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Relevant bibliographies by topics / Biological optical systems / Books

Books on the topic 'Biological optical systems'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Kao, Fu-Jen, and Peter Török. Optical imaging and microscopy: Techniques and advanced systems. Berlin: Springer, 2003.

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2

Mexican Meeting on Mathematical and Experimental Physics (2nd 2004 Mexico City, Mexico). Materials science and applied physics: 2nd Mexican Meeting on Mathematical and Experimental Physics, México City, México, 6-10 September, 2004. Edited by Hernández-Pozos J. L and Olayo-González R. Melville, N.Y: American Institute of Physics, 2005.

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3

e, Costa Fernando Almeida, ed. Advances in artificial life: 9th European conference, ECAL 2007, Lisbon, Portugal, September 10-14, 2007 ; proceedings. Berlin: Springer, 2007.

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4

Mathematical modelling in biomedicine: Optimal control of biomedical systems. Dordrecht, Holland: D. Reidel Pub. Co., 1986.

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5

Doncieux, Stéphane. From Animals to Animats 11: 11th International Conference on Simulation of Adaptive Behavior, SAB 2010, Paris - Clos Lucé, France, August 25-28, 2010. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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6

Magnenat-Thalmann, Nadia. Modelling the Physiological Human: 3D Physiological Human Workshop, 3DPH 2009, Zermatt, Switzerland, November 29 – December 2, 2009. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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7

L, Teo K., ed. Optimal control of drug administration in cancer chemotherapy. Singapore: World Scientific, 1994.

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8

Liu, Shu-Jun. Stochastic Averaging and Stochastic Extremum Seeking. London: Springer London, 2012.

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9

Joan, Cabestany, ed. Bio-inspired systems: Computational and ambient intelligence : 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, June 10-12, 2009 : proceedings. Berlin: Springer-Verlag, 2009.

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10

Hiroshi, Watanabe, and International Symposium on Dynamics of Macromolecules by Electric and Optical Methods. 1988 : Tokyo, Japan), eds. Dynamic behavior of macromolecules, colloids, liquid crystals and biological systems by optical and electro-optical methods. Tokyo: Hirokawa Publishing Company, 1988.

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11

Biological Identification: DNA Amplification and Sequencing, Optical Sensing, Lab-On-Chip and Portable Systems. Woodhead Publishing, 2018.

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12

Biological Identification: DNA Amplification and Sequencing, Optical Sensing, Lab-On-Chip and Portable Systems. Elsevier Science & Technology, 2014.

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13

Chenoweth, David M. Chemical Tools for Imaging, Manipulating, and Tracking Biological Systems: Diverse Chemical, Optical and Biorthogonal Methods. Elsevier Science & Technology Books, 2020.

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14

Chemical Tools for Imaging, Manipulating, and Tracking Biological Systems: Diverse Chemical, Optical and Bioorthogonal Methods. Elsevier, 2020. http://dx.doi.org/10.1016/s0076-6879(20)x0013-9.

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15

Mahmoud, Fallahi, Swanson Basil I, Society of Photo-optical Instrumentation Engineers., Los Alamos National Laboratory, and Air & Waste Management Association. Optical Sciences Division., eds. Advanced materials and optical systems for chemical and biological detection: 21-22 September 1999, Boston, Massachusetts. Bellingham, Wash: SPIE, 1999.

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16

1949-, Stroscio Michael A., and Dutta Mitra, eds. Biological nanostructures and applications of nanostructures in biology: Electrical, mechanical, and optical properties. New York: Kluwer Academic/Plenum Publishers, 2004.

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17

Optical Polarization in Biomedical Applications (Biological and Medical Physics, Biomedical Engineering). Springer, 2006.

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18

Chenoweth, David M. Chemical Tools for Imaging, Manipulating, and Tracking Biological Systems: Diverse Methods for Optical Imaging and Conjugation. Elsevier Science & Technology Books, 2020.

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19

Chemical Tools for Imaging, Manipulating, and Tracking Biological Systems: Diverse Methods for Optical Imaging and Conjugation. Elsevier, 2020. http://dx.doi.org/10.1016/s0076-6879(20)x0011-5.

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20

Ohtsu, Motoichi. Progress in Nano-Electro-Optics VII: Chemical, Biological, and Nanophotonic Technologies for Nano-Optical Devices and Systems. Springer, 2012.

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21

Chemical Tools for Imaging, Manipulating, and Tracking Biological Systems: Diverse Methods Based on Optical Imaging and Fluorescence. Elsevier, 2020. http://dx.doi.org/10.1016/s0076-6879(20)x0012-7.

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22

Chenoweth, David M. Chemical Tools for Imaging, Manipulating, and Tracking Biological Systems: Diverse Methods Based on Optical Imaging and Fluorescence. Elsevier Science & Technology Books, 2020.

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23

(Editor), Michael Stroscio, and Mitra Dutta (Editor), eds. Biological Nanostructures and Applications of Nanostructures in Biology: Electrical, Mechanical, and Optical Properties (Bioelectric Engineering). Springer, 2004.

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24

F, Scherer Norbert, Hicks Janice M, and Society of Photo-optical Instrumentation Engineers., eds. Laser techniques for condensed-phase and biological systems: 29-31 January 1998, San Jose, California. Bellingham, Wash., USA: SPIE, 1998.

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25

F, Bottiroli Giovanni, Karu T. I, Lubart Rachel, and Società italiana di laser chirurgia e medicina., eds. Proceedings of effects of low-power light on biological systems III: 8 September 1997, San Remo, Italy. Bellingham, Wash., USA: SPIE, 1997.

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26

F, Bottiroli Giovanni, Karu T. I, Lubart Rachel, European Optical Society, Society of Photo-optical Instrumentation Engineers., European Laser Association, and Scandinavian Society for Laser Therapy., eds. Proceedings of effects of low-power light on biological systems IV: 8-9 September 1998, Stockholm, Sweden. Bellingham, Wash., USA: SPIE, 1998.

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27

T, Kundu, and Society of Photo-optical Instrumentation Engineers., eds. Advanced nondestructive evaluation for structural and biological health monitoring: 6-8 March 2001, Newport Beach, USA. Bellingham, Wash., USA: SPIE, 2001.

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28

(Editor), Jose Luis Hernández-Pozos, and Roberto Olayo-González (Editor), eds. Materials Science and Applied Physics: Second Mexican Meeting on Mathematical and Experimental Physics (AIP Conference Proceedings). American Institute of Physics, 2005.

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29

(Editor), Mathieu Capcarrere, Alex A. Freitas (Editor), Peter J. Bentley (Editor), Colin G. Johnson (Editor), and Jon Timmis (Editor), eds. Advances in Artificial Life: 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005, Proceedings (Lecture Notes in Computer Science). Springer, 2005.

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30

(Editor), D. Floreano, J. D. Nicoud (Editor), and F. Mondada (Editor), eds. Advances in Artificial Life: 5th European Conference, ECAL'99, Lausanne, Switzerland, September 13-17, 1999 Proceedings (Lecture Notes in Computer Science). Springer, 1999.

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31

Gigan, Sylvain. Adaptive Optics and Wavefront Control for Biological Systems. SPIE, 2015.

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32

Fenno, Lief E., and Karl Deisseroth. Optogenetics and Related Technologies for Psychiatric Disease Research. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0006.

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Abstract:

Studying intact systems with simultaneous local precision and global scope is a fundamental challenge in biology. This familiar trade-off leads to important conceptual and experimental difficulties in psychiatric disease research and throughout the study of complex biological systems. Part of a solution may arise from optogenetics: the combination of genetic and optical methods to achieve gain- or loss-of-function of temporally defined events in specific cells embedded within intact living tissue or organisms. Such precise causal control within the functioning intact system can be achieved via introduction of genes that confer to cells both light-detection capability and specific effector function. A broad array of optogenetic tools and neuroscience applications have driven the wide adoption of optogenetics as a standard approach in neuroscience.

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33

Eliasmith, Chris. Neurocomputational Models: Theory, Application, Philosophical Consequences. Edited by John Bickle. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780195304787.003.0014.

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This article describes the neural engineering framework (NEF), a systematic approach to studying neural systems that has collected and extended a set of consistent methods that are highly general. The NEF draws heavily on past work in theoretical neuroscience, integrating work on neural coding, population representation, and neural dynamics to enable the construction of large-scale biologically plausible neural simulations. It is based on the principles that neural representations defined by a combination of nonlinear encoding and optimal linear decoding and that neural dynamics are characterized by considering neural representations as control theoretic state variables.

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34

Cartin-Ceba, Rodrigo, and Udaya B. S. Prakash. Rheumatoid arthritis in the critically ill. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0278.

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Rheumatoid arthritis is a progressive chronic inflammatory disease of autoimmune aetiology, characterized by joint swelling, tenderness, and destruction of synovial joints, leading to severe disability and premature mortality. The prevalence of rheumatoid arthritis has been estimated at 0.5–1% of the adult population and is the most common form of inflammatory joint disease. Rheumatoid arthritis frequently affects many non-articular systems, most commonly the cardiopulmonary, gastrointestinal, and haematological systems. Over recent years, the optimal use of disease-modifying anti-rheumatic drugs, in particular methotrexate, and the availability of several new biological agents, have dramatically enhanced the success of rheumatoid arthritis management. Multiple studies have demonstrated the efficacy of anti-TNF therapy, as well as other targeting, in reducing inflammatory activity, as well as inhibiting joint destruction in patients with active rheumatoid arthritis. A significant number of patients admitted to the intensive care unit (ICU) with autoimmune conditions have rheumatoid arthritis. The studies evaluating the outcome of rheumatoid arthritis patients admitted to the ICU have included other rheumatological conditions, but all have identified a high mortality (17–55%).

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35

Krstic, Miroslav, and Shu-Jun Liu. Stochastic Averaging and Stochastic Extremum Seeking. Springer, 2014.

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36

McDermid, Robert C., and Sean M. Bagshaw. Physiological Reserve and Frailty in Critical Illness. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199653461.003.0028.

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Physicians have long sought to define a ‘physiologic age’ distinct from chronologic age which might account for some of the variance in response to critical illness and injury. This has led to the concept of ‘physiologic reserve’ which might represent a major driver of outcome in patients requiring intensive care. The human body is a complex system that adapts to a multitude of external stressors; however, senescence or illness can reduce inherent adaptive mechanisms, reducing complexity and reducing the threshold for decompensation (i.e. acute illness or injury). This theoretical critical threshold can be considered ‘physiologic reserve’. The phenotypic expression of this process is frailty. Frailty is a condition in which small deficits accumulate which individually may be insignificant but collectively produce an overwhelming burden of disease and heightened vulnerability to adverse events. Frail patients expend a greater proportion of their reserve simply to maintain homeostasis, and seemingly trivial insults can contribute to catastrophic decompensation. While frailty has generally been described among older populations, the concept of frailty as a surrogate of physiologic reserve may have relevance to critically ill patients across a wide spectrum of age. Research is needed to characterize the biological underpinnings of frailty, optimal ways to measure it, and its importance in determining survival and functional outcomes after critical illness. The utilization of ICU resources by older patients is rising, and the prevalence of frailty in those admitted to the ICU is likely to increase.

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37

Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

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