Mitochondrial dysfunction and oxidative stress are thought to be relevant to the pathogenesis of age-related macular degeneration (AMD). Glutathione (GSH) homeostasis fulfills a number of important roles in mitochondria, such as maintenance of mitochondrial DNA and respiratory competency of cells. Although the transport of mitochondrial GSH (mGSH) is not fully understood, increasing evidence from non-ocular tissues suggests that OGC (2-oxoglutarate carrier, SLC25A11) and DIC (dicarboxylate carrier, SLC25A10) are involved in mGSH transport. However, whether OGC and DIC mediate the transfer of GSH into the mitochondria of retinal pigment epithelial cells (RPE) remains unknown. Thus, we investigated the expression, localization, and function of OGC and DIC in human RPE (hRPE) in relation to oxidative stress and GSH. Both OGC and DIC are expressed in hRPE and are localized in mitochondria. We also found a dose and time-dependent decrease of OGC and DIC expression under oxidative stress and increased expression in polarized RPE. Our data show that the downregulation of OGC and DIC resulted in increased apoptosis and mGSH depletion which can be overcome by co-treatment with GSH-MEE. These findings suggest that overexpression of OGC and DIC may be an effective strategy to decrease susceptibility to mitochondrial toxicants by elevation of mGSH.

The internal wave recognition algorithm in an ocean data buoy system can be used to realize the real-time and flexible observation of internal waves, but there is no accurate automatic recognition method. To meet the need for automatic, real-time, and reliable internal wave recognition, an automatic internal wave recognition algorithm has been proposed for a tightly profiled intelligent buoy system. The sea profile temperature data collected by the Bailong buoy system in the Andaman Sea in 2018 were used to train and test the internal wave recognition neural network model, which consists of two parts: feature extraction and feature classification. The experiment compares the long short-term memory network (LSTM), convolutional neural network (CNN) with different layers, and deep neural network (DNN) without a feature extraction network and adjusts the number of convolutional nuclei and convolutional strides to improve the feature extraction efficiency. Experiments show that the best results can be obtained when a CNN layer is used as the feature extraction network, the convolutional step length is 4, the number of convolutional kernels is 5. The recall reaches 95.31% and the precision is 97.53%. The internal wave identification delay of the algorithm is 5.0862 minutes, the number of parameters is 1593, and the number of calculations is 3024. The algorithm can be directly deployed to the ocean data buoy system to realize the demand for automatic, real-time and reliable internal wave identification at the buoy end.