Bench-Top Electrochemical Crystal Growth: In-Situ Synthesis of Extended Solids Containing Polyoxometalate Anions

A set of proof-of-concept in-situ synthesis of polyoxometalate-containing compounds is presented in this dissertation showing that room-temperature electrochemical crystal growth is a promising technique for the exploratory synthesis of new materials with functional architectures. Most polyoxometalate(POM)-based crystalline materials, if not all, have been synthesized via conventional hydrothermal/solvothermal methods. However, the high temperature and pressure required by these methods proves destructive towards thermally sensitive POM anions, resulting in greater uncertainty of the synthesis of crystalline materials based upon these plenary POM structures. We have explored a new approach for the “bench-top” synthesis of POM-containing materials at ambient conditions by employing electrochemical (e-chem) energy as a driving force for new compound formation. We have demonstrated that the e-chem approach allows for convenient synthesis of POM-containing compounds in aqueous solution without using any specialized reaction container. Compared to conventional hydrothermal/solvothermal synthesis, the new approach offers additional benefits especially towards the synthesis of POM materials that are otherwise subject to thermal decomposition. Furthermore, using e-chem methods for crystal growth facilitates a means for the selective synthesis of compounds with desired frameworks for electrical conductivity. We have had some success with e-chem crystal growth in which newly discovered compounds exhibit fascinating structures such as one-dimensional (1D), alternating POM anion and transition metal (M) cations, and two-dimensional (2D) frameworks featuring tethered POM clusters on metal-oxide chains. In addition to the use of X-ray diffraction methods to investigate iv crystal structures, we have employed TGA/DSC (Thermogravimetric Analysis /Differential Scanning Calorimetry) methods to examine the thermal behaviors of new compounds, and XPS (X-ray Photoelectron Spectroscopy) to determine the oxidation states of the metal cations and etc. Inspired by previous work, an electrochemical synthetic system for the design of POM-based complex metal oxides has been adapted to explore organic-inorganic hybrid materials. This represents a key step in adaptation of the mild reaction system for use in the synthesis of polyoxometalate-organic-frameworks (POMOFs), a class of compounds previously dominated by synthesis via conventional hydrothermal or solvothermal methods, and more recently, ionothermal methods. Applications of such materials include use in lithium ion batteries owing to the multi-electron reduction of polyoxometalate clusters and tunability of pores allowing lithiation. Interestingly, the emerging electrochemical synthetic method enables synthesis of many micrometer scale single crystals and does not require a polymer matrix, differing from previous reports of electrodeposition used to grow thin film organic-inorganic hybrids. Most notably, conventional hydrothermal techniques prove destructive to POM anions with low thermal stability, precluding synthesis or creating reliance upon self-assembly of POM anions. This bench top, one-pot reaction system allows control of potential or current density, temperature, concentration, pH, timescale, and electrode material resulting in potential for enhanced tunability. Furthermore, the electrochemical pathway utilized allows greater selectivity for the synthesis of conductive materials

A multi-marker test based on family data in genome-wide association study

<p>Abstract</p> <p>Background</p> <p>Complex diseases are believed to be the results of many genes and environmental factors. Hence, multi-marker methods that can use the information of markers from different genes are appropriate for mapping complex disease genes. There already have been several multi-marker methods proposed for case-control studies. In this article, we propose a multi-marker test called a Multi-marker Pedigree Disequilibrium Test (MPDT) to analyze family data from genome-wide association studies. If the parental phenotypes are available, we also propose a two-stage test in which a genomic screening test is used to select SNPs, and then the MPDT is used to test the association of the selected SNPs.</p> <p>Results</p> <p>We use simulation studies to evaluate the performance of the MPDT and the two-stage approach. The results show that the MPDT constantly outperforms the single marker transmission/disequilibrium test (TDT) <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Comparing the power of the two-stage approach with that of the one-stage approach, which approach is more powerful depends on the value of the prevalence; when the prevalence is no less than 10%, the two-stage approach may be more powerful than the one-stage approach. Otherwise, the one-stage approach is more powerful.</p> <p>Conclusion</p> <p>The proposed MPDT, is more powerful than the single marker TDT. When the parental phenotypes are available and the prevalence is no less than 10%, the proposed two-stage approach is more powerful than the one-stage approach.</p

Detecting susceptibility genes for rheumatoid arthritis based on a novel sliding-window approach

With the recent rapid improvements in high-throughout genotyping techniques, researchers are facing a very challenging task of large-scale genetic association analysis, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis based on a variable-sized sliding-window framework. This approach employs principal component analysis to find the optimal window size. Using the bisection algorithm in window size searching, the proposed method tackles the exhaustive computation problem. It is more efficient and effective than currently available approaches. We conduct the genome-wide association study in Genetic Analysis Workshop 16 (GAW16) Problem 1 data using the proposed method. Our method successfully identified several susceptibility genes that have been reported by other researchers and additional candidate genes for follow-up studies

A method dealing with a large number of correlated traits in a linkage genome scan

We propose a method to perform linkage genome scans for many correlated traits in the Genetic Analysis Workshop 15 (GAW15) data. The proposed method has two steps: first, we use a clustering method to find the tight clusters of the traits and use the first principal component (PC) of the traits in each cluster to represent the cluster; second, we perform a linkage scan for each cluster by using the representative trait of the cluster. The results of applying the method to the GAW15 Problem 1 data indicate that most of the traits in the same cluster have the same regulators, and the representative trait measure, the first PC, can explain a large part of the total variation of all the traits in each cluster. Furthermore, considering one cluster of traits at a time may yield more linkage signals than considering traits individually

Application of seventeen two-locus models in genome-wide association studies by two-stage strategy

The goal of this paper is to search for two-locus combinations that are jointly associated with rheumatoid arthritis using the data set of Genetic Analysis Workshop 16 Problem 1. We use a two-stage strategy to reduce the computational burden associated with performing an exhaustive two-locus search across the genome. In the first stage, the full set of 531,689 single-nucleotide polymorphisms was screened using univariate testing. In the second stage, all pairs made from the 500 single-nucleotide polymorphisms with the lowest p-values from the first stage were evaluated under each of 17 two-locus models. Our analyses identified a two-locus combination - rs6939589 and rs11634386 - that proved to be significantly associated with rheumatoid arthritis under a Rec × Rec model (p-value = 0.045 after adjusting for multiple tests and multiple models)

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared

Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data

Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measure-ments and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis Figurebased on UK Biobank data for asthma shows that the proposed method is applicable to GWAS

Characterizing Humulone Content in Beer Using Differential Mobility Spectrometry

Beer is a complex fermented beverage and several hundreds of components have been identified erenow. Hops are extremely important to beer’s organoleptic qualities, especially bitterness. Bitterness, a distinguishing component of beer’s taste, is of concern for many beer brewing companies. Chemically, the bitterness of beers is associated with the content of iso-humulone, an isomerization product of tasteless natural humulone present in hops. Thus, it is vital to characterize humulone concentration profiles because brewers can utilize these to adjust desired beer properties. However, until now, there are some uncertainties of the quantitative relation between particular hop-derived compounds and sensory bitterness. In this research, the possibility of differential mobility spectrometry (DMS) technique being employed to separate and identify different humulone isomers has been explored. Results are compared against the UV-Vis spectrometry method, which is the conventional analytical technique used to determine the bitterness level of beer. Influence of external factors on the humulone isomerization and decomposition are also studied with rationalization from theoretical calculations

Flavonoid accumulation and identification of flavonoid biosynthesis genes in Dimocarpus longan lour. by transcriptome sequencing

Dimocarpus longan Lour.&nbsp;(D. longan) is widely cultivated and is very popular around the world. Its by-products such as roots and leaves have been used as traditional Chinese medicines due to their content of important secondary metabolites, especially flavonoids. However, the economic value and application of D. longan&nbsp;roots and leaves are limited because they accumulate relatively low levels of flavonoids. Therefore, it is important to find key genes that regulate the accumulation of the predominant flavonoid compounds in D. longan&nbsp;roots and leaves. Here, we have used RNA-sequencing to describe the transcriptome of D. longan. We obtained 75,229,529 raw reads and 15.04 GB of clean data, generating 56,055 unigenes (N50 = 1,583 nt, mean length = 829.61 nt). Next, we annotated these unigenes using the various available bioinformatics databases. By this approach, we identified 6,684 genes differentially expressed between root and leaf tissues, of which thirteen were identified as flavonoid biosynthesis genes. Of these, eight genes were much highly expressed in roots (DlC4H,&nbsp;DlHCT,&nbsp;DlDFR,&nbsp;DlANS,&nbsp;DlANR,&nbsp;DlCHS,&nbsp;DlF3′H,&nbsp;and&nbsp;DlF3H), and two were much highly expressed in leaves (DlLAR&nbsp;and DlFLS). The contents of thirteen flavonoids in D. longan&nbsp;roots and leaves were measured by LC-MS, and epicatechin was found to be the predominant flavonoid in both tissues, which was significantly higher than the other flavonoids measured in the study. Its contents were 213,773.65 ng/g in roots and 22,388.71 ng/g in leaves. Our findings will facilitate efforts to increase the economic value and expand the applications of D. longan&nbsp;roots and leaves by means of genetic engineering