dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We greatly appreciate support from the following people that provided valuable contacts, information on certain species and issues regarding their trade, in addition to the published literature:  Aaron Bauer  ( Villanova University, USA ),  Jonathan Campbell  (The  University of Texas at Arlington ),  Lee Grismer  ( La Sierra University, USA ),  Maurice Isaacs (Ministry of Agriculture, Marine Resources and Local Government, Bahamas) ,  Paula Kahumbu  ( Wildlife Direct, Kenya ),  Muhammad Samar Hussain Khan , Assistant Secretary (Wildlife),  Ministry of Climate Change (Government of Pakistan) ,  Cendrine Meresse  ( Direction de l'Environment, New Caledonia ),  Asghar Mobaraki  ( Wildlife and Biodiversity Bureau Pardisan Eco-park ;  CITES Authority Iran ),  Florentino Chillopa Morales  ( SEMARNAT, Guatemala ),  Colum Muccio  ( Wildlife Rescue and Conservation Association, Guatemala ),  Mark O′Shea  ( West Midland Safari Park, United Kingdom ),  Anslem de Silva  ( Sri Lanka ),  Chris R.
Literature review We repeated the published search strategy of  [1]  on Pubmed and Embase databases, and augmented this search by manual search in the references marked as "Related to" by Pubmed; we then obtained full texts of a first selection of papers, whose "References" section was used to complete the search. Our selection was driven by the following criteria : • The device whose operating characteristics were reported in the paper had to use the same operating principle as our target device. • The paper had to report clinical use during a surgical intervention. • The paper had to report an estimation of both mean and standard deviation of the differences of paired reference (tHb) and device-derived (SpHb) measurements made at the same time, or at least to quote some indicator (such as Bland & Altman's LOA  [3] ) enabling to reconstruct these measures.
Model implementation and fitting A Bayesian implementation of this model was fitted by MCMC methods, using the Stan  [4]  modeling language through the rstan  [5]  interface to R  [6]. The model uses Eqs. (  4 ) and (  5 ) to compute the likelihood of the data and directly implements Eqs. (  2 ) and (  3 ) for series without calibrated SpHb and (  8 ) to  (11)  for series with calibrated SpHb. Using (  1 ) and (  7 ), we also sampled the relevant parameters of a new study and of a new observation within this study at each iteration of the MCMC sampling, thus obtaining a sample representative of the (predictive) distribution of measurement errors without being constrained by the particulars of any study. This simulation of the characteristics of the device in a new setting is the basis of our inferences on its performance.
Acknowledgements The authors warmly thank  Pr Bruno Falissard  ( Hôpital Paul-Brousse  ( AP-HP ) and Unité INSERM  U669 ), who declined authorship of this paper notwithstanding the importance of his contributions, criticisms and suggestions. The authors also wish to thank  Dr Shinichi Nakagawa ,  Associate Professor  of  Behavioural Ecology, University of Otago , who was kind enough to send us a copy of his paper unobtainable from french public universities library system. The authors wish to thank  Dr Ian R White ,  MRC Biostatistic Unit, and Pr Sylwia Bujkiewicz, Lecturer in Biostatistics, University of Leicester , reviewers of earlier versions of the present paper, for their insightful and helpful comments and suggestions which, among other enhancements, corrected a serious deficiency of a previous version of the presented model.
Gene expression analysis: RT-qPCR Cells were washed with PBS, scraped and total RNA was extracted using TRIzol 1 reagent (Invitrogen). After digestion of genomic DNA (RQ1 RNAse-Free DNase, Promega), RNA was extracted with phenol/chloroform, precipitated with ethanol, re-suspended in water and quantified using Nanodrop (Thermo Scientific). cDNA was synthesized from 2 μg of RNA with random hexamer primers (ThermoScript 1 RT-PCR System, Invitrogen) and stored at -20°C until used. cDNA was analyzed by quantitative PCR (Platinum 1 SYBR 1 Green qPCR SuperMix-UDG, Invitrogen) on an ABI Prism 7000 Real-Time PCR system, reactions were performed in triplicate. To compare expression levels between cell types, data obtained in at least two (and in most cases three) independent samples were processed using the ΔΔCt method  [27] , using Gapdh as a reference gene.
Primers used are listed in S1 Table. Quantitative real-time PCR on ChIPs was performed in triplicate reactions as described above and processed using the ΔCt method. Amplification was performed in 15 μl reactions using the following parameters: 50°C for 2 min, 95°C for 10 min followed by 40 cycles of (95°C 15 s, 60°C 60 s). Data presented show the aggregate of a minimum of two (and often three) separate ChIPs performed on different days, unless indicated otherwise. Enrichment levels were calculated as a percentage of immunoprecipitation relative to the input as described (www. SABiosciences. com). Data are shown as fold enrichment of a particular locus compared to control loci.
Additional data related to each of the resulting datasets is shown in Table  1 , specific ChIP using αYY2 was compared to ChIP using PreI serum. Mapping results were loaded into the software SeqMonk (Version V0. 16. 0, License GPLv3) to identify peaks. SeqMonk counts reads in fixed size windows of 100 bp (step size = 50 bp). Counts in the YY2 dataset were multiplied by 1. 62 to normalize for the number of mm9mapped reads in each dataset, and counts of 0 were substituted for 1 to allow the calculation of ratios. We assigned an arbitrary score to each peak identified, which was calculated as the number of reads in αYY2 IP divided by the number of reads in the PreI sample in the same area (unique and repeated reads combined). The peaks were then ranked from high to low numbers, and annotated with the closest gene or database feature (within a range of 100 kb).
ChIPseq data are freely available at http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE80824. To find overrepresented sequence motifs, search programs  [28]  were accessed through http://rsat. ulb. ac. be/rsat. The sequences of the top 20 peaks were used as input for CONSENSUS  [29]. The best obtained position weight matrix was found to be comparable to motifs produced by alternative software such as MEME  [30].
Identification of in vivo YY2 chromatin binding sites in TS cells Taking into account that YY2 was easily detected in TS cells (Fig  1 ) and associated with specific ERV elements in these cells (Fig 2 ; [10]), we continued our functional analysis of Yy2 in these cells. To identify YY2 binding sites in TS cell chromatin, ChIP experiments were carried out as before, and chromatin fractions obtained were sequenced (ChIPseq experiment, see M&M). YY2 binding sites were identified as regions with higher numbers of reads (peaks). A score based on the number of reads (see M&M) was assigned to each peak, and peaks were subsequently ranked from high to low numbers (Table  2 ). The total number of peaks identified with an enrichment score higher than 2 was 483 (data not shown), while 58 peaks scored above 4 (maximum 15).
As these scores were relatively low, genome-wide coverage was probably not attained. Nevertheless, this procedure yielded a list of potential in vivo YY2 binding targets. In the absence of a biological replicate dataset, we validated YY2 binding to several of the putative binding sites. As binding loci did not correspond to genes in a straightforward pattern, we assigned codes to binding sites (Table  2 ) without references to adjacent genes. When association was assayed using semi-quantitative PCR (Fig 3A ), efficient amplification was observed using control templates for each of the loci analyzed (lanes NoIP), and hardly any amplification was observed for the majority of markers with PreI serum (lanes PreI). By contrast, we observed reproducible association of YY2 to the T13T1 and T18T1 loci, as opposed to an intergenic fragment in chromosome 6 (data not shown) and the Gapdh promoter (Fig  3A ), which were not amplified above background.
Absence of YY1 association to in vivo YY2 binding sites All validated YY2 binding sites (Fig 3B ) were taken from the top 20 binding sites according to enrichment score (Table  2 ). We searched the combined sequences in this list of 20 for a potential YY2 binding motif. Analysis revealed the presence of a consensus binding site ANA-GAAGTGG (N indicating any residue), shown as a sequence logo in Fig 4A. This motif is present in all 20 sequences analyzed with a P-value 7. 38e-04, (data not shown). Furthermore, this motif shares similarity with an AANATGG motif identified previously in vitro  [1]. This data suggests that the sequences identified are bound directly by YY2 protein at a defined motif. As YY2 shares high homology in the DNA binding zinc fingers with YY1  [1] , we tested their mutual binding to several of the putative binding sites for YY2 identified in TS cells (Fig  4  and Table  2 ).
independent experiments ± SEM. No gene expression differences were observed between GFP positive and negative cells transfected with a construct carrying a non-specific shControl sequence. (D) Representation of a lncRNA locus analyzed on Chromosome 4 (GRCm38 Chr4:147018235-147491046). The primers utilized and the direction of transcription of the different transcripts is indicated with arrows and horizontal arrowheads, respectively. The location of the relevant YY2 peak number 2 (Table2) is indicated with an arrowhead. (E) Localization of the 21 YY2 binding sites identified in TS cells. Peaks are grouped according to the location relative to transcribed annotated features in the database (NCBIM37). TSS: within 2500 bp of a transcription start site; O: overlapping feature; DU: distal and upstream to TSS; DD: distal from TSS and downstream of transcribed feature; I: intergenic DNA at 100 kb from nearest annotated feature (F) Association of peaks with lncRNA genes.
Acknowledgments The authors thank  N. Brockdorf ,  A. Smith , and  H. Niwa  for generously providing cell lines. We are indebted to  C. Vallejo  and  A Benítez-Lazaro  for additional tissue culture and conditioning of media,  I. García-Tuñón  and  D. Guallar  for expert advice on TS culture and ChIP assays, respectively. We acknowledge the contributions of  Esther Gimeno  and  Eleanore Harper  to the purification of bacterial fusion proteins, and the animal facility at CIB, Madrid for rabbit maintenance, inoculations and bleeding. We acknowledge assistance from  D. García-Domingo  and the  Cell Culture Core Facility ,  IACS  and from the  Confocal Microscopy Core facility ,  IACS. Jon Schoorlemmer  wishes to express his gratitude towards the  Departamento de Anatomía, Embriología y Genética Animal, Facultad de Veterinaria, Universidad de Zaragoza  for its hospitality.
84 U7 small nuclear RNA 20 14 20428900 20429000 Gm5458 predicted gene 5458 Gene High-throughput sequencing was carried out on DNA obtained from chromatin immunoprecipitations (ChIPs) carried out with αYY2 serum. Reads were mapped to the mouse genome (Version mm9, NCBI assembly M37). The table shows data related to the 20 most significant peaks based on enrichment in the αYY2 dataset compared to the control dataset. The table lists the chromosomal localization of each peak, and the nearest annotated feature in the database within a range of 100 kb. Codes used to identify loci in ChIP assays are indicated as well. Note that several features in the table have changed names in the mm10 version of the mouse genome (NCBI GrCm38): Gm16889 has been renamed as C230088H06-Rik and Gm10348 has been replaced by first Gm17279-201 and later Gm26804-201.
To bridge this data gap, national and European innovation projects (e. g. , http://www. climate-kic-centre-hessen. org/miriade. html, MIRIADE-ANR: ANR-11-ECOT-0004) have been proposed to test lower-cost (typically ∼ EUR 1 k per unit) sensors (called hereafter low-cost medium precision -LCMP -sensors) and to develop a corresponding calibration strategy which would enable the measurement of CO 2 concentrations with a precision and an accuracy that would be acceptable for city-scale inversions (but maybe not for other scales, for which more expensive instruments may still be needed for the foreseeable future). This motivates our tests, in this study, of networks with up to 70 sensors. The principle of the inversion performance assessment, the inversion methodology and the OSSE setup are described in Sect.
H 1 The NEE simulations from C-TESSEL -the land surface model of the short-range forecasts of the European Centre for Medium range Weather Forecasts (ECMWF) at a spatiotemporal resolution of 15 km and 3 h  (Boussetta et al. , 2013)  are interpolated to derive the distribution of NEE at the spatiotemporal resolution of the atmospheric transport model. We rely on an inventory of the French emissions from the Institute of Energy Economics and the Rational Use of Energy (IER) at the University of Stuttgart to derive the distribution of sectoral fossil fuel CO 2 emissions in IDF at a high spatial resolution of 1 km × 1 km  (Latoska, 2009). It disaggregates the annual emissions of France in 2005 (according to the national inventory submissions 2007 from UNFCCC, http://www. unfccc. int), making use of extensive data from diverse databases for point, line, and area emissions and of proxy information such as population and land cover maps.
g. l. , and there are at least six layers below 250 m a. g. l. The meteorological fields driving the CHIMERE simulations come from the ECMWF analyses at 15 km resolution. The CHIMERE modeling system prepares meteorological data on its model grid by diagnosing sub-grid processes, such as turbulent mixing and convection  (Menut et al. , 2013). We use Global Land Cover Facility (GLCF) land use data at 1 km × 1 km resolution for this diagnosis. Simple urban parameterization is adopted to correct wind speed in the surface layer taking into account the increased roughness in the urban area  (Menut et al. , 2013) , since B15 found no significant differences in the simulation of CO 2 mole fractions when advanced urban scheme is used. The exchange of CO 2 between the CHIMERE 3-D regional domain and the surrounding atmosphere depends on the wind conditions from the ECMWF product and the CO 2 concentrations at the domain boundaries.
4. The CO 2 gradient computation demands selecting pairs of upwind and downwind stations. For each observation at a given time, the station at which that observation is made is first considered to be a downwind station. We then select, for that observation, a matching observation at an upwind station, based on the wind direction at the downwind station (given by the ECMWF meteorological data, also used to drive the CHIMERE model). We assume that the angle between the direction from the upwind to the downwind stations and the wind direction at the downwind station is between ±11. 25 •. The choice of such a range of angles for the gradient selection is a trade-off between the need to select enough data to constrain the inversion and the need to ensure that we do not depart too much from the objective of as-similating "downwind-upwind" gradients.
g. , ∼ EUR 18 k per year for ∼ 1 million inhabitants excluding Scope 3 emissions;  Cochran, 2015). To date, the type 1 inventories bear high and more importantly undocumented uncertainties. The type 2 inventories are those that can be derived from global or regional gridded maps of emissions estimates. They have been mainly used by the scientific community to model the atmospheric transport of CO 2. Examples are the Emissions Database for Global Atmospheric Research (EDGAR) from the European Commission Joint Research Centre (JRC) and the Netherlands Environmental Assessment Agency (http://edgar. jrc. ec. europa. eu), the global/regional inventory developed by the Institute of Energy Economics and the Rational Use of Energy (IER) at the University of Stuttgart  (Pregger et al. , 2007) , and the global fossil fuel CO 2 emission map from the Peking University (PKU-CO 2 ;  Wang et al.
, 2013). The activity data and emission factors for the compilation of type 2 inventories are usually defined from scales coarser than the city scale, which leads to large and, again, undocumented uncertainty locally. The type 3 inventories are compiled based upon local data down to the building/street scale at the urban landscape. They are arguably more realistic than the two previous ones but available for only a small number of cities to our knowledge. Examples of this type are the AIRPARIF inventory for IDF (AIRPARIF, 2013), the London Atmospheric Emissions Inventory (LAEI; GLA, 2012), and the inventory from the HESTIA project for Indianapolis  (Gurney et al. , 2012). Developing a type 3 inventory is time consuming: it usually demands institutional efforts and requires a high level of expertise.
Whatever their type (1, 2, or 3), the inventories at city scale are not frequently updated because the necessary data are usually disclosed and processed long after emissions actually happened. In case of revisions in calculation methods, such as the correction of emission factors or the addition of emitting activities that were ignored in the previous versions, the entire emission inventory has to be recomputed, which imposes a traceability framework for comparing different versions. In the case of the Paris type 3 inventory, there is only a new update every 2 years, with a 2-year lag between the date of release and the corresponding year of emissions. The IER inventory used for the practical implementation of the OSSEs in this study incorporates local data to provide a gridded inventory at 1 km and 1 h resolution.
Current atmospheric GHG monitoring programs have significant investment and operational costs; for example, the KIC Climate CarboCountCity program (http://www. climate-kic. org/projects/carbon-emissions-from-cities/) incurred costs of ∼ EUR 4 M for a 3-year project period, which included installation of a few monitoring stations in Paris and Rotterdam (less than 10 sites overall), as well as salaries and mobile campaigns. The typical cost of infrastructure instal-lation for 10-site networks, excluding the cost of the sensors, is presently of the order of EUR 200 k when deploying the network when it does not require the building of dedicated towers (as assumed in our study where stations are located at 25 m a. g. l. ), ∼ EUR 180 k euros per year for labor charges (data QA/QC, processing, and modeling), and ∼ EUR 5-10 k per year per site for maintenance and calibration.
Acknowledgements. This study is a contribution to the project MRV Sector funded by the  European Climate Knowledge and Innovation Community (Climate-KIC ). Four authors ( L. Wu ,  G. Broquet ,  F. Vogel , and  P. Ciais ) worked with the industrial chair BridGES supported by the  Université de Versailles Saint-Quentin-en-Yvelines , the  Commissariat à l'Énergie Atomique et aux Énergies Renouvelables , the  Centre National de la Recherche Scientifique ,  Thales Alenia Space, and Veolia. We thank  François-Marie Bréon  for assistance. Edited by:  R. Cohen.
Background: Observational studies are essential for assessing safety. The aims of this study were to evaluate whether results of observational studies evaluating an intervention with safety outcome(s) registered at ClinicalTrials. gov were published and, if not, whether they were available through posting on ClinicalTrials. gov or the sponsor website. Methods: We identified a cohort of observational studies with safety outcome(s) registered on ClinicalTrials. gov after October 1, 2007, and completed between October 1, 2007, and December 31, 2011. We systematically searched PubMed for a publication, as well as ClinicalTrials. gov and the sponsor website for results. The main outcomes were the time to the first publication in journals and to the first public availability of the study results (i.
Results: We identified 489 studies; 334 (68 %) were partially or completely funded by industry. Results for only 189 (39 %, i. e. 65 % of the total target number of participants) were published at least 30 months after the study completion. When searching other data sources, we obtained the results for 53 % (n = 158; i. e. 93 % of the total target number of participants) of unpublished studies; 31 % (n = 94) were posted on ClinicalTrials. gov and 21 % (n = 64) on the sponsor website. As compared with non-industry-funded studies, industry-funded study results were less likely to be published but not less likely to be publicly available. Of the 242 studies with a primary outcome recorded as a safety issue, all these outcomes were adequately reported in 86 % (114/133) when available in a publication, 91 % (62/68) when available on ClinicalTrials.
gov, and 80 % (33/41) when available on the sponsor website. Conclusions: Only 39 % of observational studies evaluating an intervention with safety outcome(s) registered at ClinicalTrials. gov had their results published at least 30 months after study completion. The registration of these observational studies allowed searching other sources (results posted at ClinicalTrials. gov and sponsor website) and obtaining results for half of unpublished studies and 93 % of the total target number of participants.
In 2007, the US Food and Drug Administration Amendments Act also required the posting of clinical trials results for all phase II to IV trials of drugs, biologic treatments and devices having at least one site in the United State at ClinicalTrials. gov no later than 1 year after the date of final collection of data for the pre-specified primary outcome  [12]. In Europe, a new law that will be implemented in 2016 will require that all clinical trials be registered on a publicly accessible European Union clinical-trials register before they can begin, with a summary of trial results posted within a year after the end of the trial. These policies have an important impact and allowed increase research value. In fact, thanks to trials registration, unpublished studies are identified, and their results could be made available through posting.
However, registration is currently mandatory only for clinical trials. Observational studies such as cohort and case-control studies are important for assessing the intervention effect  [13] [14] [15] [16]. They are particularly useful designs when randomized controlled trials (RCTs) are not feasible or when assessing rare adverse events and long-term effectiveness. Such studies represent a large part of the published literature and outnumber published RCTs  [17]. Nevertheless, prospective registration of observational studies is not currently requested  [18]. Despite not being mandatory, more than 35,000 observational studies are registered at ClinicalTrials. gov. Our hypothesis is that registration of observational studies evaluating an intervention in ClinicalTrials. gov is important to increase research value as it allows identifying unpublished studies and obtaining unpublished results.
The main objectives of this study were  (1)  to evaluate whether results of observational studies evaluating an intervention with safety outcome(s) registered at Clini-calTrials. gov were published and, if not, whether they were available through posting on ClinicalTrials. gov or the sponsor website; (2) to evaluate and compare the time to publication and to public availability of results after searching other sources by study funding source; and (3) to evaluate the completeness of reporting of the outcomes designated as safety issues.