To elucidate the transcriptomic changes of long noncoding RNAs (lncRNAs) in high-fat diet (HFD)-fed mice, we defined their hepatic transcriptome by RNA sequencing. Aberrant expression of 37 representative lncRNAs and 254 protein-coding RNAs was observed in the livers of HFD-fed mice with insulin resistance compared with the livers from control mice. Of these, 24 lncRNAs and 179 protein-coding RNAs were upregulated, whereas 13 lncRNAs and 75 protein-coding RNAs were downregulated. Functional analyses showed that the aberrantly expressed protein-coding RNAs were enriched in various lipid metabolic processes and in the insulin signaling pathway. Genomic juxtaposition and coexpression patterns identified six pairs of aberrantly expressed lncRNAs and protein-coding genes, consisting of five lncRNAs and five protein-coding genes. Four of these protein-coding genes are targeted genes upregulated by PPARα. As expected, the corresponding lncRNAs were significantly elevated in AML12 cells treated with palmitic acid or the PPARα agonist, WY14643. In Hepa1-6 cells, knockdown of NONMMUG027912 increased the cellular cholesterol level, the expression of cholesterol biosynthesis genes and proteins, and the HMG-CoA reductase activity. This genome-wide profiling of lncRNAs in HFD-fed mice reveals one lncRNA, NONMMUG027912, which is potentially regulated by PPARα and is implicated in the process of cholesterol biosynthesis.
Abstract-We introduce an approach for enabling samplingbased planners to compute motions with humanlike appearance. The proposed method is based on a space of blendable example motions collected by motion capture. This space is explored by a sampling-based planner that is able to produce motions around obstacles while keeping solutions similar to the original examples. The results therefore largely maintain the humanlike characteristics observed in the example motions. The method is applied to generic upper-body actions and is complemented by a locomotion planner that searches for suitable body placements for executing upper-body actions successfully. As a result, our overall multi-modal planning method is able to automatically coordinate whole-body motions for action execution among obstacles, and the produced motions remain similar to example motions given as input to the system.
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