Synthesis of multisubstituted halo-olefins via Pd-catalyzed cross-coupling reactions : applications in nucleoside chemistry

The enzyme S-adenosyl-L-homocysteine (AdoHey) hydrolase effects hydrolytic cleavage of AdoHcy to adenosine (Ado) and L-homocysteine (Hcy). The cellular levels of AdoHcy and Hcy are critical because AdoHcy is a potent feedback inhibitor of crucial transmethylation enzymes. Also, elevated plasma levels of Hcy in humans have been shown to be a risk factor in coronary artery disease. On the basis of the previous finding that AdoHcy hydrolase is able to add the enzyme-sequestered water molecule across the 5',6'-double bond of (halo or dihalohomovinyl)-adenosines causing covalent binding inhibition, we designed and synthesized AdoHcy analogues with the 5',6'-olefin motif incorporated in place of the carbon-5' and sulfur atoms. From the available synthetic methods we chose two independent approaches: the first approach was based on the construction of a new C5'- C6' double bond via metathesis reactions, and the second approach was based on the formation of a new C6'-C7' single bond via Pd-catalyzed cross-couplings. Cross-metathesis of the suitably protected 5'-deoxy-5'-methyleneadenosine with racemic 2-amino-5-hexenoate in the presence of Hoveyda-Grubb's catalyst followed by standard deprotection afforded the desired analogue as 5'E isomer of the inseparable mixture of 9'RIS diastereomers. Metathesis of chiral homoallylglycine [(2S)-amino-5-hexenoate] produced AdoHcy analogue with established stereochemistry E at C5'atom and S at C9' atom. The 5'-bromovinyl analogue was synthesized using the brominationdehydrobromination strategy with pyridinium tribromide and DBU. Since literature reports on the Pd-catalyzed monoalkylation of dihaloalkenes (Csp2-Csp3 coupling) were scarce, we were prompted to undertake model studies on Pdcatalyzed coupling between vinyl dihalides and alkyl organometallics. The 1-fluoro-1- haloalkenes were found to undergo Negishi couplings with alkylzinc bromides to give multisubstituted fluoroalkenes. The alkylation was trans-selective affording pure Zfluoroalkenes. The highest yields were obtained with PdCl 2(dppb) catalyst, but the best stereochemical outcome was obtained with less reactive Pd(PPh3)4 . Couplings of 1,1- dichloro-and 1,1-dibromoalkenes with organozinc reagents resulted in the formation of monocoupled 1-halovinyl product.

A generalized multidimensional index structure for multimedia data to support content-based similarity searches in a collaborative search environment

Since multimedia data, such as images and videos, are way more expressive and informative than ordinary text-based data, people find it more attractive to communicate and express with them. Additionally, with the rising popularity of social networking tools such as Facebook and Twitter, multimedia information retrieval can no longer be considered a solitary task. Rather, people constantly collaborate with one another while searching and retrieving information. But the very cause of the popularity of multimedia data, the huge and different types of information a single data object can carry, makes their management a challenging task. Multimedia data is commonly represented as multidimensional feature vectors and carry high-level semantic information. These two characteristics make them very different from traditional alpha-numeric data. Thus, to try to manage them with frameworks and rationales designed for primitive alpha-numeric data, will be inefficient. An index structure is the backbone of any database management system. It has been seen that index structures present in existing relational database management frameworks cannot handle multimedia data effectively. Thus, in this dissertation, a generalized multidimensional index structure is proposed which accommodates the atypical multidimensional representation and the semantic information carried by different multimedia data seamlessly from within one single framework. Additionally, the dissertation investigates the evolving relationships among multimedia data in a collaborative environment and how such information can help to customize the design of the proposed index structure, when it is used to manage multimedia data in a shared environment. Extensive experiments were conducted to present the usability and better performance of the proposed framework over current state-of-art approaches.

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Herbivore and nutrient impact on primary producer assemblages in a tropical marine environment

Globally, human populations are increasing and coastal ecosystems are becoming increasingly impacted by anthropogenic stressors. As eutrophication and exploitation of coastal resources increases, primary producer response to these drivers becomes a key indicator of ecosystem stability. Despite the importance of monitoring primary producers such as seagrasses and macroalgae, detailed studies on the response of these benthic habitat components to drivers remain relatively sparse. Utilizing a multi-faceted examination of turtle-seagrass and sea urchin-macroalgae consumer and nutrient dynamics, I elucidate the impact of these drivers in Akumal, Quintana Roo, Mexico. In Yal Ku Lagoon, macroalgae bioindicators signified high nutrient availability, which is important for further studies, but did not consistently follow published trends reflecting decreased δ 15N content with distance from suspected source. In Akumal Bay, eutrophication and grazing by turtles and fishes combine to structure patches within the seagrass beds. Grazed seagrass patches had higher structural complexity and productivity than patches continually grazed by turtles and fishes. Results from this study indicate that patch abandonment may follow giving-up density theory, the first to be recorded in the marine environment. As Diadema antillarum populations recover after their massive mortality thirty years ago, the role these echinoids will have in reducing macroalgae cover and altering ecosystem state remains to be clear. Although Diadema antillarum densities within the coral reef ecosystem were comparable to other regions within the Caribbean, the echinoid population in Akumal Bay was an insufficient driver to prevent dominance of a turf-algal-sediment (TAS) state. After a four year study, declining coral cover coupled with increased algal cover suggests that the TAS-dominated state is likely to persist over time despite echinoid recovery. Studies on macroalgal diversity and nutrients within this same region of echinoids indicated diversity and nutrient content of macroalgae increased, which may further increase the persistence of the algal-dominated state. This study provides valuable insight into the variable effects of herbivores and nutrients on primary producers within a tropical coastal ecosystem. Results from this work challenge many of the currently accepted theories on primary producer response to nutrients and herbivory while providing a framework for further studies into these dynamics.

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.