After the COVID-19 pandemic, over 40 million children worldwide are at risk of measles due to delayed vaccination and temporary SARS-CoV-2 viral dominance. The lasting immunosuppression caused by the disease presents a major health threat, and treatment options are urgently needed, especially for low- and middle-income countries. The manuscript by Cox et al. (2024) explores features of canine distemper virus (CDV) in ferrets, using this model as a surrogate for measles to evaluate two possible antiviral treatments, ERDRP-0519 and GHP-88309. Ferrets were infected with a lethal challenge of CDV and treated with either drug or therapeutic vaccination. We aim to characterize both the infection dynamics and efficacy of the two drug treatments using the data from the PBMC (peripheral blood mononuclear cell) associated viremia titers of CDV infected ferrets and the lymphocyte counts measured during the duration of the study. A differential mathematical model was fitted to the experimental data by minimizing the sum of squared residuals (SSR), and errors in the parameter fits were estimated using Monte Carlo Markov Chain (MCMC). We visualized the key parameter distributions for each dataset using histograms, allowing us to directly compare how each treatment influences infection dynamics. The results revealed that ERDRP-0519 reduced viral entry and enhanced clearance while GHP-88309 improved target cell growth and increased the rate of infected cell death. These findings suggest that both drugs are potentially effective measles treatment options, with ERDRP-0519 having a direct antiviral effect and GHP-88309 aiding in immune recovery. Overall, these insights provide a foundation for optimizing treatment strategies and highlight the potential for both drugs to combat measles and related morbillivirus infections, especially in areas with limited resources and vaccines.

Gliomas account for approximately 27% of all primary central nervous system tumors and exhibit highly aggressive growth patterns, making conventional treatments ineffective. Previous research has demonstrated that a replication-competent Sindbis virus (SINV) combined with cytokines (IL-7, IL-12, and GM-CSF) shows promising results in slowing down glioma progression. While prior research demonstrated that SINV combined with cytokines reduces tumor growth, a quantitative understanding of its effects remains limited. This study aims to develop and fit a mathematical model of oncolytic virus infection to data from previous research to quantify key biological processes in glioma treatment. By parameterizing the Sindbis virus-glioma interaction and estimating the effects of cytokine therapy, this model aims to evaluate the efficacy of different SINV variants, with and without cytokine combinations, in controlling tumor growth. We use an Ordinary Differential Equation (ODE) model to describe tumor growth inhibition by the oncolytic SINVs. The model includes variables for uninfected and infected tumor cells, viral load, and cytokine concentration. The data extracted from published tumor growth curves will be used to estimate key parameters, including viral replication rate, tumor growth rate, and cytokine effects. Parameter fitting will be conducted by minimizing the Sum of Squared Residuals (SSR) between model predictions and experimental data. Error in the parameters will be estimated through bootstrapping to find the best fit parameters with 95% confidence intervals. Preliminary analysis suggests that the model effectively captures tumor growth rates observed in the experimental data. Parameter estimation provides insights into the viral infection rate, cytokine-induced tumor suppression, and the timing of viral injections. These findings will help refine our understanding of how the SINVs and cytokine therapy interact in glioma treatment. This study provides a quantitative framework for evaluating the therapeutic effects of an oncolytic SINV combined with cytokines in glioma treatment. By providing parameter estimates for key biological processes, our model can help optimize treatment strategies and guide future experimental research in oncolytic virotherapy.

Mathematical modeling of viral kinetics can be used to gain further insight into the viral replication cycle and virus-host interactions. However, many virus dynamics models do not incorporate the cell-to-cell heterogeneity of virus yield or the time-dependent factor of virus production. A recent study of the kinetics of the vesicular stomatitis virus (VSV) in single BHK cells determined that both the virus production rate and the yield of virus particles vary widely between individual cells of the same cell population. We used the results of this study to determine the distribution that best describes the time course of viral production within single cells. The best distribution was then used to incorporate time-varying production into a standard model of viral kinetics. The best-fit model was determined by fitting potential distributions to cumulative viral production from single cells and comparing the Akaike Information Criterion (AIC). The results show that the best fit for most cells was log-normal. Time-dependent viral production was modeled with an integro-differential equation that incorporated the log-normal probability distribution into a standard constant production model of viral kinetics. This time-dependent model was compared to one of constant production by examining the differences between the viral peak, time of the peak, upslope, downslope, and area under the curve. These findings could have further-reaching implications for helping define the time course and nature of a particular virus infection within the human body as well as improving the dose-timing and efficacy of anti-viral treatments.

Syncytia are multinucleated cells that can occur due to virus infection of cells. Mathematical models in the form of ordinary differential equations can be used to simulate the growth of syncytia. Several novel ODE models can explain syncytia growth. Before employing these models on actual data, it is essential to analyze their structural (theoretical) and practical identifiability using computer software. Structural identifiability is an inherent property of each model and its parameters, referring to our ability to determine parameter values for the model given particular experimental measurements. Practical Identifiability analysis of a model is concerned with determining our ability to accurately determine parameter values given experimental error. Combining these two techniques enables us to determine whether or not the parameters of our syncytia models can be accurately determined. Obtaining accurate parameter values allows us to make conclusions about our data that can provide insight into the nature of the spread of syncytia. From this, we can plan experiments to parameterize the syncytia growth in the contexts of our models.

Mpox virus is a type of virus similar to smallpox that can cause diseases in humans. Several experiments have been done to collect data on how mpox evolves within an infected host. This data can be analyzed within the context of mathematical models to determine important characteristics of mpox. From this analysis, we can estimate the growth rate, reproduction number, and infecting time of mpox.  We can also construct confidence intervals to estimate the error in our predictions using bootstrapping.  Bootstrapping allows us to analyze parameter correlations within mpox data to understand how parameter values within the model affect each other in our model. From these values and confidence intervals, we can learn about how mpox evolves within the body over time. This information, in turn, may allow us to make predictions on how mpox evolves within people during infection that could inform future treatment regimens.

Iron zinc oxides are multifunctional materials with applications in luminescent devices, catalysis, spintronics, and gas sensors. Specifically, iron-doped zinc oxide (FeZnO) combines magnetic and chemical stability properties, making it suitable for technological and environmental applications. This study explores how synthesis parameters, including pH and dopant concentration, influence the morphology and properties of FeZnO nanoparticles. Hydrothermal synthesis was employed to prepare FeZnO with iron doping concentrations ranging from 1–10% and ZnO. Morphological and compositional analyses were performed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX). We also observed doped FeZnO antibacterial action for some of the synthesized samples in e-coli cultures. Future work will focus on improving dopant distribution, exploring antibacterial activity, and leveraging computational tools to refine material design for specific applications.

Star clusters are incredibly useful tools in the pursuit of understanding our Universe better. They can be used to discover how our Galaxy, the Milky Way, formed and evolved over time, delve into the secrets of how stars form and even track how the different chemistry around our Galaxy. However, determining whether a group of stars is truly a star cluster or just a group of stars is a difficult task. In this poster, we will go over what a star cluster is, how we determine membership of the star cluster and the current work we are doing to investigate galactic chemical abundance gradients using star clusters.

Oncolytic Herpes Simplex Viruses (oHSVs) target a wide range of different cells and specific mutations, allowing them to proliferate in tumor cells. Recent work has modified the virus to preferentially enter cells bearing epidermal growth factor receptors (EGFRs). This study focuses on characterizing the efficacy of different strains of EGFR-targeting oHSV by fitting a mathematical model that includes an interferon response to experimental data from U251 tumor-bearing mice. Using a combination of parameter fitting, optimization techniques, and ordinary differential equations (ODEs), we modeled tumor growth, viral dynamics, and immune response. Our findings suggest that an interferon-inclusive model best explains the growth and oHSV treatment of EGFR-bearing tumors. These results highlight the importance of immune interactions in oncolytic viral therapy and contribute to optimizing oHSV-based treatments for better clinical outcomes.

Among the most life-threatening diseases, cancer poses a major issue and affects over fifty million people worldwide. To overcome the challenges associated with conventional chemotherapy, affecting both cancerous and normal cells, here we develop folic acid-functionalized Graphene Quantum Dots (GQDs) targeted to folate receptors overexpressed in a variety of cancer cell lines. GQDs due to their high biocompatibility and intrinsic fluorescence-based imaging capabilities have recently emerged as promising theragnostic agents. In this project, we synthesized GQDs utilizing the bottom up synthesis method and functionalized them with folic acid. The efficacy of the Folic acid functionalized GQDs (FAGQDs) is evaluated through their internalization study in cancerous (HeLa) and normal (HEK-293) cells by utilizing the intrinsic fluorescence of FAGQDs.

The use of fluorescent compounds as biological markers or probes is widely used in assays for probing various properties, including but not limited to pH, temperature, or the presence of various proteins. This has allowed fluorescence to enter the fields of microscopy, diagnostics, and spectroscopy. Among the many dyes used for such applications are those that exhibit phosphorescence. Unlike fluorescence, which has a lifetime of several nanoseconds, phosphorescence lifetimes can be several seconds, allowing for the use of techniques such as gated detection, which can eliminate distracting background noise or Raman scattering. Since phosphorescence uses the triplet state rather than the singlet state, it requires less energy, which correlates with longer wavelengths. The phosphorescence emission of some dyes can extend from 425nm (blue) to 675nm (red), which encompasses almost the entire visible spectrum. This is especially useful when considering that longer wavelengths may be used when utilizing direct triplet state excitation, which allows for excitation wavelengths well into the visible range. The ability to utilize longer excitation wavelengths has numerous possibilities, among which include being safe to use with live cells, which opens the door for using phosphorescence as a technique for biological imaging. Not only does phosphorescence allow imaging to occur at longer wavelengths, which mitigates damage to cells and minimizes exposure to harmful ultraviolet radiation, but it also allows for much more affordable equipment and procedures, possibly making diagnostic care more accessible.

Multipartite viruses are a unique class of viruses that divide their genome into multiple segments, each packaged into a separate viral particle. Unlike traditional viruses, which encapsulate their entire genome within a single particle, multipartite viruses require all genome segments to infect the same host cell for successful replication. This study investigates the infection dynamics of multipartite viruses through mathematical modeling, with a focus on bipartite and tripartite viruses. By comparing their behavior to single-particle viruses, we analyze the factors influencing viral persistence and spread. Our results indicate that the higher number of particles in a virus, the harder it is to maintain an infection. While multipartite infections exhibit shorter durations of infections compared to single-particle infections, their ability to persist suggests a potential benefit. These findings can help develop an understanding into the adaptive mechanisms of multipartite viruses and contribute to a broader understanding of viral evolution and host-virus interactions.

Surface plasmon–coupled emission (SPCE) is a powerful phenomenon that utilizes the near-field interaction between excited fluorophores and thin metallic films, together with a glass substrate, to significantly improve fluorescence detection sensitivity. By coupling the fluorophore’s oscillating dipole to surface plasmons, SPCE channels a substantial fraction of the emitted photons into a defined angle, generating a highly directional and polarized emission that can achieve up to 50% light collection efficiency. This intrinsically wavelength-resolved emission not only simplifies optical system design but also elevates the signal-to-noise ratio by reducing background interference. Compared to conventional isotropic free-space fluorescence, SPCE’s strong directional control and enhanced collection enable the detection of analytes at extremely low limits. Hence, this paper elucidates how SPCE’s unique advantages can be leveraged to achieve highly sensitive detection of critical biomarkers, paving the way for more rapid and efficient diagnostic applications.

Micro- and nanoscale metal oxides are used in a variety of applications. ZnO and Ga2O3 semiconductors are two metal oxides that have a wide bandgap and find themselves used in today’s electronics, gas sensors, and photodetectors. These two materials are also used in a wide range of temperatures, which means that the chemical bond lengths, vibrational states, defect states, and band-gaps all should be variable. In our experiments, we investigate the T-dependencies of positions, intensities, and widths of Raman peaks/bands for micro- and nanoscale ZnO and Ga2O3. In our studies, in addition to the temperature-dependent Raman spectroscopy we employ scanning electron microscopy (morphology of particles), energy dispersive X-ray spectroscopy (stochiometry) and temperature-dependent photoluminescence spectroscopy (electronic structure).

Human Immunodeficiency Virus (HIV) can exist as syncytia-forming or non-syncytia-forming strains, each utilizing different mechanisms of infection. Understanding the competition between these strains is crucial, as syncytia formation has been linked to increased disease progression and immune system decline. This study develops a mathematical model to analyze their competition, incorporating parameters such as fusion rate, syncytia lifespan, and viral production. Stability analysis and simulations will determine conditions under which one strain dominates or both coexist. By varying key parameters, we aim to understand how syncytia formation influences viral dynamics and infection persistence, providing insights into HIV pathogenesis and potential treatment strategies.

Cancer remains a major global health challenge, with over 20 million new cases diagnosed annually. Conventional treatments like chemotherapy, while effective, often require high doses due to non-specific targeting, leading to severe side effects. To overcome these limitations, we developed a targeted drug delivery platform using graphene quantum dots (GQDs), which offer high biocompatibility, near-infrared (NIR) fluorescence, and photothermal properties. In this study, hyaluronic acid-conjugated GQDs HA-GQDs and RGQDs, synthesized top down from reduced graphene oxide, are loaded with doxorubicin, paclitaxel, and gemcitabine, were tested in vitro using a custom-built, fully automated system for NIR laser irradiation and real-time spectral monitoring. Drug release was triggered by GQD-mediated photothermal heating and evaluated via MTT assays and fluorescence tracking. This work presents a novel, cost-effective nanocarbon-based drug delivery system integrating targeted therapy and photothermal control for enhanced cancer treatment.

Graphene quantum dots (GQDs) have gained attention in the bioimaging community due to their biocompatibility and enhanced imaging depth in the near infra-red (NIR). Developing and optimizing a facile synthesis method of biocompatible NIR fluorescent GQDs from a variety of precursors remains, therefore, a critical task. Herein, we synthesized various GQD structures capable of fluorescing in the NIR via facile bottom-up pyrolysis of precursor materials (ascorbic acid, chitosan, citric acid, dextran, glucose, glucosamine hydrochloride, hyaluronic acid, l-glutamic acid, polyethylene glycol (PEG), sodium cholate, or sodium citrate). All synthesized GQD structures exhibit remarkable biocompatibility at concentrations of up to 1 mg/mL evaluated by an MTT assay which makes them suitable for a variety of therapeutic applications. All 11 GQD structures are successfully tracked by their NIR fluorescence in vitro bioimaging while exhibiting effective cellular internalization maximized at 12 hours in HEK293 cell line. This work provides a unique comprehensive study exploring a scalable and cost-effective process to synthesize NIR-emissive highly biocompatible GQDs from 11 precursor materials, while theoretically describing their optical properties. Due to their exceptional biocompatibility and photostable NIR emission, GQD structures developed here are expected to become prominent candidates for future clinical fluorescence imaging applications.

Between the Andromeda (M31) and Triangulum (M33) galaxies lies a population of neutral hydrogen clouds which have velocities in between M31 and M33. The origin of these clouds is unknown, and it is thought that they could represent (1) a tidal bridge that links M31 with one of its satellite galaxies, (2) an inflowing intergalactic medium stream, (3) halo gas condensations, or (4) tidally-stripped material from a population of satellite galaxies. To ascertain the origin(s) of these clouds, we embark on a UV absorption and radio-line study to constrain their chemical composition. We assessed the ionization state of the gas using photoionization modeling with Cloudy that we anchored using HI and ion column densities that we measured from our Green Bank Telescope and HST/COS datasets. Through this work, we resolve the properties of a single gaseous stream of M31 along multiple sightlines, aiding in our understanding of L* galaxy ecosystems.

Modulating the Interferon Response to Enhance Oncolytic Virotherapy in Cancer Treatment
Anushka Velala, Hana Dobrovolny Ph.D
TCU Department of Physics, Fort Worth, TX
Background: Oncolytic viruses (OVs) are a promising immunotherapeutic strategy that can selectively target and lyse cancer cells while stimulating an anti-tumor immune response. However, their efficiency is often limited by the interferon (IFN) response, which acts as a key antiviral defense mechanism in host cells. Understanding the interplay between oncolytic viruses and IFN signaling is crucial for optimizing viral-based cancer therapies that have potential of success.
Hypothesis/Objective: This study aims to investigate how oncolytic viruses interact with the IFN response in a simulated tumor microenvironment. We hypothesize that higher values of variation in the IFN modulation can significantly negatively affect viral replication and therapeutic oncolytic efficacy.
Study Design and Research Methods: An analysis was conducted using a mathematical model with systems of differential equations. This model encompasses factors such as tumor growth, oncolytic infection dynamics, viral production and clearance, and the IFN-mediated immune response. Furthermore, sensitivity analysis was conducted to assess the influence of key parameters, including viral production rate, infection rate, and IFN clearance, on the treatment outcomes.
Results: Various simulations indicate that higher IFN levels correlate with reduced viral spread, leading to diminished oncolytic activity. However, parameter variations suggest that therapeutic efficacy can be optimized by adjusting certain parameters to mitigate excessive IFN responses. For instance, higher values of IFN efficacy are correlated with stronger IFN-mediated suppression of viral production, leading to lower sustained viral loads, while lower IFN efficacy levels allow for prolonged high viral replication. Similarly, IFN clearance rate affects how long IFN-induced killing of infected cells and uninfected cells persists, which can modulate the viral load over time. The most effective interferon response is a low-level response with low IFN clearance and high values of IFN efficacy, coupled with higher values of IFN-induced killing of uninfected cancer cells.
Conclusions: These findings underscore the role of the IFN response in modulating OV therapy and suggest that targeted suppression of IFN signaling could enhance OV efficacy in resistant tumors. This research provides insights for optimizing oncolytic virotherapy and improving clinical outcomes in cancer treatment, given the rising prominence of immunotherapy.

Frustrative nonreward (FNR), an adverse reaction brought on by unexpected reward reductions or omissions, can be induced by a downshift in the quantity or quality of the reward. The consummatory successive negative contrast (cSNC) task is a well-known paradigm for studying FNR. cSNC involves monitoring the behavioral reaction to a lower reward (downshift) after exposure to a larger or better incentive. It supports the idea that an acceptable but less preferred reward will be rejected in a situation that is associated with a better and more desirable reward. The intensity of FNR depends, among other things, on the strength of the expectation of the large reward. We assumed that overtraining would enhance reward expectancy such that a reward downshift would lead to a stronger cSNC effect than that observed under regular training conditions. This would support the hypothesis that behavior (licking for sucrose) was guided by reward expectancies—an action. But overtraining often leads to habitual behavior that depends on eliciting stimuli, rather than reward expectancies. A failure to show the cSNC effect after overtraining would be consistent with the hypothesis that behavior had become automatic—a habit. Our experiment was designed to test whether overtraining in the cSNC task would result in behavior becoming either an action or a habit. In the experiment, 47 rats were exposed to different concentrations of sucrose, 32%, 16%, or 4%, and 2 training periods, overtraining for 30 sessions and regular training for 10 sessions. Animals exposed to 32% and 16% sucrose were randomly assigned to two groups depending on the amount of training they received before the downshift, either 30 (overtraining) or 10 sessions (regular training). These animals were given access to 4% sucrose after their designated training period. An unshifted control group received only access to 4% sucrose throughout training. The data obtained after 10 vs. 30 sessions of training were compared to the unshifted controls. The results showed that overtraining enhanced the cSNC effect relative to regular training, suggesting that licking was an action guided by the expectation of the current reward, rather than a habit. These results suggest that FNR induced by reward downshifts overcomes the development of a habit even after prolonged training.

Humans produce complex and learned behaviors like speech, playing musical instruments, and sports through exceptional motor abilities. These learned actions need specific motor planning and preparation. Researchers use songbirds in part because they produce a stereotyped motor sequence whenever they engage in singing behavior. Further, Zebra Finches learn their song through vocal production learning, similar to human speech acquisition; they mimic their adult male tutor's song and reproduce a similar version in adulthood. This motor learning process leads to the generation and execution of a highly skilled and stereotyped motor program production. Before the song, Zebra finches sing a sequence of introductory notes that are short-duration, non-stereotyped sounds. Previous work has speculated that these introductory notes are a form of motor preparation, but an experimental test of this hypothesis has not been conducted. This study casually examines the role of introductory notes as a motor preparation phase to help transition to executing the main song motor sequence. To distinguish motor preparation from song execution, we reasoned that presenting an external stimulus would delay preparation but not execution. We used air pressure recording to identify introductory notes and triggered white-noise playback during the introductory note performance in six birds and found that the external stimulus led to a delay, which can lead to interruption of the typical song motor pattern (e.g., abnormal pauses). Whereas the same stimulus presented during the song either caused an abnormal early termination of the motor program or did not affect the song (continuation), but it did not delay the execution of the song's motor gestures. Our findings suggest that introductory notes are flexible and modifiable by external stimuli, which is consistent with the hypothesis that they function as a preparatory motor gesture for the upcoming stereotyped song.
Understanding motor planning can provide insight into neurological, behavioral, speech, and motor disorders that are characterized by deficits in neuromuscular preparation.

Recent discussions surrounding law enforcement have highlighted varying opinions on the ability of police officers to respond effectively to mental health-related incidents. Given that 20% of police calls involve mental health or substance abuse issues, it is crucial that the general population is confident about the role of police as mental health interventionalists. The current study assessed DFW residents’ (N = 64) perceptions of the police’s ability to intervene in mental health crises utilizing in-person and online 7-point Likert scale surveys (where 1 = disagree strongly and 7 = agree strongly). Survey results showed that confidence in police’s ability to handle mental health crises increases with age, p = .04. Older participants expressed greater trust, while younger respondents were more skeptical. No significant differences were found in gender, race, ethnicity, or socioeconomic status, highlighting a generational divide in public trust. These results suggest that there is potential for improving younger individuals’ attitudes towards police intervention.

Rats use many cues when navigating to food, shelter, or a mate. The use of visual cues (e.g., landmarks) has been reported in many species. In rats, these cues include those around their start position, the experimenter, as well as landmarks located in (intramaze) or around (extramaze) the search space. In the current experiment, rats were placed into a start box with a transparent door and released onto an open field. We examined whether rats were able to discriminate between two different intramaze landmarks (wooden figurines; A and B) from the start box. Landmark A trials were reinforced with a Froot Loop© hidden in a cup behind the landmark (A+), but no Froot Loop was present on Landmark B (B-) trials or on C- trials with no landmark. Latency to the goal cup was measured and revealed no differences between the three trial types. The procedure was modified to include two response locations (to the left and right of the landmark). A+ and B+ trial types were reinforced at different cups. There was no difference in accuracy for searching the correct cup first. The use of non-visual cues, the discriminability of the landmarks, and the response cost of search will be discussed.

Bird song has been extensively investigated as a model for understanding the physiological basis for animal vocalization.. Juvenile songbirds acquire their songs and perfect them as they transition into adulthood, just as we acquire our native language by exposure and imitation of adult tutors. Scientific investigation of bird song requires the collection of hundreds of hours of audio data containing songs, calls, and cage noise. These data must be sorted into categories of interest for specific research questions, with singing behavior being the dominant behavior of interest. Data categorization is a tedious and time-consuming process, and while current software hastens this process, substantial human effort is still required. This project investigates whether or not machine learning algorithms can be used to more efficiently categorize audio data collected in songbird research. Specifically, we developed a convolutional neural network (CNN) in PyTorch to classify whether or not 0.5 second sections of audio contain bird song. Using a supervised learning paradigm, we trained the CNN using labeled spectrograms (visual representations of audio frequencies across time) acquired from recordings of the zebra finch (Taeniopygia guttata). After training the CNN, we implemented it into an algorithm that identifies song within audio recordings. We then compared the CNN based software to a pre-existing, custom-written LabVIEW template-matching algorithm to determine the relative speed and accuracy of the software. Recordings were taken in both noisy and quiet recording environments to test the strengths and limitations of the two approaches. Our data indicate that the CNN based algorithm achieves comparable levels of accuracy to the pre-existing algorithm and accomplishes the categorization using a fraction of the time required by the template matching program. . These results suggest that machine learning algorithms can effectively be used to automate and rapidly categorize stereotyped vocal patterns. Further development of this software may facilitate rapid analysies of data and be extended to categorization of a broader range of vocal patterns, including human speech.

Hormonal contraceptives can have many negative side effects that deter women from using them. One example that many women are unaware of is alcohol craving. Preliminary studies in our lab have shown that women on hormonal contraceptives have greater alcohol cravings than women who are naturally cycling. Given that this data is mostly survey-based, we aim to add a research manipulation in the current study. We are testing this through a Qualtrics survey, distributed via Amazon’s Mechanical Turk (Mturk), that is designed to prime alcohol cravings. We will ask women to rate their alcohol cravings before and after they watch a video containing alcohol. We expect that when primed with an alcohol video, women’s cravings towards alcohol will increase to a level higher than before they watched the video. Further, we expect to find that women on hormonal contraceptives will have a higher increase in cravings than women who are regularly cycling. If we do find that women on hormonal contraceptives have a stronger reaction to an alcohol cue, women would benefit from being well informed about this effect. For instance, armed with the knowledge that hormones influence craving and behavior, women may be more mindful about their drinking habits. In addition, we eventually hope that this knowledge will influence those who are developing future contraceptives to take these side effects into account.

Existential isolation can be described as the experience of feeling alone in one’s experience of the world; that no one shares your experience or comes close to understanding it (Pinel et al., 2017). Research on existential isolation remains novel, however current findings among those chronically existentially isolated has found significant associations with a host of negative affective outcomes, including increased loneliness (Pinel et al., 2017), death thought accessibility (Helm et al., 2019), depression and anxiety (Constantino et al., 2019), and reduced self-esteem and self-worth (Helm et al., 2018). Finally, there is novel and consistent evidence that existential isolation is related to insecure attachment, specifically avoidant attachment (Helm et al., 2020).

Recent literature has found that adverse early life experiences are one of the factors that can proactively influence adult well-being, resulting in consequences such as anxiety and depressive disorders, and suicidal ideation (Hays-Grudo and Morris, 2020). Additional work has also shown that childhood abuse was uniquely associated with greater loneliness among young adults (Landry et al., 2022) but the impact of early life experiences on loneliness can be impacted by a multitude of factors including, but not limited to, age, gender, socioeconomic factors, and epigenetic factors (Southwick et al., 2014). While loneliness is inherently a part of the feeling of existentially isolated, existential isolation itself is distinct in theoretically meaningful ways that this study aims to investigate through the lenses of adverse childhood experiences and childhood uncertainty. This present study aims to qualitatively examine the relationship between adverse childhood experiences and existential isolation among adult individuals. Our findings across two studies established a positive association between individuals who have experienced adverse childhood experiences and existential isolation.