Accurately describing a child’s language skills is difficult, but identifying children with atypical language development adds even more complexity. In an ordinary language assessment session, a Speech-Language Pathologist (SLP) will use both standardized, norm-referenced assessments and non-standardized assessments, like Language Sample Analysis (LSA). However, there is little research about how these different assessments relate to one another. To better understand this relationship, the language abilities of children (n=38) were assessed after turning 6-years-old and attending Kindergarten by SLPs using the following norm-referenced tests: a sound-in-words subtest from the Goldman-Fristoe Test of Articulation, 3rd Ed. (GFTA-3); core language subtests from the Clinical Evaluation of Language Fundamentals, 5th Ed. (CELF-5); and a nonverbal IQ subtest from the Kaufman Brief Intelligence Test, 2nd Ed. (KBIT-2). Then, a 10- to 20-minute language sample of the child’s spontaneous speech was collected for analysis. This project extends from previous research by including participants beyond clinical populations and using multiple sampling contexts to holistically capture the child’s naturalistic speech. I transcribed each language sample with Codes for Human Analysis of Transcripts (CHAT) and utilized Computerized Language Analysis (CLAN) software to automatically compute measures reflecting language skills from the language samples. I will conduct correlational analyses to inspect the associations between measures from norm-referenced tests and measures extracted from language samples. I expect to see significantly positive correlations between several CELF-5 measures and LSA measures of morphosyntactic development (i.e., grammar) that demonstrate a convergence between these two methods of assessment. Correlations between LSA measures and GFTA-3 measures are expected as well but to a lesser degree of association because they do not index identical elements of language. Overall, relationships discovered during this process will lend themselves to further understanding the information we gain from these common tools of language assessment.

In spring of 2023, the United States Surgeon General Dr. Vivek Murthy published an alarming report about the impacts of loneliness in America, significantly labeling loneliness as an “epidemic” of fatal impact. As a mitigation technique, Dr. Murthy advised a national response of several steps, with the first one being to improve existing local social infrastructure, such as libraries and parks. To better understand how this mitigation technique can help reduce loneliness rates, my research aims to ask: Is the number of social infrastructures in a neighborhood predictive of loneliness in vulnerable populations in Seattle? To answer this question, I am composing a composite loneliness index scale based on 13 vital social factors, measured in the U.S. Census Bureau’s American Community Survey, which have been shown to be predictive of loneliness. I am then mapping each Seattle census tract’s loneliness categorical rating, as well as libraries (from a Seattle Public Library dataset) and parks (from a Seattle Parks and Recreation dataset) to understand whether proximity to these social infrastructures can predict a lower score on this new scale. The broader aim of this project is to assess the spatial relationship of social infrastructure and loneliness in a major city so that public officials can identify vulnerable locations where social infrastructure is needed to address this epidemic. Ultimately, my goal is for this project to be replicated in other major cities so that other city governments can identify the geographies that are more prone to loneliness in their city and enact appropriate mitigation responses.

This research aims to address the lack of qualitative inquiry into the lived effects of banishment zones to address street sex work in urban criminalized contexts. Sex work is highly stigmatized, with stigma being the primary driver of discrimination, poor health, and harm to mental health for sex workers (Bateman, 2021; Lazarus et al., 2012; Armstrong, 2019). Relatedly, spatial exclusion through banishment zones renders the lives of the most vulnerable more precarious, also harming mental well-being (Becket and Herbert, 2010). The Seattle City Council recently reintroduced legislation to create a Stay Out of Areas of Prostitution (SOAP) zone in Seattle, WA along Aurora Avenue as a punitive measure to curb street prostitution occurring in public spaces. If buyers of sex, pimps, or street sex workers enter this 60-block zone, they can be arrested for violating a SOAP order and face incarceration for up to a year, and fined up to $5,000. Although the legislation seeks to target the buyers and solicitors of the sale of sex, the “end demand” approach perpetuates harm, conspiring that “immodest” women are the cause of social ills (Bateman, 2021). This study proposes a qualitative research design employing semi-structured interviews with community members involved with organizations supporting Seattle’s street sex workers to explore how the reintroduction of the SOAP legislation is felt by the affected sex workers. The proposed research seeks to fill the existing gap in understanding the confluence of spatial exclusion and street sex work within a criminalized context like Seattle, WA. 

This project explores the geography of social dance spaces (such as nightclubs) and their geographic and social responses to gentrification. Social dance spaces serve as critical sites for empowerment, self-expression, and the embodiment of identity, particularly for systematically marginalized communities, including queer and BIPOC individuals. By exploring the spatial distribution and socio-cultural significance of these venues, this research seeks to contextualize how these spaces contribute to and are affected by the gentrification and neighborhood change since the turn of the century. Through a combination of historical contextualization and qualitative interviews, the study emphasizes how life surrounding the outside of the gathering spaces, shapes the life inside these spaces. This methodology will not only showcase the physical geography of the locations in response to changes in the city over time, but also the internal meanings assigned to the spaces while these changes are happening. By looking at both the internal and external geography of these gathering spaces through ethnographic analysis, I will be able to induce information on how this approach reveals the ways in which the geographies of dance spaces both reflect and challenge dominant urban narratives, highlighting their role as counter-hegemonic sites of resistance and identity formation. By situating these venues within their broader historical and geographic context, the research provides insight into how urban spaces intersect with narratives of inclusion, power, and community belonging.

Chronic Recurrent Multifocal Osteomyelitis (CRMO), is an autoinflammatory bone disorder that is notable by the recurrent bone lesions with potential long-term complications that include growth impairment in pediatric patients. Growth impairment can be illustrated through z-scores for weight and height. Z-scores represent how far a patient’s weight and height measurements deviate from the average for their age and sex. Z-scores below -1 and -2 could indicate the negative disturbance by CRMO or inadequate treatment. Standard second-line treatments for CRMO include bisphosphonates, disease-modifying antirheumatic drugs (DMARDs), and tumor necrosis factor inhibitors (TNFi). Bisphosphonates are frequently prescribed pediatric medications for their ability to improve bone health and reduce inflammation. However, the impact of these medications – alone or in combination with DMARDs and TNFis – on growth patterns are understudied. Medication practices may demonstrate varying results on height and weight outcomes in CRMO patients. CHOIR included prospective longitudinal data from >500 patients from multiple sites across continents, which allows us to compare the effects of these treatment regimens on the change of Z-score. We expect that the proportion of patients with low Z-scores is similar across all groups before the treatments. By gathering patients based on treatment regimen, this review will compare the prevalence of lower z-scores. Understanding these correlations is vital for identifying whether certain treatments contribute to growth improvement, offering insights into optimizing care for CRMO patients.

The Thermal imaging-based Temperature After Within-limb Calibration (TAWiC) algorithm has been used and validated by our team in detection of arthritis in the knees of children. It has previously performed well through using a smart-phone FLIR thermal camera attachment. We are now hoping to explore and expand its potential by developing TAWic thresholds to find other inflammatory arthritis in the knee, ankle/subtalar, elbow and wrist joints. Patients 4 years or older with suspected active inflammatory arthritis in at least one knee, ankle/subtalar, elbow or wrist were enrolled at Harborview Medical Center or Seattle Children’s Hospital after consent was obtained. Joint exams were conducted and infrared thermal imaging was obtained through the use of a FLIR One Pro camera by doctors. I analyzed images in MATLAB by manually selecting elbow, wrists, knees, and ankle joints to generate reports and scores for virtual doctor evaluation. With 89 adults and 85 children enrolled, we found the most commonly affected joints in children to be knees while in adults, it was the wrists. Further validation of applying the TAWiC threshold to detect arthritis was conducted, and the sensitivity and specificity of this algorithm for adults with active inflammatory arthritis in the knees were 50% and 83%, respectively. To our knowledge, this is the first reported validation of the TAWiC algorithm for knee inflammatory arthritis in adults. Ongoing and future studies will seek to validate use of the TAWiC algorithm for assessing arthritis in other joints. We hope that in the future, the technology can be used remotely by patients in telehealth efforts to send imaging to minimize costs, increase efficiency, and save time in caregiving efforts.

The obsolescence of U.S. Navy parts pose significant challenges in managing diminishing manufacturing sources and material shortages (DMSMS). This research focuses on predicting and mitigating part shortages by analyzing case resolution times, leveraging machine learning and natural language processing (NLP) techniques, and developing data-driven methodologies. In collaboration with the Naval Undersea Warfare Center (NUWC) Keyport division, data is sourced from Navy systems that track part availability and supplier management, providing critical insights into supply chain vulnerabilities. To address these challenges, multiple predictive models were developed, incorporating classification, regression, and clustering techniques. Initial model development utilized publicly available datasets to refine methodologies and test various approaches. Extensive exploratory data analysis (EDA) was conducted to identify patterns in supply chain issues, with a focus on text-based insights and categorical variables with a company response factor. Sentiment analysis and machine learning techniques, including logistic regression, support vector machines (SVM), gradient boosting, and word embedding models, were explored to enhance predictive capabilities. Our work focused on refining these models using real-world Navy data, optimizing classification strategies, and expanding NLP applications for more proactive supply chain management. These advancements aim to improve operations and minimize delays by reducing the time required to resolve cases associated with obsolescent parts.

Auditory input, such as infant directed speech and music, is integral to childhood language development. However, existing research focuses primarily on examining monolingual English-speaking families, overlooking families of other cultures and languages. Hence in this study, I investigate the naturalistic auditory home environments of Latino and Hispanic infants in comparison with Pacific Northwest monolingual English speaking infants to better understand the differences in auditory exposure. This study uses audio data obtained from daylong recordings of Latino and Hispanic infants' home environments utilizing the Language Environment Analysis (LENA) technology. Infants wear the LENA recorder in a vest for up to 16 hours per day. The selection requirement for Latino/Hispanic infants is that at least one parent identifies as being of Latino or Hispanic origin. I randomly sample short snippets of recordings and upload them to Zooniverse, an online citizen science research platform, which allows volunteers to annotate for types of sounds (music or speech), its source (in-person or electronic), and target audience (infant-directed or not). I quantify the types of auditory input to compare it with an existing study of Pacific Northwest monolingual English infants to uncover differences and understand the impact that culture has on infants' language input and ultimately development.

A factor influencing the ability to tune into a single speaker in the presence of competing speech is speech rhythm. The Selective Entrainment Hypothesis suggests that attention fluctuates periodically and synchronizes with speech, a quasi-periodic stimulus. This synchronization allows the brain to predict when the most salient parts of speech will occur and direct attention towards those moments. According to the hypothesis, more rhythmic speech should be easier to synchronize with, as it is more predictable. This hypothesis has been supported by previous behavioral research, which found that altering the rhythm in the target speech stream decreased comprehension of the target speech, while rhythm distortion in the background improved comprehension, likely because it became a weaker competitor. The present study replicated and extended these findings by recording electroencephalographic (EEG) data from listeners (N = 20) to measure phase locking, or synchronization, between the target speech envelope and neural activities. I ran EEG sessions, which began by exposing participants to the target speaker’s voice on its own. Participants then listened to 300 sentence pairs, which I created by playing a sentence spoken by the background speaker and sentence from the target speaker simultaneously. The sentence pairs were divided into three rhythm alteration conditions: target-altered, background-altered, and neither-altered. After each trial the participants answered a multiple choice comprehension question to collect behavioral data. Using EEG allowed for a more direct measurement of synchronization compared to behavioral results alone. We test the hypothesis that in the conditions there will be the strongest phase locking in the background-altered condition, followed by the neither-altered, and worst in the target-altered condition, a pattern that mirrors the behavioral results. This will provide more insight into the role of rhythm in speech processing and has potential future implications for hearing aid development. 

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy with poor long-term survival rates. Cytarabine (Ara-C) is a standard chemotherapy used to treat AML patients. However, many patients relapse due to refractory disease, highlighting the need for new therapeutic strategies. Heparan sulfate proteoglycans (HSPGs) are glycoproteins that regulate key signaling pathways by interacting with growth factors and receptors. HSPG glycan chains are modified by the addition of negatively charged sulfate groups. HS2ST1 and HS6ST1 catalyze sulfate addition at the 2-O and 6-O positions of heparan sulfate chains, respectively. In AML, increased HS6ST1 expression correlates with worse patient survival, while low HS2ST1 expression is linked to adverse outcomes in certain AML subtypes, suggesting distinct roles in disease progression. To investigate the contribution of HS modifications to chemotherapy response, we generated CRISPR-edited (sgHS2ST1, sgHS6ST1, or sgControl) MOLM-13 AML cells. Compared to sgControl cells, sgHS6ST1 cells displayed increased sensitivity to Ara-C, suggesting that 6-O heparan sulfation may contribute to chemoresistance. To test whether MOLM-13 AML cells alter the expression of HS-modifying enzymes in response to chemotherapy, I performed RT-qPCR analysis at 24 and 72 hours after Ara-C treatment. Upon Ara-C treatment, HS2ST1 expression increased by 1.5-fold and HS6ST1 transcript increased by 4-fold at 24- and 72-hours post-treatment. In contrast, sulfatase 2 (SULF2) removes 6-O sulfate modifications at the cell membrane. Strikingly, compared to vehicle treatment, SULF2 expression was increased by sixfold at both time points. Our results highlight HS sulfation as a dynamic regulator of AML chemoresistance and suggest that targeting HS-modifying enzymes could enhance chemotherapy efficacy. In the future, I will create an sgSULF2 cell line to characterize the functional role of SULF2 in AML disease progression and chemotherapy resistance.