| 000 | 07238nam a2200685 i 4500 | ||
|---|---|---|---|
| 001 | 6813134 | ||
| 003 | IEEE | ||
| 005 | 20200413152907.0 | ||
| 007 | cr cn |||m|||a | ||
| 008 | 121120s2012 caua foab 000 0 eng d | ||
| 020 | _a9781598296365 (electronic bk.) | ||
| 020 | _z9781598296358 (pbk.) | ||
| 024 | 7 |
_a10.2200/S00446ED1V01Y201208BME043 _2doi |
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| 035 | _a(OCoLC)819330748 | ||
| 035 | _a(CaBNVSL)swl00401679 | ||
| 040 |
_aCaBNVSL _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQH527 _b.M243 2012 |
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| 082 | 0 | 4 |
_a612.022 _223 |
| 100 | 1 |
_aMcEachron, D. L. _q(Donald L.) |
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| 245 | 1 | 0 |
_aChronobioengineering _h[electronic resource] : _bintroduction to biological rhythms with applications. _nVolume 1 / _cDonald McEachron. |
| 260 |
_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _cc2012. |
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| 300 |
_a1 electronic text (xxiii, 262 p.) : _bill., digital file. |
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| 490 | 1 |
_aSynthesis lectures on biomedical engineering, _x1930-0336 ; _v# 43 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references. | ||
| 505 | 0 | _aPreface -- How to use this book -- Acknowledgments -- | |
| 505 | 8 | _a1. Time and time again -- 1.1 Timing is everything -- 1.2 An introduction to biological time -- 1.3 A preliminary case study -- 1.4 Why biological rhythms? -- 1.4.1 Temporal adaptation and the early bird -- 1.5 A conceptual model -- 1.6 A dynamic model -- 1.7 Rhythms and evolution -- 1.8 Chapter review -- References -- | |
| 505 | 8 | _a2. Walking on air: an empirical proof-of-concept -- 2.1 On the nature of scientific evidence -- 2.2 The evidence -- 2.2.1 On the nature of evolutionary processes -- 2.2.2 Observations on human subjects -- 2.3 Summary and conclusion for proposition 2.1 -- 2.4 Summary and conclusion for proposition 2.2 -- 2.4.1 Question 1. What is meant by the term "deleterious"? -- 2.4.2 Question 2. What is the nature of the evidence? -- 2.4.3 Question 3. What is the underlying nature of daily rhythm? -- 2.5 Chapter review -- References -- | |
| 505 | 8 | _a3. Clocktech, part 1 -- 3.1 Evolution of a mechanism -- 3.1.1 Environmentally driven rhythms -- 3.1.2 Are environmentally driven rhythms sufficient? -- 3.1.3 Limitations with environmentally driven rhythms and a new model -- 3.1.4 Multiple rhythms and phase relationships -- 3.1.5 Reaction vs. prediction in ecosystems and the new model -- 3.2 Chapter review -- References -- | |
| 505 | 8 | _a4. Clocktech II from external to internal timers -- 4.1 The cost and benefits of environmental drivers -- 4.2 A new dawn, internalizing the timers -- 4.3 Endogenous rhythms and biological clocks -- 4.3.1 Excitable tissue -- 4.4 A brief review -- 4.5 From rhythmic to autorhythmic: creating endogenous oscillators -- 4.6 Another brief review -- 4.7 Synchronizing endogenous rhythms to environmental cycles -- 4.8 The final analysis -- 4.9 Chapter review -- References -- | |
| 505 | 8 | _a5. Clocktech III, rise of the circarhythms -- 5.1 Starting forward by looking back -- 5.2 Circarhythms -- 5.3 Human studies -- 5.3.1 Travel across time zones, the phenomenon of "jet lag" -- 5.3.2 The 24/7 society, shiftwork, circadian disruption, and health issues -- 5.4 Animal studies -- 5.5 Summary and conclusion for proposition 2.3 -- 5.5.1 Measuring time's passing, the adaptive function of interval timers -- 5.6 Timing is (almost) everything -- 5.7 Overall conclusions -- References -- | |
| 505 | 8 | _a6. The circle game: mathematics, models, and rhythms -- 6.1 Introduction to mathematical modeling -- 6.2 Linear models of oscillators -- 6.3 Nonlinear models of oscillators -- 6.4 Modeling molecular networks in cells -- 6.5 Modeling external perturbations on biological oscillators: synchronization, entrainment, and other effects on rhythms -- 6.6 Phasic entrainment, parametric effects, and rhythm position -- 6.7 More detailed molecular network models -- 6.8 Conclusions and a caveat -- 6.9 Chapter review -- References -- 6.10 Additional reading -- More references -- | |
| 505 | 8 | _a7. The power of circular reasoning -- 7.1 Back to the future -- 7.2 A prototype investigative approach -- 7.2.1 Definitions and classifications -- 7.2.2 The complex case of the electroencephalogram (EEG) -- 7.2.3 The sensory-thalmacortical system -- 7.2.4 How neurons became rhythmic -- 7.2.5 Mode and level of rhythm generation of brain rhythms revisited -- 7.2.6 The in vitro evidence -- 7.2.7 Another slight digression: ionic currents and neural transmission -- 7.2.8 Relationship to electroencephalogram (EEG) -- 7.2.9 Models of oscillating neurons -- 7.2.10 Distance and lag time -- 7.3 Chapter review -- References -- | |
| 505 | 8 | _aA. Modeling approaches -- Modeling endocrine system using Matlab by Kevin Freedman -- Modeling neurons by Rajarshi Ganguly and George Neusch -- B. The end of the beginning -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aThis book represents the first in a two-volume set on biological rhythms. This volume focuses on supporting the claim that biological rhythms are universal and essential characteristics of living organisms, critical for proper functioning of any living system. The author begins by examining the potential reasons for the evolution of biological rhythms: (1) the need for complex, goal-oriented devices to control the timing of their activities; (2) the inherent tendency of feedback control systems to oscillate; and (3) the existence of stable and powerful geophysical cycles to which all organisms must adapt. To investigate the second reason, the author enlists the help of biomedical engineering students to develop mathematical models of various biological systems. One such model involves a typical endocrine feedback system. By adjusting various model parameters, it was found that creating a oscillation in any component of the model generated a rhythmic cascade that made the entire system oscillate. This same approach was used to show how daily light/dark cycles could cascade rhythmic patterns throughout ecosystems and within organisms. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF t.p. (viewed on November 20, 2012). | ||
| 650 | 0 | _aBiological rhythms. | |
| 650 | 0 |
_aBiological rhythms _xMathematical models. |
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| 650 | 0 | _aBioengineering. | |
| 653 | _abiological rhythms | ||
| 653 | _abiological clocks | ||
| 653 | _achronobiology | ||
| 653 | _aneural oscillators | ||
| 653 | _aphysiological rhythms | ||
| 653 | _amathematical modeling | ||
| 653 | _afeedback oscillation | ||
| 776 | 0 | 8 |
_iPrint version: _z9781598296358 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on biomedical engineering ; _v# 43. _x1930-0336 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813134 |
| 999 |
_c561941 _d561941 |
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