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No description available.

No description available.

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<p>We study the server allocation problem for tandem queues in the absence of intermediate buffer space. Servers are assumed to be homogeneous and non-collaborative. We provide policies to maximize the throughput. We break down our work into four stages. First, we assume that all servers are dedicated. We propose an allocation algorithm that assigns servers to stations based on the mean service times and the current number of servers assigned to each station. The algorithm is proposed for stations with exponentially distributed service times, but where the service rate at each station may be different. We further study the impact on the proposed allocation method of including service time distributions with different coecients of variation. Second, we consider tandem queues with both dedicated and flexible servers. We examine policies to dynamically assign flexible servers. When there is one flexible server and two stations each with a dedicated server, we completely characterize the optimal policy. We use the insights gained from applying the Policy Iteration algorithm on systems with three, four, and five stations to devise heuristics for systems of arbitrary size. Third, we study cases where flexibility is constrained such that flexible servers can only service two adjacent stations. We provide optimal policies for tandem queues with three and four stations and compare them with optimal policies for corresponding non-constrained cases. Fourth, we consider two parallel tandem queues with both dedicated servers and vertical flexible servers - servers that can move between corresponding stations of the two tandem queues. The workload allocations are the same for each line and each vertical flexible server moves only between two corresponding stations. We examine policies for dynamic allocation of these vertical flexible servers. When each tandem queue has two stations, each station possesses a dedicated server, and a vertical flexible server exists for each pair of stations, we specify the optimal policy. For cases with more than two stations, heuristic assignments are proposed. We also analyze the throughput improvement gained from adding flexible servers within a tandem queue or between two parallel tandem queues. Numerical results are used to verify the heuristics provided in each of the stages.</p> / Doctor of Philosophy (PhD)

Escherichia coli survives in both nutrient rich nutrient-limited environments. As such, understanding the gene and protein level activity that occurs during stationary phase is considered an important aspect of bacterial survival. Escherichia coli has been studied for decades providing substantial insight into gene expression profiles in exponential phase and recently, during adaptation to stationary phase. This led to the discovery of RpoS as a growth phase-dependent sigma factor. Further studies indicated that there are many genes that are expressed in an RpoS-independent but stationary phase-specific manner. However, proteins represent the functional molecules of the cell. Additionally, protein expression does not always correlate with the corresponding gene expression patterns. Therefore, to obtain an in depth understanding of the proteins that play a role in long-term growth in E. coli, TMT- (Tandem Mass Tags) based quantitative proteomic analysis was performed to identify proteins that are preferentially expressed during prolonged starvation. We identified proteins that were both positively and negatively regulated by RpoS during stationary phase, such as GadA and TnaA, respectively. RpoS levels peaked during early stationary phase and declined thereafter. However, proteins that were RpoS-dependent continued to increase during prolonged stationary phase. Additionally, we identified proteins that were expressed in an RpoS-independent manner during stationary phase. This suggests that protein expression during early stationary phase is distinct from prolonged stationary phase. Furthermore, RpoS-independent proteins may also play an important role during long-term growth. / Thesis / Master of Science (MSc) / Escherichia coli adapts to shifts in nutrient availability using the alternative sigma factor RpoS which controls morphological and physiological changes. Although gene expression during growth has been extensively studied, comparable information regarding changes in protein abundance during prolonged incubation is not available. We employed a quantitative proteomics approach to identify proteins that are preferentially expressed during stationary phase in E. coli. We identified classes of proteins that are upregulated and downregulated by RpoS in addition to proteins regulated independently of RpoS. Global analysis of protein expression during growth can aid in understanding the adaptation of E. coli under starvation conditions.

Cryptosporidium is a parasite that causes gastrointestinal issues such as diarrhoea and stomach pain. The main transmission routes are through contaminated water or food, between humans and from animal to humans. Cryptosporidium infects through oocysts which contain four sporozoites that releases when entering a host and can continue to breed inside the body. Cryptosporidium can cause massive outbreaks if established in a water source used for drinking water. To prevent and detect an outbreak it´s important to trace the transmission back to the source. The GP60-gene is used to identify and subtype Cryptosporidium and is a very useful tool during contact tracing. The purpose of this study was to identify species and subtype of Cryptosporidium using nanopore sequencing. In this study the GP60-gene was amplified using a Nested PCR protocol and then sequenced using nanopore sequencing. The sequences acquired where then used to make a search in BLAST to identify the species. The GP60 subtyping method uses the hypervariable region on the GP60-gene. A series of tandem repeats are used to identify the subtype. In this study seven positive Cryptosporidium faeces samples were amplified and sequenced. Nanopore sequencing was possible for five of the seven samples with C. parvum identified in four of these samples. Targeting the GP60-gene to determine species and subtype works well for the most common human pathogen species of Cryptosporidium. Further optimization is required before the method can be implemented för diagnostic use.

It is important to understand the entire spectrum of somatic variants to gain more insight into mutations that occur in different cancers for development of better diagnostic, prognostic and therapeutic tools. This thesis outlines our work in understanding somatic variant calling, improving the identification of somatic variants from whole genome and whole exome platforms and identification of biomarkers for lung cancer. Integrating somatic variants from whole genome and whole exome platforms poses a challenge as variants identified in the exonic regions of the whole genome platform may not be identified on the whole exome platform and vice-versa. Taking a simple union or intersection of the somatic variants from both platforms would lead to inclusion of many false positives (through union) and exclusion of many true variants (through intersection). We develop the first framework to improve the identification of somatic variants on whole genome and exome platforms using a machine learning approach by combining the results from two popular somatic variant callers. Testing on simulated and real data sets shows that our framework identifies variants more accurately than using only one somatic variant caller or using variants from only one platform. Short tandem repeats (STRs) are repetitive units of 2-6 nucleotides. STRs make up approximately 1% of the human genome and have been traditionally used as genetic markers in population studies. We conduct a series of in silico analyses using the exome data of 32 individuals with lung cancer to identify 103 STRs that could potentially serve as cancer diagnostic markers and 624 STRs that could potentially serve as cancer predisposition markers. Overall these studies improve the accuracy in identification of somatic variants and highlight the association of STRs to lung cancer. / Ph. D.