Memory has been widely studied for its crucial role in learning and its diverse range of expression. Although the means of acquisition differ, it is generally accepted that memory goes through three encoding stages: sensory, short-term and long-term memory. The retention of memory is important as it enables us to act with the wisdom of past experience. However, although one could not survive without memory, remembering everything is also devastating. In fact, forgetting is an important cognitive feature that allows us to adapt to the constantly changing environments. Despite its importance for cognition, little is known about the molecular nature of forgetting. Here, we investigated the genes behind forgetting by studying an olfactory memory in the nematode C. elegans. Like human, worms can modify their behavior upon acquiring unpleasant experience – their movement towards preferred odor is significantly reduced after prolonged exposure to the odor during starvation. Upon returning to food (e. coli OP50), the odor attraction slowly returns to the worms within 3-4 hours, indicating a diminishing impact of the starvation experience. By contrast, the forgetting process was significantly accelerated by 1-2 hours, when worms were cultivated on pathogenic bacteria, pseudomonas aeruginosa PA14. Our genetic studies showed that a null mutation to the daf-16 gene restored recovery time to 3-4 hours, despite of the exposure to PA14. These data indicate that daf-16 plays a positive role in accelerated memory loss upon pathogen ingestion. Because DAF-16 is involved in innate immunity and stress response, our results provide a potential connection that couples the memory to environmental stressors.

The Large Hadron Collider at CERN is built to accelerate particles to high speeds and make them collide. The curved trajectory of these particles is recorded by detecting electrical charges deposited on multiple layers of tracking equipment as a particle travels through the detector. The resulting patterns are then used to reconstruct the path traveled by the particle. The more the collisions, the higher the number of charge depositions to detect. This exponential increase in data is predicted to be the main issue with the next run of the LHC experiment, where we are set to test higher energy interactions. As traditional tracking algorithms do not scale well with this increase in data, supplementing them with machine learning provides a promising solution. Scaling high-energy particle tracking in the LHC to process petabytes of data is the focus of the Exa.TrkX project, which our study is part of. In our research, we study the robustness of Exa.Trkx models and algorithms against noise and misalignment. Robustness is judged by analyzing performance metrics like purity and efficiency of pairs of charge deposits or “doublets''. Purity is defined as the ratio of true-positives over positives, and efficiency is defined as the ratio of true positives over the number of true values. A true deposit belongs to the same trajectory as the one we are comparing it with. In this presentation, I will discuss how we proved robustness against noise by observing that the change in doublet purity and efficiency was a trivial decrease of 0.3 and 0.2 percent respectively. Our research makes sure that the Exa.TrkX models can be applied to actual LHC data. We do this by proving that the models are not affected by real-life impurities in the data like noise.

As high levels of drug use account for thousands of drug related deaths every week, it’s important to investigate the neural mechanisms behind drug escalation as well as develop possible harm reduction strategies for drug taking. Therefore, the purpose of this experiment was to examine the effects of the Kappa Opioid receptor (KOR) on cocaine escalation in the mesolimbic system. Using a preclinical model to examine this hypothesis, CRISPR/SaCas9 was utilized in the ventral tegmental area (VTA) to selectively repress expression of KOR. After four weeks, the rats underwent cocaine self-administration during short access periods and then escalation was tested in long access periods. Lastly we utilized immunohistochemistry to confirm CRISPR/SaCas9 transduction in dopaminergic neurons. We found that decreased expression of the KOR decreased escalation during long access periods. Future research will examine the role of KOR from and in specific brain regions such that KOR expression will be decreased only in dopaminergic projections from the VTA into the NAc.

Comets are cosmic snowballs of frozen gases, rock and dust that orbit the Sun. Isaac Newton suspected that comets were the origin of the life-supporting component of air and a key source for water replenishment in planetary interiors. A close-up view of comet Hartley 2 was taken by NASA's EPOXI mission during its flyby of the comet, using the spacecraft's medium resolution instrument. Comet Hartley has a novel asymmetric dumbbell-like shape. We employed mathematical models to study comet Hartley. Using calculus-based methods, we estimated various static properties, including the arc lengths (outer boundary length), surface area, and volume of Comet Hartley. Assuming a constant density, we also estimated the mass, center of mass, and moments of inertia for Comet Hartley using triple integration methods using cylindrical coordinates. The center of mass, moments of inertia, and radius of gyration form key inputs for studying the orbital mechanics of the comet in outer space. This research project provides excellent opportunities for hands-on explorations using multivariable calculus studies for engineering and space sciences applications. This research is important as studies of comets unravel secrets about the formation of the solar system.

STS-121 is a NASA space shuttle mission to the International Space Station (ISS). The ISS is a habitable satellite (Space station) in a low Earth orbit. We employ calculus-based methods to analyze and study the flightpaths, altitude, velocity, and acceleration profiles of the STS121 data reported by NASA as it travelled through outer space. Our studies unravel information about the critical points, local maxima and minima, concavity, and inflection points in the altitude data. The velocity profiles were fitted to polynomial functions using least square data fitting using linear algebra-based methods. The acceleration data involve piecewise functions which is related to the time scales involving burning of the propellent and separation of the external propellant tank as the space shuttle gets ready to move into orbit. We estimated the work done in transferring a load from Earth to the International Space station. We used optimization methods to design an optimal solar panel geometry for a satellite by minimizing the surface area. This research provides novel applications of the fundamental theorems of calculus to study motion in outer space and involves mathematical modeling, optimization, curve fitting, data analysis and data visualization.

Gliders are robotic vehicles used in the air and underwater to collect and transmit real-time data. Studies using gliders have important applications in oceanography, engineering, and remote sensing. The goal of this project was to model and identify aspects of a glider’s flight using vector-calculus and matrix-algebra based methods. We employed mathematical models to study the flightpath of a glider using vector valued functions and calculated the osculating plane of the glider. The model parameters were optimized to minimize turbulence. We studied the kinematics of underwater gliders using GPS data reported from gliders deployed by Rutgers University and the University of Washington. We analyzed the reported glider velocity data and applied vector-calculus based methods to calculate the instantaneous and average velocities and acceleration vectors. Additionally, we applied matrix-algebra based methods to translate and rotate the glider to position it at appropriate coordinates underwater for gathering data. This research provided insight into mathematical modeling of real-world data and involved applied optimization and data visualization. These studies provide novel avenues for hands on exploration and application of key mathematical concepts.

Learning and forgetting are two key processes that keep our memories in balance. In the past few decades, we have learned a great deal about mechanisms associated with memory formation and consolidation. However, little is known about the molecular mechanisms of forgetting, despite its importance in human health. Here, we take advantage of the nematode C. elegans – a living animal with a simple nervous system of 302 neurons – to explore the mechanisms behind forgetting. In particular, we focus on the decay of associative olfactory learning and the regulation of this decay after experience of pathogenic bacteria. Previous studies have shown that worms acquire an associative memory linking starvation experience and the olfactory response. After prolonged exposure to a preferred odor during starvation, worms exhibit a diminished response towards the preferred odor. However, upon returning to a food source, the attractive response toward the preferred odor recovers within 3-4 hours, indicating the loss of the associative olfactory memory. We found that the rate of memory loss, quantified by measuring the time course of recovery of the olfactory response, depends on the type of food source (bacterial strain) that worms experience. Specifically, exposing worms to pathogenic bacteria PA14, compared to the regular food source OP50, leads to a quicker loss of the associative olfactory memory. Our results further show that the acceleration of memory loss is mediated by a conserved transcription factor DAF-16/FOXO, as daf-16 mutants exhibited similar rates of memory loss regardless of OP50 or PA14 experience. Together, these findings demonstrate an unexpected role of DAF-16/FOXO in memory decay induced by exposure to pathogens. 

Arrhythmogenic cardiomyopathy (AC) is a devastating inheritable heart disease characterized by lethal heart rhythms and abnormal contractile function that can lead to sudden cardiac death or congestive heart failure. More than 70% of AC cases are due to mutations in desmosomal proteins, which are essential for maintaining structural integrity and intercellular junctions in the heart. Frameshift mutations in desmoplakin (DSP), a desmosomal protein, are a common cause of AC, therefore my project aims to use human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to model AC due to premature truncating DSP variants and to determine if the mechanism results from haploinsufficiency of DSP protein. From two unrelated patients with AC carrying different pathogenic DSP variants, p.Leu463Serfs*22 and DSP p.Arg941*, we generated patient-specific iPSC-CMs from each patient. With these cells, we created engineered heart tissues (EHTs) and found preliminary data that suggests DSP L463Sfs*22 EHTs generate less active twitch force compared to wild type, indicating that this human iPSC model can recapitulate the salient clinical phenotype. Protein analysis of these cells revealed that patients with premature truncating mutations in desmoplakin have lower desmoplakin levels when compared to wild type cells (DSP L463Sfs*22 iPSC-CMs have 38% less DSP protein levels compared to wild type iPSC-CMs). There was no evidence of the predicted truncated protein to suggest a dominant negative mechanism. Furthermore, we found a significant reduction in DSP transcript in the DSP L463Sfs*22 iPSC-CMs that was partially rescued by knocking down UPF1, a key regulator of the nonsense-mediated decay (NMD) pathway, suggesting a NMD-mediated clearance of DSP transcripts that results in haploinsufficiency. Should we also find that increasing desmoplakin protein rescues the phenotypes observed in patients, there is potential to find a novel treatment option for patients with desmoplakin-associated cardiomyopathy.

For many inhabitants of the Pacific Northwest (PNW), access and use of the multitude of public lands in and around the area is a core piece of their experience that shapes their relationships with nature. There are countless studies that have shown the health benefits of interactions with nature. Despite these benefits, data collected by many of the Federal land use agencies show that, people of color, especially Black people, are underrepresented in these spaces. Additionally, no study has yet been done for the East African immigrant communities, despite its large size including over 40,000 residents in King County alone (6th most in the nation). The objective of this study is to remedy this gap in knowledge which currently exists in our understanding of public land accessibility and utilization in the PNW by East African immigrants. My project entails developing culturally appropriate survey questionnaires, recruitment of community members, and administration of a survey, focus groups, and individual interviews. From these activities, I will explore the perceptions and accessibility of public lands from an intergenerational focus. The outcome will be the generation of rich qualitative data that will be used to identify common opportunities and barriers to accessing public lands by East African communities. In addition, the study will lay the groundwork for future larger scale studies on this topic and help inform current and future land management efforts to make public lands more equitable.

With growing concerns surrounding global warming, both pollution and alternative sources of energy have become the focus of intense research. These research efforts have addressed the need to power pollution sensors by alternative means. Harvesting energy from trees is both minimally invasive to the environment and eliminates harmful waste produced by non-green energy solutions like batteries. The intent of this research is to verify the possibility of developing an unconventional thermoelectric generator (TEG) in order to increase the energy obtained from trees. We believe that a comprehensive understanding of the temperature characteristics of trees improves the current thermoelectric harvester design. A more robust TEG produces the voltages necessary to power these IoT devices. The primary concerns in selecting materials for a specialized TEG are the thermal impedance, the dimensions and arrangement of semiconducting material, and the overall geometric composition of the TEG itself. The selection of these materials and the overall physical characteristics of the TEG depend on the analysis of the internal temperature of trees. We have developed and will soon deploy a system that captures this valuable data over time. Furthermore, we have established a theoretical TEG design tailored specifically to harvest energy from trees more effectively than previous implementations. The new harvester will be utilized to power LoRa wireless sensor networks capable of monitoring pollution and other environmental hazards. If successful, future research should be devoted to optimizing the TEG to minimize thermal resistances and parasitic heat losses, as well as efforts to maximize temperature differentials with the use of smart heat exchangers on the TEG’s ambient side. Electrical matching of the TEG and sensor node could be desirable too. Finally, integration of the TEG into other sensor applications such as wildfire monitoring is more than reasonable.

Many search engines and social media platforms employ personalization algorithms that present users with content based on their previous activity on the platform. While personalization can enhance users’ experience, critics worry that it can also reinforce human biases by constantly feeding users only one side of the viewpoint. In recent times, YouTube, the most popular video sharing platform, was accused of harboring videos promoting misinformation surrounding the 2020 presidential elections. What kind of videos are users exposed to when they search about election misinformation? What is the effect of personalization due to watch history, where it is built progressively by either watching videos promoting or debunking election fraud? Does YouTube’s up-next algorithm drive users into a rabbit hole of election fraud misinformation? Does users’ partisan bias have an effect on the election misinformation present in search results and recommendations? To answer these questions, we conducted a comprehensive audit study on YouTube by recruiting a diverse group of survey participants. Every participant installed a browser extension that enabled us to collect their personalized search results in response to search queries related to election fraud and personalized up-next trails --- Youtube’s 10 consecutive up-next videos starting from a seed video that either promote or debunked election fraud misinformation. The extension also collected unpersonalized search results and up-next trails via incognito window. By comparing the results from standard and incognito windows, we have determined the role of YouTube’s personalization algorithms in exposing users to election misinformation. Overall, our study adds to the growing body of work that examines the role of algorithms in surfacing misinformative content.

Blockchain is a distributed ledger technology that promises greater efficiency, security and transparency than traditional centralized ledger technologies through the use of consensus mechanisms, incentivized participation, and immutable smart contracts, among other features. For much of its history, Blockchain was predominantly divided into public, permissionless platforms and private, permissioned platforms, but in recent years these categories have expanded to include both consortium and hybrid blockchains. Hybrid blockchain in particular offers a unique solution for industries like supply chain management (SCM) and logistics which cannot operate on a strictly public or private basis, and struggle to establish trust between parties. Motivated by the current lack of research and example implementations of hybrid blockchain, especially in SCM and logistics contexts, the objective of this research is to contribute to the existing literature by designing and implementing a prototype hybrid blockchain-based dApp (decentralized app) focused on addressing issues within the supply chain management and logistics industry. Additionally, our secondary objective is to synthesize our review of the currently available literature on hybrid blockchain and its application in SCM and logistics into an easily digestible format, which will help outline the specific benefits hybrid blockchain provides in SCM use cases as compared to other forms of blockchain. Ultimately, our goal is to help fill the hybrid blockchain research gap by synthesizing the current literature, and providing an example hybrid blockchain implementation as reference for organizations considering implementing hybrid blockchain solutions, especially in SCM and logistics contexts. This presentation will include a brief introduction to hybrid blockchain and SCM use cases as well as an overview of our dApp design schema.

Supply chain consists of the networking between companies and suppliers to ensure the proper manufacturing of products to the consumer. A successful supply chain requires effective end-to-end traceability to ensure the authenticity and safety of the materials being processed and distributed. Challenges associated with increasing complexity, insufficient networking, and third-party trust issues have led to the search for new frameworks to satisfy supply chain management needs. Blockchain, with its out-of-the-box trust management, immutability, transparency, and decentralized nature has become a promising next step for suppliers and businesses. Popular solutions such as Ethereum, Hyperledger Fabric, and Hyperledger Sawtooth solve these challenges by improving the communication, trust management, and scalability of supply chain applications. Through the use of smart contracts and cryptographic verification, users can add logic to the supply chain without requiring an understanding of the core system. We look past this with the aim to build upon the current methodologies, by conducting a feasibility analysis on the core mechanisms and internal design choices within these applications which are overlooked in current literature. Our presentation consists of defining the preliminary terms, a literature review, and an explanation of the implementation-level parameters to provide insights into the core bottlenecks and potential vulnerabilities within these supply chain systems.

Having a female role model who possesses traditionally masculine traits like assertiveness and independence might inspire other women to join a company. However, we believe it might also signal a company's masculine work culture, and thus make applying to this company a less appealing choice for female applicants. Our research aims to test the hypothesis that women will be less interested in applying to a company that has stereotypically masculine female role models. In this study we designed with our group, participants will read either a profile of a stereotypically masculine or feminine female employee, and then answer a series of questions about their impressions of the company and their interest in working in that company. We expect the result to show that female participants are less interested in applying for a company when shown with the profile of a masculine female employee. We hope this study will expand our understanding of gender expression in the work environment and develop a novel intervention to increase gender diversity.

 As international students make up an unignorable part of the public university population in the U.S., there is a need to better understand their inclusion and involvement on campus. Inclusion in the university community and local society is not only important for international students' success in their college education, but also critical for the benefits of U.S. colleges in terms of economic revenue, diversity, and knowledge & cultural exchange (Bevis, 2002; Harrison, 2002). In this study, we are interested to learn more about the relationship between international students’ perceived community inclusion and their participation in university services and support programs. In a pilot study, 69 students (36 International, 45 Asian, 18 White, 6 Other racial groups) completed an online survey on the Psychology Online Research Pool Program at the University of Washington. Preliminary analyses show that international students show significantly less sense of inclusion in the U.S. compared to domestic students. Additionally, the sense of inclusion in the university is correlated with students’ usage of university services for international students but not for domestic students. The study has provided insights into the differences between international and domestic students’ perceived inclusion in the public university community and their opinions around university programs that aim to foster student success. It also has the potential to help public universities better understand and serve their student body.