Ablation of PGC-1β Results in Defective Mitochondrial Activity, Thermogenesis, Hepatic Function, and Cardiac Performance

The transcriptional coactivator peroxisome proliferator-activated receptor-gamma coactivator-1β (PGC-1β) has been implicated in important metabolic processes. A mouse lacking PGC-1β (PGC1βKO) was generated and phenotyped using physiological, molecular, and bioinformatic approaches. PGC1βKO mice are generally viable and metabolically healthy. Using systems biology, we identified a general defect in the expression of genes involved in mitochondrial function and, specifically, the electron transport chain. This defect correlated with reduced mitochondrial volume fraction in soleus muscle and heart, but not brown adipose tissue (BAT). Under ambient temperature conditions, PGC-1β ablation was partially compensated by up-regulation of PGC-1α in BAT and white adipose tissue (WAT) that lead to increased thermogenesis, reduced body weight, and reduced fat mass. Despite their decreased fat mass, PGC1βKO mice had hypertrophic adipocytes in WAT. The thermogenic role of PGC-1β was identified in thermoneutral and cold-adapted conditions by inadequate responses to norepinephrine injection. Furthermore, PGC1βKO hearts showed a blunted chronotropic response to dobutamine stimulation, and isolated soleus muscle fibres from PGC1βKO mice have impaired mitochondrial function. Lack of PGC-1β also impaired hepatic lipid metabolism in response to acute high fat dietary loads, resulting in hepatic steatosis and reduced lipoprotein-associated triglyceride and cholesterol content. Altogether, our data suggest that PGC-1β plays a general role in controlling basal mitochondrial function and also participates in tissue-specific adaptive responses during metabolic stress

A comparison of policy and direct practice stakeholder perceptions of factors affecting evidence-based practice implementation using concept mapping

<p>Abstract</p> <p>Background</p> <p>The goal of this study was to assess potential differences between administrators/policymakers and those involved in direct practice regarding factors believed to be barriers or facilitating factors to evidence-based practice (EBP) implementation in a large public mental health service system in the United States.</p> <p>Methods</p> <p>Participants included mental health system county officials, agency directors, program managers, clinical staff, administrative staff, and consumers. As part of concept mapping procedures, brainstorming groups were conducted with each target group to identify specific factors believed to be barriers or facilitating factors to EBP implementation in a large public mental health system. Statements were sorted by similarity and rated by each participant in regard to their perceived importance and changeability. Multidimensional scaling, cluster analysis, descriptive statistics and <it>t</it>-tests were used to analyze the data.</p> <p>Results</p> <p>A total of 105 statements were distilled into 14 clusters using concept-mapping procedures. Perceptions of importance of factors affecting EBP implementation varied between the two groups, with those involved in direct practice assigning significantly higher ratings to the importance of Clinical Perceptions and the impact of EBP implementation on clinical practice. Consistent with previous studies, financial concerns (costs, funding) were rated among the most important and least likely to change by both groups.</p> <p>Conclusions</p> <p>EBP implementation is a complex process, and different stakeholders may hold different opinions regarding the relative importance of the impact of EBP implementation. Implementation efforts must include input from stakeholders at multiple levels to bring divergent and convergent perspectives to light.</p

Co-designing the computational model and the computing substrate

Given a proposed unconventional computing substrate, we can ask: Does it actually compute? If so, how well does it compute? Can it be made to compute better? Given a proposed unconventional computational model we can ask: How powerful is the model? Can it be implemented in a substrate? How faithfully or efficiently can it be implemented? Given complete freedom in the choice of model and substrate, we can ask: Can we co-design a model and substrate to work well together? Here I propose an approach to posing and answering these questions, building on an existing definition of physical computing and framework for characterising the computing properties of given substrates

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

Equity and Efficiency Through Local Measured Service Revisited

Dale E. Lehman is an Associate Professor of Economic,. School of Business Administration at Fort Lewis College. Iqbal A Memon is an Assistant Professor of Finance School of Business Administration at Fort Lewis College