dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ). CDKN2C expression is associated with chronic liver disease. To assess whether HBV infection directly affects CDKN2C expression, we infected PHHs with HBV and evaluated CDKN2C gene expression. Interestingly, CDKN2C expression was upregulated upon HBV infection (Fig. 9a ). In line with this observation, the analysis of CDKN2C expression from patient liver tissues retrieved from the Gene Expression Omnibus database revealed an upregulation of CDKN2C in patients with active replication compared to patients with undetectable viral load and healthy patients (Fig. 9b ). Moreover, a correlation was observed between HBV viral load and CDKN2C expression in liver tissues from nine HBV-infected patients (Supplementary Fig. 6a ). Finally, CDKN2C expression appeared to be modulated in different stages of HBV infection (Fig.
Southern blot detection of HBV cccDNA was performed using digoxigenin (DIG)-labeled (Roche) specific probes as described  62. Total DNA from HBV-infected cells was extracted using the Hirt method as described  63. Specific DIG-labeled probes for the detection of HBV and mitochondrial probes for the detection of HBV and mitochondrial DNAs were synthetized using the PCR DIG Probe Synthesis Kit (Roche) and the primers as described  60. HBV total RNAs were detected by northern blot. Total RNA was purified using ReliaPrep RNA Miniprep Systems (Promega). Five µg of total RNA was subjected to electrophoresis through 2. 2 M formaldehyde and 1% agarose gel and transferred to a nylon membrane positively charged (Roche). 2  and 3. Source data are provided as a Source Data file. The membrane-bound RNA was hybridized to a 32 P-labeled RNA probe specific for detection of HBV RNA of 1200-1944 bp of viral genome (3.
Individual differential gene expression of the selected candidates was evaluated through the Z score transformation. The dataset is publicly available in the NCBI Gene Expression Omnibus database (accession number GSE132638). Analysis of gene expression using qRT-PCR. RNA was extracted as described above, and gene expression was assessed by qRT-PCR as described  60. Gene expression was normalized to GADPH expression. Primers and TaqMan® probes for quantification of GAPDH, CDKN2C, and SLC10A1 mRNA expression were obtained from ThermoFisher (TaqMan® Gene Expression Assays). Gene expression was quantified using iTaq Universal Probes Supermix (Bio-Rad). Primers for quantification of HNF4A (Fw: 5'-ACATTCGGCAAGAAGATT-3'; Re: ACTTG GCCCACTCAACGAG-3'), HLF (Fw: 5'CACCACGAAGACGATTTAG-3'; Re: 5'-CAAAAACTCCTCCAGGTCCA-3'), PPARA (Fw: 5'-GAGGGTCTCCACTGAC GTG-3'; Re: 5'-ACACTGTGTATGGCTGAGAAG-3'), and GAPDH expression (Fw: 5'-GTCTCCTCTGACTTCAACAGCG-3'; Re: 5'-ACCACCCTGTTGCTGT AGCCAA-3') were obtained from Sigma-Aldrich (Merck).
Analysis of CDKN2C expression in patients. For the analysis of CDKN2C mRNA expression in patients, CDKN2C mRNA expression was assessed in control healthy patients (n = 6), HBV-infected patients with no detectable HBV DNA (n = 32), and HBV-infected patients with detectable HBV DNA (n = 90) from GSE83148 70. Similarly, CDKN2C mRNA expression was assessed in HBV patients at different stages of virus infection, including immune tolerant phase (n = 22), immune clearance phase (n = 50), and inactive carrier phase (n = 11) from GSE65359. Alternatively, total RNA was extracted from liver tissue of nine HBV-infected patients by using the High Pure RNA Paraffin Kit (Roche) according to the manufacturer's instruction, and gene expression analysis was performed by RNAseq as previously reported 71. To analyze the correlation between CDKN2C expression and the progression of liver disease in HBV-infected patients, CDKN2C mRNA expression was assessed in HBV-related liver fibrosis patients of different stages from GSE84044 72  (n = 37 score 0, n = 33 score 1, n = 34 score 2, n = 15 score 3).
Finally, CDKN2C expression in HBV-induced HCC patients was assessed from GSE65485 73  (n = 50 tumor tissue, n = 5 non-tumor tissue) and from GSE14520 74  (n = 221 tumor tissue, n = 199 non-tumor tissue). CDKN2C mRNA expression is shown as signal intensity values. For survival analysis, expression level of CDKN2C and survival data were derived from The Cancer Genome Atlas (TCGA; https://www. cancer. gov/about-nci/organization/ccg/research/structuralgenomics/tcga) TCGA-LIHC database 75. To analyze CDKN2C expression in liver tissue of patients with chronic liver disease, fragments per kilobase of transcript per million mapped read values and clinical data were retrieved from TCGA. This dataset includes samples from HCV-infected patients (34 tumor samples including 5 paired tumor/non-tumor samples), HBV-infected patients (76 tumor samples including 7 paired tumor/non-tumor samples), patients with ALD (72 tumor samples including 8 paired tumor/non-tumor samples), and patients with NAFLD (11 tumor samples including 2 paired tumor/non-tumor samples).
Acknowledgements We thank  Claudine Ebel ,  Romain Kaiser , and  Muriel Phillips  ( IGBMC, Flow Cytometry platform, France ) for excellent technical assistance. We thank  Christelle Thibault  for the microarray analysis (IGBMC, GenomEast platform, Illkirch, France). We thank our colleague  Sarah Durand  ( U1110 ) for the excellent technical support. This work was supported by  Inserm , the  University of Strasbourg , the  European Union  ( ERC-2014-AdG-671231-HEPCIR ,  Infect-ERA hepBccc ,  EU H2020 Hep-CAR 667273 ), the  IHU Fondation ARC (French Cancer Agency) TheraHCC  program  IHU201301187  and  IHU201901299 , the  Institut Universitaire de France  and the  Agence Nationale de Recherche sur le Sida et les hépatites virales (ANRS)  and the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  under award number  R03AI131066.
FAST computes the nonlinear aerodynamic forces and moments along the turbine blade using the AeroDyn subroutine package  (Laino & Hansen, 2001). The FAST code with AeroDyn incorporated in the simulator was evaluated in 2005 by Germanischer Lloyd WindEnergie and found suitable for 'the calculation of onshore wind turbine loads for design and certification'  (Manjock, 2005). The parametric information for the FAST simulator as configured herein is available from NWTC Design Code (2013). An example of some FAST configuration parameters can be found in Table  1. The control objective is to regulate generator speed at 1800 rpm and to reject wind disturbances using collective blade pitch. The inputs to the FAST plant are generator torque, blade pitch angle, and nacelle yaw. The FAST simulator can be configured to output many different states or measurements of the plant, such as generator speed and low speed shaft velocity.
A full factorial study with two parameters is performed to determine the spanwise blade point that is sensitive to changes in stiffness in the CART2 simulator. A Matlab program is developed to run multiple simulations, allowing one parameter to be modified at a time. For each simulation, a specific node of the blade is selected and its spanwise and edgewise stiffness are reduced by a damage coefficient expressed in percentage of the initial stiffness value. The spanwise and edgewise tip deflections are recorded for each simulation. The norm of the two vectors is calculated to get the resultant tip deflection. The maximum value of the norm of each simulation is plotted according to the node and damage level. Figure  4  shows results of this study for wind inflow of 16 m/s with 3 m/s vertical shear.
For instance, a gust hitting a turbine blade at a given point of time in the wind time series would hit the blade at a different time if the rotational speed of the blade differed, as it does when the turbine is de-rated. Studies were run with constant wind having 3 m/s vertical shear. Data were analyzed after the wind turbine had reached a trim state and the start-up transients had died off. Figures (  8 ) and (  9 ) show the extreme loads of flapwise and edgewise root bending moments for an undamaged blade for 3 levels of generator settings: rated generator speed, generator set-point de-rated by 10%, and generator set-point de-rated by 20%. As can be seen in the figures, the damage equivalent loads of the blades decrease when the generator is de-rated. The de-rating of the turbine results in the blades being collectively pitched at a lower wind speed.
Cardiac imaging acquisition and analysis Two-dimensional TTEs were performed using a commercially available ultrasound machine (Vivid E9 or E95, GE Healthcare, Little Chalfont, UK). Each patient underwent comprehensive TTE according to American Society of Echocardiography (ASE) or EACVI guidelines, with particular attention given to the mitral valve  [8, 9]. Echocardiographic data were stored for off-line analysis using dedicated software (EchoPAC version 201. 0. 0; GE Vingmed Ultrasound, Horten, Norway).
Cardiological review The echocardiographic data were analysed off-line by junior and senior physicians specialized in echocardiography, as follows: quantification of MR severity and evaluation of its mechanisms and consequences were done in eight tertiary public and private hospitals (Amiens, Creteil, Lille, Rennes, Saint Denis, Toulouse and Tours) by one junior and one senior physician in each centre (i. e. 16 physicians). A junior physician was defined as a final-stage cardiology resident and a senior physician as a cardiologist specialized in cardiac imaging, who graduated more than 2 years earlier. All physicians were provided with the entire echocardiographic study, reviewed all images, and were blinded to the patient's medical history, previous MR evaluation, and MR classification (primary or secondary) by the centre that enrolled the patient.
Statistical analysis Continuous variables are presented as means with standard deviations (SD) and qualitative variables as frequencies and percentages. To determine interobserver agreement of binary or ordinal outcome variables, multirater κ statistics were computed using the macro MAGREE with SAS, Version 9. 4 (SAS Institute, Cary, North Carolina). This macro implements the methodology of Fleiss et al. , measuring the agreement when the number of raters is > 2  [10]. Kappa (κ) values were interpreted according to Landis and Koch, with κ coefficients of 0 = poor; 0. 01-0. 20 = slight; 0. 21-0. 40 = fair; 0. 41-0. 60 = moderate; 0. 61-0. 80 = substantial; and 0. 81-1. 0 = almost-perfect agreement. Multirater κ were calculated for each patient using the online κ calculator developed by Randolph.
Study limitations Despite a limited set of TTE variables, each parameter was assessed 16 times (by eight senior and eight junior physicians) and thus 400 analyses per parameter were obtained. Moreover, all TTE evaluations were performed by five senior physicians with the same commercially available ultrasound machine to limit variability due to technical/imaging quality. The definition of MR mechanism was left to the discretion of the senior echocardiographer at inclusion, without referring to a gold standard method. The dataset was restricted to two-dimensional imaging, as based on normal clinical routine. Three-dimensional TTE, transoesophageal echocardiography and multimodality imaging would have probably increased the consistency of the reviewers. Finally, we choose to include MR ≥ moderate.
The main regression model included only the strict confounder: intra uterine growth restriction (IUGR). The analyses were performed with Stata software version 13. 0. This database was approved by the National Data Protection Authority (Commission Nationale de l'Informatique et des Libertes, CNIL no. 293683). Under French regulations, this study was exempt from Institutional Review Board approval because it was an observational study using anonymized data from medical records. Women were informed that their records could be used for the evaluation of medical practices and were allowed to opt out of these studies.
Results : This study included 3083 women with a previous cesarean and with alive singleton in cephalic presentation at or after 37 gestational age (GA). We excluded 54 (1. 7%) women with a fetus with a congenital malformation and 208 (6. 7%) with planned cesarean delivery. Data for pelvimetry were missing for 270 (8. 7%) women, and neonatal data were missing for 77 (2. 5%) newborn (Figure  2 ). Thus, 2474 women were included in this analysis. Among included women, 1611 (65%) having a normal pelvic dimension at pelvimetry and 863 (35%) an abnormal dimension. Transverse diameter was less than 12 cm for 771 (31,2%) women, whose 418 (16,9%) with a transverse diameter less than 11,5 cm. Conjugate diameter was less than 10,5cm for 252  (10,2%)  women, whose 70 (2,8%) with a conjugate diameter less than 10 cm.
Discussion : Based on this population of women with trial of labor after one previous cesarean delivery, we showed that abnormal pelvic dimension at pelvimetry was not associated with an increased neonatal morbidity. Our study has some strenghts. Data were valid and exhaustive because they were collected prospectively through computer software used by all medical staff and verified every morning during collegial meeting, and for severe events such as uterine rupture, and neonatal morbidity, all medical record were analyzed. We used a severe composite neonatal criteria approved in many studies  [16, 17]. This primary outcome is pragmatic and logical, and it has good external validity. Due to the unicentric characteristic of this study, obstetrical practices were uniform in the management of patients during labor, for prenatal care and for the decision of cesarean.
Acknowledgements We are very grateful to the  RIKEN Nishina Center accelerator staff  for providing the stable and high-intensity zinc beam and to the  BigRIPS team  for the smooth operation of the secondary beams. The development of MINOS has been supported by the  European Research Council  through the  ERC  Grant No. MINOS-258567. Green's function calculations were performed using HPC resources from  GENCI-TGCC, France  ( Projects A0030507392  and  A0050507392 ) and from the DiRAC Data Intensive service at Leicester, UK (funded by the  UK BEIS  via STFC capital grants  ST/K000373/1  and  ST/R002363/1  and  STFC DiRAC Operations  grant  ST/R001014/1 ). This work (C. B. ) was also supported by the  United Kingdom Science and Technology Facilities Council (STFC)  under Grants No. ST/P005314/1  and No.
ST/L005816/1. K. O. acknowledges the support by  Grant-in-Aid for Scientific Research   JP16K05352. Y. L. S. acknowledges the support of  Marie Skłodowska-Curie Individual Fellowship  ( H2020-MSCA-IF-2015-705023 ) from the  European Union  and the support from the  Helmholtz International Center for FAIR. The valuable discussions with  C. Qi  are gratefully acknowledged. H. N. L. acknowledges the support from the  Enhanced Eurotalents program  ( PCOFUND-GA-2013-600382 ) co-funded by  CEA  and the  European Union. H. N. L. and A. O. acknowledge the support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project No. 279384907-SFB 1245. Y. L. S. and A. O. acknowledge the support from the  Alexander von Humboldt Foundation. L. X. C. and B. D. L would like to thank  MOST  for its support through the  Physics Development Program Grant  No.
ÐTÐLCN. 25/18. I. G. has been supported by  HIC for FAIR  and  HRZZ  under project No. 1257  and  7194. K. I. H. , D. K. and S. Y. P. acknowledge the support from the  NRF  grant funded by the  Korea government  (No. 2017R1A2B2012382  and  2019M7A1A1033186 ). F. B. acknowledge the support from the  RIKEN Special Postdoctoral Researcher Program. D. S. was supported by projects No. GINOP-2. 3. 3-15-2016-00034  and No. K128947. V. V. acknowledges support from the  Spanish Ministerio de Economía y Competitividad  under Contract No. FPA2017-84756-C4-2-P. V. W. acknowledges support from  BMBF  grants  05P15RDFN1 ,  05P19RDFN1  and  DFG  grant  SFB 1245. P. K. acknowledges support from  HGS-HIRe  and  BMBF  grant  05P19RDFN1. This work was also supported by  NKFIH  ( 128072 ).
Ethics committee approval The haemodynamic optimisation using the PPG (PANEX3) trial was an investigatorinitiated, randomised, parallel-group, non-blinded, clinical trial conducted in one French university hospital. The main hypothesis was analysed with respect to the superiority of PPG to decrease the number of patient suffering from at least one complication in the 30 postoperative days in comparison with that of the control group. The study protocol was approved on December 3 rd , 2014 by the official ethics committee of the University Hospital of Caen (2014-A00870-47, Comité de protection des Personnes Nord Ouest III, CHU de Caen, Avenue Côte de Nacre, Caen, France)  [13] , and recorded at clinicaltrials. gov on January 22 nd , 2015 (NCT02343601). Written informed consent was obtained before each patient was randomised the day before surgery.
Control group Patients randomised to the control group were given an intraoperative continuous infusion of balanced crystalloid fluid (Ringer lactate) at 5 mL/Kg/h and postoperative 3 mL/Kg/h. Mean arterial pressure (MAP) was measured every 5 minutes. When MAP was below 65 mmHg, patients were allowed to receive 3 mL/Kg of intravenous colloid solution (Gelatine 4%, Gelofusine, B. Braun Medical, Melsungen, Germany) in 10 minutes, ephedrine and norepinephrine (after 30 mg of ephedrine) at the discretion of the anaesthetist. In this group, PPG monitoring was blinded, but the data were recorded.
Endpoints The primary endpoint was the incidence of patients who developed at least one complication within the 30 postoperative days. Data were obtained from hospitalisation until the surgical consultation one month following surgery in our institution. Two independent experts, who were blinded to the group allocation and not involved in the study, assessed each patient a posteriori and each complication using an a priori classification following recent guidelines  [19]. The secondary endpoints were the hospital length of stay (LOS), defined as the number of days spent in the hospital from the day before surgery to hospital discharge, the total number of postoperative complications, and all-cause mortality 30 days after surgery.
The primary outcome was expressed as the percentage (95% confidence interval) of patients with at least one postoperative complication. Quantitative secondary objectives were assessed using the Mann-Whitney non-parametric test. For all tests, a P-value less than 0. 05 was considered statistically significant, and all P-values were two-tailed. Statistical analysis was carried out using R version 3. 3. 2 (Foundation for statistical computing, Vienna, Austria). A meta-analysis of randomised trials comparing non-invasive haemodynamic goaldirected treatment using PPG with control group for non-cardiac surgery adult patients was performed to assess the external validity of the present study. Following the PRISMA guidelines  [22] , PubMed, Embase, Scopus, and Google Scholar databases were searched from January 1, 2010 (beginning of publication using last upgraded PPG technology), to September 1, 2019 (Supplemental Digital Content 2).
PPG and perioperative management PPG monitoring was implemented for all patients, and the haemodynamic data were recorded for 80 (100%) patients during 319 [270-384] minutes in the PPG group and for 79 (100%) patients during 321 [276-409] minutes in the control group. The clinical complications observed at sensor removal were pain, paraesthesia and erythema in 14 (8), 8 (5) and 2 (1) patients, respectively. Throughout the recording, the mean SV was 79 + 18 mL in the interventional group and 79 + 12 mL in the control group (P = 0. 991). The mean MAP was 83 + 8 mmHg in the interventional group and 80 + 9 mmHg in the control group (P = 0. 036), and the time that the MAP was less than 65 mmHg was 25  minutes in the interventional group and 46  minutes in the control group (P = 0. 005) (Table  2 ).
After protein precipitation, the supernatant was derivatized by adding ortho-phthalaldehyde (OPA) (Sigma-Aldrich, Saint-Quentin-Fallavier, France). The HPLC separation of GSH-OPA adducts were performed by a UP3 HDO silica-based, reversed-phase C18 column (150 × 3. 60 mm, particle size 3 µm) from Phenomenex (Interchim, Montluçon, France) maintained at 37 °C, followed by fluorimetric detection at 420 nm after excitation at 340 nm (Summit HPLC system, Dionex SA, Courtaboeuf, France). Derivatives were eluted using a 10-50% acetonitrile gradient in a 25 mM phosphate buffer at pH 6 for 5 minutes. The flow rate was 0. 25 ml/min for an elution run of 20 minutes. Chromatograms were integrated using Chromeleon software from Dionex (Version 6. 80, Dionex SA, Courtaboeuf, France). GSH content, calculated using a standard curve plotted under the same conditions, was expressed in µmol/g of protein.
Data analyses. The first part of the statistical analyses were calculated using R, version 3. 2. 2. 5,93% of the data were missing. This problem was handled by imputation of the missing values using Expectation-Maximization algorithm  24  implemented in the MissMDA R package. Data were centered and scaled. A Multi-Factor Analysis  25, 26  was performed using the R package FactomineR as described in  27, 28. Eight groups were identified: muscle masses, adipose tissue masses, tumour growth, tumour oxidative status, tumour biology, inguinal adipose tissue biology, right gastrocnemius biology and plasma biology (composition of groups of variables in supplemental Table  2 ). Correlations were calculated using the corrplot R package. In order to obtain the most relevant metabolites from the first statistical analysis, a second statistical analysis was performed.
The second phase of statistical analyses was performed with GraphPad Prism 5 software (GraphPad Software, Inc. , San Diego, CA). Pairwise comparisons were calculated with a Mann Whitney t test. Comparisons of three or more groups were made with a 1-way ANOVA when only one parameter was changed or with a 2-way ANOVA when 2 factors were modified. These comparisons were followed by a Bonferroni post test. Statistical means are p ≤ 0. 05 (*), p < 0. 001 (**) and p < 0. 0001 (***). All data were presented as mean ± SEM. ethics approval. This study was approved by the Animal Experimental Committee (Comité Régional d'Ethique sur l'Expérimentation Animale, No. 01095. 02, Clermont-Ferrand, France) and carried out in accordance with the ethical guidelines.
Acknowledgements We thank  Christophe Montaurier  for the indirect calorimetric measurements. We thank  Marion Brandolini-Bunlon  and  Melanie Petera  for their advice on the multiple factorial analysis. We thank  François Senejoux ,  Catherine Felgines ,  Caroline Decombat ,  Laetitia Delort ,  Marion Vermerie ,  Marjolaine Vareille-Delarbre ,  Sophie Garcin ,  Nicolas Goncalves-Mendes  and  Jérémie Talvas  for their help with the animal sacrifices. We thank  La Ligue Contre le Cancer  and their committee for the PhD funding of  DLG. This work was supported by a PhD fellowship ( Delphine Le Guennec ) from  le Comité national de La Ligue  contre le cancer and by the local councils ( Allier, Cantal, Haute-Vienne and Haute-Loire ) and l'Institut national du cancer (INCA: projet MammAdipo;  PLBIO 13-106 ).
This dataset is ideal for finding BCGs within the cluster virial radius, and systematically characterizing the cluster galaxy properties and the local background of massive clusters up to redshift ∼ 1. This paper is organized as follows. In § 2 we introduce the data and the cluster sample. In § 3 we describe in detail the dynamical state proxies used. In § 4 we describe the tools we use to statistically characterize the galaxy population, while in § 5 we show our results. In § 6 we present our conclusions. Throughout this work, we assume a flat ΛCDM cosmology with H 0 =68. 3 km s -1 Mpc -1 and Ω M = 0. 299  (Bocquet et al. 2015).