dataesr/scientific-paragraphs-categorization huggingface dataset:
GDR provides a page that links to Prunus RNA-Seq and DNA datasets in the NCBI SRA. The expression data from selected RNA-seq analyses will be available from GDR using Tripal Analysis Expression Module  228. This module allows users to submit gene sets to generate a heatmap and also view expression patterns of a mRNA in a feature page. The published RNA-Seq and dbEST datasets are also analyzed by GDR to create a reference transcriptome (RefTrans) for major species and provides putative gene function identified by homology to known proteins. For Prunus, P. avium GDR RefTrans v1. 0 and Prunus persica GDR RefTrans v1. 0, are currently available. Currently, there is not much epigenomic data available for Rosaceae but more data are expected to come  229  and search and analysis tools for epigenomic data will be available in GDR.
With the increasing number of species with WGS, several web-based databases are available for comparative genomics. A few contain the WGS of Rosaceae species. CoGe (The Place to Compare Genomes)  230  contains data of 48,063 genomes including most of the Rosaceae genomes. Plaza 231 and the plant genome duplication database (PGDD)  232  contain some of the Rosaceae WGS, including P. persica v1. 0. Phytozome 233 currently contains P. persica v2. 0. In GDR, P. persica v2. 0 is used in a synteny analysis with eight other Rosaceae whole-genome assemblies using MCScanX  234. The synteny results are available through the new Synteny Viewer. The gene/mRNA page also lists the orthologs and paralogs identified in this analysis and provides a link to the Synteny Viewer. One of the most important roles of GDR as the community database for the Rosaceae family is to integrate all the different types of data to maximize the value.
Methods and data Terrestrial imagery from different time periods is needed to explore gully erosion mechanisms. In order to identify changes in gullies and surrounding surface conditions, early gully catchment boundaries and level terrace areas within each catchment were extracted from DOMs (digital orthophoto maps) and DSMs (digital surface models) produced using high-resolution aerial photographs from 1976; the equivalent recent data within each catchment were extracted from high-resolution 2017 UAV-derived DOMs and DSMs. Comparison of the two allowed the determination of gully volume change induced by head retreat, sidewall collapse, and incision. This allowed the calculation of erosion moduli and the determination of reasons for gully catchment area change and their impacts on gully development following three stages (Fig.
UAV flight missions were conducted in August 2017 in the same regions as the early aerial photographs from 1976. The flight heights were 170 m for S1 and 150 m for S2. UAVderived DOMs, DSMs, and point clouds were then generated for the two pilot areas using Pix4Dmapper v. 4. 2 professional image processing software (https://pix4d. com/) with spatial resolutions of 7. 10 cm for S1 and 6. 29 cm for S2. This software proved highly reliable for such data processing, as shown by previous studies  (Zhang et al. , 2018; Zhao et al. , 2017).
Acquisition and processing of early aerial photographs All 21 1976 aerial photographs were printed (at a scale of 1:16,000), so the necessary images were converted to 1200 dpi digital files using an Epson Perfection V600 Photo Scanner. Pix4Dmapper automatically processed these digital images based the image hue and texture to derive DOMs, DSMs, and point clouds. Next, ENVI v. 5. 3 software (http://www. harrisgeospatial. com/) was used to select evenly spaced ground control points (i. e. , road junctions or farm fields) in both sets of DOMs. The registered DOMs and DSMs were georeferenced using the WGS 1984 datum and UTM projection (zone 48N) based on a UAV-derived DOM as a reference. Considering the spatial resolution of early aerial photographs can not reach that of the drone, we resampled both datasets to a pixel size of 0.
Our recent study demonstrated that V B1 , V C and V A can decrease the binding affinity between Rv3291c(MtbLrpA) and DNA  [31]. In addition, in our data we found that V B1 with a final concentration of 10 mM can inhibit the growth of BCG in vitro. By considering the importance of V B1 , V A , V C and V D , we proposed that lacking certain vitamins may play a role in bacterial persistence. Therefore, in the present study, the transcriptomic and metabolomic analyses were conducted on M. bovis BCG after V B1 induction, by using RNA-sequencing and LC-MS platforms, respectively. The results indicated that V B1 exhibited an inhibitory effect on the growth of BCG in vitro. Candidate genes involved in the cell wall formation, energy metabolism and metabolic regulation were differentially expressed in BCG cultures in the absence and/or presence of V B1.
2a ). On the other hand, KEGG analysis of the transcripts was performed by BLAST against KEGG database. KEGG enrichment analysis indicated that most of the variable genes were associated with the following biological processes: "microbial metabolism in diverse environments", "biosynthesis of antibiotics", "carbon metabolism", "two-component system", "biosynthesis of amino acids", "nitrogen metabolism" and "fatty acid metabolism"(Fig. 2B ). The major identified functional groups are discussed below.
Library construction and data analysis The strand-specific library was constructed using TruSeq® Stranded Total RNA Sample Preparation kit (Illumina, USA). The ribosomal RNA was removed and the RNA fragments were cleaved, while the first and second strand cDNA were synthesized. A single ' A' nucleotide was added to the 3′ ends of the blunt DNA fragments with repaired ends, and subsequently connected with adapters. The purified libraries were quantified using Qubit® 2. 0 Fluorometer (Life Technologies, USA) and validated by Agilent 2100 bioanalyzer (Agilent Technologies, USA), in order to measure the concentration and confirm the insert size. The cluster was generated by cBot and RNA sequencing was performed using a 150 bp pair-end strategy with the Illumina Hiseq X10 platform (Illumina, USA) to generate 3 billion bases per sample.
Thereafter, raw data was acquired, followed by pretreatment using Septk1. The read length was > 90 nt. The number of average clean reads, clean reads ratio and mapping ratio for WT and V B1 -treated samples were 3. 3G, 97. 1 and 99. 6%, and 3. 2G, 97. 14 and 99. 4%, respectively. We deposited the raw data to GEO repository under accession number GSE114949. The raw sequencing data were filtered to remove rRNA reads, sequencing adapters, short-fragment reads and other low-quality reads. The cleaned paired-end reads were mapped to M. bovis BCG reference genome with accession number AP010918 by using Tophat v2. 0. 9 with two mismatches  [121]. After the completion of genome mapping, Cufflinks v2. 1. 1  [122]  was run with a reference annotation, in order to generate FPKM (Fragments Per Kilobase of exon model per Million mapped reads) values for known gene models.
Cuffdiff analysis was performed to identify the differentially expressed transcripts and genes  [118]. False discovery rate (FDR) was used to determine the threshold of P-value in multiple tests, on the basis of FDR ≤0. 05 and fold-change ≥2  [123, 124]. Furthermore, the homology of genes in M. bovis BCG was compared with M. tuberculosis H37Rv genome, by using BLAST analysis (https://blast. ncbi. nlm. nih. gov/Blast. cgi; Additional file 7: Table  S2 ). cDNA synthesis and qRT-PCR cDNA was synthesized using SuperScript III First-Strand Synthesis kit according to the manufacturer's instructions. Quantitative real-time PCR (qRT-PCR) was performed with iQ™ SYBR Green Supermix (Bio-Rad) by using the LightCycler 480 II (Roche) RT-PCR System. The PCR conditions were as follows: initial denaturation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at 55 °C for 30 s and extension at 72 °C for 10 s.
Metabolites were characterized by comparisons with reference standards or MS/MS fragment information obtained from the Human Metabolome Database (HMDB) (http://www. hmdb. ca), the Metlin Database (http:// metlin. scripps. edu), the massbank Database (http://www. massbank. jp/), the LipidMaps Database (http://www. lipidmaps. org) or the mzclound Database (https://www. mzcloud. org). We used IDA (Information Dependant Acquistion) and selected the signal intensity of the first 8 ions in the parent ion for secondary fragmentation (Additional file 8: Figure  S1 ).
Ubong et al. [27]  experimentally and numerically studied the performance of HT-PEMFCs. Their model is a single cell with a serpentine channel, and their simulation were implemented in steady-state, three-dimensional, and isothermal. Su et al. [28]  addressed the three-dimensional and isothermal simulation of an HT-PEMFC with PTFE membrane. They examined the parameters such as the distribution of saturated water, membrane conductivity, temperature, flow density, etc. on the performance of the fuel cell. Wang et al. [29]  investigated the start-up process modeling of an HT-PEMFC using Comsol software. Starting HT-PEM fuel cells from room temperature to a proper operating temperature is a challenge. In this study, the preheated air was used to increase the temperature of the cooling channel or gas channel, and the optimum state was selected by checking the start-up time and temperature behavior of the fuel cell.
Liu and Chang  [30]  experimentally investigated the effects of the micro-porous layer on the performance of an HT-PEMFC based on PBI. Using PBI electrolyte, they expressed the effect of MPL compounds, including PTFE and carbon and extracted an optimal state. The results showed that the micro-porous layer with a content of 40 wt% of PTEF and loading of 1 mg/cm 2 carbon yielded the optimum performance of the cell. Lonescu  [31]  performed a three-dimensional simulation of a single-channel HT-PEMFC in a steady state. In this research, the Comsol software was used for simulation, and the concentration of species, namely, hydrogen and oxygen, and water concentration in the cathode and anode gas channels were investigated. Liu et al. [32]  examined the effect of gas flow channel dimensions and operating conditions such as pressure and temperature on the efficiency of HT-PEMFCs.
Results and Discussion The high pectinolytic activity of the two isolates S32 and J9 justified their careful identification. As a first approach, their gapA gene was amplified by PCR and sequenced. Analysis of the gapA amplicons showed a close proximity between strains J9 and S32 (99% identity). Blast search against nr/nt NCBI databank indicated that the best scores (94 to 95% identities) for the J9 or S32 gapA amplicons are found with strains of the genus Serratia, including several strains of S. fonticola, S. liquefaciens, S. proteamaculans, S. quinivorans, and of non-identified Serratia species. The level of identity with strains of other characterized Serratia species is lower than 90%. To gain information on this water isolate, the genome sequence of strain S32 was determined. The draft genome comprises 149 contigs (N50=119,595, L50=14, 324X coverage depth) with a total length of 4,810,389 bp and a G+C content (mol%) of 53.
A new Blast search against the J11-6 T genome (NCBI assembly GCA_001976145. 1) using the sequence of the S32 or J9 gapA amplicons showed that each gapA sequence shares more than 99% identity with J11-6 T DNA, confirming the close proximity between the three strains (Figure  1 ). A high level of homology was found between the genome of S32 and that of the type strain of S. oryzae, J11-6 T. The complete genome sequence of S. oryzae J11-6 T consists of 5,012,778 bp, with G+C contents of 53. 2% and 4,733 predicted protein coding genes by automatic annotation with RAST. Using the function-based comparison tool in RAST, 88 genes were predicted to be present in S32 but absent in J11-6 T and, inversely, 64 genes were predicted in J11-6 T but absent in S32. Several S32 specific genes are related to phages or mobile elements (25 genes), or predicted to encode fimbriae (5 genes), toxins/ antitoxins (5 genes), or restriction-modification systems (5 genes) (Table  S1 ).
Acknowledgments We thank the staff of the  Pierre Vérots Foundation  for their warm welcome, and  Véronique Utzinger  for medium preparation and for assistance in implementation of phenotypic tests. This work was supported by the  Pierre Vérots Foundation  (http://www. fondation-pierre-verots. com/),  FR3728   BioEnviS  ( AAP2017 ),  Agence Nationale de la Recherche  ( ANR-15-CE21-0003 ),  CNRS , University Lyon 1, and  INSA Lyon. It has been benefited from the expertise and facilities of the platform DTAMB of  FR3728 (Villeurbanne, France ), with sequencing performed by BIOFIDAL (Vaux-en-Velin, France).
Their chemical profiles are then used for their identification and labeling, based on the current knowledge of emission chemical footprints and of atmospheric secondary processes. The availability of well-known real-world PM chemical profiles is essential to validate the attribution of factors obtained from the PMF analyses. The scarcity of local source profiles often represents a challenge for RMs in terms of both the identification of sources and the comparability between studies. In this context, libraries gathering various PM chemical source profiles obtained from previous studies are of great interest for the scientific community. The SPECIATE database has been made available by the US EPA since 1988  [10] , now containing over 3000 local source profiles. More recently, a similar effort has been conducted in Europe, leading to the SPECIEUROPE database as described by Pernigotti et al.
[11]. This database could be implemented thanks to various recent PMF studies, such as those described in Karagulian et al. [5] , Belis et al. [6] , Viana et al. [7] , Mooibroek et al. [12] , Perrone et al. [13]  and reference therein. However, the robustness of these source profiles needs to be assessed more deeply and outputs from additional source apportionment studies are still needed to increase the European coverage and to document various aerosol fractions and/or geographical areas. In particular, a very limited number of comprehensive PM 2. 5 and/or PM 10 source apportionment studies were conducted in France before 2012  [14] [15] [16] [17]. To fill this gap, the research community along with regional monitoring networks put common efforts into extensive filter samplings, off-line chemical analyses and statistical data treatments for numerous French locations  [18].
Moreover, the SOURCES project has been set-up to deliver a comprehensive overview of these filter-based source apportionment studies achieved in France for the 2012-2016 period  [19]. Here, we present some of the main results obtained within this SOURCES project, based on the outputs of PMF analyses conducted for 15 datasets corresponding to various sampling sites distributed all over France. An innovative aspect of this study is that PMF analyses have been achieved in a harmonized way, allowing for a proper comparison of the results. Furthermore, all the outcomes of the project, including time-series and source profiles along with their corresponding uncertainties, have been made available through a website interface, proposed as Supplementary Materials for this article, at http://pmsources.
PM Sampling Sites Figure  1  shows the geographical location of the 15 sampling sites for which comparable datasets (in terms of number of samples, investigated period and PM 10 chemical speciation data) were made available for the present study through the SOURCES project. Table  1  summarizes the characteristics of these sites. Most of them correspond to urban background stations and are distributed throughout France, including two Alpine cities (classified herewith as "Urban valley"). Other site typologies could also be investigated, with two urban traffic sites, one industrial, as well as one EMEP (European Monitoring and Evaluation Programme, https://www. emep. int/) rural background station. At each site, PM 10 concentrations were monitored using automated analyzers, in accordance with recommendations of EN 16450:2017  [21] , and daily (24 h) filter samples were collected every third day by employees of the corresponding regional air quality monitoring network.
Detailed information on the PMF methodology can be found elsewhere  [8, 9]. Briefly, a multivariate factor analysis was applied to decompose the matrix of the chemical dataset into two matrices: factor contributions (G) and factor profiles (F), each factor to be ascribed to a specific aerosol fraction (i. e. , factor), that may be attributed to a source. It is based on a weighted least square fit, where the weights are derived from the analytical uncertainty, and provides the optimal solution by minimizing the function Q, given by Equation (2): Q = ∑ i ∑ j e ij σ ij 2 (2) where σ ij represents the measurement uncertainty of each data point.
Input Variable and Uncertainties Selection of the input variables was done based on the percentage of values above detection limit (DL) (using 60 % as a minimum threshold), and the value of the signal-to-noise ratio (S/N) for each species  [31]. Table  2  presents the PMF input variables selected for this work. This dataset notably includes EC, major ions, molecular organic markers (MSA, levoglucosan, and mannosan), and a selection of metals or trace elements. Assuming that they mainly originate from the same sources-i. e. , primary biogenic emissions-sorbitol, arabitol and mannitol concentrations were summed and labeled as polyols  [32]. Furthermore, OC* was determined by subtracting the carbon concentrations of the specific organic markers to total OC concentrations, so that they are not accounted twice:  (3)  where the coefficient are the carbon mass par unit of mass.
Results and Discussions The outputs of the SOURCES project consist of a large database, which cannot be exhaustively discussed in a single paper. Here, we present an overview of obtained PMF's results, give some examples of factor's behaviors, and focus on the statistical evaluation of the solutions as well as on the comparability of various factors. However, all the results-including time-series and factors profiles along with their corresponding uncertainties-have been made interactively available online at http: //pmsources. u-ga. fr. This application is proposed as Supplementary Materials for the present paper. It should be noted that the harmonized methodology used for the purpose of this study may lead to slight discrepancies when compared to results obtained in the case of PMF analysis performed specifically at a given site, as discussed for instance in Favez et al.
This may be explained by using constraints that already narrowed the rotational ambiguity (lowering DISP uncertainties range) or by the sensitivity of our datasets to specific days (increasing BS uncertainties range). Table  6. Summary of the uncertainties (in µg m -3 ) obtained from the BS (5th and 95th percentiles) and DISP (min-max) runs for a subset of species in two different factor profiles, the road traffic at ROU site and the biomass burning at GRE site, for both the base and constrained runs. The dispersion of the concentrations of key species in different profiles was investigated and is briefly presented here. The external variability (from site to site) is presented in its own section below (Section 3. 6). Here, we only assessed the internal variability, i. e. , the variability of a specie at a given site for a given factor, and the coefficients of variation (CV, standard deviation over the mean) of species from the BS runs are presented in Figure  6 , averaged from all sites.
Estimation of the Uncertainties of Time Series Concentrations This section presents an attempt to express the uncertainties associated with the concentrations of a chemical species in the time series of a specific factor contribution. Even if during the BS runs both the F and G matrices were recomputed, only the F matrix was returned to the user in the EPA PMF5. 0 software. The matrix G was only used internally to map the BS factor to the base factor  [20]. As a result, the outcome was an estimation of uncertainties for the F matrix, but not for the G one. Hereafter, we propose to estimate the uncertainties for the time series of the concentrations of a chemical species by multiplying the different concentrations given by the uncertainties runs (BS and DISP) by the factor contribution of the reference run G re f : X ERRi = F ERRi • G re f , (6) where F ERRi refers either to the F matrix given by the DISP min/max run or to one of the BS runs, G re f is the contribution matrix of the reference run, and X ERRi simulates the temporal concentration of species in the run ERRi (DISP or BS).
Conclusions This paper presents results obtained from PMF analyses conducted in a harmonized way and taking advantage of datasets corresponding to more than 2200 samples collected at 15 different sampling sites in France. To the best of our knowledge, this is the first study using such a large database over several years of monitoring. Thirty-seven input variables were selected to cover a wide range of major expected PM 10 factors and to optimize total mass reconstruction at all sites. The operator-related subjectivity was reduced by using homogeneous statistical and geochemical criteria for the source discrimination and identification. Overall, the main PM 10 factors were found to be primary traffic (15 ± 7% of the PM 10 mass on a yearly average) and biomass burning (17 ± 9%) as well as two secondary aerosol fractions dominated by ammonium nitrate (17 ± 8%) and ammonium sulfate (15 ± 4%), respectively.
Supplementary Materials: The following are available online at http://www. mdpi. com/2073-4433/10/6/310/s1, Figure  S1 : Similarity plot for all pair of profiles containing a "dust" factor, Figure  S2 : Uncertainties of OC* in the Biomass burning profile according to BS for each site. In addition, http://pmsources. u-ga. fr presents interactively the whole dataset of this study. Funding: This work was conducted as part of the SOURCES project funded by ADEME (grant agreement No. 1462C0044). The PhD of Samuël Weber is funded by ENS Paris. Samples were notably collected and analyzed in the frame of various programs funded by the French Ministry of Environment through the CARA program leaded by the National Reference Laboratory for Air Quality Monitoring (LCSQA) and/or through programs (such as Primequal) managed by ADEME, as well as in the frame of actions funded by ANDRA and/or regional air quality monitoring networks (AASQAs).
Acknowledgments: The authors also acknowledge the work of many technicians and engineers in the lab at  IGE  ( A. Wack ,  C. Charlet ,  F. Donaz ,  F. Masson ,  S. Ngo ,  V. Lucaire ,  A. Vella ), at Ineris ( R. Aujay ,  A. Papin ,  V. Minguet ,  N. Chatellier ), at IMT LD (B. Malet), and at  LSCE (N. Bonnaire). Finally , the authors would like to kindly thank the dedicated efforts of many other people, notably in the AASQAs, for collecting the samples.
Statistical analysis The data from this retrospective study were described as absolute frequencies and percentages for categorical variables and median for continuous variables. The association between categorical variables were evaluated with Fisher Exact Probability test or Chi-square test, because the total of our sample was small, N = 21. A p-value < 0. 05 was considered as statistically significant. Matlab version 9. 3. 0, Release 2017b (The MathWorks, Natick, MA, USA) was used for the analysis data.