5,073 research outputs found

    Aging-associated Alteration in the Cardiac MIF-AMPK Cascade in Response to Ischemic Stress

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    An important role for a macrophage migration inhibitory factor (MIF)-AMP-activated protein kinase (AMPK) signaling pathway in ameliorating myocardial damage following ischemia/reperfusion has been described. An aging-associated reduction in AMPK activity may be associated with a decline in the ability of cardiac cells to activate the MIF-AMPK cascade, thereby resulting in reduced tolerance to ischemic insults. To test this hypothesis, _in vivo_ regional ischemia was induced by occlusion of the left anterior descending (LAD) coronary artery in young (4-6 months) and aged (24-26 months) mice. The ischemic AMPK activation response was impaired in aged hearts compared to young ones (p<0.01). Notably, cardiac MIF expression in aged hearts was lower than in young hearts (p<0.01). Dual staining data clearly demonstrated larger infarct size in aged hearts following ischemia and reperfusion compared to young hearts (p<0.05). Ischemia-induced AMPK activation in MIF knock out (MIF KO) hearts was blunted, leading to greater contractile dysfunction of MIF KO cardiomyocytes during hypoxia than that of wild type (WT) cardiomyocytes. Finally exogenous recombinant MIF significantly reversed the contractile dysfunction of aged cardiomyocytes in response to hypoxia. We conclude that an aging-associated reduction in ischemic AMPK activation contributes to ischemic intolerance in aged hearts

    Fasciolopsis buski (Digenea: Fasciolidae) from China and India may represent distinct taxa based on mitochondrial and nuclear ribosomal DNA sequences

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    Sequences of primers used to amplify fragments of Fasciolopsis buski mitochondrial genome. (DOCX 17 kb

    Improving Outfit Recommendation with Co-supervision of Fashion Generation

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    The task of fashion recommendation includes two main challenges: visual understanding and visual matching. Visual understanding aims to extract effective visual features. Visual matching aims to model a human notion of compatibility to compute a match between fashion items. Most previous studies rely on recommendation loss alone to guide visual understanding and matching. Although the features captured by these methods describe basic characteristics (e.g., color, texture, shape) of the input items, they are not directly related to the visual signals of the output items (to be recommended). This is problematic because the aesthetic characteristics (e.g., style, design), based on which we can directly infer the output items, are lacking. Features are learned under the recommendation loss alone, where the supervision signal is simply whether the given two items are matched or not. To address this problem, we propose a neural co-supervision learning framework, called the FAshion Recommendation Machine (FARM). FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information. FARM enhances visual matching by introducing a novel layer-to-layer matching mechanism to fuse aesthetic information more effectively, and meanwhile avoiding paying too much attention to the generation quality and ignoring the recommendation performance. Extensive experiments on two publicly available datasets show that FARM outperforms state-of-the-art models on outfit recommendation, in terms of AUC and MRR. Detailed analyses of generated and recommended items demonstrate that FARM can encode better features and generate high quality images as references to improve recommendation performance
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