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In this urgent decade when American democracy faces the challenge of decarbonizing the U.S. electric grid and assuring that the economic benefits of our energy transition are equitably shared, many solar energy researchers and activists are searching for new ways to partner with the civic sector. Instead of treating energy users as passive customers, experts understand the importance of engaging community as active decision-makers, beneficiaries, and communicators for a just energy transition. Distributed solar technology offers more democratic potential than small savings on individuals’ electric bills. Energy experts working on the Solar CommonsÔ community solar model at the University of Minnesota are piloting demonstration projects with community partners in Arizona and Minnesota. These solar commons aggregate savings through power purchase agreements that create 25-year peer-governed revenue streams to support mutual aid and reparative justice work in neighborhoods. This article describes a Solar Commons research project in Arizona, with a conversation among the public artists who partnered with the legal research team to co-create communication and peer governance tools that will allow DIY Solar Commons to iterate throughout the US as a new institution in our civic sector. Images of the Solar Commons public art demonstrate how the artists helped expand the vision of solar energy from the iconic individual solar panel to a technology embedded in community justice and in a complex human-more-than-human environment.

Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these “wicked” dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO).

Abstract Background Huntington's disease (HD) is a progressive neurodegenerative disorder caused by a CAG repeat expansion within the huntingtin gene. Mutant huntingtin protein misfolds and accumulates within neurons where it mediates its toxic effects. Promoting mutant huntingtin clearance by activating macroautophagy is one approach for treating Huntington's disease (HD). In this study, we evaluated the mTOR kinase inhibitor and macroautophagy promoting drug everolimus in the R6/2 mouse model of HD. Results Everolimus decreased phosphorylation of the mTOR target protein S6 kinase indicating brain penetration. However, everolimus did not activate brain macroautophagy as measured by LC3B Western blot analysis. Everolimus protected against early declines in motor performance; however, we found no evidence for neuroprotection as determined by brain pathology. In muscle but not brain, everolimus significantly decreased soluble mutant huntingtin levels. Conclusions Our data suggests that beneficial behavioral effects of everolimus in R6/2 mice result primarily from effects on muscle. Even though everolimus significantly modulated its target brain S6 kinase, this did not decrease mutant huntingtin levels or provide neuroprotection.

Abstract Background To compare the relationship of skeletal muscle mass with bone mineral content in an ethnically diverse group of 6 to 18 year old boys and girls. Methods 175 healthy children (103 boys; 72 girls) had assessments of body mass, height, and Tanner stage. Whole body bone mineral content, non-bone lean body mass (nbLBM), skeletal muscle mass, and fat mass were assessed using dual-energy X-ray absorptiometry (DXA). Muscle mass was estimated from an equation using appendicular lean soft tissue measured by DXA, weight and height. Estimates of skeletal muscle mass and adipose tissue were also assessed by whole body multi-slice magnetic resonance imaging (MRI). Linear regression was used to determine whether skeletal muscle mass assessed by DXA or by MRI were better predictors of bone mineral content compared with nbLBM after adjusting for sex, age, race or ethnicity, and Tanner stage. Results Greater skeletal muscle mass was associated with greater bone mineral content (p < 0.001). The skeletal muscle mass assessed by MRI provided a better fitting regression model (determined by R2 statistic) compared with assessment by DXA for predicting bone mineral content. The proportion of skeletal muscle mass in nbLBM was significantly associated with greater bone mineral content adjusted for total nbLBM. Conclusions This study is among the first to describe and compare the relationship of skeletal muscle to bone using both MRI and DXA estimates. The results demonstrate that the use of MRI provides a modestly better fitting model for the relationship of skeletal muscle to bone compared with DXA. Skeletal muscle had an impact on bone mineral content independent of total non-bone lean body mass. In addition, Hispanics had greater bone mineral content compared to other race and ethnic groups after adjusting for sex, age, adipose tissue, skeletal muscle mass, and height.