We previously reported that treatment with thiazolidinediones (TZDs), such as troglitazone (Tro), downregulates the protein levels of peroxisome proliferator-activated receptor gamma (PPARγ), with enhanced lipid accumulation during 3T3-L1 murine adipocyte differentiation in the presence of 3-isobutyl methylxanthine, dexamethasone, and insulin (MDI). In this study, we performed DNA microarray analysis to compare the gene expression profiles of MDI-induced and MDI/Tro-induced 3T3-L1 adipocytes to elucidate the mechanism underlying the reduction in PPARγ protein expression by Tro treatment. Apoptotic process genes of Gene Ontology were selected from the upregulated genes in MDI/Tro-induced cells and analyzed using real-time RT-PCR and western blotting. For several proteins, higher expression was detected in MDI/Tro-treated 3T3-L1 cells than in MDI-treated cells. Plasmid expression analysis using 293T cells revealed that the expression of cell death-inducing DFFA-like effector C (Cidec) or Cbp/P300-interacting transactivator with Glu/Asp-rich carboxy-terminal domain 1 (Cited1) reduced PPARγ protein expression compared with the vector control. When 3T3-L1 preadipocytes transfected with small interfering RNA targeting Cidec or Cited1 were differentiated in response to MDI or MDI/Tro treatment, the reduction in PPARγ expression in MDI/Tro-treated 3T3-L1 adipocytes was partially suppressed. Our findings indicate that the expression of PPARγ protein is regulated in part by the induction of Cidec and Cited1 in MDI/Tro-treated 3T3-L1 adipocytes.

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For effective super-resolution processing of video images, video images should not be processed as frame-by-frame twodimensional information, but as spatio-temporal information, including information in the time axis direction. Most of the proposed video super-resolution processing based on deep learning uses 2D convolutional neural networks (CNNs). Therefore, the system is based on the 2D CNNs with the additional processing related to change from frame to frame. Instead of processing video images separately in space and time, comprehensive processing as spatio-temporal information can be expected to be more flexible and effective. In this research, we propose a video super-resolution process that processes spatio-temporal information by 3D-CNNs and GAN (Generative adversarial networks). By using 3D-CNNs, the configuration does not require motion alignment as preprocessing. Since the essential purpose of the super-resolution process is to predict missing high-frequency components, we added a process that directly predicts the difference between the high-resolution image and the corresponding bicubic interpolated low-resolution image.

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