dataesr/scientific-paragraphs-categorization huggingface dataset:
Stroke prevalence was calculated representing the number of stroke survivors at the time of data collection over the total number of the sample. Estimates of stroke prevalence were also calculated for each governorate for comparative purposes. Proportions were compared using Pearson's Chisquare test, with further standardized residuals' two-by-two comparisons. Means were compared using the analysis of variance (ANOVA) test with Bonferroni correction on post hoc tests. A p value <0. 05 was deemed statistically significant. The study was conducted for observational purposes with no participants' traceability. Thus, no ethical approval was found to be necessary by the Lebanese University ethics committee.
Discussion According to our findings, age-adjusted prevalence of stroke survivors among a sample of Lebanese inhabitants on March 31, 2012 was estimated to be 0. 50% (95% CI = 0. 33-0. 66%) and 0. 60% (95% CI = 0. 42-0. 78%) for the local population and the WHO world population standardizations, respectively. However, epidemiological inference to the Lebanese population may be difficult to obtain in our study settings, although results were age-and sex-standardized, due to our data collection method and the relatively small sample size. In fact, our sample was chosen among Lebanese inhabitants that possess a landline phone number. This group may be of a relatively higher socioeconomic status or include more aged participants. Moreover, telephone interviews may have slightly underestimated the real prevalence in the country.
Experimental setup The geometrical configuration of the experimental setup is shown in Fig. 9. The laser points at the center of the micromirror and the laser spot movement is captured by a CCD camera. The data containing the coordinates of the laser spot is then processed in Simulink®. Working range has been set from 0 to 3. 5V. When the applied voltage exceeds 3. 5V, the actuators began to lose their elasticity. The mirror platform displacements during the piston motion are considered as relative displacements. The initial position of the mirror at 0V is about 220µm above the substrate. The positive z-axis translation is toward the substrate. At 0V, the displacement corresponds to 0µm. The four actuators are applied with a constant voltage of 0 to 3. 5V with 0. 5V variance. Next, we applied 2V to all actuators plus an alternative component ΔV which is applied on two opposite actuators.
SMN associates with wild-type and assembly defective PRP31 proteins Given the general role of the SMN complex in snRNP biogenesis and the role of NUFIP/R2TP in the assembly of U4-specific proteins, we tested possible links between these complexes. First, we assessed interactions by co-immunoprecipitation (Figure  7A ). Extracts of 293T cells transfected with GFP-tagged versions of PRP31, 15. 5K, NUFIP and ZNHIT3 were purified on GFP-Trap beads, and analyzed by western blotting using anti-SMN antibodies. NUFIP, as well as wild-type and mutant PRP31 proteins, associated with SMN in both RNase treated and untreated extracts (Figure  7A ). A weak association was also  detected for ZNHIT3, but none for 15. 5K (see Supplemental Figure  S3A  for longer exposure of western blots). These data show that in mammalian cells, the SMN protein associates with PRP31 and NUFIP but not with 15.
FUNDING This work was supported by the  French Centre National de la Recherche (CNRS) , the  Université de Lorraine , the ' Plan Etat Région Lorraine ', grants from  Agence Nationale de la Recherche (ANR)  [ ANR-11-BSV8-01503 ] and the  Ligue Nationale Contre le Cancer  (équipe labellisée) [30025555 Grand-Est]. A. Huttin  was a pre-doctoral fellow from the.
Homozygosity Mapping Genome-wide genotyping was undertaken using Illumina 300K single nucleotide polymorphism (SNP) mapping array beadchip (Illumina), on DNA of individuals I2 (mother), II3 (sibling-girl) and II5 (sibling-boy). The BlueFuse Multi software v3. 3 (BlueGnome) was used to identify homozygous regions in all the family members. Only the regions longer than 1Mb and carrying at least 100 consecutive homozygous SNPs were selected. In parallel, homozygous regions and inbreeding coefficients were estimated/analyzed using FSuite pipeline  [9].
Based on the assumption that the mutation underlying the HPE was recessive in this inbred family, only mutations at a homozygous state in the boy (ll5) and at a heterozygous state in the mother (l2) were retained. Resulting variants were annotated using a February 2014 build of ANNOVAR  [10]. This software maps variants to RefSeq genes, known variations and frequencies from dbSNP137; it also annotates the predicted functional consequences of missense variants using six prediction algorithms (SIFT, Polyphen2, LRT, Mutation Taster, Mutation Assessor and FATHMM) and three conservation scores (PhyloP, GERP++ and SiPhy) from the dbNSFP v2. 0  [11]. Predictions by two new ANNOVAR in-   [21, 23, 29]  except those represented under the protein, which were two compound heterozygous mutations  [28].
Sanger sequencing The STIL mutation c. 2150G>A was confirmed in the mother I2 and the boy II5, and searched for in the girl II3 by classic Sanger sequencing. This was done using the BigDye terminator cycle sequencing kit (Applied Biosystems) on an ABI3130xl sequencer (Applied Biosystems) and analyzed using SeqScape software v2. 6 (Life Technologies).
Frequencies of these mutations in public databases, bioinformatics predictions, conservation properties and physico-chemical gap between the wild-type and mutated amino acid were analyzed (Table  1 ). Regarding these elements, the 3 mutations presenting the most deleterious criteria were those located in CTRC, ORC1 and STIL. The CTRC gene encoded the serine protease chymotrypsin C, a protein produced in small quantities by pancreatic cells that degrades trypsin. Mutations in CTRC have been associated with hereditary pancreatitis  [16]. CTRC was not expressed in the mouse adult brain (Expression Atlas, European Molecular Biology Laboratory), and the only known link with development is its association with enamel development, the hard mineralized surface of teeth  [17]. The combination of these elements permitted the CTRC mutation to be discarded.
Moreover, the ORC1 mutation found in the family was already described in the 1000 Genomes Project (1000G, April 2012), and predicted tolerated both by MetaSVM and MetaLR from ANNOVAR. The mutation that presented the most deleterious criteria was the STIL homozygous mutation c. 2150G>A (p. Gly717Glu). Indeed, almost all bioinformatic tools used predicted that this mutation was likely to affect the protein function. This mutation was located in exon 12 of the gene STIL (or SIL; SCL/TAL1 interrupting locus) (RefSeqNG_012126. 1), encoding a pericentriolar and centrosomal protein. It was absent in 1000G and the Exome Sequencing Project (ESP6500, October 2012) databases, and in the Genome Management Application (University of Miami Health System) that contains genomic data of about 200 Turkish families.
Acknowledgments This work was supported by the  Agence de la Biomédecine  (to V. David) and the  Agence Nationale de la Recherche  (grant no. ANR-12-BSV1-0007-01  (to V. Dupé). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors acknowledge the  Centre de Ressources Biologiques (CRB)-Santé  (http://www. crbsante-rennes. com) of Rennes for managing patient samples and  Michelle Ware  for critical reading of the paper and providing the  Shh chick embryo.
Coexpression of IGs We performed functional annotation and classification of murine IGs (Supplemental Table  S1 ) using the DAVID bioinformatics resources (http://david. abcc. ncifcrf. gov/) and found no enrichment of specific Gene Ontology terms and KEGG pathways (Supplemental Table  S2 ), which did not support a common function for IGs. However, using a meta-analysis of microarray data  (Lee et al. 2004a) , we previously showed that 15 IGs are frequently coregulated  (Varrault et al. 2006 ). In the present study, we extended this discovery to all IGs by mining two larger meta-analyses, COXPRESdb  (Obayashi and Kinoshita 2011)  and Gemma  (Zoubarev et al. 2012) , each one using a distinct metric and relying on diverse data sets. We first searched COXPRESdb for coexpression of the 85 murine IGs present in this database, and showed that 84 are frequently coexpressed (Fig.
1A ; Supplemental Table  S3 ). Details regarding the construction of this network are given in the Supplemental Information. To test for the specificity of this network, we compared its topology, i. e. , the average number of neighbors of a node (degree) and the average edge weight (mutual rank in COXPRESdb), to coexpression networks made from other gene sets. We showed that the network of 85 IGs was topologically different to 10,000 networks obtained by randomly drawing sets of 85 genes with data in COXPRESdb (Fig. 1B ). We also constructed 50 coexpression networks from sets of 85 genes known to be functionally related, i. e. , belonging to the same Gene Ontology Biological Process (GO_BP) (Fig. 1B ; for the full list of GO_BPs used in this figure, see Supplemental Table  S22 ; for details regarding the selection of GO_BPs, see Supplemental Information).
S1 ). We compared the COXPRESdb and Gemma networks to figure out how similar they are. We first looked at the overall topology of both networks and measured the degree correlation (Supplemental Fig. S2 ). The degree of each IG in both of the databases is well correlated (r 2 = 0. 70), indicating that genes highly connected in one database were generally highly connected in the other. We then recorded edges between the 80 murine IGs jointly found in the networks displayed in Figure  1A  and C, and classified them in three categories (Supplemental Fig. S3 ): present in both databases (139 edges), present only in Gemma (176 edges), and present only in COXPRESdb (181 edges). This classification qualitatively showed that connections among a small group of highly connected IGs are well conserved in COXPRESdb and Gemma.
We also plotted the relative difference of degree in the two databases versus the average degree of each gene in the two databases (Supplemental Fig. S4 ). This quantitative analysis suggested that three groups of coexpressed IGs existed. A group of 13 IGs-i. e. , Meg3, Ndn, Grb10, Dlk1, Igf2, Cdkn1c, Plagl1, Peg3, Mest, Nnat, Asb4, H19, and Ppp1r9a-were highly connected with other IGs (average degree $15), and both the number and the identity of these connections were conserved in Gemma and COXPRESdb (relative degree difference #50%). This subnetwork was apparent in Figure  1A  as the densely connected nodes in the lower left quadrant of the figure. A second group of 10 IGs included Gnas, Magel2, Rian, Dcn, Snrpn, Airn, Cd81, Igf2os, Impact, Phlda2, and Gatm. These genes displayed intermediate connectivity, and their connections were less frequently conserved in the two databases.
Finally, the other IGs displayed less connections in at least one database (low average degree) and/or a large relative difference of degree in the two databases. We performed a similar analysis with the 86 human IGs (Supplemental Table  S5 ) present in Gemma, including 15 genes whose imprinting status is unknown but whose murine orthologs are imprinted. Again, the vast majority of IGs (80 out of 86) were frequently coexpressed (Fig. 1D ; Supplemental Table  S6 ). We verified that the network of human IGs is topologically similar to coexpression networks made from GO_BP genes and different to networks made from randomly selected genes (Supplemental Fig. S5 ). Finally, we looked at rat IG coexpression and included in the analysis the rat orthologs of murine IGs (Supplemental Table  S7 ). We showed that 58 out of 60 rat bona fide and candidate IGs were frequently coexpressed (Fig.
IGs were coregulated during cell cycle withdrawal/reentry and differentiation To gain insights into the biological function associated with this network, we searched for biological conditions under which IGs are dynamically coregulated. We first examined the data sets that displayed coexpression of many IGs and noticed that some were from cell populations whose proliferation was experimentally manipulated (data not shown). Using a collection of primers for real-time PCR, we monitored IG expression in primary mouse embryonic fibroblasts. We first induced cell cycle withdrawal by growth factor deprivation and observed the up-regulation of a large proportion of the tested IGs (Fig. 2A ). We also let the cells reach confluence in the presence of serum to induce quiescence by contact inhibition, and observed a marked induction of the expression levels of most IGs upon MEF cell cycle exit (Supplemental Fig.
The resulting synchronous proliferation took place in a different context, however, as the cells received the whole information (IDX) to undergo adipogenic differentiation. Interestingly, Figure  1. Imprinted genes are frequently coexpressed. (A) The COXPRESdb meta-analysis of microarray data was searched for coexpression among murine IGs. The resulting coexpression links are represented using Cytoscape. Node size is proportional to node degree. Edge width represents the mutual rank (see Supplemental Information) between two given nodes. (B) The average degree and average mutual rank were computed for the network of 85 IGs displayed in A (red circle). Random networks (blue lines) were generated by randomly drawing 10,000 sets of 85 Gene IDs with data in COXPRESdb and retrieving coexpression links as for the 85 murine IGs.
A similar procedure was performed with 50 sets of 85 genes found in GO Biological Processes (green triangles) and with data in COXPRESdb (for a full list of the GO BPs analyzed, see Supplemental Table  S22 ). (C ) The Gemma meta-analysis of microarray data was searched for coexpression among murine IGs. Node size is proportional to node degree. Edge width maps the number of data sets in which two given nodes are coexpressed. we again observed a rapid down-regulation of most IGs upon IDX addition (Fig. 2D ). Two to three days following IDX addition, the cells definitively exited the cell cycle (Fig. 2B, D ). It was noteworthy that only a subset of IGs was then reinduced, indicating a differential IG expression pattern depending on the reversibility of the cell cycle withdrawal. Finally, Peg10 and H19 displayed a specific pattern of expression with maximal expression during the second cell cycle that preceded definitive cell cycle exit (Fig.
Following early observations suggesting coregulation of a small number of IGs  (Hayashida et al. 1997; Yan et al. 2003) , we showed that a subset of 15 IGs is frequently coexpressed  (Varrault et al. 2006) , which was confirmed by other groups  (Lui et al. 2008; Berg et al. 2011; Zacharek et al. 2011). The present study identified significantly more coexpression among IGs by using broader and more varied data sets; e. g. , the Gemma and COXPRESdb databases comprise data sets from the nervous system, which was underrepresented in the TMM database originally explored. Networks identified through transcriptomic data may result from cell-or tissue-specific subnetworks and should thus be utilized only for sets of genes that Genome Research 361 www. genome. org are expressed in the same cell types.
Mining meta-analyses of microarray data and bioinformatics We retrieved the 124 murine IGs (Supplemental Table  S1 ) from GeneImprint (http://www. geneimprint. com), the Wamidex atlas (https://atlas. genetics. kcl. ac. uk/), and the Harwell Mousebook Imprinting Catalog (http://www. mousebook. org). We searched COXPRESdb (http://coxpresdb. hgc. jp) for murine IG data and retrieved the genes coexpressed with each of the 85 IGs present in this database. We then filtered these lists for coexpression links that involve two IGs with a mutual rank below 1200 (for a discussion of this parameter, see Supplemental Information). The resulting network was drawn using Cytoscape  (Shannon et al. 2003). To generate random murine networks from COXPRESdb, we randomly selected 10,000 sets of 85 GeneIDs from the list of genes present in this database.
We retrieved coexpression links as above and calculated the average degree and average mutual rank. We performed the same analysis with sets of 85 genes that belonged to Gene Ontology Biological Processes (GO_BPs). We selected 50 GO_BPs based only on the number of genes with data in COXPRESdb they comprised, i. e. , at least 85. In case the GO_BPs included more than 85 genes with data in COXPRESdb, we used the 85 first GeneIDs in the list of genes found in the corresponding GO_BP. To identify genes coexpressed with murine IGs in Gemma (http://www. chibi. ubc. ca/Gemma/ home. html), we retrieved the genes coexpressed with each of the 101 IGs present in this database. We then filtered these lists for coexpression links that involve two IGs in a minimum of two data sets. The resulting network was drawn using Cytoscape.
In this experiment, only reads #1 were used and mapped to the full mm9 mouse reference genome using the eland_rna module of CASAVA 1. 8. 2 (Illumina); 118. 3-160. 7 M reads per library mapped uniquely to the mouse transcriptome. Counting was performed with CASAVA's variant detection and counting module. UCSC annotation file  (refFlat. txt. gz; 2012-01-15)  was used to retrieve annotations on genes and exons. GeneIDs are from the  NCBI (gene2refseq. gz; 2012-01-19). From the 19,489 genes with at least one count in one sample, 3305 genes for which the sum of raw counts in the eight samples was less than 20 were filtered out. Counts were normalized using the RLE method as implemented in edgeR  (Robinson et al. 2010) , which was used to identify differentially expressed genes with a generalized linear model likelihood ratio test using a paired design and a FDR set at 1%.
Figure1. Imprinted genes are frequently coexpressed. (A) The COXPRESdb meta-analysis of microarray data was searched for coexpression among murine IGs. The resulting coexpression links are represented using Cytoscape. Node size is proportional to node degree. Edge width represents the mutual rank (see Supplemental Information) between two given nodes. (B) The average degree and average mutual rank were computed for the network of 85 IGs displayed in A (red circle). Random networks (blue lines) were generated by randomly drawing 10,000 sets of 85 Gene IDs with data in COXPRESdb and retrieving coexpression links as for the 85 murine IGs. A similar procedure was performed with 50 sets of 85 genes found in GO Biological Processes (green triangles) and with data in COXPRESdb (for a full list of the GO BPs analyzed, see Supplemental TableS22).
(C) The Gemma meta-analysis of microarray data was searched for coexpression among murine IGs. Node size is proportional to node degree. Edge width maps the number of data sets in which two given nodes are coexpressed. (D) The Gemma database was searched for coexpression among human IGs and orthologs of murine IGs whose imprinting status in humans is unknown or uncertain (open circles) as in C. (E) The COXPRESdb was searched for coexpression of rat IGs and orthologs of murine IGs whose imprinting status in rat is unknown (open circles) as in A.
Acknowledgments We thank  Paul Pavlidis ,  Anton Zoubarev , and  Patrick Tan  for help with the analysis of the Gemma data,  Annie Delalbre  and  Gilles Carnac  for help with notexin injection experiments,  Robert Feil  and  Satya K. Kota  for providing  JB6  and  BJ1  MEFs and primer sequences for the McrBC analysis,  Philippe Arnaud  and  Atsuo Ogura  for providing information about SNPs in murine IGs,  Julien Cau  and  Am elie Sarrazin  ( MRI imaging and cytometry facility ) for cell cytometry,  Christelle Reyn  es and  Robert Sabatier  for advice with statistical analysis of the data,  Vincent Demolombe  for bioinformatic support, and  William Ritchie ,  Gavin Kelsey , and  Marisa S. Bartolomei  for critical reading of the manuscript. This research was supported by grants from the  CNRS, INSERM, Montpellier University , and the  National Research Agency  ( ANR-07BLAN052 ).
H. A. A. , B. E. , and C. G. were supported by fellowships from the  Ministry for Higher Education and Research. B. E. and C. G. were supported by fellowships from  La Ligue contre le Cancer and L'Association pour la Recherche contre le Cancer , respectively. The  MGX-Montpellier GenomiX facility , a member of the '' France G enomique '' network, is supported by the  Languedoc-Roussillon  district and the  National Research Agency  ( ANR-10-INBS-09-06 ). Author contributions: H. A. A. , T. B. , A. V. , and L. J. conceived and designed the experiments. H. A. A. , B. E. , A. L. D. , C. G. , H. P. , T. B. , A. V. , and L. J. performed the experiments. H. A. A. , B. E. , A. L. D. , C. G. , E. D. , T. B. , A. V. , and L. J. analyzed the data. C. D. contributed analysis tools. H. A. A. , B. E. , T.
To check the reversibility of the assembly, and to discriminate the turbidity from an unspecific protein precipitation, the plate was subsequently placed on ice, and turbidity was checked every 10 minutes for 30 minutes. Data analysis was performed with Microsoft Office Excel and SigmaPlot V8. 02 equipped with Enzyme Kinetics Module V1. 1. (b) Tubulin polymerization in the presence of RAP55/LSM14 at different concentrations: 50 µM of RAP55/LSM14 in HeLa cells extracted tubulin was mixed with different concentrations of RAP55/LSM14 and the assay was performed in the presence of a single concentration of GTP. The values from individual samples were analyzed and plotted as a function of concentrations using Sigma Plot V8. 02 equipped with Enzyme Kinetics Module V1. 1. Statistical analyses. One-way analysis of variance followed by Duncan test (p < 0.
Acknowledgements We wish to thank  U836 INSERM, Grenoble, France , for offering D. M. this lifetime experience to work with them and to benefit from their available resources. The experimental work was financially supported by the  Tunisian Ministry of the Higher Education and Scientific Research  and by the  Research Unit UR   12ES08  " Signalisation Cellulaire et Pathologies " at the  Medicine Faculty of Monastir, Tunisia.
Images were processed using the L'IMAGE software (L. Nittler, Carnegie Institution, Washington, DC, USA) and corrected for detector dead time (44 ns, set electronically) and drift in X and Y positions. A type 3 kerogen standard was used to check for instrumental stability over the course of the analytical session. We chose to map the 13 C 14 N -/ 12 C 14 N -ratio (hereafter named 13 C 14 N/ 12 C 14 N) to track the 13 C isotopic label because the CN -signal is dominated by bacteria contribution; on the other hand, C -signal is overwhelmed by the polycarbonate filter contribution. Regions of interest (ROI) were manually drawn around individual cells in order to calculate their isotopic and elemental compositions. The elemental ratio 16 O -/ 12 C 14 N -(hereafter 16 O/ 12 C 14 N) was used to identify extracellular minerals and mineralized cells.
Statistical Analyses Levels of carbon assimilation by cell subpopulations or samples were analyzed by statistical comparisons of mean 13 C 14 N/ 12 C 14 N ratios (multiple and pairwise comparisons by one-way ANOVA and Welch two-sample t-tests with Bonferroni correction for multiple testing, respectively). The cell dataset used for statistical analyses is composed only of ROI located outside Fe aggregates (Supplementary Table  S1 ). Corrected p-values smaller than 0. 05 were considered significant. Parameter estimation of an exponential model was achieved by non-linear least-squares fitting; the goodness of fit was evaluated by R-squared value, calculated as 1 minus the ratio of the residual sum of squares to the total sum of squares.