Adipose tissue is crucial for the maintenance of energy and metabolic homeostasis and its deregulation can lead to obesity and type II diabetes (T2D). Using gene disruption in the mouse, we discovered a function for a RhoA-specific guanine nucleotide exchange factor PDZ-RhoGEF (Arhgef11) in white adipose tissue biology. While PDZ-RhoGEF was dispensable for a number of RhoA signaling-mediated processes in mouse embryonic fibroblasts, including stress fiber formation and cell migration, it's deletion led to a reduction in their proliferative potential. On a whole organism level, PDZ-RhoGEF deletion resulted in an acute increase in energy expenditure, selectively impaired early adipose tissue development and decreased adiposity in adults. PDZ-RhoGEF-deficient mice were protected from diet-induced obesity and T2D. Mechanistically, PDZ-RhoGEF enhanced insulin/IGF-1 signaling in adipose tissue by controlling ROCK-dependent phosphorylation of the insulin receptor substrate-1 (IRS-1). Our results demonstrate that PDZ-RhoGEF acts as a key determinant of mammalian metabolism and obesity-associated pathologies.DOI: http://dx.doi.org/10.7554/eLife.06011.001

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Electrohydraulic forming is a high-velocity forming process that deforms sheet metals with velocities above 100 m/s and strain rates more than 100 s−1. This experiment was conducted in a closed space because of safety concerns related to the high-velocity conditions; therefore, we were not able to examine the deformation process of the sheet metal. To observe the electrohydraulic forming process in detail, we performed virtual numerical simulations using accurate material properties. Therefore, in this paper, we obtained the material property of a sheet metal from a numerical estimation by using a surrogate model based on the reduced order model and the artificial neural network. The Cowper–Symonds constitutive equation was selected for the Al 6061-T6 sheet metal, and two strain rate parameters were adopted as the unknown parameters. From the two sampling techniques, the training and test samples were extracted from the specific ranges of two unknown parameters, and a numerical simulation was performed for these samples by using the LS-DYNA program. The z-axis displacements of the deformed sheet metal were obtained from the results of the numerical simulation, and two basis vectors were extracted by using principal component analysis. In addition, to predict the weighting coefficients of the two basis vectors at the defined range of parameters, we used the artificial neural network technique as a surrogate model. By comparing the surrogate model and the experimental results and calculating the root mean square error value, we estimated the optimal parameter for Al 6061-T6. Finally, the reliability of the obtained material parameters was proved by comparing the experimental results, the surrogate model, and LS-DYNA.

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