dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Financial support was provided by the  consortium projects READNA  under grant agreement  FP7 Health-F4-2008-201418 ,  ESGI  under grant agreement  262055 ,  GEUVADIS  under grant agreement  261123  of the European Commission Framework Programme 7, ICGC-CLL through the  Spanish Ministry of Science and Innovation (MICINN) , the  Instituto de Salud Carlos III (ISCIII)  and the  Generalitat de Catalunya. SD is supported by the  Parc Científic de Barcelona  through the  Torres Quevedo subprogram (MICINN)  under grant agreement  PTQ-12-05391. The  Ontario Institute for Cancer Research  has received generous support from the  Ontario Ministry of Research and Innovation. Additional financial support was provided by  Prostate Cancer Canada ,  Genome Canada  and the  Canada Foundation for Innovation.
The Synergie Lyon Cancer platform has received support from the  French National Institute of Cancer (INCa)  and from the  ABS4NGS   ANR  project ( ANR-11-BINF-0001-06 ). The ICGC RIKEN study was supported partially by  RIKEN President's Fund 2011 , and the supercomputing resource for the RIKEN study was provided by the  Human Genome Center, University of Tokyo. EH is supported by the  Research Council of Norway  under grant agreements  221580  and  218241  and by the  Norwegian Cancer Society  under grant agreement  71220 -PR-2006-0433. We want to thank  Elias Campo  for giving us access to the data of the CLL sample for BM1. 1 as well as DNA for verification.
Samples and miRNA expression analyses Blood samples were obtained from 65 healthy donors from Research Blood Components. Signed, written consent was obtained from all individuals, in accordance with the company's independent ethics committee approval. Isolation and infection of DCs with Mycobacterium tuberculosis (H37Rv) for 18 h, RNA extraction and quality verification, DNA extraction, and genome-wide genotyping have been previously described  (Barreiro et al. 2012). Genome-wide miRNA expression was profiled using the Agilent Human miRNA microarray (Release 16. 0). After a series of quality checks, preprocessing, and normalization of the data (Supplemental Methods), we identified differentially expressed miRNAs upon MTB infection by applying a linear model with a fixed effect for MTB treatment, implemented in the Bioconductor package limma  (Smyth 2004).
Mapping of expression quantitative trait loci (eQTLs) Associations between SNP genotypes (GEO accession number GSE34588)  (Barreiro et al. 2012 ) and miRNA expression levels were calculated using a linear regression model, assuming an additive effect of alleles on expression, in infected and non-infected samples. We improved the power to detect eQTLs by quantile normalization and regression of a number of principal components, to account for unknown confounders (Supplemental Fig. S9 ). FDRs were estimated by comparing the observed to a null distribution, generated using the lowest P-values observed for each miRNA in 100 permutations of expression values  (Pickrell et al. 2010; Barreiro et al. 2012). Genotype-treatment interaction effects were detected by Bayesian regression with the software BRIdGE  (Maranville et al.
2011). For trans-eQTLs, associations were calculated with both genome-wide genotyping data from the same individuals  (Barreiro et al. 2012 ) and a subset of SNPs previously identified as susceptibility loci for TB by GWAs (http:// www. genome. gov/26525384/) (Hindorff et al. 2009 ). Multiple testing corrections were performed using a Bonferroni correction at the 95% significance level. The overlap of miR-eQTLs with active genomic regions was assessed using data from the ENCODE Project (http://encodeproject. org/ENCODE/) (The ENCODE Project Consortium 2012). Enrichments were calculated using a Fisher's exact test. For the fine-mapping of miR-eQTL regions, we imputed genotypes for SNPs not present on our genotyping array with IMPUTE2  (Howie et al. 2009 ), using integrated haplotype data from Phase 1 of the 1000 Genomes Project (The 1000 Genomes Project Consortium 2012).
For gene expression analysis, genome-wide profiling of non-infected and MTB-infected samples was obtained by hybridizing RNA to Illumina HumanHT-12 v4 Expression BeadChip arrays. After a series of quality checks and preprocessing steps, differential expression analysis was performed using the Bioconductor package limma  (Smyth 2004). For quantification of cytokine and chemokine levels, we measured supernatant levels of 25 cytokines/chemokines using the Human Cytokine Magnetic 25-Plex Panel (Invitrogen). Differences in secretion levels between conditions were calculated using a Wilcoxon paired rank sum test. For details, see the Supplemental Methods. were performed by M. D. and differentiation and infection of dendritic cells by L. T. , with contributions and guidance from J. P. , G. L. -V.
Acknowledgments We thank  Robin Friedman  for critical reading of the manuscript and useful suggestions;  Yoav Gilad  for facilitating access to mRNA expression data; and  Maud Fagny ,  Eddie Loh , and  John Marioni  for helpful discussions. The laboratory of L. Q. -M. has received funding from the  Institut Pasteur , the  Centre Nationale de la Recherche Scientifique (CNRS) , the  French government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases' ' (grant no. ANR-10-LABX-62-IBEID ), and the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  grant agreement no. 281297. The laboratory of L. B. B. has received funding from the  Canadian Institute of Health Research  (grant nos.
CMB maps For the present work we have made use of the Planck foreground-cleaned CMB maps provided by the data processing centres (as described in the Planck component separation paper Planck Collaboration XII 2013). In particular, to test robustness, some of the results are presented for different cleaned CMB maps, which were constructed using four different component separation techniques: Commander-Ruler (C-R, which uses physical parametrization); NILC (an internal linear combination technique); SEVEM (a template fitting method); and SMICA (which uses spectral matching). Since the contribution of the ISW signal is only important on large scales, low resolution maps, with HEALPix  Górski et al. (2005)  parameter N side = 64, and pixel size of about 55 arcmin, have been used for most of the analyses.
Nevertheless the overall effect of the lensing is measured to better than 25 σ (Planck Collaboration XVII 2013). The additional lensing effect in the temperature power spectrum is detectable with a significance of about 10 σ (Planck Collaboration XV 2013). With Planck data, we aim at detecting a correlation between the ISW effect and the lensing potential, where the latter is a tracer of the large-scale structure at high redshift. This correlation is restricted to 9 σ, even in the ideal case, limited by cosmic variance and the smallness of the ISW effect in comparison to the primary CMB  (Lewis et al. 2011). The data products used in this study are the Planck lensing potential reconstruction, and specific lensing maps obtained from the component separation pipelines. The lensing potential is available as part of the first Planck data release.
External data sets As described in the introduction, the achromatic nature of the ISW effect requires a tracer of the gravitational potentials from the large-scale structure, so that by cross-correlating the CMB temperature map with that tracer distribution the fluctuations due to the ISW effect are singled out. The prerequisites for a tracer catalogue to be used in ISW studies are: a large survey volume; well-understood biasing properties; and low or at least wellmodelled systematics. The radio NVSS catalogue and the optical luminous galaxies (SDSS-CMASS/LOWZ) and main photometric galaxy sample (SDSS-MphG) catalogues possess these qualities. Table  7  summarizes some basic properties of these catalogues. In addition, the redshift distributions of these catalogues are shown in Fig. 2. Notice that NVSS presents the widest redshift coverage.
Marcos-Caballero et al. (2013)  proposed a theoretical model for the NVSS angular power spectrum, which also takes into account the information of the redshift distribution given by CENSORS data  (Brookes et al. 2008). The redshift distribution is parametrized by dn dz = n 0 z z 0 α e -αz/z 0 , (3) where z 0 = 0. 33 and α = 0. 37. The parameter n 0 is a constant in order to have a distribution normalized to unity. This function is represented in Fig. 2. The bias follows the prescription of Eq. 2, with M min equal to 10 12. 67 M , where the Sheth-Tormen  (Sheth & Tormen 1999)  mass function is adopted. Hereafter this model will be regarded as our fiducial model for NVSS.
SDSS luminous galaxies For this analysis we use the photometric luminous galaxy (LG) catalogue from the Baryonic Oscillation Spectroscopic Survey (BOSS) of the SDSS III. The data used consist of two subsamples: CMASS; and LOWZ. Both samples are combined to form a unique LG map (see Fig. 3 , second panel). Hereafter, these samples will be referred to as SDSS-CMASS, SDSS-LOWZ, and SDSS-CMASS/LOWZ, for the combination.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project=planck&page=Planck_Collaboration. The modal and KSW bispectrum estimator analysis was performed on the COSMOS supercomputer, part of the  STFC DiRAC HPC Facility.
M E T H O D S A N D S I M U L AT I O N S E T U P The simulations provided in this paper are based on custom numerical routines we have coded in IDL language that compute the complex visibility from a 2D intensity image of the source for the desired set of (u,v) points  (Li Causi 2008). The images of the star were constructed employing abundance maps of elements Ca, Cr, Fe, Mg, Mn, Ti, and Sr obtained with the help of DI technique in  Lueftinger et al. (2003). The LLMODELS stellar model atmosphere code  (Shulyak et al. 2004 ) was used to compute local model atmospheres. The code computes 1D, local thermodynamic equilibrium model atmospheres and can account for individual abundances of chemical elements. The Stark-broadened profiles of hydrogen lines were computed using tables of  Lemke (1997)  based on the VCS theory by  Vidal, Cooper & Smith (1973).
Local model atmospheres were calculated for each of 2244 surface elements of original DI maps (68 longitudes and 33 latitudes). Thus, each model atmosphere takes into account local abundance pattern of all mapped elements, and the solar abundance from  Asplund et al. (2009)  was assumed for all other chemical elements. The up-to-date version of VALD data base  (Piskunov et al. 1995; Kupka et al. 1999 ) was used as a source of atomic line transition parameters (including transitions originating from predicted energy levels). Theoretical fluxes were computed in a wide wavelength range from visual and IR wavelength domains with a characteristic resolution of R ≈ 500 000 in V band and R ≈ 20 000 in K band, respectively. Note that the star's surface magnetic field is weak, of the order of a few hundred Gauss  (Wade et al.
AC K N OW L E D G E M E N T S DS acknowledges financial support from CRC 963 -Astrophysical Flow Instabilities and Turbulence (project A16 and A17). CP acknowledges the support by the Belgian Federal Science Policy Office via the PRODEX Programme of ESA. OK is a Royal Swedish Academy of Sciences Research Fellow, supported by the grants from the Knut and Alice Wallenberg Foundation, Swedish Research Council, and the Göran Gustafsson Foundation. This research made use of the computer corporate facility of the Georg-August University of Göttingen and the Max-Planck-Gesellschaft (GWDG), as well as web services: SIMBAD, NASA ADS, VALD.
Background Whatever the field studied in biology, due to the prevalence of new generation technologies, retrieving relevant information from high throughput studies represents a most important challenge. In this view, five years ago, we developed Cildb, a knowledgebase that allowed data mining concerning cilia and ciliopathies (http://cildb. cgm. cnrs-gif. fr/) [1]. Cildb progressively became a reference cilium database, with a number of users reaching now 700 per month. Since its creation and publication  [1] , Cildb underwent several modifications and improvements, yielding an evolution to Version 2. 1 in 2010 and now to Version 3. 0 in 2014. Although data in Cildb are raw data treated automatically, so that false positives and false negatives may occur, results are fully informative and make easier searches on ciliary genes.
Cildb, a database for ciliary studies… and more In the early 2000's, high throughput studies started to appear concerning cilia, a re-emerging organelle at that time  [2] , and centrioles  [3] , precursors of basal bodies of cilia in metazoans. Such studies generated large amounts of data on cilia, basal body, centriole, and centrosome proteomes, on transcriptome analyses realized under various conditions (ciliogenesis etc. ), and on computation issued from comparative genomics between centric (i. e. with cilia/flagella or at least centrioles at some stage of their life cycle) and acentric organisms. Developing a way to browse these data became essential, not only from the statistician's point of view, but also for experimental biologists who want to seek information on individual proteins from the bulk of the results.
Methods The originality of Cildb was in its backbone that related on the one side a network of orthology between the whole proteomes, complete sets of protein sequences, of all the species taken pair-wise, calculated with the algorithm of Inparanoid version 4. 1 with default parameters  [4] , and on the other side the detection of each protein in a set of ciliary studies  [1]. Therefore, the database allows searches for possible ciliary properties on the whole proteome of one species, e. g. Homo sapiens, based on ciliary properties established by studies conducted in another species, e. g. flagellum proteomics in Chlamydomonas  [5]. In addition, the whole human proteome has been linked to the OMIM database (http://www. ncbi. nlm. nih. gov/omim/) that gathers all known human genetic disorders with the corresponding genes.
html), based on the format of pattern used in the PROSITE database (http://prosite. expasy. org/prosuser. html). For example, an amino acid motif such as MKK[KP]K, in which either K or P can stand at the fourth position, can be queried in the proteome of any species of Cildb. Species implemented in Cildb V3. 0 Cildb V3. 0 contains now whole proteomes of 41 eukaryotes among which 32 are centric species. Fifteen of these species were used for the 66 high throughput studies of Cildb. The 17 other species are good models for ciliary experiments although no high throughput study has been published as of yet. Nine eukaryotic acentric species which lack cilia and centrioles were also taken because they represent 'negative controls' in comparative genomics experiments: two species for which two analyses on spindle pole proteomes are available and seven species without high throughput relevant studies.
0, 45 novel studies have been introduced in Cildb. High throughput studies concerning cilia appear monthly in the literature, but computation in Cildb needs full recalculation of the database, so that it cannot be updated each time. However, if the output of a study not present in Cildb has to be compared to a study already present, this can be performed using the keyword box in the general properties filter by querying a list of gene or protein IDs bordered by '%', one per line. The limitation is that the query is slow, since this is not the main task designed for BioMart queries. Simplified interface and structure for Cildb V3. 0 For users trained with previous versions of Cildb, the most prominent change is the new interface. Indeed, it takes advantage of the novel environment provided by BioMart Version 9  [58]  (Figure  2 ).
In consequence, making an advanced search becomes much more intuitive than earlier, even for non-trained users, who can easily enter the functionalities of the database. The simplification of the interface is accompanied by a simplification of the structure of the database. First of all, the orthology calculation has been exclusively centered on Inparanoid  [4]. Formerly, users could choose between Inparanoid and Inparanoid plus 'in house' filtered blast hits. The most recent version of Inparanoid appears efficient enough to prevent the output of too many false negatives that occurred with the previous versions, so that the addition of 'in house' filtered blast hits was no more necessary, as detailed in the next section and in the legend of Table  2. We also simplified the way to filter ciliary studies and removed less useful other searches (operator 'OR', customized searches).
Acknowledgements Funding from the  Centre National de la Recherche Scientifique (CNRS)  and the  Foetocilpath grant  from the  Agence Nationale de la Recherche (ANR) , are gratefully acknowledged. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources. This work was carried out in the context of the CNRS-supported European Research Group " Paramecium Genome Dynamics and Evolution ".
It is an update of Le  Quéré et al. (2014) , including data to year 2013 (inclusive) and a projection for fossil fuel emissions for year 2014. The main changes from Le  Quéré et al. (2014)  are as follows: (1) we use 3 years of BP energy consumption growth rates  (coal, oil, gas)  to estimate E FF compared to 2 years in the previous version (Sect. 2. 1), (  2 ) we updated S OCEAN estimates from observations to 2013 extending the Surface Ocean CO 2 Atlas (SOCAT) v2 database  (Bakker et al. , 2014; Sect. 2. 4)  with additional new cruises, and (3) we introduced results from three atmospheric inverse methods using atmospheric measurements from a global network of surface stations through 2013 that provide a latitudinal breakdown of the combined land and ocean fluxes (Sect. 2. 6). The main methodological differences between annual carbon budgets are summarised in Table  3.
, 2011a). Due to availability of the input data, detailed estimates are made for the years  1997, 2001, 2004, and 2007  (using the methodology of  Peters et al. , 2011b)  using economic and trade data from the Global Trade and Analysis Project version 8. 1 (GTAP;  Narayanan et al. , 2013). The results cover 57 sectors and 134 countries and regions. The results are extended into an annual time series from 1990 to the latest year of the fossil fuel emissions or GDP data (2012 in this budget), using GDP data by expenditure in current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregates database; UN, 2014) and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). The consumption-based emission inventories in this carbon budget incorporate several improvements over previous versions (Le  Quéré et al.
, 2013; Peters et al. , 2011b Peters et al. , , 2012b ). The detailed estimates for 2004 and 2007 and time series approximation from 1990 to 2012 are based on an updated version of the GTAP database  (Narayanan et al. , 2013). We estimate the sector level CO 2 emissions using our own calculations based on the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the CDIAC estimates from the most recent carbon budget. We do not include international transportation in our estimates of national totals, but we do include them in the global total. The time series of trade data provided by GTAP covers the period 1995-2009 and our methodology uses the trade shares as this data set. For the period 1990-1994 we assume the trade shares of 1995, while for 2010 and 2011 we assume the trade shares of 2008 since 2009 was heavily affected by the global financial crisis.
Tropical deforestation is the largest and most variable single contributor to E LUC. Fire emissions associated with deforestation and tropical peat burning are based on the Global Fire Emissions Database (GFED) described in van der Werf et al. (  2010 ) but with updated burned area  (Giglio et al. , 2013)  as well as burned area from relatively small fires that are detected by satellite as thermal anomalies but not mapped by the burnedarea approach  (Randerson et al. , 2012). The burned-area information is used as input data in a modified version of the satellite-driven Carnegie-Ames-Stanford Approach (CASA) biogeochemical model to estimate carbon emissions associated with fires, keeping track of what fraction of fire emissions was due to deforestation (see  van der Werf et al. , 2010). The CASA model uses different assumptions to compute     Atlas et al.
Uncertainty assessment for E LUC Differences between the bookkeeping, the addition of firebased interannual variability to the bookkeeping, and DGVM methods originate from three main sources: the land-coverchange data set, the different approaches used in models, and the different processes represented (Table  5 ). We examine the results from the seven DGVM models and of the bookkeeping method to assess the uncertainty in E LUC. 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2009 , last decade, and last year available. All values are in GtC yr -1. The DGVM uncertainties represents ±1σ of results from the nine individual models; for the inverse models all three results are given where available. Mean (GtC yr -1 )  1960-1969 1970-1979 1980-1989 1990-1999 2000-2009 2004-2013 2013  Land-use-change emissions (E LUC ) Bookkeeping method 1.
The annual growth rate is estimated by Dlugokencky and Tans (2014) from atmospheric CO 2 concentration by taking the average of the most recent December-January months corrected for the average seasonal cycle and subtracting this same average 1 year earlier. The growth rate in units of ppm yr -1 is converted to units of GtC yr -1 by mul-tiplying by a factor of 2. 120 GtC ppm -1  (Prather et al. , 2012)  for consistency with the other components. The uncertainty around the annual growth rate based on the multiple stations data set ranges between 0. 11 and 0. 72 GtC yr -1 , with a mean of 0. 60 GtC yr -1 for 1959-1980 and 0. 19 GtC yr -1 for 1980-2013, when a larger set of stations were available  (Dlugokencky and Tans, 2014). It is based on the number of available stations, and thus takes into account both the measurement errors and data gaps at each station.
This uncertainty is larger than the uncertainty of ±0. 1 GtC yr -1 reported for decadal mean growth rate by the IPCC because errors in annual growth rate are strongly anti-correlated in consecutive years, leading to smaller errors for longer timescales. The decadal change is computed from the difference in concentration 10 years apart based on a measurement error of 0. 35 ppm. This error is based on offsets between NOAA/ESRL measurements and those of the World Meteorological Organization World Data Center for Greenhouse Gases (NOAA/ESRL, 2014) for the start and end points (the decadal change uncertainty is the 2(0. 35 ppm) 2 (10 yr) -1 assuming that each yearly measurement error is independent). This uncertainty is also used in Table  8. The contribution of anthropogenic CO and CH 4 is neglected from the global carbon budget (see Sect.