ccRCC cases were examined in an univariate (gene-by-gene) Cox model after FCMS (filter, cluster and stepwise model selection) method using SignS tool  [27]  to determine the prognostic value of the gene expression status. The obtained model was tested on the 464 RNA-Seq ccRCC TCGA samples.
Fifty-one genes correlated positively or negatively with OS (p,0. 001) across 10 crossvalidated runs. Both the K2 and TCGA series, comparing the survival curves for two, three and four groups suggest that patients fall into two groups (Figure  6 ). In addition, to evaluate whether important features were missed in the smaller gene expression microarray data, we built a separate model using only the TCGA data, with significance assessed by cross-validation. Two hundred forty six genes correlated positively or negatively with OS (p,0. 001) across 10 cross-validated runs in TCGA dataset resulting in 32 genes that were common to both analyses (File S2). Individual Cox proportional-hazard analysis of selected 51 genes with adjustment for age, tumour grade, extent of primary tumour (pathological assessment: pT) and sex revealed that 8 of these genes were associated with survival independently of age, grade and pT (Table  S8 ).
More comprehensive prognosis predictive indexes, such as MSKCC risk score (known as Motzer criteria  [28] ) for metastatic and UCLA Integrative staging system  [29]  for localized disease, were not used due to scarcity of available data (in particular ECOG performance status, Karnofsky perfor-mance status, serum lactate dehydrogenase measurement, hemoglobin measurement, and corrected serum calcium measurement). The multivariate analysis carried out on the K2 and TCGA series used the expression of the genes as a continuous variable with the log values of intensity and RPKM for the two datasets, respectively. Higher expression of these 8 genes resulted in a significantly decreased risk of death (HR 0. 16 to 0. 41 for K2 series and HR 0. 68 to 0. 84 for TCGA series). These included cysteine/ tyrosine-rich 1 (CYYR1) and LIM domain binding 2 (LDB2) genes that have been recently reported as associated with disease-free survival with higher expression in tumours that metastasized after 24 months vs.
tumours that metastasized earlier  [30]. Furthermore, expression of sphingosine-1-phosphate receptor 1 (S1PR1), ephrin-B2 (EFNB2), G protein-coupled receptor 116 (GPR116), SNF related kinase (SNRK), transmembrane protein 204 (TMEM204), C-type lectin domain family 1, member A (CLEC1A) and C-type lectin domain family 14, member A (CLEC14A) was associated with increased survival time (Table  4 ). Finally, taking into high correlation of genes (correlation. 0. 7 for K2 and TCGA datasets) the final list of genes was included together with the other covariates (age, tumour grade, pT and sex) in a multivariate Cox model and tested separately on each of the two datasets, with backwards step-wise selection to remove redundant genes. In each step, gene with the highest p-value was removed (significance level for removal from the model.
However, we found an overlap of more than 60% of differentially expressed genes in two different populations whilst applying two different high-throughput technologies. Latest reports on recurrent mutations in genes encoding several epigenetic regulators such as SWI/SNF complex gene PBRM1  [8] , methyltransferases SETD2 and MLL2, demethylases UTX (KDM6A) and JARID1C (KDM5C)  [6]  suggest possible implications  of epigenetic variation in ccRCC. The methylation status of individual genes has been examined in normal and renal tumour cells and, more recently, global profiles of gene methylation of RCC have been studied in cell lines  [49]. We analyzed 317 ccRCC and adjacent non-tumour renal tissues from genome-wide DNA methylation data from TCGA and compared it to our gene expression study. Our results confirmed hypomethylation of CA9  [50]  and identified new, at the time of preparation of the manuscript, hypomethylated genes known to be overexpressed in ccRCC, including nicotinamide N-methyltransferase (NNMT), caveolin 1 (CAV1) and cyclin D1 (CCND1).
Finally, we identified TMEM204 (CLP24), a hypoxically regulated intercellular junction protein  [56] , as predictive of overall survival. In summary, if validated in independent series and by further protein assays, some or all of the 9 genes may represent potential candidate biomarkers that can predict independently of grade the outcome in patients with ccRCC. These results indicate that an algorithm based on expression data from a subgroup of genes may form the basis of a potential prognostic tool. Further definition and validation of such an algorithm is required and other genes significant in the TCGA dataset but not in the Czech data (Table  S8 , File S2) including cyclins (A2, B2), BUB1, BIRC5, AURKB, EPAS1 among others should not be discarded if validated in larger cohorts. In summary, we confirmed alterations in HIF pathway (through upregulation of HIF-1a and HIF-2a target genes) and in the signalling pathways upstream and downstream of HIF in the Czech Republic population.
Microarray Hybridization and Data Analysis 500ng of mRNA was amplified into cRNA and biotinylated cRNA using IlluminaH TotalPrep TM -24 RNA Amplification Kit (Life Technologies) for the first batch, and two IlluminaH TotalPrep TM -96 RNA Amplification for the second and third batches (batches 2 and 3 included 2 technical replicate pairs from batch 1, and batch 3 included an additional technical replicate from batch 2). Subsequent steps included hybridization of each sample to Illumina HumanHT-12 v4 Expression BeadChips, washing, blocking, and streptavidin-Cy3 staining. Illumina's GenomeStudio software was used to generate signal intensity values from the scans and perform the initial quality controls. We found high correlation coefficients within the 5 technical replicate pairs (Pearson correlation range: 0.
96-0. 98), indicating a good repeatability of our experiments. The performance of hybridizations was evaluated by assessing the presence of outliers (very low or very high average signal intensity) and the noise-to-signal ratios by calculating P95/P05 ratios for each sample. An outlier was defined as a sample with P95/P05 ratio ,9. 5 and array intensity distribution distant from the rest of the data as identified by MDS plots (PC. 50 and PC,250) and density plots following samples normalization. One tumour and one non-tumour samples were found to be outliers and the corresponding pairs were excluded from the analysis. All samples were found to show an acceptable noise-to-signal ratio (P95/ P05. 9. 6). Supplemental quality assessment (before and after normalization) was conducted using arrayQualityMetrics package  [57]  and reached similar conclusions.
In total, 101 sample pairs were then included in the analysis. Table  1  summarizes the clinical and demographic characteristics of this K2 series of 101 patients, which included 59 men and 42 women. The majority of cases were between 55 and 74 years of age and no significant differences were observed between male and female individuals for known ccRCC risk factors. All statistical analyses were performed using R Bioconductor software, with the addition of the BRBArray tool for specific applications. The raw signal intensities of all samples were processed with lumi package applying variance-stabilizing transformation (VST) and quantile normalization  [58]. Linear Models for Microarray Analysis (limma package) using the paired t-test method was used for identification of differentially expressed probes  [59].
Significance levels (P values) were corrected using Benjamini and Hochberg (BH) false discovery rate (FDR) method to correct for multiple hypothesis testing. Probes with a FDRadjusted p-value of ,0. 05 and probes with a minimum of 2-fold change were considered significantly differentially expressed. Unsupervised Hierarchical Clustering was performed with the average linkage method while the differential gene expression was measured using centered correlation technique implemented in BRBArray tool. The Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6. 7 was used for classification of the differentially expressed genes according to biological and molecular processes  [60]. Gene Set Enrichment Analysis (GSEA) (Broad Institute) was used to evaluate the combined significance of sets of genes in ccRCC using annotations from a curated version of Biocarta.
Only gene sets represented by at least 15 genes were retained. Genes were ranked based on the limma-moderated t statistic. After Kolmogorov-Smirnoff testing, those gene sets showing FDR ,0. 25, a well-established cut-off for the identifica-tion of biologically relevant gene sets  [22]  were considered enriched between classes under comparison. The microarray experiments are MIAME compliant and have been deposited at the NCBI Gene Expression Omnibus (GEO) database (http:// www. ncbi. nlm. nih. gov/geo) under accession GSE40435. sites common to both platforms were mapped to differentially expressed genes.
Final assessment of the significance of association was performed by splitting the samples into several (2, 3 or 4) groups based on their predicted scores, and comparing survival functions of these groups (better vs. worse prognosis, tertiles, quartiles). The predicted scores were obtained from a 10-fold cross validation. Since the gene expression microarray K2 series was smaller (89 ccRCC cases), had better survival (12 deaths) and shorter follow-up time, reducing the power of tests based on this data alone, the performance of the final model was evaluated against the larger TCGA series (464 ccRCC cases). In this analysis the TCGA series was used to assess predictive performance, and not to build the model. In addition, to evaluate whether important features were missed in the smaller gene expression microarray K2 series, we build a separate model using only the TCGA series, with significance assessed by crossvalidation, and examined the overlap of the sets of selected genes.
Finally, the genes selected in the FCMS method were individually tested in a further Cox model (using STATA v 11 statistical software), where additional covariates (age, sex, tumour grade and extent of primary tumour) were included, to guard against possible confounding. Those genes not significantly associated (p. 0. 05) in this test were discarded from the model. Finally, the list of significant genes was then included together with the other covariates in a final multivariate Cox model and tested separately on each of the two datasets, with backwards step-wise selection used to remove redundant genes.
This work was supported by the  US NCI   U01  grant # CA155309. Additional support for the Czech Republic sample collection and follow-up was provided by the  EC FP7   CAGEKID  project # 241669 , and the  European Regional Development Fund  and the  State Budget of the Czech Republic (RECAMO ,  CZ. 1. 05/2. 1. 00/03. 0101 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Drinfeld doubles of finite subgroups of SU(2) and SU(3) are investigated in detail. Their modular data -S, T and fusion matrices -are computed explicitly, and illustrated by means of fusion graphs. This allows us to reexamine certain identities on these tensor product or fusion multiplicities under conjugation of representations that had been discussed in our recent paper [J. Phys. A: Math. Theor. 44 (2011), 295208, 26 pages], proved to hold for simple and affine Lie algebras, and found to be generally wrong for finite groups. It is shown here that these identities fail also in general for Drinfeld doubles, indicating that modularity of the fusion category is not the decisive feature. Along the way, we collect many data on these Drinfeld doubles which are interesting for their own sake and maybe also in a relation with the theory of orbifolds in conformal field theory.