Communication on online platforms has become one of the primary methods by which people communicate with one another. Regardless of conversation space, either online or offline, arguments can arise. Through our research, we are studying the ways in which the design of a platform influences the tenor of a conversation and the factors that lead to fights and reconciliation. We conducted in-depth in-person and phone interviews with 23 adults to understand how arguments arise online, how they end in different circumstances, and received input for possible design ideas. We followed up with the interviewees to gather demographic information about them. Through affinity diagramming, we qualitatively analyzed the 23 interview transcripts to uncover recurring themes, such as the role of time, the stages of an argument, the way people move arguments from public to private spaces, and differences between images and text in sparking arguments. Based on these themes and design suggestions from participants, we made sketches for potential design ideas that can be implemented in social media platforms to encourage constructive discourse. We are planning to use the design sketches to create prototypes to test with participants for iteration, which we will describe in a research paper to share our findings and design ideas broadly.

Mutation produces new variation in populations, and in each generation these variants are copied from parents to offspring. While almost all variants of genes are lost, they may remain in the population for many generations. We use branching process models to analyze counts of gene copies. In a population of constant size, on average, a gene copy produces one offspring copy at the next generation. An advantageous mutant will have a mean greater than 1, and a deleterious one will have a mean less than 1. It is thought that most mutations are slightly deleterious, and with high probability those variants become extinct rapidly. Nonetheless, the few deleterious mutants that are not yet extinct may achieve high numbers. Thus, we have a particular interest in those with a mean slightly less than 1. We use different probability models for the offspring distribution and consider the mutant’s survival about: the extinction probability over k generations, the expected copy count conditional on survival, and the probability of survival additional k generations conditional on surviving k already. We find that variances in addition to means of offspring distributions closely relate these statistics. By adjusting parameters of distributions, we let the mean and variance be approximately the same across distributions. Based on our simulations, when k is large and the mean and variance of offspring are the same, the mutant’s survival condition is uniform throughout. In other words, those statistics above can be estimated by the mean and variance exclusively, and the specific distribution does not affect much the conditional population dynamics. However, at the first few generations, these statistics are different for each distribution. Thus, if we know the mean and variance of a mutant, we can predict the long-term population behaviors conditional on survival without knowing the true distribution of the mutant.

The nucleus is the information center of the cell, acting as a hub for DNA storage, regulation, and replication. Therefore, transport of molecules into and out of the nucleus is vital for proper biological functioning. This transport is tightly regulated via the nuclear membrane, and a collection of proteins called nuclear pore proteins that interact with molecular signals. These signals allow molecules that cannot passively diffuse through the nuclear membrane (typically greater than 60 kD) to be shuttled into or out of the nucleus via a variety of pathways. Like all biological pathways, this process can be disrupted and lead to phenotypic abnormalities. Our lab identified two siblings with biallelic variants in NUP188, a known nuclear pore protein; we identified four additional cases in three families through collaborators. Clinical features include brain abnormalities with thin corpus callosum, progressive microcephaly, severely delayed myelination; congenital cataracts; mild dysmorphic features; and hypoventilation leading to death in infancy. In order to better understand our patients’ phenotype, we are investigating nuclear import pathways as a potential mechanism of disease. Using green fluorescent protein (GFP), we can directly visualize protein localization in living cells, without the use of additional stains such as immunohistochemistry. Based on size restriction of the nuclear pore, we used a vector construct with three repeats of GFP and our nuclear localization signal (NLS). Specifically, we are investigating four main NLS pathways: Importin α+β (SV40 NLS), Kap β2 (hnRNP NLS), Importin β (CREB NLS), and no NLS. We have been able to successfully create a viral vector for each NLS. Next steps include optimizing expression in patient cell lines. This approach will allow us to quantify and visualize discrepancies between patient and control localization patterns, leading to better understanding of causes of patient phenotype and potential novel therapies.

Research has shown that positive outcome expectancy (i.e., anticipated positive consequences as a result of engaging in a behavior) and positive affective responses to exercise play an important role in predicting exercise behavior as well as future exercise intentions. Unlike most research in which the valence (i.e., positive - negative) of outcome expectancy and affective responses are typically assessed through survey items whose valence is predetermined, the current study used a spontaneous word association test (WAT) to examine the subjective meaning of exercise-related outcome expectancy and mood before exercise, and how they are related to exercise intentions and behaviors. One-hundred and eleven undergraduate and graduate students (74 females; 64 undergraduate students) performed an exercise-related WAT in which they were asked to write down the first word or a short phrase that comes to mind when they thought of (a) things that might happen from exercising and (b) their mood right before exercising. Subsequently, they were instructed to rate their subjective valence (i.e., positive-negative) to each of their responses to the WAT. They also completed a series of online self-report surveys that included assessments of exercise intentions and past exercise behavior. Path analyses will be conducted to test our hypotheses that the subjective valence of their mood right before exercising is expected to be directly associated with exercise behavior whereas the subjective valence of outcome expectancy is expected to be indirectly associated with exercise behavior through exercise intentions. This study examines the potential value and usefulness of a spontaneous word association procedure to more accurately measure affective evaluations of an individual’s feelings and thoughts toward exercise behavior.

Development of technology, including artificial intelligence, has been controversial due to the potential negative effects. Many believe it is an auspicious industry and that technology will eventually replace the work of the human labor force. In this research project, our team analyzed the effects of technology in the labor market on the labor force within the next few decades. To accomplish our goal, we developed a model that can interpret the correlations between various factors. Specifically, this research analyzed data, including the employment rate, unemployment rate, number of occupation categories, and involvement rate of technology from varied industries. In addition, the Bureau of Labor statistics database was primarily used to regress the model. We expected to see a decrease in low-skilled occupations, such as cashier, in a short run. These types of occupations will be replace by IoT or artificial intelligence. High-skilled occupations, such as lawyer and surgeons, will still be demanded and irreplaceable in a short run, but would eventually be displaced in the long run. A shift would occur from low to high skilled occupations, due to factors such as social demand, working efficiency, and wage polarization. The employment rate goes down during the short run since more jobs are replaced by artificial intelligences and robotics. However, in a long run, more occupations will be created which will recuperate the fall in employment rate occurred during the short run. This research is very crucial and necessary because of the rapid growth of technology and its immense impact on the human labor force market. This research could ultimately provide information for governments and policy makers regarding potential growth sectors in the economy due to technological changes. Also, future generation can be more aware of this technological change, so that they can choose majors with accurately reflecting future employment trends.

In 1997, South Korea and many other Asian countries were hit with a large currency and financial crisis, with the IMF providing a $58 billion bailout program to South Korea involving large amounts of reforms and corporate restructuring involved, along with a very contractionary fiscal and monetary policy that intensified the crisis. The IMF loan had conditions of higher interest rates, higher taxes, and lower government spending, among others, on hopes that the contractionary policies will restore the foreign investors’ faith in Korea. After the crisis, the conditions of the IMF loan have been subject to much criticism in both Korea and abroad. This raises a question as to whether the IMF’s response and the conditions of the loan to Korea was appropriate for the situation. For this research, I am collecting information from many sources both from the US and in Korea, including books, journals, and newspapers. I am also acquiring various types of data as part of the research. Some of my findings are that the IMF’s response for the crisis was inappropriate as the IMF’s diagnosis of the situation was inappropriate. It is because South Korea’s situation was different from the other countries of the Asian Financial Crisis as the crisis did not arise due to overvalued exchange rates and government debt. Additionally, part of the IMF’s response in Korea can be said of as carrying over from past crises which involved the government accumulating large amounts of debt, as opposed to the Korean case, where the chaebol and the financial institutions lending to them accumulated large amounts of short-term debt and the government had almost no debt and was operating at a balanced budget. The uniqueness of the Korean crisis meant that the IMF should have reacted differently in imposing its conditions for the loan.

Court fines and fees, previously an overlooked part of the justice system, are much more impactful than once thought. Recent research has traced the rise of court mandated fines and fees, or legal financial obligations (LFOs), and detailed their various effects. LFOs have been shown to have significant, detrimental effects on poor and minority groups in relation to other parts of the population. The disparity of effects from LFOs exacerbates the already prevalent issue of wealth inequality within the United States. This study evaluates whether LFOs are higher in low tax counties than in high tax counties. This is accomplished by gathering county level tax and LFO data for four states: Florida, Pennsylvania, Washington and Wisconsin. I perform linear regression analysis of the data and performed t-tests on all variables included in the equation. Regression shows whether lower taxes are correlated with an increase of LFO severity. I anticipate that LFOs will be more severe in areas with lower taxes due to the increased financial strain on the county government. A correlation between revenues and LFOs could indicate that counties are essentially using LFOs as secondary to make up for lower tax revenues, rather than their intended purpose of disincentivizing criminal activity. With better information on potential consequences of lowering taxes, we will be able to draft smarter fiscal policies rather than arbitrarily increasing the financial burden on the poor.

Econometrics lacks an analytical framework for estimating and discussing the impact of intersectionality on economic outcomes of groups of marginalized people (e.g., women and people of color). As a result, a rich body of literature on intersectionality is left largely unused, leading to potentially misspecified regression models. In this paper, I outline an analytical framework for estimating and discussing the impact of intersectionality on economic outcomes, such as annual wage differentials between groups of marginalized and privileged people. I construct the analytical framework using principles of intersectionality theory, such as simultaneity and multiple jeopardy. I then use the framework to construct a multiple-linear regression model to investigate the following hypothesis: Do people with more marginalized identities earn less average annual wages than people with more privileged identities in the United States, all else equal? The results indicate that there is evidence to support the hypothesis, yet more in-depth investigations are necessary to better estimate the impact of marginalized and privileged identities on average annual wages.

Take Charge is a statewide family planning initiative that provides men and women in Washington State with free access to family planning services. As one of the most accessible family planning programs in the nation, Take Charge has expanded into school based clinics, allowing adolescents as young as thirteen to receive contraceptives without the knowledge of their parents. Success of family planning initiatives are typically measured by how much they reduce unintended pregnancies among their low income or minority populations, but the effect of these programs on economic indicators such as income and educational attainment are generally less well explored. Thus, the purpose of this research project is to determine the impact of having access to Take Charge on college completion for Hispanic women in Washington State. To address this question, individual level data from the Minnesota Population Center Current Population Survey were used to conduct a variety of difference-in-difference and triple difference tests to measure the effect of having access to Take Charge on college completion. The results depict that having access to this program starting at age 13 significantly increased the likelihood of completing college by 14-18% for Hispanic women. These findings imply that Take Charge can be used as a model program for other states that hope to reduce their number of unintended pregnancies and increase college completion among their low income and minority populations. 

The relationship between fertility and economic development can be modeled in many different ways. To microeconomists, fertility may determine the “quality” of children in one family, where quality is typically interpreted as a child’s level of education. These models of child quality are based on the classical quantity-quality tradeoff model by Becker, in which parents choose to substitute child quality for child quantity under some circumstances. The focus of this paper is to detect whether the quantity-quality tradeoff exists under the two-child policy in China, implemented in 2015. Since 2015, Chinese families have been permitted to have two children, and this change in the quantity of children in a family may affect child quality as well. However, the two-child policy was implemented only three years ago, so data is lacking. Thus, I chose Taiwan as my case study. I use data from Taiwan to find some the effect of fertility and apply those results to circumstances in China. By using linear regression, I find that the results from Taiwan are consistent with the classical model. Once the quantity-quality tradeoff under two-child policy is clear, scholars can further research on the short-term and long-term influence of two-child policy in China.

Despite the high growth rate of minority-owned business in the US, economics literature suggests that minority business owners have a harder time getting loans for their businesses compared to their white counterparts. If minority-owned businesses are doing well in the U.S., it is expected that loans to minority owners have high returns (or low defaults). It is paradoxical that banks would discriminate against minority loan applicants since it's against their economic interests. My research project hypothesizes that financial institutions' loan approval process is flawed for assuming that the predictors of loan defaults have the same effect on minorities and non-minorities. For example, credit score is often an important factor in predicting default, however, many minority immigrants haven't had a chance to build up their credits to the same degree as their white counterparts. In this case, a low credit score means a high probability of default for white business owners but does not predict a high default rate for non-white owners. To test the hypothesis, the research project examines the heterogeneity in the effects of common determinants of their loan repayment between minority vs. non-minority business owners through an econometric analysis of a dataset collected by the U.S Treasury Department on CDFIs(Community Development Financial Institution Fund). More specifically, my project tests if the differences in the coefficients of predictors for each group are significant. Evidence supporting the hypothesis suggests that banks are losing out on economics gains by overlooking certain factors on their applicants and actions should be taken to prevent further loss for minority business owners.

The environmental and human health damage associated with industrial waste has been an undeniable concern since the late 20th century environmental disaster at Love Canal, which had devastating effects on birth defect and miscarriage rates. Since this event, the Environmental Protection Agency (EPA) has been dedicated to the remediation of toxic waste through the Superfund program. There are Superfund sites across the country, in locations as varied as urban Tacoma, Washington and rural Evansville, Wyoming. Which toxic site should enjoy the advantages of faster cleanup? A range of factors could influence the remediation rate at these sites, yet a striking difference in site characteristics presents itself; These two sites are home to very different population densities. Population density, an emblematic proxy variable, can be used as an effective tool to investigate different aspects of socioeconomic site characteristics. Scholarly models of optimal remediation, however, often focus exclusively on the financial costs and environmental damage of cleanup. This project investigates how Superfund site remediation would change if the EPA were to incorporate specific socioeconomic criteria in its decision to remediate toxic sites. Particularly, it explores various aspects of the Superfund program and focuses on and extends a particular theoretical model for temporal remediation. Population density enters the model through its effects on costs, although other consequences of population density will be investigated. The results of the extension indicate that a higher population density corresponds to a decrease in the optimal toxic waste cleanup rate.

Major Depressive Disorder (MDD) has the largest life time prevalence (17%) of mood and anxiety-related disease. The prevalence of MDD is also 1.7 times greater in women than men. Chronic stress and anhedonia are the primary symptoms of depression, and decisions are affected. Our laboratory has shown that corticotropin releasing factor (CRF), the stress hormone, potentiates dopamine release in the core of the nucleus accumbens (NAc), suggesting a relationship between stress and reward processing. While it is known that an individual’s decision making is altered in a depressed state, the precise neurological pathway between stress and reward processing in the brain is unclear. To characterize this “stress-reward” pathway, I used a novel decision-making framework where a cohort of male and female mice performed an operant concurrent-choice task choosing between 0.1M sucrose solution or water. Mice were injected intracranially with α-CRF, a non-selective CRF antagonist, or vehicle in the NAc prior to performing the task. I measured task performance. I hypothesize that mice injected with α-CRF demonstrate less appetitive and reward-seeking behavior compared to the vehicle group, implicating a lower sucrose nosepoke percentage, sucrose choice percentage, and higher sucrose latency during the decision-making task. I also hypothesize that females have a greater reward sensitivity system than males, resulting in an augmented decline in appetitive and reward-seeking behavior compared to males. If these results indicate a significance in further elucidating the “stress-reward pathway” through the decision-making task, this can pave way for potential new treatments targeting this pathway for depression.

 This poster focuses on the relationship between afterschool programs and school discipline rates. I ask how availability of and access to school social programs affects school discipline patterns across school districts in Washington. This poster describes patterns of variation in school discipline, and summarizes how afterschool programs can affect the rate of school discipline. Using publicly available data about school districts (WA Superintendent Office of Public Instruction; U.S. Census Bureau ;), I anaylyzed the rate of school discipline as well as the availability of afterschool programs in different counties in Washington. I used ARC Gis to map the prevalence of each and then compared them in order to determine that the increase in presence of afterschool programs was correlated with a lower school discipline rate. Therefore, this poster argues that school social programs affect school discipline rates.

Noncommunicable diseases (NCDs) are responsible for the majority of all global deaths. They reduce quality of life on a mass scale and threatening the United Nation's 2030 Agenda for Sustainable Development while creating massive financial burden in the Global South and developed countries alike. However, in 2017, NCDs received only 2% of total development assistance for health (DAH) in 2017 despite causing 67% of global deaths. This is a signficant lack of alignment between disease burden and funding. Researchers state that donor groups divert DAH to other causes because they are unaware of the impact of NCDs in low and middle income countries. In addition, curbing NCDs on a mass scale requires highly complex responses in comparison to the more cost-effective and evidence-based approaches for many infectious diseases. However, these claims are based on primarily anecdotal studies and there is limited quantitative research on how donor groups allocate foreign aid for NCDs. This report aims to understand how donor groups prioritize economic, political, social, and epidemiological determinants when deciding the level of DAH to provide to countries for NCDs. The master dataset is provided by RTI International and consists of grants for NCDs from 2010 to 2015. It is merged with demographic, economic, geographic, and epidemiological data extracted from publicly available sources. I model correlations between these factors and DAH received using a multiple linear regression. As foundations and NGOs provide the majority of global funding for NCDs, I concentrate on this donor group. I hypothesize that NGOs and foundations concentrate on health indicators and level of corruption when allocating aid for NCDs versus economic indicators, such as level of trade openness. Further research of donor behavior and development assistance for health will contribute to greater transparency in funding processes to better address the emerging NCD pandemic.

Economics connects to our daily lives. It is not only about the money in the market but also people in the society. Wealth and income inequality is one of the examples. In some countries, such as in the United States, differing levels of income lead to varied benefits. This phenomenon impacts not only the quality of people’s lives but also their happiness. However, having a large amount of money is not the most important reason to increase their happiness. In this literature review, economic principles can help to understand the relationship between happiness and money. Examining Jeremy Bentham’s idea of the greatest happiness of the greatest number, the paper explores the impact of utilitarianism. There is also recent economic research related to money and happiness that is considered. One significant finding is that people’s happiness can be increased by sharing their excess income and wealth. The study of happiness and money is complex, and there are many factors not included in this paper. But, the idea above is still important to be considered and can be one way to improve economic inequality. The solutions to the problems of economic inequality may be found in this research.

It is well known that nonlinear optimization can lead to a local minimum of the loss function. As such, different initial values of the model parameters can give different values for the loss function. In other words, the existence of local minima introduces a source of variability in the loss function. Additionally, model selection often involves resampling, which in turn introduces a second source of variability. In this work, random effects models are employed to estimate these two variance components. More specifically, a neural network model is employed to examine the behavior of these variance components as a function of the variance of the initial weights and the number of hidden nodes (H). It is found that when H is small, weight initialization has a larger effect on variation of loss than cross-validation, and when H is large, these two factors contribute comparably to the variability in loss.

 The Diabetes Community Care Ambassador (DCCA) Program represents a promising intervention for improving glycemic control in youth with type 1 diabetes (T1D) struggling with diabetes management. In this novel program, lay health workers called DCCAs supported youth with T1D with poor glycemic control (HbA1 >8.5%) and their caregivers through home visits, school visits, and phone support, and connected participants with legal support. Given the findings that youth with T1D in the DCCA Program demonstrated improved glycemic control, we carried out a follow up qualitative study to understand the salient components of the DCCA program that promoted improved diabetes self-management from the youth and caregiver participant perspectives. We conducted semi-structured exit interviews with a subset of DCCA Program participants (18 caregivers, 14 youth). Participants reported on how the DCCA was helpful in supporting the youth’s diabetes management and how DCCA support compared to care provided by the multidisciplinary diabetes care team. A qualitative content analysis was carried out and emergent themes were identified. We found that within the home environment, participants felt the DCCAs helped youth address underlying weaknesses in their diabetes management routine and provided accountability for self-care goals between visits with their diabetes provider. DCCAs also provided psychosocial support that youth and caregivers believed they did not sufficiently receive from their usual multidisciplinary diabetes care team. At school, caregivers, in particular, felt that DCCAs successfully advocated on behalf of the student’s interests to facilitate diabetes management at school and also provided diabetes education to school staff. Finally in the community setting, caregivers shared that DCCAs helped to connect them to legal and support resources and better navigate the healthcare system. Our study results highlight the value of using lay health workers in providing individualized assistance for youth with T1D in supporting diabetes management outside of the ambulatory care setting.