Browsing by Author "Lin, Wei-Yu"

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Comparative microarray analyses of mono(2-ethylhexyl)phthalate impacts on fat cell bioenergetics and adipokine network
(2017-12-01)
Chiang, Huai-Chih
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Wang, Chih-Hong
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Yeh, Szu-Ching
;
Lin, Yi-Hua
;
Kuo, Ya-Ting
Cellular accumulation of mono(2-ethylhexyl)phthalate (MEHP) has been recently demonstrated to disturb fat cell energy metabolism; however, the underlying mechanism remained unclear. The study aimed to determine how MEHP influenced fat cell transcriptome and how the changes might contribute to bioenergetics. Because of the pivotal role of PPAR gamma in energy metabolism of fat cells, comparative microarray analysis of gene expression in 3T3-L1 adipocytes treated with both MEHP and rosiglitazone was performed. Pathway enrichment analysis and gene ontology (GO) enrichment analysis revealed that both treatments caused up-regulation of genes involved in PPAR signaling/energy metabolism-related pathways and down-regulation of genes related to adipokine/inflammation signals. MEHP/rosiglitazone-treated adipocytes exhibited increased levels of lipolysis, glucose uptake, and glycolysis; the gene expression profiles provided molecular basis for the functional changes. Moreover, MEHP was shown to induce nuclear translocation and activation of PPAR gamma. The similarity in gene expression and functional changes in response to MEHP and rosiglitazone suggested that MEHP influenced bioenergetics and adipokine network mainly via PPAR gamma. Importantly, adipokine levels in C57BL/6J mice with di(2-ethylhexyl)phthalate (DEHP) treatments provided in vivo evidence for microarray results. On the basis of correlation between gene expression and functional assays, possible involvements of genes in bioenergetics of MEHP-treated adipocytes were proposed.
DEEP LEARNING-BASED OBSTACLE DETECTION AND DEPTH ESTIMATION
(2019-01-01)
Hsieh, Yi-Yu
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Lin, Wei-Yu
;
Li, Dong-Lin
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Chuang, Jen-Hui
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資訊工程學系
This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains. Experimental results show that the proposed model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset, while the unified approach can actually improve both (i) and (ii) at the same time.