Identification of novel innate immune mechanisms regulating inflammation in the gastrointestinal tract

The Gastro-Intestinal (GI) tract is a unique region in the body. Our innate immune system retains a fine homeostatic balance between avoiding inappropriate inflammatory responses against the myriad commensal microbes residing in the gut while also remaining active enough to prevent invasive pathogenic attack. The intestinal epithelium represents the frontline of this interface. It has long been known to act as a physical barrier preventing the lumenal bacteria of the gastro-intestinal tract from activating an inflammatory immune response in the immune cells of the underlying mucosa. However, in recent years, an appreciation has grown surrounding the role played by the intestinal epithelium in regulating innate immune responses, both in the prevention of infection and in maintaining a homeostatic environment through modulation of innate immune signalling systems. The aim of this thesis was to identify novel innate immune mechanisms regulating inflammation in the GI tract. To achieve this aim, we chose several aspects of regulatory mechanisms utilised in this region by the innate immune system. We identified several commensal strains of bacteria expressing proteins containing signalling domains used by Pattern Recognition Receptors (PRRs) of the innate immune system. Three such bacterial proteins were studied for their potentially subversive roles in host innate immune signalling as a means of regulating homeostasis in the GI tract. We also examined differential responses to PRR activation depending on their sub-cellular localisation. This was investigated based on reports that apical Toll-Like Receptor (TLR) 9 activation resulted in abrogation of inflammatory responses mediated by other TLRs in Intestinal Epithelial Cells (IECs) such as basolateral TLR4 activation. Using the well-studied invasive intra-cellular pathogen Listeria monocytogenes as a model for infection, we also used a PRR siRNA library screening technique to identify novel PRRs used by IECs in both inhibition and activation of inflammatory responses. Many of the PRRs identified in this screen were previously believed not to be expressed in IECs. Furthermore, the same study has led to the identification of the previously uncharacterised TLR10 as a functional inflammatory receptor of IECs. Further analysis revealed a similar role in macrophages where it was shown to respond to intracellular and motile pathogens such as Gram-positive L.monocytogenes and Gram negative Salmonella typhimurium. TLR10 expression in IECs was predominantly intracellular. This is likely in order to avoid inappropriate inflammatory activation through the recognition of commensal microbial antigens on the apical cell surface of IECs. Moreover, these results have revealed a more complex network of innate immune signalling mechanisms involved in both activating and inhibiting inflammatory responses in IECs than was previously believed. This contribution to our understanding of innate immune regulation in this region has several direct and indirect benefits. The identification of several novel PRRs involved in activating and inhibiting inflammation in the GI tract may be used as novel therapeutic targets in the treatment of disease; both for inducing tolerance and reducing inflammation, or indeed, as targets for adjuvant activation in the development of oral vaccines against pathogenic attack.

Ultra-wideband cognitive interrogator network for indoor location tracking - part 1: system architecture and network performance analysis

We explore the potential application of cognitive interrogator network (CIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate source localization. We first present the CIN architecture in which the central base station (BS) continuously and intelligently customizes the illumination modes of the distributed transceivers in response to the systempsilas changing knowledge of the channel conditions and subject movements. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale CIN with randomly distributed interrogators are derived based upon the implemented cognitive intelligences. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitions on the system performance

Identification of mouse langerin/CD207 in Langerhans cells and some dendritic cells of lymphoid tissues

Human (h)Langerin/CD207 is a C-type lectin of Langerhans cells (LC) that induces the formation of Birbeck granules (BG). In this study, we have cloned a cDNA-encoding mouse (m)Langerin. The predicted protein is 66% homologous to hLangerin with conservation of its particular features. The organization of human and mouse Langerin genes are similar, consisting of six exons, three of which encode the carbohydrate recognition domain. The mLangerin gene maps to chromosome 6D, syntenic to the human gene on chromosome 2p13. mLangerin protein, detected by a mAb as a 48-kDa species, is abundant in epidermal LC in situ and is down-regulated upon culture. A subset of cells also expresses mLangerin in bone marrow cultures supplemented with TGF-beta. Notably, dendritic cells in thymic medulla are mLangerin-positive. By contrast, only scattered cells express mLangerin in lymph nodes and spleen. mLangerin mRNA is also detected in some nonlymphoid tissues (e.g., lung, liver, and heart). Similarly to hLangerin, a network of BG form upon transfection of mLangerin cDNA into fibroblasts. Interestingly, substitution of a conserved residue (Phe(244) to Leu) within the carbohydrate recognition domain transforms the BG in transfectant cells into structures resembling cored tubules, previously described in mouse LC. Our findings should facilitate further characterization of mouse LC, and provide insight into a plasticity of dendritic cell organelles which may have important functional consequences.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

On velocity-based local model networks for nonlinear identification

This paper exposes the strengths and weaknesses of the recently proposed velocity-based local model (LM) network. The global dynamics of the velocity-based blended representation are directly related to the dynamics of the underlying local models, an important property in the design of local controller networks. Furthermore, the sub-models are continuous-time and linear providing continuity with established linear theory and methods. This is not true for the conventional LM framework, where the global dynamics are only weakly related to the affine sub-models. In this paper, a velocity-based multiple model network is identified for a highly nonlinear dynamical system. The results show excellent dynamical modelling performances, highlighting the value of the velocity-based approach for the design and analysis of LM based control. Three important practical issues are also addressed. These relate to the blending of the velocity-based local models, the use of normalised Gaussian basis functions and the requirement of an input derivative.

Host-based P2P flow identification and use in real-time

Data identification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P traffic wishing to avoid identification, new strategies must be developed to detect and classify such flows. This paper introduces a new method of separating P2P and standard web traffic that can be applied as part of a data mining process, based on the activity of the hosts on the network. Unlike other research, our method is aimed at classifying individual flows rather than just identifying P2P hosts or ports. Heuristics are analysed and a classification system proposed. The accuracy of the system is then tested using real network traffic from a core internet router showing over 99% accuracy in some cases. We expand on this proposed strategy to investigate its application to real-time, early classification problems. New proposals are made and the results of real-time experiments compared to those obtained in the data mining research. To the best of our knowledge this is the first research to use host based flow identification to determine a flows application within the early stages of the connection.

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.

Identification of gene networks associated with acute myeloid leukemia by comparative molecular methylation and expression profiling

Around 80% of acute myeloid leukemia (AML) patients achieve a complete remission, however many will relapse and ultimately die of their disease. The association between karyotype and prognosis has been studied extensively and identified patient cohorts as having favourable [e.g. t(8; 21), inv (16)/t(16; 16), t(15; 17)], intermediate [e.g. cytogenetically normal (NK-AML)] or adverse risk [e.g. complex karyotypes]. Previous studies have shown that gene expression profiling signatures can classify the sub-types of AML, although few reports have shown a similar feature by using methylation markers. The global methylation patterns in 19 diagnostic AML samples were investigated using the Methylated CpG Island Amplification Microarray (MCAM) method and CpG island microarrays containing 12,000 CpG sites. The first analysis, comparing favourable and intermediate cytogenetic risk groups, revealed significantly differentially methylated CpG sites (594 CpG islands) between the two subgroups. Mutations in the NPM1 gene occur at a high frequency (40%) within the NK-AML subgroup and are associated with a more favourable prognosis in these patients. A second analysis comparing the NPM1 mutant and wild-type research study subjects again identified distinct methylation profiles between these two subgroups. Network and pathway analysis revealed possible molecular mechanisms associated with the different risk and/or mutation sub-groups. This may result in a better classification of the risk groups, improved monitoring targets, or the identification of novel molecular therapies.

Intelligent potentiostat for identification of heavy metals in situ

This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control. 

Data-Augmentation for Reducing Dataset Bias in Person Re-identification

In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.

RSSI-based environment identification for 2.45 GHz body area networks

A unique property of body area networks (BANs) is the mobility of the network as the user moves freely around. This mobility represents a significant challenge for BANs, since, in order to operate efficiently, they need to be able to adapt to the changing propagation environment. A method is presented that allows BAN nodes to classify the current operating environment in terms of multipath conditions, based on received signal strength indicator values during normal packet transmissions. A controlled set of measurements was carried out to study the effect different environments inflict on on-body link signal strength in a 2.45 GHz BAN. The analysis shows that, by using two statistical parameters, gathered over a period of one second, BAN nodes can successfully classify the operating environment for over 90% of the time.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.