BackgroundAccess to healthcare remains a major issue in the United States, particularly in rural communities. Mobile health clinics (MHCs), including those utilizing a pharmacist-driven model, are one effective solution to address access-related barriers. To our knowledge, limited information is available to aid in the development, implementation, and operation of a pharmacist-driven MHC model. This project aims to fill this gap by exploring the characteristics of existing pharmacist-driven MHCs.MethodsThis project used semi-structured interviews, guided by a 23-item interview guide, conducted with groups and individuals from pharmacist-driven mobile health programs to identify logistics for the development of a pharmacist-driven MHC model. Fifteen pharmacist-driven MHCs that met the inclusion criteria were identified through a web-based search. Of these, eight programs agreed to participate (53%). An additional two programs were identified through snowball sampling, for a total of ten participating programs. Prior to the interview, programs completed a 14-item intake questionnaire to allow for adaptation of the interview guide. Interview data was analyzed using a mixed deductive (hypothesis-driven) strategy, in which four areas of inquiry, logistics, partnerships, outcomes, and lessons learned, were identified through a literature review process and guided the analysis. In this manuscript we focus on program logistics.ResultsSixteen participants from ten pharmacist-driven MHCs completed an interview. Six subthemes were identified related to program logistics: (1) programs exist to increase access to care; (2) programs have an awareness of scope/role; (3) programs identify and meet community needs; (4) programs meet patients’ needs; (5) programs have a small staff with large volunteer-base; and (6) programs have a three-step clinical workflow.ConclusionsBy utilizing the MHC model, pharmacists may be better able to address health gaps while leveraging existing resources, and providing services tailored to the needs of the patients within a community. These findings may be used as a guide for the development, implementation, and operation of current and future pharmacy-driven MHC programs.

Community engagement represents a foundational strategy for advancing cancer research and improving health outcomes. This study examines advocacy as a form of community engagement across the cancer research continuum, aligning with ASCO's 2024-2025 presidential theme of "Driving Knowledge to Action: Building a Better Future." We present a comprehensive framework that promotes bidirectional learning, trust, and transparency at all stages of research, from conception to dissemination. The spectrum of engagement approaches is described, ranging from consultative models to fully collaborative partnerships, highlighting how each creates critical touchpoints throughout the research process. We identify significant challenges to meaningful community engagement-including institutional barriers, historical mistrust, and sustainability concerns-while offering practical solutions drawn from successful examples across diverse cancer research settings. This study concludes with actionable recommendations for advancing robust community engagement through diverse representation, mentorship programs, institutional support mechanisms, and dedicated funding channels. By integrating advocacy throughout the research continuum, we create pathways for patients, caregivers, community representatives, and emerging professionals to shape research agendas, inform study designs, and participate in translating findings into policy and practice, ultimately ensuring cancer research is inclusive, relevant, and accessible to all communities.

Corn stunt is one of the most significant corn diseases in the Neotropics, leading to severe plant stunting and substantial yield losses. Although four pathogens have been found either singly or in combination in infected plants in the Americas, the corn stunt spiroplasma (Spiroplasma kunkelii) have been the most predominant pathogen associated with the disease in the U.S., due to its widespread distribution in the Rio Grande Valley region and persistent occurrence in California and Florida. During the 2024 growing season, reports of chlorosis, leaf reddening, and stunting in corn fields in Southern, Great Plains, Central Corn Belt, and Northeastern states raised concern regarding the possibility of a more widespread distribution of corn stunt spiroplasma in the U.S. Symptomatic corn leaf samples were collected in commercial and experimental field sites across the U.S. Detection and identification of S. kunkelii were performed using a polymerase chain reaction assay targeting a section of the spiralin gene, followed by amplicon sequencing. This study provides the first report of the pathogen S. kunkelii associated with corn stunt symptoms distributed across six counties in Oklahoma, 14 counties in Kansas, two counties in Missouri and Arkansas, four counties in New York, and one county in each of Nebraska, South Dakota, Wisconsin, Minnesota, Indiana, and Alabama. All states with submitted samples had at least one confirmed case of S. kunkelii.

This study investigates how the quality of engagement auditors, assessed using the auditor’s industry expertise and size at both national and state levels, influences the likelihood of going concern opinion (GCO) issuance for U.S. banks from 2002 to 2023. We also examine how auditor quality affects the accuracy of GCOs, specifically regarding Type I (false positive) and Type II (false negative) errors in GCO issuance. Using a dataset of 4992 bank-year observations from 414 unique banks, we analyze the correlations between auditor characteristics and these error types. We find that state-level audit industry experts issue significantly more accurate GCOs, demonstrating lower rates of both Type I and Type II errors compared to their counterparts. National-level experts and larger audit firms primarily show a reduced likelihood of Type II errors, indicating a more conservative approach. Our findings underscore the importance of localized auditor expertise in assessing bank financial health and suggest that enhanced collaboration between auditors and regulators could improve the predictive power of GCOs. These results offer important implications for regulatory policy and emphasize the need for improved audit standards to bolster financial system stability.

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Landsat 8 Level 2 Collection 2 (L2C2) surface reflectance (SR) products are widely used in various scientific applications by the remote sensing community, where their accuracy is vital for reliable analysis. However, discrepancies have been observed at shorter wavelength bands, which can affect certain applications. This study investigates the root cause of these differences by analyzing the assumptions made in the Land Surface Reflectance Code (LaSRC), the atmospheric correction algorithm of Landsat 8, as currently implemented at United States Geological Survey Earth Resources Observation and Science (USGS EROS), and proposes a correction method. To quantify these discrepancies, ground truth SR measurements from the Radiometric Calibration Network (RadCalNet) and Arable Mark 2 sensors were compared with the Landsat 8 SR. Additionally, the surface pressure measurements from RadCalNet and the National Centers for Environmental Information (NCEI) were evaluated against the LaSRC-calculated surface pressure values. The findings reveal that the discrepancies arose from using a single scene center surface pressure for the entire Landsat 8 scene pixels. The pressure-related discrepancies were most pronounced in the coastal aerosol and blue bands, with greater deviations observed in regions where the elevation of the study area differed substantially from the scene center, such as Railroad Valley Playa (RVUS) and Baotao Sand (BSCN). To address this issue, an exponential correction model was developed, reducing the mean error in the coastal aerosol band for RVUS from 0.0226 to 0.0029 (about two units of reflectance), which can be substantial for dark vegetative and water targets. In the blue band, there is a smaller improvement in the mean error, from 0.0095 to −0.0032 (about half a unit of reflectance). For the green band, the reduction in error was much less due to the significantly lesser impact of aerosol on this band. Overall, this study underscores the need for a more precise estimation of surface pressure in LaSRC to enhance the reliability of Landsat 8 SR products in remote sensing applications.

ABSTRACT Families experiencing homelessness and housing instability face myriad challenges affecting the health outcomes of parents and children. Despite these hardships, resilience and family resilience among this population can contribute to positive outcomes with individuals and families emerging stronger amid adversity. This study systematically reviews research examining resilience and family resilience within families experiencing homelessness and/or housing instability. We searched five electronic databases with search terms related to family, homelessness, housing instability, and resilience. A total of 27 studies fit the inclusion criteria. Summaries of study designs, samples, demographics, and resilience-related findings were synthesized followed by quality assessments. The 27 studies, comprising quantitative (n = 10), qualitative (n = 15), and mixed-method designs (n = 2), revealed resilience-related factors within distinct domains. The first domain included individual factors such as perseverance, optimism, and problem-solving. Domain two focused on interpersonal factors including social support and parenting approaches. The third domain included community and environmental factors related to the impact of organizational staff. Findings demonstrate resilience-related factors at the individual, interpersonal, community, and environmental levels. Notably, this review found that both individual and interpersonal resilience factors were most prevalent, and demonstrated the need for integrating individual, interpersonal, and community components into both research and practice.