Human Promoter Recognition Based on Principal Component Analysis

This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combined frequency matrices, and principal components (PCs) of each matrix are generated to construct the new feature space. ANN built classifiers are used to test the discriminability of each feature space. Finally, the 3 and 5-mer feature matrix is selected as the context feature in this model. Two proposed schemes of HPR-PCA model are discussed and the implementations of sub-modules in each scheme are introduced. The context features selected by PCA are III used to build three promoter and non-promoter classifiers. CpG-island modules are embedded into models in different ways. In the comparison, Scheme I obtains better prediction results on two test sets so it is adopted as the model for HPR-PCA for further evaluation. Three existing promoter prediction systems are used to compare to HPR-PCA on three test sets including the chromosome 22 sequence. The performance of HPR-PCA is outstanding compared to the other four systems

Performance Enhancement of Organic Light-Emitting Diodes with an Inorganically Doped Hole Transport Layer

Organic light-emitting diodes (OLEDs) are generally considered as the next generation display and lighting sources owing to their many attractive properties, including low power consumption, wide viewing angle, vibrant color, high contrast ratios and compatibility with flexible substrates. The research and development of OLEDs has attracted considerable interest and has led to significant progress during the last two decades. The use of OLEDs in small-area displays such as cell phone screens, digital cameras, and wearable devices has become a reality. However, the OLED technology is still far from mature, posing a challenge for their widespread acceptance for applications in large-area displays and solid-state lighting. In particular, the lifetime of OLEDs is too short for many commercial applications, and the degradation mechanisms are still under debate. This work aims to improve the OLED device lifetime by doping of organic hole transport materials with inorganic transition metal oxides (TMOs), and to reduce the cost by simplifying the device layer structure and manufacturing procedure.;First, stress tests under continuous wave and pulsed currents were conducted to gain a better understanding of the key factors governing the degradation process of phosphorescent OLEDs. Through comparative studies of the aging behaviors of OLEDs with different hole transport layers (HTLs) under different stressing conditions, we have found that joule heating plays an important role in device degradation when a large energy level misalignment exists at the indium-tin-oxide (ITO) anode/HTL interface. The heating was effectively suppressed by reducing the interfacial energy barrier, leading to a prolonged lifetime of the OLEDs.;P-type doping of hole transport materials with TMOs was then developed as an effective way to reduce the interfacial energy barrier and the operational voltage of OLED devices. A systematical study was carried out on the effects of doping 4,4\u27-Bis(N-carbazolyl)-1,1\u27-biphenyl (CBP), a wide bandgap organic hole transport material, with WO3 and MoO3. The optimal doping conditions including the doping level and doping thickness have been determined by fabricating and characterizing a series of hole-only devices. Integrating the doped HTL into green phosphorescent OLEDs has resulted in a simplified structure, better optoelectronic characteristics, and improved device reliability.;Finally, selective doping of organic materials with the TMOs was developed and the concept of delta doping was applied to OLEDs for the first time. Selective doping was achieved by simple sequential deposition of the organic host and TMO dopant. Hole-only devices with a HTL comprising alternative 0.5 nm TMO-doped/3-10 nm undoped CBP layers exhibited greatly enhanced hole transport and had a turn-on voltage as low as 1.1 V. Simple fluorescent tris-(8-hydroxyquinoline) aluminum (Alq3)-based green OLEDs with a selectively doped CBP HTL showed a lower voltage and longer lifetime under constant-current stressing compared to similar OLEDs with an undoped HTL. Furthermore. delta doping was realized in more thermally stable organic materials, resulting in a marked conductivity increase along the plane of the doped layers by several orders of magnitude. The delta doping effects were explained by hole accumulation in potential wells formed in nanometer-thick doped regions, as revealed by high-resolution secondary ion mass spectrometry (SIMS) measurements

Human Promoter Recognition Based on Principal Component Analysis

This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combined frequency matrices, and principal components (PCs) of each matrix are generated to construct the new feature space. ANN built classifiers are used to test the discriminability of each feature space. Finally, the 3 and 5-mer feature matrix is selected as the context feature in this model. Two proposed schemes of HPR-PCA model are discussed and the implementations of sub-modules in each scheme are introduced. The context features selected by PCA are III used to build three promoter and non-promoter classifiers. CpG-island modules are embedded into models in different ways. In the comparison, Scheme I obtains better prediction results on two test sets so it is adopted as the model for HPR-PCA for further evaluation. Three existing promoter prediction systems are used to compare to HPR-PCA on three test sets including the chromosome 22 sequence. The performance of HPR-PCA is outstanding compared to the other four systems

Animal welfare deserts: human and nonhuman animal inequities

Residents of distressed areas of inner cities have less access to many of life’s necessities and amenities than their more well-off counterparts. Geographic proximity has been identified as a primary barrier to accessing care for pets potentially creating animal welfare deserts. This project addresses three questions: Are there visible animal welfare deserts in distressed urban centers?; What human inequities are most strongly related to animal welfare deserts?; and What might be done to address these inequities? Using business location and census data in the city of Detroit, this research identifies distinct animal welfare deserts finding that more prosperous areas have more pet support resources and that the need for services is not related to the location of pet stores and veterinary offices. The study concludes that the overlap between human economic distress and pet resource deserts presents a threat to the goals of One Health. Potential policy solutions are proposed to address inequities in the distribution of animal welfare resources

A Neural Network Based Wide Area Monitor for a Power System

With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an online wide area monitor (WAM) using a neural network called the wide area neuroidentifier (WANI). The WANI is used to predict ahead the speed deviations of generators in the different areas using phasor measurement unit (PMU). Results are presented to show the effectiveness of the wide area monitor for different disturbances