The HuBMAP Human Reference Atlas (HRA) effort aims to develop a common coordinate framework (CCF) for the healthy human body, see HRA Portal at https://humanatlas.io. An international team of organ experts across 25+ consortia are authoring so-called ASCT+B tables that capture the partonomy and typology information for anatomical structures, cell types, and biomarkers used to identify cell types. The ASCT+B tables are used to revise and extend existing CCF-relevant ontologies. In close collaboration with NIAID at NIH, a 3D Reference Object Library was compiled that provides semantically annotated 3D representations of major anatomical structures captured in the ASCT+B tables. The HRA can be extended and explored using several interactive user interfaces: The Registration User Interface (RUI) supports tissue data registration and annotation across 50+ 3D reference organs. The Exploration User Interface (EUI) supports the exploration of semantically and spatially explicit data—from the whole body to the single cell level. The Cell Distance Explorer (CDE) computes and visualizes distance distributions between different cells, cell types, and anatomical structures; and cell types and morphological features. For an introduction to HuBMAP goals, data, and code visit the Visible Human MOOC (VHMOOC). This talk details how HuBMAP, SenNet, GTEx, and other data is mapped to the HRA and how the HRA can be used to harmonize data at scale in support of precision health and precision medicine. 

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A disease is rarely a consequence of an abnormality in a single gene but reflects perturbations to the complex intracellular network. Network medicine offers a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho) phenotypes. As an application, I will explore how we use network medicine to uncover the role of individual food molecules in our health. Indeed, our current understanding of how diet affects our health is limited to the role of 150 key nutritional components systematically tracked by the USDA and other national databases in all foods. Yet, these nutritional components represent only a tiny fraction of the over 135,000 distinct, definable biochemicals present in our food. While many of these biochemicals have documented effects on health, they remain unquantified in any systematic fashion across different individual foods. Their invisibility to experimental, clinical, and epidemiological studies defines them as the ‘Dark Matter of Nutrition.’ I will speak about our efforts to develop a high-resolution library of this nutritional dark matter, and efforts to understand the role of these molecules on health, opening novel avenues by which to understand, avoid, and control disease.

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Drawing from across cultures and across scholarly disciplines, Places & Spaces: Mapping Science demonstrates the power of maps to address vital questions about the contours and content of human knowledge. An interdisciplinary and international advisory board chose each one of the works in the Places & Spaces exhibit as an outstanding example of how visualization can bring patterns in scientific data into focus. The exhibit is curated by the Cyberinfrastructure for Network Science Center at Indiana University. The exhibit has been on display at over 382 venues in 28 countries on 6 continents. It showcases the work of 248 mapmakers who hail from 17 different countries.

This presentation highlights the current applications of Computed Tomography (CT) in diagnosing and managing coronary artery disease. It examines how photon-counting CT technology offers higher-resolution imaging with reduced radiation doses, enhancing patient outcomes. Artificial intelligence is explored as a tool for improving image acquisition, interpretation, and predictive accuracy in cardiovascular care. The session also delves into emerging innovations that may further optimize the precision and efficiency of cardiac imaging. Ultimately, attendees will gain insight into today’s imaging practices and the technological trends that are shaping the future of cardiac CT imaging.

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A disease is rarely a consequence of an abnormality in a single gene but reflects perturbations to the complex intracellular network. Network medicine offers a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho) phenotypes. As an application, I will explore how we use network medicine to uncover the role of individual food molecules in our health. Indeed, our current understanding of how diet affects our health is limited to the role of 150 key nutritional components systematically tracked by the USDA and other national databases in all foods. Yet, these nutritional components represent only a tiny fraction of the over 135,000 distinct, definable biochemicals present in our food. While many of these biochemicals have documented effects on health, they remain unquantified in any systematic fashion across different individual foods. Their invisibility to experimental, clinical, and epidemiological studies defines them as the ‘Dark Matter of Nutrition.’ I will speak about our efforts to develop a high-resolution library of this nutritional dark matter, and efforts to understand the role of these molecules on health, opening novel avenues by which to understand, avoid, and control disease.

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In this talk, I will give an overview of the computational steps in the analysis of single-cell-based large-scale microscopy experiments. First, I will present a novel microscopic image correction method designed to eliminate illumination and uneven background effects which, left uncorrected, corrupt intensity-based measurements. New single-cell image segmentation methods will be presented using differential geometry, energy minimization, and deep learning methods (www.nucleAIzer.org). I will discuss the Advanced Cell Classifier (ACC), a machine-learning software tool capable of identifying cellular phenotypes based on features extracted from the image. It provides an interface for a user to efficiently train machine learning methods to predict various phenotypes. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. To improve the learning speed and accuracy, we propose an active learning scheme that selects the most informative cell samples.

Our recently developed single-cell isolation methods, based on laser-microcapturing and patch clamping, utilize the selection and extraction of specific cell(s) using the above machine learning models. I will show that we successfully performed DNA and RNA sequencing, proteomics, lipidomics, and targeted electrophysiology measurements on the selected cells.

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