Public Health

Public Health

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Nelms Institute Contact: Swarup Bhunia
Consumers and law enforcement are equally concerned about the increase in adulterated consumables that are present in the market. In addition to counterfeited substances, authorities also have to deal with new illegal drugs. These drugs are imported into the country in various ways; often, these substances are available in small quantities and require rapid detection on the field. Previously, we proposed a portable NQR system (known as iNQR) that can be carried onto the field to detect and quantify various chemicals. This system includes all the required components for a portable detection system, including the detector, data accumulator, digital signal processor (DSP), and user interface. However, since this setup can be cumbersome for regular use and challenging to move quickly in the field, here we propose a new portable version of the iNQR system within a hand-held form factor.



Nelms Institute Contact: Swarup Bhunia
Face masks provide effective, easy-to-use, and low-cost protection against airborne pathogens or infectious agents, including SARS-CoV-2. Existing masks are all passive in nature, i.e., simply act as air filters for the nasal passage and/or mouth. This article presents a new “active mask” paradigm, in which the wearable device is equipped with smart sensors and actuators to both detect the presence of airborne pathogens in real time and take appropriate action to mitigate the threat. The proposed approach is based on a closed-loop control system that senses airborne particles of different sizes near the mask and then makes intelligent decisions to reduce their concentrations. In the current implementation, an on-board controller determines ambient air quality via a commercial particulate matter sensor, and if necessary, activates a piezoelectric actuator that generates a mist spray to load these particles, thus causing them to fall to the ground. The system communicates with the user via a smart phone application that provides various alerts, including the need to recharge and/or decontaminate the mask prior to reuse. The application also enables a user to override the on-board control system and manually control the mist generator if necessary. Experimental results from a functional prototype demonstrate significant reduction in airborne PM counts near the mask when the active protection system is enabled.



Nelms Institute Contact: Yuguang “Michael” Fang
This project serves as a training ground for educating future decision-makers and workforce on privacy-preserving healthcare technologies. This multidisciplinary research advances the state-of-the-art public health by combining multi-scale data collection and analysis. Specifically, the project redesigns current healthcare monitoring systems for both severe infectious diseases and long-term environment-related diseases and their exacerbation (e.g., air pollutant-induced pulmonary diseases, such as chronic obstructive pulmonary disease and lung cancer). By considering the high sensitivity and distributed manner of the data from patients and users, this project addresses the privacy preservation in two-fold: 1) completely redesign efficient collaborative classification schemes by applying novel metrics without leaking individual’s privacy; and 2) introduce new architectures to perform crowdsourcing data analysis by using light-weighted and verifiable encryption schemes. This project also grounds the theoretical outcomes to actual crowdsensing systems and social networks for validation. Finally, a new methodology on public health prediction model is developed with practical systematic implementation in healthcare systems.



Nelms Institute Contact: Ruogu Fang
Human emotions are dynamic, multidimensional responses to challenges and opportunities, which emerge from network interactions in the brain. Disruptions of these network interactions underlie emotional dysregulation in many mental disorders including anxiety and depression. In the process of carrying out our current NIH funded research on how human brain processes emotional information, we recognize the limitation of empirical studies, including not being able to manipulate the system to establish the causal basis for the observed relationship between brain and behavior. Creating an AI-based model system that is informed and validated by known biological findings and that can be used to carry out causal manipulations and allow the testing of the consequences against human imaging data will thus be a highly significant development in the short term. In the long term, the model can be further enriched and expanded so that it becomes a platform for testing a wider range of normal brain functions, as well as a platform for testing for how various pathologies affect these functions in mental disorders.



Nelms Institute Contact: Ruogu Fang
The three distinct neurodegenerative disorders — Parkinson’s disease; multiple system atrophy Parkinsonian variant, or MSAp; and progressive supranuclear palsy, or PSP — can be difficult to differentiate because they share overlapping motor and non-motor features, such as changes in gait. But they also have important differences in pathology and prognosis, and obtaining an accurate diagnosis is key to determining the best possible treatment for patients as well as developing improved therapies of the future. Previous research has shown that accuracy of diagnosis in early Parkinson’s can be as low as 58%, and more than half of misdiagnosed patients actually have one of the two variants. Testing of the new AI tool, which will include MRI images from 315 patients at 21 sites across North America, builds upon more than a decade of research in the laboratory of David Vaillancourt, Ph.D., a professor and chair of the UF College of Health & Human Performance’s department of applied physiology and kinesiology, whose work is focused on improving the lives of more than 6 million people with Parkinson’s disease and Parkinson’s-like syndromes. To differentiate between the forms of Parkinsonism, Vaillancourt’s lab has developed a novel, noninvasive biomarker technique using diffusion-weighted MRI, which measures how water molecules diffuse in the brain and helps identify where neurodegeneration is occurring. Vaillancourt’s team demonstrated the effectiveness of the technique in an international, 1,002- patient study published in The Lancet Digital Health in 2019.



Nelms Institute Contact: Ruogu Fang
The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer’s disease. Existing efforts at characterizing brain dynamics have predominantly centered on ‘isolated’ models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.



Nelms Institute Contact: Sanjeev Koppal
Most cameras today indiscriminately photograph their entire visual field. In contrast, animal eyes, have fast mechanical movements that control how the scene is imaged in detail by the fovea, where visual acuity is highest. In computer vision, this idea of actively selecting where to look — i.e. active vision — has been mostly demonstrated with slow, power-hungry mechanical options for changing camera pose, such as pan-tilt-zoom motors or robot motion. The key challenge to conducting active vision for small mobile platforms is to provide fast, camera control of the physical properties that influence image formation, such as wavelength, resolution, polarization, viewpoint, exposure time, etc. This project focuses on active vision algorithms for fast adaptive resolution, generalizing the foveation capability found in animals. I propose new designs called *foveating cameras*, which work by capturing reflections off a tiny, fast mirror whose scan path allows for selective scene viewing. Foveating cameras will revolutionize sensing in mobile systems and robotics, since algorithms such as visual state estimation or object recognition can now use imagery with high resolution on every region of interest, even if these are at different depths and viewing directions.



Nelms Institute Contact: Dapeng Oliver Wu
Despite the advent of combined antiretroviral therapy, the ongoing HIV epidemic still defies prevention and intervention strategies designed to reduce significantly both prevalence and incidence worldwide. Phylodynamic analysis has extensively been used in the HIV field to track the origin and reconstruct the virus demographic history both at local, regional and global level. However, such studies have been so far only retrospective, with little or no power to make predictions about future epidemic trends. The overarching goal of the project is to develop an innovative computational framework coupling phylodynamic inference and behavioral network data with artificial intelligence algorithms capable of predicting HIV transmission clusters future trajectory, and informing on key determinants of new infections. We will achieve this goal by carrying out three specific aims: 1. Develop a phylodynamic-based PRIDE module to forecast HIV infection hotspots [the infected]; 2. Develop a behavioral network-based PRIDE module for risk of HIV infection [the uninfected], and 3. Carry out focus groups for deploying the new PRIDE forecasting technology into public health, and implement prevention through the peer change agent model.



Nelms Institute Contact: Yong-Kyu “YK” Yoon
A micromachined smart pseudopalate system equipped with an electrode sensor array and wireless module is proposed for the linguistic and brain function disorder such as Parkinson’s disease. The system is incorporated with AI/ML algorithm to analyze the detected signals. It is a minimally invasive system and will have huge impacts on both linguistic applications such as lenition study, accent reduction, and medical applications such as Alzheimer disease, concussion etc.