JOINT ANALYSIS OF MULTIPLE PHENOTYPES IN ASSOCIATION STUDIES

Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. This dissertation contains three chapters and the three chapters include three new methods we developed for jointly analyzing multiple phenotypes. In the first chapter of this dissertation, we propose an Adaptive Fisher’s Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. In the second chapter, we propose an Allele-Based Clustering (ABC) approach for the joint analysis of multiple non-normal phenotypes in association studies. In the ABC method, we consider the alleles at a SNP of interest as a dependent variable with two classes, and the correlated phenotypes as predictors to predict the alleles at the SNP of interest. In the third chapter, we develop a novel variable reduction method using hierarchical clustering method (HCM) for joint analysis of multiple phenotypes in association studies. HCM involves two steps. The first step applies a dimension reduction technique by using a representative phenotype for each cluster of phenotypes. Then, existing methods are used in the second step to test the association between genetic variants and the representative phenotypes rather than the individual phenotypes. We perform extensive simulations to evaluate performances of AFC, ABC, and HCM methods and compare the powers of our methods with the powers of some existing methods. Our simulation studies show that the proposed methods have correct type I error rates and are either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodologies AFC and HCM by analyzing whole-genome genotyping data from a lung function study. The results of real data analysis demonstrated that the proposed methods have great potential in GWAS on complex diseases with multiple phenotypes

The Impact of Family Background on Subject Selection in Senior Secondary School Students in the Context of the Reform of College Entrance Examination: An Empirical Study Based on Survey Data from Zhejiang Province

Subject selection is one of the key components in the new college entrance examination system. This study analyzed the data from a sample survey of college entrance examination participants in Zhejiang Province between 2017 and 2020 using descriptive statistics, factor analysis, cross analysis, and binary logistic regression in an effort to research into the impact of family background on subject selection and academic achievements in senior secondary school students. Research findings showed that: (i) Students from families of higher social and economic status were more likely to be academically high-achieving; (ii) Student family background had a significant impact on their subject selection, specifically represented by a positive effect of home economic capital on student choice of subjects requiring higher learning costs, a positive effect of family perception of the importance of mathematics, physics, and chemistry on student choice of science subjects, and a positive effect of family cultural environment on student choice of liberal arts subjects; (iii) Personal factors such as gender and academic results are crucial for students’ decisions on subject selection, with male and high-performing students showing preferences for science subjects

An intelligent, distributed and collaborative DDoS defense system

The Distributed Denial-of-Service (DDoS) attack is known as one of the most destructive attacks on the Internet. With the advent of new computing paradigms, such as Cloud and Mobile computing, and the emergence of pervasive technology, such as the Internet of Things, on one hand, these revolutionized technologies enable the availability of services and applications to everyone. On the other hand, these techniques also benefit attackers to exploit the vulnerabilities and deploy attacks in more efficient ways. Latest network security reports have shown that distributed Denial of Service (DDoS) attacks have been growing dramatically in volume, frequency, sophistication and impact, making it one of the most challenging threats in the Internet. An unfortunate state of affairs is that the remediation strategies have fallen behind attackers. The severe impact caused by recent DDoS attacks strongly indicates the need for an effective DDoS defense system. We study the current existing solution space, and summarize three fundamental requirements for an effective DDoS defense system: 1) an accurate detection with minimal false alarms; 2) an effective inline inspection and instant mitigation, and 3) a dynamic, distributed and collaborative defense infrastructure. This thesis aims at providing such a defense system that fulfills all the requirements. In this thesis, we explore and address the problem from three directions: 1) we strive to understand the existing detection strategies and provide a survey of an empirical analysis of machine learning based detection techniques; 2) we develop a novel hybrid detection model which ensembles a deep learning model for a practical flow by flow detection and a classic machine learning model that is aware of the network status, and 3) we present the design and implementation of an intelligent, distributed and collaborative DDoS defense system that effectively mitigate the impact of DDoS attacks. The performance evaluation results show that our proposed defense system is capable of effectively mitigating DDoS attacks impacts and maintaining a limited disturbing for legitimate services

Improving Adversarial Attacks on Latent Diffusion Model

Adversarial attacks on Latent Diffusion Model (LDM), the state-of-the-art image generative model, have been adopted as effective protection against malicious finetuning of LDM on unauthorized images. We show that these attacks add an extra error to the score function of adversarial examples predicted by LDM. LDM finetuned on these adversarial examples learns to lower the error by a bias, from which the model is attacked and predicts the score function with biases. Based on the dynamics, we propose to improve the adversarial attack on LDM by Attacking with Consistent score-function Errors (ACE). ACE unifies the pattern of the extra error added to the predicted score function. This induces the finetuned LDM to learn the same pattern as a bias in predicting the score function. We then introduce a well-crafted pattern to improve the attack. Our method outperforms state-of-the-art methods in adversarial attacks on LDM

MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image Translation by Prompts Redescription and Beyond

Recently, text-to-image diffusion models become a new paradigm in image processing fields, including content generation, image restoration and image-to-image translation. Given a target prompt, Denoising Diffusion Probabilistic Models (DDPM) are able to generate realistic yet eligible images. With this appealing property, the image translation task has the potential to be free from target image samples for supervision. By using a target text prompt for domain adaption, the diffusion model is able to implement zero-shot image-to-image translation advantageously. However, the sampling and inversion processes of DDPM are stochastic, and thus the inversion process often fail to reconstruct the input content. Specifically, the displacement effect will gradually accumulated during the diffusion and inversion processes, which led to the reconstructed results deviating from the source domain. To make reconstruction explicit, we propose a prompt redescription strategy to realize a mirror effect between the source and reconstructed image in the diffusion model (MirrorDiffusion). More specifically, a prompt redescription mechanism is investigated to align the text prompts with latent code at each time step of the Denoising Diffusion Implicit Models (DDIM) inversion to pursue a structure-preserving reconstruction. With the revised DDIM inversion, MirrorDiffusion is able to realize accurate zero-shot image translation by editing optimized text prompts and latent code. Extensive experiments demonstrate that MirrorDiffusion achieves superior performance over the state-of-the-art methods on zero-shot image translation benchmarks by clear margins and practical model stability.Comment: A prompt re-description strategy is proposed for stabilizing the diffusion model in image-to-image translation. Code and dataset page: https://mirrordiffusion.github.io

Performance-based plastic design method of high-rise steel frames

Under major earthquakes, high-rise steel moment frames designed according to the current codes will experience an inelastic deformation, which is difficult to predict and control. According to the principle of work-energy balance, a performance-based plastic design (PBPD) methodology is put forward for the design of high-rise steel frames in this study. In this method, the target drift and yield mechanisms are pre-selected as key performance criteria. The design base shear in a given earthquake level is calculated based on the work-energy balance principle that the work required to push the structure monotonically to the target drift is equal to the energy needed by an equivalent single degree of freedom to reach the same state. The plastic design is utilized to design the frame components and connections so as to attain the desired yield mechanism and behavior. The method has been adopted to design a ten-story steel moment resisting frame, and has been validated by nonlinear dynamic time history analyses and pushover analysis. The results indicate that the frames develop targeted strong column sway mechanisms, and the story drifts are less than the target values, thus satisfying the anticipated performance objectives. The addressed method herein can form a basis for the performance-based plastic design of high-rise steel moment resisting frames

Tunable Magnetism and Valleys in VSiZ3_3 monolayers

Two-dimensional magnetism and valleys have recently emerged as two significant research areas, with intriguing properties and practical uses in advanced information technology. Considering the importance of these two areas and their couplings, controllable creations of both the magnetism and valley polarization are highly sought after. Based on first-principles calculations, we propose a new class of two-dimensional monolayers with a chemical formula of MAZ$_3$, which is viewed as a 2H-MZ$_2$ trilayer passivated by the A-Z bilayer on its one side. Taking VSiN$_3$ as an example, the MAZ$_3$ monolayers are found to exhibit tunable magnetism and valleys. For the intrinsic VSiN$_3$ monolayer, it is a non-magnetic semiconductor, with multiple degenerate valleys and trigonal warping near $K_\pm$ points in the band structure. Besides, the bands have spin splittings owing to the spin-orbit coupling. Under a moderate carrier doping, the monolayer becomes a Stoner ferromagnet, which enhances the spin splittings of the valence band and generates valley splittings. Moreover, the Berry curvature is valley contrasting, leading to distinct valley-spin related anomalous Hall currents as the doping concentration increases. Our work opens up new way to modulate the spin splittings and valley splittings via electric means, and provides opportunities for exploring advanced spintronic and valleytronic devices.Comment: 6 pages, 4 figure

Association of Nicotine with Osteochondrogenesis and Osteoarthritis Development: The State of the Art of Preclinical Research

The deleterious effects of nicotine on various health conditions have been well documented. Although many orthopedic diseases are adversely affected by nicotine, little is known about its preclinical effects on chondrogenesis or osteogenesis, cartilage formation, osteoarthritis (OA), and osteochondral repair. A systematic review was conducted examining the current scientific evidence on the effects of nicotine on chondrogenesis or osteogenesis in vitro, possible consequences of prenatal nicotine exposure (PNE) on cartilage and OA susceptibility in the offspring, and whether nicotine affects OA development and osteochondral repair in vivo, always focusing on their underlying mechanisms. The data reveal dose-dependent effects on articular chondrocytes and on the chondrogenesis and osteogenesis of medicinal signaling cells in vitro, with lower doses often resulting in positive effects and higher doses causing negative effects. PNE negatively affects articular cartilage development and induces OA in the offspring without or with nicotine exposure. In contrast, protective effects on OA development were only reported in monosodium iodoacetate-induced small animal models. Finally, nicotine repressed MSC-based osteochondral repair in vivo. Future studies need to investigate dose-dependent clinical effects of smoking on cartilage quality in offspring, OA susceptibility and progression, and osteochondral repair more in detail, thus identifying possible thresholds for its pathological effects