dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of microRNA host genes Our RNA-seq dataset allows us to estimate expression levels for all long, poly-adenylated transcripts, thus in principle including miRNA primary transcripts. We extracted coordinates of miRNA transcripts from Ensembl 82  (Cunningham et al, 2015) , and we analyzed their overlap with exons of Ensembl-annotated or de novo-detected non-coding RNA genes. We found a total of 70 lncRNAs that had exonic overlap with miRNAs, which we classified as potential "miRNA host genes" and included in further analyses (Dataset EV11). We did not include in this analysis genes with intronic overlap with miRNAs.
Antiviral effector datasets We specifically analyzed the expression patterns of a subset of ISGs that were previously proposed to act as antiviral effectors through a large-scale over-expression screening approach in Huh-7. 5 cells  (Schoggins et al, 2011). We extracted the list of genes whose overexpression had a negative on HCV replication in this experimental setup, at both 48-h and 72-h time points, and matched them with current Ensembl annotations by gene names. Genes that appeared as having both positive and negative effects on HCV replication (depending on the time points) were excluded. We added to this list of genes a dataset of ISG antiviral effectors previously described  (Metz et al, 2013) , including genes whose antiviral properties were predicted with a knockdown approach  (Metz et al, 2012).
Differentially expressed genes in HCV-infected Huh7 cells We also analyzed a set of genes previously shown to be differentially expressed following HCV infection in Huh-7. 5 cells  (Walters et al, 2009). We matched the list of genes with Ensembl 82 annotations using the RefSeq accession number provided in this dataset  (Walters et al, 2009). The gene lists and their differential expression patterns are provided in Dataset EV5.
Tissue expression patterns from GTEx We analyzed the spatial expression patterns of the various gene lists identified in our study, using the gene expression dataset of the GTEx consortium, release v6  (Mele et al, 2015). We downloaded the pre-computed median RPKM values per tissue from the GTEx server. We used this dataset to estimate the tissue in which each gene reaches its maximum expression, for genes for which the maximum RPKM value in GTEx tissues was at least 1.
Mature microRNA expression Mature microRNA expression data were generated starting from the same total RNA preparations that were used for RNA-seq experiments. Equal amount of input total RNA was used in each reaction. RNA was diluted to 4 ng/ll in water and cDNA was generated from 8 ng of total RNA using TaqMan TM Advanced miRNA cDNA Synthesis Kit (Applied Biosystems TM cat# A28007) according to manufacturer's instructions. Briefly, mature miRNAs were first extended on both ends via 3 0 polyA tailing and 5 0 RNA-adaptor ligation. Next, a reverse transcription (RT) reaction was performed using universal RT primers (supplied with the kit) that recognize the universal sequences present on both the 5 0 and 3 0 extended ends of the mature miRNAs. One-sixth of RT reaction product was then used to preamplify the cDNA using the universal primers (supplied with the kit) that recognize the universal sequences added to all mature miRNAs on the 5 0 and 3 0 ends.
Acknowledgements This work was supported by  Swiss National Science Foundation (SNF)  grants  310030B_147089  and  310030_166202  to  M. H. Heim ,  SNF Ambizione  grant  PZ00P3_142636  to  Anamaria Necsulea ,  SNF MD-PhD  stipend  323530_145255  to  Tujana Boldanova , and a  SCIEX  grant  13. 296  to  Aleksei Suslov. The computations were performed at the  Vital-IT  (http://www. vitalit. ch)Center for high-performance computing of the  SIB Swiss Institute of Bioinformatics.
Horizontal bars represent median values. Boxes represent the inter-quartile (25-75%) ranges of the distribution. Boxplot whiskers extend to the most extreme data point found within 1. 5 times the inter-quartile distance from the box. Outlier points are not shown. Tujana Boldanova et al HCV infection and IFN-a in human liver EMBO Molecular Medicine targets predicted computationally with TargetScan (Agarwal et al, 2015; Dataset EV4; Materials and Methods). We found that miR-122 targets had significantly higher fold changes (median 0. 045) than targets of other expressed miRNAs (median À0. 025, Wilcoxon rank sum test, P-value 1e-3) and than genes not targeted by this set of liver miRNAs (median À0. 15, Wilcoxon rank sum test, P-value < 1e-10; Fig 2D). These observations are compatible with the reported subtle de-repression of miR-122 target genes in the pres- ence of HCV infection ª 2017 The Authors EMBO Molecular Medicine Vol 9 | No 6 | 2017.
Cufflinks to estimate gene expression levels using all TopHataligned reads, assigning reads with multiple alignments to each gene depending on gene expression levels estimated with unique reads (default multiple read correction procedure in Cufflinks). All gene expression estimates are provided with our GEO submission (accession number GSE84346). EMBO Molecular Medicine HCV infection and IFN-a in human liver Tujana Boldanova et al EMBO Molecular Medicine Vol 9 | No 6 | 2017 ª 2017 The Authors.
GeneMapper software version 4. 1 was used to score alleles. Two different people scored the alleles. Since reading of size might be difficult for hexaploid plants  [9] , we performed a second independent run and an independent score of alleles. The second dataset was used to assess the reproducibility of the results. Analyses were computed independently on the two datasets, and overall agreement was shown between datasets (see details in the supplementary file). In addition, we used previously genotyped accessions from Tropical America and Oceania  [24]. To allow comparison with our sampling number, we randomly sampled 132 accessions both for Tropical America and Oceania. The number of alleles recorded was used to statistically compare diversity in the three areas, West Africa, Tropical America and Oceania.
So, PIC = 1-Pi 2 with Pi the frequency of allele i. The neighbor joining tree method was used with the Euclidean genetic distance among accessions as implemented in PowerMarker 3. 25  [26]. The phylogram was visualized using the international tree of life: Itoel Program v2  [27]. Genetic structure. Discriminant analysis of principal components (DAPC) implemented in the ADENEGET package  [28]  using R software 3. 1. 1 [29]  was used to assess the distribution of genetic diversity. For DAPC, we first performed a principal component analysis (PCA) to capture the large number of allele absence/presence (0/1) matrices on the most significant PCA axes. The number of PCA axes retained was based on (1) the a score optimization procedure proposed in the ADEGENET package  [28] , (2) the cumulative percentage of variance explained by the first selected axes.
We then assessed the optimal number of clusters supported by the genetic data using the k-means procedure  [28]. A Bayesian information criterion (BIC) was calculated with each model (K = 1 to 20). Membership probabilities for each individual were plotted using the compoplot function of R. We also used STRUCTURE 2. 3. 4 software  [30, 31]. We ran the admixture model with a number of populations from K = 1 to 20 with 15 runs using a 100. 000 burn in period and 400. 000 Markov Chain Monte Carlo (MCMC) steps. Analysis of the composition of the genetic groups. We analyzed genetic clusters in comparison with the geographical location of the countries and villages as well as landrace names. To assess relationships between agro-morphological traits and genetic clustering, we performed Kruskall-Wallis nonparametric tests.
We ran this test with the 21 morphological traits recorded. We considered the genetic group to be associated with morphological differences when the test was significant with a Bonferroni corrected p-value of 0. 0024. Wilcoxon paired tests were then performed for each significant trait to assess which specific genetic group differed significantly from the others. We also assessed the relationship between the clustering and climate variables. To do so, for each accession we downloaded the BIOCLIM data  [32]  composed of 19 bioclimatic variables and monthly climate data (temperature and precipitation). We performed a principal component analysis to obtain synthetic uncorrelated climatic contrast. The PCA axes were then used to check for a relationship between climate variables and inferred cluster groups using Kruskall-Wallis nonparametric tests.
Structure of sweet potato populations With the a-score optimization function applied to the dataset, the number of PCA to be retained ranged from two to four. The cumulative variance explained by the 12 first PCs axes was 74. 7%, and consequently we retained these 12 axes (S1 Fig) In terms of clustering between DAPC and STRUC-TURE, the inferences of ancestry between groups were very similar (S6 Table ). When K = 5 (Fig  3 ), the first group contained 18 accessions, 15 from Togo and three from Senegal. The second group, called "Tropical accessions", contained 27 accessions all from Togo. The third group was composed of 60 accessions of which only four were from Senegal. All the accessions from the northern part of Togo were in this group, named "Tropical arid accessions". Group four contained eight accessions all from Senegal called "Sahelian accessions".
More thorough sampling throughout Africa is now needed to assess such hypotheses. S7 Table. Individuals found in the same group when the two datasets were run. This table shows that the two datasets produce approximately the same results: 92% of the samples are allocated to same group in the two datasets. The color or the letter for the same column of the same K for the two datasets was not identical, this show that the samples have changed cluster. Using this method, only 12 individuals have changed their place for K = 3, 6 for K = 4 and 16 for K = 5. (PDF) S8 Table. Wilcoxon-paired test between the genetic groups. A p-value < 5% was considered to be significant. The trait is specific to one group when the p-value is significant in each paired comparison with the same trait. (PDF).
In type 3 aphthous lesions are present almost continuously  [22]. Depending on the number of lesions per episode, the RAS patients were classified as group A (1-3 lesions per episode) or group B (over 3 lesions per episode). Blood samples were collected to determine the serum zinc concentration in all participants. The zinc concentration was measured by flame atomic absorption spectroscopy (F AAS; Jena AG; ZEEnit700E) using an analytical method described by Arora et al. [12] , Khademi and Shaikhiany  [19] , Orbak et al. [23]  and Özler  [24]. The results were statistically analyzed with non-parametric tests, as the data were not normally distributed according to Shapiro-Wilk normality test. Kruskal-Wallis test was utilized to compare mean serum zinc levels in Mi-, Ma-and HeRAS subgroups as in mild, moderate and severe RAS subgroups.
A neutron inelastic scattering experiment was performed at the GELINA (Geel Electron LINear Accelerator) neutron source of the European Commission Joint Research Centre Geel (EC-JRC Geel) with the aim of determining the reaction cross sections for the stable isotopes of natural titanium. A 235 U fission chamber was used to monitor neutrons with energies up to 20 MeV. The GAINS (Gamma Array for Inelastic Neutron Scattering) spectrometer was employed to detect the γ rays resulting from the decay of the excited nuclei. We determined the γ -ray production cross sections of the first transitions in the 46,48,49,50 Ti isotopes. The experimental values were compared with previous reported results and also with theoretical calculations performed with the TALYS 1. 8 code using the default input parameters.
Another motivation comes from the fact that the first transition in 48 Ti (E γ = 983. 5 keV) is investigated in order to establish a possible γ -ray reference cross section for neutron-induced reactions  [4] [5] [6]. Looking in the experimental nuclear reaction database EXFOR  [7]  we observe that most of the neutron inelastic scattering experiments on the titanium isotopes were performed between 1960 and 1980 in a limited energy range. The energy range was extended by Dashdorj et al. [8]  in 2007 when they determined the γ -production cross sections of the transitions observed from the inelastic scattering of the neutrons on 48 Ti up to E n = 200 MeV. Our goal was to measure the γ -production cross sections for the most important transitions in all of the stable Ti isotopes. Cross section data from the present a e-mail: aolacel@tandem.
The detectors are placed at backward angles with respect to the sample in order to reduce the impact of the γ -flash. In order to normalize the data, the neutron flux was monitored using a 235 U fission chamber (FC) centered on the flux at about 146. 8 cm upstream the sample. It contains 8 uranium layers of 70 mm diameter on five Al foils of 20 µm thickness supported by 84 mm diameter ring  [16]. The FC uses as a counting gas a P10 mixture at atmospheric pressure. The neutrons produce fission reactions in the uranium layers. The main working principle of the FC consists of applying a hight voltage in order to collect the charge generated by the fission fragments moving through the P10 mixture. For extensive descriptions of the FC see Refs. [16, 17]. The acquisition of the data is digital for the HPGe detectors and analoque for the FC.
The digital acquisition uses Acqiris DC440 digitizers running at 420 × 10 6 samples/s (2. 38 ns/sample), with 12-bit amplitude resolution (4096 channels full range)  [18]. The acquired data is transfered to a PC and for each signal the time and the amplitude is computed and recorded. The energy of the incident neutron is determined from the time information, while the γ energy is determined from the amplitude of the corresponding event. The time is calculated as the difference between the moment when the neutrons produce the inelastic reaction in the sample and the γ ray is emitted and detected and the moment when the neutrons are created.
Theoretical considerations Our experimental results are compared with theoretical calculations performed with the TALYS 1. 8 code using the default input parameters. These calculations use the semiempirical model and parameters that were obtained from global optimizations. For calculating the level densities it uses the approach of Gilbert and Cameron  [21]  together with the constant temperature model at lower energies and the back shifted Fermi gas model with one energy dependent level density parameter which accounts for the damped shell effect at higher energies. The γ -ray strength functions are described using the generalized Lorentzian form by Kopecky and Uhl  [22]  for E1 transitions. For the other type of transitions it uses the Brink-Axel option  [23, 24]. The Reference Input Parameter Library  [25]  is employed to derive the nuclear structure and the decay table used in the modeling of de-excitation of the nuclei.
Conclusions Using the neutrons provided by the GELINA neutron source and the GAINS spectrometer in combination with a fission chamber we performed an inelastic scattering experiment in which we studied the γ -ray transitions corresponding to the five stable isotopes of a natural titanium sample. We reported here the γ -production cross sections for the first transitions in 46,48,49,50 Ti and comparison with the theoretical calculations performed with the TALYS 1. 8 code and previous experimental results if available. For most of the transitions discussed here the total relative uncertainty was around 5%. This work was supported by the European Commission within the Seventh Framework Program through Fission-2013-CHANDA (project No. 605203). The authors would like to thank the GELINA operators for their help and support during the measurements.
45. The bioapplication Target Activation Bioapplication was used to quantify HuR protein levels in KC3AC1 cells expressing Scr_ or HuR_shRNA. Briefly, a secondary nuclear mask was created based on the the primary nuclear mask (DAPI) and the mean fluorescence intensity within this mask was determined from mean values obtained for >5000 cells. Homology modelling. The sequence of the mouse FL and Δ6 MR LBDs were aligned to that of the human MR. Theses sequence alignments were then used together with the X-ray crystal structure of the human MR LBD (Protein Data Bank identification number 2AA2)  25  as templates to generate the three-dimensional homology models of the FL and and Δ6 MR using the Modeller9v8 program  46. Investigations in mice. Animals were provided with free access to water, except for the water deprivation group, which did not receive any water for 18 hours.
Acknowledgements This work was supported by grants from the  Institut National de la Santé et de la Recherche Médicale , the  Université Paris-Sud  and  Agence Nationale de la Recherche  (Grant  11-BSV1-028-01 ). Ingrid Lema  held a fellowship from the  Cardiovasculaire-Obésité-Rein-Diabète-Domaine d'Intérêt Majeur (Région Ile-de-France)  and  HAC Pharma & SFE  ( Société Française d'Endocrinologie). The authors are indebted to  Julie Sappa  for English editing.
Aiguille du Midi and bedrock temperature data Studies on rock wall permafrost started at the end of 2005 in the Mont Blanc massif with the progressive installation of nine rock surface temperature (RST) sensors at the Aiguille du Midi summit (AdM), a set of three granite pillars. The AdM is accessible by cable car throughout the year (with approximately 500 000 visitors per year). As a pilot site in high-elevated permafrost research, the AdM is now equipped with a variety of instruments to measure rock wall temperature  (Magnin et al. , 2015c) , snow cover  (Magnin et al. , 2017)  and mechanics with extensometers  (Ravanel et al. , 2016). Three 10 m-deep boreholes of 15-node thermistor chains are installed in the AdM bedrock and have recorded temperature with 3 h time steps since December 2009 (NW and SE faces) and April 2010 (NE face).
Grands Montets and ERT data The Grands Montets (GM) is a summit culminating at 3296 m a. s. l. , to the north and about 800 m below the Aiguille Verte (4122 m a. s. l. ). In 1962-1963 a cable car was installed on its 60 • -steep north face to transport skiers up to the glaciated area. In May 2011, a RST logger was installed (GEOPrecision PT1000, sensor accuracy ±0. 1 • C) at the foot of the highly fractured NW face (3058 m a. s. l. ) in a 85 • -steep rock wall portion. It recorded the rock temperature at depths of 3, 10, 30 and 55 cm until January 2013. The 2012 mean annual rock surface temperature (MARST) at a depth of 3 cm was -1. 4 • C. In 2012 and 2013, electrical resistivity tomography (ERT) soundings were conducted along the NW face of the GM and four other sites of the massif  (Magnin et al.
, 2009; Hipp et al. , 2014; Myhra et al. , 2015). The increasing amount of available RST time series in the European Alps has permitted the formulation of such statistical model for the entire Alpine range  (Boeckli et al. , 2012). This last model has been applied on a 4 mresolution DEM of the French part and of the Swiss and Italian borders of the Mont Blanc massif with local air temperature input data to map the mean annual rock surface temperature (MARST; Figs. 1 and 2,  Magnin et al. , 2015a). In our modelling procedure, we use the MARST map available for the French part of the Mont Blanc massif to generate the initial RST condition. We run the transient simulations in the commercial hydrogeological software DHI-WASY FEFLOW version 7. 0 by forcing the RST with climate time series from 1850 to 2100 and solving the heat conduction equation in 2-D with consideration of freeze and thaw processes in the bedrock interstices.
Equation (  1 ) accounts for only one fluid phase and only one solid phase (water and rock respectively), which is the default use of FEFLOW in saturated conditions such as in this study. The piFreeze plug-in working with Feflow7. 0 adds the ice fraction to the solid phase in order to modify only the parameters of solid thermal conductivity and of solid thermal heat capacity in Eq. (  1 ). The addition of the ice in the whole modelled medium is expressed throughout the bulk volume as ε a + ε w + ε i + ε r = 1, with ε a , ε w , ε i and ε r the bulk fractions of air (ε a = 0 in our case), water, ice and rock respectively. A relation is established between the bulk volume of ice and the bulk volume of liquid, which is the mass fraction per bulk volume of the unfrozen liquid to the total liquid mass, also called the freezing function F  (Clausnitzer and Mirnyy, 2015) : F = ε w ρ w ε w ρ w + ε i ρ i ( 2 ) where ρ is the density of the corresponding phase (subscript i for ice and w for water).
The IPSL-CM5A-MR model was chosen because its basic state is very close to the recent observational records during the first years of the 21st century (Fig. 4 ), and its response to the radiative forcing throughout the century is close to the median of the CMIP5 models, ensuring a representative behaviour to estimate long-term evolutions. Forcing time series are obtained by extracting data at the closest grid-point (1336 m a. s. l. ) to the Mont Blanc Massif.
Acknowledgements. This work was founded by the  French Agence Nationale de la Recherche  in the scope of the  VIP Mont Blanc project  ( ANR-14-CE03-0006 ) and by the  EU Interreg V-A France-Italy  ALCOTRA 2014-2020 no. 342   PrévRisk Haute Montagne  project. We acknowledge  Peter Schätzl  and  Carlos Rivera  from  Feflow's staff  for their help with the Pi-Freeze plug-in. We.
Data availability. Data are available upon request to the first author. The borehole data are visible online at http://gtnp. arcticportal. org/  (Magnin, 2015b)  and on http://www. permasense. ch/en. html where they can be downloaded as well. The ERT data will be uploaded on similar data repository in within the next year. The MARST map used for the model initialisation can be downloaded at https://hal-sde. archives-ouvertes. fr/hal-01120617 (Magnin et al. , 2015b).
In order to determine the main technical features of a water spray system, a Phase Doppler Interferometer (PDI) device is generally used to measure the droplets size distributions and the droplets velocities allowing to perform accurate local measurements. Nevertheless, some usual water spray models (  [1] ,  [6] ) can require as input parameters only an overall droplet size distribution and an initial velocity. Thus it is needed to process some statistical methods over these experimental data to determine them. A second difficulty is linked to the high sensibility of droplets size distribution and droplets initial velocity due to the pressure effect at nozzle head. Indeed, an initial characterization of a water spray system is performed at a given pressure for the nozzle head before using in fire scenario.
At this elevation, the droplets are underrelaxed and so the velocity measurements do not correspond strictly to initial velocity of droplets. As an alternative way for defining the features of spray nozzles, a numerical method for determining droplets size distributions and droplets initial velocity is then developed using both the SYLVIA two-zone model  [25]  and experimental data  [11]. Indeed, this approach proposes to perform a calibration of spray modeling by means on full-scale fire tests in enclosure, for which the fire power and the ventilation system are perfectly controlled. After a brief description of SYLVIA software (see also the appendix) and of the water spray modeling, this work addresses a new calibration approach for assessing the input parameters of this modeling by using large-scale scale fire tests in DIVA facility  [11].