2011 WSU Annual CGRS Abstracts

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    Dopaminergic toxicity of 1-methyl-4-phenylpyridinium (MPP+): Model for Parkinson’s disease
    (2011-02-17) Le, Viet Q.; Wimalasena, Kandatege
    Parkinson's disease (PD) is among the most common neurodegenerative diseases. Approximately 60,000 Americans are diagnosed with this disease every year. The cause or cure for PD remains unknown. MPP+ is a dopaminergic neurotoxin that induces symptoms similar to PD and commonly used as a good model to study the molecular causes of PD. The specific dopaminergic toxicity of MPP+ is proposed to be due to the uptake through dopamine transporter (DAT) followed by the inhibition of complex I of the electron transport chain leading to cellular energy starvation. However, this proposal has not been conclusively established. We investigated the mechanism of MPP+ toxicity using MN9D (neuronal) and HepG2 (non-neuronal) cell models. Our studies show that both cells take-up substantial levels of MPP+ under similar experimental conditions, while only MN9D cells are susceptible to MPP+toxicity. MPP+ toxicity is independent of DAT in MN9D cells. Extracellular Ca2+decreases MPP+ uptake into MN9D cells, but has no effect on the toxicity. Voltage-gated Ca2+ channel blockers decrease the MPP+ uptake into MN9D cells, but again do not protect the MN9D cell from MPP+ toxicity. These and other findings suggest a novel mechanism in which MPP+ perturb intracellular Ca2+ leading to neuronal cell death. The understanding of the causes of PD at the molecular level could lead to the development of therapeutic and preventive measures.
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    Mixed Integer Non-linear Programming (MINLP) formulation of energy-efficient location routing problem for electric-powered vehicles
    (2011-02-17) Mirzaei, Shokoufeh; Krishnan, Krishna K.
    Electric vehicles (EVs) in the robotic context and in the future of logistics networks will play an important role as a sustainable and emission-free tool of transportation. The recent development of EVs such as Nissan LEAF and Chevrolet Volt is a turning point in the modification of transportation networks. Aligned with this green movement, the first EV Charging Station for the State of Kansas has been installed on December, 2010. It happened five months after unveiling the first EV charging station in USA, and within a week of the release of the above mentioned EVs. These actions show the potential promise in the use of EVs in the state. Although there are obvious benefits to the use of EVs, one of their main restrictions is the limited stored energy. Thus, energy-efficient Location-Routing Problem (LRP) becomes an important problem which has not been investigated vastly in literature. This paper provides a novel formulation of LRP which finds the best location-allocation and routing plan of EVs with the objective of minimizing the total energy cost. The vehicle weight and travelled distance are the major contributing factors in the energy consumption. Each vehicle energy limit is enforced in the model so that the vehicle does not get discharged before its travel completion. This research helps to use EVs in the most energy-efficient way. The proposed model can also be applied to situations when there is a mixed-fleet (hybrid, gas, or electric powered). A case study is presented to explain the model.
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    Nanomonitors: a miniature electronic biosensor for early disease diagnosis
    (2011-02-17) Brandigampala, Savindra Madhavi; Prasad, Shalini
    Heart disease more specifically vulnerable coronary plaque rupture, which is the cause of acute coronary syndromes stroke, peripheral vascular limb ischemia, and other end-organ ischemic diseases, is one of the leading causes of death in Kansas. Approximately 50% of Kansas population lives in rural areas and 13% of Kansas population are above 65 years, an age group where ACI (Acute Coronary Insufficiency) is one of the major causes of death. More importantly approximately 13% of Kansas population does not have health insurance. Hence, it becomes essential to develop technologies, which enable rapid and cost effective diagnosis of ACI in a pre-symptomatic state. The primary purpose of this research is to develop an inexpensive and user friendly ‘point- of- care’ (POC) device for pre-symptomatic diagnosis of ACI through the detection of two proteins that have been identified as biomarkers for this disease. Inflammation and thrombosis are key mediators of vulnerable coronary plaque and NT-BNP and Troponin-T are two proteins which are biomarkers of this condition. We have utilized nanoporous alumina membranes to generate high surface area to volume structures for trapping protein biomolecules. We employ the protein specific capacitance measurement method as the basis for protein biomarker detection. We demonstrate device performance parameters for protein biomarker detection in purified and spiked serum samples to be comparable to the current gold standard: ELISA.
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    Study and analysis of Cognitive Radio channel scanning technology for Wi-Fi networks
    (2011-02-17) Syeda, Reshma Sultana; Namboodiri, Vinod
    Wi-Fi has become such a ubiquitous wireless technology in a relatively short period that it is not surprising; we are in the midst of at least one Wi-Fi network no matter wherever we are. Each one of us has a Laptop/Smartphone/Netbook competing against each other for the Wi-Fi bandwidth and thus has to compromise on data speeds in order to share the limited Wi-Fi spectrum. Contrary to this Wi-Fi crowding phenomenon which is yet to worsen with the ongoing explosive growth of wireless devices, studies show that 90% of the time, spectrum designated to legacy technologies like the TV spectrum was found unoccupied and not every channel was in use always. Cognitive Radio Technology is the riposte to this paradoxical situation. Cognitive Radios (CR) are envisioned to solve the challenge of spectrum scarcity when communication technologies have increasingly started relying on the wireless medium. CR is an intelligent radio which scans the radio spectrum for free channels and uses them to its own advantage. Though CR technology definitely sounds a promising candidate to deal with this crowding Wi-Fi spectrum, not much is known on how energy efficient these CRs and their Spectrum Scanning processes are. Our work is to study and analyze these energy-intensive Spectrum Scanning processes and further propose techniques to make them more energy efficient, thereby making battery constrained portable devices operate for longer durations. Our work also increases the reliability and availability of wireless networks in rural areas of Kansas where opportunity of recharging batteries is limited.
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    Multi-sensor health diagnosis using Deep Belief Network based state classification
    (2011-02-17) Tamilselvan, Prasanna; Wang, Pingfeng
    Kansas is one of the headquarters of major aircraft manufacturing industries. Due to large human life risks involved in flight journey, safety and operational reliability of aircraft is more critical. This research proposes a novel multi sensor health monitoring and failure diagnosis for Kansas industries to manufacture most reliable and failure preventive aircrafts to the world. Aircraft reliability depends on continuous monitoring of current system health status and health state detection is a key factor for prevention of performance degradation at different stages of damage. Due to nature of observed data and the available knowledge, health diagnostic methods are often a combination of statistical inference and machine learning. A novel artificial intelligent technique, Deep Belief Networks (DBN), has been quite effective in some applications such as image recognition and audio classification with promised advantages such as fast inference, fast learning, and the ability to encode higher order networks. This paper proposes the use of DBN for structural health monitoring applications of aircraft and develops multi-sensor health diagnosis method. DBN works based on Restricted Boltzmann machine and it learns layer by layer considering priors and network posteriors. Enhanced diagnostic system can be structured in three stages: first, collection of data from different sensors and preprocessing of the data; second, development of DBN classifier model based on nature of the system; third, training of DBN with data for different possible health states of the system. Classification effectiveness of this network is evaluated using experimental data for various real time practical conditions.