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- Marazioti, Antonia, et al.
(author)
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KRAS signaling in malignant pleural mesothelioma
- 2022
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In: EMBO Molecular Medicine. - : EMBO. - 1757-4684 .- 1757-4676. ; 14:2
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Journal article (peer-reviewed)abstract
- Malignant pleural mesothelioma (MPM) arises from mesothelial cells lining the pleural cavity of asbestos-exposed individuals and rapidly leads to death. MPM harbors loss-of-function mutations in BAP1, NF2, CDKN2A, and TP53, but isolated deletion of these genes alone in mice does not cause MPM and mouse models of the disease are sparse. Here, we show that a proportion of human MPM harbor point mutations, copy number alterations, and overexpression of KRAS with or without TP53 changes. These are likely pathogenic, since ectopic expression of mutant KRASG12D in the pleural mesothelium of conditional mice causes epithelioid MPM and cooperates with TP53 deletion to drive a more aggressive disease form with biphasic features and pleural effusions. Murine MPM cell lines derived from these tumors carry the initiating KRASG12D lesions, secondary Bap1 alterations, and human MPM-like gene expression profiles. Moreover, they are transplantable and actionable by KRAS inhibition. Our results indicate that KRAS alterations alone or in accomplice with TP53 alterations likely play an important and underestimated role in a proportion of patients with MPM, which warrants further exploration.
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- Luces, Mario, et al.
(author)
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An Emulator-Based Prediction of Dynamic Stiffness for Redundant Parallel Kinematic Mechanisms
- 2016
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In: Journal of Mechanisms and Robotics. - : ASME International. - 1942-4310 .- 1942-4302. ; 8:2
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Journal article (peer-reviewed)abstract
- The accuracy of a parallel kinematic mechanism (PKM) is directly related to its dynamic stiffness, which in turn is configuration dependent. For PKMs with kinematic redundancy, configurations with higher stiffness can be chosen during motion-trajectory planning for optimal performance. Herein, dynamic stiffness refers to the deformation of the mechanism structure, subject to dynamic loads of changing frequency. The stiffness-optimization problem has two computational constraints: (i) calculation of the dynamic stiffness of any considered PKM configuration, at a given task-space location, and (ii) searching for the PKM configuration with the highest stiffness at this location. Due to the lack of available analytical models, herein, the former subproblem is addressed via a novel effective emulator to provide a computationally efficient approximation of the high-dimensional dynamic-stiffness function suitable for optimization. The proposed method for emulator development identifies the mechanism's structural modes in order to breakdown the high-dimensional stiffness function into multiple functions of lower dimension. Despite their computational efficiency, however, emulators approximating high-dimensional functions are often difficult to develop and implement due to the large amount of data required to train the emulator. Reducing the dimensionality of the approximation function would, thus, result in a smaller training data set. In turn, the smaller training data set can be obtained accurately via finite-element analysis (FEA). Moving least-squares (MLS) approximation is proposed herein to compute the low-dimensional functions for stiffness approximation. Via extensive simulations, some of which are described herein, it is demonstrated that the proposed emulator can predict the dynamic stiffness of a PKM at any given configuration with high accuracy and low computational expense, making it quite suitable for most high-precision applications. For example, our results show that the proposed methodology can choose configurations along given trajectories within a few percentage points of the optimal ones.
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