Characterizing Intense Pulsed Light-Elicited Effects on Escherichia coli and Non-Fat Dry Milk Through Metabolomic and Chemometric Analysis

University of Minnesota M.S. thesis.September 2019. Major: Nutrition. Advisor: Chi Chen. 1 computer file (PDF); viii, 83 pages.Disinfecting powder food is challenging due to their low water activity. Intense-pulsed light (IPL) is advantageous in achieving efficient bacterial reduction. Its mechanisms of mediating bactericidal effect has been characterized as inducing DNA damage and disrupting cell structure integrity. A novel IPL platform is being constructed and studied to achieve high disinfecting efficacy while maintaining the physiochemical properties of the powder food. However, little is known about its influence on cell metabolism, which is essential for cell survival and growth. E.coli K-12 culture from overnight incubation were treated with a bactericidal dose of IPL for different durations. After centrifugation, the metabolites in bacterial pellets were extracted by a mixture of chloroform, methanol, and water. Aqueous and lipid extracts were examined by liquid chromatography-mass spectrometry (LC-MS)-based metabolomic analysis. The principal components analysis (PCA) of LC-MS data indicated that the metabolome of E. coli was dramatically affected by IPL treatment in a time-dependent pattern. Multiple nucleotides, antioxidants, and membrane components, including adenosine monophosphate, glutathione, and menaquinone-8, were identified as the metabolites sensitive to IPL treatment. These markers revealed IPL-induced membrane damage and oxidative stress. Additional markers suggest IPL hindered ability of repairing DNA damage. New information from untargeted metabolomic analysis provides useful insights on the mechanism of IPL-elicited bactericidal activities. An ideal IPL treatment is expected to achieve pasteurization with minimal influences on physical, chemical, and nutritional properties of powdered food. While IPL showed effective bactericidal effect, it is also essential to evaluate its influence on the food matrix. IPL-irradiated non-fat dry milk was prepared by solvent extraction and acid hydrolysis, and then examined by liquid chromatography-mass spectrometry (LC-MS) analysis. Targeted and untargeted chemometric analysis were performed to determine the chemical compositions of prepared samples and the effects of IPL treatment. Targeted chemometric analysis indicated that IPL treatment in this study did not significantly affect the amino acid composition of non-fat dairy milk powder. However, the multivariate models constructed by untargeted chemometric analysis of extracted samples revealed the dose-dependent chemical changes after IPL treatment. IPL treatment directly degraded riboflavin, and led to formation of peptides as a result of photolysis of milk proteins. Untargeted chemometric analysis on the chemical effects of IPL treatment will provide useful information to guide the development of IPL disinfection technology

PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to {\omega}-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes

Near-Field Sparse Channel Estimation for Extremely Large-Scale RIS-Aided Wireless Communications

A significant increase in the number of reconfigurable intelligent surface (RIS) elements results in a spherical wavefront in the near field of extremely large-scale RIS (XL-RIS). Although the channel matrix of the cascaded two-hop link may become sparse in the polar-domain representation, their accurate estimation of these polar-domain parameters cannot be readily guaranteed. To tackle this challenge, we exploit the sparsity inherent in the cascaded channel. To elaborate, we first estimate the significant path-angles and distances corresponding to the common paths between the BS and the XL-RIS. Then, the individual path parameters associated with different users are recovered. This results in a two-stage channel estimation scheme, in which distinct learning-based networks are used for channel training at each stage. More explicitly, in stage I, a denoising convolutional neural network (DnCNN) is employed for treating the grid mismatches as noise to determine the true grid index of the angles and distances. By contrast, an iterative shrinkage thresholding algorithm (ISTA) based network is proposed for adaptively adjusting the column coherence of the dictionary matrix in stage II. Finally, our simulation results demonstrate that the proposed two-stage learning-based channel estimation outperforms the state-of-the-art benchmarks.Comment: This paper has been accepted for publication in the IEEE GLOBECOM 2023 Workshops Proceeding