Toward Controlling Cardiac Tissue Pacing Using Modified mRNA

Arrhythmia is a common heart disease that happens when the heart is beating too fast, too slow, or irregularly. To study the mechanisms and treatments of this disease, it is important to acutely control the beating rate of the model as it will help distinguish the contribution of different potassium currents and drug-induced action potential in cardiomyocytes. The current method of tissue pacing, electrical pacing, causes contamination and corrosive damage to tissues, thus the tissues fail to be used repeatedly or in future studies. In this study, red-shifted channelrhodopsin (ReaChR) is applied as a non-chemical means to control the beating rate. ReaChR is a light-gated ion channel that opens and allows potassium to enter cardiomyocytes when excited by red lights. To deliver ReaChR into micro tissues, modified mRNA is chosen because of its higher transfection rate comparing to the plasmid, and lower cell toxicity comparing to the virus. Reducible polycation (RPC) is synthesized and used as transfection reagent to acquire a better transfection rate and modifiable structure. The results show successful modified mRNA synthesis and enhanced transfection efficiency with modified mRNA comparing to plasmid in both cells and tissues. The improved transfection efficiency of modified mRNA into iPSC-derived cardiomyocytes and iPSC- derived micro heart muscle using RPC is achieved. The results presented in this thesis demonstrate the potential of using modified mRNA to control the beating rate of the tissue and eventually control other physiological properties in cells

Modeling of Swimming Cells from Nano-Scale to Micro-Scale

Certain human genetic diseases -- primary ciliary dyskinesia, infertility, and hydrocephalus -- are characterized by changes in beat frequency and waveform of cilia and flagella. Chlamydomonas reinhardtii, which is a single-cell green alga about ten micrometers in diameter that swims with two flagella, serves as an excellent biological model because its flagella share the same structure and genetic background as mammalian cilia and flagella. This study uses the finite element method to investigate the behavior of C. reinhardtii swimming from nano-scale to micro-scale. At the device-level, micro-scale modeling indicates that well-designed acoustic microfluidic devices can be used to trap groups of C. reinhardtii, and then the apparent spread of a C. reinhardtii population can be correlated to swimming capability through knowledge of the acoustic trapping strength. This finite element model is validated against cell-trapping experiments. At the organism-level, a static nano-scale model is used to study the passive structures of flagella, and a dynamic nano-scale model confirms the theory that steady dynein force (active structures), combined with fluid-structure interactions, can induce flagellar oscillation. These two studies will be connected by a particle tracing model that incorporates the dynamic nano-scale model and the micro-scale model to enable further study of both propulsive forces and the acoustic cell-trapping mechanism

Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees

A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses

Robust multi-objective optimization for islanded data center microgrid operations

Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi-objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.</p

Robust multi-objective optimization for islanded data center microgrid operations

Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi-objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.</p