Objectives/Goals: Here, we utilize deep learning to automate the analysis of dual X-ray absorptiometry (DEXA) scans in the UK Biobank (UKB) imaging dataset to enable a large-scale assessment of lumbar spine disc degeneration, low back pain, and socioeconomic status. Methods/Study Population: Study Population: The UKB is a biomedical database that includes lateral spine DEXA imaging for 50,000 participants. Deep Learning Model Development: A computer vision model was developed that receives a DEXA scan as input and outputs a quadrilateral that corresponds to the corners of 5 lumbar vertebral bodies. The model is a deep, fully convolutional, encoder–decoder network using DeepLabV3. Statistical Analysis: To determine our preliminary model accuracy, we used the intersection over union (IoU) metric.We analyzed data using an ordinal regression model to determine the relationship between income/ neighborhood level multiple deprivation index (MDI) and low back pain (LBP), as well as a mixed effects model to estimate the relationship between income/MDI and disc height index (DHI). Results/Anticipated Results: Our model predicted vertebral body quadrilaterals in training and unseen test data (train IoU  =  0.96, test IoU  = .91) and was used to infer data for 10,440 participants. Confirming previous studies, there were significant relationships (p0.05) between income or MDI and DHI (Figure 2). Discussion/Significance of Impact: Low back pain is the world’s leading cause of disability, and socioeconomic factors play an important role. We found no relationship between disc height index and socioeconomic status. Thus, disc degeneration may not be a factor in this low back pain phenotype.

Regardless of method, political scientists often seek to develop arguments that can be generalized to a population of cases. But is this the only way to think about how cases speak to one another? We advocate for a new way to think about how qualitative research produces broadly applicable insights: translation. Much like linguistic translation, the goal of translation in political science is to develop ideas that are intelligible in a different context, even as the context will change how an idea or political practice is interpreted or enacted. Translation offers at least three benefits. It allows us to (1) rethink how we form and deploy concepts; (2) rethink what a generalizable argument is by carrying parts of an argument, instead of entire causal chains to other cases; and (3) rethink how we conceptualize knowledge accumulation to include an abductive process where generating theory is the primary goal.

Archimedes screw generators are a small-scale, eco-friendly hydropower technology. Despite their promise as a sustainable energy technology, the design specifics of the technology are not well documented in the published literature. Existing performance prediction models often fail to accurately forecast power loss, particularly as it relates to the outlet of the screw generator. To address this, a comprehensive computational fluid dynamic model was developed and evaluated using both laboratory-scale experiments and real-world data. This yielded an extensive dataset that covered wide variations in design parameters. The dataset was then used to inform the development and evaluation of an outlet power loss prediction model. The resulting model significantly improved the accuracy of overall performance predictions, reducing average error to 13.68 % compared with nominal experimental data – a substantial improvement over previous models, which averaged around 42.55 % error for the same test cases. Notably, the new model achieved an absolute error of 5 % or less in over 26 % of comparison points, marking a remarkable advancement by predicting outlet power loss by more than 28.8 %.

The North American Heritage at Risk (NAHAR) collaborative, which formed during the COVID-19 pandemic, allowed for heritage-at-risk partners to shift from reactive to proactive strategies. The result was the creation of the NAHAR research pipeline to respond to landscapes at risk. The pipeline includes modeling of environmental changes to the landscape; monitoring sites to verify location and assess condition; meeting with the public, descendant community, land managers, and transdisciplinary experts in their field to discuss climate change impacts to their heritage in the next 10 years; methodizing by means of a workflow organizer using data from the modeling, monitoring, and meeting; and when appropriate, mitigating areas identified during the methodizing process. In 2020, the Florida Public Archaeology Network—along with partners in Georgia, South Carolina, Washington, Texas, and Louisiana—began the Science Collaborative People of Guana project at the Guana Tolomato Matanzas Estuarine Research Reserve (GTM NERR) north of St. Augustine, Florida. Using a collaborative science mindset, the project team applied the NAHAR pipeline to gain a better sense of how resources were used in the past and how they currently are being used by communities to ensure responsive resource management and relationship building with visitors, descendants, and other community stakeholders, such as the Gullah/Geechee Nation. This article will provide the building blocks for other collaborative teams to follow the NAHAR pipeline and share lessons learned from the two-year project.

The results of an experimental investigation of smooth-body adverse pressure gradient (APG) turbulent boundary layer flow separation and reattachment over a two-dimensional ramp are presented. These results are part of a larger archival smooth-body flow separation data set acquired in partnership with NASA Langley Research Center and archived on the NASA Turbulence Modeling Resource website. The experimental geometry provides initial canonical turbulent boundary layer growth under nominally zero pressure gradient conditions prior to encountering a smooth, two-dimensional, backward facing ramp geometry onto which a streamwise APG that is fully adjustable is imposed. Detailed surface and off-surface flow field measurements are used to fully characterize the smooth-body APG turbulent boundary layer separation and reattachment at multiple spanwise locations over the ramp geometry. Unsteady aspects of the flow separation are characterized. It is shown that the first and second spatial derivatives of the streamwise static surface pressure profile are sufficient to determine key detachment and reattachment locations. The imposed streamwise APG gives rise to inflectional mean velocity profiles and the associated formation of an embedded shear layer, which is shown to play a dominant role in the subsequent flow development. Similarity scaling is developed for both the mean velocity and turbulent stresses that is found to provide self-similar collapse of profiles for different regions of the ramp flow. Despite the highly non-equilibrium flow environment, a new similarity scaling proved capable of providing self-similar turbulent stress profiles over the full streamwise extent of flow separation and downstream reattachment.

Insomnia’s impact on psychological functioning is known to increase suicide risk. The underlying mechanisms of this association are unclear. This study explored psychological factors including depression, emotion dysregulation, perceived burdensomeness and thwarted belongingness as possible mechanisms in the association between insomnia and suicidal ideation in a nationally representative sample for age, sex and race in the United States. Participants (N = 428) completed a Qualtrics survey of demographics, Insomnia Severity Index, Difficulties in Emotion Regulation Scale, Interpersonal Needs Questionnaire, Frequency of Suicidal Ideation Inventory and PROMIS-Depression and PROMIS-Anxiety short forms. Regression analyses and structural equation modeling were used. Insomnia severity was associated with greater suicidality (p < 0.001, CI = 0.19–0.31). When accounting for depression severity, emotion dysregulation and perceived burdensomeness fully mediated insomnia–suicidal ideation frequency association (β = 0.04, p = 0.045; β = 0.24, p < 0.001). Insomnia has major implications on psychological functioning, which may serve as mechanisms through which insomnia confers risk for suicidality. Our model posits that insomnia prevents regional sleep restoration in brain regions involved in psychological functioning, thereby conferring risk for suicidality. Insomnia may be an ideal upstream target for reducing suicidality and its risk factors, including depression, emotion dysregulation and perceived burdensomeness.

We study the exact Hausdorff and packing dimensions of the prime Cantor set, $\Lambda _P$, which comprises the irrationals whose continued fraction entries are prime numbers. We prove that the Hausdorff measure of the prime Cantor set cannot be finite and positive with respect to any sufficiently regular dimension function, thus negatively answering a question of Mauldin and Urbański (1999) and Mauldin (2013) for this class of dimension functions. By contrast, under a reasonable number-theoretic conjecture we prove that the packing measure of the conformal measure on the prime Cantor set is in fact positive and finite with respect to the dimension function $\psi (r) = r^\delta \log ^{-2\delta }\log (1/r)$, where $\delta $ is the dimension (conformal, Hausdorff, and packing) of the prime Cantor set.

As increasing proportions of our global population age, transgender people are experiencing higher rates of dementia, and many are afraid to enter long-term care. Structural interventions such as advance directives may help mitigate fears around entering long-term care by managing specific anxieties that transgender people may have about dementia, loss of decision-making capacity, and discrimination in long-term care settings.