dataesr/scientific-paragraphs-categorization huggingface dataset:
To guide the reader's eye, we mark, for each profile in the figure, the position of the pressure jump as derived from the analysis of the y signal (See Table  3 ). For both sectors we find that the X-ray surface brightness and radio profiles show relatively sharp features at the same position as the steepening of the Planck y profiles. This is also the case for the temperature profile of the west sector. For the south-east sector, however, this evidence is less clear. Because it is located in a much lower signal-to-noise region of the cluster, the error of the outermost temperature bin is too large to be able to put a stringent constraint on a possible temperature jump. To check if the X-ray features are also consistent with the hypothesized presence of a discontinuity in the cluster pressure profile we simultaneously fit the observed X-ray surface brightness and temperature profiles using the following discontinuous 3D density and temperature models: n = n 0 × D n (r/r X ) -ξ 1 r < r X (r/r X ) -ξ 2 r > r X ; (8) Table  4 : Main parameters of the fit of the temperature and density models to the XMM-Newton data (see Eqs.
4% confidence level errors (Table  3  and Table 4  ). This agreement near the discontinuity is also seen in Fig. 7  which, in addition, shows that the 3D pressure profiles for the west sector derived from the SZ and the X-ray data are consistent not only near the jump, but also over a much wider radial range. These results indicate that the feature seen by Planck is produced by a shock induced by supersonic motions in the cluster's hot gas atmosphere. Assuming Rankine-Hugoniot pressure jump conditions across the fronts ( §85 of  Landau & Lifshitz 1959) , the discontinuity in the density, temperature and pressure profiles are uniquely linked to the shock Mach number. Table  4  shows that the Mach number obtained from the SZ and X-ray pressure profiles are also consistent within the ±1σ confidence level errors.
Even though this analysis is based on only about half of the data collected by Planck, our results represent a substantial step forward in the study of the physics of the Coma cluster. The full set of data collected by Planck, will not only improve the signalto-noise by another factor ∼ √ (2) but also significantly improve our understanding of instrumental effects. Thus, we will be able to generate more accurate y maps, and more thoroughly unveil Coma's two-dimensional SZ structure and its filamentary environment.
Notice that the fact that the projection of the 3D pressure distribution onto a plane converts the sharp jump into a curved surface brightness profile allows us to recover the position of r J with an accuracy higher than the angular resolution of Planck. 2 clearly shows that the range of radii affected by the r J value is of the order of r J itself. Since r J > 10 ′ , many independent data points with large signal-to-noise ratio are contributing to the determination of r J , driving the uncertainty well below the corresponding projected y profile, smoothed with the 10 ′ beam. The dotted black line shows the expected y profile due to the inner power component, while the dashed line shows the contribution of the outer power law component. The black solid line is the sum of these two components.
Acknowledgements. A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. rssd. esa. int/PlanckCollaboration. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN  and  JA (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  DEISA (EU). Partial support for this work for  L. Rudnick  comes from  U. S. NSF  Grant  09-08668  to the  University of Minnesota. We would also like to acknowledge useful conversations with  G.
In this paper we present PLAY Platform, a Web-oriented distributed semantic middleware that serves as an Event Marketplace: the place where heterogeneous events can be integrated and combined. The purpose of the platform is to derive useful information from diverse real-time sources such as collaborative processes. The platform provides technology where instant results are needed or where heterogeneous data must be integrated on the fly or where the data arrive fast enough to require the stream processing nature of our approach. The main advantages of the platforms are its scalability (cloud-based nature) and the expressivity of the event combinations that can be defined (using both real-time and historical data). The platform has been applied in a use case about Personal data management. In this paper we present some results from the validation, focusing on smartphone and social media integration.
Introduction Recently, there has been a significant paradigm shift towards real-time computing. Previously, requests for Web sites just like queries against databases were concerned with looking at what happened in the past. On the other hand, complex event processing (CEP) is a technology concerned with processing real-time events, i. e. , CEP is concerned with what has just happened. An event is something that has happened, or is contemplated as having happened  [8]. For example, an event may signify a sensor reading, and so forth. Using complex event processing this paper wants to outline a framework for dynamic and complex, event-driven interaction for the Web. This infrastructure leads to the concept of the Event Marketplace (similar to a service marketplace) where events coming from different event producers (as illustrated above) can be arbitrary combined by different event consumers.
Big Data Processing Language To combine real-time data and contextual (historic) data we propose a language called Big Data Processing Language (BDPL). The language is suited to be deployed in a distributed setting. We modelled BDPL as close to SPARQL 1. 1 as possible. Bound variables in the query are allowed to join events based on values from the individual event instances. From the SPARQL 1. 1 language we use the following subset: CONSTRUCT queries without operators UNION and subqueries  5  , OPTIONAL or LIMIT  6  clauses. We distinguish graph patterns (using syntax EVENT and GRAPH) into real-time data and historic data respectively. The real-time parts may be combined with temporal operators such as time windows from  [10]  to enable temporal processing. Intuitively, a query is fulfilled if there is a mapping  [13]  for all variables from the real-time parts and a compatible mapping for the historic data at the time of the real-time answer.
g. temporal) expressivity such as relative and vague time is not supported by the state of the art in real-time processing engines. C-SPARQL is a language and a system to process streaming RDF data incrementally  [11]. RDF triples are events. Timestamps are attached to the triples implicitly when the events enter the system. Sets of events are matched in windows. This means that the approach has a set-at-a-time semantics. EP-SPARQL  [9]  is built on top of the Prolog-based event processing engine ETALIS  [10]  like our work. EP-SPARQL supports more event processing operators than C-SPARQL including event-at-a-time operators like the sequence of two events which require no mandatory window definition and are thus more declarative. Like C-SPARQL, however, this approach considers events as triples not as objects with further structure.
All patients gave written informed consent to participate in the study. In part A of the study, the safety, tolerability, and renal effects of oral BAY 94-8862 [2. 5, 5, or 10 mg once daily (q. d. )] compared with placebo were investigated in patients with HFrEF and mild CKD. The effects on serum potassium concentration, eGFR, and albuminuria were assessed. An independent data monitoring committee (DMC) reviewed data from part A to confirm the safety and tolerability of BAY 94-8862 in patients with HFrEF and mild CKD before the initiation of part B. In part B, the primary endpoint was the change in serum potassium concentration after treatment with oral BAY 94-8862 or placebo in patients with HFrEF and moderate CKD, measured at visits 6 and 7 [i. e. the mean of the changes from baseline to visits 6 (day 22 + 2) and 7 (day 29 + 2)] to reduce the variability associated with isolated measurements.
Statistical considerations Statistical analyses were performed using SAS version 9. 2 (SAS Institute, Inc. , Cary, NC, USA). Analyses are presented for two sets: the safety set (all patients who had received at least one dose of study drug) and the full analysis set (all patients from the safety set of part B with baseline and one or more post-baseline serum potassium measurements). Unless otherwise specified in the text, missing values were not imputed. For part A, all variables were analysed descriptively in the safety set. For part B, the full analysis set was used for the primary analysis. Five different dose-response models were fitted to the serum potassium data from the placebo and BAY 94-8862 groups,  8  but none was considered to provide a better model than analysis of covariance (adjusted for centre and baseline serum potassium concentration).
BAY 94-8862 will be compared with eplerenone in a separate clinical trial (ARTS-HF; ClinicalTrials. gov identifier: NCT01807221). The trend towards a greater reduction in BNP and NT-proBNP levels with BAY 94-8862 5 mg b. i. d. and 10 mg q. d. compared with spironolactone 25 or 50 mg suggests that BAY 94-8862 may exert greater effects on cardiac function and haemodynamic stress at these doses than spironolactone. Studies in rodents have demonstrated that the concentrations of spironolactone and eplerenone in renal tissue are at least six-fold and three-fold higher, respectively, than in the myocardium. 13 In contrast, BAY 94-8862 distributes equally to the kidney and the heart in rats (unpublished data). This suggests that BAY 94-8862 may have cardiac effects even at relatively low dosages and may in part explain the lower incidence of hyperkalaemia with BAY 94-8862 compared with spironolactone.
Acknowledgements Study coordinators:  Catherine Salt ,  Cat Taylor  ( Bayer Plc, Newbury, UK );  Veronique Smets  ( Bayer SA-NV, Diegem, Machelen, Belgium ). Statisticians:  Nina Kimmeskamp-Kirschbaum  and  Ulrich Gehrmann  ( Bayer HealthCare Pharmaceuticals, Wuppertal, Germany );  Daniel Aurasence ,  Bayer ,  BG-Medicine, Cytopherx, Gambro, Lilly, Mesoblast ,  Novartis ,  Pfizer , scTherapeutics,  Sticares InterACT , and Takeda; stock options from  Aurasence, BG-Medicine , and scTherapeutics; and travel support from  Bayer, Gambro, Mesoblast , and  Pfizer. B. P. also has a patent pending (site-specific delivery of eplerenone to the heart). L. K. has received payment from  Servier  for speaking at a symposium. P. P. has received consulting fees/honoraria and support for travel from  Bayer  and  Pfizer , payment for consultancy from  Novartis ,  Johnson & Johnson ,  BMS , and  Coridea , and payment for lectures from  Novartis and BMS.
1 M sodium cacodylate buffer, was added directly after the fixation step. The blocks were mounted on SEM stubs, coated with platinum/palladium and examined under a Helios NanoLab dual beam microscope (FEI Company). Samples were milled at a thickness of 20 nm per slice using the FIB (30 kV, 700 pA) and imaged with a backscattered imaging mode (3 kV, 1 nA). Tomograms and 3D models were computed using IMOD software (http://bio3d. colorado. edu/imod/) [24].
Fluorescence recovery after photobleaching (FRAP) In FRAP experiments, time series were acquired using a spinning disk microscope (Roper Scientific) based on a CSU-X1 Yokogawa head mounted on the lateral port of an inverted Ti-Eclipse Nikon microscope equipped with a 10061. 4NA Plan-Apo objective and a fibered 491 nm 50 mW DPSS laser (Roper Scientific). Images were obtained with a Photometrics Coolsnap HQ2 CCD camera (Photometrics, Tucson, AZ). Bleaching was performed using a combined FRAP-4D module at the back illumination port of the microscope, previously developed by some of us and commercialized under the name of Ilas1 (Roper Scientific). All the imaging modalities of this system were controlled with Metamorph 7. 1. 7 software (MDS Analytical Technologies, Sunnyvale, CA). Stacks of images (4 to 6 planes) were taken approximately every 1.
6 s. Following the first 5 to 12 images, a selected region of interest (ROI) was bleached at about 60 to 80% acousto-optic tunable filters-controlled laser power in ,75 ms. The recovery of the fluorescent signal within the ROI and the total fluorescence intensity within whole single cell were recorded over the next 120 to 200 s, depending on the experimental sample. The fluorescence in the bleached ROI and in the whole cell was quantified at every time point using ImageJ software and the normalized intensity at time t was obtained using the equation F t = (T 0 6B t )+(T t 6B 0 ), where F t is the normalized fluorescence in the ROI at time point t, T 0 and T t are the fluorescence in the whole cell at time points 0 and t, and B 0 and B t are the fluorescence in the bleached ROI at time points 0 and t, respectively.
Normalized intensity versus time plots were generated using GraphPad Prism 5 software. The mobile (F M ) and immobile (F I ) fractions of fluorescent Langerin molecules were estimated with the following equations: F M = (I E -I 0 )+(I I -I 0 ) and F I = 1-F M , where I I , I E and I 0 represent the normalized fluorescence at the immediate pre-bleaching time point, the first post-bleaching time point and the plateau, respectively. Means and 95% confidence intervals were calculated with Excel software.
This work was supported by  EFS-Alsace  and grants from  ARMESA (Association de Recherche et de Developpement en Medecine et SantePublique)  to (C. L. ). This work was partly funded by the  ''Canceropole de la region ile de France '' (www. canceropole-idf. fr). B. C. was a recipient of a  Synchrotron Soleil grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
However, due to the very small field of view of the XRT instrument, X-ray observations of the whole prompt emission phase are highly unlikely. Since its launch, INTEGRAL has observed 22 GRBs (out of ∼ 90 GRBs localised by IBIS) with an off-axis angle equal to or smaller than ∼ 5 •. GRBs at such small off-axis angles can be observed, during their prompt emission, by the JEM-X (3-35 keV) instrument on board INTEGRAL, providing additional spectral information for the prompt emission and the transition to the afterglow phase. The availability of simultaneous X-ray data for some INTEGRAL bursts motivated a search for X-ray afterglow emission in the sample of 22 GRBs detected by JEM-X. Post-GRB emission in this context is defined as emission occurring after the T 90 burst duration in the γ-ray band (20-200 keV).
Transition from prompt to afterglow emission Fig. 3  shows the X-ray GRB emission for two GRBs from the JEM-X sample considered in this analysis (GRB 051105B and GRB 081003A, in the left and right panel respectively). As shown in these two examples, the transition and the early afterglow seen in the JEM-X data can be quite different from case to case. In the case of GRB 051105B (left panel Fig. 3 ), the X-ray decay differs from the observed temporal shape seen at hard X-rays with IBIS/ISGRI, where the burst ends more abruptly. The X-ray decay phase is observed up to T 0 +50 s with δ =-2. 4 ± 0. 1. This value agrees with the average decay seen in FRED GRB tails of ∼ 2. 44  [8]. GRB 081003A shows post-GRB emission with different decaying phases (right panel Fig. 3 ). The early post-burst X-ray emission, from T 0 +22 s to T 0 +70 s, decays as a power-law of slope -.
, 1986). However, in spite of coconut water, incentive prices, therapeutic virtues and high consumption, its sensory characteristics are not much studied, with few exceptions  (Lapitan and Mabesa, 1983 ). Besides, these previous studies do not always treat cultivars intra and inter variations. Also, they are generally centered on hedonic characteristics of fruits, without indications on their sensory profiles. Thus, sensory methods have an important place among analytical techniques because they use human as instrument measure  (Raoux, 1998). Moreover, in Ivory Coast, sensory data of coconuts are non existent. So, the present document studies the sensory precisely gustatory parameters of coconuts water during nuts ripening. Values will be compared to sugars contents in order to characterize specifically coconut water and give indicators for their efficient promotion.
Descriptive tests Panelists performed descriptive tests of the coconut water on a scale of notation in 5 points according to norm NF  V09-16 (AFNOR, 1984). Tested parameters were sweet, salty and sour tastes. Concentrations of the tasting witnesses were prepared by addition of the witness substance in the mineral water (Table  1 ). Their maximum values were determined according to bibliographic data  (Campos et al. , 1996). For each tested taste, every subject savored successively the sample and the tasting witnesses. The intensity of the taste was immediately noted on the form. Gathered numerical informations were converted into corresponding concentrations of the substances witnesses. Indeed, for a given taste, 5 points on the scale represented respectively 5 successive concentrations of the tasting witness.
Statistical analyses Data were treated with statistical software GenStat (GenStat Discovery, Edition 2). So, an analysis of variances (ANOVA) with two classification criteria was performed at 1 and 5% significant level for treatments of the coconut water sensory profiles and sugar contents. Criteria of classification were cultivars and maturity stages, Campaigns constituted blocks. Averages values were compared by the least significant difference (LSD). Results of hedonic tests were compared by Duncan test.
DNA copy number determination Copy number alterations were detected as previously described, using Affymetrix SNP-array 6. 0 (Supplementary Materials and Methods) (3). Data were normalized, analysed and visualized using Partek Genomic Suite version 6. 6 (Partek Inc. , St Louis, MO, USA) and the Genome Alteration Print (GAP) software  (24). Microarray data were deposited in the ArrayExpress database (accession: GBM11, E-MEXP-3285; SW613-Tu1, E-MEXP-3278; SW613-3, E-MEXP-3277).
A key ingredient to searches for physics beyond the Standard Model in B 0 s mixing phenomena is the measurement of the B 0 s -B 0 s oscillation frequency, which is equivalent to the mass difference ∆m s of the B 0 s mass eigenstates. Using the world's largest B 0 s meson sample accumulated in a dataset, corresponding to an integrated luminosity of 1. 0 fb -1 , collected by the LHCb experiment at the CERN LHC in 2011, a measurement of ∆m s is presented. A total of about 34,000 B 0 s → D - s π + signal decays are reconstructed, with an average decay time resolution of 44 fs. The oscillation frequency is measured to be ∆m s = 17. 768 ± 0. 023 (stat) ± 0. 006 (syst) ps -1 , which is the most precise measurement to date.
Another key ingredient of this measurement is the determination of the flavour of the B 0 s meson at production, which relies heavily on good particle identification and the separation of tracks from the primary interaction point. The observed particle and antiparticle states B 0 s and B 0 s are linear combinations of the mass eigenstates B H and B L with masses m H and m L and decay widths Γ H and Γ L , respectively  [11]. The B 0 s oscillation frequency is equivalent to the mass difference ∆m s = m H -m L. The parameter ∆m s is an essential ingredient for all studies of timedependent matter-antimatter asymmetries involving B 0 s mesons, such as the B 0 s mixing phase φ s in the decay B 0 s → J/ψ φ  [12]. It was first observed by the CDF experiment  [6]. The LHCb experiment published a measurement of this frequency using a dataset, corresponding to an integrated luminosity of 37 pb -1 , taken in 2010  [13].
Materials and methods We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures. Results Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project. Conclusions We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework.
INTRODUCTION Biomedical research increasingly relies on the integration of information from multiple data sources, obtained either primarily for the purposes of research, such as trial data and genetic samples, or through secondary use of routinely collected data, for example, electronic health records (EHR). ] [3]  Two levels of heterogeneity can be distinguished: structural and terminological. 5] [6]  Variation in their forms and approaches generates structural heterogeneity of the data models. Second, numerous medical coding systems (terminologies) are used to represent diagnoses, procedures, and treatments in health databases,  7  frequently with many-to-many mappings between them, creating semantic heterogeneity, sometimes also referred to as terminological heterogeneity. 8 ector  8  mentions that these two types of heterogeneity, structural and semantic, are not independent as there are mutual constraints between the information models and coding systems.
Approaches to structural heterogeneity can be grouped in two categories: extract-transform-load (ETL) systems and mediators systems. In the former, the different data sources to be integrated (eg, data warehouses) are all expected to conform to some structural model. This is achieved by carrying out an ETL process on an existing relational database to transfer the data into a single target model. Multiple projects have been built on this approach. The shared health research information network (SHRINE) aims at bringing together various i2b2 clinical data repositories. 13 18 19 The i2b2 model is also used by other projects like TRANSMART. 20 The Stanford translational research integrated database environment, an initiative from Stanford, uses the HL7 reference information model as a foundation for their model while EU-ADR developed its own common model.
33 34 None of these implementations use vocabulary services to support their structural aspects. The terminological needs of various projects are handled internally. The SHRINE project uses a pivot terminology and BIRN stores term mappings in a relational database. 35 36 The smart open services for European patients (epSOS) project is developing an ontology to address the multilingual and mapping needs of its community. 37 38 Nevertheless, terminology servers are often involved like Apelon DTS in FURTHeR and Bioportal in ONCO-I2B2. 39 40 he LexEVS terminology server, having originally been developed in the context of the caBIG initiative, is being used by several projects (eg, ePCRN, NCI thesaurus browser). 24 41 42 he web-based server bioportal also uses it as part of its infrastructure. 43 LexEVS permits unification of all loaded terminologies under the LexGrid format (including ontologies expressed as ontology web language).