Refine
Has Fulltext
- yes (2) (remove)
Year of publication
- 2021 (2) (remove)
Document Type
- Doctoral Thesis (2) (remove)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
- data assimilation (2) (remove)
Institute
The Earth's electron radiation belts exhibit a two-zone structure, with the outer belt being highly dynamic due to the constant competition between a number of physical processes, including acceleration, loss, and transport. The flux of electrons in the outer belt can vary over several orders of magnitude, reaching levels that may disrupt satellite operations. Therefore, understanding the mechanisms that drive these variations is of high interest to the scientific community.
In particular, the important role played by loss mechanisms in controlling relativistic electron dynamics has become increasingly clear in recent years. It is now widely accepted that radiation belt electrons can be lost either by precipitation into the atmosphere or by transport across the magnetopause, called magnetopause shadowing. Precipitation of electrons occurs due to pitch-angle scattering by resonant interaction with various types of waves, including whistler mode chorus, plasmaspheric hiss, and electromagnetic ion cyclotron waves. In addition, the compression of the magnetopause due to increases in solar wind dynamic pressure can substantially deplete electrons at high L shells where they find themselves in open drift paths, whereas electrons at low L shells can be lost through outward radial diffusion. Nevertheless, the role played by each physical process during electron flux dropouts still remains a fundamental puzzle.
Differentiation between these processes and quantification of their relative contributions to the evolution of radiation belt electrons requires high-resolution profiles of phase space density (PSD). However, such profiles of PSD are difficult to obtain due to restrictions of spacecraft observations to a single measurement in space and time, which is also compounded by the inaccuracy of instruments. Data assimilation techniques aim to blend incomplete and inaccurate spaceborne data with physics-based models in an optimal way. In the Earth's radiation belts, it is used to reconstruct the entire radial profile of electron PSD, and it has become an increasingly important tool in validating our current understanding of radiation belt dynamics, identifying new physical processes, and predicting the near-Earth hazardous radiation environment.
In this study, sparse measurements from Van Allen Probes A and B and Geostationary Operational Environmental Satellites (GOES) 13 and 15 are assimilated into the three-dimensional Versatile Electron Radiation Belt (VERB-3D) diffusion model, by means of a split-operator Kalman filter over a four-year period from 01 October 2012 to 01 October 2016. In comparison to previous works, the 3D model accounts for more physical processes, namely mixed pitch angle-energy diffusion, scattering by EMIC waves, and magnetopause shadowing. It is shown how data assimilation, by means of the innovation vector (the residual between observations and model forecast), can be used to account for missing physics in the model. This method is used to identify the radial distances from the Earth and the geomagnetic conditions where the model is inconsistent with the measured PSD for different values of the adiabatic invariants mu and K. As a result, the Kalman filter adjusts the predictions in order to match the observations, and this is interpreted as evidence of where and when additional source or loss processes are active.
Furthermore, two distinct loss mechanisms responsible for the rapid dropouts of radiation belt electrons are investigated: EMIC wave-induced scattering and magnetopause shadowing. The innovation vector is inspected for values of the invariant mu ranging from 300 to 3000 MeV/G, and a statistical analysis is performed to quantitatively assess the effect of both processes as a function of various geomagnetic indices, solar wind parameters, and radial distance from the Earth. The results of this work are in agreement with previous studies that demonstrated the energy dependence of these two mechanisms. EMIC wave scattering dominates loss at lower L shells and it may amount to between 10%/hr to 30%/hr of the maximum value of PSD over all L shells for fixed first and second adiabatic invariants. On the other hand, magnetopause shadowing is found to deplete electrons across all energies, mostly at higher L shells, resulting in loss from 50%/hr to 70%/hr of the maximum PSD. Nevertheless, during times of enhanced geomagnetic activity, both processes can operate beyond such location and encompass the entire outer radiation belt.
The results of this study are two-fold. Firstly, it demonstrates that the 3D data assimilative code provides a comprehensive picture of the radiation belts and is an important step toward performing reanalysis using observations from current and future missions. Secondly, it achieves a better understanding and provides critical clues of the dominant loss mechanisms responsible for the rapid dropouts of electrons at different locations over the outer radiation belt.
While patients are known to respond differently to drug therapies, current clinical practice often still follows a standardized dosage regimen for all patients. For drugs with a narrow range of both effective and safe concentrations, this approach may lead to a high incidence of adverse events or subtherapeutic dosing in the presence of high patient variability. Model-informedprecision dosing (MIPD) is a quantitative approach towards dose individualization based on mathematical modeling of dose-response relationships integrating therapeutic drug/biomarker monitoring (TDM) data. MIPD may considerably improve the efficacy and safety of many drug therapies. Current MIPD approaches, however, rely either on pre-calculated dosing tables or on simple point predictions of the therapy outcome. These
approaches lack a quantification of uncertainties and the ability to account for effects that are delayed. In addition, the underlying models are not improved while applied to patient data. Therefore, current approaches are not well suited for informed clinical decision-making based on a differentiated understanding of the individually predicted therapy outcome.
The objective of this thesis is to develop mathematical approaches for MIPD, which (i) provide efficient fully Bayesian forecasting of the individual therapy outcome including associated uncertainties, (ii) integrate Markov decision processes via reinforcement learning (RL) for a comprehensive decision framework for dose individualization, (iii) allow for continuous learning across patients and hospitals. Cytotoxic anticancer chemotherapy with its major dose-limiting toxicity, neutropenia, serves as a therapeutically relevant application example.
For more comprehensive therapy forecasting, we apply Bayesian data assimilation (DA) approaches, integrating patient-specific TDM data into mathematical models of chemotherapy-induced neutropenia that build on prior population analyses. The value of uncertainty quantification is demonstrated as it allows reliable computation of the patient-specific probabilities of relevant clinical quantities, e.g., the neutropenia grade. In view of novel home monitoring devices that increase the amount of TDM data available, the data processing of
sequential DA methods proves to be more efficient and facilitates handling of the variability between dosing events.
By transferring concepts from DA and RL we develop novel approaches for MIPD. While DA-guided dosing integrates individualized uncertainties into dose selection, RL-guided dosing provides a framework to consider delayed effects of dose selections. The combined
DA-RL approach takes into account both aspects simultaneously and thus represents a holistic approach towards MIPD. Additionally, we show that RL can be used to gain insights into important patient characteristics for dose selection. The novel dosing strategies substantially reduce the occurrence of both subtherapeutic and life-threatening neutropenia grades in a simulation study based on a recent clinical study (CEPAC-TDM trial) compared to currently used MIPD approaches.
If MIPD is to be implemented in routine clinical practice, a certain model bias with respect to the underlying model is inevitable, as the models are typically based on data from comparably small clinical trials that reflect only to a limited extent the diversity in real-world patient populations. We propose a sequential hierarchical Bayesian inference framework that enables continuous cross-patient learning to learn the underlying model parameters of the target patient population. It is important to note that the approach only requires summary information of the individual patient data to update the model. This separation of the individual inference from population inference enables implementation across different centers of care.
The proposed approaches substantially improve current MIPD approaches, taking into account new trends in health care and aspects of practical applicability. They enable progress towards more informed clinical decision-making, ultimately increasing patient benefits beyond the current practice.