Titlebook: Recent Trends in Materials and Devices; Proceedings of ICRTM Vinod Kumar Jain,Sunita Rattan,Abhishek Verma Conference proceedings 2020 The

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reatment. In a second experiment, relevant carbohydrate metabolites, amino acid levels and selected carbohydrate metabolizing enzyme activities were determined in hepatic tumors from rats which had received phenobarbital as the promoting drug. Serine dehydratase and glucose 6-phosphatase were strong

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Virender Sharma,C. V. Kannan,Yash Pal,Hrishikesh Dhasmana,Amit Kumar,Abhishek Verma,V. K. Jainligand and shunted to the lysosomes where both the receptor and its ligand appears to be degraded. A minor fraction of the ligand is secreted to the bile. The main findings of the present study are that:.The expression of IGF-I is lower than normal in nodules and carcinomas. IGF-II appears in the ma

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Safiya Nisar,Sonal Chauhan,Sunita Rattan first argument for the implication of a change in Ca. Channels in hypertension is the blood pressure-lowering effect of dihydropyridine Ca. antagonists (MacGregor et al. 1982; Kazda and Knorr 1990). These antagonists have also been shown to suppress the myogenic active tone displayed by blood vesse

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Anil Kumar,Amit Kumar,Abhishek Verma,D. P. Singh,Harish Mudila,Deepak Kumar,Ritu Srivastavahe same review criteria. The labs represented scientific challenges based on new data setsand real world problems in multimodal and multilingual information access. In addition to this, 10 benchmarking labs reported results of their yearlong activities in overview talks and lab sessions. The papers

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Omita Nanda,Jampana Gayathri,A. M. Biradar,Kanchan Saxenahe same review criteria. The labs represented scientific challenges based on new data setsand real world problems in multimodal and multilingual information access. In addition to this, 10 benchmarking labs reported results of their yearlong activities in overview talks and lab sessions. The papers

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Heena Yadav,Subhra Das,Sudip Majumderualizing the learned attention maps. To our knowledge, this is the first study to venture into such dependencies modeling and interpret the respective neural net for plant classification. Finally, we show that our proposed method outperforms the conventional CNN approach on the PlantClef2015 benchma

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