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    Hearing Aid Audio Processing Model Benchmarking with Binary Environmental Classification

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    Gemstone Team ECHOCurrent hearing aid audio processing models are trained to filter out noise and amplify specific sounds such as speech. However, real-world audio environments contain a multitude of sounds that users may want to hear that cannot exhaustively be defined within the model. Team ECHO’s project proposes a method for hearing aid audio processing models to account for these unknown sounds without explicit definition. The team developed an open source audio dataset containing unfiltered-environmental audio that is categorized by a set of 4 binary features (indoors, crowded, walking, speaking) which are applicable to any audio environment. The team collected audio data using over-the-ear microphones at different locations centered around the UMD College Park campus and in Washington DC. The team also constructed a benchmarking platform for researchers to compare the performance and efficiency of different models in different environments based around this dataset. The platform evaluates models on 6 different metrics (e.g., Noise Floor, Signal-to-Noise Ratio, Dynamic Range, and Crest Factor). Results from testing various audio processing models demonstrated significant differences in performance metrics across unique noise environments. The developed and tested benchmark serves as a groundwork for future audio processing research tailored specifically for hearing aid comfort and realism

    Selective cleavage and measurement of mucin with proteases: treatment and diagnostic.

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    Airway epithelial mucus is a key component of pathogen clearance and immune response as mucin complexes entrap irritants within the mucus hydrogel and expel them through coughing. For mucus to successfully function, it requires a proper ratio of mucins MUC5AC and MUC5B within the hydrogel complex to form the ideal viscosity. The underlying pathology of asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis include significant overexpression of MUC5AC, causing highly viscous airway mucus and interfering with disease clearance and respiration. We explore the application of designed proteases to selectively cleave MUC5AC and restore the healthy mucin ratio in MUC5AC upregulated samples. Furthermore, we are investigating a protease-substrate complex that triggers the release of a fluorescent molecule as cleavage occurs as a possible diagnostic tool to measure concentrations of MUC5AC across samples

    Prevalence of Depressive and Loneliness Symptoms in ASD & Non-ASD Youth by Race

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    Autism Spectrum Disorder (ASD) is associated with elevated depression rates, yet racial and ethnic disparities in mental health outcomes remain understudied. This study examined differences in depressive and loneliness symptoms among White, Non-Hispanic and Non-White adolescents with and without autism. As part of a larger study on anxiety and psychophysiology in autistic and nonautistic adolescents, we investigated whether depressive and loneliness symptom rates varied across groups. The sample included 34 adolescents (aged 11-14). Participants completed the Child Depression Inventory-2 (CDI-2; Kovacs, 2011) and the Asher Loneliness Scale (Asher & Wheeler, 1985), self-reporting their experiences over the past two weeks. Of note, lower scores on the Loneliness scale indicate more loneliness. Independent samples t-tests compared symptom severity between racial/ethnic groups. No differences between groups (i.e., Non-White, White Non-Hispanic) were identified regarding average depression scores (p = 0.27) or average loneliness scores (p = 0.27). Specifically, Non-White adolescents reported an average of 10.8 (SD = 5.6) on the depression scale whereas White, Non-Hispanic adolescents reported an average of 8.71 (SD = 4.86). White, Non-Hispanic adolescents reported an average of 58.6 (SD = 7.8) on the loneliness scale compared to Non-White Hispanic adolescents who reported an average of 54.7 (SD = 10.80). Although Non-White Hispanic individuals with and without ASD exhibited higher average depression symptom scores, further research is needed to determine whether systemic factors contribute to mental health disparities. Future studies should incorporate larger, more diverse samples and investigate socioeconomic and environmental influences on mental health outcomes among autistic youth.Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award number K08MH133879 to Dr. Yarger. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH

    Cleansing Data and Bias within Predictive Policing Algorithms

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    Gemstone Team GAHSPHot spots policing—the allocation of police resources toward high-crime areas—has been revolutionized by machine learning. Instead of relying on historical crime hot spots, predictive policing algorithms allow departments to allocate officers to where crime is expected to occur next. This has led to their increasing adoption by especially large police departments, as well as modest reductions in crime. However, predictive policing algorithms have thus been shown to exhibit similar biases to traditional policing methods. A vast literature has shown that nonwhite areas are more frequently policed, and that laws are disproportionately enforced against nonwhites in these communities. This creates a problem for predictive policing; since these algorithms are trained on historical crime data which reflects these racial biases, predictions come to perpetuate racial bias into the future. As such, our team has built a new predictive algorithm which not only uses more contemporary machine learning techniques, but directly accounts for demographic fairness in its predictive judgments. Using real crime data, we then tested our model against PredPol, a state-of-the-art predictive policing software, comparing them on predictive accuracy and on racial bias in their predictions. Our results showed that our model outperformed PredPol in both predictive accuracy and fairness, demonstrating that it is possible to make policing more equitable without sacrificing the predictive accuracy of these algorithms

    Polly Smith's Library: Processing the Books of Jim Henson's Costumer Designer at the Michelle Smith Performing Arts Library

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    Polly Smith generously donated her costume design library to the Michelle Smith Performing Arts Library. These books will eventually be a great boon to costume design students but first they have to be processed. I have spent the Spring semester going through these books and assessing their condition and content to be added into the collection. This poster will explore the project, the steps I took, and the future of Polly Smith's library. It will show interesting books and talk about the challenges I faced

    Advanced Characterization of Engineered Living Materials

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    Engineered Living Materials (ELMs) are biological structures composed of cells embedded in a self-produced protein matrix [Molinari et al. 2021]. Our lab pioneered de novo ELMs made of elastin-like polypeptides (ELPs) displayed on the cell surface [Molinari et al. 2022]. Previous studies show that a fraction of ELPs detach from the outer membrane to be secreted into the media, contributing to the formation of macroscopic ELP-cell aggregates [Molinari et al. 2022]. Since the secretion of ELPs was not intentional, it is impossible to calculate the exact ELP-cell ratio within ELMs. However, this ratio is critical for the control of final ELM mechanical properties. To characterize the ELP-cell ratio within ELMs, I inserted a Tobacco Etch Virus protease (TEVp) cut site between the ELP and the surface anchor. This allows the ELP to be cleaved from the cell surface by adding TEVp to the media. In this way, I will quantify the protein content via immunoblotting and the cell number by measuring colony-forming units and the production of fluorescent proteins

    2024 Slavery Law and Power Transcription, Introduction, and Original Images

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    XML and PDF files for Slavery, Law, and Power grant documents for the 2024 NHPRC. Original images have been given copyright permissions by their respective archival institution and transcription and introductions are original scholarship from SLP project staff. Transcriptions and Introductions are stored using long term XML plaintext encoding while the images are stored as PDFs

    Inferring the Early History of Northern South America through Mitochondrial DNA Analysis

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    Prior to the mass colonization of the Americas, early peopling consisted of Indigenous Americans who, over the course of tens of thousands of years, came to occupy the continents that would later be settled by the ambitious European powers of the time. Through the use of mitochondrial DNA (mtDNA) sequencing throughout numerous samples, as well as temporal analysis of genetic mutations within samples of the same haplogroup, the migrations of different early indigenous populations can be tracked across the geography of the continent, and the ethnographic composition of different regions can be reconstructed with respect to the time period. Given that haplogroups belonging to maternal Amerindian descent are identified by haplogroups A, B, C, and D, and that genetic variations in haplogroups can be tracked over time, it is possible to approximate a model of the early genetic composition of Colombia and other regions of northern South America through the use of samples from model haplogroups like A2al, B2d14, C1d2, and D4h3a. For this study, 94 samples across these haplogroups have been surveyed, and individual phylogenetic trees have been constructed, which each infer a genetic timeline for the mutations found under each haplogroup. With the construction of a phylogenetic tree for each of the four haplogroups, a larger, cumulative phylogenetic tree was also constructed, which in combination with the known presences of haplogroups across the geography of Colombia, can provide insight into the early migration and settling patterns of these early pre-columbian Indigenous Americans

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