dataesr/scientific-paragraphs-categorization huggingface dataset:
Alongside this, ACR50 (reaching 57% at week 12), ACR70 (reaching 37% at week 16), strong skin responses (PASI75 and PASI100 of 100% and 87%, respectively, at week 8) and rapid onset of action (some responses observed within 2 weeks of first infusion) concord to indicate robust therapeutic efficacy of bimekizumab within the contextual limitations of a PoC trial. When evaluating these data, the size and duration of the PoC trial limits the interpretation of the safety dataset; furthermore, the small sample size warrants cautious interpretation of the observed therapeutic effect size. Because patients received only three doses of bimekizumab, further optimisation of the dosing regimen may impact the onset, level and durability of clinical responses in joint and skin. Of note, in the current trial, modifications of background medication were allowed after week 8, potentially explaining the increase in ACR responses seen in the placebo group between weeks 8 and 20.
for work on blockade experiments in synoviocytes. Additional statistical support was provided by  Emma Jones of Veramed and ros Walley of UCB. the authors would like to acknowledge  Ailsa dermody , phd of iMed Comms, an  Ashfield Company , part of  UdG Healthcare plc , for medical writing support that was funded by  UCB pharma  in accordance with Good publication practice (Gpp3) guidelines. study was supported by  UCB pharma. , tB, MG, SG, LI, AdGL, AM, ro, SS, FS, pV, MILW are employees of  UCB pharma. dB, tB, AdGL, pV hold stocks and/or stock options in UCB pharma. dB is a part-time employee of UCB pharma and holds a part-time position at the  Academic Medical Center/University of Amsterdam. dB received a grant from  UCB  pharma to conduct preclinical experiments; dB received grants and/or consultant or investigator fees from the following organizations outside of the submitted work:  AbbVie , pfizer, MSd, roche, BMS, novartis,  Eli Lilly ,  Boehringer Ingelheim  and  Glenmark.
Differentiated DCs were seeded in fresh medium prior to LPS stimulation for 24 h. Glucose consumption and lactate production were monitored to assess the input and output rates of glycolysis. We found that LPS stimulation increased both the glucose consumption and the lactate production by DCs, indicative of an enhanced glycolytic flux (Fig. 5a, b ). Treatment of cells with FP7 prior to LPS stimulation inhibited these metabolic changes induced by LPS up to 100 ng/ml. At the highest LPS concentration, FP7 could not completely block the increase of the glycolytic flux, consistent with cytokine secretion data (Fig. 2 ). TLR4-specific blocking antibody also prevented the increase of glucose consumption induced by LPS (Fig. 5c ). These results indicate that TLR4 stimulation induced an increased glycolytic flux in monocyte-derived human DCs that can be inhibited by FP7.
FP7 treatment prevented lung pathology induced by influenza infection (Fig. 7b, c ). Influenza induces expression of cytokines, i. e. , TNFα , IL-1β and IFN-β and chemokines such as KC (murine IL-8) that is inhibited by Eritoran therapy  38, 39. To assess the effect of FP7 on PR8-induced cytokine gene expression, mice were infected at day 0 and treated daily for 5 days with FP7 starting 2 days post-infection and were sacrificed on day 7 post-infection. Total RNA was extracted from lung homogenates and gene expression measured by qRT-PCR. FP7 led to a significant reduction in influenza-induced gene expression for TNF-α , IL-1β , IFN-β , KC (P < 0. 01; Fig. 8a-c ), IL-6, and RANTES (P < 0. 05; Fig. 8d ). These data were confirmed and extended by measuring cytokine protein levels and HMGB1 levels in lung homogenates from mice that were PR8-infected and treated with vehicle only or with FP7.
Briefly, peripheral blood mononuclear cells were isolated by standard density gradient centrifugation on Ficoll-Hypaque (Eurobio, Courtaboeuf, France). Mononuclear cells were separated from peripheral blood lymphocytes (PBL) by centrifugation on a 50% Percoll solution (GE Healthcare, Villacoublay, France). Monocytes were purified by immunomagnetic depletion using pan-mouse IgG Dynabeads (ThermoFisher Scientific) with a cocktail of monoclonal antibodies anti-CD19 (4G7 hybridoma), anti-CD3 (OKT3 hybridoma, ATCC, Manassas, VA, USA) and anti-CD56 (NKH1, Beckman Coulter, Fullerton, CA, USA). Monocyte purity was > 90% as assessed by CD14 labelling without CD3 + , CD19 + and CD56 + contaminating cells (data not shown). Monocytes were resuspended at 1 × 10 6 cells/ml in RPMI 1640 medium with GlutaMAX supplemented with 10% foetal calf serum (FCS, BioWhittaker, Lonza, Belgium) and 40 μ g/ml gentamycin.
Acknowledgements European Commission-granted TOLLerant  H2020-MSC-ETN-642157  project,  NIH  grants  R56 AI18797  (SNV and KAS) and  R01 AI104541  (SNV and JCB),  Inserm  " Projet Ebola ,  I3M " (VL). We would like to acknowledge the contribution of  Alice Machado  for expert technical assistance. We thank  Dr. F. Neumann  ( Innaxon Biosciences ) for providing TLR-4 blocking antibodies.
Antibodies and Reagents Antibodies used were: anti-T6BP (HPA024432) anti-NDP52 (HPA023195), anti-LC3B (L7543), anti-actin (A2066), and anti-ATG5 (A0856), all from Sigma-Aldrich, anti-OPTN (Abcam, ab23666 Paris, France), anti-SQSTM1/p62 (Santa Cruz Biotechnology, sc-28359, Heidelberg, Germany), and anti-p65/RelA (Millipore #06-418, Molsheim, France). Anti-MeV-N (mouse monoclonal, clone 120) and anti-MeV-P (rabbit polyclonal, clone J37171) were used. Anti-CD46 conjugated to the PE antibody (8E2 clone) was from ThermoFisher Scientific (12-0469-42, Courtaboeuf, France). Secondary antibodies used were: anti-Mouse conjugated to Peroxydase (A2304) from Sigma-Aldrich and anti-Rabbit conjugated to HRP (NA9340). Pharmacological agents used were Bafilomycin A1 (25 nM) (InvivoGen #tlrl-baf1, Toulouse, France) and Chloroquine (25 µM or 12.
Immunofluorescence-coupled Flow Cytometry For CD46 staining, 0. 25-0. 5 × 10 6 cells were incubated in microtiter U-bottom plates with saturating concentrations of labeled monoclonal antibody (mAb) in 20 µL PBS 2% FCS/0. 1% NaN3for 30 min on ice. Cells were washed twice and analyzed immediately, without fixation. The anti-human CD46 mAb used was the phycoerythrin (PE)-conjugated 8E2 clone (mouse IgG1κ) from eBioscience. A LSRII flow cytometer (Becton Dickinson, Pont-de-Claix, France) and the FlowJo software (Tristar, Ashland OR, USA) were used to collect and analyze the data. Nonviable cells were excluded using forward and side scatter electronic gating. In some experiments, results were confirmed by using the fluorescein isothiocyanate (FITC)-conjugated anti-human CD46 E4. 3 mAb (mouse IgG2aκ) from BD Biosciences (Pont-de-Claix, France).
Acknowledgments: We thank  D. C. Rubinsztein  for providing us with mRFP-GFP-LC3-Hela cells, and  Hadrien Kronenberger  for technical help. We acknowledge the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0042 ) of  Université de Lyon  operated by the  French National Research Agency  ( ANR-11-IDEX-0007 ). We thank the flow cytometry and confocal microscopy facilities of the  SFR Biosciences Gerland. This work was supported by la  Ligue Contre le Cancer (LCC) ,  Institut Universitaire de France (IUF) ,  ANR-14-CE14-0022 ,  Fondation pour la Recherche Médicale (FRM) , and  Association François Aupetit. DSP is the recipient of a fellowship from  La Ligue Contre le Cancer.
, USA). The counting of stained positive cells was carried in at least 3 different microscopic fields of 1 mm 2 surface area by tissues on at least 3 different tissues by conditions by using image analysis software (NDP, view, Hamamatsu). 2. 5. RNA Isolation and RT-qPCR. The protocol and conditions for RNA extraction, RT-PCR, and qPCR were similar as reported earlier by our laboratory  [5, 24]. The specific primers used are presented in Supplementary Table  1  available online at https://doi. org/10. 1155/2017/1359064. The relative gene expression was normalized against 18S gene expression. The mean expression of control mice in each treatment group served as a reference for mRNA expression (control mRNA level was arbitrarily taken as 1). For a wide transcriptomic study, the 1 μg RNA was obtained after pooling equal amount of RNA from different mouse liver from the same conditions.
The cells were then incubated with appropriate dilutions of various combinations of the following fluorochrome-conjugated antibodies/reagents: CD1d tetramer-α-GalCer-APC (provided by Maria Leite-De-Moraes), CD11c-APC (clone HL3), anti-Gr-1-FITC (clone RB6-8C5), anti-CD11b-PE Cy™7 (clone M1/70), anti-CD69-PE (clone H1. 2F3) or anti-CD3ε-Pacific Blue (clone 500A2), anti-NK1. 1-PerCP-Cy-5. 5 (clone PK136), and anti-CD8α-APC-Cy7 (clone 53-6. 7) antibodies, all purchased from BD PharMingen. The cells were then washed; fixed in PBS containing 2% FCS, 0. 01 M sodium azide, and 2% formaldehyde; and analyzed on FACSAriaTM II flow cytometer using BD FACSDiva software (BD Bioscience), and the data were processed using CXP software (Beckman Coulter). Dead cells and doublet cells were excluded on the basis of forward and side scatter.
2. 9. Statistical Analysis. Each dot on the graph represents results obtained for each mouse individually. The Mann-Whitney U test using GraphPad Prism5 software was done to compare the parameters between WT and IL-33 KO mice in in vivo studies. # represents a significant difference with WT PBS condition with # p < 0 05, ## p < 0 01, and ### p < 0 001. * represents a significant difference between WT and IL-33 KO conditions at the same time PI with * p < 0 05, * * p < 0 01, and * * * p < 0 001. $ represents significant difference between two conditions in the same background mice with $ p < 0 05, $$ p < 0 01, and $$$ p < 0 001.
Acknowledgments This work was supported by  INSERM  and the  University of Rennes 1 , the " Contrat de Projet Etat Région "  CPER Infectio 2016-2022. The authors would like to thank the  H2P2  platform for immunohistochemistry analysis and the BIOSIT technological platform for flow cytometry analysis and animal house facilities. Muhammad Imran Arshad  has been supported by a research grant funded by  Higher Education Commission  under  NRPU  (Project no. 20-4613/NRPU/ R&D/HEC/14/45 ) scheme at the  University of Agriculture, Faisalabad, Pakistan. Muhammad Farooq  was supported by a Ph. D. fellowship from the  Government of Pakistan (Higher Education Commission, University of Veterinary and Animal Sciences, Lahore). Virginie Carrière  has been supported by  Fondation de la recherche médicale (FRM), France.
INTRODUCTION 2] [3] [4] [5] [6]  Tenofovir alafenamide (TAF) is a novel TFV prodrug that is associated with 91% lower plasma TFV levels compared with TDF,  7  which leads to less adverse impact on bone and kidneys. 8, 9 Recent HIV treatment guidelines have either replaced TDF with TAF or include both as part of recommended initial regimens. 10, 11 n this large, double-blind, multicenter trial, we compared the switch to fixed-dose emtricitabine (FTC) with TAF versus continued use of fixed-dose FTC with TDF in virologically suppressed HIV-infected patients (ClinicalTrials. gov number NCT02121795). Results from week 48 were previously reported and demonstrated that switching to FTC/ TAF was noninferior to continued use of FTC/TDF while remaining on the same third agent in maintaining viral suppression and led to improvements in markers of bone and renal safety.
ACKNOWLEDGMENTS The authors thank the patients of the Study  GS-US-311-1089  and their partners and families. They also thank  Sandra Friborg ,  Cecilia Tran-Muchowski ,  Lijie Zhong ,  Wei Dong ,  Ling Guo , and the complete  GS-US-311-1089  study team, as well as  Anna Kido  for providing editorial assistance. The authors also thank the principal investigators and their study staff for Study GS-US-311-1089: t  J. Angel  ( Ottawa Hospital ),  N. B. ( Southwest Infectious Disease Associates ),  P. Benson  ( Be Well Medical Center ),  C. Brinson.
Cutoff Definition and Statistical Analysis For the statistical analysis, an antigen-specific cutoff was calculated for each antigen. This included the identification of outliers by using the interquartile range (IQR). Values were treated as outliers if values were not between Q1 -1. 5 × IQR and Q3 + 1. 5 × IQR. Outliers were excluded from the cutoff calculations. For the calculations, data of 63 H. pylori negative sera were used. Except for FliD, a cutoff for each antigen was defined as the mean MFI of negative sera + X × SD. The individual X for an antigen was determined by best results for sensitivity and specificity at the lowest possible value for X. The best result for the antigens gGT, GroEL, HpaA, CagA, and the H. pylori lysate, was defined by the highest possible value of the sum of sensitivity and specificity.
The best result for the antigens VacA and HP0231 was defined by the highest possible specificity. Due to the problem that mean and SD of FliD were zero, an arbitrary cutoff was chosen for FliD. Dividing the antigen-specific MFI value of the samples by the antigen-specific cutoff yielded the signal-to-cutoff (S/CO) ratios. Antigens with a S/CO ratio > 1 were considered to be reactive (positive). To perform statistical analysis, MFI or S/CO values were used in the software RStudio (Version 3. 2. 16, RStudio Inc. , Boston, MA, USA, 2015) and WEKA (Version 3. 8. 0, The University of Waikato, Hamilton, New Zealand, 2016). Significant differences between two distributions were shown using the Mann-Whitney U test. Odds ratios (ORs) and 95% confidence intervals (CI) were determined, to quantify the association between two properties.
Therefore, 5 min was set as the final incubation time for the detection antibody. Proteomes 2017, 22, 24 5 of 12 MFI value of the samples by the antigen-specific cutoff yielded the signal-to-cutoff (S/CO) ratios. Antigens with a S/CO ratio > 1 were considered to be reactive (positive). To perform statistical analysis, MFI or S/CO values were used in the software RStudio (Version 3. 2. 16, RStudio Inc. , Boston, MA, USA, 2015) and WEKA (Version 3. 8. 0, The University of Waikato, Hamilton, New Zealand, 2016). Significant differences between two distributions were shown using the Mann-Whitney U test. Odds ratios (ORs) and 95% confidence intervals (CI) were determined, to quantify the association between two properties.
Metabolic parameters of reactive and hyporesponsive NK cells mTOR is a well-known regulator of the cell metabolism. We thus asked whether the higher activity of mTOR measured in reactive NK cells resulted in detectable changes in metabolic activity. We first measured cell size and granularity using the FSC and SSC flow-cytometry parameters. Reactive NK cells from C57BL/6 control mice presented a slight but significant increase of both morphological indicators when compared to hyporesponsive NK cells of B2m À/À mice (Figure  6A ). Similarly, their mitochondrial content as well as glucose and fatty-acid uptake capacities estimated by measure of the uptake of the glucose fluorescent analog 2-NBDG or the fatty-acid fluorescent analog Bodipy  FL-C16 were significantly higher (Figure  6B ). In contrast, mitochondrial ROS production, lipid droplet content or lipid peroxidation were comparable in both cell types (data not shown).
Flow cytometry Single cell suspensions of spleens were obtained and stained. Intracellular stainings for phosphorylated proteins were done using Lyse/Fix and PermIII buffers (BD Bioscience). Measurement of glucose uptake was performed as described  (Marcais et al. , 2014). Mitochondrial content was measured using Mitotracker Green (Molecular Probes, 1 mM) incubated for 10 min at 37 ˚C in PBS. Lipid uptake was measured using BodipyFL C16 (Molecular Probes, 1 mM) incubated for 30 min at 37 ˚C in complete medium. Surface staining were then performed to identify the different populations. Flow cytometry was carried out on a FACS LSR II or on a FACS Fortessa (Becton-Dickinson). Data were analysed using FlowJo (Treestar). The following mAbs from eBioscience, BD Biosciences or Biolegend were used: anti-CD19 (ebio1D3), anti-CD3 (145-2 C11), anti-NK1.
Statistical analysis Statistical analyses were performed using Prism 5 (Graph-Pad Software). Two tailed unpaired t-test, and ANOVA tests with Bonferroni correction were used as indicated in the figure legends. Significance is indicated as follows: *p<0. 05; **p<0. 01; ***p<0. 001. The heatmap presented in Figure  1A  was established as follow: we first selected the phosphoepitopes for which the MFI (Mean Fluorescence Intensity) was significantly above the one of the FMO control (Student T-test). The MFI of the 15 selected phosphoepitopes for the 4 NC sub-populations defined in Figure  1 -figure supplement 2 was then normalized to the MFI value of the NKG2A + Ly49C + populations in the C57BL/6 mice and the values obtained were averaged to calculate the means for each populations. These values were used to establish the Heatmap using the Multiple Experiment Viewer application.
We used the R statistical language to manage our database and carry out the statistical analysis (R version 3. 3. 2). We splited the database into six datasets (2 Mouse strains * Differentiation subsets), each containing the 15 phospho-epitopes. We performed an ANOVA for each phospho-epitope to test for the phosphorylation difference between the 4 NC sub-populations. The parameters of the ANOVA Type I SS were adapted to control for the experiment effect. The Bartlett Homogeneity of Variances Test was applied first, when it failed to reject its H0, then the phospho-epitope was retained for the ANOVA test. The normality of the residuals of the ANOVA model was checked graphically and numerically with the Shapiro-Wilk Normality Test. When this test failed to reject its H0 then the adjusted P values for multiple comparisons were extracted with the Tukey's 'Honest Significant Difference' method.
Acknowledgements The authors acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  ENSL ,  UCBL ,  US8/ INSERM ) facilities, in particular the  Plateau de Biologie Experimentale de la Souris , and the flow cytometry facility. We would like to thank  Pr. Suzanne Lemieux  ( INRS, Institut Armand-Frappier, Laval, Quebec, Canada ) for providing us with the 4LO3311 hybridoma and  Pr. David Raulet  for advice on its purification. We would like to thank  Pr. Kristin Hogquist  for the Nur77 GFP mice. The TW lab is supported by the  Agence Nationale de la Recherche  ( ANR JC SPHINKS  to TW and  ANR JC BaNK  to AM), the  ARC foundation  (equipe labellisee), the  European Research council (ERC-Stg.
Acknowledgements This work was supported by funds from  Agence Nationale de la Recherche (ANR) ,  Canc eropôle Ile de France ,  Fondation Gustave Roussy ,  French National Agency for Research on AIDS and viral Hepatitis (ANRSH) ,  Institut National du Cancer (INCA), Laboratory of Excellence (LabEx) LERMIT ,  NATIXIS  and the SIDACTION (to J-L. P. ). S. Q. R is supported by  Higher Education Commission (Pakistan)  and by the  LabEx LERMIT  with a grant from  ANR  ( ANR-10-LABX-33 ) under the program " Investissements d'Avenir "  ANR-11-IDEX-0003-01. I. M. and L. V. are funded by  INCA  ( INCA-DGOS-INSERM 6043  and  2015-1-PL BIO-07-IGR-1 ). H. D and D. D. are respectively recipients of PhD fellowships from  LabEx LERMIT and Fondation Philantropia. r e f e r e n c e s.
In contrast to our previous study with an exploratory cohort, we have included in our multivariate analyses all the currently admitted confounding variables (leukocytosis, age, cytogenetics, number of inductions), and NKp46 appeared as the most important risk factor compared with other variables in multivariate models. We then assessed the impact of NKp46 expression at diagnosis on clinical outcome after allo-SCT. Our data suggest that the clinical outcome after allo-SCT is strongly dependent on NKp46 status at the time of diagnosis. Indeed, the clinical benefit of allo-SCT is exclusively observed in the subgroup of patients with high NKp46 expression, whereas in the subgroup of patients with low NKp46 expression, there was no significant effect of allo-SCT on survival and relapse. These results support the idea that NKp46 can be considered as a predictive biomarker for clinical outcome after allogeneic transplantation, since the observed benefit only occurs in case of treatment by allo-SCT.
The median age at induction was 48 y (range: 19-59). All patients were included in the LAM2006IR prospective multicenter randomized trial between November 2007 and April 2012 (NCT00860639). All patients had previously untreated AML with intermediate cytogenetics, as defined by Slovak et al. 43  Patients received conventional 3C7 induction chemotherapy with or without the addition of Gemtuzumab Ozogamicin. 44 atients with APL AML, patients above 66 y and patients without CR after one or two courses of induction chemotherapy were not eligible for this study. The median age at induction was 47 y (range: 18-65). Out of 180 patients, 22 had favorable cytogenetics (12. 2%), 139 had intermediate cytogenetics (77. 2%) and 19 had unfavorable cytogenetics (10. 6%). Sixty-six (36. 7%) patients received allo-SCT in their CR1.
Flow cytometry For the IPC cohort, analyses were performed in the Biopathology department of the Paoli Calmettes Institute. A FACS Canto II (BD Biosciences), and FACS Diva Software (BD Biosciences) were used for flow cytometry. For the GOELAMS cohort, analyses were performed in the Immunomonitoring platform of the Paoli Calmettes Institute. A LSR Fortessa (BD Biosciences) was used for flow cytometry. Cells were immunostained with Krome Orange (KOTM)-or allophycocyanin (APC)-conjugated anti-CD45, fluorescein isothiocyanate (FITC)-conjugated or Phycoerythrin-Cyanine 7 (PC7)-or Phycoerythrin-Texas Red-xTM (ECD)-conjugated anti-CD3, and PC7-, APC AF700-or APCconjugated anti-CD56. Triggering receptor expression NKp46 was measured with Phycoerythrin-Cyanine 5 (PC5)-conjugated monoclonal antibodies.
Isotype controls were mouse immunoglobulin G conjugated to PC5. All antibodies used in this study were kindly provided by Beckman-Coulter. For total blood, red blood cells were lysed with BD FACS Lysing solution (BD Biosciences) before data acquisition. The NKp46 mean fluorescence intensity (MFI) ratio (NKp46 MFI / isotype control MFI, referred to as rMFI) was calculated for each patient. Assays were performed blinded to the study end point.
Statistical analysis Statistical analyses were performed using Graph Pad Prism (Graph Pad Software, San Diego, CA) and R software (http://www. r-project. org). The limit of significance was set at p < 0. 05. OS from CR was defined by the time between CR achievement after induction therapy until death from any cause, and PFS as time between CR achievement and relapse or death, whatever occurred first. Patients without an event were censored at the time of their last follow-up. Survival times were estimated by the Kaplan-Meier method and compared using the log-rank test. For OS and PFS stratified by post-remission therapy, hazard ratios for OS and PFS were determined by Cox regression analysis, while treating allo-SCT as a time-dependent covariate. The cumulative incidences of leukemia relapse and death in CR1 were calculated using the Prentice estimator, considering relapse and death in CR1 as mutually competing events.
Acknowledgments The authors thank  Beckman Coulter  for their technical advices and for providing the antibodies used in this study. The authors thank the  Paoli Calmettes Institute immunomonitoring platform  for their valued contributions to this work. The authors thank the  Groupe Ouest Est d'Etude des Leuc emies Aigu€ es et autres Maladies du Sang (GOELAMS) , the  FILOtheque AML  ( N BB-0033-00073 ), and  Lamya Haddaoui , for their implication in this study.