dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction and purity control Cells were sorted directly in RLT lysis buffer from RNeasy Micro Kit (Quiagen) and total RNA was extracted according to manufacturer's instructions. Prior to RNA-seq, the purity of the 4 sorted samples and the eventual cross contaminations were evaluated by RT-PCR. The expression patterns of Ginip, mCherry, Mrgprd, Tafa4, Trpv1 and TrkA transcripts were analyzed and found accurate (data not shown). However, SP cells were found to be highly contaminated by CD31-expressing endothelial cells that also bind IB4 and thus were excluded from the RNA-seq experiment (data not shown). The quality of the RNA obtained from each sorted sample was determined with respect to RNA profiles generated by the Agilent Bioanalyzer 2000 (Agilent Inc. ).
RNA-Sequencing and quality control For each sample, 50 ng of high quality total RNAs were amplified using the Amino Allyl Message Amp II amplification kit (Life Technologies) and 100 ng of resulting amplified RNAs were used for libraries building with TrueSeq Sample preparation (low throughput protocol) kit from Illumina. Libraries were sequenced in 100-bp paired-end cycles using the Illumina HiSeq2000 system with SBS technology. Image analysis and base calling were performed using the HiSeq Control Software and Real-Time Analysis component provided by Illumina. Quality of the sequencing was assessed using FastQC and the Illumina software SAV (Sequence Analysis Viewer); a representative example is shown in Fig. 2. For each sample, the median and the mean distribution of the quality scores was above 30 and data acquisition was not affected by base loss (Fig.
Data analysis Base calling yielded between 40 and 50 million reads (Table  1 ). RNAseq reads were aligned to Mus musculus genome (UCSC mm10) using the eland_rna module from CASAVA (refFlat. txt file downloaded from UCSC on October 10, 2012). As CASAVA software is not designed for pair-end analysis, only the first pair was used to generate the counts. Reads mapping to contaminants including 28S, 18S and 5S ribosomal RNAs, mitochondrial chromosome, PhiX genome (Illumina control) and Illumina adaptors as well as reads mapping to multiple splicing positions and with no match, were removed. Gene counting was carried with the counting module from CASAVA. Table  1  gives an overview of the number of counts generated and shows those that mapped to exons that were used for the creation of the Raw Data file.
Genes generating less than 10 counts were excluded from the subsequent statistical analysis. The first normalization method used was the Reads Per Kilobase per Million of mapped reads (RPKM)  [6]. Analysis of RPKM values generated for genes expected to be enriched in each defined subset, showed substantial and accurate enrichment in Mrgprd and P2rx3 in DP subset Tafa4 and Th in C-LTMRs and Cgrp and Sp in DP subset, providing a proof of concept of the RNA-seq data presented in our study (Fig. 4A ). Because our aim was to determine the sets of differentially expressed genes (DEGs) in one subset as compared to the two others and that the RPKM normalization method was shown to be inappropriate for such an analysis  [3] , we chose the Trimmed Mean of M-values (TMM) normalization factors provided by edgeR package (p b 0,01, fold change N2).
Data validation Only DEGs that generated at least 1000 reads in both biological replicates were kept for downstream data validation. As a result, data validation was carried on 156, 184 and 784 DP-, C-LTMRs-and DN-DEG, respectively. For this, we performed an extensive in situ hybridization (ISH) screen on DRG cryosections followed by either a) GINIP or IB4 immunostaining for DP and C-LTMRs datasets or b) GINIP and TrkA.
Discussion We described here a unique dataset encompassing the combinatorial gene expression enriched in MRGPRD + , C-LTMRs and DN DRG sensory neurons. We highlight here that our published RNA-seq data are of good quality and concordant with our validation strategy. These data provide a wealth of information regarding the presence and the prevalence of transcripts in MRGPRD + , C-LTMRs and DN sensory neurons, they highlight the differential gene expression in two functionally opposite classes of cutaneous afferents and they allow open-access monitoring the cellular distribution of over 100s of genes  [7].
Acknowledgments We are grateful to the members of the laboratory for scientific discussions, to  M. Barad ,  A. Zouine  and  M. Malissen  from  CIML flow cytometry facility  for FACS assistance. We thank  S. Alonso  for comments on the manuscript,  M. Deage  for the pilot experiment on Tafa4 DRG neurons as well as IBDM imaging and animal facilities for assistance. This work has been funded by  ERC-Starting  grant paineurons  260435  to A. M.
The dependency of the PGEA on the fluence rate of the incident light I was then assessed by varying I at each value of the initial angle A 0. PGEA can be plotted as a function of I for the different values of A 0 (and the corresponding A P = A 0 + π/2). PROT1 The position of the PGEA was also measured for different values of A 0 , where A P = A 0 + π/2. However, the fluence rate of the incident light I was tuned experimentally so that the angle of the straight organ was immediately steadied at A R = A 0 stated in terms of the A R C model. This provides an estimate of the value of I(A R = A 0 ) at photogravitropic equilibrium. The results of both protocols can be used to independently assess the A a R ðIÞC model. The data produced in  [17]  according to the two protocols have been reprocessed.
Back Close Full scheme has been included, adapted from the GEMS/MACC aerosol scheme  (Morcrette et al. , 2009; Benedetti et al. , 2011; Michou et al. , 2014). It includes the same five aerosol species that can be directly emitted from the surface for dust and seasalt particles, or from external emission datasets for black carbon, organic matter and sulfate precursors. The spatial domain of our simulations has consequently been extended compared to the previous study of  Nabat et al. (2014) , in order to include all the sources generating aerosols that can be transported over the Mediterranean basin. No aerosol is indeed included in the lateral boundary forcing. Sea salt aerosols are generated by wind stress on ocean surface, either because of air bubbles bursting at the sea surface, or from spume droplets directly torn off the wave crests by the wind.
(2001) , that propose a simplified formulation of dust emission, based on the wind speed and thresholds according to the fraction of bare soil and soil moisture. In ALADIN-Climate, this function has been replaced by the  Marticorena and Bergametti (1995)  parameterization, that takes into account more soil characteristics coming from the ECOCLIMAP database  (Masson et al. , 2003) , which provides information on the erodible fraction and the sand and clay fractions, allowing a classification of the soil textures. After the determination of an erosion threshold based on the soil distribution, the soil moisture and the roughness caused by nonerodible elements, the horizontal saltation flux is cal-Figures.
Back Close Full culated, proportionally to the third power of the wind friction velocity. The vertical flux is then inferred from this saltation flux, according to an empirical relationship given by  Marticorena and Bergametti (1995) , which notably depends on the soil clay content. The emitted dust size distribution is based on the work of  Kok (2011). More details about this dust emission parameterization can be found in  Nabat et al. (2012) , who have used the same dust emission scheme in RegCM4. Once emitted dust particles are integrated in the three dust size bins of the scheme: 0. 01 to 1. 0 µm, 1. 0 to 2. 5 µm and 2. 5 to 20 µm. The external emission datasets for the three other aerosol types come from Lamarque et al. (  2010 ), who have provided inventories at 0. 5 • resolution of different species for climate models.
Observation data For the evaluation of the aerosols and their direct radiative effects, different observed datasets are used in the present work. Simulated AOD is compared to satellite data from the MODerate resolution Imaging Spectroradiometer (MODIS, collection 5. 1, standard and Deep Blue algorithms, 1 • resolution,  Tanré et al. , 1997; Levy et al. , 2007) , the Multiangle Imaging SpectroRadiometer (MISR, Level3,  Kahn et al. , 2005 Kahn et al. , , 2010) )  and the SEVIRI radiometer onboard the geostationary satellite Meteosat Second Generation (MSG). For the latter instrument, we use the algorithm of  Carrer et al. (2010) , which provides high-resolution AOD over both ocean and land surfaces. Nowadays, this algorithm is being implemented on the production chain of the ICARE thematic center (http://www.
3, from the north-east to the south-west. Sea-salt particles are essentially simulated over the Atlantic ocean, as well as the western Mediterranean Sea in lower quantities. In greater detail, some differences can be noted between the model and satellite data. CNRM-RCSM5 AOD is closer to MISR over northern Africa, where a large zone of AOD higher than 0. 5 can be identified in both datasets, while MODIS and especially AERUS-GEO show lower AOD. Similar conclusions can be drawn for the Arabian peninsula. Dust export over the Atlantic ocean is on the contrary in very good agreement between the five products (AOD between 0. 5 and 0. 7). Over western and central Europe, MISR AOD is lower than MODIS, AERUS-GEO, MACC and CNRM-RCSM5. Large differences in AOD are also present in Eastern Europe and Russia, where MODIS shows higher AOD than the other datasets.
However, this region is in the limit of the domain seen by SEVIRI (lower values in AERUS-GEO), and is also close to the border of the domain used in CNRM-RCSM5, so that aerosols over this region may come from outside the domain. Finally, AOD over the northern Atlantic ocean is higher in CNRM-RCSM5 and MACC than in satellite products, but the presence of numerous clouds in this area limits the quality of the satellite data there. Figures.
Back Close Full Averages for summer 2012 are given in Table  1 , for CNRM-RCSM5 and different model and satellite datasets: the MACC reanalysis, the AOD climatology from  Nabat et al. (2013) , named NAB13 thereafter, MODIS, MISR and AERUS-GEO. NAB13 and MACC, which present AOD components for each aerosol type, are based on both model and satellite data for the first one, and on model and data assimilation for the second one. NAB13, which gives reliable estimations of the different AOD components, is only available on the 2003-2009 period, so that the average over this period with the minimum and maximum values have been indicated. Averages have been calculated on the three domains defined in  Nabat et al. (2013) : Europe, the Mediterranean Sea and northern Africa. Over Europe, CNRM-RCSM5 is very close to NAB13 for total AOD (0.
18 on average) and the five aerosol types, even if the sharing between organic matter and sulfate aerosols is slightly different. MACC simulate more dust and sulfate particles, but the three satellite data have lower AOD (between 0. 15 and 0. 16), so that CNRM-RCSM5 AOD is median. Over the Mediterranean Sea, a good agreement is shown between CNRM-RCSM5 (0. 23 for total AOD), MACC (0. 24) and NAB13 (0. 23). In addition, the proportion between the different aerosol types is similar in the three datasets. However, as in Europe, satellite data have lower AOD (between 0. 18 and 0. 22). More variability is noted with regards to AOD over northern Africa, notably because of the dust component. CNRM-RCSM5 shows higher AOD (0. 45) than NAB13 (0. 41), MACC (0. 32) and the satellite data (between 0. 21 and 0.
Generalization A similar composite study has been carried out for other stations (defined in Table  5 ) where daily radiation and temperature data were available. Figure  14  presents the results per station for six parameters (AOD, solar and thermal surface radiation, cloud cover, 2 m and soil temperature) for the NO, PROG-M and PROG simulations, as well as for observations when available, while the average composites are given in Table  7. As in Murcia, the difference in AOD between dusty days and the set of all the days is for every station similar in observations (0. 23 on average) and the PROG simulation (0. 22). The difference in PROG-M comes only from the number of dusty days varying from one month to another. As a consequence, measurements reveal that downward SSR is on average 28 W m example in Mallorca, an increase of 6 % in cloud cover on dusty days amplifies the dimming due to aerosol loads.
We would like to thank Meteo-France for the financial support of the first author, and the surface radiation and temperature in French stations. This work is part of the Med-CORDEX initiative (www. medcordex. eu) and a contribution to the HyMeX and ChArMEx programmes. ChArMEx is the atmospheric component of the French multidisplinary program MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales). ChArMEx-France was principally funded by INSU, ADEME, ANR, CNES, CTC (Corsica region), EU/FEDER, Météo-France, and CEA. The aircrafts were operated by SAFIRE. TRAQA was funded by ADEME/PRIMEQUAL and MISTRALS/ChArMEx programmes and Observatoire Midi-Pyrénées. This research has received funding from the French National Research Agency (ANR) projects ADRIMED (contract ANR-11-BS56-0006) and REMEMBER (contract ANR-12-SENV-0001), as well as from the FP7 European Commission project CLIMRUN (contract FP7-ENV-2010-265192).
Acknowledgements. We would like to thank Meteo-France for the financial support of the first author, and the surface radiation and temperature in French stations. This work is part of the Med-CORDEX initiative (www. medcordex. eu) and a contribution to the HyMeX and ChArMEx programmes. ChArMEx is the atmospheric component of the French multidisplinary program MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales). ChArMEx-France was principally funded by INSU, ADEME, ANR, CNES, CTC (Corsica region), EU/FEDER, Météo-France, and CEA. The aircrafts were operated by SAFIRE. TRAQA was funded by ADEME/PRIMEQUAL and MISTRALS/ChArMEx programmes and Observatoire Midi-Pyrénées. This research has received funding from the French National Research Agency (ANR) projects ADRIMED (contract ANR-11-BS56-0006) and REMEMBER (contract ANR-12-SENV-0001), as well as from the FP7 European Commission project CLIMRUN (contract FP7-ENV-2010-265192).
AgroParisTech professors and librarians in Food Science and Technology have developed information literacy instruction to help students find and use relevant information. The course trains students to develop information literacy competencies: to identify the information needed and define its nature and extent; to access the information with efficiency because they select and use methods, resources and retrieval systems most appropriate to the topic; to evaluate the used methods, information, and sources; to search, access and use information ethically and legally; to read, use and write bibliographic references. AgroParisTech trains two kinds of students. For Higher Education students (engineering students), the training takes place at their arrival in AgroParisTech, including courses and practical classes: role and management of information in organizations; information on plagiarism; resources in environment, food science and technology; use of relevant tools and methods for searching in databases; reference writing.
For each competency, AgroParisTech librarians and professors have fixed the knowledge required for their students. To identify the information needed and define its nature and extent Figure  1  shows the sequences for finding information and data on a given topic. Often students use the resources available in libraries or online but they do not necessarily choose the best one according to their questions. Therefore, the training ensures that they ask the right research questions before searching for documents. Firstly, the documents that are used depend on the kind of work. A scientific review mainly requires many papers while a general presentation requires books, reviews or syntheses. Secondly, there are specific resources where to find each type of document. For papers, bibliographic databases will be the most relevant; for books, library catalogues are the best resources.
The library represents a quiet working and learning place for all the users, with available re-IJFS October 2015 Volume 4 pages 126-133 The information literacy competencies acquired during studies will be extended all along the life with significant time spent to look for information. The development of the information society and the knowledge economy, and lifelong learning must be considered to ensure that students will share data and experience, will communicate ethically and legally to be actors of the society  (Pochet et al. , 2013). Johnston and Webber (2003)  think that information literacy is a key discipline of the information society. They develop a model of the information literate student in the changing information society and even speak of the information literate university where all the members would become information literate.
Acknowledgements  J. Dequéant ,  D. Gasparotto ,  A. Lesueur ,  Y. Maresse  and  M. -C. Pattier , librarians,  P. Cansell , consultant, are involved in the training of Engineering Higher Education students. Prof. Marie-Noëlle Maillard  analyses the scientific article with the masters students. The authors acknowledge  Will Raiser  for the English language correction.
Physics results Table  1  shows the details of the five CNGS runs. The total number of neutrino interactions recorded in the OPERA detector is 19505. For runs 2008 and 2009, all predicted events have been searched for in the two bricks with the highest probability of containing the neutrino interaction. Only the most probable brick has been analysed so far for runs 2010 -2012. In the    present analysis, the whole sample of events without a reconstructed muon in the electronic detectors (0 μ) have been considered. A cut on the muon momentum (p μ < 15 GeV/c) has been applied for 1 μ events to improve the signal to noise ratio. Details on the dataset are summarised in Table  2. The total number of fully analysed neutrino interactions is 4685.
Atmospheric muons The underground Gran Sasso laboratory is a privileged location to study TeV-scale cosmic rays. Between 2008 and 2012, OPERA collected more than 3 million atmospheric muon events, out of them about 110000 multiple muon bundles were detected. Profiting of the inversion of the magnet polarity that was performed on purpose during 2012 run, the atmospheric muon charge ratio R μ = N μ+ /N μ-in the TeV region was measured both for single and for multiple muon events. The combination of the two data sets collected with opposite magnet polarities allowed minimizing the systematic uncertainties, thus reaching the most accurate measurement in the high energy region to date  [21] : R μ (n μ = 1) = 1. 377 ± 0. 006(stat. ) +0. 007 -0. 001 (syst. ) R μ (n μ > 1) = 1. 098 ± 0. 023(stat. ) +0.
)-(3). The central coefficient P A on the other hand, includes the contributions from all the neighbours and the transient term. In some of the cases, where sources term linearization was applied, it also contained part of the source terms. φ b contains all the terms those are treated as known (source terms, differed corrections and part of the unsteady term). The momentum, pressure correction and temperature are solved sequentially using an ILU-preconditioned GMRES procedure implemented in the IML++ library  [14]. All of the computational meshes were generated using the open-source software Gmsh  [15].
Background: Methylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation. Aberrant promoter methylation is considered a hallmark of cancer involved in silencing of tumor suppressor genes and activation of oncogenes. However, recent studies have also challenged the simple model of gene expression control by promoter methylation in cancer, and the precise mechanism of and role played by changes in DNA methylation in carcinogenesis remains elusive. Results: Using a large dataset of 672 matched cancerous and healthy methylomes, gene expression, and copy number profiles accross 3 types of tissues from The Cancer Genome Atlas (TCGA), we perform a detailed meta-analysis to clarify the interplay between promoter methylation and gene expression in normal and cancer samples.
Classification of genes based on their CGI methylation profiles in normal and cancerous tissues We first assess how promoter methylation profiles differ between genes, when for each gene we consider the average methylation profile across normal or cancerous samples. For that purpose, we collected highdensity methylation datasets from the cancer genome atlas (TCGA) data portal providing more than 485K CpG methylation levels for 672 normal and cancerous samples from three tissues of origin: breast, colon and lung (Table  1 ). For each CGI, we combine the probes in the CGI and in the shore and shelves of the CGI, defined as the regions up to 4kb outside of the CGI  [11] , in a unique CGI, shores and shelves (CGI+SS) methylation profile. We restrict our analysis to the 1827 CGI+SS where at least 20 CpG probes are measured by the technology in order to have high enough coverage to measure the methylation variation within each CGI+SS.
01) and technical ones. Another way to assess how methylation impacts expression is to look, for each given gene, how variation in expression across samples correlates with variations in methylation of nearby CGIs. For each set of samples (split by tissue of origin and normal/cancerous state), we measure the strength of association between methylation and expression for each gene by computing a predictive goodness of fit R 2 which represents the level of gene expression variation explained by CGI+SS methylation variation (see Materials and methods). This coefficient is calculated either when the CGI+SS methylation status is summarized by the mean methylation values of all the probes, or by using the full CGI+SS methylation information of each probe. We observe that the full CGI+SS methylation profile is predictive of gene expression for a subset of genes in each dataset, and that this predictive power is significantly higher than using only the average CGI+SS methylation (Fig.
The impact of CGIs located outside of promoter regions, such as intragenic CGIs is still unclear as it does not seem to contribute significantly to global gene expression regulation. Yet, a few studies point at their potential role in modulating alternative promoters  [34]  or in long-range regulation  [35]. Reproducing this methodology on different datasets allowed us to compare the variations of gene expression regulation by methylation in normal and cancerous tissues but also between different types of tissues. Our results suggest that genes targeted by methylation are not only very different between different normal tissues, but more importantly that they are very different between normal and cancerous samples of a given tissue suggesting a shift of epigenetic regulation between normal and cancerous tissues.
CNVs have been widely analyzed as a source of genetic variation that plays an important role in complex phenotypes such as cancer  [31, 40]. While CNV contribution has been characterized on a genome-wide scale, the link with other regulation mechanisms, particularly DNA methylation, is still unclear  [41, 42]. We showed that the impact of both processes in gene expression regulation seems to be non-redundant. The relatively large dataset size gives us confidence in the statistical validity of the results, which are however limited to a fraction of the total genes because of uneven coverage. Methylome sequencing has already been performed and also supports the complexity of methylation patterns but is still limited to very small datasets  [32]. Undoubtedly, larger methylome datasets available in the near future will further improve our understanding of the role of DNA methylation in gene expression regulation.