dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Prism (version 7. 0a, GraphPad, San Diego, CA, USA) was used for statistical analyses. Normal distribution of the data was determined using the Shapiro-Wilk test. To assess differences between total volumes using OSIRIX and VolSEQ, a non-parametric Mann-Whitney U test was subsequently used as the data were not normally distributed as well as a Bland-Altman plot. For fat volume comparisons between sexes and sides and fat volume comparisons between age groups, an ANOVA test or Kruskal-Wallis test was employed depending on the distribution of the data. Age correlations were assessed using a twotailed Spearman-r test as data was non-parametric. A perfect correlation was de ned as equal to 1. 0, very strong as ≥ 0. 8, moderate as ≥ 0. 6, fair as ≥ 0. 3, and weak ≥ 0. 1 based on Chan  26.
Utilising the exported numerical values of the ROIs originally computed by OSIRIX allows the Python algorithm to rely on dependable values. Compared to other available software, VolSEQ also allows for the quanti cation of tissues to a pixel level, revealing values that would be di cult, sometimes impossible to identify manually. The interface allows for the selection of the range of HU, so it can also be applied to other targeted tissues for volumetric analysis in CT scans in future studies. Additionally, the novelty of this study was that it quanti es the total volume of the PSIJ and the fat volume within a larger age span (16 to 82 years) than has been reported in the literature to this date. Its use could be valuable in the detection and quanti cation of fat content / in ltration in structures like the SIJ and aid in the potential diagnosis of related conditions.
In addition, the posterior sacroiliac ligament and the inferior boundary of the articular cartilage were sometimes di cult to determine and, in some instances, had to be separated manually, drawing a line between the ilium and the most posterior part of the sacrum acting as the posterior sacroiliac ligament biasing the annotation process. This approach may have altered the true shape of the structure. Furthermore, the program is not fully independent of manual input and requires the use of OSIRIX or similar software for the creation of the original ROIs. The BMIs of the patients were unknown; therefore, it was impossible to consider the impact of BMI on fat volume within the PSIJ. Further research should investigate this variable to understand its potential in uence on altered loads and stresses on the joint.
As shown in Extended Data Fig. 8 , both sets of technique-specific regions have comparable ATACseq signal, while the G4access-specific showed more apparent NDRs and better nucleosome positioning in MNase-seq data. We also note that common ChIP-G4access areas are the most open, possibly because they are more enriched for promoter regions. To investigate how global chromatin perturbation might impact G4access signals, we treated Raji cells with TSA, a HDAC inhibitor. While TSA globally relaxes chromatin through histone hyperacetylation (Extended Data Fig. 9a ), it can reduce ATAC-seq signals at open chromatin regions  29, 30. We confirmed this effect in MNase-seq following 24h TSA treatment where nucleosome densities increased at NDR locations. At both promoter and non-promoter G4access locations, we also observed consistent reduction of the signals where predicted G4s are present, indicating that nucleosomes tend to re-position to G4 sites, thus presumably reducing their formation (Extended Data Fig.
Nascent Chr-RNA-seq Chromatin-associated RNAs (ChrRNAs) presented in ED Figure  3g  were either analyzed from GSE90238 or isolated from 2x10  7  Raji cells as follows. Nuclear fractionation was performed by incubating cells in 4mL of Buffer I (10mM Tris-HCl pH 7. 5, 10 mM NaCl, 2. 5mM MgCl2, 0. 5% IGEPAL CA-630) on ice for 5 minutes. Next, we carefully underlaid 1 mL of Buffer II (10mM Tris-HCl pH 7. 5, 10 mM NaCl, 2. 5mM MgCl2, 0. 5% IGEPAL CA-630, 10% sucrose) before harvesting the nuclear fraction at 1400 rcf for 5 minutes at 4°C. Nuclei were resuspended with 125µL of NUN1 buffer (20mM Tris-HCl pH 8, 75mM NaCl, 0. 5mM EDTA, 50% Glycerol) followed by 1. 2mL of NUN2 buffer (20mM HEPES-KOH pH 7. 6, 300mM NaCl, 0. 2mM EDTA, 7. 5mM MgCl2, 1% IGEPAL CA-630, 1M Urea). After 15 minutes of vigorous vortexing, the chromatin was centrifuged at 15000 rcf for 10 minutes at 4°C and washed with 500µL of NUN2 buffer.
qPCR reactions were performed in triplicates. Cycling conditions were 95°C for 10 min. , then 40 cycles of 95°C for 30 sec. /65°C for 30 sec. /72ºC for 15 sec. , followed by melting curve analysis. Results presented on Extended Data Fig. 1c  are displayed as the ratio of enrichment of the G4 to non G4 regions. FRET melting profiles FRET melting profile assays were performed on 6 G-quadruplexes and one hairpin to test their stabilites in MNase digestion conditions. FRET buffers were lithium cacodylate supplemented with 140mM KCl and 10mM KCl with 45mM NaCl for physiological and NMase conditions, respectively. The sequences tested, F21CTAT (GGGCTAGGGCTAGGGCTAGGG), EBR1 (GGGCAGGGGGTGATGGGGAGGAGCCAGGG), F21T (GGGTTAGGGTTAGGGTTAGGG), F25cebT (AGGGTGGGTGTAAGTGTGGGTGGGT), FAG3AT (AGGGAAGGGAAGGGAAGGGA), FmycT (TTGAGGGTGGGTAGGGTGGGTAA), and FdxT (TATAGCTAT-PEG-TATAGCTATA) were double labeled with FAM and TAMRA to follow their unfolding by fluorescence.
FRET-MC, Th-T and NMM massive G4 validation assays in vitro Target selection for the design of 596 oligonucleotides was performed by first overlapping human G4access peaks common to HaCaT, K562 and Raji cell lines. This allowed the isolation of 4743 regions of various sizes (Extended Data Fig. 2B ) from which we extracted the maximum G4Hunter score, using a window of 30 nt within the G4Hunter algorithm. To perform our large-scale in vitro assays, we subsequently selected 596 fragments from this list, with a score distribution comparable to that of the initial pool (not shown). The list of peaks with genomic coordinates and oligonucleotides is presented in Supplementary Tables  2  and 3. The library of oligonucleotides was synthetized and purchased at Eurogentec (Seraing, Belgium) with RP cartridge gold purification, and further used for FRET-MC, ThT and NMM.
Genomic data set processing, peak calling and differential analysis For both native and re-analyzed published data sets considered in this study, raw sequencing reads were aligned using Bowtie2 44 (version 2. 1. 0) to the human (hg19), mouse (mm9), drosophila (dm6) and yeast (sc3) genomes. Aligned reads were elongated in silico using the DNA fragment size inferred using an in-house developed PASHA (version 0. 99. 21) R (version 3. 3. 1) pipeline  45  or using MACS2 46 (version 2. 1. 2) which also allows peak calling for G4-ChIP and G4access (peaks were considered below a p-value of 10 -10 from the narrow peaks table). PASHA was used for ChIP-seq and MNase-seq datasets, using drosophila spike-in for ChIP normalization  47  (Fig. 4d ), and MACS2 was used for G4access and G4 ChIP for the sake of consistency in comparison with previously published G4 ChIP analyses.
MACS2 was run using input DNA as control and with recommended settings  46. Bedgraph files generated by MACS2 were then converted to wig files (bin10) and scaled using the sequencing depth with PASHA. Wiggle files representing average enrichment score every 10bp were generated. For nucleosome positioning analyses (midpoints), to determine the average nucleosome positions, wiggle files representing the central nucleotides of DNA fragments were also generated (Fig. 1a , 4a and Extended Data Fig. 6a, 7a, 9c ). Finally, for nucleosome densities representation and analyses (MNase-seq), we smoothed the signal by replacing each 10bp bin by the average of the 5 surrounding bins on each side and using an in-house script. To assess differences in G4access peak intensities, DESeq 48 (version 1. 26. 0) was used with the MACS2 peak definition as genomic references.
Differences between the conditions (control versus treated cells) were called at a p-value below 0. 05. RNA-seq datasets analyzed or re-analyzed in this study were processed by aligning sequencing reads to mouse genome (mm9) or human genome (hg19) using TopHat2 for RNA-seq and Chr-RNA-seq. Alignment files were then treated using Cufflinks (v2. 2. 1) to generate RPKM used in ED Fig. 3g. SNP analyses for mouse RNA-seq data from Fig. 5a  are described further below.
Motif search analysis and NDRs determination To analyze motifs associated to open chromatin/NDRs at promoters, we focused on CGI-containing promoters since those display an established intrinsic property to exclude nucleosomes 2. NDRs and deepest NDR points were defined using an inhouse script. First by creating an inversed track of the MNase-seq signal (y=1/x, 0 values were replaced by the minimal value found in the region -300/+100 of annotated TSSs in the sequencing signals). Then, peaks and peak summits corresponding to NDRs and their deepest points were determined. The peak-calling was performed using wigpeakcaller  49  (https://doi. org/10. 7554/eLife. 02105. 022 ) fixing a threshold based on the peak height and the gap between two adjacent signals (Fig. 1a  and Extended Data Fig. 7c ).
De novo motif discovery analyses were thus performed at the lowest nucleosome density (deepest point of MNase-seq signal) at promoters overlapping CGIs between -500 to TSS (Fig. 1a ) and on G4access or G4-ChIP (Extended Data Fig. 4 ) datasets using MEME-ChIP 50 (default settings, version 5. 0. 2). Fragments from -30 to +30 bp centered on the deepest point of MNase-seq signal or peak summits for the other datasets were used and tested using the jaspar 2020 core non redundant database. Fragments overlapping the annotated TSS region (-200 bp to the TSS) were defined using Bedtools (version 2. 21. 0). For yeast datasets (Extended Data Fig. 10a ), TSS determined by CAGE were used (http://www. yeastss. org/download/). The First 3 motifs are displayed ranked by site numbers (Fig. 1a , Extended Data Fig.
Allelic data analysis of mESC Raw sequencing reads were aligned strictly to the mm9 or JF1 51 genomes using Bowtie2. Reads overlapping distinguishable SNPs (https://molossinus. brc. riken. jp/mogplus/#JF1) between the two genomes were considered to assess allelic signals of G4 access and RNA-seq datasets. At these loci, reads overlapping SNP from each allele were determined and counted using HTSeqcount (version 0. 6. 1p1) at G4access peaks (for differential G4access analysis) and within gene annotations (for differential gene expression analysis). Assessment of statistical analyses of differential signals were performed using DESeq 48 (version 1. 26. 0) (Fig. 5b-d ; Supplementary Table  1 ). The list of 31 murine imprinted DMRs, all corresponding to CGIs, was in-house curated by the Feil laboratory and is largely based on previous reports by others  [52] [53] [54]  together with Feil laboratory's own compilation of imprinted DMRs.
Computation of G4Hunter scores G4Hunter scores were computed using a previously published principle  55  with specific functions (see provided R scripts). First, G4Hunter hits were extracted from the reference genomes (hg19, mm9, dm6 and sc3; with a window of 25 and a minimal score of 0. 5). Each hit is characterized by its genomic coordinates and a "max_score" reflecting the score of the highest G4Hunter within this window. These scores reflect the relative G4 propensity of the peaks. Peaks with no overlapping G4Hunter hit have a score of 0. Random genomic regions of same size distribution were used as control. To assess GC richness effects in G4Hunter score distributions, all peaks were resized at 90bp around their summits. Shuffled DNA sequences using the same pool of nucleotides and the same size distribution were generated and compared to G4access or G4-ChIP datasets using G4Hunter analysis at 25 bp window settings and selecting the best scores in the fragments 6 (Extended Data Fig.
GC and CpG contents, G-tracks and number of G per track analyses For number of G-tracks and number of G per G-track analyses, all peaks were resized at 90bp around their summits (Extended Data Fig. 3c-d ). To assess GC and CpG contents, G or C and dinucleotide CG were determined in the specified windows. For promoters, windows from -200bp to gene TSSs were used (Extended Data Fig. 3e ).
Gene expression and gene ontology analyses Genes exhibiting G4access peaks in their promoters (within -200bp and their TSS) were determined using Bedtools 56. Fragments Per Kilobase per Million (FPKM) of all genes were determined using Cufflinks 57 (version 2. 2. 1) (Extended Data Fig. 3g ). Charts were drawn from all expressed genes defined as genes with FPKM>0. (RNAseq dataset used are indicated in Supplementary Table  4 ). Gene ontology was performed using DAVID webtool 58 (https://david. ncifcrf. gov/) on the top 3000 promoters ranked by G4access signals (Extended Data Fig. 3f ). Methylation analysis WGBS datasets were analyzed using Bismarck (version 0. 22. 3)  59  for Fig. 6a -b (K562 cells) and the computed beta files provided in ref  60  (GSE186458) for Fig. 6c-d  (human granulocytes).
2 and/or QP longloops were considered. The longloop predictions were generated using the Quad-Parser consensus. In the gene expression analysis presented in Extended Data Fig. 3g , genes that are not expressed (RPKM=0) were not included. In MNase-seq heatmaps, saturated or absence of signals expanding all along the displayed genomic areas were considered as artifacts or outliers and removed. Replication Experiments were repeated in replicates as indicated in the manuscript, (Between 2 and 4 times). All replicates were successful. Please note that Extended Data Fig. 1b  has been performed as technical replicates only. Randomization Randomization of genome sequences were repeated 10 times for enrichment analyses. In Fig. 1g , 6a, Extended Data Fig. 2d -e,6c-d,7c and 10a-b genomic sequences were randomly selected and compared to experimental datasets of same sizes.
This work was supported in the  JCA  lab by grants from the ( ANR-20-CE12-0023 ), FRM ( AJE20130728183 ),  INCA PLbIO  ( N°2020-117 ) and  CNRS   80prime 2021  (DeciphG4). This project has received financial support from the  CNRS  through the  MITI interdisciplinary programs. CE was supported in part by an  ARC grant  (retour postdoc). We thank  Beatrice Loriod  and the  Transcriptomics and Genomics Marseille-Luminy (TGML)  platform for sequencing the G4access samples. We are grateful to  David Monchaud  for providing us with G4 interfering molecules in exploratory experiments. TGML is a member of the  France Génomique  consortium ( ANR-10-INBS-0009 ). EGO, TM and SB were supported by grants from the  Epigenesys Labex of excellence  and EGO in part by  ANR-18-CE12-0019. We acknowledge the financial support from the  France Génomique National Infrastructure , funded as part of " Investissement d'Avenir" program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ) for the MGX sequencing platform facility in Montpellier.
). Individual datasets, mean with SEM are presented (n=2 for G4access, n=2 for RNA-seq for each cross BJ and JB). c, d-Example of G4access enrichment at the TSS-proximal DMR of the Meg3 (chr12: 110. 775. 000-110. 825. 000) and Peg13 (chr15: 72. 635. 500-72. 642. 500) imprinted locus. G-stretches involved in predicted G4 formation (G4Hunter score 1. 76 for Meg3 and 1. 52 for Peg13 genes, orange bars) within the DMR are highlighted in red below the track. Allelic G4 and RNA signals (normalized read count) are indicated in the dashed rectangle for each of the strains and the maximum corresponds in each case to the active allele.
H. was below 35%. The remaining spray drop deposits were subjected to wetting (i. e. , when the R. H. was above the POD, which likely rose slightly with the lower air temperature recorded overnight) and drying cycles during the experimental period. None of the six K salts applied led to a significant increase in leaf K concentration, compared to the untreated leaves (Table  2 ). Similarly, the K and C concentrations in the leaves did not vary significantly between harvest times (3 and 24 h) for any of the treatments. Table  2. Potassium and C concentrations of olive leaves collected 3 and 24 h after 130 mM K-phosphate, K-nitrate, K-sulphate, K-chloride, K-carbonate and K-bicarbonate spraying. Data are means ± SD (N = 3; F (p) = Fisher test, with significance level in parentheses). In columns, different letters indicate significant differences (p < 0.
Regarding the concentration of K recovered in the leaf washing liquid, significantly higher concentrations were obtained in all foliar K treatments compared to untreated leaves, both 3 and 24 h after foliar application (Table  4 ). Table  4. Potassium concentrations recovered in the washing liquid of olive leaves treated with 130 mM K-phosphate, K-nitrate, K-sulphate, K-chloride, K-carbonate, and K-bicarbonate foliar sprays. Samples were collected 3 and 24 h after foliar spraying. Data are means ± SD (N = 3; F (p) = Fisher test, with significance level in parentheses). In the columns, the different letters indicate significant differences (p < 0. 05) according to Tukey's HSD test.
Samples were collected 3 and 24 h after foliar spraying. Data are means ± SD (N = 3; F (p) = Fisher test, with significance level in parentheses). In columns, different letters indicate significant differences (p < 0. 05) according to Tukey's HSD test. Treatment [Cl -] (mg L -1 m -2 ) [NO 3 -] (mg L -1 m -2 ) [SO 4 2-] (mg L -1 m -2 ) [PO4 -3 ] (mg -1 m -2 ).
Chemicals The following potassium (K) salts were supplied as foliar sprays at a concentration of 130 mM: K-phosphate tribasic (K 3 PO 4 , reagent grade, ≥98%, Sigma-Aldrich, Germany); K-sulphate (K 2 SO 4 ACS reagent, ≥99. 0%, Sigma-Aldrich); K-nitrate (KNO 3 , ACS reagent, ≥99. 0%, Sigma-Aldrich); K-chloride (KCl, ACS reagent, 99. 0-100. 5%, Sigma-Aldrich); K-carbonate (K 2 CO 3 , ACS reagent, ≥99. 0%, Sigma-Aldrich); and K-bicarbonate (KHCO 3 , ACS reagent, 99. 7%, Sigma-Aldrich). The solubility (g kg -1 ) in water (at 25 • C) of all the compounds was taken from Haynes  [32]. Estimations of the solubility (M) of the different chemicals (Table  1 ) and geochemical calculations were performed using the PHREEQC code and the Pitzer database  [26]. All foliar treatments contained 0. 1% Genapol X-80 (Sigma-Aldrich), which provided a mean surface tension of 27.
Data Analysis Firstly, exploratory analyses of the data were carried out, to verify compliance with the assumptions of homoscedasticity, and the normality of the data for each situation was evaluated. To verify the homoscedasticity of the variances of the data, a Bartlett's test was performed. In cases where the variances were not homogeneous, data transformations were performed. The normality of the data was checked with the Kolmogorov-Smirnov (KS) test. Differences in cation and anion concentrations in the leaves and washing liquids were analysed by performing two-way ANOVAs. Tukey HDS tests were carried out for estimating differences between factors when F-values were significant (p < 0. 05). Statistical analyses were carried out using SPSS 15. 0 (SPSS Inc. , Chicago, IL, United States).
Potassium and C concentrations of olive leaves collected 3 and 24 h after 130 mM K-phosphate, K-nitrate, K-sulphate, K-chloride, K-carbonate and K-bicarbonate spraying. Data are means ± SD (N = 3; F (p) = Fisher test, with significance level in parentheses). In columns, different letters indicate significant differences (p < 0. 05) according to Tukey's HSD test.
The battery management unit ensures the safe and reliable operation of battery pack. The real time data are first collected via embedded sensors and is then fed to battery modeling and state estimation algorithms (stored on the computer of the ECU). The crucial information about the state of the battery is, in turn, furnished, such as charge (SoC), health (SoH), temperature (SoT), power capability, etc. [20]. Based on the provided states, appropriate decisions are taken at the right time: temperature regulation of the battery pack based on ambient temperature and that of the battery; external charge control based on battery capability, the SoC, and information provided by the external charger; battery cell equalization and balancing; fault diagnosis and prognostic; safety warnings, protection measures, and bidirectional human-media interaction, etc.
The data-driven methods, especially machine (and deep) learning, have found recent applications for estimating battery states (health, temperature, etc. )  [152]. Some of the applications of data-driven methods are as follows: vector machines [153], artificial neural networks [154], long-short-term memory networks, etc. A recent study exhibits the application of a data-driven approach to optimally configure the essential components of modern microgrids such as battery storage considering their degradation  [155]. The efficacy of data-driven approaches is evident from satisfactory results in a diverse range of operating scenarios. The recent applications depict the general robustness of data-driven models compared to their conventional counterparts, though the requirement of training datasets and the associated computational burden are the major bottlenecks  [156].
Among data-driven models, deep neural networks have witnessed recent attention. An application of a modified convolution neural network (CNN) is reported in  [157] , where techniques such as transfer learning and network pruning are exploited to address the shortcomings of conventional CNN, including training data dependency and computational burden. The accuracy of online capacity estimation is thereafter presented for four different LFP battery packs. Another innovative work is reported in  [158] , where the authors have extended the conventional CNN approach for estimating the complete charging curves just by collecting thirty points within ten minutes. Furthermore, the approach is generic in a way that it can be applied to any battery with a few training datasets by using a transfer learning process.
Data-Driven Estimators Data-driven methods are considered a viable future owing to the following advantages: black-box nature (model-independent), resistance to parameter variation, and scalability. However, these advantages come with a higher computational burden and need for a significant amount of training data with appropriate quality (for example labelling) in the context of estimation accuracy. Data-driven methods have found several recent applications for battery state estimation, such as neural networks, vector machines, fuzzy inference, metaheuristic [152,153,170,171], etc. Among the major shortcomings is that an inconsistent dataset can lead to an unavoidable estimation inaccuracy. For this, a fractional-order physics-informed recurrent neural network is proposed in  [172]  for battery state estimation, where the performance is enhanced by incorporating fractional-order gradient functions during the backpropagation step.
Mostly, the applications are estimating SoH and capacity fade, and predicting the remaining useful life of battery packs. A group of researchers in  [63]  reviewed the datadriven machine learning approaches for predicting battery states. To enhance the accuracy of battery health estimation, least-square vector machine and model-based Pf are hybridized as an effective joint supervision approach  [173]. Similarly, an application of a global metaheuristic optimizer (non-dominating sorting genetic algorithm) is illustrated in [174] for data-driven SoH estimation. Deep learning neural networks are also very promising for battery state estimation. For example, a convolutional neural network-based estimator is proposed in [157] to provide fast yet accurate battery capacity forecasting by employing a small dataset.