dataesr/scientific-paragraphs-categorization huggingface dataset:
This relative expression was normalized using the corresponding geometric average of three reference genes with geNorm v3. 4  [16] : β-actin, transmembrane p24 trafficking protein 4 (TMED4) and ribosomal protein L19 (RPL19) genes that were respectively slightly, moderately and highly expressed and not regulated during follicular development or in the compartment. The significance of the relative expression data on genes involved in canonical pathways was tested using the one-way ANOVA model of the R statistical software system (the Comprehensive R Archive National, http://www. cran. r-project. org) after logarithm transformation of the data. For each gene, an ANOVA model was fitted using two factors: "stage" (4 levels) and "compartment" (2 levels) and their interactions. A backward variable selection procedure was applied, as previously described  [11].
Biological trends Ingenuity 1 Pathway Analysis software (IPA; http://www. ingenuity. com) was used to examine the functional enrichment in differentially expressed genes. The significance of the association between the differential gene lists and functional categories was determined by a P value calculated using Fisher's exact test corrected for multiple testing (FDR <0. 05 (Benjamini-Hochberg test)). Pathway based analysis was performed using Webgestalt (http://bioinfo. vanderbilt. edu/ webgestalt/ Kegg, Wikipathway and Pathwaycommons databases) and GeneCodis (http:// genecodis. cnb. csic. es/ Kegg pathway and Panther databases) software programs. Significantly enriched pathways were identified using a hypergeometric test followed by a multiple testing correction of p-values using the Benjamini-Hochberg test (FDR <0.
05). Then, to ensure that pathways obtained from computational biology were coherent and gave a good interpretation of experimental results  [14, 17] , only Webgestalt significant pathways identified using the two softwares (Webgestalt and GeneCodis) or from two databases, were conserved (Kegg/Wikipathway/ Pathwaycommons databases). IPA downstream Effects Analysis was then used to identify the expected effects of our observed gene expression changes on the functions (expected to increase or decrease). This analysis examined genes in our dataset that were known to affect functions, compared the directions of change of the genes with those expected based on findings in the literature, and then issued a prediction for each function based on the direction of change (in the differential gene expression list).
In the same way, IPA Upstream Regulator analysis was used to understand the reasons for the gene expression changes observed. It identified the cascade of upstream transcriptional regulators that might explain the gene expression changes observed in the dataset and predicted the activity of their encoded protein (cf. Methods section). Taking account of the gene expression profile (expressed and/or differentially expressed) for each predicted upstream regulator provided more evidence for the biological mechanism. The expression and/or differential expression of the transcriptional regulator in the dataset produced more evidence for a particular biological mechanism. These analyses were described by Bonnet et al. [12].
Biomarker selection Biomarkers of the compartments and stages of early folliculogenesis were determined using the DESeq package  [14]  of R software for multi-factorial design (R 2. 14. 0; DESeq release 1. 6. 1). The genes mostly expressed in a specific cell type and stage (PDO, PMO, SCO, SAO, PDG, PMG, SCG, SAG) were selected using pairwise comparisons (nbinom Test) with other samples, including multi-tissue samples (FDR<5% and a fold change >3 for granulosa samples and a fold change >10 for oocytes samples (for example, PDO / PMO + SCO + SAO + PDG + PMG + SCG+SAG + MT)). Theca cells were included in multi-tissue samples to ensure the selection of specific expression in oocytes or granulosa cells and to prevent contamination. The accuracy of biomarker selection was checked using qRT-PCR and the significance of differential gene expression was evaluated using Student's test after a fourth square transformation.
Global differential gene expression during early follicular development This study was carried out using the gene profiles of 15,349 genes generated by RNA-seq experiment (http://www. ebi. ac. uk/arrayexpress/ under accession number E-MTAB-1587)  [12]. This experiment used a laser capture microdissection method to split and produce granulosa cells or oocyte transcriptomes for each stage of early follicular development: primordial (PD), primary (PM), secondary (SC) and small antral (SA) follicle stages. The experiment included three/four biological replicates per sample. Previous study applied a generalized linear model (DESeq GLM) to this dataset to explore the differential expression and the specificity between each compartment (oocyte, granulosa cells). We also observed 19. 6% of genes that were significantly differentially expressed (DEG) during early development (3,015 genes, FDR<5%)  [12].
At the secondary stage, IPA analysis highlighted major functional change and two biological functions relative to protein metabolism (degradation, synthesis) as being mainly over-represented in the secondary stages of granulosa cells but not enriched in differentially expressed genes during oocyte development. Pathways. Pathway based analyses were investigated using Webgestalt software and revealed significant pathway enrichment in the lists of differentially expressed genes (FDR<0. 05; S4 Table ). These pathways encompassed cell processing, signaling pathways and those involved in cell communication that were slightly enriched in primary follicles and mainly enriched from the secondary stage when compared to primordial stage (Fig  3 ). However, some pathways were only significantly enriched at the small antrum stage.
Finally, members of the PDGF receptor family (such as platelet derived growth factor D (PDGFD), platelet-derived growth factor receptors (PDGFRA and PDGFRB), etc. ) and members of FGF Functional enrichment analysis for sets of differentially expressed genes during early follicular development was performed in silico for each compartment using Ingenuity Pathway Analysis (IPA) software. Statistical significance was determined by a P value calculated using Fisher's exact test corrected for multiple testing correction (Benjamini-Hochberg test, FDR <0. 05). This analysis identified nine main cellular function categories as being significantly enriched in differentially expressed genes. Bar colors correspond to enriched functions at primary stage (compared to primordial stage), secondary stage (compared to primary and primordial stages), and small antrum stage (compared to secondary, primary and primordial stages).
Biomarkers of early folliculogenesis Biomarker selection. A number of protocols have been developed for domestic species such as cattle, buffalo and sheep regarding the performance of in vitro functional analysis, manipulation and embryo production, or the evaluation of cryopreservation prior to cancer treatments. The identification of biomarkers is therefore an important step because they can be used to monitor follicular development and survival. For this purpose, we performed a pair-wise comparison between each stage dataset and the other datasets including multi-tissue samples (FDR<5%, FC>3-10) and selected the 29 most differentially expressed genes (see Materials and Methods). The expression of this gene selection was then checked by qRT-PCR. Statistical analysis confirmed the differential expression observed in the RNA-seq data for 24 out of the 29 genes tested (S7 Table ).
Statistical analysis Data were summarized as the mean and the standard deviation (SD). The data were analyzed using Statistica 7. 1 software. After testing for the normal distribution (Kolmogorov-Smirnov test), differences within and between the NW, OW and OB groups were analyzed using a twoway analysis of variance for repeated measurements. After confirming significant group differences over time, the Newman-Keul's post hoc test was performed. The power of the correlation analyses was calculated using a Pearson test (for parametric data). A value of p<0. 01 was accepted as the minimal level of statistical significance.
Of the 32 HIV-infected patients who received primary immunization, 7 two were not currently undergoing regular monitoring at the service, and one received an additional dose of hepatitis A vaccine. Thus, a total of 29 patients infected with HIV (HIV Group) were included in this study. Of the 27 patients exposed to HIV who were non-infected and received primary immunization, 7 only 10 (ENI Group) are being followed at the service and were included in the present study. Hence, 39 of these subjects who were regularly followed at the service were included. Clinical data of patients were obtained from the review of medical records: clinical and immunological category for HIV infection (CDC 1994)  9  ; weight and height measurements used to calculate the Z-score for body mass index (BMI z-score) and z-scores for height/age (H/A z-scores) according to the reference standards and recommendations by the World Health Organization 10 ; clinical events; hospitalizations, and age at start of antiretroviral therapy prescribed to the HIV Group.
As part of the routine clinical follow-up, patients in the HIV group underwent assessment of CD4+ T lymphocyte count and viral load (VL) of HIV every four months. CD4+ T cells were assessed by flow cytometry (FACS Calibur, BD Biosciences, Franklin Lakes, USA). The HIV viral load quantification was performed through RT-PCR (Cobas Amplicor) HIV-1 Monitor kit (Roche Diagnostics System, Basel, Switzerland) and Bayer Versant human immunodeficiency virus type 1 (HIV-1) RNA 3. 0 (B-DNA) (Bayer, Tarrytown, New York). The following data were collected: CD4 + T lymphocyte count and HIV viral load quantification 30 days after primary vaccination and seven years later. Quantification of total antibodies against HAV was performed using electrochemiluminescence by means of a commercial Elecsys Anti-HAV kit (Roche Diagnostics, Indianapolis, USA) seven years after the primary immunization.
The variables VL seven years after the PV and HAV AB after PV were included in the adjustment as numerical variables, since in the univariate analysis, VL seven years after PV produced no adjustment in the Odds Ratio when categorized, and HAV AB after PV showed wide confidence interval when categorized. The level of statistical significance was set at p<0. 05. Statistical analysis was performed using the Statistical Package for Social Sciences 16. 0 (SPSS Inc. Chicago, IL, USA).
7mIU/mL for HAV. As he started to show evidence of HIV viral replication, the antibody titers for HAV decreased again (Fig. 2 ), even though he received a 2 nd booster dose and showed an increase in CD4+T cell count (from 65 cells/ mm 3 to 285 cells/mm 3 in the period between the 1 st and 2 nd revaccination doses). The small number of sample subjects, particularly in the ENI Group, who typically abandon follow-up soon after their uninfected status is verified, and the great variability of data may have impaired the statistical analysis of our findings. Still, it was possible to demonstrate the importance of temporal monitoring of antibody levels after hepatitis A vaccine in HIV-infected patients. Although all adolescents exposed and not infected with HIV remained seropositive for HAV, 21% of the HIV-infected Figure  2  Dynamics of plasma levels of hepatitis A antibodies after revaccination in 6 patients from the HIV group without protective antibody levels seven years after primary immunization.
The protocol was approved by the independent ethics committee of Pitié-Salpêtrière Hospital on December 1 st , 2010, and obtained the authorization of the French agency for the security of medications and health products-Afssaps changed to ANSM on 2 November 2010. A written informed consent was obtained from all patients before inclusion. This clinical trial was registered before enrollment of participants in the EU Clinical trials Register under the identification number: ID RCB 2010-A00776-33. It was also registered at ClinicalTrials. gov under the identification number: NCT01530685 (URL: clinicaltrials. gov/ show/NCT01530685?displayxml = true) later due to some technical problems with the sponsor of the study. The authors confirm that all ongoing and related trials for this drug/intervention are registered.
Power calculation The trial was designed to demonstrate superiority of the dietary supplement over placebo. Assuming a 7% change in FPG in the active treatment group based on internal unpublished data and a previous study  [21] , i. e. 0. 39 mmol/L for changes in FPG between the dietary supplement and placebo groups during the treatment periods, with a standard deviation (SD) of 0. 41 mmol/L (for changes between 4 months and baseline), a sample of 25 evaluable subjects per group was considered to have 90% power to detect a between-treatment difference in FPG change with a type I error rate of 5%. Assuming that 15 to 20% of subjects would be nonevaluable for the primary efficacy outcome, at least 60 subjects (30 per group) were to be included.
The baseline values of the identified two groups of subjects (separated by the correlation line) were compared using the nonparametric Mann Whitney test to identify the two different phenotypes. Correlations between changes in fat-free mass, changes in insulin sensitivity (revised QUICKI), and changes in free fatty acids were also explored. Statistical analyses were performed using R software version 2. 13 (http://www. r-project. org), with the following R packages: plotrix 3. 4-5  [35]  and nlme 3. 1-105  [36]. Two-tailed pvalues of less than 0. 05 were considered statistically significant.
Acknowledgments We are grateful to  Sophie Gougis , registered dietician, for dietary counselling to the patients and dietary data analysis. We thank  Christine Baudoin  ( APHP-CRNH-Pitié Salpêtrière ) for facilitating patient recruitment,  Laëtitia Béraud  ( Biochemistry and Hormonology Department, Tenon Hospital ) for technical assistance in performing inflammatory biomarker.
ARLEQUIN v. 3. 5  [34]  was used for different purposes: haplogroup (h) and haplotype (H) diversities; Ewens-Watterson non-parametric tests; population pairwise F ST between first and second surnames; and mutational steps between Y-C haplotypes. Other data analyses were performed using SPSS v. 22 program (IBM Corporation, New York, USA). Contingency tables have been used to test if observed values of R1b1a2-M269 lineage is significantly associated with surname occurrences. Data were arranged into six groups in order to avoid classes with low number of cases. The designed interval classes were: 21-8, 7-5, 4, 3, 2 and 1. The database of the 2010 Spain census (www. ine. es/, accessed 07/12/2011) has been chosen since it would be the less affected by most recent immigration towards Spain. Surnames were coded to anonymize donors.
This comparison must be a routine for the study of human populations. Surname distributions in populations of most countries are known from national population censuses or from other data sources such as telephone and/or poll lists. Although neutrality tests cannot be performed on these huge data sets-hundreds of thousands-of surnames (alleles) (e. g. Ewens-Watterson test is currently limited to 1000 different alleles, ARLEQUIN program v. 3. 5) almost surely national surname distributions would not fit well the IAM, due to similar causes than those acting on our study sample. However, a chi-square test between sampled and those all recorded population surnames is a priori feasible though it would not provide probably a significant difference due to the very high value of the chi-square limit.
This research was funded by the  Spanish Ministry of Economy and Competitiveness (MINECO , http://www. mineco. gob. es/portal/site/mineco/idi)Project  CGL2010-15191/BOS  to RC. CLH has a predoctoral fellowship granted by  Complutense University of Madrid. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
NS5A direct sequencing and sequence analysis The nested PCR products were purified using the Micro-spin™ S-400 HR Columns, GE Healthcare, Buckinghamshire, UK) according to the manufacturer's instructions. DNA sequencing was performed using the Big Dye® Terminator v 3. 1 Cycle Sequencing Kit with a DXL 3500 DX sequencer (Appied Biosystem, Courtaboeuf, France). The same primers used in the two semi-nested PCR for the amplification of the NS5A region for both subtypes 1a and 1b were used for sequencing in this region. Nucleotide sequences were aligned with reference sequences of different subtypes with SeqScape v 2. 7 (Applied Biosystem, Courtaboeuf, France) and analyzed for the presence of previously identified mutations conferring resistance to NS5A inhibitors. GeneBank accession numbers for NS5A reference sequences are AF009606 for subtype 1a and AY045702 for genotype 1b.
Acknowledgements The authors thank the patients who participated in this clinical study and are particularly grateful to  Mr Ray Cooke  for linguistic assistance. This study was supported by the  CNRS-UMR 5234 (Microbiologie fondamentale et Pathogénicité) Bordeaux University  and the  Tunisian Ministry of Higher Education, Scientific Research and Technology (LR "Epidémiologie  et diversité génétique des virus hépatiques et entériques humain,  Institut Pasteur de Tunis " -Contract:  LR11-IPT-09 ).
Muscle mRNA was extracted using 30 mg of muscle with the RNeasy Fibrous Tissue Mini Kit (Qiagen, Hilden, Germany) and retrotranscribed to cDNA by RT-PCR using polythiamine primers (Invitrogen, Carlsbad, USA). The full DMD transcript was amplified and sequenced in 20 overlapping amplicons with our own primers and others described previously  [15]. Sequence analysis was performed by Sanger sequencing method (Big Dye 3. 1 chemistry and equipment 3500xL ABI, Applied Biosymtems, Foster City, USA). The impact of the DNA variants detected on mRNA splicing and protein functionality was evaluated by in silico analysis using computational algorithms. Altered splicing sites were analyzed using positional weight matrices included in the Human Splicing Finder and Alamut softwares. Pathogenicity of amino acid substitutions was evaluated using Polyphen-2  [16]  and SIFT  [17]  algorithms.
All substitutions with a Polyphen-2 score below 1 and a SIFT score below 0. 05 were considered damaging substitutions. Moreover, when an aminoacid substitution was detected in any of the 100 healthy controls, pathogenicity was rejected. In addition, we used LOVD database (Leiden Open Variation Database: www. dmd. nl) as a reference to determine if the substitution consisted in a missense mutation or a non-synonymous SNP. The nucleotide position was determined according to the DMD reference sequence (RefSeq NM_004006. 2) and mutation nomenclature followed the guidelines of the Human Genome Variation Society (HGVS). The mutation rate per nucleotide and per generation for each mutation type was calculated according to the formula μx = mn x / Nt x , as previously described  [18]. MARCOIL program was used to predict the existence and location of potential coiled-coil domains in protein sequences  [19].
Small mutations We identified 98 mutations in 105 unrelated individuals (18. 2%, 105/576), 55 of which have not been previously identified in the LOVD database. We identified a variety of mutations: 54 nonsense mutations, 15 microdeletions, 11 insertions/duplications, 20 splice site mutations, 4 missense, and one deep intronic mutation. The frequency of mutation types differed according to the clinical phenotype (Fig  3 ). The vast majority of DMD patients presented frameshift or nonsense mutations, causing a premature stop codon and a truncated protein rapidly degraded by the NMDA process. (81%). Nonsense substitutions are the most common type in patients with DMD/IMD (56. 1%). However, most mutations in BMD patients affected mRNA splicing process (43. 8%), while nonsense and frameshift mutations accounted for 31.
Missense and in-frame mutations Only four missense mutations and one in-frame deletion of a single amino acid (4. 8% of point mutations and 0. 9% of total mutations) were identified. Four of these mutations were associated with DMD and IMD phenotypes, while only one was associated with an atypical BMD patient. Two of the four were located at the N-terminal actin binding domain (ABD1): p. Leu53Arg in a DMD patient with irregular dystrophin reduction and p. Gly166Val in an IMD The small mutations identified in this work will be submitted to LOVD database.
According to previous publications  [39] , there are no hot spots for small insertions/deletions, although the study of the mutated sequence reveals that in most cases the presence of repeated motifs is implicated in the etiology of such mutations. The sequencing of the coding and flanking intron regions allowed us to identify 119 SNPs (S1 and S2 Tables), 21 of which had not been previously described in LOVD database, dbSNP and HapMap. The study of haplotypes obtained by sequencing reveals the variability in the DMD gene. Just as the point mutations study reveals that 56% are private mutations, study of the SNPs shows that the vast majority of unrelated families present a unique haplotype for DMD locus. This probably reflects the high rate of DMD recombination linked to its large size. Of the 119 identified SNPs, 93 are located in intron regions and 26 in coding exons.
For example, are these methylation sites functional? Do they preferentially regulate downstream genes with certain gene functions? Do they interact with different TFs? To address these questions, we utilized datasets of genome-wide tissue-specific DNA methylation and gene expression to perform a detailed survey of potential regulatory roles of tissue-specific differentially methylated sites (T-DMRs). The genome-wide methylation profiles were obtained by the improved comprehensive high-throughput array for relative methylation (CHARM) array  [11] , which has no bias to promoter and CpG islands and requires only small amount of tissue samples. We find that the T-DMRs that are positively correlated with gene expression are likely to be functional and that the genes associated with these T-DMRs are enriched for those that negatively regulate transcription and metabolism, suggesting a novel, two-layer mechanism of DNA methylationdependent gene regulation.
A significant number of T-DMRs positively correlate with gene expression In our previous genome-wide profiling of DNA methylation profiles of mouse retina and brain  [11] , we identified 2498 T-DMRs. To explore the potential regulatory role of these T-DMRs, we integrated the methylation dataset with a genome-wide gene expression dataset obtained from the same tissues  [12]. We compared the two tissues and calculated the difference in gene expression (ΔE) and the difference of DNA methylation (ΔM) of a T-DMR within a gene region (proximal T-DMR) from 4 kb upstream of the TSS to the end of transcription. We did not limit our interest to gene promoters since it has been shown that gene-body DNA methylation also plays a role in tissue-specific gene regulation  [13]. In total, 952 unique proximal T-DMRs are associated with genes whose expression showed at least 25% fold change between the two tissues.