Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation

Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets because the large number of distinct event types within such data prevents effective aggregation. A common coping strategy for this challenge is to group event types together as a pre-process, prior to visualization, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a dynamic hierarchical aggregation technique that leverages a predefined hierarchy of dimensions to computationally quantify the informativeness of alternative levels of grouping within the hierarchy at runtime. This allows users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings from within a large-scale hierarchy of event types, and a scatter-plus-focus visualization that supports interactive hierarchical exploration. While these contributions are generalizable to other types of problems, we apply them to high-dimensional event sequence analysis using large-scale event type hierarchies from the medical domain. We describe their use within a medical cohort analysis tool called Cadence, demonstrate an example in which the proposed technique supports better views of event sequence data, and report findings from domain expert interviews.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias--when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, with a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of the drift for each cohort, which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201

Breast Cancer Detection via Microwave Imaging

poster abstractBreast cancer is one of the major common diseases among women and takes about 40,000 lives every year. Early detection of breast cancer greatly increases the chance of survival. The norm for today’s detection of breast cancer consists of mammograms, magnetic resonance imaging (MRI), and ultrasonic examination. Unfortunately, the process is a fraction of completeness despite its feeling of discomfort, high cost, and exposure to ionizing radiation which poses cumulative side effects respectively. The present research investigates the efficiency and implementation of microwave imaging to be used in the detection of breast cancer. Microwave imaging (MWI) is a process that illuminates the breast with microwave signals, and receives and analyses scattered signals for breast cancer detection and imaging. The electromagnetic waves that are scattered within the breast provide information that are transmitted and received via microstrip patch antennas, providing an image of detected lesions. In the presented poster, design of a patch antenna and simulation results are presented. In the event of designing, the overall goal was to obtain a voltage standing wave ratio (VSWR) less than 2 at 2.4 GHz signal frequency. To receive the intended results, the dimensions and design of the microstrip patch were important factors given the substrate parameters. Currently, the project is in the prototyping stage for the validation of simulation results and further optimization and development of the antenna for microwave breast cancer detection and imaging applications

Towards Device Agnostic Detection of Stress and Craving in Patients with Substance Use Disorder

Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored

Towards Device Agnostic Detection of Stress and Craving in Patients with Substance Use Disorder

Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored

Cartridge Filter Testing and Development

Problem statement The goal is to determine the parameters which produce the optimal filters. Rationale Melt-blowing is a process by which Delta Pure uses to manufacture water filters. Several production parameters affect the characteristics of these filters and the goal is to determine which variables impact these characteristics the most. Approach Three different production parameters were chosen; air pressure, polymer temperature, and extruder speed. Eight filters were produced with each parameter set to high and low points, as well as one filter which served as a midpoint for the data. These filters were run in the testing rig (pictured above) to find the differential pressure and the flow rate through the filter. The filters were then subjected to a compression test to find the load that the filters could take. Afterwards, statistical software was run to determine the parameters which most affected the filters’ rigidity, differential pressure, and flow rate. Anticipated results and conclusions • Selection of parameters for testing of Filters by setting up a 3x3 DOE • Test pressure differential, flow rate, and rigidity of the filters • Use statistical software to find the optimal conditions of filter production Results • Of the three chosen parameters, air pressure and extruder speed had the greatest impact on the filters’ characteristics. • Changes in flow rate were negligible across all the tested filters.https://scholarscompass.vcu.edu/capstone/1048/thumbnail.jp

Harnessing high-dimensional hyperentanglement through a biphoton frequency comb

Quantum entanglement is a fundamental resource for secure information processing and communications, where hyperentanglement or high-dimensional entanglement has been separately proposed towards high data capacity and error resilience. The continuous-variable nature of the energy-time entanglement makes it an ideal candidate for efficient high-dimensional coding with minimal limitations. Here we demonstrate the first simultaneous high-dimensional hyperentanglement using a biphoton frequency comb to harness the full potential in both energy and time domain. The long-postulated Hong-Ou-Mandel quantum revival is exhibited, with up to 19 time-bins, 96.5% visibilities. We further witness the high-dimensional energy-time entanglement through Franson revivals, which is observed periodically at integer time-bins, with 97.8% visibility. This qudit state is observed to simultaneously violate the generalized Bell inequality by up to 10.95 deviations while observing recurrent Clauser-Horne-Shimony-Holt S-parameters up to 2.76. Our biphoton frequency comb provides a platform in photon-efficient quantum communications towards the ultimate channel capacity through energy-time-polarization high-dimensional encoding

Refractory Abdominal Pain in a Patient with Chronic Lymphocytic Leukemia: Be Wary of Acquired Angioedema due to C1 Esterase Inhibitor Deficiency.

Acquired angioedema due to C1 inhibitor deficiency (C1INH-AAE) is a rare and potentially fatal syndrome of bradykinin-mediated angioedema characterized by episodes of angioedema without urticaria. It typically manifests with nonpitting edema of the skin and edema in the gastrointestinal (GI) tract mucosa or upper airway. Edema of the upper airway and tongue may lead to life-threatening asphyxiation. C1INH-AAE is typically under-diagnosed because of its rarity and its propensity to mimic more common abdominal conditions and allergic reactions. In this article, we present the case of a 62-year-old male with a history of recently diagnosed chronic lymphocytic leukemia (CLL) who presented to our hospital with recurrent abdominal pain, initially suspected to hav

Engineered Recombinant Single Chain Variable Fragment of Monoclonal Antibody Provides Protection to Chickens Infected with H9N2 Avian Influenza

Passive immunisation with neutralising antibodies can be a potent therapeutic strategy if used pre- or post-exposure to a variety of pathogens. Herein, we investigated whether recombinant monoclonal antibodies (mAbs) could be used to protect chickens against avian influenza. Avian influenza viruses impose a significant economic burden on the poultry industry and pose a zoonotic infection risk for public health worldwide. Traditional control measures including vaccination do not provide rapid protection from disease, highlighting the need for alternative disease mitigation measures. In this study, previously generated neutralizing anti-H9N2 virus monoclonal antibodies were converted to single-chain variable fragment antibodies (scFvs). These recombinant scFv antibodies were produced in insect cell cultures and the preparations retained neutralization capacity against an H9N2 virus in vitro. To evaluate recombinant scFv antibody efficacy in vivo, chickens were passively immunized with scFvs one day before, and for seven days after virus challenge. Groups receiving scFv treatment showed partial virus load reductions measured by plaque assays and decreased disease manifestation. These results indicate that antibody therapy could reduce clinical disease and shedding of avian influenza virus in infected chicken flocks