Kalman Filtering with Uncertain Noise Covariances

In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the standard Kalman filter. This new filter is used in simulation to estimate the health parameters of an aircraft gas turbine engine

HILIC-LC/MS method for non-derivatized amino acid analysis in spent media

Spent media analysis is vital to vaccine upstream process development and optimization. Determination of amino acid (AA) concentrations in culture media is essential to understand the changes in cell culture conditions over time and also to design optimal feed strategy to improve production economics. Amino acids are highly polar and most have low UV absorbance. Therefore, derivatization by Ortho Phthalaldehyde (OPA) and Fluorenylmethoxy Chloroformate (FMOC) is necessary to improve retention on reverse phase columns and to enhance sensitivities on UV/florescence detectors. This process is labor intensive and time consuming. In the current approach, we applied hydrophilic interaction chromatography (HILIC) and triple quadrupole mass spectrometry (MS) to quantitate non-derivatized AAs extracted from culture media. Samples are collected from the bioreactor at different time points. Prior to extraction, a mixture of C13/N15 labeled AAs is spiked as internal standards (IS) to normalize variabilities in extraction recoveries. AAs and IS are extracted by adding 4 parts of 50% acetonitrile in 20mM Ammonium formate in water (pH=3) to 1 part of culture media sample. The samples are vortexed and centrifuged at 12000 RPM for 5 min to remove the cells and protein precipitates. Supernatants are directly injected on to HILIC–Z column (Agilent Technologies, 2.1X100mm; 2.7uM) connected to SCIEX API 4000 triple quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) mode. The method uses 20 minute normal phase gradient (buffer system: 20mM Ammonium formate in water and 20mM Ammonium formate in 90% acetonitrile) at a flow rate of 0.5ml/min to resolve 17 amino acids. Data analysis is performed by Analyst software. Ratios of AA and corresponding IS peak areas vs. concentrations are plotted to generate calibration curves for each AA. Our method demonstrated nano-molar detection sensitivities and a linear range of 75nM to 2.5µM for most of the AAs. This method also exhibited excellent resolution between isobaric AAs Leucine and Isoleucine. In the poster presentation, we will present method qualification data in detail. We will also discuss application of this method to two independent vaccine upstream process development studies, one is associated with microbial fermentation process and another involved mammalian cell culture process

Acoustic signal-based underwater oil leak detection and localization

Underwater Wireless Sensor Networks (UWSNs) have been becoming popular for exploring offshore, natural resource development, geological oceanography, and monitoring the underwater environment. The acoustic channel characteristics in underwater impose challenges, including limited bandwidth, signal attenuation, and propagation delay that limits UWSN utilization. The marine environment is under threat from pollution, which impacts human life and activities. Compared to other pollution types, the oil leak is a significant threat to the marine ecosystem. When the leaked oil or other petroleum products mix with water in the ocean, significant biological and economic impacts could result. Although much research has focused on improving the reception and processing of acoustic signals, increasing performance, and reducing packet delay, no significant research results have been reported on finding an effective early-stage leak detection method using acoustic signal processing. Accurate information about oil spill location and its characteristics is much needed for oil spill containment and cleanup operations. Developing an efficient under- water oil leak detection and localization algorithm is still challenging in UWSNs because of the impairments of the acoustic channel. In this thesis, we propose a technique that detects the presence of an oil leak in the underwater environment at an early stage. We also propose a localization algorithm that determines the approximate location of the oil leak. Firstly, we review the propagation properties of acoustic signals to understand acoustic communication in the marine environment better. We then discuss the transmission of sound in terms of reflection and refraction. We propose a leak detection technique based on the range estimation method to detect oil leak at an early stage before reaching the ocean sur- face. We perform a two-dimensional analysis for evaluating the performance of the proposed detection technique. To investigate the proposed technique, we perform evaluation with different network sizes and topologies. We discuss the detection ratio, network scalability, power and intensity of the received signal. We then perform a three-dimensional analysis to evaluate the performance of the proposed technique. We conduct theoretical analysis to investigate the proposed technique in terms of detection ratio, network scalability, power and intensity of the received signal. We assess the efficiency of the proposed detection method by considering an oil leak at different ocean levels. Finally, we propose a cooperative localization algorithm for localizing the leak in the UWSN. We then evaluate the proposed localization algorithm for two different topologies. Our results show that our proposed technique works well for an underwater network with concentric hexagonal topology. We can extend the proposed method for other types of targets with different shapes and sizes

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

<p>Abstract</p> <p>Background</p> <p>There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.</p> <p>Methods</p> <p>Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.</p> <p>Results</p> <p>Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.</p> <p>Conclusions</p> <p>We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.</p

identification using an extended

membership function optimization for syste

Fuzzy Membership Function Optimization for System Identification Using an Extended Kalman Filter

The generation of membership functions for fuzzy systems is a challenging problem. In this paper, we use an extended Kalman filter to optimize the membership functions for system modeling, or system identification. We describe the algorithm and then show the result as sub-optimal novel method of system identification. The ideas described in this paper are illustrated for system identification of a nonlinear dynamic system of a permanent magnet synchronous motor. The other interesting observation made is that the proposed system acts as a noise-reducing filter. We demonstrate that the extended Kalman filter can be an effective tool for identifying the parameters of a fuzzy system model

The sweet and sour of serological glycoprotein tumor biomarker quantification

Abstract Aberrant and dysregulated protein glycosylation is a well-established event in the process of oncogenesis and cancer progression. Years of study on the glycobiology of cancer have been focused on the development of clinically viable diagnostic applications of this knowledge. However, for a number of reasons, there has been only sparse and varied success. The causes of this range from technical to biological issues that arise when studying protein glycosylation and attempting to apply it to practical applications. This review focuses on the pitfalls, advances, and future directions to be taken in the development of clinically applicable quantitative assays using glycan moieties from serum-based proteins as analytes. Topics covered include the development and progress of applications of lectins, mass spectrometry, and other technologies towards this purpose. Slowly but surely, novel applications of established and development of new technologies will eventually provide us with the tools to reach the ultimate goal of quantification of the full scope of heterogeneity associated with the glycosylation of biomarker candidate glycoproteins in a clinically applicable fashion