Computationally efficient inference for center effects based on restricted mean survival time

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151995/1/SIM_8356-Supp-0001-Supp_Info_revised_Xin_paper_2_SIM_30MAY2019.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151995/2/sim8356_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151995/3/sim8356.pd

Coherent modulation of the electron temperature and electron-phonon couplings in a 2D material

Ultrashort light pulses can selectively excite charges, spins and phonons in materials, providing a powerful approach for manipulating their properties. Here we use femtosecond laser pulses to coherently manipulate the electron and phonon distributions, and their couplings, in the charge density wave (CDW) material 1T-TaSe$_2$. After exciting the material with a short light pulse, spatial smearing of the electrons launches a coherent lattice breathing mode, which in turn modulates the electron temperature. This indicates a bi-directional energy exchange between the electrons and the strongly-coupled phonons. By tuning the laser excitation fluence, we can control the magnitude of the electron temperature modulation, from ~ 200 K in the case of weak excitation, to ~ 1000 K for strong laser excitation. This is accompanied by a switching of the dominant mechanism from anharmonic phonon-phonon coupling to coherent electron-phonon coupling, as manifested by a phase change of $\pi$ in the electron temperature modulation. Our approach thus opens up possibilities for coherently manipulating the interactions and properties of quasi-2D and other quantum materials using light.Comment: 15 pages, 4 figure

Establishment of Rab-11 Induced Inflammatory Regulation as Therapeutic Targets in Colon Cancer Progression

Colon cancer is the third-deadliest cancer in the United States. Better understanding the cancer microenvironment/niches is crucial to the development of successful therapeutic targets. An RNAi screening using enterocyte specific driver was performed in Drosophila melanogaster intestine to search for niches regulating the intestine stem cell homeostasis. A small GTPase, Rab11 caused strong intestine stem cell (ISC) proliferation and tissue hyperplasia upon knockdown, due to increased production of inflammatory cytokines and growth factors. Increased inflammatory cytokines and proliferation were also observed in mouse Rab11a knockout (KO) intestine, indicating Rab11 regulatory role in the inflammation-induced hyperplasia is evolutionarily conserved and may also apply to human. We hypothesized that Rab11 is required to maintain cytokines in an appropriate state and its expression is down regulated in cancers. We investigated dextran sulfate sodium and chemical induced mouse colon cancer. Rab11 was largely reduced/absent in cancer tissues whereas well present in the normal tissue. We also investigated the correlation of Rab11 level and human cancer progression by immunofluorescence staining, and found that close to 50% and 40% of the cases studied had reduced Rab11 level by 20% and 30%, respectively. The greater the reduction is, the higher chance it is associated with more progressed cancer. Rab11, therefore, functions to suppress cancer progression and can be potentially developed towards a better diagnosis and treatment target for colon cancer. We will screen FDA approved drugs for ISC proliferation regulation, using a fly intestine tumor model established by expressing a human activated RAFGOFgene and a luciferase gene in the fly gut precursor cells. Selected drugs will be applied to test the Rab11 induced hyperplasia in fly, and further validated by mouse and human organoids derived from Rab11 KO mouse or human colon cancer tissues

The temporal and long‐term impact of donor body mass index on recipient outcomes after kidney transplantation – a retrospective study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153284/1/tri13505_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153284/2/tri13505.pd

Destination image: a consumer-based, big data-enabled approach

Purpose - This research aimed to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places. Design/methodology/approach - Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then employed to group destinations by attributes, and ANOVA provided comparisons. Findings - Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic, and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages, and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers. Originality/value - This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations. Practical implications - Destinations should determine into which category they belong and then appeal to the real needs of tourists. DMOs should provide the essential attributes and pay attention to creating a unique social life atmosphere

Inhibition of Histone Deacetylases Prevents Cardiac Remodeling After Myocardial Infarction by Restoring Autophagosome Processing in Cardiac Fibroblasts

Background/Aims: Histone deacetylases (HDACs) play a critical role in the regulation of gene transcription, cardiac development, and diseases. The aim of this study was to investigate whether the inhibition of HDACs improves cardiac remodeling and its underlying mechanisms in a mouse myocardial infarction (MI) model. Methods: The HDAC inhibitor trichostatin A (TSA, 0.1 mg/kg/day) was administered via daily intraperitoneal injections for 8 consecutive weeks after MI in C57/BL mice. Echocardiography and tissue histopathology were used to assess cardiac function. Cultured neonatal rat cardiac fibroblasts (NRCFs) were subjected to simulated hypoxia in vitro. Autophagic flux was measured using the tandem fluorescent mCherry-GFP-LC3 assay. Western blot was used to detect autophagic biomarkers. Results: After 8 weeks, the inhibition of HDACs in vivo resulted in improved cardiac remodeling and hence better ventricular function. MI was associated with increased LC3-II expression and the accumulation of autophagy adaptor protein p62, indicating impaired autophagic flux, which was reversed by TSA treatment. Cultured NRCFs exhibited increased cell death after simulated hypoxia in vitro. Increased cell death was associated with markedly increased numbers of autophagosomes but not autolysosomes, as assessed by punctate dual fluorescent mCherry-green fluorescent protein tandem-tagged light chain-3 expression, indicating that hypoxia resulted in impaired autophagic flux. Importantly, TSA treatment reversed hypoxia-induced impaired autophagic flux and led to a 40% decrease in cell death. This was accompanied by improved mitochondrial membrane potential. The beneficial effects of TSA therapy were abolished by RNAi intervention targeting LAMP2; likewise, in vivo delivery of chloroquine abolished the TSA-mediated cardioprotective effects. Conclusion: Our results provide evidence that the HDAC inhibitor TSA prevents cardiac remodeling after MI and is dependent on restoring autophagosome processing of cardiac fibroblasts

Novel Statistical Methods for Restricted Mean Survival Time and Patient Preference Augmented Dynamic Treatment Regimes in Observational Studies

In this dissertation, we develop three new statistical methods and estimating procedures in survival analysis with restricted mean survival time and in evaluating the optimal treatment decision rules by involving patient preference. Restricted mean survival time (RMST) is a clinically interpretable and meaningful survival metric defined as the patient's mean survival time up to a pre-specified time horizon of interest, denoted as L. No existing RMST regression method allows for the covariate effects to be expressed as functions over time, which is a considerable limitation in light of the many hazard regression models that do accommodate such effects. To address this void in the literature, in the first project of my dissertation, we propose an inference framework for directly modeling RMST as a continuous function of L. We apply our method to kidney transplant data obtained from the Scientific Registry of Transplant Recipients (SRTR). The second and third projects of my dissertation consider personalized treatment decision strategies in the management of chronic diseases, such as end stage renal disease, which typically consists of sequential and adaptive treatment decision making. This can be formulated through a dynamic treatment regime (DTR) framework, where the goal is to tailor treatment to each individual given their medical history in order to maximize a desirable health outcome. We develop a new method, Augmented Patient Preference incorporated Reinforcement Learning (APP-RL), to incorporate a patient's latent preferences through data augmentation into a tree-based reinforcement learning method to estimate optimal dynamic treatment regimes for multi-stage, multi-treatment settings. For each patient at each stage, we derive their posterior distribution of preferences given responses to a questionnaire, and then subsequently weight multiple outcomes with the estimated preferences to identify the optimal stage-wise personalized decision. APP-RL is robust, efficient, and leads to interpretable DTR estimation. We further extend the APP-RL ideas into the survival setting with censored data in the last project. We investigate a two-stage treatment setting where patients have to decide between quality of life and survival restricted at maximal follow-up. We successfully develop a method that incorporates the latent patient preference into a weighted utility function that balances between quality of life and survival time, in a Q-learning model framework. We further propose a corresponding m-out-of-n Bootstrap procedure to accurately make statistical inferences and construct confidence intervals on the effects of tailoring variables, whose values can guide the personalized treatment strategies.PHDBiostatistics PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/172753/1/zhongych_1.pd

Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare

In this paper, we consider personalized treatment decision strategies in the management of chronic diseases, such as chronic kidney disease, which typically consists of sequential and adaptive treatment decision making. We investigate a two-stage treatment setting with a survival outcome that could be right censored. This can be formulated through a dynamic treatment regime (DTR) framework, where the goal is to tailor treatment to each individual based on their own medical history in order to maximize a desirable health outcome. We develop a new method, Survival Augmented Patient Preference incorporated reinforcement Q-Learning (SAPP-Q-Learning) to decide between quality of life and survival restricted at maximal follow-up. Our method incorporates the latent patient preference into a weighted utility function that balances between quality of life and survival time, in a Q-learning model framework. We further propose a corresponding m-out-of-n Bootstrap procedure to accurately make statistical inferences and construct confidence intervals on the effects of tailoring variables, whose values can guide personalized treatment strategies

Identification and immunoinfiltration analysis of key genes in ulcerative colitis using WGCNA

Objective Ulcerative colitis (UC) is a chronic non-specific inflammatory bowel disease characterized by an unclear pathogenesis. This study aims to screen out key genes related to UC pathogenesis. Methods Bioinformatics analysis was conducted for screening key genes linked to UC pathogenesis, and the expression of the screened key genes was verified by establishing a UC mouse model. Results Through bioinformatics analysis, five key genes were obtained. Subsequent infiltration analysis revealed seven significantly different immune cell types between the UC and general samples. Additionally, animal experiment results illustrated markedly decreased body weight, visible colonic shortening and damage, along with a significant increase in the DAI score of the DSS-induced mice in the UC group in comparison with the NC group. In addition, H&E staining results demonstrated histological changes including marked inflammatory cell infiltration, loss of crypts, and epithelial destruction in the colon mucosa epithelium. qRT-PCR analysis indicated a down-regulation of ABCG2 and an up-regulation of IL1RN, REG4, SERPINB5 and TRIM29 in the UC mouse model. Notably, this observed trend showed a significant dependence on the concentration of DSS, with the mouse model of UC induced by 7% DSS demonstrating a more severe disease state compared to that induced by 5% DSS. Conclusion ABCG2, IL1RN, REG4, SERPINB5 and TRIM29 were screened out as key genes related to UC by bioinformatics analysis. The expression of ABCG2 was down-regulated, and that of IL1RN, REG4, SERPINB5 and TRIM29 were up-regulated in UC mice as revealed by animal experiments