Novel applications of mass spectrometry for quantitation and reaction mechanism elucidation

Mass spectrometry (MS) has been growing as one of the most widely used tools in the field of analytical chemistry. Various applications have been developed to harness the high sensitivity and specificity of mass spectrometric analysis. In this dissertation, two major challenges are addressed. By developing mass spectrometric-based methods, absolute quantitation of proteins/peptides have been achieved. Elucidation of various reaction mechanisms are also enabled. These are the focuses of this dissertation. In Chapters 2 to 4, a novel quantitation method is developed, titled as coulometric mass spectrometry (CMS). The strength of this method is that no reference standard or isotope-labeled compound is required for absolute quantitation. The method relies on electrochemical oxidation of an electrochemically active target compound to determine the amount of the oxidized compound using Faraday\u27s Law. On the other hand, the oxidation reaction yield can be determined based on the MS signal change following electrolysis. Therefore, the absolute amount of the analyte can be calculated. In the project for quantifying the mixture of dopamine and serotonin, this method is optimized and proved to quantify the compounds in a mixture after the chromatographic separation. Gradient elution is used for separation and each compound can be quantified using the electrochemical mass spectrometry method. Furthermore, the tyrosine-containing peptides are targeted and electrochemically oxidized to generate electric current for successful quantitation by CMS method. In addition, the CMS method is further applied to absolute quantitation of proteins, as proteins can be digested into peptides. The results for surrogate peptide quantity measured by our method and by traditional isotope dilution method are in excellent agreement, with the discrepancy of 0.3-3%, validating our CMS method for absolute protein quantitation. Due to the high specificity and sensitivity of MS and no need to use isotope-labeled peptide standards, the CMS method would be of high value for the absolute proteomic quantification. In Chapter 5, elucidation of ion dissociation patterns for structural analysis is presented by using an atmospheric pressure thermal dissociation mass spectrometry (APTD-MS) technique. By using this technique, neutral CO resulting from amino acid and peptide ion dissociation is detected. In the future, more meaningful analytes can be investigated by APTD-MS to study dissociation mechanisms at the ambient environment. In Chapter 6, a gold-catalyzed oxidative coupling reaction is reported via electrochemical approach. Oxidation of Au(I) to Au(III) can be achieved through anode oxidation, which facilitates facile access to conjugated diynes via homo-coupling or cross-coupling. Besides, transient reaction intermediates are detected and confirmed by mass spectrometry which provides evidence to mechanistic studies. In Chapter 7, a novel and rapid method is developed for antibody characterization. By which, multiple reactions (e.g., reduction, digestion and deglycosylation) can take place on antibodies in microseconds in the microdroplets. The resulting antibody fragments can be either collected or online analyzed by mass spectrometry. It suggests that microdroplet environment is a powerful reactor for both exploring large molecule reactions and speeding their analysis

Immigration, Ethnicity, and Citizenship: The Words and Faces of the Chinese of North America

In this dissertation, I have analyzed the migrant experience of Chinese immigrants in North America through their representation in literature and photography. Each of its three chapters focuses on three major ethnic issues affecting the lives and identity of Chinese immigrants and their offspring in North America: the first concerns the ways in which occupation, home, and family affect the destinies of Chinese immigrants; the second deals with the role of language in the lives of Chinese immigrants and the career of Chinese migrant writers; the third addresses stereotypes about Chinese immigrants and their offspring and the redefinition of their identity. In this interdisciplinary study, literature inspires us to picture verbally Chinese immigrants’ struggles under the discriminatory laws and prejudices of society, and their search for respect and equal rights. As for the medium of photography, it provides ample visual evidence that reinforces and complements the literary representations of them. I have chosen to study the literary works by Frank Chin, Maxine Hong Kingston, Qiu Xiaolong, Ha Jin, Fae Myenne Ng, David Henry Hwang, Li-Young Lee, Wayson Choy, and Ying Chen. All of them are pivotal figures and explorers of contemporary Chinese ethnic literature in the United States and Canada. Their work offers a multifaceted history of the Chinese immigrants in North America from the late nineteenth century to the present. Along with the study of Chinese American photographers, Mary Tape, Benjamen Chinn, Corky Lee, and Wing Young Huie, I have added a discussion of the work of two American photographers, Arnold Genthe and George Grantham Bain. The contrasting views that emerge help to illuminate the processes of stereotyping as well as identity construction. The work of the Americans focuses on the immigrants’ “Chineseness”, while that of the Chinese Americans seeks to present Chinese immigrant life and the fight for equality from within the Chinese American community. My discussion of the work of these writers and photographers will bring further attention to the difficulties and the challenges facing the Chinese ethnic group in North America

An embedded two-layer feature selection approach for microarray data analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.<br /

A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.<br /

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.<br /

Increasing phosphorus recovery from dewatering centrate in microbial electrolysis cells

Additional file 2: Figure S2 (A) EDS analysis results for precipitants recovered from the MEC operation (Set C). (B) EDS analysis results for pure struvite (99.999% purity)

SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications. However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers. To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors. Based on such approach, we present SlimYOLOv3 with fewer trainable parameters and floating point operations (FLOPs) in comparison of original YOLOv3 (Joseph Redmon et al., 2018) as a promising solution for real-time object detection on UAVs. We evaluate SlimYOLOv3 on VisDrone2018-Det benchmark dataset; compelling results are achieved by SlimYOLOv3 in comparison of unpruned counterpart, including ~90.8% decrease of FLOPs, ~92.0% decline of parameter size, running ~2 times faster and comparable detection accuracy as YOLOv3. Experimental results with different pruning ratios consistently verify that proposed SlimYOLOv3 with narrower structure are more efficient, faster and better than YOLOv3, and thus are more suitable for real-time object detection on UAVs. Our codes are made publicly available at https://github.com/PengyiZhang/SlimYOLOv3