Browsing by Author "Wang, Jing (TTU)"
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Item ANKYRIN REPEAT-CONTAINING PROTEIN 2A Is an essential molecular chaperone for peroxisomal membrane-bound ASCORBATE PEROXIDASE3 in Arabidopsis(2010) Shen, Guoxin (TTU); Kuppu, Sundaram (TTU); Venkataramani, Sujatha (TTU); Wang, Jing (TTU); Yan, Juqiang (TTU); Qiu, Xiaoyun (TTU); Zhang, Hong (TTU)Arabidopsis thaliana ANKYRIN REPEAT-CONTAINING PROTEIN 2A (AKR2A) interacts with peroxisomal membrane-bound ASCORBATE PEROXIDASE3 (APX3). This interaction involves the C-terminal sequence of APX3 (i.e., a transmembrane domain plus a few basic amino acid residues). The specificity of the AKR2A-APX3 interaction suggests that AKR2A may function as a molecular chaperone for APX3 because binding of AKR2A to the transmembrane domain can prevent APX3 from forming aggregates after translation. Analysis of three akr2a mutants indicates that these mutant plants have reduced steady state levels of APX3. Reduced expression of AKR2A using RNA interference also leads to reduced steady state levels of APX3 and reduced targeting of APX3 to peroxisomes in plant cells. Since AKR2A also binds specifically to the chloroplast OUTER ENVELOPE PROTEIN7 (OEP7) and is required for the biogenesis of OEP7, AKR2A may serve as a molecular chaperone for OEP7 as well. The pleiotropic phenotype of akr2a mutants indicates that AKR2A plays many important roles in plant cellular metabolism and is essential for plant growth and development. © 2010 American Society of Plant Biologists.Item Intermodulation-Based Nonlinear Smart Health Sensing of Human Vital Signs and Location(2019) Mishra, Ashish (TTU); McDonnell, William (TTU); Wang, Jing (TTU); Rodriguez, Daniel (TTU); Li, Changzhi (TTU)This paper discusses the use of a nonlinear sensing technology based on radio frequency (RF) intermodulation response to track both the vital signs and location of human subjects. Smart health sensing was realized through the use of a wearable nonlinear tag and an intermodulation-based nonlinear sensor operating in both Doppler and frequency shift keying (FSK) modes. The Doppler mode was used to detect the heartbeat and breathing of the target subject while human subject localization was achieved in the FSK mode. One of the key advantages of this nonlinear smart sensor system was clutter rejection. This system identified the signal reflected from the wearable nonlinear tag and suppressed undesired signals and interferences that were reflected from other objects. The wearable tags used for the experiments were passive, hence they did not require any battery or power supply for their operation. Since the respiration signal is typically stronger than the heartbeat signal, the nonlinear detection setup was designed such that the respiratory signal receives less gain to avoid its sidelobes and harmonics from interfering heartbeat signal detection. This enhanced the heartbeat signal quality so that the cardiac activity could be easily tracked. Four types of experiments were performed on multiple subjects to demonstrate the advantages of this intermodulation-based nonlinear smart health sensing system. Previously, 2 nd order harmonics were utilized for target localization and vital sign monitoring. However, these 2 nd order harmonics suffer from high path loss and licensing issues. In this paper, target localization and smart health sensing were realized using 3 rd order intermodulation with less path loss and no licensing issues compared with its harmonic counterparts. The experiment performed in nonlinear FSK mode was able to detect and locate the source of motion with high accuracy. Similarly, vital signs were recorded in the nonlinear Doppler mode. The design effectively made the amplitude of the heartbeat signal component more prominent, so that the sidelobes and harmonics of respiration do not suppress heartbeat signal.Item Some asymptotic theory for functional regression and classification(2011) Ruymgaart, Frits (TTU); Wang, Jing (TTU); Wei, Shih Hsuan (TTU); Yu, Li (TTU)Exploiting an expansion for analytic functions of operators, the asymptotic distribution of an estimator of the functional regression parameter is obtained in a rather simple way; the result is applied to testing linear hypotheses. The expansion is also used to obtain a quick proof for the asymptotic optimality of a functional classification rule, given Gaussian populations. © 2011 Frits Ruymgaart et al.