dataesr/scientific-paragraphs-categorization huggingface dataset:
NICE NEWs IN thE ANNALs Ann R Coll Surg Engl 2013; 95: 535-537 c) It was suggested that NICE should keep its lists of society officers up to date more efficiently. NICE will consider whether it is feasible to achieve an annual update of the lead contact in each of the numerous societies on its database and, at a minimum, will aim to do this for the most frequently used societies. This will require accurate information from each society about who is best placed to deal with requests for advice. d) Societies suggested that NICE should inform them and their nominated advisors when draft guidance is being made available for public consultation on its website. NICE has always done this routinely and will continue to do so, informing the society's lead contact, but will review its processes for doing this.
The ERA-Interim/Land is a global land-surface dataset covering the period 1979-2010 and describing the evolution of the soil (moisture and temperature) and snowpack. ERA-Interim/Land is the result of a single 32 yr simulation with the latest ECMWF land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on GPCP v2. 1. ERA-Interim/Land preserves closure of the water balance and includes a number of parameterisations improvements in the land surface scheme with respect to the original ERA-Interim dataset, which makes it suitable for climate studies involving land water resources. The quality of ERA-Interim/Land, assessed by comparing with ground-based and remote sensing observations is discussed. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of sites measurements.
Dataset and methods The experimental set-up makes use of offline (or stand-alone) land simulations, which represents a convenient framework for isolating benefits and deficiencies of different land surface parameterizations  (Polcher et al. , 1998). In addition, in terms of computational cost, given the complexity of the coupling with the atmosphere, offline simulations are much more cost-effective (faster) to run than a full atmospheric-land assimilation system. Figures.
Back Close Full In this study, offline runs are performed both at the global and point scales. All the 3 hourly meteorological forcing parameters were linearly interpolated in time to the land surface model integration time step of 30 min. The land-use information has been derived from the United States Geophysical Survey -Global Land Cover Classification (USGS-GLCC) and the United Nations -Food and Agriculture Organization (UN-FAO) data set at the same resolution as the forcing data. A comprehensive description of the land surface model and the ancillary datasets is given in the IFS documentation (2012, Part IV, chapters 8 and 11, http://www. ecmwf. int/research/ifsdocs/CY37r2/index. html).
GPCP v2. 1 precipitation The GPCP dataset merges satellite and rain gauge data from a number of satellite sources including the Global Precipitation Index, the Outgoing Long-wave Radiation, Precipitation Index, the Special Sensor Microwave/Imager (SSM/I) emission, the SSM/I scattering, and the TIROS Operational Vertical Sounder (TOVS). In addition, rain gauge data from the combination of the Global Historical Climate Network (GHCN) and the Climate Anomaly Monitoring System (CAMS), as well as the Global Precipitation Climatology Centre (GPCC) dataset which consists of approximately 6700 quality controlled stations around the globe interpolated into monthly area averages, are used over land. More details on the datasets and the method used to merge these data are provided by  Adler et al. (2003).
FLUXNET land energy fluxes Available observational data for the year 2006 from the Boreal Ecosystem Research and Monitoring Sites (BERMS,  Betts et al. , 2006) , the FLUXNET project  (Baldocchi Figures Back  Close Full As part of the CEOP program, reference site observations from the Amazonian region also belonging to the LBA experiments (the Large Scale Biosphere-Atmosphere Experiment in Amazonia) are available for scientific use. In this study, observations are taken from flux towers located within an evergreen broadleaf forest (Manaus) and a woody savannah region (Brasilia). The FLUXNET observations used in this study are part of the LaThuile dataset, which provides flux tower measurements of latent heat flux (LE), sensible heat flux (H) and net ecosystem exchange (NEE) at high temporal resolution (30 to 60 min).
The GTS-SYNOP observing network The SYNOP datasets provide daily observations of the main weather parameters and selected land surface quantities such as snow depth, at a large number of sites worldwide. The snow data are acquired at a minimum frequency of once a day and represent the only quantitative snow-depth measurement on the ground (remote sensing observations have difficulties in representing snow properties). These data are operationally used at ECMWF for the daily global snow analysis as described in Drusch et al. (  2004 ) and de Rosnay et al. (2013a).
The GRDC river discharge dataset The Global Runoff Data Centre (GRDC) operates under the auspice of the World Meteorological Organization and provides data for verification of atmospheric and hydrologic models. The GRDC database is updated continuously, and contains daily and monthly discharge data information for over 3000 hydrologic stations in river basins located in 143 countries. Over the GSWP-2 period the runoff data of 1352 discharge gauging stations was available and used for verification of the soil hydrology  (Balsamo et al. , 2009). Pappenberger et al. (2009)  and  Balsamo et al. (2010b)  used the GRDC daily discharge to evaluate a coupled land surface -river discharge scheme for river flood prediction. In this study river discharges from GRDC data set, clustered by large continental areas, are used to evaluate the overall hydrological consistency and improvements.
Soil hydrology A revised soil hydrology in TESSEL was proposed by van den  Hurk and Viterbo (2003)  for the Baltic basin. These model developments were in response to known weaknesses of the TESSEL hydrology: specifically the choice of a single global soil texture, which does not characterize different soil moisture regimes, and a Hortonian runoff scheme which produces hardly any surface runoff. Therefore, a revised formulation of the soil hydrological conductivity and diffusivity (spatially variable according to a global soil texture map) and surface runoff (based on the variable infiltration capacity approach) were operationally introduced in IFS in November 2007. Balsamo et al. (2009)  verified the impact of the soil hydrological revisions from field site to global atmospheric coupled experiments and in data assimilation.
Results The resources dynamical range in the past 3 decades associated with snow and unsaturated soil layers. The evolution of ERA-Interim/Land along the 32 yr of this dataset and its differences with respect to ERA-Interim are illustrated in Figs. 3  and 4  for both soil moisture and snow water equivalent. The stability and the differences with respect to ERA-Interim can be appreciated in the plots of Figs. 3a and 4a for snow water equivalent and Figs. 3b  and 4b  for the top 1 m soil moisture. The snow changes in Fig. 4a  are mainly consequence of the new snow scheme and highlight both a snow mass increase in high latitudes and a slight reduction in mid-latitudes. The soil moisture presents large differences in Fig. 4b  than can be attributed to the soil hydrology revisions. In these runs observational constraints on the snow and soil water reservoirs such as those applied by data assimilation is totally absent, however the resulting water reservoirs and the fluxes both towards the atmosphere (heat and moisture) and the river discharges, are shown to improve with respect to the original ERA-Interim output.
Snow The verification of snowfields considers two different observational datasets to evaluate the snow evolution in ERA-Interim and ERA-Interim/Land: (i) the SYNOP daily snow depth and (ii) datasets from the former USSR. The 1979-1993 former USSR dataset was used in  Brun et al. (2013)  to evaluate simulated snow properties, such as density, which is not routinely measured at SYNOP stations. Dutra et al. (2010)  attributed the largest improvement in the new snow scheme to the snow density representation. This is confirmed by the verification results on a large number of sites where snow density was measured for the typical Northern latitudes snow season (October to June) average for 1979-1993 period  (Balsamo et al. , 2012). In ERA-Interim, as well as ERA-Interim-Land, the snow density is not at all constrained by data assimilation due to a lack of observations and therefore it relies solely on the capacity of the land surface Figures model to represent the seasonal evolution, from about 100 kg m -3 at the beginning of the winter season to more than 300 kg m -3 towards the end of the snow season.
Back Close Full within the EU-funded ERA-CLIM project, moreover it can open up new possibilities of considering more advanced data assimilation schemes (e. g. Fowler and van Leeuwen, 2012) , especially designed for non-linear systems. The skill of an ERA-Interim/Land variant (with no precipitation readjustment) together with other model-based and remote-sensing datasets for the detection of soil moisture climate trends in the past 30 yr is evaluated in  Albergel et al. (2013). This study, using the methodology described in this paper, represents an attempt to gain insights on soil water reservoirs and its evolution in response to natural and anthropogenic forcing.
The ERA-Interim/Land added value components encompass soil, snow and vegetation description upgrades, as well as a bias correction of the ERA-Interim monthly-accumulated precipitation based on GPCP v. 2. 1. In the Northern Hemisphere the precipitation correction is shown to be effective in reducing the bias over US and rather neutral over Eurasia, while in the tropical land benefits are visible in the river discharge. The new land surface reanalysis has been verified against several datasets for the main water reservoirs, snow and soil moisture, together with the energy and water Introduction.
, 2012). The ERA-Interim/Land dataset has been used operationally at ECMWF since 2010 for the initialization of the past reforecasts needed for the monthly forecasting  (Vitart et al. , 2008)  and the seasonal prediction systems  (Molteni et al. , 2011). Ongoing research effort includes the extension of this dataset beyond 2010 using a different dataset for precipitation based on the latest GPCC collections and application of the described methodology to future ECMWF reanalyses.
Acknowledgements. Authors thank  R. Riddaway  from  ECMWF  for his valuable comments on the text and  C. O'Sullivan  and  A. Bowen  are thanked for their help in improving the figures. Eric Brun  is thanked for his interest and precious advise on ERA-Interim/Land snow verification. This work used eddy covariance data acquired by the FLUXNET community that is greatly acknowledged. TU Wien  provided the ISMN data for soil moisture verification and we thank them for their important effort. We thank the  GRDC  for data provision of global river discharge. The  ECMWF User-Support team  is acknowledged for making the data easily accessible.
Data analysis Approximately 15 × 10 6 Pb-Pb events, recorded during the first LHC heavy-ion run in 2010 at √ s NN = 2. 76 TeV, were analyzed. A minimum bias trigger was used, requiring two pixel chips hit in the SPD in coincidence with a signal in the VZERO-A and VZERO-C detectors. Measurements were also made with the requirement changed to a coincidence between signals from the two sides of the VZERO detectors. An offline event selection was also applied in order to reduce the contamination from background events, such as electromagnetic and beamgas interactions. All events were required to have a reconstructed vertex position along the beam axis (V z ) with |V z | < 10 cm from the nominal interaction point. The data were sorted according to centrality classes, reflecting the geometry of the collision (i.
In both Fig. 2  and Fig. 3  as well as in the next figures, the error bar of each point corresponds to the statistical uncertainty (typically the size of the marker). The systematic uncertainty is represented by the shaded band around each point. The origin and the value of the assigned systematic uncertainty on the width of the balance function, calculated for each centrality and for both ∆η and ∆ϕ, will be discussed in the next paragraph. The data sample was analyzed separately for two magnetic field configurations. The two data samples had comparable statistics. The maximum value of the systematic uncertainty, defined as half of the difference between the balance functions in these two cases, is found to be less than 1. 3% over all centralities. In addition, we estimated the contribution to the systematic uncertainty originating from the centrality selection, by determining the centrality not only with the VZERO detector but alternatively using the multiplicity of the TPC tracks or the number of clusters of the second SPD layer.
Centrality dependence The width of the balance function (Eq. 3) as a function of the centrality percentile is presented in Fig. 4. Central (peripheral) collisions correspond to small (large) centrality percentile. The width is calculated in the entire interval where the balance function was measured (i. e. 0. 0 < ∆η < 1. 6 and 0 o < ∆ϕ < 180 o ). Both results in terms of correlations in the relative pseudorapidity ( ∆η , upper panel, Fig. 4-a ) and the relative azimuthal angle ( ∆ϕ , lower panel, Fig. 4-b ) are shown. The experimental data points, represented by the full red circles, exhibit a moving from right to left along the x-axis) for both ∆η and ∆ϕ. Our results are compared to different model predictions, such as HIJING  [28]  and different versions of a multi-phase transport model (AMPT)  [29].
To further quantify the previous observation, Fig. 7  presents the relative decrease of ∆η (a) and ∆ϕ (b) from peripheral to central collisions as a function of the mean number of participating nucleons, N part , for the highest SPS 3  [9]  and RHIC  [17]  energies, compared to the values reported in this Letter. In this figure, central (peripheral) collisions correspond to high (low) number of N part. The choice of the representation as a function of N part is mainly driven by the apparent better scaling compared to the centrality percentile. It is seen that in terms of correlations in relative pseudorapidity the data points at the different energies are in fairly good agreement 2 We do not compare our results to the data from the NA49 experiment at SPS in this figure, for two reasons. Firstly, the balance function in that experiment was not measured at mid-rapidity.
Contrary to classical interpolation approaches (linear, cubic, B-spline, …), which result in blurred reconstructions, the nonlocal patch-based strategy can reconstruct high frequency information by enforcing pattern regularity rather than imposing some smoothness constraint, thus yielding better image quality reconstructions. Since its introduction, this type of prior has demonstrated very high performance compared to classical interpolation methods for MRI. In this paper, we propose to extend and to adapt the PBSR method to DW datasets. First, we propose a locally adaptive optimization scheme in order to improve the reconstruction quality. Based on a coarse to fine optimization scheme, we use the local image context to drive the reconstruction process. Second, we propose a collaborative approach where the b0 image is used to constrain DW image reconstruction.
• CLASR: Collaborative and locally adaptive super-resolution As mentioned above, it is possible to improve SR reconstruction by using HR priors from another image. Originally proposed for the SR reconstruction of LR T2-w data using HR T1-w MRI, we propose to apply this type of approach to DW imaging. Here, we first propose to reconstruct the b0 image using LASR and then to use this reconstructed HR b0 image to drive the reconstruction of the individual DW images. By adding b0 image priors, the reconstruction process will be more constrained and thus limit the introduction of artifacts. Therefore, the reconstruction process of a DW image can be rewritten as follows: (9) where b0 and dw indicate the image type. By using Eq (9), higher weights are given to voxels with similar neighborhoods in the b0 image and at the same time with similar neighborhoods in the DW image under study.
Second, 10 repetitions were averaged to ensure a very high SNR for our gold standard (see Fig. 2 ). The advantages of performing image processing validation on DW gold standard instead of using synthetic data has been already demonstrated for DW image denoising purposes (Tristan-Vega and Aja-Fernandez, 2009). Finally, the gold standard construction was achieved by first denoising all the 10 DW images with the LPCA filter  (Manjon et al. , 2012)  (see middle of Fig. 2 ) and then by fusing the 10 denoised DW images using the robust a mean estimator  (Coupe et al. , 2010). The LR images used during experiment were simulated by down-sampling our gold standard by a factor 2 using nearest neighbor interpolation along each axis (i. e. , [2 2 2]) what resulted in simulated LR images of 2. 4x2. 4x2.
Implementation details All our experiments were performed using MATLAB 7. 4 (Mathworks Inc. ) on a 6-core Intel(R) Xeon(R) CPU W3670 at 3. 2GHz with 16 GB RAM. Both LASR and CLASR were implemented using multithreaded C-MEX. Image intensities were scaled linearly between 0-255 and rescaled to original intensity range after processing. The patch size used was 3x3x3 voxels and the size of search volume was 7x7x7 voxels during all the experiments. The global stopping criterion for convergence was defined as:  (10)  where was set to 1. 2. In addition, for the local stopping criterion , was set to 0. 1. As done for denoising  (Coupe et al. , 2008) , we also used patch pre-selection based on local mean to avoid useless computation. The used condition was defined as:  (11)  where is the mean value of the patch and was empirically set to 0.
Impact of the choice of priors on image reconstruction To investigate the advantage of using b0 image priors more deeply, we performed two additional experiments. First, we designed an experiment to analyze the role of the high SNR of b0 image (i. e. , higher than DW images) in CLASR reconstruction. Second, we proposed an experiment to study the role of the additional image-features provided by b0 image priors compared to DW image priors. The first experiment consisted in comparing the reconstruction quality of CLASR when using high SNR b0 image and low SNR b0 image. A first dataset was built by adding synthetic Rician noise to the simulated LR image to obtain usual higher SNR for b0 image than for DW images. A second dataset was built by forcing the SNR of b0 image to be equal to the SNR of DWI.
The intensity range of b0 image was scaled to the intensity of DWI before adding the same synthetic Rician noise as previously. Both datasets were then processed with the proposed pipeline including the denoising step. In addition to CLASR reconstruction, LASR reconstruction was achieved on both datasets to be used as baseline in order to remove differences resulting from denoising step. Figure  5  shows the results of the experiment in terms of PSNR and MSSIM improvements compared to LASR method. As expected, when using b0 image with lower SNR as priors, the quality of reconstruction decreased since the denoised b0 image had lower image quality. More interestingly, the CLASR reconstruction using low SNR b0 image still outperformed LASR reconstruction. It seems that in addition to the advantage of the higher SNR of b0 image compared to DW image, b0 image priors also provide useful additional image features that help reconstructing DW images.
In a second experiment, we compared the CLASR reconstruction using b0 image as a prior and the first DW image as a prior. The DW image used as prior was removed from datasets of DW images to be reconstructed. Figure  6  shows the results of the experiment in terms of PSNR and MSSIM improvements compared to LASR method for both datasets. By using b0 image priors better reconstruction quality was obtained. This indicates that b0 image content provides more useful information than DW image content to reconstruct other DW images. This might be attributable to better tissue contrast in b0 image than in DW images (see Figure  1 ).
• Impact of misalignment on image reconstruction quality As mentioned previously, DW datasets c a n b e c o r r u p t e d b y m o t i o n a r t i f a c t s a n d g e o m e t r i c distortions, making the alignment assumption of b0 and DW images used by CLASR no longer valid. Therefore, we studied the impact of misalignments on the quality of reconstruction obtained with CLASR. First, the displacements between b0 and DW images were obtained with FSL eddy current correction  (Smith et al. , 2004). The estimated mean displacements in mm are presented on Figure  7. Then, we analyzed the relationship between image quality in terms of PSNR and the estimated mean displacements. We did not find any significant linear relationship between the quality of CLASR reconstruction and the estimated mean displacements.
In all cases studied, CLASR produced the lowest angular error. However, one can note that angular profiles agree closely with the gold standard f o r all three methods. In the 6-7% of non-agreement, the underestimation/overestimation is on average equal to 1. 0, i. e. there is no strong bias towards underestimation or overestimation of single and crossing configurations. Finally, while for FA and GFA the B-spline interpolant provided the worst results, B-spline interpolation better preserved orientation than trilinear interpolation for the angular study. 10 : FA maps estimated on the gold standard and the simulated LR image (top). FA maps obtained on reconstructed dataset using trilinear interpolation, 3 rd order B-spline interpolation and CLASR (bottom). Visually, the FA map obtained using CLASR is closer to the FA of the gold standard.
In this experiment, we limited the reconstruction at 0. 4x0. 4x0. 4mm 3 for practical reasons in order to keep reconstruction time and memory requirement reasonable during fiber tracking. Figure  15  shows reconstruction results using only the first acquisition of the 10 datasets used for the gold standard construction. First, as expected, by using one HR acquisition, the SNR of the DW dataset was quite low and thus, resulted in a very noisy FA map estimation and fiber tracking result (see Figs  15  and 16 ). After denoising, better FA map estimation and fiber tracking were obtained. The denoising filter also removed the intensity bias introduced by Rician noise. This is visible on Figure  15  where the intensity range of FA maps used as background changed after denoising. At the end of the pipeline, the reconstruction provided by CLASR reveals fine details not visible on the original noisy HR DWI.
Finally, in order to verify the reproducibility of our fiber tracking results, we performed the same experiment using the second acquisition of our dataset. The results are presented in Figure  17. As for the first acquisition we observed that fiber tracking is improved after denoising and that a larger extent of CR is obtained after SR. However, while for FA estimation, the use of one acquisition seems to produce satisfying results, the use of 2-3 acquisitions might be required for fiber tracking in order to reveal the full potential of the proposed framework. This aspect will be investigated in future work. 3 , the denoised HR DW image using  (Manjon et al. , 2012) , the super-resolution of the denoised HR DW image at 0. 6x0. 6x0. 6mm 3 and the super-resolution of the denoised HR DW image at 0.