dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Prof. Steven Goodman ,  Dr. Philippe Boussès , and  Dr. Nohal Elissa  for scientific discussions about the south-western Indian Ocean culicidian fauna. This study was conducted as part of the  RIFT-OI  research project on emerging infectious diseases transmitted by arthropod vectors in the geographical area of the Indian Ocean, and financed by the  Centre de Recherche et de Veille sur les maladies émergentes dans l'Océan Indien (CRVOI)  and the  Institut Pasteur de Madagascar.
During the R&D phase in the period 2007-2012 many tests of prototypes of the telescope were performed under beam at LNL-INFN , LNS-INFN and GANIL, obtaining very good performances in terms of identification capabilities (mass and charge) and thresholds. In December 2014 there was the commissioning of the first complete block and in June 2015 there was the first physics experiment with 4 blocks; data analysis is in progress. In December 2015 the second physics experiment will take place and, starting from 2016, 12 blocks of FAZIA (the so-called FAZIETTO) will be moved to GANIL, to be coupled with INDRA  [2]. After 2020 FAZIETTO will be probably moved to LNL in order to exploit the first beams of SPES.
7 % of all mapped reads, "probable CpGs" in Additional file 1a) were not retained for analysis. Visualization by the Integrated Genome Browser (IGB) software showed that SCL-exo signal at id CpGs correlated well with hMeDIPseq peaks (Fig. 2b ). Similarly, reads containing CpHs and no CpGs in the first 10 bases (47 % of all mapped reads, Additional file 1a) were used to build a. wig file putatively reflecting non-CpG hydroxymethylation. IGB visualization indicated that the putative CpH hydroxymethylation signal was distributed quite uniformly with no detectable peaks corresponding to hMeDIP peaks (Additional file 1b). In addition, no correlation (Pearson's correlation coefficient [r] = 0. 04) was detected between possible CpH signals from two technical replicates (Additional file 1c). Collectively, these data strongly suggest that, as shown for mouse ES cells  [11] , most hydroxymethylated cytosines are found in a CpG context in RA-treated P19 cells and that the occurrence of SCL-exo reads not containing CpGs in their first 10 bases was probably due to a suboptimal processing by the exonuclease.
The status of hydroxymethylation of 27 selected CpGs from this set (consensus id CpGs) and included in a MspI CCGG restriction site was next verified by using the EpiMark 5-hmC and 5-mC Analysis Kit (New England Biolabs) which allows a quantitative determination of the percentage of hydroxymethylation of CpGs thanks to the insensitivity of glucosylated ChmCGG sites to MspI cleavage. A strong correlation (r = 0. 79) between EpiMark and SCL-exo data was observed (Fig. 2h  and Additional file 1e). This is in the range of what has been observed when Aba-seq and EpiMark data were compared (r = 0. 72) for hydroxymethylated CpGs in ES cells  [16]. Interestingly, the hydroxymethylated status of SCL-exo id CpGs not included in hMeDIP peaks was systematically validated with the EpiMark kit, thus indicating that those were not false positive (Additional file 1e).
Although TET1 enrichment was strongly correlated to CpG density (Fig. 3a ), such a correlation was not observed for the SCL-exo signal at id CpGs (Fig. 3b-d ). Indeed, id CpGs in regions with a unique CpG in 300 bp had a fairly high mean coverage   3d ). Conversely, hMeDIP-seq signal was clearly dependent on CpG density and regions with a unique hydroxymethylated CpG were not immunoprecipitated (Fig. 3e ). Accordingly, inclusion of SCL-exo id CpGs in hMeDIP peaks increased as a function of CpG density (Fig. 3f  ). These data indicate that SCL-exo is more sensitive than hMeDIP for detecting hydroxymethylated CpGs at low density. We next implemented SCL-exo with genomic DNA from E14 mouse ES cells (see sequencing statistics in Additional file 2a) and compared the data with previously published TAB-seq or Aba-seq datasets in E14 ES cells  [16, 21].
Collectively, these data suggest that the E14 mESC hydroxymethylome might be extremely variable from cell to cell, a conclusion which is in accordance with the known variability of the methylome of ES cells  [22] [23] [24]. Despite this variability, a significant fraction of the CpGs identified either by SCL-exo or by TAB-seq overlapped, especially at high coverage (Fig. 4c ). This was also observed when comparing SCL-exo and Abaseq data (Fig. 4c ). Importantly, as noticed for RAtreated P19 cells, a large fraction of SCL-exo id CpGs fell within hMeDIP-seq peaks (Fig. 4d , left panel). This was also true for TAB-seq id CpGs (Fig. 4d , right panel), but variability was still observed between TAB-seq and SCL-exo id CpGs overlapping with hMeDIP peaks (Additional file 2d). These data indicate that, although cell to cell heterogeneity in E14 ESCs might hinder reproducible identification of unique 5hmCpGs, SCL-exo and TAB-seq identify similar regions as being hydroxymethylated in E14 ESCs.
Accordingly, functional annotation of the SCL-exo and TAB-seq id CpGs overlapping hMeDIP peaks in E14 cells using GREAT (http://bejerano. stanford. edu/great/) generated similar terms for both sets of CpGs (Additional file 2e). However, analysis of the bulk id CpGs from both techniques (i. e. without selecting those overlapping with hMeDIP peaks) using standard settings of the GREAT annotation tool retrieved functional annotation terms only for SCL-exo id CpGs (Fig. 4e ). Aside from generating information on the location of 5hmC-enriched regions in the genome, single-CpG resolution of SCL-exo allows to interrogate databases for particular TFBS motif enrichment with high precision. To this aim, sequences including hydroxymethylated CpG positions in P19-derived NPLCs were searched for motifs with the SeqPos motif tool from Cistrome (http:// cistrome.
Enhancers are genomic regions enriched in multiple TFBSs which are characterized by their conservation across species  [31]. In order to further investigate a potential relationship between hydroxymethylated CpGs and transcription factor binding, the conservation status of SCL-exo id CpGs from a subset of enhancers encompassing ChIP-seq-identified Meis1-bound TGACAG binding sites in NPLCs was examined. Although id CpGs were included in highly conserved genomic regions (Fig. 6a , left panel), these CpGs were themselves poorly conserved (Fig. 6a , close-up view, right panel). As an example, Fig. 6b  shows that, although embedded in a highly conserved region, the observed SCL-exo id CpG is present only in the mouse genome. The lack of conservation of hydroxymethylated CpGs could also be inferred from TAB-seq data in E14 ES cells (Additional file 3a), indicating that it is not an artifact due to the SCL-exo method and, most importantly, that it is also true for ES cells.
Conversely, Meis1-bound TGACAG motifs in P19 cells showed high conservation, as did c-Myc-and N-Myc-bound CACGTG motifs in ES cells, whereas SCL-exo id CpGs overlapping with CACGTG sites showed a lack of conservation (Additional file 3b-e). These data indicate that hydroxymethylated CpGs are not conserved among vertebrates and highlight the possibility that transcription factorbound CpGs are protected from loss during evolution by a lack or a high turnover of cytosine modifications. In support of this hypothesis, c-Myc-and N-Myc-bound CACGTG motifs in ES cells were not found to be enriched in 5hmC by TAB-seq (Additional file 3f ), and only 14 N-Myc-bound motifs (out of 313) were identified by SCL-exo (Additional file 3g). Of note, these 14 motifs showed a drop in conservation at the level of the CpG dinucleotide (Additional file 3h).
Conclusions Collectively, our data indicate that 5hmC can be mapped at single-CpG resolution by SLC-exo. Contrary to methods based on the use of restriction enzymes, SCL-exo has the advantage of being unbiased in terms of sequence context requirement. Using SCL-exo, we demonstrate here that TET enzymes mainly target unconserved CpGs, suggesting that cytosine hydroxymethylation at enhancers might serve a structural role at the level of chromatin rather than having a direct effect on transcription factor binding to DNA.
High confidence SCL-exo id CpGs (178,218 CpGs) were called when covered more than 8× and found in at least two out of three replicates. For the analysis of the reproducibility of SCL-exo identification of CpGs as a fonction of read density (Additional file 1d), 5000 CpGs identified in two replicates and 5000 CpGs identified in only one replicate out of two were randomly selected. TFBS motif search was run with the SeqPos motif tool from Cistrome  [25]  (which does not accept datasets with more than 5000 regions) within 100 bp windows centered either on 3682 id CpGs with a SCL-exo signal above 45 reads and overlapping with a hMeDIP peak or on 3682 randomly selected CpGs (Fig. 5 ). A SeqPos search for motifs according to the conservation of SCL-exo id CpGs was run on a pool of 6163 id CpGs from mouse chr11, covered at least 20×, and sorted into three groups according to their PhastCons scores: High conservation (1072 id CpGs with a PhastCons score between 0.
Datasets used in this study TET1 ChIP-seq and hMeDIP-seq data from RA-treated P19 cells were from the Gene Expression Omnibus repository (GEO -http://www. ncbi. nlm. nih. gov/geo) datasets GSM941681 and GSM941665, respectively. MEIS1 chip-seq data from RA-treated P19 cells were from GSM819083. Mouse ESC CpG hydroxymethylation data were extracted from datasets of two technical replicates of TAB-seq (GSM1180306 and GSM1180307) and another biological replicate (GSM118308). Aba-seq and hMeDIP-seq data from E14 mouse ESCs were from GSE42898 and GSM1087009, respectively. N-Myc and c-Myc bound CACGTG sites in mouse ES cells were extracted from GSM288356 and GSM288356 GEO datasets, respectively.
Acknowledgments We are grateful to  M. Bizot ,  G. Palierne ,  F. Percevault , and  E. Jullion  for technical assistance. We thank  R. Métivier  and  C. Le Péron  for critical reading of the manuscript. GS acknowledges support from the  Centre National de la Recherche Scientifique , the  University de Rennes 1, The Ligue Contre le Cancer , and  Cancéropole Grand Ouest. MW is supported by the  EpiGeneSys network of excellence , the  INCa , and the  European Research Council  ( ERC.
On the other, the source of the dispersion is important for policy as it could be due to imposed constraints or the result of certain groups, such as women with young children, choosing to spend less time at work. This paper represents a first step in trying to understand those questions. We use data for the US, the UK, Germany and France to decompose earnings inequality and asses the roles played by the dispersion of wage and by inequality in hours in explaining cross-country differences and changes over time. By definition, an individual's earnings are the product of her hourly wage rate and her hours of work. Using as our inequality index the mean log deviation (MLD), an inequality index belonging to the general entropy family, we decompose earnings inequality into the dispersion of hourly wages and a component capturing the contribution of hours.
Databases The harmonized dataset constructed for this paper is based on different national surveys collected from national statistical institutes. We use household or labor surveys for the US, the UK, Germany and France, covering two decades starting around 1990. In particular, we use the Current Population Survey for the US, the British Household Panel Survey and, from 2009, Understanding Society for the UK, the German Socio-Economic Panel for Germany, and the Enquete Emploi for France (which becomes the Enquete Emploi en temps continue in 2003), all of them surveys that have been widely used in the empirical literature on inequality. For example, CPS data was used by Murphy and Welch (1992) in their seminal paper on wage inequality in the US, while GSOEP has been employed by Bell and Freeman (2001) and the other three surveys by Blundell, Bozio and Laroque (2013) to perform international comparisons of hours of work.
Although the design of the surveys changes over time and across countries, those datasets have a core set of questions that can be harmonized. They are, in fact, the primary source for several projects that provide harmonized data for a number of countries, such as the Luxemburg Income Study (LIS) and the Cross-National Equivalent File (CNEF) dataset. 6 We chose not to use the already harmonized data provided by these projects as they were not suitable for our purposes. The LIS data are available every five years only, and since for several countries they start only about 25 years ago, we would have had only 5 observations, making it harder to identify time trends. The CNEF dataset, instead, has annual observations, but covers a shorter time span than that available in the original date sources.
For these reasons we resorted to using the original surveys. For the US, we had the choice between two datasets, CPS and the Panel Study of Income Dynamics (PSID) data. A number of papers have used PSID to examine the evolution of earnings; see, for example, Gottschalk and Moffitt (1994), Moffitt and Gottschalk (2002), and   Haider (2001). The PSID data is attractive because of its panel dimension, but its small sample size is a major drawback for our purposes, specially since we intend to examine time trends for disaggregated workers, implying that results for the subgroups may not be representative. A second reason to prefer CPS is that it asks the same question we find in the European surveys, namely 'how many hours do you usually work per week in your current job'. PSID had traditionally not included such a question, focusing on annual hours, although the question was included in some recent surveys starting in 2003.
Earnings The measure of earnings that we employ is the usual gross income from labor that the individual receives over a week from the main current job. For employees this means contractual wages plus overtime pay. This variable is present in all the datasets, yet some important differences need to be highlighted. Our main concern is that income from self-employment is difficult to measure in household surveys, mostly because the self-employed tend to have high non-response and under-reporting rates; in addition, income from self-employment varies considerably over time. For these reasons, the self-employed are not asked about current usual earnings in the CPS, and in the BHPS over one fifth of self-employed respondents either refuse to give information or do not know how much they earn. 7 We therefore decided to remove the self-employed from our sample.
8 A second concern is that three countries report gross earnings, while France only provides earnings net of social security contributions (but not of income taxes). However, since such contributions are roughly proportional to gross earnings, this difference should have little effect on measured inequality. 9 Survey frequency and the period of time covered by the questions also varies across datasets. The US and France, for instance, collect data monthly and quarterly respectively and the questions concern current employment. Instead, the UK and Germany survey once a year, asking questions about current earnings and also about the employment situation during the previous year. Note that although there are differences in survey frequency (monthly, quarterly, and annual), we always use questions concerning the same reference period -current job -and not questions concerning earnings last year, available in some of the surveys.
Therefore the periodicity over which the survey is conducted does not create comparability problems in terms of the variable we are using. Our selection rule is to select the month of March or the first quarter of the year, and we do so for the US and for France. However, in the case of Germany such a choice implies a small number of observations. Since keeping the largest possible sample is crucial given our intention of decomposing the population by gender and educational groups, we use data for the entire year. For the UK, BHPS data was collected in October/November and thus we are forced to use this period. In all cases we checked that at the aggregate level (i. e. before dividing into population subsamples) annual data and first-quarter/March data gave results that were not significantly different.
Hours worked Hours worked can be measured in different ways, capturing contract hours, actual hours or usual hours. For most of the databases we use the question concerning "usual hours worked in the main current job". Some databases also ask about the number of hours actually worked during the previous week. Although this variable may have less measurement problems, we were concerned with seasonality and we hence decided against its use. 11 The harmonization of this variable was not straightforward due to coding problems. First, we had to make sure that it included both contractual hours and overtime. Second, it is a variable that is often truncated. In particular, Germany truncates at 80 hours per week and the US at 99 hours. Given the issue we are interested in, this may be a concern as truncation affects the upper tail of the distribution of hours worked.
The United States: data sources and definitions Before proceeding to examine the data for the four countries, we consider in detail data sources for the US. Figure  1  above presents the distribution of hours of work for the United States, with hours being highly concentrated around 40 and both the upper and lower tails being rather thin. This pattern did not match our expectations, our prior being that the US would exhibit a fat upper tail capturing the workaholic culture that we often find discussed in the popular press; see Schor (2008). Our results raise the question of whether the data we are using is the most suitable one, and if other variable definitions or data sources would yield a different picture. In order to address this concern, we consider a number of additional measures. First, we consider the CPS data and our core variable of weekly hours but do not restrict our sample by age, and compute the distribution of hours for this group.
Second, we consider PSID as an alternative dataset. In order to asses the accuracy of our chosen data, we compute measures of hours inequality for both CPS and PSID for the years 2003, 2005, and 2007. These are the only three years for which we have data for the question 'how many hours do you work per week in your current or most recent job' in PSID. 12 We use two measures of hours. The first are hours worked the previous week, i. e. the most comparable measure to the one in CPS, denoted J1 in the survey. We also use the variable Hann which are total annual hours worked during the previous year. We divide the data by 50 weeks (the modal working weeks in a year in the US) in order to get a measure comparable with the others. probably reflecting the fact that very young individuals work less.
We find a substantially higher degree of inequality, as captured by the two measures of dispersion. In particular I h goes from 0. 04 to 0. 07, probably due to the low working hours of young individuals (notably, students). The top panel of figure  2    (20, 40, 45, 50. ). Clearly, the PSID data imply that American workers are highly concentrated around certain working hours, and the similarities between this pattern and that found for the CPS indicates that our sample choice is representative and well-suited to our purposes. In contrast, a very different picture is obtained when we look at annual hours worked (the variable Hann divided by 50), which are reported in the bottom panel of table 1. A major problem of this measure is that individuals may have unemployment spells and/or may have had several jobs during the year, yet we have no information of how the hours were split between the various jobs.
R 2 = 0. 63 and 0. 44, respectively for daily values). The slope of the temporal relationship between daily δ 18 O and 2 m air temperature is approximately 2 times smaller (0. 49 ‰ • C -1 ) than the average Antarctic spatial (0. 8 ‰ • C -1 ) relationship initially used for the interpretation of EPICA Dome C records. In accordance with results from precipitation monitoring at Vostok and Dome F, deuterium excess is anticorrelated with δ 18 O at daily and monthly scales, reaching maximum values in winter. Hoar frost precipitation samples have a specific fingerprint with more depleted δ 18 O (about 5 ‰ below average) and higher deuterium excess (about 8 ‰ above average) values than other precipitation types. These datasets provide a basis for comparison with shallow ice core records, to investigate post-deposition effects.
Pioneering studies have been conducted in coastal Antarctica and on the central East Antarctic plateau, where deep ice cores have been retrieved (Fig. 1 , Table  1 ). For instance,  Schlosser et al. (2004)  used fresh snow samples collected after each snowfall event at Neumayer Station, situated in a coastal area. They investigated the relationships between snow δ 18 O and temperature as a function of the synoptic situation and air mass trajectory classes, based on 5-day back trajectories. The δ 18 O-temperature relationship was different for different trajectory classes and strongly dependent on the presence of sea ice along air mass pathways. Fujita and Abe (2006)  used a 1-year dataset of daily precipitation measurements at Dome Fuji Station, on the East Antarctic plateau, to depict a strong linear relationship between stable isotope ratios and air temperature, coherent with the regional geographical relationship using 10 m snow temperature.
Ekaykin et al. (2004)  reported a significant correlation between monthly values of δD and air temperature for 1year snowfall sampling performed at Vostok. They obtained a temporal slope significantly lower than the spatial one (Table 1). Precipitation measurements are also important for the evaluation of atmospheric circulation models in their abil-ity to capture snowfall timing, amount and isotopic composition. So far, the ability to reproduce Antarctic isotopic signals has been benchmarked mostly using spatial patterns (e. g. Werner et al. , 2011; Risi et al. , 2010). Obtaining highresolution snowfall datasets is important to test whether models correctly resolve synoptic-scale processes. Here, we use the first and so far only multi-year series of daily precipitation measurements and sampling from the French-Italian Concordia Station (Fig.
1 ), located at Dome C in East Antarctica (75 • 06 S, 123 • 21 E; elevation: 3233 m a. s. l. ; temperature at -10 m: -54. 5 • C; snow accumulation rate: ≈ 25 mm w. e. year -1 ; w. e. denotes water equivalent), where the longest continuous ice core records have been retrieved in the framework of the EPICA project  (Jouzel et al. , 2007). Snow particle observations provided an identification of precipitation types (diamond dust, hoar frost and snowfall), allowing us to explore potential impacts on their isotopic composition. We also used our dataset and accumulation measurements to assess the capacity of one operational meteorological model to resolve accumulation, and one atmospheric general circulation model equipped with water stable isotopes to resolve isotope-temperature relationships at the daily to monthly scale.