dataesr/scientific-paragraphs-categorization huggingface dataset:
We use the full two-body problem code (General Use Binary Asteroid Simulator (GUBAS)  11  , Methods) that implements coupled rotational and orbital dynamics to numerically determine Δv T for each sampled combination of input parameters. Coupled dynamics are necessary because of the non-spherical shapes of Didymos and Dimorphos and their close proximity relative to their sizes. A range of values for M is generated by combining the volumes of the sampled ellipsoid shape parameters with values for Dimorphos's density. The Monte Carlo approach is summarized by Extended Data Fig. 1. As Dimorphos's density has not been directly measured and has a large uncertainty, we treat it as an independent variable and uniformly sample a wide range of possible values between 1,500 and 3,300 kg m -3 , a range that encompasses the 3σ uncertainty  1.
Then, a second secant search algorithm is used to determine the Δv T required to achieve the sampled post-impact orbit period. We match the pre-and post-impact orbit periods because these are directly measured by ground-based observations and are thus the best-constrained parameters of the system 2. Given the non-Keplerian nature of the Didymos system, we use the GUBAS to numerically propagate the binary asteroid dynamics. GUBAS is a well-tested full two-body problem (F2BP) code that can model the mutual gravitational interactions between two arbitrarily shaped rigid bodies with uniform mass distributions  11, 38. GUBAS has been benchmarked against other F2BP codes 39 and used extensively in previous dynamical studies of the Didymos system (for example, refs. 10,37,40). Finally, the mass of Dimorphos is calculated from its ellipsoidal shape and Dimorphos's density.
Acknowledgements We thank  S. Marchi ,  K. T. Ramesh ,  P. Sanchez ,  S. Schwartz ,  J. Steckloff ,  M. B. Syal  and  G. Tancredi  for their comments on the manuscript. M. Z. , I. G. , D. M. and P. T. acknowledge  D. Lubey ,  M. Smith  and  D. Mages  for the useful discussions and suggestions regarding the operational navigation of LICIACube. This work was supported by the  DART mission ,  NASA  contract no. 80MSFC20D0004. S. D. R. and M. J. acknowledge support from the  Swiss National Science Foundation  (project number  200021_207359 ). S. D. R. , M. J. , R. L. , P. M. , N. M. and K. W. , acknowledge the funding from the  European Union 's  Horizon 2020 research and innovation programme , grant agreement no. 870377  (project no. NEO-MAPP ). P. M. acknowledges support from  CNES ,  ESA  and the  CNRS  through the  MITI interdisciplinary programmes.
L. acknowledges the support provided by  NASA  through grant no. HST-GO-16674  from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract no. NAS 5-26555 , and the support from  NASA   DART Participating Scientist Program , grant no. 80NSSC21K1131. R. N. acknowledges support from  NASA/FINESST  (grant no. NNH20ZDA001N ). J. M. T. -R. acknowledges financial support from project no. PID2021-128062NB-I00  funded by  MCIN/AEI (Spain). Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. Lawrence Livermore National Laboratory  is operated by  Lawrence Livermore National Security, LLC , for the  US Department of Energy, National Nuclear Security Administration  under contract nos.
Data availability The dynamical simulations were carried out using GUBAS, which is publicly available on Github (https://github. com/meyeralexj/gubas). The Dimorphos orbital velocity direction vector components presented in Extended Data Table  1  were computed using the dimorphos_s501. bsp and sb-65803-198. bsp (Didymos) data files available at https://dart. jhuapl. edu/SPICE_kernels/spk. The DART incident velocity vector components presented in Extended Data Table  1  were computed using those two files in combination with the DART_2022_269_1241_ops_v01_impact. bsp data file, available at the same URL. Data availability at that URL is planned until summer 2023, after which those data may be found at https://naif. jpl. nasa. gov/naif/data. html.
Patients A total of 147 biopsies from treatment-naive FF (discovery cohort) and 93 FFPE (validation cohort) tumor samples from immunocompetent EBV À PCNSL were recollected from different hospitals across France (see Supplementary Table  S1 , available at https://doi. org/10. 1016/j. annonc. 2022. 11. 002). None of the patients received corticosteroids for 1 week before the brain biopsy. All patients had a complete systemic evaluation to rule out secondary central nervous system DLBCL. Diagnoses and section selection for sequencing were established at the reference institution by specialized (neuro)pathologists. The tumor cell content for each sample was estimated to be at least 60% based on histomorphological evaluation. FFPE tissue sections available for all data (FF and FFPE cohorts) were used for immunophenotypic characterization with antibodies directed against CD20, CD10, BCL6, CD3, Ki67, and MUM1/IRF4.
Multi-omic data integration for PCNSL molecular subtyping The multi-omic cohort comprised 85 FF samples from which we obtained six data types including mutations, CNA events, gene expression, immune cell proportions, TCR/BCR clonotypes, and fusion transcripts (Figure  1A ). The most appropriate cluster number was obtained by clustering prediction index (CPI) and Gap-statistics analyses. 3] [34] [35] [36] [37] [38] [39] [40] [41] [42]  Clustering performance was evaluated for each cluster number (n ¼ 2-8) using the weighted silhouette width. 43 Genomic data were deposited at the European      The study design including the workflow and the data composition for each cohort is demonstrated. WES was carried out using tumor tissue and matched peripheral blood samples for 98 PCNSL patients, while for 17 patients it was tumor-only.
For the methyl-seq, we also included four control peripheral blood samples. Different types of information were available for each omic data, as seen inside each bar plot. The multi-omic cohort consisted of 85 samples that had WES, RNA-seq, and clinic data within the discovery fresh-frozen cohort (red dashed box). (B) The number of affected samples within the top driver genes (identified by dNdScv algorithm) in the cohort of 115 PCNSL patients is shown. Bar plots are filled according to mutation type (missense, nonsense, splice site, frameshift, multihit, or other) or CNA events (gain, deletion, or deep deletion). The frequency of affected samples within the cohort is annotated at the top of each bar plot for each driver gene. (C) The boxplot comparison of the proportion of mutations attributed to c-AID or SBS9 mutational processes when occurring at clonal times versus subclonal times is shown.
org/ 10. 1016/j. annonc. 2022. 11. 002). We obtained a Cohen's k coefficient of 0. 90 (P < 0. 001, Supplementary Figure  S8A , available at https://doi. org/10. 1016/j. annonc. 2022. 11. 002) when evaluating the accuracy of correctly assigning patients from the multi-omic cohort. Additionally, when expanding to the FF-RNA complete set (n ¼ 123) or when using the FFPE cohort (n ¼ 93), we observed the same behaviors regarding clinical outcome (global P < 0. 001) across molecular subtypes in both univariate and multivariate models using OS and PFS (Figure  2D , Supplementary Figure  S8 , available at https://doi. org/10. 1016/j. annonc. 2022. 11. 002). Next, we evaluated the contribution of each of the six data types to outcome prediction models by using Harrell's concordance index (C-index).
When analyzing the TME and epigenetic features across subtypes, we found similar results to those previously described by Kotlov et al. in DLBCLs where the hypermethylated subtypes (CS1/CS2) had a depleted TME. 50 n top of this and given the importance of routine clinical implementation, we propose RBraLymP (RNA-based Brain Lymphoma Profiler), which uses gene expression data from either FFPE or FF tissue, to identify the PCNSL molecular subtypes associated with multi-omic features. The RBraLymP algorithm is publicly accessible at https://github. com/iS4i4S/PCNSL-RBraLymP such that existing and new therapy efforts can be directed to the most appropriate patients. Our study presents various limitations. Identification of subtypes was achieved using relevant features obtained from different static bulk-omics data, and hence does not respond to tumor intra-heterogeneity or single-cell's crosstalk as other approaches like single-cell RNA-seq or spatial transcriptomics would potentially do.
Another limitation would be the absence of a validation PCNSL cohort having   The locus overlap (LOLA) region set enrichment analysis for hypermethylated promoters across the four PCNSL subtypes is shown. The x-axis presents the targets followed by the database. P values were calculated using a twosided Fisher's exact test and then adjusted for multiple testing by the FDR method. CS, cluster; FDR, false discovery rate; GO, Gene Ontology; PCNSL, primary central nervous system lymphoma; TF, transcription factors. the same data types as the discovery cohort; however, this is at the moment difficult given the rarity of the disease. Finally, our analysis on DLBCL cell lines would benefit from further validations using PCNSL-derived cell lines, organoids, or patient-derived xenograft models with drug sensitivity and RNA-seq data.
ACKNOWLEDGEMENTS We thank  Yannick Marie  and  Emeline Mundwiller  ( Paris Brain Institute ),  Lucile Armenoult , and  Mira Ayadi  ( Carte d'Identité de Tumeurs, Ligue contre le Cancer ) for the help with exome, RNA sequencing, and bisulfite DNA sequencing libraries' preparation;  Silvia Duran  for helpful discussions; Figure 7 was created with biorender. comand all the clinicians, pathologists, researchers, and patients associated with the  French National 'Lymphome Oculo-Cérébral ' included in Rare cancers of the central nervous system,  RENOCLIP-LOC Network , approved by the  French National Institute of Cancer (INCa).
NoFigure 1. 1 Figure 1. Study design and PCNSL mutational landscape. (A)The study design including the workflow and the data composition for each cohort is demonstrated. WES was carried out using tumor tissue and matched peripheral blood samples for 98 PCNSL patients, while for 17 patients it was tumor-only. For the methyl-seq, we also included four control peripheral blood samples. Different types of information were available for each omic data, as seen inside each bar plot. The multi-omic cohort consisted of 85 samples that had WES, RNA-seq, and clinic data within the discovery fresh-frozen cohort (red dashed box). (B) The number of affected samples within the top driver genes (identified by dNdScv algorithm) in the cohort of 115 PCNSL patients is shown. Bar plots are filled according to mutation type (missense, nonsense, splice site, frameshift, multihit, or other) or CNA events (gain, deletion, or deep deletion).
Insert Table 2 here The semi-structured interviews with these participants focused on their role in SEE, main competencies that students should develop, components of SEE education (underpinned by values), methods of teaching, changes in approaches to SEE, capitals required, and issues surrounding reflexivity, and approaches and challenges of social entrepreneurship education. From these, we produced interview transcripts and field notes. Field notes included records of our initial hunches, important themes that emanated from the interview conducted, and with some potential connections between data points, for instance, how we could ask participants to elaborate on a certain theme further in the subsequent interviews. The interview data were analysed inductively by teasing out key themes in order of significance, corresponding to the research questions.
Bourdieusian theory, mainly forms of capitals, as aggregate themes, has formed our overall bridging framework for analysis and interpretation. Drawing on these constructs, we moved back and forth between emic (data-driven)  (Tatli and Ozbilgin, 2012)  and etic (theoretically driven) readings of the empirical material. In other words, an iterative and S o c i a l E n t e r p r i s e J o u r n a l reflexive approach to data analysis was followed whereby we have analyzed, revisited the data and connected them to emerging findings, and progressively refined our focus and understanding  (Tracy, 2013)  to address our key research questions. Our questions have entailed a closer examination of characteristics and aspects of capital in the context of SEE, and the kind of habitus that the SEE field generates for the accumulation of multiple capitals and their conversion as part of a transformative and reflexive experience, leading to meaningful outcomes for social entrepreneurship.
2a  and in Supplementary Fig. 1 ). Simultaneous fits to all data sets yield transition temperatures T 2 = 25. 7(2) K and T 1 = 20. 8(1) K respectively, in good agreement with refs. 27 and 28. We note that the critical exponents for the two order parameters are clearly different. Below T 2 , the neutron intensity increases linearly with decreasing temperature which means a critical exponent of  1  2 as assumed fixed in the fit. This corresponds to the critical exponent resulting from long-range interactions or from a secondary order parameter. At T 1 , (0, 1, 0) displays a power law behavior with β = 0. 32(3) which is comparable to the critical exponent of a 3D Heisenberg, XY or Ising system. To unambiguously determine the spin orientations, we performed a polarized neutron diffraction experiment using the triple axis spectrometer 4F1 and with scattering vector Q = (0, K, L) in the horizontal scattering plane.
Acknowledgements This work was supported by the  European Research Council  through the  Synergy network HERO  (Grant No. 810451 ). We thank the  EU Interreg program  MAXESS4FUN for  Cross Border  and Society for funding the simulation work. We are grateful for neutron beamtime received for this project at the instruments TriCS and RITA-II at the SINQ neutron spallation source at the  Paul Scherrer Institute , at the E5 diffractometer at the  BER-II  research reactor at the  Helmholtz-Zentrum Berlin , and at the 4F1 spectrometer at the research reactor at the  Laboratoire Leon Brillouin. We acknowledge  Diamond Light Source  for beamtime on I16 (Proposal No. MM30817-1 ). This project was supported by the  Danish national Council for Research infrastructure (NUFI)  through  DANSCATT  and the  ESS-Lighthouse Q-MAT.
New trends in scintillators for RadIT applications include inorganic and organic scintillator composites or heterostructures, liquid phase synthesized perovskites and single-crystal micrometerthick films, use of multi-physics models and data science to guide scintillator and RadIT optimization, structural innovations such as photonic crystals, nano-scintillators enhanced by the Purcell effect, heterostructural scintillating fibers, and multilayer configurations. RadIT have also been recognized as powerful tools for scintillator discovery and development.
2D photodetectors and more recently data science  [22]. Besides topics such as scintillators for space applications, scintillator development and discovery are poised to enter a new phase through big data mining, multiphysics models, new experimental information derived from automated, high throughput, and in-situ RadIT, including multi-modal RadIT, as discussed in Sec. ??. It is clear that the interdisciplinary marriage between scintillator science and technology and RadIT offers many exciting opportunities for innovation in the coming decade, as summarized at the end in Sec. V.
The light emission also depends on the type of radiation, as discussed in more details in Sec. III for X-rays of different energies, neutrons, and charged particles. Due to the complex interplay among different metrics and material properties, quantitative mappings between scintillator metrics and RadIT metrics usually require multi-physics codes as Geant4  [56] , [57] and MCNP  [58]. First-principles simulations of full RadIT systems are still beyond the current scope of Geant4 and MCNP due to the lack of sufficient accuracy for data interpretation and object reconstruction from images and tomographic measurements. In addition to improvements in RadIT system specific modeling and simulations, laboratory experiments to characterize scintillators, scintillatorphotodetector prototypes (see Sec.
III, Sec. IV) remain essential in scintillator selection for RadIT applications and system optimization. Recently, data science and use of RadIT such as neutron imaging for in-situ scintillator development and characterization are highlighted as examples of emerging topics in Sec. ??. Table. III in the appendix lists metrics requirements for specific applications. It is obviously impossible to meet all the scintillator requirements simultaneously in most applications. A trade-off between different metrics are often necessary, as briefly summarized here. 1) Stopping power: The stopping power or linear attenuation coefficient depends on density, material composition (effective atomic number), and cross sections. A thicker scintillator corresponds to high stopping power. However, the spatial resolution usually degrades due to a larger volume of scintillator light emission, increases in scattering and straggling.
032 g/cm 3. The data are from the NIST/PSTAR database. In addition to lower dose, proton and heavy ion IT have better material density resolution (∼ 0. 1%) than those of Xray CT (∼ 1%); however, higher cost and larger footprint have limited proton and heavy ion IT to existing proton and ion accelerator facilities. The energy ranges of such facilities are divided into three bands: low energy or < 300 MeV per nucleon (mostly medical facilities for cancer therapy)  [116] , medium energy ∼ 1 to a few GeV per nucleon  [117] -  [119] , and high energy > 10 GeV per nucleon  [120]. A magnetic lens allowed flash proton radiography of thick objects (up to 50 g cm -2 ) with a time/spatial resolution below 200 ns/200 µm  [121]. The spatial resolution was further enhanced by the implementation of a ×3 magnetic lens, that effectively shrinks the FoV for a higher spatial resolution to 65 µm  [122].
The spallation neutron source delivered short pulses of neutrons at 20 -60 Hz. The neutrons propagated over 10-15 m path length towards the sample and the detector. The energy of each registered neutron is reconstructed from its time of flight. The short duration of the neutron pulses is therefore crucial for measuring the neutron transmission spectrum in each pixel of the imaging dataset. Some growth parameters (such as the location and the shape of liquid-solid interface, qualitative uniformity of elemental composition, location of defects) can be measured with regular neutron imaging, where a white transmission spectrum is measured for each pixel. A wider range of parameters can be investigated with a pulsed neutron beam where transmission spectra that depend on the isotopic composition can be measured as a function of neutron time of flight for each pixel.
16 : Schematic diagram of the energy-resolved neutron imaging experiment. A crystal growth furnace is placed directly in the neutron beam in front of a neutron counting detector. Thousands of images are acquired simultaneously, each corresponding to a specific neutron energy. Neutron transmission spectrum is thus measured for each pixel of the transmission dataset. Reproduced with permission from  [190]. the temperature profile and the speed of translation. Moreover, formation of two phases within the melt was observed during in-situ growth of Cs 2 LiLaBr 6 :Ce crystal, as shown in Fig. 17. In the very first in-situ growth of this crystal, it was found that Cs-rich/Li-deficient layer forms in the melt just above the solid phase. Good scintillator material was only grown after the Csrich layer is settled over about an 11-hour period.
Two or more color X-ray CT have already been used successfully in biology and medicine  [222]. Examples of neutron and X-rays, neutrons and proton imaging can be found in  [108]. From the signal collection perspective, light intensity / particle counts can also be used in conjunction with energy, momentum, polarization and other light/particle properties for multi-messenger RadIT. From the signal processing perspective, signals in real physical space can be extended to the phase space for multi-dimensional RadIT, e. g. X-ray ptychography  [87]. Below we give additional examples of detectors and data processing for MM-RadIT  [22]. One of the key ingredients that enable dual-energy  [222]  and multicolor CT is energy-resolved X-ray photon counting detectors at high flux (∼10 9 ph•mm -2 •s -1 ).
IV. RECENT SCINTILLATORS AND CONCEPTS In addition to progress in new inorganic scintillators, Sec. IV-A, we shall highlight results and progress in structured scintillators presented during the SCINT22 conference, ranging from nanostructures, Sec. IV-B, micrometer-thick thin films, Sec. IV-C, to bulk composite structures, Sec. IV-D. As presented, each topic emphasizes a particular application: Sec. IV-A on new inorganic scintillators for fast timing, Sec. IV-B on nanostructures for light guiding and higher light yield, Sec. IV-C on thin film for high X-ray imaging resolution, Sec. IV-D on bulk composites for fast neutrons. Many other new applications in RadIT may be found for each of the scintillators and novel structures. Sec. IV-E discusses the emerging opportunities associated with machine learning and data science.
This framework has been applied to predict a range of properties that are either directly or indirectly connected to the performance of luminescent materials, including scintillators  [279] -  [283]. Zhuo et al. [280]  employed a tree-based ensemble learning algorithm, along with elemental features (such as the average electronegativity, average polarizability), local configurational information, and the relative dielectric permittivity of the host medium, to train a ML model that could reliably predict 5d level centroid shift of Ce 3+ substituted inorganic phosphors, a quantity that is critical in predicting the light yield and thermal response of rare-earth substituted inorganic luminescent materials. In a different study, Zhuo et al. [281]  developed a ML regression model using a set of 134 experimentally-measured temperature-dependent Eu 3+ emission spectra of phosphors to rapidly estimate the thermal quenching temperature -defined as the temperature when the emission intensity is half of the initial value -and subsequently used the model to screen more than 1000 oxide Eu 3+ doped host compounds to select five new candidates, which were not included in the training dataset, with predicted thermal quenching temperatures > 423 K (see Fig.
22 ). These compounds were eventually synthesized to validate this informatics approach. Closely following along the similar lines of research, Park et al. [283]  reported an integrated ML platform, consisting of 18 different learning algorithms, to evaluate and compare the performance of different models towards predicting the band gap, the excitation and emission wavelengths of Eu 2+ -activated luminescent materials. Going beyond the surrogate models for predictions of scintillation-related properties that are otherwise expensive to compute or time-and resource-intensive to measure directly, data-enabled approach has also been applied to extract new insights and practically useful design parameters from scintillator materials databases. As a recent example, Pilania et al. [282]  employed a curated dataset of scintillation light yield and response time measurements for twenty-five Ce-or Eudoped scintillator compounds to discover a strong correlation between the lattice contribution to the dielectric constant and the light yield, regardless of the specific composition or crystal structure of the host material, as depicted in Fig.