dataesr/scientific-paragraphs-categorization huggingface dataset:
Contributions: R. B. designed and conducted experiments, acquired and analyzed data, wrote and edited the manuscript; M. B. designed and conducted experiments, acquired and analyzed data, and edited the manuscript; S. C. , T. G. , and I. N. conducted experiments, analyzed data, and edited the manuscript; T. P. provided ABCG2 null samples; B. C. and J. -J. K. provided MPN blood samples, discussed results, and edited the manuscript; A. G. d. B. and S. A. designed experiments, analyzed data, and edited the manuscript; W. E. N. designed research, analyzed data, wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: The work was supported by Institut National de la Santé et de la Recherche Médicale (Inserm), Institut National de la Transfusion Sanguine (INTS), the University of Paris, and grants from Laboratory of Excellence (Labex) GR-Ex, reference No.
ANR-11-LABX-0051. The Labex GR-Ex is funded by the IdEx program "Investissements d'avenir" of the French National Research Agency, reference No. ANR-18-IDEX-0001. R. B. was funded by the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 675115-RELEVANCE-H2020-MSCA-ITN-2015. M. B. was funded by Ministère de l'Enseignement Supérieur et de la Recherche at the BioSPC Doctoral School. R. B. and M. B. also received financial support from Société Française d'Hématologie (SFH) and Club du Globule Rouge et du Fer (CGRF).
Author Contributions: R. B. designed and conducted experiments, acquired and analyzed data, wrote and edited the manuscript; M. B. designed and conducted experiments, acquired and analyzed data, and edited the manuscript; S. C. , T. G. , and I. N. conducted experiments, analyzed data, and edited the manuscript; T. P. provided ABCG2 null samples; B. C. and J. -J. K. provided MPN blood samples, discussed results, and edited the manuscript; A. G. d. B. and S. A. designed experiments, analyzed data, and edited the manuscript; W. E. N. designed research, analyzed data, wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: The work was supported by Institut National de la Santé et de la Recherche Médicale (Inserm), Institut National de la Transfusion Sanguine (INTS), the University of Paris, and grants from Laboratory of Excellence (Labex) GR-Ex, reference No.
Rockfall Activity To simulate rockfall events and study their interaction with the protection forest, we used the probabilistic process-based model Rockyfor3D  [26]. A field survey was conducted in summer 2018 in order to characterize the rockfall activity within the protection forest and to collect data required for the calibration of the rockfall model. Two transects were identified in the transit zone, one following the line of slope (vertical transect) and the other perpendicular to it (horizontal transect). The transects were 10 m wide and long enough to cover the entire length and width of the protection forest, equal to 440 and 175 m, respectively. Stakes were installed along the transects (one stake every 20-30 m) to aid relocation over time. The positions of the stakes were determined through RTK-DGPS measurements (TopCon HiPer V DGPS system, TopCon Positioning SRL, Tokyo, Japan).
Slope Surface Parameters To define ground and forest stand parameters required as input in the software package Rockyfor3D, field investigations were conducted during summer 2018, setting up 16 circular plots with a radius of 12 m (Figure  2 ). In each plot, we collected the position of the center (using a TopCon DGPS system, TopCon Positioning SRL, Tokyo, Japan). Within each plot, we recorded species, DBH, and relative position to the center for each tree with a DBH larger than 7. 5 cm. We also collected the heights of the five trees closest to the center, in order to create a local tree height-diameter curve. For each plot, we evaluated the surface roughness (hereafter RG), expressed as the size of the material covering the slope's surface and assessing the three size probability classes named rg70, rg20, and rg10; these parameters are an expression of the different MOHs (Mean Object Heights,  [27] ).
Remote Sensing Data The area has been flown over with different sensors both in pre-event and post-event conditions. A first campaign was performed by means of a high-density LiDAR scan in 2015 (appr. 12 pts/m 2 ) and a UAV flight that provided high-resolution imagery and a Structure-from-Motion (SfM) point cloud. Because of the storm event, during 2019 the area was re-scanned with LiDAR sensor, adding a new very highly detailed data source (appr. 25 pts/m 2 for the study site).
Data Pre-Processing Rockyfor3D simulations require a stack of raster layers with the same resolution and extent. For this specific purpose, the resolution has been set to a pixel size of 2 meters. The DTM (dem. asc) derived from the ground-classified point cloud collected during 2015 was used as a base layer. We selected the four roads crossing the study area as local checkpoints (calculation screens) to evaluate the number of passages and rock energy, while the conifer percentage (conif_percent. asc) and soil type (soiltype. asc) parameters were derived from field measurements and photo interpretation. Differently from the usual subdivision of the study area into subzones with homogeneous values, the RG files (rg10. asc, rg20. asc, and rg70. asc) were produced by hypothesizing a linear relation between slope and RG values measured in the field, offering a more realistic distribution across the site.
Hence, in order to be consistent with the Rockyfor3D parameter, the RG values were calculated as the 10th, 20th, and 70th percentile of the points' distribution starting from the maximum value (i. e. , 2. 5 m) to the lowest and then regularized according to the standard thresholds (see Manual's Annex I  [26] ). Finally, to validate the new RG rasters, we collected in the field the values of rg10, rg20, and rg70 at 103 different locations in the study area. Field sampled data and LiDAR data were compared in order to understand the pros and cons of both methodologies. For the statistical analysis, we used the free software PAST  [28]. The rasters obtained through this methodology provides values of RG with a resolution of 2 m, allowing the interaction between falling blocks and deadwood to be investigated.
Simulation of the 2DEG Density To investigate the two-dimensional electron gas (2DEG) properties of Al-rich Al-GaN channel heterostructures, various barrier thicknesses and Al content into the AlGaN channel were simulated using the commercial software for semiconductor nanodevices, "Nextnano". The solver includes both piezoelectric and spontaneous polarization contributions  [27]. The layers are considered pseudomorphic on AlN. After optimizing the meshing parameters, especially around the 2DEG, a good agreement between the electron density values from a variety of measured literature data could be achieved. Figure  1  depicts the 2DEG density as a function of the Al content on the channel for various barrier thicknesses (10 nm, 20 nm, and 30 nm). We observed a slight increase of the electron density with the AlN barrier thickness.
Funding: This work was supported by the  French RENATECH network , the  French National  grant  ANR-17-CE05-00131  within the project called  BREAKUP  and the  ANR-11-LABX-0014  representing GaNeX, as well as the project  InRel-NPower , which received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 720527. H. M. is partially supported by  JSPS KAKENHI Grants  ( JP16H06415 ) and  JST SICORP-EU  ( JPMJSC1608 ).
Study design The French population-based CONSTANCES cohort enrolled volunteers since 2012, aged 18-69 years at baseline, and according to a random sampling scheme strati ed on age, gender, socioeconomic status and region of France  17. Among the different procedures conducted with participants, they completed annual self-administered questionnaires on their lifestyle, health, social, and personal characteristics. All the procedures are detailed at www. constances. fr. CONSTANCES has obtained the authorization of the National Data Protection Authority, was approved by the Institutional Review Board of the National Institute for Medical Research (Authorization number 910486) and was performed in accordance with relevant guidelines and regulations. All participants signed an informed consent form to be included in the cohort.
Then, the analyses were strati ed for sex and age, separately. The following age groups were considered: 18-29; 30-39; 40-49; 50-59; ≥60. Lastly, sensitivity analyses were performed for diet rich in sugar and fat since this outcome had only one available follow-up endpoint, 2017 and was assessed over 5 years. First, we additionally adjusted for duration of follow-up in the model. Duration of follow-up was assessed continuously as the difference between 2017 and at baseline. Second, we tested for statistical interaction between physical exertion at work and duration of follow-up since duration of follow-up could be an effect modi er between the association between physical exertion and diet rich in sugar and fat. If a signi cant interaction was found, strati ed analyses were done. Missing data were handled by multiple imputations.
ACKNOWLEDGMENTS The authors thank the team of the "Population-based Epidemiologic Cohorts Unit" (Cohortes en population) that designed and manages the Constances Cohort Study. They also thank the National Health Insurance Fund ("Caisse nationale d'assurance maladie", CNAM) and its Health Screening Centres ("Centres d'examens de santé"), which are collecting a large part of the data, as well as the National Old-Age Insurance Fund ("Caisse nationale d'assurance vieillesse", Cnav) for its contribution to the constitution of the cohort, ClinSearch, Asqualab and Eurocell, which are conducting the data quality control.
Fatty Acids Analysis Total lipids were extracted from PLSO according to the method described by Moilanen and Nikkari  [60]. C19:0 was used as the internal standard in the experiments realized in LR12ES05, Lab-NAFS 'Nutrition-Functional Food & Vascular Health', Monastir, Tunisia. Lipids were trans-methylated with 14% boron trifluoride in methanol (BF 3 -MeOH) using the method of  Morrison and Smith [61]. Subsequently, fatty acid methyl esters were analyzed by gas chromatography (GC) under the same conditions described by Zarrouk et al. [62]. Fatty acids were identified by comparison with synthetic standards. This experiment was performed in triplicate and the data expressed either as g/100 g of oil (C19:0 used as internal standard) or as percentages of total fatty acids.
Measurement of Plasma Membrane Permeability with Propidium Iodide Propidium iodide (PI) was used to evaluate the plasma membrane permeability and cell death. This dye penetrates cells with damaged plasma membranes considered as dead cells  [71]. After 24 h of treatment with or without 7β-OHC (50 µM) in the presence or absence of PLSO (100 µg/mL) or α-tocopherol (400 µM), C2C12 cells (adherent and non-adherent cells) were stained with a PI solution (1 µg/mL of PBS) for 5 min at 37 • C, and then immediately analyzed on a BD Accuri™ C6 flow cytometer (BD Biosciences, San Jose, CA, USA). The red fluorescence was selected on a 630 nm band-pass filter and 10,000 cells were acquired for each sample. Data analyses were performed using FlowJo software (Carrboro, NC, USA).
Measurement of Oxidative Stress Evaluation of Reactive Oxygen Species Production with Dihydroethidium Dihydroethidium (DHE) was used to detect ROS, mainly superoxide anion (O 2 •-) production. DHE, a dye that can freely diffuse across cell membranes, is rapidly oxidized under the action of ROS to fluorescent ethidium. This latter exhibits an orange/red fluorescence (λ Ex max = 488 nm; λ Em max = 575 nm)  [72]. After 24 h of treatment, C2C12 cells (adherent and non-adherent cells) were stained with a 2 µM DHE solution for 15 min at 37 • C and then analyzed on a BD Accuri™ C6 flow cytometer (BD Biosciences). The fluorescent signals of the DHE-stained cells were collected through a 580 nm band-pass filter and 10,000 cells were acquired for each sample. Data analyses were performed using FlowJo software.
Evaluation of Mitochondrial Function Measurement of Transmembrane Mitochondrial Potential with DiOC 6 (3) The variation in the mitochondrial transmembrane potential (∆Ψm) was detected using 3,3 -dihexyloxacarbocyanine iodide (DiOC 6 (3))  [67]. After 24 h of treatment, adherent and non-adherent C2C12 cells were pooled, stained with a solution of DiOC 6 (3) (Invitrogen/Thermo Fisher Scientific, Montigny le Bretonneux, France) at 40 nM (15 min; 37 • C), and then analyzed on a BD Accuri™ C6 flow cytometer (BD Biosciences). The loss of ∆Ψm is indicated by a decrease in the green fluorescence intensity collected through a 520 ± 10 nm band-pass filter. For each sample, 10,000 cells were acquired, and data analyses were performed with FlowJo software.
Measurement of Mitochondrial Reactive Oxygen Species with MitoSOX-Red Mitochondrial ROS production, including superoxide anion (O 2 •-), was measured by flow cytometry after staining with MitoSOX-Red (Thermo Fisher Scientific, Asheville, NC, USA). Once in the mitochondria, this probe is oxidized and exhibits an orange/red fluorescence (λ Ex max = 510 nm; λ Em max = 580 nm)  [78]. After 24 h of treatment, adherent and non-adherent C2C12 cells were polled and stained with a 5 mM MitoSOX-Red solution for 15 min at 37 • C and then analyzed on a BD Accuri™ C6 flow cytometer. The fluorescent signals were collected through a 580 ± 20 nm band pass filter. For each sample, 10,000 cells were acquired, and data analyses were performed using FlowJo software.
Cells were washed and incubated in the dark (30 min, RT) with a goat anti-rabbit 488-Alexa antibody (Santa-Cruz Biotechnology, Santa Cruz, CA, USA) diluted at 1/500 in PFS buffer. After washing in PBS, cells were stained with Hoechst 33342 (1 µg/mL) and then mounted in Dako fluorescent mounting medium (Dako, Copenhagen, Denmark). The slides were stored in the dark at 4 • C and examined with structured illumination microscopy (Apotome). The images were realized with ZEN imaging software (Zeiss).
Statistical Analysis The experimental results were statistically analyzed with GraphPad Prism 8. 0 software (GraphPad Software, San Diego, CA, USA). In vitro data were expressed as the mean ± standard deviation (SD) and compared with an ANOVA test followed by a Tukey's test, which allows multiple comparisons and permits to assess any interaction. Clinical data were compared with a Student's t-test. A p-value less than 0. 05 was considered statistically significant. The heatmap representation was realized with GraphPad Prism 8. 0 software.
The levels of C22:0, C24:0, and C26:0 were determined by GC-MS, and the corresponding elongase activity index, which could correspond to ELOVL1, 3, and 6 activity (ratio (C24:0/C22:0) (A) and ratio (C26:0/C22:0) (B)) were calculated. Data are the mean ± SD of two independent experiments. A multiple comparative analysis between the groups, taking into account the interactions, was carried out using an ANOVA test followed by a Tukey's test. A p-value less than 0. 05 was considered statistically significant. The statistically significant differences between the groups, which are indicated by different letters, take into account the vehicle used    Control cells (Figure  9A ), α-tocopherol (400 mM)-treated cells (Figure  9C ), and PLSO (100 µg/mL)-treated cells (Figure  9E ) have a well-preserved cell morphology with a fusiform shape and a large central round nucleus containing some nucleoli; in the cytoplasm, they have small empty vacuoles and morphologically normal mitochondria and peroxisomes.
They could protect polyunsaturated fatty acids (PUFA) from oxidation by scavenging lipid peroxyl radicals (ROO • )  [119]. In PLSO, α-tocopherol is present in the highest quantity and contributes to the natural conservation of PLSO. It is important to highlight that PLSO is an excellent source of vitamin E, which is constituted of four tocopherols and four tocotrienols  [120]. In another hand, the antioxidant potential of PLSO, evaluated by DPPH, FRAP, and FIC assays, demonstrated an important antioxidant potential of this oil, reinforcing our interest to study the cytoprotective properties of this oil in vitro. Consequently, in the context of sarcopenia, we evaluated the protective properties of PLSO against 7β-OHCinduced cytotoxicity on a model of murine C2C12 myoblast cells. Indeed, several oxysterols, including 7β-OHC, are present at increased levels and high amounts in the plasma and tissues of patients with age-related diseases  [31, 121] , and our preliminary data obtained by GC-MS on the plasma from subjects with and without sarcopenia have revealed significant higher plasma levels of 7β-OHC in sarcopenic patients.
Acknowledgments: The authors would like to thank the association " Mediterranean Nutrition and Health (NMS: Nutrition Méditerranéenne & Santé )". Emmanuelle Prost-Camus,  Philippe Durand , and  Michel Prost  (PLSO polyphenols analysis and KRL assay;  Laboratoire LARA-Spiral, Couternon, France ),  Lucy Martine  and  Niyazi Acar  (PLSO fatty acids and phytosterols analysis;  Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon ,  CNRS ,  INRAE ,  Université Bourgogne Franche-Comté, Dijon, France) ,  Imed Cheraief  (PLSO fatty acids analysis;  University of Monastir, Faculty of Medicine ,  LR12ES05 ,  Lab-NAFS, Monastir, Tunisia ),  Valerio Leoni  and  Claudio Caccia  (analysis of very-long-chain fatty acids;  laboratory of Clinical Chemistry, Hospitals of Desio, ASST-Brianza  and the  Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy ) and  Jean-Paul Pais de Barros  (α-tocopherol analysis in PLSO; plasma oxysterols analysis;  Lipidomic Facility ,  Université de Bourgogne Franche-Comté (UBFC) , 21078 Dijon, France) are warmly thanked for their technical support.
2B ). GldL-TMH1 and GldM-TMH specifically formed homodimers but no interaction was detected between GldL-TMH1 and GldM TMH (Fig. 2C ). To test the interaction with GldL-TMH2, which exhibits an out-to-in topology, we used the BLA assay  (Julius, Laur et al. 2017 , Schanzenbach, Schmidt et al. 2017). GldL-TMHs and GldM-TMH were fused to either the N-or C-terminal domain of b-lactamase (Bla). If an interaction between TMHs occurs, a functional b-lactamase is reconstituted and its activity can be quantified using a chromogenic substrate-based assay. In this assay, GldL-TMH2 homodimerization was not observed. However, GldL-TMH2 specifically interacted with both GldL-TMH1 and GldM-TMH (Fig. 2D ). Taken together, our data show that GldL TMH1 and TMH2 interact with each other and GldL TMH2 interacts with GldM single TMH in the motor complex.
Sequence alignment showed that three acidic residues are conserved in the GldL and GldM N-terminal regions (Supplementary Fig. S2A  and S2B ): a glutamate at position 31 in the GldM TMH (GldM-E31) and two glutamates in GldL, one located in TMH2 (E49; strictly conserved in all GldL homologs in the OrthoDB database), and one located between TMH2 and the cytoplasmic domain (E59) (Supplementary Fig. S2C ). Substitution of GldM E31 (GldM E31A ) did not exhibit any defect in gliding motility compared to wild-type GldM, indicating that this residue does not play a significant role in T9SSdependent secretion or gliding (Fig. 4D ). By contrast, cells producing GldL E49A failed to adhere to the glass surface, while substitution of GldL E59 abolished gliding motility without affecting adherence (i. e.
Image analysis -Images were analyzed using ImageJ (http://imagej. nih. gov/ij/). The MicrobeJ plugin  (Ducret, Quardokus et al. 2016 ) was used to detect and track cells during gliding. The Trackmate plugin  (Tinevez, Perry et al. 2017 ) was used to detect SprB fluorescence and analyze its dynamics. Statistical dataset analysis was performed using Excel and the R software environment (https://www. r-project. org/). Kymographs were generated using the KymoResliceWide plugin (https://imagej. net/KymoResliceWide, E. Katrukha and L. Young). For fluorescence recovery quantification, images were corrected for bleaching using histogram matching prior to signal recovery quantification. combined from technical triplicates of four independent measurements (2 colonies from two independent transformations each).
Acknowledgements We thank the members of the  Cascales team  for insightful discussions and support,  Jean-Pierre Duneau  for discussion regarding peptide solubilization,  Moly Ba ,  Isabelle Bringer ,  Annick Brun ,  Olivier Uderso ,  Mathilde Valade  and  Audrey Gozzi  for technical assistance. This work was supported by the  Aix-Marseille Université (AMU) , the  Centre National de la Recherche Scientifique (CNRS) , and grants from the  Agence Nationale de la Recherche  ( ANR-15-CE11-0039  and  ANR-20-CE11-0017 ), from the  Excellence Initiative of Aix-Marseille University  ( A*MIDEX ,  A-M-AAP-ID-17-33-170301-07. 22 ), a  French "Investissements d'Avenir" programme , and from the  Fondation Bettencourt-Schueller. M. S. V. was supported by a doctoral fellowship from the  French Ministère de la Recherche , and an end-of-thesis fellowship from the  Fondation pour la Recherche Médicale  ( FDT2018-05005242 ).
Histological analyses. In line with a previous study  40  , jejunal segments were emptied and fixed in 4% paraformaldehyde for 24 h at 4 °C until serial dehydration in ethanol and embedding in paraffin for further morphometry analysis. Villus length and crypt depth were determined on hamatoxylin-eosin-stained jejunal sections (5 µm). A minimum of 15-20 well-oriented crypt-villus units were measured under a light microscope (Nikon Eclipse E400; Nikon Instruments, France) using image analysis software (ImageJ, version IJ1. 44). All morphological measurements were conducted by the same investigator (MF), who was blinded to the minipig group from which each section was taken.
Statistical analysis. All analyses were performed using the open-source R statistical software (R: A Language and Environment for Statistical Computing; R Core Team, R Foundation for Statistical Computing, Vienna, Austria; https:// www. r-proje ct. org/). Quantitative variables were expressed as medians with interquartile range or as means with SEM. For simple comparisons, we ran nonparametric Mann-Whitney U tests. We ran Type-III ANOVAs to test the Surgery type × Time interaction with post hoc Tukey tests for repeated meas-.
Clinical and laboratory data Data on baseline characteristics including demographics, physiological variables, the presence of medical comorbidities, SOFA and SAPS II score, and laboratory values including oxygenation parameters, full blood count, coagulation parameters, and inflammatory markers (C-reactive protein, interleukin 6, and ferritin) were collected on admission to ICU. Ventilatory variables including plateau pressure (Pplat), total positive end-expiratory pressure (PEEP), tidal volume (Vt) driving pressure (Pplat-PEEP), and static respiratory compliance [(Vt/(Pplat-PEEP)] were also captured. Use of neuromuscular blocking agents, prone positioning, vasopressors, renal replacement therapy, anti-viral treatments, tocilizumab, and methylprednisolone were collected. The decision of anti-viral, tocilizumab and methylprednisolone administrations was at the discretion of the treating physician.