Unified Approaches for Frequentist and Bayesian Methods in Two-Sample Clinical Trials with Binary Endpoints

Two opposing paradigms, analyses via frequentist or Bayesian methods, dominate the statistical literature. Most commonly, frequentist approaches have been used to design and analyze clinical trials, though Bayesian techniques are becoming increasingly popular. However, these two paradigms can generate divergent results even in analyses of the same trial data, which may harm the scientific interpretability of the trial. Therefore, it is crucial to harmonize analyses under each approach. In this dissertation, novel unified approaches for one-sided frequentist and Bayesian hypothesis testing problems comparing two proportions in fixed-sample and group-sequential clinical trials are proposed. When a frequentist design with desired type I and II error rates are given, the unification is achieved by deriving specific Bayesian decision thresholds and sample sizes. Similarly, when a Bayesian design is given, the unification is achieved by deriving corresponding frequentist characteristics. In addition, theoretical methods to determine the Bayesian decision threshold, sample size and power are provided. Numerical results show that the unified approach can yield the same type I and II error rates for frequentist and Bayesian hypothesis tests through a numerical study. Further, detailed evaluations suggest that Bayesian priors specifications, allocation ratios, number of analyses can affect the resulting Bayesian sample sizes and decision thresholds. Overall, the unified approach can be adopted into the current clinical trial setting and is helpful to make trial results translatable between frequentist and Bayesian methods

Extracting Rural Crash Injury and Fatality Patterns Due to Changing Climates in RITI Communities Based on Enhanced Data Analysis and Visualization Tools (Phase I)

Traffic crashes cause considerable incapacitating injuries and losses in Rural, Isolated, Tribal, or Indigenous (RITI) communities. Compared to urban traffic crashes, those rural crashes, especially for those occurred in RITI communities, are heavily associated with factors such as speeding, low safety devices application (for instance, seatbelt), adverse weather conditions and lacking maintenance and repairers for road conditions, inferior lighting conditions, and so on. Therefore, there exists an urgent need to investigate the unique attributes associated with the RITI traffic crashes based on numerous approaches, such as statistical methods, and data-driven approaches. This project focused on extracting rural crash injury and fatality patterns due to changing climates in RITI communities based on enhanced data analysis and visualization tools. Three new interactive graphic tools were added to the Rural Crash Visualization Tool System (RCVTS), to enhance the visualization approach. A Bayesian vector auto-regression based data analysis approach was proposed to enable irregularly-spaced mixture-frequency traffic collision data interpretation with missing values. Moreover, a finite mixture random parameters model was formulated to explore driver injury severity patterns and causes in low visibility related single-vehicle crashes. The research findings are helpful for transportation agencies to develop cost-effective countermeasures to mitigate rural crash severities under extreme climate and weather conditions and minimize the rural crash risks and severities in the States of Alaska, Washington, Idaho, and Hawaii

EXTRACTING RURAL CRASH INJURY AND FATALITY PATTERNS DUE TO CHANGING CLIMATES IN RITI COMMUNITIES BASED ON ENHANCED DATA ANALYSIS AND VISUALIZATION TOOLS (PHASE II)

This report documents the research activities to investigate the traffic crashes in Rural, Isolated, Tribal, or Indigenous (RITI) communities involving considerable incapacitating injuries and fatalities. The traffic crashes occurring in RITI communities, are different from urban traffic crashes, and are related more to the features like speeding, low application of safety devices (for instance, seatbelt), adverse weather conditions and lacking maintenance and repairs for road conditions, and inferior lighting conditions. Thus, it is necessary to study the properties and attributes of traffic crashes at the RITI area using data analysis methods, such as statistical methods, and data-driven methods. This project is trying to analyze the rural crash injury and fatality patterns caused by changing climates in RITI communities based on enhanced data analysis using latest mathematical method. The mixed logit model to examine the risk factors in determining driver injury severity in four crash configurations in two-vehicle rear-end crashes on state roads based on seven-years of data from the Washington State Department of Transportation. The differences between the MLM and the LCM are investigated for exploring the relationships between driver injury severity in the rain-related rural single-vehicle crash and its corresponding risk factors. Moreover, this project develops a latent class mixed logit model with temporal indicators to investigate highway single-vehicle crashes and the effects of significant contributing factors to driver injury severity. The results of this research will be beneficial to transportation agencies to propose effective methods to improve rural crash severities under special climate and weather conditions and minimize the rural crash risks and severities

Developing an Interactive Baseline Data Platform for Visualizing and Analyzing Rural Crash Characteristics in RITI Communities

This project focused on developing an interactive baseline crash data platform, termed as Rural Crash Visualization Tool System (RCVTS), to visualize and analyze rural crash characteristics in RITI communities. More than 975 thousand crash records were collected in the state of Alaska, Idaho, and Washington, from 2010 to 2016. Data fusion is applied to unify the collected data. In the proposed RCVTS platform, three main functions are defined: crash data visualization, data analysis, and data retrieval. Crash data visualization includes an on-street map based crash location tool and a graphic query tool. Data analysis involves a number of visualization approaches, including static charts— i.e., the scatter chart—the line chart, the area chart, the bar chart, and interactive graph— i.e., the sunburst chart. Users are allowed to generate customized analytical graphs by specifying the parameters and scale. The three types of authorized users are defined to download crash information in the data retrieval section following corresponding limitations. The proposed RCVTS was illustrated using a sample case with crash records of the State of Alaska. It showed that the proposed RCVTS functions well. Recommendations on future research are provided as well

SENP1 regulates IFN-γ−STAT1 signaling through STAT3−SOCS3 negative feedback loop

Interferon-γ (IFN-γ) triggers macrophage for inflammation response by activating the intracellular JAK−STAT1 signaling. Suppressor of cytokine signaling 1 (SOCS1) and protein tyrosine phosphatases can negatively modulate IFN-γ signaling. Here, we identify a novel negative feedback loop mediated by STAT3−SOCS3, which is tightly controlled by SENP1 via de-SUMOylation of protein tyrosine phosphatase 1B (PTP1B), in IFN-γ signaling. SENP1-deficient macrophages show defects in IFN-γ signaling and M1 macrophage activation. PTP1B in SENP1-deficient macrophages is highly SUMOylated, which reduces PTP1B-induced de-phosphorylation of STAT3. Activated STAT3 then suppresses STAT1 activation via SOCS3 induction in SENP1-deficient macrophages. Accordingly, SENP1-deficient macrophages show reduced ability to resist Listeria monocytogenes infection. These results reveal a crucial role of SENP1-controlled STAT1 and STAT3 balance in macrophage polarization

Fermentation improves flavors, bioactive substances, and antioxidant capacity of Bian-Que Triple-Bean Soup by lactic acid bacteria

The ancient traditional Chinese drink Bian-Que Triple-Bean Soup made by fermentation (FTBS) of Lactococcus lactis subsp. lactis YM313 and Lacticaseibacillus casei YQ336 is a potential functional drink. The effect of fermentation on the flavor and biological activity of FTBS was evaluated by analyzing its chemical composition. Five volatile flavors were detected in modified FTBS. Fermentation decreased the proportion of nonanal (beany flavor substances) but significantly increased the total flavone contents, phenol contents and many bioactive small molecule substances in FTBS. The changes of these substances led to the significant improvement of FTBS sensory evaluation, antioxidant activity and prebiotic potential. This research provides a theoretical basis for the application of Lactic acid bacteria (LAB) in the fermentation of edible plant-based foods and transformation from traditional food to industrial production

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A dual AAV system enables the Cas9-mediated correction of a metabolic liver disease in newborn mice

Many genetic liver diseases present in newborns with repeated, often lethal, metabolic crises. Gene therapy using non-integrating viruses such as AAV is not optimal in this setting because the non-integrating genome is lost as developing hepatocytes proliferate1,2. We reasoned that newborn liver may be an ideal setting for AAV-mediated gene correction using CRISPR/Cas9. Here we intravenously infuse two AAVs, one expressing Cas9 and the other expressing a guide RNA and the donor DNA, into newborn mice with a partial deficiency in the urea cycle disorder enzyme, ornithine transcarbamylase (OTC). This resulted in reversion of the mutation in 10% (6.7% – 20.1%) of hepatocytes and increased survival in mice challenged with a high-protein diet, which exacerbates disease. Gene correction in adult OTC-deficient mice was lower and accompanied by larger deletions that ablated residual expression from the endogenous OTC gene, leading to diminished protein tolerance and lethal hyperammonemia on a chow diet