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Streptococcus pneumoniae is a common commensal of the human upper respiratory tract flora living peacefully without causing any harm to the host in normal conditions. S. pneumoniae can cause serious life threatening diseases in certain circumstances and usually affects children, the elderly and immunocompromised people. S. pneumoniae is a highly transformable pathogen and newly emerging antibiotic resistant strains are becoming commoner. Developing multi-drug resistant strains is a serious threat to the community, and the available drug treatment and vaccines do not provide full protection. Some newer strategies such as harnessing the immunological response to S. pneumoniae must be adopted to overcome this invasive pathogen. The human immune system has evolved in a way to successfully detect, isolate and eliminate invading pathogens. It provides a generalised and rapid response during invasion of infectious agents and is usually enough to stop infections. Normally immunological and inflammatory responses against invading pathogens and foreign antigens are under strict control. However, they are potentially dangerous and may cause autoimmune diseases. Autophagy is an emerging pathway associated with the innate immune system. It has an important role in infection control and maintains a fine balance in inflammatory responses to protect the host from harmful effects. Autophagy is basically a homeostatic pathway for degradation of unwanted protein aggregates at a cellular level, but has an important role in innate immunity. Autophagy-related (Atg) proteins have a crucial role in the body’s immune system and take part in innate and adaptive immunity. This pathway is considered to prevent the body’s immune system from attacking self-tissues and suppression of the auto-immune inflammatory responses. In this thesis I present that infection with S. pneumoniae strain D39 WT and its pneumolysin deficient counter-part D39 ΔPly induces autophagy in primary murine bone marrow derived macrophages and human neutrophils in-vitro and in- vivo. We confirmed autophagy by a classical marker protein LC3 through immunofluorescence and western blot. The associated inflammasome activation in S. pneumoniae infection has an inhibitory effect on autophagy induction as was observed using WT and pneumolysin deficient strains. Similarly inflammasome inhibition with pharmacological and genetic methods up-regulates autophagy in S. pneumoniae infection. I also present here that autophagy is associated with phagocytosis and intracellular killing of S. pneumoniae and both these pathways are used as innate immune mechanisms for clearing infection. Previous research links autophagy and phagocytosis, and the phagocytosed microbe is targeted to the lysosome for degradation and killing. Our findings here in this thesis demonstrate that these pathways are also influenced by the virulence factors of S. pneumoniae. Pneumolysin have some inhibitory effects on autophagy induction and phagocytosis which may be a direct effect or indirectly through the inflammasome activation. Next, I present here a novel extracellular killing pathway in human neutrophils, the neutrophil extracellular traps generation or NETosis. S. pneumoniae infection induces NET generation, that is autophagy-dependent and can be inhibited by blocking autophagy pharmacologically or genetically. NET generation is morphologically the same in S. pneumoniae D39 WT and D39 ΔPly but pneumolysin helps in pathogen escape from NET entrapment which is a novel finding and needs further exploration. I present here the role of different pattern recognition receptors in S. pneumoniae induced autophagy signalling. S. pneumoniae infection induces autophagy independent of TRIF, MyD88, TLR4, TLR2 and NOD2 pathways. P38MAP kinase was also explored and has no association with autophagy induction in S. pneumonia infection. Autophagy induction in S. pneumoniae infection may be associated with some unknown signalling pathway which needs further exploration.

Traditional wireless location verification (authentication) is only feasible under the assumption that radio propagation is described by simple time-independent mathematical models. A similar situation applies to location acquisition, albeit to a lesser extent. However, in real-world situations, channel conditions are rarely well-described by simple mathematical models. In this thesis, novel location verification and acquisition techniques that integrate machine learning algorithms into the decision process are designed, analysed, and tested. Through the use of both simulated and experimental data, it is shown how the novel solutions developed remain operational in unknown time-varying channel conditions, thus making them superior to existing solutions, and more importantly, deployable in real-world scenarios. Location verification will be of growing importance for a host of emerging wireless applications in which location information plays a pivotal role. The location verification solutions offered in this thesis are the first to be tested against experimental data and the first to invoke machine learning algorithms. As such, they likely form the foundation for all future verification algorithms.