Browsing by Author "Chen, Yingying"

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    Exploring the Sensing Capability of Wireless Signals
    Du, Changlai (Virginia Tech, 2018-07-06)
    Wireless communications are ubiquitous nowadays, especially in the new era of Internet of Things (IoT). Most of IoT devices access the Internet via some kind of wireless connections. The major role of wireless signals is a type of communication medium. Besides that, taking advantage of the growing physical layer capabilities of wireless techniques, recent research has demonstrated the possibility of reusing wireless signals for both communication and sensing. The capability of wireless sensing and the ubiquitous availability of wireless signals make it possible to meet the rising demand of pervasive environment perception. Physical layer features including signal attributes and channel state information (CSI) can be used for the purpose of physical world sensing. This dissertation focuses on exploring the sensing capability of wireless signals. The research approach is to first take measurements from physical layer of wireless connections, and then develop various techniques to extract or infer information about the environment from the measurements, like the locations of signal sources, the motion of human body, etc. The research work in this dissertation makes three contributions. We start from wireless signal attributes analysis. Specifically, the cyclostationarity properties of wireless signals are studied. Taking WiFi signals as an example, we propose signal cyclostationarity models induced by WiFi Orthogonal Frequency Division Multiplexing (OFDM) structure including pilots, cyclic prefix, and preambles. The induced cyclic frequencies is then applied to the signal-selective direction estimation problem. Second, based on the analysis of wireless signal attributes, we design and implement a prototype of a single device system, named MobTrack, which can locate indoor interfering radios. The goal of designing MobTrack is to provide a lightweight, handhold system that can locate interfering radios with sub-meter accuracy with as few antennas as possible. With a small antenna array, the cost, complexity as well as size of this device are reduced. MobTrack is the first single device indoor interference localization system without the requirement of multiple pre-deployed access points (AP). Third, channel state information is studied in applications of human motion sensing. We design WiTalk, the first system which is able to do fine-grained motion sensing like leap reading on smartphones using the CSI dynamics generated by human movements. WiTalk proposes a new fine-grained human motion sensing technique with the distinct context-free feature. To achieve this goal using CSI, WiTalk generates CSI spectrograms using signal processing techniques and extracts features by calculating the contours of the CSI spectrograms. The proposed technique is verified in the application scenario of lip reading, where the fine-grained motion is the mouth movements.
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    Investigate the Metabolic Reprogramming of Saccharomyces cerevisiae for Enhanced Resistance to Mixed Fermentation Inhibitors via ¹³C Metabolic Flux Analysis
    Guo, Weihua; Chen, Yingying; Wei, Na; Feng, Xueyang (PLOS, 2016-08-17)
    The fermentation inhibitors from the pretreatment of lignocellulosic materials, e.g., acetic acid and furfural, are notorious due to their negative effects on the cell growth and chemical production. However, the metabolic reprogramming of the cells under these stress conditions, especially metabolic response for resistance to mixed inhibitors, has not been systematically investigated and remains mysterious. Therefore, in this study, ¹³C metabolic flux analysis (¹³C-MFA), a powerful tool to elucidate the intracellular carbon flux distributions, has been applied to two Saccharomyces cerevisiae strains with different tolerances to the inhibitors under acetic acid, furfural, and mixed (i.e., acetic acid and furfural) stress conditions to unravel the key metabolic responses. By analyzing the intracellular carbon fluxes as well as the energy and cofactor utilization under different conditions, we uncovered varied metabolic responses to different inhibitors. Under acetate stress, ATP and NADH production was slightly impaired, while NADPH tended towards overproduction. Under furfural stress, ATP and cofactors (including both NADH and NADPH) tended to be overproduced. However, under dual-stress condition, production of ATP and cofactors was severely impaired due to synergistic stress caused by the simultaneous addition of two fermentation inhibitors. Such phenomenon indicated the pivotal role of the energy and cofactor utilization in resisting the mixed inhibitors of acetic acid and furfural. Based on the discoveries, valuable insights are provided to improve the tolerance of S. cerevisiae strain and further enhance lignocellulosic fermentation.
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    Transcriptional profiling reveals molecular basis and novel genetic targets for improved resistance to multiple fermentation inhibitors in Saccharomyces cerevisiae
    Chen, Yingying; Sheng, Jiayuan; Jiang, Tao; Stevens, Joseph; Feng, Xueyang; Wei, Na (2016-01-13)
    Background Lignocellulosic biomass is a promising source of renewable biofuels. However, pretreatment of lignocellulosic biomass generates fermentation inhibitors that adversely affect the growth of industrial microorganisms such as Saccharomyces cerevisiae and prevent economic production of lignocellulosic biofuels. A critical challenge on developing S. cerevisiae with improved inhibitor resistance lies in incomplete understanding of molecular basis for inhibitor stress response and limited information on effective genetic targets for increasing yeast resistance to mixed fermentation inhibitors. In this study, we applied comparative transcriptomic analysis to determine the molecular basis for acetic acid and/or furfural resistance in S. cerevisiae. Results We recently developed a yeast strain YC1 with superior resistance to acetic acid, furfural, and their mixture through inverse metabolic engineering. In this study, we first determined transcriptional changes through RNA sequencing in YC1 versus the wild-type strain S-C1 under three different inhibitor conditions, including acetic acid alone, furfural alone, and mixture of acetic acid and furfural. The genes associated with stress responses of S. cerevisiae to single and mixed inhibitors were revealed. Specifically, we identified 184 consensus genes that were differentially regulated in response to the distinct inhibitor resistance between YC1 and S-C1. Bioinformatic analysis next revealed key transcription factors (TFs) that regulate these consensus genes. The top TFs identified, Sfp1p and Ace2p, were experimentally tested as overexpression targets for strain optimization. Overexpression of the SFP1 gene improved specific ethanol productivity by nearly four times, while overexpression of the ACE2 gene enhanced the rate by three times in the presence of acetic acid and furfural. Overexpression of SFP1 gene in the resistant strain YC1 further resulted in 42 % increase in ethanol productivity in the presence of acetic acid and furfural, suggesting the effect of Sfp1p in optimizing the yeast strain for improved tolerance to mixed fermentation inhibitor. Conclusions Transcriptional regulation underlying yeast resistance to acetic acid and furfural was determined. Two transcription factors, Sfp1p and Ace2p, were uncovered for the first time for their functions in improving yeast resistance to mixed fermentation inhibitors. The study demonstrated an omics-guided metabolic engineering framework, which could be developed as a promising strategy to improve complex microbial phenotypes.