Abstract: Physical exercise has long been recommended as a treatment for osteoarthritis (OA), though its effects vary based on the exercise protocol. Here, we examined whether environmental lighting conditions influence the anti-inflammatory benefits of exercise in a rat model of OA. Moderate-intensity treadmill exercise (Ex) was performed for six weeks under a 12:12 h light/dark (L/D) cycle, and compared against rats housed in a 24 h continuous light (L/L) environment. L/L conditions were associated with serological changes shortly after OA induction, which exacerbated the inflammatory microenvironment in the joint. Differentiation capacity was also impaired in bone precursor cells isolated from normal rats maintained under L/L conditions, despite elevated inflammatory responses. Exercise training under L/L conditions led to increased corticosterone levels in the blood, which exacerbated the progression of cartilaginous and synovial lesions. Osteoporotic phenomena were also observed in exercise-trained rats maintained under L/L conditions, along with inflammation-induced catabolism in the gastrocnemius muscle. Aberrant light/dark cycle conditions were also found to be associated with suppression of splenic Cry1 expression in exercise-trained rats, leading to dysregulation of immune responses. Taken together, these data suggest that lighting condition may be an important environmental factor influencing the exercise-induced benefits on OA.

Abstract: Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.