The Experts below are selected from a list of 61167 Experts worldwide ranked by ideXlab platform
Kenneth E. Aupperle - One of the best experts on this subject based on the ideXlab platform.
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crossroads spontaneous organizational reconfiguration a historical example based on xenophon s anabasis
Organization Science, 1996Co-Authors: Kenneth E. AupperleAbstract:This paper examines an ancient historical event that has profound implications regarding the role of organizational culture in facilitating spontaneous organizational reconfiguration. Xenophon's Anabasis documents the successful retreat of a Greek army trapped in Persia in a setting that is comparable to the hypercompetition of today. Spontaneous reconfiguration is seen here to be a vital survival element in hypercompetitive environments, past and present. As a result, a historical case is used as a time-bridge to reveal the importance of rapid and substantive organizational redesign when confronting highly competitive and quickly shifting environments. The Anabasis is also used to animate several of Gareth Morgan's (Morgan, G. 1986. Images of Organization. Sage Publications, Newbury Park.) metaphors. In particular, metaphors pertaining to a Biological Organism, the brain, and culture are used to parallel the Greek emphasis on body, mind, and spirit. While Xenophon's army is depicted here in terms of bein...
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Crossroads—Spontaneous Organizational Reconfiguration: A Historical Example Based on Xenophon's Anabasis
Organization Science, 1996Co-Authors: Kenneth E. AupperleAbstract:This paper examines an ancient historical event that has profound implications regarding the role of organizational culture in facilitating spontaneous organizational reconfiguration. Xenophon's Anabasis documents the successful retreat of a Greek army trapped in Persia in a setting that is comparable to the hypercompetition of today. Spontaneous reconfiguration is seen here to be a vital survival element in hypercompetitive environments, past and present. As a result, a historical case is used as a time-bridge to reveal the importance of rapid and substantive organizational redesign when confronting highly competitive and quickly shifting environments. The Anabasis is also used to animate several of Gareth Morgan's (Morgan, G. 1986. Images of Organization. Sage Publications, Newbury Park.) metaphors. In particular, metaphors pertaining to a Biological Organism, the brain, and culture are used to parallel the Greek emphasis on body, mind, and spirit. While Xenophon's army is depicted here in terms of bein...
Alexander J. Malkin - One of the best experts on this subject based on the ideXlab platform.
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High-Resolution Spore Coat Architecture and Assembly of Bacillus Spores
2011Co-Authors: Alexander J. Malkin, Selim Elhadj, Marco PlompAbstract:Elucidating the molecular architecture of bacterial and cellular surfaces and its structural dynamics is essential to understanding mechanisms of pathogenesis, immune response, physicochemical interactions, environmental resistance, and provide the means for identifying spore formulation and processing attributes. I will discuss the application of in vitro atomic force microscopy (AFM) for studies of high-resolution coat architecture and assembly of several Bacillus spore species. We have demonstrated that bacterial spore coat structures are phylogenetically and growth medium determined. We have proposed that strikingly different species-dependent coat structures of bacterial spore species are a consequence of sporulation media-dependent nucleation and crystallization mechanisms that regulate the assembly of the outer spore coat. Spore coat layers were found to exhibit screw dislocations and two-dimensional nuclei typically observed on inorganic and macromolecular crystals. This presents the first case of non-mineral crystal growth patterns being revealed for a Biological Organism, which provides an unexpected example of nature exploiting fundamental materials science mechanisms for the morphogenetic control of Biological ultrastructures. We have discovered and validated, distinctive formulation-specific high-resolution structural spore coat and dimensional signatures of B. anthracis spores (Sterne strain) grown in different formulation condition. We further demonstrated that measurement of the dimensional characteristics of B. anthracis sporesmore » provides formulation classification and sample matching with high sensitivity and specificity. I will present data on the development of an AFM-based immunolabeling technique for the proteomic mapping of macromolecular structures on the B. anthracis surfaces. These studies demonstrate that AFM can probe microbial surface architecture, environmental dynamics and the life cycle of bacterial and cellular systems at near-molecular resolution under physiological conditions.« less
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Spore Coat Architecture of Clostridium novyi NT Spores
Journal of Bacteriology, 2007Co-Authors: Marco Plomp, Ian Cheong, J. Michael Mccaffery, Chetan Bettegowda, Shibin Zhou, Xin Huang, Kenneth W. Kinzler, Bert Vogelstein, Alexander J. MalkinAbstract:Spores of the anaerobic bacterium Clostridium novyi NT are able to germinate in and destroy hypoxic regions of tumors in experimental animals. Future progress in this area will benefit from a better understanding of the germination and outgrowth processes that are essential for the tumorilytic properties of these spores. Toward this end, we have used both transmission electron microscopy and atomic force microscopy to determine the structure of both dormant and germinating spores. We found that the spores are surrounded by an amorphous layer intertwined with honeycomb parasporal layers. Moreover, the spore coat layers had apparently self-assembled, and this assembly was likely to be governed by crystal growth principles. During germination and outgrowth, the honeycomb layers, as well as the underlying spore coat and undercoat layers, sequentially dissolved until the vegetative cell was released. In addition to their implications for understanding the biology of C. novyi NT, these studies document the presence of proteinaceous growth spirals in a Biological Organism.
Marco Plomp - One of the best experts on this subject based on the ideXlab platform.
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High-Resolution Spore Coat Architecture and Assembly of Bacillus Spores
2011Co-Authors: Alexander J. Malkin, Selim Elhadj, Marco PlompAbstract:Elucidating the molecular architecture of bacterial and cellular surfaces and its structural dynamics is essential to understanding mechanisms of pathogenesis, immune response, physicochemical interactions, environmental resistance, and provide the means for identifying spore formulation and processing attributes. I will discuss the application of in vitro atomic force microscopy (AFM) for studies of high-resolution coat architecture and assembly of several Bacillus spore species. We have demonstrated that bacterial spore coat structures are phylogenetically and growth medium determined. We have proposed that strikingly different species-dependent coat structures of bacterial spore species are a consequence of sporulation media-dependent nucleation and crystallization mechanisms that regulate the assembly of the outer spore coat. Spore coat layers were found to exhibit screw dislocations and two-dimensional nuclei typically observed on inorganic and macromolecular crystals. This presents the first case of non-mineral crystal growth patterns being revealed for a Biological Organism, which provides an unexpected example of nature exploiting fundamental materials science mechanisms for the morphogenetic control of Biological ultrastructures. We have discovered and validated, distinctive formulation-specific high-resolution structural spore coat and dimensional signatures of B. anthracis spores (Sterne strain) grown in different formulation condition. We further demonstrated that measurement of the dimensional characteristics of B. anthracis sporesmore » provides formulation classification and sample matching with high sensitivity and specificity. I will present data on the development of an AFM-based immunolabeling technique for the proteomic mapping of macromolecular structures on the B. anthracis surfaces. These studies demonstrate that AFM can probe microbial surface architecture, environmental dynamics and the life cycle of bacterial and cellular systems at near-molecular resolution under physiological conditions.« less
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Spore Coat Architecture of Clostridium novyi NT Spores
Journal of Bacteriology, 2007Co-Authors: Marco Plomp, Ian Cheong, J. Michael Mccaffery, Chetan Bettegowda, Shibin Zhou, Xin Huang, Kenneth W. Kinzler, Bert Vogelstein, Alexander J. MalkinAbstract:Spores of the anaerobic bacterium Clostridium novyi NT are able to germinate in and destroy hypoxic regions of tumors in experimental animals. Future progress in this area will benefit from a better understanding of the germination and outgrowth processes that are essential for the tumorilytic properties of these spores. Toward this end, we have used both transmission electron microscopy and atomic force microscopy to determine the structure of both dormant and germinating spores. We found that the spores are surrounded by an amorphous layer intertwined with honeycomb parasporal layers. Moreover, the spore coat layers had apparently self-assembled, and this assembly was likely to be governed by crystal growth principles. During germination and outgrowth, the honeycomb layers, as well as the underlying spore coat and undercoat layers, sequentially dissolved until the vegetative cell was released. In addition to their implications for understanding the biology of C. novyi NT, these studies document the presence of proteinaceous growth spirals in a Biological Organism.
Gu M. - One of the best experts on this subject based on the ideXlab platform.
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Thermodynamic Machine Learning through Maximum Work Production
2021Co-Authors: Boyd A. B., Crutchfield J. P., Gu M.Abstract:Adaptive systems -- such as a Biological Organism gaining survival advantage, an autonomous robot executing a functional task, or a motor protein transporting intracellular nutrients -- must model the regularities and stochasticity in their environments to take full advantage of thermodynamic resources. Analogously, but in a purely computational realm, machine learning algorithms estimate models to capture predictable structure and identify irrelevant noise in training data. This happens through optimization of performance metrics, such as model likelihood. If physically implemented, is there a sense in which computational models estimated through machine learning are physically preferred? We introduce the thermodynamic principle that work production is the most relevant performance metric for an adaptive physical agent and compare the results to the maximum-likelihood principle that guides machine learning. Within the class of physical agents that most efficiently harvest energy from their environment, we demonstrate that an efficient agent's model explicitly determines its architecture and how much useful work it harvests from the environment. We then show that selecting the maximum-work agent for given environmental data corresponds to finding the maximum-likelihood model. This establishes an equivalence between nonequilibrium thermodynamics and dynamic learning. In this way, work maximization emerges as an organizing principle that underlies learning in adaptive thermodynamic systems.Comment: 29 pages, 10 figures, 6 appendices; http://csc.ucdavis.edu/~cmg/compmech/pubs/tml.ht
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Thermodynamic Machine Learning through Maximum Work Production
2020Co-Authors: Boyd A. B., Crutchfield J. P., Gu M.Abstract:Adaptive thermodynamic systems -- such as a Biological Organism attempting to gain survival advantage, an autonomous robot performing a functional task, or a motor protein transporting intracellular nutrients -- can improve their performance by effectively modeling the regularities and stochasticity in their environments. Analogously, but in a purely computational realm, machine learning algorithms seek to estimate models that capture predictable structure and identify irrelevant noise in training data by optimizing performance measures, such as a model's log-likelihood of having generated the data. Is there a sense in which these computational models are physically preferred? For adaptive physical systems we introduce the organizing principle that thermodynamic work is the most relevant performance measure of advantageously modeling an environment. Specifically, a physical agent's model determines how much useful work it can harvest from an environment. We show that when such agents maximize work production they also maximize their environmental model's log-likelihood, establishing an equivalence between thermodynamics and learning. In this way, work maximization appears as an organizing principle that underlies learning in adaptive thermodynamic systems.Comment: 27 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/tml.ht
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Thermodynamic Machine Learning through Maximum Work Production
eScholarship University of California, 2020Co-Authors: Ab Boyd, Jp Crutchfield, Gu M.Abstract:Adaptive thermodynamic systems -- such as a Biological Organism attempting to gain survival advantage, an autonomous robot performing a functional task, or a motor protein transporting intracellular nutrients -- can improve their performance by effectively modeling the regularities and stochasticity in their environments. Analogously, but in a purely computational realm, machine learning algorithms seek to estimate models that capture predictable structure and identify irrelevant noise in training data by optimizing performance measures, such as a model's log-likelihood of having generated the data. Is there a sense in which these computational models are physically preferred? For adaptive physical systems we introduce the organizing principle that thermodynamic work is the most relevant performance measure of advantageously modeling an environment. Specifically, a physical agent's model determines how much useful work it can harvest from an environment. We show that when such agents maximize work production they also maximize their environmental model's log-likelihood, establishing an equivalence between thermodynamics and learning. In this way, work maximization appears as an organizing principle that underlies learning in adaptive thermodynamic systems
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Thermodynamic Machine Learning through Maximum Work Production
2020Co-Authors: Boyd A. B., Crutchfield J. P., Gu M.Abstract:Adaptive systems -- such as a Biological Organism gaining survival advantage, an autonomous robot executing a functional task, or a motor protein transporting intracellular nutrients -- must model the regularities and stochasticity in their environments to take full advantage of thermodynamic resources. Analogously, but in a purely computational realm, machine learning algorithms estimate models to capture predictable structure and identify irrelevant noise in training data. This happens through optimization of performance metrics, such as model likelihood. If physically implemented, is there a sense in which computational models estimated through machine learning are physically preferred? We introduce the thermodynamic principle that work production is the most relevant performance metric for an adaptive physical agent and compare the results to the maximum-likelihood principle that guides machine learning. Within the class of physical agents that most efficiently harvest energy from their environment, we demonstrate that an efficient agent's model explicitly determines its architecture and how much useful work it harvests from the environment. We then show that selecting the maximum-work agent for given environmental data corresponds to finding the maximum-likelihood model. This establishes an equivalence between nonequilibrium thermodynamics and dynamic learning. In this way, work maximization emerges as an organizing principle that underlies learning in adaptive thermodynamic systems.Comment: 29 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/tml.ht
Mario Giacobini - One of the best experts on this subject based on the ideXlab platform.
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validating a threshold based boolean model of regulatory networks on a Biological Organism
Evolutionary Computation Machine Learning and Data Mining in Bioinformatics, 2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean update functions attached to each element, to validate a previously proposed threshold-based additive update function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean function to match this regime. Results show that there is a higher degree of overlap between the original function and the additive function than with random update function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.
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EvoBio - Validating a threshold-based boolean model of regulatory networks on a Biological Organism
Evolutionary Computation Machine Learning and Data Mining in Bioinformatics, 2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean update functions attached to each element, to validate a previously proposed threshold-based additive update function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean function to match this regime. Results show that there is a higher degree of overlap between the original function and the additive function than with random update function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.