Background: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. 
Methods: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. 
Results: We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefis, as well as the limitations, of the approach. 
Keywords: visualization; visual computing; rule-based modeling; cell signaling

The TLR4 family of models. A similarity comparison between models TLR4 v15 and TLR4 RPS v1 in the TLR4 family.

MOSBIE: A Tool for Comparison and Analysis of Rule-Based Biochemical Models

Background: Molecular and systems biologists are tasked with the comprehension and analysis of incredibly complex networks of biochemical interactions, called pathways, that occur within a cell. Through interviews with domain experts, we identified four common tasks that require an understanding of the causality within pathways, that is, the downstream and upstream relationships
between proteins and biochemical reactions, including: visualizing downstream consequences of perturbing a protein; finding the shortest path between two proteins; detecting feedback loops within the pathway; and identifying common downstream elements from two or more proteins. 
Results: We introduce ReactionFlow, a visual analytics application for pathway analysis that emphasizes the structural and causal relationships amongst proteins, complexes, and biochemical reactions within a given pathway. To support the identified causality analysis tasks, user interactions allow an analyst to filter, cluster, and select pathway components across linked views. Animation is used to highlight the flow of activity through a pathway. 
Conclusions: We evaluated ReactionFlow by providing our application to two domain experts who have significant experience with biomolecular pathways, after which we conducted a series of in-depth interviews focused on each of the four causality analysis tasks. Their feedback leads us to believe that our techniques could be useful to researchers who must be able to understand and analyze the complex nature of biological pathways. 
ReactionFlow is available at <a href="https://github.com/CreativeCodingLab/ReactionFlow">https://github.com/CreativeCodingLab/ReactionFlow</a>. 
Keywords: Pathway visualization; Biological networks; Causality analysis; Topological ordering

Downstream consequences if a protein/complex is &dlquo;knocking out&drquo; in the defects in vitamin and cofactor metabolism pathway: (a) GIF protein (b) TCll:Cbl complex (c) MTRR:MTR complex.

ReactionFlow: An Interactive Visualization Tool for Causality Analysis in Biological Pathways

Background: Molecular activation pathways are inherently complex, and understanding relations across many biochemical reactions and reaction types is difficult. Visualizing and analyzing a pathway is a challenge due to the network size and the diversity of relations between proteins and molecules. 
Results: In this paper, we introduce PathwayMatrix, a visualization tool that presents the binary relations between proteins in the pathway via the use of an interactive adjacency matrix. We provide filtering, lensing, clustering, and brushing and linking capabilities in order to present relevant details about proteins within a pathway. 
Conclusions: We evaluated PathwayMatrix by conducting a series of in-depth interviews with domain experts who provided positive feedback, leading us to believe that our visualization technique could be helpful for the larger community of researchers utilizing pathway visualizations. 
PathwayMatrix is freely available at <a href="https://github.com/CreativeCodingLab/PathwayMatrix">https://github.com/CreativeCodingLab/PathwayMatrix</a>. 
Keywords: Pathway visualization; Biological networks; Matrix visualization; Binary Relationships

Visualizing protein binary relationships of the HIV life cycle by different tools: BioFabric (top panel), Cytoscape (middle panel), Hive Plot (bottom left panel), and PathwayMatrix (bottom right panel).

PathwayMatrix: Visualizing Binary Relationships between Proteins in Biological Pathways

PhD candidate Paul Murray presents his research in biological data visualization techniques. 
The Pathway Commons web service brings together several large biological databases, with tens of thousands of biological entities and interactions that can be traversed and queried through the Pathway Commons API. Query results are returned using the BioPax format (which is an RDF/XML OWL file), and each BioPax file contains a number of different relationship types. Rather than try to encompass a wide variety of node types and link types into a single visualization, a linked-view visualization approach is employed that presents each type of relationship in its own view, with linked interactions between views. This system is being integrated with the Pathway Commons API to enable effective visual queries of complex biological data.

Screenshot of linked-view visualization approach.

Exploring BioPax and the Pathway Commons API Using Linked Views

Dynamic network visualization has been a challenging topic for dynamic networks analysis, especially for spatially embedded networks like brain networks.  In this paper, we present an animated interactive visualization design that combines enhanced node-link diagrams and distance matrix layouts to assist neuroscientists in their exploration of dynamic brain networks and that enables them to understand how functional connections relate to the spatial structure of the brain.  Our visualization also provides the ability to observe the evolution of a network, the change in the community identities, and node behavior over time.

Screenshot of our visualization tool showing the coordinated dual-representation of dynamic network data.

Visualizing Dynamic Brain Networks Using an Animated Dual-Representation

Background: Biologists make use of pathway visualization tools for a range of tasks, including investigating inter-pathway connectivity and retrieving details about biological entities and interactions. Some of these tasks require an understanding of the hierarchical nature of elements within the pathway or the ability to make comparisons between multiple pathways. We introduce a technique inspired by LineSets that enables biologists to fulfill these tasks more effectively. 
Results: We introduce a novel technique, Extended LineSets, to facilitate new explorations of biological pathways. Our technique incorporates intuitive graphical representations of different levels of information and includes a well designed set of user interactions for selecting, filtering, and organizing biological pathway data gathered from multiple databases. 
Conclusions: Based on interviews with domain experts and an analysis of two use cases, we show that our technique provides functionality not currently enabled by current techniques, and moreover that it helps biologists to better understand both inter-pathway connectivity and the hierarchical structure of biological elements within the pathways.

Comparison of the same dataset realized with (a) LineSets, showing set information and network information simultaneously leads to visual clutter; (b) Extended LineSets, mitigates visual clutter via a more effective depiction of category information.

Extended LineSets: A visualization technique for the interactive inspection of biological pathways

This paper describes an artwork created in response to a question about the role of the artist in communicating climate change issues. The artwork, titled Turbulent World, incorporates turbulence and surprise as a means to visualize the potential instability of our culture and the environment due to climatic changes indicated by increased worldwide temperatures. The artwork makes use of a custom fluid engine that can represent any amount of turbulence and energy. A dataset encoding a simulation of rising surface-air temperatures over the next century is mapped to the turbulence system; and the visualization is updated as the months and years flow by, based on the projected temperatures at different areas of the world. That is, the increased turbulence of the system causes a representation of a map of the world to become distorted in different ways. A secondary view is overlaid, showing numerical data and providing a more dispassionate display of the inexorable increase in world temperature. 
Keywords: climate change, art-science, software art

Screen capture from the artwork as it shows the average surface-air temperature across the world for September 2067, using the projected values generated by the CM2.1 model developed by the Geophysical Fluid Dynamics Laboratory.

TurbulentWorld: An Artwork Indicating the Impact of Climate Change

Thanks to advances in non-invasive technologies such as functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), highly-detailed maps of brain structure and function can now be collected. In this context, brain connectomics have emerged as a fast growing field that aims at understanding these comprehensive maps of brain connectivity using sophisticated computational models. In this paper we present BRAINtrinsic, an innovative web-based 3D visual analytics tool that allows users to intuitively and iteratively interact with connectome data. Moreover, BRAINtrinsic implements a novel visualization platform that reconstructs connectomes&rsquo; intrinsic geometry, i.e., the topological space as informed by brain connectivity, via dimensionality reduction. BRAINtrinsic is implemented with virtual reality in mind and is fully compatible with the Oculus Rift technology. Last, we demonstrate its effectiveness through a series of case studies involving both structural and resting-state MR imaging data. 
Keywords: connectomics, connectome datasets, intrinsic geometry, neuroimaging

Intrinsic geometry of the tractography-derived structural and the resting-state fMRI connectome, as well as the locations of rich-club regions in these spaces.

BRAINtrinsic: A Virtual Reality-Compatible Tool for Exploring Intrinsic Topologies of the Human Brain Connectome

Senior data analyst, Wei Xie in the Alibaba Safety and Security Intelligence Center will present Alibaba's work on large-scale data visualization and analytics aimed at identifying and analyzing risks and frauds in customer transaction data records. The presentation will provide and introduction to The Alibaba Group, and overview of the cyber-crime challenge currently being faced in China, and a discussion of big data vs. cyber-crime with prospective approaches and solutions. 
Speaker: Wei Xie 
Wei Xie, director of Alibaba's Business Safety Agency, is the leader of SSIC (Safety and Security Intelligence center). SSIC of Alibaba group is China’s first data visualization center based on real-time operation data in the area of online safety and security. Mr. Xie has over 15 years of experiences in the field of IT as well as safety and security. He is also the expert on anti-fraud, data safety. Conducting university collaborations is one of his main responsibilities as well.

When Big Data Meets Cyber-Crime

EVL PhD alumna and director of the University of Arkansas Little Rock&rsquo;s Emerging Analytics Center, Carolina Cruz-Neira hosts the inaugural open house of her latest Virtual Reality Center. 
Among Cruz-Neira&rsquo;s accomplishments is as the co-creator of the CAVE&trade; Virtual Realtiy Environment, developed as part of her PhD research at EVL. She is also recognized by having founded and led very successful virtual reality research centers, like the Virtual Reality Applications Center at Iowa State University, the Louisiana Immersive Technologies Enterprise and now the Emerging Analytics Center (EAC). 
EAC&rsquo;s work is centered on interdisciplinary projects and it covers a wide range of topics from the development and use of large-scale one-of-a-kind systems to every day portable technologies such as smart phones, glasses and tablets as well as pioneering applications for these technologies in many disciplines and markets. 
EAC&rsquo;’s projects are in areas such as large data visualization, data analytics, training environments, software architectures for visualization and virtual reality, technology development, modeling and simulation, museum installations, and tech-transfer to many of our sponsors and collaborators. 
Join Cruz-Neira and EAC faculty, researchers, and students at the facility open house October 16-17, 2015 at the Emerging Analytics Center, UALR EIT - 420. 
Register: <a href="discovervirtualreality.eventbrite.com">discovervirtualreality.eventbrite.com</a>

EAC Open House Flyer

Carolina Cruz-Neira

EVL Alumna Opens Emerging Analytics Center at University of Arkansas

The week of October 25, 2015, the international IEEE Visualization (VIS) 2015 conference is being held in downtown Chicago, and the Electronic Visualization Laboratory (EVL), part of the Computer Science (CS) department at the University of Illinois at Chicago, has a leadership role in helping ensure its success. 
EVL director Maxine Brown serves as General Co-chair with Michael E. Papka of Argonne National Laboratory (who is also a CS/EVL alumnus). EVL CS faculty G. Elisabeta Marai is co-chair of the Doctoral Colloquium. EVL CS faculty Angus Forbes and EVL School of Design faculty Daria Tsoupikova are co-chairs of the Arts Program (VISAP). EVL director of research and CS faculty Andy Johnson serves on the Program Committees of the Scientific Visualization (SciVis) and VISAP tracks. EVL senior research scientist and director emeritus Dan Sandin is participating in VISAP as an invited artist and panelist. 
EVL CS students are also participating. Jillian Aurisano and Tim Luciano are serving as Student Volunteers. Aurisano also has two accepted posters: &ldquo;Show Me Data,&rdquo; which received an honorable mention, is about a conversational interface in visual data exploration that is being done with CS advisor Andy Johnson and professor Barbara Di Eugenio; and, &ldquo;AlignmentVis: Visual Analytics for Ontology Matching,&rdquo; which describes her work from the Data and Web Semantics course taught by professor Isabel Cruz. Angus Forbes&rsquo; student Alessandro Chetta is presenting a paper in the Workshop on Visual Analytics in Health Care. Forbes&rsquo; student Kyle Almryde received honorable mention in the SciVis Data Contest for his web-based 3D visualization application entitled &ldquo;Halos in a Dark Sky: Interactively Exploring the Structure of Dark Matter Halo Merger Trees.&rdquo; 
In addition to the above, many EVL alumni are also involved in organizing this conference, and many more will be attending the one week event. The UIC CS department is one of the sponsors. 
The conference press release below provides additional details about <a href="http://ieeevis.org">IEEE VIS 2015</a>. The <a href="http://visap.uic.edu/2015/">IEEE VIS Arts Program</a> is open to the public for two weeks, from October 19-30, and is being held at the School of the Art Institute of Chicago&rsquo;s (SAIC) LeRoy Neiman Center, 37 S. Wabash. 
* * * * * * * * * * * * * * * * 
2015 IEEE Visualization Conference Connects Latest in Research to Real World Practice 
CHICAGO, 12 October 2015 - Over a thousand data visualization specialists are expected at the 2015 IEEE Visualization (VIS) conference in Chicago this month. For one week starting October 25, VIS will host the largest annual gathering of academics, practitioners, and researchers focused on making every variety of dataset visually understandable. 
After its successful meeting in Paris last year, the international VIS conference is returning to the United States, where organizers hope to attract a record number of young practitioners seeking opportunities to meet and mingle with the senior members and leaders in their field. This year&rsquo;s program includes several returning events aimed at encouraging such interactions, such as the Doctoral Colloquium and Digital Compass activities, along with a new industry-targeted Practitioner Event. 
<ldquo;This conference is the best opportunity all year for the data visualization community to see and share the latest advancements, tools, and techniques,rdquo; said Michael E. Papka, VIS 2015 co-chair. &ldquo;From the academic to the applied, VIS has something to offer everybody working in the field today.&rdquo; 
VIS 2015 papers talks and panels are organized into three tracks: Visual Analytics Science and Technology (VAST), Information Visualization (InfoVis), and Scientific Visualization (SciVis). These main track offerings and a three-day exhibition begin on Tuesday October 27. Three co-located symposiums are held on the first two days of VIS 2015: Visualization in Data Science (VDS), Large Data Analysis and Visualization (LDAV), and Visualization for Cyber Security (VizSec). The VIS offerings on Sunday October 25 and Monday October 26 also include eight workshops and nine tutorials. 
The VIS 2015 Arts Program features a papers track and a two-week public art exhibition at the School of the Art Institute of Chicago&rsquo;s (SAIC’s) LeRoy Neiman Center, which is located across the street from the conference venue, the Palmer House Hilton, in Chicago&rsquo;s downtown Loop district. 
IEEE VIS is sponsored by IEEE’s Computer Society. Additional support comes from academic, industry, and government research organizations. <a href="http://ieeevis.org">Register and learn more about the conference program</a>. Follow us on Twitter, like our Facebook page, or visit our LinkedIn page. 
CONTACT: 
Laura Wolf (Argonne National Laboratory) 
IEEE Visualization 2015 Coordinating Chair 
<a href="mailto:info@ieeevis.org">info@ieeevis.org</a>

&ldquo;Halos in a Dark Sky: Interactively Exploring the Structure of Dark Matter Halo Merger Trees,&rdquo; received honorable mention in the IEEE VIS 2015 SciVis Data Contest.

EVL has leadership roles in international IEEE Visualization 2015 conference

PhD, 2019

Timothy Luciani

Every day, travelers use Public Transport Systems to reach their destinations. Public Transport Authorities generally use passive way-finding devices, including ambient displays, to provide useful information for travelers, such as the number of vehicles in the vicinity of a stop and their estimated times of arrival. However, due to both the complexity of the transport networks and the lack of sophistication in the design of these displays, the information provided by these devices is limited. We present TransitTrace, a visualization that exploits interaction-free ambient displays to provide travelers with more detailed information and ultimately to help them to navigate the city using a Public Transport System. Specifically, the proposed design makes use of a novel animation strategy to aid travelers in route planning tasks. In this paper, we describe details about the system and visualization design of TransitTrace, as well as its initial implementation using transportation data provided by the City of Chicago. 
Categories and Subject Descriptors 
H.2.8 [Database Applications]: Spatial databases and GIS; H.5.1 [Multimedia Information Systems]: Animations 
Keywords 
Public Transport Systems, Geotemporal Visualization, Ambient Displays

The animation begins by showing the current position of all vehicles that will be arriving in the users&rsquo; vicinity within a given window of time.

TransitTrace: Route Planning using Ambient Displays

Accessibility is an important element in urban transportation planning. Accessibility measures combine mobility and land use measures to provide a more complete picture of the transportation-land use nexus than either of these measures alone. By providing insights into the varying degrees to which different areas of a region are connected to opportunities by the transportation system, accessibility analysis helps urban planners to understand the relationship between transportation and land use, and provides reference for them to improve the equality of the residents. Calculating accurate accessibility values and visualizing them in an efficient way is a complex and challenging process. In this paper, we present a web-based system that visualizes multimodal
accessibility to multiple land uses of Chicago metropolitan area, as the first step of an effort to build an integrated platform for accessibility analysis tasks. We also discuss some use cases of this system, and show its effectiveness by providing experts feedback of this prototype. 
Categories and Subject Descriptors 
I.3.8 [Computer Graphics]: Applications; H.5.2 [Information Interfaces and Presentation]: User Interfaces|graphical user interfaces 
General Terms 
Design 
Keywords 
accessibility analysis, geographic visualization, urban transportation planning

Travel Time View. Users can investigate the travel time from any block group to other block groups showed as an isochrone map.

Understanding Transportation Accessibility of Metropolitan Chicago Through Interactive Visualization

The use of electronic health records (EHRs) in clinical environments provides new opportunities for nurses to integrate data analyses into their practice. While having access to these records has many benefits, the act of recording, retrieving, and analyzing this data can nonetheless introduce communication issues, as navigating and interpreting large amounts of heterogeneous data can be difficult, and conclusions can be hard to validate. In this paper, we describe a series of integrated visual interfaces to help nurses document and reason about patient data and about clinicians&rsquo; understanding of patient data. The interfaces present the output of a predictive algorithm that makes use of historical EHR data, patient vital signs, and nurse handoff reports in order to classify a patient in terms of their likelihood of experiencing clinical events. Furthermore, the interfaces enable the nurses to quickly explore the original data and to examine other nurses&rsquo; interpretation of patient activity during previous shifts. We present a series of usage scenarios that introduce our interactive visualization tools in the context of real-world healthcare situations. 
CCS Concepts 
Applied computing -> Health care information systems; Health informatics; Human-centered computing -> Visual analytics; Information visualization

Screenshot of the augmented  ow sheet timeline.

Augmenting EHR Interfaces for Enhanced Nurse Communication and Decision Making

This paper introduces a novel web application that visualizes dark matter halo merger trees and their evolution through space and
time. Our application enables users to interact with individual halos within these trees in order to perform a range of visual analysis
tasks, including: identifying the substructure and superstructure of the halos; observing the movement of halos across a specified range of time steps; and comparing the branching characteristics of multiple trees. Central to our application is the ability to navigate the halos by interactively &ldquo:jumping&rdquo; from tree to tree. By clearly marking halos that have &ldquo;tributaries&rdquo; - that is, halos that split off into multiple halos or that merge with one or more halos - we make it easy for the user to traverse the complex structure of the universe. Our application is publicly available online and runs at interactive rates on the browser.

A screen capture of our web application showing a complete dark matter halo merger tree.

Halos in a Dark Sky: Interactively Exploring the Structure of Dark Matter Halo Merger Trees

We present the design and implementation of a visual mining and analysis tool to help identify protein mutations across family structural models, and to help discover the effect of these mutations on protein function. We follow a client-server approach in which distributed data sources for 3D structure and non-spatial sequence information are seamlessly integrated into a common visual interface. Multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues. We evaluate our tool on the triosephosphate isomerase (TIM) family structural models and sequence data, and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins. 
Keywords: Molecular Sequence Analysis, Molecular Structural Biology, Computational Proteomics

scTIM/dTIM comparison.

Fixing TIM: Identifying Functional Mutations in Protein Families through the Interactive Exploration of Sequence and Structural Data

A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data from higher dimensions onto a lower-dimensional space. This article examines the use of motion to retain an accurate representation of the point density of clusters that might otherwise be lost when a multidimensional dataset is projected into a two-dimensional space. Specifically, we consider different types of density-based motion, where the magnitude of the motion is directly related to the density of the clusters. We investigate how users interpret motion in two-dimensional scatterplots and whether or not they are able to effectively interpret the point density of the clusters through motion. We conducted a series of user studies with both synthetic and real-world datasets to explore how motion can help users in completing various multidimensional data analysis tasks. Our findings indicate that for some tasks, motion outperforms the static scatterplots; circular path motions in particular give significantly better results compared to the other motions. We also found that users were easily able to distinguish clusters with different densities as long the magnitudes of motion were above a particular threshold. Our results indicate that incorporating density-based motion into visualization analytics systems effectively enables the exploration and analysis of multidimensional datasets. 
Keywords: Visual analytics, visual clustering, user study, spatial layout, scatterplots, multidimensional visualization,
perception

Examples from our user study in which a user is asked to detect a cluster with a different magnitude of motion.

Density-based motion

Visualizing and analyzing the relationships between taxonomic entities represented in multiple input classifications is both challenging and required due to recurrent new discoveries and inferences of taxa and their phylogenetic relationships. Despite the availability of numerous visualization techniques, the large size of hierarchical classifications and complex relations between taxonomic entities generated during a multi-taxonomy alignment process requires new visualizations. This paper introduces ProvenanceMatrix, a novel tool allowing end users (taxonomists, ecologists, phylogeneticists) to explore and comprehend the outcomes of taxonomic alignments. We illustrate the use of ProvenanceMatrix through examples using taxonomic classifications of various sizes, from a few to hundreds of taxonomic entities and hundreds of thousands of relationships. 
Keywords: Taxonomic classification, multi-taxonomy alignment, phylogenetic relationship, matrix representation, glyph-based visualization

Abstract toolkit input and output example rendered with GraphViz.

ProvenanceMatrix: A Visualization Tool for Multi-Taxonomy Alignments

This paper explores the specialized nature of research-oriented web applications that enable interactions with and the visual analysis of &ldquo;Big Data,&rdquo; i.e., large, heterogeneous scientific datasets. We introduce a pragmatic methodology for the design and evaluation of scientific workflows in research-oriented web applications. Through an in-depth usability study of the CoGe web application, a system that provides a rich set of tools for exploring genomic datasets, we demonstrate: how to identify bottlenecks in multi-step tasks; how to analyze these bottlenecks in order to provide effective solutions for improving user experience; and how these solutions may more generally apply to similar research-oriented websites in other scientific domains that also enable scientific workflows. Specifically, we provide details regarding: our user interviews, the visualization system we created to analyze complex tasks associated with scientific workflows, and how this analysis directly leads to suggestions for improvements in the current implementation of the CoGe web application. A follow-up study was carried out which indicates that our suggestions improved the ability of CoGe users to navigate and complete custom workflows, leading us to believe that our approach could also be applied to other research-oriented web applications that utilize scientific datasets. 
Keywords: Scientific workflows, Big Data, user evaluation, visual analytics.

Comparison of typical workflow for user completion of Task.

Designing and Evaluating Scientific Workflows for Big Data Interactions

This paper describes the creation of SwarmVision, a system of autonomous robotic cameras that functions both as the basis for an art installation and as an instrument for generating novel engineering research. Through a series of interviews with engineers who examined the project, we illustrate how experimentation with the instrument could lead to potential new research directions in computer vision, machine learning, swarm robotics, remote collaboration, and visualization. This suggests that an unstructured and aesthetic approach to research can inform and inspire new engineering research directions, within and beyond the scope of this particular project. 
Keywords: Media arts methodologies, computational photography, swarm robotics, autonomous robotic cameras, arts and engineering.

Camera view of autonomous image capture process viewed in the 3D virtual scene overview.

Generation of Engineering Research Directions through Artistic Process

Images from two articles by EVL&rsquo;ers made the cover of the January/February issue of JIST (Journal of Imaging Science and Technology)! 
COVER - TOP IMAGES (white background): 
Chihua Ma, Angus Forbes, Daniel Llano, Tanya Berger-Wolf, Robert V. Kenyon, &ldquo;SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks,&rdquo; Journal of Imaging Science and Technology, 60(1), January/February 2016, pp. 010405-1 - 010405-13, DOI: <a href="http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010405">http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010405</a> 
COVER - BOTTOM IMAGES (black backgrounds): 
G. Elisabeta Marai, Timothy Luciani, Adrian Maries, S. Levent Yilmaz, Mehdi B Nik, &ldquo;Visual Descriptors for Dense Tensor Fields in Computational Turbulent Combustion: A Case Study,&rdquo; Journal of Imaging Science and Technology, 60(1), January/February 2016, pp.010404-1 - 010404-11, DOI: <a href="http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010404">http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010404</a>

Cover art of JIST January/February 2016 issue features EVL research

The thesis work presented here focuses on the optimization of the similarity computation among nodes for spatio-temporal networks. The study case in this work is the computation of spatio-temporal networks on brain. The aim of the first part of the work presented is to built a GPU-based computation system able to speed up the networks definition. In the second part of the work we developed an interactive tool to visualize the networks computed using the GPU-based method developed in this first part. 
Nowadays, with the increase of computational analysis in sciences such as biology and neuroscience, the computational aspect is the most challenging one. Scientists need tools able to process large amounts of data simultaneously. Previously, scientific computations were performed using clusters of computers, but this type of infrastructure is very expensive and complex to build. The work in this thesis is part of a greater project that has the aim to apply spatio-temporal networks inference techniques to perform network analysis studies in different fields. One of the problems of spatio-temporal network applications is the computational time, and it becomes impractical to keep developing studies when it takes a long time to analyze and compute the results. We aim to help the researchers to get results in a reasonable amount of time, so they can be focused more on their studies. There are many ways to speed up the computation process, one of these is to exploit the paradigm of parallel computation using Graphic Processor Unit (GPU). GPUs represent a low-cost solution to this problem since the level of parallelization they have is really high and GPUs hardware match exactly the requirements for the problem addressed. With the first part of this thesis work we present a GPU solution and we evaluate the performance of spatio-temporal networks definition. 
The second part of the thesis work uses the results computed in the first part to propose an interactive visualization tool for the spatio-temporal networks computed. With a decrease in computation time, it is easier to achieve a comparison between the results. The proposed visualization tool allows for the changing of parameters at run-time and visualizing the differences in the resulting networks. This process is made possible thanks to a great work of pre-computation of all the data that must to be visualized using the GPU-based system developed. This second part of the work goals to improve the understanding of the results computed by the algorithm. In this study, we focus on brain network computation and visualization. 
We mentioned that this thesis work is composed by two different parts. One of them is used to compute the similarity measure needed to define the DN. While, the other aims to visualize interactively the DN computed, leaving one degree of freedom to the
final user. One of the goals of this work is the creation of a pipeline that goes from the collected data to the visualization of the result, which in our case are the networks computed previously. In fact the two piece of software developed are part of the pipeline that defines the framework we present. This framework want to avoid the dependability problem given by that the different software are not able to communicate among each others.

Network comparison, it shows the comparison between two different data session loaded in the system, each one is represented by a color in the network.

GPU Acceleration and Interactive Visualization for Spatio-Temporal Networks

Monday, May 2, 2016, 9:30 AM 
Room 1000, Science & Engineering Office Building, UIC 
UIC Computer Science and EVL exchange student from the Politecnico di Milano in Italy, Andrea Purgato presents his thesis research that explores the optimization of the similarity computation among nodes for spatio-temporal networks. 
This research is divided into two parts. The first aspect is to develop a GPU-based computation system to speed up the networks definition. The second uses the GPU-based method developed initially to develop an interactive visualization tool focused specifically on brain network visualizations.

GPU Acceleration and Interactive Visualization for Spatio-Temporal Network

Brain network visualization interface showing networked communities plotted.

Andrea Purgato Thesis Presentation: &ldquo;GPU Acceleration and Interactive Visualization for Spatio-Temporal Network&ldquo;

While most academic conferences attract people who want to learn, the annual IEEE Visualization (VIS) conference attracts people who want to be challenged. Throughout the year, the conference sponsors several contests - to encourage the best visualizations and designs of complex datasets. Awards and recognition are announced at the annual conference, which is taking place this week in Baltimore, Maryland. 
Faculty and students of the Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago were surprised and elated to learn they won Honorable Mention in the IEEE Visualization and Graphics Technical Committee&rsquo;s (VGTC) third International Data Visualization Contest, organized by the IEEE Visualization Pioneers Group (VPG). This year, the contest was done in collaboration with ACT, Inc., the company that administers the standardized test for college readiness assessment. 
Early in the Fall 2016 semester, UIC Computer Science (CS) associate professor G. Elisabeta Marai gave students in her CS 526 Interactive Scientific Visualization course a design assignment based on the IEEE VGTC VPG Data Visualization Contest. The students - including Timothy Luciani, Juan Trelles Trabucco, Chihua Ma, Andrew Burks, Manu Thomas, Krishna Bharadwaj, Shiwangi Singh, Peter Hanula, and Lei Di - spent several weeks designing, implementing, and integrating pieces for a group submission to the contest. Serendipitously, their visualization effort, called &ldquo;Multi-Scale Voronoi-Based ACT Assessment&rdquo; was recognized as one of the winning entries by the judges. The group was honored at the VPG Reception at VIS 2016. 
ACT operates nearly 9,000 testing centers annually to deliver the ACT Assessment test to over two million high school students on six different Saturdays each year as part of its open, &ldquo;National&rdquo; program. Use of the test data by schools and government agencies has grown significantly in recent years, and is increasing the demands on the ACT network and test seat availability. Thus, ACT seeks to use data it collects to better manage its system and its operating costs. 
The 2016 challenge was to develop a static visual representation that shows how geographic and seasonal changes in the availability of the ACT test affects nearby or adjacent testing sites, by moving students or assessments, changing dates, or some other strategy. For example, how does the addition or subtraction of a testing center affect demand in neighboring zip codes, or what is the optimal test center network in a particular season? 
The students who worked on the winning entry are UIC CS PhD, MS and BS students, many of whom have EVL Research Assistantships or Research Experiences for Undergraduate (REU) support. 
<a href="http://vacommunity.org/ieeevpg/viscontest/2016/">IEEE VGTC VPG third International Data Visualization Contest information.</a> 
# # # # # # 
IMAGE CAPTION AND CREDITS 
TOP LEFT: Choropleth maps showing national-level costs and utilization. The large green map shows aggregate expenses (2012-2015). California, Texas, and Florida have the three highest expenses. The small multiple below shows the expense distribution per year (no significant variation). The purple maps show, similarly, the percent utilization (Assigned/Capacity), and a reasonably fair distribution nationwide. 
BOTTOM: Average bar charts. Two bar charts display the average expenses and utilization per state (2012-2015). The charts provide a quantitative comparison across states and complement the choropleths. 
TOP MIDDLE: Voronoi visualization of test center distribution at regional level (Illinois). Voronoi diagrams partition the region based on distances to test centers. The Voronoi cell intensity is mapped to Assigned/Capacity. The darker the cell, the higher demand in that region. The partially filled red circle in each cell double-encodes utilization. Circle size encodes the Center capacity, and the filled area shows the assigned number. Orange markers show population sizes per Zipcode. The region with the most test centers in Illinois is the greater Chicago area.K 
TOP MID-RIGHT: Side-by-side Voronoi analysis of center addition (Cook County). Two side-by-side Voronoi maps show county-level changes before and after the addition of two new test centers (yellow). The saturation of cells with a yellow boundary captures the effect over nearby centers. Lighter Voronoi cells in the South Side of Chicago indicate lower participation in those neighborhoods. 
TOP RIGHT: Seasonal Optimized Network. Four maps show seasonal population density per Zipcode (Chicago region). Partially filled red glyphs encode Centers and show the Assigned/Capacity per season. The Center network shown is a custom optimized network in which under-utilized Centers were iteratively removed and the nearest Center capacity was increased. Summer and Fall are popular in Chicago, and have dense Center networks.

IEEE VIS'16 VGTC VPC Data Visualization Contest winners - Juan Trelles, Chihua Ma, Manu Thomas, Timothy Luciani, G.E. Marai, Shiwangi Singh, Lei Di, Peter Hanula, Krishna Bharadwaj, Andrew Burks; EVL/UIC

UIC/EVL Receives Honorable Mention in IEEE VIS 2016’s VGTC VPG Data Visualization Contest

MS Graduate 2018

Manu Mathew Thomas

In this paper we introduce TimeArcs, a novel visualization technique for representing dynamic relationships between entities in a network. Force-directed layouts provide a way to highlight related entities by positioning them near to each other. Entities are brought closer to each other (forming clusters) by forces applied on nodes and connections between nodes. In many application domains, relationships between entities are not temporally stable, which means that cluster structures and cluster memberships also may vary across time. Our approach merges multiple force-directed layouts at different time points into a single comprehensive visualization that provides a big picture overview of the most significant clusters within a user-defined period of time. TimeArcs also supports a range of interactive features, such as allowing users to drill-down in order to see details about a particular cluster. To highlight the benefits of this technique, we demonstrate its application to various datasets, including the IMDB co-star network, a dataset showing conflicting evidences within biomedical literature of protein interactions, and collocated popular phrases obtained from political blogs. 
Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Interfaces and Presentation]: User
Interfaces-Graphical user interfaces

TimeArcs visualization for interactions around OPSD protein. (1) and (2) are conflicting evidences of OPSD and K+ interaction in 2003 and 2012 publications.

TimeArcs: Visualizing Fluctuations in Dynamic Networks

Adjacency matrices can be a useful way to visualize dense networks. However, they do not scale well as the network size increases due to limited screen space, especially when the number of rows and columns exceeds the pixel height and width of the screen. We introduce a new scalable technique, MultiLayerMatrix, to visualize very large matrices by breaking them into multiple layers. In our technique, the top layer shows the relationships between different groups of clustered data while each sub-layer shows the relationships between nodes in each group as needed. This process can be applied iteratively to create multiple sub-layers for very large datasets. We illustrate the usefulness of MultiLayerMatrix by applying it to a network representing similarity measures between 2,048 characters in the Asteraceae taxonomy, a rich dataset that describes characteristics of species of flowering plants.We also discuss the scalability of our technique by investigating its effectiveness on a large synthetic dataset with 20,000 columns by 20,000 rows.

Visualizing 2048 nodes in a regular adjacency matrix (left) and in a MultiLayerMatrix of two layers: The middle panel shows the first layer, and the right panel shows an example of the second layer.

MultiLayerMatrix: Visualizing Large Taxonomic Datasets

Node-link diagrams are widely used for visualizing relational data in a wide range of fields. However, in many situations it is useful to provide set membership information for elements in networks. We present BranchingSets, an interactive visualization technique that uses visual encodings similar to Kelp Diagrams in order to augment traditional node-link diagrams with information about the categories that both nodes and links belong to. BranchingSets introduces novel user-driven methods to procedurally navigate the graph topology and to interactively inspect complex, hierarchical data associated with individual nodes. Results indicate that users find the technique engaging and easy to use. This is further confirmed by a quantitative study that compares the effectiveness of the visual encodings used in BranchingSets to other techniques for displaying set membership within node-link diagrams, finding our technique more accurate and more efficient for facilitating interactive queries on networks containing nodes that belong to multiple sets.

A prototype application that uses the BranchingSets technique to facilitate the navigation of a complex, hierarchical biological pathway dataset where both nodes and links are members of one or more sets.

BranchingSets: Interactively Visualizing Categories on Node-Link Diagrams

UIC Computer Science Associate Professor and Electronic Visualization Laboratory faculty member, G. Elisabeta Marai with collaborators from the University of Texas, University of Iowa, and University of Minnesota has been awarded an NIH R01 from the National Cancer Institute. 
&ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; will develop a computational methodology that supports the construction of a validated precision model of patient-specific outcomes for head and neck cancers, based on demographics, toxicity, and complex imaging data. The methodology will be the first in the field to include both complex imaging and non-imaging data, while taking into account large-scale biological and clinical correlates. A web-based environment will further allow practitioners to interactively explore and communicate the model mechanics and outcomes to stakeholders. The approach is innovative through its leverage of big imaging data repositories and through its unique blend of computational modeling principles from bioengineering, statistics, and computer science (medical imaging and visualization).

CS/EVL Associate Professor Marai awarded NIH R01 from the National Cancer Institute

This article presents the main features and components of iCardia - an innovative mHealth platform designed to support remote monitoring and health coaching of cardiac rehabilitation (CR) patients, through Fitbit wearable sensor devices, smartphones, and personalized SMS text messages. The design and development of iCardia were based on an iterative, user-centered design process and an open-service architecture to ensure rapid scalability and adherence to evidence-based guidelines for easier transition into clinical practice. iCardia has the potential to enable a paradigm shift towards a collaborative CR environment that utilizes mHealth technologies to engage patients to more effectively self-manage their cardiovascular disease.

iCardia platform components and architecture

Development of an innovative mHealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients

&ldquo;SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks,&rdquo; authored by Ma, C., Forbes, A. G., Llano, D. A., Berger-Wolf, T., Kenyon, R. V. was recognized by the Society for Imaging Sciences and Technology as the recipient of the 2017 Charles E. Ives Journal Award. 
The Charles E. Ives Award is given in recognition of an outstanding contribution in the area of basic or applied science or engineering published in a Society journal (JIST or JEI) during the preceding calendar year.

Flow diagram: (a) mouse brain, (b) camera, (c) time-series imaging data of mouse brain slices, (d) generate dynamic communities using DNA and (e) present the analysis results using the SwordPlots visualization.

EVL PhD Candidate Chihua Ma Receives Charles E. Ives Journal Award

EVL PhD Candidate, Timothy Luciani presents his research &ldquo;Contextual Similarity Abstraction Techniques for Spatial Data Analysis&rdquo; during his preliminary exam. 
Date: Thursday July 13, 2017 
Time: 10:00am 
Room: ERF 2068 (Cybercommons) 
Committee: Dr. G. Elisabeta Marai (Chair), Dr. Robert Kenyon, Dr. Andrew Johnson, Dr. Angus Forbes, Dr. Klaus Mueller (Stony Brook University, NY) 
Abstract: 
Comparative analysis of modern large-scale datasets is a core task in scientific visualization. This task entails identifying both global similarities among entire data collections, and local similarities between smaller data subsets to help provide critical insights into data trends and potentially lead to classification, validation and discovery. To this day, Shneiderman&rsquo;s visual information-seeking mantra remains the most common approach to designing comparative visualization applications: Overview first, zoom and filter, then details on demand. However, for large-scale datasets, this mantra becomes infeasible due to the high volume (number of entries and dimensionality) of the data. Van Ham and Perer explore an alternative, context-based model that emphasizes the use of contextual information as a means of data reduction: Search, show context, expand on demand. While powerful, this paradigm has not been well studied outside the domain of large-scale, dense graphs. Specifically, little is known on how well this approach transfers to domain-specific, datasets that are heterogeneous, spatially and statistically dense yet locally sparse, and possess spatial/non-spatial properties. 
To help explore this problem space, I propose a set of comparative analysis techniques which extend the Van Ham and Perer paradigm to spatial, scientific data. These techniques employ a blending of derived, quantitative similarity metrics and high-density visual abstractions - implemented through various integrated systems - to handle the inherent global data density and local attribute sparsity found in these datasets. Each of these approaches improves the likelihood of discovering patterns and similarities by first limiting the data to a user-defined subset before providing abstracted, contextual overviews.

&ldquo;Contextual Similarity Abstraction Techniques for Spatial Data Analysis&rdquo;

One of the significant challenges of care transitions in Intensive Care Units (ICUs) is the lack of effective support tools for outgoing
clinicians to find, filter, organize, and annotate information that can be effectively handed off to the incoming team. We present a large display interactive multivariate visual approach, aimed towards supporting clinicians during the transition of care. We first provide a characterization of the problem domain in terms of data and tasks, based on an observation session at the University of Illinois Hospital, and on interviews with several biomedical researchers and ICU clinicians. Informed by this experience, we design a scalable, interactive visual approach that supports both overview and detail views of ICU patient data, as well as anomaly detection, comparison, and annotation of the data. We demonstrate a large-display implementation of the visualization on an existing anonymized ICU dataset. Feedback from domain experts indicates this approach successfully meets the requirements of effective care transitions. 
Index Terms: ICU care transition, large display visualization, shared cognition, collaborative decision making

(Left) Two colocated testers establish a common information space using Echo on a tiled large display wall that runs SAGE2 middleware. (Right) A group of testers run Echo synchronously on a large display screen, on a laptop and on a tablet.

Echo: A Large Display Interactive Visualization of ICU Data for Effective Care Handoffs

Background: Visualizing the complex probability landscape of stochastic gene regulatory networks can further
biologists&rsquo; understanding of phenotypic behavior associated with specific genes. 
Results: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. 
Conclusions: We demonstrate the effectiveness of the tool through two case studies on the computed probabilisti landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases. 
Keywords: Systems biology, Feature detection and tracking, Network analysis, Modelling/Simulation

The PRODIGEN interface consists of several visual components.

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space

Mini Challenge 2 of the VAST Challenge 2017 focused on a small industrial area south of the fictional Mistford preserve, specifically around four manufacturing factories. Our main goal was to develop a visual analytics tool to explore the spatio-temporal chemical readings and wind data. Specifically, we wanted to determine which factories were responsible for emitting which chemicals and to determine the performance of the nine sensors in the area. In order to help achieve this goal, we developed a web-based application that utilizes interactive visualizations and path line analysis for revealing sensor errors and chemical reading spikes, along with pinpointing the possible sources of chemical reading spikes.

EVL Project Team (from left to right): Andrew Burks, Joe Borowicz, Elisabeta Marai (faculty advisor), Joshua Castor and Manu Thomas

MC2 - Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach

Our web-based image analysis tool for the VAST 2017 Mini-Challenge 3 combines small multiple views of satellite images, linked semantic zooming and image intensity histograms, along with filter controls. The resulting tool allow users to interactively analyze spatio-temporal changes in the preserve area.

EVL Project Team (from left to right): Bart Kupiec, Andrew Burks, Elisabeta Marai (faculty advisor) and Vijayraj Mahida

MC3 - A Web-Based Interactive Image Explorer for Temporal Analysis of Satellite Images

Research Experience Undergraduate, BS 2018

Joe Borowicz

SEPTEMBER 11, 2017 - UIC Computer Science (CS) Associate Professor G. Elisabeta (Liz) Marai loves to inspire and educate next-generation computer scientists. For several years now, she has selected the best and brightest undergraduates from her Fall and Spring courses and, pending financial support, offers them both summer research experiences and extracurricular activities to accelerate their knowledge of visualization and visual analytics technologies and techniques. 
Earlier this year, Marai reached out to CS faculty, and particularly CS faculty affiliated with the Electronic Visualization Laboratory (EVL), to ask if they would join her in hiring her undergrads on grants, with funding provided by the NSF Research Experiences for Undergraduates (REU) program or by private donors. While the students would be paid to work on a variety of projects, Marai would also provide them with extracurricular work - participating in the 12th Visual Analytics Science and Technology (VAST) Challenge, hosted annually by the international IEEE Visualization (VIS) conference. 
Months prior to the IEEE VIS conference, the VAST Challenge publishes several problems (a Grand Challenge and three Mini-Challenges) around one theme. This year, it focused on a fictitious mid-size city with a small industrial area located near a wildlife preserve, where it was discovered that the population of a specific local bird was decreasing. Each of the VAST problems provided different datasets, with different questions about &ldquo;who-what-where-why-when–how&rdquo;. For each problem, the goal was to design and develop specific visual analytics tools to help the local ornithologist determine what was happening to the birds. 
How do undergrad sophomores and juniors deal with such a competition? They work together in teams, and they learn a lot about visualization, visual analytics, programming, and web design and development. And, they are mentored by Marai&rsquo;s PhD/MS students, as well as other EVL faculty, staff and students. 
This year&rsquo;s VAST Challenge received 56 submissions from individuals and teams in industry, government, and academia. EVL student teams submitted three entries, one for each Mini-Challenge; two EVL submissions received Honorable Mention awards and one was well reviewed but did not receive an award. Given these submissions were led by undergraduates, most of whom had never done visualization before (most were students from Marai&rsquo;s CS342 software design course), that&rsquo;s a huge accomplishment! 
All the student teams wrote two-page papers about their entries that will be published in the IEEE VIS 2017 electronic conference proceedings. Marai will fund two students to attend this year’s IEEE VIS conference, to be held October 1-6, 2017 in Phoenix, AZ, to present their solutions, and to receive their Honorable Mention certificates on behalf of their teammates. 
 
# # # # # # 
 
Here is a summary of the three projects. 
Mini-Challenge #2: Honorable Mention for Clarity in Visual Communication 
Project: &ldquo;Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach&rdquo; 
UIC Participants: Undergraduates Joshua Castor, Joseph Borowicz and Andrew Burks; graduate students Manu Thomas and Timothy Luciani; and, faculty mentor Liz Marai. 
The dataset provided several months of meteorological data (wind speed and direction) and chemical data emitted by four industrial factories and captured by nine sensing stations. To explore the spatio-temporal chemical readings and wind data, specifically which factories emitted what chemicals and how the nine sensors in the area were performing, the team developed a web-based analytics tool with interactive visualizations and path line analysis to reveal sensor errors and chemical reading spikes, as well as pinpoint possible sources of chemical reading spikes. The goal was to help the local ornithologist determine whether or not the factories were compliant with environmental regulations. 
Mini-Challenge #3: Honorable Mention for Good Interactive Image Explorer for Temporal Analysis 
Project: &ldquo;Web-Based Interactive Image Explorer for Temporal Analysis of Satellite Images&rdquo; 
UIC Participants: Undergraduates Vijayraj Mahida, Bartosz Kupiec and Andrew Burks; graduate student Timothy Luciani; and, faculty mentor Liz Marai. 
The dataset provided satellite imaging data from commercial multi-spectral imagers that routinely cover the nature preserve every few weeks. The team developed a web-based image analysis tool that combined small, multiple views of satellite images, linked semantic zooming and image intensity histograms, along with filter controls. The goal was to help the local ornithologist interactively analyze spatio-temporal changes to understand the health of the wildlife preserve and alert him to possible conditions that might be impacting the birds. 
Mini-Challenge #1: No IEEE award, but kudos to the students for participating! 
Project: &ldquo;A Bespoke Analysis Tool for Spatio-temporal Park Traffic Data&rdquo; 
UIC Participants: Undergraduates Dimitar Kirilov, Isabel Lindmae and Andrew Burks; graduate student Chihua Ma; and, faculty mentor Liz Marai. 
The dataset provided resident and tourist vehicle traffic in the preserve, for day trips, overnight camping, etc., as a data stream with vehicle types and entry/exit timestamps. The team developed a web-based traffic-data analysis tool that consisted of two linked heat maps showing daily activity for vehicles, as well as a histogram showing the total time spent by vehicles in the park. Combined, these views enabled the analysis of both spatial and temporal patterns in the wildlife preserve, helping the local ornithologist identify traffic patterns, correlations, or anomalies to aid his investigations. 
 
# # # # # # 
 
The IEEE VIS 2017 VAST Challenge Awards are listed on the IEEE VIS 2017 website: 
<a href="http://ieeevis.org/year/2017/info/awards/vast-challenge-awards">http://ieeevis.org/year/2017/info/awards/vast-challenge-awards</a> 
The IEEE VIS VAST Challenge 2017 website: 
<a href=http://www.vacommunity.org/VAST+Challenge+2017>http://www.vacommunity.org/VAST+Challenge+2017</a> 
Note: At IEEE VIS 2016, Marai and EVL undergraduate and graduate students won Honorable Mention in the third International Data Visualization Contest organized by the IEEE Visualization and Graphics Technical Committee&rsquo;s (VGTC) Visualization Pioneers Group (VPG). See: <a href=" https://www.evl.uic.edu/entry.php?id=2247">https://www.evl.uic.edu/entry.php?id=2247</a>

Shown here: (back row, left to right) Manu Thomas, Dimitar Kirilov, Joseph Borowicz, Bartosz Kupiec, Vijayraj Mahida and Andrew Burks; (front row, left to right) Joshua Castor, G.E. Marai and Chihua Ma. Not pictured: Isabel Lindmae and Timothy Luciani.

UIC/EVL Receives Two Honorable Mentions in IEEE VIS 2017 VAST Challenge

This paper describes a web-based traffic data analysis tool developed for the VAST 2017 Mini Challenge 1. The tool consists of two linked heat maps which allow for the inspection of daily activity for vehicles, as well as a histogram which allows for the analysis of total time spent by vehicles in the park. Combined, these views allow for the analysis of both spatial and temporal patterns in the park preserve.

Traffic data analysis tool, with a large overview interactive heat map and a detailed heat map to the side; the maps capture spatio-temporal patterns A stacked histogram allows for temporal analysis; bins can be zoomed in for closer inspection.

MC1: A Bespoke Analysis Tool for Spatio-temporal Park Traffic Data

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle. 
Index Terms - Dynamic networks, biological data visualization, rule-based modeling, protein-protein interaction networks.

A screenshot of the DIN-Viz application for analyzing the dynamics of a rule-based model of a protein-protein interaction network (i.e., a Dynamic Influence Network).

Dynamic Influence Networks for Rule-based Models

Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage-and its evaluation-of existing, higher-level visualization design models. In a departure from existing visualizationdesign models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature. 
Index Terms - Design studies, Tasks and requirements analysis, Visualization models, Domain characterization, Activity-centered design, Functional specifications 
<iframe src="https://player.vimeo.com/video/230835822" width="640" height="360" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>
<a href="https://vimeo.com/230835822">[VIS17 Preview] Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization</a> from <a href="https://vimeo.com/vgtcommunity">VGTCommunity</a> on <a href="https://vimeo.com">Vimeo</a>.

Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization

This collaborative research project led by the colleagues at the University of Delaware aims to develop and advance tools for using image-data appearing in scientific publications, in addition to text, in order to support beneficial, targeted access to the biomedical literature. 
UIC's principal investigator, Computer Science associate professor, G. Elisabeta Marai will be responsible for the image analysis algorithms to distinguish between gel and plate images, the image analysis algorithms to distinguish between Electronic Microscopy vs Fluorescent Microscopy vs Light Microscopy and X-Rays, and the design of the visual interface to present results to end users. She will lead the design, development and implementation of the feature extraction and selection tools and techniques related to this research project.

Incorporating Image-based Features into Biomedical Document Classification

The Englewood Data Hub (EDH) is a project that brings together both crowdsourced and public data surrounding the Englewood area into a single place. 
The <a href="https://englewooddatahub.org/">EDH Resource Directory</a> makes use of the crowdsourced service and school data to display an interactive map with their locations. Its user interface (UI) allows for an exploratory experience that offers filtering of services by names and type and displays detailed information about the services available in Englewood. 
The <a href="https://englewooddatahub.org/analytics">EDH Analytics Tool</a> combines the crowdsourced service and school data with public data sets to display visualizations; specifically, the public datasets used are 2010 Census data along with Vacant Lot and Crime data from the Chicago Data Portal. 
The Englewood Data Hub has been developed by a group of researchers at the Electronic Visualization Laboratory, University of Illinois at Chicago under the direction of Prof. G. Elisabeta Marai. 
Team members include EVL research experience undergraduate students (REUs): Andrew Burks, Joshua Castor, and Isabel Lindmae. 
This project is funded by the The Joseph and Bessie Feinberg Foundation and the University of Illinois at Chicago.

Englewood Social Service Finder and Englewood Analytics

Committee: 
G. Elisabeta Marai (Chair) 
Robert Kenyon 
Andrew Johnson 
Angus Forbes (University of California, Santa Cruz) 
Tamara Munzner (University of British Columbia) 
Date & Time: Feb 5th, 2018, 3:30 pm - 5:30 pm 
Location: 2036 ERF (Electronic Visualization Laboratory CAVE2) 
Abstract: 
Recent neuroscience research indicates that cognitive operations are performed not by individual brain regions working in isolation, but by networks that consist of several discrete brain regions. These discrete brain regions act in synchrony and share functional connectivity. The study of brain networks contains both non-spatial data - dynamic functional connections within the brain - and spatial data - 2D/3D medical scans of the brain. To further understand the relationships between dynamic functional connectivities and physical structures of the brain, researchers analyze dynamic brain networks over multiple experiments or with different models. Similarly, bioinformatics research also contains problems about dynamic biological networks that require the integration of spatial and nonspatial data and multi-run comparison. Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their biological data through visual exploration and analysis. 
In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.

Doctoral Defense Announcement: &ldquo;Visual Analysis Techniques for Dynamic Biological Networks&rdquo;

Drug-drug interaction (DDI) alerts safeguard patient safety during medication prescribing, but are often ignored by physicians. Despite attempts to improve the usability of such alerts, physicians still mistrust the relevance of simplistic computerized warnings to support complex medical decisions. By building on prior fieldwork, this paper evaluates novel designs of trust - eliciting cues in DDI alerts. A sequential mixed-method study with 70 physicians examined what trust cues improve compliance, promote reflection, and trigger appropriate actions. In a survey, 52 physicians rated the likelihood of compliance and usefulness of redesigned alerts. Based on these findings, alerts were assessed in a scenario-based simulation with 18 physicians prescribing medications in 6 patient scenarios. Our results show that alerts embodying expert endorsement, awareness of prior actions, and peer advice were less likely to be overridden than current alerts, and promoted reflection, monitoring, or order modifications - thus building towards greater attention to patient safety. 
Keywords: healthcare information systems; health informatics; clinical alerts; trust in socio-technical systems; interaction design; decision-making 
<ahref="https://doi.org/10.1093/iwc/iwx020">doi:10.1093/iwc/iwx020</a>

DDI alerts were displayed in an experimental setting that simulated an inpatient meeting room where physicians use a workstation to prescribe medications. Interruptions were simulated using a second computer and screen interactions recorded using Camtasia

Design and Evaluation of Trust - Eliciting Cues in Drug - Drug Interaction Alerts

Recent neuroscience research indicates that cognitive operations are performed not by individual brain regions working in isolation, but by networks that consist of several discrete brain regions. These discrete brain regions act in synchrony and share functional connectivity. The study of brain networks contains both non-spatial data dynamic functional connections within the brain and spatial data 2D/3D medical scans of the brain. To further understand the relationships between dynamic functional connectivities and physical structures of the brain, researchers analyze dynamic brain networks over multiple experiments or with different models. Similarly, bioinformatics research also contains problems about dynamic biological networks that require the integration of spatial and nonspatial data and multi-run comparison. Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their biological data through visual exploration and analysis. 
In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.

RemBrain 2.0 implements a summary+detail visual approach for the analysis for comparative analysis of multiple brain networks at four scales: groups of subjects, individual subjects, activations of a subject, and ROIs of an activation.

Visual Analysis Techniques for Dynamic Biological Networks

We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient&rsquo;s probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration. 
Index Terms - Visual analytics, precision medicine, design studies, nomograms, parallel coordinate plots, activity-centered design. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190,&ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo; 
National Science Foundation: 
NSF-CNS-1625941, &ldquo;MRI - Development of Continuum: A Virtualized Attentive Environment for Amplified Collaboration&rdquo; 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Radiomics is one such &ldquo;big data&rdquo; approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between &ldquo;favorable&rdquo; and &ldquo;unfavorable&rdquo; clusters were noted. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190,&ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo; 
National Science Foundation: 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

Investigation of radiomics-based signature for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Conditional survival (CS), the survival beyond a pre-defined time interval, can identify periods of higher mortality risk for patients with locally advanced laryngeal cancer who face treatment-related toxicity and comorbidities related to alcohol and smoking in the survivorship setting. Using Weibull regression modeling, we analyzed retrospectively abstracted data from 638 records of patients who received radiation to identify prognostic factors for overall survival (OS) and recurrence free survival (RFS) for the first 3 years of survival and for OS conditional upon 3 years of survival. The CS was iteratively calculated, stratifying on variables that were statistically significant on multivariate regression. Predictive nomograms were generated. The median total follow up time was 175 months. The 3- and 6- year actuarial overall survival (OS) was 68% (95% confidence interval [CI] 65-72%) and 49% (CI 45-53%). The 3-year conditional overall survival (COS) at 3 years was 72% (CI 65-74%). Black patients had worse COS over time. Nodal disease was significantly associated with recurrence, but after 3 years, the 3-year conditional RFS converged for all nodal groups. In conclusion, the CS analysis in this patient cohort identified subgroups and time intervals that may represent opportunities for intervention. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo;

Conditional Survival Analysis of Patients With Locally Advanced Laryngeal Cancer: Construction of a Dynamic Risk Model and Clinical Nomogram

Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that &lsquo;radiomics&rsquo;, or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual meeting. These public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set. 
Support: 
National Science Foundation: 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

Radiomics involves the application of image processing algorithms to define a series of quantitative image characteristics. We sought to develop a clinically usable radiomics signature for primary oropharyngeal cancers (OPC), using the gross tumor volume (GTV) regions of interest (ROIs) to derive a &ldquo;phenotypic profile&rdquo; associated with time to local recurrence (LR). 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190,&ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

Head and neck cancer treatment and toxicity Big Data collected at U Texas MD Anderson Cancer Center; jointly analyzed at EVL, MDACC, UMN and Iowa.

Development of a Predictive Quantitative Contrast Computed Tomography-Based Feature (Radiomics) Profile for Local Recurrence in Oropharyngeal Cancers

Professors J. Aerts (Leuven University, Belgium), N. Gehlenborg (Harvard), G.E. Marai (UIC), and K. Nieselt (University of Tuebingen, Germany) organized a week-long international meeting on &ldquo;Biological Data Visualization: Crossroads&rdquo; at Dagstuhl, at the Leibniz Center for Informatics, Germany, with support from the German federal and local government. The center hosts invitation-only week-long meetings on cutting-edge/current research topics in computer science. 45 invitees from academia, research labs, industry, bioinformatics, biology and computer science attended the event from April 15-20th. 
Motivation 
The rapidly expanding application of experimental high-throughput and high-resolution methods in biology is creating enormous challenges for the visualization of biological data. To meet these challenges, a large variety of expertise from the visualization, bioinformatics and biology domains is required. These encompass visualization and design knowledge, algorithm design, strong implementation skills for analyzing and visualizing big data, statistical knowledge, and specific domain knowledge for different application problems. 
In particular, it is of increasing importance to develop powerful and integrative visualization methods combined with computational analytical methods. Furthermore, because of the growing relevance of visualization for bioinformatics, teaching visualization should also become part of the bioinformatics curriculum. 
With this Dagstuhl Seminar we want to continue the process of community building across the disciplines of biology, bioinformatics, and visualization. We aim to bring together researchers from the different domains to discuss how to continue the BioVis interdisciplinary dialogue, to foster the development of an international community, to discuss the state-of-the-art and identify areas of research that might benefit from joint efforts of all groups involved. 
 Thus during the seminar we envision to address the following topics: 
<ul><li>Challenges in the integrative and visual analysis of high-dimensional and complex medicine data</li>
<li>Visual analysis of stochastic biological networks</li>
<li>Beyond genomes: visual representations of metagenomes and pangenomes</li>
<li>On the BioVis crossroads: Continued collaborations between the visualization and bioinformatics communities</li>
<li>Designing a curriculum for teaching visualization in bioinformatics</li></ul> 
While emphasis of topics is put on different, highly challenging problems in biology, it is also envisioned to identify newly needed visualization paradigms and developments that can help to solve these challenges. Thus further topics may be added, depending on the specific interest of the participants. 
 As one outcome of the seminar, we plan to summarize the results in a white paper, in which the computational, the visualization and the application domain aspects are collected and published to not only ensure that a broad audience is reached but also that our crossroads journey may continue.

Prof. Marai co-organizes Dagstuhl meeting on visualization of biological data

We present the design of a novel framework for the visual integration, comparison, and exploration of correlations in spatial and non-spatial geriatric research data. These data are in general high-dimensional and span both the spatial, volumetric domain - through magnetic resonance imaging volumes - and the non-spatial domain, through variables such as age, gender, or walking speed. The visual analysis framework blends medical imaging, mathematical analysis and interactive visualization techniques, and includes the adaptation of Sparse Partial Least Squares and iterated Tikhonov Regularization algorithms to quantify potential neurology-mobility connections. A linked-view design geared specifically at interactive visual comparison integrates spatial and abstract visual representations to enable the users to effectively generate and refine hypotheses in a large, multidimensional, and fragmented space. In addition to the domain analysis and design description, we demonstrate the usefulness of this approach on two case studies. Last, we report the lessons learned through the iterative design and evaluation of our approach, in particular those relevant to the design of comparative visualization of spatial and non-spatial data. 
Index Terms: Design studies; methodology design; task and requirements analysis; integrating spatial and non-spatial data visualization; visual comparison; high-dimensional data; applications of visualization 
Support: 
NSF CAREER IIS-1541277

GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

We introduce a deep learning approach for the identification of shock locations in large-scale tensor field datasets. Such datasets are typically generated by turbulent combustion simulations. In this proof of concept approach, we use deep learning to learn mappings from strain tensors to Schlieren images which serve as labels. The use of neural networks allows for the Schlieren values to be approximated more efficiently than calculating the values from the density gradient. In addition, we show that this approach can be used to predict the Schlieren values for both two-dimensional and three-dimensional tensor fields, potentially allowing for anomaly detection in tensor flows. Results on two shock example datasets show that this approach can assist in the extraction of features from reacting flow tensor fields. 
Support: 
NSF CAREER IIS-1541277

Networks for 3D Sod dataset (left) and the 2D Blast dataset (right).

A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields

Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

A Composite Clinical/Serially-derived Parotid Gland Radiomics-based Model can be Correlated with Post-RT Xerostomia at 6 months in Oropharyngeal Cancer Patients Treated with Image-Guided Radiation Therapy

Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&ldquo;

A Combination of Serial Tumor Radiomics Features and key clinical attributes can predict the response of Head & Neck Cancers Treated with Image-Guided Radiation Therapy

Parotid gland irradiation is a major risk factor for xerostomia in patients undergoing RT for head and neck cancers. We hypothesize that texture analysis allows for quantization of changes in parotid gland structure. 28 predominantly male patients diagnosed with locally advanced OPC who received concurrent chemoradiation constituted our cohort. The corresponding AUCs and CIs for predictive models suggest that the functional approach yields a superior ROC compared to using either the baseline radiomics feature or using the delta ratio between the mid and initial time points. Textural kinetic trajectories from consequential intra-treatment CT scans can predict for subsequent early and late radiation-induced toxicities. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

Actual Martingale residual by predicted value from random forest plot of time-to-local failure for the three sets based on the radiomic signature.

Normal Tissue Radiomic Feature Kinetics in Oropharygeal Cancers Treated with Image-Guided Radiation Therapy: Assessment of Parotid Glands Textural Changes on Daily Non-contrast CT

Recent data demonstrate that human papillomavirus (HPV)-associated oropharyngeal cancers (OPC) have discrete biology and clinical behavior as compared to HPV-negative OPC. We hypothesize that some phenotypic image features can be correlated to tumor biology in a non-invasive fashion. Some imaging features, specifically shape and intensity features can make a distinction between HPV-associated and HPV-negative OPC. Quantitative information from pretreatment imaging of intact tumor can give a better idea on tumor biology. Further studies are needed to validate the correlation between these radiomic biomarkers and other intrinsic tumor characteristics. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

Flowchart of patient selection for inclusion.

Predicting the HPV P16 Status of Oropharyngeal Cancer Patients Using Radiomics and an Ensemble of Random Forests

The computational study of the dynamic and stochastic natures of gene regulatory networks is a challenging topic in systems biology. Visualizing ensemble time-evolving probability landscapes of stochastic gene networks can further biologists&rsquo; understanding of phenotypic behavior associated with specific genes. We present a web-based visual analysis tool for the exploration of peak distributions over state space and simulation time in such stochastic networks, and the comparison of peak distributions between multiple simulations. Our approach combines multiple linked views to capture ensemble time-evolving probability landscapes. A peak trajectory cube provides users an overview of peak spatiotemporal distributions between six simulations. A peak projection map shows the exact peak locations of multiple simulations at the user selected time. At a more detailed level, users can inspect a particular state in the peak projection map to view for each simulation both the probability values over time, and the local probability landscape shapes. This information is displayed in a small multiple using two glyphs: profile glyphs and arrow glyphs. The arrow glyph indicates that a state is a peak when all the glyph eight arrows point towards the glyph center. In the figure, a disagreement between the arrow glyphs and the peak projection map demonstrates that probability distributions over genes in this system are not independent of each other. Our visual analysis tool allows bioinformatics researchers to explore and compare the time evolving changes of probability landscapes from multiple simulations efficiently, without running many small scripts and computing all characteristics separately. 
Keywords: Systems biology, Feature detection and tracking, Modelling/Simulation, Ensemble visualization, Spatiotemporal visualization

Visualizing ensemble time-evolving probability landscapes of stochastic networks

Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: &ldquo;Overview first, zoom and filter, then details on demand&rdquo;. However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model. 
Index Terms - theory, visualization design, details-first model, discourse paper, computational fluid dynamics 
<a href="https://youtu.be/q-_KM_oRgxk">See a video on this project.</a>

The Details-first, Show Context, Overview Last model supports the interactive, web-based exploration of ensemble simulations.

Details-First, Show Context, Overview Last:  Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Authors: Hesham Elhalawani, Timothy A Lin, Stefania Volpe, Abdallah S.R. Mohamed, Aubrey L. White, James Zafereo, Andrew Wong, Joel E. Berends, Shady Abohashem, Bowman Williams, Jeremy M. Aymard, Aasheesh Kanwar, Subha Perni, Crosby D. Rock, Luke Cooksey, Shauna Campbell, Pei Yang, Khanh Nguyen, Rachel Ger, Carlos Eduardo Cardenas, Xenia Fave, Carlo Sansone, Gabriele Piantadosi, Stefano Marrone, Rongjie Liu, Chao Huang, Kaixian Yu, Tenfei Li, Yang Yu, Youyi Zhang, Hongtu Zhu, Jeffrey S. Morris, Veerabhadran Baladandayuthapani, John W. Shumway, Alokananda Ghosh, Andrei Pöhlmann, Hady Ahmady Phoulady, Vibhas Goyal, Guadalupe Canahuate, G. Elisabeta Marai, David Vock, Stephen Y. Lai, Dennis S. Mackin, Laurence E. Court, John Freymann, Keyvan Farahani, Jayashree Kalpathy-Cramer, Clifton D Fuller 
Abstract: Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants
were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants’ feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyperdimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings. 
Keywords: machine learning, radiomics challenge, radiation oncology, head and neck, big data

Workflow of radiomics challenges.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons from Open-Source Radiomics Challenges

Pollution exposure assessment at the population level is an established enterprise for environmental scientists and public health officials - but efforts to help individuals monitor and track their personal pollution exposures have just begun to garner research interest. Self-tracking pollution exposure is challenging for several reasons, including current limitations in sensor size, accuracy, and cost, frequent calibration requirements, and that people&rsquo;s daily activities often interfere with data quality in wearable sensing. The goal of this research is to develop a human-centered computing framework for the emerging field of personal pollution exposure assessment. To that aim, in this position paper, we propose a Bayesian approach to combine environmental sensing data from different spatiotemporal resolutions, such as from citywide national monitoring stations, neighborhood-wide lightweight sensing nodes, and personal wearables. 
Author Keywords: Environmental sensing; pollution exposure profiling; self-tracking; pollution monitoring.

The phone application of myCityMeter, lookup (a), personal exposure profiles (b), and suggested actions (c).

Toward a Bayesian Approach for Self- Tracking Personal Pollution Exposures

Recent epidemiological studies suggest that age-related cognitive decline - particularly, the stages between normal cognitive changes in aging and early dementia - is adversely affected by environmental exposures, such as long-term air pollution and traffic noise. Although monitoring outdoor air pollution is now commonplace, and smart home solutions for monitoring indoor air quality is becoming prevalent, ways that the elderly can record long-term environmental exposures and adopt healthy lifestyle changes are little explored. We present myCityMeter, a pollution exposure management tool for older adults and their caregivers. myCityMeter measures the pollutants shown to be associated with cognitive impairment in older adults: PM2.5 and ambient noise. Using a set of neighborhood-level stationary and personal mobile sensors, myCityMeter helps users to monitor their environmental exposures, know potential exposures when planning activities, journal cognitive performances, and take day-to-day actions to avoid the environmental risk factors for early dementia. 
Author Keywords: Older adults; pollution exposure management; pollution monitoring; cognitive impairment; environmental sensing; pm2.5; noise; array of things.

An overview of myCityMeter, a personal pollution exposure monitoring tool that collects data from three monitoring sources.

myCityMeter: Helping Older Adults Manage the Environmental Risk Factors for Cognitive Impairment

A November 27, 2018 story on the University of Illinois Cancer Center&rsquo;s website profiles leading-edge cancer and computer science research being conducted by G. Elisabeta (Liz) Marai, a UIC Associate Professor of Computer Science and the department&rsquo;s Electronic Visualization Laboratory (EVL), and a member of the Cancer Center. 
Specifically, this article highlights two major grants to Marai from the National Institutes of Health (NIH) and National Cancer Institute (NCI) to work with collaborators at other institutions on the design and development of computer programs to assist medical professionals determine the best treatment options for cancer patients, specifically those suffering from head and neck cancer. 
With collaborators at MD Anderson Cancer Center, University of Iowa, and University of Minnesota (NIH/NCI R01 grant CA214825-01), Marai and her graduate students are developing a computing methodology that will precisely determine health outcomes for patients with head and neck cancers based on demographics, toxicity and complex imaging data. While there are &ldquo;large, rich repositories of patient data… risk prediction algorithms for cancers are rudimentary and incorporate minimal patient characteristics, largely due to a lack of computational methodologies....This research has the potential to change the standard of care and outcomes of treatment,&rdquo; Marai explains. 
A second NIH/NCI grant (R01 CA225190-01) enables Marai and the above-mentioned collaborators to develop computer models that combine imaging data, such as radiation dosage and spatial distribution, and nonspatial data, such as demographics and toxicity, in order to identify similar patients and assist medical professionals predict optimal treatment options. This research has the potential to improve the standard of care and the quality of life of surviving patients - not only for head and neck, lung, or brain cancer, but for other chronic conditions, such as mental health disorders, substance abuse diseases, or diabetes. 
<a href="https://cancer.uillinois.edu/marai-developing-computer-programs-to-aid-cancer-patients/">Read the entire story.</a>

Liz Marai with graduate students Tim Luciani and Juan Trelles Trabucco standing in EVL&rsquo;s CAVE2 hybrid reality system with diagrams of patient data depicting head and neck lymph nodes affected by disease spread.

UIC Computer Science/EVL Marai Developing Computer Programs to Aid Cancer Patients

I present an automated method for computing the spatial similarity of tumor location with respect to organs at risk between multiple head and neck cancer patients and ranking the patients accordingly. I use this method and resulting metric to predict, for each patient, a radiation dose distribution across organs in the head and neck. Furthermore, I go over the design and implementation of a visual analysis tool used to help validate the method. 
The method uses as input DICOM (Digital Imaging and Communications in Medicine) files provided by our collaborators at MD Anderson Cancer Center. We preprocess these files to extract primary data components and translate them into an efficient format for the automated method and web-based application to read in. The method is written in Python, and utilizes the Structural SIMilarity (SSIM) algorithm for computing similarities. A JSON (JavaScript Object Notation) file is generated containing all patients, and for every patient a dose prediction and ordered list of most similar patients is produced. The web-based GUI (Graphical User Interface) reads in the JSON file and lets users visually explore the data, analyze the method, and gather insights for possible radiation therapy plans. 
The resulting method and interface, CAMP-RT (Correlations Across Multiple Patients in Radiation Therapy) is continuously being evaluated by domain experts from the MD Anderson Cancer Center. I report their qualitative feedback regarding the method and interface. I further evaluate quantitatively the results of the prediction using a dataset of 101 head-and-neck cancer patients. The results indicate the method has good predictive capabilities.

Main View of Application

CAMP - RT: A Patient Similarity-Based Method for Predicting Radiation Dosage in Head and Neck Radiation Oncology

This article provides a 25 year-long perspective on Immersive Analytics through the lens of first-in-kind technological advancements introduced at the Electronic Visualization Laboratory, University of Illinois at Chicago, along with the challenges and lessons learned from multiple Immersive Analytics projects. 
The Electronic Visualization Lab (EVL) at the University of Illinois at Chicago has been at the forefront of Virtual Reality research since 1992, when it introduced the CAVE&trade; Automatic Virtual Environment, the first projection-based virtual reality system in the world. Since then, the lab has developed a wide range of immersive systems and technologies to be used for visualization and visual analysis tasks, such as PARIS&trade;, ImmersaDesk&trade;, CAVE2&trade;, and SAGE2&trade;. These technologies paved the way for the resurgence in virtual reality in the past few years, and lead to the introduction of Immersive Analytics - which investigates how new interaction
and display technologies can be used to support analytical reasoning by immersing the users in data.

CAVE2 immersive exploration of dark matter formation in the universe. While little information can be shown on a typical desktop display, the high-resolution tiled environment lets analysts examine details without losing the context of the larger dataset.

Immersive Analytics Lessons from the Electronic Visualization Laboratory: a 25 Year Perspective

Through the use of open data portals, cities, districts and countries are increasingly making available energy consumption data. These data have the potential to inform both policymakers and local communities. At the same time, however, these datasets are large and complicated to analyze. We present the activity-centered-design, from requirements to evaluation, of a web-based visual
analysis tool to explore energy consumption in Chicago. The resulting application integrates energy consumption data and census data, making it possible for both amateurs and experts to analyze disaggregated datasets at multiple levels of spatial aggregation and to compare temporal and spatial differences. An evaluation through case studies and qualitative feedback demonstrates that this visual analysis application successfully meets the goals of integrating large, disaggregated urban energy consumption datasets and of supporting analysis by both lay users and experts. 
Keywords: Interactive Visualization; Visual Design; Sustainability

Interactive, web-based, open-source energy consumption explorer for the city of Chicago.

Visual Analysis of a Smart City&rsquo;s Energy Consumption

Cancer has a major impact on society all around the world. It affects people of all population groups though certain groups bear more burden of cancer. Some of the contributing factors for cancer disparities are socioeconomic status, geographic location, race/ethnicity. The objective of this project is to provide an interface to show cancer incidents and address the effect of different socioeconomic factors on cancer incidents in the areas of Chicago. 
The web-based application was developed by Computer Science Master degree-seeking graduate student Yashika Goyal, under the advisement of EVL and Computer Science associate professor, G. Elisabeta Marai.

Screenshot of Cancer Disparities application

Cancer Disparities in Chicago

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely. 
Keywords: Biomedical and Medical Visualization, Spatial Techniques, Visual Design, High-Dimensional Data 
Funding: NIH Award #NCI-R01CA225190: QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data (Marai, PI) 
NIH, National Cancer Institute #1R01CA214825-01: SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment (Marai, PI)

Screen shot of visual interface for cohort-based radiation therapy (RT) prediction results.

Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

PhD Graduate Student

Carla Floricel

Biological network figures are ubiquitous in the biology and medical literature. On the one hand, a good network figure can quickly provide information about the nature and degree of interactions between items, and enable inferences about the reason for those
interactions. On the other hand, good network figures are difficult to create. In this paper, we outline 10 simple rules for creating biological network figures for communication, from choosing layouts, to applying color or other channels to show attributes, to the use of layering and separation. These rules are accompanied by illustrative examples. We also provide a concise set of references and additional resources for each rule.

Two representations of proteins involved in glioblastoma multiforme (GBM).

10 simple rules to create biological network figures for communication

Objectives: We sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. 
Methods: We reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at >=12 months after IMRT. Doses to auto segmented regions of interest (ROIs; inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50–59, 60–69, or >=70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. 
Results: Chronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose-response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 + ] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD.<bt> 
Conclusions: Age at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients. 
Funding: NIH Award #NCI-R01CA225190: QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data (Marai, PI) 
NIH, National Cancer Institute #1R01CA214825-01: SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment (Marai, PI)

Example of auto-segmented ROIs on axial, sagittal, and coronal views with dose wash overlay.

Chronic Radiation-Associated Dysphagia in Oropharyngeal Cancer Survivors: Towards Age-Adjusted Dose Constraints for Deglutitive Muscles

In head and neck (HN) cancer treatment, radiotherapy (RT) plans assigned to a given patient depend in large measure on the tumor location with respect to surrounding organs at risk (OARs). With the emergence of Big Data repositories, RT data from patients with
similar tumor locations could be used to predict the RT plan for a new patient, as opposed to clinicians or institutional memory alone. 
We propose an automated technique to evaluate RT plan similarity between HN cancer patients who have previously received radiotherapy, based on similarities in their tumor location with respect to OARs. We use this technique to predict and evaluate the RT dose distribution across organs in a new HN patient, using similar patients as a prior. 
Funding: NIH Award #NCI-R01CA225190: QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data (Marai, PI) 
NIH, National Cancer Institute #1R01CA214825-01: SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment (Marai, PI)

Visual Informatics

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