dataesr/scientific-paragraphs-categorization huggingface dataset:
Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively).
The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round-robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO 2 , all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4. 7 ppm) than the other algorithm products (2. 4-2. 5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.
They are computationally more demanding than their proxy counterparts, but their dependence on models is reduced  (Butz et al. , 2011). All algorithms are still under continuous development, and indeed in some cases have already released an updated version (e. g. Guerlet et al. , 2013; Oshchepkov et al. , 2013). This paper deals with the versions submitted to the GHG-CCI round-robin data pool.
, 2013). These are monitored by performing regular lamp measurements with a low-pressure HCl gas cell. Another issue which could contribute to the uncertainty is the bias caused by faulty laser sampling boards in the Bruker 125HR instruments  (Messerschmidt at al. , 2010). These have all since been replaced, but the historical data set remains somewhat compromised. Dohe et al. (2013)  have devised a correction scheme, but this still needs to be implemented. In the meantime the TCCON community offer estimated bias corrections for various stations and periods in time. The strongest suggested correction is -1. 2 ppm XCO 2 for pre-17 June 2009 Bremen data, while other stations were unaffected. We have not applied these corrections, since all the retrieval algorithms in this study used the equally uncorrected TC-CON data for the assessment of their bias-correction procedures and the impact of any such correction on the reported network accuracy (which applies to the uncorrected data set used and should be taken into account when interpreting the validation results) is still unknown.
Methodology The scope of the round-robin algorithm-TCCON comparisons was to identify any remaining shortcomings in the data products generated with the competing algorithms and determine any inter-algorithm quality differences. Therefore the methodology has been kept straightforward and simple, but identical for all algorithms involved. Complicating the validation is the fact that both TCCON and satellite measurements provide best estimates of the true atmospheric state, based on their own individual sensitivities and a priori information. According to  Rodgers (2000) , one can correct for the different a priori profiles used in the TC-CON and satellite retrieval algorithms. Here we have opted to use the TCCON a priori as the common a priori profile for all measurements. Using  Rodgers (2000) , x cor = x + 1 m 0 i m i A i -1 (ap i x -ap i T ) (3) in which x cor and x are the a-priori-corrected and original column-averaged dry-air mole fractions; i is the vertical layer index; and m i corresponds to the mass of dry air in layer i, which is directly derived from p i /g i.
Considering this aspect in the TCCON-satellite comparisons, both of which yield only total column information, requires a reasonable estimate of the true atmospheric variability, which is not available on a global scale. In  Wunch et al. (2011b)  a detailed assessment of this issue was made, comparing ACOS-GOSAT XCO 2  (O'Dell et al. , 2012)  with TCCON measurements. The study was limited to data taken at the Lamont station only, where the real atmospheric variability could be derived from regular aircraft observations. They found that smoothing the TCCON profile with the ACOS-GOSAT averaging kernel at Lamont induced a bias of about 0. 6 ppm with no significant seasonal cycle or airmass dependence. The a priori correction on the other hand did feature a seasonal and latitudinal dependence, as expected, given that, contrary to the TCCON a priori, the ACOS a priori does not feature a seasonal cycle.
No such evaluation has yet been made for a XCH 4 retrieval product, nor can it currently be made for any other station. With no ad hoc information on what best represents the true state for all stations, we limit ourselves to the a priori correction described above. After the a priori correction, all available time series have been trimmed so as to work, in each given comparison round, with data that have matching temporal coverage. As with every satellite versus FTS comparison we defined a collocation time and area in which satellite and groundbased measurements can be paired. Ideally these criteria are as strict as possible in order to minimize the impact of spatial The typical variability (1σ ), including random errors and real atmospheric variability, of the FTS measurements within this 4 h overlap time frame is on average 2.
5 ppb for XCH 4 and 0. 4 ppm for XCO 2. Relaxing the overlap criteria does have a significant impact on the variability, and at ± 6 h the variability increases to 3. 5 ppb (XCH 4 ) and 0. 5 ppm (XCO 2 ). From these data pairs we derived various statistical parameters. In the figures and tables within this article, N corresponds to the number of collocated data pairs; R is the Pearson's r correlation coefficient; Bias is the average satellite-FTS difference: Bias = mean(X sat -X FTS ); (4) while the scatter corresponds to the standard deviation of said difference: Scatter = std(X sat -X FTS ). (5) Note that the single measurement precision requirements for inverse modelling, set forward by the users, is < 8 ppm for XCO 2 and < 34 ppb in the case of XCH 4  (Buchwitz et al. , 2012). All these parameters have been calculated using the individual data pairs as well as daily and monthly means.
Thus to test for normality, we performed a Shapiro-Wilk normality test  (Shapiro and Wilk, 1965) , on a 0. 05 confidence level, on all the relevant data samples. All data samples passed the test apart from the SRA samples from OCPR XCH 4 and BESD XCO 2. However an analysis of the quantile-quantile probability showed no clear departure from normality.
SCIAMACHY XCO 2 The two competing algorithms are BESD and WFMD. Table 3 and Fig. 3a  show the evolution of the bias over the different stations. The error bars in Fig. 3a  correspond to the 95 % confidence bands of the bias. Note that there are no data for Karlsruhe since the TCCON measurements there commenced in 2010, while there were no post-2009 WFMD data at the time of this analysis. The overall bias is slightly smaller for BESD, but the variability of the bias (i. e. relative accuracy) is almost identical (1. 28 versus 1. 29 ppm). The most significant differences between both data sets are the scatter and data density (Fig. 3b  and d ). While the overall scatter for BESD is significantly lower (2. 5 vs. 4. 7 ppm for WFMD), its data density is also lower (9674 vs. 31 818 data pairs). Interestingly this makes the uncertainty on the overall bias, i.
e. the standard error (σ/ √ N ), very similar (0. 025 versus 0. 026 ppm for BESD and WFMD, respectively). The higher scatter for WFMD also reveals itself in the generally lower correlation coefficients. Note that for the Lauder station, situated in New Zealand, BESD only offers three data pairs, all of which are measured on the same day (hence the lack of a daily correlation coefficient for this station). Both algorithms fail the above-stated quality requirements at this site, and if we thus exclude the Lauder station from our analysis, the relative accuracy (RA in the Table ) of BESD improves to 0. 63 ppm, while that of WFMD (slightly) deteriorates to 1. 36 ppm. The time series in Figs. 4 (BESD) and 5 (WFMD) are collocated daily averaged FTS and satellite measurements from Bialystok (a), Lamont (b) and Darwin (c).
Comparing these figures, it is clear that BESD features substantially fewer data than WFMD, due to its more restrictive filtering process (particularly a very strict MERIS cloud mask). Also clearly visible is the extremely limited (if any) seasonal cycle in the Darwin data. BESD data clearly exhibit lower scatter, but some outliers can be identified. This has been identified as an issue related to the SCIAMACHY Level 1 version 7 consolidation product (L1v7u), used in the retrieval. Tests with the new L1v7w data show that these outliers are eliminated, which should further increase BESD's precision. The seasonality of Lamont, Darwin and the overall results are slightly in favour of BESD as well as the SRA value (see Table  4 ). Keep in mind however that these parameters are derived from a limited data sample.
Acknowledgements. This work was primarily funded by  ESA/ESRIN (GHG-CCI ), with additional funds from  EU FP7  (grant agreement no. 283576 ,  MACC-II) ,  DLR (SADOS) , the  State  and the  University of Bremen  and  PRODEX SECPEA/A3C. We thank the members of the  GOSAT  Project (JAXA,  NIES , and  Ministry of the Environment (MoE), Japan ) for providing GOSAT Level 1B and Level 2 data products (GOSAT  RA1PI  project  CONSCIGO ). We also thank TCCON and related funding organizations ( NASA  grants  NNX11AG01G ,  NAG5-12247  and  NNG05-GD07G ;  NASA   Orbiting Carbon Observatory Program ;  DOE ARM program ; the  Australian Research Council ,  DP0879468  and  LP0562346 ; the  EU projects IMECC, InGoS and GEOmon ; and the  Senate of Bremen ).
2011 ) and 2FGL  (Nolan et al. 2012) , do not include a counterpart of 1ES 1312-423 at HE (between 100 MeV and 100 GeV). However, motivated by the HESS detection at VHE, a careful modelling of the Centaurus A giant lobes emission by the Fermi-LAT collaboration, using 3. 5 yr of data, reveals a faint HE source coincident with 1ES 1312-423 (see Section 2. 2). The HE and VHE spectra derived by the Fermi-LAT and HESS collaborations are combined with multiwavelength data from Swift X-Ray Telescope (XRT) and Ultraviolet/Optical Telescope (UVOT), Automatic Telescope for Optical Monitoring (ATOM) and Australia Telescope Compact Array (ATCA) (see Sections 2. 3, 2. 4, 2. 5 and 2. 6) with the purpose of understanding the properties of the source. A standard one-zone SSC model and a blackbody emission model for the host galaxy are used to describe the current and the archival data, detailed in Section 3.
9 arcsec) inner radius, thus excluding 58 per cent of the events. The XRTMKARF tool is used to generate a dedicated Ancillary Response Function (ARF) at the location of the source in the FoV, along with the latest spectral redistribution matrices from the calibration database (CALDB). The Swift/XRT spectrum is rebinned to have at least 20 counts per bin using grppha, yielding a usable energy range between 0. 3 and 7. 0 keV. Multiple model spectra are tested with PYXSPEC V. 1. 0. 1, the response functions swxpc0to12s6_20010101v013 and the dedicated ARF. Systematic errors on the Swift/XRT spectra and absolute flux are less than 3 and 10 per cent, respectively  (Godet et al. 2009). The XRT spectrum is first studied using the weighted average column density of Galactic H I N H = 7. 8 × 10 20 cm -2 that is Table  1.
Swift/UVOT analysis Simultaneously with Swift/XRT observations, the Swift/UVOT  (Roming et al. 2005 ) took six snapshots of 1ES 1312-423 with the filter uvm2 (224. 6 nm). They are integrated with the UVOTIMSUM tool of the package HEASOFT 6. 12 and analysed using the UVOTSOURCE tool, with circular ON and OFF regions of radius 5 arcsec. The background is estimated from different OFF regions, at least 25 arcsec away from the source. A change of the background regions impacts the reconstructed flux at the percent level. The magnitudes are dereddened according to the extinction laws of  Draine (2003)  with a total absorption at 224. 6 nm A λ = 1. 1 mag corresponding to the IRAS extinction mentioned in Section 2. 3. The flux densities are computed from the magnitude according to the zero-points of  Poole et al.
Comparison of archival and current data The 1σ Fermi-LAT butterfly (300 MeV < E < 300 GeV) and the HESS spectrum are represented by the empty and filled dark butterflies in Fig. 6. The HESS butterfly includes the systematic un-Figure  6. Fermi-LAT and HESS spectra, represented by the empty magenta and filled dark red butterflies, respectively. The HESS (resp. Fermi-LAT) spectrum includes statistical and systematic errors on the flux and index, for reference the 1σ statistical butterfly is shown in dashed light red (resp. hatched light magenta). The Fermi-LAT butterfly is extrapolated to VHE (dashed magenta), taking into account the EBL absorption according to  Franceschini et al. (2008). certainties on the index (∼0. 20) and on the flux (∼20 per cent), added quadratically at the decorrelation energy to the statistical one.
The systematic uncertainties on the Fermi-LAT spectral parameters are propagated in the same way. The Fermi-LAT spectral extension to VHE (dashed line), which is absorbed by the extragalactic background light (EBL) according to  Franceschini, Rodighiero & Vaccari (2008) , overshoots the HESS 1σ confidence contour. The difference between the spectral indexes is explained by intrinsic curvature, supported by the empirical relation of  Abdo et al. (2010e)  between the peak frequency ν IC peak of the HE bump and the HE photon index , which reads log ν IC peak [Hz] = -4 + 31. 6 = 26. 2 ± 1. 4, corresponding to a peak energy between ∼25 GeV and ∼15 TeV. The low statistics of the Fermi-LAT data at high energies (three photons above 100 GeV) does not enable a straightforward characterization of this peak energy.
The data are modelled assuming a blackbody emission in the infrared-optical band while the radio and optical emission of the core as well as the X-ray, HE and VHE spectra are described with a one zone homogeneous SSC emission. Infrared to UV data, which are dereddened consistently with Sections 2. 32. 4 and 2. 5, are extracted from the Wide-field Infrared survey (WISE,  Wright et al. 2010) , from the 2MASS All-Sky Catalogue of Point Sources  (Cutri et al. 2003) , from the 6dF galaxy survey  (Jones et al. 2009 ) and from the ultraviolet survey performed with Galaxy Evolution Explorer (GALEX;  Martin et al. 2005). The UV fluxes measured with GALEX and with UVOT differ by a factor of 3. Both measurements are dereddened and the discrepancy can hardly be explained by the known uncertainties. A high-amplitude variability could explain the difference, though it is barely observed in the other energy bands on the time-scale of years.
C O N C L U S I O N The HESS collaboration reports the discovery of the blazar 1ES 1312-423 in the VHE γ -ray domain. In spite of being one of the faintest VHE γ -ray sources ever detected, with a differential flux at 1 TeV equivalent to 0. 5 per cent of the Crab nebula differential flux, the long observation campaign on its neighbour Centaurus A unveiled VHE γ -ray emission from 1ES 1312-423 at the ∼6σ level. The analysis of 3. 5 years of data from Fermi-LAT brought to light an HE spectrum that is one of the hardest derived for a blazar by the Fermi-LAT Collaboration, though with a large uncertainty on the index. The combination of these HE and VHE spectra together with ATCA, ATOM and Swift measurements allows for the first time the broad-band SED of this HBL-type blazar to be investigated.
AC K N OW L E D G E M E N T S The support of the Namibian authorities and of the University of Namibia in facilitating the construction and operation of HESS is gratefully acknowledged, as is the support by the German Ministry The Fermi LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l'Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K.
A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d' Études Spatiales in France. The ATCA is part of the Australia Telescope National Facility which is funded by the Commonwealth of Australia for operation as a National Facility managed by CSIRO. This research was funded in part by NASA through Fermi Guest Investigator grant NNH09ZDA001N (proposal number 31263). This research was supported by an appointment to the NASA post-doctoral Programme at the Goddard Space Flight Center, administered by Oak Ridge Associated Universities through a contract with NASA. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Then, trans-mitral inflow in pulsed-wave Doppler from apical four chamber view and tissue Doppler imaging (TDI) on basal segments of septal and lateral walls in apical four chamber view were taken as previously described  [19]. A cine-loop of LV parasternal short axis view with high frame rate was obtained. All acquisitions were performed by the same operator. All images were digitally stored on hard disks for off-line analysis (EchoPac Q-analysis software, GE Healthcare). Measurements were made on five cardiac cycles and averaged for each data value. The following parameters were determined as recommended by the American Society of Echocardiography  [18] : LV end diastolic and systolic diameters (LVEDD and LVESD), diastolic posterior wall thicknesses (dPWth). LV end diastolic and systolic volumes (LVEDV and LVESV) were calculated from the Teichholz method in order to assess LV ejection fraction (LVEF), whereas LV shortening fraction (LVSF) was calculated from LVEDD and LVESD previously measured.
5. 7 Software System (EMKA Technologies). Data were analysed using Datanalyst software (EMKA Technologies). The following parameters were obtained: LV end diastolic pressure (LVEDP), LV contraction and relaxation velocities (dP/dt max and dP/dt min , respectively), the index of LV relaxation constant (Tau). The animals were thereafter sacrificed by injection of a pentobarbital overdose.
Chemiluminescence was visualized using an Amersham Image-Quant RT-ECL camera (GE Healthcare) and band signals were assessed by densitometry with ImageQuant TL software (GE Healthcare). For each lane, a ratio to the corresponding GAPDH band intensity was calculated; the use of GAPDH as reference was validated by checking the abundance stability of GAPDH protein between Ctrl and Dox-CM groups. Antibody references and conditions used in this study are summarized in Table  1.
Cardiac parameters were recorded using IOX1. 5. 7 software (EMKA Technologies): inotropism, lusitropism and chronotropism were respectively evaluated by measuring the contraction velocity (dP/dt max ), the relaxation velocity (dP/dt min ) and the heart rate (HR). Classically, PTX is administered to rats two or three days before experiments  [22]. However, due to high mortality in Dox-CM rats pre-treated with PTX, we were not able to apply this protocol and used another one consisting in pre-treating heart with PTX (4 mg. L 21 ) in a closed system during 30 min, as described by other groups  [23]. Data were analysed using Datanalyst software (EMKA Technologies). After Langendorff experiments, hearts were weighed, as well as LV free walls which were carefully separated from the heart.
Genetic analysis The presence of at least 50% tumor cells in samples was evaluated histologically. Genomic DNA was extracted using DNAeasy Blood & Tissue DNA isolation kit or QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). Genotyping of EGFR exons 18, 19, 20, and 21 was performed by pyrosequencing method with PyroMark Q24 and CE-IVD-marked Therascreen EGFR Pyro associated Kit (Qiagen) or length analysis of fluorescently labelled PCR products for exons 19 and 21. To reveal the presence of p. L858R, PCR product of exon 21 was digested by Sau96I (New England Biolabs, Evry, France). Genotyping was carried out using 3500 Genetic Analyzer (Applied Biosystems, Foster City, CA) and results were interpreted using Genemapper V4. 1 software (Applied Biosystems). Genotyping of KRAS exon 2 (codons 12 and 13) was performed by pyrosequencing method with the PyroMark Q24 and CE-IVD-marked Therascreen KRAS Pyro associated kit (Qiagen) or allelic discrimination using the 7500 Fast Real-Time PCR platform (Applied Biosystems).
The design of sequences of the TaqMan probes was kindly provided by Pr Laurent-Puig  [30]. Genotyping of BRAF exon 15 and HER-2 exon 20 was performed by pyrosequencing method with sequences designed using PyroMark Assay Design Software (Qiagen). Pyrosequencing was done according to the manufacturer's instructions (Qiagen). Results were interpreted using PyroMark Q24 2. 0 software (Qiagen). The primers sequences and PCR conditions are available on request.
Materials and Methods This study was based on data from a prospective cohort study conducted in the medical ICU of a teaching hospital. One hundred consecutive patients between January 2007 and December 2011 with a diagnosis of CAP requiring invasive mechanical ventilation were included. All patients $18 years of age admitted to our medical ICU with the diagnosis of CAP were prospectively identified and monitored until discharge. The study was designed retrospectively but data were prospectively collected. The study was approved by the hospital's ethical committee (Institut Federatif de Recherche 48), and the requirement for informed consent was waived because of the observational nature of the study.