Opportunistic Access in Frequency Hopping Cognitive Radio Networks

Researchers in the area of cognitive radio often investigate the utility of dynamic spectrum access as a means to make more efficient use of the radio frequency spectrum. Many studies have been conducted to find ways in which a secondary user can occupy spectrum licensed to a primary user in a manner which does not disrupt the primary user\u27s performance. This research investigates the use of opportunistic access in a frequency hopping radio to mitigate the interference caused by other transmitters in a contentious environment such as the unlicensed 2.4 GHz region. Additionally, this work demonstrates how dynamic spectrum access techniques can be used not only to prevent interfering with other users but also improve the robustness of a communication system

Advancing Structure-Property Relationships in Functional Materials Via Thiol-ene Photopolymerization

Thiol-ene photopolymerizations provide a robust and versatile synthetic pathway to functional materials, and owing to the radical step-growth nature of polymerization and the resulting homogenous network structure, provide non-convoluted insight into how network chemistry influences and dictates macromolecular properties. The first facet of this dissertation focuses on the design and synthesis of bio-inspired, thin film adhesives for dry and aqueous adhesion. Drawing inspiration from the intertidal marine mussel, Chapter II details the synthesis of adhesive networks containing a monofunctional catechol-based monomer. The inclusion of a catechol group resulted in significant improvements in adhesion on a variety of substrates. In Chapter III, the inclusion of simple hydrophobic groups in the adhesive thiol-ene networks to improve underwater adhesion is reported. The presence of hydrophobic groups effectively push water away from the adhesive resin/substrate interface, facilitating adhesive interaction underwater. Further, the influence of the catechol (a known radical scavenger) and the hydrophobic groups (commonly considered non-adhesive) on polymerization kinetics, thermal mechanical, and mechanical properties was determined. The second facet of this dissertation focuses on the synthesis of semi-fluorinated polymer networks, as outlined in Chapter IV. Fluorine groups impart several advantageous properties to polymeric materials including increased mechanical strength, chemical and thermal stability, and unique optical and wetting properties. As such, the inclusion of the trifluorovinyl ether group in a thiol-ene photopolymerization resulted in the rapid and efficient synthesis of semi-fluorinated networks, exhibiting significant increases in thermomechanical and mechanical properties as a function of fluorine content

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

Machine learning algorithms for cognitive radio wireless networks

In this thesis new methods are presented for achieving spectrum sensing in cognitive radio wireless networks. In particular, supervised, semi-supervised and unsupervised machine learning based spectrum sensing algorithms are developed and various techniques to improve their performance are described. Spectrum sensing problem in multi-antenna cognitive radio networks is considered and a novel eigenvalue based feature is proposed which has the capability to enhance the performance of support vector machines algorithms for signal classification. Furthermore, spectrum sensing under multiple primary users condition is studied and a new re-formulation of the sensing task as a multiple class signal detection problem where each class embeds one or more states is presented. Moreover, the error correcting output codes based multi-class support vector machines algorithms is proposed and investigated for solving the multiple class signal detection problem using two different coding strategies. In addition, the performance of parametric classifiers for spectrum sensing under slow fading channel is studied. To address the attendant performance degradation problem, a Kalman filter based channel estimation technique is proposed for tracking the temporally correlated slow fading channel and updating the decision boundary of the classifiers in real time. Simulation studies are included to assess the performance of the proposed schemes. Finally, techniques for improving the quality of the learning features and improving the detection accuracy of sensing algorithms are studied and a novel beamforming based pre-processing technique is presented for feature realization in multi-antenna cognitive radio systems. Furthermore, using the beamformer derived features, new algorithms are developed for multiple hypothesis testing facilitating joint spatio-temporal spectrum sensing. The key performance metrics of the classifiers are evaluated to demonstrate the superiority of the proposed methods in comparison with previously proposed alternatives

A Macromolecular Approach to Eradicate Multidrug Resistant Bacterial Infections while Mitigating Drug Resistance Onset

Polymyxins remain the last line treatment for multidrug-resistant (MDR) infections. As polymyxins resistance emerges, there is an urgent need to develop effective antimicrobial agents capable of mitigating MDR. Here, we report biodegradable guanidinium-functionalized polycarbonates with a distinctive mechanism that does not induce drug resistance. Unlike conventional antibiotics, repeated use of the polymers does not lead to drug resistance. Transcriptomic analysis of bacteria further supports development of resistance to antibiotics but not to the macromolecules after 30 treatments. Importantly, high in vivo treatment efficacy of the macromolecules is achieved in MDR A. baumannii-, E. coli-, K. pneumoniae-, methicillin-resistant S. aureus-, cecal ligation and puncture-induced polymicrobial peritonitis, and P. aeruginosa lung infection mouse models while remaining non-toxic (e.g., therapeutic index—ED50/LD50: 1473 for A. baumannii infection). These biodegradable synthetic macromolecules have been demonstrated to have broad spectrum in vivo antimicrobial activity, and have excellent potential as systemic antimicrobials against MDR infections

Synthesis of Bicyclic Thieno[2,3-d]Pyrimidines, Tricyclic Thieno[2,3-d]Pyrimidines and Thieno[3,2-d]Pyrimidines as Classical and Nonclassical Antifolates

An introduction, background and research progress in the areas of antifolates and antimitotic agents has been reviewed and discussed. Thymidylate synthase (TS), dihydrofolate reductase (DHFR) and glycinamide ribonucleotide formyltransferase (GARFTase) are important folate dependent enzymes that are targets for cancer chemotherapy and the treatment of infectious diseases. As a part of this study, thirty-five novel compounds were designed and synthesized on the basis of existing clinically active compounds and their crystal structures. These compounds were synthesized and evaluated as single and/or muliple targeted classical and nonclassical antifolates to decrease toxicity and improve the antitumor activity and selectivity of existing therapeutic agents. In addition, bicyclic substituted thieno[2, 3- italic d /italic ]- and theino[3, 2- italic d /italic ]pyrimidines were synthesized as antimitotic agents. These compounds allowed potent inhibition of tumor cells in culture and extended the structure-activity relationship in the antimitotic area

Synthesis of furo[2,3-d]pyrimidines, thieno[2,3- d]pyrimidines, pyrrolo[2,3-d]pyrimidines as classical and nonclassical antifolates, receptor tyrosine kinase (RTK) inhibitors and antimitotic agents

An introduction, background and research progress in the areas of antifolates, receptor tyrosine kinase (RTK) inhbitors and anti mitotic agents has been discussed. Thymidylate synthase (TS), dihydrofolate reductase (DHFR) and glycinamide ribonucleotide formyltransferase (GARFTase) are important folate dependent enzymes that are targets for cancer chemotherapy and the treatment of infectious diseases. Classical antifolates, in most cases, are substrates for folypoly - γ - glutamate synthase (FPGS) and rely on folate transporter systems to enter cells. As a part of this study, twenty - eight compounds were designed on the basis of existing clinically active compounds and crystal structures, synthesized and evaluated as single and/or muliple targeted classical and nonclassical antifolates to decrease toxicity and improve the activity and selectivity of existing therapeutic agents. In additio n, these structures provides an extension to the structure activity relationship in the antifolate area. RTK inhibitors and antimitotic agents are important antitumor agents and are extensively used in the clinic for the treament of various types of cancer s. Pgp overexpression is one of the common reasons for drug resistance to existing antitumor agents and consequently the reason for some chemotherapeutic failures. A furo[2,3- d ]pyrimidine compound was discovered to have dual RTK inhibitory activity along with antimitotic activity that circumvent pgp over expression. Antimitotic activity via the binding at the colchicine site is one of the mechanisms of action. Molecular modelling and biological evaluation suggest the importance of conformational restriction for activity. Fifty - seven furo[2,3- d ]pyrimidines and six thieno[2,3- d ]pyrimidines were designed on iv the basis of crustal structures and synthesized as potential RTK inhibitors with antimitotic antitumor activity. Four pyrrolo[2,3- d ]pyrimidines were design ed and synthesized as antimitotic anticancer agents that also reverse pgp action

Synthesis and Molecular Modeling Studies of Bicyclic Inhibitors of Dihydrofolate Reductase, Receptor Tyrosine Kinases and Tubulin

The results from this work are reported into two sections listed below: Synthesis: Following structural classes of compounds have been designed, synthesized and studied as inhibitors of pjDHFR, RTKs and tubulin: 1. 2,4-Diamino-6-(substituted-arylmethyl)pyrido[2,3-d]pyrimidines 2. 4-((3-Bromophenyl)linked)-6-(substituted-benzyl)-7H-pyrrolo[2,3-d]pyrimidin-2-amines 3. 6-Methyl-5-((substitutedphenyl)thio)-7H-pyrrolo[2,3-d]pyrimidin-2-amines A total of 35 new compounds (excluding intermediates) were synthesized, characterized and submitted for biological evaluation. Results from these studies will be presented in due course. Bulk synthesis of the potent lead compound 170 was carried out to facilitate in vivo evaluation. Docking Studies Docking studies were performed using LeadIT, MOE, Sybyl or Flexx for target compounds listed above and for other compounds reported by Gangjee et al. against the following targets: 1. Dihydrofolate reductase: human, P. carinii, P. jirovecii (pjDHFR) and T. gondii (tgDHFR) 2. Thymidylate synthase: human (hTS) and T. gondii (tgTS) 3. Receptor tyrosine kinases: VEGFR2, EGFR and PDGFR-β 4. Colchicine binding site of tublulin. Novel homology models were generated and validated for pjDHFR, tgDHFR, tgTS, PDGFR-β and the F36C L65P pjDHFR double mutant. The tgTS homology model generated in this study and employed to design novel inhibitors shows remarkable similarity with the recently published X-ray crystal structures. Docking studies were performed to provide a molecular basis for the observed activity of target compounds against DHFR, RTKs or tubulin. Results from these studies support structure-based and ligand-based medicinal chemistry efforts in order to improve potency and/or selectivity of analogs of the docked compounds against these targets. Novel topomer CoMFA models were developed for tgTS and hTS using a set of 85 bicyclic inhibitors and for RTKs using a set of 60 inhibitors reported by Gangjee et al. The resultant models could be used to explain the potency and/or selectivity differences for selected molecules for tgTS over hTS. Topomer CoMFA maps show differences in steric and/or electronic requirements among the three RTKs, and could be used, in conjuction with other medicinal chemistry approaches, to modulate the selectivity and/or potency of inhibitors with multiple RTK inhibitory potential. Drug design efforts that involve virtual library screening using these topomer CoMFA models in conjunction with traditional medicinal chemistry techniques and docking are currently underway