dataesr/scientific-paragraphs-categorization huggingface dataset:
Polymorphisms at HLA-G 3'UTR To perform these analyses, we evaluated 340 individuals (153 Brazilian and 187 French subjects) who presented data regarding 3'UTR allele, genotype, haplotype, diplotype. The eight previously reported  [12] , HLA-G 3'UTR polymorphic sites [14-bp Ins/Del (rs1704), +3003 C/T (rs1707), +3010C/G (rs1710), +3027A/C (rs17179101), +3035 C/T (rs17179108), +3142 C/G (rs1063320), +3187 A/G (rs9380142) and +3196 C/G (rs1610696)] were observed in Brazilian and French individuals. No other polymorphic site was observed in this region. The allele and genotype frequencies of the eight HLA-G 3'UTR polymorphic sites observed for both populations are shown in Table  1. No significant differences were observed when these frequencies were compared between the two populations. Haplotype reconstruction of the 3' UTR polymorphic sites using the EM and PHASE algorithms agreed in 99.
Associations between HLA-G 3'UTR polymorphic sites and plasma sHLA-G levels To perform these analyses, we evaluated 259 individuals (150 Brazilian and 109 French subjects) who presented data regarding 3'UTR allele, genotype, haplotype, diplotype and sHLA-G levels. Soluble HLA-G levels did not fit a Gaussian distribution in the Brazilian (P <0. 0001) or French (P <0. 0001) populations or in both populations together (P<0. 0001). No significant correlations were observed between sHLA-G levels and age of the individuals (rS = 0. 0169, P =0. 8638) or between sHLA-G levels and gender of the individuals (median for men = 23. 2 ng/mL and median for women = 25. 6 ng/mL; P =0. 8111). Overall, the median sHLA-G levels observed for French (25. 2 ng/mL) and Brazilian (26. 8 ng/mL) individuals were closely similar (P =0.
We recently developed a unique high-throughput RT-PCR platform to probe the entire repertoire of over 3,000 alternative splice events annotated in the RefSeq database without bias against medium-and low-expressed genes  22  , and further used this technique to probe the involvement of 81 different RNA-binding proteins in epithelial-tomesenchymal transition by comparing the splicing profiles of epithelial and mesenchymal tissues with those of the cells lines treated with siRNAs against the 81 proteins  23. Here we have used high-throughput RT-PCR to implicate the RNA-binding protein Muscleblind (MBNL1) in differentiation of stem cells, and we show that essential induction of MBNL1 late during mesoderm differentiation controls a splicing programme that induces and represses specific exons at the RNA level.
Results Alternative splicing programs in fibroblasts and stem cells. To analyse the programme of alternative splicing changes occurring in human pluripotent stem cells during stem cell induction, maintenance and differentiation, we designed a large-scale screening strategy. Using high-throughput PCR, we previously screened the entire NCBI Reference Sequence (RefSeq) database for the presence or absence of both of two predicted isoforms at thousands of loci. From these, detected, alternatively spliced events we selected 303 high-quality PCRs from across the alternative exons, using only purity and yield of the two PCR products in 10 different cell lines as parameters We used these high-quality PCRs to study alternative splicing in pluripotent stem cells in more detail because they occur in an essentially random and functionally diverse crosssection of genes  22.
Furthermore, there is a very good correlation (R ¼ 0. 824, Supplementary Fig. S2 ) between percent-spliced-in values obtained with RNA-Seq data  28  and our PCR-based methodology for those events in which RNA-seq data had enough read depth to produce confident PSI estimates (104 out of 303 mapped events; Supplementary Data 1). To establish if these alternative splicing changes were a general feature of stem cells we performed manual end-point PCRs with the 15 switch-like splicing events on three different pluripotent stem cells and their resulting differentiated and parental fibroblasts. Samples included the well-characterized H9 embryonic stem cells and their redifferentiated fibroblasts. In all cases we observed a near complete and fully reversible shift from one isoform to the other between stem cells and fibroblasts (Fig.
Most of the target genes described here are novel targets of the MBNL1 splicing factor. Exceptions include ATP2A1 exon 22 splicing, which was previously shown to be controlled by MBNL1 (refs 38,39), as was the auto-regulation of MBNL1 exon 5 (ref. 40). The high-throughput PCR method we used identifies many robust splicing shifts starting from all the alternative splice events in the highly curated NCBI Reference Sequence database  41. As the PCR method used (35 cycles) amplifies all expressed genes, even low-and medium-expressed ones, in the precise regions of alternative splicing, it creates lists that are complementary to those generated by RNA-Seq and our findings that stem cell splicing mirrors knockdown of MBNL1 have also been observed by this method  28. Interestingly, although genes differentially expressed between iPSCs and fibroblasts are enriched in the same physiological pathways  42  , genes regulated at the expression level did not overlap with any of the switch-like target exons identified here.
For the 15 RBFOX2/MBNL1 selected targets, the amplified products were loaded on 1. 5% agarose gel. All primers are detailed in Supplementary Table  S1  and Supplementary Data 1. Immunofluorescence analysis. Cells plated on cover slips were fixed for 10 min in 3. 7% formalin (in PBS) followed by a 2-min permeabilization with 0. 1% Triton X-100 (in PBS) and incubation in PBS containing 0. 1% bovine serum albumin. F-actin was revealed using rhodamin-conjugated phalloidin (Sigma-Aldrich) and nuclei were stained using Hoechst 33342 (Sigma-Aldrich). Cells were washed in PBS and water, mounted with DAKO mounting medium and observed under the fluorescence microscope. Cell imaging was performed with a Leica DM6000 (Leica, Wetzlae, Germany) with PL APO grade oil Â 20 objective. Images were captured with a Coolsnap HQ2 camera (Roper Scientific Inc.
Acknowledgements This work was supported by the collaborative laboratory Splicos therapeutics, and  Institut National du Cancer (Canceropole Ile-de-France). L. L. was supported by a fellowship from  Fondation pour la Recherche Medicale (FRM)  grant (Equipe FRM 2011 -no. DEQ20111223745 ,  DCR20091217183 ), l' Association pour la Recherche contre le Cancer ARC  ( A09/4/5062  and  SFI20111203957 ) and la Ligue regionale contre le Cancer (LNCC PYRENEES-ORIENTALES 2010, 2012). This work was also funded by grants from the  Canadian Cancer Society  and the  Canadian Institute for Health Research. S. Abou Elela  is the  Canada Research Chair in RNA Biology and Cancer Genomics. B. Chabot is the  Canada Research Chair in Functional Genomics. This research using hESC has been authorized by the  French Biomedical Agency (ABM)  for  JM Lemaitre Laboratory , (NOR:  SJSB0721833S ).
Background: To externally validate and assess the robustness of two nomograms to predict the recurrence risk of women with endometrial cancer (EC). Methods: Using an independent, multicentre external patient cohort we assessed the discrimination and calibration of two nomograms -the 3-year isolated loco-regional (ILRR) and distant (DR) recurrence nomograms -in women with surgically treated stage I-III EC. Results: Two hundred and seventy one eligible women were identified from two university hospital databases and the Senti-Endo trial. The median follow-up and initial recurrence time were 38. 1 (range: 12-69) and 22. 0 (range: 8. 3-55) months, respectively. The overall recurrence rate was 13. 8% (37 out of 271). Predictive accuracy according to the discrimination was 0. 69 (95% CI, 0. 58-0.
These nomograms are built on clinicopathological parameters and were internally validated using cross-validation and bootstrapping methods. The nomograms included the following covariates: age at diagnosis, FIGO stage (2009), grade, lymphovascular invasion, histological type, depth of myometrial invasion and peritoneal cytology. However, external validation in an independent set of women is required to ensure applicability to patients from different institutions  (Awtrey, 2012). The aim of this study was to externally validate these recently introduced nomograms in a population of women with EC using large databases of academic cancer centres.
MATERIALS AND METHODS Patients. In the current study, data of all women with EC who received primary surgical treatment between January 2003 and December 2009 were abstracted from two institutions with prospectively maintained EC databases (Tenon University Hospital and Reims University Hospital, both in France) and from the Senti-Endo trial  (Ballester et al, 2011)  with the same inclusion criteria as those of the Kondalsamy-Chennakesawan et al (2012) cohort. Electronic medical records and surgery notes were also reviewed. To be included for validation analysis, the women had to have all nomogram variables documented. They were treated with upfront surgery according to the international guidelines. Boardapproved gynaecological pathologists assessed the pathological specimens. Histological staging and grading were performed according to the 2009 FIGO classification  (Petru et al, 2009)  system on the basis of the final evaluation of the pathological specimen.
Data analysis All data were recorded on standardized case report forms using Excel spreadsheets, stored in an Access database (Microsoft, Redmond, USA) and exported for analysis in SPSS 17. 0. Analysis for real-time PCR results was done using CFX Manager™ Software Version 1. 0, Bio-Rad. Chi-square distribution for the risk of being infected with a Plasmodium species, age, residence, gender and sub microscopic infection were calculated as a proportion of parasite infections detected in these groups. Continuous variables were described by the range and median of all PCR-positive cases and were compared between groups by the Mann Whitney-Non Parametric Test. Statistical significance was considered at p-value <0. 05.
These measurements are performed with the Compact Muon Solenoid (CMS) detector at the LHC using proton-proton collision data at √ s = 7 TeV. The differential cross section measurements are normalized to the Z-peak region (60-120 GeV). This normalization cancels out the effect of multiple interactions per bunch crossing (pileup) on the reconstruction, and the uncertainty in the integrated luminosity, acceptance, and efficiency evaluation. The measurements in this paper are corrected for the effects of resolution, which cause event migration between bins in mass and rapidity. The observed dilepton invariant mass is also corrected for final-state photon radiation (FSR). This effect is most pronounced below the Z peak. The differential cross sections are measured separately for both lepton flavors within the detector acceptance and are extrapolated to the full phase space.
The DY signal samples are generated with the NLO generator POWHEG  [30]  interfaced with the PYTHIA v6. 4. 24  [31]  parton shower generator (a combination referred to as POWHEG). Both tt and single-top-quark samples are produced with the POWHEG generator, and the τ-lepton decays are simulated with the TAUOLA package  [32]. The tt sample is rescaled to the NLO cross section of 157 pb. Diboson samples (WW/WZ/ZZ) and QCD multijet background events are produced with PYTHIA. An inclusive single-W-boson sample (W+jets) is produced using POWHEG. The proton structure is defined using the CT10  [19]  parton distribution functions. All samples are generated using the PYTHIA Z2 tune  [33]  to model the underlying event. Pileup effects are taken into account in the MC samples, which are generated with the inclusion of multiple proton-proton interactions that have timing and multiplicity distributions similar to those observed in data (average of 9 interactions per bunch crossing).
Muon selection The first step in the muon selection is the trigger. The muon trigger thresholds depend on the instantaneous luminosity, and, since the instantaneous luminosity increased during 2011 data taking period, the trigger thresholds also increased. In the L1 trigger and HLT processing the data from the multiple detection layers of the CSC and DT muon chambers are analyzed to provide an estimate of the muon momentum. For data taken in the earlier part of the 2011 run, the trigger selects dimuon events where each muon has a transverse momentum of at least 6 GeV. For the subsequent running periods, the trigger selects events where one muon has p T > 13 GeV and the other muon has p T > 8 GeV. The HLT then matches these candidate muon tracks to hits in the silicon tracker to form HLT muon candidates.
Dimuon background estimation In the dimuon channel, the QCD multijet background is evaluated using control data samples. This method makes use of the muon isolation and the sign of the charge as two independent discriminating variables to identify a signal region and three background regions in the twodimensional space defined by the muon charge sign and the isolation. The background estimation is then based on the ratio between the number of signal and background events in the above regions  [37]. The tt background, which is the dominant process at high masses, is estimated from data using a sample of events with eµ pairs. The estimated number of µ + µ -events can be expressed as a function of observed e ± µ ∓ events based on acceptance and efficiencies determined from simulation. The electron and muon candidates in the eµ sample are required to satisfy the DY → e + e -and DY → µ + µ -selection criteria, respectively.
Dimuon efficiency The scale factor ρ accounts for the differences in both the single-muon and the dimuon selections. The single-muon properties (including the trigger) are determined using Z → µ + µ - events in data and simulation, where one muon, the tag, satisfies the tight selection requirements, and the selection criteria are applied to the other muon as a probe (tag-and-probe method  [37] ). An event sample with a single-muon trigger (the tag) is used to evaluate this scale factor. The dimuon selection scale factor is based on the dimuon vertex efficiency as measured in data and simulation after the rest of the selection is applied. The total event selection efficiency in the dimuon channel is factorized in the following way: ε = ε(µ 1 ) • ε(µ 2 ) • ε(dimuon) • ε(event, trig), (5) where • ε(µ) is the single muon efficiency; • ε(dimuon) is the efficiency that the two muon tracks of the selected dimuon event come from a common vertex and satisfy the angular requirement between them; • ε(event, trig) is the efficiency of triggering an event in both L1 and HLT.
Dimuon systematic uncertainties The main uncertainty in the dimuon signal comes from the efficiency scale factor ρ that reflects systematic deviations that vary up to 2% between the data and the simulation. As discussed in Section 4. 4, single-muon efficiencies of several types are measured with the tag-and-probe procedure and are combined into event efficiency scale factors. The tag-and-probe procedure yields the efficiency of each type and an associated statistical uncertainty. A variety of possible systematic biases in the tag-and-probe procedure has been investigated, such as dependence on binning in single-muon p T and η, dependence on the assumed shape of signal and background in the fit model, and others. Appropriate systematic uncertainties in the single-muon efficiency scale factors have been assigned.
Third, the diagonal covariance of each MC efficiency factor is obtained from the statistical uncertainty. The covariance of the pre-FSR cross section is obtained from the covariance of the post-FSR cross section via error propagation. After the FSR unfolding some covariances with the Z-boson peak region become negative. The contribution from the statistical uncertainty of the FSR unfolding matrix is negligible. The covariance of the normalized cross section is derived from the covariance of the unnormalized cross section, taking the uncertainty of the Z-peak bin (Table  6 ) and assuming no correlation between the cross section value in a particular mass bin and the normalization factor. The covariance matrices are included in the HEPDATA record for this paper.
1. b2 (described above) is included as an additional correction factor and the ratio between data and theoretical prediction is shown in the middle plot. Differences between NLO and NNLO values in theoretical expectations are significant in the low-mass region, as reported in  [15]. Although this measurement is sensitive to NNLO effects, it does not provide sufficient sensitivity to distinguish between different PDFs. In addition to the fully corrected dσ/dm measurement, we report the cross sections within the detector acceptance (r i pre-FSR, det ) and the post-FSR cross sections (r i post-FSR , r i post-FSR, det ). The corresponding definitions are r i post-FSR = 1 ∆m i • N i u A i i ρ i N norm u A norm norm ρ norm , (16) r i pre-FSR, det = 1 ∆m i • N i u i ρ i N norm u norm ρ norm , (17) r i post-FSR, det = 1 ∆m i • N i u i ρ i N norm u norm ρ norm , (18) m [GeV] ] -1 /dm [GeV The shaded uncertainty band for the theoretical calculation includes the statistical uncertainty from the FEWZ calculation and the 68% confidence level uncertainty from PDFs combined in quadrature.
Double-differential cross section d 2 σ/dm d|y| measurement The result of the double-differential cross section measurement for the dimuon channel is presented as the following ratio: R ij pre-FSR, det = N ij u ij ρ ij N norm u norm ρ norm. ( 19 ) The quantities N ij u , ij , ρ ij are defined in a given bin (i, j), with i corresponding to the binning in invariant mass, and j corresponding to the binning in absolute rapidity; N norm u , norm , and ρ norm refer to the Z-peak region within |y| < 2. 4 in the muon acceptance. The normalization factors from our measurement and each theoretical prediction from various PDF sets are available in Table  10. As shown in this table, the normalization factors between data and theoretical predictions are in good agreement within the uncertainty, except for JR09.
The statistical uncertainty (solid band) is smaller than the PDF uncertainty and the latter is the dominant uncertainty in the FEWZ calculations. In general, the PDF uncertainty assignment is different for each PDF set. For instance, CT10 (NLO) and CT10 (NNLO) PDF uncertainties correspond to a 90% CL, so, to get a consistent comparison to other PDF sets the uncertainties are scaled to the 68% CL. In the low-mass region and the Z-peak region, we observe good agreement between data and theory. The NNLO effects are more significant in the low-mass region. The corrections for the γγ-initiated processes calculated with FEWZ 3. 1. b2 are negligible in the double-differential cross section measurement, because the effects are approximately constant over the investigated rapidity range and statistical fluctuations or other systematic uncertainties are much larger across the invariant-mass range of the measurement.
04 2. 93 ± 0. 06 3. 01 ± 0. 19 1. 8-1. 9 2. 68 ± 0. 14 3. 16 ± 0. 10 1. 13 ± 0. 05 2. 51 ± 0. 05 2. 39 ± 0. 18 2. 89 ± 0. 41 1. 9-2. 0 2. 40 ± 0. 13 2. 63 ± 0. 09 0. 94 ± 0. 05 2. 03 ± 0. 05 2. 21 ± 0. 19 2. 0-2. 1 2. 10 ± 0. 11 2. 10 ± 0. 09 0. 77 ± 0. 05 1. 57 ± 0. 05 1. 39 ± 0. 15 1. 84 ± 0. 38 2. 1-2. 2 1. 60 ± 0. 09 1. 50 ± 0. 08 0. 49 ± 0. 03 1. 09 ± 0. 04 1. 00 ± 0. 14 2. 2-2. 3 1. 04 ± 0. 08 0. 89 ± 0. 06 0. 34 ± 0. 04 0. 64 ± 0. 04 0. 53 ± 0. 10 0. 41 ± 0. 16 2. 3-2. 4 0. 39 ± 0. 04 0. 31 ± 0. 03 0. 12 ± 0. 02 0. 20 ± 0. 01 0. 20 ± 0. 05 where N ij u , N norm u , ρ ij , and ρ norm do not contain the FSR correction. All the r shape measurements are summarized in Tables  11 12. r ij post-FSR, det = 1 ∆y j • N ij u ij ρ ij N norm u norm ρ norm , (20) These double-differential DY measurements will impose constraints on the quark and antiquark PDFs in a wide range of x, and in particular, should allow the replacement of fixed-target DY data with modern collider data in PDF analyses.
The measurements are corrected for the effects of resolution, which cause event migration between bins in mass and rapidity. The observed dilepton mass is also corrected for final-state photon radiation. The dσ/dm differential cross section results are given separately for both lepton flavors in the fiducial region and are extrapolated to the full phase space. Since the electron and muon results are consistent, they are combined. The results are in good agreement with the standard model predictions, calculated at NNLO with the program FEWZ using the CT10 PDF set. The d 2 σ/dm d|y| measurement is compared to the NLO prediction calculated with FEWZ using the CT10 PDFs and the NNLO theoretical predictions as computed with FEWZ using the CT10, NNPDF2. 1, MSTW2008, HERAPDF15, JR09, ABKM09, and CT10W PDFs.
Oenocyte analysis Images were processed with a Motic Images Plus camera 2300. For each group of treatment, all the oenocytes of each section from the different larvae were analyzed using ImageJ software 1. 43u (US NIH) to measure the cell (Ac) and nucleus (An) areas. The mean number of oenocytes analyzed per larvae was 81630. The nucleo-cytoplasmic ratio NCR was estimated by the following formula: NCR = An/(Ac -An) For the global statistical analysis, data were processed by a generalized linear model (GLM) with a log link function for analyzing the effect of the different experimental factors (i. e. , larvae stage, Paraquat concentration and NAC). In order to assess the effects of the factor levels, statistical differences between treatment groups were analyzed with a multi-T-test, after normality had been checked to fit the distribution of observed measurements at each factor level.
Results Figure  1  is an overview of our study design. Measures of baseline AHR in 31 mouse strains found that there was up to a 4. 2-fold change in AHR slope across strains, with the smallest slope (i. e. 0. 40 SEM 0. 10) corresponding to the C57BL/6J strain and the largest slope (i. e. 1. 67 SEM 0. 20) corresponding to the KK/H1J strain [Figure  2 ]. The complete AHR phenotype results are freely available as project ''Berndt1'' in the Mouse Phenome Database of the Jackson Laboratory. The EMMA results for AHR slope did not contain any P-values that were significant after correction for multiple comparisons (i. e. ,0. 05/281,300 = 1. 8e-07) [Figure  3 ]. Nonetheless, some genomic regions had P-values that were nominally significant and may indicate true associations with AHR slopes. The set of 227 mouse genes within 50 kb of SNPs with EMMA P-values ,0.
5e-04) was for SNP rs17526969. We next attempted to replicate the asthma association findings in two human cohorts: Sepracor/LOCCS/LODO/Illumina and GABRIEL. We found that there was gene level replication for KCNIP4, and, considering the genomic interval spanning four genes, some regional replication for the PDZD2/GOLPH3/ MTMR12/ZFR association [Figure  6 ]. Each asthma GWAS had a different top hit in each region, and only KCNIP4 showed evidence of SNP-level replication at a nominally significant threshold [Table  1 ]. Based on the replication results in GABRIEL and Sepracor/LOCCS/LODO/Illumina, we attempted to replicate the KCNIP4 findings in DAG. The lowest EVE P-value of 2. 9e-04 at rs6833065 did not replicate in Sepracor/LOCCS/ LODO/Illumina or DAG, and data for this SNP was not available in GABRIEL.
Rather, there are weak effects observed at multiple sites that implicate the gene. It is possible that more thorough genotyping of KCNIP4 in the populations where single or few SNPs did not replicate primary findings would uncover regions of association that further support gene-or SNP-level replication. Future studies may also be able to investigate whether KCNIP4 variants are associated with AHR among non-asthmatics, as the mouse data found that KCNIP4 variants were associated with baseline AHR. Based on the mouse and human association results, the Kv channel interacting protein 4 (KCNIP4) emerged as the gene most likely to be related to asthma and AHR. Current information for this gene was gathered with the AceView tool  [34]. KCNIP4 is found on human chromosome 4, at 4p15. 32, covering 1220.