Covalent protein adduction by drugs of abuse

Recreational abuse of the drugs cocaine, methamphetamine, and morphine continues to be prevalent in the United States of America and around the world. While numerous methods of detection exist for each drug, they are generally limited by the lifetime of the parent drug and its metabolites in the body. However, the covalent modification of endogenous proteins by these drugs of abuse may act as biomarkers of exposure and allow for extension of detection windows for these drugs beyond the lifetime of parent molecules or metabolites in the free fraction. Additionally, existence of covalently bound molecules arising from drug ingestion can offer insight into downstream toxicities associated with each of these drugs. This research investigated the metabolism of cocaine, methamphetamine, and morphine in common in vitro assay systems, specifically focusing on the generation of reactive intermediates and metabolites that have the potential to form covalent protein adducts. Results demonstrated the formation of covalent adduction products between biological cysteine thiols and reactive moieties on cocaine and morphine metabolites. Rigorous mass spectrometric analysis in conjunction with in vitro metabolic activation, pharmacogenetic reaction phenotyping, and computational modeling were utilized to characterize structures and mechanisms of formation for each resultant thiol adduction product. For cocaine, data collected demonstrated the formation of adduction products from a reactive arene epoxide intermediate, designating a novel metabolic pathway for cocaine. In the case of morphine, data expanded on known adduct-forming pathways using sensitive and selective analysis techniques, following the known reactive metabolite, morphinone, and a proposed novel metabolite, morphine quinone methide. Data collected in this study describe novel metabolic events for multiple important drugs of abuse, culminating in detection methods and mechanistic descriptors useful to both medical and forensic investigators when examining the toxicology associated with cocaine, methamphetamine, and morphine.

Covalent Protein Adduction by Drugs of Abuse

Recreational abuse of the drugs cocaine, methamphetamine, and morphine continues to be prevalent in the United States of America and around the world. While numerous methods of detection exist for each drug, they are generally limited by the lifetime of the parent drug and its metabolites in the body. However, the covalent modification of endogenous proteins by these drugs of abuse may act as biomarkers of exposure and allow for extension of detection windows for these drugs beyond the lifetime of parent molecules or metabolites in the free fraction. Additionally, existence of covalently bound molecules arising from drug ingestion can offer insight into downstream toxicities associated with each of these drugs. This research investigated the metabolism of cocaine, methamphetamine, and morphine in common in vitro assay systems, specifically focusing on the generation of reactive intermediates and metabolites that have the potential to form covalent protein adducts. Results demonstrated the formation of covalent adduction products between biological cysteine thiols and reactive moieties on cocaine and morphine metabolites. Rigorous mass spectrometric analysis in conjunction with in vitro metabolic activation, pharmacogenetic reaction phenotyping, and computational modeling were utilized to characterize structures and mechanisms of formation for each resultant thiol adduction product. For cocaine, data collected demonstrated the formation of adduction products from a reactive arene epoxide intermediate, designating a novel metabolic pathway for cocaine. In the case of morphine, data expanded on known adduct-forming pathways using sensitive and selective analysis techniques, following the known reactive metabolite, morphinone, and a proposed novel metabolite, morphine quinone methide. Data collected in this study describe novel metabolic events for multiple important drugs of abuse, culminating in detection methods and mechanistic descriptors useful to both medical and forensic investigators when examining the toxicology associated with cocaine, methamphetamine, and morphine.

Improving storage performance through layout optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds—parent-to-child or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5–54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3–127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Improving Storage Performance Through Layout Optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds - parent-tochild or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5-54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3-127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Wireless Sensor Network Deployment

Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work.