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Shah, S A,Yoon, G H,Chung, S S,Abid, M N,Kim, T H,Lee, H Y,Kim, M O Nature Publishing Group 2017 Molecular psychiatry Vol.22 No.3
<P>Extensive evidence has indicated that a high rate of cholesterol biogenesis and abnormal neuronal energy metabolism play key roles in Alzheimer's disease (AD) pathogenesis. Here, for we believe the first time, we used osmotin, a plant protein homolog of mammalian adiponectin, to determine its therapeutic efficacy in different AD models. Our results reveal that osmotin treatment modulated adiponectin receptor 1 (AdipoR1), significantly induced AMP-activated protein kinase (AMPK)/Sirtuin 1 (SIRT1) activation and reduced SREBP2 (sterol regulatory element-binding protein 2) expression in both <I>in vitro</I> and <I>in vivo</I> AD models and in Adipo<SUP>−/−</SUP> mice. Via the AdipoR1/AMPK/SIRT1/SREBP2 signaling pathway, osmotin significantly diminished amyloidogenic Aβ production, abundance and aggregation, accompanied by improved pre- and post-synaptic dysfunction, cognitive impairment, memory deficits and, most importantly, reversed the suppression of long-term potentiation in AD mice. Interestingly, AdipoR1, AMPK and SIRT1 silencing not only abolished osmotin capability but also further enhanced AD pathology by increasing SREBP2, amyloid precursor protein (APP) and β-secretase (BACE1) expression and the levels of toxic Aβ production. However, the opposite was true for SREBP2 when silenced using small interfering RNA in APPswe/ind-transfected SH-SY5Y cells. Similarly, osmotin treatment also enhanced the non-amyloidogenic pathway by activating the α-secretase gene that is, <I>ADAM10</I>, in an AMPK/SIRT1-dependent manner. These results suggest that osmotin or osmotin-based therapeutic agents might be potential candidates for AD treatment.</P>
Prediction of compressive strength of concrete using neural networks
Yousef A. Al-Salloum,Abid A. Shah,Saleh H. Alsayed,Tarek H. Almusallam,M.S. Al-Haddad,H. Abbas 사단법인 한국계산역학회 2012 Computers and Concrete, An International Journal Vol.10 No.2
This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.