Skeletal muscle atrophy is a debilitating response to fasting, disuse, cancer, and other systemic diseases. In atrophying muscles, the ubiquitin ligase, atrogin-1 (MAFbx), is dramatically induced, and this response is necessary for rapid atrophy. Here, we show that in cultured myotubes undergoing atrophy, the activity of the PI3K/AKT pathway decreases, leading to activation of Foxo transcription factors and atrogin-1 induction. IGF-1 treatment or AKT overexpression inhibits Foxo and atrogin-1 expression. Moreover, constitutively active Foxo3 acts on the atrogin-1 promoter to cause atrogin-1 transcription and dramatic atrophy of myotubes and muscle fibers. When Foxo activation is blocked by a dominant-negative construct in myotubes or by RNAi in mouse muscles in vivo, atrogin-1 induction during starvation and atrophy of myotubes induced by glucocorticoids are prevented. Thus, forkhead factor(s) play a critical role in the development of muscle atrophy, and inhibition of Foxo factors is an attractive approach to combat muscle wasting.

Skeletal muscle atrophy is a debilitating response to starvation and many systemic diseases including diabetes, cancer, and renal failure. We had proposed that a common set of transcriptional adaptations underlie the loss of muscle mass in these different states. To test this hypothesis, we used cDNA microarrays to compare the changes in content of specific mRNAs in muscles atrophying from different causes. We compared muscles from fasted mice, from rats with cancer cachexia, streptozotocin-induced diabetes mellitus, uremia induced by subtotal nephrectomy, and from pair-fed control rats. Although the content of >90% of mRNAs did not change, including those for the myofibrillar apparatus, we found a common set of genes (termed atrogins) that were induced or suppressed in muscles in these four catabolic states. Among the strongly induced genes were many involved in protein degradation, including polyubiquitins, Ub fusion proteins, the Ub ligases atrogin-1/MAFbx and MuRF-1, multiple but not all subunits of the 20S proteasome and its 19S regulator, and cathepsin L. Many genes required for ATP production and late steps in glycolysis were down-regulated, as were many transcripts for extracellular matrix proteins. Some genes not previously implicated in muscle atrophy were dramatically up-regulated (lipin, metallothionein, AMP deaminase, RNA helicase-related protein, TG interacting factor) and several growth-related mRNAs were down-regulated (P311, JUN, IGF-1-BP5). Thus, different types of muscle atrophy share a common transcriptional program that is activated in many systemic diseases.

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We consider the forward problem of uncertainty quantification for the generalised Dirichlet eigenvalue problem for a coercive second order partial differential operator with random coefficients, motivated by problems in structural mechanics, photonic crystals and neutron diffusion. The PDE coefficients are assumed to be uniformly bounded random fields, represented as infinite series parametrised by uniformly distributed i.i.d. random variables. The expectation of the fundamental eigenvalue of this problem is computed by (a) truncating the infinite series which define the coefficients; (b) approximating the resulting truncated problem using lowest order conforming finite elements and a sparse matrix eigenvalue solver; and (c) approximating the resulting finite (but high dimensional) integral by a randomly shifted quasi-Monte Carlo lattice rule, with specially chosen generating vector. We prove error estimates for the combined error, which depend on the truncation dimension s, the finite element mesh diameter h, and the number of quasi-Monte Carlo samples N . Under suitable regularity assumptions, our bounds are of the particular form O(h 2 + N −1+δ ), where δ > 0 is arbitrary and the hidden constant is independent of the truncation dimension, which needs to grow as h → 0 and N → ∞. As for the analogous PDE source problem, the conditions under which our error bounds hold depend on a parameter p ∈ (0, 1) representing the summability of the terms in the series expansions of the coefficients. Although the eigenvalue problem is nonlinear, which means it is generally considered harder than the source problem, in almost all cases (p = 1) we obtain error bounds that converge at the same rate as the corresponding rate for the source problem. The proof involves a detailed study of the regularity of the fundamental eigenvalue as a function of the random parameters. As a key intermediate result in the analysis, we prove that the spectral gap (between the fundamental and the second eigenvalues) is uniformly positive over all realisations of the random problem.

The United States Energy Information Administration releases an Annual Energy Outlook (AEO) projecting future supply, demand, and resources for energy and electricity in the U.S. It is widely relied upon for policymaking. This study assesses twelve years of these projections of generation and capacity for six classes of renewable technologies. It finds consistent under projections for most renewable energy types in the mid-and long-term, due to inaccuracies, limitations, and inconsistencies in the underlying model. It identifies and evaluates five hypotheses that may explain such inaccuracy: inaccurate modelling of state renewable energy mandates, expiration of renewable tax credits, flaws in modelling structure, a biomass cofiring assumption, and capital cost projections. Unless AEO's projections of renewable energy are greatly improved, the reliability of its sector-wide electricity projections is inherently low. Key modifications suggested by this study include: fully valuing financial and non-financial benefits of renewables, improving cost innovation expectations for renewable energy, and, perhaps most importantly, properly modelling state renewable energy mandates.

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