No abstract

Parkinson's disease (PD) is an age-associated neurodegenerative disorder characterized by the death of dopaminergic neurons in the substantia nigra pars compacta. Activation of 5′-adenosine monophosphate-activated protein kinase (AMPK) has been suggested to be associated with PD pathogenesis. The aim of the present study was to investigate the effects of the aberrant expression of microRNA-185 (miR-185) in PD. A 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced in vitro model of PD was generated using the human SH-SY5Y dopaminergic neuroblastoma cell line, in order to examine the potential molecular mechanisms underlying the roles of miR-185 in PD. miR-185 expression was assessed in MPTP-treated SH-SY5Y cells using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In addition, MPTP-treated SH-SY5Y cells were transfected with a miR-185 mimic or scramble miRNA, and flow cytometry was used to evaluate the level of cellular apoptosis. The expression of autophagy markers, including Beclin 1, microtubule-associated protein light chain 3 (LC3) I and LC3II, as well as key molecules involved in the AMPK/mechanistic target of rapamycin (mTOR) signaling pathway, such as phosphorylated (p)-AMPK and p-mTOR, was examined using RT-qPCR and western blot analyses. In addition, SH-SY5Y cells were treated with the AMPK inhibitor, Compound C, and its effects on cellular apoptosis were assessed. The results demonstrated that miR-185 was significantly downregulated in SH-SY5Y cells treated with MPTP at concentrations of >100 µM when compared with untreated controls. Following transfection with a miR-185 mimic, miR-185 expression in SH-SY5Y cells was significantly increased when compared with blank control cells. Notably, miR-185 overexpression was revealed to significantly reduce the MPTP-induced increase in cellular apoptosis. In addition, the expression levels of Beclin 1, LC3I/II, p-AMPK and p-mTOR were significantly upregulated in MPTP-treated SH-SY5Y cells; whereas miR-185 overexpression significantly downregulated the expression of these factors. Furthermore, miR-185 overexpression significantly suppressed apoptosis of SH-SY5Y cells treated with MPTP plus Compound C when compared with the Compound C group. In conclusion, the results of the present study suggest that overexpression of miR-185 may inhibit autophagy and apoptosis of dopaminergic cells in PD potentially via regulation of the AMPK/mTOR signaling pathway. Therefore, AMPK/mTOR-mediated autophagy and apoptotic signaling pathways may be potential novel therapeutic targets for the development of alternative strategies for the treatment of patients with PD.

In marine exploration seismology, surface-related multiples are usually treated as noise mainly because subsequent processing steps, such as migration velocity analysis and imaging, require multiple-free data. Failure to remove these wavefield components from the data may lead to erroneous estimates for migration velocity or result in strong coherent artifacts that interfere with the imaged reflectors. However, multiples can carry complementary information compared to primaries, as they interact with the free surface and are therefore exposed more to the subsurface. Recent work has shown that when processed correctly multiples can improve seismic illumination. Given a sufficiently accurate background velocity model and an estimate for the source signature, we propose a new and computationally efficient two-way wave-equation based linearized inversion procedure that produces accurate images of the subsurface from the total upgoing wavefield including surface-related multiples. Modelling of the surface-related multiples in the proposed method derives from the well-known surface-related multiple elimination method. We incur a minimal overhead from incorporating the multiples by having the wave-equation solver carry out 2 Tu and Herrmann the multiple predictions via the inclusion of an areal source instead of expensive dense matrix-matrix multiplications. By using subsampling techniques, we obtain high-quality true-amplitude least-squares migrated images at computational costs of roughly a single reverse-time migration with all the data. These images are virtually free of coherent artifacts from multiples. Proper inversion of the multiples would be computationally infeasible without using these techniques that significantly brings down the cost. By promoting sparsity in the curvelet domain and using rerandomization, out method gains improved robustness to errors in the background velocity model, and errors incurred in the linearization of the wave-equation with respect to the model. We demonstrate the superior performance of the proposed method compared to the conventional reverse-time migration using realistic synthetic examples.

Seismic trace interpolation is an important technique because irregular or insufficient sampling data along the spatial direction may lead to inevitable errors in multiple suppression, imaging, and inversion. Many interpolation methods have been studied for irregularly sampled data. Inspired by the working idea of the autoencoder and convolutional neural network, we have performed seismic trace interpolation by using the convolutional autoencoder (CAE). The irregularly sampled data are taken as corrupted data. By using a training data set including pairs of the corrupted and complete data, CAE can automatically learn to extract features from the corrupted data and reconstruct the complete data from the extracted features. It can avoid some assumptions in the traditional trace interpolation method such as the linearity of events, low-rankness, or sparsity. In addition, once the CAE network training is completed, the corrupted seismic data can be interpolated immediately with very low computational cost. A CAE network composed of three convolutional layers and three deconvolutional layers is designed to explore the capabilities of CAE-based seismic trace interpolation for an irregularly sampled data set. To solve the problem of rare complete shot gathers in field data applications, the trained network on synthetic data is used as an initialization of the network training on field data, called the transfer learning strategy. Experiments on synthetic and field data sets indicate the validity and flexibility of the trained CAE. Compared with the curvelet-transform-based method, CAE can lead to comparable or better interpolation performances efficiently. The transfer learning strategy enhances the training efficiency on field data and improves the interpolation performance of CAE with limited training data.

Human immunodeficiency virus (HIV) infection significantly affect neurodevelopmental and behavioral outcomes. We investigated whether alterations of gray matter organization and structural covariance networks with vertical HIV infection adolescents exist, by using the GAT toolbox. MRI data were analysed from 25 HIV vertically infected adolescents and 33 HIV-exposed-uninfected control participants. The gray matter volume (GMV) was calculated, and structural brain networks were reconstructed from gray matter co-variance. Gray matter losses were pronounced in anterior cingulate cortex (ACC), right pallidum, right occipital lobe, inferior parietal lobe, and bilateral cerebellum crus. The global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in frontal, temporal, basal ganglia, cerebellum, and temporal lobes. Brain hubs in the HIV-infected subjects increased in number and tended to shift to sensorimotor and temporal areas. In the HIV-infected subjects, decreased GMVs in ACC and bilateral cerebellum were related to lower Mini-Mental State Examination scores; the CD4 counts were positively related to the GMVs in ACC and sensorimotor areas. These findings suggest that focally reduced gray matter, disrupted nodal profiles of structural wirings, and a shift in hub distribution may represent neuroanatomical biomarkers of HIV infection on the developing brain.