Sensitivity of a data-assimilation system for reconstructing three-dimensional cardiac electrical dynamics

Modelling of cardiac electrical behaviour has led to important mechanistic insights, but important challenges, including uncertainty in model formulations and parameter values, make it difficult to obtain quantitatively accurate results. An alternative approach is combining models with observations from experiments to produce a data-informed reconstruction of system states over time. Here, we extend our earlier data-assimilation studies using an ensemble Kalman filter to reconstruct a three-dimensional time series of states with complex spatio-temporal dynamics using only surface observations of voltage. We consider the effects of several algorithmic and model parameters on the accuracy of reconstructions of known scroll-wave truth states using synthetic observations. In particular, we study the algorithm’s sensitivity to parameters governing different parts of the process and its robustness to several model-error conditions. We find that the algorithm can achieve an acceptable level of error in many cases, with the weakest performance occurring for model-error cases and more extreme parameter regimes with more complex dynamics. Analysis of the poorest-performing cases indicates an initial decrease in error followed by an increase when the ensemble spread is reduced. Our results suggest avenues for further improvement through increasing ensemble spread by incorporating additive inflation or using a parameter or multi-model ensemble. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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Accounting for land model error in numerical weather prediction ensemble systems: toward ensemble-based coupled land/atmosphere data assimilation

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0016.1 ◽

2021 ◽

Author(s):

Clara Sophie Draper

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Model Error ◽

Model Parameters ◽

Ensemble Spread ◽

Numerical Weather ◽

Vegetation Fraction ◽

Ensemble Systems

AbstractThe ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.

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A novel localization based biosensor, SEDAR, visualizes spatio-temporal dynamics of activated Ras in endogenous Drosophila tissues

10.1101/432997 ◽

2018 ◽

Author(s):

Rishita Changede

Keyword(s):

Plasma Membrane ◽

Growth Factor ◽

Real Time ◽

Tyrosine Kinases ◽

Temporal Dynamics ◽

Three Dimensional ◽

Growth Factor Receptor ◽

Leading Edge ◽

Border Cells ◽

Spatio Temporal

AbstractChemokine signaling via growth factor receptor tyrosine kinases (RTKs) regulates development, differentiation, growth and disease implying that it is involved in a myriad of cellular processes. A single RTK, for example the Epidermal Growth Factor Receptor (EGFR), is used repeatedly for a multitude of developmental programs. Quantitative differences in magnitude and duration of RTK signaling can bring about different signaling outcomes. Understanding this complex RTK signals requires real time visualization of the signal. To visualize spatio-temporal signaling dynamics, a biosensor called SEnsitive Detection of Activated Ras (SEDAR) was developed. It is a localization-based sensor that binds to activated Ras directly downstream of the endogenous activated RTKs. This binding was reversible and SEDAR expression did not cause any detectable perturbation of the endogenous signal. Using SEDAR, endogenous guidance signaling was visualized during RTK mediated chemotaxis of border cells in Drosophila ovary. SEDAR localized to both the leading and rear end of the cluster but polarized at the leading edge of the cluster. Perturbation of RTKs that led to delays in forward migration of the cluster correlated with loss of SEDAR polarization in the cluster. Gliding or tumbling behavior of border cells was a directly related to the high or low magnitude of SEDAR polarization respectively, in the leading cell showing that signal polarization at the plasma membrane provided information for the migratory behavior. Further, SEDAR localization to the plasma membrane detected EGFR mediated signaling in five distinct developmental contexts. Hence SEDAR, a novel biosensor could be used as a valuable tool to study the dynamics of endogenous Ras activation in real time downstream of RTKs, in three-dimensional tissues, at an unprecedented spatial and temporal resolution.

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A model-independent data assimilation (MIDA) module and its applications in ecology

Geoscientific Model Development ◽

10.5194/gmd-14-5217-2021 ◽

2021 ◽

Vol 14 (8) ◽

pp. 5217-5238

Author(s):

Xin Huang ◽

Dan Lu ◽

Daniel M. Ricciuto ◽

Paul J. Hanson ◽

Andrew D. Richardson ◽

...

Keyword(s):

Data Assimilation ◽

Ecological Models ◽

Model Parameters ◽

Independent Data ◽

Posterior Distributions ◽

Earth System ◽

Technical Requirements ◽

Efficient Data ◽

Model Independent ◽

Parameter Values

Abstract. Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting.

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The Effect of Model Error Identification on the Fast Reservoir Simulation by Capacitance-Resistance Model

SPE Journal ◽

10.2118/202475-pa ◽

2020 ◽

Vol 25 (06) ◽

pp. 3349-3365

Author(s):

Azadeh Mamghaderi ◽

Babak Aminshahidy ◽

Hamid Bazargan

Keyword(s):

Data Assimilation ◽

History Matching ◽

Water Injection ◽

Calibration Procedure ◽

Model Error ◽

Original Model ◽

Model Parameters ◽

Problem Solution ◽

Training Time ◽

Resistance Model

Summary Using fast and reliable proxies instead of sophisticated and time-consuming reservoir simulators is of great importance in reservoir management. The capacitance-resistance model (CRM) as a fast proxy has been widely used in this area. However, the inadequacy of this proxy for simplifying complex reservoirs with a limited number of parameters has not been addressed appropriately in related works in the literature. In this study, potential uncertainties in the modeling of the waterflooding process in the reservoir by the producer-based version of CRM (CRMP) are formulated, leading to embedding a new error-related term into the original formulation of the proxy. Considering a general form of the model error to represent both white and colored noises, a system of a CRMP-error equation is introduced analytically to deal with any type of intrinsic model imperfection. Two approaches are developed for the problem solution including the following: tuning the additional error-related parameters as a complementary stage of a classical history-matching procedure, and updating these parameters simultaneously with the original model parameters in a data-assimilation approach over model training time. To validate the model and show the effectiveness of both solution schemes, the injection and production data of a water-injection procedure in a three-layered reservoir model are used. Results show that the error-related parameters can be matched successfully along with the model original variables either in a routine model calibration procedure or in a data-assimilation approach by using the ensemble-based Kalman filter (EnKF) method. Comparing the average of the obtained range for the liquid rate as the problem output with true data demonstrates the effectiveness of considering model error. This leads to substantial improvement of the results compared with the case of applying the original model without considering the error term.

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Transformation of spatiotemporal dynamics in the macaque vestibular system from otolith afferents to cortex

eLife ◽

10.7554/elife.20787 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 19

Author(s):

Jean Laurens ◽

Sheng Liu ◽

Xiong-Jie Yu ◽

Raymond Chan ◽

David Dickman ◽

...

Keyword(s):

Temporal Dynamics ◽

Translational Motion ◽

Three Dimensional ◽

Cerebellar Nuclei ◽

Thalamic Nuclei ◽

Cortical Areas ◽

Temporal Properties ◽

Spatio Temporal ◽

Translation Signals ◽

Self Motion

Sensory signals undergo substantial recoding when neural activity is relayed from sensors through pre-thalamic and thalamic nuclei to cortex. To explore how temporal dynamics and directional tuning are sculpted in hierarchical vestibular circuits, we compared responses of macaque otolith afferents with neurons in the vestibular and cerebellar nuclei, as well as five cortical areas, to identical three-dimensional translational motion. We demonstrate a remarkable spatio-temporal transformation: otolith afferents carry spatially aligned cosine-tuned translational acceleration and jerk signals. In contrast, brainstem and cerebellar neurons exhibit non-linear, mixed selectivity for translational velocity, acceleration, jerk and position. Furthermore, these components often show dissimilar spatial tuning. Moderate further transformation of translation signals occurs in the cortex, such that similar spatio-temporal properties are found in multiple cortical areas. These results suggest that the first synapse represents a key processing element in vestibular pathways, robustly shaping how self-motion is represented in central vestibular circuits and cortical areas.

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Development of a semi-distributed hydrological model on a tidal-affected river: application to the Adour catchment, France.

10.5194/egusphere-egu2020-13582 ◽

2020 ◽

Author(s):

Valentin Mansanarez ◽

Guillaume Thirel ◽

Olivier Delaigue ◽

Benoit Liquet

Keyword(s):

Hydrological Model ◽

Potential Evapotranspiration ◽

River Mouth ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Natural Systems ◽

Tidal Influence ◽

Novel Approach ◽

Spatio Temporal ◽

Parameter Values

Streamflow estimation from rain events is a delicate exercise. Watersheds are complex natural systems and their response to rainfall events is influenced by many factors. Hydrological rainfall-runoff modelling is traditionally used to understand those factors by predicting discharges from precipitation data. These models are simplified conceptualisations and thus still struggle when facing some particular processes linked to the catchment. Among those processes, the tide influence on river discharges is rarely accounted for in hydrological modelling when estimating streamflow series at river mouth areas. Instead, estimated streamflow series are sometimes corrected by coefficients to account for the tide effect.In this presentation, we explored a semi-distributed hydrological model by adapting it to account for tidal-influence in the river mouth area. This model uses observed spatio-temporal rainfall and potential evapotranspiration databases to predict streamflow at gauged and ungauged locations within the catchment. The hydrological model is calibrated using streamflow observations and priors on parameter values to calibrate each model parameters of each sub-catchments. A drift procedure in the calibration process is used to ensure continuity in parameter values between upstream and downstream successive sub-catchments.This novel approach was applied to a tidal-affected catchment: the Adour&#8217;s catchment in southern France. Estimated results were compared to simulations without accounting for the tidal influence. Results from the new hydrological model were improved at tidal-affected locations of the catchment. They also show similar estimations in tidal-unaffected part of the catchment.

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An exact solution of a diffusive predator–prey system

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2004.1404 ◽

2005 ◽

Vol 461 (2056) ◽

pp. 1029-1053 ◽

Cited By ~ 32

Author(s):

Sergei Petrovskii ◽

Horst Malchow ◽

Bai-Lian Li

Keyword(s):

Exact Solution ◽

Large Time ◽

Temporal Dynamics ◽

Nonlinear Partial Differential Equations ◽

Predator Prey ◽

Change Of Variables ◽

Spatio Temporal ◽

Parameter Values ◽

The Impact ◽

Large Time Limit

We consider a system of two nonlinear partial differential equations describing the spatio-temporal dynamics of a predator–prey community where the prey per capita growth rate is damped by the Allee effect. Using an appropriate change of variables, we obtain an exact solution of the system, which appears to be related to the issue of biological invasion. In the large-time limit, or for appropriate parameter values, this solution describes the propagation of a travelling population front. We show that the properties of the solution exhibit biologically reasonable dependence on the parameter values; in particular, it predicts that the travelling front of invasive species can be stopped or reversed owing to the impact of predation.

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Examining differences in streamflow estimation for gauged and ungauged catchments using evolutionary data assimilation

Journal of Hydroinformatics ◽

10.2166/hydro.2013.193 ◽

2013 ◽

Vol 16 (2) ◽

pp. 392-406 ◽

Cited By ~ 2

Author(s):

Gift Dumedah ◽

Paulin Coulibaly

Keyword(s):

Data Assimilation ◽

Estimation Accuracy ◽

Model Parameters ◽

Ungauged Catchments ◽

Model Parameter ◽

Soil Moisture Accounting ◽

Measurement Models ◽

Daily Streamflow ◽

Parameter Values ◽

Ungauged Watersheds

Data assimilation has allowed hydrologists to account for imperfections in observations and uncertainties in model estimates. Typically, updated members are determined as a compromised merger between observations and model predictions. The merging procedure is conducted in decision space before model parameters are updated to reflect the assimilation. However, given the dynamics between states and model parameters, there is limited guarantee that when updated parameters are applied into measurement models, the resulting estimate will be the same as the updated estimate. To account for these challenges, this study uses evolutionary data assimilation (EDA) to estimate streamflow in gauged and ungauged watersheds. EDA assimilates daily streamflow into a Sacramento soil moisture accounting model to determine updated members for eight watersheds in southern Ontario, Canada. The updated members are combined to estimate streamflow in ungauged watersheds where the results show high estimation accuracy for gauged and ungauged watersheds. An evaluation of the commonalities in model parameter values across and between gauged and ungauged watersheds underscore the critical contributions of consistent model parameter values. The findings show a high degree of commonality in model parameter values such that members of a given gauged/ungauged watershed can be estimated using members from another watershed.

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Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

10.5194/gmd-2019-47 ◽

2019 ◽

Cited By ~ 1

Author(s):

Theo Baracchini ◽

Philip Yifei Chu ◽

Jonas Šukys ◽

Gian Lieberherr ◽

Stefan Wunderle ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Satellite Remote Sensing ◽

Three Dimensional ◽

Model Performance ◽

Remote Sensing Data ◽

Temporal Scales ◽

Localization Scheme ◽

Spatio Temporal

Abstract. The understanding of lakes physical dynamics is crucial to provide scientifically credible information for ecosystem management. We show how the combination of in-situ data, remote sensing observations and three-dimensional hydrodynamic numerical simulations is capable of delivering various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we present a flexible framework for DA into lakes three-dimensional hydrodynamic models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraints of operational systems and near real-time operations (e.g. integration into http://meteolakes.ch).

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Numerical model of the spatio-temporal dynamics in a water strider group

Scientific Reports ◽

10.1038/s41598-021-96686-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexander Kovalev ◽

Alexander E. Filippov ◽

Stanislav N. Gorb

Keyword(s):

Loss Rate ◽

Temporal Dynamics ◽

Complex Dynamics ◽

Point Of View ◽

Stored Energy ◽

Water Strider ◽

Population Status ◽

Appearance Rate ◽

Spatio Temporal ◽

Food Assimilation

AbstractThe water strider group demonstrates a very complex dynamics consisting of competition for the food items, territoriality and aggression to the conspecific individuals, escaping from the predators, etc. The situation is even more complex due to the presence of different instars, which in most water strider species live in the same habitat and occupy the same niche. The presented swarm model of water striders demonstrates the realistic population dynamics. For the swarm formation in the model, attraction and repulsion forces were used. Animal motion in the model takes into account inertia and kinetic energy dissipation effects. The model includes three different rates related to the growth of individuals: food appearance rate, food assimilation rate, and stored energy loss rate. The results of our modeling show that the size distribution of individuals seems to be an adequate measure for population status, and it has a characteristic shape for different model parameter combinations. Distribution of the distances between nearest neighbors is other important measure of the population density and its dynamics. Parameters of the model can be tuned in such a way, that the shape of both distributions in a steady phase coincides with that shape observed in a natural population, which helps to understand the factors leading to particular momentary distribution of both parameters (size and distance) in the population. From this point of view, the model can predict how both distributions can further develop from certain state depending on particular combination of factors.

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