Computational Approaches for Analyzing High-Throughput Genomic Data

With the improvement of high-throughput technologies, association studies related to molecular phenotypes have become increasingly significant. Associated genetic variants found from studies based on high-throughput omics experiments provide valuable information to help understand biological mechanisms behind complex traits. While analyses using high-throughput data can play a crucial role to study complex traits, many analytical challenges remain unresolved. This dissertation primarily focuses on two outstanding issues in genetic association analysis of high-throughput sequence data. First, when incorporating functional annotations into multi-SNP association analyses and the number of candidate SNPs increases, computational burden increases. Second, there is a need to identify reproducible signals between studies. Measuring reproducibility between assays in high-throughput experiments and association results between studies is crucial to assess the quality of the overall procedures and the association evidence. In Chapter 2, we propose an algorithm to incorporate functional annotations into Bayesian multi-SNP analysis based on a probabilistic hierarchical model. The proposed algorithm, name as deterministic approximation of posteriors (DAP), shows superior accuracy and computational efficiency over the existing methods, including Markov Chain Monte Carlo (MCMC) algorithms to fit a sparse Bayesian variable selection model. In Chapter 3, we propose a probabilistic quantification of association evidence, accounting for linkage disequilibrium (LD). By identifying a set of SNPs in LD and representing a single association signal, we are able to construct credible sets and perform appropriate false discovery rate (FDR) control in Bayesian multi-SNP association analysis. We also derive a set of sufficient summary statistics that lead to equivalent inference results as using individual-level data. In Chapter 4, we propose a set of computational methods to measure reproducibility among high-throughput sequencing experiments. In particular, we propose a statistical approach to take advantage of the fact that a strong and genuine signal is expected to show the same directional effects in multiple studies.We design a novel Bayesian hierarchical model and estimate the posterior probability of each testing unit (e,g, SNP) being reproducible under a proposed set of prior probabilities. We also propose visualization tools and quantification measures tool to assess the overall reproducibility among multiple experiments. In three chapters of the dissertation, we discuss several issues in studies utilizing high-throughput data and propose computational methods to deal with these issues.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147631/1/yejilee_1.pd

Visible light photocatalysis alkene-alkyne [2+2] cycloaddition by energy transfer

Department of ChemistryThe alkyne-alkene [2+2] cycloaddition is straightforward approach to cyclobutene derivatives. Cyclobutenes are widely present in numerous natural products and pharmacological compounds with various biological properties. In addition, Structural characteristics of cyclobutene such as the high ring strain and unsaturation give the ability for many useful synthetic transformations. because the [2+2] cycloaddition is thermally forbidden, it can be achieved by photochemically. But this process has mostly been developed under UV light irradiation condition. The excited state of molecules has different reactivity from ground state. straightforward way to access the excited state of molecules is through a direct photoexcitation approach. Most organic molecules need high energy source for direct excitation such as UV light. But using high energy source like UV light has disadvantage for selectivity of the reaction, functional group resistance and general applicability. Recently, Visible light photocatalysis has attracted much attention from organic synthetic society because of its environmentally friendly and mild condition. Visible light mediated energy transfer process is a method to access excited (triplet) state in mild condition. Herein, we develop visible light photocatalysis [2+2] cycloadditions of alkene-alkyne by energy transfer mechanism. Under Blue LED light by using Iridium catalysts as photocatalyst, diverse cyclobutenes can be accessed in mild condition in moderated to excellent yields. Also, in intramolecular reaction, highly substituted 1,3-Dienes are observed by tandem intramolecular [2+2] cycloaddition - ring opening reaction. Moreover, further synthetic transformations to valuable structure like extended ??-systems were explored based on our synthesis method.clos

Adult Civic Educators’ Educational Needs Assessment in the Context of South Korea

This study measured current and required levels of educational capacities of adult civic educators in South Korea. The pairwise comparisons provided implications for more urgent and important professional development areas

Towards Efficient Neural Scene Graphs by Learning Consistency Fields

Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video) with multiple objects. Nevertheless, computationally heavy ray marching for every image frame becomes a huge burden. In this paper, taking advantage of significant redundancy across adjacent frames in videos, we propose a feature-reusing framework. From the first try of naively reusing the NSG features, however, we learn that it is crucial to disentangle object-intrinsic properties consistent across frames from transient ones. Our proposed method, \textit{Consistency-Field-based NSG (CF-NSG)}, reformulates neural radiance fields to additionally consider \textit{consistency fields}. With disentangled representations, CF-NSG takes full advantage of the feature-reusing scheme and performs an extended degree of scene manipulation in a more controllable manner. We empirically verify that CF-NSG greatly improves the inference efficiency by using 85\% less queries than NSG without notable degradation in rendering quality. Code will be available at: https://github.com/ldynx/CF-NSGComment: BMVC 2022, 22 page

Factors Related to Smoking Status Among Young Adults: An Analysis of Younger and Older Young Adults in Korea

Objectives Young adulthood represents a critical developmental period during which the use of tobacco may begin or cease. Furthermore, differences in smoking behaviors between younger (aged 18-24 years) and older (aged 25-34 years) young adults may exist. This study aimed to characterize patterns related to current smoking in younger and older young adults. Methods This study used data acquired from the Sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2014. A total of 2069 subjects were categorized as younger (712 subjects) and older (1357 subjects) young adults. The chi-square test was used to assess the relationships between smoking status and socio-demographic, health-related, and smoking-related factors. Multivariable logistic regression models were constructed to assess the factors affecting current smoking in these age groups. Results The current smoking prevalence was 18.3% among the younger young adults and 26.0% among the older young adults. Sex, education level, occupation, perceived health status, alcohol consumption, and electronic cigarette use were related to current smoking in both age groups. Secondhand smoke exposure at home and stress levels showed significant relationships with smoking in younger and older young adults, respectively. Conclusions Strong correlations were found between the observed variables and smoking behaviors among young adults. Determining the factors affecting smoking and designing interventions based on these factors are essential for smoking cessation in young adults

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

Tropical cyclones (TCs) are destructive natural disasters. Accurate prediction and monitoring are important to mitigate the effects of natural disasters. Although remarkable efforts have been made to understand TCs, operational monitoring information still depends on the experience and knowledge of forecasters. In this study, a fully automated geostationary-satellite-based TC center estimation approach is proposed. The proposed approach consists of two improved methods: the setting of regions of interest (ROI) using a score matrix (SCM) and a TC center determination method using an enhanced logarithmic spiral band (LSB) and SCM. The former enables prescreening of the regions that may be misidentified as TC centers during the ROI setting step, and the latter contributes to the determination of an accurate TC center, considering the size and length of the TC rainband in relation to its intensity. Two schemes, schemes A and B, were examined depending on whether the forecasting data or real-time observations were used to determine the initial guess of the TC centers. For each scheme, two models were evaluated to discern whether SCM was combined with LSB for TC center determination. The results were investigated based on TC intensity and phase to determine the impact of TC structural characteristics on TC center determination. While both proposed models improved the detection performance over the existing approach, the best-performing model (i.e., LSB combined with SCM) achieved skill scores (SSs) of +17.4% and +20.8% for the two schemes. In particular, the model resulted in a significant improvement for strong TCs (categories 4 and 5), with SSs of +47.8% and +72.8% and +41.2% and +72.3% for schemes A and B, respectively. The research findings provide an improved understanding of the intensity- and phase-wise spatial characteristics of TCs, which contributes to objective TC center estimation

Burnout and peritraumatic distress of healthcare workers in the COVID-19 pandemic

Background To evaluate the current status of emotional exhaustion and peritraumatic distress of healthcare workers (HCWs) in the COVID-19 pandemic, and identify factors associated with their mental health status. Methods An online survey involving 1068 of consented HCWs that included nurses, physicians, and public health officers was conducted in May 2020. Descriptive statistics and multivariate regression analyses were performed on the collected data. Results Although no significant difference in peritraumatic distress was observed among the surveyed HCWs, the workers’ experience of emotional exhaustion varied according to work characteristics. Respondents who were female, older, living with a spouse, and/or full-time workers reported higher levels of emotional exhaustion. Public health officers and other medical personnel who did not have direct contact with confirmed patients and full-time workers had a higher level of peritraumatic distress. Forced involvement in work related to COVID-19, worry about stigma, worry about becoming infected, and perceived sufficiency of organizational support negatively predict emotional exhaustion and peritraumatic distress. Conclusions Job-related and emotional stress of HCWs should not be neglected. Evidence-based interventions and supports are required to protect HCWs from mental illness and to promote mental health of those involved in the response to the COVID-19 pandemic.This work was supported by the Gyeonggi-do Public Health Policy Institute, funded by the Gyeonggi-do Province Government, South Korea. The funding body had roles in the design of the study, data collection and analysis

Nano-biosupercapacitors enable autarkic sensor operation in blood

Today’s smallest energy storage devices for in-vivo applications are larger than 3 mm3 and lack the ability to continuously drive the complex functions of smart dust electronic and microrobotic systems. Here, we create a tubular biosupercapacitor occupying a mere volume of 1/1000 mm3 (=1 nanoliter), yet delivering up to 1.6 V in blood. The tubular geometry of this nano-biosupercapacitor provides efficient self-protection against external forces from pulsating blood or muscle contraction. Redox enzymes and living cells, naturally present in blood boost the performance of the device by 40% and help to solve the self-discharging problem persistently encountered by miniaturized supercapacitors. At full capacity, the nano-biosupercapacitors drive a complex integrated sensor system to measure the pH-value in blood. This demonstration opens up opportunities for next generation intravascular implants and microrobotic systems operating in hard-to-reach small spaces deep inside the human body