dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression in peripheral blood mononuclear cells To assess a possible genotype-phenotype correlation, we selected the three associated genes-PLCL2, NFKB1, and IRF8-to perform gene expression assays of their mRNA. Moreover, because of compelling data on IRF8-associated pathways, we also investigated IFN-γ and type I IFN (IFIT1) mRNA gene expression. We randomly selected peripheral blood nuclear cell (PBMC) samples from 39 patients (who did not receive immunosuppressive drugs) and 24 healthy controls. The total RNA was obtained by using an RNA extraction kit (RNeasy Mini Kit; Qiagen, Hilden, Germany) followed by a c-DNA reverse transcription step (SuperScript II Reverse Transcriptase; Invitrogen, Waltham, MA, USA). Gene expression was performed by using quantitative Real-Time Polymerase Reaction (Universal Master Mix II; Applied Biosystems, Waltham, MA, USA).
Statistical analysis Statistical analyses were performed by using PLINK (Harvard and MIT version 1. 07, available at  [9] ). Power calculation was assessed by using a two independent binomial module combined with a one-sided Fisher exact test (StatXact 8 software; Cytel, Cambridge, MA, USA). Tests for conformity with Hardy-Weinberg equilibrium were performed by using a standard chi-square test (1 degree of freedom). Homogeneity between the two cohorts was confirmed by using the Breslow-Day method and therefore the combined data were subsequently analyzed by calculating the pooled odds ratios (ORs) by using a Cochran-Mantel-Haenszel test under fixed effects. Individual association analyses of all SNPs were performed by comparing cases and controls with a two-sided Fisher exact test on allelic distribution.
All ORs are provided with their 95% confidence intervals (CIs). To test SNP × SNP epistasis, the allele frequencies were compared in patients and control subjects. The PLCL2*T alleles, NF-kB*G alleles, and IRF8*A alleles were considered binary random variables, and the dependency of disease on pairwise combinations of these covariates was tested with a logistic regression model. The fitted model included each one of two tested factors as first-order components and an interaction term reflecting the extent of the allelic by allelic epistasis. We applied a logistic regression assuming a double-dominant model. Exact calculations of ORs and P values were made with Logxact 8 software (Cytel). We applied the Bonferroni correction although it is very conservative; 16 tag SNPs were considered to test the association between each SNP and SSc (P values were therefore multiplied by 16 to get the adjusted ones).
Acknowledgements We would like to thank those members of the  GENESYS Consortium  who contributed to the recruitment of French subjects:  P. Carpentier  and  J. L. Cracowski  in Grenoble;  J. Sibilia  in Strasbourg; A. Cosnes in Créteil; and  J. Cabane ,  K. Tiev ,  Z. Amoura ,  L. Mouthon , and  C. Frances  in Paris. We thank  J. Benessiano  and  B. Grandchamp  ( Hôpital Bichat Claude-Bernard, Paris, France ) for their assistance in setting up the French Caucasian control sample. We thank the Italian centers that contributed to the control populations of HYPER-GENES study cohort in Italy. This work was funded by  Association des Sclerodermies de France. We thank  Pr Javier Martin ,  Grenada (Spain) , who provided us the genotypic data from his previous studies on some SNP markers herein investigated.
This is a prerequisite for records submitted to the Barcode of Life Database (BOLD) to achieve Barcode status  (Ratnasingham and Hebert, 2007) , but not yet a requirement of GenBank  (Federhen et al. , 2009). The increasing emphasis on producing at least partially validated sequence data is a step towards reference sequences for species and to a degree combats the proliferation of unauthenticated sequence data Table  3  Molecular markers used for phylogenetic analysis within Culicoides.
Genomic region Molecular marker held on GenBank which, where investigated, has been found to be unreliable (James  Harris, 2003; Nilsson et al. , 2006). Conflict between species assignments and genetic similarities have already been identified for several Culicoides entries in GenBank  (Ander et al. , 2013; Bellis et al. , 2013a) , which could be the result of either misidentifications, evidence of synonymous species or an inability of COI analyses to separate these species. The CBOL initiative also emphasises the importance of retaining morphological 'vouchers' for all sequenced specimens as these constitute an essential link between the genetic data and the taxonomic classification enabling verification of identifications and cross-referencing of any subsequent specimens  (Pleijel et al.
Phylogenetics to phylogenomics With increasing technical capabilities to produce sequence data and corresponding increases in computational capacity, phylogenetic analysis is beginning to enter a new era, moving from the analysis of evolutionary relationships on the basis of single genes or small numbers of genes towards the use of genome-scale  (Stencel and Crespi, 2013)  datasets to infer evolutionary relationships  (Barrett et al. , 2013; Brito and Edwards, 2009; Narum et al. , 2013; Xi et al. , 2012) , so called 'phylogenomics'  (Delsuc et al. , 2005). Following on from initial preliminary physical mapping of the Culicoides genome  (Nunamaker et al. , 1999)  progress began in 2013 on producing the first annotated whole genome dataset for a species of Culicoides  (Fife et al. , 2013; Nayduch et al.
, 2014) , targeting the American BTV vector C. sonorensis  Wirth and Jones, 1957. In addition to aiding the design of appropriate primers for other gene regions not yet investigated in Culicoides, the publication of this first Culicoides genome represents a vital step to increasing the ease in which additional Culicoides species genomes can be assembled, annotated and incorporated into future phylogenomic studies. The usefulness of these datasets for taxonomic purposes was recently demonstrated with the production and analysis of reference genome assemblies for 16 species of Anopheles  (Neafsey et al. , 2013)  which have revealed discord between the relative placement of Anopheles complex species based on phylogenomic data  (Besansky, 2014)  in comparison to relationships based on more limited numbers of genetic markers  (Obbard et al.
, 2013; Beaman and Cellinese, 2012; Smith and Blagoderov, 2012) , which is particularly important where movement of specimens is problematic due to biodiversity legislation  (Schindel, 2010). Bioinformatics tools for the free online dissemination of linked morphological and genetic data exist via BOLD  (Ratnasingham and Hebert, 2007) , and data from several Culicoides studies have already been deposited and made publicly available via BOLD  (Bellis et al. , 2013a (Bellis et al. , , 2013b (Bellis et al. , , 2014a)). GenBank also provides the utility, although more limited than that of BOLD, to link morphological specimen data to sequence data records  (Federhen et al. , 2009).
Progress in integrative taxonomy in Culicoides Datasets of sequences for national checklists of Culicoides morphospecies are starting to be published  (Ander et al. , 2013; Matsumoto et al. , 2009b; Slama et al. , 2014) , although often with a limited number of specimens per morphospecies. A dramatic increase in the number of specimens analysed per morphospecies and the range of geographic areas from which these samples are collected is required in order to investigate within-species haplotype diversity and reassess species deliminations using cladistic analysis. There is clearly a need for combining morphological and genetic analyses in taxonomic studies of Culicoides with the assessment of congruence between morphological and molecular phylogenetic data, so called 'integrative taxonomy'  (Will et al.
Support and recognition by funding bodies and institutions of the importance of having a stable and well resolved taxonomy for Culicoides and other significant pathogen vectors is also utmost if research into vector-borne diseases is to progress beyond the use of model species  (Agnarsson and Kuntner, 2007; Besansky et al. , 2003; Godfray, 2007; Mallet and Willmott, 2003; Wilson, 2003). A cladistic reinterpretation of the subgeneric classification of Culicoides and species delimitations based on molecular phylogenetic analyses with phenotypic and bionomic data overlaying the cladograms, should be the ultimate goal of Culicoides taxonomy. While this may seem a distant target, the increasing use of molecular techniques to investigate the evolutionary history of Culicoides will allow some aspects of Culicoides species delimination and relationships to be resolved.
Acknowledgements The authors would like to thank  Eric Denison ,  Ian Roper  and  Craig O'Brien  for their contribution to the preparation and production of the Culicoides wing images, and  Simon Carpenter  for his valuable comments during the preparation of this manuscript. The work of LEH is supported by funding from the  Horserace Betting Levy Board (HBLB)  ( Vet/PRJ/766 ). GAB acknowledges support received from  Lyn Cook  and  Alan Dyce. The authors are grateful to the anonymous referees whose comments greatly improved this manuscript.
Identification of electrophysiologically active volatiles. Identification of FID peaks showing elec- trophysiological activity was performed by positive ion electron impact gas chromatography-mass spectrometry (EI GC-MS) on an HP 6890 GC coupled to an HP 5973 MS detector. One microliter samples were injected at 250 °C onto the HP-5MS column. These volatiles were tentatively identified by comparing mass spectra with those of authentic samples in the NIST 2005 database and were further confirmed by co-injecting the collection of plant volatiles with commercially available authentic standards on both non-polar HP-5MS and polar DB-WAX columns (30 m × 0. 25 mm × 0. 25 μ m, Agilent Technologies, Palo Alto, CA, USA), with peak enhancement indicating co-elution. Quantification of identified compounds was achieved by injecting authentic standards at four known concentrations onto the HP-5MS column and recording the peak area.
Each standard was injected four times, and a calibration curve was plotted for each compound, which was used to determine the amounts of each active volatile from the 18 tested plant species during both flowering and non-flowering (= vegetative) periods. To compare the amounts of each active volatile, six samples were analyzed at flowering and non-flowering stages of each plant species, respectively. Electroantennography. The EAG apparatus (Syntech Ltd. , Hilversum, The Netherlands) was linked to a desktop computer with an IDAC-02 data acquisition interface board, on which EAG responses were recorded, stored and quantified. Electrical signals from the antennae were amplified using a high-impedance DC amplifier and a signal connection interface box (Auto Spike, IDAC2/3; Syntech). The antennae of female and male A.
Acknowledgements We are grateful to  Drs. Scott Armstrong  ( USDA Agricultural Research Service, USA ),  Toby Bruce  ( Rothamsted Research, United Kingdom ),  Sean Halloran  ( Swiss Institute of Science and Technology, Switzerland ) and  Jianing Wei  ( Institute of Zoology, Chinese Academy of Sciences, China ) for their helpful comments on early draft of the manuscript. This research was supported by the  National Basic Research Program of China  (No. 2012CB114104 ), and the  National Natural Science Funds of China  (No. 30900949 ,  31321004 ).
g. [6] ) λ hφ = g sin 2θ m 2 h 2 -m 2 h 1 4vm A , λ φ = 4 λ 2 hφ sin 2 2θ v 2 m 2 h 2 -m 2 h 1 m 2 h 2 m 2 h 2 -m 2 h 1 -sin 2 θ , (58) where we have used ṽ = 2m A /g. This implies that both λ hφ and λ φ become large for heavy h 2 or for light dark matter. As a result, Eq. (  57 ) imposes an important constraint on our model. We further require the standard perturbativity constraint g2 4π < 1 , (59) which we find less significant for our purposes. Our results for U(1) DM are presented in Fig. 2 , upper row. We include the constraints from PLANCK  [23]  (relic abundance), LUX  [24]  (direct detection), FERMI  [25]  M (GeV)  (indirect detection) and perturbativity of λ i. The area between the red lines is consistent with the thermal relic DM abundance measured by PLANCK. The LUX data provide the strongest constraint on the allowed parameter space.
Y. M. acknowledges partial support from the European Union FP7 ITN INVISIBLES (Marie Curie Actions, PITN-GA-2011-289442) and the ERC advanced grants Higgs@LHC and MassTeV. This research was also supported in part by the Research Executive Agency (REA) of the European Union under the Grant Agreement PITN-GA2012-316704 ("HiggsTools"). The authors would like to thank the Instituto de Fisica Teorica (IFT UAM-CSIC) in Madrid for its support via the Centro de Excelencia Severo Ochoa Program under Grant SEV-2012-0249, during the Program "Identification of Dark Matter with a Cross-Disciplinary Approach" where some of the ideas presented in this paper were developed.
Acknowledgements. The authors are indebted to  Sasha Pukhov  and  Genevieve Belanger  for their help with the new version of micrOMEGAs 4. 1. 8. C. G. and O. L. acknowledge support from the  Academy of Finland , project " The Higgs boson and the Cosmos ". This work was also supported by the  Spanish MICINN 's  Consolider-Ingenio 2010 Programme  under grant  Multi-Dark   CSD2009-00064 , the contract  FPA2010-17747.
Acknowledgements: PD is on leave from  CNRS ,  Institut de Mathématiques de Toulouse, France , and GD is on leave from  Université Paul Sabatier ,  Institut de Mathématiques de Toulouse, France , where part of this research was conducted. TBNM wishes to thank the hospitality of the  Department of Mathematics, Imperial College London  for its hospitality. NW wishes to thank the  Université Paul Sabatier, Institut de Mathématiques de Toulouse, France , for its hospitality. This work has been supported by the  French.
A large domain, encompassing northern Africa and Europe, is designed to take into account all possible ozone precursors and aerosol sources and allows for contributions from long-range transport. The model results are compared to the available measurements and scores are presented. Having in mind the models' performances, the ozone and aerosol composition is further analyzed. For ozone, the variability is quantified both at the surface and in altitude. For aerosol, the model provides additional information such as composition and size distribution. Section 2 presents the experimental framework of the ADRIMED campaign and the whole set of data (surface, soundings, aircraft measurements, satellite) used in this study. Section 4 presents the modeling system and the settings. Section 5 ana- lyzes the meteorological situation using E-OBS data and the WRF model.
Observations In order to characterize the meteorological situation and the atmospheric composition, and to estimate the realism of the models, many observations are used in this study. An overview of the pollution over the area is done with the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite AOD measurements. With the surface measurements of the E-OBS database, the temperature and the precipitation are characterized. Using the EEA (European Environmental Agency) network, the surface ozone and particulate matter concentrations are studied. The optical depth is quantified using the AERONET (Aerosol Robotic Network) network. Temperature and ozone variabilities are estimated in the troposphere thanks to the aircraft observations of the ADRIMED project. The location of the measurement sites used in this study is summarized in Table  1.
EEA chemical measurements For regulatory pollutants, many measurements are routinely performed and well organized in quality-checked databases. The EEA  (Guerreiro et al. , 2013)  is responsible for the Air-Base database used in this study. It contains surface concentration measurements and information submitted by the participating countries throughout Europe (http://www. eea. europa. eu/). For this study, we focused on ozone and PM 10. In order to calculate scores and to study time series, a subset of data is used, including eight "coastal background" and nine "continental background" stations. Their location is displayed in Fig. 2  and details about their coordinates are provided in Fig. 1. They were chosen to be representative of various locations around the western Mediterranean Sea: Spain, France and Italy, including the Baleares, Corsica and Lampedusa islands.
AERONET measurements The AERONET photometer measurements  (Holben et al. , 2001)  are used to characterize the observed AOD and the volume aerosol size distribution (ASD). The AOD data are recorded by numerous stations deployed around the world and hourly values are available. Several quality levels are proposed on the AERONET database (http://aeronet. gsfc. nasa. gov/). In this study, the level 2. 0 is used for AOD and the level 1. 5 for ASD  (Dubovik and King, 2000). The sta-  tions used in this study are listed in Table  1  and their location is displayed in Fig. 2.
Air quality during the ADRIMED period The first step is to evaluate if the ADRIMED period was highly polluted or not, compared to previous summers in this region. To quantify this air quality, we used the results of the MACC-II (Monitoring Atmospheric Composition and Climate) and MACC-III projects for the surface O 3 and PM 10. The surface concentrations are calculated from a multiensemble of reanalysis based on seven European models (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM) and mostly relying upon validated in situ observation data sets for the period between 2007 and 2012 and near-real time observations for 2013. Details of the models in their operational setup are presented in  Marécal et al. (2015). This production has been established by the GEMS FP6 project and its follow-up FP7 projects (MACC, MACC-II and MACC-III).
The IFS meteorological fields from ECMWF, the TNO-MACC emission inventory,  (Kuenen et al. , 2014) , and the global chemical boundary conditions from MACC reanalysis or forecasts according to availability are used to force the models. Then, each model uses its own assimilation system (from optimal interpolation to 4D-VAR) to compute the analyses which allow building the ensemble. The ensemble hourly values are produced throughout Europe from the median value of the Ensemble and at 0. 1 • × 0. 1 • resolution. To estimate the relative air quality of the summer 2013, compared to previous summers, two indicators are computed. These indicators are both estimated for locations corresponding to highly urbanized cities, around the Mediterranean Basin. A subset of data is selected to focus on the months of June, July and August of each year.
The aerosol size is represented using 10 bins, from 40 nm to 40 µm in diameter. The aerosol life cycle is completely represented with nucleation of sulfuric acid, coagulation, adsorption/desorption, wet and dry deposition and scavenging. This scavenging is represented by both coagulation with cloud droplets and precipitation. The formation of SOA is also taken into account. The photolysis rates are explicitly calculated using the FastJX radiation module (version 7. 0b;  Wild et al. , 2000; Bian et al. , 2002). The modeled AOD is calculated by FastJX and for several wavelengths: 200, 300, 400, 600 and 1000 nm. For the comparisons with the AERONET measurements, we selected the AOD calculated at 600 nm. This cal-culation includes absorption by ozone (over the whole atmospheric column), Rayleigh scattering, Mie diffusion by liquid water and ice water clouds, absorption and Mie diffusion by aerosols.
In this model version, the aerosol is dry. A complete analysis of the improvement obtained in the model with this online calculation is fully described in a companion paper  (Mailler et al. , 2015). At the boundaries of the domain, climatologies from global model simulations are used. In this study, outputs from LMDz-INCA  (Szopa et al. , 2009)  are used for all gaseous and aerosol species, except for mineral dust. For this species, simulations from the GOCART model are used  (Ginoux et al. , 2001). The anthropogenic emissions are estimated using the same methodology as the one described in  (Menut et al. , 2012)  but with the HTAP (hemispheric transport of air pollution) annual totals as input data. These masses were prepared by the EDGAR team, using inventories based on MICS-Asia, EPA-US/Canada and TNO databases (http://edgar.
jrc. ec. europa. eu/national_reported_data/htap. php). Biogenic emissions are calculated using the MEGAN emissions scheme  (Guenther et al. , 2006)  which provides fluxes of isoprene and monoterpenes. In addition to this version, several processes were improved and added in the framework of this study. First, the mineral dust emissions are now calculated using new soil and surface databases,  (Menut et al. , 2013b)  and with a spatial extension of potentially emitting areas in Europe as described in  Briant et al. (2014). Emission fluxes produced by biomass burning are estimated using the new high-resolution fire model presented in  Turquety et al. (2014). Taking into account these fluxes is a major improvement in the CHIMERE model. Figure  6  presents the locations of burned areas during the summer of 2013, as detected by the MODIS satellite-based instrument (MCD64 product at 500m resolution, processed as described in  Turquety et al.
Analysis of ozone concentrations The first comparisons between measured and modeled atmospheric composition are undertaken for the analysis of ozone concentrations near the surface and in altitude. Ozone reflects the amount of photo-oxidant pollution, especially during summertime periods. Two kinds of data are used in this section: (i) the routine surface measurements of the AirBase background stations and (ii) the airborne measurements done for ADRIMED with the ATR aircraft. The AirBase measurements are regular in time (hourly), and are used to quantify if the model is able to simulate both the background values and the peaks during high-pollution events. However, being only at the surface, these measurements are not dedicated to provide information on the model behavior in the whole troposphere.
Comparisons between MODIS and CHIMERE As a first step, the satellite measurements are here only used to check if the main AOD patterns are well retrieved by the model. The measured AOD at 550 nm is extracted from the MODIS satellite data over the period from 6 June to 15 July 2013. Observations are interpolated on the model grid and comparisons are done for collocated data in space and time, as displayed in Fig. 13. The MODIS map includes the AOD retrieved over ocean and over land, proposed on the NASA Giovanni database. MODIS AOD products have been used for many years to study the amount and origin of aerosols in the Mediterranean troposphere. Barnaba and Gobbi (2004)  used these data to split relative contributions of aerosols on AOD. They showed that for the same particle size, its origin (maritime, continental or desert dust) may induce an AOD variability of one order of magnitude.
However, considering all these potential problems and compared to the AOD of the AERONET measurements, the modeled AOD has not an important bias and follows the observed day-to-day variability. The discrepancies are studied in the last section in terms of the aerosol size distribution. Table  8  corresponds to statistical scores calculated over these AERONET stations. The number of observations is very variable from one station to another: if the Izana station has 516 measurements, Forth Crete has only 108. These differences are certainly due to the cloud screening algorithm applied on the raw sunphotometer data to ensure that provided AODs are only due to aerosols. The correlation is variable from one site to another with values ranging from 0. 20 (Ilorin) to 0. 77 (Izana) and 0. 79 (Lampedusa).