dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample characteristics Of the 345 CHR-P individuals participating in EU-GEI, 324 provided data on the number of cigarettes smoked per day. Of these 324 individuals, 39 (12. 0%) were assessed with the CIDI and CAARMS at 6-months follow-up, 174 (53. 7%) at 1 year and 127 (39. 2%) at 2 years follow-up, respectively. Median follow-up period in days was 196 (range 21-272) for 6 months, 380 days (range 187-580) for 1-year, and 757 days (min = 535 and max = 993) for 2-year assessments. See flow-chart Supplementary Figure  1. Data regarding missingness at baseline, and comparisons between dropouts and completers at 1-year are presented as Supplementary Sections 2, 3. Comparing completers and dropouts at 1-year follow-up showed no significant differences in number of cigarettes smoked per day, age, gender, current employment, GAF disability scores, experienced childhood trauma and current cannabis use at baseline.
Second, according to the ejecta velocity field given by a shock physics simulation, a surface is constructed to isolate the compact material from the fast-moving ejecta using the α-shape-construction algorithm  (Ballouz et al. 2019). Third, this surface then is used to carve out the DEM regolith bed and match the velocity and mass of particles with the shock physics simulation outcome. Finally, the fast-moving ejecta is added as individual particles. With a combination of two numerical schemes, Bern's SPH and the DEM code pkdgrav, this handoff procedure was developed recently for end-to-end impact cratering modeling and has been validated against the Hayabusa2 SCI impact experiments on asteroid Ryugu  (Zhang et al. 2021a ). The simulation results show that, in this low-gravity regime, the excavation process lasted about 300 s.
The results are reported in terms of particle rotational frequencies, velocity dispersion, and spatial dispersion under the same input impact conditions. In addition, velocity dispersion owing to different particle temperatures and collisions between particles is discussed. The three codes that have been used to study the additional dynamical effects are as follows. 1. GRAINS  (Ferrari et al. 2017 (Ferrari et al. , 2020)) : a DEM N-body code that models the ejecta fragments as tiny polyhedra with a randomized shape. Therefore, it has an advantage over other N-body codes such as pkdgrav or Rebound in that it allows for more complex particle shapes that may significantly affect the nature of particle collisions. The full six-dof dynamics, including both center-of-mass (translational) and attitude (rotational) motion of ejecta, are considered.
Nonetheless, the largest difference between the three SRP models can only be appreciated further away from the central body, where the ratio between the amplitude of the SRP and the gravitational accelerations becomes larger. The LIMARDE code can study the motion of irregular dust particles considering their rotation, temperature, initial velocity and torque, and orientation. Here we focus on some additional dynamical effects that might come from the different physical properties of the particles, such as temperature, shape, and density. The code is able to compute the rotational motion of the ejected particles as a function of the active forces at any instance/distance during the propagation of the plume. Torque is computed from the law of variation of the angular momentum by using the Euler dynamic equations.
We then use a script to process a specified cube file's contents into image data arrays for a specified camera with its associated camera specifications. Table  1  gives the relevant camera specifications used for the case study of the next section. This script loops over the nonempty cubes (be they base cubes or subcubes) in the cube file and maps onto pixels of the image plane (a) the volume of space within the current cube intersected by each pixel column, (b) the number of particles in that volume of space, and (c) the flux from those particles computed using a simple photometric light scattering model. This script can accommodate binning of the pixels in the image plane for efficiency, i. e. , forming larger pixels out of n × n raw pixels. SPICE Toolkit  (Acton et al. 2018 ) calls are used to calculate the observation geometry, including the positions of the camera, the Sun, Earth, Dimorphos, Didymos, and the Didymos system barycenter (with respect to which the origin of the cube file's base grid is specified in the cube file header).
Finally, images are both plotted as magnitude per pixel with various descriptive metadata overlaid and written out in Flexible Image Transport System (FITS) format containing flux per pixel for delivery to the DART SOC web page. This is done both with and without applying convolution with the appropriate camera point-spread function (PSF). For LEIA, a PSF was provided by LICIACube science team members, but for LUKE, a lack of characterizing the PSF of the actual flight article prelaunch led us to assume a simple Gaussian PSF with an FWHM of 1 pixel. The PSF matching use of the F350LP filter on WFC3-UVIS on HST was generated by one of the coauthors using the software TinyTIM  (Krist et al. 2011 ).
Avenues for future work include, as mentioned earlier, (a) careful testing and implementation of the handoff procedure based on velocity field in order to make ejecta initialization more realistic; (b) in the interface of ejecta dynamical simulation to synthetic image generation, simultaneous improvement of both the sharpness of definition of the ejecta cone in close proximity to Dimorphos and uniformity of the ejecta field at a greater distance from Dimorphos; and (c) incorporation of a more realistic phase curve including both geometric and diffraction effects and, ultimately, use of a proper radiative transfer code to handle multiple scattering as well. Ongoing development of image analysis applicable to the Earth-vicinity/HST vantage point images will also aim to accomplish proper estimation/inversion of the total ejecta mass and cSFD slope, if they can be disentangled by leveraging other DART encounter data sets.
This work was supported in part by the  DART mission ,  NASA  contract No. 80MSFC20D0004  to  JHU/APL. Some of this work was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). P. M. , S. D. R. , and Y. Z. acknowledge funding support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ). P. M. acknowledges funding support from the  French space agency CNES. F. F. acknowledges funding from the  Swiss National Science Foundation (SNSF) Ambizione  grant No. 193346. S. R. S. acknowledges support from the  DART   Participating Scientist Program , grant no. 80NSSC22K0318. A. R. and S. I. acknowledge financial support from the  Agenzia Spaziale Italiana  (ASI; contract.
Study population All patients who underwent SLKT at our institution between January 2008 and December 2018 were included retrospectively. A pre-SLKT serum sample was available for all patients, for the detection of single antigens and CDC cross-matching. Deceased-donor transplants were matched for ABO compatibility. Data were obtained from the French national registry of the "Agence de la Biomédecine, " and from electronic clinical, laboratory, and pathology reports. All recipients received induction therapy with either basiliximab (Novartis R ) or polyclonal antithymoglobulins (ATG) (Genzyme R ) and intravenous methylprednisolone, followed by a triple immunosuppressive regimen combining prednisone, tacrolimus, and mycophenolate mofetil. The maintenance regimen included tacrolimus (8-10 ng/ml for the three first months and 4-8 ng/ml thereafter).
e. , without anti-human globulin enhancement step), looking for IgG and IgM cytotoxic DSA. Only IgG positive crossmatches were considered as positive crossmatches in the present work. No autologous crossmatches were performed. The presence and specificity of anti-HLA antibodies were assessed prospectively on recipient serum samples collected before transplantation, with LABScreen (One Lambda, Canoga Park, CA, United States) reagents LSM12 for the screening step, and LS1A04 and LS2A01 for class I and II single-antigen beads on a Luminex platform. Pretransplant, screening was performed first and followed by SAFB assay in both classes when found positive in at least one class. Post-transplant, only single antigen assays were performed. Anti-HLA antibody levels were expressed as mean fluorescence intensity (MFI) values after normalization of the non-specific binding using the Baseline formula of the Fusion R (One Lambda) software.
Thus, a foundation set of consensus SAS profiles has been obtained for benchmarking scattering-profile prediction from atomic coordinates. Additionally, two sets of SAXS data measured at different facilities to q > 2. 2 A ˚À1 showed good mutual agreement, affirming that this region has interpretable features for structural modelling. SAS measurements with inline size-exclusion chromatography (SEC) proved to be generally superior for eliminating sample heterogeneity, but with unavoidable sample dilution during column elution, while batch SAS data collected at higher concentrations and for longer times provided superior statistical precision. Careful merging of data measured using inline SEC and batch modes, or low-and high-concentration data from batch measurements, was successful in eliminating small amounts of aggregate or interparticle interference from the scattering while providing improved statistical precision overall for the benchmarking data set.
, 2018). The full utilization of SAS data to gain biological insights, however, depends upon the ability to accurately predict or simulate the SAS profile from atomic coordinates for comparison with measurements. Since the publication of the first programs to calculate small-angle X-ray scattering (SAXS;  Svergun et al. , 1995)  and small-angle neutron scattering (SANS;  Svergun et al. , 1998)  profiles from atomic coordinates there has been an ongoing acceleration in the rate of biomolecular SAS publications and citations  (Trewhella, 2022). Since these first programs, there have been numerous further developments and new approaches to SAS profile prediction (see, for example,  Grishaev et al. , 2010; Poitevin et al. , 2011; Chen & Hub, 2014; Schneidman-Duhovny et al. , 2016; Grudinin et al.
, 2017; Hub, 2018) , including an extension to ensembles for dynamic and multistate systems (see, for example,  Bernadoet al. , 2007; Schneidman-Duhovny et al. , 2016; Cordeiro et al. , 2017). Each developer has chosen a preferred set of experimental data against which to test their approach as there is no standard set of data to evaluate the differences among the different approaches or to test new approaches in a standard way. In addition, the very use of SAS data for structural analysis implies an assumption of their reproducibility: data sets collected independently using different instruments from the same biomolecule in the same solution conditions are assumed to coincide within experimental error. However, no such demonstration has ever been carried out. The aim of this project was to generate a set of experimental SAS profiles for proteins of known structure that can be used to benchmark different approaches to calculating SAS profiles from atomic coordinates while also testing the intrinsic reproducibility of the experiment.
To this end, SAS profiles for five proteins were measured on different beamlines using a common source for each protein and standard buffers. Each protein was measured using SAXS as well as SANS with H 2 O and D 2 O buffers. These three sets of data are influenced by distinct scattering-contrast values for the protein and its hydration layer with respect to the bulk solvent and potentially could be used to test different models of the hydration layer  (Svergun et al. , 1998; Zhang et al. , 2012; Kim & Gabel, 2015). Sets of data were submitted to the project coordinators (JT and PV) for assessment as scattered intensity I as a function of the momentum transfer or scattering-vector amplitude q [i. e. I(q) versus q, where q = (4 sin)/, is half of the scattering angle and is the wavelength of the radiation] with associated standard errors for [Sample + Solvent],  [Solvent]  and [Sample + Solvent] À  [Solvent].
Criteria for selection of proteins The selection of suitable proteins initially focused on identifying structures that were relatively rigid in order to avoid possible complications due to flexible regions or structural inhomogeneity. Further, there should be high-resolution crystal structures of good quality for each protein, and a range of sizes and shapes was desirable. Also, the selected proteins needed to be readily available at high purity with conditions for optimal SAS data collection available from previous studies to minimize the potential for interparticle interference or aggregation that would bias the results. The search for proteins that could meet the above conditions proved to be challenging, not least because dynamics to some degree play an essential role in nearly all of biology.
Ribbon representations of the crystal structures of each protein demonstrate the relative sizes and shapes of each protein and the fact that urate oxidase has a large central cavity that is solvent-accessible (Fig. 1 ). Bovine serum albumin was considered given its popularity as an intensity standard for SAS studies of proteins, but was not selected due to the flexibility of the loop connecting its two domains and its known tendency to oligomerize in solution over time  (Bujacz, 2012; Trewhella et al. , 2017). Details of each of the selected proteins are provided in Table  1  and Supplementary Table S1 , while Supplementary Table  S2  gives the sequences, with modifications and bound ligands, of the proteins used for measurement.
Xylanase and xylose isomerase were supplied as a 43%(v/v) glycerol stock (0. 5 ml of 36 mg ml À1 protein) and an ammonium sulfate microcrystalline precipitate (0. 5 ml of 33 mg ml À1 protein in 0. 91 M ammonium sulfate), respectively. Prior to SAS measurements, these protein stocks were dialyzed with 3 Â 2 h changes and a 1:50 volume ratio against locally prepared buffer (50 mM Tris pH 7. 5, plus 1 mM MgCl 2 for xylose isomerase only) with progressively decreasing glycerol [20%, 10% and 0%(w/v)] or salt (500, 250 and 150 mM NaCl), respectively. A final dialysis of 2 Â 6 h changes was then performed with a 1:100 volume ratio against the measurement buffer provided together with the protein. This sequence ensures the sufficient removal of glycerol from the xylanase and of ammonium salt from the xylose isomerase.
RNaseA and lysozyme, which were supplied as commercial powders, were dissolved directly in the measurement buffer and dialyzed against the measurement buffer with 2 Â 2 h changes and a 1:100 volume ratio. According to local practice, the solutions were spun in an Eppendorf centrifuge (or equivalent) for 5 min to remove potential dust/particles. 3. 1. 4. Preparation of urate oxidase and its buffer. Urate oxidase in complex with its very high affinity inhibitor 8-azaxanthine (molar mass 153. 10 g mol À1 ) with all four sites bound was specially prepared according to  Retailleau et al. (2004). For SAS measurements, $5 mg of the complex in the measurement buffer (as 0. 5 ml of a 10 mg ml À1 solution) with sufficient buffer for SEC-SAS and batch SAS measurement was shipped on ice to each laboratory on 11 June 2019.
The   Gasteiger et al. , 2005). ‡ Experimentally determined. § Calculated using SEDNTERP  (Philo, 1997) ; see also Section S2 and Supplementary Table  S2. } Calculated using MULCh  (Whitten et al. , 2008). † † Performed at Sydney Mass Spectrometry. Note: urate oxidase shows a second resolved peak at 34 169  Da. protein is known to be extremely stable in the measurement buffer at 4 C (Commission du Medicament et des Dispositifs Medicaux Steriles, 2005). At the concentrations used for SAS measurements there is insignificant free inhibitor and hence there is no need for free inhibitor in the measurement buffer. Prior to shipment, urate oxidase was subjected to sizeexclusion high-performance liquid chromatography (SE-HPLC) using an S200 column to confirm that it was the pure tetramer with no significant higher order oligomers.
Prior to batch SAS measurements, it was recommended that SEC could be performed on an aliquot to evaluate the monodispersity of the sample following transport and, if indicated, a SEC purification step with concentration as needed could be performed. 3. 1. 5. SANS sample preparation. Compared with SAXS, SANS measurements typically require larger samples, longer data-acquisition times and preparation of samples in D 2 O. Each facility optimized their sample preparation locally. Also, lysozyme is more soluble in H 2 O compared with D 2 O  (Broutin et al. , 1995) , and to minimize the formation of aggregates or possible gelling it was dissolved first in H 2 O and then dialyzed into D 2 O or subjected to exchange on a column. At the Institut Laue-Langevin (ILL) exchange of buffers was accomplished by recovering samples after SEC-SANS and reconcentrating as needed to measure in batch mode.
Data acquisition and initial data-evaluation protocol Full details of sample handling prior to SAS measurements and SAS data acquisition at each facility are provided in Supplementary Table  S3   S4a ). Guinier and P(r) analyses for the submitted data sets were analyzed by the project coordinators (JT and PV) using a standard protocol to facilitate the initial comparison of results. autoRg and autoGNOM (from ATSAS 3. 0 and 3. 1;  Franke et al. , 2017; Manalastas-Cantos et al. , 2021)  were used, and quoted errors are as reported by these routines. Generally, minimal adjustments were made to the selected Guinier ranges, mostly to make q max R g ' 1. 3, while maximum linear dimension (d max ) values were rounded to whole numbers (in A ˚). Also, where indicated the d max value selected by auto-GNOM was manually refined to avoid apparent truncation or overextension of the r range by ensuring that P(r) approaches d max smoothly as a horizontal tangent.
Developing the datcombine tool to combine data in a standard way To develop a consensus scattering profile for each protein the datcombine tool was created and is now available in ATSAS 3. 1. 0. Data at the various instruments were generally collected on an arbitrary intensity scale and over a range of concentrations, thus requiring the application of a multiplicative scale factor before combination. It was also necessary to apply an additive constant to account for differences in the background due to small inaccuracies in solvent subtraction. These adjustments are ideally performed for data on the same q-scale. The datcombine tool thus takes a set of data recorded at the various participating facilities for a given protein and first re-grids them onto a common q-scale. For the SAXS data, a uniform Áq = 0.
Statistical error estimates for the contributed data were validated by comparing all pairwise solvent measurements. The program first sorts the data at each q i value by the magnitude of the errors, starting from the smallest, and proceeds to add data with errors of increasing magnitude that do not increase the propagated average. We note here that there are alternatives to using the average error to exclude high-uncertainty data, for example by using a maximumlikelihood error estimate and an error-weighting scheme. Resolution of the optimal approach is complicated, however, by the facts that the data have been re-gridded and experimental errors in neighbouring q channels are correlated to some extent. That said, the differences in the magnitude of propagated errors for the plain average or the maximumlikelihood approaches are expected to be small, and for the purposes here we chose to implement datcombine using the subroutines within the ATSAS program package, which have been thoroughly tested over a long period of time.
Modelling There are several approaches to calculating SAS scattering profiles from atomic coordinates, and it is not within the scope of this study to review or evaluate all of the different approaches taken. Further, the consensus data from this study by themselves provide no basis for concluding that any one method is preferred. Rather, the aim here is to provide a set of data that could be used to improve any given approach. Nevertheless, it was relevant to see how well the consensus profiles compared with prediction, and so from the readily available methods we considered examples that took different approaches to modelling the hydration layer and its contribution to the scattering. WAXSiS  (Chen & Hub, 2014; Knight & Hub, 2015)  uses explicit-solvent all-atom moleculardynamics (MD) simulations to account for the hydration layer and excluded solvent, and when fitting to an experimental curve an optional additive constant can be applied.
CRYSOL  (Svergun et al. , 1995) , CRYSON  (Svergun et al. , 1998) , Pepsi-SAXS/Pepsi-SANS  (Grudinin et al. , 2017)  and FoXS (Schneidman-Duhovny et al. , 2016) represent the hydration layer as a shell of uniform contrast surrounding the atomic structure. When optimizing the fit to experimental data, free parameters are refined to account for the excluded volume of bulk solvent by the protein and for the contrast of the hydration shell, and there is an optional constant subtraction to account for errors in background subtraction. The modelling presented here used CRYSOL and CRYSON as implemented in ATSAS online (version 3. 1. 0; https://www. embl-hamburg. de/ biosaxs/atsas-online/), Pepsi-SAXS run locally (Linux version 3. 0), Pepsi-SANS as implemented on the ILL Pepsi home site (https://pepsi.
app. ill. fr/) and FoXS (version 2. 16. 0) via the FoXS website (https://modbase. compbio. ucsf. edu/foxs/). To improve the convergence of the predicted SAS profiles, custom WAXSiS-type calculations for SAXS and SANS, incorporating either X-ray form factors or neutron scattering factors, were performed locally using GROMACS  (Abraham et al. , 2015)  and were run for longer times compared with the version available on the WAXSiS website (http:// waxsis. uni-goettingen. de/) that uses YASARA  (Krieger & Vriend, 2015 ; Section S1 has full details of the MD simulation systems, which are openly shared at Zenodo at https://doi. org/ 10. 5281/zenodo. 7057567). The atomic coordinates of crystal structures deposited in the Protein Data Bank (PDB) with accession codes 7rsa  (Wlodawer et al. , 1988) , 2vb1  (Wang et al.
, 2007) , 2dfc  (Watanabe et al. , 2006) , 3l8w  (Gabison et al. , 2010)  and 1mnz (E. Nowak, S. Panjikar & P. A. Tucker, unpublished work) were used to calculate SAS profiles for RNaseA, lysozyme, xylanase, urate oxidase and xylose isomerase, respectively. In addition, the NMR solution structure of RNaseA in the RECOORD database (https://www. ebi. ac. uk/pdbe/recalculated-NMR-data), which is a 32-model ensemble (PDB entry 2aas;  Santoro et al. , 1993) , was considered. For xylose isomerase, a single N-terminal Met missing from the crystal structure was added to the coordinate file using PyMOL (version 2. 3. 3; Schro ¨dinger). For urate oxidase the crystal structure with PDB entry 3l8w contains the inhibitor xanthine, whereas the inhibitor in the SAS samples was 8-azaxanthine, which differs from xanthine by just one atom (a C to N substitution, 1 Da molecular mass) and binds in the same way.