Duchenne Muscular Dystrophy (DMD) is a severe muscle wasting disease caused by the deficiency of dystrophin protein affecting ~1 in 3500 boys. Exercise has been investigated as a potential therapy but has shown conflicting effects on dystrophic muscle. Animal models that have been studied but do not fully mimic the disease and cardiac phenotype. Here, we provide the first known evaluation of exercise in a new DMD mdx rat model that mimics skeletal muscle and cardiac pathology. Having an animal model that better reflects the DMD cardiac phenotype is crucial in establishing physical activity guidelines that minimize potential damage to the heart. In Aim I of this pilot study, DMD mdx rats (n=3) and sibling wildtypes (WT; n=3) were assigned to wheel or treadmill exercise training groups (high intensity or low intensity) or unexercised (sedentary) for 6-weeks. Throughout this 6-week period, I was responsible for executing the rat treadmill exercise training protocol at low-to-moderate intensity (5x/week), and monitored the rat wheel running activity. Physiological measurements were conducted pre- and post- training (wheel running, treadmill testing, grip testing, echocardiography, and hindlimb force testing). Compared to wheel running or unexercised rats, treadmill training produced the greatest gain in treadmill exercise endurance testing and in fatigue-resistance in in-vivo hindlimb force testing for DMD rats. However, the treadmill program was associated with severe cardiac effects in DMD mdx, indicated by significant fibrosis and inflammation and echocardiography (Myocardial Performance Index, % fractional shortening and ejection fraction), a finding which will be investigated in subsequent cohorts.

Parkinson’s Disease (PD) is a neurodegenerative disease that affects movement and often leads to tremor, rigidity, and balance issues. Speech impairment, or dysarthria, is also common and may influence many aspects of speech production, such as respiration, articulation, phonation, and prosody. There are currently multiple methods for measuring speech impairment severity over time. Often, severity of dysarthria is captured by neurologists during administration of a global measure of PD severity, the Unified Parkinson’s Disease Rating Scale (UPDRS). This scale uses one question to assess speech impairment severity on a perceptual level, but does not formally evaluate specific characteristics, such as intelligibility. Alternatively, speech-language pathologists may use measures of speech intelligibility to assess dysarthria severity. This method systematically quantifies the extent that speech was understood by a listener. The connection between these two approaches is currently unknown. Therefore, the purpose of this project is to examine the relationship between UPDRS scores and speech intelligibility metrics, two commonly-used indicators of dysarthria severity in PD. Connected speech samples for 27 individuals with idiopathic PD were collected. UPDRS ratings were completed independently by a speech expert who was blinded to intelligibility scores. Speech intelligibility ratings were based on 100-word speech samples and averaged across three listeners. Statistical analyses will include Pearson correlation to determine the strength of this relationship. Expected findings include a significant negative correlation, suggesting that increased severity on the UPDRS (higher score) corresponds to increased severity on the speech intelligibility calculation (lower score). Determining the relationship between these two indicators of speech severity will unite clinical understanding of speech impairment across the fields of neurology and speech-language pathology. This will also indicate whether a single speech severity question equates to more time-intensive intelligibility ratings.

Approximately 10-20% of adolescents globally struggle with mental health conditions. Due to multiple physical, social and emotional changes, adolescents are particularly vulnerable to mental health illnesses. Untreated or inadequately treated mental health conditions can be exacerbated by stigma, discrimination, and isolation. The inability to effectively address mental health problems can manifest both short-term and long-term consequences into adulthood, impairing both quality of life and having negative impacts on health outcomes and mortality. Mental Health for Every Adolescent (MHEA) is a programme we, as students at the University of Washington, created as an early intervention to destigmatize and educate students about mental health. Through this project, we sought to understand the impact and need for community based mental health intervention for young adults. We partnered with faculty to create curricula that addressed stress, anxiety, depression and other mental health related topics. We then recruited volunteers through social media advertising and virtually trained them to facilitate workshops. Using feedback we obtained from local stakeholders at our workshop sites, we modified our curricula to ensure they were culturally appropriate and sensitive. Over the course of 3 years, we completed 74 workshops across 3 countries - India, the United Arab Emirates and the US - and we reached more than 3100 middle school and high school students. We found that the needs of students varied across communities and therefore, the workshops had to be tailored appropriately. In April 2020, the MHEA team leveraged local relationships and published a COVID-specific guide aimed at adolescents which was launched at 6 school districts in the United States. The MHEA project describes a successful implementation of a community-based mental health intervention for children and young adults. Programmatic materials developed can be disseminated and incorporated into middle and high-school curriculum. Future work will evaluate the impact on participants in the program.

The goal of this project is to develop a dynamically evolving connectionist model that more closely resembles the brain through its information-processing. Over the years, AI has shifted from the first generation of feedforward systems to the use of recurrent or convolutional Neural Networks. The third and newest generation of AI models, the brain-based models, and the Spiking Neural Network (SNN), attempts to bridge the gap between Neuroscience and ML using biologically realistic models like Θ-model, LIF, Izhikevich, HR, HH. These models, however, are still a black box leaving very little control or understanding on the learning process within the system without the access to the inner structure of the network. In addition, these systems are highly inefficient, slow, and very complex due to the limitations imposed by the hardware and explicit simulation of partial differential equations. Real world problems require “flexible learning and dynamically adaptive connectionist systems” that are capable to adapt and accommodate new input in real time. Current solutions have focused on varying the weights within a system rather than focusing on how connections within the system are formed. Based on our understanding from organismal brain structures, our approach, called biomimetic information codec, .bic, is a morphologically-adaptive coding hierarchical network that form in accordance with energy minimization - driven by dissipation of "heat" generated by the training data - constructing cortices and connectome for processing of information. My first objective herein is to quantitatively compare detailed structures between biological (fly brain) and .bic. networks using a random matrix approach.

Systems of non-linear differential equations are often difficult to analytically solve, control, and analyze, whereas systems of linear differential equations are relatively straightforward to solve, control, and analyze because we possess tools to study systems of linear differential equations. Koopman analysis allows us to transform a system of non-linear differential equations into a linear system. The caveat is that sometimes, the resulting linear system is infinite-dimensional, meaning that the mapping between the original space and the Koopman space is infinite-dimensional. This poses a computational challenge because infinite dimensional vector spaces are difficult to computationally work with. Koopman analysis has traditionally been done on one fixed point (equilibrium). Previously, work has been done to study specific examples of systems that have closure i.e. systems with a finite-dimensional Koopman operator. In this project, we looked at how to apply Koopman operators to systems with multiple fixed points. We found effective eigenfunctions that linearize low-dimensional non-linear dynamical systems analytically if possible, computationally otherwise. Under the assumption that the right-hand side of the differential equations are polynomials, we identified appropriate eigenfunctions that linearize the Koopman space with a possible invertible mapping. Previously, we have been able to find a closed-form solution that generates eigenfunctions for one-dimensional systems that have a polynomial form. However, in practice, the resulting integral equation can be difficult to computationally solve with current methods, and edge cases such as singularities and asymptotes are not well understood. Using implicit SINDy (an algorithm that approximates dynamical systems given data), we attempted to find polynomial decompositions that allowed us to describe the eigenfunctions with rational polynomial functions. This work is significant because better understanding dynamical systems allows us to better understand dynamic fields such as natural disaster detection, the firing of neurons, and the spread of pandemics.

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.

Skin can detect a wide range of stimuli through the touch system, which is mediated by specific cells and structures. Merkel cells, one of these specialized types of skin cells, sense gentle touch and texture. In mammals, Merkel cells are identified by the expression of the transcription factors Sox2 and Atoh1. Recently, the Rasmussen lab identified Merkel cells in the zebrafish skin that share many characteristics of mammalian Merkel cells, including expression of Sox2 and Atoh1a. Interestingly, a paper by Konig in 2018 also described a novel cell type that expresses serotonin (5-HT), calretinin, and synaptic vesicle glycoprotein 2 (SV2), termed “HCS” cells, within the zebrafish skin. These researchers proposed that HCS cells are a different sensory population than Merkel cells, because of differing visual characteristics between these cells. However, the paper did not present any conclusive evidence, making the relationship between HCS cells and Merkel cells still uncertain. I hypothesized that Merkel cells and HCS cells are actually the same population of cells. I tested my hypothesis using antibody staining and confocal imaging of zebrafish skin. I found that Atoh1a-positive Merkel cells express serotonin and SV2, demonstrating that Merkel cells in zebrafish are the same cells as HCS cells. Overall, my results resolve the identity of recently described sensory cell types in the zebrafish skin. In future research, I hope to use zebrafish Merkel cells as a promising model to better understand touch system development and regeneration.

I invite you to close your eyes and take a deep breath—5 seconds in, 5 seconds out if you can. Deep breathing relaxes us as humans. Through technology I was able to help instigate relaxation, or at least get you thinking about it. I think that’s a beautiful thing technology can do; it can remind us to take a breath, drink water, educate us on perspectives we may never have known, make us observers of art and music. However, it’s no secret that many parts of the online universe have been created to be addicting; the AI algorithms programmed to show each user exactly what will make users scroll another ten minutes. We also know that technology has been a hotbed for hate speech in recent years as well as a place where unrealistic beauty standards (monetized by diet companies) have thrived. In my project I refer to “enchantment" as a bodily connection to the mind, the senses, and to a community. I use this idea of enchantment to explore how various technologies, both modern and traditional, can instigate enchantment, instead of the opposing dis-enchantment. What is technology's connection to catalyzing (re)enchantment in our modern lives and communities? Depression rates are increasing at an alarming rate, especially among teens; I have to wonder if teenagers’ access to social media could be a partial cause of depression, or if for some teens, social media can act as a place of salvation from other possible causes of depression (e.g., school bullying, family issues, etc.). How we interact with the world and how we view ourselves is increasingly dependent on the internet and our global interconnectedness. Technology is becoming more prominent in our daily lives than ever before, how can we make these technological experiences ones that better our lives?

The historical record of the Community, Environment, and Planning (CEP) major, a student-governed program, exists in various formats and locations. The fragmented storage and lack of cohesive narrative prevents prospective students from learning about the major, current students from documenting their experience, and past students from staying connected. This project answers the question "What are the best practices of documenting and sharing the history of an organization, using the Community, Environment, and Planning (CEP) major at the University of Washington as a case study?" The creation of an archive allows for cohesion between cohorts, continuity in storytelling and program improvements, and increased program awareness. The methods used include background research about archives as both a source of information and a subject in their own right, physical and digital archival work, and the creation of a webpage. Analyzing these methods and documenting their application to CEP also informed a framework that guides future archival efforts. Building the archive on the CEP website utilizes existing infrastructure and current visitors to the site. Through digital media and technologies, students can add their stories, access those of past students, and establish connections between them. This archive demonstrates the impact the major has had on students and the greater Seattle area and will provide a platform to help secure programmatic resources, generate prospective student interest, and strengthen alumni connections.

The research question at hand asks how block parties can be used as an empowerment tool to positively affect civic engagement for the Black community. The project lies within the contexts of outreach strategies for underrepresented minority communities; the power of civic engagement and identity; and the inherently political history of the Black community and public space. The primary methods include an in-depth literature review to adequately frame the topic; the interviewing of Black community leaders in applicable disciplines across the United States for their experience and insight; and a creative depiction of the block party re-envisioning into one that centers Black empowerment, learning, and civic amplification. The findings propose a potential solution to magnifying Black voices in civic processes by taking a contemporary approach to the traditional idea of a block party. The final product is the literature review and representation of the Bl*ck Empowerment Party through a virtual zine. These products aim to provide individuals of underrepresented identities with the framework and inspiration to implement the project through discovering innovative methods of public outreach and empowerment rooted in the histories of their own communities in hopes of encouraging engaged citizens. The Bl*ck Empowerment Party addresses the inequities within city structures and development processes through offering a creative solution that honors the rightful space for affected communities to be empowered stakeholders and catalysts.

In the past decade, there has been an increasing focus on the sexual lives of college students. Through this focus, both academic literature and popular culture perpetuate a narrative of “hookup culture” (i.e. environments that accept and facilitate casual sex encounters without romantic or emotional intentions) within this demographic. Though many theories support the importance of studying the sexual lives of college students, this narrative neglects the emotional aspects of intimate relationships, limiting the field’s understanding of intimacy in college. By conducting a thematic analysis of anonymous, written responses from college students (n = 59), this project studied how college students’ perceptions of intimacy differ from the popular narrative of hookup culture and casual sex. Using Erik Erikson’s stages of developmental theory and previous sociological research, I hypothesized that college students will emphasize the emotional components more than the sexual components of their intimate relationships and perceptions. The results of this study expand our understanding of intimate relationships among college students and challenge the limiting narrative of hookup culture and casual sex as ways to study and understand this demographic’s intimate relationships.