The Experts below are selected from a list of 29487 Experts worldwide ranked by ideXlab platform
Holger Gohlke - One of the best experts on this subject based on the ideXlab platform.
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constraint network analysis cna a Python Software package for efficiently linking biomacromolecular structure flexibility thermo stability and function
Journal of Chemical Information and Modeling, 2013Co-Authors: Christopher Pfleger, Prakash Chandra Rathi, Doris L Klein, Sebastian Radestock, Holger GohlkeAbstract:For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based Software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis Software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity a...
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constraint network analysis cna a Python Software package for efficiently linking biomacromolecular structure flexibility thermo stability and function
Journal of Chemical Information and Modeling, 2013Co-Authors: Christopher Pfleger, Prakash Chandra Rathi, Doris L Klein, Sebastian Radestock, Holger GohlkeAbstract:For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based Software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis Software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/Software for nonprofit organizations.
Christopher Pfleger - One of the best experts on this subject based on the ideXlab platform.
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constraint network analysis cna a Python Software package for efficiently linking biomacromolecular structure flexibility thermo stability and function
Journal of Chemical Information and Modeling, 2013Co-Authors: Christopher Pfleger, Prakash Chandra Rathi, Doris L Klein, Sebastian Radestock, Holger GohlkeAbstract:For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based Software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis Software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity a...
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constraint network analysis cna a Python Software package for efficiently linking biomacromolecular structure flexibility thermo stability and function
Journal of Chemical Information and Modeling, 2013Co-Authors: Christopher Pfleger, Prakash Chandra Rathi, Doris L Klein, Sebastian Radestock, Holger GohlkeAbstract:For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based Software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis Software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/Software for nonprofit organizations.
Perraudin Max - One of the best experts on this subject based on the ideXlab platform.
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New Features of P3$\delta$ Software: Partial Pole Placement via Delay Action
HAL CCSD, 2021Co-Authors: Boussaada Islam, Mazanti Guilherme, Niculescu Silviu-iulian, Leclerc Adrien, Raj Jayvir, Perraudin MaxAbstract:International audienceThis paper presents the Software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python Software with a friendly user interface for the design of parametric stabilizing feedback laws with time-delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy and coexisting real roots-induced-dominancy. After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delays, the paper presents the main features of the current version of P3$\delta$. We detail, in particular, the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$
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New Features of P3$\delta$ Software: Partial Pole Placement via Delay Action
HAL CCSD, 2021Co-Authors: Boussaada Islam, Mazanti Guilherme, Niculescu Silviu-iulian, Leclerc Adrien, Raj Jayvir, Perraudin MaxAbstract:International audienceThis paper presents the Software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python Software with a friendly user interface for the design of parametric stabilizing feedback laws with time delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy (MID) and coexisting real roots-induced-dominancy (CRRID). After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delay, the paper presents the main features of the current version of P3$\delta$. We detail in particular its new features, such as the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of the input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$
Boussaada Islam - One of the best experts on this subject based on the ideXlab platform.
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New Features of P3$\delta$ Software: Partial Pole Placement via Delay Action
HAL CCSD, 2021Co-Authors: Boussaada Islam, Mazanti Guilherme, Niculescu Silviu-iulian, Leclerc Adrien, Raj Jayvir, Perraudin MaxAbstract:International audienceThis paper presents the Software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python Software with a friendly user interface for the design of parametric stabilizing feedback laws with time delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy (MID) and coexisting real roots-induced-dominancy (CRRID). After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delay, the paper presents the main features of the current version of P3$\delta$. We detail in particular its new features, such as the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of the input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$
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New Features of P3$\delta$ Software: Partial Pole Placement via Delay Action
HAL CCSD, 2021Co-Authors: Boussaada Islam, Mazanti Guilherme, Niculescu Silviu-iulian, Leclerc Adrien, Raj Jayvir, Perraudin MaxAbstract:International audienceThis paper presents the Software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python Software with a friendly user interface for the design of parametric stabilizing feedback laws with time-delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy and coexisting real roots-induced-dominancy. After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delays, the paper presents the main features of the current version of P3$\delta$. We detail, in particular, the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$
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Partial Pole Placement via Delay Action: A Python Software for Delayed Feedback Stabilizing Design
HAL CCSD, 2020Co-Authors: Boussaada Islam, Mazanti Guilherme, Niculescu Silviu-iulian, Huynh Julien, Sim Franck, Thomas MatthieuAbstract:International audienceThis paper presents a new Python Software for the parametric design of stabilizing feedback laws with time delays, called Partial Pole Placement via Delay Action (P3$\delta$). After an introduction recalling recent theoretical results on the multiplicity-induced-dominancy (MID) and coexisting real roots-induced-dominancy (CRRID) properties and their use for the feedback stabilization of control systems operating under time delays, the paper presents the current version of P3$\delta$, which relies on the MID property to compute delayed stabilizing feedback laws for scalar differential equations with a single delay. We detail in particular its graphical user interface (GUI), which allows the user to input the necessary information and obtain the results of the analysis done by the Software. These results include the parameters stabilizing the closed-loop system, graphical representations of the spectrum of the closed-loop system, simulations of solutions in the time domain, and a sensitivity analysis with respect to uncertain delays
Giovanni Volpe - One of the best experts on this subject based on the ideXlab platform.
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digital video microscopy enhanced by deep learning
Optica, 2019Co-Authors: Saga Helgadottir, Aykut Argun, Giovanni VolpeAbstract:Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python Software package, which can be easily personalized and optimized for specific applications.
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digital video microscopy enhanced by deep learning
arXiv: Soft Condensed Matter, 2018Co-Authors: Saga Helgadottir, Aykut Argun, Giovanni VolpeAbstract:Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard current methods rely on algorithmic approaches: by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python Software package, which can be easily personalized and optimized for specific applications.
