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您的检索式：作者名="Rajesh AMIN"
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| 1 | Adiponectin downregulation is associated with volume overload-induced myocyte dysfunction in rats显示文摘目的: Adiponectin 被报导了在在体积的病理学的室的改变,而是 adiponectin 的角色期间施加保护的效果导致超载的心失败遗体不清楚。在这研究,我们在心脏的 myocyte 上调查了 adiponectin 的效果在 rats.Methods 的可收缩的机能障碍追随者体积超载: 体积超载被 infrarenal 主动脉静脉 cava 通过手术在老鼠导致管。老鼠是在 2- 的静脉内地管理的 adenoviral adiponectin, 6 星期、 9 星期的后面的管。adiponectin, adiponectin 受体(AdipoR1/R2 和 T-cadherin ) 和 AMPK 活动的蛋白质表示用西方的污点分析被测量。孤立的室的 myocytes 在管以后的 12 个星期被准备检验 myocytes 和细胞内部的 Ca 2+ transient.Results 的可收缩的表演: A-V 管在浆液和心肌的 adiponectin 层次导致了重要减小,心肌的 adiponectin 受体(AdipoR1/R2 和 T-cadherin ) 层次,以及心肌的 AMPK 活动。与这些变化一致,孤立的 myocytes 在房间弄短和细胞内部的 Ca 2+ 展出了重要消沉短暂。adenoviral adiponectin 的管理显著地增加了浆液 adiponectin 层次并且阻止了 myocyte 在管老鼠的可收缩的机能障碍。而且,有 recombinant adiponectin 的孤立的 myocytes 的预告的处理(2.5 μ g/mL ) 显著地在管老鼠改进了他们的可收缩的性能,但是没在控制或 adenoviral 有效果管理 adiponectin 的 rats.Conclusion : 这些结果表明在 adiponectin downregulation 和体积之间的积极关联导致超载的室的改变。Adiponectin 在体积起一个保护的作用导致超载的心失败。 | Li-li WANG Dori MILLER Desiree WANDERS Gayani NANAYAKKARA Rajesh AMIN Robert JUDD Edward E MORRI-SON Ju-ming ZHONG | 2016 | Acta Pharmacologica Sinica2016，37，2： | 4 |
| 2 | Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques显示文摘Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry. | Abdus Saboor Arif Hussain Bless Lord Y。Agbley Amin ul Haq Jian Ping Li Rajesh Kumar | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
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