Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Identification of statistical patterns in complex systems via symbolic time series analysis

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.     

Evidence for an elders' advantage in the naive product usability judgments of older and younger adults

OBJECTIVE: To determine whose naive judgments of consumer product usability are more accurate--those of younger or older adults. Accuracy is here defined as judgments compatible with results from performance-based usability tests. BACKGROUND: Older adults may be better able to predict usability problems than younger adults, making them particularly good participants in studies contributing to the user-centered design of products. This advantage, if present, may stem from older adults' motivation for more usable products or from their experience adapting their own environments to meet their changing physical, cognitive, and sensory needs. METHOD: Sixty older participants (ages 65-75 years) and 60 younger ones (ages 18-22 years) evaluated illustrations of consumer products on specific criteria (e.g., readability, learnability, or error rates). They either rated a single design for each product or ranked six alternative designs. They also explained their choices, indicated which features were most critical for usability, and selected usability-enhancing modifications. RESULTS: Although there was no reliable age difference in the amount of usability information provided in the open-ended explanations, older adults were more accurate at ranking alternative designs, selecting the most usability-critical features, and selecting usability-enhancing modifications (all ps < .05). CONCLUSION: The usability judgments of older adults are more accurate than those of younger adults when these judgments are solicited in a fixed-alternative, but not open-ended, format. APPLICATION: Because older adults are more discerning about potential product usability problems, they may be particularly valuable as research participants in early-stage design research (prior to the availability of working prototypes).     

Isoperimetric graph partitioning for image segmentation

Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.     

Odour emission ability (OEA) and its application in assessing odour removal efficiency

Odourous emissions from sewer networks and wastewater treatment plants (WWTPs) can significantly impact a local population. Sampling techniques such as wind tunnels and flux hood chambers are traditionally used to collect area source samples for subsequent quantification of odour emission rates using dilution olfactometry, however these methods are unsuitable for assessing liquid samples from point sources due to the large liquid volumes required. To overcome this limitation, a gas phase sample preparation method was developed for assessing the total Odour Emission Ability (OEA) from a liquid sample. The method was validated using two volatile organic sulphur compounds (VOSCs), dimethyl-trisulphide (DMTS) and bismethylthiomethane (BMTM) that are frequently detected from sewers and WWTPs and are relatively stable compared with common VOSCs like mercaptan or methyl mercaptan. The recovery rates of DMTS and BMTM were quantified by injecting a known volume of a standard liquid sample into Tedlar bags using a static injection and a dynamic injection methodology. It was confirmed that both dynamic and static injection methods at ambient condition achieved high recovery rates with no need to consider increasing evaporation by elevating the temperature. This method can also be used to assess odour removal effectiveness of liquids by comparing the OEA before and after the treatment tests. Two application examples were presented.     

3D model based tracking for omnidirectional vision: A new spherical approach

The current work addresses the problem of 3D model tracking in the context of monocular and stereo omnidirectional vision in order to estimate the camera pose. To this end, we track 3D objects modeled by line segments because the straight line feature is often used to model the environment. Indeed, we are interested in mobile robot navigation using omnidirectional vision in structured environments. In the case of omnidirectional vision, 3D straight lines are projected as conics in omnidirectional images. Under certain conditions, these conics may have singularities.In this paper, we present two contributions. We, first, propose a new spherical formulation of the pose estimation withdrawing singularities, using an object model composed of lines. The theoretical formulation and the validation on synthetic images thus show that the new formulation clearly outperforms the former image plane one. The second contribution is the extension of the spherical representation to the stereovision case. We consider in the paper a sensor which combines a camera and four mirrors. Results in various situations show the robustness to illumination changes and local mistracking. As a final result, the proposed new stereo spherical formulation allows us to localize online a robot indoor and outdoor whereas the classical formulation fails.     

Adaptive ROC-based ensembles of HMMs applied to anomaly detection

Hidden Markov models (HMMs) have been successfully applied in many intrusion detection applications, including anomaly detection from sequences of operating system calls. In practice, anomaly detection systems (ADSs) based on HMMs typically generate false alarms because they are designed using limited amount of representative training data. Since new data may become available over time, an important feature of an ADS is the ability to accommodate newly acquired data incrementally, after it has originally been trained and deployed for operations. In this paper, a system based on the receiver operating characteristic (ROC) is proposed to efficiently adapt ensembles of HMMs (EoHMMs) in response to new data, according to a learn-and-combine approach. When a new block of training data becomes available, a pool of base HMMs is generated from the data using a different number of HMM states and random initializations. The responses from the newly trained HMMs are then combined to those of the previously trained HMMs in ROC space using a novel incremental Boolean combination (incrBC) technique. Finally, specialized algorithms for model management allow to select a diversified EoHMM from the pool, and adapt Boolean fusion functions and thresholds for improved performance, while it prunes redundant base HMMs. The proposed system is capable of changing the desired operating point during operations, and this point can be adjusted to changes in prior probabilities and costs of errors. Computer simulations conducted on synthetic and real-world host-based intrusion detection data indicate that the proposed system can achieve a significantly higher level of performance than when parameters of a single best HMM are estimated, at each learning stage, using reference batch and incremental learning techniques. It also outperforms the learn-and-combine approaches using static fusion functions (e.g., majority voting). Over time, the proposed ensemble selection algorithms form compact EoHMMs, while maintaining or improving system accuracy. Pruning allows to limit the pool size from increasing indefinitely, thereby reducing the storage space for accommodating HMMs parameters without negatively affecting the overall EoHMM performance. Although applied for HMM-based ADSs, the proposed approach is general and can be employed for a wide range of classifiers and detection applications.     

Cell formation in group technology using constraint programming and Boolean satisfiability

Cell formation consists in organizing a plant as a set of cells, each of them containing machines that process similar types or families of parts. The idea is to minimize the part flow among cells in order to reduce costs and increase productivity. The literature presents different approaches devoted to solve this problem, which are mainly based on mathematical programming and on evolutionary computing. Mathematical programming can guarantee a global optimal solution, however at a higher computational cost than an evolutionary algorithm, which can assure a good enough optimum in a fixed amount of time. In this paper, we model and solve this problem by using state-of-the-art constraint programming (CP) techniques and Boolean satisfiability (SAT) technology. We present different experimental results that demonstrate the efficiency of the proposed optimization models. Indeed, CP and SAT implementations are able to reach the global optima in all tested instances and in competitive runtime.     

Quantification of condition indicator performance on a split torque gearbox

The requirement for higher energy density transmissions (lower weight) in helicopters has led to the development of the split torque gearbox (STG) to replace the traditional planetary gearbox by the drive train designer. This may pose a challenge for the current gear analysis methods used in health and usage monitoring systems (HUMS). Gear analysis uses time synchronous averages to separates in frequency gears that are physically close to a sensor. The effect of a large number of synchronous components (gears or bearing) in close proximity may significantly reduce the fault signal (reduce signal to noise ratio) and therefore reduce the effectiveness of current gear analysis algorithms. In this paper, quantification of condition indicator performance on a split torque gearbox is reported. The vibration signatures are processed through a number of gear analysis algorithms to quantify the gear fault performance. The performance metric is separability.