dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The various sleep studies were performed to address the effects of night to night variability, home and sleep laboratory environments, and full attended PSG versus PM measurements. The main endpoint was to compare the difference of PM at home and PSG with 2 consecutive PSGs. The sample size calculation was performed based upon the data of White  18  with a standard deviation of 6. 0. Assuming a broader range in AHI, a standard deviation of 9. 0 was selected. It was assumed that the minimum important difference to detect was an AHI of 5. With a power of 90% and an alpha of 0. 05, ~70 patients were required to use the Alice PDX at home before or after PSG. A combined dropout rate and equipment failure or data loss rate of 12% for the unattended home study was predicted so that ~80 patients had to be recruited.
Intraclass correlation (ICC) was performed by using a 2-way random model measuring absolute agreement for single measures. Bland-Altman plots of the AHI data were generated for visualization of the bias and limits of agreement. Receiver operating characteristic (ROC) analyses were performed at AHI cutoffs of 5, 15, and 30 to compute the area under the curve (AUC), as well as sensitivity, specificity, positive and negative likelihood ratios, and PPVs and negative predictive values (NPVs) for the given device-detected event values. The rates of diagnostic agreement, overestimation, and underestimation were calculated according to the criteria described in Table  3. The data were analyzed by using SPSS (Version 20; IBM Corporation, Armonk, NY, USA) and Analyse-it (Version 3. 8; Leeds, UK). Statistical trends were considered significant at p<0.
Results One hundred and twenty-six individuals were screened between September 2009 and January 2012 and in that sample 120 were eligible but 22 refused to participate. Therefore, 98 individuals were randomized. Eighty-five participants (74 OSA and 11 simple snorers) had complete data and were included in the final data analysis (Figure  1 ). Baseline characteristics are shown in Table  1. Participants were middle aged, predominantly male, with a mean BMI approaching the threshold for obesity with a large neck circumference. Hypertension (systolic blood pressure >140 mmHg or diastolic blood pressure >90 mmHg) was present in 24% of the participants. Other than the simple snorers being significantly younger, the demographic variables were not significantly different from participants with a suspicion of OSA.
28). In addition data analysis was performed using a categorical cut <1. 0 to define an abnormal CPR. To account for repeat measurements, associations were estimated using generalized estimating equations (GEE) with an exchangeable working correlation structure (binary outcomes)  [45]. Analyses were adjusted a priori for fetal sex  [18] , gravidity and GA. Interactions (effect measure modification) were tested for nutritional indicators (BMI, MUAC, short stature), anaemia, inflammation, gravidity, and fetal sex as well as potential risk behaviour (tobacco smoking, betel nut chewing and alcohol) using multiplicative terms (equivalent to factorial interactions). P < 0. 15 for the interaction term was used to define presence of effect measure modification.
Doppler velocimetry measurements Of 396 women, 361 had UA Doppler studies: 282 had one, 67 had two and 12 had three studies (total: 452). MCA studies were performed on 298 women who had 370 MCA Doppler scans: 235 women were studied once, 55 women twice, seven women three times, and one woman four times. CPR data was available for 252 women, who had 303 paired UA and MCA studies. Of these, 206 women were studied once, 41 women twice and five women three times.
Discussion In this first Doppler ultrasound study from PNG to determine factors related to abnormal fetoplacental and intrafetal blood flow, peripheral P. falciparum ). The shaded areas are the intervals between which 95% (grey) and 80% (light green) of the data are located, equivalent to the 2. 5th/97. 7th and 10th/90th centiles and corresponding to Eqs. (1b, 2b, 3b) calculated as per Royston  [18]  ◂ parasitaemia was associated with an increased UARI and a decreased MCAPI. Associations were strongest when the impact of overall malaria exposure (peripheral infection during any stage of pregnancy) on flow was evaluated. Here, increased UARI and intrafetal flow redistribution were observed amongst women with microscopic or sub-microscopic P. falciparum infections. No such effects were observed for P.
Acknowledgements We would like to thank the participating women and health centres. Specific thanks go to  Dupain Singirok ,  Julie Elizah ,  Dr. Leanne Robinson ,  Desmond Sui ,  Dr. Eline Kattenberg ,  Dr. Anna Rosanas-Urgell ,  Professor Peter Siba , and the  Madang Scientific Officer's Association (MSOA)  for constructive feedback on the manuscript.
Funding This research was supported by the  Malaria in Pregnancy Consortium , through a grant from the  Bill & Melinda Gates Foundation (46099) ;  Pfizer Inc  (investigator-initiated research grant  WS394663 ), a  PNGIMR Internal Competitive Research Award  to MO, and the  Pregvax  Consortium, through a grant from the  European Union 's  Seventh Framework Programme   FP7-2007-HEALTH (PREGVAX 201588 ). SK received a  National Health and Medical Research Council (NHMRC)   Early Career Fellowship  (# 1052960 ), and IM received an  NHMRC   Senior Research Fellowship  (# 1043345 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2. Muons are measured in gas-ionization detectors embedded in the steel Figure  1 : Feynman diagrams for dark matter pair production in a vector boson fusion process (left) and for bottom squark pair production (right). Given a nearly degenerate bottom squark and LSP, the final state b-quarks are too soft to be observed. flux-return yoke outside the solenoid. A more detailed description of the CMS detector, together with a definition of the coordinate system used (including the azimuthal angle φ) and the relevant kinematic variables can be found in Ref. [22]. The data sample was collected using an online event selection requiring events with p miss T > 65 GeV and at least two jets with p T > 35 GeV, with a VBF topology. This online selection has an efficiency of more than 98% for the analysis.
For background samples, the response of the CMS apparatus is simulated using GEANT4 (v9. 4p03)  [38] , while for the signal samples a fast simulation program  [39]  is used. The signal acceptance and dijet mass distribution are cross checked with the GEANT4-based simulation and the acceptance is corrected for the small differences (<5%) observed. To simulate the effect of pileup, additional pp collisions with the multiplicity distribution matching that in data are superimposed on the hard-scattering event. Event yields are normalized to the integrated luminosity of the collision data using next-to-next-to-leading order cross section calculations, except in the case of signal samples for which next-to-leading order ( b bjj)  [40]  and leading order (χχj) cross sections  [32]  are used. The strategy for the background estimation is to use Monte Carlo (MC) simulations to model the p miss T distributions, and jet and lepton vetoes.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;.
Titin transcript processing is controlled by the DCMassociated splicing factor RBM20  [21] [22] [23] [24] , which targets a number of additional DCM-associated genes. The myosin heavy chain locus represents a well characterized example of transcriptional regulation of both protein-coding  [25]  and noncoding DCM-associated genes  [26]. In this study we surveyed the cardiac transcriptome of left ventricular tissue of DCM patients and non-diseased donors. These datasets were used to characterize the impact of regulatory variation on gene expression and splicing in the heart, and its relation to the biology of DCM. We analyzed the differences in expression levels between diseased and non-diseased cardiac tissue, identifying 228 differentially expressed genes. Furthermore, we identified regulatory variants impacting gene and exon expression levels.
Genetic effects on the transcriptome We characterized the impact of naturally occurring genetic variation on the regulation of gene expression and splicing. To this end genotype data were obtained from SNP arrays for all samples. After stringent quality control (see Methods: Additional file 1: Table  S7 ), we imputed variants from the 1000 Genomes project  [36]. Imputation quality was assessed using genotype calls obtained from the RNA-seq reads and was high for variants with minor allele frequency (MAF) >10% (Additional file 1: Figure  S3 ). We therefore selected only variants with MAF >10%, resulting in 1,851,329 high confidence imputed variants for quantitative trait locus (QTL) analysis. Cis expression QTL (eQTL) analysis of protein-coding and long noncoding transcripts uncovered widespread genetic effects on gene expression levels.
Genetic effects on splicing Since alternative splicing is hypothesized to play an important role in the etiology of DCM  [21, 24, 32]  and other cardiovascular diseases, we set out to identify splicing QTL (sQTL) using an exon-based model similar to DEXSeq  [33]. In addition we also used a gene level test that associates changes of relative transcript isoform abundance with genotypes  [41]  to identify transcript ratio QTL (trQTL). We found evidence for extensive genetic regulation of splicing, with 11. 8% of tested exons and 14. 6% of tested genes showing sQTL and trQTL, respectively (Table  2 ). In both approaches we ruled out confounding by RBM20 expression by estimating that the upper limit for the fraction of significant trans associations of sQTL SNPs to RBM20 was below 1%. We compared the exon-based (sQTL) and the transcript-based (trQTL) approaches in terms of overlapping genes and overlapping SNPs with significant QTL.
We trained a multilocus genetic risk score (see "Methods") using DCM GWA data  [51] , which comprises 909 DCM cases and 2120 controls from Germany. We selected the most predictive variables from covariates (sex and age), eQTL SNPs, sQTL SNPs, and the DCM GWA SNP rs9262636. In a tenfold cross validation we found that the selection based on all candidate variables yielded risk scores with the largest area under the ROC curve (median AUC = 0. 70; Additional file 1: Figure  S9 ). A model based only on the GWA SNP yielded a median AUC = 0. 65 and a model based on age and sex only resulted in median AUC = 0. 63. Overall we observed a moderate improvement of the risk score when including eQTL and sQTL, indicating that these SNP sets encoded relevant information on DCM risk.
Although we apply a ten-fold cross validation, this is not a true replication and is still sensitive to overfitting. In its current form this risk score therefore has limited use for clinical applications. It does, however, demonstrate that QTL variants together encode biological information that significantly improves the prediction of the DCM phenotype. More generally, we found that 20% of all GWA loci for heart-related phenotypes published to date alter gene expression levels. Compared to the 13% identified in the GTEx data set, this increase constitutes a substantial improvement in candidate gene prioritization, an important bottleneck in GWA study follow-ups.
Left ventricular samples from patients with DCM Left ventricular tissue samples from patients with endstage DCM were retrieved during left ventricular device implantation or/and cardiac transplantation in the period between 1993 and 2011. They were snap-frozen and stored in liquid nitrogen in a tissue bank at the Royal Brompton and Harefield Hospitals NHS Foundation Trust. The set of 128 DCM cases originally considered for this study consisted of end-stage non-ischaemic DCM patients for whom good quality RNA from left ventricular tissue was available for RNA-sequencing analysis. Out of all these included cases, less than 10% report a family history. The diagnosis of non-ischaemic DCM was confirmed from medical records, but additional clinical data were not available. After genotype quality control (see below and Additional file 1: Table  S1 ) 97 samples were used for data analysis.
Processing of RNA-seq data The paired-end RNA-seq reads were aligned against the human genome assembly GRCh37 using TopHat version 1. 4. 1 with option -r 0. This specifies the mate inner distance, which is expected to be zero, since we have 200 bp fragment size and 100-bp reads. In addition we specified option -M that removes multimapping reads before aligning to the transcriptome. The remaining options were set to their default values. We have supplied transcript annotations from Ensembl version 66, which specifies known splice junctions and exon boundaries. In addition we also enabled TopHat's coverage-based search for novel exons and splice junctions.
Quantification of the transcriptome To quantify transcriptome features we have used the Gencode annotation version 19 and augmented it with annotation of the MHRT lncRNA locus  [26]  and a custom TTN annotation  [20]. For gene level quantification we used htseq-count version 0. 5. 3p3 and the 'intersection-nonempty' mode that is suited to quantify overlapping transcripts on different strands. Transcript levels were estimated as fragments per kilobase per million sequenced (FKPM) using cufflinks (version 2. 2. 1)  [70]  with the same gene models as above. Exon coverage was determined using intersectBed from bedtools version 2. 15. 0. The 'percent spliced in' index (PSI)  [34]  was computed using scripts from  [35]  on non-overlapping exonic parts derived from the Gencode annotation version 19 using the script dexseq_prepare_annotation.
Cells were pooled, collected by centrifugation at 1100 rpm, and resuspended in DMEM supplemented with 10% FCS. To selectively enrich for cardiomyocytes, cells were preplated for 1 h in a T75 flask during which period cardiofibroblasts attached readily to the bottom of the flask. The supernatant was then seeded in 15 cm dishes (~1 × 107 cells/dish) and cardiomyocytes were cultured for 3 days in DMEM supplemented with 10% FCS. Fibroblasts were cultured for two passages in 5 days to increase cell purity by overgrowing non-proliferating myocytes. RNA-sequencing was performed using the same procedures as for the human samples. Reads were processed as the human data using the rat reference genome assembly rn4 and Ensembl version 66. Differentially expressed genes were identified using DESeq  [71].
DCM patients DNA isolated from peripheral blood samples was used for genotyping on the Affymetrix GW6 platform at the Max Delbrück Center in Berlin according to the manufacturer's protocol. Genotype calls for 906,600 SNPs were obtained from the Affymetrix genotyping console software version 4. 1. 4. 840 using the birdseed2 algorithm with default settings. Prior to imputation, quality control was performed using GenABEL. We checked for sex mismatches and removed related individuals and individuals with admixed or non-european ancestry (Additional file 1: Table  S1 ). After quality control (QC) we retained 97 DCM patients for the analysis.
Non-diseased donors Genotyping and QC of genotypic data and post-QC processing of the left ventricular samples obtained from donors has been described in detail previously  [56]. Genome-wide SNP genotyping was performed using Illumina HumanOmniExpress Beadchips interrogating 733,202 genetic markers. QC was carried out in the GenABEL package in the statistical programming language R using default settings. Only the data of the 108 samples for which RNA-seq data were generated were used in the present study, all of which passed QC and were part of the original 129 samples used in the original study  [56]. Using data of both populations we checked for population structure and computed the first three principal components for inclusion in our models as covariates. As independent quality control for both DCM patients and donors, we obtained SNP calls from RNA-seq data for known SNP positions in exons based on the 1000 Genomes data set phase 1 version 3.
Genotype imputation Since the genotypic data in DCM patients and donors was obtained on different genotyping arrays, which have an overlap of around only 200,000 SNPs, we used genotype imputation to increase the resolution of our genetic map. The cases were typed on the Affymetrix GW6 array with 906,600 SNPs, while controls were typed on the Illumina HumanOmniExpress with 733,202 SNPs. Both genotype data sets were first filtered according to the following quality criteria. In each data set, we required SNPs with minor allele frequency (MAF) greater than 5% to have a call rate of at least 95% and SNPs with lower MAF to have a call rate of 99%. Moreover the test for Hardy-Weinberg equilibrium had to be P > 0. 0001. We selected the set of 195,386 SNPs that were typed on both platforms and passed the quality criteria as the input for the genotype imputation.
The reference haplotypes were obtained from the 1000 Genomes data set phase 1 version 3 that comprises reference haplotypes for 1092 individuals. We applied shapeit v1 for the prephasing of the genotypes. We used impute v2 for the actual genotype imputation. We assessed the quality of the imputed genotype calls using two data sets. The first data set was based on the genotype calls from the RNA-seq described in the section genotyping. The second data set consisted of all genotypes from the SNP array that were not used for the imputation because they were specific to one of the two arrays. Imputation quality was measured as overall genotype accuracy (fraction of correct genotype calls), non-reference accuracy (fraction of correct minor allele genotype calls), and imputation efficacy (fraction of individuals with genotype confidence P > 0.
95). Additional file 1: Figure  S3  shows the imputation quality based on the RNA-seq data. Overall, we achieved a good efficacy (Additional file 1: Figure  S3a ) and also a good accuracy (Additional file 1: Figure  S3b ). The non-reference accuracy, however, shows a bimodal distribution with very high values, but very low values in some instances. This is expected to occur for low MAF variants when all individuals are assigned the major allele. Indeed Additional file 1: Figure  S3d  shows that the very low non-reference accuracy values occur at low MAF. Therefore, to achieve good non-reference accuracy, we use imputed variants with MAF >0. 1 only. Similar results were obtained using the SNP array-based evaluation (data not shown). In order to avoid artifacts in QTL analyses caused by rare genotypes that coincide with outliers in the expression data, we substituted homozygous minor alleles that occurred less than three times by the heterozygous genotype.
sQTL mapping For the sQTL analysis we used exon expression levels of exon counting bins defined as in  [33]  using all transcripts from GENCODE v19 that also had a Refseq model. We used an adaptation of the DEXSeq model  [33]  for sQTL analysis. The original DEXSeq model was designed for rather small data sets, so it was too slow for analyzing large numbers of SNPs for each exon and too sensitive for large sample sizes, detecting very small effects that are likely false positives. To reduce runtime, we used a regular linear model with Gaussian error terms instead of a generalized linear model for count data. We therefore quantilenormalized exon expression levels across all exons within each sample such that they follow a standard normal distribution. We encoded SNP genotypes as factors by rounding the imputed genotype dosages.
This simple model assumes that the best SNP is also the 'causal' SNP for each target. Since other significantly associated SNPs might also be causal and thus functionally relevant, or the causal SNP might not be the most significantly associated SNP, these mislabeled data points might dilute the enrichment results when annotated as 'not causal'. Therefore, we removed all data points where the SNP was also significantly associated with the target but not the top hit. The distance between SNP and target is an important predictor for QTL  [42] , so we grouped the distances into five bins of size 10 kb, starting from 1 bp, and used these features for sQTL and eQTL analysis. For both QTL analyses we used chromatin state annotations from the 25 state chromHMM segmentation from roadmap. In particular, we used the states 1) heterochromatin; 2) TSS; 3) bivalent promoter; 4) promoter; 5) DNAse; 6) polycomb; 7) weakly transcribed.
DCM risk score We estimated and evaluated different multilocus genetic risk models trained from varying sets of input variables. The original DCM GWA  [51]  genotype data of 292,367 SNPs for 909 DCM cases and 2120 control was generously provided by the authors. Our method is based on feature selection by regularized logistic regression (LASSO) as implemented in the glmnet R package  [76]. We prepared sets of candidate variables for selection into the risk model. The following SNP sets were considered: 11,771 SNPs with eQTL (P < 10e-5), 9134 SNPs with sQTL (FDR <0. 05), one GWA SNP (rs9262636), and the empty set. Furthermore we considered combinations of eQTL + GWA, sQTL + GWA, eQTL + sQTL + GWA. All sets were extended to include the covariates sex and age. To evaluate the risk model we performed tenfold crossvalidation.
Analysis of heart GWA SNPs To assess the value of our eQTL and sQTL results for nomination of candidate genes for mediating the effect at loci identified in GWA of heart-related traits, we obtained published GWA results from the GWAS catalog (accessed 11. 12. 2015). We selected all traits that were annotated as heart disease (EFO_0003777) or cardiovascular measurement (EFO_0004298) in the experimental factor ontology. We removed cardiovascular measurements that were not directly related with the heart. For the eQTL we intersected the GWA loci with eQTL data of both our own study and the GTEx study. For each of the studies, we used proxies (LD >0. 8) for GWA SNPs that were not in the dataset. To avoid double counting because of LD, we selected only the best proxy SNPs for each pair of GWA SNP and potential cis eQTL gene.
LD information was obtained from the SNAP database  [53]. sQTL were analyzed in the same way, but using only sQTL data from our study. For eQTL-GWA analysis we used genes with RPKM >1 in >5% of the samples, to select candidates that are amenable to biological follow-up analysis. Enrichment of heart GWA SNPs among eQTL was assessed as follows. We considered all SNPs tested for cis eQTL for the selected genes as the basic population. Then we classified each SNP as eQTL (P < 1e-5) or non-eQTL and GWA (GWA SNP or its best) or non-GWA. Finally, we applied Fisher's exact test to determine the significance.