In 2020 marijuana use among college students was at an all-time high with 44% reporting using in the past year. Research demonstrates individuals with ADHD are at risk for increased marijuana use later in life. Additionally, some studies demonstrate self-reported symptoms of ADHD among undiagnosed college students prospectively relate to increased marijuana use, which was mediated by changes in perceptions of typical peer marijuana use (i.e., descriptive norms). However, this is complicated by frequent marijuana use being linked to issues with attention, impulsivity, and memory; all captured by self-reported ADHD measures. This study will evaluate bidirectional relations between ADHD-related symptoms, marijuana descriptive norms, and marijuana use among college students. Data collection is in progress and will be completed in March 2022 as part of a larger study. Students completed assessments at baseline, 6 months, and 12 months. A cross-lagged panel model will be conducted in R using the lavaan package to evaluate the bidirectional associations over a 1-year period, as well as test whether marijuana norms at 6 months mediate the longitudinal associations between ADHD-related symptoms and marijuana use from baseline to 12 months.  We hypothesize those reporting more ADHD-related symptoms will use marijuana more frequently 6 months later. Additionally, those who use marijuana more frequently will report more ADHD-related symptoms after 6 months. We also hypothesize that those experiencing more ADHD-related symptoms at baseline will have increased marijuana norms at 6 months and subsequent greater marijuana use at 12 months. This study attempts to disentangle the bidirectional associations between ADHD-related symptoms, marijuana norms, and marijuana use over a one-year period. Conclusions and future directions for research will be presented. Additionally, this study may have implications for norms-based interventions to better address memory, impulsivity, and other ADHD-related symptoms in association with marijuana use and perceptions.

Human adenoviruses (HAdVs) are known to infect and cause diseases in multiple organ systems. HAdV serotypes are associated with particular diseases, giving us reason to believe there is a genetic link to tissue tropism. To understand this relationship, we are using mouse adenoviruses (MAdVs). Like HAdVs, MAdV serotypes are associated with distinct tissue tropisms: MAdV-1 infects macrophages whereas MAdV-2 infects intestinal epithelial cells. By swapping genes between MAdV-1 and MAdV-2, we hope to uncover the genetic basis of tissue tropism in MAdVs. These studies are aided by the availability of a cell culture system that recapitulates the cellularity of the intestinal epithelium and supports MAdV-2 replication but not MAdV-1 replication. Phylogenic analysis indicates that MAdVs are the oldest lineage of Mastadenoviridae, the genus that includes AdVs of mammals. Thus, functions of viral genes that are conserved among viruses within the genus and important for MAdV tissue tropism are likely to apply to HAdVs. A major determinant of viral cellular tropism is receptor usage, which may also play a primary role in tissue tropism. For both MAdV species, the trimeric fiber protein that extends from the icosahedral capsid is thought to be the viral attachment protein that binds to a host receptor. MAdV-1 has been shown to use specific cell adhesion proteins called integrins as a receptor for viral entry. Although the receptor for MAdV-2 is unknown, it differs from MAdV-1. To gain insight into tissue tropism, I have designed a chimeric MAdV-1-M2f virus, where the fiber gene of MAdV-2 has been inserted in place of the native fiber gene in the MAdV-1 genome. A prior student in the Smith lab created the inverse chimera. Using these viruses, we can infect intestinal epithelial cells to measure replication. Doing so will help uncover whether the fiber/receptor interaction plays a central role in determining tissue tropism.

Soldiers who are transitioning to civilian life have alarmingly high rates of suicide, alcohol abuse, and mental illness. There are multiple facets that may influence these poor outcomes for veterans. Organizational dissatisfaction is a risk factor for poor mental health and lack of access to mental health support for veterans, but this has yet to be studied in-depth. The aim of this project is to assess how demographic variables and organizational dynamics impact mental health during the transition process. First, we will use preliminary data from The Network Study, one of the first longitudinal studies to follow the mental health, alcohol use, and socioeconomic stressors of service members as they transition out of the Army. Multiple regression will be used to analyze the influence of interpersonal support and conflict within the Army on depressive and anxiety symptoms. Mental health symptoms were measured by the PHQ-9 and GAD-7, two common screening questionnaires used to briefly assess depressive and anxiety symptoms, respectively. We will additionally assess various demographic variables as moderators. Next, findings will be used to develop questions for qualitative interviews to be conducted with recent veterans. We predict there will be a positive association between organizational support and positive mental health outcomes, and qualitative interviews will show lower work-related stress and increased access to mental health supports as major themes for those with positive work relationships. In line with previous research, we predict these associations and themes will be strongest for women, racial and ethnic minorities, and those of lower rank. Support and intraunit conflict are potentially modifiable factors, so our research demonstrates a potential pathway for prevention of negative mental health outcomes for veterans.

Cancer is a disease that is characterized by uncontrolled cell growth and proliferation. As these cells spread throughout the body they can invade and damage major organs resulting in death. It has been shown that most cancers arise from genetic changes in the signaling pathways that control the cell growth and proliferation. One major challenge cancer researchers face is to understand the behavior of these pathways. As an example, the behavior of the Endothelial Growth Factor receptor (EGFR) pathway, a commonly mutated pathway in cancer, has a broad range of behaviors. Under different conditions, the pathway can exhibit bistability, amplification and even oscillations in protein levels. Predicting such behaviors can significantly aid our understanding of how cancer arises as well as provide us with insights to therapeutically treat patients. The goal of this project was to develop a high accuracy machine learning model that is capable of predicting the behaviors of signaling pathways. Over the first quarter, I generated over 3000 artificial signaling networks utilizing existing software written by the lab. For each of these networks I then extracted the network architecture, the connections and relationships between species in the pathways, into matrices such as the stoichiometry matrix and scaled Jacobian matrix. These matrices were then treated as pseudo-images and used to train a convolutional neural network to predict oscillatory behavior. We expect this model to have greater than 70 percent accuracy given preliminary models trained on similar smaller datasets. The development of this high accuracy model provides cancer researchers with valuable insights into the behavior of these signaling pathways and how they can best therapeutically treat patients.

The deformation processes in the brittle-ductile transition region are poorly constrained and critical to understanding both the magnitude of earthquakes within the subduction seismogenic zone, and slow slip and tremor downdip of this region. Here we present field observations and microstructural data in the form of photomicrographs, thin-section scale x-ray maps and electron backscatter diffraction (EBSD) to constrain the rheology of a paleosubduction interface in the Central Alps during deformation and fluid-rock interaction. We studied three samples across a 10 meter transect within the lowest 10 meters of the overring Austroalpine plate at the contact with the Penninic subduction plate shear zone. Our granodiorite samples are exhumed from paleodepths of 30-35 km. Cataclasites, microfractures, and crack-seal quartz ± calcite veins are evidence of brittle deformation while a ductile foliation fabric indicates viscous deformation at conditions of 400-430ºC & 0.80-0.95 GPa. EBSD data shows progressive increase in viscous deformation and mineralogic changes closer to the contact. As the samples progress towards the shear zone the crystallographic preferred orientation and grain misorientation of quartz and albite decreases suggesting the controlling deformation process changes from dislocation creep to diffusion creep due to pinning of the albite and quartz by phengite. This coincides with progressive fluid-rock modification observed in photomicrographs and chemical x-ray maps. We observe a decrease in albite replaced by an increase in quartz and phengite across the three samples. The breakdown of albite, and its replacement with more water rich minerals like phengite, the quartz +/- calcite veins, and the progressive shift from dislocation to fluid-aided diffusive mechanisms demonstrates increased fluid-rich interactions nearer to the plate interface contact. Increased fluid-rock interaction impacts the viscous deformation mechanisms and strength of these rocks. Diffusion creep occurs in low stress environments, suggesting that fluid- rock interactions lead to a low stress environment below the locked seismogenic zone.

According to the World Health Organization, 10% of pregnant women and 13% of recent mothers suffer from adverse mental health, particularly depression. The pregnancy process is an already tumultuous experience as mothers undergo many physical and psychological changes, so the addition of other prenatal stressors like limited resources that disproportionately affect low-income mothers can further negatively impact maternal mental health, birth outcomes, and an infant's cognitive and emotional development. The purpose of the study is to examine the impact of maternal mental health on low-income mothers and children and how mindfulness can be a potential tool of intervention. I carried out a literature review using preliminary outcomes of an ongoing, longitudinal study examining maternal well-being following a mindfulness-based intervention among low-income new mothers with data acquired from the New Moms Connect Lab. Early findings show prenatal mindfulness resulted in lower depression and lower anxiety caused by pregnancy, however its effects were not long-term. Postnatal mindfulness did not yield conclusive results due to lower attendance. Social- Emotional Attunement (SEACAP) resulted in reduced depression in comparison to all three. Overall, mindfulness practices reduced maternal stress more than the control group. Research is still ongoing and more data is still being collected regarding the long term effects of postnatal mindfulness. This research is important as it provides data on the maternal health of low-income mothers who have been grossly underrepresented in previous similar research. These findings also indicate the potential benefits of mindfulness programs geared towards serving low-income communities.

Global climate change is likely to have an effect on plants, pollinators, and the interactions between the two ecological groups. Recent studies have suggested that climate change may affect the growth and development of these organisms in the long-term, but variations in temperature could also change the short-term behavior of pollinators when they visit plants. For instance, pollinators with certain physical traits might be better suited to forage in areas with higher or lower temperatures. In this study, I explored this idea with the assumption that the foraging decisions of pollinators are indicative of the temperatures that they are flying at. I addressed the questions of 1) how variation in temperature affects pollinator traits? and 2) are certain pollinator traits correlated with differences in the identities and traits of the plants that they are captured visiting? To answer these questions, I measured the size of bees collected in the Rocky Mountains during the summer of 2021, and I compared these sizes to the temperatures at the sites at which the bees were caught. I predicted that 1) pollinator traits would correlate with temperature, and 2) the interactions in my data analysis would show trait matching between the pollinators and the plants that they were visiting when caught. The results of this study should help illustrate how the impacts of climate change might affect plant-pollinator communities, which could help to identify potentially advantageous traits as well as interactions that may be flexible or persistent during changing climatic conditions.