dataesr/scientific-paragraphs-categorization huggingface dataset:
MSL binds mainly to gene bodies, while NSL to promoter regions in mESCs To characterize the genome-wide role of MSL and NSL complexes, we carried out ChIP-seq analysis in mESCs using the above-characterized anti-Msl1 and anti-Nsl1 antibodies. The obtained binding maps of Msl1 and Nsl1 in mESCs were then compared at the UCSC genome browser to publicly available ChIP-seq data for Mof, H4K16 acetylation (H4K16ac), RNA Polymerase II (Pol II), and DNAse hypersensitive sites (DHS). At a representative genomic locus, Nsl1 peaks were detected at the TSSs of four expressed genes, where they co-localized with Mof, Pol II and DHSs, whereas Msl1 binding peaks were usually broader and together with H4K16ac downstream of Pol II peaks (Figure  2A ). As previously reported, Mof is present at promoters, gene bodies (GBs) and intergenic regions  (Li et al.
MSL binds to pluripotency genes regulated by Mof To better understand the function of genes bound by MSL and NSL in mESCs, genome-wide expression changes were analysed by microarrays. To this end total RNA was isolated from control mESCs, or mESCs depleted for either Msl1, or Nsl1 (Figure  2 -figure supplement 2A-D). In shMsl1 KD cells, 275 genes were found to be downregulated (with Msl1 itself is in the downregulated list (Figure  7 -source data 1)), and 500 genes upregulated, as compared to control KDs. By comparing Msl1-bound and Msl1-regulated genes (Figure  7A ), we found that Msl1 is present at about 30% (105 genes) of all downregulated and at 20% (107 genes) of all upregulated genes. In Nsl1 KD conditions, 1158 genes are downregulated (including Nsl1 itself) and 429 are upregulated, as compared to KD controls (Figure  7 -source data 1).
By comparing the genome-wide binding (ChIP-seq) and expression data changes following KD, we show that Nsl1 is present at 43% (441 genes) of all   (Anders and Huber, 2010). The table represents genes expressed only in mESCs (when compared to NPCs). The overlap with all Msl1 positive, all Nsl1 positive or only complex specific genes was calculated. Statistical analyses in Figure  5 -figure supplement 2A-D indicates significant enrichment of Msl1 at mESC-specific genes. (D and E) Bound genes were divided into three categories: Common (category 1), specific for Nsl1 (category 2) and specific for Msl1 (category 3). From these categories genes were chosen for ChIP-qPCR using the anti-Msl1 (D) or anti-Nsl1 (E) antibodies. Fold enrichment higher than five was defined as specific binding and Msl1 and Nsl1 presence was analysed at the indicated genes in each category.
Proteins were identified by database searching using SEQUEST  (Eng et al. , 1994)  within ThermoProteome Discoverer 1. 3 and 1. 4 (ThermoFischerScientific). Tandem mass spectra were searched against a Mus musculus protein sequence database containing 16,604 entries (from the Swissprot 2013-04-03 release). In all searches, cysteine residues were considered to be fully carboxyamidomethylated (+57 Da statically added) and methionine considered to be oxidized (+16 Da dynamically added). Proteins were considered as specific in a given IP data set if they were absent or 10-fold minimum enriched as compared to a MOCK IP, performed on the same protein input by using a non-specific antibody targeting yeast TAF90. Relative protein abundance for each protein in either the anti-Msl1, or the anti-Nsl1 IPs was estimated by the calculation of a Normalized Spectral Abundance Factor (NSAF)  (Zybailov et al.
ChIP-seq 10 ng of precipitated DNA obtained from ChIP was used for Solexa sequencing. To create a genomic library, we followed the instructions of NEXTFlex v12. 03 (BIO Scientific) for Msl1 and the NEBNext protocol (E6240; Biolabs) for Nsl1. Libraries were validated with the Agilent Bioanalyzer. Single reads run sequencing was conducted with the HiSeq 2000. Image analysis and base calling were done with the Illumina pipeline (1. 8. 2). The July 2007 Mus musculus genome assembly (NCBI37/mm9) from NCBI was used for the sequence alignment by the software Bowtie (0. 12. 7)  (Langmead, 2010). All analyses were conducted with unique reads. Bed files were used to create read density (wig) files by extending reads to 200 bp length and creating 25 bp bins. We further included following sequencing datasets, which were obtained from Gene Expression Omnibus (www.
Peak detection and annotation To detect Msl1 and Nsl1 peaks, the algorithm MACS14  (Zhang et al. , 2008)  was applied using default parameters with slight modifications. For Msl1 peak detection the p-value cutoff was set to 10 -5 , no shifting model was built and the shift size was defined as 200. The annotation was based on the ENSEMBL 67 database (mm9). Peaks were annotated to genomic features (TSS, TTS, CDS Exons, 5'UTR, 3′UTR, Introns and intergenic) using the software HOMER (4. 2)  (Heinz et al. , 2010)  with default parameters.
Data analysis To calculate the Msl1, Nsl1, or Pol II enrichment at a given gene either the peak tag density of the nearest peak to the TSS (in a region of +2 kb), was obtained through MACS14  (Zhang et al. , 2008)  or the total tag density around the TSS (+2 kb) was taken. Further analysis and graphical representation were conducted using the software R. Density profiles around the TSS and GB were obtained through seqMINER  (Ye et al. , 2011). For the comparison and analysis of genomic features between data sets, the software BEDTools (2. 17. 0)  (Quinlan and Hall, 2010)  was used. Scatter plots and Pearson correlations with Pearson p-values were obtained by calculating the log2 values of read densities normalized to the control at the given peaks or around ENSEMBL transcription start sites.
RNA-seq Gene expression levels are based on the ENSEMBL 67 database (mm9). Raw data of mESCs and NPCs were taken from Gene Expression Omnibus (GSE34473) and processed using the software tools TopHat  (Trapnell et al. , 2009)  and HTSeq with default parameters. FPKM (fragments per kilobase of exon per million fragments mapped) values were calculated with Cufflinks  (Roberts et al. , 2011). Differentially expressed genes (DE) in mESCs and NPCs were identified with the bioconductor package DESeq (1. 14. 0)  (Anders and Huber, 2010)  using default parameters. shRNA interference shRNA approaches were conducted with pLKO. 1 puro shRNA vectors (Sigma-Aldrich, France) of the TRC2 library. For Ns1 KD the TRCN0000241466 shRNA clone and for Msl1 KD the TRCN0000241378 shRNA clone was used. Double KD of Msl1 and Nsl1 was conducted with equal amount of the TRCN0000241466 and TRCN0000241378 shRNA clones.
Cell cycle analysis 7 days after lentiviral infection 5 × 10 5 mESCs were dissolved in 1 ml PBS (0. 1% NaCitrate and 0. 1% TritonX 100). Propidium iodide (50 μg/ml) was added and after 4 hr incubation on ice cells were analysed by the FACS calibur. Data were analysed using the CellQuest software.
Gene expression profiles and statistics Experiments were designed with three independent biological replicas. Biotinylated cDNA targets were prepared, starting from 150 ng of total RNA, using the Ambion WT Expression Kit (Cat 4411974), and the Affymetrix GeneChip WT Terminal Labelling Kit (Cat 900671) according to Affymetrix recommendations. Following fragmentation and end-labeling, 3 μg of cDNAs were hybridized on GeneChip Mouse Gene 2. 0 ST arrays (Affymetrix, UK) for whole-transcript expression profiles. Washed and stained chips were scanned with the GeneChip Scanner 3000 7 G (Affymetrix) at a resolution of 0. 7 μm. Obtained raw data (. CEL intensity files) were processed with Affymetrix Expression Console software version 1. 1 to calculate probe set signal intensities using Robust Multi-array Average (RMA) algorithms with default settings.
Acknowledgements We are grateful to  JW Conaway ,  ME Torres Padilla ,  V Pavet-Portal  and  D Langer  for materials, helpful discussions and advice. We thank  F Klein  for the help in the gelfiltration experiment,  M Gerard ,  D Devys , and  A Krebs  for critically reading the manuscript and for helpful comments, the IGBMC microarray and sequencing platform data generation and bioinformatics support;  G Duval  for antibody generation; the mass-spectrometry facility;  M Hestin  and  G Rossi  for help in ESC culturing. SR was supported by a fellowship from  ARC. This work was supported by funds from  CNRS ,  INSERM ,  Strasbourg University, and ANR  ( ANR-09-BLAN-0266 ;  ANR-09-BLAN-0052 ) grants. This study was also supported by the grant  ANR-10-LABX-0030-INRT , under the frame programme Investissements  ANR-10-IDEX-0002-02.
Replication Cohort-AddNeuroMed, Alzheimer's Research UK, and Dementia Case Registry The replication cohort was drawn from a combination of AddNeuroMed (ANM) and the Alzheimer's Research UK/Maudsley BRC Dementia Case (ARUK/DCR) Registry at King's Health Partners Alzheimer's Disease research cohorts (referred to collectively as ANM + ARUK + DCR) at King's College London with SOMAscan data available as previously described  (15). A total of 677 individuals (both males and females) were included in the analysis. All samples and information were collected with written and signed informed consent. All studies were approved by the local research ethics committees.
somalogic. com/Products-Services/SOMAscan/ FAQs. aspx). TUK proteomic data were collected using SOMAscan v3, measuring 1,129 proteins, whereas ANM + ARUK + DCR proteomic data were collected using SOMAscan v2, measuring 1,001 proteins. All except 21 of the proteins measured by SOMAscan v2 are also measured by SOMAscan v3. TUK proteomics data are publicly available upon request on the department website (http://www. twinsuk. ac. uk/data-access/accessmanagement/). We aim to make the ANM + ARUK + DCR data available to the public as soon as possible. In the short term, the authors can be contacted and collaborations established under an agreement shared with SomaLogic. Quality control was performed at the sample and SOMAmer level, and involved the use of control SOMAmers on the microarray and calibration samples.
Step 4. Influence of expression on age-related proteins (TUK) Rank normalized reads per exon were used to assess the age effect on exon expression. A linear mixed model was fitted to examine age effect on gene expression in R (http://www. r-project. org/) with the lmer function in the lme4 package  (18). Confounding factors in the models included as fixed effects were primer insert size, GC content mean, and batch. Random effect confounding factors included primer index, date, family relationship, and zygosity. The p values to assess significance for age effect were calculated from the Chi-square distribution with 1 degree of freedom using likelihood ratio as the test statistic, while comparing a null model (expression ~ fixed covariates + random covariates) versus a full model with age (expression ~ age + fixed covariates + random covariates).
Funding SOMAscanTM and SOMAmerTM are trademarks of SomaLogic, Inc. TwinsUK: This work was supported by: the EU Framework We would like to acknowledge Abhishek Dixit for his help with data management and access. S. J. K. is supported by an MRC Career Development Award in Biostatistics (MR/L011859/1). This paper presents independent research part funded by NIHR. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. Finally, we wish to express our appreciation to all study participants of the TwinsUK, ANM, ARUK, and DCR.
Following the next wash, 50 ml/well streptavidin-peroxidase (Roche Diagnostics, Mannheim, Germany; 1:5,000) was incubated for 45 minutes at room temperature. After the final wash, 50 ml/ well of substrate solution containing TMB (tetramethylbenzidine, Sigma-Aldrich) was used and the reaction was stopped with 25 ml/ well 2N H 2 SO 4 (Sigma-Aldrich). The plates were measured using the SpectraMAX ELISA reader (Molecular Devices, Sunyvale, U. S. ) with wavelength correction (450 nm and 570 nm). Data were analyzed with SOFTmax Pro 3. 0 software. Pooled plasma from 15 patients was used to generate standard calibration curves which were assigned arbitrary units. This standard was used for each measurement of anti-filarial antibodies. In plasma from healthy European donors O. volvulus-specific IgG1-4 and IgE were not detectable (data not shown).
Statistical analysis Statistical analyses were performed using the software SPSS (IBM SPSS Statistics 20; Armonk, NY), the PRISM 5 programme (GraphPad Software, Inc. , La Jolla, USA) and SAS version 9. 2 (SAS Institute Inc. Cary, NC, USA). Since most of the variables did not show a normal distribution, the following tests were chosen: to compare three groups a Kruskal-Wallis-test was performed and, if significant, followed by a Mann-Whitney-U test for a further comparison of the groups; for comparisons of two binary variables the Fisher's exact test and for comparisons of continuous parameters the Spearman correlation was used. The immune response data were also assessed using a generalized linear model. Initially, parameters including age, gender, nodules and co-infections were included as covariates but the relevant associations were revealed with regional, IVM and MF related parameters.
This work was funded by through a grant from the  Liverpool School of Tropical Medicine  as part of the  A-WOL Consortium  funded by the  Bill and Melinda Gates Foundation. In addition, the study was supported by the  European Commission  (grant No. 242121 , EPIAF), the  German Research Foundation  (DGF, grants  Ho2009/8-1 ), the  BONFOR intramural funding program of the Medical Faculty of Bonn University , and a  European Foundation Initiative into African Research in Neglected Tropical diseases (EFINTD)  awarded to AYD (grant  1/81995  and  86 52 ). AH is a member of the  Excellence Cluster Immunosensation (DFG ,  EXC 1023 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
System description The instrument used to measure polarized skylight in this study is the ground-based CIMEL Dual-Polar sun/sky radiometer, CE318-DP. It is equipped with two rotating wheels mounting filters and polarizers in the optical sensor head. There are three sets of triplet polarizers on the polarizer wheel, and each of them is composed of three polarizers. This instrument has polarization observation capability in an extended spectrum with center wavelengths from 340 to 1640 nm by combining the rotations of polarizer and filter wheels  [6]. Some bands share the same set of polarizer triplets, for example, 340, 380, 440, and 500 nm; 675 and 870 nm; 1020 and 1640 nm. We should note that it is not always like this for different instruments. In this study, polarized observation of the CE318-DP labeled #350 in the AERONET database is adopted.
Acknowledgments The authors acknowledge the support of the  National Basic Research Program of China  ( 973 Program ) under grant  2010CB950800  ( 2010CB950801 ),  Sino-German Science Foundation  (Grant no. GZ-659 ) and Director 's Foundation for Youth Scholars of RADI, CAS  (Grant no. Y3SJ7300CX ). We acknowledge the  CIMEL Electronique  for support in Polarized Almucantar measurements. Li Li  thanks  Vladimir V. Rozanov ,  Xiaobing Sun  for discussions. We are also grateful to the anonymous reviewers for their valuable comments and suggestions.
Communication between all implicated experts will facilitate the comp ilation of data from patients coming from a large number of countries and this will be very useful for research because the data on RA that can be obtained in each individual country are not high enough for obtaining meaningful conclusions. Finally, ENERCA will contribute to the harmonization of the haematology and paediatric (haematology) specialities curricula throughout the EU (re: "European haematology curriculum/passport" accessible on the European Haematology Association website). This will be achieved by fostering the active participation of expert centres in the organization of training courses on biological and clinical aspects of these medical specialties. The permanent contact with the European School of Haematology (ESH), one of the ENERCA 3 associated partners, will be a guarantee of success.
Networking of CoE This is a pivotal activity of ENERCA undertaken by the EGRA in the preparation of ENERCA White Book. It is well known that differences exist between national regulations and, in the same way, differences can be also found regarding sample management and biobanking. These differences may become , in some cases, a barrier to networking. Accordingly , networking is imperative for the exchange of knowledge, patient's samples and personal data transfer in both clinical management and research, as well as in legal and ethical issues. Providing patients with adequate information before they give their consent to the clinical procedure or involvement in research seems to be a key way to overcome this problem. This information should include the possibility of integrating clinical data in a network and its consequences, as well as the description of the future research foreseen.
11 http://camb. info Planck Collaboration: Cosmological parameters has been very well tested (and improved) by the community. Numerical stability and accuracy of the calculation at the sensitivity of Planck has been explored in detail  (Hamann et al. 2009; Lesgourgues 2011b; Howlett et al. 2012) , demonstrating that the raw numerical precision is sufficient for numerical errors on parameter constraints from Planck to be less than 10% of the statistical error around the assumed cosmological model. (For the high multipole CMB data at > 2000 introduced in Sect. 4, the default camb settings are adequate because the power spectra of these experiments are dominated by unresolved foregrounds and have large errors at high multipoles. ) To test the potential impact of camb errors, we importance-sample a subset of samples from the posterior parameter space using higher accuracy settings.
This confirms that differences purely due to numerical error in the theory prediction are less than 10% of the statistical error for all parameters, both with and without inclusion of CMB data at high multipoles. We also performed additional tests of the robustness and accuracy of our results by reproducing a fraction of them with the independent Boltzmann code class  (Lesgourgues 2011a; Blas et al. 2011 ). In the parameter analysis, information from CMB lensing enters in two ways. Firstly, all the CMB power spectra are modelled using the lensed spectra, which includes the approximately 5% smoothing effect on the acoustic peaks due to lensing. Secondly, for some results we include the Planck lensing likelihood, which encapsulates the lensing information in the (mostly squeezed-shape) CMB trispectrum via a lensing potential power spectrum (Planck Collaboration XVII 2014).
For poorly constrained parameters the actual value of the bestfit parameters is not very numerically stable and should not be over-interpreted; in particular, highly degenerate parameters in extended models and the foreground model can give many apparently different solutions within this level of accuracy. The best-fit values should be interpreted as giving typical theory and foreground power spectra that fit the data well, but are generally non-unique at the numerical precision used; they are however generally significantly better fits than any of the samples in the parameter chains. Best-fit values are useful for assessing residuals, and differences between the best-fit and posterior means also help to give an indication of the effect of asymmetries, parameter-volume and prior-range effects on the posterior samples.
ΛCDM model from Planck In this section we discuss parameter constraints from Planck alone in the ΛCDM model. Planck provides a precision measurement of seven acoustic peaks in the CMB temperature power spectrum. The range of scales probed by Planck is sufficiently large that many parameters can be determined accurately without using low-polarization information to constrain the optical depth, or indeed without using any other astrophysical data. However, because the data are reaching the limit of astrophysical confusion, interpretation of the peaks at higher multipoles requires a reliable model for unresolved foregrounds. We model these here parametrically, as described in Planck Collaboration XV (2014), and marginalize over the parameters with wide priors. We give a detailed discussion of consistency of the foreground model in Sect.
5. 1 and Planck Collaboration XVII 2014) provides a different probe of the perturbation amplitudes and geometry at late times. CMB lensing can break degeneracies inherent in the temperature data alone, especially the geometric degeneracy in nonflat models, providing a strong constraint on spatial curvature using only CMB data. The lensing reconstruction constrains the matter fluctuation amplitude, and hence the accurate measurement of the temperature anisotropy power can be used together with the lensing reconstruction to infer the relative suppression of the temperature anisotropies due to the finite optical depth to reionization. The large-scale polarization from nine years of WMAP observations  (Bennett et al. 2012)  gives a constraint on the optical depth consistent with the Planck temperature and lensing spectra.
Nevertheless, the WMAP polarization constraint is somewhat tighter, so by including it we can further improve constraints on some parameters. We therefore also consider the combination of the Planck temperature power spectrum with a WMAP polarization lowmultipole likelihood  (Bennett et al. 2012 ) at ≤ 23 (denoted WP), as discussed in Planck Collaboration XV (2014) 14. We refer to this CMB data combination as Planck+WP. Table  2  summarizes our constraints on cosmological parameters from the Planck temperature power spectrum alone (labelled "Planck"), from Planck in combination with Planck lensing (Planck+lensing) and with WMAP low-polarization (Planck+WP). Figure  2  shows a selection of corresponding constraints on pairs of parameters and fully marginalized one-parameter constraints compared to the final results from WMAP  (Bennett et al.
Acoustic scale The characteristic angular size of the fluctuations in the CMB is called the acoustic scale. It is determined by the comoving size of the sound horizon at the time of last-scattering, r s (z * ), and the angular diameter distance at which we are observing the fluctuations, D A (z * ). With accurate measurement of seven acoustic peaks, Planck determines the observed angular size θ * = r s /D A to better than 0. 1% precision at 1 σ: θ * = (1. 04148 ± 0. 00066) × 10 -2 = 0. 596724 • ± 0. 00038 •. (10) Since this parameter is constrained by the positions of the peaks but not their amplitudes, it is quite robust; the measurement is very stable to changes in data combinations and the assumed cosmology. Foregrounds, beam uncertainties, or any systematic effects which only contribute a smooth component to the observed spectrum will not substantially affect the frequency of the oscillations, and hence this determination is likely to be Planck's most robust precision measurement.
15 , we can infer that the combination of Planck and BAO measurements will lead to tight constraints favouring Ω K = 0 (Sect. 6. 2) and a dark energy equation-of-state parameter, w = -1 (Sect. 6. 5). Since the BAO measurements are primarily geometrical, they are used in preference to more complex astrophysical datasets to break CMB parameter degeneracies in this paper. Finally, we note that we choose to use the 6dF+SDSS(R)+BOSS data combination in the likelihood analysis of Sect. 6. This choice includes the two most accurate BAO measurements and, since the effective redshifts of these samples are widely separated, it should be a very good approximation to neglect correlations between the surveys.