Objectives/Goals: Depression is common among people living with HIV (PLWH). This study explored the link between reduced metacognitive awareness and depression in PLWH. It utilized a positive emotion regulation task to compare brain activation during viewing versus upregulating positive emotions. Methods/Study Population: Depressed PLWH (N = 24; mean age  =  53; HAM-D mean  =  19) participated in an emotion regulation task while blood oxygen-level-dependent (BOLD) responses were recorded. In the emotional regulation task, participants were shown the International Affective Picture System (IAPS) a series of positive, negative, and neutral images. Participants were asked to view these images and given instructions to either negatively reappraise (RN) or positively reappraise (RP). In the RP condition, participants were no longer shown the image and asked to upregulate their positive emotional responses associated with it. Ten onset times were included for each trial. Results/Anticipated Results: A one-sample t-test was conducted to analyze contrasts between reappraisal of positive images and viewing positive images (RP > VP). Results showed significantly greater activation in the posterior cingulate and angular gyrus during the RP condition (peak MNI: 18, -52, 34; p < 0.001, uncorrected, k > 10 voxels). In comparing the reappraisal of negative images to viewing negative images (RN > VN), there was increased activation in the right supramarginal gyrus (peak MNI: 50, -28, 22; p < 0.001, uncorrected, k > 10 voxels). When contrasting the reappraisal of positive to negative images (RP > RN), BOLD signals were higher in the left dorsolateral prefrontal cortex (peak MNI: 40, -38, 32; p < 0.001, uncorrected, k > 10 voxels). Discussion/Significance of Impact: Findings underscore that depressed PLWH demonstrates BOLD responses in brain regions linked to appetitive motivation and meta-cognitive awareness during the RP condition which demands more executive resources among those with depression, highlighting the complexity of emotional regulation in this population.

There is a growing trend for studies run by academic and nonprofit organizations to have regulatory submission requirements. As a result, there is greater reliance on REDCap, an electronic data capture (EDC) widely used by researchers in these organizations. This paper discusses the development and implementation of the Rapid Validation Process (RVP) developed by the REDCap Consortium, aimed at enhancing regulatory compliance and operational efficiency in response to the dynamic demands of modern clinical research. The RVP introduces a structured validation approach that categorizes REDCap functionalities, develops targeted validation tests, and applies structured and standardized testing syntax. This approach ensures that REDCap can meet regulatory standards while maintaining flexibility to adapt to new challenges. Results from the application of the RVP on recent successive REDCap software version releases illustrate significant improvements in testing efficiency and process optimization, demonstrating the project’s success in setting new benchmarks for EDC system validation. The project’s community-driven responsibility model fosters collaboration and knowledge sharing and enhances the overall resilience and adaptability of REDCap. As REDCap continues to evolve based on feedback from clinical trialists, the RVP ensures that REDCap remains a reliable and compliant tool, ready to meet regulatory and future operational challenges.

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

The biopsychosocial model remains a key paradigm for healthcare, despite widely recognised scientific and philosophical shortcomings. Here we report on recent updates integrating evolutionary theory with the biopsychosocial model to provide a more comprehensive and scientifically complete approach to understanding the multiple relevant levels of causation of medical and psychiatric problems.

Empowering the Participant Voice (EPV) is an NCATS-funded six-CTSA collaboration to develop, demonstrate, and disseminate a low-cost infrastructure for collecting timely feedback from research participants, fostering trust, and providing data for improving clinical translational research. EPV leverages the validated Research Participant Perception Survey (RPPS) and the popular REDCap electronic data-capture platform. This report describes the development of infrastructure designed to overcome identified institutional barriers to routinely collecting participant feedback using RPPS and demonstration use cases. Sites engaged local stakeholders iteratively, incorporating feedback about anticipated value and potential concerns into project design. The team defined common standards and operations, developed software, and produced a detailed planning and implementation Guide. By May 2023, 2,575 participants diverse in age, race, ethnicity, and sex had responded to approximately 13,850 survey invitations (18.6%); 29% of responses included free-text comments. EPV infrastructure enabled sites to routinely access local and multi-site research participant experience data on an interactive analytics dashboard. The EPV learning collaborative continues to test initiatives to improve survey reach and optimize infrastructure and process. Broad uptake of EPV will expand the evidence base, enable hypothesis generation, and drive research-on-research locally and nationally to enhance the clinical research enterprise.

The GINI project investigates the dynamics of inequality among populations over the long term by synthesising global archaeological housing data. This project brings archaeologists together from around the world to assess hypotheses concerning the causes and consequences of inequality that are of relevance to contemporary societies globally.

The density structure of firn has implications for hydrological and climate modelling, and ice-shelf stability. The structure of firn can be evaluated from depth models of seismic velocity, widely obtained with Herglotz–Wiechert inversion (HWI), an approach that considers the slowness of refracted seismic arrivals. However, HWI is strictly appropriate only for steady-state firn profiles and the inversion accuracy can be compromised where firn contains ice layers. In these cases, full waveform inversion (FWI) may yield more success than HWI. FWI extends HWI capabilities by considering the full seismic waveform and incorporates reflected arrivals. Using synthetic firn density profiles, assuming both steady- and non-steady-state accumulation, we show that FWI outperforms HWI for detecting ice slab boundaries (5–80 m thick, 5–80 m deep) and velocity anomalies within firn. FWI can detect slabs thicker than one wavelength (here, 20 m, assuming a maximum frequency of 60 Hz) but requires the starting velocity model to be accurate to ±2.5%. We recommend for field practice that the shallowest layers of velocity models are constrained with ground-truth data. Nonetheless, FWI shows advantages over established methods, and should be considered when the characterisation of firn ice slabs is the goal of the seismic survey.

OBJECTIVES/GOALS: To test the effect of a trusted Community Health Worker (CHW) support model to increase accessibility, feasibility and completion of COVID-19 home-testing in Native American and Latino communities. METHODS/STUDY POPULATION: We conducted a multi-site pragmatic randomized controlled trial among adult Native Americans and Latinos from the Flathead reservation in Montana and Yakima Valley in Washington. Participants were block randomized by site location and age to either an active or passive study arm. Participants in the active arm received assistance with online COVID-19 test kit registration and virtual swabbing support from CHWs, while the passive study arm received the standard-of-care support from the COVID-19 home testing kit vendor. Simple and multivariate logistic regression modeled the association between home-testing distribution mechanism and test completion. Multivariate models included community and sex as covariates. Descriptive feedback was collected in a post-test survey. RESULTS/ANTICIPATED RESULTS: Overall, 63% of the 268 enrolled participants completed COVID-19 tests, and 50% completed tests yielding a valid result. Active arm participants had significantly higher odds of test completion (OR 1.66, 95% CI: [1.01, 2.75], p-value=0.04). Differences were most pronounced among adults ≥60 years, with 84% completing testing kits in the active arm, compared to 58% in the passive arm (p=0.07). Ease of use and not having to leave home were top positive aspects of the home-based test while transporting and mailing samples to lab and long/overwhelming instructions were cited as negative aspects. Most test completers (93%) were satisfied with their experience and 95% found CHW assistance useful. Sample expiration and insufficient identifiers were top causes of non-valid test results. DISCUSSION/SIGNIFICANCE: While test completion rates were low in both study arms, the CHW support led to a higher COVID-19 test completion rate, particularly among older adults. Still, CHW support alone does not fully eliminate testing barriers. Socio-economic differences must be accounted for in future product development for home-based testing to improve health equity.

OBJECTIVES/GOALS: Despite efforts to improve COVID-19 health outcomes through testing and vaccination, SARS-CoV-2 has exacerbated health disparities in underserved populations. Through this study we examined socio-contextual factors impacting decisions to test for COVID-19 among Native Americans in the Flathead Reservation and Hispanics in the Yakima Valley. METHODS/STUDY POPULATION: A series of 28 key informant interviews and 6 focus groups (N=39 focus group participants) were completed with community and tribal leaders using an interview guide informed by the Theory of Planned Behavior, Social Cognitive Theory, and the Social Contextual Factor Frameworks. The interview guide was designed to examine the socio-contextual factors impacting decisions to test for COVID-19 among Native Americans and Hispanics in the Northwest. A codebook was developed to apply deductive coding to informant responses, followed by an inductive, constant comparison approach. Three analysts met to refine the codebook and conduct inter-rater agreement. RESULTS/ANTICIPATED RESULTS: Five themes (social, cultural, health, religious and political factors) were identified that impacted testing for COVID-19. For social factors, participants discussed the influence of families and friends and unfair employment practices influencing decisions to test. Cultural factors included deep rooted distrust for the government and historical trauma. Health factors participants reported included the importance of testing to save lives, distrust for medical system, and health communications around COVID-19 affecting decisions to test. There was some interaction between religious and political factors. While participants mentioned beliefs in putting things in God’s hands, some decisions to test seemed to be affected by their political views. DISCUSSION/SIGNIFICANCE: Several socio-cultural factors influence decisions to test for COVID-19. Understanding the community’s perception of COVID-19 testing is critical for successful implementation of preventive strategies.

Distributed Acoustic Sensing (DAS) is increasingly recognised as a valuable tool for glaciological seismic applications, although analysing the large data volumes generated in acquisitions poses computational challenges. We show the potential of active-source DAS to image and characterise subglacial sediment beneath a fast-flowing Greenlandic outlet glacier, estimating the thickness of sediment layers to be 20–30 m. However, the lack of subglacial velocity constraint limits the accuracy of this estimate. Constraint could be provided by analysing cryoseismic events in a counterpart 3-day record of passive seismicity through, for example, seismic tomography, but locating them within the 9 TB data volume is computationally inefficient. We describe experiments with data compression using the frequency-wavenumber (f-k) transform ahead of training a convolutional neural network, that provides a ~300-fold improvement in efficiency. In combining active and passive-source and our machine learning framework, the potential of large DAS datasets could be unlocked for a range of future applications.

OBJECTIVES/GOALS: The COVID-19 pandemic impacted health systems and exposed disparities in access to health care among underserved populations. We examined how the pandemic shaped social, mental, and physical health among Native American and Latino communities in rural and underserved areas. METHODS/STUDY POPULATION: Using Theory of Planned Behavior, Social Cognitive Theory, and Social Contextual Factor frameworks, we developed interview guides to examine perceptions of the COVID-19 pandemic on social, mental, and physical health among community members. Stakeholders of the Confederated Salish and Kootenai Tribes of the Flathead Reservation in Montana and the Hispanic/Latinx population in Yakima Valley in Washington were selected through purposeful community-engagement. A total of six focus group discussions and 30 key informant interviews were administered in both communities. A codebook was developed and deductive coding was applied to informant responses, followed by an inductive, constant comparison approach. The codebook was further refined and inter-rater agreement was completed by three analysts. RESULTS/ANTICIPATED RESULTS: Four themes were highlighted as areas impacted by the COVID-19 pandemic (mental and physical health, family dynamics, and social disruptions) with few differences among geographic areas or between focus group (n=39) and key informant (n=28) participants. Perceived impacts on mental health included increased stress, anxiety, and depression, while pandemic-related lifestyle or family changes impacted physical health. Participants reported changes to family routines and dynamics due to staying home, social distancing, and more frequent interactions inside or limited interactions outside the household respectively. Social distruptions reported included impacts on finances, employment, and household staples, though participants highlighted how many community members stepped up to help those in need. DISCUSSION/SIGNIFICANCE: The COVID-19 pandemic had similar impacts on two geographically distinct underserved communities in Montana and Washington. Understanding the community’s experience with the COVID-19 pandemic is critical to identify strategies to support families, community needs, and mental and physical health in underserved communities.

The density structure of firn has implications for hydrological and climate modelling and for ice shelf stability. The firn structure can be evaluated from depth models of seismic velocity, widely obtained with Herglotz-Wiechert inversion (HWI), an approach that considers the slowness of refracted seismic arrivals. However, HWI is appropriate only for steady-state firn profiles and the inversion accuracy can be compromised where firn contains ice layers. In these cases, Full Waveform Inversion (FWI) can be more successful than HWI. FWI extends HWI capabilities by considering the full seismic waveform and incorporates reflected arrivals, thus offering a more accurate estimate of a velocity profile. We show the FWI characterisation of the velocity model has an error of only 1.7% for regions (vs. 4.2% with HWI) with an ice slab (20 m thick, 40 m deep) in an otherwise steady-state firn profile.