dataesr/scientific-paragraphs-categorization huggingface dataset:
Pharmacokinetic, Toxicology, and Lipinski Test The pharmacokinetic properties of OBEO main components were predicted using the SwissADME web server (http://www. swissadme. ch/. accessed date on 15 July 2022). In addition, the PreADMET web server (https://preadmet. bmdrc. kr/) was used to examine the toxicological characteristics of small molecules to predict the carcinogenicity of compounds in mouse and rat models and for the estimation of possible inhibition of the human ether-à-go-go related gene (hERG) channel in the heart. The PubChem database (https://pubchem. ncbi. nlm. nih. gov. accessed date on 15 July 2022) was used to assess the drug character of O. basilicum main compounds according to the theoretical method of pharmacokinetic parameters based on the rule of five (RO5) defined by Lipinski  [63].
Docking Analysis AutoDock software version 4. 0 (http://autodock. scripps. edu. accessed date on 15 July 2022) was used to perform the docking analysis. The AutoDock tool predicts the conformation of the receptor-bound ligand with a predicted Gibbs free binding energy. The grid options were defined as follows; spacing (0. 575 Å), X dimension  (40) , Y dimension  (40) , and Z dimension  (40). The critical amino acids (AAs) are located in the active site of DNA gyrase B. These acids were selected as docking boxes  [64]. Finally, BIOVIA Discovery Studio viewer version 21. 1. 0 was used to visualize the interaction mode between the ligand and the active site residues.
Molecular Docking of EO Compounds against E. coli 2. 9. 1. Molecular Structure Preparation The three-dimensional structure of E. coli DNA gyrase B protein was obtained from the Research Collaboratory for Structural Bioinformatics (https://www. rcsb. org, accessed date on 15 July 2022, PDB ID: 4DUH) with an X-ray resolution of 1. 75 Å [62]. Swiss-PdbViewer, version 4. 1. 0 (http://www. expasy. org/spdbv) was used for energy minimization (EM) of DNA gyrase B methyl chavicol (PubChem ID: 637563), and trans-anethole (PubChem ID: 637563) were considered as candidates for DNA gyrase B inhibition. The structures of SDF (structure-data file) format were obtained from the European Commission database. Pub-Chem Pymol software was used to convert the SDF to protein data bank (PDB) format. The ligand was structurally and geometrically optimized by EM using HyperChem software, version 8.
2. 9. 9 Molecular Docking of EO Compounds against E. coli 2. 9. 1. Molecular Structure Preparation The three-dimensional structure of E. coli DNA gyrase B protein was obtained from the Research Collaboratory for Structural Bioinformatics (https://www. rcsb. org, accessed date on 15 July 2022, PDB ID: 4DUH) with an X-ray resolution of 1. 75 Å [62]. Swiss-PdbViewer, version 4. 1. 0 (http://www. expasy. org/spdbv) was used for energy minimization (EM) of DNA gyrase B methyl chavicol (PubChem ID: 637563), and trans-anethole (PubChem ID: 637563) were considered as candidates for DNA gyrase B inhibition. The structures of SDF (structure-data file) format were obtained from the European Commission database. Pub-Chem Pymol software was used to convert the SDF to protein data bank (PDB) format. The ligand was structurally and geometrically optimized by EM using HyperChem software, version 8.
7×, and all material left was recovered. Final libraries were quantified by qPCR using the KAPA Library Quantification Kit (KAPA Biosystems Inc. , Wilmington, MA, USA) before amplification with Illumina's cBot. Libraries were sequenced 1 × 50 + 8 bp on Illumina's HiSeq2500. The bioinformatic analyses to identify miRNA sequences were carried out as follows: the adapter from raw data was removed using a skewer  (31) , and reads with a size ranging from 15 to 30 bases were aligned to a plant-based reference genome (Prunus persica, NCBIv2, and annotation NCBIv2. 52 restricted to miRNAs) (hhttps://plants. ensembl. org/Prunus_persica/Info/ Index; https://mirbase. org/results/?query=prunus+persica), with miRNA annotation from miRBase (version 22) using ShortStack  (32) , allowing a maximum of two mismatches in the seed area.
Bioinformatic target gene prediction analysis The RNA target analysis servers psRNATarget scoring schemas V1 and V2 (https://www. zhaolab. org/psRNATarget/)  (35)  and TAPIR (http://bioinformatics. psb. ugent. be/webtools/tapir/)  (36)  were used to predict human putative target genes of all the plantderived miRNAs identified by NGS. The cDNA library chosen for both psRNATarget and TAPIR servers was the "Homo sapiens (human), transcript, Human genomic sequencing project, " available at psRNATarget, and the bioinformatic predictions were performed with the default parameters. Applying psRNATarget scoring schemas V1 and V2, the following parameters for each output were reported: target accession number of the putative target gene, expectation (penalty for the mismatches), mRNA target aligned fragment, and the inhibitory effect (cleavage or translation inhibition)  (35).
The scoring Schema V1 also showed the unpaired energy (UPE), which is the energy required to open a mRNA secondary structure  (35). The results showed applying the TAPIR program includes the score (penalty for mismatches, gaps, and G-U pairs), the minimum free energy (MFE) ratio, which is a ratio between the free energy of the miRNA-mRNA duplex vs. the free energy of this duplex with perfect matches, and the mRNA target aligned fragment  (36). The GeneCodis4 program (https://genecodis. genyo. es/) was used to perform the Gene Ontology (GO) biological process analysis of candidate putative target genes.
miRNA name miRBase accession number Assay reference (GenGlobe ID) Assay sequence ( ′ -′ ) hsa-miR-141-3p MIMAT0000432 YP00204504 5 ′ -UAACACUGUCUGGUAAAGAUGG-3 ′ hsa-miR-103a-3p MIMAT0000101 YP00204063 5 ′ -AGCAGCAUUGUACAGGGCUAUGA-3 ′ ppe-miR482f MIMAT0031512 YP02105458 5 ′ -UCUUUCCUACUCCACCCAUUCC-3 ′ ppe-miR482c-5p MIMAT0027283 YP02114485 5 ′ GGAAUGGGCUGUUUGGGAUG-3 ′ UniSp4 N/A YP00203953 N/A MiRNA names, accession number, and sequences of miRCURY LNA miRNA PCR Assays (Qiagen). N/A, non-applicable.
Cytotoxicity assays. Detection of edible plant-derived miR f and miR c-p in biological human samples Plant-derived miRNA analyses from serum and fecal samples were performed as a proof of concept from anonymous volunteers (>18 yr), who qualitatively consumed plant-based products. The samples were donated by nine volunteers (five men and four women) anonymously, and no personal data were collected from these subjects. Specifically, volunteers usually consumed these plant-based food groups: legumes, fruits, vegetables, cereals, and fats and oils, in the variety and proportion they preferred. Blood samples were collected through a venous puncture in BD Vacutainer R SST TM II Advance tubes (BD Vacutainer Systems, Plymouth, United Kingdom). Samples were kept at room temperature for 30 min and centrifuged at 2,000 g for 15 min at 4 • C.
Statistical analysis The statistical analyses were performed using GraphPad Prism 6. 0 for Windows (GraphPad Software Inc. , La Jolla, CA, USA). For in vitro studies, comparisons between the miRNA negative control and the experimental groups were performed using Student's ttest. Student's t-test was selected because the aim was to determine differences between plant-derived miR482f and miR482c-5p with the negative control, as independent studies, and not to establish a comparison between the three groups. Statistical significance was considered at p-value <0. 05.
Results. Plant-derived miRNAs are present in edible plant-based products, and miR f and miR c-p could potentially inhibit the expression of pro-inflammatory human genes We performed NGS analysis to identify miRNAs in edible plant-based foods, including fruits (apple, orange, and pear), vegetables (spinach and tomato), and fats and oils (walnut and olive). After the alignment of the reads with the peach genome, as a reference genome, 176 miRNAs were identified in the overall pool of plant-based food samples analyzed (Supplementary Table  S1 ). The bioinformatic analysis with psRNATarget and TAPIR predicted potential human target genes of the 176 miRNAs detected in the plant-based food samples (data not shown). In the present study, we only focused on miR482f (Ensembl gene: ENSRNA049996209) and miR482c (Ensembl gene ENSRNA049996546) because human gene prediction analyses revealed their potential role in targeting inflammation-related human genes.
The alignment of the miR482f mature sequence with the human transcriptome revealed that 6 (psRNATarget scoring schema V1), 24 (psRNATarget scoring schema V2), and 5 (TAPIR) transcripts could be putative targets (Supplementary Table  S2 ). Altogether, 26 different transcripts (corresponding to 22 different genes) were predicted as putative targets of miR482f. Only three transcripts appeared in the three algorithms. These were as follows: the transcript NM_145912, which codifies for NFAT activating protein with ITAM motif 1 (NFAM1), and the transcripts NM_197949 and NM_022570, which codify for two isoforms of the C-type lectin domain containing 7A (CLEC7A) protein (Supplementary Table  S2 , Supplementary Figure  S1 ). The alignment of the plant-derived miR482c-5p mature sequence and the human transcriptome reported that 2 (psRNATarget scoring schema V1), 24 (psRNATarget scoring schema V2), and 5 (TAPIR) transcripts could be putative target genes (Supplementary Table  S3 ).
A total of 27 different transcripts (22 different genes) were identified as putative targets of plant-derived miR482c-5p. The only common targets of the three prediction programs were the transcript NM_001160332 of the neurofascin (NFASC) protein and the transcript NM_006068, which encodes for the Toll-like receptor 6 (TLR6) (Supplementary Table  S3 , Supplementary Figure  S2 ). According to the bibliography, the combination of psRNATarget and TAPIR programs gives highly accurate predictions  (42). Therefore, we selected for subsequent analyses the common target genes to psRNATarget (schemas V1 and V1) and TAPIR: NFAM1 and CLEC7A as putative target genes of miR482f, and NFASC and TLR6 as putative target genes of miR482c-5p. To explore the biological functions shared between the predicted target genes, we performed Gene Ontology (GO) biological process analysis with GeneCodis4 (Supplementary Figure  S3 ).
The positive expression of the spike-in UniSp4 in both serum and feces samples confirmed the accuracy of the experimental procedure (RNA isolation and expression analysis) (data not shown). We measured expression levels of plant-derived miR482f and miR482c-5p using qPCR in nine fecal samples (Figure  5 ). Both miR482f and miR482c-5p were detected in all the samples, suggesting that plant-derived miRNAs are present in the gastrointestinal tract and resist digestion. The expression of the human hsa-miR-141-3p was used as an endogenous control to determine the quality of the fecal samples. We analyzed the levels of plant-derived miR482f and miR482c-5p in serum samples from the same volunteers in which the two plant-derived miRNAs were present in feces. Neither miR482f nor miR482c-5p were detected in serum samples in any of the volunteers (data not shown).
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the  Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition (CIBERobn , grant No. CB12/03/30002 ),  Ministerio de Asuntos Económicos y Transformación Digital  ( PID2022-141766OB-I00 ,  RTI2018-102205-B-I00 , and  BFU2015-65937-R  projects), and a  Center for Nutrition Research pre-doctoral grant  awarded to ED-S.
Funding This work was supported by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (Unistra) , and the  French government funds through the "Agence Nationale de la Recherche " in the framework of the  Investissements d'Avenir program  [ ANR-10-INBS-07 PHENOMIN  to YH]. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Similarly M w , the moment magnitude is calculated from L km the length of the rupture in kilometers: = + 1. 667log( ), for > 2. 5 for DS in SCR (5) where a μ = 4. 32, a -σ = 4. 12 and a +σ = 4. 51. The D av and M w estimates are given in Table  S2  in Supporting Information S1 alongside the segment lengths L and the average observed displacements 𝐴𝐴 D𝐷. These relationships result in slightly larger offsets and magnitudes than empirical relationships based on datasets dominated by earthquakes in more rapidly deforming regions (e. g. ,  Wells & Coppersmith, 1994). On the other hand, as noted above, it is likely that the fault lengths used may underestimate the true length by a few kilometers resulting in the possible underestimation of these values. In addition, the majority of the events the  Leonard (2014)  regressions are based on are reverse faults rather than normal faults.
, 2020). However, in combination with the Hebron scarp (see G. Salomon et al. , 2022) , our work suggests that some distributed extension extends along the western margin of the Kalahari Craton following structures within the Gariep and Namaqua-Natal Belts, rather than all of the deformation occurring within the Damara Belt or continuing along the Walvis Ridge. Recent compilations of reverse-faulting earthquake scarps in Australia also found striking congruence between lineaments in aeromagnetic datasets and recent earthquake ruptures in an SCR setting  (King et al. , 2019; Yang et al. , 2021). In most cases the surface traces of these faults were more complex than those observed here, though  Yang et al. (2021)  suggest that some ruptures (such as the Petermann earthquake) were relatively straight when there was a single dominant tectonic fabric which was well-aligned to the regional stresses.
For ChIP-qPCR, enrichment relative to the input of immunoprecipitated chromatin fragments was determined using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) on a LightCycler480 (Roche) using the software supplied with the machine (v1. 5. 1. 62) or CFX384 Touch (Bio-Rad) using the built-in software (version number not provided). Enrichment analysis was performed using Microsoft Excel for Mac (v16. 75. 2). qPCR results are from two PCR experiments each on two biological replicates. Changes in CTCF enrichment at CBS 20326 are expressed relative to a CTCF binding site near the Igf2 gene on chromosome 7. Primer sequences in Supplementary Data 4.
ChIP-seq and SLIM-ChIP data analysis ChIP-seq datasets were mapped to the ENSEMBL Mouse genome assembly GRCm38. p6 (mm10) using BWA (v0. 7. 15)  73  with default parameters. Rad21 ChIP-seq data from mESCs were obtained from the GEO repository [GSE33346 (https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE33346)]  74. For ChIP-seq data, duplicate reads, reads with multiple alignments and low-quality reads were removed, followed by the calculation of densities for combined biological and technical replicates. All densities include data from at least two biological replicates, with the exception of the tracks used for visualization in Fig. 6a. For visualization, data densities were further binned in 200 bp windows. CTCF binding peaks were identified from the combined biological and technical replicates with MACS2 (v2.
25. The list of identified CTCF peaks and their significant CTCF binding motifs is provided in Supplementary Data 1. CTCF SLIM-ChIP data from mESCs were obtained from the GEO repository [GSE108948 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE108948)]  47. All MNase-digested datasets (1. 5 u, 6. 25 u, 25 u, 100 u and Mix) were downloaded and mapped as paired-end reads to the ENSEMBL Mouse assembly GRCm38. p6 (mm10) using bowtie2 (v2. 4. 2)  78  with default parameters, except that maximum fragment length was set to 2 kb. Mapped reads were filtered for mapping quality >20, followed by removal of duplicate reads using the fixmate and markdup options in SAMtools (v1. 10, using htslib v1. 10. 2)  79. Filtered SLIM-ChIP reads were then used to generate genome wide coverage using the BEDtools suite (v2.
30. 0)  80  , followed by narrow peak calling using MACS2 (v2. 2. 7. 1)  75  with default parameters. Out of the five datasets, MACS2 was unable to build a peak model from the 100 u MNase-digested dataset, which we excluded from our downstream data analysis. The remaining four MNase-digested datasets (1. 5 u, 6. 25 u, 25 u and Mix) were merged using SAMtools (v1. 10), followed by filtering out all the read2 (3'-position within the stranded read) from the paired-end alignment and converting the start points of the read1 alignments into a single base-pair coverage (5'-position within the stranded read). The SLIM-ChIP read distribution in the 100 bp up-and downstream from the midpoint of the CTCF binding motif was next calculated by counting the number of forward strands and reverse strands in each single base-pair window.
Reanalysis of Hi-C data and intersection with ChIP-seq data Hi-C data from mESCs were obtained from the GEO repository [GSE96107]  45. Reads were mapped to ENSEMBL Mouse assembly GRCm38. p6 (mm10) and processed to aligned reads using HiC-Pro (v2. 9. 0) and Bowtie2 (v2. 3. 0), with default settings to remove duplicates, assign reads to DpnII restriction fragments, and filter for valid interactions  78, 81. Hi-C interaction matrices, at 10 kb resolution, were generated from the valid interactions and were normalized using the Iterative Correction and Eigenvector decomposition method (ICE) implemented in HiC-Pro. TAD boundaries were called using TADtool (v0. 76)  82  , with window size 500 kb and insulation score cut-off value 21. 75, resulting in a high degree of genome-wide overlap with TAD borders as reported previously  45.
For the visualization of Hi-C matrices, for the creation of 'virtual 4 C plots' and for the determination of signal densities within TADs, custom R-scripts were used. Intersection of CTCF ChIP-seq peaks with TAD boundaries was done using the BEDtools suite (v2. 26. 0)  80. Coordinates of TADs and genome-wide insulation scores are provided in Supplementary Data 5 and 6.
4C-seq and data analysis Chromatin fixation, cell lysis, and 4 C library preparation were done as previously described using 15 million cells per experiment  46. NlaIII (New England Biolabs, R0125) was used as the primary restriction enzyme and DpnII (New England Biolabs, R0543) as the secondary restriction enzyme. For 4C-seq library preparation, 800 ng of 4 C library was amplified using 16 individual PCR reactions with inverse primers including Illumina TruSeq adapter sequences (primers in Supplementary Data 4). Multiplexed Illumina sequencing was done using 86 bp single-end reads on the Next-Seq 550 system (Illumina) according to the manufacturer's instructions at the high-throughput sequencing core facility of the I2BC (Gif-sur-Yvette, France). 4C-seq data were mapped to ENSEMBL Mouse assembly GRCm38.
The final Agencourt RNAClean XP beads purification step was done using 24 μl of beads instead of 40 μl for a more stringent size selection. Direct-RNA sequencing was done for 48-72 h, using FLO-MIN106 flowcells (R9 chemistry) on a MinION (MK 2. 0) sequencing device with the MinKNOW software (Oxford Nanopore Technologies, latest version available at the time of the experiments).
Nano-C data analysis Direct-RNA sequencing reads (fast5 format) were basecalled using Guppy (v4. 0. 11; Oxford Nanopore Technologies) followed by default quality filtering (QC score > 7), which primarily removes short reads. The resulting RNA fastq files were then converted to DNA fastq using an in-house Perl script. To reliably identify contacts within our error-prone and complex 3C reads, we devised a two-step mapping and filtering approach. Read quality was evaluated using the MinIONQC tool (v1. 4. 2)  87. In the first step, reads in the DNA fastq files were mapped to a synthetic genome only consisting of the different viewpoints present in the run [viewpoint sequences obtained from ENSEMBL Mouse assembly GRCm38. p6 (mm10)] (Supplementary Data 3 for viewpoints contained in each run). We used BWA-MEM (v0.
4a ). Based on the notion that the large majority of interactions in 3C experiments are intra-chromosomal  89  , we assumed that the abundant low quality inter-chromosomal mappings mostly consisted of randomly mapped reads. Individual mapping segments within our viewpoint containing reads were therefore filtered for mapping with quality scores over 25 (MQ ≥ 25). In-house developed Perl scripts were used to assign the individual segments from each read to NlaIII fragments in the genome, to merge segments falling in the same or neighboring NlaIII fragment and to determine the total number of multi-contacts within the reads. Reads that only mapped to the viewpoint were removed. Numbers of contacts per viewpoint and run are provided in Supplementary Data 3. Multi-contact information (NlaIII fragments) per read was organized according to the Interact Track Format, with multi-contact reads spanning over multiple lines and additional information provided in non-essential columns: • Column 4 (Name): unique identifier assigned to each multicontact read.
• Column 7 (Exp): multi-contacts in the read (viewpoint + n other contacts). • Column 8 (Color): random color assigned to each multi-contact read. #000000 if the originally identified viewpoint was not identified upon mapping to the masked genome. • Column 12 (sourceName): indication if the viewpoint was identified upon mapping to the masked genome (1: yes, 0: no). • Column 17 (targetName): Nano-C runID, indicating the experiment in which the multi-contact read was identified. Interact files can be downloaded from the Mendeley Data repository (https://data. mendeley. com/datasets/g7b4z8957z/4). Interact files are based on 3C libraries from at least two biological replicates. Nano-C multi-contact plots were generated from these Interact files after filtering for specific genomic intervals using a custom R-script, with sorting of reads based on the contact that is nearest to the viewpoint.
For benchmarking of Nano-C against Multi-contact 4C (MC-4C) and Tri-C  49, 50  , the following data sets were obtained from the ENA and GEO repositories: • MC-4C: Hbb-b1 viewpoint (ENA study: PRJEB23327; entries: ERR2190825, ERR2190826, ERR2190827, ERR2190831 and ERR2190832). • Tri-C: combined R2, HS-39, HS2 and 3'HS1 viewpoints (GEO study: GSE107940; entries: GSM2878084, GSM2878085, GSM2878087, GSM2878088, GSM2878090, GSM2878091, GSM2878092, GSM2878093, GSM2878094, GSM2878095, GSM2878096, GSM2878097, GSM2878098 and GSM2878099). For optimal comparison, MC-4C and Tri-C DNA fastq files were subjected to our newly developed Nano-C data analysis pipeline, with experiment-specific adjustments where needed: for MC-4C, the interactions were translated to DpnII fragments and for Tri-C, the mapping results from both ends of the paired-end reads were combined for further downstream analysis.
Specimen thickness t was determined as ranging from 50 to 75 nm from stereo pairs recorded at tilt angles ±10°, as described  91. Images were recorded with a nominal defocus of 3 μm, on the camera (pixel size 0. 29 or 0. 236 nm) or on Kodak SO163 negative films scanned with a Coolscan 9000 (Nikon) using the Super Coolscan software at a resolution of 4000 pixels per inch. Images were denoised by wavelet filtration in ImageJ ('A trous filter' plugin, with k 1 = 20, k n>1 = 0). DNA contour lengths projected onto the image plane (L // ) were segmented and measured in ImageJ (v1. 53) using the freehand line tool. The conformation of DNA complexes confined within ice layers of thickness t in the range of the persistence length of the molecule (l p ≈ 50 nm) is described by the Odijk regime of confinement  92, 93  : the projected length L // relates to the DNA contour length L c as L // ≈ 0.