An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the role of a character in a fictional virtual world. The interactive narrative systems usually employ a drama manager (DM), an omniscient background agent that monitors the fictional world and determines what will happen next in the players' story experience. Prevailing approaches to drama management choose successive story plot points based on a set of criteria given by the game designers. In other words, the DM is a surrogate for the game designers. In this dissertation, I create a data-driven personalized drama manager that takes into consideration players' preferences. The personalized drama manager is capable of (1) modeling the players' preference over successive plot points from the players' feedback; (2) guiding the players towards selected plot points without sacrificing players' agency; (3) choosing target successive plot points that simultaneously increase the player's story preference ratings and the probability of the players selecting the plot points. To address the first problem, I develop a collaborative filtering algorithm that takes into account the specific sequence (or history) of experienced plot points when modeling players' preferences for future plot points. Unlike the traditional collaborative filtering algorithms that make one-shot recommendations of complete story artifacts (e.g., books, movies), the collaborative filtering algorithm I develop is a sequential recommendation algorithm that makes every successive recommendation based on all previous recommendations. To address the second problem, I create a multi-option branching story graph that allows multiple options to point to each plot point. The personalized DM working in the multi-option branching story graph can influence the players to make choices that coincide with the trajectories selected by the DM, while gives the players the full agency to make any selection that leads to any plot point in their own judgement. To address the third problem, the personalized DM models the probability that the players transitioning to each full-length stories and selects target stories that achieve the highest expected preference ratings at every branching point in the story space. The personalized DM is implemented in an interactive narrative system built with choose-your-own-adventure stories. Human study results show that the personalized DM can achieve significantly higher preference ratings than non-personalized DMs or DMs with pre-defined player types, while preserve the players' sense of agency.

Central to the goals of public health are obtaining and interpreting timely and relevant information for the benefit of humanity. In this dissertation, we propose methods to monitor and assess the spread HIV in a more rapid manner, as well as to improve decisions regarding patient treatment options. In Chapter 1, we propose a method, extending the previously proposed dual-testing algorithm and augmented cross-sectional design, for estimating the HIV incidence rate in a particular community. Compared to existing methods, our proposed estimator allows for shorter follow-up time and does not require estimation of the mean window period, a crucial, but often unknown, parameter. The estimator performs well in a wide range of simulation settings. We discuss when this estimator would be expected to perform well and offer design considerations for the implementation of such a study. Chapters 2 and 3 are concerned with obtaining a more complete understanding of the impact of treatment in randomized clinical trials in which multiple patient outcomes are recorded. Chapter 2 provides an illustration of methods that may be used to address concerns of both risk-benefit analysis and personalized medicine simultaneously, with a goal of successfully identifying patients who will be ideal candidates for future treatment. Riskbenefit analysis is intended to address the multivariate nature of patient outcomes, while “personalized medicine” is concerned with patient heterogeneity, both of which complicate the determination of a treatment’s usefulness. A third complicating factor is the duration of treatment use. Chapter 3 features proposed methods for assessing the impact of treatment as a function of time, as well as methods for summarizing the impact of treatment across a range of follow-up times. Chapter 4 addresses the issue of meta-analysis, a commonly used tool for combining information for multiple independent studies, primarily for the purpose of answering a clinical question not suitably addressed by any one single study. This approach has proven highly useful and attractive in recent years, but often relies on parametric assumptions that cannot be verified. We propose a non-parametric approach to meta-analysis, valid in a wider range of scenarios, minimizing concerns over compromised validity.

Mitochondrial diseases involving dysfunction of the respiratory chain are the most common inborn errors of metabolism. Mitochondria are found in all cell types besides red blood cells; consequently, patients can present with any symptom in any organ at any age. These diseases are genetically heterogeneous, and exhibit maternal, autosomal dominant, autosomal recessive and X-linked modes of inheritance. Historically, clinical genetic evaluation of mitochondrial disease has been limited to sequencing of the mitochondrial DNA (mtDNA) or several candidate genes. As human genome sequencing transformed from a research grade effort costing $250,000 to a clinical test orderable by doctors for under $10,000, it has become practical for researchers to sequence individual patients. This thesis describes our experiences in applying "MitoExome" sequencing of the mtDNA and exons of >1000 nuclear genes encoding mitochondrial proteins in ~200 patients with suspected mitochondrial disease. In 42 infants, we found that 55% harbored pathogenic mtDNA variants or compound heterozygous mutations in candidate genes. The pathogenicity of two nuclear genes not previously linked to disease, NDUFB3 and AGK, was supported by complementation studies and evidence from multiple patients, respectively. In an additional two unrelated children presenting with Leigh syndrome and combined OXPHOS deficiency, we identified compound heterozygous mutations in MTFMT. Patient fibroblasts exhibit severe defects in mitochondrial translation that can be rescued by exogenous expression of MTFMT. Furthermore, patient fibroblasts have dramatically reduced fMet-\(tRNA^{Met}\) levels and an abnormal formylation profile of mitochondrially translated \(COX_1\). These results demonstrate that MTFMT is critical for human mitochondrial translation. Lastly, to facilitate evaluation of copy number variants (CNVs), we developed a web-interface that integrates CNV calling with genetic and phenotypic information. Additional diagnoses are suggested and in a male with ataxia, neuropathy, azoospermia, and hearing loss we found a deletion compounded with a missense variant in D-bifunctional protein, \(HSD_{17}B_4\), a peroxisomal enzyme that catalyzes beta-oxidation of very long chain fatty acids. Retrospective review of metabolic testing from this patient revealed alterations of long- and very-long chain fatty acid metabolism consistent with a peroxisomal disorder. This work expands the molecular basis of mitochondrial disease and has implications for clinical genomics.

As new therapeutic targets and drugs are discovered for B-cell lymphoma and other cancers, companion diagnostics are also needed to determine target engagement, therapeutic efficacy, and patient segmentation for clinical trials. We first employed synthetic chemistry to build a platform for modifying small molecule drugs into imaging probes, using the poly(ADP-ribose) polymerase 1 (PARP1) inhibitor AZD2281 (Olaparib) as a model for technology development. Our results showed that small-molecule companion imaging drugs can be used for fluorescence imaging in cells, as well as for pharmacokinetic studies and positron emission tomography (PET) imaging in vivo, without significantly perturbing their target binding properties or cellular uptake. To apply this approach to B-cell lymphoma drugs currently in clinical trials, we modified an irreversible inhibitor of Bruton's Tyrosine Kinase (BTK), PCI-32765 (Ibrutinib), with the fluorophore Bodipy FL (BFL), and used it for imaging in cells and in a mouse window-chamber xenograft model. The excellent co-localization of our probe (Ibrutinib-BFL) with BTK demonstrated its utility for studying additional BTK inhibitors and as a companion imaging probe. In parallel, we hypothesized that central nervous system (CNS) lymphoma diagnosis from paucicellular cerebrospinal fluid (CSF) samples could be improved with molecular profiling of putative lymphoma cells trapped in a customized microfluidic chip. Following fabrication and characterization of a polydimethylsiloxane (PDMS) diagnostic device containing an array of affinity-free single-cell capture sites, we were able to efficiently recover >90% of lymphocytes, perform immunostaining on chip, and apply an image-processing algorithm to group cells based on their molecular marker expression, such as kappa/lambda light chain restriction. Additionally, in combination with Ibrutinib-BFL or other imaging drugs, we demonstrated the potential for on-chip drug imaging for use in conjunction with drug development. Finally, we applied bioorthogonal conjugation chemistries on cellulose paper for potential applications in lowering the cost of drug screening. We anticipate that these approaches will enable direct, molecular information for personalized treatment decisions in B-cell lymphomas, as well as provide a roadmap for the development of companion diagnostic probes and devices for additional indications.

The ‘Blood-Type’ diet advises individuals to eat according to their ABO blood group to optimize health without the support of science evidence. The objective of this study was to determine whether consumption of a diet in accordance with an individual’s ABO genotype is associated with various biomarkers of cardiometabolic health. Study subjects (n=1,455) were participants of the Toronto Nutrigenomics and Health study. Dietary intake was assessed using a one-month, 196-item food frequency questionnaire and a diet score was calculated to determine relative adherence to each of the four blood type diets. ABO blood group was determined by genotyping rs8176719 and rs8176746 in the ABO gene. The results show that adherence to the Type-A, Type-AB, and Type-O diets were associated with favourable profile of certain cardiometabolic risk factors (P<0.05); however, these dietary effects were not dependent on someone’s ABO blood group. Therefore, the findings do not support the “Blood-Type” diet hypothesis

The ‘Blood-Type’ diet advises individuals to eat according to their ABO blood group to optimize health without the support of science evidence. The objective of this study was to determine whether consumption of a diet in accordance with an individual’s ABO genotype is associated with various biomarkers of cardiometabolic health. Study subjects (n=1,455) were participants of the Toronto Nutrigenomics and Health study. Dietary intake was assessed using a one-month, 196-item food frequency questionnaire and a diet score was calculated to determine relative adherence to each of the four blood type diets. ABO blood group was determined by genotyping rs8176719 and rs8176746 in the ABO gene. The results show that adherence to the Type-A, Type-AB, and Type-O diets were associated with favourable profile of certain cardiometabolic risk factors (P<0.05); however, these dietary effects were not dependent on someone’s ABO blood group. Therefore, the findings do not support the “Blood-Type” diet hypothesis

Lung cancer is the leading cause of cancer-related deaths among men and women. Non-Small Cell Lung Cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. In the past decade, discovery of Epidermal Growth Factor Receptor (EGFR) mutations have heralded a new paradigm of personalized treatment for NSCLC. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Despite these advances, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. This thesis demonstrates the development of a predictive computational model for personalized therapeutic interventions in advanced NSCLC. The findings suggest that the combination of clinical and genetic data significantly improves the model’s predictive performance for tumor response than clinical data alone.

Knowledge is often tacit and "sticky", i.e. highly context-specific and therefore costly to transfer to a different setting. This paper examines the methods used by firms to facilitate cross-site knowledge sharing by "thinning" knowledge, that is, by stripping knowledge of its contextual richness. An interview-based study of cross-site knowledge sharing in three industries (consulting, industrial materials, and high-tech products) indicated that highly developed knowledge-sharing systems do not necessarily involve extensive codification and recombination of personalized knowledge. Many multinational firms evidently conceive their knowledge-sharing systems with more modest objectives in mind than any large-scale "learning spirals" featuring iterative conversion of personalized knowledge into codified knowledge and vice-versa. A typology of knowledge-thinning systems was derived by interpreting the field study results from the perspective of knowledge-thinning methods used in earlier eras of history. The typology encompasses topographical, statistical and diagrammatic knowledge-thinning systems. (authors' abstract)

Software practitioners make technical and business decisions based on the understanding they have of their software systems. This understanding is grounded in their own experiences, but can be augmented by studying various kinds of development artifacts, including source code, bug reports, version control meta-data, test cases, usage logs, etc. Unfortunately, the information contained in these artifacts is typically not organized in the way that is immediately useful to developers’ everyday decision making needs. To handle the large volumes of data, many practitioners and researchers have turned to analytics — that is, the use of analysis, data, and systematic reasoning for making decisions. The thesis of this dissertation is that by employing software analytics to various development tasks and activities, we can provide software practitioners better insights into their processes, systems, products, and users, to help them make more informed data-driven decisions. While quantitative analytics can help project managers understand the big picture of their systems, plan for its future, and monitor trends, qualitative analytics can enable developers to perform their daily tasks and activities more quickly by helping them better manage high volumes of information. To support this thesis, we provide three different examples of employing software analytics. First, we show how analysis of real-world usage data can be used to assess user dynamic behaviour and adoption trends of a software system by revealing valuable information on how software systems are used in practice. Second, we have created a lifecycle model that synthesizes knowledge from software development artifacts, such as reported issues, source code, discussions, community contributions, etc. Lifecycle models capture the dynamic nature of how various development artifacts change over time in an annotated graphical form that can be easily understood and communicated. We demonstrate how lifecycle models can be generated and present industrial case studies where we apply these models to assess the code review process of three different projects. Third, we present a developer-centric approach to issue tracking that aims to reduce information overload and improve developers’ situational awareness. Our approach is motivated by a grounded theory study of developer interviews, which suggests that customized views of a project’s repositories that are tailored to developer-specific tasks can help developers better track their progress and understand the surrounding technical context of their working environments. We have created a model of the kinds of information elements that developers feel are essential in completing their daily tasks, and from this model we have developed a prototype tool organized around developer-specific customized dashboards. The results of these three studies show that software analytics can inform evidence-based decisions related to user adoption of a software project, code review processes, and improved developers’ awareness on their daily tasks and activities.

Background: Human head and neck squamous cell carcinoma (HNSCC) fundamentally vary in their susceptibility to different cytotoxic drugs and treatment modalities. There is at present no clinically accepted test system to predict the most effective therapy for an individual patient. Methods: Therefore, we established tumor-derived slice cultures which can be kept in vitro for at least six days. Upon treatment with cisplatin, docetaxel and cetuximab, slices were fixed and paraffin sections were cut for histopathological analysis. Results: Apoptotic fragmentation, activation of caspase 3, and cell loss were observed in treated tumor slices. Counts of nuclei per field in untreated compared to treated slices deriving from the same tumor allowed estimation of the anti-neoplastic activity of individual drugs on an individual tumor. Conclusion: HNSCC-derived slice cultures survive well in vitro and may serve to improve personalized therapies, but also to detect mechanisms of tumor resistance by harvesting surviving tumor cells after treatment.