Sun protection behaviors of state park workers in the southeastern USA

© The Author(s) 2019. Published by Oxford University Press on behalf of the British Occupational Hygiene Society. Background: Due to the nature of their work, state park workers receive substantial exposure to sunlight, putting them at an increased risk of developing skin cancer. Increased use of sun protection behaviors can reduce this risk. Objectives: Using the health belief model (HBM) as a theoretical framework, the purpose of this study was to assess factors associated with sun protection behaviors among state-park workers. Methods: In this cross-sectional study, a convenience sample of participants were recruited from 23 state parks in the Southeastern USA to complete a self-administered questionnaire based on the constructs of the HBM. Results: The sample comprised 310 state park workers. The majority of participants were non-Hispanic White (61.6%), male (63.5%), and were aged 39.56 (±13.97) years on average.The average duration of sun exposure during the workday was reported as 3.51 h (±1.88). Nearly 12% of the participants reported that their workplace had a sun-safety policy and ~10% reported receiving sun-safety training at their workplace.The majority of participants reported that they did not sufficiently use sun protection methods. Factors associated with sun protection behaviors included the HBM constructs of perceived benefits outweighing perceived barriers (standardized coefficient = 0.210, P = 0.001), self-efficacy (standardized coefficient = 0.333, P \u3c 0.001), and cues to action (standardized coefficient = 0.179, P = 0.004). Conclusion: Future research should explore the barriers to adopting and enforcing sun-safety policies in the workplace. HBM appears to be efficacious in explaining sun protection behaviors among state park workers. HBM constructs should be considered in future interventions aimed at increasing sun protection behaviors in this population

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn and propagate streamlines directly from T1 and anatomical contexts. Training for this network has previously relied on high-resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical protocols. Then, we define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. Under this metric, T1 tractography generated by CoRNN reproduces diffusion tractography with approximately two millimeters of error

Predicting Age from White Matter Diffusivity with Residual Learning

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural MRI data has become an important task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest. The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 years for cognitively normal participants and MAE of 6.62 years for cognitively impaired participants, while the second method achieves MAE of 4.69 years for cognitively normal participants and MAE of 4.96 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.Comment: SPIE Medical Imaging: Image Processing. San Diego, CA. February 2024 (accepted as poster presentation

The Northern Black Swift: Migration path and wintering area revealed

Winter ranges have been identified for most neotropical migrant bird species, those that spend the winter months in Central and South America and summer months in North America. Published accounts and specimen collections of the Northern Black Swift (Cypseloides niger borealis) during spring and fall migration are extremely limited and winter records are nonexistent. We placed light-level geolocators on four Black Swifts in August 2009, and retrieved three a year later. Data from the geolocators revealed initiation of fall migration (10 to 19 Sep 2009), arrival dates at wintering areas (28 Sep to 12 Oct 2009), departure dates from wintering areas (9 to 20 May 2010), and return dates to breeding sites (23 May to 18 Jun 2010) for Northern Black Swifts breeding in interior North America (Colorado, USA). Northern Black Swifts traveled 6,901 km from the Box Canyon breeding site and 7,025 km from Fulton Resurgence Cave to the center of the wintering area. The swifts traveled at an average speed of 341 km/day during the 2009 fall migration and an average speed of 393 km/day during the 2010 spring migration. This is the first evidence that western Brazil is the wintering area for a subset of the Northern Black Swift, extending the known winter distribution of this species to South America

Blood metabolite markers of cognitive performance and brain function in aging

We recently showed that Alzheimer's disease patients have lower plasma concentrations of the phosphatidylcholines (PC16:0/20:5; PC16:0/22:6; and PC18:0/22:6) relative to healthy controls. We now extend these findings by examining associations between plasma concentrations of these PCs with cognition and brain function (measured by regional resting state cerebral blood flow; rCBF) in non-demented older individuals. Within the Baltimore Longitudinal Study of Aging neuroimaging substudy, participants underwent cognitive assessments and brain (15)O-water positron emission tomography. Plasma phosphatidylcholines concentrations (PC16:0/20:5, PC16:0/22:6, and PC18:0/22:6), cognition (California Verbal Learning Test (CVLT), Trail Making Test A&B, the Mini-Mental State Examination, Benton Visual Retention, Card Rotation, and Fluencies—Category and Letter), and rCBF were assessed. Lower plasma phosphatidylcholine concentrations were associated with lower baseline memory performance (CVLT long delay recall task—PC16:0/20:5: (−2.17)–1.39(−0.60) p = 0.001 (β with 95% confidence interval subscripts)) and lower rCBF in several brain regions including those associated with memory performance and higher order cognitive processes. Our findings suggest that lower plasma concentrations of PC16:0/20:5, PC16:0/22:6, and PC18:0/22:6 are associated with poorer memory performance as well as widespread decreases in brain function during aging. Dysregulation of peripheral phosphatidylcholine metabolism may therefore be a common feature of both Alzheimer's disease and age-associated differences in cognition