dataesr/scientific-paragraphs-categorization huggingface dataset:
Supplementary analyses Considering an increase of 15% in ICS adherence as clinically significant, we compared the percentage of patients reaching at least this change at 12 months in each group. The percentage of patients who reported as having a not well-controlled asthma (ACQ score of 1. 5 or above) was also compared between MI and UC groups at 12 months. All tests were two sided and significance level was set at P0. 05. Data analysis was performed using SAS version 9. 3.
Study limitations and strengths The present study has some limitations that warrant caution in the interpretation of findings. First, our primary measure of adherence used pharmacy refill data, which does not assess actual medication consumption. However, assessing pharmacy refill data is considered to be one of the most reliable methods of assessing adherence relative to self-reports, dose/ pill counters, and biological assessments,  69  the former two of which have been shown to be subject to reporting bias and/or tampering when the participant is aware that they are being monitored. 67 Second, patients were recruited from a single tertiary care center, so results may not generalize to asthmatics treated in primary care or community samples. However, a sample of moderate-severe asthmatics was deliberately targeted given their relatively greater needs for ICS medication and the importance of regular adherence to achieve control.
The medium ES (d=0. 20) and high patient acceptability (postintervention ratings for pertinence, usefulness, enjoyableness, feasibility, and likely to change adherence behavior all 8. 9/10) suggest that a full randomized controlled trial is warranted, and this pilot data will permit an accurate estimation of the sample size needed to reach statistical significance on both primary and secondary outcomes. Despite these limitations, this study also has several important strengths. First, it is the first adherence intervention among adult asthmatics to test the feasibility of a theory-driven intervention that was specifically designed to promote behavior change. Second, it measured baseline and follow-up adherence using a well-validated, objective measure (% pharmacy refills), which reduces the likelihood of recall bias (self-reports) and subject tampering (dose counters and biological assessments) and improves the overall reliability of the measure.
Acknowledgments Funding support for this study was provided by an unrestricted, investigator-initiated grant from  GlaxoSmithKline  and salary awards from the  Fonds de la recherche du Québec -Santé (FRQS)  (KLL and SLB) and the  Canadian Institutes of Health Research  (KLL and SLB). Scholarship support was also provided by the  FRQS (GM). The authors thank  Dr Jean-Luc Malo, MD ;  Guillaume Lacoste , BA;  Sandra Pelaez, PhD ;  Nicole Rutamucero , MSc; and  Melanie Beland, MSc , for their invaluable assistance with data collection;  Wendy Smith, PhD , for conducting the MI intervention; and  Catherine Laurin, PhD , for conducting intervention integrity evaluations.
Assessment of linearity Linearity of the dose response of NDL-PCBs concentration to ingested NDL-PCBs was assessed by regression analysis performed by means of the REG procedure of SAS (9. 1, SAS Institute, Cary, NC, USA). The linear and the quadratic effects of PCBs ingested (ng g À1 BW d À1 ) were tested. Where not significant (P > 0. 10) the quadratic term was removed from the model and the response was declared linear.
Incidence of condensation of OM on bioavailability In order to evaluate the incidence of contaminated matrices on bioavailability a variance analysis were performed. For each congener, values below the limit of quantification (LOQ) were replaced by LOQ/2 to avoid missing data. Statistical analyzes were carried out using SAS (9. 1, SAS Institute). Each piglet was considered as an experimental unit. Concentrations of each NDL-PCB congener in liver, muscle, and adipose tissue were analyzed using the GLM procedure. The model included the exposure media (AC, FA, SS, SO, n = 3 and C, n = 2). A Tukey-Kramer test was used for comparison of means. Differences were considered significant for P < 0. 05.
Acknowledgments The authors are grateful to the  French Ministry for Agriculture and Agence de l'Environnement et de la Maîtrise de l'Energie  for their financial support. They thank  S. Denys  ( ANSES ) for scientific comments, T. Duval (URAFPA) and  C. Grandclaudon  ( URAFPA ) for NDL-PCBs analyzes and  P. Hartmeyer  (URAFPA) and  H. Toussaint  ( URAFPA ) for providing care to piglets.
The debates then turned to the putative lack of overlap between datasets from different laboratories using SNS enrichment with l{exonuclease purification, even for the same cell line, suggesting that the method was probably inaccurate. However this argument is almost entirely based on a comparison of two data-sets for the same cell line  [1, 6] , but the results of the first study  [6]  have repeatedly been shown to display a marked lack of overlap with those of other studies, whereas the results of the second  [1]  overlap significantly with those of other studies  [7, 8]. Agreement on a consensual protocol for SNS enrichment and quantification has also become a critical issue as the scale of investigation of replication origins has changed profoundly in recent years. Beginning with investigations of individual loci, and continuing with the microarray technology, there has recently been another technological shift in this field towards the use of ultra-deep sequencing  [8].
We took this opportunity to confront different genome-wide detections of replication origins based on different methods and protocols, which had never been done before. Comparisons between SNS-based origins and bubble trapping based origins on different cell lines show a good agreement between maps. Furthermore these comparisons indicate that the sensitivity and specificity of the detection of origins based on SNS data is significantly improved with our dedicated method compared to previously used chIP-Seq tools. Now that a consensus set of replication origins has been identified, the time has come to unravel the genomic and epigenetic characteristics that make these particular loci replication origins. To proceed we focus on the connections between the spatial and temporal programs of replication at fine scales.
These results also highlight the difficulties involved in assessing the impact of specific modifications on normal S-phase progression. We hypothesize that the imprecise mapping of origin positions has hampered the search for specific epigenetic signatures. In this study we integrated data collected in several genome-wide studies aiming to map DNA replication timing domains and chromatin states. We provide unique datasets including origin position, efficiency and timing, and the local genomic characteristics of each origin (histone modifications, sequence characteristics). Overall, our findings make it possible to define new mechanisms potentially involved in defining of origin sub types activated sequentially during S phase.
Author Summary Replication is the mechanism by which genomes are duplicated into two exact copies. Genomic stability is under the control of a spatiotemporal program that orchestrates both the positioning and the timing of firing of about 50,000 replication starting points, also called replication origins. Replication bubbles found at origins have been very difficult to map due to their short lifespan. Moreover, with the flood of data characterizing new sequencing technologies, the precise statistical analysis of replication data has become an additional challenge. We propose a new method to map replication origins on the human genome, and we assess the reliability of our finding using experimental validation and comparison with origins maps obtained by bubble trapping. This fine mapping then allowed us to identify potential regulators of the replication dynamics.
Comparing genome-wide detections of replication origins: A matter of resolution To evaluate the reproducibility of replication origin detections, we compared our results with those of two previously published studies  [10, 11]. The first dataset was obtained with the same SNS purification protocol but in a different laboratory  [10] , on different cell lines (IMR-90, HeLa, human embryonic stem cell H9, induced pluripotent stem cells from IMR90 (IPS)). These datasets were comparable with ours, despite coverage differences (Table  1 ). In the original publication, detections were made using SoleSearch on these data  [9]. For comparison we re-analyzed them using the scan method (Table  1 ). The second dataset corresponds to origins detected using bubble trapping, which is based on the sequencing of EcoR1 fragments containing at least one bubble (on GM06990 cells).
In this case detections were based on a background read depth distribution  [11]. For the sake of simplicity, the three data sets will be referred to as SNS-scan, SNS-SoleS and Bubble origins. The three methods differ widely in the number of detected origins, with about 2 to 3 times more SNS-SoleS origins than Bubble and SNS-scan origins, even for data from the same cell lines (Table  1 ). SNS-SoleS origins are 760 bp long on average and cover ,6% of the genome, whereas SNS-scan origins are longer (,4 kb on average) and cover ,12% of the genome, and Bubble origins (6. 4 kb long on average) cover ,25% of the genome (Table  1 , Figure  1-C ). The strong contrast between SNS-detected origins and Bubble origins reflects differences in the level of resolution of the methods: whereas SNS data allow the detection of origins at relatively high resolution (based on 1.
Comparison of SNS and Bubble trapping detections Then we compared SNS origins with Bubble origins to assess the overlap between experimental protocols. Here 45-46% of SNS origins (SNS-SoleS or SNS-scan) overlap with Bubble origins and 36-37% of Bubble origins overlap with SNS origins (vs. 5-7% expected by chance, Table  3 ). Given the strong difference in resolution between the two methods (Table  1 , Supp. Figure  S1 ), we repeated the comparison after having clustered SNS origins (so that to obtain a resolution comparable to that of Bubble origins (Table  3 ). With this procedure, 65% (51%) of SNS-scan (SNS-SoleS) origins overlap with Bubble origins and 44% (37%) of Bubble origins overlap with SNS-scan (SNS-SoleS) origins, compared to 6-7% expected by chance (Table  3 ). We note that the cross-validation of SNS-detected origins by Bubble origin data is stronger when we used SNS-scan origins than SNS-SoleS origins, which suggests that the scan approach achieves a better sensitivity and specificity than SoleSearch.
It should be noticed that the comparisons between replication origins detected by SNS or by bubble trapping were performed on different cell lines, and hence underestimate the true overlap between the different methods. The matter of resolution appears central in a fair comparison between datasets, and was partly assessed by considering clustered SNS origins. However the distributions of origins length still differ between methods, despite comparable on average (Figure  1-D ). Nevertheless, this study demonstrates a good agreement between SNS-based and bubble trapping-based replication origin maps, with at least 65% of SNS origins confirmed by bubble trapping. Most early origins are constitutive, efficient, and strongly associated with CGIs The first attempts to unravel the spatial program of replication showed that replication origins were associated with specific genomic features, such as high GC content, CpG islands (CGIs)  [1, 16, 17]  and G-quadruplexes  [10, 18].
Moreover, a recent study showed that origins could be divided into subgroups  [10] : constitutive origins, which are common to all cell lines investigated; specific origins, which are found in only one cell type; and common origins, which are neither constitutive nor specific. This suggests that the same genomic and epigenomic constraints may apply to all constitutive origins (and possibly to other types of origins too). We confirmed the strong overlap of datasets from different cell lines, as constitutive origins accounted for 57%, 35%, and 35% of origins in K562, HeLa and IMR90 cell lines respectively, whereas specific origins accounted for only 15, 13, and 9%. We then showed that the clustering of origins previously described  [10]  was specific to constitutive origins, as the size distribution was skewed towards large sizes (average sizes of 4.
Moreover, the extension of the study to several cell lines showed that 42% of CGIs were very efficiently recognized as sites of replication initiation in all cell types, thus defining a very robust and efficient subset of replication origins with a tendency to fire early in S phase. The key role of CGIs was also highlighted by the clear increase in the percentage of CGIs overlapping at least one origin in one cell line with the number of cell lines investigated (Figure  2-D ). With the datasets for five cell lines used here, we were able to associate ,76% of CGIs with at least one origin, but the trend clearly suggests that most CGIs are potential origins of replication.
This result is in agreement with the observation that on one model origin two G4 motifs cooperate to drive initiation very efficiently  [26]. We investigated the importance of G4s for origin selection further, by determining whether CGIs associated with origins (Ori-CGIs) displayed a higher level of enrichment in G4s than CGIs not associated with origins (nonOri-CGIs). The human genome contains a total of 28,691 CGIs (from the UCSC database-hg19), 50% of which overlap with constitutive origins (76% overlapped with origins in at least one of the cell lines investigated). As mentioned above, constitutive origins are the most efficient and tend to fire in early S phase. We found that CGIs overlapping with constitutive origins displayed a greater enrichment in G4 L1-7 and L1-15 than nonOri-CGIs (Figure  3-D ).
Discussion We aimed to identify sets of replication origins in a reliable manner, paying particular attention to the development of tools with good specificity and sensitivity. By comparing raw datasets obtained in two independent laboratories applying the same protocol to five different cell lines, we validated the SNS enrichment method, showing it to be highly reproducible. Moreover, the comparison of SNS-based detections with bubble trapping-based detections shows the validity of genome-wide detections of replication origins by two independent protocols. By applying this method to the data obtained for five different cell lines, we identified a subclass of constitutive origins common to all five cell lines, constituting a very robust set of human replication origins. These origins were enriched in CGIs and almost all of the CGI-Oris found in a given cell type were found to be constitutive.
SNS preparation, sequencing, and data access Short nascent strands were purified as previously described  [32] , but with minor changes to the protocol. We pooled fractions 18 to 24. These fractions contained single-stranded DNA molecules of various sizes, from 1. 5 to 2. 5 kb. We used 500 U of a custom-made l-exonuclease (Fermentas (50 U. ml-1)) for each preparation. For the genome-wide mapping of origins, eight SNS preparations were obtained independently from 10 8 cells each and then pooled. SNS were made double-stranded by random priming with the Klenow exo-polymerase (# EP0421, Fermentas) and random primers (#48190011, Invitrogen). Adjacent strands were then ligated with Taq DNA Ligase (M0208L, Biolabs). Two libraries were constructed with Illumina protocols and five deep sequencing runs were performed with a Solexa/Illumina GA I genome analyzer generating 75 bp reads.
The detection is performed at level a by setting threshold d a such that Prfmax t DR(t,u) ð Þ w d a gƒa. To account for coverage heterogeneities, we segment the coverage process (N) into regions of constant intensities (constant l). We use a segmentation model for this purpose, based on the Poisson distribution adapted from segmentation models for array CGH data analysis  [42]. This segmentation step has two main advantages: First, it automatically detects regions of constant coverage (constant l) and regions with extremely low coverage that are excluded from the study. Second, it allows our significance thresholds to adapt to coverage variations. An example of detection is provided in Figure  1 -A. Our method is available at http://pbil. univ-lyon1. fr/members/fpicard/research. html. Threshold a was calibrated using independent input DNA from public databases since input DNA was not available at the time of the experiment.
Genomic and chromatin feature extraction TSS, CpG islands, and chromatin mark positions were downloaded from the UCSC Genome Browser (http://genome. ucsc. edu/). Details on the datasets are provided in Supp. Table  S3. The positions of G-quadruplexes were determined by applying Quadparser on hg19  [43] , specifying the length of the spacer between 4 tracks of GGG or CCC with spacer of size 1-7, 1-15, 1-20, and 1-30. All results and tables can be downloaded from http://pbil. univ-lyon1. fr/members/fpicard/research. html.
Determining mean replication timing profiles We determined the mean replication timing profiles throughout the complete human genome from Repli-Seq data  [19, 21] , as previously described  [44]. Repli-Seq tags for six FACS fractions were downloaded from the NCBI SRA website (study accession number: SPR0013933) for the erythroid K562 cell line, and from the UCSC ENCODE website http://hgdownload. cse. ucsc. edu/ goldenPath/hg19/encodeDCC/wgEncodeUwRepliSeq/for the IMR90 fetal lung fibroblast cell line. For the HeLa cell line, we calculated the mean replication timing (MRT) rather than the S50 (median replication timing)  [19, 20]. Timing categories were determined by dividing timing values into 6 intervals (0,1=6, 1=6,2=6, early origins, 2=6,3=6, 3=6,4=6, mid-S origins, 4=6,5=6, 5=6,6=6, late origins).
Discriminant analysis for determining combination of marks We consider a linear discriminant analysis to find a linear combination of chromatin features which characterize early, mid-S phase and late origins  [46]. We consider the data matrix with rows corresponding to origins detected in K562 cells and columns corresponding to linear distances to chromatin marks (datasets links are provided in Supp. Table  S3 ). The interpretation of a discriminant analysis is based on two key ingredients (similarly to Principal Component Analysis): the position of the origins on the discriminant axis (Figure  6-A ) and the correlations of the chromatin features with the discriminant axis (Figure  6-B,C ). DA1 is the discriminant axis best discriminating between timing categories. It comprises the distances of origins to open chromatin marks (negative correlation with distances to H2AZ, H3k9ac, H3k4me3) and to H4k20me1/H3K27me3 (Table  7 ).
Part of this work was supported by the  ABS4NGS   ANR  project ( ANR-11-BINF-0001-06 ). The team of MNP is supported by grants from the  ARC  and the  Ligue Contre le Cancer  and belongs to the network of Excellence Who am I? ( ANR-11-LABX-0071 )/ Investments for the Future program   ANR-11-IDEX-0005-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The 32 P-labeled products were separated and purified on 7. 5% PAGE gels that contained 0. 5% (w/v) SDS. The phosphorothioate linkages in the transcripts were cleaved by treatment with 1 mM iodine for 3 min at 37 • C. The resulting cleavage products were resolved by denaturing PAGE (we used 9-20% PAGE gels to resolve different RNA sizes) and quantified with a Typhoon Trio imager and Im-ageQuant TL v8 software (GE-healthcare) using the full, undistorted dynamic range of the gel scans (gamma settings were adjusted only for gel visualization). Analysis of NAIM experiments was performed as described previously by converting raw NAIM signals obtained from gel band ratios into normalized interference factors  (19, 22, 23).
Genotyping Genomic DNA was extracted from blood samples, using the QIamp DNA blood Mini Kit (Qiagen, Hilden, Germany). Genotyping was performed with the Illumina HumanCy-toSNP12v2. 1 Panel, containing 299,140 single nucleotide polymorphisms (SNPs). Quality control (QC) for the data was performed with PLINK software (http://pngu. mgh. harvard. edu/ ,purcell/plink/). For genetic markers, we applied the following QC criteria: call rate $95%, minor allele frequencies (MAF) $ 0. 05, p-value for Hardy-Weinberg equilibrium $10 25 and no Mendelian errors. In total, 255,341 high-quality SNPs were considered for further analysis. For individuals, we excluded three samples based on a call rate ,98% and/or the presence of Mendelian errors, resulting in the limitation of the analysis to 414 subjects. For linkage analyses, we selected 40,783 common SNPs (MAF$0.
As genetic models, we considered a diallelic locus (minor allelic frequency of 0. 45) influencing a quantitative phenotype with an additive effect and leading to a trait heritability ranging from 0. 3 to 0. 5. We generated 100 replicates for each heritability value and we performed linkage analysis in each replicate. The power of our sample to observe a suggestive linkage was 37%, 50%, 66%, 83%, and 97% for heritability values of 0. 3, 0. 35, 0. 4, 0. 45, and 0. 5, respectively. Family-based association tests (FBATs) were carried out on linked regions with FBAT software, version 2. 0. 3, to test for association in the classical transmission disequilibrium test with the empirical variance, to account for linkage  [9]. Pairwise LD measures (r 2 ) were calculated across the genome with HAPLO-VIEW  [10].
Haplotypes of genotyped variants in the linkage region were phased with ShapeIT  [11]  and untyped variants were imputed with Impute2 v. 2. 1. 2 software (http://mathgen. stats. ox. ac. uk/impute_v2. html), based on the 1000 Genomes phase I reference panel. Variants with a MAF$0. 05 were included in subsequent analyses if they had an imputation information score greater than 0. 6. We also performed a specific imputation for HLA class II alleles using HIBAG, the method described by Zheng et al  [12]. Family-based association tests (FBATs) for high-quality imputed variants were performed by a new analytical approach (FBATdosage) accounting for genotype uncertainty through the use of the expected allele count, also known as allele dosage, in the general FBAT framework  [13].
We then investigated whether our associated SNPs had been described as expression quantitative trait loci (eQTLs), using data from the GTEx eQTL database (http://www. ncbi. nlm. nih. gov/ gtex/GTEX2/gtex. cgi), which explores eQTL loci in lymphoblastoid cells. Eight of the 86 SNPs from our cluster had been reported to be significantly associated with mRNA levels for HLA-DQA1, HLA-DQA2, and HLA-DQB1 (Table  S2 ). One of the most significant findings was the association between rs9268403 and HLA-DQA1 expression. The 27 SNPs reported in the previous study  [3]  were eQTLs for the same three genes and for HLA-DRB5 (Table  S2 ). Interestingly, rs2516049, the SNP we were able to replicate in our sample, had also been identified as an eQTL for HLA-DQA1. Among these HLA class II loci of interest we were able to impute reliable four digits HLA-DQA1 and HLA-DQB1 alleles  [12]  in ,85% of our study subjects.