dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this work, we define homogeneous samples of high-quality astrophysical parameters by exploiting many Gaia data products that appear in Gaia DR3, while focusing on the sources and data products in the astrophysical_parameters and the astrophysical_parameters_supp tables which were produced by the Apsis software  (Creevey et al. 2023; Delchambre et al. 2023; Fouesneau et al. 2023). We consider different regimes of stars all across the HR diagram. In the first part of this work, we consider large samples of young massive disc OBA stars (Sect. 3), FGKM spectral-type stars (Sect. 4), and faint ultracool dwarfs (UCDs, Sect. 5). We then focus on smaller samples of specific object types; carbon stars (Sect. 6), solar analogues (Sect. 7), and the Gaia spectrophotometric standard stars (SPSS;  Pancino et al.
2021, Sect. 8). Concerning the latter, this paper provides the first homogeneous determination of the SPSS dataset to date. We validate each of the samples using the Gaia data themselves and external catalogues, and our results are published in six tables that appear alongside Gaia DR3; see Sect. 9 and Table  7. In Sect. 10, we demonstrate some use cases of these samples of stars. We use a subset of the OBA sample to illustrate its usefulness in analysing the Milky Way rotation curve (Sect. 10. 1). We then use the properties of the FGKM stars to analyse known exoplanet systems including the determination of planet radii and masses (Sect. 10. 2). We then predict the colours of the Sun in various passbands using the solar analogue sample (Sect. 10. 3). Finally, we analyse the ages of some unseen UCD-companions to the FGKM stars  (Sect.
As illustrated in this work, many science cases can be explored with these data. DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement. The full list of funding agencies and grants is given in Appendix A. This research has used NASA's Astrophysics Data System, and the VizieR catalogue access tool (CDS, Strasbourg, France). The data processing and analysis made use of matplotlib  (Hunter 2007) , NumPy  (Harris et al. 2020) , the IPython package  (Pérez & Granger 2007) , Vaex  (Breddels & Veljanoski 2018) , TOPCAT  (Taylor 2005) , pyphot (http://github. com/mfouesneau/pyphot), R (R Core Team 2013), Astropy  (Astropy Collaboration 2013 , 2018) , CmdStanPy (https:// github. com/stan-dev/cmdstanpy), and ArviZ  (Kumar et al.
Chemin); -the National Natural Science Foundation of China (NSFC) through grants 11573054, 11703065, and 12173069, the China Scholarship Council through grant 201806040200, and the Natural Science Foundation of Shanghai through grant 21ZR1474100; -the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lausanne and the project TTP-2018-07-1171 'Mining the Variable Sky', with the funds of the Croatian-Swiss Research Programme; -the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; -the Danish Ministry of Science; -the Estonian Ministry of Education and Research through grant IUT40-1; -the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network; -the European Cooperation in Science and Technology (COST) through COST Action CA18104 'Revealing the Milky Way with Gaia (MW-Gaia)'; -the European Research Council (ERC) through grants 320360, 647208, and 834148 and through the European Union's Horizon 2020 research and innovation and excellent science programmes through Marie Skłodowska-Curie grant 745617 (Our Galaxy at full HD -Gal-HD) and 895174 (The build-up and fate of selfgravitating systems in the Universe) as well as grants 687378 (Small Bodies: Near and Far), 682115 (Using the Magellanic Clouds to Understand the Interaction of Galaxies), 695099 (A sub-percent distance scale from binaries and Cepheids -CepBin), 716155 (Structured ACCREtion Disks -SACCRED), 951549 (Sub-percent calibration of the extragalactic distance scale in the era of big surveys -UniverScale), and 101004214 (Innovative Scientific Data Exploration and Exploitation Applications for Space Sciences -EXPLORE); -the European Science Foundation (ESF), in the framework of the Gaia Research for European Astronomy Training Research Network Programme (GREAT-ESF); -the European Space Agency (ESA) in the framework of the Gaia project, through the Plan for European Cooperating States (PECS) programme through contracts C98090 and 4000106398/12/NL/KML for Hungary, through contract 4000115263/15/NL/IB for Germany, and through PROgramme de Développement d'Expériences scientifiques (PRODEX) grant 4000127986 for Slovenia; -the Academy of Finland through grants 299543, 307157, 325805, 328654, 336546, and 345115 and the Magnus Ehrnrooth Foundation; -the French Centre National d'Études Spatiales (CNES), the Agence Nationale de la Recherche (ANR) through grant.
Acknowledgements. We thank the referee for their constructive comments on the manuscript. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the  ANR-10-IDEX-0001-02  for the ' Investissements d'avenir' programme , through grant  ANR-15-CE31-0007  for project ' Modelling the Milky Way in the Gaia era ' (MOD4Gaia), through grant  ANR-14-CE33-0014-01  for project 'The  Milky Way disc formation in the Gaia era ' (ARCHEOGAL), through grant  ANR-15-CE31-0012-01  for project ' Unlocking the potential of Cepheids as primary distance calibrators' (UnlockCepheids ), through grant  ANR-19-CE31-0017  for project ' Secular evolution of galxies ' (SEGAL), and through grant  ANR-18-CE31-0006  for project ' Galactic Dark Matter' (GaDaMa ), the  Centre National de la Recherche Scientifique (CNRS)  and its  SNO Gaia of the Institut des Sciences de l'Univers (INSU) , its  Programmes Nationaux: Cosmologie et Galaxies (PNCG) ,  Gravitation Références Astronomie Métrologie (PNGRAM) ,  Planétologie (PNP) ,  Physique et Chimie du Milieu Interstellaire (PCMI) , and  Physique Stellaire (PNPS) , the ' Action Fédératrice Gaia' of the Observatoire de Paris , the  Région de Franche-Comté , the  Institut National Polytechnique (INP)  and the  Institut National de Physique nucléaire et de Physique des Particules  ( IN2P3 ) co-funded by  CNES ; -the  German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
While machine learning from single-cell datasets can make these predictions for individual cell types  9, 10  , they generally cannot account for changes resulting from cell-cell interactions. Moreover, these predictive methods are data-driven and unable to predict cellular states from prior biological knowledge or mechanism alone. Beyond filling the gaps between measurement times, computational techniques for temporal analysis of cellular states provide the potential to predict the future state of multicellular systems. More advanced computational tools are needed to model unseen emergent behaviors in complex biological systems comprised of multiple cell types. Mechanistic mathematical modeling can address the key challenge of extending from static high-resolution data to forecasting the dynamics of multicellular systems.
Furthermore, the ability to run simulations at scale across diverse biological conditions with ABMs can refine biological understanding and predict future cellular behaviors in these complex systems. As a result, they provide a computational means to prioritize bench experiments or clinical trials to address the costs and practical constraints of real-world experimentation. Widespread application of ABMs for modeling biological systems currently has two major limitations; the highly technical nature of most software implementations, and the limited ability to integrate molecular data to ground simulations in the real-world. The former issue gatekeeps ABM away from those without significant mathematical and computer-programming experience. This technical requirement limits widespread application, including by many potential users who have extensive working knowledge of the biological system they are investigating.
Even for users with advanced computational expertise, the custom coding required can limit reproducibility. Disease etiology and operating biological hypotheses are often hidden deep in source code, obscuring the assumptions about the system and, thus, the full set of hypotheses that are being simulated and tested. These technical limitations to ABM modeling also limit the ability to embed molecular datasets, which are often too high-dimensional to manually encode into equations of agents and rules. A conceptual framing that can generally abstract cellular phenotypes and their interactions-combined with a simplifying coding infrastructure-is essential for the integration of molecular measurements to personalize model predictions. Here, we sought a way for an expert's knowledge base to be condensed and standardized into a set of clear biological statements that can be readily mapped onto biophysically motivated ABM parameters that describe their biological system, with or without molecular and cellular measurements.
Facilitating this requires an intuitive language to concisely express expert knowledge as plain text descriptions of the rules of cell interactions that "encode" a system of study, and software to translate these plain text descriptions into mathematical expressions and executable models for immediate exploration of a digitized copy of the biological system  44. While we use PhysiCell 44 as a reference implementation, the grammar can independently represent expert knowledge for further analysis or import into other simulation frameworks  45- 50. Thus, the grammar allows for broad application of ABMs and addresses the need to be reproducible, modular, and extensible. We demonstrate how this grammar can encode expert biological knowledge through sample models in cancer biology spanning tumor cell growth, invasion, and response to immunotherapy.
g. , observing changes in expression of immune cells in the tumor microenvironment). We demonstrate how the data-driven rules enable for the first time direct transfer of bulk and single-cell RNA-seq and spatial transcriptomics into ABMs for personalized predictions. Assimilating multi-omics and spatial molecular data into agent-based models can ground simulations in the real-world in such a way that ABMs can serve as an abstract digital copy of the biological system as it changes over time.
RESULTS Agent-based simulation frameworks 13 like PhysiCell 44 model individual cells as software agents with independent states (e. g. , position, volume, cycle status) and processes (e. g. , volume regulation, motility, secretion); see Fig. 1A. Each cell agent respond to stimuli (signals) in their microenvironment that effect changes in their behaviors (Fig. 1B ). Cell hypotheses relating cell behavioral responses to signals are written in a grammar that can be translated into mathematics and executable code. (Fig. 1C ). Hypotheses can be drawn from a variety of sources, including domain expertise, natural language processing / mining of prior literature, and analysis of transcriptomic and other data. Due to our uniform knowledge representation, all these rules can be compatibly integrated into a growing library of cell hypotheses.
Example 2: Forecast tumor progression in pancreatic cancer from an initial state defined by spatial transcriptomics We sought to extend our rules framework from our toy model of tumor cell growth in Example 1 to a personalized, data-driven model of tumor progression. For these models, we leveraged the rules-based framework to encode rules of cell-cell interactions and initial conditions of cells from spatial transcriptomics and single-cell RNA-seq datasets. Due to the dense stroma that is characteristic of PDAC, we sought to use our computational model to investigate the influence of cancer-associated fibroblast (CAF) content on tumor progression (Fig. 3a ). Our previous studies of cell-cell interactions in single-cell RNA-seq analysis and organoid co-cultures demonstrated two distinct transcriptional phenotypes in neoplastic cells  68.
Other pancreatic cells in the spatial transcriptomics data were approximated as steady state (no net proliferation, death, motility, or secretion) and are assumed to be essentially inert with regards to carcinogenesis. Agents were initialized from these cellular annotations of the Visium data matrix as described in Methods. Each tumor's development was forecasted for 90 days. Included in this count is a ring of bounding pancreatic cells. Simulation results are shown in (Fig. 3c ). We observe a transitory state in which the tumor cells transition from mixed epithelial and mesenchymal states to become nearly uniformly mesenchymal due to interactions with the fibroblasts. Subsequently, masses of epithelial tumor cells arise in both models, consistent with extensive tumor growth and the observed transition between PDAC transcriptional subtypes during tumorigenesis and invasion  71.
Example 5: Using experimental insight to model combination immune-targeted therapies in the pancreatic cancer microenvironment While the literature contains a wealth of prior knowledge of cellular roles in cancer, distilling cell behaviors into quantifiable networks for modeling previously required extensive manual curation. New singlecell datasets are uncovering novel cellular mechanisms of tumors in the microenvironment and their response to therapy. As we showed in example 2, data-driven cell-cell interaction rules derived from these datasets provide the opportunity to refine knowledge-driven or literature-based rules in the context of individual tumors. Integrating genomics datasets into ABMs can further personalize virtual prediction of therapeutic outcomes. An ongoing platform neoadjuvant clinical trial  87  is evaluating combination therapies in human pancreatic cancer to rewire the immune system through systematic, rational combination therapeutic strategies.
[ Fig. 6 ] The limited responsiveness of PDAC to immunotherapy has led to new combination therapeutic strategies to further enhance T cell function. To test the mechanisms whereby different therapies can alter T cells, we next simulate monotherapy and combination therapy on a single patient's hypothetical pancreatic microenvironment. Our group has recently observed tumor response associated with prolonged disease-free survival through the addition of CD137 agonist therapy to a combination of an irradiated, granulocyte-macrophage colonystimulating factor (GM-CSF)-secreting, allogeneic PDAC vaccine (GVAX)  90  and immune checkpoint inhibitor therapy  87  , consistent with the promise shown by this approach in preclinical studies. Previously, our ligand-receptor analysis of the PDAC atlas single-cell data further predicted increased interferon expression in CD137 high CD8 T cells  88.
As inferred in the bulk RNA-seq data, PD-1lo CD4+ T cells secrete chemokines that can attract CD8+ T cells to cause a bias in migration. Based on the single-cell RNA-seq-derived rules, CD137hi CD8+ T cells further secrete proinflammatory factor, allowing for further motility bias (i. e. , more directed migration) and tumor sensing. We model treatment by including CD4+ T cells following the vaccine treatment, and further altering the proportion of PD-1lo cells to simulate Nivolumab therapy and CD137hi cells to simulate the impact of Urelumab. In contrast to example 2, fibroblasts were excluded from our model due to their underrepresentation in the reference bulk and single-cell datasets from which these rules of cell-cell interactions were derived. In the first simulation, we model tumor-immune interactions occurring after immune priming via GVAX cancer vaccine alone; this is the first row in Fig.
Further, PhysiCell is open-source, built and maintained by a large community of mathematicians, statisticians, and scientists. From their efforts and from the user community, a vast amount of knowledge is encoded in the system at baseline; however, everything is completely customizable, extensible, and modifiable. Even when hypotheses are not fully executed in simulations, the cell behavior grammar affords an opportunity to systematically collect, annotate, curate, and grow our knowledge of behaviors of many cell types. Additionally, we have added the ability to directly translate parameters from spatial transcriptomics datanamely spatial location and annotated identity-to an ABM, and therefore use spatial data to initialize an ABM directly. Thus, models can now exactly match the tissue structure and transcriptional profile of samples directly.
Spatial relationships between cells matter immensely and spatial relationships can have a great impact on simulated (and real) outcomes. This strong dependence of many cancer systems and ABM trajectories on initial conditions can complicate model inquiry and impact critical system behaviors and model parameters obtained through inference; by leveraging robust single-cell spatial transcriptomics tissue profiles as initial conditions in the digital modeling stage, the hypothesis-driven rules modeling paradigm is grounded in precise referential data but also offers a path to both stronger model inquiry and more confident mathematical inference. These models nonetheless still require annotation of a finite number of agents identified in spatial molecular data, often annotating cells into broad phenotypes and abstracting cellular subtypes.
Future work must evaluate the sensitivity of models to the granularity of cellular phenotypes in these high-throughput datasets and accurate inference of parameters in the resulting higher-dimensional models. Moreover, future work must facilitate simple and robust identification of cell types and model parameterization, starting by identifying which parameters are most critical, and curating best parameter estimates for community reuse. We demonstrate a variety of models built using the hypothesis grammar, and two also informed by genomics-derived rules, which we hope will serve as helpful references for users. These examples cover multicellular behavior, demonstrated through the case studies of carcinogenesis and immune response to tumor growth. In some cases, all agents follow the same rules and their fate is decided by the actions of those around them.
Cell behaviors To build this grammar, we require clear abstractions of key cell behaviors that frequently occur in multicellular observations and corresponding reference models. In this context, a cell behavior is a cell-scale process, such as cycling, death, or phagocytosis. Generally, each behavior can be represented by a small number of continuous phenotypic parameters, describing the rate, magnitude, or frequency of the behavior. In earlier work, Sluka et al. developed the Cell Behavior Ontology (CBO)  51  as a controlled vocabulary of individual cell behaviors. More recently, we worked with a multidisciplinary coalition to extend and structure behaviors from the CBO and other sources into MultiCellDS 52 (multicellular data standard). In particular, this work defined a behavioral cell phenotype that collects of biophysical characterizations of a cell's behavior, organized hierarchically by function: cycling, death, volume, mechanics, secretion (including uptake), and motility.
Coordinate transforms and spatial transcriptomics integration To simulate cellular profiles based on initial conditions defined from VISIUM single-cell spatial transcriptomics data in PhysiCell, we interpret a given tissue profile 𝒜-a collection of the form B𝑥. , 𝑦. E ∈ ℝ + arranged in a triangular lattice with uniform spacing d = 71. 000 𝜇𝑚-as an (𝑛 × 2) (𝑛 × 2) matrix. Choosing a prospective center location 𝑋 ! = (𝑥 ! , 𝑦 ! ) ∈ ℝ + and using a hypothesized average cell diameter 𝑑 3 in the tissue, we apply a linear transformation 𝑇 (4 % ,5 & ) on 𝑋 to produce a scaled cell coordinate matrix 𝑋 3 = 𝑇 (4 % ,5 & ) (𝑋) describing a digital tissue with center (0,0) = 𝑇 (4 % ,5 & ) (𝑋 ! ) and ideal lattice spacing on the order of 𝑑 3 to encode cell signals and behaviors in the PhysiCell stage at scale.
Spatial transcriptomics data for example 2 Two resected pancreatic lesions were subjected to the commercial Visium spatial transcriptomics sequencing FFPE protocol. Slides were stained with H&E and imaged prior to RNA extraction, and image analysis was performed in parallel with transcriptomic analysis. An artificial intelligence method for annotation of pancreatic tumor tissue regions called CODA 70 was used to annotate acinar cells, islet cells, smooth muscle cells, and the distribution of collagen. This method was also used to distinguish normal ductal, neoplastic, and tumor cells from the H&E imaging, which were further visually confirmed by a pathologist (E. D. T. ). Spots with greater than 70% purity of ductal cells were further annotated to assign agent types for the associated tumor and normal cells in each spot.
For this annotation, we used our transfer learning method ProjectR 126 version 1. 8. 0 to distinguish proliferative signaling (modeled as an epithelial phenotype) from co-occurrence of EMT and inflammatory signaling (modeled as the mesenchymal phenotype) as defined in CoGAPS non-negative matrix factorization analysis of our reference scRNA-seq atlas of PDAC tumors using methods described previously  69, 89, 127. To locate fibroblasts, Seurat version 4. 1. 0 was used to compute module scores from a pan-CAF gene signature as described previously  69. Two python scripts were used to translate from annotated ST data into a PhysiCell-readable csv file containing coordinates and categorical cell types available in the github repository as extract_visium_coordinates. py and pull_physicell_init. py.
Single-cell RNA-seq PDAC atlas data as a reference dataset for example 5 This example uses our PDAC single-cell RNA-seq atlas data to further define immune cell subtypes in reference tumors  130, 131. Cell-cell communication analysis was performed using the Domino package  132- 134. To determine the by-tissue cell counts, the single-cell RNA sequencing data was preprocessed, clustered, and annotated using the Seurat R package 135. Cell identity clusters of interest ("Activated_CD4", "B cell", "CD4", "CD8", "Effector_CD8", "Epithelial_cancer", "Macrophage", "Mast", "Neutrophil", "NK/CTL", "T cell", "Treg cell") were then thresholded based on median normalized expression of genes of interest (here CD274/PD-L1, PDCD1/PD-1, TNFRSF9/CD137) and the number of cells falling into lo/hi categories were reported as described previously  88.
These cell numbers were then used to initialize the PhysiCell model in example 5, while Domino analysis informed the model rules. Whereas the cell phenotypes from the single-cell PDAC atlas data for example 2 are based on data from GSE155698 131 and CRA001160, example 5 uses only the former dataset due the immune enrichment of this sample cohort 130. other grant support from Genentech, BMS, NeoTX, and Break Through Cancer outside the submitted work. Dr. Jaffee is the Dana and Albert "Cubby" Broccoli Professor of Oncology. EJF is on the Scientific Advisory of Resistance Bio/Viosera Therapeutics, a paid consultant for Merck and Mestag, and receives research funds from Abbvie Inc and Roche/Genetech.