dataesr/scientific-paragraphs-categorization huggingface dataset:
Each muscle strip was then electrically stimulated by an S48 stimulator (Grass Technologies, West Warwick, RI) with square-wave pulses (70Hz, 250-ms train duration, 0. 5-ms pulse duration) for 5 min at 37˚C, following the previous contractile protocols  [25]. Muscle force (mN/mg) was measured by a stationary force transducer (force range 0 to 1,600 mN)  [26]. The tetanic contractions were recorded and converted to digital data by an A-D converter (model ML826; AD instruments, Colorado Springs, CO). LabChart 7. 3. 1 was employed to analyze the data. Cyt c was added to the strip during the 5-min contraction with or without the addition of SOD. Cyt c can be reduced by O 2 •to generate an enhanced absorbance at 550 nm, which was measured via a spectrophotometer (Nanodrop 2000, Thermal Scientific, MA, USA).
Tubes were capped and vortexed for 1 min, then centrifuged at 3000 rpm at 4˚C for 5 min. Four ml of the upper hexane layer was removed to 13×100 glass tubes and evaporated under nitrogen for at least 30 min. Later, 200 μL of ethanol was added to the dried hexane fraction, then wells was rinsed, and the mixture was transferred to 0. 5 mL Eppendorf safety-lock tubes and stored at -80˚C until analysis. Additionally, vitamin C was separated on a 150 × 4. 6 mm, 5 μm Supelcosil LC18-DB column and eluted by 95:5 (v/v) methanol-0. 1 M lithium acetate (25 mM, pH 4. 75, flow rate of 1. 3 mL/min). α-tocopherol was measured using HPLC with electrochemical detection (Model 5200A Coulochem II; ESA, Inc. , Chemlford, MA) and a Model 5011 high sensitivity analytical cell. Data was extracted using TotalChrom Navigator software.
The apoptotic index (AI; %) was determined by the number of apoptotic cells over 100 respective cells that were randomly selected. Each sample was counted in duplicates and AI was recorded as the average of two slides. If counts varied by more than 5%, the slides in question were recounted  [30]. Statistics. The student's t test was used to analyze the mouse data for statistical significance between the control and stretched muscles as well as to compare ROS data with and without SOD treatments via IBM SPSS software. The human data was analyzed using a mixed model ANOVA with repeated measures followed by adjusted Tukey's post hoc tests using SAS  (9. 3). All values were presented as mean ± SE (IBM SPSS Statistics 21). p < 0. 05 was considered statistically significant. Compliance of supplementation.
Interestingly, Close et al. observed an increase in ROS, as measured by electron spin resonance (ESR), only after the peak muscle function decline and delayed onset of muscle soreness which followed eccentric downhill running in human subjects. Therefore, it has been suggested that ROS are not major contributors to muscle damage in eccentric exercise  [65]. However, the exact source of ROS in lengthening exercise has not been fully investigated in human studies. By monitoring the ROS formation in skeletal muscle ex vivo, the data from current studies showed a marked release of O 2 •from stretched muscle during contraction. The inconsistencies found between the animal and human studies may be due to the differences in contractile protocol and the methods used for detecting ROS. The animal stretching model is aimed to further explore the redox relations of the impaired muscle function following stretch preconditioning of the muscle.
The human portion of this study was supported by  Wastl Human Performance Laboratory, Donald L. Corrigan Professional Development Grant ,  Purdue Research Foundation , and  Purdue University Bilsland Strategic Initiative Fellowship. The animal portion of this study was funded by The  Ohio State University College of Medicine-Health and Rehabilitation Sciences Fund   013000  and  American Physiology Society S&R Foundation Ryuji Ueno Award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Processing Each reflection is indexed based on its 2 angle and its position on the diffraction ring, defined by its η angle along this ring. In addition the size and the total intensity of each reflection is determined. Figure  3  shows an EDF (ESRF Data Format) frame that was collected during the experiment. It records the intensity of each of the 2,048 × 2,048 pixels of the detector. The X-ray beam is small enough for the diffraction rings to break up in individual grain reflections. The Al and Cu layers show up as strong rings in the corners. In Table  S1  the reflections of a few grains and their properties are listed. To identify and follow grain reflections over time Matlab R was used to process the experiment data consisting of the collected EDF frames for each experiment. Three steps were followed: (1) finding grain reflections per frame, (2) labeling grain reflections in different frames and (3) add meta data which includes the number of reflections, initial, minimum and maximum 2 values.
The two strains were maintained in pure culture in aerobic nutrient medium (5. 0 g liter Ϫ1 peptone and 3. 0 g liter Ϫ1 meat extract) at 30°C prior to DNA extraction and genome sequencing, which was performed by MicrobesNG (https://microbesng. uk/) using the Illumina MiSeq platform with 2 ϫ 250-bp paired-end reads. Trimmed reads were produced using Trimmomatic version 0. 30 (http://www. usadellab. org/cms/?page ϭtrimmomatic) with a sliding window quality cutoff of Q15, and de novo assembly was performed using SPAdes version 3. 7 (http://cab. spbu. ru/software/spades/) (12, 13). Coverage of 30ϫ was achieved during sequencing, and genome annotation was performed using the RAST annotation server version 2. 0 (http://rast. nmpdr. org/rast. cgi) via the classic RAST pipeline  (14). The draft genome of strain Acidovorax sp.
strain BoFeN1 consisted of 4. 06 Mb in 184 contigs (N 50 ϭ 37,384), with a GC content of 63. 77%. A total of 3,794 protein-coding sequences were predicted from the annotated genome, with 50 tRNAs identified using the ARAGORN version 1. 1 tRNA detection program (http://mbio-serv2. mbioekol. lu. se/ARAGORN/)  (15). The closest match for the 16S rRNA gene was identified in the GenBank database, by using the BLASTN program (https://blast. ncbi. nlm. nih. gov/Blast. cgi), as Acidovorax defluvii strain BSB411 (99% similarity; GenBank accession number NR_026506). The draft genome of Paracoccus pantotrophus strain KS1 consisted of 4. 16 Mb in 227 contigs (N 50 ϭ 36,889), with a GC content of 67. 69%. A total of 3,941 protein-coding sequences and 54 tRNAs were predicted from the annotated genome. The nearest 16S rRNA gene sequence was identified using BLASTN, with a best match as Paracoccus pantotrophus (100%; accession number AB098590).
Data availability. DDBJ/ENA/GenBank accession numbers have been assigned for Acidovorax sp. strain BoFeN1 (QOZT00000000) and Paracoccus pantotrophus strain KS1 (QOZU00000000). Raw sequencing reads for Acidovorax sp. strain BoFeN1 (SRP157586) and Paracoccus pantotrophus strain KS1 (SRP157588) are available in the NCBI Sequence Read Archive.
Methods Clinical material. Human liver biopsies from a clinical series of PSC and PBC cases (paraffin-embedded samples, n = 7 per group) were anonymized and assessed histologically using H&E by an expert pathologist. Diagnoses were based on clinical data and confirmed by histology (see Supplementary Table  1 ). Normal human liver samples were obtained from autopsy cases from non-hepatic disease with normal liver histology (n = 7). Informed consent was obtained from the patients for the tissue to be used in the Tissue Governance Group, College of Medicine and Veterinary NHS Lothian Public Health Office, under project license TGU-LAB-SR299 approved by the Local Commission for Medical Ethics, University of Edinburgh. Animal models. Mice were housed in a specific pathogen-free environment in open cages (NKP, M3-sloping front) with Aspen chips as bedding at 21 °C.
2% Triton X-100 for 10 min. Images were acquired the same day at 647 nM exposure. 5-Bromo-2′-deoxyuridine incorporation. 5-Bromo-2′-deoxyuridine (BrdU) (Invitrogen) is added to the cells at 3 μg/ml and cultured 3 h at 37 °C. Cells were fixed in cold Methanol:Acetone (1:1) for 10 min and treated with 2 N HCl 30 min at RT. Immunofluorescence was routinely performed (Supplementary Table  3 ). Nuclear staining was performed with DAPI. Immunofluorescence for cells. Cells were fixed in cold Methanol:Acetone (1:1) for 10 min and permeabilized with 0. 1% Triton A-100 (Sigma) during 15 min before immunofluorescence was routinely performed. Antibodies used are listed in Supplementary Table  3. Nuclear staining was performed with DAPI. Microscopy and cell counting. Images were acquired using a Nikon Eclipse e600 and Retiga 2000R camera (Q-Imaging, Image Pro premier software) or Perkin Elmer Operetta high content imaging system.
For single-cell quantification in tissue, images were acquired in up to four fluorescent channels at ×20 magnification, and analysed using Fiji ImageJ or Columbus Image Data Storage and Analysis System (Perkin Elmer) software depending on the microscope used. Histological sections were assigned a randomized blinded code prior to quantification by a separate researcher, and the randomization decoded at the time of the final data analysis. Quantification of different liver populations from the images was performed manually on 30 random non-overlapping fields per mouse (×200 magnification). Ductopenic index was calculated by dividing the number of K19-positive bile ducts per portal tract. Any ratio below a 0. 5 threshold was considered ductopenia  42. Cells were identified based on DAPI stained-nuclei, morphology and specific staining for each population.
Illumination correction and background For pixel analysis, ImageProPremier software was used to select regions of positivity and automatically analysed using a macro-instruction. Results are expressed as the mean percentage of positive pixels per field. PiSR analysis used an AxioScan Z. 1 (Zeiss) to acquire tiled images at ×20 magnification. Images were then analysed using a standard colour threshold in Fiji Image J. DAPI, Alexafluor 488, and 555 were detected using band paths of 415-480, 495-540 and 561-682 nm for 405, 488 543 nm lasers respectively. All scale bars denote 50 μm. Isolation of hepatocytes. Mice were killed and their livers were perfused in situ with Liver Perfusion Medium (Gibco) and Liver Digest Medium (Gibco). The liver was then disrupted to yield a cell suspension, filtered through a 50 μm filter (BD Biosciences) and hepatocytes were pelleted by centrifugation at 135 × g 1 min.
RNA concentration and quality was assessed using a Nanodrop spectrophotometer (Thermo-Fisher). Reverse transcription was performed using QuantiTect Reverse transcription kit (Qiagen). Real time-QPCR was performed on a LightCycler 480 II (Roche) with commercial primers from Qiagen's QuantiTect (stated in Supplementary Table  4 ). Each gene expression was assessed with its own standard curve and normalized using Peptidylprolyl Isomerase A (PPIA) as housekeeping gene. Samples were run in triplicate. Statistical analysis. A minimum of four mice were included per experimental group. For cell culture experiments, a minimum of four independent biological replicates were performed. Prism software version 5. 0a (GraphPad Software, Inc) was used for all statistical analyses. Normal distribution of data was determined using D'Agostino and Pearson omnibus normality test with Welch's correction if variances differed (F test).
Acknowledgements The authors would like to thank  Prof. Lozano  ( Department of Cancer Genetics, University of Texas ) for Mdm2flox/flox mice; and  F. Lemaigre  and  I. Leclercq  for OPNi-CreERT2R26RYFP mice. S. F. -G. was funded by  Principal 's  Career Development Ph. D. Scholarships  for Ph. D. students and via the  MRC Centre for Regenerative Medicine Centre grant. This work was supported by the  UK Medical Research MRC  ( MR/ K017047/1 ), the  Computational and Chemical Biology of Stem Cell Niche  ( MR/L012766/ 1 ) and the  UK Regenerative Medicine Platform  ( MR/K026666/1 ).
The TssK SN -TssF-TssG complex was purified using Ni-NTA affinity chromatography and eluted with 300 mM imidazole. Subsequently, it was further purified by gel-filtration chromatography using a Superose 6 10/300 GL column (GE Life Sciences) equilibrated in 20 mM Tris-HCl (pH 7. 8), 150 mM NaCl, 5% Glycerol, and 0. 5 mM TCEP. The purity of the sample was assessed by Coomassie-stained sodium dodecyl sulfate-polyacrylamide gel electrophoresis. using Dog Picker 51 within the Appion pipeline  52. Movie frame alignment was carried out with dose weighting using MotionCorr2  53. Defocus parameters were estimated with GCTF  54. An initial model was obtained using cryoSPARC  55  and all 2D and 3D classifications and refinements were performed using RELION 2. 1. Subsequently, Relion3. 0beta was used to refine per-particle defocus values before reclassification of the data focusing on the TssF-TssG C-terminal domain.
Finally, multi-body refinement  30  was used with signal subtraction to produce a map focused on the two TssK trimers and a map focused on the (TssF) 2 -(TssG) 1 heterotrimer. The reported resolution is based on the gold-standard FSC = 0. 143 criterion  56, 57  and Fourier shell correlation curves were corrected for the effects of soft masking by high-resolution noise substitution  58. ; Hcp: magenta (PDB 5OJQ); VgrG-PAAR: green-olive (PDB ID 4MTK and 4JIV)  9  Model building and refinement. TssK was modeled based on the available X-ray crystal structures  24  , whereas TssF and TssG were modeled de novo by initially building a backbone trace of their C-terminal domains into a preliminary 4. 2 Å resolution reconstruction. Modeling of TssF and TssG relied on both the global map and the focused map obtained using multi-body refinement.
Acknowledgements Research reported in this publication was supported by the  National Institute of Allergy and Infectious Diseases  ( HHSN272201700059C  to DV), the  National Institute of General Medical Sciences  ( R01GM123089  to FD), a  Pew Biomedical Scholars Award  (DV), and an Investigators in the  Pathogenesis of Infectious Disease Award  from the  Burroughs Wellcome Fund  (DV). This work was also partly supported by the  Arnold  and  Mabel Beckman  cryoEM center.
Data availability The sharpened and unsharpened cryoEM reconstruction of TssK SN -TssF-TssG, the focused reconstructions of TssK and TssF-TssG, and atomic model have been deposited in the Electron Microscopy Data Bank and the Protein Data Bank with accession codes EMD-9341 (TssK SN -TssF-TssG map), EMD-9342 (TssK focused map), EMD-9343 (TssF-TssG focused map), and PDB 6N38. Other data are available from the corresponding author upon reasonable request.
This voltage minimizes the electrostatic tip-sample forces and is plotted in the LCPD map. Unless stated otherwise, all nc-AFM and KPFM data are unfiltered raw data. DFT Calculations of hBN/Ir(111). We used the CP2K software package,  68  in particular the QuickStep module,  69  for the DFT calculations of the structure of hBN/Ir(111). The vdW-DF2-B86r approximation  70, 71  from the LibXC library  72  was employed for the exchange-correlation term in the Kohn-Sham scheme. The Gaussian plane wave method  73  was used to solve the electronic structure selfconsistently, where the basis set to expand the wave functions was DZVP-MOLOPT-SR-GTH  74  and a cutoff energy of 700 Ry was used for expanding the density in plane waves, with five grids and a relative cutoff of 70 Ry. The Goedecker-Teter-Hutter (GTH) type pseudopotentials  75  were employed, with 17 valence electrons in Ir.
ACKNOWLEDGMENTS We thank  Leo Gross  and  Nikolaj Moll  for valuable scientific discussions and  John Tracey  for support with the nc-AFM simulations. O. K. appreciates help from  Prokop Hapala  on the programming of the enhanced probe particle code. This research made use of the  Aalto Nanomicroscopy Center (Aalto NMC ) facilities and was supported by the  European Research Council  ( ERC-2011-StG No. 278698  " PRECISE-NANO ") and the  Academy of Finland  (Project nos. 305635 ,  311012 ,  314877 , and  314882 , and  Centres of Excellence Program  project nos. 284594  and  284621 ). J. R. and A. S. F. were supported by  EU  project  PAMS  (contract no. 610446 ), and acknowledge use of the  CSC, Helsinki , for computational resources. A. P. S. acknowledges the computational resources at  CSC ,  Espoo , project  2000606 , and  Centro Svizzero di Calcolo Scientifico (CSCS) ,  Lugano , project  uzh11.
Organ shortage leads to using non-optimal liver grafts. Thus, to determine the graft quality, the Donor Risk Index and the Eurotransplant Donor Risk Index have been proposed. In a previous study we showed that neither could be validated on the French database. Our aim was then dedicated to propose an adaptive Donor Quality Index (DQI) using data from 3961 liver transplantation (LT) performed in France between 2009 and 2013, with an external validation based on 1048 French LT performed in 2014. Using Cox models and three different methods of selection, we developed a new score and defined groups at risk. Model performance was assessed by means of three measures of discrimination corrected by the optimism using a bootstrap procedure. An external validation was also performed in order to evaluate its calibration and discrimination.
However, they could not be validated on the French LT database  [4] [5] [6]. Major donor and recipient differences observed between those databases may explain this result  4, 5. If re-calibrating a model can be considered  7, 8  , however, the discrimination cannot be altered  6. Thus, the current DRIs could not be used on our dataset. A general policy is to try to give the best organs to the more severe patients with liver failure  3. Looking for the best match between a graft and its recipient requires preliminary information about the graft quality prior to LT. A donor quality index (DQI), which scores the characteristics of the graft prior to LT, is one of the key factors for a better matching to a recipient. The covariates used to compose the DQI are those available at the time of the harvest in order to define the intrinsic characteristics of the graft.
More, in order to better orient the decision of using a graft, a liver donor quality index is of importance. In 2014, in the United-States, 9. 6% 9 of donor livers were not transplanted. This rate was 6. 7% in France for our study period (2009-2013) (Supplementary Tables  S1  and S2  provide reasons why some grafts were not collected and why some collected organs were not transplanted during the study period, respectively). Moreover, the DQI may be of interest for choosing which graft might benefit from novel preservation techniques. Since we showed that the existing DRIs did not fit to the French database  4, 5  , the primary objective of the present study was to generate a DQI and to perform an internal and external validation.
Material Derivation Dataset. Information relating to LT performed in France between January 4, 2009 and December 31, 2013 was obtained from the ABM (https://www. agence-biomedecine. fr/Organes). The study was conducted with the approval of the Independent Ethics Committee (L 1121-1 to L 1126-11 articles of the Public Health Code). Authorization was also obtained from the "Commission Nationale de l'Informatique et des Libertés" (agreement No. 915206). The data provided were de-identified beforehand. In accordance with previous works  1, 2  , recipients under 18 years, and recipients of multiple organ transplants were not included. Of note, no donation after cardiac death was performed during the retained period (https:// www. agence-biomedecine. fr/annexes/bilan2016/donnees/organes/01-prelevement/synthese.
htm#figP1). The recipients' follow-up began at LT and ended with the occurrence of one of the following events: lost to follow-up, death, graft-loss (re-transplantation) or at the end of the study, as of December 31, 2014. The outcome was death or graft-loss. Patients with incomplete covariates were not retained as specified in the flow diagram presented in Fig. 1. Finally, 3961 LT were analyzed. Donor and recipient characteristics are shown in Table  1. This Table contains all the covariates included in the DRI and ET-DRI, and available in the French database  1, 2, 4.
Results The DQI. The retained full model included all the variables present in at least two of the three models and the set of covariates retained in addition (see methods section) (Table  3 ): the lowest MDRD creatinine clearance; the liver type; the MELD exception; donor and recipient's blood groups. The liver type covariate was retained since it was present in both DRI and ET-DRI. The MELD exception and donor and recipient's blood groups covariates were also retained, but for adjustment only. Hence the proposed DQI score was as follows: In Table  4 , the progression of the score through different combinations is presented. In our dataset, the average score was 1. 83 (SD: 0. 44) with a median of 1. 76 and a range between 1 and 3. 12. Risk groups. Three groups were obtained according to the following values (Fig.
External validation. An external validation  4, 6  was performed in the validation dataset, which was constituted of grafted patients in 2014. This dataset was independent of the derivation dataset. To calculate the DQI in the validation dataset, we followed the same procedures as proposed previously in the construction steps. Calibration and discrimination were evaluated through seven steps. Comparing the databases. The comparison of the datasets appears in Table  5. The distribution of the DQI from the derivation and validation datasets is presented in Fig. 2S  (see Supplementary Figures). It appears that the DQI is higher in the validation dataset than in the construction dataset. This might be due to older donors and more frequent strokes.
Regression on prognostic index (PI). The slope on the PI, i. e. ln(DQI), was 0. 88 (SD = 0. 29). The slope was not different from 1 (P-value = 0. 68, likelihood-ratio test); thus, the discrimination was preserved in the validation dataset. Check model misspecification/fit. Since the estimation of the β* coefficients were not different from 0 (p = 0. 64), no adjustment on the DQI covariates was needed. We tested if there was a lack of fit for the adjustment covariates, i. e. recipients' covariates. The β ⁎ adjustment were different from 0 (p < 0. 01), an adjustment on the adjustment covariates was needed. The assumption of proportional hazards was verified for all covariates. Measures of discrimination. The Harrell C-index was 0. 587 (0. 545-0. 628), the Gönen and Heller K statistic was 0. 612 (0.
594-0. 630) and the Royston and Sauerbrei R D 2 was 0. 416 (0. 309-0. 523). Discrimination measures on the validation and derivation datasets were close. Kaplan-Meier curves for groups at risk. Failure free survival per DQI categories for derivation and validation datasets is given in Fig. 3. The follow-up in the derivation data was truncated to 800 days. First, Kaplan Meier curves were well separated. Then, the risk groups were discriminative. Second, Kaplan Meier curves from derivation and validation data were close. For greater accuracy, we then plotted three survival curves for each risk group with confidence intervals in derivation and validation datasets (Fig. 3 ). Validation  curves are within the confidence interval of the derivation dataset curves (except for group 1: from 300 to 500 days and the tail of group 2).