dataesr/scientific-paragraphs-categorization huggingface dataset:
Section 2 shows that in most continental western European and Nordic countries, social protection is predominantly provided through public systems, whereas in other industrialised countries as, for example, the United States the role of private social benefits is relatively large. Second, considering gross (before tax) public and private social cash transfers and services does not account for the considerable impact tax systems can have on a) on the real value public social spending, and b) what part of an economy's domestic production recipients of social benefits draw on. Section 3 shows that the tax system affects the net (after tax) value of social benefits as governments partially "claw back" benefit expenditure through taxation. Furthermore, Governments can pursue social policy objectives through the tax system, and the value of these measures is not reflected in the gross public social expenditure totals as in the OECD Social Expenditure Database or the National Accounts  (OECD, 2001, and 2001a).
Towards a Social Purpose The purpose of the benefits is one factor in the delineation of what is social and what not. The OECD Social Expenditure Database groups benefits with a social purpose in 13 policy areas: old-age cash benefits; disability cash benefits; occupational injury and disease; sickness benefits; services for the elderly and disabled; survivors; family cash benefits; family services; active labour market policies; unemployment compensation; housing benefits; public health expenditure; and other contingencies, e. g. , cash benefits to those on low income (OECD, 2001). Thus, for example, public support to general savings programmes or life insurance arrangements is not considered social. Similarly, Austrian Journal of  Statistics,Vol. 32 (2003), Number 3, 201-224  fiscal support towards children is considered social, whereas favourable tax-treatment because of marital status is not.
5% of GDP in Austria and Japan, to almost 1% of GDP in Denmark and Germany, and exceeds 3% of GDP in the Netherlands, the UK, and the US. All these countries know employmentbased tax advantaged pension benefits, while benefits stemming from individual-based tax-advantaged private pensions are recorded for the United Kingdom and the United States. Data on current private pension payments do not, however, fully reflect the importance of private pension programmes. Often current contributions (and assets held by pension funds) by far exceed the magnitude of current benefits. Hence, the importance of these private pension benefits is expected to grow with maturing pension plans. Other examples of private social benefits include supplementary unemployment compensation (in the United States), employer-provided child-care support, maternity pay and parental leave provisions.
Non-commercial non-government organisations (NGOs) also provide important social support. However, comprehensive data on employer-benefits for parents and social services provided by NGOs that is comparable with the detailed information in the OECD Social Expenditure Database is not available, but for a few countries. Available information on private social expenditure is generally considered to be of lesser quality than information on public spending. That is no surprise given the voluntary nature of many benefits. In the absence of compulsion, there are often no strong incentives for institutions that provide voluntary social benefits to report relevant information to a central authority. National Statistical Offices often obtain information on pension spending from organisations that supervise or represent pension funds.
g. average tax paid on public pension income. Subsequently, the AITRs were applied to the detailed equivalent gross spending item as recorded in the OECD Social Expenditure Database  (OECD, 2001). The AITRs were estimated on basis of a variety of national sources including, administrative data on basis of tax records, micro data sets and/or "microsimulationmodels". The most reliable source of information is administrative data held by tax offices (or social insurance funds for social security contributions) or sufficiently detailed information published by national statistical offices. In the case of Austria, information on the taxation of old age, disability and survivor pensions was taken from income and earnings statistics as published by Statistics Austria  (Statistik Österreich, 2000 and 2000a).
Information needed to estimate the AITR on continued wage payments in case of sickness could have been taken from the same source. However, in order to obtain the highest possible degree of cross-country comparability, AITRs on such benefits are obtained by using the tax equations underlying the OECD Taxing Wages publication  (OECD, 2000e). Although the latter does not capture the earnings distribution in countries, the differences in the AITRs so calculated were very small (29. 5 vis-à-vis 28. 2%). Information on the taxation of benefit income for Germany, the United Kingdom and the United States was obtained Austrian Journal of  Statistics,Vol. 32 (2003), Number 3, 201-224  from national Tax authorities. German and Japanese data were obtained through social policy ministries who co-operated with national tax authorities.
Tax Breaks that Mirror Cash Benefits Tax breaks which mirror the effect of cash benefits can be substantial. For example, in Germany the value of tax allowances for families with children approximated $32. 5 billion in 1997. Tax authorities in Austria operate similar support measures to families with children. However, for the largest part these benefits are paid in cash, whereas in Germany these benefits are mainly offset against tax liabilities. Hence, the Austrian child benefit is included in the OECD Social Expenditure Database, whereas the German measure is considered here. The "Earned Income Tax Credit" (EITC) in the United States also illustrates the relationship between direct cash transfers and tax breaks for social purposes. In 1997, the cost of this programme amounted to almost $30.
, 2016, in prep. ), in prep. ) , and  Wylezalek et al. (2013)  show that the CARLA cluster member candidates are, on average, centrally concentrated around the target RLAGN. This paper is organized as follows. Section 2 briefly presents the CARLA sample and the overall strategy of our HST/CARLA program. Section 3 presents the analysis strategy carried out on the HST data, including detection limits. Section 4 presents the results, including cluster membership, star-formation rates (SFRs), and stellar masses. In Section 5 we compare the HST results with the CARLA selection method and discuss the results. We summarize our work in Section 6. We also present details on the data analysis in Appendix A. Appendices B and C list the line properties of structure members and non-members, respectively, and we present notes on individual sources in Appendix D.
Total exposure times on the field around MRC 2036-254 (B3 0756+406) reached 4023 sec (4123 sec) in grism mode and 1023 sec (1073 sec) in direct imaging mode. Table  1  lists the observation dates, exposure times, and orientation angles. We refer to the spectroscopically confirmed structures by their CARLA names, CARLA J2039-2514 and CARLA J0800+4029. Each visit was divided into four dithered blocks of exposures, with the direct images taken just after the grism exposures to enable wavelength calibration of the spectra based on source position. We retrieved calibrated and flat-fielded individual exposures (FLT) 15 from MAST 16 , which uses the most up-to-date calibration files for WFC3. These were our primary data products before further reduction and extraction of the spectra.
Data Reduction Our grism data contain traces of several hundred objects (see Figure  2 ), including first and zeroth orders and even additional orders (-1, +2) in the case of bright objects. Therefore, in addition to standard reduction procedures (cosmic-ray rejection, sky subtraction, etc. ) spectra need to be carefully extracted. We generally follow the steps presented in the WFC3 IR grism cookbook 17  (v1. 3), applying identical methods for both fields. We combine the individual F140W exposures using the aXe software (v2. 2. 4) to create deep drizzled direct images of the fields. From these images we perform source extraction using SExtractor  (Bertin & Arnouts 1996). In this step we create two catalogs: a deep catalog cleaned by hand from spurious detections (the master catalog), and a shallow catalog with a more conservative magnitude limit.
Emission Line Fitting We determine the redshift of the sources and emission line fluxes using the python version of mpfit 22  , a Levenberg-Marquardt least-squares minimization fitting procedure. When required (e. g. , for all cluster members), we fit the [O III] doublet (λ4959, λ5007) with two Gaussians constrained to have the same width, same redshift, and a 1:3. 2 flux ratio  (Osterbrock 1989). Simultaneously, we fit the continuum using a third order polynomial and include a third Gaussian corresponding to Hβ, constrained to have the same redshift as [O III]. We substitute [O III] with, or add to the model, other Gaussians to account for Hα or [O II] when required (e. g. , for non-cluster members). Table  3  shows the redshifts and emission line fluxes of our cluster members, and Table  4  presents the redshifts of non-cluster members.
Leftward arrows denote sources below our detection limit at 3. 6µm, and upward arrows in the optical (2σ); their positions in the color-color plane are set using these limits. Williams et al. (2009)  (see also  Labbé et al. 2005 , Wuyts et al. 2007 , Whitaker et al. 2011 ) have shown that it is possible to use rest-frame U V J colors to separate the SF, dusty SF and passive populations. To build our color-color diagrams (Fig. 7 ), we use observed z i -F140W vs. F140W -[3. 6]. Following  Mei et al. (2009)  and using Mei's codes and the python version of EZGAL (Mancone & Gonzalez 2012), we transform  Williams et al. (2009)  color limits into our observed apparent colors. We use a Bruzual & Charlot (2003) SSP model with galaxy formation redshifts averaged between z f = 3 and 8, and metallicities equal to 40% solar, solar and 2.
Red Sequences and SF Populations In the CMDs (Fig. 8 ), the orange and gray shaded areas show estimates of the expected color of L * earlytype galaxies computed with a Bruzual & Charlot (2003) passive evolution model of solar metallicity and a Salpeter (1955) IMF 27. These were calculated using the python version of EZGAL (Mancone & Gonzalez 2012) with the appropriate filter transmission curves. We evolve a SSP (i. e. , delta-burst), shown by the gray shaded areas, and an exponentially decaying stellar population of characteristic time-scale τ = 1 Gyr, shown by the orange shaded areas. The thickness of these regions correspond to formation redshifts over the range 3 < z f < 8. Both model evolutions are normalized to match the typical CARLA L * at z = 2. 0  (Wylezalek et al. 2014) , and assume slopes of -0.
3. Considering the total background-subtracted midinfrared light from the two structures, we find that the inferred total stellar masses are comparable to the most massive clusters known at slightly lower redshift (z ∼ 1. 7). This analysis crudely implies these structures have total masses of M 500 ∼ 10 14 M ⊙. 4. With just two orbits, we only confirm emissionline sources with SFRs > 20 M ⊙ yr -1. We show that HST grism spectroscopy is efficient at confirming galaxy clusters at high-redshift even with shallow observations, but that deeper spectroscopic data is required to confirm the clusters' passive population. 5. We study the two cluster color-magnitude relations. While CARLA J2039-2514 shows a population of red and quiescent galaxies where we would expect a red sequence at these redshifts, CARLA J0800+4029 does not exhibit such a population up to F 140W < 23 mag.
S. M. acknowledges financial support from the  Institut Universitaire de France (IUF) , of which she is senior member. E. A. C acknowledges the support of the  STFC. N. A. H acknowledges support from  STFC  through an  Ernest Rutherford Fellowship. Facilities: HST (WFC3; STScI), Spitzer (IRAC;  JPL/Caltech) ,  Palomar  (DBSP; Caltech).
Human expression data Gene expression profiling was performed using Illumina's Human Ref-8 Sentrix Bead Chip arrays (Illumina, San Diego, CA) containing 24,516 probes corresponding to 18,311 distinct genes and 21,793 RefSeq annotated transcripts. mRNA was amplified and labeled using the Illumina Total Prep RNA Amplification Kit (Ambion, Austin, TX). After hybridization, array images were scanned using the Illumina BeadArray Reader, and probe intensities were extracted using the Gene expression module (version 3. 3. 8) of Illumina's Bead Studio software  (version 3. 1. 30). Expression signals were background corrected using GenomeStudio software. Probes were included in the analysis if their expression was considered detected (Illumina detection P , 0. 01) in at least 90% of samples. After removing nonwell-characterized probes, a total of 15,539 probes corresponding to 12,502 distinct genes remained for the analysis.
Variance stabilization transformation was applied to the raw intensities and quantile normalization was done in the R statistical environment with the Lumi package  (Lin et al. 2008; Du et al. 2008). Principal variance components analysis was used to identify main factors contributing to the variability of expression data. Given the strong influence of the variables center, sample batches, date of hybridization, and microarray, we performed an adjustment on these factors using the function Combat implemented in the sva R package  (Leek et al. 2012). Human genotype data CTS participants were typed for genome-wide genotype data using the Human Custom 1. 2 M and the Human 610 Quad Custom arrays from Illumina. SNPs with genotyping call rate , 99%, minor allele frequency (MAF) , 0. 01, or showing significant (P , 10 25 ) deviation from Hardy-Weinberg equilibrium were filtered out.
This led to 506,290 quality control (QC)-validated autosomal SNPs. Individuals were excluded according to the following criteria: genotyping rate , 95% close relatedness as suspected from pairwise clustering of identity by state distances and multidimensional scaling implemented in PLINK  (Purcell et al. 2007) , and genetic outliers of non-European ancestry detected by principal components analysis as implemented in the EIGENSTRAT program  (Price et al. 2006). The 506,290 QC-checked SNPs were then used for imputing 11,672,179 autosomal SNPs from the 1000 Genomes February 2012 release reference dataset. For this, MACH (version 1. 0. 18. c) software was used  (Li et al. 2010). All SNPs with acceptable imputation quality r 2. 0. 3  (Johnson et al. 2013 ) and imputed MAF. 0. 01 were kept for genotype-expression association analysis  (N = 8,989,527).
Macrophage mouse expression study Macrophages were primary intraperitoneal macrophages in control conditions, isolated and processed as in  Orozco et al. (2012). Total RNA extracted from 86 strains was profiled with Affymetrix Mouse Genome HT MG-430A arrays. The image data were processed using the Robust Multichip Average method to determine the hybridization signal for each gene. A total of 17,962 probes corresponding to 12,242 genes were available for further analysis.
Data combination Human and mouse macrophage gene expressions have been preprocessed separately, as described above. The probe-level measurements were converted into gene-level measurements in both datasets to allow comparison across different platforms. The probe within a gene that had the maximum average expression across samples was used to represent that gene. In order to compare gene expressions between the different species, the ENSEMBL Gene ID was used to derive mouse orthologous to human genes. The result of this step was an overall of 7890 genes commonly expressed in human and in mouse gene expression datasets. Human and mouse macrophage samples were clustered separately, based on their Euclidian distance to detect outlier observations. A total of 19 human and 7 mouse samples were removed as outliers for further analysis.
Gene coexpression network construction for human macrophages A weighted gene coexpression network analysis (WGCNA) was conducted on the human macrophage expression dataset, composed of 7890 genes and 665 samples, to identify modules of coexpressed genes. To construct the network, the absolute values of correlation coefficients [biweight midcorrelation  Zheng et al. (2014)]  were calculated for all possible gene pairs. Values were entered into a matrix, and the data were weighted into an adjacency matrix such that it followed an approximate scale-free topology (estimated b power = 5). Finally, the topological overlap matrix (TOM) was converted from the adjacency matrix and used to derive a TOM-based distance matrix for the next hierarchical clustering of expressions. We performed an average hierarchical clustering with the TOM-based metric as distance and identified groups of highly correlated human genes cutting the branches of dendrogram by dynamic tree cut algorithm  (Langfelder et al.
Preservation analysis on mouse macrophages dataset In order to assess how well a human module was preserved in mouse macrophage data, we used mouse expression data to compute the ME and kME metrics derived from the human modules genes' composition. Preservation of human modules in mouse data was then determined using both human and mouse kME values. "Consistent genes" between species were then defined as the set of genes in each human module that had concordant sign of kME values in both datasets. Then, the percentage of consistent genes between species was computed for each human module; the higher the percentage, the more preserved the modules. A permutation procedure was used to assign a P-value to this measure of preservation between the two datasets. The null hypothesis was that the proportion of consistent genes observed for each human module was no better than the corresponding proportion of consistent genes of modules derived from random clustering.
Genomes imputation-based expression quantitative trait loci analysis in human macrophages Associations between imputed genotypes and expression were computed using a linear regression model where the imputed allele dosage was used as covariate to assess SNP effect. Analyses were conducted by use of the MatrixEQTL R package  (Shabalin 2012) , adjusting for sex, age, and potential contaminations cell types (i. e. , CD4+, CD8+, CD19+, CD56+, CD66b+, erythroblasts, and megakaryocytes counts). Expression quantitative trait loci (eQTL) effects were considered as cis if the SNP was located within a 10 6 bp distance upstream or downstream from probe sequence. Otherwise, they were considered as trans. A statistical threshold of 5 • 10 28 was used to declare significance. A total of 576 individuals with both imputed genotypes and macrophage gene expressions were available for eQTL analysis.
Data availability Mouse macrophage data used in this study are deposited in the NCBI GEO repository (http://www. ncbi. nlm. nih. gov/geo/) under the accession number GSE38705. Cardiogenics macrophage expression data are deposited in the European Genome-phenome Archive (https://www. ebi. ac. uk/ega/) under the accession number EGAS00001000411.
Gene expression modules in human macrophage Human gene expression data were first investigated using WGCNA  (Langfelder and Horvath 2008)  to identify modules (or clusters) of genes whose expressions were highly correlated (see Materials and Methods). Twenty seven modules encompassing 6802 genes (86%) were identified (Table  1 ). The remaining 1088 genes were only weakly correlated with other genes and were not included in any module. The size of the identified modules, labeled with numbers to allow their distinction, ranged from 62 (M27) to 967 (M1) (Table  1 ). Each module was then characterized by its first principal component (ME)  (Langfelder et al. 2011 ) computed from the covariance matrix of expression levels of the genes in the module. The percentage of module expression variability explained by MEs ranged from 17% (M3 module of size 449) to 30% (M26 module of size 67) (Table  1 ).
Preservation analysis of human modules in mouse In a second step, we assessed whether the 27 modules identified in human macrophages were preserved in the mouse model. For this, we first partitioned the mouse genes into the same module assignments as in humans. We then computed mouse-specific MEs and kMEs. For each module, we then calculated the percentage of genes exhibiting similar kME between human and mouse (see Figure  S1  for illustrative examples). Consistent genes are those with similar kME sign across the two species. A higher percentage of consistent genes indicates the preservation of human module in the mouse dataset. Figure  2  shows the distribution of the percentage of consistent genes for each human modules. This percentage ranged from 42% for the M18 module to 93% for the M19 module, with a mean of 65%.
Enrichment analysis of preserved modules Pathway analysis was then applied to the six most preserved modules to assess whether they were enriched for genes belonging to specific biological pathways. Enrichment analysis was performed using the DAVID software  (Huang et al. 2009 ) interrogating the GO, KEGG, REACTOME, and PANTHER databases (see Materials and Methods). Results of the enrichment analysis are provided in Table  2. At a false discovery rate (FDR) of 5%, three modules were found to be significantly enriched in genes belonging to specific biological pathways. The M21 module was significantly enriched (FDR = 8. 85 • 10 227 ) for genes coding for ribosomal-associated proteins and the M15 module for proteasome-related genes (FDR = 1. 37 • 10 23 ). The M15 was also significantly enriched (FDR = 0.
However, none of them satisfied our QC criteria for detection P-values, the associated detection P-values being. 0. 20 for. 95% of the samples. As a consequence, we were not able to test whether rs192804963 associates with PARK2 macrophage expression in our study. The PARK2 rs192804963 is intronic and common, with an MAF of 0. 20, and was inferred with a correct imputation quality of 0. 66. According to public databases, it is in complete linkage disequilibrium (LD) (D9 = +1) with many other 39 PARK2 SNPs, including the genotyped rs75203550. The MAFs of the rs192804963 and rs75203550 slightly differed (0. 21 vs. 0. 13) in CTS, leading to a moderate pairwise LD r 2 of 0. 55. Nevertheless, the rs75203550 demonstrated a pattern of association with macrophage OXPHOS gene expressions similar to that observed with rs192804963 (Table  S7 ).
We performed sensitivity analyses by modifying these parameters and observed similar findings (data not shown). Third, we report here the results of the preservation of human modules in the mouse dataset. We also conducted the module identification in mouse (despite the much smaller sample size) and assessed their preservation in human. Similar findings were observed; for example, modules enriched for ribosome genes (FDR of 10 28 ) and oxidative phosphorylation (FDR of 10 25 ) were identified as preserved. Fourth, our preservation analysis and genetic investigations relied on the use of the ME approach. This strategy may not fully detect the preserved modules and the genetic variations underlying their expression variability, as the percentage of module expression variability explained by the ME was rather moderate.