dataesr/scientific-paragraphs-categorization huggingface dataset:
Pretransplant and posttransplant DSA formation presents a further challenge for improving long-term outcomes in liver transplantation. Our understanding of the impact of DSAs, and acute and chronic AMR, is still evolving. The occurrence of de novo DSA in liver transplantation is low; however, its occurrence is associated with double the risk of death. 4 This presents a particular challenge for the screening of patients for de novo DSA, making the most cost-effective approach to improving outcomes not only through testing but also through prevention. In the future, utilization of biomarkers will aid us with identification of patients at risk of de novo DSA formation and injury, and thereby guide us with regard to appropriate immunosuppression minimization. The concept of tacrolimus minimization as a protective factor in liver transplantation is only partly based on evidence or data from RCTs in liver transplantation, but also seemingly on traditional views of CNI-related side effects (predominantly on kidney function and posttransplant lymphoproliferative disorder).
Editorial support was funded by  Astellas Pharma Europe Ltd. A. L. , work. B-G. E reports nonfinancial support from  Astellas , during the development of this supplement; honoraria and consultancy fees from  Astellas ,  Pfizer  and  Novartis  and clinical trial support from  Novartis  and  Astellas , outside of the submitted work. P. Astellas , during the development of this supplement; nonfinancial support from  Astellas , and personal fees from  Astellas ,  Pfizer  and  Novartis , outside the submitted work. M. M. reports nonfinancial support from  Astellas , during the development of this supplement; nonfinancial support and personal fees from  Astellas , outside of the submitted work. M. G. reports nonfinancial support from Astellas during the development of this supplement; nonfinancial support from  Astellas  and personal fees from  Astellas , outside the submitted work.
We present the VLA-COSMOS 3 GHz Large Project based on 384 hours of observations with the Karl G. Jansky Very Large Array (VLA) at 3 GHz (10 cm) toward the two square degree Cosmic Evolution Survey (COSMOS) field. The final mosaic reaches a median rms of 2. 3 µJy beam -1 over the two square degrees at an angular resolution of 0. 75. To fully account for the spectral shape and resolution variations across the broad (2 GHz) band, we image all data with a multiscale, multifrequency synthesis algorithm. We present a catalog of 10,830 radio sources down to 5σ, out of which 67 are combined from multiple components. Comparing the positions of our 3 GHz sources with those from the Very Long Baseline Array (VLBA)-COSMOS survey, we estimate that the astrometry is accurate to 0. 01 at the bright end (signal-to-noise ratio, S/N 3GHz > 20).
Survival analysis on our data combined with the VLA-COSMOS 1. 4 GHz Joint Project catalog yields an expected median radio spectral index of α = -0. 7. We compute completeness corrections via Monte Carlo simulations to derive the corrected 3 GHz source counts. Our counts are in agreement with previously derived 3 GHz counts based on single-pointing (0. 087 square degrees) VLA data. In summary, the VLA-COSMOS 3 GHz Large Project simultaneously provides the largest and deepest radio continuum survey at high (0. 75 ) angular resolution to date, bridging the gap between last-generation and next-generation surveys.
Calibration Calibration of the data was performed via the Astronomical Image Processing System (AIPS) based data reduction pipeline AIPSLite  (Bourke et al. , 2014)  developed for the Caltech-National Radio Astronomy Observatory (NRAO) Stripe 82 Survey  (Mooley et al. , 2016). This pipeline was adapted for the VLA-COSMOS 3 GHz Large Project (as described below) and it follows, in general, the procedures outlined in Chapter E of the AIPS Cookbook 1. In brief, the data are first loaded with the Obit 2  task BDFIn. Band edges, and to a larger extent IF edges, were then flagged with the task UVFLG. SPWs 2 and 3, found to be irreparably corrupted by radio frequency interference (RFI) in all observations (see Fig. 3 ), were entirely flagged using the task UVFLG. After flagging, FRING, BPASS, SETJY, CALIB, GETJY, and CLCAL tasks were used to derive the delay, bandpass, and complex gain solutions.
Polarization calibration was performed using the tasks RLDLY, PCAL, and RLDIF as detailed in Sect. 7 of Chapter E in the AIPS Cookbook. The task RFLAG was used to flag all target pointings and the flags were applied using the UVCOP task. The derived calibration was applied and the calibrated data set was produced with the SPLAT task. Finally, the calibrated uv data were saved to disk using the task FITTP. During the pipeline process several diagnostic plots were generated to assess the quality of the calibration: bandpass solutions, antenna gains as a function of time, calibrated spectrum of the gain calibrator, and calibrated amplitude versus phase plots of the gain calibrator per pointing. In Table  1  we list the statistics for the amplitude of the phase calibrator in each SPW for all observing blocks.
The calibrated data set was generated by applying RFLAG and imaging the target fields, including applying flags (using the task UVCOP), calibration (using the task SPLAT), and exporting the uv data and maps (using the task FITTP). The pipeline performance and output were tested by i) manually reducing separate blocks of VLA-COSMOS observations and comparing the results with the pipeline output, and ii) comparing the output to the Common Astronomy Software Applications (CASA 3 )-based NRAO reduction pipeline for randomly selected data taken in the A-and C-array configurations. No obvious differences were found. As the pipeline used here was tailored specifically to the COSMOS field (e. g. , it includes polarization calibration), after this verification it was further applied to the remaining VLA-COSMOS data sets.
The calibrated uv data sets output by the pipeline for each observing block were first run through the AIPS task UVFIX to assure accurately computed positions. We note that applying UVFIX at the end of calibration has the same effect as applying it at the beginning of calibration. They were then further processed in CASA by clipping each calibrated uv data set in amplitude 3 CASA is developed by an international consortium of scientists based at the NRAO, the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), the CSIRO Australia Telescope National Facility (CSIRO/ATNF), and the Netherlands Institute for Radio Astronomy (ASTRON) under the guidance of NRAO. See http://casa. nrao. edu; (McMullin et al. , 2007)  (above 0. 4 Jy) using the task FLAGDATA 4  , splitting the individual pointings using the task SPLIT, and concatenating all existing observations of the same pointing using the task CONCAT.
Radio spectral indices Given the wide bandwidth of our VLA-COSMOS 3 GHz survey and the existence of previous COSMOS radio surveys, we approached radio spectral index calculations in two ways. The first method uses the MSMF algorithm to construct spectral indices directly from our observed data by fitting a two-term Taylor polynomial to amplitudes between 2 and 4 GHz (MSMF-based spectral index or α MSMF hereafter). The second method uses the cataloged monochromatic flux densities at 3 GHz in combination with the values taken from the 1. 4 GHz joint catalog  (Schinnerer et al. , 2010)  to calculate spectral indices between these two frequencies (1. 4-3 GHz spectral index or α 1. 4-3 GHz hereafter). In Sect. 4. 1 we investigate systematics in the MSMF spectral index maps, and compare the differently derived spectral indices.
Summary and conclusions We presented the VLA-COSMOS 3 GHz Large Project based on 384 hours of observations with the Karl G. Jansky Very Large Array at 3 GHz (10 cm) toward the two square degree COS-MOS field. Our final mosaic, imaged per pointing with the multiscale multifrequency algorithm and self-calibration, reaches a median rms of 2. 3 µJy beam -1 over the two square degrees, at an angular resolution of 0. 75. We further presented a catalog of 10,830 radio sources. Combining our data with the 1. 4 GHz VLA-COSMOS Joint Project data using survival analysis, we found the expected median spectral index α of -0. 7. Comparing the positions of our 3 GHz sources with those from the highresolution VLBA imaging at 1. 4 GHz, we estimated that the astrometry is accurate to 0. 01 at the bright end.
Acknowledgements. We thank the referee,  Jim Condon , for insightful comments that helped improve the manuscript. Based on observations with the  National Radio Astronomy Observatory  which is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. This research was funded by the  European Union 's  Seventh Frame-work programs  under grant agreements  333654  (CIG, 'AGN feedback') and  337595  ( ERC Starting Grant , 'CoSMass'). MB and PC acknowlege support from the  PRIN-INAF   2014. AK and FB acknowledge support by the  Collaborative Research Council 956 , sub-project  A1 , funded by the  Deutsche Forschungsgemeinschaft (DFG). BM and FB acknowledge support through  DFG priority program  1573 founded by the  DFG.
A subsidiary anglebinned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.
Algorithm overview The Level 3 SM (L3SM) processor consists of a set of several algorithms. The forward model in L3SM uses the same physically based forward models as the ESA SMOS Level 2 SM processor, but in a MO retrieval context. A short summary of the main features of this processor is provided here and a detailed description can be found in  Kerr et al. (2012). The SMOS L2 retrieval can be divided into two main components: 1. The first component is a physical model that computes TB at the antenna reference frame forced by ancillary data (land classification and soil properties) and physical parameters (skin or near-surface temperature and soil temperature). The selected physical model for the SMOS mission is L-MEB from  Wigneron et al. (2007). The main features of the L-MEB physical model implementation in the SMOS operational processor are as follows: -Effective scattering albedo is considered.
-SM and VOD are jointly retrieved over nominal (bare soil and low vegetation) surfaces using angular signature and polarisation information. -Dual polarisation is used. Full polarisation data are only used to take into account the Faraday rotation and geometric rotation to transform modelled TB from the top of atmosphere (TOA) to the antenna reference frame. -The mean antenna pattern  (Kerr et al. , 2012)  is used in the iterative retrieval algorithm. The mean weighting function expresses the average contributions for all angular acquisitions. The -3 dB footprints are about 20 km in radius. This corresponds to the nominal resolution of the synthetic aperture. This also corresponds to 86 % of the signal if a homogeneous surface is considered (Al  Bitar et al. , 2012). -Surface heterogeneity is considered through aggregated TB contributions from 4 × 4 km 2 surface units.
Preprocessing and post-processing steps are implemented to filter the input and output data for undesired effects like the decrease in quality due to spatial sampling or radio frequency interferences (RFIs)  (Oliva et al. , 2012; Richaume et al. , 2014). The physical approach at Level 3 MO is the same as that of Level 2 SO. In fact the core processing uses the same implementation of the L-MEB radiative transfer model. The main difference in Level 3 is the use of several orbits, rather than one, to retrieve SM and VOD. This has an impact first on the post-processing steps for selecting the orbits and second on the optimisation scheme to retrieve the parameters. Since the Level 2 retrieval is a multi-parameter retrieval, the Level 3 is thus a multiorbit multi-parameter retrieval. The reasons that motivated the use of the MO approach are the following: -The angular sampling and radiometric accuracy at the border of the swath are reduced.
The main input TB for the processing is generated from the snapshot-based L1B products, which are TBs in the Fourier domain. This consists of an inverse fast Fourier transform (IFFT) to make the transition from the Fourier domain to the spatial domain using the L3 EASE-Grid 2. 0. In a subsequent step, TB measurements corresponding to the same grid point are selected from the different snapshots (for a given grid point, the incidence angle of the observation is different for each snapshot) to construct a grid-point-based product similar to the ESA L1C TB product but in EASE-Grid 2. 0. The alternative is to interpolate the ESA L1C TB dataset from the 15 km Icosahedral Snyder Equal Area (ISEA) grid to the 25 km EASE-Grid 2. 0 grid. This option was excluded because it could have generated interpolation artefacts on the TB products that would have propagated through the processing chain.
Orbit selection The selection of orbits is needed to select TBs at high latitudes where a sub-daily revisit is available and to generate the time series dataset on the EASE-Grid 2. 0 as input to the MO retrieval. The following criteria are applied for the selection of revisits: -Ascending and descending orbits are processed separately since the impact of RFI  (Oliva et al. , 2012)  and sun corrections  (Khazâal et al. , 2016)  between ascending and descending orbits are very different. -TB products are filtered at high latitudes where more than one revisit per day occurs (latitudes above 60 • N and 60 • S). A maximum of one revisit per day is considered. The selection criterion is the minimum distance from the centre of the swath because the radiometric accuracy and resolution is best at the centre.
Cost function and retrieval Observed TBs at the antenna reference frame from the precedent, actual and succeeding dates are assembled for each node. The forward algorithm is run to generate the modelled TB for each of the TB dataset records. The ancillary data and parameters are independently considered for each record. A Bayesian cost function that includes the aforementioned MO observed TB and modelled TB is then constructed. This is achieved by incorporating in the retrieval approach a temporal autocorrelation function for the VOD. The cost function is as follows: Cost = (TB M -TB F ) t • COV -1 TB • (TB M -TB F ) + p (P -P 0 ) t • COV -1 p • (P -P 0 ) , (1) where COV TB = σ 2 TB is the error covariance matrix of TB data when assuming no auto-temporal correlation, TB M is the measured TB from SMOS, TB F is the forward modelled TB using L-MEB, P is the vector of retrieved parameters (SM and VOD) at the three times of acquisition, COV P is the error covariance matrix for parameter P and P 0 is the a priori value of parameter P.
The CATDS Level angle-binned TB processor The objective of this algorithm is to generate a product containing fixed-angle full-polarisation brightness temperatures at top of atmosphere (TOA) but with the polarisations expressed in the ground reference frame (horizontal and vertical components) over the EASE-Grid 2. 0. The main input for this algorithm is the snapshot dataset mentioned in the previous section. The algorithm consists of four steps: (a) filtering, (b) interpolation, (c) reference frame transformation and (d) angle binning. However, note that before being projected to a ground reference frame, the data are processed in the instrument reference frame. Thus, TBs are labelled TB Y and TB X to express that the polarisations are at satellite level, while once processed they will be provided in the ground reference frame and will be labelled TB H and TB V.
TB interpolation The acquisition sequence of SMOS is shown in Table  2. At each epoch an acquisition can be co-polarised (X, Y ) or combined cross (XY , Y X) and co-polarised. The table shows that there is no complete dataset for any epoch. A weighted linear interpolation is used to compute the missing acquisitions based on adjacent ones. The weighting function accounts for the two following elements: -The TB acquisitions have different accuracy levels since the integration time is longer when only co-polarisation is acquired (pure acquisition) when compared to the case where combined cross and co-polarisation are acquired. -The time span between two acquisitions in the same mode is not constant. Acquisitions closer in time are considered more reliable than farther ones, taking into consideration that the synthetic antenna weighting function rotates and that the incidence angle changes.
SMOS DPGS Level 2 soil moisture product The ESA L2 Soil Moisture User Data Product (SMUDP;  Kerr et al. , 2012) , which is a SO retrieval product, is used in this study for comparison purposes. This product is a halforbit swath-based dataset of physical variables (SM, VOD, dielectric constant imaginary and real parts, etc. ), processing parameters (percentage of forest cover, type of surface model, etc. ) and quality indicators (probability of RFI, X 2 , etc. ) over continental surfaces. Ascending and descending orbits are processed separately in the current configuration. The SMUDP product is delivered in the BinX format over the ISEA discrete global grid  (Carr et al. , 1997) , with a hexagonal partitioning of aperture 4 at a resolution of 9 km known as ISEA4H9. The grid point centres have a fixed separation distance of around 15 km.
SMOS CATDS Level 3 brightness temperature products The SMOS CATDS full-polarisation angle-binned daily brightness temperature products (CDF3TA/D) version 310, were downloaded from the same database as the L3 MO SM. These products consist of global 1-day maps of fullpolarisation TB over fixed-angle bins with their associated accuracies. Detailed computation was described above in Sect. 3. The product also contains auxiliary data like the geometric angles, Faraday angles, length of major semi-axis and length of minor semi-axis. Quality flags are also provided in the product. The TB H and TB V records are extracted for the 40 • bin. No additional filtering is done over these products.
In situ datasets In this study, the SMOS SM products are evaluated against in situ SM from two networks with spatially distributed SM data at the footprint scale (USDA Watershed and AMMA-CATCH). The in situ soil moisture data from probes installed near the surface are used. These sites provide a soil moisture reading, representative of the first 5 cm of the top soil layer, as they are vertically installed. This may lead to a mismatch between the sensor sampling depth and the expected representative depth 0-2 or 0-3 cm of the L-band microwave radiometers  (Escorihuela et al. , 2010). This mismatch induces errors that are to be considered in conjunction with the sampling errors due to the spatial heterogeneity. The choice of the sites is made to cover contrasting environments over two different continents to provide an overview of the SM MO processor performances.
AMMA dataset The AMMA long-term observing system (AMMA-  CATCH, 1996 and 2005)  includes three mesoscale sites located in Niger, Benin and Mali that are representative of the West African ecoclimatic gradient  (Cappelaere et al. , 2009; Mougin et al. , 2009). The AMMA-CATCH soil moisture network is a well-established network in terms of satellite product assessment  (de Rosnay et al. , 2009; Pellarin et al. , 2009; Louvet et al. , 2015). The Niger and Benin sites are selected for this study. The Niger site, centred at 13. 645 • N-2. 632 • E, is mainly composed of tiger bush on the plateau and fallow savannah and pearl millet crop fields on the sandy slopes  (Cappelaere et al. , 2009). The Benin site, located at 1. 5-2. 8 • E, 9-10. 2 • N, is mainly composed of woody savannah and tropical forest.
USDA -watersheds The United States Department of Agriculture (USDA) Agricultural Research Service monitors a network of watersheds across the US using a high number of instruments. Surface soil moisture (5 cm) is monitored across the watersheds and has been recorded on an hourly basis since 2002. The USDA provides estimates of the average soil moisture over an area that is approximately the size of a SMOS footprint. Two of the watersheds have been selected for this study: Walnut Gulch (WG), Arizona, USA  (Keefer et al. , 2008) , and Little Washita (LW), Oklahoma, USA  (Elliott et al. , 1993). Soils in WG can be classified as sandy loam. The original datasets are available from https://www. tucson. ars. ag. gov/ dap/ for WG and from http://ars. mesonet. org/webrequest/ for LW. Over LW the soil properties are more heterogeneous with a loam, clay and sand textures.
Previous studies on calibration and scaling have quantified the uncertainty of the in situ measurements over the sites to be lower than 0. 01 m 3 m -3 when compared to gravimetric measurements. The basin-scale weighted average is based on the Thiessen polygon method and has a standard deviation between 0. 05 and 0. 10 m 3 m -3. A detailed description of the site characteristics is provided in  Jackson et al. (2010) , and details on the averaging procedure are provided in  Jackson et al. (2012). This network has been used for validation of remote sensing soil moisture datasets (including SMOS) in many studies  (Sahoo et al. , 2008; Jackson et al. , 2012; Leroux et al. , 2014). Information on land use and topography of these sites is provided in Table  5.
From Fig. 10a  it is clear that the coverage area of the L3SM product is higher in these areas. Figure  9a  and b show the difference (MO-SO) in the number of successful soil moisture retrievals between the L3SM and L2SM products. The general behaviour shows a systematic increase in the number of retrievals of the MO with respect to the SO retrievals. The number of retrievals moderately increases in desert and plain areas (10-20 retrievals per year per orbit). The increase is much higher for forested areas. The L2SM showed a higher number of successful retrievals in the area between 62-70 • longitude and 35-55 • latitude. This is due to an anomaly in the processing of TB prod-ucts. The ancillary data containing the TEC are not properly used over this region. This has been corrected and all operational products are now properly processed.
They seem to increase at the majority of the sites. The comparison shows a slight negative bias for the two datasets. The absolute value of bias is less than 0. 04 m 3 m -3 , except for the L3 retrieval over the Benin site, which is 0. 058 m 3 m -3. Thus, in general the bias is within the uncertainty of the in situ data if we consider the combined errors from sensor errors, spatial heterogeneity and sensing depth mismatch. The correlation values range from 0. 65 to 0. 88 for the different sites. Increased correlation was found for the L3SM products over the Niger site and slightly over WG in descending overpasses. The majority of the correlation values remain high with L3SM retrieval with no significant difference between L2SM and L3SM. More in-depth analysis can be obtained by inspecting the time series of soil moisture.