The pathophysiology of eosinophilic esophagitis: Altered mechanisms of antigen detection in the esophagus

Recently, a chronic idiopathic disease of the esophagus has emerged, which is now known as eosinophilic esophagitis (EoE). Incomplete knowledge regarding the pathogenesis of EoE has limited treatment options. EoE is known to be a Th2-type immune-mediated disorder. Based on previous studies in both patients and experimental models, it is possible that an abnormal reaction to antigen mediates the pathophysiology of EoE. In this thesis, symptoms and signs unique to EoE were identified by an age-matched, case-controlled study of 326 patients with EoE and gastroesophageal reflux disease. The molecular mechanisms involved in antigen detection in the esophagus, in relation to EoE were then investigated. Esophageal epithelial cells were found, for the first time, to be capable of acting as non-professional antigen presenting cells, with the ability to engulf, process and present antigen on MHC class II to T helper lymphocytes. Antigen presentation by esophageal epithelial cells was induced by interferon-γ, which is increased in biopsies from patients with EoE. Next, it was discovered that esophageal epithelial cell lines expressed functional toll-like receptor (TLR) 2 and TLR3, but in esophageal mucosal biopsies only infiltrating immune cells (including eosinophils) expressed TLR2 and TLR3. Finally, the potential involvement of IgE in the pathogenesis of esophageal inflammation was investigated. IgE in the esophagus was found to be present on mast cells, which are increased in density in the esophageal mucosae of patients with EoE and especially those with a history of atopy. Mechanisms of antigen detection may mediate the pathophysiology of EoE in the esophagus through antigen presentation by epithelial cells, detection by TLRs on immune cells and detection through IgE on mucosal mast cells. Together, these findings demonstrate that mechanisms of antigen detection may actually contribute to the pathophysiology of EoE. Through increased understanding of the mechanisms of EoE, the results of this thesis may contribute to future therapy.

Microbial analysis of soil and groundwater from a gasworks site and comparison to a sequenced biological reactive barrier remediation process (SEREBAR)

Aims: To investigate the distribution of a polymicrobial community of biodegradative bacteria in (i) soil and groundwater at a former manufactured gas plant (FMGP) site and (ii) in a novel SEquential REactive BARrier (SEREBAR) bioremediation process designed to bioremediate the contaminated groundwater. Methods and Results: Culture-dependent and culture-independent analyses using denaturing gradient gel electrophoresis (DGGE) and polymerase chain reaction (PCR) for the detection of 16S ribosomal RNA gene and naphthalene dioxygenase (NDO) genes of free-living (planktonic groundwater) and attached (soil biofilm) samples from across the site and from the SEREBAR process was applied. Naphthalene arising from groundwater was effectively degraded early in the process and the microbiological analysis indicated a dominant role for Pseudomonas and Comamonas in its degradation. The microbial communities appeared highly complex and diverse across both the sites and in the SEREBAR process. An increased population of naphthalene degraders was associated with naphthalene removal. Conclusion: The distribution of micro-organisms in general and naphthalene degraders across the site was highly heterogeneous. Comparisons made between areas contaminated with polycyclic aromatic hydrocarbons (PAH) and those not contaminated, revealed differences in the microbial community profile. The likelihood of noncultured bacteria being dominant in mediating naphthalene removal was evident. Significance and Impact of the Study: This work further emphasizes the importance of both traditional and molecular-based tools in determining the microbial ecology of contaminated sites and highlights the role of noncultured bacteria in the process.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

PLS modelling and fault detection on the Tennessee Eastman benchmark

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process for modelling and fault detection. Two methods are applied : linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. The performance of the RBF networks is enhanced through the use of a recently developed training algorithm which uses quasi-Newton optimization to ensure an efficient and parsimonious network; details of this algorithm can be found in this paper. The PLS and PLS/RBF methods are then used to create on-line inferential models of delayed process measurements. As these measurements relate to the final product composition, these models suggest that on-line statistical quality control analysis should be possible for this plant. The generation of `soft sensors' for these measurements has the further effect of introducing a redundant element into the system, redundancy which can then be used to generate a fault detection and isolation scheme for these sensors. This is achieved by arranging the sensors and models in a manner comparable to the dedicated estimator scheme of Clarke et al. 1975, IEEE Trans. Pero. Elect. Sys., AES-14R, 465-473. The effectiveness of this scheme is demonstrated on a series of simulated sensor and process faults, with full detection and isolation shown to be possible for sensor malfunctions, and detection feasible in the case of process faults. Suggestions for enhancing the diagnostic capacity in the latter case are covered towards the end of the paper.

Detection of improper installation from the sensor signal of vortex flowmeters

Effects of inappropriate installation can bias the measurements of flowmeters. For vortex flowmeters, a method is proposed to detect inappropriate installation of the flowmeter from the oscillatory signal of the vortex sensor. The method is based on assuming the process of vortex generation to be a generic, noisy, nonlinear oscillation, describable by a noisy Stuart-Landau equation, with a corresponding sensor signal that also contains higher harmonic excitations. By making use of the scaling properties of the Navier-Stokes Equation, the method was designed to be robust with respect to uncertainties in the fluid properties. The diagnostic functionality is demonstrated on measurement data. In the experiments, installation effects that lead to more than 0.5% error in the output of the flowmeter could clearly be detected. (C) 2003 Elsevier Ltd. All rights reserved.

Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

Pterin detection using surface-enhanced Raman spectroscopy incorporating a straightforward silver colloid-based synthesis technique

Optical techniques toward the realization of sensitive and selective biosensing platforms have received considerable attention in recent times. Techniques based on interferometry, surface plasmon resonance, and waveguides have all proved popular, while spectroscopy in particular offers much potential. Raman spectroscopy is an information-rich technique in which the vibrational frequencies reveal much about the structure of a compound, but it is a weak process and offers poor sensitivity. In response to this problem, surface-enhanced Raman scattering (SERS) has received much attention, due to significant increases in sensitivity instigated by bringing the sample into contact with an enhancing substrate. Here we discuss a facile and rapid technique for the detection of pterins using SERS-active colloidal silver suspensions. Pterins are a family of biological compounds that are employed in nature in color pigmentation and as facilitators in metabolic pathways. In this work, small volumes of xanthopterin, isoxanthopterin, and 7,8-dihydrobiopterin have been examined while adsorbed to silver colloids. Limits of detection have been examined for both xanthopterin and isoxanthopterin using a 10-s exposure to a 12 mW 532 nm laser, which, while showing a trade-off between scan time and signal intensity, still provides the opportunity for the investigation of simultaneous detection of both pterins in solution. (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3600658]

Automatic detection and classification of football players

Color segmentation of images usually requires a manual selection and classification of samples to train the system. This paper presents an automatic system that performs these tasks without the need of a long training, providing a useful tool to detect and identify figures. In real situations, it is necessary to repeat the training process if light conditions change, or if, in the same scenario, the colors of the figures and the background may have changed, being useful a fast training method. A direct application of this method is the detection and identification of football players.

Software-defined sphere decoding for FPGA-based MIMO detection

Sphere Decoding (SD) is a highly effective detection technique for Multiple-Input Multiple-Output (MIMO) wireless communications receivers, offering quasi-optimal accuracy with relatively low computational complexity as compared to the ideal ML detector. Despite this, the computational demands of even low-complexity SD variants, such as Fixed Complexity SD (FSD), remains such that implementation on modern software-defined network equipment is a highly challenging process, and indeed real-time solutions for MIMO systems such as 4 4 16-QAM 802.11n are unreported. This paper overcomes this barrier. By exploiting large-scale networks of fine-grained softwareprogrammable processors on Field Programmable Gate Array (FPGA), a series of unique SD implementations are presented, culminating in the only single-chip, real-time quasi-optimal SD for 44 16-QAM 802.11n MIMO. Furthermore, it demonstrates that the high performance software-defined architectures which enable these implementations exhibit cost comparable to dedicated circuit architectures.

Introduction to Wireless Intrusion Detection Systems

The IDS (Intrusion Detection System) is a common means of protecting networked systems from attack or malicious misuse. The development and rollout of an IDS can take many different forms in terms of equipment, protocols, connectivity, cost and automation. This is particularly true of WIDS (Wireless Intrusion Detection Systems) which have many more opportunities and challenges associated with data transmission through an open, shared medium.
The operation of a WIDS is a multistep process from origination of an attack through to human readable evaluation. Attention to the performance of each of the processes in the chain from attack detection to evaluation is imperative if an optimum solution is to be sought. At present, research focuses very much on each discrete aspect of a WIDS with little consideration to the operation of the whole system. Taking a holistic view of the technology shows the interconnectivity and inter-dependence between stages, leading to improvements and novel research areas for investigation.
This chapter will outline the general structure of Wireless Intrusion Detection Systems and briefly describe the functions of each development stage, categorised into the following 6 areas:
• Threat Identification,
• Architecture,
• Data Collection,
• Intrusion Detection,
• Alert Correlation,
• Evaluation.
These topics will be considered in broad terms designed for those new to the area. Focus will be placed on ensuring the readers are aware of the impact of choices made at early stages in WIDS development on future stages.

Next generation planar waveguide detection of microcystins in freshwater and cyanobacterial extracts, utilising a novel lysis method for portable sample preparation and analysis

The study details the development of a fully validated, rapid and portable sensor based method for the on-site analysis of microcystins in freshwater samples. The process employs a novel lysis method for the mechanical lysis of cyanobacterial cells, with glass beads and a handheld frother in only 10min. The assay utilises an innovative planar waveguide device that, via an evanescent wave excites fluorescent probes, for amplification of signal in a competitive immunoassay, using an anti-microcystin monoclonal with cross-reactivity against the most common, and toxic variants. Validation of the assay showed the limit of detection (LOD) to be 0.78ngmL and the CCß to be 1ngmL. Robustness of the assay was demonstrated by intra- and inter-assay testing. Intra-assay analysis had % C.V.s between 8 and 26% and recoveries between 73 and 101%, with inter-assay analysis demonstrating % C.V.s between 5 and 14% and recoveries between 78 and 91%. Comparison with LC-MS/MS showed a high correlation (R=0.9954) between the calculated concentrations of 5 different Microcystis aeruginosa cultures for total microcystin content. Total microcystin content was ascertained by the individual measurement of free and cell-bound microcystins. Free microcystins can be measured to 1ngmL, and with a 10-fold concentration step in the intracellular microcystin protocol (which brings the sample within the range of the calibration curve), intracellular pools may be determined to 0.1ngmL. This allows the determination of microcystins at and below the World Health Organisation (WHO) guideline value of 1µgL. This sensor represents a major advancement in portable analysis capabilities and has the potential for numerous other applications.

Dual Enlargement of Gold Nanoparticles: From Mechanism to Scanometric Detection of Pathogenic Bacteria

A mechanism of dual enlargement of gold nanoparticles (AuNPs) comprising two steps is described. In the first step, the AuNPs are enlarged by depositing Au atoms on their crystalline faces. In this process, the particles are not only enlarged but they are also observed to multiply: new Au nuclei are formed by the budding and division of the enlarged particles. In the second step, a silver enhancement is subsequently performed by the deposition of silver atoms on the enlarged and newly formed AuNPs to generate bimetallic Au@Ag core-shell structures. The dual nanocatalysis greatly enhances the electron density of the nanostructures, leading to a stronger intensity for colorimetric discrimination as well as better sensitivity for quantitative measurement. Based on this, a simple scanometric assay for the on-slide detection of the food-born pathogen Campylobacter jejuni is developed. After capturing the target bacteria, gold-tagged immunoprobes are added to create a signal on a solid substrate. The signal is then amplified by the dual enlargement process, resulting in a strong color intensity that can easily be recognized by the unaided eye, or measured by an inexpensive flatbed scanner. In this paper, dual nanocatalysis is reported for the first time. It provides a valuable mechanistic insight into the development of a simple and cost-effective detection format.