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Upon certain stimuli, microglia undergo different degrees of transformation in order to maintain homeostasis of the CNS. However, chronic microglia activation has been suggested to play an active role in the pathogenesis of neurodegenerative diseases. The density of microglia and the degree of microglia activation vary among brain regions; such differences may underlie the brain region-specific characteristics of neurodegenerative diseases. In this study, we aim to characterize the temporal and spatial profiles of microglia activation induced by peripheral inflammation in male C57BL/6J mice. Our results showed that, on average, microglia densities were highest in the cortex, followed by the limbic area, basal nuclei, diencephalon, brainstem and cerebellum. Among the 22 examined brain nuclei/regions, the substantia nigra had the highest microglia density. Microglia morphological changes were evident within 3 h after a single intraperitoneal lipopolysaccharides injection, with the highest degree of changes also in the substantia nigra. The lipopolysaccharide-induced microglia activation, determined by maximal cell size, was positively correlated with density of microglia and levels of TNFα receptor 1; it was not correlated with original microglia cell size or integrity of blood-brain barrier. The differential response of microglia also cannot be explained by different types of neurotransmitters. Our works suggest that the high density of microglia and the high levels of TNFα receptor 1 in the substantia nigra make this brain region the most susceptible area to systemic immunological insults.

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The design of an adaptive load-shedding strategy by executing an artificial neural network (ANN) and transient stability analysis for an electric utility system is presented. To prepare the training data set for an ANN, the transient stability analysis of an actual power system has been performed to solve for minimum load shedding with various operation scenarios without causing the tripping problem of generators. The Levenberg-Marquardt algorithm has been adopted and incorporated into the back-propagation learning algorithm for training feedforward neural networks. By selecting the total power generation, total load demand and frequency decay rate as the input neurons of the ANN, the minimum amount of load shedding is determined to maintain the stability of power systems. To demonstrate the effectiveness of the proposed ANN minimum load-shedding scheme, a utility power system has been selected for computer simulation and the amount of load shedding is verified by stability analysis.

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Cobalt nitride thin films could be prepared by employing a direct current reactive sputtering deposition on (100) silicon substrates in mixtures of fixed Ar (4×10−1 Pa) and N2 at various partial pressures. The CoxN thin films could be tailored by appropriately controlling the partial pressure of the reactive nitrogen. With adequately increasing nitrogen to argon partial pressure, a series of sequence phase formation from α-Co, Co4N, Co3N, Co2N, and CoN could be observed. The phase transition sequence was accompanied by a substantial refinement and improvement of the films’ grain structure. Rapid thermal annealing of cobalt nitride thin films exhibited a stepwise decomposition via the dissociating of Co4N→Co3N+β-Co(N), Co3N→Co2N+β-Co(N), and Co2N→CoN+β-Co(N) with increasing the elevated temperature. Phase formation, thermal decomposition, electrical resistivity, and microstructure of reactive sputtered cobalt nitride films were discussed in this study.

Atomic diffusion bonding of wafers with thin nanocrystalline metal films J. Vac. Sci. Technol. B 28, 706 (2010); 10.1116/1.3437515 Thermal conductivity measurement and interface thermal resistance estimation using Si O 2 thin film Rev. Sci. Instrum. 79, 054902 (2008); 10.1063/1.2927253Thermal wave studies of thin metal films and structures AIP Conf.