dataesr/scientific-paragraphs-categorization huggingface dataset:
Neonatal CD8 + T Cells Have a Distinctive Transcriptome Signature To investigate the nature of the high sensitivity of neonates to intracellular pathogens, we assessed the transcriptome of naive CD8 + T cells from four human neonatal and four adult donors (GEO: GSE61570). To ensure that we were evaluating only the naive circulating populations of CD8 + T cells, peripheral blood mononuclear cells (PBMCs) or cord blood mononuclear cells (CBMCs) were first submitted to negative selection to eliminate monocytes, B cells, CD4 + T cells, gd T cells, and natural killer (NK) and NKT cells. PBMCs also were depleted of effector and memory populations with antibodies against CD44 and CD45RO. CBMCs were negative for these markers. Cells were then purified with anti-CD8 antibodies. The resulting populations were about 98% CD3+CD8+, over 95% negative for CD45 RO and 100% positive for CD45 RA.
Global analysis of our samples using the gene set enrichment analysis (GSEA) software, which calculates significance as related to the number of genes altered in a given pathway, corroborated that neonatal CD8 + T cells were enriched in pathways associated with cell cycle, cell maturation, and inflammation, while adult cells were enriched in TCR signaling and function (Figures  S2B  and S2C ). Consistent with these observations, reactome network analysis showed that adult-specific transcripts formed a network of pathways related to TCR signaling, while neonate-specific transcripts formed webs associated with cell cycle, antiviral pathways, and innate immunity (Figure  S3 ). Differential expression of genes of these main signature pathways also was validated by comparing our microarray analysis with RNA-seq data from Blueprint (neonatal CD8 + T cells)  (Adams et al.
The transcriptome data also showed that neonatal CD8 + T cells were enriched in gene expression signatures characteristic of innate immunity, particularly antiviral and inflammatory responses (Figure  4A ). The genes enriched in the antiviral pathway participate in nucleus-to-cytoplasm transport (RAE1, NUP37, NUP133, and KPNA3) and RNA maturation (SRSF6, WDR77, and GEMIN4), and they are important cellular targets during viral infections (NEDD4) (Figure  4A ). Among the genes overexpressed in the neonatal T cells, we found several genes associated with inflammatory responses, such as those for defensins and cathelicidins. GSEA showed that neonatal CD8 + T cells were significantly enriched in inflammation-related genes (Figure  S2C ). Because some of these genes are characteristic of the neutrophil immune response, we used the database of inflammation-induced genes in human neutrophils  (Surmiak et al.
Differential Epigenetic Programming of Neonatal versus Adult CD8 + T Cells We took advantage of available data from the Roadmap  (Kundaje et al. , 2015)  and Blueprint  (Adams et al. , 2012)  international consortia (which have generated extensive epigenomic maps of naive CD8 + T cells from adult and neonatal origins) to evaluate the epigenetic regulation of these two types of samples. First, we compared the expression of genes from our adult and neonatal transcriptome signatures with the RNAseq data obtained from the above consortia (Figures  6A  and S1B ). We observed a highly significant difference between the relative expression of genes from the adult and neonatal signatures (Figure  6A , left panel). We also found that the signature profile of neonatal versus adult CD8 + T cells from RNA-seq data from Blueprint and Roadmap was very similar to our tran-scriptome data, thus cross-validating the different datasets (Figures  S1B  and S4 ).
, 2012). Therefore, part of the differences encountered in this analysis could be explained by this fact. However, in a transgenic mice model where RTEs were labeled, it was reported that neonatal and adult CD4 + T cell RTEs have phenotypic and functional differences. Adult cells did not proliferate in response to IL-7 and produced less of the Th1/Th2 cytokines as compared to neonatal RTEs  (Opiela et al. , 2009). Our work suggests a function of neonatal cells within the innate immune response. The consistency between our observations and the transcriptomic and epigenetic data from the Roadmap and Blueprint consortia strengthens our conclusions. Further analysis will determine whether the innate immunity role of CD8 + T cells could be either an ancient role of these cells, possible pointing to an evolutionary functional origin of CD8 + T cells, or an acquired role adapted to the transition of birth in mammals.
Transcriptome Analysis The labeled RNA was hybridized and scanned on an Agilent platform in an 8X60K human Agilent microarray. Raw data are available at GEO: GSE61570. 17, 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 November 15, 2016 2157  For bioinformatic analysis, first the raw fluorescence data were normalized by the R package (AGIND). Later, analysis was performed using software Limma, Multiple Experiment Viewer (MEV) and R program, with an alpha of 0. 05 to obtain the significantly changed genes. The heatmaps were obtained with the software MEV, with base 2 logarithmic transformations and analysis of arithmetic average for each gene.
Cell Reports GSEA  (Subramanian et al. , 2005)  was performed regardless of Limma analysis. Reactome analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and GO analysis was performed with the ClueGO plug-in under Cytoscape 3. 0  (Shannon et al. , 2003) , with the Database for Annotation, Visualization and Integrated Discovery (DAVID) software  (Huang et al. , 2009).
qRT-PCR The cDNA was synthesized from total RNA using the Superscript Vilo cDNA synthesis kit (11755050, Life Technologies). The qPCRs were performed using SYBR Green PCR Master Mix (4309155, Life Technologies). The mRNA levels were calculated from standard curves for each gene and relative to those of the GAPDH gene, used as a standard. Processing of ChIP-Seq and RNA-Seq Data RNA-seq and chromatin immunoprecipitation sequencing (ChIP-seq) (H3K4me3, K3K27ac, and H3K27me3) samples from adult naive CD8 + T cells (Donor_101_8) were obtained from the ROADMAP project. The RNA-seq (donor C002YM) and ChIP-seq (H3K4me3, K3K27ac, and H3K27me3; donor C0066P12) samples from neonatal naive CD8 + T cells were obtained from the Blueprint project (see Table  S1  for more information). Gene expression was assessed by quantifying RefSeq gene exons signal from RNA-seq BigWig files using BigWig-Tools extract option  (Pohl and Beato, 2014) ; then, for each gene, the corresponding exon signal was summed up and normalized using the Normalizer package  (Glusman et al.
, 2013)  with the quartiles option. Histone modifications within promoter regions (related to Figure  6A ) were assessed by extracting the average of the top 25% signal from ChIP-seq BigWig files from H3K4me3, H3K27ac, and H3K27me3 histone marks using BigWig-Tools, in a region around 1. 5 kb to the transcription start site (TSS) RefSeq genes, then the signal was normalized as described above. For distal H3K27ac analysis (related to Figure  6C ), peakcalling for H3K27ac ChIP-seq from neonatal CD8 + T cells was obtained directly from the Blueprint database. Peak-calling for H3K27ac ChIP-seq from adult CD8 + T cells (Roadmap) was performed with MACS2  (Zhang et al. , 2008) , using the Blueprint parameters. The unique no-overlapping peaks between the two populations (each ChIP-seq data) were selected using Bedops  (Neph et al.
Chromatin Segmentation CD8 neonatal segmentations and visualization with 12 states for samples C002YMH1, C0066PH1, and S00C2FH1 were obtained from the BLUEPRINT project (2015-01-28 release, more details are given in Table  S1 ). CD8 adult samples (naive primary cells: Donor_100_7 and Donor_101_8) were obtained from ROADMAP. ChIP-seq data for six histone modifications (H3K4me3, H3K4me1, H3K27ac, H3K36me3, H3K27me3, and H3K9me3) were used (see Table  S1 ). For the segmentation, we used the implementation described in the ChromHMM software (version 1. 10)  (Ernst and Kellis, 2012). The input data were the ChIP-seq bed files with the genomic coordinates and strand orientation of mapped sequences (after remove duplicate reads). The genome was divided in consecutive 200-bp non-overlapping intervals and independently assigned present (1) or absent (0) for each of the six chromatin modifications.
The assignment was based on the count of tags mapping to the interval and on the basis of a Poisson background model using a threshold of 10-4, as explained  (Ernst and Kellis, 2012) , after binarization, and for segmentation we used the 12 states model established by the Blueprint Consortium (2015-01-28 release, see Table  S1 ). Further, we computed the probability that each location is in a given chromatin state, and then we assigned each 200-bp interval to its most likely state for each sample. In addition, visualization files were generated with ChromHMM software (version 1. 10).
ACKNOWLEDGMENTS This project was specifically supported by a joint EcosNord-Anuies-SEP- Conacyt  project ( M11S01 ). Work in the M. A. S. laboratory is supported by grants from  Consejo Nacional de Ciencia y Tecnologı ´a  ( CONACYT ;  CB-2011-01 168182 ) and  Programa de Mejoramiento del Profesorado  ( PROMEPSI-UAEM/13/342 ). Work in the S. S. laboratory is supported by recurrent funding from the  Inserm and Aix-Marseille University  and by specific grants from the  European Union 's  FP7 Program  (agreement 282510-BLUEPRINT), the  Association pour la Recherche contre le Cancer (ARC)  (project  SFI20111203756 ), and the  Aix-Marseille initiative d'excelence (A*MIDEX)  project  ANR-11-IDEX-0001-02. We thank  Centro Estatal de la Transfusio  ´n Sanguı ´nea in Cuernavaca for the donation of leukocyte concentrates and the mothers and babies of  Hospital General Parres in Cuernavaca  for the donation of cord blood.
This study makes use of data generated by the  Blueprint and Roadmap consortia. A full list of the investigators who contributed to the generation of the data is available from www. blueprint-epigenome. eu and http://www. roadmapepigenomics. org/. Funding for the Blueprint project was provided by the  European Union 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  282510 -BLUEPRINT. The Roadmap consortium is financed by the  NIH. We are grateful to  Professor C. I. Pogson  for critical reading of the manuscript.
Acknowledgements We are indebted to the curators  Sergio Bravi  ( CSMNF, Napoli, Italy ),  Abel Prieur ,  Emmanuel Robert  ( FSL, Villeurbannes, France ),  Martina Kölbl-Ebert  ( JME, Eichstätt, Germany ),  Jean-Michel Pacaud  ( MNHN, Paris, France ),  Alessandro Garassino  ( MSNM, Milano, Italy ),  Claire Mellish  ( NHMUK, London, United-Kingdom ),  Matt Riley  ( SMCU, Cambridge, United-Kingdom ),  Martin Nose  ( SNSB-BSPG, Munich, Germany ),  Jean-Bernard Caron ,  Peter Fenton  and  Janet Waddington  ( ROM, Toronto, Canada ),  Fabienne Giraud-Guillot  ( UJF-ID, Grenoble, France ),  Isabelle Rouget  ( UPMC, Paris, France ) for their work in the collections, without which this study would not have been possible. We also acknowledge the contribution of Philippe Loubry, who realized the SEM micrograph and  Denis Poinsot  ( Université Rennes 1 ) who offered insightful advices on statistical analysis.
JTH is currently kindly funded by the  German Research Foundation  ( DFG; Ha 6300/3-1 ). CH is supported by a  Bavarian Equal Opportunities Sponsorship  at the  LMU Munich. CH and JTH are kindly supported by  J. Matthias Starck , Munich. We especially thank all private fossil collectors for their long-lasting support and all people involved in providing open source, open access or low cost software. We are glad to acknowledge the precious help of two anonymous referees: their detailed review significantly improved this article.
4 fb -1 recorded with a trigger requiring a photon candidate with p T > 30 GeV measured within |η| < 1. 44 and E miss T > 25 GeV, where the E miss T is calculated using calorimeter information without correcting for muons. Compared to the available triggers in the full 2012 data set, which require a photon candidate with p T > 135 GeV or events with E miss T > 120 GeV, these low trigger thresholds enable a high signal sensitivity to electroweak production and compressed mass spectrum scenarios. The data set was recorded during the second half of the 2012 data-taking period but only reconstructed during the Long Shutdown 1 of the LHC as part of the so-called "parkeddata" program  [41].
Its magnitude is referred to as E miss T. The E miss T is calculated using all particles identified by the PF algorithm. Filters against anomalously high E miss T from instrumental effects are applied  [50]. The dimensionless variable E miss T -significance (E miss,signif T )  [50]  is a measure of the probability that the E miss T in a given event arises from genuine noninteracting stable particles, such as neutrinos or gravitinos, and not as a consequence of the limited energy resolution of objects, such as jets, photons, or leptons. The E miss,signif T is proportional to the logarithm of the likelihood ratio of both hypotheses. Energy and angular resolutions of the relevant objects, determined by data and simulation studies, are taken into account. For processes where the reconstructed E miss The SM ttγ and QCD multijet production samples, as well as the TChiNg and TChiWg signal scenarios, are simulated with the MADGRAPH 5.
1. 3 generator  [51]. The diboson event samples and the electroweak GGM signal scan are generated using PYTHIA 6. 4  [52]. All Monte Carlo (MC) samples incorporate the CTEQ6L1  [53]  parton distribution functions (PDF) and use the PYTHIA program to describe the parton showering and the hadronization. The GEANT4  [54]  package is used to model the detector and detector response. The cross sections of the electroweak GGM signal scan are calculated at next-to-leading-order (NLO) accuracy using the PROSPINO 2. 1. [55]  program, the cross sections for the TChiNg and TChiWg signal points are calculated at NLO+NLL (next-to-leading logarithm) accuracy  [56] [57] [58].
Conclusion We have searched for electroweak production of gauginos in the framework of gauge mediated supersymmetry breaking in final states with photons and E miss T. A dataset, corresponding to an integrated luminosity of 7. 4 fb -1 , recorded with a special trigger with low thresholds is used. The data are found to agree with the SM expectation. The analysis is sensitive to electroweak production and compressed mass spectra which are characterized by minimal hadronic activity in the final state, complementing previously published searches. Limits in the TChiNg scenario are set for the first time, excluding NLSP masses below 570 GeV at 95% CL. In the TChiWg scenario, NLSP masses below 680 GeV are excluded at 95% CL, increasing the previous mass limit in this scenario  [31]  by 140 GeV. In the general gauge mediation model for compressed mass spectrum scenarios with e.
(2008). For moderately to heavily absorbed sources (N H ∼ 10 23 cm -2 ), the expected count rates are more strongly suppressed using the Chandra response (as the NuSTAR instrument response is more strongly weighted to higher energies). The intrinsic fluxes are therefore underestimated for such sources with Chandra when a single conversion factor is assumed. We have verified that this effect leads to an slope difference in the expected log N -log S at f 3-8keV ∼ 10 -14 -10 -13 erg s -1 cm -2 that matches the discrepancy seen between the NuSTAR measurements and the Chandra and XMM-Newton measurements shown in Figure  3  (left). Figure  4  -  The differential number counts for the observed 3 -8 keV band. The data points (black squares) show measurements from the combined NuSTAR survey fields, where error bars are 1σ equivalent (i.
SUMMARY AND CONCLUSIONS We have presented measurements of the number counts of AGN with NuSTAR in two bands, from 3 -8 keV and 8 -24 keV. The data span a broad range in flux, with good constraints covering 10 -14 < ∼ S (8 -24 keV; erg s -1 cm -2 ) < ∼ 10 -13. The 3 -8 keV differential source number densities are in agreement with measurements from Chandra and XMM-Newton, although the slope measured by NuSTAR is somewhat steeper. The slope difference results from the fact that the NuSTAR effective area curve is weighted to significantly higher energies compared to XMM-Newton and Chandra, which results in the observed discrepancy when using a simple counts to flux conversion factor. In the 8 -24 keV band we present the first direct measurement of the AGN number counts that includes data above ∼10 keV and reaches down to flux levels ∼ 3 × 10 -14 erg s -1 cm -2.
2011) , and continuation of the serendipitous survey, which will better constrain the slope of the number counts distribution above the break and improve spectral constraints on the resolved AGN population. This work was supported under NASA Contract No. NNG08FD60C, and made use of data from the NuSTAR mission, a project led by the California Institute of Tech-nology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software (NuS-TARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). JA acknowledges support from ERC Advanced Grant FEEDBACK at the University of Cambridge and a COFUND Junior Research Fellowship from the Institute of Advanced Study, Durham University.
Background: In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution.
Results: We assessed the performance of our method, named MI-MFA, on two real omics datasets. Incomplete artificial datasets with different patterns of missingness were created from these data. The MI-MFA results were compared with two other approaches i. e. , regularized iterative MFA (RI-MFA) and mean variable imputation (MVI-MFA). For each configuration resulting from these three strategies, the suitability of the solution was determined against the true MFA configuration obtained from the original data and a comprehensive graphical comparison showing how the MI-, RI-or MVI-MFA configurations diverge from the true configuration was produced. Two approaches i. e. , confidence ellipses and convex hulls, to visualize and assess the uncertainty due to missing values were also described. We showed how the areas of ellipses and convex hulls increased with the number of missing individuals.
A free and easy-to-use code was proposed to implement the MI-MFA method in the R statistical environment. Conclusions: We believe that MI-MFA provides a useful and attractive method for estimating the coordinates of individuals on the first MFA components despite missing rows. MI-MFA configurations were close to the true configuration even when many individuals were missing in several data tables. This method takes into account the uncertainty of MI-MFA configurations induced by the missing rows, thereby allowing the reliability of the results to be evaluated.
Missing row values for a table of variables are challenging to handle because most statistical methods cannot be directly applied to incomplete datasets. In the multiple multivariate framework, several approaches have already been proposed to deal with missing row values  [2]. The only methods widely available for analyzing incomplete data focus on removal of the missing rows, either by ignoring subjects with incomplete information or by replacing the missing items with plausible values (e. g. , means of the observed cases). In multivariate statistical analysis, case deletion procedures can be very inefficient, discarding an unacceptably high proportion of subjects because even if the per-table rates of missing rows are low, only a few subjects may have complete data for all tables. In addition, case-deletion procedures may bias the results if the remaining subjects providing the complete data are unrepresentative of the entire sample  [3, 4].
Here we consider an alternative method, involving a multiple imputation approach adapted to the MFA framework, and called MI-MFA. Multiple imputation (MI) was proposed by Rubin (1987)  [3]  in order to estimate both the parameters of interest and their variability in a data missingness framework. It relies on the principle that a single value cannot reflect the uncertainty of the estimation of a missing value. First, MI is used to generate plausible synthetic data values, called imputations, for missing values in the data. This step results in a number (M) of imputed datasets in which the missing data are replaced by random draws of plausible values according to a specific statistical model. The second step consists of analyzing each imputed dataset using a statistical method that estimates the parameters of interest.
This step results in M analyses (instead of just one) which differ only because the imputations differ. Finally, MI combines all the results together to obtain a single consensus estimate, thereby combining variation within and across the M imputed datasets. Under fairly liberal conditions, this last step results in statistically valid estimates that properly reflect sampling variability. The major challenge in MI involves generating possible values for each missing observation. Statistically advanced imputation procedures can therefore be used for this. Two general approaches are often used for imputing multivariate data: joint modeling (JM)  [12]  and fully conditional specification (FCS), also known as multivariate imputation by chained equations (MICE)  [13, 14]. JM involves specifying a multivariate distribution for the missing data and drawing imputations from their conditional distributions by Markov chain Monte Carlo (MCMC) techniques.
FCS specifies the multivariate imputation model on a variable-by-variable basis using a set of conditional models, one for each incomplete variable. The key issue in JM is appropriate specification of the multivariate distribution. A multivariate normal model has often been used as it is computationally tractable (because only the mean vector and the variancecovariance matrix need to be estimated). This model has even been used when some of the variables are not Gaussian. However, the main weakness of JM is that it can only be applied when the imputation involves a small number of variables. This is not very common in omics datasets that are often composed of tens of thousands of variables, or more. FCS allows greater flexibility than JM in creating multivariate models. Indeed, FCS can use specialized imputation models by separately defining the conditional densities for each variable, even if this can require a considerable amount of work.