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Molecular mechanisms underlying the promotion of wound repair by coenzyme Q10: PI3K/Akt signal activation via alterations to cell membrane domains
Get PDF鳥取大学博士(医学
Analysis and Design of Active Cobalt Species on MFI Zeolite for Catalytic Methylation of Benzene with Methane
Get PDF鳥取大学博士(工学
Alterations in the omics profiles in mevalonate pathway-inhibited cancer cells
Get PDFAims: Statins, cholesterol-lowering drugs, are potential therapeutic agents for inhibiting cancer proliferation. However, the mechanisms that mediate the effects of statins, the homeostatic responses of tumor cells to statin therapy, and the modes underlying the antitumor effects of statins remain unclear. Main methods: To uncover the effects of statins on cancer cells in vitro, we performed transcriptome and metabolome analyses on atorvastatin-treated statin-resistant and statin-sensitive lung cancer cells. Key findings: The results of Gene Ontology terms and pathway enrichment analyses showed that after 24 h of atorvastatin treatment, the expression of cell cycle- and DNA replication-related genes was significantly decreased in the statin-sensitive cancer cells. The results of metabolome analysis showed that the components of polyamine metabolism and purine metabolism, glycolysis, and pentose phosphate pathway were decreased in the statin-sensitive cancer cells. Significance: Differences in cellular properties between statin-sensitive and statin-resistant cancer cells revealed additional candidates for therapeutic targets in statin-treated cancer cells and suggested that inhibiting these metabolic pathways could improve efficacy. In conclusion, combining statins with inhibitors of polyamine metabolism (cell proliferation and protein translation), purine metabolism (DNA synthesis), glycolytic system (energy production), and pentose phosphate pathway (antioxidant stress) might enhance the anticancer effects of statins
Gender Recognition Using a Gaze-Guided Self-Attention Mechanism Robust Against Background Bias in Training Samples
Get PDFWe propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples
