Racial/ethnic minorities are less likely to use higher-quality hospitals than whites. We propose that a higher level of information-related complexity in their local hospital environments compounds the effects of discrimination and more limited access to services, contributing to racial/ethnic disparities in hospital use. While minorities live closer than whites to high-volume hospitals, minorities also face greater choice complexity and live in neighborhoods with lower levels of medical experience. Our empirical results reveal that it is generally the overall context associated with proximity, choice complexity, and local experience, rather than differential sensitivity to these factors, that provides a partial explanation of the disparity gap in high-volume hospital use.
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http://dx.doi.org/10.1177/0046958014545575 | DOI Listing |
Inorg Chem
January 2025
Department of Chemistry University of Tennessee, Knoxville, Tennessee 37996-1600, United States.
A series of 2-pyridone[α]-fused BOPHYs - were prepared via a two-step procedure involving the preparation of enamine, followed by an intramolecular heterocyclization reaction. In addition to being fully conjugated with the BOPHY core pyridone fragment, BOPHYs and have a pyridine group connected to the BOPHY core via one- or two -CH- groups. New BOPHYs were characterized by spectroscopy as well as X-ray diffraction.
View Article and Find Full Text PDFJ Neurosurg
January 2025
1Department of Bioengineering, George Mason University, Fairfax, Virginia.
Objective: The complex mix of factors, including hemodynamic forces and wall remodeling mechanisms, that drive intracranial aneurysm growth is unclear. This study focuses on the specific regions within aneurysm walls where growth occurs and their relationship to the prevalent hemodynamic conditions to reveal critical mechanisms leading to enlargement.
Methods: The authors examined hemodynamic models of 67 longitudinally followed aneurysms, identifying 88 growth regions.
PLoS One
January 2025
Management School, Harbin University of Commerce, Harbin, Heilongjiang, China.
Road traffic congestion on the cold chain logistics not only increase the cost and time, but also creates certain negative impact on the national carbon emissions. To fully utilize the traffic resources, this study has classified urban road traffic congestion and defined the various vehicle delivery speeds with dynamic congestion levels. Simultaneously, it has developed the cold chain products replenishment strategy by considering delivery route, multi-depot condition and even vehicle types, aiming to minimize the total cost and carbon emissions, and maximizing the cold chain products freshness.
View Article and Find Full Text PDFPLoS One
January 2025
School of Information Science and Engineering, Xinjiang University, Urumqi, China.
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect.
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