Novel statistical approaches to integrate multi-omic data

Our ultimate goal is to better understand the biological mechanisms that lead to disease pathogenesis, by investigating the regulatory and mediating processes across multiple layers of biological processes. Recent advances in high-throughput technologies have generated an unprecedented amount of multi-omics data. Multi-omics measurements, including genomics data such as genotype and copy number variation (CNV), transcriptomics data such as messenger ribonucleic acid (mRNA) and microRNA expression, as well as epigenomics data such as deoxyribonucleic acid (DNA) methylation, have been traditionally analyzed separately. Multi-omics measurements profiled on different layers of biological processes are interconnected and the biological features identified on different layers influence clinical outcomes at different levels. Integrative analysis borrows strength across multiple levels of omics data by incorporating the regulatory relationship. This could substantially increase the power to reveal the underlying biology of the associations. This dissertation includes 3 projects on multi-omic data integration. In project 1, we propose a novel approach for clustering gene expression with the information from gene regulators that are measured on different but overlapping samples. This allows integration of multiple types of omics data from different but overlapping samples. Genes with similar expression patterns under various conditions may imply co-regulation or relation in functional pathways. In project 2, we study the causal relationships between omics layers and complex traits using association summary statistics as data in multivariable Mendelian randomization (MVMR) and extend the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method to detect horizontal pleiotropy in the MVMR setting. In project 3, we further optimize the MVMR-PRESSO method so that it yields higher power to detect horizontal pleiotropy under situations of directional pleiotropy – when effects of pleiotropic genetic variants on the outcome are distributed with a non-zero mean. All of these approaches will facilitate the integration of multi-omics data and lead to a better understanding of biological mechanisms underlying complex diseases

Characteristics and sources of water-soluble organic aerosol in a heavily polluted environment in Northern China

Water-soluble organic aerosol (WSOA) in fine particles (PM2.5) collected during wintertime in a polluted city (Handan) in Northern China was characterized using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS). Through comparing with real-time measurements from a collocated Aerosol Chemical Speciation Monitor (ACSM), we determined that WSOA on average accounts for 29% of total organic aerosol (OA) mass and correlates tightly with secondary organic aerosol (SOA; Pearson's r = 0.95). The mass spectra of WSOA closely resemble those of ambient SOA, but also show obvious influences from coal combustion and biomass burning. Positive matrix factorization (PMF) analysis of the WSOA mass spectra resolved a water-soluble coal combustion OA (WS-CCOA; O/C = 0.17), a water-soluble biomass burning OA (WS-BBOA; O/C = 0.32), and a water-soluble oxygenated OA (WS-OOA; O/C = 0.89), which account for 10.3%, 29.3% and 60.4% of the total WSOA mass, respectively. The water-solubility of the OA factors was estimated by comparing the offline AMS analysis results with the ambient ACSM measurements. OOA has the highest water-solubility of 49%, consistent with increased hygroscopicity of oxidized organics induced by atmospheric aging processes. In contrast, CCOA is the leastwater soluble, containing 17% WS-CCOA. The distinct characteristics of WSOA from different sources extend our knowledge of the complex aerosol chemistry in the polluted atmosphere of Northern China and the water-solubility analysis may help us to understand better aerosol hygroscopicity and its effects on radiative forcing in this region. (C) 2020 Published by Elsevier B.V.Peer reviewe

Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem

The typical inverse ECG problem is to noninvasively reconstruct the transmembrane potentials (TMPs) from body surface potentials (BSPs). In the study, the inverse ECG problem can be treated as a regression problem with multi-inputs (body surface potentials) and multi-outputs (transmembrane potentials), which can be solved by the support vector regression (SVR) method. In order to obtain an effective SVR model with optimal regression accuracy and generalization performance, the hyperparameters of SVR must be set carefully. Three different optimization methods, that is, genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO), are proposed to determine optimal hyperparameters of the SVR model. In this paper, we attempt to investigate which one is the most effective way in reconstructing the cardiac TMPs from BSPs, and a full comparison of their performances is also provided. The experimental results show that these three optimization methods are well performed in finding the proper parameters of SVR and can yield good generalization performance in solving the inverse ECG problem. Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs

Effect of Same-Temperature GaN Cap Layer on the InGaN/GaN Multiquantum Well of Green Light-Emitting Diode on Silicon Substrate

GaN green LED was grown on Si (111) substrate by MOCVD. To enhance the quality of InGaN/GaN MQWs, same-temperature (ST) GaN protection layers with different thickness of 8 Å, 15 Å, and 30 Å were induced after the InGaN quantum wells (QWs) layer. Results show that a relative thicker cap layer is benefit to get InGaN QWs with higher In percent at fixed well temperature and obtain better QW/QB interface. As the cap thickness increases, the indium distribution becomes homogeneous as verified by fluorescence microscope (FLM). The interface of MQWs turns to be abrupt from XRD analysis. The intensity of photoluminescence (PL) spectrum is increased and the FWHM becomes narrow

ELISA for Aging Biomarkers Induced by Telomere Dysfunction in Human Plasma

Background. We identified cathelicidin related antimicrobial protein (CRAMP) secreted from telomere dysfunctional bone marrow cells of late generation telomerase knockout mice (G4mTerc−/−), increased in blood and various tissues. It can represented human aging and disease. The main aim of this study is to investigate the sensitive direct enzyme-linked immunosorbent assay (ELISA) method to analyze the human aging and disease in plasma and the detailed methods to quantify the direct ELISA of these aging biomarkers. Methods. Telomere lengths of 50 healthy persons are measured with real-time PCR in blood cells. Plasma samples from all subjects are analyzed using direct ELISA. Results. From 25 years old person to 78 years, the telomere length becomes shorter during aging. In blood plasma, the expression levels of CRAMP increases during human aging. There is the reverse correspondence between the telomere length and the plasma CRAMP level. We also find that the fresh plasma, the frozen plasma which thawed less than 3 times, and the plasma kept in the room temperature less than 3 hours are better for the ELISA analyze of CRAMP in the plasma. Conclusion. This CRAMP ELISA could become a powerful tool for investigating the relationship between human aging and telomere length shortening

Entropy as a Gene‐Like Performance Indicator Promoting Thermoelectric Materials

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138909/1/adma201702712.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138909/2/adma201702712-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138909/3/adma201702712_am.pd

High-performance non-Fermi-liquid metallic thermoelectric materials

Searching for high-performance thermoelectric (TE) materials in the paradigm of narrow-bandgap semiconductors has lasted for nearly 70 years and is obviously hampered by a bottleneck of research now. Here we report on the discovery of a few metallic compounds, TiFexCu2x-1Sb and TiFe1.33Sb, showing the thermopower exceeding many TE semiconductors and the dimensionless figure of merits comparable with the state-of-the-art TE materials. A quasi-linear temperature (T) dependence of electrical resistivity in 2 K - 700 K and the logarithmic T-dependent electronic specific heat at low temperature are also observed to coexist with the high thermopower, highlighting the strong intercoupling of the non-Fermi-liquid (NFL) quantum critical behavior of electrons with TE transports. Electronic structure analysis reveals the existence of fluctuating Fe-eg-related local magnetic moments, Fe-Fe antiferromagnetic (AFM) interaction at the nearest 4c-4d sites, and two-fold degenerate eg orbitals antiferromagnetically coupled with the dual-type itinerant electrons close to the Fermi level, all of which infer to a competition between the AFM ordering and Kondo-like spin compensation as well as a parallel two-channel Kondo effect. These effects are both strongly meditated by the structural disorder due to the random filling of Fe/Cu at the equivalent 4c/4d sites of the Heusler crystal lattice. The magnetic susceptibility deviates from ideal antiferromagnetism but can be fitted well by x(T) = 1/({\theta} + BT{\alpha}), seemingly being consistent with the quantum critical scenario of strong local correlation as discussed before. Our work not only breaks the dilemma that the promising TE materials should be heavily-doped semiconductors, but also demonstrates the correlation among high TE performance, NFL quantum criticality, and magnetic fluctuation, which opens up new directions for future research.Comment: 19 pages with 6 figure