dataesr/scientific-paragraphs-categorization huggingface dataset:
However, there are several novel aspects in our study, which include not only the discovery of a new G4containg gene but also the functional relevance of this finding through intervention with small molecules, both in cell cultures and in animal model. Specifically, we found that a number of chemically unrelated G4 ligands  (26, 39, 41, 42)  are able to bind to and stabilize the G4 structure identified within the promoter of vegfr-2, providing an explanation for their inhibitory effect on VEGFR-2 expression and on EC proliferation, migration, differentiation and in vivo angiogenesis. The effects of G4 ligands have been observed in human-and mouse-derived EC consistently with the presence of the G4 structure in both cell types  (54)  [as determined by the G4 prediction software QGRS  (33) ].
ACKNOWLEDGEMENTS The authors thank  Dr Stefano Alemaof the  Institute of Cell Biology and Neurobiology CNR (Monterotondo, Rome ) and  Prof Stefano Cacchione  and  Dr Alessandra Galati  of  University 'La Sapienza' of Rome  for their kind help with Rho and Rac GTPases activity assays and for DMS footprinting assay, respectively. FUNDING  Italian Association for Cancer Research  [ 11567  and # 9979  to A. B. ]; S. I. is recipient of a fellowship from the  Italian Foundation for Cancer Research  (F. I. R. C. ); and A. R. is recipient of a fellowship from the  Veronesi Foundation. Funding for open access charge:  Italian Association for Cancer Research  [ 11567  to A. B. ].
Patients' samples and cell lines 7] [18]  The SKMM, OPM2, LP1 and RPMI8226 HMCL were purchased from ATTC (LGC Promochem, France). These HMCL were recently fully characterized from molecular and phenotypic points of view. 19 The MMC of 206 patients with previously untreated multiple myeloma were included in this study. These 206 patients were treated with high-dose therapy and autologous stem cell transplantation and this cohort is termed in the following the Heidelberg-Montpellier cohort. The patients' characteristics are presented in Online Supplementary Table  S1. For 12 patients, MMC were harvested both at diagnosis and at relapse. Gene expression profiling of purified MMC was performed using Affymetrix U133 2. 0 plus microarrays as described elsewhere  20  and data were normalized using the MAS5 Affymetrix algorithm with a scaling factor of 100.
The. CEL and MAS5 files are deposited in the ArrayExpress public database (http://www. ebi. ac. uk/arrayexpress/), under accession number E-MTAB-362. Interphase-fluorescence in situ hybridization (iFISH) analysis was performed according to our previously reported standard protocol. 21 We also used publicly available Affymetrix data (http://www. ncbi. nlm. nih. gov/geo, GSE2658) on purified MMC from a cohort of 345 previously untreated patients from the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA). These patients were treated with total therapy 2  22  and this cohort is termed in the following the UAMS-TT2 cohort. Normal bone marrow plasma cells (BMPC) were obtained from healthy donors after they had given informed consent. 23, 24 Peripheral blood CD14 + monocytes were purified by a FACS cell sorter from healthy donors' peripheral blood buffy coats purchased from the French Blood Center (France) as indicated elsewhere.
KLF4 expression in normal and malignant plasma cells KLF4 was expressed in purified CD14 + monocytes, memory B cells as well as in BMPC of healthy individuals and was not expressed in in-vitro generated plasmablasts using Affymetrix microarrays (probe set 221841_s_at). These data were confirmed using real-time PCR (Online Supplementary Figure  S1A ), with a high correlation between KLF4 real-time PCR expression and Affymetrix microarray expression (probe set 221841_s_at) in three normal BMPC samples, three monocyte samples, and five primary MMC samples (r=0. 83, P=0. 001, results not shown). KLF4 gene expression was highly variable in primary myeloma cells from 206 newly diagnosed patients, with log 2 Affymetrix signals ranging from 2 to 13 (Figure  1A ). For 12 patients, gene expression profiles of MMC were determined at both diagnosis and relapse, and no significant change in KLF4 expression was found (P=0.
XG19-TR-KLF4 cells were cultured with or without doxycycline (dox) for 4 days and cell cycle was quantified using DAPI staining and BrdU incorporation, labeling with anti-BrdU antibody and flow cytometry analysis. (A) XG2-TR-KLF4, XG7-TR-KLF4, XG19-TR-KLF4 and LP1-TR-KLF4 were analyzed as described for XG19-TR-KLF4 cells. The figures represent data from three independent analyses. (B) *Indicates a significant increase in G0/G1 phases; **A significant decrease in S-phase; ***A significant increase in G2/M phases using a Wilcoxon test for pairs (P<0. 05).
Acknowledgements This work was supported by grants from  ARC  ( SL220110603450 ,  Paris France) , the  European Community  ( FP7-OVERMYR ), the  Tumorzentrum Heidelberg/Mannheim, Germany , the  Deutsche Krebshilfe, Bonn, Germany , and the  Deutsche Forschungsgemeinschaft  ( Transregio TRR 79 ),  Bonn, Germany. MS is supported by a grant from  Guillaume Espoir association (Saint-Genis-Laval, France).
One mg of total RNA was used for preparation of biotinylated cRNA and 15 mg of cRNA were hybridized. The Affymetrix Fluidics Station 450 was used for washing and staining. Arrays were scanned using the GeneChip Scanner 3000 (Affymetrix). Affymetrix CEL files were analyzed in R using the Bioconductor suite of packages. Raw probe signals were background corrected, normalized and summarized using the RMA procedure. Linear models were applied using the limma package in order to identify genes with potentially significant change in expression in response to time effect or R1881 treatment at each duration (model formula: , Duration + Duration:R1881). The empirical Bayes method was used to compute moderated p-values that were then corrected for multiple comparisons using the Benjamini and Hochberg's false discovery rate (FDR) controlling procedure.
The microarray data have been deposited and described, in accordance with MIAME guidelines, in Gene Expression Omnibus under the accession number GSE29232 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc = GSE29232). To assess that the relative RNA expression levels of 14 regulated-transcripts were consistent with the microarray data, RT-qPCR was performed in triplicates from the same RNA as in microarray experiments using QuantiTect SYBRH Green PCR Kit (Qiagen 204145). Primers provided from Qiagen: Hs_EGFR (QT00085701), SOX2 (QT00237601), Hs_NDRG1 (QT02290232), Hs_MME (QT00048755), Hs_TFPI2 (QT00062804), Hs_CDK5R1 (QT00231644), Hs_SCNN1G (QT00063217), Hs_RHOB (QT00227409), Hs_PRDM1 (QT00060494), Hs_IL7R (QT00053634), Hs_SERPINB2 (QT00077497), Hs_PAX9 (QT00023317), Hs_FST (QT01665853), Hs_ADAMTS1 (QT00098588) and Hs_TPB (QT00000721) as housekeeping gene to normalize raw data.
Identification of candidate biomarker genes Potential candidate genes were selected with the following criteria: 1) androgen-regulated: considering log2 fold-change cut off value to 1. 2 and the FDR (false discovery rate) with P ,0. 05; 2) expressed at significantly higher levels after treatment (3 h and/or 24 h duration); 3) Gene Ontogeny categories and relevant pathways using Ingenuity Pathway Analysis (IPAH) software (Ingenuity Systems). A total of 36 gene targets were chosen for confirmation of (differential) expression in patient samples.
Statistical analysis Real-time data. The threshold cycle (Ct) was automatically given by the SDS2. 2H software package (Applied Biosystems). Relative quantities (RQ) were determined using the equation: RQ = 2-DDCt. All data were generated in duplicate for each gene expression per sample. The TaqMan Array Cards data were simultaneously analyzed for differential expression using the Integromics RealTime StatMiner 4. 0H package which integrates BioConductor R software. The different steps of the workflow process of PCR data analysis: quality control, selecting the most stable endogenous control, data normalization and relative quantification, were achieved with this bioinformatic tool. As controls for the presence of prostate cells in the prostatic samples, two prostate-specific markers, members of the kallikrein family, i.
Values for the ROC curves were -DCt normalized using the geometric mean of TBP, KLK2 and KLK3. The 95% confidence interval (95% IC) of the AUC values were calculated as described  (13). For this expression study, potential biomarkers were considered valuable if AUCs $0. 9  (13). All these statistical calculations were performed using STATAH11. 0 software (College Station, Texas).
They also retained the ability to develop, particularly in Matrigel 3D cultures, well-polarized hollow spheroids undergo and eventually acinar differentiation as a response to growth factors and as a result of interactions with extra-cellular matrix  [17, 18, 19]. To reinforce androgen-dependent gene activation programs, we used stably transfected RWPE-1 cells with the wild-type AR gene  [14]. The resulted RWPE-I-AR cells were strongly androgensensitive as checked by proliferation assays. These cells were treated by the non metabolisable synthetic androgen R1881 for 3 h and 24 h. Androgen-induced stimulation was first checked by quantitative RT-PCR of a panel of known target AR genes including KLK3/PSA, MME (macrophage metalloelastase), and TMPRSS2 (data not shown). Extracted cDNAs from cells treated or not with R1881 were then hybridized on Affymetrix Human 133 plus 2.
The results confirmed that the relative RNA expression levels were consistent with the microarray data (Table  S1 ). To identify and prioritize biomarker candidates among genes found to be differentially expressed after 3 h and/or 24 h R1881 treatment, we based in silico analyses on biological characteristics considered to be the most relevant in both literature survey and Ingenuity Pathway Analysis (IPAH). Analyses with this software returned the following information: 1/ the relevant functions associated with the dataset and 2/ affected signaling and metabolic pathways associated with the dataset. In addition to high foldchange in microarray dataset at 3 h and/or 24 h of R1881 exposition, genes were designed as of interest if they have at least one these functional annotations or disease associations in the Ingenuity Knowledge database: possible detection in urine, wellknown tissue expression and, in particular, differential expression in prostate cancer cell lines, in prostate benign tissues and/or in prostate cancer, and/or strong association with carcinogenetic processes.
PCA3 is indeed now recognized as a valuable biomarker of prostate cancer (revue in  [27, 28] ) and has been shown to be specifically expressed in up to 95% of PCa  [3, 29]. PCA3 was therefore used as a positive control, reflecting PCa-specific expression panel. TMPRSS2  [30]  and DLX1  [31, 32]  are also potential PCa biomarkers, as disclosed by examining microarray data using the Oncomine database, and their expression was similarly evaluated as positive controls. In addition, we evaluated expression of 6 potential housekeeping genes: 18S, ACTB, HMBS, RPL13A, TBP, and UBB, as well as 2 genes considered as prostate-specific genes: KLK2 and KLK3/ PSA. As a global marker of androgen-sensitive tissues (prostate and seminal vesicles), we also selected AR gene for analysis (Table  2 ). Whether PCa and PTZ tissues express similar RNA amounts is unknown.
Normalization by a housekeeping gene was therefore warranted. Recent reports showed that housekeeping genes can in fact present serious differences between tissues, cell lines or clinical samples  [33]. We therefore first sought to determine which potential housekeeping genes were stably expressed under the different conditions from among the 6 we chose. TBP was found as the most stable endogenous control using the NormFinder algorithm of the StatMinerH package and was used as the normalization gene in the following parts of the study. The prostate-specific genes KLK2 and KLK3/PSA also disclosed clear expression stability and were used together with TBP as the endogenous control (geometric mean). We compared expression of all the selected genes in 26 matched normal prostate PTZ and PCa tissues, obtained from radical prostatectomy specimens.
In our study, we observed a strong overexpression in PCa from 50 to 210 fold, as compared to normal prostate and BPH, respectively. DLX1 showed the same expression pattern, as previously observed  [31]. ALCAM and CLDN7 both encode proteins localized to the inter-cellular junctions of diverse epithelia and have previously been suggested as androgen-regulated and overexpressed in PCa  [38, 39]. RasD1 was initially described as a dexamethasone-inducible ras-related protein and potential actor in preventing aberrant cell growth in several cell lines  [40]. According to Ingenuity Pathway AnalysisH, among the 14 genes thus found to be differentially expressed in BPH and prostate cancer, all were involved in a significantly represented functional network -cell growth and proliferationexcept PCA3 (little is known about its function, but PCA3 in the control of PCa cell survival, in part through modulating AR signaling  [41] ) and CDK5R1 (rather involved, along with 8 other genes, in a significantly represented biological function: cell death and survival).
Adding tissues from normal seminal vesicles was therefore considered in the present study, to reinforce expression comparison between prostate cancer and a reference benign tissue, highly protected against cancer transformation. We therefore evaluated gene expression in 35 matched seminal vesicle and PCa tissues using the same selected genes (Table  2 ). The lack of seminal vesicle involvement by prostate cancer was carefully pathologically ascertained before use in the study. TBP was found as the most stable endogenous control using the NormFinder algorithm of the StatMinerH package and was used as the normalization gene. As expected, the prostatespecific KLK2 and KLK3 genes were underexpressed in seminal vesicle as compared to prostate tissue (P,0. 01). Ten other genes were also significantly overexpressed in PCa as compared to seminal vesicle: ALCAM, LOX, BNIP3, CLDN8, AQP3, FOXA2, and NDRG1, as well as the expected TMPRSS2, DLX1 and PCA3 (Figure  1C  and Figure  S3 ).
We also favored the DLX1 gene, since we found it to be always overexpressed in prostate cancer tissues. Other genes also significantly overexpressed in PCa as compared to other tissues were also evaluated: ALCAM, RASD1 and CLDN7. The AKR1C1/2 gene was also considered because of its functional role in androgen bioavailability  (20). At last, CDK5R1 was overexpressed in BPH as compared to PCa, but also when comparing BPH to seminal vesicle and normal PTZ (data not shown). As a potential specific marker of BPH, it was therefore also included in the evaluation of diagnostic potential since BPH constitutes the most frequent diagnostic challenger to prostate cancer in the context of elevated serum PSA. Receiver Operating Characteristic (ROC) curves were constructed using normalized Ct values and the areas under curve (AUCs) were calculated (Figure  2 ).
These AUCs represent an appropriate means to evaluate global discriminative properties, an AUC greater than 0. 90 being considered as excellent  [46]. As expected, PCA3 was found to be highly discriminating, with an AUC of 0. 91. This was highly reminiscent of the results obtained in the expression study that accompanied the pioneer report of urine PCA3 test: prostate tumor and normal prostate tissue specimens were analyzed for PCA3 expression and the resulting AUC-ROC value was 0. 98  [22]. DLX1 gave similar results (AUC: 0. 96), whereas none of the 3 potential prostate cancer-specific genes, ALCAM, RASD1 and CLDN7, gave AUC values higher than 0. 9. While AKR1C1/2 and CDK5R1did not, CRYAB, KCNMA1 and SDPR gave significantly high AUCs. Access to Oncomine database (using cancer vs normal analysis with PCa as the cancer type) confirmed that all 3 genes have already been found in several expression data sets as significantly underexpressed in prostate cancer when compared to non-cancerous tissues.
Data Collection and Definitions Assessments were conducted during specific home visits by a team of four trained psychologists, working independently from the psychologists performing the CAPEDP intervention and with no knowledge of whether the women being evaluated were in the intervention group or the control group. Assessment took place at baseline for demographic and health characteristics and then at 3, 6, 12, 18 and 24 months after the child's birth for both groups. The assessment team received specific training on each of the assessment instruments. Individual and group supervision was provided for all members of the assessment team, to support them in handling difficult situations during evaluation, for example in situations of abuse or neglect, developmental delay, suicidal ideation in the mother or serious social problems.
Population characteristics At baseline and, when appropriate, at follow-up visits, the following data were collected: demographic data included age, sex, marital status, ethnicity, family characteristics, household composition, characteristics of the partner and, if different, the father of the coming child, whether the pregnancy was planned or not, number of years of schooling, educational level attained, employment status, and income; health variables included PND, self-perceived state of health, and use of tobacco, alcohol or drugs.
They were therefore not included in the present analysis. Although analysis of the eligibility criteria revealed no apparent difference with regard to the other women included in the modified ITT analysis, the benefits of randomization may well have been compromised to some extent. For this reason, due to the fact that mean EPDS scores differed between the two groups at baseline, both primary and secondary analyses were adjusted for baseline EPDS scores using linear regression. All in all, data concerning 183 women from the control group and 184 women from the intervention group were involved in the final analyses. For the primary analysis, the between-group absolute difference in the EPDS score at three months was analyzed using Student's t test. For the secondary analyses, data that were normally distributed were compared between groups using Student's t test for continuous data and the chi-square test for categorical data.
Results A total of 440 women accepted to participate in the study and signed the consent form in the maternity ward at the prenatal visit. Of these, 73 (16. 6% of the initial 440) then proceeded to withdraw their consent or could not be contacted during the first year of the study. These women were therefore not included in the modified intent to treat population (see Figure  1 : flowchart diagram) because no data were available for them apart from initial eligibility criteria. At the second evaluation point, when the children were three months old, a further 88 mothers could not be interviewed, i. e. an additional 20% of the total initial sample. Comparisons between the 277 who presented at this interview and the 88 who did not, revealed that those who presented were more likely to have only one of the three additional risk factors at inclusion rather than having two or all three (p = 0.
Acknowledgments The authors would like to thank the  440  families who participated in the study, the members of the home-visiting team and the research assistants, without whom this project would have been impossible:  Joan Augier ,  Amel Bouchouchi ,  Anna Dufour ,  Cecile Glaude, Audrey Hauchecorne, Gae ¨lle Hoisnard ,  Virginie Hok ,  Alexandra Jouve ,  Anne Legge , Celine Menard,  Marion Milliex ,  Alice Tabareau ,  Francine Messeguem. We also thank the team of the  Binet Children Community Mental Health Center, Sebastien Favriel and the research team of l'EPS Maison Blanche  for technical support,  Estelle Marcault  for logistical support and research implementation, Veronique Laniesse,  Alexandra Avonde ,  Dominique Granger  and  Sebastien Favriel  for administrative support,  Cecile Jourdain ,  Pierre Arwidson ,  Beatrice Lamboy and Gerard Guillemot  for help with research administration,  Nathalie Fontaine ,  George Tarabulsy  and  Michel Boivin , for assistance with developing the research and intervention instruments, and the members of the supervision team:  Laure Angladette ,  Drina Candilis ,  Judith Fine ,  Alain Haddad ,  Joana Matos ,  Anne-Sophie Mintz ,  Marie-Odile Perouse de Montclos ,  Diane Purper-Ouakil ,  Francoise Soupre ,  Susana Tereno ,  Bertrand Welniarz  and  Jaqueline Wendland.
Acknowledgements We thank financial support from  ANPCyT  ( PICT 25209 ,  PME 01113  for X-ray diffractometer),  UBACyT  ( X057 ),  Secyt-Ecos  ( A01E05 ) and Conicet (fellowships for AZ and Cecchi). AZ, PA and FDC are members of the research staff of Conicet.
Experimental Crystal data  Hydrogen-bond geometry (A ˚, ). D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA N3-H3Á Á ÁCl2 i 0. 79 (3) 2. 60 (3) 3. 369 (3) 165 (3) C4-H4Á Á ÁCl1 i 0. 95 2. 74 3. 604 (3) 152 Symmetry code: (i) x þ 1 2 ; Ày þ 1 2 ; z À 1 2. Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT; program(s) used to solve structure: SIR2004  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). We are grateful to all personel of the LCATM laboratory, UniversiteOum El Bouaghi, Algeria, for their assistance. Thanks are due to MESRS (Ministere de l'Enseignement Superieur et de la Recherche Scientifique -Algeria) for financial support via the PNR program.
Computing details Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT  (Bruker, 2011) ; program(s) used to solve structure: SIR2004  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
Data and Study Sites We analyzed original data on pre-control nodule prevalence in adult males (N = 7,525 individuals) and mf prevalence in the population aged five years and above (N = 29,775 individuals) from 148 villages in seven geographical areas including countries in the former OCP area, and foci in Cameroon, Nigeria, and Uganda, which are part of APOC (Table  1 , Figure  1 ). Most of these data have been previously published  [9, 14, 18, 19] , except for part of the data from Cameroon. The simuliid vectors responsible for transmission in each area have been described previously (Table  1 )  [9, 19, 20, 21, 22, 23]. In all areas, data on nodule and mf prevalence had been collected simultaneously (except for Nigeria, where nodule palpation took place six to twelve months after skin snipping, though still before the start of control interventions).
A sensitivity analysis showed that the model fit and model predictions did not change when assuming different values for minimum sensitivity of nodule palpation at low infection levels (60%, 80%, or 100%). This is explained by the fact that sensitivity is most important for high prevalence settings (for which we assume sensitivity is high anyway), and far less important in low prevalence settings (where Table  1. Characteristics of data used for modeling the association between prevalence of nodules and microfilariae. misclassification is largely governed by specificity). Therefore, we simplified the final model by leaving out the parameter for sensitivity, effectively assuming 100% sensitivity of nodule palpation for all infection levels. Based on the model described above, we estimated the conditional distribution of mf prevalence in a hypothetical village outside the dataset, given an estimate of the 'true' nodule prevalence in adult males (i.