dataesr/scientific-paragraphs-categorization huggingface dataset:
001 were considered as significant Supplementary Data 1 and 2). To create bigwig files reads from Watson and Crick strands were selected using SAMtools (v0. 1. 9) and provided to the bam2wig. py script from the RSeQC program suite (v2. 6. 4). ChIP and ChIP-seq. Cells were cross-linked with 1 % formaldehyde at room temperature for 10 mins followed by quenching with Glycine at 250 mM. Chromatin was sonicated using Bioruptor® Pico from 10 to 20 cycles (30 s ON, 30 s OFF) at 4 °C. The ChIP for the IRF9 TF was performed with 5 × 10 6 K562 cells stimulated with IFNa for 6 h and 5 μL of IRF9 Rabbit mAB (Cell signal, D2T8M) in 300 μL of ChIP dilution buffer. ChIP for the histone modifications was performed with 5 × 10 5 of K562 cells non-stimulated and stimulated with IFNa for 6 h, or 5 × 10 5 of in vitro differentiated THP-1 cells non-stimulated and stimulated with LPS for 6 h, and 1 μg of mAB against H3K27ac, H3K4me3 or H3K4me1 (Diagenode, C15210016, C15410003 and C15410194, respectively) in 300 μL of ChIP dilution buffer.
Chromatin was incubated with the antibody for overnight at 4 °C followed by the addition of 20 μL of Dynabeads protein G (Life technologies, 10004D). Bound immune complexes was washed three times and finally incubated in ChIP elution buffer (1% SDS, 0. 1 M NaHCO3) and 2 μL of proteinase K (20 mg/ mL, Life technologies 25530049) at 65 °C for overnight. DNA was then purified using QIAquick PCR Purification Kit (Quiagen, 28104). qPCR analyses were performed using the SYBR green Master Mix (Thermo Fisher Scientific) on the QuantStudio 6 instrument (Thermo Fisher Scientific). The relative expression was analyzed using the relative standard curve method and values were assessed as percentage of the input. Primers used are listed in Supplementary Data 7. ChIP-seq libraries were generated with the MicroPlex Library Preparation Kit (Diagenode), according to the manufacturer's instructions.
The libraries were sequenced in paired-end 50/30nt mode using the NextSeq® 500/550 (Illumina), according to the manufacturer's instructions (Supplementary Data 4). Reads were trimmed with Sickle v1. 33 and aligned to the hg19 genome assembly using Bowtie v2. 3. 4. 3 and default parameters. ChIP-Seq coverage tracks (bigwig) were generated with deepTools 66 v3. 2. 1 using -normalize RPKM. Peaks for ChIP-Seq were called using Macs2. ChIP-seq data for the IRF1, STAT1, and STAT2 TFs in K562 cells stimulated with IFNa were obtained from the ENCODE Consortium (Supplementary Data 4). Median average profiles were generated using the SeqPlots tool  67  for the 2 Kb and 10 Kb regions centered at the TSS of the selected genes for the TFs (IRF9, IRF1, STAT1, and STAT2) and the histone modifications (H3K27ac and H3K4me3), respectively.
Clones with homologous deletion were those showing a mutant band of the expected size and no wild-type band. For the CRISPR controls of the deletion of the OAS3 Epromoter, we transfected 1 × 10 6 K562 wild-type cells with 1 µg of each of the gRNA used for the OAS3 Epromoter deletion. Sanger sequencing identified one clone with an inert mutation (singlebase insertion) in the first intron of the OAS3 gene. Primers and gRNAs are listed in Supplementary Data 7. OAS3 gene expression recovery. To recover the expression of the OAS3 gene, we transfected 1 × 10 6 K562 ΔOAS3 clones 1 and 2 with the human ORF clone of OAS3 (ORIGENE, ref. RC222722) using the Neon Transfection System (Thermo Fisher Scientific; pulse voltage 1450 V, pulse width 10, pulse number 3). We used the expression plasmid of GFP (addgene, #46956) as a transfection negative control.
After 18 h, half of the cell population was treated with 100 ng of IFNα (Sigma Aldrich, 50 ng/mL) for 6 h. Data were normalized to Renilla values and represented as the fold-change of relative light units over the wildtype pOAS3-luc or pSV40-luc-pOAS3 vector from non-stimulated K652 cells. Experiments were performed in triplicate. Genomic analyses of interferon-dependent enhancers in Hela cells. Interferondependent and independent enhancers in Hela-S3 cells were retrieved from the Muerdter et al. study  15. The dataset comprises a genome-wide STARR-seq enhancer screening in HeLa-S3 cells treated or not with a combination of two inhibitors of type I interferon response. The annotation of enhancer regions with respect to the closest RefSeq TSS was performed using HOMER's annotatePeaks. pl tool  73.
Motif enrichment analyses. Known motif enrichment and de novo motif discovery analyses for TFs-binding sites were performed using the findMotifsGenome. pl program in HOMER suit  73  using the default motif lengths and 200 nt long sequences centered on each enhancer region as input. The ten enriched known motifs with the lowest adjusted P-value from each condition and dataset were extracted. For Muerdter et al. dataset, all proximal and distal enhancers for each condition were used as a background. A heatmap was plotted using the -log 10 (adjusted P-value). Lower P-value corresponds to higher enrichment in dark color. For motif enrichment in K562 promoters, all promoters analyzed by CapSTARRseq were used as a background. The top de novo motif with the lowest P-value from each promoter category was demarcated.
ISRE binding across the promoter regions. The identified promoter regions were analyzed with respect to the enrichment of the interferon-stimulated response element (ISRE) and TFs individually. The binding site's information of TFs, IRF family (IRF1-9), and STAT1/2 of the human genome (hg19) were extracted from the latest version of the JASPAR database  74. The individual TFs-binding regions were generated by bedtools merge (at least 1 base pair overlap), whereas the general ISRE was generated using the bedtools merge in all the IRF family of TFs and STAT1/STAT2. The TF-binding occurrences in the different categories of promoters were computed by counting intersects (bedtool intersect) of individual TFsbinding regions and ISRE across corresponding promoter regions. Positional distribution of IFNa-specific TFs-binding sites.
Overlapping binding sites of STAT1, STAT2, and IRF9 in IFNa-stimulated K562 cells were defined using the 'intersect' parameter from BEDTools v2. 28. 0  75  , based on ChIP-seq data. For each induced gene, we identified the closest merged peak. The location of the closest merged peak was categorized into three groups: (i) the same promoter, located within ±1 kb from the TSS of the induced gene; (ii) intergenic, located at >1 kb from the TSS of any RefSeq gene and; (iii) another promoter, located ±1 kb from the TSS of any other RefSeq gene. Statistical significance of the positional distribution was assessed using the OLOGRAM tool  76  from the Pygtftk package  77. Proximity of IFNa-induced genes in the human genome. To find the distribution of induced genes in the genome, the same number of IFNa-stimulated genes was extracted randomly from the human genome (hg19).
The randomly selected genes were further clustered based on distance (≤100 kb) between the TSS of two induced genes. Further, the distance density distributions of observed and randomly selected genes were compared using Kolmogorov-Smirnov (KS) test. Identification of IFNa-dependent clusters. The TSS coordinates of IFNa-induced genes or associated with induced Epromoters in K562 cells (as defined in Supplementary Data 2) were extracted with reference to the RefSeq annotations. The distance between each pair of TSSs was computed using a custom Python script. A cluster of IFNa-induced loci was defined if at least two TSS of induced genes or Epromoters were found to be closer than 100 kb. The clusters were then classified in function of the type of genes they contained: "induced gene only", "induced gene & Epromoter", "induced Epromoter only", or whether they contained a constitutive Epromoter (Supplementary Data 1).
To assess whether the clustered genes were located within the same TAD, we used the coordinates of TAD borders identified in K562 cells from highly resolutive Hi-C data  78. Overlapping domains were merged into one single TAD domain as described in  79. The detailed clusters are summarized in Supplementary Data 5. Identification of LPS-dependent clusters in primary macrophages. LPSinduced genes (log 2 (FC) > 1; P-val. <0. 01) and binding sites of interferonassociated TFs (IRF1, IRF8, STAT1) were extracted from the Mancino et al. study  29. The dataset comprises gene expression and TF-binding sites (mm9) in mouse primary macrophages before and after LPS stimulation for 4 h. The TSS coordinates of LPS-induced genes were extracted with reference to the RefSeq annotations. LPS-induced clusters were identified based on distance (≤100 Kb) as described for the IFNa-response clusters.
The promoters binding with TF(s) were considered only if the TF-binding site(s) overlapped at least 1 bp with the promoter region (±1 Kb from the TSS). We then determined the number of promoters per cluster that bind a given TF. The detailed description of the LPS-induces clusters is described in Supplementary Data 6. 3D chromatin organization of the IL15RA/IL2RA locus. CTCF ChIP-seq and Hi-C data in THP-1 + PMA cells from the data of Phanstiel et al. study  35  were visualized using the New WashU Epigenome Browser (epigenomegateway. wustl. edu)  80. TADs called from Hi-C experiments (HindIII) THP-1 cells were taken from  81. DNA interactions centered on the IL15RA promoter with the associated CHiCAGO score were taken from published Promoter Capture Hi-C 36 and visualized using the New WashU Epigenome Browser.
Acknowledgements We thank the  Transcriptomics and Genomics Marseille-Luminy (TGML)  platform for sequencing the CapSTARR-seq and ChIP-seq samples and the Marseille-Luminy cell biology platform for the management of cell culture. TGML is a member of the  France Genomique consortium  ( ANR-10-INBS-0009 ). Work in the laboratory of S. Spicuglia was supported by recurrent funding from  Institut National de la Santé et de la Recherche Médicale and Aix-Marseille University  and by specific grants from  Canceropôle PACA ,  A*MIDEX  ( ANR-11-IDEX-0001-02 ),  INCA  ( PLBIO018-031 INCA_12619 ),  Ligue contre le Cancer  ( Equipe Labellisée 2018 ),  ANR  ( ANR-18-CE12-0019  and  ANR-17-CE12-0035 ),  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  (grant agreement No  860002 ),  Institut Marseille Maladies Rares (MarMaRa) , and  Bettencourt Schueller Foundation  (Prix coup d'élan pour la recherche française).
Data availability The data that support this study are available from the corresponding author upon reasonable request. The RNA-Seq, CapSTARR-seq and ChIP-Seq data generated in this study have been submitted to the Gene Expression Omnibus (GEO) under the accession code GSE159462. Public datasets can be found under accession codes: ENCSR000FAU, ENCSR000FBC, ENCSR000EGL, ENCSR669GJD, GSE183296, GSE63525, GSE56123, GSE96800, GSE89663. All generated and publicly available datasets are listed in the Supplementary Data 4. Public databases used in this study are: Jaspar 2020 [http://genome. ucsc. edu/cgi-bin/hgTrackUi? hgsid=1169499883_qhnKn3jHG401qgfVk2LXUvfvZ2rU&db=hg38&c=chr19&g=hu-b_186875_JASPAR2020_TFBS_hg 38 ]; Interferome database [http://www. interferome. org/ interferome/home. jspx]. Source data are provided with this paper.
The purification occurs via dialysis. It has been demonstrated that AGuIX is an effective MRI-positive contrast agent with a longitudinal relaxivity (r 1 ) up to three times higher than that of DOTAREM, e. g. , 11. 4 mmol -1 s -1 and a ratio transverse relaxivity (r 2 )/r 1 of 1. 14 at 1. 4 T (2). This compound, AGuIX, is also produced for human use. The synthesis and purification permit to remove free gadolinium atoms. AGuIX is presently used in phase I to II clinical trials (NCT02820454, NCT03818386, and NCT03308604) as visible in www. clinicaltrials. gov. In previous mice experiments, these nanoparticles showed a half-life time in blood of 21. 6 min and an excellent fast renal elimination reaching a maximum of renal accumulation 4 h after injection, thus limiting most of toxicity consequence in mice body  (26).
Data Processing and Computational Platform The full data sets were reconstructed using the SYRMEP TomoProject software  (45). The phase was retrieved using Paganin's algorithm  (46) , and the slices were reconstructed by applying the Filtered Back Projection algorithm. The reconstructed images exhibit an effective pixel size equal to 3 µm and a spatial resolution equal to 6 µm. The images were further analyzed using FIJI image-processing package, through which the images of the projections of maximum and minimum intensity values were created and combined with each other. Maximum and minimum intensities are known visualization techniques that compress a 3D volume into a 2D image  (47). An image of maximum intensity projection is determined by the brightest pixel intensities along a projection path; in our experiment it corresponds to the densest objects.
The open-source software VolView (https://www. kitware. com/volview/) was used to create surfacerendered images and to segment 3D volumes in false color scale, by establishing a transfer function between the gray levels of the image and a color map. The CNR was estimated with reference to the tumor/metastases regions loaded with NPs and to the healthy brain tissue without NPs. The calculations were done using the equation described in  (48) : CNR = I NPs -I BG (1=2)(s 2 NPs + s 2 BG ) ½ 1=2 (1) I NPs and I BG are the mean gray values of a 5 × 5 pixels region of interest (ROI) selected within the tumors with NPs clusters and within the background, while s NPs and s BG are the standard deviations referred to the two different ROIs. The same calculation was repeated considering the mean gray values and the standard deviation of a 5 × 5 pixels ROI selected in the brain tissue without NPs.
FUNDING This work was partially funded by the  VOXEL  project (European Union's  Horizon 2020 research and innovation program  under grant agreement no  665207 ) and  COST Action MP1203. The MRI facility IRMaGe is partly funded by the  French program  " Investissement d'Avenir " run by the  French National Research Agency , grant " Infrastructure d'avenir en Biologie Sante " [ ANR-11-INBS-0006 ]. EL and IG gratefully acknowledge the financial support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Projektnummer 192346071,  SFB 986 , project  Z2. LS and PZ acknowledge projets interdisciplinaire du CNRS  80 PRIME 2019.
Acknowledgments and Data ExoMars is a space mission of the  European Space Agency (ESA)  and  Roscosmos. The NOMAD experiment is led by the  Royal Belgian Institute for Space Aeronomy (IASB-BIRA ), assisted by Co-PI teams from  Spain (IAA-CSIC), Italy (INAF-IAPS) , and the  United Kingdom (Open University ). This project acknowledges funding by the  Belgian Science Policy Office (BELSPO) , with the financial and contractual coordination by the  ESA Prodex Office  ( PEA 4000103401 ,  4000121493 ), by the  Spanish MICINN  through its  Plan Nacional  and by European funds under grants  PGC2018-101836-B-I00  and  ESP2017-87143-R (MINECO/FEDER) , as well as by  UK Space Agency  through grants  ST/V002295/1 ,  ST/V005332/1  and  ST/S00145X/1  and.
There are now other reliable methods including genetic analysis  [21] [22] [23] , which requires the use of databases and fresh tissue or targeted chemical analyses based on a calculated ratio of quercotriterpenosid (QTT)/glycosylated bartogenic acid (GluBA) content that defines a triterpenoid index used to determine the oak species  [24, 25]. Other non-targeted analysis methods have been developed to identify metabolite families and highlight discriminating compounds of oak species  [26, 27]. Still others try to determine the probability of a species' distribution by non-parametric classification approaches  [28]. In recent years, molecular networks emerged. This approach consists of organizing and visualizing tandem mass spectrometry data through spectral similarities. Compounds presenting a similar fragmentation pathway are grouped in clusters that highlight the structural relationship between compounds belonging to the same molecular family  [29, 30].
Crude extracts of various origins containing many metabolites can thus be more quickly characterized by comparing experimental spectral data to each other and to databases. Moreover, molecular networks help to point out compounds of interest in one or several extracts in comparison in order to focus the analysis on these compounds specifically without spending time on known compounds. The aim of the present study was to develop a new method able to compare sessile and pedunculate oak samples and to determine the species of unknown samples. Firstly, HHPLCMS/MS analyses of oak wood extracts from sessile and pedunculate oaks were compared using the Venn diagram combined with molecular networking to highlight the differences in profiles between the two species. Secondly, these species profiles were then assessed to determine the species of unknown individual samples due to their distribution between extracts of sessile and pedunculate oaks in the Venn diagram and in the network.
Comparison of Sessile and Pedunculate Oak Chemical Composition Sessile and pedunculate chemical compositions were first compared using a Venn diagram, presented in Figure  1. For both samples, more than 1700 ions were detected in negative ionization mode. The Venn diagram shows that the majority of the ions (1644) are common to sessile and pedunculate oaks, corresponding to molecules related to the genus Quercus  [31]. Some ions are specific to each of the species, namely, 129 for sessile oaks and 130 for pedunculate oaks. In order to correlate this first comparison of the dataset with the mass spectra and to describe the ions that compose it, a molecular network was created using MS/MS analysis of pedunculate and sessile oak samples. The aim was to determine the differences in molecular content between these two species without focusing on a particular molecular family.
For both samples, more than 1700 ions were detected in negative ionization mode. The Venn diagram shows that the majority of the ions (1644) are common to sessile and pedunculate oaks, corresponding to molecules related to the genus Quercus  [31]. Some ions are specific to each of the species, namely, 129 for sessile oaks and 130 for pedunculate oaks. In order to correlate this first comparison of the dataset with the mass spectra and to describe the ions that compose it, a molecular network was created using MS/MS analysis of pedunculate and sessile oak samples. The aim was to determine the differences in molecular content between these two species without focusing on a particular molecular family. First, MS/MS spectra were collected and processed on MetaboScape. Then, a quantitative molecular network was generated with Global Natural Product Social Molecular Networking (GNPS) and was visualized using Cytoscape.
Molecules generating similar MS/MS spectra are clustered due to similarities in their fragmentation patterns. The resulting molecular networks, presented in Figure  2 , visualize chemical relationships of the compounds contained in pedunculate (in red) and sessile (in green) extracts and their relative abundance in the two samples by the proportion of red and green in each node. For both samples, more than 1700 ions were detected in negative ionization mode. The Venn diagram shows that the majority of the ions (1644) are common to sessile and pedunculate oaks, corresponding to molecules related to the genus Quercus  [31]. Some ions are specific to each of the species, namely, 129 for sessile oaks and 130 for pedunculate oaks. In order to correlate this first comparison of the dataset with the mass spectra and to describe the ions that compose it, a molecular network was created using MS/MS analysis of pedunculate and sessile oak samples.
The aim was to determine the differences in molecular content between these two species without focusing on a particular molecular family. First, MS/MS spectra were collected and processed on MetaboScape. Then, a quantitative molecular network was generated with Global Natural Product Social Molecular Networking (GNPS) and was visualized using Cytoscape. Molecules generating similar MS/MS spectra are clustered due to similarities in their fragmentation patterns. The resulting molecular networks, presented in Figure  2 , visualize chemical relationships of the compounds contained in pedunculate (in red) and sessile (in green) extracts and their relative abundance in the two samples by the proportion of red and green in each node. Standards were not available for each cluster, so the clusters were annotated thanks to online databases (PubChem, Lotus, SciFinder, GNPS), the interpretation of the spectra, and the literature.
For each cluster, two attempts of annotations are reported in Table  1. Molecules with the same fragmentation pattern and therefore belonging to the same mo- First, MS/MS spectra were collected and processed on MetaboScape. Then, a quantitative molecular network was generated with Global Natural Product Social Molecular Networking (GNPS) and was visualized using Cytoscape. Molecules generating similar MS/MS spectra are clustered due to similarities in their fragmentation patterns. The resulting molecular networks, presented in Figure  2 , visualize chemical relationships of the compounds contained in pedunculate (in red) and sessile (in green) extracts and their relative abundance in the two samples by the proportion of red and green in each node. Standards were not available for each cluster, so the clusters were annotated thanks to online databases (PubChem, Lotus, SciFinder, GNPS), the interpretation of the spectra, and the literature.
Genetic Analyses for Species Assignation To distinguish oak species, genetic analyses were conducted by the CGAF-ONF laboratory (UMR BioForA, Orléans, France). For this study, total genomic DNA was extracted on the same trees on the same forest stands and cambial tissues from the same cooperage samples as previously, using the NucleoSpin ® 96 Plant II Kit (Macherey-Nagel, Hoerdt, France). Genotype data were obtained on the capillary of an ABI 3500 automatic sequencer (Applied Biosystems, Foster City, CA, USA) using 18 nuclear microsatellite markers developed by Guichoux et al. and analyzed with GeneMapper™ software v4. 1 (Applied Biosystems)  [45]. The determination of the species was carried out using an assignment method from Pritchard et al. using structured software. The method consists in comparing the multilocus genotypes with the reference data for each species.
The MS and MS/MS experiments were carried out on a maXis UHR-Q-TOF mass spectrometer (Bruker, Bremen, Germany) with an electrospray ion source (ESI), working in negative ionization mode that allows for better detection and isolation of the molecular ion compared to the positive ionization mode. The pressure of nebulizing gas was set to 2 bar, and the flow rate and temperature of dry gas were set at 9. 0 L min -1 and 200 • C. The capillary voltage was set at 4 kV. Mass spectra were summed during 400 ms in the m/z range 50-2250. All the MS data were processed using DataAnalysis software version 4. 4 (Bruker). Molecular formulae were generated using the SmartFormula algorithm with an elemental composition of C, H, O to an infinite number and N ≤ 4 with a mass accuracy ≤3 ppm and were submitted to the SciFinder, PubChem, Lotus, and GNPS databases in order to propose compound structures.