The Experts below are selected from a list of 22545 Experts worldwide ranked by ideXlab platform
Ursula Gaedke - One of the best experts on this subject based on the ideXlab platform.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
Frontiers in Ecology and Evolution, 2019Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
bioRxiv, 2018Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
Benjamin Rosenbaum - One of the best experts on this subject based on the ideXlab platform.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
Frontiers in Ecology and Evolution, 2019Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
bioRxiv, 2018Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
Uwe Hoffmann - One of the best experts on this subject based on the ideXlab platform.
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Cardiorespiratory kinetics: comparisons between athletes with different training habits
European Journal of Applied Physiology, 2019Co-Authors: Jessica Koschate, Laura Gerlich, Veronika Wirtz, Lutz Thieschäfer, Uwe Drescher, Uwe HoffmannAbstract:PurposeFast muscular oxygen uptake ( $${\dot{V}}{\text{O}_\text{2musc}}$$ V ˙ O 2musc ) kinetics are limiting factors for high exercise capacities. It is hypothesized that $${\dot{V}}{\text{O}_\text{2musc}}$$ V ˙ O 2musc and heart rate (HR) kinetics would be faster in individuals, performing long-distance endurance training (CONT) compared with athletes performing predominantly interval-based sports (INT).Methods17 subjects (INT: n = 7, 24 ± 5 years, 183 ± 7 cm, 85 ± 10 kg, 6 ± 3 h of training per week, CONT: n = 10, 37 ± 7 years, 175 ± 9 cm, 69 ± 10 kg, 6 ± 3 h of training per week) completed a treadmill work rate (WR) protocol with pseudo-randomized WR changes with velocities of 6.5 and 9.5 km h^−1. $${\dot{V}}$$ V ˙ O_2musc and the Respective kinetics were estimated from the measured pulmonary oxygen uptake and HR combined with a circulatory model. Kinetics information were calculated using time series analysis. Higher maxima of the cross-correlation function (CCF) of WR and the Respective Parameter ( $${\dot{V}}{\text{O}_\text{2musc}}$$ V ˙ O 2musc , HR) indicate faster kinetics responses.ResultsThe kinetics of HR (INT: 0.23 ± 0.04 vs. CONT: 0.42 ± 0.18; P = 0.001), $${\dot{V}}$$ V ˙ O_2pulm (0.30 ± 0.05 vs. 0.53 ± 0.20; P = 0.005) and $${\dot{V}}$$ V ˙ O_2musc (0.31 ± 0.06 vs. 0.53 ± 0.16; P = 0.005) were significantly slower in INT compared with the CONT athletes.ConclusionsIt seems that at least in the long-term CONT exercise, training without the need of changing intensities is favorable for fast $${\dot{V}}$$ V ˙ O_2 and HR kinetics compared with INT exercise including frequently changing intensities.
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Faster heart rate and muscular oxygen uptake kinetics in type 2 diabetes patients following endurance training.
Applied Physiology Nutrition and Metabolism, 2016Co-Authors: Jessica Koschate, Uwe Drescher, Christian Brinkmann, K. Baum, Thorsten Schiffer, Joachim Latsch, Klara Brixius, Uwe HoffmannAbstract:Cardiorespiratory kinetics were analyzed in type 2 diabetes patients before and after a 12-week endurance exercise-training intervention. It was hypothesized that muscular oxygen uptake and heart rate (HR) kinetics would be faster after the training intervention and that this would be detectable using a standardized work rate protocol with pseudo-random binary sequences. The cardiorespiratory kinetics of 13 male sedentary, middle-aged, overweight type 2 diabetes patients (age, 60 ± 8 years; body mass index, 33 ± 4 kg·m−2) were tested before and after the 12-week exercise intervention. Subjects performed endurance training 3 times a week on nonconsecutive days. Pseudo-random binary sequences exercise protocols in combination with time series analysis were used to estimate kinetics. Greater maxima in cross-correlation functions (CCFmax) represent faster kinetics of the Respective Parameter. CCFmax of muscular oxygen uptake (pre-training: 0.31 ± 0.03; post-training: 0.37 ± 0.1, P = 0.024) and CCFmax of HR (p...
Guntram Weithoff - One of the best experts on this subject based on the ideXlab platform.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
Frontiers in Ecology and Evolution, 2019Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
bioRxiv, 2018Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
Michael Raatz - One of the best experts on this subject based on the ideXlab platform.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
Frontiers in Ecology and Evolution, 2019Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a continuous-time dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
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estimating Parameters from multiple time series of population dynamics using bayesian inference
bioRxiv, 2018Co-Authors: Benjamin Rosenbaum, Michael Raatz, Guntram Weithoff, Gregor F Fussmann, Ursula GaedkeAbstract:Empirical time series of interacting entities, e.g. species abundances, are highly useful to study ecological mechanisms. Mathematical models are valuable tools to further elucidate those mechanisms and underlying processes. However, obtaining an agreement between model predictions and experimental observations remains a demanding task. As models always abstract from reality one Parameter often summarizes several properties. Parameter measurements are performed in additional experiments independent of the ones delivering the time series. Transferring these Parameter values to different settings may result in incorrect parametrizations. On top of that, the properties of organisms and thus the Respective Parameter values may vary considerably. These issues limit the use of a priori model parametrizations. In this study, we present a method suited for a direct estimation of model Parameters and their variability from experimental time series data. We combine numerical simulations of a dynamical population model with Bayesian inference, using a hierarchical framework that allows for variability of individual Parameters. The method is applied to a comprehensive set of time series from a laboratory predator-prey system that features both steady states and cyclic population dynamics. Our model predictions are able to reproduce both steady states and cyclic dynamics of the data. Additionally to the direct estimates of the Parameter values, the Bayesian approach also provides their uncertainties. We found that fitting cyclic population dynamics, which contain more information on the process rates than steady states, yields more precise Parameter estimates. We detected significant variability among Parameters of different time series and identified the variation in the maximum growth rate of the prey as a source for the transition from steady states to cyclic dynamics. By lending more flexibility to the model, our approach facilitates parametrizations and shows more easily which patterns in time series can be explained also by simple models. Applying Bayesian inference and dynamical population models in conjunction may help to quantify the profound variability in organismal properties in nature.
