dataesr/scientific-paragraphs-categorization huggingface dataset:
P. 134: Fibrinogen β chain as potential biomarker for an intra-vitam scrapie diagnosis Maria Mazza,  1  Chiara Guglielmetti,  1  Francesca Martucci,  1  Simona Carrella,  1  Marianna Pagano, 1 Chiara D'Ambrosio, 2 Andrea Scaloni, 2 Giovanni Candiano, 3 Maria Gabriella Perrotta,  4  and Pier Luigi Acutis Materials and methods. Ovine plasma samples were collected from four different Italian naturally scrapie-affected flocks. Twenty animals were chosen, ten positive and ten negative sheep, coupled by age, sex and breed. To increase the visibility of low abundant proteins, plasma samples were subjected to ProteoMiner™ (Biorad) before 2D-PAGE. Spots were detected by colloidal Comassie blue staining; image analysis and gel comparison were carried out by BioNumerics ® software; proteins were identified by nanoLC-ESI-LIT-MS/MS (Thermo) analysis and database search by MASCOT software package (Matrix Sciex).
Acknowledgments. This work was funded by a grant from the  University of Zaragoza  ( UZ2010-BIO-10 ). Acknowledgments. This research was supported by grant  RF-2009-1474624  and  IZSPLV06/10RC  Acknowledgments. Support:  NIAID-NIH AI-4893-02/FDA  Acknowledgments. Support:  NIH-RO1-NS-061902 ;  Morris Animal Foundation   D12ZO-045  Acknowledgments. This work was funded by The  UK Department of Health  ( 007/0162 ) and  Fondation Alliance Biosecure Acknowledgments. Funding by:  Department of Health, UK  ( 007/0162 )  NHS Blood and Transplant  ( NBS1024/G/AP ) Acknowledgments. Supported by the  Dutch Ministry of Economic Affairs.
Performance Evaluation To show the relevance and the type of performance that can be expected by combining the decision from several individuals, we consider data obtained from a P300 speller experiment where the goal is to detect ERPs. The performance will be reported with the Area Under the ROC curve (AUC)  [32]  and the information transfer rate (ITR)  [33]  in bits per minute (bpm) defined by: ITR = 60 T • ϑ ( 8 ) where ϑ is the capacity of the channel between the interface and the user's brain: ϑ = log 2 (N out ) + P log 2 (P ) + (1 -P )log 2 ( 1-P Nout-1 ) (9) with P being the probability of a good detection (i. e. , the accuracy), N out being the number of possible different outputs, and T being the time in seconds of recorded EEG signal that is required to make the decision among the N out outputs.
Spatial filters are obtained through the Rayleigh quotient by maximizing the SSNR  [34]. During the experiments, four spatial filters (N f = 4) were used. The input vector for the classifier was obtained by the concatenation of the N f time-course signals across spatial filters for a single subject. The detection score, O i , was obtained with the Bayesian linear discriminant analysis (BLDA) classifier  [37]. The training and test database contained 4800 trials for each subject (800 for target, 4000 for non-target). We evaluated the impact of the number of subjects on single-event detection by considering the classifier outputs from the different subjects with Monte-Carlo simulations (combination of trials corresponding to same events, e. g. , the presentation of a visual stimulus, and combination of trials from different subsets of subjects).
The best electrodes are O 2 and P 8. The electrode selected for each subject with SS individual are depicted in Figure  6. The electrode subsets are estimated with data from the first session, which contain 400 patterns corresponding to the presentation of a target and 2000 patterns for the presentation of a non-target. The remaining data are considered for evaluating classifier performance (AUC). We define the AUC performance auc(i, j) by the AUC obtained by considering i electrodes, 1 ≤ i ≤ N s and j subjects, 1 ≤ j ≤ N sub. auc(i, j) is computed by considering the mean AUC with the O sum rule of all the possible combinations of j subjects (N sub !/((N sub -j)! • j!), where ! is the factorial function). Figure  7  represents the AUC as a function of the number of electrodes and number of subjects that are involved in the classification for SS common.
In addition to hardware issues for setting a cooperative BCI, BCI frameworks should embed features for transferring signals and decisions online. These features are already available in current BCI frameworks, e. g. , BCI2000, OpenViBE, MATLAB+Simulink  [47, 48] , facilitating the development of cooperative BCIs. With cooperative BCIs for healthy people, it would be more efficient to have several users with relatively cheap systems with few electrodes than to have a single user with a very expensive system, e. g. , with 32 channels. By considering the EEG signals from different users, a remaining issue is the synchronization of the different EEG signals to the different visual stimuli to avoid jitter effects.
In this paper, we describe a void-finding algorithm and its application to data from VIPERS (VIMOS Public Extragalactic Redshift Survey;  Guzzo et al. , 2014). VIPERS offers a unique opportunity to look for cosmic voids at higher redshift thanks to its wide angular coverage and efficient sampling rate. We structure the paper as follows: in Sec. 2 we briefly describe the VIPERS survey and the samples we derived from the data; in Sec. 3, we describe the mock galaxy catalogues used in this analysis; in Sec. 4, we describe the tests carried out to eval-uate the impact of observational effects on low-density regions detection process; in Sec. 5, we describe the void-search algorithm and its application to VIPERS; in Sec. 6, we present the void-galaxy correlation function; and Sec. 7, gives our discussion of the results and conclusions.
Galaxy samples The first public data release of VIPERS was made available in October 2013 and included 64% of the final dataset  (Garilli et al. , 2014). In this study we use the internal release version 4. 0 catalogue, which increases the area of the W1 field by 30% and  represents 75% of the final coverage. The angular size of the fields are 7. 4 deg 2 in W4 and 9. 9 deg 2 in W1. We select galaxies with confident spectroscopic redshifts, corresponding to zflag ≥ 1. 5, yielding 34965 objects in W1 and 27258 in W4. For a detailed description of the meaning of VIPERS flag values see  Guzzo et al. (2014). We wish to perform a consistent void search across the redshift range of the survey. Therefore, we extracted two B-band rest-frame volume-limited samples from the VIPERS catalogue, one for each field (W1 and W4).
4 < z < 1. 3 and the same apparent magnitude limit as VIPERS (i AB < 22. 5). We replicated the VIPERS target selection procedure on the mock catalogues obtaining the following samples: -100% mocks: containing all mock galaxies brighter than the chosen absolute magnitude limit within the survey border (indicated by the green lines in Fig. 1 ) but without internal gaps. -100% mocks real space: the same as above but using the real space positions of galaxies. -VIPERS-like mocks: including the survey mask and accurately reproducing the galaxy number density in the VIPERS volume limited sample that we considered. We matched the galaxy number density in the mocks to the VIPERS data taking into account the apparent magnitudedependent sampling rate described in  Guzzo et al. (2014). The mocks well reproduce the trends observed in the real data.
We also refer the reader to  Cucciati et al. (2014)  for a detailed study of the impact of observational systematics on the density field estimate. We chose the cell size to match the sizes of voids that may be found in VIPERS. As will be discussed in Sec. 5. 4, the void sizes that can be reliably probed in the VIPERS data range in radius from 15 r 30Mpc. To compute the counts-in-cells, we randomly place a sufficient number of spherical cells inside the unmasked and undistorted mock to massively over-sample the available volume, keeping only those for which at least 80% of the volume is inside the survey borders (green lines in Fig. 1 ). The amount of overlap allowed between the cells and the survey boundaries is somewhat arbitrary, we find that a 20% limit provides us with enough cells to calculate robust statistics whilst not being so conservative as to dramatically reduce the effective survey volume.
Statistical significance To assess the statistical significance of maximal spheres, we compare the occurrence of these spheres to the occurrence of maximal spheres in Poisson-distributed catalogues with the same number density and mask as VIPERS. The confidence level with which we detect a maximal sphere in the galaxy distribution (over the Poisson distribution) is then given by P(r) = 1 - N rnd (≥ r) N rnd,tot , (4) where N rnd (≥ r) is the number of maximal spheres found in the random sample with radius equal to or greater than r, and N rnd,tot is the total number of maximal spheres in the random sample. The closer P(r) is to 1, the less likely a maximal sphere is expected to occur in a random distribution. The number density of galaxies in the VIPERS data is not constant with redshift (see Fig.
The smaller spheres could also fall below the significance limit (see Sec. 5. 4), which means a loss of detected volume. Our selection biases prevent us from drawing conclusions from the actual void size distribution, but we can directly compare the void size distribution as observed in the VIPERS data with that observed in the mocks, as both are affected by the same selection biases. Using our of maximal spheres, we compute the void-galaxy cross-correlation function. We find an enhancement in the correlation function along the line of sight. This anisotropy is a clear signature of the velocity flows induced by the voids and matches well the signal measured in mock catalogues. In the next phase of this work, we will fit the mean density profile of the voids and model the void-galaxy cross-correlation function  (Hawken et al.
Acknowledgements This work is based on observations collected at the  European Southern Observatory, Cerro Paranal, Chile , using the  Very Large Telescope  under programs  182. A-0886  and partly  070. A-9007. Also based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT) , which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This work is based in part on data products produced at TERAPIX and the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and  CNRS. The VIPERS web site is http://www.
vipers. inaf. it/. We acknowledge the crucial contribution of the  ESO  staff for the management of service observations. In particular, we are deeply grateful to  M. Hilker  for his constant help and support of this program. Italian participation to VIPERS has been funded by  INAF  through  PRIN 2008  and 2010 programs. DM gratefully acknowledges financial support of  INAF-OABrera. LG, AJH, and BRG acknowledge support of the  European Research Council  through the  Darklight ERC Advanced Research Grant  (#  291521 ). AP, KM, and JK have been supported by the  National Science Centre  (grants  UMO-2012/07/B/ST9/04425  and  UMO-2013/09/D/ST9/04030 ), the  Polish-Swiss Astro  Project (co-financed by a grant from  Switzerland , through the  Swiss Contribution  to the enlarged European Union), and the  European Associated Laboratory Astrophysics Poland-France HECOLS.
0 software. The viral reads corresponding to the mumps virus genome were selected and assembled using ABySS and CAP3 in order to obtain the full-length genome. Our MuV genome is composed of 15,263 nucleotides, and genomic analysis showed the classical organization of Paramyxoviridae, with seven transcription units containing seven genes and two noncoding regions located at the 5= and 3= ends of the genome. Although many cases of mumps virus in several African countries were reported to the WHO, the circulating genotypes were rarely known. The genotype of this Gabonese MuV strain was determined based on a phylogenetic analysis of the partial sequence of 316 nucleotides of the SH gene compared to the sequences available in the databases. Our strain has 97% amino acid identity with sequences belonging to genotype G strains and shows the highest amino acid identity with strain accession no.
EU606261 circulating in the United Kingdom in 2005. In these cases reported in Franceville, MuV was detected mainly in children who had received two doses of the measles-mumps-rubella vaccine. The introduction of MuV genotype G in Gabon is due to a decrease in immunization coverage in children Ͻ1 year old based on the data available for the measles virus vaccine  (8). Nucleotide sequence accession number. This whole-genome shotgun project has been deposited in DDBJ/ENA GenBank under the accession no. KM597072.
The precision of the astrometric and photometric measurements will depend on the brightness and spectral type of the stars. At G = 15 mag the end-of-mission precision in parallaxes will be ∼ 25 µas. At G = 20 mag the final precision will drop to ∼ 300 µas, while for the brightest stars (6 < G < 12 mag) it will be ∼ 10 µas. The end-of-mission G-photometric performance will be at the level of millimagnitudes. For radial velocities the precisions will be in the range 1 to 15 km s -1 depending on the brightness and spectral type of the stars  (Katz et al. 2011). For a detailed description of performances see the Gaia website 2. 1 SDSS catalogue from  Eisenstein et al. (2006)  added 9316 WDs to the 2249 WDs in  McCook & Sion (1999). A more recent publication  (Kleinman et al. 2013) , using data from DR7 release, almost doubles that amount, with of the order of 20 000 WDs.
(1995)  and using the improved high-density physics of  Kowalski et al. 2007 (medium and long dashed lines). The dotted vertical lines show the limits where the Gaia transmission is higher than 5%. We additionally used pure-He models drawn from  Bergeron et al. (2011) , which cover a range 5  of 3500 K < T eff < 40 000 K. The cooler pure-He DC 6 models are described in more detail in  Kilic et al. (2010a) , and their main feature is the non-ideal equation of state of  Bergeron et al. (1995). In recent years, new pure-He models of  Kowalski et al. (2007) , which include improved description of non-ideal physics and chemistry of dense helium, have also been used in the analysis of the data  (Kilic et al. 2009b). These models include a number of improvements in the description of pure-He atmospheres of very cool WDs.
About 95% of the isolated WDs brighter than G = 20 will have parallaxes more precise than 20%. Expected end-of-mission parallax uncertainties were also computed for two observational datasets extracted from SDSS data. The first sample includes 125 cool (T eff < 7000 K) WDs analysed by  Kilic et al. (2010a) , the second data set includes almost 3000 hot (6000 K < T eff < 140 000 K), non-magnetic and single WDs from  Tremblay et al. (2011). The relative error of their parallaxes, derived from predicted distances, are shown in Fig. 6. For these samples, we computed the values quoted in the bottom part of Table  2. As can be seen, 94% of the WDs will have parallax determinations better than 10%, corresponding to absolute magnitudes with uncertainties below 0. 2 mag, which allows a clear distinction between WDs and MS stars.
While the masses for the sample of  Tremblay et al. (2011)  are known from spectroscopic fits, the masses of the cool WDs from  Kilic et al. (2010a)  are not constrained, and therefore Gaia will be able to provide this information for the first time. Gaia will also observe (and discover new) binaries containing WDs (according to simulations by GUMS shown in Table  9 , only 36% of WDs detected by Gaia will be single) and orbital solutions will be achieved for a significant number of them. Table  2. Approximate number of WDs (all temperatures) with parallaxes better than a certain percentage, derived using the GUMS dataset and real WD SDSS datasets (extracted from  Kilic et al. 2010a; Tremblay et al. 2011).
GUMS All WDs Single WDs  Therefore, Gaia will provide independent mass determinations of WDs. These data are desirable to check and calibrate the currently available mass estimates of WDs based on the models and photometric/spectroscopic data. Among all these binaries, eclipsing binaries will be extremely useful to determine the radius of the WD. We currently know more than 2000 binary pairs composed of a WD and an MS star  (Rebassa-Mansergas et al. 2011). Only 34 systems of this sample are eclipsing binaries. About a thousand more will be discovered by Gaia. σ π / π N %.
Classification and parametrisation The spectrophotometric instrument onboard Gaia has been designed to allow the classification of the observed objects and their posterior parametrisation. The classification and parametrisation will be advantageous in front of other existing or planned photometric surveys because of the combination of spectrophotometric and astrometric Gaia capabilities. The extremely precise parallaxes will permit one to decontaminate the WD population from cool MS stars or subdwarfs. Parallaxes are especially important for very cool WDs (T eff < 5000 K) since Gaia  will provide the necessary data to derive the masses of already known WDs (for the first time) and newly discovered cool WDs. The classification and a basic parametrisation of the sources will be provided in the intermediate and final Gaia data releases in addition to the spectrophotometry and integrated photometry.
This agrees with the results obtained by the SPY project  (Pauli et al. 2006) , showing that hot WDs (T eff > 10 000 K) that belongs to the halo population have masses in the range 0. 45-0. 50 M. The predicted HR diagram in Gaia observables is shown in Fig. 17. According to Fig. 18  in  Robin et al. (2012) , the low-mass MS can be perfectly separated from the WD branch (at least for T eff > 3000 K). Therefore, we will be able to separate cool WDs from cool MS stars in the Gaia catalogues. We did not aim to discuss which of the two simulations considered here better reproduces the reality, but only to provide an estimate of the number of WDs in different stellar populations predicted to be observed by Gaia. On the other hand, a comparison of the true Gaia data with our simulations will improve our knowledge about the Milky Way formation and validate the models and assumptions.
Acknowledgements. J. M. Carrasco ,  C. Jordi  and  X. Luri  were supported by the  MINECO (Spanish Ministry of Economy) -FEDER  through grant  AYA2009-14648-C02-01 ,  AYA2010-12176-E, AYA2012-39551-C02-01  and  CONSOLIDER CSD2007-00050. GUMS simulations have been performed in the supercomputer  MareNostrum at Barcelona Supercomputing Center -Centro Nacional de Supercomputación  (The  Spanish National Supercomputing Center). S. Catalán  acknowledges financial support from the  European Commission  in the form of a  Marie Curie Intra European Fellowship  ( PIEF-GA-2009-237718 ). P. -.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. We thank the  Aeronet, PHOTONS, RIMA  and  WRC  staff for their scientic and technical support. Financial support was provided by: the  Spanish CICYT  ( CGL2009-09740  and  CGL2011-23413 ,  CGL2011-13085-E ). The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 262254  [ACTRIS]. We also thank the  Environmental Council of the CyL Regional Government  ( Consejera de MedioAmbiente, Junta de Castilla y Len ) for supporting this research. The publication of this article is financed by  CNRS-INSU. ACPD ACPD ACPD.
Baseline evaluation and data management Data collected at baseline included demographics, medical history, risk factors, lifestyle, and current symptoms. Patients were examined and HR was determined both by pulse palpation and most recent ECG (performed within the last six months). Blood test results (e. g. haemoglobin, fasting glucose, haemoglobin A1c, creatinine, cholesterol, triglycerides) and current chronic medical treatment (drugs taken regularly more than seven days before entering the study) were also recorded. A standardised, international, electronic case report form translated into the local language was used for data collection. It was sent electronically to the data management centre where completeness, internal consistency, and accuracy were checked.
Statistical analysis All data is collected and analysed at the Robertson Centre for Biostatistics, University of Glasgow, UK, which is responsible for managing the database, performing all analyses, and storing the data according to regulations. Baseline variables are summarised as mean and standard deviation (SD), or median, lower and upper quartiles for continuous data depending on the distribution of the data; and as counts and percentages for categorical data. Differences between the groups were tested using one-way analysis of variance (ANOVA), or Kruskal-Wallis test for continuous variables, again depending on the distribution of the data, and Pearson's χ 2 test or Fisher's exact test for categorical variables. A multivariable analysis of independent correlates of HR ≥ 70 bpm was performed using a logistic regression model.
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil-fuel combustion and cement production (E FF ) are based on energy statistics, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models.
With this projection, cumulative emissions of Earth Syst. Sci. Data, 6, 235-263, 2014   www. earth-syst-sci-data. net/6/235/2014/ C. Le Quéré et al. : Global carbon budget 2013 237 CO 2 will reach about 535 ± 55 GtC for 1870-2013, about 70 % from E FF (390 ± 20 GtC) and 30 % from E LUC (145 ± 50 GtC). This paper also documents any changes in the methods and data sets used in this new carbon budget from previous budgets (Le Quéré et al. , 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10. 3334/CDIAC/GCP_2013_V2. 3). C. Le Quéré et al. : Global carbon budget 2013 Table 5. Comparison of the processes included in the E LUC of the global carbon budget and the DGVMs. See Table 6 for model references. All models include deforestation and forest regrowth after abandonment of agriculture (or from afforestation activities on agricultural land).
We adopt a range of ±1 standard deviation (σ ) to report the uncertainties in our estimates, representing a likelihood of 68 % that the true value will be within the provided range if the errors have a Gaussian distribution. This choice reflects the difficulty of characterising the uncertainty in the CO 2 fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty of updating the CO 2 emissions from LUC. A likelihood of 68 % provides an indication of our current capability to quantify each term and its uncertainty given the available information. For comparison, the Fifth Assessment Report of the IPCC (AR5) generally reported a likelihood of 90 % for large data sets whose uncertainty is well characterised, or for long time intervals less affected by year-to-year variability.