Human Immunodeficiency Virus 1 (HIV-1) is a lentivirus and the causative agent of Acquired Immunodeficiency Syndrome (AIDS). HIV encodes a viral protease, the function of which is required for viral replication. The host innate immune sensor CARD8 detects HIV protease activity, leading to inflammasome activation during HIV infection. Inflammasomes are cytosolic innate immune complexes that recruit Caspase-1 and lead to secretion of pro-inflammatory cytokines and lytic cell death. Humans encode a single CARD8 gene; however Old World Monkeys (OWMs), the hosts of Simian Immunodeficiency Viruses (SIVs) encode two copies of CARD8. The function of the OWM CARD8 is unknown. To characterize the function of OWM CARD8s, I cloned CARD8 from representative OWMs and tested their responses to HIV and SIV protease in two ways. First, I determined if OWM CARD8s are capable of forming an inflammasome in response to HIV-1, a panel of SIVs, or the broad CARD8 activator VbP. I found that most, but not all, OWM CARD8s are functional but not responsive to HIV-1/SIVs. Human CARD8 senses HIV-1 through viral protease cleavage of its N-terminus. To determine if this lack of response of OWM CARD8s is due to the absence of viral protease cleavage, I will next perform western blots comparing human and OWM CARD8 proteolysis in the presence of absence of HIV/SIVs. My data suggests that the species-specific differences in CARD8 alters its capacity to detect viral proteases. We speculate that the absence of HIV-like pathogenesis in OWMs with endemic SIV may in part be due to the absence of CARD8 inflammasome activation to SIV protease.

The Allan Hill region of Antarctica has produced the oldest ice core samples ever recovered, which provide insights into Earth’s climate history prior to the existing 800,000 year ice core record. However, the highly disturbed nature of this ice complicates straightforward dating and interpretation. Understanding the scales of preserved climate records in this old ice will enable deeper insights into the variability of climate over the last 2 million years. Here I study a section of ice from ALHIC1901, an ice core recovered from the Allan Hills in 2019. This section has three parallel sets of water isotope measurements, and they all show a small but significant dip. However, the cause of this dip remains unclear. The goal of this study is to test whether this isotope change could be a glacial-interglacial transition preserved for 1.3 million years, or whether diffusion should have eliminated any climate signal. To investigate this question, I apply a simple water isotope diffusion model that takes as inputs temperature, an initial water isotope profile, and a thinning history, and provides as an output the resulting water isotope profile after a given number of years. I identify the range of possible temperature, thinning, and initial water isotope signals for this ice. I use these as inputs for the diffusion model, and compare the results to the ice core record to evaluate if the observed water isotope signal can be climatically driven. Constraining the cause of this water isotope signal will improve our understanding of fine scale paleoclimate proxy changes in the extremely old Allan Hills ice cores, enabling new insights into past climate variability.

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart defect, requiring patients to undergo multiple invasive cardiac procedures, including pulmonary valve replacement (PVR). However, with recent clinical advances, new tools are needed to optimize PVR timing. We believe noninvasively collected cardiopulmonary exercise testing (CPET) data can provide insight into a patient’s need for PVR. Specifically, we hypothesize that patients with a more severe stage of pulmonary valve dysfunction have a limited ability to increase their stroke volume during exercise, an abnormal response that can be assessed by analyzing the behavior of the oxygen pulse (O2-pulse) curve during CPET. A ‘flattening’ of this curve suggests impaired augmentation of stroke volume and potentially a more urgent need for PVR. This research aims to identify metrics that can characterize patterns in O2-pulse. Data were collected from 44 participants with TOF undergoing CPET PVR evaluation and 10 healthy individuals. To find a maximum O2-pulse, we fit a penalized bilinear regression model to this curve. We extracted 8 parameters to mathematically describe the O2-pulse curve, as well as 20 traditional CPET performance metrics. One important parameter that was calculated is the ‘lost area under the curve’ (LAUC), defined as the area under the two calculated regression lines over time subtracted from the area under the curve as determined if the first regression line were to continue on the same slope as is typically expected during a maximal CPET. This value captures both the change in slope and when participants transitioned from a steep increase in O2-pulse to a relatively flattened O2-pulse. The LAUC, among our other identified metrics, can potentially provide insight into the optimal timing of PVR in patients with TOF. Unsupervised machine learning may be a useful tool to characterize patterns in these metrics and search for clinically relevant patient phenotypes.

Western U.S. wildfires are a growing threat to human lives, societal infrastructure, and global climate. While it is well known that meteorological factors impact wildfire intensity and growth rate, quantitative relationships between meteorology and wildfire are scale-dependent. For example, a recent study evaluating all recently observed California wildfires found that explosive fire growth was strongly related to short periods of strong winds and dryness. However, that study used data from a global atmospheric reanalysis (which cannot resolve local winds). As such, even the strong relationships found between meteorology and wildfire growth may have been underestimated. Given the potential consequences involved in predicting and mitigating future wildfires, it is important to understand the real-world accuracy of previously determined fire-environment relationships. To do so, this project compares how local meteorological observations from Remote Automated Weather Stations (RAWS) differ from reanalysis observations during known wildfires. The seasonal and spatial variation in the different relationships is also evaluated. Analysis has shown that the RAWS network is dense enough to adequately represent conditions at each fire being examined. Early results indicate that RAWS and reanalyses have similarly timed wind events during the max growth period. These results are promising, as they indicate that global atmospheric reanalyses can be used as a proxy for ground observations in remote terrain when analyzing periods of extreme wildfire growth.