Monitoring the physiology of patients with cardiovascular conditions is useful in preventing or predicting adverse symptoms. However, known predictors of symptoms, such as blood pressure, are difficult to monitor throughout the day. We aim to address this problem in individuals with Postural Orthostatic Tachycardia Syndrome (POTS), which is a condition where the body cannot properly regulate its blood vessels causing lightheadedness, fainting, and spikes in heart rate with little warning. To do so, we will (1) survey individuals with POTS to develop an understanding of what key physiological signals are valuable to monitor, (2) develop an unobtrusive wearable device in a convenient sleeve form-factor, (3) perform a case study with our device on people with POTS to test its efficacy. The primary signal we will collect is pulse transit time (PTT), which is the time for a pulse wave to travel between two points. Prior work has shown PTT is strongly correlated with blood pressure, which is a known predictor of adverse symptoms for POTS. We calculate PTT with two optical sensors consisting of an LED and photodiode at opposite ends of the sleeve. Each sensor allows us to monitor the changes in blood volume and determine when a pulse wave arrives at that point (i.e., PTT). Beyond helping to predict symptoms, we believe our device can help fill knowledge gaps about the physiology behind POTS, as the condition is still not fully understood, and provide physicians with a rich dataset to make medical conditions such as POTS more easily diagnosable.

People who spend early life at high altitude have an increased capacity for aerobic work due to developmental adaptations to hypoxic environments. Since the effects of altitude on aerobic performance became evident after the 1968 Mexico City Olympics, altitude training has been implemented to increase red blood cell carrying capacity and improve performance. However, it is unclear how early development at altitude and current training at altitude may differently advantage endurance athletes. In order to test for possible mechanisms by which altitude enhances endurance performance, this research compares personal records and biomarkers of oxygen carrying capacity among endurance athletes who experienced early development at altitude or sea-level and are currently training at altitude or sea-level. The study aims to determine if the altitude in which athletes developed and are currently training at will be associated with faster gender-adjusted personal records and greater lung capacity. I conducted a cross-sectional observational study with 23 endurance athletes in Seattle, WA and Boulder, Colorado. Participants self-collected chest circumference (CC) at maximum inhalation and completed online questionnaires about running performance, family history, and personal motivations for competing. I devised a gender-adjusted personal record percentile score for each subject’s 5k times based on the top 500 US men’s and women’s 5k times recorded during the 2019 season. This presentation discusses the results on differences in personal records and CC in relation to early development and current training altitude. I examined athlete’s motivation for competing through the qualitative analysis of open-ended interview responses to explore how motivation works synergistically with physiological biomarkers. These findings will be discussed in terms of existing research and consideration for endurance training at altitude and sea-level.

Venezuelan Equine Encephalitis Virus (VEEV) is an arthropod-borne virus spread by mosquitos. It causes a range of acute disease from mild flu-like symptoms to more severe illnesses such as encephalitis. Certain species of rodents serve as a reservoir host for VEEV and display no symptoms, whereas horses, as amplification hosts, often exhibit lethal symptoms, suggesting that differential immune responses in these hosts contribute to differences in pathogenesis. Because reservoir and amplification hosts exhibit distinct pathogenesis profiles, this suggests VEEV replicates differently in these hosts and may be able to better evade the equine innate immune system. We have shown that changes in 3’ untranslated region (UTR) sequence and structure affect the ability of VEEV to evade host innate immune sensors such as IFIT2 and RIG-I. We generated mutant viruses which encode 3’-UTR sequences from either pathogenic or attenuated strains of VEEV and are using these to investigate the role of RNA structures in the 3’ UTR in limiting VEEV replication in equine cells. By stimulating the interferon response in equine fibroblast cells (NBL-6 cells) using a synthetic double-stranded RNA analog, Poly I:C, we predict equine antiviral responses will inhibit VEEV replication. Furthermore, we hypothesize pathogenic chimeras of VEEV will replicate more efficiently in Poly I:C-treated cells than attenuated chimeras. These studies will expand our fundamental understanding of the molecular mechanism of how RNA structure in the VEEV genome contributes to host innate immune evasion and has implications in future antiviral therapeutics and vaccination strategies.

Herpes simplex virus type 2 (HSV-2) is a sexually transmitted pathogen that is estimated to infect around 23 million people per year and there are not any approved vaccines that are therapeutic (that act in infected subjects) or preventative (that would prevent an infection). Most vaccines that we use today rely on injecting antigens subcutaneously or intramuscularly in order to elicit an adaptive immune response. However, with mucosal pathogens, mucosal immunity might be preferential because having local immunity in the site of the first pathogen exposure has the best chance at preventing the spread of infection beyond the pathogen portal of entry. My hypothesis is that a mucosal immunization would prime memory cells to reside in vaginal tissues and provide better protection for HSV-2 than other routes of immunization. From preliminary data, intranasal immunization was found to be effective in protecting mice from an HSV-2 infection. I have also characterized the role of CD8+ T cells in preventing infection in order to provide insight into the role of mucosal immunization for HSV-2. This work contributes to the efforts to make an effective vaccine to prevent HSV-2 mediated disease. 

Neurons in the brain are dynamical in nature, maintaining constantly changing states. Neurons modulate voltage based on input currents and produce spikes when the voltage exceeds a certain threshold. Additional dynamics after spiking, called evoked after-spike currents, are important for computation and memory over time scales. The diversity of neuronal dynamics and the variability in parameters underlying them give rise to rich and varied dynamics across networks. We hypothesize that the complexity and diversity of biological dynamics in the brain play a critical role in predictive coding of temporally complex systems, and that diverse forms of after-spike currents enable computation over variable timescales. Current artificial neural networks (ANNs), that emulate the structure of biological neural networks, successfully learn relationships between static patterns but have difficulty learning dynamic patterns that change over time. We aim to incorporate complex biological dynamics and diversity in ANNs and thereby systematically explore the function of such dynamics in network computation and learning. To this end, we construct ANNs that express biologically realistic dynamics, developing methods to learn dynamics-generating parameters, such as membrane capacitance and threshold, in individual neurons. Theoretically, diverse dynamics of individual neurons will enable even more complex dynamics when combined in networks and may improve performance on tasks requiring computation over complex timescales, such as determining actions based on temporal patterns of cues. Hence, we hypothesize that when trained on temporally challenging tasks, our networks will learn diverse dynamics across neurons. We present the diversity of parameters learned and the resulting distribution of firing patterns and compare performance between our neural networks and traditional networks that only learn connection weights. This research will inform learning methods for training novel biologically inspired neural networks and will also shed light on the physiological role of diversity in the brain.