Digital Commons Network^™

Changes In Lean Body Mass With Glucagon-Like Peptide-1-Based Therapies And Mitigation Strategies, Ian J. Neeland

Faculty Scholarship

Weight loss induced by glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dual glucagon-like peptide-1 receptor (GLP-1R)/glucose-dependent insulinotropic polypeptide receptor agonists is coming closer to the magnitudes achieved with surgery. However, with greater weight loss there is concern about potential side effects on muscle quantity (mass), health and function. There is heterogeneity in the reported effects of GLP-1-based therapies on lean mass changes in clinical trials: in some studies, reductions in lean mass range between 40% and 60% as a proportion of total weight lost, while other studies show lean mass reductions of approximately 15% or less of total weight lost. There …

Full Hill-Type Muscle Model Of The I1/I3 Retractor Muscle Complex In Aplysia Californica, Qianxue Chen, Jiayi Shen, Samantha Pao, Yu Huan, Gregory P. Sutton, Jeffery P. Gill, Hillel J. Chiel

Faculty Scholarship

The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems’ neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia’s grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia’s grasper, has not been characterized. Because of the importance …

Internalizations Of Decorated Bicategories Via Π₂-Indexings, Juan Orendain

Faculty Scholarship

We treat the problem of lifting bicategories into double categories through categories of vertical morphisms. We consider structures on decorated 2-categories allowing us to formally implement arguments of sliding certain squares along vertical subdivisions in double categories. We call these structures π₂ indexings. We present a construction associating, to every π₂ indexing on a decorated 2-category, a length 1 double internalization.

The Effect Of Early Postnatal Auditory Stimulation On Outcomes In Preterm Infants, Juliann M. Di Fiore, Gloria Liu, Kenneth A. Loparo, Cynthia F. Bearer

Faculty Scholarship

Abstract: Preterm infants are deprived of in utero sensory stimulation during the third trimester, an important period of central nervous system development. As a result, maturational trajectories are often reduced in infants born preterm. One such system affected is the brain including the auditory and respiratory control pathways. During normal pregnancy the intrauterine environment attenuates external auditory stimuli while exposing the fetus to filtered maternal voice, intra-abdominal sounds, and external stimuli. In contrast, during the third trimester of development, preterm infants are exposed to a vastly different soundscape including non-attenuated auditory sounds and a lack of womb related stimuli, both …

Indefinitely Flat Circular Velocities And The Baryonic Tully-Fisher Relation From Weak Lensing, Tobias Mistele, Stacy Mcgaugh

Faculty Scholarship

We use a new deprojection formula to infer the gravitational potential around isolated galaxies from weak gravitational lensing. The results imply circular velocity curves that remain flat for hundreds of kiloparsecs, greatly extending the classic result from 21 cm observations. Indeed, there is no clear hint of a decline out to 1 Mpc, well beyond the expected virial radii of dark matter halos. Binning the data by mass reveals a correlation with the flat circular speed that closely agrees with the baryonic Tully-Fisher relation known from kinematic data. These results apply to both early- and late-type galaxies, indicating a common …

The Baryonic Tully-Fisher Relation. I. Wise/Spitzer Photometry, Francis Duey, Stacy Mcgaugh

Faculty Scholarship

We present Wide-field Infrared Survey Explorer (WISE) W1 photometry of the Spitzer Photometry and Accurate Rotation Curves sample. The baseline of near-IR fluxes is established for use by stellar mass models, a key component to the baryonic Tully-Fisher relation and other kinematic galaxies scaling relations. We focus this paper on determination of the characteristics of the W1 fluxes compared to IRAC 3.6 μm fluxes, internal accuracy limitations from photometric techniques, external accuracy by comparison to other work in the literature and the range of W1 to IRAC 3.6 μm colors. We outline the behavior of SDSS g, W1 and IRAC …

Extreme Value Statistics Analysis Of Process Defects In Additive Manufacturing Materials, Ayorinde E. Olatunde, Kristen Hernandez, Austin Ngo, Arafath Nihar, Thomas G. Ciardi, Rachel Yamamoto, Pawan K. Tripathi, Roger H. French, John J. Lewandowski, Anirban Mondal

Faculty Scholarship

Fatigue and fracture studies focused on process defects that occur in Additive Manufacturing (AM) materials have shown that defect populations possess features which are better measured with extreme value statistics (EVS). In AM alloys, defect occurrences increase with material volume. This situation facilitates the need to model process defects in the path of fatigue crack growth with suitable statistical tools, such as EVS, which is more cost-effective when compared to destructive experiments. The application of EVS on defect space features helps determine the difference in defects present on fracture surfaces. As the fatigue quality of any material depends on its …

Geometric Representation Of Classes Of Concave Functions And Duality, Elisabeth M. Werner

Faculty Scholarship

Using a natural representation of a 1/s-concave function on R ᵈ as a convex set in R ᵈ⁺¹, we derive a simple formula for the integral of its s-polar. This leads to convexity properties of the integral of the s-polar function with respect to the center of polarity. In particular, we prove that the reciprocal of the integral of the polar function of a log-concave function is log-concave as a function of the center of polarity. Also, we define the Santaló regions for s-concave and log-concave functions and generalize the Santaló inequality for them in the case the origin is …

Differences In Perceived Energy And Macronutrient Requirements Across Divisions In Ncaa Athletes, K. Michelle Singleton, Andrew Jagim, Jamie Mcallister-Deitrick, Marcos Daou, Chad M. Kerksick

Faculty Scholarship

Background

Sports nutrition is an impactful component to sports performance. The purpose of the current study was to investigate the sports nutrition knowledge of National Collegiate Athletic Association collegiate athletes and assess self-reported perceived requirements for energy and macronutrient intake. A secondary aim was to evaluate the awareness of physical and emotional perceptions associated with mindful eating.

Methods

Participants included NCAA Division I (DI, n = 45), II (DII, n = 31), and III (DIII, n = 47) athletes. Athletes completed a validated questionnaire designed to assess sports nutrition knowledge and were asked questions about their perceived dietary energy and …

Supernatural Attributions For Extraordinary Events: Examining Cognitive And Contextual Predictors, Joshua A. Wilt, Julie J. Exline, Nick Stauner

Faculty Scholarship

Why do people make supernatural attributions (SA) for events? This article focused on five possible predictors: stressfulness, unusual events, religiousness, seeing a supernatural explanation as meaningful, and seeing supernatural entities as powerful. We also predicted that people would be more likely to adopt supernatural explanations seen as highly accessible, motivating, and plausible. We focused primarily on SA in general and secondarily on specific entities. We tested preregistered hypotheses using survey data in two samples of undergraduates: one reporting life-changing events (N = 594) and another reporting on an event with a supernatural air, or supernaturally perceived events (N = 475). …

The Mrnacalc Webserver Accounts For The N1-Methylpseudouridine Hypochromicity To Enable Precise Nucleoside-Modified Mrna Quantification, Sarah E. Krul, Sean J. Hoehn, Carlos E. Crespo-Hernández

Faculty Scholarship

Nucleoside-modified messenger RNA (mRNA) technologies necessarily incorporate N1-methylpseudouridine into the mRNA molecules to prevent the over-stimulation of cytoplasmic RNA sensors. Despite this modification, mRNA concentrations remain mostly determined through the measurement of UV absorbance at 260 nm wavelength (A260). Herein, we report that the N1-methylpseudouridine absorbs approximately 40% less UV light at 260 nm than uridine, and its incorporation into mRNAs leads to the under-estimation of nucleoside-modified mRNA concentrations, with 5%–15% error, in an mRNA-sequence-dependent manner. We therefore examined the RNA quantification methods and developed the mRNACalc webserver. It accounts for the molar absorption coefficient of modified nucleotides at 260 …

Image-Based Fracture Surface Defect Characterization Methods For Additively Manufactured Ti-6al-4v Tested In Fatigue, Austin Ngo, Kristen Hernandez, Ayorinde E. Olatunde, Thomas G. Ciardi, Alex Harding, Arafath Nihar, Anirban Mondal, Roger H. French, Laura S. Bruckman, John J. Lewandowski

Faculty Scholarship

Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based …

Shea Position Statement On Pandemic Preparedness For Policymakers: Building A Strong And Resilient Healthcare Workforce, Robin L.P. Jump

Faculty Scholarship

Throughout the COVID-19 pandemic, many areas in the United States experienced healthcare personnel (HCP) shortages tied to a variety of factors. Infection prevention programs, in particular, faced increasing workload demands with little opportunity to delegate tasks to others without specific infectious diseases or infection control expertise. Shortages of clinicians providing inpatient care to critically ill patients during the early phase of the pandemic were multifactorial, largely attributed to increasing demands on hospitals to provide care to patients hospitalized with COVID-19 and furloughs.¹ HCP shortages and challenges during later surges, including the Omicron variant-associated surges, were largely attributed to HCP …

Excess Prevalence Of Preexisting Chronic Conditions In Older Adults With Incident Epilepsy, Siran M. Koroukian, Hannah L. Fein, Long Vu, Wyatt P. Bensken, Nicholas K. Schiltz, Martha Sajatovic

Faculty Scholarship

Objective: Prior studies have examined chronic conditions in older adults with prevalent epilepsy, but rarely among those with incident epilepsy. Identifying the chronic conditions with which older adults present at epilepsy incidence assists with the evaluation of disease burden in this patient population and informs coordinated care development. The aim of this study was to identify preexisting chronic conditions with excess prevalence in older adults with incident epilepsy compared to those without. Methods: Using a random sample of 4 999 999 fee-for-service Medicare beneficiaries aged >65 years, we conducted a retrospective cohort study of epilepsy incidence in 2019. Non-Hispanic Black …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

D-Hacking, Emily Black, Talia B. Gillis, Zara Hall

Faculty Scholarship

Recent regulatory efforts, including Executive Order 14110 and the AI Bill of Rights, have focused on mitigating discrimination in AI systems through novel and traditional application of anti-discrimination laws. While these initiatives rightly emphasize fairness testing and mitigation, we argue that they pay insufficient attention to robust bias measurement and mitigation — and that without doing so, the frameworks cannot effectively achieve the goal of reducing discrimination in deployed AI models. This oversight is particularly concerning given the instability and brittleness of current algorithmic bias mitigation and fairness optimization methods, as highlighted by growing evidence in the algorithmic fairness literature. …

Is John Stuart Mill's On Liberty Obsolete?, Vincent A. Blasi

Faculty Scholarship

In On Liberty, published in 1859, John Stuart Mill argues for the “absolute” protection of the “liberty of thought and discussion.” Ever the empiricist, he maintains that such uncompromised freedom, not for all communication or self-expression but for the subset of those activities that qualifies as thought and discussion, would generate the best overall consequences for societies such as Great Britain and the United States. The advent of digital technology has altered how thought and discussion is generated, distributed, and received in ways that might problematize some of the empirical assumptions upon which Mill's argument in On Liberty is …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Adoption As Substitute For Abortion?, Malinda L. Seymore

Faculty Scholarship

In Dobbs v. Jackson Women’s Health, Justice Samuel Alito relied on adoption as part of the justification for holding that abortion is not constitutionally protected: “States have increasingly adopted ‘safe haven’ laws, which generally allow women to drop off babies anonymously; and that a woman who puts her newborn up for adoption today has little reason to fear that the baby will not find a suitable home.” Using adoption as an adequate substitute for abortion is a long-standing strategy for the antiabortion movement; but it is often embraced by pro-choice advocates as well. This position is supportable only if the …

The Subfederal In Immigration Polarization, Huyen Pham, Pham Hoang Van

Faculty Scholarship

The framing of subfederal immigration regulation as a red- blue divide is conventional wisdom. As more states, cities, and counties have engaged in the regulation of immigrants within their jurisdictions, it is not particularly surprising to see deep-red states like Texas enacting laws that restrict the rights of immigrants in their jurisdictions (e.g., requiring police within the state to honor detainers issued by United States Immigration and Customs Enforcement (ICE)) or deep-blue states like California enacting laws that protect immigrants’ rights (e.g., issuing driver’s licenses without requiring proof of lawful immigration status).

Rather than only reflecting national polarization on immigration …

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Critical Race Theory Bans And The Changing Canon: Cultural Appropriation In Narrative, Susan Ayres

Faculty Scholarship

Thirty-five states have enacted critical race theory bans at the level of elementary and secondary public education, and seven states have extended these to the university level. One way to resist these attempts to repress a healthy democracy by whitewashing history is through a pedagogy of antiracism, including literary works. The question of what that would look like involves questions of cultural appropriation, which occurs when one takes from another culture, such as a writer creating a narrative about a character outside of the writer’s cultural identity. This Article considers the story of Ota Benga, brought from the Congo to …

Putting The Lawyer First: Framing Well-Being In Law As An Ethical Dilemma, Aric Short

Faculty Scholarship

A disturbingly high percentage of our students continue to be unwell. In the most recent and comprehensive survey of law student well-being in 2021, almost 70% of law students responded that, in the past twelve months, they believed they needed to seek help for emotional or mental health problems. Embedded screening tools in the survey suggested that 34% of respondents were clinically depressed and 54% suffered from clinical anxiety. 44% of respondents reported being drunk in the past thirty days, 33% had engaged in binge drinking in the preceding two weeks, and 38% had smoked marijuana in the past twelve …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

A Data Integration Framework Of Additive Manufacturing Based On Fair Principles, Kristen J. Hernandez, Erika I. Barcelos, Jayvic C. Jimenez, Arafath Nihar, Pawan K. Tripathi, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Abstract: Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data …

Exploring 2d X-Ray Diffraction Phase Fraction Analysis With Convolutional Neural Networks: Insights From Kinematic-Diffraction Simulations, Weiqi Yue, Mohommad Redad Mehdi, Pawan K. Tripathi, Matthew A. Willard, Frank Ernst, Roger H. French

Faculty Scholarship

Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting β-phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure α- or pure β-phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how …