Dramatic changes taking place locally, regionally, globally, demand that we rethink strategies to improve public health, especially in disadvantaged communities where the cumulative impacts of toxicant exposure and other environmental and social stressors are most damaging. The emergent field of Sustainability Science, including a new bioregionalism for the 21st Century, is giving rise to promising place-based (territorially rooted) approaches. Embedded in this bioregional approach is an integrated planning framework (IPF) that enables people to map and develop plans and strategies that cut across various scales (e.g. from regional to citywide to neighborhood scale) and various topical areas (e.g. urban land use planning, water resource planning, food systems planning and "green infrastructure" planning) with the specific intent of reducing the impacts of toxicants to public health and the natural environment. This paper describes a case of bioregionally inspired integrated planning in San Diego, California (USA). The paper highlights food-water-energy linkages and the importance of "rooted" community-university partnerships and knowledge-action collaboratives in creating healthy and just bioregions.
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http://dx.doi.org/10.1515/reveh-2015-0050 | DOI Listing |
J Magn Reson Imaging
January 2025
Department of Radiology, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine (Shenzhen Traditional Chinese Medicine Hospital), Shenzhen, China.
Background: Multifrequency MR elastography (mMRE) enables noninvasive quantification of renal stiffness in patients with chronic kidney disease (CKD). Manual segmentation of the kidneys on mMRE is time-consuming and prone to increased interobserver variability.
Purpose: To evaluate the performance of mMRE combined with automatic segmentation in assessing CKD severity.
Lymphology
January 2025
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFSurg Radiol Anat
January 2025
Anatomy Department, University of Western Brittany (UBO), Brest, France.
Purpose: The aim was to establish a functional MRI protocol for analyzing human stereoscopic vision in clinical practice. The feasibility was established in a cohort of 9 healthy subjects to determine the functional cortical areas responsible for virtually relief vision.
Methods: Nine healthy right-handed subjects underwent orthoptic examination and functional MRI.
Front Sports Act Living
January 2025
Department of Public Health and Sport Sciences, University of Inland Norway, Elverum, Norway.
Introduction: Physical inactivity is a global health challenge, exacerbated by increased screen time and sedentary behaviors. Enhancing physical activity levels at schools offers a promising approach to promote lifelong healthy habits.
Methods: This protocol paper outlines the MOVE12 pilot study, a 12-week intervention study designed to increase physical activity among Norwegian upper secondary school students through 6-7-min daily MOVE-breaks integrated into lessons.
Mach Learn Clin Neuroimaging (2024)
December 2024
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called ntrastive Learning-based ph Generalized nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis.
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