The Experts below are selected from a list of 20544 Experts worldwide ranked by ideXlab platform
Glantz Spencer - One of the best experts on this subject based on the ideXlab platform.
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Bioinformatics Discovery and Functional Characterization of Lipidbinding LOV Photoreceptors
ScholarlyCommons, 2017Co-Authors: Glantz SpencerAbstract:The light–oxygen–voltage sensitive (LOV) domain subset of the PAS superfamily is a ubiquitous photoreceptor class that enables organisms across multiple kingdoms to sense blue light. LOV photoreceptors are modular flavin-binding proteins that consist of Discrete Sensor and effector domains. Blue-light drives a LOV Sensor to trigger a conformational change in photoreceptor structure that ultimately regulates the biochemical function of one or more of its fused effectors. The nature by which LOV photoreceptors vary in their Sensor-effector domain combinations allows for light-gated regulation by a single-photoreceptor class of diverse physiological processes across species in varied ecological settings that underlie circadian rhythms, virulence, phototropism, and stress responses. Here, we report the bioinformatics identification of over 6,700 candidate LOV domains and their annotation for Sensor-effector topology and inferred ontological function. In addition to nearly tripling the number of reported LOV sequences, we identified several classes of LOV proteins with predicted Sensor-effector pairings that were previously unknown or considered rare and thus have yet to be functionally characterized, including photoreceptors with LOV Sensors and Regulator of G-protein signaling (RGS) and PAS homology effectors (“RGS-LOV-PAS”) in dikarya fungi and brown algae. We report the experimental characterization of two bioinformatics-identified fungal RGS-LOV-PAS photoreceptors, BcRGS5 from B cinerea and CeRGS from C. europaea, that rapidly localize from cytoplasm to the plasma membrane upon blue-light illumination in a heterologous mammalian cell expression system. Dynamic membrane localization by BcRGS5 is mediated by a light-switchable high affinity electrostatic interaction with anionic phospholipids. The seconds-timescale membrane-recruitment, likely driven by a conserved lipid-binding amphipathic helix, may serve to potentiate RGS effector activity on membrane-bound binding partners in the native fungal organism. As neither LOV photoreceptors nor RGS proteins nor PAS Sensory proteins are known to traffic by light-gated and direct association with phospholipids, this work establishes a novel photoSensory signaling mechanism for multiple protein classes and highlights the value of applying genomic technologies to diverse organisms to capture photoSensory protein diversity that is vastly important in adaptation, photobiology, and optogenetics
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Bioinformatics Discovery And Functional Characterization Of Lipid-Binding Lov Photoreceptors
ScholarlyCommons, 2017Co-Authors: Glantz SpencerAbstract:The light–oxygen–voltage sensitive (LOV) domain subset of the PAS superfamily is a ubiquitous photoreceptor class that enables organisms across multiple kingdoms to sense blue light. LOV photoreceptors are modular flavin-binding proteins that consist of Discrete Sensor and effector domains. Blue-light drives a LOV Sensor to trigger a conformational change in photoreceptor structure that ultimately regulates the biochemical function of one or more of its fused effectors. The nature by which LOV photoreceptors vary in their Sensor-effector domain combinations allows for light-gated regulation by a single-photoreceptor class of diverse physiological processes across species in varied ecological settings that underlie circadian rhythms, virulence, phototropism, and stress responses. Here, we report the bioinformatics identification of over 6,700 candidate LOV domains and their annotation for Sensor-effector topology and inferred ontological function. In addition to nearly tripling the number of reported LOV sequences, we identified several classes of LOV proteins with predicted Sensor-effector pairings that were previously unknown or considered rare and thus have yet to be functionally characterized, including photoreceptors with LOV Sensors and Regulator of G-protein signaling (RGS) and PAS homology effectors (“RGS-LOV-PAS”) in dikarya fungi and brown algae. We report the experimental characterization of two bioinformatics-identified fungal RGS-LOV-PAS photoreceptors, BcRGS5 from B cinerea and CeRGS from C. europaea, that rapidly localize from cytoplasm to the plasma membrane upon blue-light illumination in a heterologous mammalian cell expression system. Dynamic membrane localization by BcRGS5 is mediated by a light-switchable high affinity electrostatic interaction with anionic phospholipids. The seconds-timescale membrane-recruitment, likely driven by a conserved lipid-binding amphipathic helix, may serve to potentiate RGS effector activity on membrane-bound binding partners in the native fungal organism. As neither LOV photoreceptors nor RGS proteins nor PAS Sensory proteins are known to traffic by light-gated and direct association with phospholipids, this work establishes a novel photoSensory signaling mechanism for multiple protein classes and highlights the value of applying genomic technologies to diverse organisms to capture photoSensory protein diversity that is vastly important in adaptation, photobiology, and optogenetics
R Gueli - One of the best experts on this subject based on the ideXlab platform.
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predator prey model for Discrete Sensor placement
Eighth Annual Water Distribution Systems Analysis Symposium (WDSA), 2008Co-Authors: R GueliAbstract:A metaheuristic approach is proposed to design the optimal placement of monitoring stations, for aiming an early detection of the intentional water distribution networks contamination. The approach is based on the use of a predator-prey model, that is applied to multi-objective optimization. The proposed algorithm is used to solve the Sensor constrained contamination detection problem, which is polynomially equivalent to the asymmetric k-center problem, so it is NP-Hard In particular the predator-prey model is applied to find the optimal Sensors placement evaluated, according to the four design objectives which are described in the Battle of Water Sensor Networks (BWSN) manifesto. Both predators and preys are subjected to an evolution process. The competing coevolution approach has been chosen to avoid the problem of designing the fitness function or, in other words, to avoid the problem of locating the most representative contamination events. The candidate solutions and tests, which are used to evaluate these solutions, evolve simultaneously to find the optimal evaluation set, and as a consequence to minimize the number of needed checks, during the selection of the optimal solution.
Jared Saia - One of the best experts on this subject based on the ideXlab platform.
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Discrete Sensor placement problems in distribution networks
Mathematical and Computer Modelling, 2005Co-Authors: Tanya Y Bergerwolf, W E Hart, Jared SaiaAbstract:We consider the problem of placing Sensors in a network to detect and identify thesource of any contamination. We consider two variants of this problem:0(1)Sensor-constrained: we are allowed a fixed number of Sensors and want to minimize contaminationdetection time; and (2)time-constrained: we must detect contamination within a given time limit and want to minimize the number of Sensors required. Our main results are as follows. First, we give a necessary and sufficient condition for source identification.Second, we show that the Sensor and time constrained versions of the problem are polynomially equivalent. Finally, we show that the Sensor-constrained version of the problem is polynomially equivalent to the asymmetric k-center problem and that the time-constrained version of the problem is polynomially equivalent to the dominating set problem.
Suresh G Advani - One of the best experts on this subject based on the ideXlab platform.
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a methodology for using long period gratings and mold filling simulations to minimize the intrusiveness of flow Sensors in liquid composite molding
Composites Science and Technology, 2002Co-Authors: Sylvia R M Kueh, Richard S Parnas, Suresh G AdvaniAbstract:In liquid composite molding (LCM), a fiber preform is placed in the mold and a thermoset resin is injected into the closed mold cavity to occupy the spaces in between the fibers. A significant number of LCM parts are rejected on account of inadequate resin impregnation of the reinforcement preform during the mold filling stage. Hence, implementing Sensors and active control can improve the cost efficiency of mold filling. In this paper, long-period grating Sensors are utilized to monitor flow front progression. A methodology has been devised to compare the performances of different Sensor configurations in the mold. Sensor performance evaluation is based on comparing the resin arrival times between the ideal mold filling situation and several simulated non-ideal scenarios. This methodology can be applied to any other Discrete-Sensor technology with an on/off response to the presence of resin and for other mold-filling anomalies. Filling simulations of a square plaque mold with a curved edge are used to illustrate the methodology.
Tanya Y Bergerwolf - One of the best experts on this subject based on the ideXlab platform.
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Discrete Sensor placement problems in distribution networks
Mathematical and Computer Modelling, 2005Co-Authors: Tanya Y Bergerwolf, W E Hart, Jared SaiaAbstract:We consider the problem of placing Sensors in a network to detect and identify thesource of any contamination. We consider two variants of this problem:0(1)Sensor-constrained: we are allowed a fixed number of Sensors and want to minimize contaminationdetection time; and (2)time-constrained: we must detect contamination within a given time limit and want to minimize the number of Sensors required. Our main results are as follows. First, we give a necessary and sufficient condition for source identification.Second, we show that the Sensor and time constrained versions of the problem are polynomially equivalent. Finally, we show that the Sensor-constrained version of the problem is polynomially equivalent to the asymmetric k-center problem and that the time-constrained version of the problem is polynomially equivalent to the dominating set problem.