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Titlebook: Methods in Chemical Ecology Volume 2; Bioassay Methods Kenneth F. Haynes,Jocelyn G. Millar Book 1998 Springer Science+Business Media New Yo

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書目名稱Methods in Chemical Ecology Volume 2
副標(biāo)題Bioassay Methods
編輯Kenneth F. Haynes,Jocelyn G. Millar
視頻videohttp://file.papertrans.cn/633/632280/632280.mp4
圖書封面Titlebook: Methods in Chemical Ecology Volume 2; Bioassay Methods Kenneth F. Haynes,Jocelyn G. Millar Book 1998 Springer Science+Business Media New Yo
描述Identification of chemicals that affect the naturally occurring interactions be- tween organisms requires sophisticated chemical techniques, such as those docu- mented in volume 1, in combination with effective bioassays. Without an effective bioassay, the identification becomes akin to looking for a needle in a haystack, but without any idea of what a needle looks like. To a large extent serniochemical identifications must be driven by bioassays. The design of bioassays for use in chemical ecology is governed by the sometimes conflicting objectives of ecological relevance and the need for simplic- ity. Bioassay design should be based on observations of the interactions between organisms in their natural context, a theme that appears throughout this volume. As a result, this volume is as much about ecology and behavior as it is about specific methods. It is impossible to design a relevant bioassay, whether it is simple or complex, without understanding at least the fundamentals of how chemical cues or signals mediate the interaction in nature. Thus, the development of bioassay methods must be driven by an understanding of ecology and a knowledge of the natural history of the organi
出版日期Book 1998
關(guān)鍵詞Allelopathy; biology; ecology; environment; microorganism
版次1
doihttps://doi.org/10.1007/978-1-4615-5411-0
isbn_softcover978-1-4613-7471-8
isbn_ebook978-1-4615-5411-0
copyrightSpringer Science+Business Media New York 1998
The information of publication is updating

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Kelly M. Jenkins,Paul R. Jensen,William Fenicalent called from it is not tested. In this chapter, we propose software reliability growth models based on such components‘ characteristics. Especially, these models reflecting the different testing-environment for composed components are formulated by nonhomogeneous Poisson processes. Furthermore, u
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Mark E. Hay,John J. Stachowicz,Edwin Cruz-Rivera,Stephan Bullard,Michael S. Deal,Niels Lindquistch as crack growth, vibration, corrosion, wear and lubricant condition. With the measured data, the predictive maintenance policy determines the optimum threshold level at which maintenance action is performed to bring the system to a “better” condition, if not as good as new, in order to maximize s
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J. Daniel Harelly, the role of the amine neuromodulatory systems (dopamine, serotonin, norepinephrine, and acetylcholine) in impulsive decision-making and reinforcement learning processes is discussed. Ultimately, the integration of reinforcement learning algorithms with sophisticated behavioral and neuroscience
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