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Previous nutritional studies have shown that insulin regulation is different between DT and A strains of gibel carp. As leptin plays a pivotal role in the effects of insulin, we hypothesized that leptin regulation of glucose and lipid metabolism would differ between the two strains. To test our hypothesis, recombinant human leptin was injected into two strains. The results showed that leptin activated PI3K-AKT, AMPK-ACC, and JAK2-STAT signaling pathways in both strains. Hypoglycemia induced by leptin might be due to higher glucose uptake by the liver and muscles together with enhanced glycolytic potential and reduced gluconeogenic potential. Decreased lipogenesis and up-regulated fatty acid oxidation were induced by leptin. In terms of genotype, the PI3K-AKT signaling pathway was more strongly activated by leptin in the muscle tissue of the A strain, as reflected by the heightened phosphorylation of AKT. Furthermore, glycogen content, glycolytic enzyme activity, and gluconeogenic capability were higher in the A strain than the DT strain. Strain A had higher levels of fatty acid synthesis and lipolytic capacity in the liver than the DT strain, but the opposite was true in white muscle. Regarding leptin-genotype interactions, the DT strain displayed stronger regulation of glucose metabolism in the liver by leptin as compared to the A strain. Moreover, a more active JAK2-STAT signaling pathway accompanied by enhanced inhibition of fatty acid synthesis by leptin was observed in the DT strain. Overall, the regulation of glucose and lipid metabolism by leptin differed between the two strains, as expected.
Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures.
Do you have the tools to address recent challenges and problems in modern computer networks? Discover a unified view of auction theoretic applications and develop auction models, solution concepts, and algorithms with this multidisciplinary review. Devise distributed, dynamic, and adaptive algorithms for ensuring robust network operation over time-varying and heterogeneous environments, and for optimizing decisions about services, resource allocation, and usage of all network entities. Topics including cloud networking models, MIMO, mmWave communications, 5G, data aggregation, task allocation, user association, interference management, wireless caching, mobile data offloading, and security. Introducing fundamental concepts from an engineering perspective and describing a wide range of state-of-the-art techniques, this is an excellent resource for graduate and senior undergraduate students, network and software engineers, economists, and researchers.