<p> The study of proteasome is a rapidly evolving field with multifaceted implications in neuroscience, aging, and cancer. Recent developments structural biology of the proteasome machinery has catapulted the drug discovery and targeted protein degradation. The success of proteasome inhibitors like Bortezomib and Ixazomib has also led to new interests in developing more precise inhibitors for the various proteasome isoforms. Proteasome activation is a relatively new field, and much has to be done in the field. The 20S CP is an emerging target in chemical biology and drug discovery for its implications in maintaining protein homeostasis and immune regulation. The central theme of the thesis is to study the proteasome in cellular contexts to develop new chemical biology tools to study the proteasome and its modulation by small molecules and probes in cellular contexts to ameliorate protein accumulation-mediated neurodegeneration </p>

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Due to an increasing prevalence of bacterial resistance to antibiotic drugs and the overuse of commercial antibiotics, the need to discover novel antibacterial compounds is becoming more urgent. There is one promising avenue of novel drug discovery which has begun to be explored; the analysis of secondary metabolites. Rhodococcus is a genus of gram-positive bacterium known for their ability to catabolize a wide range of compounds, and more notably for its ability to produce bioactive secondary metabolites. Rhodococcus belongs to the class actinobacteria. A species of Rhodococcus, MTM3W5.2, has been discovered in Morristown, Tennessee and was found to produce a metabolite with inhibitory activity against closely related species. The aim of this study is to elucidate the structure of the inhibitory metabolite by first isolating and purifying it, and then characterizing it using spectroscopic techniques. The compound was isolated from MTM3W5.2 RM broth cultures using n-butanol extraction, which yielded an active crude extract. The crude extract was then subjected to fractionation using a Sephedex LH-20 column with a 100% methanol solvent. The inhibitory activity of the fractions was tested through disk diffusion assay using Rhodococcus erythropolis as an indicator of inhibitory activity. Further preparation was completed using preparative reverse-phase high-performance liquid chromatography. Advanced purification was conducted using multiple rounds of analytical reverse-phase HPLC and activity was tested at each subsequent step using disk diffusion assay. Throughout the study, the HPLC fractions were characterized and the stability was monitored using UV-Visible spectroscopy. Two pure samples at 58.63 and 72.72 minutes from HPLC (High-performance liquid chromatography) collections were selected for further structural identification and are currently being studied using spectroscopic techniques, most notably 2D NMR spectroscopy (two-dimensional nuclear magnetic resonance).

This research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complimentary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engineà ­s performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engineà ­s operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications. / Ph. D.

The natural immune system embodies a wealth of information processing capabilities that can be exploited as a metaphor for the development of artificial immune systems. Chief among these features is the ability to recognize previously encountered substances and to generalize beyond recognition in order to provide appropriate responses to pathogens not seen before. This thesis presents a new supervised learning paradigm, resource limited artificial immune classifiers, inspired by mechanisms exhibited in natural and artificial immune systems. The key abstractions gleaned from these immune systems include resource competition, clonal selection, affinity maturation, and memory cell retention. A discussion of the progenitors of this work is offered. This work provides a thorough explication of a resource limited artifical immune classification algorithm, named AIRS (Artificial Immune Recognition System). Experimental results on both simulated data sets and real world machine learning benchmarks demonstrate the effectiveness of the AIRS algorithm as a classification technique.

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