Objectives/Goals: Magnetic resonance imaging (MRI) reports are stored as unstructured text in the electronic health record (EHR), rendering the data inaccessible. Large language models (LLM) are a new tool for analyzing and generating unstructured text. We aimed to evaluate how well an LLM extracts data from MRI reports compared to manually abstracted data. Methods/Study Population: The University of California, San Francisco has deployed a HIPAA-compliant internal LLM tool utilizing GPT-4 technology and approved for PHI use. We developed a detailed prompt instructing the LLM to extract data elements from prostate MRI reports and to output the results in a structured, computer-readable format. A data pipeline was built using the OpenAI Application Programming Interface (API) to automatically extract distinct data elements from the MRI report that are important in prostate cancer care. Each prompt was executed five times and data were compared with the modal responses to determine variability of responses. Accuracy was also assessed. Results/Anticipated Results: Across 424 prostate MRI reports, GPT-4 response accuracy was consistently above 95% for most parameters. Individual field accuracies were 98.3% (96.3–99.3%) for PSA density, 97.4% (95.4–98.7%) for extracapsular extension, 98.1% (96.3–99.2%) for TNM Stage, had an overall median of 98.1% (96.3–99.2%), a mean of 97.2% (95.2–98.3%), and a range of 99.8% (98.7–100.0%) to 87.7% (84.2–90.7%). Response variability over five repeated runs ranged from 0.14% to 3.61%, differed based on the data element extracted (p Discussion/Significance of Impact: GPT-4 was highly accurate in extracting data points from prostate cancer MRI reports with low upfront programming requirements. This represents an effective tool to expedite medical data extraction for clinical and research use cases.

Many experiments have demonstrated the power of norm enforcement— peer monitoring and punishment—to maintain, or even increase, contributions in social dilemma settings, but little is known about the underlying norms that monitors use to make punishment decisions, either within or across groups. Using a large sample of experimental data, we empirically recover the set of norms used most often by monitors and show first that the decision to punish should be modeled separately from the decision of how much to punish. Second, we show that absolute norms often fit the data better than the group average norm often assumed in related work. Third, we find that different norms seem to influence the decisions about punishing violators inside and outside one's own group.

The spotted hyaena Crocuta crocuta is relatively understudied across its range despite evidence of widespread declines. It is therefore essential that robust baseline population density assessments are conducted to inform current management and future conservation policy. In Mozambique this is urgent as decades of armed conflict followed by unchecked poaching have resulted in large-scale wildlife declines and extirpations. We conducted the first robust population density estimate for a spotted hyaena population in Mozambique using spatially explicit capture–recapture methodologies. We recorded a relatively low population density of 0.8–2.1 hyaenas/100 km2 in the wildlife management area Coutada 11 in the Zambezi Delta of central Mozambique in 2021. These densities are well below the estimated carrying capacity for the landscape and are comparable to published densities in high human-impact, miombo woodland-dominated and arid environments. The combination of historical armed conflict, marginal trophy hunting and bushmeat poaching using wire snares and gin traps (with physical injuries evident in 9% of identified individuals) presents persistent anthropogenic pressure, limiting the post-war recovery of this resident hyaena population. We provide insights into the dynamics of hyaena population status and recovery in such post-war landscapes, adding to mounting evidence that the species is less resilient to severe anthropogenic disturbances than previously believed. We recommend long-term monitoring of this and other carnivore populations in post-war landscapes to ascertain demographic trends and implement effective conservation interventions for population recovery.

We use an experiment to evaluate the effects of participatory management on firm performance. Participants are randomly assigned roles as managers or workers in firms that generate output via real effort. To identify the causal effect of participation on effort, workers are exogenously assigned to one of the two treatments: one in which the manager implements a compensation scheme unilaterally or another in which the manager cedes control over compensation to the workers who vote to implement a scheme. We find that output is between seven and twelve percentage points higher in participatory firms.

Engineering machines are becoming increasingly complex and possess more control variables, increasing the complexity and versatility of the control systems. Different configurations of the control system, named a policy, can result in similar output behavior but with different resource or component life usage. There is therefore an opportunity to find optimal policies with respect to economic decisions. While many solutions have been proposed to find such economic policy decisions at the asset level, we consider this problem at the fleet level. In this case, the optimal operation of each asset is affected by the state of all other assets in the fleet. Challenges introduced by considering multiple assets include the construction of economic multi-objective optimization criteria, handling rare events such as failures, application of fleet-level constraints, and scalability. The proposed solution presents a framework for economic fleet optimization. The framework is demonstrated for economic criteria relating to resource usage, component lifing, and maintenance scheduling, but is generically extensible. Direct optimization of lifetime distributions is considered in order to avoid the computational burden of discrete event simulation of rare events. Results are provided for a real-world case study targeting the optimal economic operation of a fleet of aerospace gas turbine engines.

By coupling long-range polymerase chain reaction, wastewater-based epidemiology, and pathogen sequencing, we show that adenovirus type 41 hexon-sequence lineages, described in children with hepatitis of unknown origin in the United States in 2021, were already circulating within the country in 2019. We also observed other lineages in the wastewater, whose complete genomes have yet to be documented from clinical samples.

Single-molecule techniques to analyze proteins and other biomolecules involving labels and tethers have allowed for new understanding of the underlying biophysics; however, the impact of perturbation from the labels and tethers has recently been shown to be significant in several cases. New approaches are emerging to measure single proteins through light scattering without the need for labels and ideally without tethers. Here, the approaches of interference scattering, plasmonic scattering, microcavity sensing, nanoaperture optical tweezing, and variants are described and compared. The application of these approaches to sizing, oligomerization, interactions, conformational dynamics, diffusion, and vibrational mode analysis is described. With early commercial successes, these approaches are poised to have an impact in the field of single-molecule biophysics.

Migrants and refugees face elevated risks for mental health problems but have limited access to services. This study compared two strategies for training and supervising nonspecialists to deliver a scalable psychological intervention, Group Problem Management Plus (gPM+), in northern Colombia. Adult women who reported elevated psychological distress and functional impairment were randomized to receive gPM+ delivered by nonspecialists who received training and supervision by: 1) a psychologist (specialized technical support); or 2) a nonspecialist who had been trained as a trainer/supervisor (nonspecialized technical support). We examined effectiveness and implementation outcomes using a mixed-methods approach. Thirteen nonspecialists were trained as gPM+ facilitators and three were trained-as-trainers. We enrolled 128 women to participate in gPM+ across the two conditions. Intervention attendance was higher in the specialized technical support condition. The nonspecialized technical support condition demonstrated higher fidelity to gPM+ and lower cost of implementation. Other indicators of effectiveness, adoption and implementation were comparable between the two implementation strategies. These results suggest it is feasible to implement mental health interventions, like gPM+, using lower-resource, community-embedded task sharing models, while maintaining safety and fidelity. Further evidence from fully powered trials is needed to make definitive conclusions about the relative cost of these implementation strategies.

The United States Government (USG) public-private partnership “Accelerating COVID-19 Treatment Interventions and Vaccines” (ACTIV) was launched to identify safe, effective therapeutics to treat patients with Coronavirus Disease 2019 (COVID-19) and prevent hospitalization, progression of disease, and death. Eleven original master protocols were developed by ACTIV, and thirty-seven therapeutic agents entered evaluation for treatment benefit. Challenges encountered during trial implementation led to innovations enabling initiation and enrollment of over 26,000 participants in the trials. While only two ACTIV trials continue to enroll, the recommendations here reflect information from all the trials as of May 2023. We review clinical trial implementation challenges and corresponding lessons learned to inform future therapeutic clinical trials implemented in response to a public health emergency and the conduct of complex clinical trials during “peacetime,” as well.

In the current study we evaluated an afterschool nutrition education programme, called Vetri Cooking Lab (VCL), for promoting healthy and diverse eating habits among at-risk children in the Greater Philadelphia area. To understand potential programme impacts, we conducted a longitudinal analysis of survey data collected before and after participation in VCL. Main study included cooking confidence, cooking knowledge, changes in dietary consumption behaviours, and changes in vegetable preferences. Participants included students in grades 3–11 enrolled in VCL during the 2018–19 school year at VCL sites (n = 60) throughout Philadelphia, PA, and Camden, NJ. Eligible participants completed surveys both before and after participating in the programme. We found that students’ confidence and knowledge increased (P < 0.001) after the cooking intervention. Knowledge and confidence were positively associated (r = 0.55; P < 0.001). Confidence was correlated with consumption behaviour changes (r = 0.18; P = 0.022). Confidence was positively associated with consumption changes in both our adjusted (OR = 1.81; P < 0.001) and unadjusted models (aOR = 1.88; P = 0.013). Compared to Black students, White students were more likely to report consumption changes (aOR = 5.83; P = 0.013). Hispanic/Latino participants and participants who spoke Spanish had nearly three times higher odds of consumption behaviour changes (Hispanic/Latino OR = 2.55; P = 0.007; Spanish OR = 3.04; P = 0.005). Student age and gender were not associated with behaviour changes. Our research demonstrates that programmes integrating practical cooking skills education along with nutrition, food, and cooking education can improve confidence and knowledge about healthy food choices amongst children driving an overall improvement in children’s eating habits.

The role of housing in providing a welfare asset has been widely explored. With the growth in home ownership between 1979 and 2008 and erosion of the welfare state, housing wealth has become part of the welfare mix in the UK. Here, we present analysis of housing outcomes, as measured in the UK Household Longitudinal Survey (UKHLS), among people who identify as lesbian, gay, or bisexual in Great Britain. This shows that lesbian, gay, and bisexual (LGB) people have poorer housing outcomes than heterosexual counterparts: they are less likely to be homeowners; more likely to be private renters; and more likely to be social renters. With growing intergenerational inequalities in access to home ownership, we argue that, as openly LGB (and broader trans and queer) people being on average younger than the rest of the population, this could lead to LGB people, as a group, being excluded from asset-based welfare in the future as they age.

We present radio observations of the galaxy cluster Abell S1136 at 888 MHz, using the Australian Square Kilometre Array Pathfinder radio telescope, as part of the Evolutionary Map of the Universe Early Science program. We compare these findings with data from the Murchison Widefield Array, XMM-Newton, the Wide-field Infrared Survey Explorer, the Digitised Sky Survey, and the Australia Telescope Compact Array. Our analysis shows the X-ray and radio emission in Abell S1136 are closely aligned and centered on the Brightest Cluster Galaxy, while the X-ray temperature profile shows a relaxed cluster with no evidence of a cool core. We find that the diffuse radio emission in the centre of the cluster shows more structure than seen in previous low-resolution observations of this source, which appeared formerly as an amorphous radio blob, similar in appearance to a radio halo; our observations show the diffuse emission in the Abell S1136 galaxy cluster contains three narrow filamentary structures visible at 888 MHz, between $\sim$80 and 140 kpc in length; however, the properties of the diffuse emission do not fully match that of a radio (mini-)halo or (fossil) tailed radio source.

Marine litter poses a complex challenge in Indonesia, necessitating a well-informed and coordinated strategy for effective mitigation. This study investigates the seasonality of plastic concentrations around Sulawesi Island in central Indonesia during monsoon-driven wet and dry seasons. By using open data and methodologies including the HYCOM and Parcels models, we simulated the dispersal of plastic waste over 3 months during both the southwest and northeast monsoons. Our research extended beyond data analysis, as we actively engaged with local communities, researchers and policymakers through a range of outreach initiatives, including the development of a web application to visualize model results. Our findings underscore the substantial influence of monsoon-driven currents on surface plastic concentrations, highlighting the seasonal variation in the risk to different regional seas. This study adds to the evidence provided by coarser resolution regional ocean modelling studies, emphasizing that seasonality is a key driver of plastic pollution within the Indonesian archipelago. Inclusive international collaboration and a community-oriented approach were integral to our project, and we recommend that future initiatives similarly engage researchers, local communities and decision-makers in marine litter modelling results. This study aims to support the application of model results in solutions to the marine litter problem.

This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.11. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression, along with a powerful portfolio allocation framework for dynamic multi-period asset investment management.

The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: CovRegpy.py, CovRegpy_DCC.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These six scripts contain implementations of software features including multivariate covariance time series models based on the regularized covariance regression (RCR) framework, dynamic conditional correlation (DCC) framework, risk premia parity (RPP) weighting functions, singular spectrum analysis (SSA), singular spectrum decomposition (SSD), and X11 decomposition framework, respectively.

These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting, and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks.

We examine, herein, two real-world case studies for actuarial practitioners. The first of these is a modification (demonstrating the regularization of covariance regression) of the original example from Hoff & Niu ((2012). Statistica Sinica, 22(2), 729–753) which modeled the covariance and correlative relationship that exists between forced expiratory volume (FEV) and age and FEV and height. We examine this within the context of making probabilistic predictions about mortality rates in patients with chronic obstructive pulmonary disease.

The second case study is a more complete example using this package wherein we present a funded and unfunded UK pension example. The decomposition algorithm isolates high-, mid-, and low-frequency structures from FTSE 100 constituents over 20 years. These are used to forecast the forthcoming quarter’s covariance structure to weight the portfolio based on the RPP strategy. These fully funded pensions are compared against the performance of a fully unfunded pension using the FTSE 100 index performance as a proxy.

OBJECTIVES/GOALS: DR-TB care in South Africa includes decentralized treatment with shorter, all-oral regimens. Treatment guidelines direct regular clinical and laboratory evaluation to assess patient improvement. We therefore measured sputum collection frequency and follow-up time to assess fidelity to these guidelines in Gauteng Province, South Africa. METHODS/STUDY POPULATION: We included Rifampicin-resistant (RR) sputum specimens from the South African National Health Laboratory Service, which provides pathology services to 80% of the population, submitted between August 2022-September 2023. Patient data were obtained from a DR-TB registry and additional sputum specimen data were collected from follow-up laboratory worksheets. Follow-up spanned from first sputum collection date (baseline) to patient outcome date (e.g., completion, lost) or study closure date (if still on treatment). Monthly sputum submission rate was measured for those with ≥1 additional sputum submitted. We compared patient data by treatment site: at the specialized hospital vs. any other site, using Wilcoxon ranksum and χ2 tests. RESULTS/ANTICIPATED RESULTS: Baseline RR-TB specimens were available for 142 patients, of whom 28 (20%) had specimens submitted from the specialized hospital. Patients at the specialized hospital were older (median age 41 vs. 35.5 years, p=0.03), had higher baseline fluoroquinolone resistance (10% vs. 1%, p=0.01), and longer follow-up (median 5.2 vs. 3.5 months, p=0.01) compared to patients elsewhere. Further, 43 (30%) patients had ≥1 additional sputum submitted during follow-up. Among these, monthly sputum collection rates did not differ by site (0.3 vs. 0.3 sputum per month, p=0.89). We anticipate that increased sputum frequency will be associated with successful TB treatment outcomes based on preliminary findings. DISCUSSION/SIGNIFICANCE: These findings highlight ongoing challenges with routine laboratory follow-up according to DR-TB guidelines across treatment sites in South Africa. Future research is needed to determine reasons for low sputum collection rates, such as low patient adherence, variation in practice of healthcare workers, loss to follow-up, and clinical challenges.

Globally, mental disorders account for almost 20% of disease burden and there is growing evidence that mental disorders are socially determined. Tackling the United Nations Sustainable Development Goals (UN SDGs), which address social determinants of mental disorders, may be an effective way to reduce the global burden of mental disorders. We conducted a systematic review of reviews to examine the evidence base for interventions that map onto the UN SDGs and seek to improve mental health through targeting known social determinants of mental disorders. We included 101 reviews in the final review, covering demographic, economic, environmental events, neighborhood, and sociocultural domains. This review presents interventions with the strongest evidence base for the prevention of mental disorders and highlights synergies where addressing the UN SDGs can be beneficial for mental health.