Mycobacterium tuberculosis peptides presented by HLA-E molecules are targets for human CD8 T-cells with cytotoxic as well as regulatory activity

Tuberculosis (TB) is an escalating global health problem and improved vaccines against TB are urgently needed. HLA-E restricted responses may be of interest for vaccine development since HLA-E displays very limited polymorphism (only 2 coding variants exist), and is not down-regulated by HIV-infection. The peptides from Mycobacterium tuberculosis (Mtb) potentially presented by HLA-E molecules, however, are unknown. Here we describe human T-cell responses to Mtb-derived peptides containing predicted HLA-E binding motifs and binding-affinity for HLA-E. We observed CD8(+) T-cell proliferation to the majority of the 69 peptides tested in Mtb responsive adults as well as in BCG-vaccinated infants. CD8(+) T-cells were cytotoxic against target-cells transfected with HLA-E only in the presence of specific peptide. These T cells were also able to lyse M. bovis BCG infected, but not control monocytes, suggesting recognition of antigens during mycobacterial infection. In addition, peptide induced CD8(+) T-cells also displayed regulatory activity, since they inhibited T-cell proliferation. This regulatory activity was cell contact-dependent, and at least partly dependent on membrane-bound TGF-beta. Our results significantly increase our understanding of the human immune response to Mtb by identification of CD8(+) T-cell responses to novel HLA-E binding peptides of Mtb, which have cytotoxic as well as immunoregulatory activity.

Immune-inspired adaptive information filtering

Adaptive information filtering is a challenging research problem. It requires the adaptation of a representation of a user’s multiple interests to various changes in them. We investigate the application of an immune-inspired approach to this problem. Nootropia, is a user profiling model that has many properties in common with computational models of the immune system that have been based on Franscisco Varela’s work. In this paper we concentrate on Nootropia’s evaluation. We define an evaluation methodology that uses virtual user’s to simulate various interest changes. The results show that Nootropia exhibits the desirable adaptive behaviour.

Human Capital Decisions and Employee Satisfaction at Selected Hotels in India

Understanding the role of human capital is one of the key considerations in delivering and sustaining competitiveness. Managing employees in the hospitality industry is particularly a challenging task as the industry is considered to be labor intensive. High turnover and increasing employee demands are among the problems that are identified as threats to maintaining a strong competitive position. Successful hotels attempt to retain their best employees in an effort to adapt to changing environments and increased competition. Effective hotel human resource systems can produce positive outcomes, through effective employee retention strategies that focus on work force motivation, attitudes and perception. The positive implementation of these strategies can influence and create employee satisfaction. This study aims to focus on the relationship between the mediating variables of motivation, attitudes, perception and their effect on employee satisfaction. These findings are based upon an extensive survey carried out between April 2009 and June 2009 in the small mountainous state of Uttarakhand, located within the Indian sub-continent. Although the area of study is confined to the Kumaon region of Uttarakhand, the authors contend that the findings and implications can be applied to other remote developing tourist destinations in other regions.

Human-driven benthic jellyfish blooms: Causes and consequences for coastal marine ecosystems

Coastal marine ecosystems are among the most impacted globally, attributable to individual and cumulative effects of human disturbance. Anthropogenic nutrient loading is one stressor that commonly affects nearshore ecosystems, including seagrass beds, and has positive and negative effects on the structure and function of coastal systems. An additional, previously unexplored mechanistic pathway through which nutrients may indirectly influence nearshore systems is by driving blooms of benthic jellyfish. My dissertation research, conducted on Abaco Island, Bahamas, focused on elucidating the role that benthic jellyfish have in structuring systems in which they are common (i.e., seagrass beds), and explored mechanistic processes that may drive blooms of this taxa. ^ To establish that human disturbances (e.g., elevated nutrient availability) may drive increased abundance and size of benthic jellyfish, Cassiopea spp., I conducted surveys in human-impacted and unimpacted coastal sites. Jellyfish were more abundant (and larger) from human-impacted areas, positively correlated to elevated nutrient availability. In order to elucidate mechanisms linking Cassiopea spp. with elevated nutrients, I evaluated whether zooxanthellae from Cassiopea were higher from human-disturbed systems, and whether Cassiopea exhibited increased size following nutrient input. I demonstrated that zooxanthellae population densities were elevated in human-impacted sites, and that nutrients led to positive jellyfish growth. ^ As heightened densities of Cassiopea jellyfish may exert top-down and bottom-up controls on flora and fauna in impacted seagrass beds, I sought to examine ecological responses to Cassiopea. I evaluated whether there was a relationship between high Cassiopea densities and lower benthic fauna abundance and diversity in shallow seagrass beds. I found that Cassiopea have subtle effects on benthic fauna. However, through an experiment conducted in a seagrass bed in which nutrients and Cassiopea were added, I demonstrated that Cassiopea can result in seagrass habitat modification, with negative consequences for benthic fauna. ^ My dissertation research demonstrates that increased human-driven benthic jellyfish densities may have indirect and direct effects on flora and fauna of coastal marine systems. This knowledge will advance our understanding of how human disturbances shift species interactions in coastal ecosystems, and will be critical for effective management of jellyfish blooms.^

Human-driven Benthic Jellyfish Blooms: Causes and Consequences for Coastal Marine Ecosystems

Coastal marine ecosystems are among the most impacted globally, attributable to individual and cumulative effects of human disturbance. Anthropogenic nutrient loading is one stressor that commonly affects nearshore ecosystems, including seagrass beds, and has positive and negative effects on the structure and function of coastal systems. An additional, previously unexplored mechanistic pathway through which nutrients may indirectly influence nearshore systems is by driving blooms of benthic jellyfish. My dissertation research, conducted on Abaco Island, Bahamas, focused on elucidating the role that benthic jellyfish have in structuring systems in which they are common (i.e., seagrass beds), and explored mechanistic processes that may drive blooms of this taxa. To establish that human disturbances (e.g., elevated nutrient availability) may drive increased abundance and size of benthic jellyfish, Cassiopea spp., I conducted surveys in human-impacted and unimpacted coastal sites. Jellyfish were more abundant (and larger) from human-impacted areas, positively correlated to elevated nutrient availability. In order to elucidate mechanisms linking Cassiopea spp. with elevated nutrients, I evaluated whether zooxanthellae from Cassiopea were higher from human-disturbed systems, and whether Cassiopea exhibited increased size following nutrient input. I demonstrated that zooxanthellae population densities were elevated in human-impacted sites, and that nutrients led to positive jellyfish growth. As heightened densities of Cassiopea jellyfish may exert top-down and bottom-up controls on flora and fauna in impacted seagrass beds, I sought to examine ecological responses to Cassiopea. I evaluated whether there was a relationship between high Cassiopea densities and lower benthic fauna abundance and diversity in shallow seagrass beds. I found that Cassiopea have subtle effects on benthic fauna. However, through an experiment conducted in a seagrass bed in which nutrients and Cassiopea were added, I demonstrated that Cassiopea can result in seagrass habitat modification, with negative consequences for benthic fauna. My dissertation research demonstrates that increased human-driven benthic jellyfish densities may have indirect and direct effects on flora and fauna of coastal marine systems. This knowledge will advance our understanding of how human disturbances shift species interactions in coastal ecosystems, and will be critical for effective management of jellyfish blooms.

Concept maps as a tool to analyze the ideas of teacher training students regarding what to teach on human nutrition in Primary Education

Concept maps are a technique used to obtain a visual representation of a person's ideas about a concept or a set of related concepts. Specifically, in this paper, through a qualitative methodology, we analyze the concept maps proposed by 52 groups of teacher training students in order to find out the characteristics of the maps and the degree of adequacy of the contents with regard to the teaching of human nutrition in the 3rd cycle of primary education. The participants were enrolled in the Teacher Training Degree majoring in Primary Education, and the data collection was carried out through a training activity under the theme of what to teach about Science in Primary School? The results show that the maps are a useful tool for working in teacher education as they allow organizing, synthesizing, and communicating what students know. Moreover, through this work, it has been possible to see that future teachers have acceptable skills for representing the concepts/ideas in a concept map, although the level of adequacy of concepts/ideas about human nutrition and its relations is usually medium or low. These results are a wake-up call for teacher training, both initial and ongoing, because they shows the inability to change priorities as far as the selection of content is concerned.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

'On Affinity Measures for Artificial Immune System Movie Recommenders'

Abstract. We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity measures, Kendall's Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender. We compare the results with those published previously and show that that Weighted Kappa is more suitable than others for movie problems. We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.

'Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection'

Abstract. Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Integrating Innate and Adaptive Immunity for Intrusion Detection

Network Intrusion Detection Systems (NIDS) monitor a net- work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

Price Trackers Inspired by Immune Memory

In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm’s ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently.

Introducting Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomaly Detection

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound imnological concepts.