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Titlebook: Smooth Muscle; Edwin E. Daniel,David M. Paton Book 1975 Springer Science+Business Media New York 1975 biochemistry.chemistry.drug.physiolo

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發(fā)表于 2025-3-21 19:18:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Smooth Muscle
編輯Edwin E. Daniel,David M. Paton
視頻videohttp://file.papertrans.cn/870/869154/869154.mp4
圖書封面Titlebook: Smooth Muscle;  Edwin E. Daniel,David M. Paton Book 1975 Springer Science+Business Media New York 1975 biochemistry.chemistry.drug.physiolo
描述The study of the actions of drugs on smooth muscle has been a preoccupation of many pharmacologists almost from the beginning of the discipline. To a con- siderable degree, the development of theories to explain drug actions on smooth muscle has occurred somewhat independently of the development of our knowledge of the physiology, biochemistry, and biophysics of smooth muscle. This knowledge has developed rapidly in the past decade, and some of its consequences for our understanding of drug-receptor interactions in smooth muscle have not always been fully appreciated or accepted. One of the purposes of this volume is to provide pharmacologists with some understanding of the physiology, biophysics, and bio- chemistry of smooth muscle and of related advances in methodology so as to facilitate the incorporation of such knowledge and related methods into future pharmacological studies of smooth muscle and drug interactions. Another purpose of the book is to provide both graduate students and in- vestigators in pharmacology and related disciplines with a summary of the numerous methods that have evolved or are available for the study of drug and smooth muscle interactions, and, in parti
出版日期Book 1975
關(guān)鍵詞biochemistry; chemistry; drug; physiology; smooth muscle
版次1
doihttps://doi.org/10.1007/978-1-4684-2751-6
isbn_softcover978-1-4684-2753-0
isbn_ebook978-1-4684-2751-6
copyrightSpringer Science+Business Media New York 1975
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Ruth M. Henderson network (WSN) technology can be used in the geological disaster remote monitoring system by deploying sensor nodes in the mountainous area, the difficulties faced in long-term and large-area monitoring, such as limited battery energy of sensor nodes and high energy consumption of long-distance tran
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Lucas S. Van Orden IIItools leads to reduce the efficiency of CPU or GPU. Meanwhile, they give up active defense and increase the security risk. Unfortunately, the existing intrusion detection systems seldom adjust the defense policy according to the host’s performance and the time when the attack might occur. Thus, diff
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G. Burnstock attack detection method based on machine learning. A large-scale network internal attack detection model is constructed to analyze the degree of network internal attack. The model is trained by machine learning method to improve the convergence speed of model detection. After determining the attack
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