Parkinson's disease (PD) is characterized by selective and progressive degeneration of dopamine (DA)-producing neurons in the substantia nigra pars compacta (SNpc) and by abnormal aggregation of α -synuclein. Previous studies have suggested that DA can interact with α -synuclein, thus modulating the aggregation process of this protein; this interaction may account for the selective vulnerability of DA neurons in patients with PD. However, the relationship between DA and α -synuclein, and the role in progressive degeneration of DA neurons remains elusive. We have shown that in the presence of DA, recombinant human α -synuclein produces non-fibrillar, SDS-resistant oligomers, while β-sheet-rich fibril formation is inhibited. Pharmacologic elevation of the cytoplasmic DA level increased the formation of SDS-resistant oligomers in DA-producing neuronal cells. DA promoted α -synuclein oligomerization in intracellular vesicles, but not in the cytosol. Furthermore, elevation of DA levels increased secretion of α-synuclein oligomers to the extracellular space, but the secretion of monomers was not changed. DA-induced secretion of α -synuclein oligomers may contribute to the progressive loss of the dopaminergic neuronal population and the pronounced neuroinflammation observed in the SNpc in patients with PD.

Weekend sleep extension may have biological protective effects in preventing sleep-restriction induced or related obesity. The results suggest a simple population-level strategy to minimize effects of sleep loss.

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In this study, an aerostruetural analysis using a proper orthogonal decomposition vrith a neural network is proposed for accurate and efficient aerostructural wing design optimization using tbe reduced-order model. Because reducedorder-model basis weighting estimation bas a limitation in that its robustness cannot be guaranteed by various design variables and wing deformation due to fluid structure interaction, this study employs tbe neural network, which is capable of perceiving tbe relationship between tbe input variables and reduced variables for the proper orthogonal decomposition to complement tbe defects. To construct tbe proper orthogonal decomposition with a neural network, tbe neural network is learned using pairs of design variables and reduced variables from snapsbot data obtained from tbe aerostructural analysis. Because the proposed aerostructural analysis using a proper orthogonal decomposition with a neural network is applied to validation cases and its results are compared to those of the full-order analysis, it is investigated that tbe proposed analysis algorithm has tbe capability to accurately and efficiently predict tbe aerodynamic and structural performances of wings that are considered about wing deformation. Furthermore, because tbe design optimization problem minimizing tiie weigbt of a wing design is performed with tbe analysis algorithm, it is confirmed that it can be a more efficient design than a conventional design method using a second-order polynomial model, wbicb consists of a greater number of experiment designs than the number of snapshots.'^spnng L/D Nomenclature = weights in a neural network model = spatial correlation matrix = force generated to springs = vector of the hidden nodes = actual snapshot = spring stiffness coefficient = lift-to-drag ratio