dataesr/scientific-paragraphs-categorization huggingface dataset:
In the TCGA dataset, we observed similar demographic characteristics as in the 3SEQ cohort, with TCGA C2 like 3SEQ subtype III being significantly associated with uterine LMS (p < 0. 0001), while TCGA C3 (subtype I) and TCGA C1 (subtype II) were overrepresented in extrauterine sites (Supplementary Table  S2 ). These results correlate well with our findings on 51 LMS cases from our previous dataset analyzed by gene microarrays, where the ratio of uterine LMS (13) also was higher in subtype III (10/24) than in subtype I (1/13) and subtype II (3/12), but did not reach significance (p = 0. 1178, Chisquare test). Within the 3SEQ cohort, 18%  (18)  of tumors are low grade, 19%  (19)  are intermediate grade, and 63% (62) are high grade by histologic analysis. Low grade lesions were more frequent in subtype I LMS, with 10/35 cases showing low grade histology while 3/22 of subtype II and 0/13 of subtype III LMS cases are low grade respectively, however there was no statistically significant correlation between grade and molecular subtype (Table  1 , p = 0.
Functional annotation of LMS molecular subtypes based on subtype-specific genes in the 3SEQ dataset SAMSeq (24) was used to identify genes that were significantly over/under-expressed in each subtype of LMS analyzed by 3SEQ, again using only those cases with a positive Silhouette value. In subtype I LMS, 5900 genes are relatively overexpressed while 1900 genes are under-expressed compared to the other two subtypes (Supplementary Table  S5 ). The overexpressed genes were enriched in Gene Ontology (GO) Biology processes that included muscle contraction, muscle system processes, and cytoskeleton organization. In addition, a number of genes (LMOD1, MYOCD, CALD1, DES, CNN1, ACTG2, SLMAP and CASQ2) known to be involved in smooth muscle function were identified in this list indicating that subtype I was most associated with normal smooth muscle function (Supplementary Table  S6 ).
Consistent with this finding, 4 samples of normal myometrium and 3 cases of benign uterine leiomyoma were grouped with subtype I LMS in principal component analysis (Figure  2D ) and clustered with subtype I when analyzed by unsupervised hierarchical clustering with the 70 LMS core samples (Supplementary Fig. S2A ). As stated above, the overexpression of genes associated with muscle function was also noted on the mRNA level in corresponding subtype C3 from the TCGA dataset, while the subtype I identified previously through gene microarray analysis had high levels of expression for these genes at both the mRNA and protein level  (13). In 3SEQ data on subtype II LMS, 5100 genes are significantly overexpressed and 2087 genes are expressed at lower levels as compared to the other two subtypes of LMS.
Analysis of 3SEQ data showed high levels of mRNA expression for ARL4C in subtype II LMS. Using a FFPE compatible antibody strong staining was seen in a subset of LMS cases (Figure  3C ) with positive staining in 30 LMS cases, 17 of which were subtype II LMS. Negative staining was seen in 28 LMS cases, 23 of which were subtype I or subtype III LMS. This IHC result validates ARL4C to be a subtype II LMS biomarker not only at the mRNA level (as determined by SAMSeq) but also at the protein level (p = 0. 0033, Chisquare test; r = 0. 5277, p < 0. 0001, Spearman Correlation) (Supplementary Table  S7 ). Comparison between 3SEQ mRNA levels and ARL4C immunohistochemistry showed a good association (p = 0. 0046) (Figure  3D ). Comparison between LMOD1 and ARL4C protein expression and mRNA expression levels varied as expected across the LMS subtypes as defined by 3SEQ (Supplementary Fig.
S4 ). While in individual cases these markers may not definitely identify a case as belonging to a specific subtype, when combined these markers do allow us to distinguish large numbers of cases represented on a TMA in distinct molecular groups. No outcome data were available for the LMS samples used for 3SEQ analysis. To correlate the assignment of LMS subtypes with outcome we used immunostaining data on TA-201 that contains 127 cases of LMS with known clinical outcome  (27, 34). In this analysis, 48 of 127 (38%) LMS cases were defined as subtype I LMS by their coordinate expression of all 5 subtype I reactive antibodies. These cases demonstrated a better disease specific survival (DSS) when uterine and extrauterine LMS were analyzed together and compared to the remaining LMS cases (p = 0.
Potential clinical applications of LMS subtyping To explore the potential for clinical implications in the recognition of LMS subtypes we compared the genes that are specifically overexpressed in each LMS subtype with genes from the TARGET V2 database known to be activated by mutations or amplifications  (35). This database contains genes for which targeted therapy is either currently available or under development. A large number of these genes were found to be expressed at different levels between the 3 molecular subtypes (Table  2 ). This suggests that LMS subtypes may respond differentially to those targeted therapies, an observation that may play a role in determining the success rate of novel therapies in clinical trials. For several of these targets there may be an incomplete correlation between mRNA levels for the mutated or amplified target gene and response to drug, but this analysis nevertheless forms a first step towards personalization of LMS patient care.
Conventional chemotherapy and radiation therapy only have limited effects and surgical resection remains the best option for treatment. Characterization of molecular subtypes in malignancies can lead to better individualization of treatment. In a prior study on 51 cases of LMS for which frozen tissue was available, we used 44k spotted cDNA arrays for gene expression profiling and obtained data that indicated that at least 3 molecular subtypes exist in LMS (  13 ). This study relied on the availability of fresh frozen tissues and as a result only small numbers of LMS cases could be studied. The 3SEQ approach can use FFPE materials and renders a digital rather than an analogue readout for gene expression levels with a higher signal to noise ratio and allows for accurate measurement of genes expressed at a wide range of levels, including those with low levels of expression.
3SEQ analysis on 99 samples of LMS and data on a third cohort of 82 LMS cases from the TCGA study were obtained. Despite the differences in methodology and sample selection between these three cohorts, remarkable similarities were apparent and indicated the existence of three molecular subtypes in LMS. Subtype I LMS expresses most genes associated with muscle function by Gene Ontology annotation. The similarity of subtype I LMS to smooth muscle differentiation was also supported by the fact that subtype I cases clustered with samples of leiomyoma and myometrium. Finally, we observed high expression levels for LMOD1 in subtype I LMS, a smooth muscle cell-restricted gene that is preferentially expressed in differentiated smooth muscle cells  (40). The expression of smooth muscle markers in subtype I suggest a tumor subtype that is closer to the smooth muscle cells than subtype II and III.
Financial support MvdR was supported by the  National Institutes of Health  ( CA 112270 ) and IE was supported by the  Fund for Health Research  ( FIS PI12/01645 ). We thank the  Stanford Center for Genomics  and Personalized Medicine with help on Illumina sequencing, and thank  Alayne Brunner ,  Patricia Marian Robinson ,  Joanna Przybyl  and other labmates for sample collection, helpful discussions and experimental design.
Genome-Wide Search of Prdm Genes and Definition of Metazoan Prdm Subfamilies We identified a total number of 976 Prdm genes in the fully sequenced genome of 93 metazoan species that represent a significant sampling of metazoan diversity (table  1 ). Supplementary tables S1-S6, Supplementary Material online, list all the identified genes (with accession numbers, presence of characteristic domains, sources of the genomic data, and taxonomic information about the studied species). The sequences of all the corresponding proteins can be found in supplementary data set S1, Supplementary Material online. The number of Prdm genes found per species ranges from 2 in the sponge Oscarella carmela, the placozoan Trichoplax adhaerens, and the nematode Caenorhabditis elegans to 19 in teleosteans (table  1 ).
Moreover, this earlier work taxonomic sampling included species such as Drosophila melanogaster and C. elegans which were characterized here to have lost several ancestral Prdm genes. Our findings are however not surprising, as similar important early metazoan diversifications have been observed for many genes families involved in development including TFs and cell signaling molecules (e. g. ,  Kusserow et al. 2005; Jager et   Evolution of Prdm Genes. doi:10. 1093/molbev/msv260 MBE diversification of Prdm genes is rendered difficult, in part by the still relative paucity of sequenced nonbilaterian genomes, and even more importantly by the uncertainties about the phylogenetic relationships of nonbilaterian phyla between each other and bilaterians. Our data nevertheless suggest a two-step increase in the number of Prdm genes: A first one at the origin of metazoans and a second one in the cnidarian/ bilaterian lineage.
We constructed a character matrix (supplementary table S13, Supplementary Material online) that compiles the number of members for the seven aforementioned Prdm subfamilies in all studied species, with a multistate code (0 = no member, 1 = one member, 2 = two members, 3 = three or more members). This matrix was used to infer the number of members of each of these subfamilies at the different nodes of the metazoan tree. For this purpose, we used both unbiased parsimony and ML approaches as implemented in the ancestral state reconstruction tools of Mesquite (see Materials and Methods for details). Results of these analyses obtained by parsimony and ML were very consistent and are shown in supplementary tables S14 and S15, Supplementary Material online. By comparing the number of members at different nodes, we can both infer the position of gene duplications and identify potential secondary gene  found with all tested species samplings FIG.
6. Phylogenetic analysis of the Prdm3/16 subfamily. An ML tree is shown. Midpoint rooting has been used. A similar tree topology has been obtained by BI. Statistical supports (aLRT and aBayes values for ML; posterior probabilities for BI) are indicated on the nodes by color circles (color code is indicated in the figure). Nodes without color circles are not statistically supported and/or not congruent between ML and BI methods. Species names are abbreviated using the first letter of the genus name followed by the three first letters of the species name. All abbreviations can be found in table 1. Black arrows indicate pairs of paralogs that are closely related in the phylogenetic tree, whereas asterisks denote paralogs that are not closely associated in the phylogenetic tree. Duplications that likely occurred in Gnathostomata, Pancrustacea, and Hymenoptera are indicated.
In most cases, sequence similarity to Prdm genes was low. None of the encoded proteins contains both a SET domain and Zn fingers, whereas many of the proteins lack these domains entirely. Three of the proteins contain a SET domain (supplementary table  S17 , Supplementary Material online)-however, in a phylogenetic analysis with a larger sample of SET domain proteins (see below), these sequences do not cluster with Prdm proteins (not shown). We determine that it is unlikely that bona fide Prdm genes exist outside metazoans and therefore conclude that the Prdm genes constitute a metazoan-specific family of TFs. Seventeen of the retrieved nonmetazoan genes encode proteins with one or more Zn fingers and we further studied these genes. We noticed that, when making BLAST searches of Prdm genes in animals, we also often retrieved non-Prdm Zn finger genes with some similarity to Prdm genes.
We started from the SET domain proteins identified by  Sun et al. (2008)  in Human, Drosophila, Saccharomyces, and Schizosaccharomyces and retrieved their putative orthologs by BLAST searches in 14 additional species belonging to Unikonts (supplementary table S18 and data set S4, Supplementary Material online). Orthology relationships were assessed by reciprocal best BLAST hit and only the putative orthologs that encode a protein with a recognizable SET domain were retained for further analysis. We then performed phylogenetic analyses using a multiple alignment of the SET domain of the 419 retrieved proteins. We found a well-supported monophyletic and exclusive group of Prdm proteins, in addition to several other monophyletic groups (fig. 8 ). This phylogenetic tree is largely consistent with previous analyses of SET domain proteins made with different species samples  (Sun et al.
Whole-Genome Analysis For all the studied species, genomic sequences (genome contigs and/or gene predictions and/or peptide predictions) were retrieved from the various sources listed in supplementary tables S2-S6, Supplementary Material online. BLAST searches  (Altschul et al. 1997)  were performed using the KoriBlast and ngKLAST softwares (Korilog Company, Muzillac, France) on local databases constructed from the downloaded genomic sequences. We first used the 17 described Prdm genes from Homo sapiens  (Fumasoni et al. 2007 ) as queries for these BLAST searches. To enhance the comprehensiveness of our searches, we next used the Prdm sequences of some additional species as queries in BLAST searches against the genome of species from the same taxonomic group. We used the Prdm sequences from the wellassembled and annotated Drosophila and Tribolium genomes as queries to search the Prdm genes in the other arthropod genomes; the Danio Prdm sequences were used to screen the genomes of the other teleosts; and the Prdm genes from Amphimedon and Nematostella were used for searches in the nonbilaterian and nonmetazoan genomes.
The resulting multiple alignments were manually improved. Multiple alignments were handled using SEAVIEW 4  (Gouy et al. 2010). BoxShade (http://www. ch. embnet. org/ software/BOX_form. html, last accessed November 25, 2015) was used to generate printouts of some multiple alignments. WebLogo (http://weblogo. berkeley. edu, last accessed November 25, 2015) was used to generate sequence logos. ML analyses were performed using the online available PHYML software (http://www. atgc-montpellier. fr/phyml/, last accessed November 25, 2015;  Guindon and Gascuel 2003; Guindon et al. 2005). Le and Gascuel amino acid substitution model was used  (Le and Gascuel 2008). Equilibrium frequencies, proportion of invariable sites, and gamma shape parameter were estimated from the data. We used four substitution rate categories.
The starting trees were generated using BIONJ and the NNI type of tree improvement was used. Statistical supports for the different internal branches were determined by approximate likelihood-ratio test (aLRT), a Bayesian-like transformation of aLRT (aBayes) and in some cases bootstrap resampling  (Anisimova and Gascuel 2006; Anisimova et al. 2011). BI was performed using MRBAYES 3. 2  (Huelsenbeck and Ronquist 2001; Ronquist et al. 2012)  with JTT + G model  (Jones et al. 1992). Two independent Markov chains were sampled every 200 generations. The trees obtained in the two runs were mixed and the first 25% of the trees were discarded as "burn-in. " Convergence was assessed by looking at the average standard deviation split frequencies and potential scale reduction factor values, following the software's instructions.
Phylogenetic trees were visualized and rooted using FigTree 1. 4 developed by Andrew Rambaud (http://tree. bio. ed. ac. uk/software/figtree/, last accessed November 25, 2015). We also performed AU test  (Shimodaira 2002). We generated trees with different topologies by rearranging the branching order of the ML trees produced by PhyML. Likelihoods of these test trees and best PhyML tree were compared by the AU test using CONSEL  (Shimodaira and Hasegawa 2001). A given topology was considered as rejected if the P value of this tree was inferior to 0. 05.
Acknowledgments The authors are grateful to  Maja Adamska  for giving them access to the Sycon ciliatum genomic data. They also thank the Genoscope and the  EMBL  for giving access to unpublished Platynereis expressed sequence tags and genomic sequences. They also thank  Eric Bellefroid  for stimulating discussions about Prdm gene functions and their evolution. They thank  Eve Gazave  and  B. Duygu Ozpolat  for critical reading of the manuscript. They are grateful to  Adrien Demilly  for help with the figures. This work was funded by the  CNRS , the  Agence Nationale de la Recherche (France)  ( ANR grant Blanc METAMERE ), the  Institut Universitaire de France  and the  Who am I? laboratory of excellence  (No. ANR-11-LABX-0071 ) funded by the  French Government.
Results: The DSFQ discriminated IBS from healthy subjects with a significant difference (P < 0. 001) between groups (estimated mean difference = 5. 58; 95% CI, 4. 91-6. 28). On the basis of the ROC curve (AUC = 0. 9479), a cutoff value of 5 gives a sensitivity of 92% and a specificity of 84%, with a positive likelihood ratio of 5. 75. Composite score of symptoms correlated strongly (P < 0. 0001) with digestive discomfort measured by FDDQL (À0. 816), digestive comfort measured by FBA (À0. 789), and the IBS-SSS score (0. 762). Conclusions: Measurement of digestive symptom frequency by means of the DSFQ can differentiate IBS from healthy subjects, and shows a good correlation with other validated questionnaires (clinical trial #NCT01457378).
Main Study Patients with IBS (n = 100; 18 y and above), meeting Rome III criteria (all subtypes),  5  and 100 age-matched (18  to 35, 36 to 50, and 51 to 65 y) and sex-matched healthy control (HC) subjects were included in the study. Participants were recruited by public advertisement and from the database of 2 clinical centers in France (Eurofins Optimed, Gie`res; and RPS France, Caen). Diagnosis of IBS was established using the Rome III questionnaire. 5  Patients were required to have a score of >75 on the IBS-SSS at the time of the study (active disease). Patients were excluded if they had clinical signs of alarm (eg, rectorragy, fever, recent weight loss), a known organic disease, including inflammatory bowel disease or cancer, or if they were on antidepressant or analgesic drugs. Healthy subjects were excluded if they had consulted a general practitioner or a gastroenterologist for any functional bowel disorder, had a known organic disease, or received any chronic medical treatment that could influence the GI tract.
Ancillary Study In addition, a larger cohort of subjects (521 women, age 18 to 60 y), who had participated in 2 previous independent randomized controlled trials using the same questionnaires, were analyzed to assess the stability of the questionnaire  14, 15  ; these subjects recruited from the general population reported minor GI discomfort, defined by the presence of mild digestive symptoms (ie, abdominal discomfort/pain, bloating, flatulence/passage of gas, borborygmi/rumbling stomach) in the past month, but did not fulfill the criteria of functional digestive disorders. The study protocol was approved by the Ethics Committee Sud Est III (Lyon, France). All participants gave written informed consent before inclusion in the study. The study is registered at Clinicaltrials. gov: NCT 01457378.
The closer this similarity value is to 0, the worse the association is. The relevance network with similarity values >0. 5 was considered as a relevant association. Reproducibility of the questionnaire (stability over time), in the ancillary study, was measured by the intraclass correlation coefficient (ICC) between 2 consecutive assessments 1 week apart during the baseline period of 2 randomized controlled trials. During this period, subjects are considered in stable healthy status. Statistical analyses were performed using SAS Software (SAS Institute Inc. ) version 9. 2 and or R software version 2. 15. 2.
First, the role of politics for public service prices is assessed using a large dataset on Austrian water provision. This involves testing the effect the political budget cycle, partisan politics and the intensity of political competition on water prices. Second, to account for potential strategic interactions between jurisdictions, the influence of neighbouring jurisdictions is assessed. These empirical tests help to evaluate if political price distortions are a relevant phenomenon of public monopolies and whether yardstick competition exists between Austrian municipalities. Testing both hypotheses jointly should also clarify price setting in public monopolies. This paper contributes to the literature by showing that vote-seeking politicians also use public service prices in an opportunistic manner.
The finding of such opportunistic behaviour suggests that although theoretically utility prices should be closely connected to costs, mandated both by national and European regulations, political reasons lead to deviations from this principle. In addition, the paper shows that yardstick competition may be interpreted as a conditionality of the political budget cycle, thus confirming the notion that the intensity of opportunistic behaviour depends on the institutional and political situation (see  Persson and Tabellini 2003; Akhmedov and Zhuravskaya 2004; Shi and Svensson 2006; Brender and Drazen 2007). The paper proceeds as follows. The following Section 2 briefly sketches the related literature connecting political incentives and fiscal policy. After that the paper tests the hypotheses that politics influence public monopoly behaviour using a large dataset on Austrian water providers in Section 3.
statistical analysis Bacteriological data were subjected to the statistical analyses described below. As usual, bacterial concentrations were log-transformed to avoid the extreme skewness effect of the original concentration. The TiO 2 coating and ultraviolet exposure were defined as binary variables. Bacterial concentrations were determined at six time points, ie, 0, 15, 30, 45, 60, and 90 minutes. For each observation, bacterial concentrations were counted on three different disks with a double reading. The reliability of the two bacterial concentration readings and the three disk counts was almost perfect, with all intraclass correlation coefficients being above 0. 9. The effects of TiO 2 coating, duration of ultraviolet exposure, and the staphylococcus strain used were tested in a generalized linear model, using bacterial concentration as the response variable, which included some first-order interactions, notably between the TiO 2 coating and ultraviolet exposure and between time variable and each of the aforementioned factors.
The variance of the Chapman estimator can be computed using the following formula  (Wittes, 1972) : var( N) = (a + b + 1) • (a + c + 1) • b • c (a + 1) 2 • (a + 2) Although the Chapman estimator and its associated normal based confidence interval are not difficult to compute once the contingency table is populated, the freely available Survey Toolbox software 1  provides a user-friendly package for their computation. To date, the Chapman estimator is the most widely used in two-source applications  (Hook and Regal, 1995). A comparison of the performance of alternative estimators can be found in  Brittain and Böhning (2009). To ensure unbiased estimation, some assumptions need to be satisfied. In disease surveillance, the two most critical assumptions are the need for direct and indirect independence between surveillance components.