Glucagon-like peptide-2 (GLP-2) is an intestinal hormone that promotes gut growth through an insulin-like growth factor (IGF)-1 and intestinal epithelial (IE)-IGF-1 receptor (R)-dependent pathway. GLP-2 also promotes epithelial barrier function by as yet unknown mechanisms. I hypothesized that GLP-2-mediated effects on barrier function requires the IE-IGF-1R. Chronic GLP-2 treatment enhanced barrier function by decreasing gastrointestinal permeability in vivo and increasing jejunal resistance ex vivo. These responses were abolished in inducible IE-IGF-1R knockout (KO) animals. Additionally, epithelial sealing tight junctional proteins claudin-3 and -7 were upregulated by GLP-2 in control but not KO mice. Moreover, IE-IGF-1R deletion induced a shift in occludin localization from apical to intracellular domains. In contrast, in irinotecan-induced enteritis, GLP-2 normalized epithelial barrier function in control animals, but continued to be ineffective in KO mice. Collectively, the effects of GLP-2 on barrier function are dependent on the IE-IGF-1R and involve modulation of the tight junctional complex.

Dynamic Bayesian Networks (DBNs) are temporal probabilistic models for reasoning over time. They often formulate the core reasoning component of intelligent systems in the field of machine learning. Recent studies have focused on the development of some DBNs such as Hidden Markov Models (HMMs) and their variants, which are explicitly represented by highly skilled users and have gained popularity in speech recognition. These varieties of HMMs represented as DBNs have contributed to the baseline of temporal modelling. However they are limited in their expressive power as they are approximated and pose difficult challenges for users when choosing the appropriate model for diverse real-life applications. To worsen the situation further, researchers and practitioners have also stressed that applications often have difficulties when evolving (or learning) such network models from environments captured as massive datasets, due to the ongoing predominance of computational intensity (or nondeterministic polynomial (NP) time hard). Finding solutions to these challenges is a difficult task. In this thesis, a new class of temporal probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic Bayesian Networks (DBNs) are ideally suited to achieve situation awareness, in which elements in the environment must be perceived within a volume of time and space, their meaning understood, and their status predicted in the near future. The use of Dynamic Bayesian Networks in achieving situation awareness has been poorly explored in current research efforts. This research completely evolves DBNs automatically from any environment captured as multivariate time series (MTS) which minimizes the approximations and mitigates the challenges of choice of models. This potentially accommodates both highly skilled users and non-expert practitioners, and attracts diverse real-world application areas for DBNs. The architecture of our EDBN uses a combined strategy as it resolves two orthogonal issues to address the challenging problems: (1) evolving DBNs in the absence of domain experts and (2) mitigating computational intensity (or NP-hard) problems with economic scalability. Most notably, the major contributions of this thesis are as follows: the development of a new class of temporal probabilistic modeling (EDBN), whose architecture facilitates the demonstration of its emergent situation awareness (ESA) and emergent future situation awareness (EFSA) technologies. The ESA and its variant reveal hidden patterns over current and future time steps respectively. Among other contributions are the development and integration of an economic scalable framework called dynamic memory management in adaptive learning (DMMAL) into the architecture of the EDBN to emerge such network models from environments captured as massive datasets; the design of configurable agent actuators; adaptive operators; representative partitioning algorithms which facilitate the scalability framework; formal development and optimization of genetic algorithm (GA) to emerge optimal Bayesian networks from datasets, with emphasis on backtracking avoidance; and diverse applications of EDBN technologies such as business intelligence, revealing trends of insulin dose to medical patients, water quality management, project profitability analysis, sensor networks, etc. To ensure the universality and reproducibility of our architecture, we methodically conducted experiments using varied real-life datasets and publicly available machine learning datasets mostly from the University of California Irvine (UCI) repository.

High mortality rates from cardiovascular disease underscore the need for improved therapies. Thus, it is important to further our understanding of factors and mechanisms promoting cardiac protection and repair. Fibroblast growth factor-2 (FGF-2), administered to the heart before or during injury exerts beneficial effects such as cytoprotection and angiogenesis. However, the effects of a chronic elevation in endogenous FGF-2 on recovery/remodeling after ischemic injury are not known. My hypothesis was that chronic elevation in endogenous FGF-2 expression (in FGF-2 overexpressing transgenic mice) exerts beneficial effects such as improved function after isoproterenol-induced injury in vivo. The first study showed that treatment with the β-adrenergic agonist isoproterenol resulted in exaggerated levels of cellular infiltration and myocardial disarray in transgenic FGF-2 versus non-transgenic mouse myocardium. This was suggestive of increased cardiac injury in transgenic FGF-2 mice. Inhibition of T cells using the immunosuppressants cyclosporine A or antibodies against CD3ε attenuated cellular infiltration in transgenic FGF-2 mice, to levels comparable to those of non-transgenic mice, suggesting a T lymphocyte-mediated effect. Overall morphological data suggested that chronic FGF-2 elevation might have created an adverse outcome after cardiac injury. In a follow-up study the effect of chronic FGF-2 elevation on cardiac function was examined, as measured by tissue Doppler imaging (TDI), after isoproterenol administration. FGF-2 overexpressing mice displayed improved cardiac function compared to controls, after isoproterenol, both acutely (24 h) and in a sustained fashion (2-4 weeks). The FGF-2 associated functional improvement at 2-4 weeks was attenuated following immunosuppression with cyclosporine A, but not treatment with anti-CD3ε antibodies. The FGF-2–associated functional improvement may be partially attributed to a cyclosporine A-sensitive (but anti-CD3-insensitive) infiltrating cell population. Thus cellular infiltration, in response to elevated FGF-2, may have a net beneficial effect. In a third study, non-transgenic mice were put through a brief swimming protocol (exercise) prior to isoproterenol. This acute bout of exercise resulted in significant improvement in TDI function, compared to control groups, measured at 24 hours up to 4 weeks post-isoproterenol. In conclusion, increased endogenous cardiac FGF-2 expression, or an acute bout of exercise, exert sustained beneficial effects against isoproterenol-induced cardiac injury.

Glucagon-like peptide-2 (GLP-2) is an intestinal hormone that promotes gut growth through an insulin-like growth factor (IGF)-1 and intestinal epithelial (IE)-IGF-1 receptor (R)-dependent pathway. GLP-2 also promotes epithelial barrier function by as yet unknown mechanisms. I hypothesized that GLP-2-mediated effects on barrier function requires the IE-IGF-1R. Chronic GLP-2 treatment enhanced barrier function by decreasing gastrointestinal permeability in vivo and increasing jejunal resistance ex vivo. These responses were abolished in inducible IE-IGF-1R knockout (KO) animals. Additionally, epithelial sealing tight junctional proteins claudin-3 and -7 were upregulated by GLP-2 in control but not KO mice. Moreover, IE-IGF-1R deletion induced a shift in occludin localization from apical to intracellular domains. In contrast, in irinotecan-induced enteritis, GLP-2 normalized epithelial barrier function in control animals, but continued to be ineffective in KO mice. Collectively, the effects of GLP-2 on barrier function are dependent on the IE-IGF-1R and involve modulation of the tight junctional complex.

The Arizona Child Acoustic Database consists of longitudinal audio recordings from a group of children over a critical period of growth and development (ages 2-7 years). The goal of this database is to 1) document acoustic changes in speech production that may be related to physical growth 2) inform development of a model of speech production for child talkers. This work was funded by NSF BSC-1145011 awarded to Kate Bunton, Ph.D. and Brad Story, Ph.D, Principal Investigators. This database contains longitudinal audio recordings of 55 American English speaking children between the ages of 2-7 at 3-month intervals. Since children began the study at different ages, some children have fewer recording sessions than others. The database can also be used to provide cross-sectional data for children of a specific age. Please refer to the subject data table for information on specific sessions available here http://arizona.openrepository.com/arizona/handle/10150/316065. All children were recorded using the same protocol; therefore, task numbers are consistent across children and sessions. A calibration tone is included as Record 1 for all sessions. The speech protocol focused on production of English monopthong and diphthong vowels in isolation, sVd, hVd, and monosyllabic real words. In addition, the protocol includes several nonsense vowel-to-vowel transitions. Speakers were prompted either verbally by investigators or by graphical prompts. Details of the protocol with reference to task numbers can be found in the protocol spreadsheet available here http://arizona.openrepository.com/arizona/handle/10150/316065. Details on data recording: All samples were recorded digitally using an AKG SE 300B microphone with a mouth to mic distance of approximately 10 inches. Signals were recorded digitally using a Marantz PMD671, 16 bit PCM (uncompressed) at 44.1KHz. Recordings are made available in .wav format. Individual zip files contain all recordings from a single session.

The Arizona Child Acoustic Database consists of longitudinal audio recordings from a group of children over a critical period of growth and development (ages 2-7 years). The goal of this database is to 1) document acoustic changes in speech production that may be related to physical growth 2) inform development of a model of speech production for child talkers. This work was funded by NSF BSC-1145011 awarded to Kate Bunton, Ph.D. and Brad Story, Ph.D, Principal Investigators. This database contains longitudinal audio recordings of 55 American English speaking children between the ages of 2-7 at 3-month intervals. Since children began the study at different ages, some children have fewer recording sessions than others. The database can also be used to provide cross-sectional data for children of a specific age. Please refer to the subject data table for information on specific sessions available here http://arizona.openrepository.com/arizona/handle/10150/316065. All children were recorded using the same protocol; therefore, task numbers are consistent across children and sessions. A calibration tone is included as Record 1 for all sessions. The speech protocol focused on production of English monopthong and diphthong vowels in isolation, sVd, hVd, and monosyllabic real words. In addition, the protocol includes several nonsense vowel-to-vowel transitions. Speakers were prompted either verbally by investigators or by graphical prompts. Details of the protocol with reference to task numbers can be found in the protocol spreadsheet available here http://arizona.openrepository.com/arizona/handle/10150/316065. Details on data recording: All samples were recorded digitally using an AKG SE 300B microphone with a mouth to mic distance of approximately 10 inches. Signals were recorded digitally using a Marantz PMD671, 16 bit PCM (uncompressed) at 44.1KHz. Recordings are made available in .wav format. Individual zip files contain all recordings from a single session.

The Arizona Child Acoustic Database consists of longitudinal audio recordings from a group of children over a critical period of growth and development (ages 2-7 years). The goal of this database is to 1) document acoustic changes in speech production that may be related to physical growth 2) inform development of a model of speech production for child talkers. This work was funded by NSF BSC-1145011 awarded to Kate Bunton, Ph.D. and Brad Story, Ph.D, Principal Investigators. This database contains longitudinal audio recordings of 55 American English speaking children between the ages of 2-7 at 3-month intervals. Since children began the study at different ages, some children have fewer recording sessions than others. The database can also be used to provide cross-sectional data for children of a specific age. Please refer to the subject data table for information on specific sessions available here http://arizona.openrepository.com/arizona/handle/10150/316065. All children were recorded using the same protocol; therefore, task numbers are consistent across children and sessions. A calibration tone is included as Record 1 for all sessions. The speech protocol focused on production of English monopthong and diphthong vowels in isolation, sVd, hVd, and monosyllabic real words. In addition, the protocol includes several nonsense vowel-to-vowel transitions. Speakers were prompted either verbally by investigators or by graphical prompts. Details of the protocol with reference to task numbers can be found in the protocol spreadsheet available here http://arizona.openrepository.com/arizona/handle/10150/316065. Details on data recording: All samples were recorded digitally using an AKG SE 300B microphone with a mouth to mic distance of approximately 10 inches. Signals were recorded digitally using a Marantz PMD671, 16 bit PCM (uncompressed) at 44.1KHz. Recordings are made available in .wav format. Individual zip files contain all recordings from a single session.

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The purpose of this study was to explore associations between early-life factors and the development of youth onset type 2 diabetes mellitus (T2DM). Until 1990, T2DM was seldom reported in youth, however rates of youth onset T2DM are rising worldwide. This retrospective case-control study utilized repository data housed at the Manitoba Centre for Health Policy to review perinatal exposures of Manitoba youth with and without T2DM. The mean age at time of diagnosis was 13.1 years and 61% of youth onset T2DM cases were female. The majority of youth with T2DM resided in rural areas at time of diagnosis. This study found a 14-fold increase in the risk of youth onset T2DM when the mother had pre-gestational diabetes, and 6.5-fold increase in that risk if the mother had gestational diabetes. Breastfeeding was found to be protective, and a lower income quintile at time of birth was found to be more significantly associated with the development of youth onset T2DM than increasingly higher income quintiles . The findings emphasize that efforts aimed at preventing T2DM in youth must begin in the pre-conception period and continue throughout pregnancy.