Reduced order parameterized viscous optimal flow control problems and applications in coronary artery bypass grafts with patient-specific geometrical reconstruction and data assimilation

Coronary artery bypass graft surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease (CAD). In this thesis, we will construct a numerical framework combining parametrized optimal flow control and reduced order methods and will apply to real-life clinical case of triple coronary artery bypass grafts surgery. In this mathematical framework, we will propose patient-specific physiological data assimilation in the optimal flow control part, with the aim to minimize the discrepancies between the patient-specific physiological data and the computational hemodynamics. The optimal flow control paradigm proves to be a handy tool for the purpose and is being commonly used in the scientific community. However, the discrepancies between clinical measurements and computational hemodynamics modeling are usually due to unrealistic quantification of hard-to-quantify outflow conditions and computational inefficiency. In this work, we will utilize the unknown control in the optimal flow control pipeline to automatically quantify the boundary flux, specifically the outflux, required to minimize the data misfit, subject to different parametrized scenarios. Furthermore, the challenge of attaining reliable solutions in a time-efficient manner for such many-query parameter dependent problems will be addressed by reduced order methods

Reduced order methods for parametric optimal flow control in coronary bypass grafts, toward patient-specific data assimilation

Coronary artery bypass grafts (CABG) surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease. In this work, we apply a numerical optimal flow control model to patient-specific geometries of CABG, reconstructed from clinical images of real-life surgical cases, in parameterized settings. The aim of these applications is to match known physiological data with numerical hemodynamics corresponding to different scenarios, arisen by tuning some parameters. Such applications are an initial step toward matching patient-specific physiological data in patient-specific vascular geometries as best as possible. Two critical challenges that reportedly arise in such problems are: (a) lack of robust quantification of meaningful boundary conditions required to match known data as best as possible and (b) high computational cost. In this work, we utilize unknown control variables in the optimal flow control problems to take care of the first challenge. Moreover, to address the second challenge, we propose a time-efficient and reliable computational environment for such parameterized problems by projecting them onto a low-dimensional solution manifold through proper orthogonal decomposition-Galerkin

Quantification of sirolimus by liquid chromatography-tandem mass spectrometry using on-line solid-phase extraction

Quantification of the new immunosuppressant sirolimus (syn. rapamycin; Rapamune((R))) in whole blood by chromatography is essential for its clinical use since no immunoassay is available although monitoring is mandatory. Here we report on a rapid and convenient liquid chromatography (LC)-tandem mass spectrometry method and describe our practical experience with its routine use. Whole blood samples were hemolyzed and deproteinized using an equal volume (150 mul) of a mixture of methanol/zinc sulfate solution containing the internal standard desmethoxy-rapamycin. After centrifugation, the clear supernatants were submitted to an on-line solid-phase extraction procedure using the polymeric Waters Oasis HLB(R) material, with elution of the extracts onto the analytical column in the back-flush mode by column switching. For analytical chromatography a RP-C18 column was used with 90/10 methanol/2 mM ammonium acetate as the mobile phase. A 1:10 split was used for the transfer to the mass spectrometer, a Micromass Quattro LC-tandem mass spectrometry system equipped with a Z-spray((R)) source and used in the positive electrospray ionization mode. The following transitions were recorded: sirolimus, 931>864 m/z, and desmethoxy-rapamycin (I.S.), 901>834 m/z. The analytical running time was 5 min, including on-line extraction. The method has a linear calibration curve (r>0.99; range 1.6-50 mug/l) and is rugged and precise with monthly CVs <7% at a sirolimus concentration of 13.1 mug/l in routine use; the instrumentation proved to be reliable and required minimal maintenance

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain parameters is very rare in realistic applications. We discuss and compare four different methods tested for data-driven uncertainty quantification based on a benchmark scenario of carbon dioxide storage. In the benchmark, for which we provide data and code, carbon dioxide is injected into a saline aquifer modeled by the nonlinear capillarity-free fractional flow formulation for two incompressible fluid phases, namely carbon dioxide and brine. To cover different aspects of uncertainty quantification, we incorporate various sources of uncertainty such as uncertainty of boundary conditions, of conceptual model definitions and of material properties. We consider recent versions of the following non-intrusive and intrusive uncertainty quantification methods: arbitary polynomial chaos, spatially adaptive sparse grids, kernel-based greedy interpolation and hybrid stochastic Galerkin. The performance of each approach is demonstrated assessing expectation value and standard deviation of the carbon dioxide saturation against a reference statistic based on Monte Carlo sampling. We compare the convergence of all methods reporting on accuracy with respect to the number of model runs and resolution. Finally we offer suggestions about the methods' advantages and disadvantages that can guide the modeler for uncertainty quantification in carbon dioxide storage and beyond

Extension of the crRNA enhances Cpf1 gene editing in vitro and in vivo.

Engineering of the Cpf1 crRNA has the potential to enhance its gene editing efficiency and non-viral delivery to cells. Here, we demonstrate that extending the length of its crRNA at the 5 end can enhance the gene editing efficiency of Cpf1 both in cells and in vivo. Extending the 5 end of the crRNA enhances the gene editing efficiency of the Cpf1 RNP to induce non-homologous end-joining and homology-directed repair using electroporation in cells. Additionally, chemical modifications on the extended 5 end of the crRNA result in enhanced serum stability. Also, extending the 5 end of the crRNA by 59 nucleotides increases the delivery efficiency of Cpf1 RNP in cells and in vivo cationic delivery vehicles including polymer nanoparticle. Thus, 5 extension and chemical modification of the Cpf1 crRNA is an effective method for enhancing the gene editing efficiency of Cpf1 and its delivery in vivo

Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

An Algebraic Framework for Compositional Program Analysis

The purpose of a program analysis is to compute an abstract meaning for a program which approximates its dynamic behaviour. A compositional program analysis accomplishes this task with a divide-and-conquer strategy: the meaning of a program is computed by dividing it into sub-programs, computing their meaning, and then combining the results. Compositional program analyses are desirable because they can yield scalable (and easily parallelizable) program analyses. This paper presents algebraic framework for designing, implementing, and proving the correctness of compositional program analyses. A program analysis in our framework defined by an algebraic structure equipped with sequencing, choice, and iteration operations. From the analysis design perspective, a particularly interesting consequence of this is that the meaning of a loop is computed by applying the iteration operator to the loop body. This style of compositional loop analysis can yield interesting ways of computing loop invariants that cannot be defined iteratively. We identify a class of algorithms, the so-called path-expression algorithms [Tarjan1981,Scholz2007], which can be used to efficiently implement analyses in our framework. Lastly, we develop a theory for proving the correctness of an analysis by establishing an approximation relationship between an algebra defining a concrete semantics and an algebra defining an analysis.Comment: 15 page