Integration of Functional Genomic Data in Genetic Analysis

Identifying disease risk genes is a central topic of human genetics. Cost-effective exome and whole genome sequencing enabled large-scale discovery of genetic variations. However, the statistical power of finding new risk genes through rare genetic variation is fundamentally limited by sample sizes. As a result, we have an incomplete understanding of genetic architecture and molecular etiology of most of human conditions and diseases. In this thesis, I developed new computational methods that integrate functional genomics data sets, such as epigenomic profiles and single-cell transcriptomics, to improve power for identifying genetic risks and gain more insights on etiology of developmental disorders. The overall hypothesis that disease risk genes contributing to developmental disorders are bottleneck genes under normal development and subject to precise transcriptional regulations to maintain spatiotemporal specific expression during development. In this thesis I describe two major research projects. The first project, Episcore, predicts haploinsufficient genes based on a large integrated epigenomic profiles from multiple tissues and cell lines by supervised machine learning methods. The second one, A-risk, predicts plausibility of being risk genes of autism spectrum disorder based on single-cell RNA-seq data collected in human fetal midbrain and prefrontal cortex. Both methods were shown to be able to improve gene discovery in analysis of de novo mutations in developmental disorders. Overall, my thesis represents an effort to integrate functional genomics data by machine learning to facilitate both discovery and interpretation of genetic studies of human diseases. We believe that such integrative analysis can help us better understand genetic variants and disease etiology

The Future of Healthy Living: Improving the impact of urban living environments on residents

Healthy living is always a significant part of community well-being. The quality of human living spaces plays an important role in promoting health and wellbeing. Increasingly, there are many mental and physical health concerns in multicultural, complex urban living environments. Living environments, which include dwellings and community areas, become important examples when considering potential leveraging points to improve the quality of healthy urban living. Considering the current rapid technological development and cultural shifts, there are many significant changes in people’s lifestyles that will affect their living space needs. This project begins by describing the current development of residential living space in urban cities, such as Toronto, and analyzing the dynamics at play and their patterns of social support. Utilizing foresight methods, systemic analysis, and generative research methodology, this project will research urban ecologies to understand the relationship between humans and their living spaces and how space could shape their wellbeing, highlighting the physical and mental issues that impact people today in urban city living spaces. Through an investigation of various aspects of current urban development patterns, possible future directions and solutions for the living environment may be proposed. This research will focus on intervention strategies and key design principles for future residential design innovations as a guide, catalyst, and opportunity for future residential design inspiration

A Dual Cox Model Theory And Its Applications In Oncology

Given the prominence of targeted therapy and immunotherapy in cancer treatment, it becomes imperative to consider heterogeneity in patients' responses to treatments, which contributes greatly to the widely used proportional hazard assumption invalidated as in several clinical trials. To address the challenge, we develop a Dual Cox model theory including a Dual Cox model and a fitting algorithm. As one of the finite mixture models, the proposed Dual Cox model consists of two independent Cox models based on patients' responses to one designated treatment (usually the experimental one) in the clinical trial. Responses of patients in the designated treatment arm can be observed and hence those patients are known responders or non-responders. From the perspective of subgroup classification, such a phenomenon renders the proposed model as a semi-supervised problem, compared to the typical finite mixture model where the subgroup classification is usually unsupervised. A specialized expectation-maximization algorithm is utilized for model fitting, where the initial parameter values are estimated from the patients in the designated treatment arm and then the iteratively reweighted least squares (IRLS) is applied. Under mild assumptions, the consistency and asymptotic normality of its estimators of effect parameters in each Cox model are established. In addition to strong theoretical properties, simulations demonstrate that our theory can provide a good approximation to a wide variety of survival models, is relatively robust to the change of censoring rate and response rate, and has a high prediction accuracy and stability in subgroup classification while it has a fast convergence rate. Finally, we apply our theory to two clinical trials with cross-overed KM plots and identify the subgroups where the subjects benefit from the treatment or not

Three-dimensional single gyroid photonic crystals with a mid-infrared bandgap

A gyroid structure is a distinct morphology that is triply periodic and consists of minimal isosurfaces containing no straight lines. We have designed and synthesized amorphous silicon (a-Si) mid-infrared gyroid photonic crystals that exhibit a complete bandgap in infrared spectroscopy measurements. Photonic crystals were synthesized by deposition of a-Si/Al2O3 coatings onto a sacrificial polymer scaffold defined by two-photon lithography. We observed a 100% reflectance at 7.5 \mum for single gyroids with a unit cell size of 4.5 \mum, in agreement with the photonic bandgap position predicted from full-wave electromagnetic simulations, whereas the observed reflection peak shifted to 8 um for a 5.5 \mum unit cell size. This approach represents a simulation-fabrication-characterization platform to realize three-dimensional gyroid photonic crystals with well-defined dimensions in real space and tailored properties in momentum space

Stabilization and current-induced motion of antiskyrmion in the presence of anisotropic Dzyaloshinskii-Moriya interaction

Topological defects in magnetism have attracted great attention due to fundamental research interests and potential novel spintronics applications. Rich examples of topological defects can be found in nanoscale non-uniform spin textures, such as monopoles, domain walls, vortices, and skyrmions. Recently, skyrmions stabilized by the Dzyaloshinskii-Moriya interaction have been studied extensively. However, the stabilization of antiskyrmions is less straightforward. Here, using numerical simulations we demonstrate that antiskyrmions can be a stable spin configuration in the presence of anisotropic Dzyaloshinskii-Moriya interaction. We find current-driven antiskyrmion motion that has a transverse component, namely antiskyrmion Hall effect. The antiskyrmion gyroconstant is opposite to that for skyrmion, which allows the current-driven propagation of coupled skyrmion-antiskyrmion pairs without apparent skyrmion Hall effect. The antiskyrmion Hall angle strongly depends on the current direction, and a zero antiskyrmion Hall angle can be achieved at a critic current direction. These results open up possibilities to tailor the spin topology in nanoscale magnetism, which may be useful in the emerging field of skyrmionics.Comment: 31 pages, 6 figures, to appear in Physical Review

Contribution of Msh2 and Msh6 Subunits to the Asymmetric ATPase and DNA Mismatch Binding Activities of \u3cem\u3eSaccharomyces cerevisiae\u3c/em\u3e Msh2–Msh6 Mismatch Repair Protein

Previous analyses of both Thermus aquaticus MutS homodimer and Saccharomyces cerevisiae Msh2–Msh6 heterodimer have revealed that the subunits in these protein complexes bind and hydrolyze ATP asymmetrically, emulating their asymmetric DNA binding properties. In the MutS homodimer, one subunit (S1) binds ATP with high affinity and hydrolyzes it rapidly, while the other subunit (S2) binds ATP with lower affinity and hydrolyzes it at an apparently slower rate. Interaction of MutS with mismatched DNA results in suppression of ATP hydrolysis at S1—but which of these subunits, S1 or S2, makes specific contact with the mismatch (e.g., base stacking by a conserved phenylalanine residue) remains unknown. In order to answer this question and to clarify the links between the DNA binding and ATPase activities of each subunit in the dimer, we made mutations in the ATPase sites of Msh2 and Msh6 and assessed their impact on the activity of the Msh2–Msh6 heterodimer (in Msh2–Msh6, only Msh6 makes base specific contact with the mismatch). The key findings are: (a) Msh6 hydrolyzes ATP rapidly, and thus resembles the S1 subunit of the MutS homodimer, (b) Msh2 hydrolyzes ATP at a slower rate, and thus resembles the S2 subunit of MutS, (c) though itself an apparently weak ATPase, Msh2 has a strong influence on the ATPase activity of Msh6, (d) Msh6 binding to mismatched DNA results in suppression of rapid ATP hydrolysis, revealing a “cis” linkage between its mismatch recognition and ATPase activities, (e) the resultant Msh2–Msh6 complex, with both subunits in the ATP-bound state, exhibits altered interactions with the mismatch

Role of Cytokines and Chemokines in Alcohol-Induced Tumor Promotion

Excessive chronic alcohol consumption has become a worldwide health problem. The oncogenic effect of chronic alcohol consumption is one of the leading concerns. The mechanisms of alcohol-induced tumorigenesis and tumor progression are largely unknown, although many factors have been implicated in the process. This review discusses the recent progress in this research area with concentration on alcohol-induced dysregulation of cytokines and chemokines. Based on the available evidence, we propose that alcohol promotes tumor progression by the dysregulation of the cytokine/chemokine system. In addition, we discuss specific transcription factors and signaling pathways that are involved in the action of these cytokines/chemokines and the oncogenic effect of alcohol. This review provides novel insight into the mechanisms of alcohol-induced tumor promotion

Integrated prediction of green bond return under the dual risks of climate change and energy crisis

Prediction of bond return is a classic problem in financial area, providing an important basis for portfolio construction and risk management. The sustainable investment attribute of green bonds has been favored by investors, so that green bonds have become an important component for major asset allocation. However, due to the specific investment focus of green bonds, investors’ return expectations are influenced not only by traditional corporate bond factors, but also by related factors such as climate change and energy transition. Against the backdrop of increasingly severe climate risks and the global energy crisis, this paper analyses the volatility characteristics of China’s green bonds at multiple time scales, and introduces exogenous variables such as returns of the alternative financial assets, climate risks and returns of energy markets for prediction. Based on the LSTM model, the volatility of green bond yield at different time scales is separately predicted using optimal exogenous variable before integration. It is found that the new integrated prediction model can significantly improve the forecasting performance compared to traditional single LSTM models and simple decomposition-integrated models. Further, both climate risks and energy markets variables have a significant improvement effect on predicting green bond in low-frequency item, while energy markets variables also have a better predictive effect on trend items. Building on the use of only LSTM model, it could be further enhanced by integrating more algorithms to select the best single model for each component, further improve the prediction accuracy and provide a more effective quantitative tool for investment decision-making and risk management in related fields