dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  S. Cox  and  S. Patrick  for correcting our English. We also thank the field workers involved in long-term demographic studies since 1966 at Possession Island and  Dominique Besson  for invaluable help with data management. The long-term demographic study at Crozet was supported by the  French Polar Institute IPEV  (programme  N°109 ),  Terres Australes et Antarctiques Franc ßaises, and Zone Atelier Antarctique (CNRS-INEE ). The  Ethics Committee of IPEV and Comit e de l'Environnement Polaire  approved the field procedures. The study is a contribution to the Program EARLYLIFE funded by a  European Research Council   Advanced Grant  under the European Community's  Seven Framework Program   FP7/2007-2013 (Grant Agreement ERC-2012-ADG_20120314  to H. W. ).
SO 2 concentration was finally calculated by means of a photomultiplier. Data sets were provided by the Observatoire Réunionnais de l'Air (ORA). The SO 2 analyser resolution was about 0. 5 ppb, and outside eruptive periods it never exceeded this threshold (Fig. A1 ). The corresponding data were used only to list days that were characterised by the presence of the volcanic plume at the Maïdo observatory. CO monitoring was performed using a PICARRO G2401 analyzer which is compliant with international ambient atmospheric monitoring networks, including the World Meteorological Organization (WMO) and the Integrated Carbon Observation System (ICOS; https://www. icos-ri. eu/, last access: 25 June 2018). It was the property of BIRA-IASB (Belgian Institute for Space Aeronomy). Figure  2  shows the availability of data for the main aerosol and gas-phase parameters used in this study.
Calculations The classification of event days was achieved visually using the contour plot of the DMPS size distribution. The positive and negative ion size distributions provided by the AIS confirmed the status of the event when available. Days were classified and separated into three main groups: undefined, (UN) non-event (NE), and event (E) days according to Dal Maso et al. (2005). The monthly event frequency, f m , was calculated as the ratio of event to numbers of days in months, after having excluded undefined, missing and plume days (PD) according to Eq. (  1 ): f m = no. of E no. of days in month -(missing data days + PD) × 100, (1) The condensation sink (CS; s -1 ), which represents the loss rate of vapours on pre-existing particles, was calculated from the DMPS size distributions according to  Kulmala et al.
(2005). The method searches, usually over the AIS channel size, for the time that corresponds to the maximum concentration in each size channel. As the DMPS offers a much more extended data set than the AIS, we applied the method to the DMPS 12-19 nm size range for which the lower limit (12 nm) was defined by the smallest channel of the device. In order to detect the concentration maximum, a normal distribution was fitted to the time evolution of the concentration for each channel. GR corresponded to the slope of the linear regression on the time-diameter pairs. The formation rate of 12 nm particles, J 12 , (cm -3 s -1 ) was calculated using the following Eq. (  4 ) given by  Kulmala et al. (2007) : J 12 = dN 12-19 dt + CoagS 12 × N 12-19 + GR 12-19 7 nm × N 12-19 , (4) where N 12-19 is the concentration corresponding to 12 to 19 nm particle diameters, CoagS 12 represents the coagulation of 12-19 nm particles on pre-existing larger diameter particles and GR 12-19 corresponds to the growth rate estimated between 12 and 19 nm.
Dynamics of the NPF events at the Maïdo observatory The origin of the newly formed particles at a given site can be attributed to the local environment only if newly formed particles are detected at the smallest size range, when clusters just formed and had no time yet to grow. Thus, we used both the AIS and DMPS size distribution to investigate the evolution of NPF event at Maïdo. The AIS covers the size range between 0. 90 and 46. 2 nm while the DMPS covers the size range between 11. 78 and 706. 77 nm. Temporal interpolation was first performed to harmonise the DMPS and the AIS data sets to a 5 min resolution. For visual inspection of the consistency of the two data sets, hybrid plots were drawn up showing the AIS negative ion concentration up to 12 nm and then the particle concentration from the DMPS for larger sizes.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  654109  ( ACTRIS-2 ), from the  French programme SNO-CLAP , and from the  OMNCG/OSU-R programme of La Réunion University. We also wish to thank the  ORA (Observatoire Réunionnais de l'Air)  for providing the SO 2 data set as Jean Sciare who provides the processed data from the aethalometer. Edited by: Fangqun Yu Reviewed by:  Fangqun Yu  and two anonymous referees.
STAT5 regulates the expression of miR-21 in K562 cells and is necessary for the imatinibinduced miR-21 regulation In order to study the mechanism underlying the imatinib-induced regulation, we cloned a 517 bp genomic DNA fragment corresponding to the proximal miR-21 promoter  [13]  into the luciferase vector pGL4. 10. Luciferase activity expressed by K562 cells transfected with this construct suggested that the 517 bp DNA fragment contains regulatory element(s) necessary for the action of imatinib on the expression of miR-21 (Supplementary Figure  5 ). Two of the transcription factors binding sites identified in this 517 bp fragment through a bioinformatics analysis (Matinspector program of the Genomatix suite) concern factors of the Signal transducer and activator of transcription (STAT) family (Figure  3A ): the first one (TTACAGGAA, position 410-418) is not related to a specific subtype of STAT ; the second one (TTCTGAGAA, position 441-449) is qualified as a "STAT5" site by Matinspector.
Images were processed with Feature Extraction (version 10. 7). Data were quantile-normalized for inter-array comparisons and analyzed using the "BRB-Array Tools" package, version 4. 2. 0  [38]. Genes that were differentially expressed among the groups were identified using a F-test (Class Comparison Between groups of arrays Package, BRB-Array Tools). Normalized data for all samples have been deposited in NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE78037. For RT-qPCR experiments, RNAs were prepared from Trizol homogenates by precipitation from the aqueous phase following the supplier's recommendations. For RT-qPCR of miR-21 and miR-21*, RNA (200ng) were polyadenylated and reverse-transcribed using the High Specificity miRNA First Strand cDNA synthesis kit (Agilent).
Monitoring of K562 cell growth and sensitivity to imatinib To monitor cell growth, transfected or transduced cells were seeded in 96-well white plates (5000 cells per well). The ATP contents of the wells were assessed every day using the Cell Titer Glo reagent (Promega). For the imatinib sensitivity studies, serial dilutions of imatinib were added to the wells directly after the seeding. ATP contents were measured 48h later. The IC 50 were calculated using GraphPad software.
Mobile phases were a mix of solvent A (0. 1% formic acid in 5% ACN) and solvent B (0. 1% formic acid in 80% ACN). Elution was performed using a 5-40% linear gradient of solvent B for 105 min. The separation flow rate was set at 200 nL/min. Data were acquired in a data-dependent mode that alternated an MS scan survey over an m/z range of 300-2000, and three MS/MS scans of top 3 ions both in CID and PQD fragmentation mode. Monocharged ions were rejected and the dynamic exclusion duration was set to 20 sec. Data were searched by SEQUEST through Proteome Discoverer 1. 3 (Thermo Fisher Scientific Inc. ) against Homo sapiens Complete Proteome Set (Uniprot version 2011-01: 58009 entries). Spectra from peptides higher than 5000 Da or lower than 350 Da were rejected. The search parameters were as follows: mass accuracy of the monoisotopic peptide precursor and peptide fragments was set to 10 ppm and 0.
ACKNOWLEDGMENTS The authors thank the staff of Vect'UB, the vectorology platforms (INSERM US  005 -CNRS UMS 3427-TBM-Core ,  Université de Bordeaux, France)  for the lentivirus productions,  Dr A. Bedel  ( INSERM U1035 ) for the gift of BCR-ABL1-positive and -negative iPS cells cDNA and  Dr I. Dusanter-Fourt  ( Institut Cochin, Paris ) for the STAT5-and control-shRNA lentiviral vectors.
This method represents a relatively standardized approach to cognitive debriefing and reduces the chance of the interviewer introducing bias into the responses. This method also encourages the participants to explain their rationale behind each answer and to identify how a particular item may be problematic. 34 ollowing each interview, the recordings were transcribed verbatim, and all identifying characteristics were removed from the transcripts to ensure confidentiality and anonymity. Analysis of the concept elicitation part was performed using software designed for qualitative analysis (Atlas. ti 7. 0; Scientific Software Development GmbH, Berlin, Germany). 35 To ensure that all relevant concepts were captured, "concept saturation" was considered complete "when no new relevant or important information emerges and collecting additional data will not add to the understanding of how patients perceive the concept of interest.
38 The cataplexy diary and the modified version of the ESS-CHAD are being used as outcome measures in a Phase III, placebocontrolled clinical trial of sodium oxybate in children and adolescents with narcolepsy (NCT02221869). Limitations of this study include the small sample size, and a sample that may not necessarily be broadly representative of pediatric narcolepsy patients due to some limitations in racial and ethnic diversity and the fact that the study was conducted in only one language, English. However, there is no clinical rationale for believing that the signs and symptoms of cataplexy and narcolepsy would differ because of these factors.
Acknowledgments The authors gratefully acknowledge the contributions of the investigators and the patients and families who participated in the study. This study was funded by  Jazz Pharmaceuticals. Under the direction of the authors,  E Jay Bienen, PhD , of the  Curry Rockefeller Group, LLC (CRG) , provided medical writing assistance for this publication. Editorial assistance in formatting, proofreading, copy editing, and fact checking was also provided by  CRG. Jazz Pharmaceuticals  provided funding to CRG for medical writing and editorial support of this manuscript.
Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH 4 ) budget over  2000  -2012 (Saunois et al. , 2016)) , we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH 4 emissions. The GCP dataset integrates results from topdown studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years.
However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods  2002-2006 and 2008-2012  differs from one atmospheric inversion study to another. However, all topdown studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4. 2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13 CH 4.
However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH 4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH 4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13 CH 4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants.
, 2013; Bohn et al. , 2015; Poulter et al. , 2017) , rice paddy emissions  (Zhang et al. 2016) , termite emissions  (Sanderson, 1996; Kirschke et al. , 2013, Supplement)  and soil uptake  (Curry, 2007) , data-driven approaches for other natural fluxes (e. g. Bastviken et al. , 2011; Etiope, 2015) , atmospheric chemistry climate model for methane oxidation by OH  (John et al. , 2012; Naik et al. , 2013; Voulgarakis et al. , 2013; Holmes et al. , 2013) , bottom-up inventories for anthropogenic emissions (e. g. Emission Database for Global Atmospheric Research, EDGAR; US Environmental Protection Agency, USEPA; Food and Agriculture Organization, FAO; Greenhouse Gas -Air Pollution Interactions and Synergies model, GAINS), observation-driven models for biomass burning emissions (e. g. Global Fire Emissions Database, GFED) and finally by atmospheric inversions, which optimally combine methane atmospheric observations within a chemistry transport model, and a prior knowledge of sources and sinks (inversions are also called top-down approaches, e.
fossil and fire emissions, or to separate regions with a dominant source (e. g. agriculture in India versus wetlands in Amazonia), but δ 13 C-CH 4 alone cannot further separate microbial emissions between agriculture, wetlands, termites, or freshwaters with enough confidence due to uncertainties in their close isotopic signatures. The Global Carbon Project (GCP) has provided a collaborative platform for scientists from different disciplinary fields to share their individual expertise and synthesise the current understanding of the global methane budget. Following the first GCP global methane budget published by  Kirschke et al. (2013)  and using the same dataset as the budget update by  Saunois et al. (2016)  for 2000-2012, we analyse here the results of an ensemble of top-down and bottom-up approaches in order to determine the robust features that could explain the variability and quasi-decadal changes in CH 4 growth rate since 2000.
Methods The datasets used in this paper were those collected and published in The Global Methane Budget 2000-2012  (Saunois et al. , 2016). The decadal budget is publicly available at http://doi. org/10. 3334/CDIAC/Global_Methane_Budget_ 2016_V1. 1 and on the Global Carbon Project website. Here, we only describe the main characteristics of the datasets and the reader may refer to the aforementioned detailed paper. The datasets include an ensemble of global top-down approaches as well as bottom-up estimates of the sources and sinks of methane.
Bottom-up studies The bottom-up approaches gather inventories for anthropogenic emissions (agriculture and waste handling, fossilfuel-related emissions, biomass burning emissions), land surface models (wetland emissions), and diverse data-driven approaches (e. g, local measurement upscaling) for emissions from fresh waters and geological sources (   2012). They report methane emissions from the following major sources: livestock (enteric fermentation and manure management); rice cultivation; solid waste and wastewater; fossil fuel production, transmission, and distribution. However, they differ in the level of detail by sector, by country, and by the emission factors used for some specific sectors and countries  (Höglund-Isaksson et al. , 2015). The Food and Agriculture Organization (FAO) FAOSTAT emissions dataset (FAOSTAT, 2017a, b) contains estimates of agricultural and biomass burning emissions  (Tubiello et al.
, 2013 (Tubiello et al. , , 2015)). Biomass burning emissions are also taken from the Global Fire Emissions Database  (version GFED3, van der Werf et al. , 2010, and version GFED4s, Giglio et al. , 2013; Randerson et al. , 2012) , the Fire Inventory from NCAR (FINN;  Wiedinmyer et al. , 2011) , and the Global Fire Assimilation System (GFAS,  Kaiser et al. , 2012). For wetlands, we use the results of 11 land surface models driven by the same dynamic flooded area extent dataset from remote sensing  (Schroeder et al. , 2015)  over the 2000-2012 period. These models differ mainly in their parameterisations of CH 4 flux per unit area in response to climate and biotic factors  (Poulter et al. , 2017; Saunois et al. , 2016).
The bottom-up inventories do reflect some of this variation, such as in 2009 when gas and oil methane emissions slightly decreased (EDGARv4. 2FT2010 and EDGARv4. 2EXT, Fig. S7 ). Methane emissions from agriculture and waste are continuously growing in the bottom-up inventories at the global scale. The observed activity data underlying the emissions from agriculture estimated in this study, as reported by countries to FAO via the FAO-STAT database (FAO, 2017a, b), exhibit inter-annual variabilities that partly explain the variability in methane emissions discussed herein. Livestock methane emissions from the Americas (mainly South America) increased mainly between 2000 and 2004 and remained stable afterwards (estimated by FAOSTAT, Fig. S12 ). Additionally, Asian (India, China, and South and East Asia) livestock emissions mainly increased between 2004 and 2008 and also remained rather stable afterwards.
S3 ), while some suggest constant to decreasing emissions from fossil fuel use and production (Fig. S4 ). The latter result seems surprising in view of large increases in coal production during 2000-2012, especially in China. However, this recent period is characterised by major reorganisations in the Chinese coal industry, including evolution from many small gassy mines to fewer mines with better safety and emission control. The trend in biomass burning emissions is small but barely significant between 2000 and 2012 (-0. 05 ± 0. 05 Tg CH 4 yr -2 , Fig. 3 ). This result is consistent with the GFED dataset (both versions 3 and 4s) for which no significant trend was found over this 13-year period. However, between 2002 and 2010, a significant negative trend of -0. 5 ± 0. 1 Tg CH 4 yr -2 is found for biomass burning, both from the topdown approaches (Fig.
, 2016). Indeed, both the flooded dataset used in  Poulter et al. (2017)  and the one used in  Mc Norton et al. (2016b)  based on an improved version of the topography-based hydrological model  (Marthews et al. , 2015)  show decreasing wetland extents from the 2000s to the 2010s. However, resulting decreasing methane emissions are not in agreement with top-down studies even when constrained by satellite data. Thus, as has been concluded in most land model CH 4 inter-comparisons and analyses, more efforts are needed to better assess the wetland extent and its variations (e. g. Bohn et al. , 2015; Melton et al. , 2013; Xu et al. , 2016). Even though top-down approaches may attribute the emissions increase between  2002-2006 and 2008-2012  to tropical regions (and hence partly to wetland emitting areas) due to a lack of observational constraints, it is not possible, with the evidence provided in this study, to rule out a potential positive contribution of wetland emissions in the increase in global methane emissions at the global scale.
Indeed, the recent acceleration of the methane atmospheric growth rate in 2014 and 2015 (Ed Dlugokencky; NOAA ESRL, www. esrl. noaa. gov/gmd/ccgg/trends_ch4/) adds more challenges to our understanding of the methane global budget. The next Global Methane Budget will aim to include data from these recent years and make use of additional surface observations from different tracers and satellite data to better constrain the time evolution of atmospheric methane burden. Data availability. The datasets used in this paper are those collected for The Global Methane Budget 2000-2012  (Saunois et al. , 2016). The decadal budget is publicly available at http://doi. org/ 10. 3334/CDIAC/Global_Methane_Budget_2016_V1. 1 and on the Global Carbon Project website. The full time series of the mean surface atmospheric methane mixing ratios are available in the Excel spreadsheets for the four networks.
GRB Sample Our GRB sample contains events distributed through the Gamma-ray Coordinates Network (GCN) system, 145  supplemented by the Swift 146    (Lien et al. 2016 ) and Fermi 147   (Gruber et al. 2014; von Kienlin et al. 2014 ) trigger pages and the IPN  (Hurley et al. 2003). Events distributed through the GCN system are ingested into the GW candidate event database (GraceDB) 148  within seconds of publication. The dedicated Vetting Automation and Literature Informed Database (VALID; Coyne 2015) cross-checks their time and localization parameters against the tables relative to each satellite and against the published catalog, and with automated literature searches. In total there are 110 bursts recorded in the GCN and the IPN database during the period of interest (2015 September 12 to 2016 January 19).
Keeping in mind that this search requires at least 660 s of data in coincidence from the two GW detectors (see Section 4. 3), we note that the number is in line with the 40 ~% duty cycle of the two Advanced LIGO detectors during O1. In total, with the two methods, we were able to process 41 GRB events, that is, 41% of the events recorded during O1 that had sky location information available. Eight of these events were analyzed in single-detector mode by the modeled search for NS binaries: the ability of this search to run with data from only one detector thus allows us to significantly increase our sample.
Further details on the methods used to search for NS binary signals in coincidence with short GRBs can be found in  Harry & Fairhurst (2011)  and  Williamson et al. (2014). The efficiency of the NS binary search method for recovering relevant GW signals is evaluated via the addition in software of simulated signals to the data. In order to assess performance, these data are filtered with the same bank of templates used for the search. This provides a means of placing constraints on the short GRB progenitor in the event of no detection in the on-source. All simulated signals are modeled using the "TaylorT2" time-domain, post-Newtonian inspiral approximant  (Blanchet et al. 1996). We note that this approximant differs from the one used to build the templates. This choice is designed to account for the disagreement among existing inspiral waveform models in our efficiency assessment (see  Nitz et al.