dataesr/scientific-paragraphs-categorization huggingface dataset:
Architecture of Verified JavaBIP The architecture of Verified JavaBIP is shown in Figure  1. The user starts with a JavaBIP model, optionally with verification annotations. The user then has two choices: verify the model with VerCors, or execute it with the JavaBIP Engine. We extended VerCors to automatically transform a JavaBIP model to the internal representation of VerCors, Common Object Language (COL), which is roughly equal to the union of the input languages of VerCors. This representation is then verified. An example of this transformation is given in Listings 2 and 3. If verification succeeds, the JavaBIP model is memory safe, has no data races, and all its components respect the specified properties. In this case no extra run-time verification is needed. If verification fails, there are either memory safety issues, or components violate properties.
By doing such sensitivity studies, the robustness of the reaction scheme can be tested more deeply, and the reaction rate values can be tuned in a more relevant way, making sure that the final model is not only able to represent one regime, but rather a whole variety of situations. In addition, predicting thrombin kinetics under pathological conditions (like factor deficiencies) is the ultimate goal of most mathematical models. In the present work, we evaluated two well-established mathematical models of the coagulation cascade from Chatterjee et al. [2]  for the contact pathway and from Hockin et al. [1]  and Butenas et al. [3]  for the TF pathway. Three clotting factor deficiencies were studied to investigate if the models could reproduce the dynamics of thrombin generation. For each model, the numerical simulations were compared with experimental data using calibrated automated thrombography (CAT)  [17].
Acknowledgements The authors would like to thank  Prof. Jean-François Schved  for his insightful comments. This research was supported by  CONACyT, Mexico scholarship and the LabEx Numev  (convention  ANR-10-LABX-20 ). This work was publicly funded through  ANR  (the  French National Research Agency)  under the " Investissements d'avenir " programme with the reference  ANR-16-IDEX-0006.
The photometry on the UVOT images was done with uvotsource in HEASoft and an aperture encircling the entire galaxy. For JWST photometry, we used a 6-arcsec circular aperture, which allows us to gather all the observed light observed in JWST filters from the host galaxy. All measurements are summarised in Table  7. 4. To derive the main physical properties of the host galaxy, such as its stellar mass, we employ two separate methodologies based on the photometric and spectroscopic data available for the host, and finally compare the results to assess the robustness of our conclusions. We first fit the multi-wavelength (0. 1-4. 4 µm) dataset using the prospector python package  [126] , which allows us to model the host galaxy spectrum starting from its main constituents, namely a set of stellar population base spectra, built from the Flexible Stellar Population Synthesis (FSPS) package  [127] , and combined with a specific star-formation history (SFH) model.
We attribute the strongest observed line at 2. 15 microns to tellurium (Te) III from an M1 line list of heavy elements presented in  [45] , where the line wavelengths are experimentally calibrated according to the NIST database  [156]. Te belongs to the second r-process peak. With the M1 line list, we model kilonova emission line spectra under the assumption that photons from forbidden lines produced outside the photosphere freely escape from the ejecta. The collision strengths of Te III are taken from an R-matrix calculation  [43]  and those of other ions are obtained by using an atomic structure code HULLAC  [157]. The abundance pattern is chosen to be the solar r-process but we separate "light" and "heavy" elements at an atomic mass of 85 and introduce a parameter, the abundance ratio of the two (see figure  14 ).
Acknowledgements We dedicate this paper to  David Alexander Kann , who passed on March 10. The final messages he sent were regarding follow-up of GRB 230307A, and we hope it would satisfy his curiosity to know the final conclusions. This work is based on observations made with the  NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes  at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with program #4434 and 4445. Support for Program numbers 4434 and 4445 was provided through grants from the  STScI  under  NASA  contract  NAS5-03127. This paper is partly based on observations collected at the  European Southern Observatory  under  ESO programme  110.
B. Metzger is supported in part by the NSF (grant AST-2002577). J. H. and D. L. were supported by a VILLUM FONDEN Investigator grant (project number 16599). G. P. L. is supported by a Royal Society Dorothy Hodgkin Fellowship (grant Nos. DHF-R1-221175 and DHF-ERE-221005). G. L. was supported by a research grant (19054) from VILLUM FONDEN. K. H. is supported by JST FOR-EST Program (JPMJFR2136) and the JSPS Grant-in-Aid for Scientific Research (20H05639, 20H00158, 23H01169, 20K14513). KEH acknowledges support from the Carlsberg Foundation Reintegration Fellowship Grant CF21-0103. S. Schulze acknowledges support from the G. R. E. A. T. research environment, funded by Vetenskapsrådet, the Swedish Research Council, project number 2016-06012. The Cosmic Dawn Center (DAWN) is funded by the Danish National Research Foundation under grant No.
140. JPUF is supported by the Independent Research Fund Denmark (DFF-4090-00079) and thanks the Carlsberg Foundation for support. SJS acknowledges funding from STFC Grant ST/X006506/1 and ST/T000198/1. VSD and ULTRACAM are funded by STFC grant ST/V000853/1 AAB acknowledges funding from the UK Space Agency. MN is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 948381) and by UK Space Agency Grant No. ST/Y000692/1. H. F. S is supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures program. DS acknowledges funding from STFC grants ST/T000406/1, ST/T003103/1, ST/X001121/1. MER acknowledges support from the research programme Athena with project number 184. 034.
The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual highthroughput screening campaigns. In addition, the most sophisticated methods, e. g. , those based on configurational sampling by molecular dynamics, require significant pre-and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations.
e. , 3D reconstruction, protonation/ tautomeric state assignment, partial charge determination, etc. ), modeling of the protein-ligand complex, and compound ranking and prioritization. Given the number of molecules to be processed, structure-based vHTS of ultralarge chemical libraries is challenging and requires efficient and fully automated approaches. Recently, the preparation and docking of a chemical library of 1. 4 billion commercially available compounds by the open-source software VirtualFlow was reported  9  (also see ref 10 for a recent review on parallelization algorithms for virtual screening). Moreover, the computational cost underlying virtual screening imposes severe limitations on the affordable level of theory, which results in fast but inaccurate scoring functions for ranking.
, is left to the user. ChemFlow is shipped with three independent modules: DockFlow, LigFlow, and ScoreFlow (Figure  1 ). In coordination with the standalone software PrepFlow,  27  which was designed to handle chemical library preparation robustly and efficiently, hundreds of thousands of ligands are prepared, docked, prioritized, and finally reranked using simplified free energy calculations by ChemFlow. Starting from the output of PrepFlow, DockFlow performs virtual high-throughput docking against one or multiple targets using four popular docking codes: AutoDock Vina,  28  QVINA,  29  SMINA,  30  and PLANTS. 31  LigFlow provides compound parametrization by atom-typing to GAFF2  32  and partial charge assignment to AM1-BCC  33  or RESP  34  charges by quantum-mechanical calculations, which is required for running MD.
■ RESULTS The use of ChemFlow is illustrated using a subset of the Greenidge dataset. 26  The resulting dataset is highly relevant for virtual screening purposes because (1) it features 626 proteinligand complexes with drug-like organic compounds; (2) it includes 234 distinct proteins, 25 of which cocrystallized with ≥5 ligands; (3) it was curated by experts from the pharmaceutical industry; (4) it provides high-resolution structures (X-ray) and thermodynamic data (pK d ) for all complexes; and (5) it comes with in silico predictions for benchmarking computational methods (see Tables  S6  and S7  for details). To emulate a typical vHTS campaign, the ligands were extracted and processed by PrepFlow. 27  The chemical library was prepared using GAFF2 parameters and RESP charges and forwarded to DockFlow, which prioritized 10 binding modes per ligand via PLANTS and the ChemPLP scoring  39  The best binding pose per ligand along with the coordinates of the protein were postprocessed by ScoreFlow.
For this purpose, explicit-water MD for 20 ns was performed and analyzed using the one-average MM-GBSA method. For the MD simulations, the Amberff14SB 40 (explicitsolvent) and Amber99SB-ILDN  41  (implicit-solvent) force fields were used for the protein, GAFF2  32  was used for the ligand, and the TIP3P model 42 was used for water. For the MM/GBSA calculations, the Amber GB2 model 43 that implements the OBC implicit-solvent model  44  was used with an internal dielectric constant of 4 (see the Supporting Information (SI) for details). The numerical predictions for 626 protein-ligand complexes extracted from the Greenidge dataset with no entropy contribution are compared to experimental binding affinities in Figure  1. The data show that docking with free energy rescoring provides binding affinity results that are significantly correlated with experiments with a Pearson correlation coefficient R = 0.
■ DATA AND SOFTWARE AVAILABILITY The ChemFlow software is freely distributed at the following link: https://github. com/IFMlab/ChemFlow. A tutorial on the installation and use of ChemFlow for protein-ligand binding affinities with α-thrombin is available at https:// github. com/IFMlab/ChemFlow/blob/master/docs/Tutorial-ChemFlow. rst. ChemFlow interfaces several programs whose availability is indicated below. Conda is open-source and free of charge for not-for-profit institutions. The docking software Autodock Vina, QVINA, and SMINA are open-source and free of charge. The docking program PLANTS is available to academic not-for-profit users under a specific license agreement. The Open Babel tool is open-source and free of charge. For MD simulations and MM/PB(GB)SA calculations, the AmberTools suite is free of charge, and its components are mostly released under the GNU General Public License (GPL).
To evaluate the actual post-streaming distribution 𝑓 from the updated velocity moments, we chose a third-order Hermite expansion to improve upon previous works (which all used a second-order one) while keeping the collision evaluation simple. Indeed, the resulting effect of the collision encoded by Eq. (  18 ) ends up being quite straightforward: the post-collision velocity moments do not require the lengthy evaluation of (and multiplications by) matrices 𝑴 and its inverse, as the closed-form expressions of the three velocity moments (found with Matlab [Matlab 2023]) are 𝜌 (𝒙, 𝑡 + 1) = 𝜌 * ; (21) 𝑢 𝛼 (𝒙, 𝑡 + 1) = 𝑢 * 𝛼 + 1 2𝜌 * 𝐹 𝛼 ; ( 22 ) 𝑆 𝑥 𝑦 (𝒙, 𝑡 + 1) = (1 -1 𝜏 )𝑆 * 𝑥 𝑦 + 1 𝜏 𝑢 * 𝑥 𝑢 * 𝑦 + 2𝜏 -1 2𝜏 𝜌 * (𝐹 𝑥 𝑢 * 𝑦 + 𝐹 𝑦 𝑢 * 𝑥 ) ; (23) 𝑆 𝑥𝑥 (𝒙, 𝑡 + 1) = 𝜏 -1 3𝜏 (2𝑆 * 𝑥𝑥 -𝑆 * 𝑦𝑦 -𝑆 * 𝑧𝑧 ) + 1 3 𝑢 * 𝑥 2 + 𝑢 * 𝑦 2 + 𝑢 * 𝑧 2 + 1 3𝜏 2𝑢 * 𝑥 2 -𝑢 * 𝑦 2 -𝑢 * 𝑧 2 + 1 𝜌 * 𝐹 𝑥 𝑢 * 𝑥 + 𝜏 -1 3𝜏𝜌 * (2𝐹 𝑥 𝑢 * 𝑥 -𝐹 𝑦 𝑢 * 𝑦 -𝐹 𝑧 𝑢 * 𝑧 ) ; 𝑆 𝑦𝑦 (𝒙, 𝑡 + 1) = 𝜏 -1 3𝜏 (2𝑆 * 𝑦𝑦 -𝑆 * 𝑥𝑥 -𝑆 * 𝑧𝑧 ) + 1 3 𝑢 * 𝑥 2 + 𝑢 * 𝑦 2 + 𝑢 * 𝑧 2 + 1 3𝜏 2𝑢 * 𝑦 2 -𝑢 * 𝑥 2 -𝑢 * 𝑧 2 + 1 𝜌 * 𝐹 𝑦 𝑢 * 𝑦 + 𝜏 -1 3𝜏 𝜌 * (2𝐹 𝑦 𝑢 * 𝑦 -𝐹 𝑥 𝑢 * 𝑥 -𝐹 𝑧 𝑢 * 𝑧 ) ; 𝑆 𝑧𝑧 (𝒙, 𝑡 + 1) = 𝜏 -1 3𝜏 (2𝑆 * 𝑧𝑧 -𝑆 * 𝑥𝑥 -𝑆 * 𝑦𝑦 ) + 1 3 𝑢 * 𝑥 2 + 𝑢 * 𝑦 2 + 𝑢 * 𝑧 2 + 1 3𝜏 2𝑢 * 𝑧 2 -𝑢 * 𝑥 2 -𝑢 * 𝑦 2 + 1 𝜌 * 𝐹 𝑧 𝑢 * 𝑧 + 𝜏 -1 3𝜏𝜌 * (2𝐹 𝑧 𝑢 * 𝑧 -𝐹 𝑥 𝑢 * 𝑥 -𝐹 𝑦 𝑢 * 𝑦 ).
Implementation details We implemented our approach (see pseudocode in Alg. 1) in C++ and CUDA, using a structure-of-arrays (SOA) data structure to store 20 variables per grid node: 10 per grid node to store the velocity moments, with two copies to facilitate the time update. For our solid boundary treatment, we follow the approach of  Li and Desbrun [2023]  by first constructing a bounding volume hierarchy tree structure for the 3D mesh model on the GPU; for dynamic objects, we use cut-cell flags and bounding boxes to accelerate link-mesh intersection. Given the low peak memory and efficiency of our HOME-LBM scheme, turbulent flow simulation with static objects is achieved with only one GPU kernel pass which contains streaming, boundary treatment (link-mesh intersection) and collision. Though not limited to such a choice, we use 8×4×4 blocks and assign each node to one CUDA thread.
ACKNOWLEDGMENTS The tractor model in Fig. 1 is from grabcad, the octopus in Fig. 2 is from Thingi10k, the fern in Fig. 5 is from cgtrader, the leaf in Fig. 13 is from archive3d, the comb in Fig. 15 is from cgtrader, while the propeller in Figs. 6 and 16 is from free3d. Mathieu Desbrun  acknowledges the generous support of  Ansys  and Adobe Research, as well as a  Choose France Inria  chair.
However, the choice of hyperparameters may greatly influence its performance  [11]. BayesPrism  [15]  applies Markov chain Monte Carlo to jointly estimate gene expression profiles of cell types and states together with their proportions in ST spots. An alternative statistical strategy for deconvolution is to use log-normal  [10]  or weighted linear regression  [13] , utilizing the pre-computed gene expression levels for cell types as explanatory variables. Finally, Tangram  [14]  learns a mapping function that transfers scRNA-seq (or single nuclei RNA-seq) expression to the tissue space measured using ST, so that spatial correlation across the genes between the two techniques is maximized. Integrating data obtained via two different experimental techniques such as scRNAseq and ST may be prone to bias arising from cross-platform and batch effects, such as differences in the sequencing depth or only a partial overlap between sequenced cell types and genes.
Moreover, such a reference data set measuring gene expression in cells of different types, ideally taken from exactly the same tissue sample, may not be accessible. This calls for methods designed for ST data that do not require an external reference in the form of scRNA-seq data. To this end, STdeconvolve  [16]  uses latent Dirichlet allocation (LDA) to simultaneously infer gene expression profiles and proportions of cell types in each ST spot. However, it does not rely on any external reference and the cell type decomposition is based solely on patterns of gene co-expression. Hence, there is a dearth of methods for cell type deconvolution in ST data, which do not require a reference scRNA-seq dataset, but instead incorporate prior, domain knowledge that limits their hypothesis space and guides toward feasible solutions  [17, 18].
To address these needs, here we present Celloscope-a novel method for decomposing cell type mixtures in ST spots, enabling to spatially map cell types in tissues examined with ST technology. Importantly, the innovative character of Celloscope involves using prior qualitative information on marker genes. Such marker genes are expected to be more highly expressed in their respective cell types compared to other types  [19]. There is a large body of expert knowledge on marker genes for different cell types, established by multiple independent studies (as of  19. 11. 2022 , PubMed search for "cell type marker genes" phrase returned 67,327 entries). Celloscope can leverage this wealth of knowledge and, as a consequence, is fully independent of scRNA-seq reference datasets. The expression level for each marker gene in each cell type is estimated as part of the model's inference from ST data.
Moreover, we estimate the admixture of additional, a priori unknown cell types. The results of extensive experiments on simulated data demonstrated the excellent Celloscope's performance. On top of that, Celloscope surpassed other methods that use scRNA-seq data as a reference to compute gene expression profiles in a simulation study. To further validate the correctness of the Celloscope's assumptions and prove its usefulness, we applied it to data on the anterior section of the mouse brain. We also showed that Celloscope can be used to elucidate the cause of inflammation in a human prostate tissue  [20]. These results indicate that Celloscope successfully leverages biological knowledge of cell type marker genes to accurately decompose cell type mixtures in ST data.
Celloscope model overview We propose Celloscope, a novel Bayesian probabilistic graphical model of gene expression in ST data, which deconvolutes cell type composition in ST spots, and a method to infer model parameters based on an MCMC algorithm (Methods). Apart from gene expression measurements per each spot localized in the analyzed tissue, ST data come also with corresponding images of hematoxylin and eosin (H&E) staining of the same sample. The first step of the Celloscope's pipeline is an analysis of those H&E images (Fig. 1A ). In this step, the total number of cells for each spot is estimated. Optionally, regions of interest in the tissue (e. g. , an inflammation) are annotated by a pathologist, so that further analysis steps are restricted to these regions. Prior knowledge of marker genes for considered cell types is encoded as a binary matrix (Fig.
1B ). The types correspond to such cells that are expected to be found in the examined tissue or (optionally) in the selected area of interest. Additionally, we assume Optionally, regions of interest are annotated. H&E image source: 10x Genomics. B Prior knowledge on marker genes is given as the model's input, in a form of a binary matrix, together with ST data on marker gene expression in spots (C). D The graphical representation of Celloscope. Gray nodes correspond to the observed variables, double circled to deterministic ones, while the remaining nodes correspond to hidden variables. Arrows represent probabilistic dependencies. Model's variables are described in Table  1  and hyperparameters in Table  2. E Cell type decomposition in each spot using Celloscope is performed via MCMC inference the presence of a dummy type accounting for novel or unknown cell types.
For each scenario, 15 datasets were simulated, assuming 800 spots, 7 cell types (including the dummy type), and 149 marker genes. We first computed the average absolute error across spots for each replicate. For a single spot, the absolute error value ranges between 0 (when the model perfectly predicts the simulated fractions of the spot occupied by each cell type) and 2/T, where T denotes the number of types. The upper bound for this error can easily be seen by considering a case when the simulated fractions would be such that the spot would be fully dominated by one cell type with fraction 1, while the model would predict full domination by a different cell type. Thus, for T = 7 , we expect that the average absolute error across spots will be in the range [0, 0. 29]. In this evaluation, Celloscope achieved excellent performance, with error levels between 0.
Knowledge of gene expression profiles of cell types does not have a large impact on model accuracy We next assessed the hypothetical improvement in accuracy coming from including gene expression profiles of cell types obtained from an additional, external data source, such as a scRNA-seq reference dataset. To this end, instead of estimating hidden variables , as in the default setting, we run Celloscope with set to their true, simulated values (Fig. 2A ). In the case of the dense scenario, the performance stayed at the comparable level to the default Celloscope's setting. However, we observed some error drop for the sparse scenario (from median 0. 027 for the default to 0. 022). Still, this improvement was smaller compared to the one obtained from knowing the cell counts per spot (i. e. , when the corresponding hidden variables were fixed to their true values).
Celloscope performs favorably over preceding approaches We compared Celloscope's performance to previously published Stereoscope  [8] , RCTD  [7] , and SpatialDWLS  [13]. These methods, unlike Celloscope, require a reference scRNA-seq dataset to compute gene expression profiles for the analyzed cell types. Importantly, all considered methods were applied on exactly the same simulated datasets, for which the ground truth was known. Thus, we were in possession of the true marker gene expression levels across cell types, which were provided to RCTD, Stereoscope, and SpatialDWLS  [13] , and used by these methods for the estimation of cell type proportions in simulated spots (see Additional file 1: Sections S1, S2, and S3 for run settings used for RCTD Stereoscope and SpatialDWLS, respectively).
Decomposition of cell types in a sagittal mouse brain section Celloscope was applied to mouse brain data  [22]  and was able to successfully indicate brain structures (Fig. 3A ). Specifically, an analysis of spatial transcriptomics data on sagittal mouse brain slices (an anterior section) was performed. In contrast to simulated data, for this dataset, there is no ground truth specifying the exact, underlying compositions of cell types in each spot. We can, however, expect that some of the known cell types will dominate in specific brain regions and that some other non-specific cell types will be prevalent across the entire brain tissue. Thus, to evaluate the quality of cell type deconvolution by Celloscope, we first compared the obtained spatial cell type distribution to regions as specified by the mouse brain atlas  [23].
Celloscope's results obtained for mouse brain coronal section data are in agreement with the results for the sagittal section Further, we performed an analysis of spatial transcriptomics data for a coronal mouse brain slice  [31]  that is orthogonal to the sagittal section analyzed above (Fig. 4A ). As we were analyzing the same tissue type, we were locating the same cell types, namely oligodendrocytes, astrocytes, GABAergic neurons, glutamatergic neurons, choroid plexus epithelial cells, endothelial cells, microglia, and vascular and leptomeningeal cells. However, since in our previous analyses (Fig. 3A ) a portion of cells was assigned to the dummy type, we also considered two additional cell types, i. e. , cholinergic neurons, peptidergic cells, and di-mesencephalon neurons. The markers for the considered cell types were found using the marker identification procedure based on lead genes (Methods), taking candidate marker genes from the dataset of Zeisel et al.