dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients and procedures The BENEFIT study comprised a prospective, 2-year, international, multicentre, randomised, double-blind, placebo-controlled, parallel-group, phase 3 study with a preplanned follow-up phase of up to 5 years, followed by an observational extension study (NCT00544037) for a maximum follow-up of 8. 7 years. 4] [5]  Eligible patients had experienced a first neurological event suggestive of MS (CIS) and had at least two clinically silent lesions on T2-weighted brain MRI. Within 60 days, patients were randomly assigned in a 5:3 ratio to IFNB1b 250 mg or placebo subcutaneously every other day. Patients completed the placebo-controlled phase when CDMS was diagnosed using Poser criteria  6  or after 2 years in the study (whichever came first) and were then eligible to enter the open-label, single-arm, follow-up study for up to 5 years during which all patients were offered IFNB1b, but could also opt to take other or no medication.
Statistical analyses Statistical analyses were conducted on the dataset of the entire BENEFIT study period. All variables were analysed by descriptive statistical methods. Efficacy was analysed in three domains: relapse-based, disability-based and patient-reported outcomebased variables (see online supplemental appendix 2). Most variables in the first domain were time-to-event variables described by Kaplan-Meier estimates, and treatment groups were compared by a log-rank test and proportional hazards regression (covariates: randomised treatment, steroid use during the first clinical event, type of disease onset and categorised number of T2 lesions on BENEFIT screening MRI). The variables in the last two domains were mainly analysed using non-parametric and/or parametric longitudinal modelling.
3% of patients and was worse in 31. 7%. Non-parametric longitudinal modelling found no effects of time ( p=0. 28) or treatment ( p=0. 9008) on EDSS scores. No significant differences between treatment groups, and a slight overall decrease ( p=0. 027) in MSFC mean z-scores including the subscores for the 9-hole peg test and 25-foot walk, were observed over the 8-year study period, although a significant increase ( p=0. 0453) in PASAT z-score in favour of early treatment was maintained over the whole study period (see online supplemental figure S2). Throughout the 8-year observational period, QoL as measured by FAMS-TOI (see online supplemental figure S3) and EQ-5D rating scales remained high, without significant differences between the two treatment groups (data not shown).
Neutralising antibodies Only 39 (n=25 (14. 1%) IFNB1b arm; n=14 (13. 2%) placebo arm) of the patients enrolled in BENEFIT Extension were NAb+ at their last assessment in the interventional follow-up phase of BENEFIT. When data were stratified into four groups (NAb:-<20 NU/mL, NAb+: ≥20 to <100 NU/mL, ≥100 to <400 NU/mL, ≥400 NU/mL), no differences in EDSS or conversion to CDMS were identified. Median EDSS was 1. 5, 1. 5, 2. 0 and 1. 5, respectively, in these groups at their last visit in the Figure  3  Annualised relapse rate (ARR) by year in the total number of patients observed overall and during each study year. ARR was significantly lower in the early treatment group than in the delayed treatment group over the entire study period as well as in years 1 and 8. ARR was lower among patients in the early treatment than the delayed treatment group for years 1 and 2 (0.
RS: Interpreted data and actively contributed to the writing and reviewing of the submitted manuscript. Member of the study steering committee. Sponsor's responsible clinician for the placebo-controlled study phase. DP: Drafted the statistical analysis plan, interpreted data and drafted and reviewed the manuscript. Sponsor's responsible clinician for the extension study phase. Funding This study was funded by Bayer HealthCare Pharmaceuticals Inc. Competing interests GE has received honoraria for lectures or consulting from Biogen Idec, Merck Serono and Sanofi Aventis, received personal compensation for serving on the BENEFIT scientific advisory board, and for speaking from Bayer Schering Pharma AG. GE has also received research support from Serono: grant to University Hospital to support a research programme on MRI in MS and from Teva: grant to support a research programme on anti-IBF neutralising antibodies; LK has lectured at medical conferences or in public and received honoraria for participation in advisory boards and steering committees which have been exclusively used for funding research for his department.
Acknowledgements We are grateful to the patients and the BENEFIT investigators for their continuing contributions to the study. Additionally, we thank  Charlotte Stolz  ( PAREXEL International GmbH ) for statistical planning and programming support. Robert C Ristuccia, PhD , provided medical writing assistance in preparing the manuscript (Precept Medical Communications) that was funded by  Bayer HealthCare Pharmaceuticals. Merck Serono, MediciNova ,  Novartis ,  Novonordisk ,  Octapharma ,  Peptimmune ,  Roche ,  Sanofi Aventis ,  Santhera Pharmaceuticals ,  Teva , Xenoport,  UCB ,  Wyeth  and by grants from the  Swiss MS Society , the  Swiss National Research Foundation , the  European Union , the  Gianni Rubatto Foundation ,  Novartis , and  Roche Research Foundations. XM has received speaking honoraria and travel expenses for scientific meetings, has been a steering committee member of clinical trials or participated in advisory boards of clinical trials in the past years with  Bayer, Biogen Idec, EMD ,  Genentech ,  Genzyme ,  Merck Serono, Neurotec ,  Novartis ,  Sanofi Aventis ,  Teva Pharmaceuticals  and  Almirall ; CHP has received personal compensation from  Biogen Idec ,  Bayer Schering Pharma AG ,  Teva Pharmaceutical Industries ,  Merck Serono, MorphoSys ,  Novartis Pharmaceutical Corporation ,  GlaxoSmithKline ,  Actelion Pharmaceuticals Ltd ,  UCB ,  Receptos , and  Roche  for consulting services.
Germination experiment Trials were conducted in a set of seven controlledtemperature cabinets. Dormancy-release responses were compared under humid (seeds on dry filter paper at 80 ^10% relative humidity), saturated (seeds on wet filter paper, but no free water) and wet (seeds submersed in water) conditions at constant  (20, 25, 30, 35, 40 and 458C)  and diurnally fluctuating (20/408C, 12 h/12 h) temperatures (only 20, 40 and 20/408C for the humid treatment). Experiments were conducted under constant (24 h) light conditions, as light has previously been shown not to affect release from physical dormancy or subsequent germination  (Baskin and Baskin, 1998). Temperatures were checked daily with minimum -maximum thermometers to ensure the settings were accurate, and relative humidity in the humid treatment was confirmed using an iButton data logger (DS1921 G-F5; iButtonLink Technology, East Troy, Wisconsin, USA).
Acknowledgements We thank  Natasha Burrows  ( M. pigra ) and  Kunjithapatham Dhileepan  (A. nilotica) for providing seeds,  Manon Griffith  and  Jo Vitelli  for access to controlled temperature cabinets,  Anne Bourne  for data analyses, and  Tony Grice  and  Chengyuan Xu  for comments on a draft manuscript. Funding was provided by  Ile de France Region  through  International Mobility Aid.
Sequences of primers are given in Supplementary Table  S2. A typical run included duplicates of each IP and input DNA, and serial dilutions of one input DNA to create a standard curve and determine the efficiency of the amplification. Data were analysed with the LightCycler 480 software using the 'second derivative maximum' method for quantification. The level of occupancy at a specific DNA locus is calculated as the percentage of DNA present in the immunoprecipitate relative to the total input. Relative occupancy was obtained by normalizing to occupancy at the IME2 locus.
5 vol absolute ethanol. After centrifugation, the RNA pellet was washed with absolute ethanol, air-dried and resuspended in water. Reverse transcription (RT)-quantitative PCR was conducted following a two-step procedure using the RevertAid H Minus M-MuLV reverse transcriptase (Thermo Scientific) and random hexamers for priming, and the SYBR Green I Master mix and LightCycler 480 system (Roche) for real-time PCR. Sequence of primers is given in Supplementary Table  S2. Primer extension was performed as follows. Ten microliters containing 10 mg of RNA, 1 pmol of 32 P-5 0 -labelled gene-specific oligonucleotides and 20 nmol of each dNTP was heated for 15 min at 65 C and cooled down on ice. Ten microliters containing 200 units of SuperScript II Reverse Transcriptase (Invitrogen) in 2Â first-strand buffer and 0.
ACKNOWLEDGEMENTS We thank  Michel Strubin ,  Dieter Kressler ,  Lisete Fernandes , and  Ge´rard Faye  for plasmids and strains. We are grateful to  Laetitia Cormier  for constructing the strains  Y400  and  Y402 ,  Christophe Leroy  for constructing the strain Y933 and  Lisete Fernandes  for her comments on the manuscript.
FUNDING  Centre National de la Recherche Scientifique , and grants from  Agence Nationale pour la Recherche  [ ANR-06-JCJC-0045  and  ANR-08-BLAN-0229 ]; and  Association pour la Recherche sur le Cancer  [subvention 4901]. Funding for open access charge:  Centre National de la Recherche Scientifique.
HPLC analysis of azithromycin AZ content and dissolution profile were determined by an HPLC system composed of 515 HPLC Pump Waters, Waters 2487 Dual Absorbance Detector and Waters 717 plus Autosampler (Waters, France). Data were managed using Millenium 32 Chromatography manager (Waters, France). HPLC method was based on  Gaudin et al. (2011)  method. AZ was eluted using Luna C8 EC 5 m, 150 mm × 4. 6 mm column (Phenomenex, France) thermostated with Crococil oven (CIL, Saint Foy la Grande, France) at 45 • C. The mobile phase at apparent pH of 9 was composed by methanol/phosphate buffer 15 mM (80/20, v/v) at flow rate of 1. 2 ml min -1. The injected volume was 10 l and AZ was detected at 215 nm.
Differential scanning calorimetry (DSC) analysis DSC analysis was performed using a differential scanning calorimeter (Mettler Toledo TA controller and DSC30, Switzerland) with STAR e software. DSC method consisted in a heating rate of 5 • C min -1 in the range of 30-180 • C for thermograms, or 2 • C min -1 in the range of 30-150 • C for melting point determination (n = 3 for each formulation). Precisely weighted samples of 6-8 mg were in aluminium pans, sealed and perforated with a pin. An empty sealed perforated aluminium pan was used as reference. For comparison, theoretical melting points were calculated as a mean of individual melting points taking into account the relative content.
Infrared analysis Spectra of AZ, PEG 1500 and 4000 and suppositories were analyzed by Fourier transformed infrared (FTIR), using Mattson model Genesis II FTIR spectrometer controlled by Winfirst software from Mattson instruments Inc. (Winlab. Instruments, Bagnolet, France). Samples were ground and mixed with potassium bromide at 1% weight ratio and then compressed at a pressure of 8 tonnes during 5 min (hydraulic press). Single beam spectra were recorded after averaging 20 scans at a resolution of 0. 5 cm -1 , between 4000 and 400 cm -1 and corrected against the background spectrum of the atmosphere. All spectra were obtained in the absorbance mode and then converted in transmittance. Calibration of the instrument was repeated periodically during operation.
They were collected at predose, then 12 samples in 48 h post-administration and kept on crushed ice before centrifugation at 1200 × g during 10 min within 30 min after collection. Only supernatants (at least 500 l) were transferred into cryo tubes and conserved at -80 • C prior analysis. The maximal concentration (C max ), and the time to reach the maximal concentration (T max ) of each formulation were taken directly from mean plasma concentration-time profile curves, whereas the area under the concentration-time curve (AUC) was calculated using trapezoidal rule. Standard deviations are given for all PK parameters. Absolute and relative bioavailability were estimated from mean AUC 0-24 h and AUC 0-48 h. Data for 24 h were analyzed by comparison with previously obtained parameters for suspended AZ suppositories  (Kauss et al.
Acknowledgments The authors thank  Dr. Mathieu Marchivie , ( Univ. Bordeaux ,  FRE 3396 CNRS Pharmacochimie, Bordeaux, France ) for his expertise in phase diagram analysis and interpretation and  Mrs Anne-Marie Lagueny  ( Univ. Bordeaux, Laboratory of Galenic pharmacy and biopharmacy, Bordeaux, France)  for her technical support during laboratory development. This work was supported in part by the  Wellcome Trust foundation ,  Wellcome Trust   Feasibility Award  ref:  085242/Z/08/Z. P. Olliaro  is a staff member of the WHO; the authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of the WHO.
coli (Ec), Yersinia pestis (Yp), Shewanella oneidensis (So), Vibrio vulnificus (Vv), Rhodospirillum rubrum (Rr), Herbaspirillum seropedicae (Hs), Mycoplasma capricolum subsp. capricolum ATCC 27343 (Mc), Synechocystis sp. PCC 6803 substr. Kazusa (Sk), Caenorhabditis elegans (Ce), and Azotobacter vinelandii (Av). A very hydrophobic patch within the W56 loop was found in many clades of the AAA + proteins (highlighted in grey). (b and c) Two views of the PspF 1-275 hexamer modelled by Rappas with ATP-bound monomers (PDB entry 2C96) based on energy minimisation. 5, 6 The PspF 1-275 hexamer was depicted in grey ribbons with one protomer highlighted in green to delineate the boundaries of the self-association interface. Key residues (the YLW patch, blue) in the W56 loop (orange), L1 (red), linker 1 (magenta), and ATP (rainbow) were highlighted in the hexameric structure.
The predicted PspA 1-186 structure and its relevance to function The lack of a PspA structure has been an impediment to the understanding of its docking and inhibition mechanisms. By using the I-TASSER server, we predicted the tertiary structure of the PspA 1-186 in silico and attempted to explain the docking event with PspF based on previous and current biochemical data. The predicted PspA 1-186 structure has a coiled-coil conformation (Fig. 7 ), consistent with previous predictions,  20, 41  and shows structural similarity to the alpha spectrin [Protein Data Bank (PDB) entry 1CUN]. The confidence C-score for this model, as calculated based on the significance of the threading alignments and the cluster density, is -2. 55. A C-score typically ranges from -5 to + 2, with a higher value reflecting a model of better quality.
Computational analyses The PspF 1-275 sequence was searched against AAA + proteins using NCBI protein BLAST †. The tertiary structure of PspA 1-186 was predicted using the I-TASSER online server ‡ using the following crystal structures as templates: PDB entries 1CUN, 3S84, 2OTO, 1S35, 2QIH, 3NA7, 3na7A, 1U4Q, 2B5U, and 2YFA. The hexameric PspF 1-275 structure was previously modelled by M. Rappas with the ATP-bound monomeric PspF 1-275 crystals (PDB entry 2C96) and subsequently used in two research papers. 5, 6 Protein rendering was performed in Chimera (UCSF).
Acknowledgements We thank  Dr. G. Jovanovic  and  Dr. C. M. Engl  for their stimulating comments on the manuscript. We thank all the past and present MB group membersfor their friendly support. This work was supported by  Biotechnology and Biological Sciences Research Council  project grants ( BB/ J002828/1  and  BB/G001278/1 ) and a  Wellcome Trust  grant ( WT093044MA ) to M. B.
PRELP gene expression The expression of PRELP mRNA was tested by RT-PCR in tumor cells from CLL patients and other hematological malignancies as well as in PBMC and leukocyte subsets from healthy control donors. First, we tested PRELP mRNA expression in PBMC from CLL patients and healthy donors. PBMC from all CLL patients (n = 30) expressed PRELP at the mRNA level irrespective of clinical phase (non-progressive/progressive) (Figure  1 ). PRELP mRNA was not expressed in isolated CLL Tcells (0/10). Neither was PRELP expressed in fresh PBMC (lymphocytes and monocytes) of healthy donors (0/20), enriched normal blood B cells (0/6), T cells (0/4), or granulocytes (0/5). The results are summarized in Supplementary Table  S1. Sequencing of cDNA from CLL patients (n = 10) revealed no major mutations in the PRELP gene (data not shown).
Being able to predict the Li-ion battery degradation is necessary for applications such as electric vehicles (EVs) and hybrid ones (HEVs). Most of the time, battery life prediction is based on accelerated cycling datasets obtained under different conditions. However, cell aging occurs not only during cycling but also at rest (calendar mode), the latter representing about 90 % of its lifetime. In this work, an empirical model of a 12 Ah commercial graphite/nickel-manganese-cobalt (C/NMC) cell accounting for calendar aging is presented. An innovative accelerated aging protocol representative of a battery usage likely to be encountered in real-world is also proposed. Experimental results tend to prove that a state-of-charge (SoC) range management can extend the battery lifetime significantly, mainly due to the calendar aging effect.
Calendar aging data Aging datasets upon storage under static conditions (at rest) were obtained in the framework of the SIMCAL project  [2]. The SIMCAL research program aims to study and to understand the calendar aging mechanisms of a wide variety of new battery technologies since these mechanisms are still unclear (especially for new generation of LIBs). Two stress factors have been investigated: the State of Charge (SoC) and the temperature of storage. Three SoC and three temperatures are considered, respectively 30 %, 65 %, 100 % and 30°C, 45°C, 60°C. During storage, a substantial part of the capacity loss can be recovered after a full charge using the CC/CV protocol defined in Error! Reference source not found. Therefore, three discharge/charge cycles at 1C nom at 25°C were performed.
Conclusion Simulating performance loss of LIBs is a fundamental step for design and commercialisation of EVs and HEVs. As most of the battery life is spent under storage, studying battery degradation during storage seems particularly relevant for this application. Launched in 2009, the SIMCAL research program has provided highly valuable calendar aging datasets, as well as in-depth analysis of aging phenomena of commercial high power lithium-ion cells. In this work, a degradation model capturing temperature and state of charge was fit to calendar aging data of a 12 Ah high power cell. Besides static calendar aging tests, complementary tests aging tests mixing both storage and usage conditions were performed. Aging protocol of these tests was specially selected to match a realistic repartition of an EV application where up to 90 % of time is spent at rest.
DATA ANALYSIS The abiotic characteristics were analyzed according to a generalized linear mixed model  (Pinheiro and Bates, 2000)  to account for the hierarchical sampling design and to test for the effect of the habitat type (In vs. Out), bedrock type (calcareous vs. siliceous), elevation (continuous, from approximately 2000 to 3000 m) and their interactions ("lmer" function of the "lme4" R package). Population (nested in Transect) and Transect (nested in Massif) levels were included as random factors, and elevation was defined as varying between transects. We tested the null model with random effects only and models that included all of the interactions terms for the fixed effects. The normality of the random factor and residuals were checked. The best models were selected according to AIC criteria  (Akaike, 1974).
ACKNOWLEDGMENTS This research was conducted at the long-term research site  Zone Atelier Alpes , in the  ILTER-Europe network. We are grateful to the  Station Alpine Joseph Fourier (SAJF)  for providing field logistics and facilities during the field campaign. We would like to thank  Guillaume Côté Maurais  and  Fabien Quétier  for their help during field sampling and  Armelle Monier  for her help during soil DNA extraction and PCR amplification. We thank the  CNRS-LECA  for providing funding to a group of young researchers to run the " Vertical Ecology" project. The  ANR  project  Diversitalp  ( ANR-07-BDIV-014 ) also contributed to this project. We thank the three anonymous reviewers and the editor for their helpful suggestions on an earlier version of this manuscript. J. Roy  received a grant from the  French Ministère de la Recherche.
Spectral Index Concerning the different CTA arrays, Fig. 3. 3 shows that the best performing arrays at high energies are C, D, H, I, J, K. While it may be of more interest to find out if they would detect sufficient photons on which to perform tests for time-delay, we note that there are a number of unbinned methods that can cope with sparse datasets [see, e. g. , 171, 165, 177] so that 10 photons of E>=10 TeV are required in order to be able to begin to test for LIV.
Also, the uncertainties on dark matter density will be significantly reduced by new measurements of individual stellar velocities available after the launch of the GAIA mission 9. Stacking-methods of Fermi-LAT dSphs data were proven valid to make constraints more stringent  [49, 215, 216]. The application of these methods for CTA is currently under study. The search for DM signatures in galaxy clusters, investigated in Section 1. 2 was performed for two representative clusters, Perseus and Fornax. The former one is thought to have the highest CR-induced photon yield, and the latter is thought to have the strongest DM-induced signatures. Compared to dSphs, the gamma-ray signatures of galaxy clusters have several contributions: in the first place, the DM signal is expected from an extended region that can be larger than a few degrees, and secondly, gamma-rays induced by interactions of accelerated cosmic rays with the ambient fields and/or by individual cluster galaxies are an irreducible background to the DM signal, as recently shown in Refs.
Acknowledgment We gratefully acknowledge support from the following agencies and organisations:  Ministerio de Ciencia, Tecnología e Innovación Productiva (MinCyT) ,  Comisión Nacional de Energía Atómica (CNEA)  and  Consejo Nacional de Investigaciones Científicas y Técnicas  ( CON-ICET) Argentina ;  State Committee of Science of Armenia; Ministry for Research, CNRS-INSU  and  CNRS-IN2P3 ,  Irfu-CEA ,  ANR, France ;  Max Planck Society ,  BMBF, DESY, Helmholtz Association, Germany ; MIUR, Italy;  Netherlands Research School for Astronomy (NOVA) ,  Netherlands Organization for Scientific Research (NWO) ;  Ministry of Science and Higher Education  and the  National Centre for Research and Development, Poland ; MICINN support through the  National R+D+I, CDTI  funding plans and the CPAN and MultiDark Consolider-Ingenio 2010 programme, Spain;  Swedish Research Council, Royal Swedish Academy of Sciences  financed, Sweden;  Swiss National Science Foundation (SNSF) ,  Switzerland ;  Leverhulme Trust ,  Royal Society, Science and Technologies Facilities Council, Durham University, UK ;  National Science Foundation ,  Department of Energy, Argonne National Laboratory, University of California, University of Chicago, Iowa State University, Institute for Nuclear and Particle Astrophysics (INPAC-MRPI program ),  Washington University McDonnell Centre for the Space Sciences, USA.
We thank  I. Freire ,  S. Funk ,  W. Hofmann ,  A. Murphy ,  A. Pinzke ,  S. Sarkar ,  D. Torres  and  F. Zandanel  who provided comments on the manuscript. D. Emmanoulopoulos acknowledges the  Science and Technology Facilities Council  ( STFC ) for support under grant  ST/G003084/1. This work was partially supported by the  Spanish Consolider-Ingenio CPAN  ( CPAN09-PD13 ) and  Multidark  ( CSD2009-00064 ). A. Jacholkowska  and  J. Bolmont  acknowledge the support of GdR PCHE in France.