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

Ultracapacitor character analysis and its application in unified power quality conditioner as energy storage system

This dissertation focuses on the Ultracapacitor (UCAP) character analysis and its application in Unified Power Quality Conditioner (UPQC) as an Energy Storage System (ESS) for improved UPQC performance. It includes three parts as described below. The first part is Paper I. The UCAP is a popular choice for the ESS because of its distinct characters. In the application UCAP\u27s transient behavior need to be studied for design purpose. Usually these transient characters are not shown in the product data sheet. In this paper UCAP frequency analysis is performed, and based on the test data the equivalent model of the UCAP is built. The fitting result shows that using multi-level ladder circuit can perfectly fit the UCAP transient characters. The second part is Paper II. A UPQC is to compensate both source voltage sag and load current imperfections in power distribution system. With the UCAP based Energy Storage System, the UPQC has an optimized power flow between UPQC and system during the transit state, also the UPQC\u27s serial and shunt part has an improved performance with Ucap. The impact of UCAP model on its control and simulation is analyzed. From the analysis, UCAP based Energy Storage System can well fulfill the requirement of UPQC to provide high active power during transit time and improve the UPQC overall performance. The third part is Paper III. Conventionally the PI control method is applied in UPQC, including DVR and APF part. With ESS the H∞ control method is applied in UPQC. This paper shows that the two methods have their own advantages and disadvantages. In the practical application the control method can be chosen by considering the different transit states --Abstract, page iv

DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

The volume-wise labeling of 3D medical images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL) is highly desirable for training with limited labeled data. Imbalanced class distribution is a severe problem that bottlenecks the real-world application of these methods but was not addressed much. Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D medical image segmentation. Specifically, we propose two loss weighting strategies, namely Distribution-aware Debiased Weighting (DistDW) and Difficulty-aware Debiased Weighting (DiffDW), which leverage the pseudo labels dynamically to guide the model to solve data and learning biases. The framework improves significantly by co-training these two diverse and accurate sub-models. We also introduce more representative benchmarks for class-imbalanced semi-supervised medical image segmentation, which can fully demonstrate the efficacy of the class-imbalance designs. Experiments show that our proposed framework brings significant improvements by using pseudo labels for debiasing and alleviating the class imbalance problem. More importantly, our method outperforms the state-of-the-art SSL methods, demonstrating the potential of our framework for the more challenging SSL setting. Code and models are available at: https://github.com/xmed-lab/DHC.Comment: Accepted at MICCAI202

FSDiffReg: Feature-wise and Score-wise Diffusion-guided Unsupervised Deformable Image Registration for Cardiac Images

Unsupervised deformable image registration is one of the challenging tasks in medical imaging. Obtaining a high-quality deformation field while preserving deformation topology remains demanding amid a series of deep-learning-based solutions. Meanwhile, the diffusion model's latent feature space shows potential in modeling the deformation semantics. To fully exploit the diffusion model's ability to guide the registration task, we present two modules: Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided Module (SDG). Specifically, FDG uses the diffusion model's multi-scale semantic features to guide the generation of the deformation field. SDG uses the diffusion score to guide the optimization process for preserving deformation topology with barely any additional computation. Experiment results on the 3D medical cardiac image registration task validate our model's ability to provide refined deformation fields with preserved topology effectively. Code is available at: https://github.com/xmed-lab/FSDiffReg.git.Comment: Accepted as a conference paper at Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference 202

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