dataesr/scientific-paragraphs-categorization huggingface dataset:
Magnetic resonance imaging recordings The MR acquisitions were performed on a horizontal 17. 2 T small animal MRI scanner (Biospec, Bruker Biospin, Etlingen, Germany) using a volume coil for mouse brain imaging (Rapid Biomedical, Rimpar, Germany). Good B0 homogeneity was ensured through a MAPSHIM correction in the region of interest. fMRI data were acquired using a 2D GE-EPI sequence with the following acquisition parameters: flip angle ¼ 40 deg, field of view ¼ 1. 6 × 1. 6 cm 2 , in plane resolution ¼ 200 × 200 μm 2 , number of slices = 3, slice thickness ¼ 300 μm, echo time = 10 ms, repetition time = 1000 ms, and acquisition time = 16 min. At the end of the fMRI session, high-resolution anatomical images of the same slices were acquired using a rapid acquisition with relaxation enhancement (RARE) pulse sequence: field of view ¼ 1.
Acknowledgments Financial support was provided by the  Institut national de la santé et de la recherche médicale (Inserm) , the  European Research Council  ( ERC-2013-AD6 ;  339513 ), the  Fondation pour la Recherche Médicale  ( EQU201903007811 ), the  Agence Nationale de la Recherche  ( NR-16-RHUS-0004  [ RHU TRT_cSVD ] and CE37_2020_TF-fUS-CADASIL), the  Fondation Leducq Transatlantic Networks of Excellence program  ( 16CVD05 , Understanding the role of the perivascular space in cerebral small vessel disease), and the  Fondation Alzheimer France  ( M21JRCN009 ) and the  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ] supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program. M. T. was supported by a fellowship from the  Fondation pour la Recherche Médicale  ( SPF201909009103 ).
Image Acquisition and Quantification Images were acquired using a confocal laser microscope (LSM) 800 airyscan (Zeiss, Oberkochen, Germany) equipped with Zen software. YAP quantification in HEK293 cells were performed using automatic image J macro developed to allow the quantification of mean cytoplasmic intensity, mean nuclear intensity and nucleo-cytoplasmic ratio by dividing the two precedent parameters. This quantification was done by analyzing 2 to 3 ×200 magnification images per well representing 500 to 1,000 cells per images.
RNA Nanostring Technology RNA (50 ng) was used from WT or YAP -/-HEK293 treated or not with TNF (n = 3 per groups). The fibrosis gene panel was used including 770 genes involved in fibrotic processes. All quality controls were performed following manufacturer instructions. Normalization was performed using five housekeeping genes that were not affected by the experimental conditions. Count detection limit was determined using threshold based on negative controls. Data analysis was performed using the nSolver ™ package (version 3. 0) and Advanced Analysis module (version 1. 0. 36). Differential expression and pathway analysis were performed using the nSolver advance analysis module according to the guidance given by manufacturers. Genes with a p-value below 0. 05 were considered as being significantly differentially expressed.
Statistical Analysis Data are presented as single values, with mean and standard deviation and are expressed as percentage of the mean of control values. Three independent experiments were always performed, with at least 2 biological replicates for each experiment. No data exclusion was performed except if samples were impossible to use due to low quality. Multiple comparisons were performed by one way ANOVA or Kruskal-Wallis test (according to normality), post hoc comparisons were corrected with the false discovery rate (FDR) method of Benjamini and Hochberg. Results were considered significantly different when p <0. 05. All statistical analyses were performed on GraphPad Prism v9. 2. 0 software or nsolver advanced software (for Nanostring results).
Lifetime and Relaxation of Resistance States As mentioned above, the OFF state corresponds to the equilibrium state of the junctions, while the ON state is metastable and relaxes toward the OFF state over a time scale that depends on the temperature. This is shown in Figure  6a , which displays the evolution of the conductance as a function of time after the junction has been set in the ON state. In order to quantify the relaxation dynamics, we carried out the same measurement protocol for all temperatures: first, we systematically preconditioned the junction at T = 100 K, by initially switching the junction into the OFF and then into the ON state. After that, we ramped the temperature to a desired T and, once T was stable, recorded the conductance G 0 as a function of time t. The data have been normalized as G norm (t) ≡ (G 0 (t) -G OFF )/(G ON -G OFF ), so that G norm = 1 in the ON state and G norm = 0 in the OFF state.
Acknowledgements This work was supported by  ERC  grant no. 647100  " SUSPINTRONICS ,"  ERC  grant no. 966735  " SUPERMEM," French ANR  grant  ANR-17-CE30-0018-04  " OPTOFLUXONICS," Spanish AEI  grant  PID2020-118078RB-I00 , and  Flag-ERA ERA-NET grant  " To2Dox. "D. P. has benefited support from the  initiative of excellence IDEX-Unistra  ( ANR-10-IDEX-0002-02 ) from the  French national program Investment  for the future.
Cell Culture and Treatment Peripheral B cells were purified from blood samples using the RosetteSep human enrichment kit (catalog number 15064; Stem Cell Technologies, Vancouver, BC, Canada). Lymphoblastoid cell lines (LCL) were generated in this study by infection of primary B cells from different donors with EBV. The myeloma-derived RPMI-8226 cells (http://web. expasy. org/cellosaurus/CVCL_0014, accessed on 23 February 2022) and the BL-derived cell lines, including the BL EBV(-) Louckes cell line (http://web. expasy. org/cellosaurus/CVCL_8259, accessed on 23 February 2022), were obtained from the International Agency for Research on Cancer (IARC) Biobank. Primary and immortalized B cells were cultured in RPMI 1640 medium (Gibco, Invitrogen Life Technologies, Cergy-Pontoise, France) supplemented with 10% fetal bovine serum, 100 U/mL penicillin G, 100 mg/mL streptomycin, 2 mM L-glutamine, and 1 mM sodium pyruvate (PAA; Pasching, Austria).
DNMTs Assays Nuclear proteins were extracted by using the Nuclear Extraction Kit (ab1134474 from Abcam) according to the manufacturer's instructions. Ten µg of nuclear extract was then used to perform the DNMT1 Assay using the DNMT1 Assay Kit (ab113469-Abcam) and the DNMT activity assay (DNMT Activity Quantification Kit (Colorimetric) ab113467-Abcam). DNMT activity was further measured on the ELISA reader Multiskan GO (Thermo scientific) with the software: SkanIt Software Version 5. 0.
EBV and AFB1-Related BL Methylome Profiling Identifies Differentially Methylated Genes In order to define an eBL-specific DNA methylation profile, we performed a comparative methylome analysis of primary and in vitro-EBV-immortalised B lymphocytes (LCL cells) with previously identified differentially methylated positions (DMPs) of EBV -positive and negative Burkitt lymphoma-derived cell lines  [30]. DNA from the abovementioned samples was interrogated for CpG methylation with the Illumina 450 K bead array (as described in the methods). The resulting heatmap, which illustrates a methylome pattern capable of discriminating samples according to their cell transformation status, highlights DNA methylation changes associated with the EBV-positive lymphoma condition (Figure  1a ). Since AFB1 is associated with eBL, we further investigated any common DMP with infection status by integrating publicly available data referring to white blood cell samples from a Gambian population heavily exposed to AFB1 (Figure  1b ).
Although other genes may also have a valuable role in the pathogenesis of eBL, TGFBI is often deregulated in several cancer types by aberrant DNA methylation mechanisms, and there is currently no evidence regarding its possible role in lymphoma development. other genes may also have a valuable role in the pathogenesis of eBL, TGFBI is often deregulated in several cancer types by aberrant DNA methylation mechanisms, and there is currently no evidence regarding its possible role in lymphoma development. These data reveal that both EBV and AFB1 might influence DNA methylation status on the same CpGs sites within the TGFBI gene. from the comparative analysis of methylomes associated with the conditions previously illustrated in (a) with AFB1 exposure. The Venn Diagram illustrates the intersection of DMPs associated respectively with aflatoxin B1 exposure condition ("AFB1 in vivo") provided by a publicly available dataset  [42] , with the eBL-specific ones ("BL EBV+") identified in a previous study  [30]  and, ultimately, with the specific signature of B cell transformation state ("BL").
Validation of the Differential Methylation in the EBV and AFB1-Related BL Cells To substantiate the finding of methylome analysis and the AFB1-related in silico data, we aimed to perform validation in vitro. To this end, endemic Burkitt lymphoma-derived cell lines and human eBL samples were subjected to targeted pyrosequencing. The results showed that eBL cell lines exhibited significantly higher methylation levels at the TGFBI CpG sites previously identified as differentially methylated (CG21, CG11, and CG00) in comparison to primary human B cells (Figure  2a ). The consistent findings were obtained by evaluating TGFBI methylation levels in human eBL samples and comparing them to those in lymphoid tissue from healthy controls (Figure  2b ). Similarly, to validate previous in silico data on AFB1 exposure-associated TGFBI hypermethylation, verifying its specific effect on the B cell subpopulation, EBV-negative cell lines were in vitro-treated with the mycotoxin and analyzed by pyrosequencing at the three CpG sites of interest.
40PIL-IL shows the lowest T g of -92 °C, which contributes to its highest ionic conductivity. The Li + transference numbers of the PIL-IL electrolytes were measured using the Bruce-Vincent method in symmetric lithium cells. [58, 59] The cells were subjected to a 30 mV polarization, and electrochemical impedance spectra were measured at initial and steady state. Figure  2b  shows the measurement result for 40PIL-IL, and Figure  S5 , Supporting Information, shows the result for 50PIL-IL and 60PIL-IL. For samples 40PIL-IL, 50PIL-IL, and 60PIL-IL, Li + transference numbers of 0. 44, 0. 31, and 0. 17 were determined, respectively. The decrease in the transference number in this series is due to the decrease in Li + mobility as supported by the  7  Li NMR data and the reduction of the ratio between Li + and FSI -in the PIL-IL electrolytes.
The electrolyte-infiltrated glass microfiber filters were dried in a fume hood overnight and then further dried in vacuum at 80 °C for 6 h. Material Characterization: SEM images were taken at a beam energy of 30 keV (FEI Nova Nano SEM 230). FTIR analysis was performed in an argon-filled glovebox using a Bruker Alpha spectrometer (Bruker Daltonics Inc. , Bremen, Germany) averaging 32 scans for each spectrum within 400-4000 cm -1. The 1 H,  7  Li, and  13  C MAS NMR experiments were conducted on a Bruker Avance III NMR spectrometer with a 4 mm CP/ MAS probe at 400. 2, 155. 5, and 100. 6 MHz, respectively, applying the following parameters for the three nuclei: 4. 9 µs (90°)/1. 0 µs (10°)/2. 0 µs (15°) pulses, 16/32/256 scans, and 15/10/10 recycle delays ensuring the acquisition of quantitative 1 H and  7  Li NMR data ( 13 C NMR data not quantitative).
All NMR spectra were recorded at 10 000 Hz MAS rotation rates. In the case of  13  C, the proton frequency was decoupled with 71 kHz SPINAL-64 during data acquisition. The 1 H and  13  C NMR chemical shifts were externally referenced to the signals at 1. 8 and 39. 5 ppm of adamantane, respectively, and  7  Li was referenced to a solution of 9. 9 m LiCl in D 2 O. The solid polymer samples were prepared by drying the PIL-IL solutions in the 4 mm MAS rotors, and Teflon inserts were used to ensure proper rotation. TGA and differential scanning calorimetry (DSC) were measured by using Netzsch STA449 F3 Jupiter thermal analyzer under argon with a ramp rate of 5 °C min -1. XPS measurements were performed by a PHI Quantum 2000 using a monochromated Al Kα X-ray source (1486. 6 eV) with a pass energy of 30 eV.
XPS data were processed with CasaXPS software and quantified using the corrected cross sections with the instrument parameters. Spectra were calibrated by setting the CC peak in C 1s spectrum to 284. 8 eV. Electrochemical Characterization: Electrochemical measurements were all performed in CR2032 coin cells. Each cell contains a 1 mm and a 0. 5 mm thick stainless steel spacer and a stainless steel 1. 4 mm thick spring. For conductivity measurement, the PIL-IL films were sandwiched in between two stainless steel blocking electrodes with a diameter of 16 mm, which was then sealed in coin cells. Electrochemical impedance spectra was performed at 25, 40, 50, 60, 70, and 80 °C by Novocontrol broadband dielectric/impedance spectrometer. The CV and LSV of the PIL-IL electrolyte were measured by a Biologic VMP3 potentiostat with a lithium counter electrode with a diameter of 12 mm, a stainless steel working electrode with a diameter of 16 mm.
Here we propose a discrete-in-scale universal multifractal (UM) cascade based approach. Daily, Hourly and six-minute rainfall time series datasets (with lengths ranging from 100 to 15 years) over three regions (Paris, Nantes, and Aix-en-Provence) in France that are characterized by different climates are analyzed to identify scaling regimes and estimate corresponding UM parameters (α,C 1 ) required by the UM cascade model. Suitable renormalization constants that correspond to the P ,D,T values of reference rainfall are used to simulate an ensemble of reference rainfall scenarios, and the simulations are finally compared with datasets. Although only purely temporal simulations are considered here, this approach could possibly be generalized to higher spatial dimensions as well.
, 2012; Lovejoy and Schertzer, 2013; Gires et al. , 2013; Hoang et al. , 2014). The objective of this paper is to simulate region specific reference rainfall scenarios which could be used as realistic inputs (as they exhibit larger variability and intermittency over a wide range of scales compared to uniform rainfall or synthetic hyetograms) to hydrological models for optimally designing storm-water management infrastructures. This paper attempts to understand the possibility of simulating reference rainfall ensembles characterized by the required properties (P,D,T) while exhibiting temporal variability and intermittency close to that of observed rainfall data. Section 2 discusses the different regions considered in France, their corresponding reference rainfall regulations and the observational datasets used.
These rainfall datasets are analysed via multifractal techniques as shown in Section 3 to identify scaling regimes and corresponding UM parameters necessary to simulate rainfall. Section 4 gives a brief recollection about discrete-in-scale UM cascades, explains in detail the procedure used here to simulate reference rainfall scenarios, and finally defines four metrics to quantitatively compare the simulations with corresponding datasets. Finally, the conclusions of this study along with its limitations and some future scope (extension to higher dimensions and other regions) are discussed in Section 5. 2 Regions considered and observational datasets used French regional storm-water management/discharge regulations are usually expressed in relation with some reference rainfall events expressed in terms of precipitation P , duration D, return period T values.
The rainfall datasets used for the three regions (Paris, Nantes and Aix-en-Provence) were obtained from MeteoFrance. Different time series were collected according to the time step (6-minute, hourly, daily). Figure. 2 shows the selected conurbations and their climatological rainfall data. These three regions were selected for this study as their monthly cumulative rainfall climatology (computed from daily data sets) are quite different from each other: while Paris receives around 40-60 mm monthly rainfall, Nantes receives a higher monthly rainfall from around 40-90 mm, Aix-en-Provence on the other hand receives a more variable monthly rainfall from around 10-80 mm. Cities are chosen here since storm-water management is more vital in urban areas due to their limited infiltration capacity. Information about the datasets used for each city/conurbation are given in Table  3.
Since the proportion of data missing is low, replacing these values with zeros will probably not result in any significant change to the actual data. For the sake of simplicity, we shall henceforth refer to the daily, hourly and 6-minutes datasets of Paris, Nantes and Aix as PD1, PD2, PD3, ND1, ND2, ND3, AD1, AD2, AD3 respectively. The concept of universality in complex systems states that only a few parameters (out of many) are relevant for defining the system since the same dynamical process is repeated scale after scale or the process interacts with many independent processes over a range of scales resulting in this reduction  (Schertzer and Lovejoy, 1987; SCHERTZER et al. , 1991; Schertzer and Lovejoy, 1997). In the UM framework only three parameters α,C 1 ,H (therefore referred to as UM parameters) are necessary.
We consider each observational dataset to be a single sample (to avoid any reduction in the largest scale considered which may lead to different multifractal characteristics). However, there is a drawback due to this small sample size: the estimate of spectral slope β is unreliable (coefficient of determination of the straight line fit is too low). Since H = β+K(2)-1 2 , consequently the H values estimated using β are also not very accurate. Therefore H is estimated by considering the first order (q = 1) un-normalized trace moment ⟨R λ 1 ⟩ (initially) assuming that the field is non-conservative ⟨R λ 1 ⟩ = λ -H (3) where once again equality sign is used for a possible asymptotic equivalence (λ → ∞). It turns out that for all the datasets (from PD1 to AD3) the slope of a straight line fitted through a log-log plot of ⟨R λ 1 ⟩ vs.
Figure. 3 shows the results of this analysis done on all the datasets (PD1 to AD3): there are two scaling regimes having distinct slope or K(q) with a scaling break (the scale where K(q) changes abruptly and distinctly) at around 2 to 4 weeks. This break in temporal scaling can be attributed to the synoptic maximum  (Tessier et al. , 1996)  or in other words the lifetime of planetary scale atmospheric structures. The similarity of scaling breaks observed in all the datasets justify the dependence of scaling break on the value of the largest planetary spatial scale (and its corresponding eddy turnover time or lifetime). All these scaling ranges of both the first and second scaling regimes are tabulated in Table. 4. Henceforth the scaling moment functions of the first and second scaling regime are denoted as K 1 (q) and K 2 (q) respectively.
Double Trace Moment (DTM) Analysis Although the TM analysis helps in estimating K(q), it does not provide explicit estimates of UM parameters α, C 1. To do this the DTM analysis  (Lavallee et al. , 1993)  is used: DT M λ = λ η α K(q) (5) where η is the power to which the field is raised. Eq. (  5 ) suggests that when K(q, η) vs. η is plotted in log-log coordinates, the slope of a fitted straight line gives the estimate of α, whereas C 1 is calculated using this α estimate and the y-intercept of the fitted straight line. While performing the usual DTM analysis it is found that the α estimates are larger than 2 (thereby exceeding the limits in Eq. 2) in the first scaling regime for all the datasets considered here. Generally this could be due to two different issues: (i) an incorrect α estimation procedure, or (ii) an incorrect assumption about the processes conservativeness.
However, for the datasets considered here the first possibility seems more likely due to the fact that the H estimates are negligibly small (as shown in Table. 4 and discussed earlier in section 3) and that Fourier analysis of these datasets are unreliable due to the small sample size chosen (N s = 1). Therefore, to overcome this issue an iterative DTM procedure is used here. More technical details about this procedure is given in the Appendix A. Table. Comparison Index (MCI) based on the difference in the theoretical maximum observable singularity from a finite-sized sample γ s  (Hubert et al. , 1993; Douglas and Barros, 2003 ) MCI = 1 6 3 j=1 2 i=1 γ s,obs(j) (i) -γ s,sel(j) (i) (6) based on the difference between UM parameter values observed from datasets and selected for simulations (as indicated by the subscripts obs and sel) with the analytical expression γ s = C 1 α α -1 1 C 1 α-1 α - 1 α (7) with respect to α and C 1 , the indices i,j denote the scaling regime (first or second) and the dataset (6-minutes, hourly or daily) used respectively.
Simulating reference rainfall scenarios To have the same P ,D,T characteristics of the reference rainfall, a simulated rainfall series with largest (temporal) scale (T sim ) needs to have a specific number (ρ) of peak values of rainfall (≥ P ) accumulated over specific durations (D) so that their return period T = Tsim ρ. A simple way to do this is to multiply the simulated multifractal time series (with largest scale T sim = ρT, ∀ ρ ∈ Z + ) by an appropriate renormalization constant (RC): P divided by the ρ-th highest value in the multifractal series aggregated over duration D. Therefore, the simulated rainfall series are dependent on these P ,D,T values, resulting in 1 rainfall series for Paris, 11 rainfall series for Nantes, and 3 rainfall series for Aix. Since the observed datasets have two scaling regimes it is necessary to use a double cascade: a coarser resolution cascade using parameters α 1 ,C 11 for the first scaling regime and a finer resolution cascade using parameters α 2 ,C 12 for the second scaling regime.
Let the smallest scale observed and simulated be δ (here δ = 6 minutes for all three regions). The largest temporal scale selected from observed datasets T sel is related to the largest scale that can be simulated T (s,sim) (largest scale in simulated sample which is a power of λ 1 = 2 and ≤ T sim ): T s,sim = δ2 ⌊log 2 T sel δ ⌋ ; T s,sim ≤ T sim (9) where ⌊x⌋ denotes the integer part of x. The RC computed using Eq. (  10 ) when multiplied to ε Λ δ gives the final rainfall series that has characteristics corresponding to the reference rainfall. This entire procedure is repeated n e times to generate an n e member ensemble of possible reference rainfall scenarios (Fig. 5. schematically presents the whole simulation method). Here n e = 10, i. e. an ensemble of 10 members (m1 to m10) are simulated.
Comparing simulations with observational datasets Four metrics possessing different properties have been defined to compare the stochastic simulations of rainfall to the actual datasets. The first metric is the Multifractal Comparison Metric (MCM), the second metric is the Rainfall Comparison Metric (RCM), the third metric is the Singularity Comparison Metric (SCM), whereas the fourth and final metric is the Codimension Comparison Metric (CCM). These metrics are defined with the general idea that lower metrics correspond to better simulations and vice-versa.
Multifractal Comparison Metric (MCM) The MCM is a theoretical metric and is computed based on the maximum observable theoretical singularity γ s (Eq. 7) from a finite sample size N s ≈ λ Ds where D S is the sample dimension  (Schertzer and Lovejoy, 1992)  in each dataset and in each simulated member of the ensemble MCM = 1 6 3 j=1 2 i=1 γ s,obs (i) - 1 10 10 k=1 γ s,sim(k) (i) (11) where j indicates the dataset used (daily, hourly or 6 minute), i denotes the scaling regime (first or second), k indicates the ensemble member. MCM is closely related to MCI defined earlier, the only difference between them is that MCM uses the UM parameters estimated from DTM analysis of simulated members, whereas MCI directly uses the UM parameters selected for simulations from the observed datasets. Therefore, as expected the MCM computed for all the simulations are very low (shown in Figure.