The immune system must be able to mount a response against pathogens and transformed cells while remaining tolerant to healthy host tissue. A key process for ensuring this self-tolerance is the negative selection of self-reactive thymocytes. Expression of Programmed Death-1 (PD-1), a co-inhibitory member of the CD28 family associated with dampened peripheral immune responses,was found to be upregulated in 20-40% of thymocytes undergoing negative selection in the HYcd4model of thymic development. Although analysis of gene and protein expression directly ex vivo indicates that PD-1- and PD-1+ thymocytes are equally apoptotic, PD-1+ thymocytes appear to be protected from apoptosis in an in vitro stimulation assay. Analysis of HYcd4PD-1-/- mice indicates that thymocytes receive a higher intensity signal in the absence of PD-1. Future work utilizing HYcd4PD-1-/- mice will increase our understanding of the role of PD-1 in thymic negative selection. / Immunology

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The negative selection algorithm developed by Forrest et al. was inspired by the manner in which T-cell lymphocytes mature within the thymus before being released into the blood system. The resultant T-cell lymphocytes, which are then released into the blood, exhibit an interesting characteristic: they are only activated by non-self cells that invade the human body. The work presented in this thesis examines the current body of research on the negative selection theory and introduces a new affinity threshold function, called the feature-detection rule. The feature-detection rule utilises the inter-relationship between both adjacent and non-adjacent features within a particular problem domain to determine if an artificial lymphocyte is activated by a particular antigen. The performance of the feature-detection rule is contrasted with traditional affinity-matching functions currently employed within negative selection theory, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming-distance rule. The performance will be characterised by considering the detection rate, false-alarm rate, degree of generalisation and degree of overfitting. The thesis will show that the feature-detection rule is superior to the r-chunks rule and the hamming-distance rule, in that the feature-detection rule requires a much smaller number of detectors to achieve greater detection rates and less false-alarm rates. The thesis additionally refutes that the way in which permutation masks are currently applied within negative selection theory is incorrect and counterproductive, while placing the feature-detection rule within the spectrum of affinity-matching functions currently employed by artificial immune-system (AIS) researchers. / Dissertation (MSc)--University of Pretoria, 2009. / Computer Science / Unrestricted

Protein sequences may evolve to avoid highly hydrophobic local regions of sequence, in part because such sequences promote nonnative aggregation. Hydrophobic local sequences are avoided in proteins even in buried regions, where native structure requirements tend to favor them. In this dissertation, I describe three explorations of this hydrophobic suppression. In Chapter 2, I examine the occurrence of hydrophobic and polar residues in completely buried β-strand elements, and find evidence for hydrophobic suppression that decreases as a β-strand becomes more exposed. In Chapter 3, I present a generalized study of the tendency of local sequences to deviate from the hydropathy (hydrophobicity) expected based on their solvent exposure. First, I examined the hydropathy of local and nonlocal sequence groups over a large range of solvent exposures, within folded protein domains in the ASTRAL Compendium database; second, I calculated the tendency of residues within 10 positions of a nonpolar or polar reference residue to deviate from the hydropathy expected based on their structural environment. Both analyses suggested that protein sequences exhibit 'local hydropathic balance' across a range of 6-7 residues, meaning that polar and nonpolar residues are more dispersed in the sequence than expected based on solvent exposure patterns. This balance occurs in all major fold classes, domain sizes and protein functions. An unexpected finding was that it partly arises from a tendency of buried or exposed residues to be flanked by polar or nonpolar residues, respectively. This relationship may result from evolutionary selection for folding efficiency, which might be enhanced by reduced local competition for buried or exposed sites during folding. Finally, in Chapter 4, I present several exploratory analyses, including a decision-tree approach, to visualize the influence of a large number of sequence-structure properties on residue hydrophobicity. Overall, the work in this dissertation confirms that hydrophobic suppression and local hydropathic balance in general are intrinsic properties of folded proteins. I speculate that local hydropathic balance results from selection for reduced aggregation propensity, increased folding efficiency and increased native state specificity. The concept of local hydropathic balance might be used to improve the properties of designed and engineered proteins.

A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion.

Network security has always been an area of priority and extensive research. Recent years have seen a considerable growth in experimentation with biologically inspired techniques. This is a consequence of our increased understanding of living systems and the application of that understanding to machines and software. The mounting complexity of telecommunications networks and the need for increasing levels of security have been the driving factor. The human body can act as a great role model for its unique abilities in protecting itself from external, foreign entities. Many abnormalities in the human body are similar to that of the attacks in wireless sensor networks (WSN). This paper presents basic ideas drawn from human immune system analogies that can help modelling a system to counter the attacks on a WSN by monitoring parameters such as energy, frequency of data transfer, data sent and received. This is implemented by exploiting two immune concepts, namely danger theory and negative selection. Danger theory aggregates the anomalies based on the weights of the anomalous parameters. The objective is to design a cooperative intrusion detection system (IDS) based on danger theory. Negative selection differentiates between normal and anomalous strings and counters the impact of malicious nodes faster than danger theory. We also explore other human immune system concepts and their adaptability to Wireless Sensor Network Security.

Artificial Immune Systems (AIS) are class of computational intelligent methods developed based on the principles and processes of the biological immune system. AIS methods are categorized mainly into four types according to the inspired principles and processes of immune system. These categories are clonal selection, negative selection, immune network and danger theory. The approach of negative selection algorithm (NSA) is one of the major AIS models. NSA is a supervised learning algorithm based on the imitation of the T cells maturation process in thymus. In this imitation, detectors are used to mimic the cells, and the process of T cells maturation is simulated to generate detectors. Then, NSA classifies the specified data either as normal (self) data or as anomalous (non-self) data. In this classification task, NSA methods can make two kinds of classification errors: a self data is classified as anomalous, and a non-self data is classified as normal data. In this thesis, a novel negative selection method, variable shaped detector (V-shaped detector), is proposed to increase the classification accuracy, or in other words decreasing classification errors. In V-shaped detector, new approaches are introduced to define self and represent detectors. V-shaped detector uses the combination of Local Outlier Factor (LOF) and kth nearest neighbor (k-NN) to determine a different radius for each self sample, thus it becomes possible to model the self space using self samples and their radii. Besides, the cubic b-spline is proposed to generate a variable shaped detector. In detector representation, the application of cubic spline is meaningful, when the edge points are used. Hence, Edge Detection (ED) algorithm is developed to find the edge points of the given self samples. V-shaped detector was tested using different data sets and compared with the well-known one-class classification method, SVM, and the similar popular negative selection method, NSA with variable-sized detector termed V-detector. The experiments show that the proposed method generates reasonable and comparable results.

Structural health monitoring (SHM) systems have been technologically advancing over the past few years. Improvements in fabrication and microelectronics allow the development of highly sophisticated sensor arrays, capable of detecting and transmitting an unprecedented amount of data. As the complexity of the hardware increases, research has been performed in developing the means to best utilize and effectively process the data. Algorithms from other computational fields are being introduced for the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to calculate energy ratios, which are then classified using a neural network and an AIS algorithm known as negative selection (NS). The effectiveness of both methods are validated using simulated acceleration data of a four story structure exhibiting various damage states via computer simulation.

Security assurance in a computer system can be viewed as distinguishing between self and non-self. Artificial Immune Systems (AIS) are a class of machine learning (ML) techniques inspired by the behavior of innate biological immune systems, which have evolved to accurately classify self-behavior from non-self-behavior. This work aims to leverage AIS-based ML techniques for identifying certain behavioral traits in high level hardware descriptions, including unsafe or undesirable behaviors, whether such behavior exists due to human error during development or due to intentional, malicious circuit modifications, known as hardware Trojans, without the need fora golden reference model. We explore the use of Negative Selection and Clonal Selection Algorithms, which have historically been applied to malware detection on software binaries, to detect potentially unsafe or malicious behavior in hardware. We present a software tool which analyzes Trojan-inserted benchmarks, extracts their control and data-flow graphs (CDFGs), and uses this to train an AIS behavior model, against which new hardware descriptions may be tested.

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