dataesr/scientific-paragraphs-categorization huggingface dataset:
01 to 1. 0 min and an RT wavelet range from 0. 01 to 0. 08 min. Isotopes were detected using the isotope peak grouper with an m/z tolerance of 5. 0 ppm, an RT tolerance of 0. 05 min (absolute) and the maximum charge set at 1 and the representative isotope used was the most intense. The resulting peak list was filtered using the peak-filter option (height: 1. 0 E 4 to 1. 0 E 7 , data points: 8 to 50, tailing factor: 0. 5 to 2. 00). Last, using the peak-list rows filter option, features without an MS 2 spectrum associated were removed. The resulting peak list, containing 283 features, was exported using the GNPS Export/Submit module to an. mgf file and a quantitation table in csv format.
ucsd. edu/ProteoSAFe/status. jsp?task=9e5e99ef164a4fa0bbe218c73de3ff26. Visualization of the results was carried out in Cytoscape 3. 8. 0 [91]. The output of the GNPS was used to annotate against the in silico ISDB-DNP  [35]  and then the script for taxonomically informed metabolite annotation  [36]  was used to re-rank and clean out the output based on the taxonomy.
Conclusions The metabolite profiling workflow based on UHPLC-MS/MS provided a comprehensive survey of the phenolic composition of the methanolic extract of Stryphnodendron pulcherrimum leaves. This workflow identified 30 compounds through spectral matching against experimental data from GNPS. Additionally, 19 compounds were putatively assigned via the same spectral matching but against in silico databases. Compounds 1, 4, 6, 13, 24, 31, 42 and 45 have been previously reported in the genus Stryphnodendron. Gallic acid (1), (epi)gallocatechin (6), (epi)gallocatechin gallate  (13) , myricitrin  (31)  and myricetin  (42)  were reported previously in the species by our group  [49]. To the best of our knowledge, 44 of these compounds are reported herein for the first time in the species Stryphnodendron pulcherrimum and 41 are described for the first time in the genus.
Supplementary Materials: The following are available online at https://www. mdpi. com/2218-1 989/11/5/281/s1, Compounds annotated in GNPS; Table  S1. In-house database including 74 compounds reported in the literature from Stryphnodendron. Figure  S1. Matches between spectrum MS/MS from G1. Figure  S2. Matches between spectrum MS/MS from G2. Figure  S3. Matches between spectrum MS/MS from G3. Figure  S4. Fragmentation scheme of (epi)gallocatechin gallate  (13). Figure  S5. Fragmentation scheme of O-coumaroyl O-galloyl-hexoside  (21).
Acknowledgments: P. W. P. G. is thankful to the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Process:  141680/2018-0 , Modality:  Doctoral Scholarship-GD Graduate Program. L. M. Q. G. is thankful to the  Ministerio de Ciencia, Tecnología y Telecomunicaciones, MICITT , from  Costa Rica  for the scholarship provided (N •  214171-025 ).
As other regions of Morocco, people living in North-Eastern Morocco have a common cultural past that dates back to the Arab civilization in the seventh century. The original cumulative culture has maintained a well-developed traditional knowledge of medicinal plants' uses that form the basis of the traditional medical system existing until now  [6, 7]. Unfortunately, this local cultural and natural heritage is threatened with extinction. The decrease of these phyto-therapeutic practices and the degradation of phyto-genetic resources are due to several factors, mainly the lack of documentary databases related to traditional medical practices and the scarcity of ethnobotanical information archives aggravate this natural and cultural heritage loss. Regarding, these ancestral medical practices in this country, we found that there are many investigations carried out in different regions of Morocco that deal with traditional use of medicinal plants for the treatment of renal diseases.
Ethnobotanical Survey In order to collect the traditional knowledge about medicinal plants used by people living in the study area, an ethnobotanical survey was conducted from February 2018 to January 2020 in thirteen rural communes of the North-Eastern region of Morocco (Table  2 ) spread over six provinces (Guercif, Taourirt, Jerada, Berkane, Nador, Oujda-Angad). The ethnobotanical data were randomly selected at thirteen sites visited by conducting semi-structured interviews with 476 respondents from the local population and 12 traditional herbalists. The application of simple random sampling achieved the selection of informants. This sampling technique has the main advantage of ensuring the representativeness of the population. Informants who do not live in the study area are excluded from this study.
Identification of Medicinal Plant Species All local names of plants collected during this study were translated into botanical names, based on the following references  [7, 190]. For the authentication and the accuracy of plant names listed in this paper of scientific names, we consulted documents specializing in the taxonomy of Moroccan flora (Then, the identification was performed by using standard floras available in Morocco  [191] [192] [193] [194] [195] [196]. For the accuracy and authentication of the scientific nomenclature, the plants recorded were checked against database available online: Catalogue of Life: 2019 Annual Checklist (https://www. catalogueoflife. org/col/) (accessed on 13 April 2020), the Plant List (http://www. theplantlist. org/) (accessed on 13 April 2020) and African Plant Database (http://www.
ville-ge. ch/musinfo/bd/cjb/ africa/recherche. php) (accessed on 15 April 2020). Only the plant names accepted in these databases were retained. Following the Angiosperm Phylogeny Group IV (2016), the plant families listed in this paper were checked with database APG-IV 2016  [197]. Once the name of each plant species selected was identified correctly, the whole or a part of the picked plants were pressed with a plant press and dried properly. A voucher number was attributed to each specimen and deposited in the Herbarium (HUMPOM), at Mohammed first University, Oujda, Morocco. To give the relative importance of each plant species known locally to be used in popular medicine, we calculated the use-value (UV) for each species. This index was calculated using the following formula  [198] : UV = ∑ U N ( 1 ) where UV = use value of species, U = number of quotations per species, N = number of informants.
Statistical treatment Principal component analysis (PCA), correlation matrix, pairwise p-values and heatmap were performed with the R software (packages corrplot and FactoMineR,  [25] ). The Pearson correlation was used to assess both the correlation matrix and p-values. Two types of statistical analyses were performed; (i) with DTR occurrence only (n = 152) and (ii) with DTR occurrence, granulometry and TOC analyses (n = 139 samples). During statistical analysis a concentration of 0 was attributed to DTR concentrations < LOD, and for DTR concentrations between LOD and LOQ, a concentration of LOQ/2 was attributed  [26].
Acknowledgements The authors gratefully acknowledge  C. Morio  for sampling authorization. The authors also wish to thank  C2FN  ( L. Augustin  and  L. Ardito ) for their support during coring operations. Post-graduate students, and specifically  A. Thibault  and  M. Réty  are acknowledged for their participation in data acquisition. R. Boscardin  is acknowledged for her long-standing technical support.
Acknowledgements This work was supported by a grant from the  Agence Nationale de la Recherche (ANR)  with  PRCI  funding ( ANR-17-CE07-0048-01 ) and by the  University of Padova P-DiSC # 11BIRD2020-UNIPD ,  CariParo Foundation ,  Synergy -Progetti di Eccellenza 2018  (L. D). Giovanni Salvagno  is acknowledged for preliminary experiments. Prof. Tommaso Carofiglio  and  Dr Paolo Zardi  are acknowledged for technical assistance.
Analysis methods Scanning Electron Microscopy (SEM) images were recorded on a FEI Nova NanoSEM 450 (Hillsboro, Oregon, USA) at an acceleration voltage of 10 kV using an in-lens secondary electron detector. To prevent charge accumulation, Furosemide/PVP particles were coated with a 10 nm thick layer of gold by sputtering before the analysis. The size distribution and particle mean size of Furosemide/PVP sub-micron particles were determined by measuring 260 particles using ImageJ software. The particle size distribution was fitted with a Gaussian line shape using OriginPro 2019b (version 9. 6. 5. 169).
Far-Field and SERS Investigations of Pure Furosemide and PVP In order to facilitate the interpretation of Raman spectra from Furosemide/PVP composites, far-field Raman spectra and maps of pure, commercial Furosemide and PVP particles are characterized first. Afterwards, specific marker bands allowing to clearly distinguish between Furosemide and PVP signals in the composite particles spectra and images are identified from these datasets. Ideally, marker bands should be selected in wavenumber regions where no (or nearly no) signal superposition occurs. 2] For Furosemide, the chosen marker band is the in-plane ring bending vibration at 686 cm -1. 39 For PVP, the selected marker band is the most intense CH2 asymmetric stretching vibrations of the saturated PVP chains peak at 2924 cm -1. 40 The far-field Raman spectra and the marker bands of pure Furosemide (red) and PVP (green) compounds are shown in Figure  3.
Acknowledgement The authors thank their colleague  Cédric Martin  ( Spinofrin SAS, Boulogne-Billancourt, France ) for recording the presented SEM image. The Synthesis, Irradiation, and Analysis of Materials (SIAM), technological platform of  University of Namur  is acknowledged for XPS measurements. The authors also thank the  Direction Générale de l'Armement (DGA, France)  and the  Bundesamt für Ausrüstung, Informationstechnik und Nutzung der Bundeswehr (BAAINBw, Germany ). ISL is a joint initiative of the  Ministère de la Défense (France)  and the  Bundesministerium der Verteidigung (Germany)  and is actively supporting the NS3E joint laboratory including the  Centre National de la Recherche Scientifique (CNRS)  and the  University of Strasbourg (UNISTRA).
Fra and Methyl-Fra Derivatives Prevail to Fra-Glycosylated Forms An increase in the polarity of given compounds typically results from their oxidation by oxygenases or glycosylation by glycosyltransferases. This metabolization enables then the sequestration of phenolic compounds into the primary cellular vacuole as described with Fra  [22, 37]. To identify metabolites of Fra, Qui, Emo, Mad, and VH characterized by the fluorescence spectra detected by SImaging, non-targeted metabolomics of methanolic extracts from treated and non-treated BY-2 cells was carried out using UPLC-HRMS/MS. The results were compared to a database including the reference compounds and related metabolites deduced from in silico biotransformation. In this regard, 54 metabolites absent in the control extracts were annotated according to m/z of the parent ion and isotopic profile as compared to references or putative catabolites.
Spectral Data Analysis The dataset consisted of 10 spectra/cell collected from five cells in three independent biological replicates for each treatment, at 25 and 50 µM, after 5 min and 18 h. Intensities per channels of each spectrum were averaged per cell and normalized to 1 before statistical analysis with R software V4. 0. 0 (GNU GPLv2, R Core Team) and RStudio V 1. 2. 5001 (AG-PLv3, RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA http://www. rstudio. com/) using the ChemoSpec package V5. 2. 12. A distant matrix was established for each dataset applying the Pearson's correlation coefficient, and a robust principal component analysis (PCA) was performed. The same procedure was used with solutions of pure compounds to analyze 10 spectra/acquisition in triplicate.
The spectrometer was equipped with an electrospray ionization (ESI) source and operated in positive ion mode on a mass range from 20 to 1000 Da with a spectra rate of 2 Hz in AutoMS/MS fragmentation mode. The end plate offset was set at 500 V, capillary voltage at 2500 V, nebulizer at 2 Bar, dry gas at 8 L. min-1 and dry temperature at 200 • C. The transfer time was set at 20-70 µs and MS/MS collision energy at 80-120%. The MS/MS cycle time was set to 3 s, absolute threshold to 816 cts and active exclusion was used with an exclusion threshold at 3 spectra, release after 1 min and precursor ion was reconsidered if the ratio current intensity/previous intensity was higher than 5. Raw data were processed in MetaboScape 4. 0 software (Bruker): molecular features were considered and grouped into buckets containing one or several adducts and isotopes from the detected ions with their retention time and MS/MS information when available.
Acknowledgments: We thank  Gilles Ulrich  ( Institut de Chimie des Procédés pour l'Énergie, l'Environnement et la Santé, Strasbourg ) for access to the spectrofluorometer to establish reference spectra and quantum yields. We thank to  Thomas J. Bach  ( Institut de Biologie Moléculaire des Plantes, Strasbourg ) for sharing the collection of phenolic compounds and to  Dimitri Heintz  for access to mass spectrometry equipment (metabolomics facility, IBMP). We are also grateful to  Stephanie Riché  and  Sridevi Ramanoudjame  ( Laboratoire d'Innovation Thérapeutique, Strasbourg ) for excellent technical assistance to purify vismione H. We acknowledge helpful criticism and discussion with  Romain Duval  and  Elisabeth Davioud-Charvet.
Full treatment was a combination of pre-and posttreatments. In the prevention assay, HepG2 cells were treated with medium, vehicle, ribavirin, or various concentrations of  [6] -gingerol at 37 °C and 5% CO 2 for 1 hour before being infected with CHIKV at 37 °C and 5% CO 2 for 1 hour. After washing, the cells were supplemented with fresh medium and incubated at 37 °C and 5% CO 2 for 48 hours. In all treatments, the supernatant was collected after 48 hours of incubation, as shown in Turnip et al. [27] , and measured for viral titers by standard plaque assay. 2. 6. Statistical Analysis. All statistical analysis was done using the SPSS Statistics program (Ver. 24), and the two-tailed t -test for independent samples was used. A p value of less than 0. 05 was considered as statistically significant.
The primary objective of the present study was to evaluate the frequency of positive tests for alcohol and drugs during roadside testing or after road accidents among drivers in the Brittany region of France. The study's secondary objective was to describe the blood concentrations of the substances found during these tests, in order to provide a scientific basis for the establishment or modification of legislative threshold values for road injuries prevention. We performed a cross-sectional study of a database compiled by Rennes University Hospital's toxicology laboratory in the Brittany region of France between 2010 and 2018. Driver's age, sex, and test status (positive or negative), and blood levels of ethanol, 9-tetrahydrocannabinol (THC), methylene dioxymethamphetamine (MDMA), amphetamine, benzoylecgonine and 6-monoacetylmorphine (6-MAM) were collected.
Data source and design Following a request by the police, Rennes University Hospital's toxicology laboratory carries out assays on blood samples taken (i) after a crash, (ii) in the context of checkpoints (such as raves and festivals) or (iii) during random roadside testing, at any time of day and any location. In the last two cases, blood samples were taken after positive oral fluid test or alcohol tester. Since the proportion of each of these three origins is unknown, we pooled the results together. The requested drug screens include ethanol, the cannabinoid THC, MDMA, other amphetamines, the cocaine metabolite benzoylecgonine, and the heroin metabolite 6-MAM. For each sample, several data are recorded in the toxicology laboratory's database: the tested person's sex and date of birth, the date and place of the roadside testing or the crash, the substances sought (ethanol, THC, MDMA, amphetamine, benzoylecgonine, and/or 6-MAM), the date of the analytical result, the result (positive or negative), and the blood concentration of analyte in positive tests.
Although morphine has been assayed in the laboratory, there is still uncertainty as to the source of this substance; morphine can come either from medical care (morphine or codeine) or from narcotics. In order to be sure of the narcotic intake, we chose to keep only 6-MAM in this study. We conducted a cross-sectional study of data extracted from the laboratory's database for the period between January 1st, 2010, and November 22nd, 2018.
Statistical analysis We analyzed (i) the dataset as a whole, (ii) each substance class, and (iii) samples that were positive for two or more substances. All analyses were performed using SAS software (version 9. 4, SAS-institute, Cary, NC, USA) and SAS Enterprise Guide®. Quantitative variables (age and plasma concentration) were described as the mean, median, standard deviation (SD), range and interquartile range (IQR), and qualitative variables (sex, assay request, assay positivity, multiple substance abuse) were described as the number (percentage). Fig. 1  was created using Prism software (version 5. 0, GraphPad Software, La Jolla, CA, USA).
Results Between 2010 and 2018, a total of 25,998 toxicological analysis results (concerning 12,497 individuals) were logged in the laboratory's database. The general characteristics of the tested drivers and the requests for alcohol and drug tests are summarized in Table  1. The age of the tested individuals ranged from 15 to 94, with a median of 29 and an IQR of 23 to 38. Over this 9-year period, most of the tested subjects fell into the 15-24 and 25-34 age groups (corresponding respectively to 37. 8% and 31. 1% of the 25,998 requests for analyses). The male/female sex ratio was 6. 5 (NB: the sex was not reported in 0. 2% of cases). Cannabis and alcohol were the most frequently tested-for substances during roadside testing; they accounted for 33% and 20. 3% of the assay requests, respectively (Table  1 ).
Discussion The results of this large, cross-sectional analysis of data collected between 2010 and 2018 by Rennes University Hospital's toxicology laboratory provides an interesting overview of DUIA and DUID in the Brittany region of France. >40% of the blood assays were positive for at least one psychoactive substance -mainly ethanol and cannabis. To the best of our knowledge, the present study is the first of this magnitude to have explored the patterns of psychoactive substance consumption (including multiple substance abuse) among drivers in France, the consumption profiles, and the blood concentrations. The great majority of the blood tests assessed in the present study (86. 5%) concerned men. In the literature, male sex is associated with more time spent driving, a greater frequency of speeding violations, and greater alcohol consumption (relative to females)  (Norris et al.
Specifically, we assumed a model with exponential growth in phase 1 and a transition to densitydependent competitive Lotka-Volterra-type dynamics in phase 2. By fitting our model to the homotypic growth curves, we inferred the values of the phase 1 and phase 2 growth rates and the within-type interaction parameters (see section "Materials and Methods"). To infer the between-type interactions, we used additional data from 72-h competition assays, covering a wide range of initial ratios of the two cell types. Although this latter data set comprises only the initial and final proportions (at the beginning of phase 1 and the end of phase 2), we were able to infer the proportions at the beginning of phase 2 by adjusting for the exponential growth of both types during phase 1. We then used these inferred proportions and our previously inferred parameter values to estimate the remaining interaction parameters (see section "Materials and Methods").
The initial 4T07 proportion (horizontal axis) is measured at the start of the respective period and the growth rate (vertical axis) is averaged over the period. Phase 1 data are from time-lapse microscopy. Other data points in the first column are from serial competition assays, such that each point corresponds to the slope of a thin gray line in panel (B). Data points in the middle column are obtained from the competition assay data by adjusting for exponential growth during phase 1 (see section "Materials and Methods"). Curves are the results of our mathematical model (see section "Materials and Methods") with parameter values inferred from data (Table  1 ). (B) 4T07 frequency dynamics across serial competition assays. Thick solid lines are averaged data (means of replicates with similar initial 4T07 proportions) and thick dashed lines are results of our mathematical model with parameter values inferred from data.
Thin gray lines are data for individual experiments. A total of 10 5 cells were seeded in co-cultures and harvested and replated as indicated. 4T07 parental cells were transduced either with an empty retroviral vector (4T07P) or labeled with a GFP-encoding retrovirus (4T07G). The ratios of GFP to unlabeled cells were estimated by flow cytometry. (C) Logit-transformed 4T07 frequency dynamics. This panel shows the same data as panel (B) but with a logit-transformed vertical axis so that the slope of each curve is equal to the mean net growth rate difference (the gain function, as described in section "Materials and Methods" and Supplementary Figure  7 ). Finally, having inferred all the evolutionary parameter values, we calculated net growth rates of the two cell types, averaged over different time periods.
79) drastically decreased the 168FARN growth rate. Moreover, buffering the same medium at pH 7. 0 restored the proliferative capacity of 168FARN culture (Figure  7B ). We conclude that the loser clone is indeed highly sensitive to medium acidification. Taken together our data suggest that the decrease in the growth rate of 168FARN observed in heterotypic conditions is triggered by 4T07 mediated extracellular acidification. (A) 168FARN and 4T07 cells were seeded at the indicated initial densities in six-well plates and cultured for 3 days. Culture media were removed, immediately covered with a layer of mineral oil to prevent oxidation and the pH was measured. (B) 10 5 168FARN cells were grown for 3 days. Medium was then replaced by conditioned media from cultures grown at low or high density, as indicated.
EdU Staining Cells were incubated with 10 µM EdU for 2 h, harvested and processed using the Click-iT TM EdU Alexa Fluor TM 647 Flow Cytometry Assay Kit (Thermo Fisher Scientific #C10424) following manufacturer instructions. Labeled cells were then analyzed on a FACSCalibur flow cytometer using CellQuest Pro software (BD Biosciences).
Apoptosis Quantification To determine the percentage of apoptotic cells with externalized phosphatidylserine (PS), adherent and floating cells were collected and labeled with the Annexin V-Cy3 Apoptosis Detection Kit (Abcam, Cambridge, United Kingdom, #ab14143) according to the manufacturer's instructions. Labeled cells were then analyzed on a FACSCalibur flow cytometer using CellQuest Pro software (BD Biosciences). β-Hydroxybutyrate Quantification β-Hydroxybutyrate concentration was measured by an enzymatic kit (Sigma-Aldrich MAK041) following the manufacturer instructions. Briefly, β-hydroxybutyrate present in the culture medium was determined by a coupled enzyme reaction, resulting in a colorimetric (450 nm) product, proportional to the β-hydroxybutyrate concentration. The absorbance was measured on a spectrophotometer.