dataesr/scientific-paragraphs-categorization huggingface dataset:
The final NGS libraries were quantified using Agilent Bioanalyzer DNA Chip 1000 then pooled; 11 libraries per pool. Paired-end 100 bp deep sequencing was carried out on HiSeq 2500 (Illumina). Using a customized analysis pipeline (Figure  S1B ) all samples were run together on a virtual machine. The analysis pipeline implemented as a bash shell script is composed of TopHat  34  , Picard, (http://picard. sourceforge. net/index. shtml), Samtools  35  and Cuffdiff  34. TopHat aligns RNA-seq reads to the reference genome (GRCh37). Picard removes PCR duplicates and Samtools creates indices for bam files. We ran the Cuffdiff function from the Cufflinks package v2. 1. 1. 34 to find differentially expressed genes and transcripts in the samples using the default settings. Results from Cuffdiff were plotted in R using the Bioconductor CummeRbund package  34.
Information for mapped, unmapped and duplicate reads was extracted using Samtools. The length of the transcript in base pairs was calculated by summing up depth per genome position. The covered fraction of transcript was computed by dividing the total number of bases in mapped reads with the length of the transcript. Depth per genome position was calculated using GenomeCov function from BedTools  36. RNA quality control was performed with RNA-SeQC  37. Read metrics are summarized in Table  S1B. Sample preparation for mass spectrometry. Subcellular fractionation was performed using the Nuclear Extract Kit (ActiveMotif) resulting in cytosolic (CYT), nuclear (NUC) and chromatin-bound (CH) proteins. All extraction buffers were supplemented with protease inhibitors (Complete EDTA-free, Roche) and phosphatase inhibitors (PhosStop, Roche).
Mass spectrometry. Nano LC-MS/MS analysis. Of the resolubilized peptide solution, 6 10 μl were subsequently analyzed by liquid chromatography (LC) using an EASY nLC-II coupled to a Q Exactive mass spectrometer (Thermo Scientific, Bremen, Germany)  33. Emitter columns were packed in-house with ReproSil-Pur 120 C18-AQ 3 μ m diameter beads (Dr. Maisch GmbH, Germany). Liquid chromatography analysis was performed with a segmented gradient from 10 to 60% mobile phase B over 100 min at a flow rate of 250 nl/min. Mobile phase composition was as follows: mobile phase A, H 2 O with 0. 5% acetic acid; mobile phase B, H 2 O: acetonitrile (20:80 volume ratio with 0. 5% acetic acid). A data dependent mass spectrometric method was employed, where the 10 most intense ions, excluding unassigned charge states and singly charged ions, detected in the preceding full scan are isolated (3 Th isolation width) and fragmented using higher energy collisional dissociation (HCD) (normalized collision energy 25).
Precursor scans (MS 1 level) were acquired at a resolution of 70,000 (m/z 300) and an AGC target value of 3,000,000 charges (maximum ion injection time 20 ms). Fragmentation spectra were acquired at a resolution of 17,500 (m/z 300) and an AGC target value of 100,000 charges (maximum ion injection time 120 ms). All scan events were recorded in profile mode. A dynamic exclusion list of 25 s was employed and the exclude isotopes functionality was activated. Data analysis. MS data was analyzed by MaxQuant suite of algorithms version 1. 4. 1. 2 40 using a Homo Sapiens database downloaded from UniprotKB on the 27 th of November 2013 and comprising 88,473 protein isoforms entries. The following default search parameters were employed: first search mass accuracy tolerance 20 ppm, main search mass accuracy tolerance 4.
5 ppm, FTMS MS/MS tolerance 20 ppm, minimum peptide length of 7 amino acids, peptide spectrum match FDR and protein FDR both set to 0. 01 as calculated by the revert database approach  41. Protein quantification was based on a minimum of two ratio counts, originating from unique or razor peptides only. Additionally, unless explicitly stated otherwise, other parameters of the data analysis were not changed from their MaxQuant 1. 4. 1. 2 default value. Multiplicity was set to 3 with Arg6/Lys4 and Arg10/Lys8 for medium and heavy labels respectively as required by the triple SILAC approach and the search was conducted using Trypsin/P enzyme specificity, allowing for maximum two missed cleavages, N-terminal acetylation and methionine oxidation as variable modifications and cysteine carbamidomethylation as fixed modification.
Both re-quantify and match between runs (match time: 1 min, alignment time window: 20 min) functionalities were enabled. MaxQuant analysis was performed both on the combined (36 MS runs per sample) and separate organelle fractions (12 MS runs per sample). Despite reducing the ampholyte concentration during in-solution isoelectric focusing (0. 1% instead of the recommended 1%) and additional washing on C 18 STAGETips, ampholytes eluted in the beginning of the chromatogram, interfering with timely MaxQuant processing. Raw files were thus preprocessed with the RecalOffline application (ThermoFisher Scientific) to exclude the first 35 min of acquisition. Bioinformatics and statistical analysis. Proteomics data sets were analyzed with the empirical Bayes moderated T test implemented by the limma bioconductor package in the R environment.
P values were corrected for multiple hypothesis testing using the Benjamini-Hochberg method (FDR ≤ 0. 05). Differential expression was calculated on normalized log 2 ratios (nano LC-MS/MS). Differential expression for RNA-sequencing data was performed with the Cufflinks package  34  using the Cuffdiff function. PCA analysis was performed with the FactoMineR package v. 1. 29 within the R environment on FPKM values for RNA and on non-normalized intensities derived from mass spectrometry for PROT. Differentially expressed genes and proteins were processed using the in-house developed Comics R package for enrichment analysis. Data availability. The mass spectrometry proteomics data, including all raw files and data analysis tables produced by MaxQuant analysis, have been deposited to the ProteomeXchange Consortium (http://proteomecentral.
All three proteins were measured by mass spectrometry and their intensity profiles (Figure  S4E ) validated the western blot data and confirmed the specificity of the organelle fractionation technique employed. In the combined analysis (CYT, NUC and CH together), 8408 proteins were identified with 7129, 7023 and 7136 quantified proteins per replicate (FDR ≤ 1% on both peptide and protein level) (Figure  S4F , Table  S2 ). As an internal control, the data set was screened for proteins expected in ESC and MSC, respectively. Pluripotency-associated proteins identified include SOX2, POU5F1, SALL4, UTF1, DNMT3B, DPPA4, LIN28A, and GRB7. As classical MSC phenotyping surface markers  49  we identified ALCAM (CD116), ITGB1 (CD29), CD44, ENG (CD105), NT5E (CD73), and THY1 (CD90) (Figure  S4G ). As expected, expression patterns observed followed the cell type origin.
87 for BM-MSC, while the two MSC subtypes correlate in the range 0. 94-0. 96. The close relationship between ESC-MSC and BM-MSC is also validated by PCA analysis, where approximately 95% of the variance is explained by the first two components separating ESC and MSC (Fig. 2d ). Comparing our proteomic dataset on ESC and MSC to the two most comprehensive human proteome maps published to date, which contain together 19608 proteins and cover a wide range of tissues from adult and fetal origin  51, 52  , we uniquely identified 54 proteins and 5409 peptides, which map to 3024 proteins (Figure  S5A ). Our study thus provides new additional data to the human proteome. Given their likely stem cell specificity and the origin of the data in the maps, the 54 proteins are unsurprisingly largely uncharacterized and thus lack functional data.
In comparison to the most comprehensive studies available on human ESC  53, 54  (Figure  S5B ), our data set contains 4500 proteins commonly expressed within the three studies which may be seen as an ESC core proteome. The data set adds 1661 unique identifications, expanding the ESC proteome and contributing to ESC biology. Finally, comparing our data set to the most comprehensive MSC proteomics study available to date  55  , our relative contribution to the reported MSC proteome is even larger adding 3331 unique identifications (Figure  S5C ). Among all proteins identified, 62 were classified as uncharacterized proteins including KIAAs and ORFs, from which 20 were significantly differentially expressed, hinting at a role in stem cell biology. A large proportion of transcription-related proteins in our dataset are zinc finger proteins (ZNF) (207) which is believed to be the largest family of transcription factors in mammalians.
The potentially best suited MSC markers are TNFSF1A, LIFR, JAG1 and PVRL3, when considering tissues, or TNFRSF10D, TNFRSF1A, CD248 and LIFR, when considering cell lines. In general, most discriminative are TNFRSF1A (CD120a) and TNFRSF10D (CD264), and their use, or more generally our findings, may prove helpful in refining the definition of MSC as given by  67, 68. To broaden our search for potential MSC surface markers, our dataset was further filtered for proteins integral to plasma membrane; 293 candidates identified by nano LC-MS/MS in both MSC subtypes were thus obtained. S3 ). Their normalized log 10 protein expression levels are shown across a wide range of tissues (top panel) and cell lines (bottom panel) according to ProteomicsDB. CD molecules up-regulated solely in ESC-MSC are highlighted in purple, whereas those upregulated only in BM-MSC are highlighted in red.
The higher number of SDE features measured by RNA sequencing may also relate to translational and post-translational regulation not propagating into protein abundance differences. Nonetheless, a 60% correlation coefficient between the two techniques was achieved, validating literature reports and confirming the quality of the presented dataset. Based on PCA and correlation analysis, the presented data suggests equivalency between bone marrow-and embryonic stem cell-derived MSC, with differences similar to previous reports on donor-derived MSC from different tissues. This interpretation is further supported by the observation that expression profiles of BM-MSC from younger donors correlate better to those of ESC derived MSC. The reported differences between ESC and MSC reflect the expected cell behavior: cell cycle centered for the former versus extracellular processes driven for the latter.
Development of moderate intestinal inflammation during BaP treatment. The development of intestinal inflammation in mice was determined by histological examination. Despite that no effect was observed on mouse growth phenotypes (data not shown), moderate inflammatory signs were observed in the ileal and colonic mucosa of C57BL/6 mice exposed to BaP (Fig. 1A, B ) compared to the control and vehicle groups, Nevertheless, histological examinations of the mouse intestinal epithelium revealed a significantly lower inflammatory score in the colon than in the ileum (mean histological scores of 2. 9 and 6. 2 for the colonic and ileal mucosa, respectively) (p-value = 0. 0013). Infiltration of the epithelium by polynuclear cells was significantly increased in these two intestinal mucosa tissues from mice exposed to orally administered BaP (Fig.
Paired-end sequencing (2 × 250 bp) was performed at MR DNA (www. mrdnalab. com, Shallowater, TX, USA) on a MiSeq platform. High-throughput sequencing data analysis. Paired-end reads were joined using fastq-join from the ea-utils software package  55. Sequences were analysed using the open source software package Quantitative Insights Into Microbial Ecology (QIIME v. 1. 8. 0)  56. After demultiplexing and quality filtering of sequences, chimeras were detected and removed using USEARCH 6. 1 (http://www. drive5. com/usearch). The resulting high quality sequences were processed to generate OTUs with a 97% similarity threshold that were then taxonomically assigned on Greengenes database using UCLUST (http://www. drive5. com/uclust). A variable number of sequences was obtained per sample (range [20,641-231,849] and [5,804-418,919] for stool and mucosa samples, respectively).
Therefore, for fair comparison, the sequence number of each sample was randomly normalized to the same sequencing depth (20,500 sequences per stool sample and 5,800 sequences per mucosal sample). The alpha (Chao 1, Shannon's diversity index, phylogenetic diversity (PD)) and beta-diversity (Principal Coordinate Analyses (PCoA) on a weighted Unifrac distance matrix analyses) were performed using QIIME. Statistical Analysis. Statistical analyses of the histological scores were performed using a Kruskal-Wallis nonparametric test with GraphPad Prism 5 software (San Diego, CA, USA) with a statistical significance of p < 0. 05. Analysis of Similarity (ANOSIM) was applied to test for significant differences between sample groupings. Significant variations in bacterial population richness, diversity and abundance were assessed using Mann-Whitney (p < 0.
Acknowledgements We thank  Dr. Abdelkrim Alloui  for animal care ( Animal Facilities, Clermont-Ferrand, France ) and  Lorraine Gameiro  from the  CICS platform (Université Auvergne ) for technical assistance with tissue preparation for microscopy analysis. We thank  Cecile Lepère  for reviewing the English version of the manuscript. CR received a graduate grant from the  "Ministère de l'Enseignement Supérieur et de la Recherche" of France.
Considering large and complex areas like the Tibetan Plateau, an analysis of the spatial distribution of the solar radiative budget over time not only requires the use of satellite remote sensing data, but also of an algorithm that accounts for strong variations of topography. Therefore, this research aims at developing a method to produce time series of solar radiative fluxes at high temporal and spatial resolution based on observed surface and atmosphere properties and topography. The objective is to account for the heterogeneity of the land surface using multiple land surface and atmospheric MODIS data products combined with a digital elevation model to produce estimations daily at the kilometric level. The developed approach led to the production of a three-year time series (2008)(2009)(2010) of daily solar radiation budget at one kilometer spatial resolution across the Tibetan Plateau.
Numerous algorithms have been developed to estimate solar radiative fluxes using satellite radiometric measurements  [6] [7] [8] [9] [10] [11] [12] [13] [14]  and some have led to the production of regional or global datasets  [15]. To the knowledge of the authors, among the best known are the Surface Radiation Budget project (SRB)  [16] , the Clouds and the Earth's Radiant Energy System (CERES)  [17] , the International Satellite Cloud Climatology Project (ISCCP)  [18] , and the Global LAnd Surface Satellite (GLASS) radiation products  [19]. To date, most of the available radiation products have high temporal resolution but too coarse spatial resolution (30-280 km scale) to investigate local-scale radiation balances  [20]. Moreover, they are often not suitable to characterize rugged areas such as the Tibetan Plateau  [21] , even if some of those algorithms have been adapted to tilted surfaces  [22] [23] [24] [25].
In that context, the objective is to evaluate the potential of using existing satellite data products, such as the ones provided by MODIS, to estimate the solar radiation budget over the Tibetan Plateau. The idea is to assess the accuracy that can be achieved only by using those existing satellite data products over heterogeneous areas. To do so, MODIS (Moderate-Resolution Imaging Spectroradiometer) satellite data products time series at a kilometer resolution are used to estimate the atmospheric transmittance as well as the land surface albedo and a digital elevation model (DEM) is used to compute the local illumination angle, leading to a new approach to model daily radiation fluxes at higher spatial resolution. The algorithm provides instantaneous fluxes computed for all sky conditions which are then aggregated into daily averages.
Study Area and Validation Data The Tibetan Plateau, lying between the Himalayan range to the south and the Taklimakan desert to the north (Figure  1 ), is the largest and highest plateau area in the world spreading over 1. 2 million km 2  and presenting an average elevation over 5000 meters. Ground measurements, coming from four radiative balance stations located on the Tibetan Plateau (Figure  1 ): BJ (now Nagqu), Linzhi, NamCo, and Qomolangma, are used to evaluate the modelled solar radiation budget estimates. The information concerning the stations' sites and instruments is taken from Babel et al. [26]. The BJ site is set over relatively flat terrain in a large valley at 4502 m. The vegetation is very low, sometimes interspersed with bare soil, but quite homogeneous on the landscape scale.
Solar Radiation Budget Using MODIS Data Products MODIS data were selected because they are freely, widely and, for most of the products, have a kilometric resolution, and are available daily. However, when this study was conducted, MODIS albedo products were only available as eight-day products. Additionally, they are reliable and more than 10 years of archives are available allowing for the computation of historical averages and anomaly detection  [3]. The methodology to estimate solar radiative fluxes over the Tibetan Plateau is implemented using Terra-MODIS atmospheric and land products level 2 and 3. All the atmospheric products are not related to the spectral wavelength and the albedo is a broadband product. Then, no narrow-to-broadband conversion is needed in this case as the produced solar radiative fluxes are directly broadband estimates.
The surface pressure, atmospheric optical depth (AOD), the precipitable water, ozone thickness, and cloud properties are extracted from the level 2 atmospheric products. To compute the solar incident angle, the local satellite overpass time is necessary and is taken from MOD11A1 day view time. The black-sky and white-sky albedo are obtained from MCD43B3. Then, the blue-sky albedo is derived according to the method given in Román et al. [57]. All the required MODIS products are presented in Table  1. All the data products used during the solar radiative fluxes computation, except for the albedo product, are daily products and include missing data. Thus, a data filling plan was implemented in order to fill the gaps before using the data in the computation. The methodology applied is based on spatial and temporal gap filling.
When there is less than 10% of missing data in a scene, local spatial average are used to fill the gaps. When there is more than 10% of missing data and if the corresponding eight-day or monthly MODIS level 3 products are available, the latter are used to fill the gaps. If missing pixels remain after this operation or if no eight-day or monthly MODIS products are available, the average value of the surrounding pixels is used. If there are still some pixels without value, the last option is to fill the pixel with the average value of the whole image (Figure  2 ). In parallel, a quality flag dataset is produced to provide information about the reliability of the estimates retrieved using those filled datasets. The characteristics of the topography are retrieved at 1 km resolution from the DEM provided by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), i.
e. , the Global Eight-day and monthly MODIS datasets are available for AOD, precipitable water, and ozone thickness. There is no such product available for the surface pressure data but this daily product is relatively complete, as shown later on (Section 3. 4), then the use of the data filling procedure based on the local average is sufficient. The filling plan used the data presented in Table  2. The characteristics of the topography are retrieved at 1 km resolution from the DEM provided by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), i. e. , the Global Digital Elevation Model (GDEM) version 2. ASTER-GDEM2 is one of the most complete high-resolution digital topographic dataset in the world to date. The ASTER-GDEM2 covers land surfaces between 83 ˝N and 83 ˝S at a spatial resolution of 1 arc-second.
Solar Radiation Budget Once the MODIS data products required for the estimation of the solar radiation budget are extracted, registered, and gap filled, the solar radiation budget can be computed in three main steps summarized in the workflow presented in Figure  3. First the TOA irradiance is calculated using the latitude and longitude values of each pixel, the solar constant corrected for the sun-earth distance, the satellite overpass time derived from MOD11A1 and integrating the slope and aspect of the terrain derived from the DEM. Second, the atmospheric transmissivity is computed for clear sky using the surface pressure, AOD, precipitable water, ozone thickness extracted from MOD06, MOD04, MOD05, and MOD05, respectively. Combined with the TOA irradiance, the surface irradiance is computed for clear sky.
Solar Radiation Budget Three-Year Time Series Based on the methodology presented in Section 2. 3 and using a database containing the required gap filled MODIS products, a time series of instantaneous and daily at-surface solar radiative fluxes from 2008 to 2010 was produced at one kilometer resolution. Figure  5  shows the three-year time series of instantaneous solar irradiance and reflected fluxes estimated for the four pixels corresponding to the four ground stations, namely BJ, Linzhi, NamCo, and Qomolangma (noted as Qomo). The instantaneous values are estimated at satellite overpass time which is between 10. 30 and 11 am local time depending on the day. Figure  5  shows that the instantaneous solar incident and reflected radiative fluxes estimated at BJ, Linzhi and NamCo fluctuates more than the one obtained over Qomolangma.
Validation Against Ground Measurements The radiative fluxes estimates are validated using the ground measurements recorded at the four stations from 2008 to 2010, considering all skies and clear sky days separately. The distinction between clear sky and cloudy conditions is made on the basis of the cloud fraction provided by MODIS. Some incoherent values were observed in the ground measured reflected fluxes, then the data have been filtered which also masked out some possible snow events. The validation results are presented in the scatter plot in Figure  7.
Validation Against Ground Measurements The radiative fluxes estimates are validated using the ground measurements recorded at the four stations from 2008 to 2010, considering all skies and clear sky days separately. The distinction between clear sky and cloudy conditions is made on the basis of the cloud fraction provided by MODIS. Some incoherent values were observed in the ground measured reflected fluxes, then the data have been filtered which also masked out some possible snow events. The validation results are presented in the scatter plot in Figure  7. According to Figure  7 , better results are obtained when the solar radiative fluxes are estimated for clear sky, especially the instantaneous irradiance. The estimation of the latter performs also better than for the instantaneous reflected fluxes and the daily net radiations.
For clear sky incoming fluxes, most of the values are located close to the trend line (Figure  7 ) while few pixels show large difference between measured and estimated fluxes. Those large differences have a strong impact on the RMSE. Furthermore, part of the RMSE is explained by the bias which is, in some cases, important as compared to the RMSE. The observed bias may be caused by the fact that the instantaneous fluxes are compared to ground measurements interpolated from 30 min average values. As the cloud cover at a specific location can evolve rapidly, its impact on data integrated over 30 minutes can be important  [60]. Moreover, at this time of the day, the solar radiative fluxes can increase rapidly in 30 min. Finally, the correlation between estimated and measured instantaneous and daily fluxes is mostly significant at the station of Qomolangma and only for clear sky conditions.