Doctor of Philosophy

dissertationThermal ablation is widely used, first line local-regional therapy for unresectable hepatocellular carcinoma (HCC). Although high temperature delivered by thermal energy results in efficient coagulation necrosis in tumor cells, various factors including tumor size, shape, location, and cirrhosis can lead to un-uniform heat distribution and inefficient cell damage. As a result, the incomplete ablation causes high rates of tumor recurrence and poor survival for HCC patients. Cells that are not completely ablated can induce heat shock proteins (HSPs), which are cellular gatekeepers to protect tumor cells from thermal damage and prepare them for future neoplastic growth. Synchronous adjuvant chemotherapy targeting those cells can achieve more complete tumor abrogation and prevent future tumor recurrence. This dissertation describes a strategy to combat postablation recurrence by synchronous inhibition of heat shock protein 90 (HSP90) by thermo-responsive, elastin-like polypeptide (ELP)-based biopolymer conjugates. ELP copolymer carries high concentrations of a potent HSP90 inhibitor, geldanamycin (GA), which inhibit the induction of HSP90 and further destabilize numerous HSP90 client proteins critical for cell survival. It is hypothesized that combination of thermal ablation with concomitant inhibition of HSP90 via ELP-GA conjugates can achieve synergistic anticancer effect. Specifically, the ablation-created hyperthermia will sensitize tumor cells to be more vulnerable to the drug, which will be conjugated with high concentrations through thermally targeted, ELP-based biopolymer systems. The ELP conjugates, in turn, will reach and kill the remaining viable cells to prevent future recurrence. ELP-GA conjugates that ferry multiple GAs and rapidly respond to hyperthermia were synthesized, characterized, and evaluated for activity in HCC models. The cytotoxicity of ELP-GA conjugates was enhanced with hyperthermia treatment, and effective HSP90 inhibition was achieved in HCC cell lines. In a tumor-bearing mouse model, electrocautery-based thermal ablation offered effective destruction of tumor core and created a hyperthermia zone for targeted delivery and accumulation of ELP-GA conjugates. Results demonstrate that the combination of thermal ablation and targeted HSP90 inhibition can enhance the anticancer effect and cellular delivery of macromolecular chemotherapeutics to achieve safe, synergistic, and long-term anticancer effect with no tumor recurrence observed. The combination approach paves the way for developing molecular-targeted intervention to increase the efficacy of first-line local-regional therapies for HCC

CAN THE INTERNET HELP RELIEVE DEPRESSION?

Depression is a very serious problem in our modern society. It can influence people negatively in many ways. Although many scientists are trying to find efficient ways to deal with depression, there are still no final conclusions. Using data from the China Health and Retirement Longitudinal Study (CHARLS), this study shows that in China, internet use among older people (aged 45 and above) helps relieve their depression symptoms. Combining this finding with steadily growing cellphone use, this suggests that developing specially APPs for older people to use smart phones can help combat depression in China. Other ways to reach the rural population via the internet can also be powerful tools to help with depression

Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning

A backdoor attack allows a malicious user to manipulate the environment or corrupt the training data, thus inserting a backdoor into the trained agent. Such attacks compromise the RL system's reliability, leading to potentially catastrophic results in various key fields. In contrast, relatively limited research has investigated effective defenses against backdoor attacks in RL. This paper proposes the Recovery Triggered States (RTS) method, a novel approach that effectively protects the victim agents from backdoor attacks. RTS involves building a surrogate network to approximate the dynamics model. Developers can then recover the environment from the triggered state to a clean state, thereby preventing attackers from activating backdoors hidden in the agent by presenting the trigger. When training the surrogate to predict states, we incorporate agent action information to reduce the discrepancy between the actions taken by the agent on predicted states and the actions taken on real states. RTS is the first approach to defend against backdoor attacks in a single-agent setting. Our results show that using RTS, the cumulative reward only decreased by 1.41% under the backdoor attack

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the base-learners as multiple versions of "ground truths". Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semi-supervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.Comment: To appear in AAAI-2019. The first three authors contributed equally to the pape

The residential coal consumption : disparity in urban-rural China

We appreciate the support of the Program for Major Projects in Philosophy and Social Science Research of the Ministry of Education of China (No. 14JZD031), Key Program of National Social Science Fund of China (No. 15AJY005), National Natural Science Foundation of China (Nos. 71473203, 71171001, and 71471001), and New Century Excellent Talents in University (No. NCET-12-0595).Peer reviewedPostprin

Environmental regulations and corporate cash holdings

The impact of environmental regulations on corporate performance and decisions has attracted significant attention from academics, practitioners and policymakers. We extend this line of research to examine the impact of regional environmental regulations on firms’ cash holdings. We find that environmental regulations motivate firms to increase cash holdings. Further analyses reveal that firms increase cash holdings due to having less debt financing, decreased sales and more green innovation, all caused by environmental regulations. Under regulatory pressure, firms operating in more competitive industries, facing more financial constraints, having more environmental expenditure and belonging to the secondary sector tend to hold more cash than other firms, while firms with better CSR performance do not maintain as high cash holdings as their counterparts. We further demonstrate that increased cash holdings caused by the imposition of environmental regulations increase firm value