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Visualization Challenge

Armadillo Room: 1 - 1:55 p.m.

Hydrodynamical Simulations of Black Hole Star Collisions

Fulya Kiroglu, PhD student, Weinberg College of Arts & Sciences
Additional Authors: Kyle Kremer

Recent analyses have shown that close encounters between stars and black holes occur frequently in dense star clusters. Depending upon the distance at the closest approach, these events can lead to a fully disrupted star with roughly half of the stellar material forming an accretion disk around the black hole which can potentially be detected through electromagnetic signatures from the disk. This animation shows a hydrodynamic simulation of close encounter between a black hole and a sun-like star using the Smoothed Particle Hydrodynamics code StarSmasher. The star is partially disrupted on first passage and tidally captured by the black hole. The partially disrupted remnant returns to pericenter for a second time roughly 4 days after the first passage and undergoes 4 total passages before ultimately being disrupted completely by the black hole. The video is made by the package SPLASH which uses the SPH smoothing kernel to render plots of density and other physical quantities, giving a smooth representation of the data.

Aurora Is a Web Application for Visualizing Non-Linear Graph (NLG)

Yangyang Li, PhD student, Feinberg School of Medicine

Aurora is a web application for visualizing Non-Linear Graph (NLG). This application allows you to upload JSON data representing graphs and visualize them with various layout options. The tool is useful for visualizing graph JSON data and helps us to understand the graph data in depth.

Data Visualization of the Impact of Metal-Organic Framework Topology on Cryogenic Hydrogen Storage

Kunhuan Liu, PhD student, McCormick School of Engineering and Applied Science

Figure caption: Distribution of textural properties of MOFs grouped by topologies, for (a) volumetric deliverable capacity (VDC), (b) pore volume (PV), (c) gravimetric surface area (GSA), (d) volumetric surface area (VSA). The rank zero denotes the topology with the highest mean VDC and higher rank means lower mean VDC. The grey line represents the range of the property, dark blue represents the intermediate (25/75) quartile range (IQR), the light blue denotes the median, and the yellow denotes the mean value of respective textural properties. Rectangle denotes notable groups of topologies, namely (e) crowded topologies that generate top performing MOFs while mean VDC is low and (f) vacant topologies that have high void fraction.

Figure story: In this analysis, we want to compare the data points (MOF structures) grouped by a categorical variable, the underlying topology, which has over 500 values, and identify any relationship or patterns between properties of interest, namely the hydrogen deliverable capacity (shown as predicted VDC), MOF porosity (PV, GSA, and VSA), and the underlying topology. We examine the three porosity descriptors because scientific community at which this study aims has described the correlation between these descriptors and VDC. Little is known in the community how MOF topology influences VDC, or the porosity. Due to the nature of my data, grouping topology by shapes, by colors, or creating box plots for each topology value will all result in overplotting. Using the average for each topology leads to an overly simplified conclusion that is trivial. To address these challenges, I came up with using lines with colored segments to retain as much information as possible for each topology, while showing trends and groups across topologies. Lines highlighted in segments combined with visible bubbles (open circles) easily denote the range, quantiles, median and mean for the readers. This leads to the otherwise overlooked new insight that best performing materials (shown by the upper range) have the topologies that have the poorest performance on average. All colors are tested to be colorblind-friendly using Viz Palette developed by Elijah meeks and Susie Lu.

Automated Surveying of Supernovae with the Zwicky Transient Facility

Nabeel Rehemtulla, PhD student, Weinberg College of Arts & Sciences

My research is focused on time-domain astronomy, (TDA) the study of astrophysical objects which change on timescales of seconds to ~years rather than the millions or billions of years which is typical in the cosmos. TDA has recently undergone what some call a data revolution; new observatories create data at unprecedented rates (in some cases, multiple terabytes per night) and entirely new techniques are required to extract maximal knowledge from this Big Data. In my animation, I am presenting a workflow I have helped create in the Bright Transient Survey (BTS), a team who seeks to discover and classify as many supernovae (SNe) as possible to understand their large scale statistics and properties.

I ease the audience into the research by starting with a simplified explanation of how the discovery and classification of SNe works. Subtle elements like the size of the gray squares where either telescope images conveys key concepts of this process in an intuitive way: we first search with large a field-of-view and then conduct detailed study with a smaller field-of-view once we know where to look. The sky animation conveys the magnitude of our sample, and, together with the scrolling timeline, the illustration that this work has required a half decade of effort. From my experience, non-astronomers frequently query why there are gaps in our sky map, so, in an effort to not confuse the audience, I make sure to explain this before moving on. The scatter plot presents one way astronomers frequently study SNe, plotting brightness versus time. I progressively introduce different populations to best show their separation and prevent overwhelming the audience. Different types of SNe have different colors and symbols to ensure accessibility for those with color-vision deficiency. The last frame of content describes the fully-automated workflow. I reintroduce the previously used telescope assets to draw from the information conveyed earlier. Arrows and progressively revealing subsequent elements are intended to help guide the eye through the different stages of the otherwise complex workflow. The four elements presented are all machine learning (ML) tools; I aimed to make the visuals supporting them as simple as possible, so as to not assume any ML knowledge from my audience. Lastly, I present appropriate references, credits, and a link to the source code for creating the visualization in Manim.

Health-Related Quality of Life in Medicare Advantage Beneficiaries With Heart Failure or Cancer

Kriti Shah, MD-PhD student, Feinberg School of Medicine

Heart failure (HF) affects approximately 6.7 million U.S. adults while cancer affects approximately 17 million Americans. In recent years, HF experts have been increasingly comparing HF with cancer to reframe approaches to patient care. While both profoundly impact physical, mental, and social health, in the public perception, cancer seems more morbid than HF. Secondly, even though both conditions require complex management, multidisciplinary care has been adopted and enforced primarily in cancer care. Lastly, both conditions have high rates of comorbidities with other debilitating conditions like depression and anxiety. Therefore, truly understanding quality of life in both conditions can help prioritize patient comfort and minimize distress.

To see how quality of life differed between patients with cancer and patients with HF, we looked at data from the Medicare Health Outcomes Survey from 2016-2020. This database consisted of >1.1 million Medicare Advantage users, of which 71,025 users had HF. We included people >=65 years old who self-reported having either HF or breast, prostate, colorectal, and lung cancer; anyone younger had serious disability and were less representative of the broader HF sample.

To measure quality of life, we used a well-validated self-report measure called the Veterans Rand-12 (VR-12), which measured mental and physical health of respondents with a mental component score and physical component score. Lower scores indicate poorer health. As can be seen in the visualization, those with HF had lower physical and mental component scores than those with lung, colorectal, breast, or prostate cancer, indicating poorer physical and mental health. And, overall, patients with HF experienced significantly worse physical health than the average U.S. population.

Given that the intended audience of this research and visualization are researchers in the HF and cancer space, the visualization’s aim is to not only inform viewers about the direness of HF on life, but to also encourage further discussion regarding the discrepancies that may be causing such findings and the need for targeted interventions. These discrepancies include differences in uptake and adherence to treatment regimens, referrals to multidisciplinary care including palliative support, utilization of rehab services, screening of risk factors of poor self-care, and more.

This visualization was created using R 4.2.1 and PowerPoint. As can be seen in the design, the purpose of this central illustration was to clearly show the worse health outcomes of HF. The bar graphs highlight HF in red in contrast to the grayed-out cancer bars. The visualization further represents a progression of direness through its color scheme with the gold-yellow “caution”-colored heading showing less differences between HF and cancer as compared to the red “danger” heading between HF and the general population. The visualization further includes a caption for those who are unfamiliar with VR-12 to explain the findings.

For more information on the research behind this visualization, please see the final published paper: Shah, K. P., Khan, S. S., Baldridge, A. S., et al. (2023). Health Status in Heart Failure and Cancer: Analysis of the Medicare Health Outcomes Survey 2016-2020. JACC. Heart failure, S2213-1779(23)00678-9. Advance online publication

Electrophysiological profiles of neurons derived from patients with KCNQ2-related developmental epilepsies

Syed Wafa, MD-PhD student, Feinberg School of Medicine

Research: KCNQ2 is a gene that encodes a protein responsible for regulating the activity of neurons in the brain. Mutations in this gene are associated with developmental epilepsies and cognitive impairments. However, the neuronal impact of genetic perturbations in KCNQ2 has yet to be elucidated. Here, we harvested blood cells from 5 patients with different KCNQ2 mutations and reprogrammed these blood cells into stem cells; we then used gene-editing technologies to correct each mutation. We grew neurons from each pair of mutated (disease) and mutation-corrected (control) stem cell lines and recorded their electrophysiological activity over 4 weeks in culture using multi-electrode arrays. The overall goal of this research is to discover electrophysiological biomarkers that drive the etiology and progression of KCNQ2-related developmental epilepsies. Data collection, processing, and visualization: We generated 5 pairs of control and diseased stem cell lines (total of 10 stem cell lines). For each pair, we grew neurons in 3 batches (i.e. 3 biological replicates) and had at least 18 total technical replicates per line (across the 3 batches). In total, we had 282 technical replicates across the 3 batches for all 10 stem cell lines. We performed 5 minute-recordings over 4 weeks in culture (27 timepoints in total). We extracted 41 electrophysiological features from spike times measured in each recording. Overall, we had a matrix of 282 x 41 x 27 for downstream processing. For each batch, we separately performed linear interpolation for each feature of each technical replicate across timepoints to get a complete set of timepoints and scaled features to the mean of the final time point of the controls. Figure A portrays representative spike raster plots from a technical replicate for each cell line on day 21 in culture. Each row represents an electrode, and each colored line represents a spike detected during the recordings. Figure B is an illustration showing the building blocks of the 41 extracted features. To generate heatmaps (figure C), we calculated the scaled difference between disease and controls for each batch separately. Intended audience: The data processing, analysis, and visualization is intended for (1) the stem cell community, where researchers record electrical activity to quantify electrophysiological properties of brain and heart cells, (2) the precision medicine community, where researchers and clinicians study the etiology and progression of diseases and impact of pharmacological interventions using patient-derived cells. Hardware and software: All analyses were performed in a MacOS system (MacBook Pro 2.7 GHz Quad-Core Intel Core i7). MATLAB 2024a (with standard in-built toolboxes) was used for signal processing and generation of spike raster plots. The ComplexHeatmap package in R 4.3.1 was used for generation of heatmaps.

Seeing Beyond the CT Scan: Enhancing Radiotherapy Pneumonitis Prediction through Grad-CAM Visualization Tools

Zhuoyang Zou, PhD student, McCormick School of Engineering and Applied Science
Additional Authors: P. T. Troy Teo, Mohamed Abazeed, Amulya Yalamanchilli

Motivation. Radiation therapy is indispensable for lung cancer treatment, but it carries the inherent risk of harming surrounding healthy tissues, leading to potentially severe side effects such as radiation-induced pneumonitis—an inflammation that can result in permanent lung damage. This situation underscores an urgent, unmet clinical need to assess patient susceptibility to pneumonitis before initiating treatment. In response, we have developed advanced deep-learning (DL) models that utilize pre-treatment CT images to pinpoint patients at heightened risk of developing pneumonitis. However, the complex nature of DL model interpretability presents a significant hurdle, especially for clinicians and medical practitioners not well-versed in the nuanced mechanisms of artificial intelligence. To close this gap and make the models' decision-making processes more transparent, we introduced visualization tools designed to elucidate how deep-learning models learn and predict pneumonitis. This progression from predicting individual 2D slices to compiling them into an integrated 3D image volume provides insights into the prediction of pneumonitis for a lung cancer patient. Data and Visualization Technique. In our quest to enhance radiation-toxicity prediction, we leverage CT images from patients, encoded in DICOM format for learning. To elucidate the models' decision-making processes, we integrated the DL prediction model with the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. Grad-CAM generates insightful heat maps for each image slice, shedding light on critical regions deemed significant by the model, thereby cultivating a more profound comprehension and trust in the predictive power of the DL model. Further enhancing our approach, we employed a Recurrent Neural Network (RNN) that calculates the likelihood of pneumonitis across sequential image slices. The innovative combination of 2D Grad-CAM heat maps superimposed on corresponding CT slices, coupled with RNN-derived probabilities, offers a dynamic view into how DL models anticipate pneumonitis onset. This dual-phase visualization—from individual 2D slices to an aggregated 3D image volume—allows for the extrapolation of cumulative probabilities, providing a comprehensive patient-specific pneumonitis risk prediction. Visualization Impact on Model Accuracy. The use of Grad-CAM-generated heatmaps and sequential probability generation enables the optimization of parameters for both the deep learning model and RNN. Starting with a slice-level prediction ROC-AUC of 0.6713, the aggregation of slice-level prediction probabilities significantly enhances pneumonitis prediction, elevating the ROC-AUC from 0.6713 at the slice level to 0.8285 for patient-level diagnostics. Users, Impact, and Innovation. Our visualizations, derived from a robust CNN-RNN network, not only demystify AI's role in predictive diagnostics but also mark a significant leap toward personalized patient care in radiation oncology. By offering clinicians interpretable analysis of radiation toxicity through gradient-based heatmaps on DICOM images, these visualizations directly impact treatment planning and patient outcomes. They pinpoint potential pneumonitis areas, allowing for precise radiation targeting, reducing severe pneumonitis risk, and ultimately enhancing patient quality of life. Moreover, these visual aids enhance patient education about their treatment, promote informed consent, and may reduce treatment-related anxiety. Ultimately, our innovative application of visualization aids in pneumonitis prediction is poised to enhance clinical decision-making, align treatments with patient-specific risk profiles, and improve overall patient care.

USMLE Medical Knowledge Graph

Flynn Chen, MD-PhD student, Feinberg School of Medicine

As a Feinberg medical student studying for the United States Medical Licensing Exam Step 1, I've always been curious about the topology of information that I am studying, and how to convey the complex and vastness of the content to friends and family outside of medicine. The design principle of this visualization is to create an interactive exhibit where users can freely roam and explore each piece of information, and see how one idea connects with others. This visualization is accomplished using high yield USMLE information extracted from Anki flash cards. Each flash card is a node, and the node is colored by its relevant organ or disease system. The nodes are linked by the intersectionality of organ or disease systems, and the linkage for each node is equilibrated in a 3D force graph ( This piece is meant to be an interactive exhibit that chronicle the progress we've made as medical students, and our aspiration for using knowledge for the betterment of others.