Browsing by Author "Zhou, Bin"

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    Computational Analysis of LC-MS/MS Data for Metabolite Identification
    Zhou, Bin (Virginia Tech, 2011-11-30)
    Metabolomics aims at the detection and quantitation of metabolites within a biological system. As the most direct representation of phenotypic changes, metabolomics is an important component in system biology research. Recent development on high-resolution, high-accuracy mass spectrometers enables the simultaneous study of hundreds or even thousands of metabolites in one experiment. Liquid chromatography-mass spectrometry (LC-MS) is a commonly used instrument for metabolomic studies due to its high sensitivity and broad coverage of metabolome. However, the identification of metabolites remains a bottle-neck for current metabolomic studies. This thesis focuses on utilizing computational approaches to improve the accuracy and efficiency for metabolite identification in LC-MS/MS-based metabolomic studies. First, an outlier screening approach is developed to identify those LC-MS runs with low analytical quality, so they will not adversely affect the identification of metabolites. The approach is computationally simple but effective, and does not depend on any preprocessing approach. Second, an integrated computational framework is proposed and implemented to improve the accuracy of metabolite identification and prioritize the multiple putative identifications of one peak in LC-MS data. Through the framework, peaks are likely to have the m/z values that can give appropriate putative identifications. And important guidance for the metabolite verification is provided by prioritizing the putative identifications. Third, an MS/MS spectral matching algorithm is proposed based on support vector machine classification. The approach provides an improved retrieval performance in spectral matching, especially in the presence of data heterogeneity due to different instruments or experimental settings used during the MS/MS spectra acquisition.
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    Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System
    Ye, Zhoujing; Yan, Guannan; Wei, Ya; Zhou, Bin; Li, Ning; Shen, Shihui; Wang, Linbing (MDPI, 2021-04-10)
    Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.
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    Salmonella inactivation and cross-contamination on cherry and grape tomatoes under simulated wash conditions
    Bolten, Samantha; Gu, Ganyu; Luo, Yaguang; Van Haute, Sam; Zhou, Bin; Millner, Pat; Micallef, Shirley A.; Nou, Xiangwu (2020-05)
    Washing in chlorinated water is widely practiced for commercial fresh produce processing. While known as an effective tool for mitigating food safety risks, chlorine washing could also represent an opportunity for spreading microbial contaminations under sub-optimal operating conditions. This study evaluated Salmonella inactivation and cross-contamination in a simulated washing process of cherry and grape tomatoes. Commercially harvested tomatoes and the associated inedible plant matter (debris) were differentially inoculated with kanamycin resistant (KanR) or rifampin resistant (Rim) Salmonella strains, and washed together with uninoculated tomatoes in simulated packinghouse dump tank (flume) wash water. Washing in chlorinated water resulted in significantly higher Salmonella reduction on tomatoes than on debris, achieving 2-3 log reduction on tomatoes and about 1 log reduction on debris. Cross-contamination by Salmonella on tomatoes was significantly reduced in the presence of 25-150 mg/L free chlorine, although sporadic cross-contamination on tomatoes was detected when tomatoes and debris were inoculated at high population density. The majority of the sporadic cross-contaminations originated from Salmonella inoculated on debris. These findings suggested that debris could be a potentially significant source of contamination during commercial tomato washing.