dataesr/scientific-paragraphs-categorization huggingface dataset:
These differential measurements are presented normalised to the total Z+jet cross-section. The data are compared to predictions at O(α s ) and O(α 2 s ) using different PDF parametrisations. The remainder of this article is organised as follows: Sect. 2 describes the LHCb detector and the simulation samples used; Sect. 3 provides an overview of jet reconstruction at LHCb; Sect. 4 describes the selection and reconstruction of candidates and the determination of the background level; Sect. 5 describes the cross-section measurement; the associated systematic uncertainties are discussed in Sect. 6; the results are presented in Sect. 7; Sect. 8 concludes the article.
Jet detection and reconstruction efficiencies The jet detection efficiency is determined from simulation and is defined as the efficiency for a jet to be reconstructed with transverse momentum greater than 7. 5 GeV, satisfying the jet selection criteria, given that a true jet is reconstructed in the same event. This efficiency is determined as a function of the true jet transverse momentum and shows little variation in the central region of the LHCb detector. Reweighting the simulation to have the same jet pseudorapidity distribution as data has a negligible effect on the efficiency. This efficiency is about 75 % for jets with transverse momentum of about 10 GeV, but rises to about 96 % for high transverse momentum jets, as shown in Fig. 1. The drop in efficiency at low transverse momentum is mainly due to the jet identification requirements having a larger effect in this region.
Summary A measurement of the pp → Z(→ µ + µ -) + jet production cross-section at √ s = 7 TeV is presented, using a data sample corresponding to an integrated luminosity of 1. 0 fb -1 recorded by the LHCb experiment. The measurement is performed within the kinematic acceptance, p µ T > 20 GeV, 2. 0 < η µ < 4. 5, 60 < M µµ < 120 GeV, 2. 0 < η jet < 4. 5 and ∆r(µ, jet) > 0. 4. The cross-sections are determined for jets with transverse momenta exceeding two thresholds, 20 and 10 GeV. The differential cross-sections are also measured as a function of various variables describing the Z boson kinematic properties, the jet kinematic properties, and the correlations between them. The measured cross-sections show reasonable agreement with expectations from O(α 2 s ) calculations, for all the PDF parametrisations studied.
Members of the CURE Hemostasis Group and a skilled research coordinator collected prospective data from the time of presentation using standardized data forms. All patients underwent clinical evaluation about bleeding symptoms: hematemesis, melena, hematochezia; bleeding severity: hypotension, shock, need for pressors; and cirrhosis data: etiology of cirrhosis, severity of cirrhosis (Child-Pugh class and Model of End Stage Liver Disease -MELD score) from the time of presentation. Laboratory data (creatinine, hematocrit, hemoglobin and coagulation tests) and endoscopic findings were also recorded from the time of presentation. The patients were prospectively followed-up from the date of diagnosis to hospital discharge, death, or liver transplantation. Coagulopathy was defined as values of prothrombin time (PT), international normalized ratio (INR), or platelet count outside the institutional normal ranges.
RESULTS A total of 1249 patients were registered for severe UGIH in the data basis, 461 (36. 9%) were cirrhotic patients. Epistaxis was determined to be the source of severe hemorrhage in 20 patients with ESLD hospitalized for severe UGIH. Based on the number of cirrhotic patients evaluated for severe UGIB during the same period of time, we estimated that the epistaxis diagnosis represented 4. 3% (20/461) of all cirrhotic patients hospitalized for suspected severe UGIH. Most patients (18/20 = 90%) developed what was clinically diagnosed as inpatient UGIH after hospitalization for worsening liver disease and did not have signs of UGIH on admission. In this particular subgroup of inpatients with cirrhosis and severe UGI hemorrhage, the prevalence of epistaxis was higher and was estimated to be 15.
Synchrotron radiation experiments A λ = 0. 08321 nm monochromatic X-ray beam was focused on a ~170(h) × 130(v) nm 2 spot by Si refractive lenses 7 with a flux of ~2 × 10 9 photons s -1 at the sample position. Experiments were performed in transmission geometry with the beam normal to the substrate (Fig. 1B ). XRF spectra were recorded in the horizontal scattering plane using a Si-drift detector. XRD data were collected using a CCD camera with an X-ray converter screen and 2K × 2K pixels of 50 × 50 μm 2 each. The sample was step-scanned through the beam using an x/y/z piezo stage (ESI †). Background scattering from the substrate was very small and did not contribute discrete peaks or short-range order to the XRD patterns.
Acknowledgements We wish to thank  F. Gentile  ( IIT-Genova ) and  E. Di Fabrizio  ( KAUST ) for very helpful suggestions in the context of the current work. We also thank  J. L. Pellequer  ( CEA Marcoule (IBEB, Service de Biochimie et Toxicologie Nucléaire, Bagnols sur Cèze, France) ) for the gift of the TMV particle solution and  M. Reynolds  ( ESRF ) for the gold nanoparticle solution. I. Snigireva  ( ESRF Imaging Laboratory ) collected the gold nanoparticle residue SEM image. All other SEM images were recorded at  IIT-Genova.
Over the Mediterranean region, elevated tropospheric ozone (O 3 ) values are recorded, especially in summer. We use the thermal Infrared Atmospheric Sounding Interferometer (IASI) and the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to understand and interpret the factors and emission sources responsible for the high O 3 concentrations observed in the Mediterranean troposphere. Six years (2008)(2009)(2010)(2011)(2012)(2013) of IASI data have been analyzed and results show consistent maxima during summer, with an increase of up to 22 % in the [0-8] km O 3 column in the eastern part of the basin compared to the middle of the basin. We focus on summer 2010 to investigate the processes that contribute to these summer maxima. Using two modeled O 3 tracers (inflow to the model domain and local anthropogenic emissions), we show that, between the surface and 2 km, O 3 is mostly formed from anthropogenic emissions, while above 4 km it is mostly transported from outside the domain or from stratospheric origins.
In Sect. 4 we focus on summer 2010, as an example year, and validate the WRF-Chem model simulation with surface O 3 and IASI data, and then use the WRF-Chem model to assess the sources of O 3 in the troposphere. In Sect. 5, we use IASI and WRF-Chem free-tropospheric O 3 data to investigate potential STE events. Discussion and conclusions are given in Sect. 6.
EMEP data The EMEP (European Monitoring and Evaluation Programme) O 3 hourly data (http://ebas. nilu. no/) are used to validate the WRF-Chem model at the surface. All ozone measurements within EMEP are done with UV monitors. In this study, measurements at eight ground rural background sites during the summer of 2010 are used. Details on the EMEP observation system can be found in  Hjellbrekke et al. (2012). The geographic locations of the eight stations used for validation are plotted in Fig. 2 , and the corresponding details are listed in Table  1. Two more station data sets were available, GR01-Aliartos (38. 37 • N, 23. 11 • E) and IT01-Montelibretti (42. 1 • N, 12. 63 • E), for the same period. We disregarded the data from these stations because they show a strong diurnal variation of 80-90 ppbv amplitude and recurrent near-zero O 3 concentrations throughout the period of the study, and were thus considered unreliable.
Comparison to IASI observations Averaged data for summer 2010 are used for the comparison of WRF-Chem and IASI O 3 [4-10] km free-tropospheric column. The modeled profile is first linearly interpolated to the time and location of the retrieval. Then, the averaging kernels associated with each IASI measurement and its a priori profile are applied to the interpolated modeled profile (of around seven layers between 4 and 10 km). Figure  7  shows    (Pfister et al. , 2013). On the other hand, the high discrepancies seen over northern Africa might be due to poor spectral fits from IASI above surfaces with sharp emissivity variations, particularly above the desert  (Hurtmans et al. , 2012) , leading to a possible overestimation of the real profile. We analyzed the IASI total retrieval error for the [4-10] km partial column (not shown here) and found that it is on average around 7 % in the model domain, and between 7 and 12 % where the discrepancies between the model and IASI are the highest.
4 and 1. 4 pvu and specific humidity values between 0. 01 and 2 g kg -1 between 700 and 250 hPa during July and August to the east of the basin, in accordance with our results for summer 2010 at 4, 6, and 8 km. Doche et al. (2014)  using IASI data at 3 km height also showed 6-year recurrent O 3 summer maxima in July to the east of the basin. Since IASI has a lower sensitivity in the lower troposphere and above the sea, the anthropogenic emission contribution to the boundary layer O 3 is not well captured by the instrument. However, IASI is able to detect high tropospheric O 3 values in the free to upper troposphere, where its sensitivity is the highest, to the east of the basin during the 6 years. Focusing on summer 2010, we use IASI and the regional chemical transport model WRF-Chem to interpret these maxima.
Acknowledgements. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI L1 data are distributed in near-real time by  EUMETSAT  through the EumetCast distribution system. The authors acknowledge the French Ether atmospheric database administrators (www. pole-ether. fr)for providing the IASI L1C data and L2 temperature data. This work was undertaken under the auspices of the  O3M-SAF  project of the  EUMETSAT  and supported by the  European Space Agency  (ozone  CCI  project). The French scientists are grateful to  CNES  and  Centre National de la Recherche Scientifique (CNRS)  for financial support. The research in Belgium is funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA Prodex arrangement ).
DU in July 2010 at 30 • E. This period coincides with the 2010 Russian heat wave (Schubert et al. , 2011) , which caused severe fires with high O 3 precursors emissions (R'Honi et al. , 2013). Further discussion is provided in Sect. 4. 24 O 3 budget from the WRF-Chem model during summer 2010From this section onwards, we focus our analysis on summer 2010, the year of the anthropogenic emission inventory used in the model. We evaluate the model then we discuss the O 3 budget at different altitude levels in the Mediterranean troposphere. 4. 1 Model evaluation: comparison to EMEP and IASIThe model is evaluated by comparing O 3 concentrations with ground O 3 data from the EMEP stations (Sect. 2. 2) and then free-tropospheric O 3 data from IASI (Sect. 2. 3). www. atmos-chem-phys. net/14/10119/2014/ Atmos.
A second paper  (Aharonian et al. 2008b , hereafter HUL2) listed 14 upper limits based on 94 hours of observation spanning  2005-2007. In this paper, which follows extensive compilation efforts from previous-generation instruments such as Whipple  (Horan et al. 2004)  or HEGRA  (Aharonian et al. 2004) , 47 selected candidates are studied, with observations spanning April 2004 to December 2011, for a total live time of approximately 400 hours. The candidates and the data selection are presented in Sec. 2, the event and spectral analyses are examined in Sec. 3, and the constraints on the VHE emission are discussed in Sec. 4, together with the target-selection strategy.
Selected candidate VHE emitters The sample of targets consists of the AGN observed with H. E. S. S. until December 2011, for which more than an hour of corrected live time was recorded (see Sec. 3). Only objects located away from the Galactic plane, that is above a Galactic latitude of 10 o , were taken into account. Neither datasets on potential or detected H. E. S. S. sources 2 are included, nor those where upper limits based on the entire dataset have already been published (HUL1, HUL2). The objects listed in the 2LAC that are located in the same FoV as selected targets and are potentially associated with AGN were also studied. These criteria yield a total of 42 AGN and 5 unknown-type Fermi-LAT or EGRET sources, as listed in Table  2. Pointed observations were performed for 33 objects, while 14 are visible in the FoVs.
2FGL J1959. 1-4245 and 2FGL J2219. 1+1805 are neighbors of PKS 2004-447 and RBS 1888, respectively. 2FGL J0505. 8-0411 and 2FGL J0540. 4-5415 were jointly observed with BZB J0543-5532. The classification of the targets is also primarily based on BZCAT. For objects not listed there, the 2LAC catalog was fol-  lowed, yielding two AGN of unknown type in addition to BL Lac and FSRQ objects: 1FGL J0506. 9-5435 and 2FGL J0537. 7-5716, called AGU in Table  2 , following the 2LAC nomenclature. Following HUL1 and HUL2, 3C 120 and Pictor A are classified as Fanaroff-Riley I (FR I) and II (FR II) radio galaxies. Searching the SIMBAD database 3 , Seyfert 1 nuclei (Sey I) are hosted by these two objects and by NGC 7469, while PKS 1345+125 is classified as a Seyfert 2 (Sey II). To summarize, most of the targets are blazars, with 13 FSRQ and 23 BL Lac objects, including PKS 0352-686 and 1FGL J0022.
Acknowledgments This work has been carried out within the  SIRTA project  " Boundary Layer Process Studies " and within the  SIRTA science initiatives of multi-instrument soundings of diurnal cycle of ABL depths. SIRTA is a French national atmospheric experimental facility located in Palaiseau (France) at Ecole Polytechnique. A major part of the work was performed when the author was affiliated with  Laboratoire de Météorologie Dynamique (LMD) ,  CNRS-Ecole Polytechnique, Palaiseau, France. The author appreciates the knowledgeable comments of  Martial Haeffelin  (Director, SIRTA) at various stages of this study, and the support of SIRTA staff members. The author thanks four anonymous reviewers for their objective assessments and useful suggestions, which certainly helped improve the scientific and the technical content of the article.
1 M HCl, which typically resulted in an intensity of ∼15 V/ppm on 28 Si (using a 10 11 Ω resistor). Measurements were conducted on the interference-free low-mass side of the three Si isotopes. The most critical interference, caused by 14 N 16 O on the least 4 times during a sequence; each sample or standard was measured in dynamic mode for 30 cycles with an integration time for each cycle of 4 s for Si as well as for Mg with an idle time of 3 s after magnet switching. Pure 0. 1 M HCl solutions were measured before and after each standard-samplestandard block and were used for on-peak zero correction. Typical intensities of 28 Si in blank solutions were below 5 mV. We report Si isotope data relative to the standard reference material NBS28 (quartz sand) in the delta notation according to  Coplen (2011)  as δ( 29/28 Si) NBS28 and δ( 30/28 Si) NBS28 expressed in per mill ( ) by multiplication of Equation 1 and 2 with a factor of 10 3 : δ( 29/28 Si) NBS28 =    29 Si 28 Si sample 29 Si 28 Si NBS28 -1    (1) δ( 30/28 Si) NBS28 =    30 Si 28 Si sample 30 Si 28 Si NBS28 -1    (2) All reported errors on delta values are the 95% confidence interval (CI) calculated according to Eq.
The B-LNG data are first corrected for energy variations. Calibration factors are then determined for each wavelength and for each flight by searching for areas with very low aerosol content and by assuming that the Rayleigh contribution controls the lidar signal. These areas are chosen, as far as possible, in the upper altitude range close to the aircraft where bias due to the aerosol transmission does not play a significant role. The consistency of the calibration factor is checked using different aerosol free areas and several flights, whenever possible. This is the major source of error in the calculation of R(z), and the uncertainty (error and bias, but mostly due to bias) was found to be less than 15 % at 532 nm and less than 30 % at 1064 nm. These numbers were derived from a sensitivity study using different possible calibration factors and different flights.
Comparison of airborne lidar with in situ measurements Aerosol and carbon monoxide (CO) in situ measurements available on the ATR-42 aircraft are described in  Quennehen et al. (2012)  and ADV2010. For the aerosols, a condensation particle counter (CPC-3010) measured the number of submicronic particles, while the aerosol concentrations in different size bins were measured by a Passive Cavity Aerosol Spectrometer Probe (PCASP SPP-200), a GRIMM (model 1. 108), and a scanning mobility particle sizer (SMPS) with a lower time resolution (150 s). In this paper we have used the SMPS and the GRIMM data to compute the aerosol mean geometrical diameter with the 150 s time resolution. Comparisons of the CPC concentrations with the integrated concentrations of the eight size bins of the GRIMM between 0.
We thus considered a comparison between airborne and spaceborne CALIOP L1 observations as a first step. In ADV2010, the AL2 CALIOP products were analysed for one particular flight of the POLARCAT campaign using layers detected at 80 km horizontal resolution and with a 3 % threshold value for the layer optical depth at 532 nm. Comparisons between the CALIOP AL2 and airborne lidar PCR then showed larger values for CALIOP in the aerosol layers of the 11 April flight. Considering the large uncertainty in the weak aerosol layers detected in the AL2 product over the Arctic, averaging of the L1 version 3. 01 CALIOP data is used in this paper to analyse the 45 CALIPSO tracks available in the aircraft campaign domain. The comparison of the aerosol parameter PDF obtained for the campaign period and the campaign area is considered as more appropriate to validate the satellite aerosol data than relying on optimized collocations of aircraft and satellite data, which would give a very small number of cases.
Gridded latitudinal distributions with a 1. 25 • resolution in the campaign area are used to check the coherency of the two data sets. The CALIOP L1 attenuated backscatter coefficients β 1064 and β 532 are available with 333 m horizontal resolution up to the 8. 2 km vertical level and with 1 km resolution at higher altitude. Before making any horizontal or vertical averaging of these data, it is necessary to apply a cloud mask on the L1 data set. This cloud mask is based on the cloud mask features available in the level 2 version 3. 01 CALIOP cloud (CL2) data products for the 5 km horizontal resolution. Additional checks have, however, been added to verify that cloud layers are not misclassified. First, ice cloud layers, detected in the 80 km horizontal resolution profile, must have a pseudocolour ratio > 0.
For semitransparent clouds with smaller ODs (< 0. 9), a transmission correction is performed. The data are also excluded in the 100 m layer just above the cloud top to avoid any error in the cloud top estimate. The cloud filtering is then very conservative in order to exclude a possible bias in the aerosol parameters measured below clouds when the spectral variation of the overlaying cloud attenuation has to be taken into account. The cloud-filtered 333 m attenuated backscatter vertical profiles are then over 80 km and vertically over 150 m with a low pass second-order polynomial filter second-order polynomial filter to improve the signal-to-noise ratio. The 80 km mean attenuated backscatter ratio R 532 (z) and R 1064 (z), the mean aerosol colour ratio and the mean 532 nm volume depolarization ratio are finally calculated using the molecular density and ozone vertical profiles available at 33 standard altitudes in the CALIOP data products.
With the lidar 1064 nm calibration factor used in the version 3. 0 CALIOP L1 data products (see top figures in Fig. 6 ), the ratio between R 532 (z) -1 and R 1064 (z) -1 in this upper layer leads to CR a near 1 for both examples. This would mean that large dust-like aerosols contribute in both cases to the tropospheric aerosol in the European Arctic sector no matter which latitude band is chosen, which does not seem to be credible. Furthermore, depolarization remains low (< 5 %). The 1064 calibration in the version 3. 0 CALIOP data set is based on the assumption that for a specific set of cirrus clouds, the cloud colour ratio is equal to 1 allowing the 532 nm calibration to be transferred to the 1064 channel. This is detailed in a large number of publications  (Vaughan et al. , 2010 (Vaughan et al.
01. This uncertainty of the mean is calculated assuming an error of 0. 4 for a single CALIOP measurement (i. e. the width of the distribution for the negative values) and assuming 1700 independent layers out of 28 872 data points available in the 0 and 7 km altitude range above the campaign domain (i. e. considering a 1 km vertical sampling instead of the 60 m vertical resolution to ensure independence). Since we compare patchy data, it is also important to assess how the averaging of aerosol layers with observed clear air scenes may explain this difference. For example, the difference between the airborne and CALIOP R 532 averages can be explained if there are twice as many layers with low aerosol load (R 532 < 1. 05) in the CALIOP data set. This may be related to the fact that in our CALIOP data processing we remove all the total backscatter values below clouds.
The wider longitude range chosen for the CALIOP data set do not compensate for this difference in the observed air masses. Since the difference in the magnitude of the 532 nm backscatter ratio is not only related to a calibration uncertainty in one instrument or both, but also to differences in the number of observations with low aerosol content in the altitude range 1. 5 to 4 km, we choose not to apply any correction to the 532 nm CALIOP data set.
Specific averaging methods can then be applied. The cloud screening, needed when using L1 lidar data, is based on CL2 CALIOP data products and the IR CALIPSO radiometer data. A recalibration of the CALIOP R 1064 in the Arctic was chosen to reduce the positive bias of the CALIOP data with respect to airborne observations of the colour ratio. A fixed factor was applied to the 1064 nm attenuated backscatter data, of 1. 3 and 1. 4, respectively, for night-time and daytime orbits. This value could be significantly smaller if a small negative bias of the 532 nm CALIOP lidar signal is also corrected, but this hypothesis was not applied in this work. The use of the new version 4. 0 data which will be available very soon would certainly help to address this question. -Comparisons of the statistical distributions in the altitude range 0-7 km show no significant bias for R 1064 when correcting the CALIOP 1064 nm data but a -8 % difference between the CALIOP and B-LNG R 532 data.
The latter might be related to a calibration problem of either the B-LNG or the CALIOP instrument. However, being largest in a specific altitude range between 1. 5 and 4 km, the differences of the spatial averaging of airborne and satellite data are also to be considered. The difference in the magnitude of CR a is mainly related to this overestimation of R 532 in the B-LNG data. The depolarization ratio is not measured at the same wavelength and its spectral variation follows that of hygroscopic aerosol often at a size small enough to be detected only at 355 nm  (Gross et al. , 2012). -The latitudinal distribution of the colour ratio and the depolarization ratio is similar for the B-LNG and the CALIOP data sets, especially considering the limited number of aircraft flights. It is a good indication that, despite possible bias in these two parameters when comparing them, airborne and satellite data are still valuable for the analysis of the aerosol growth or the relative fraction of dust or volcanic ashes using CALIOP observations.