A global rise in humanitarian emergencies, driven by conflict, poses significant health challenges, especially for children under five years old. While the source of such crises and the challenges affected healthcare systems face may be confined to man-made borders, the resulting spread of health problems such as antimicrobial resistant (AMR) infections and diarrheal diseases are not bound geographically. To address concerns in such dynamic environments, healthcare workers utilize simple, fast acting solutions to save as many children as possible. For diarrheal diseases, this entails initially treating with zinc supplements and oral rehydration solutions (ORS), saving antibiotics for the cases that do not respond to this treatment. Determining who needs this care is often assessed through proxy data tracked via routine vaccination records, such as “zero dose communities”. However, both protocols are not without their shortcomings. The goal of this thesis is to examine their risks. We first examine how zinc might impact resistance development in Escherichia coli in vitro. We further demonstrate by computational modeling that slight changes in fitness have disproportionate changes on the rate of resistance onset. After discovering that the use of zinc for diarrhea treatment may be contributing to the AMR crisis, we next focus on ensuring that children suffering from diarrheal diseases can access treatment. We find that using zero dose communities as a means of determining which children could access care, while suitable for other services, is ultimately insufficient for diarrheal diseases in crisis settings such as Democratic Republic of Congo, Afghanistan and Bangladesh. Finally, we look at developing a tool that could be used to better understand access to care patterns for diarrheal disease and show the impacts that conflict, weather and travel infrastructure have on altering access to care in Yemen, which has been in the midst of the world’s worst humanitarian crisis. Overall, this body of work demonstrates how both current treatment practices and access to care assessments for diarrheal diseases have previously overlooked risks which can contribute to poor health outcomes especially for children under five years old living in areas affected by humanitarian crises.

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The overall objective of this research consists of two main parts: (1) provide evidence that autocrine production of IGF-I modulates tight junction permeability and (2) demonstrate the ability of IGFBPs to regulate IGF-I delivery across cell layers. To meet the first objective, parental and IGF-I secreting bovine mammary epithelial cells were tested for cell layer permeability, tight and adherens junction proteins, IGF-IR, and a downstream signaling components of IGF-IR. In comparison with parental cells, IGF-I secreting cells had high levels of IGF-IRs, but low levels of the junction components E-cadherin, b-catenin, and occludin. The differences in parental and IGF-I secreting cells was not due to extracellular stimuli since inclusion of IGF-I, IGFBP-3, or co-culture with SV40-IGF-I cells did not alter the barrier properties of parental cells, suggesting that intracrine signaling may alter cell connectivity. The second objective focused on exogenous rather than endogenous IGF-I and the role of IGFBPs and IGF-IRs in ligand transcytosis. Bovine aortic endothelial cells (BAECs) cultured on surfaces optimized to minimize paracellular transport were utilized to investigate the kinetics involved in the transport of insulin-like growth factor-I from the apical side of confluent monolayers to the basolateral side. Binding competitors were used to determine the role of the cell surface insulin-like growth factor-I receptor (IGF-IR) and cell surface insulin-like growth factor binding proteins (IGFBPs) in this transport process. Although IGFBPs initially retard delivery of IGF-I, using a computation model, this report shows that pulse durations of less than 6 hrs resulted in enhanced delivery of IGF-I in the presence of IGFBPs, above that for delivery in the absence of IGFBPs. In addition, the model was utilized to identify key parameters to target when developing engineered growth factors for the treatment of diseases. It is shown that the sorting factions and internalization rates are reasonable targets for the design of engineered growth factors. Since the sorting fractions are dictated by binding affinities in the acidic environment of the endosomes, it may be beneficial to design and analog of IGF-I that is more resistant to changes in pH, similar to those develop from epidermal growth factor. / Ph. D.

Understanding the complex growth process of dendritic arbors is essential for the medical field and disciplines like Biology and Neurosciences. The establishment of the dendritic patterns has received increasing attention from experimental researchers that seek to determine the cellular mechanisms that play a role in the growth of neural trees. Our goal in this thesis was to prove the recurrence formula for the probability distribution of all possible neural trees, as well as the formulas of the expected number of active branches and their variances. We also derived formulas for the spatial locations of the optimal targeting region for a tree with branching probability. These formulas were necessary for the simplified stochastic computational model that Osan et al have developed in order to examine how changes in branching probability influence the success of targeting neurons located at different distances away from a starting point.

This thesis aims to investigate how well associative learning can account for human sequence learning under incidental conditions. It seems that we can learn complex sequential information about events in our environment, for example language or music, incidentally, without being aware of it. Awareness is, however, a complex issue with arguments for (Dienes, 2012) and against (Shanks, 2005) the existence of implicit learning processes. A dual process account proposes that there exist two different learning systems, one based on conscious, controlled reasoning and rules, and the other based on automatic association formation, which can take place outside of awareness (McLaren, Green, & Mackintosh, 1994). This thesis attempts to use the predictions of an associative account in conjunction with a suitable method for investigating implicit learning: sequence learning (Destrebecqz & Cleeremans, 2003). The research involves a collection of serial reaction time (SRT) tasks whereby participants respond to on-screen stimuli that follow a sequence that they were (intentional learning) or were not (incidental learning) informed of. Following on from the experimental design of Jones and McLaren (2009) this thesis provides evidence that humans differ in their ability to learn different sequential contingencies. After training sequences of trials where the current trial location was twice as likely to be either: the same as (Same rule); or different to (Different rule) the location two trials before this, participants were far better at learning the latter rule. I found that this result was not adequately simulated by the benchmark associative model of sequence learning, the Augmented SRN (Cleeremans & McClelland, 1991), and present a revised model. This model, amongst other attributes, represents all the stimuli experienced by participants and can therefore learn stimulus-response contingencies. These seem to block learning (to some extent) about the Same rule thus providing an associative explanation of the advantage for acquisition of the Different rule. Further predictions regarding the role of additional stimuli alongside sequence learning were then derived from this associative account and tested on human participants. The first of these was that additional stimuli within the task will interact with sequence learning. I found that human participants show increased Same rule learning when additional, concurrently presented stimuli follow the previous element in the sequence. I demonstrate that when participants perform an SRT task where responses are predicted by the colour of a cue, they are able to learn about this relationship in the absence of awareness. Using this cue-response learning I further investigate cue-competition between sequences and colours under incidental conditions and find evidence that suggests between cue associations may alter the influence of cue competition. These results altogether suggest that stimuli – both simple and sequential – can be learned under incidental conditions. This thesis further proposes that learning about simple and more complex relationships between stimuli interacts according to the predictions of an associative account and provides evidence that contributes to a dual process understanding of human learning.

The analysis of hemodynamic parameters and functional reactivity of cerebral capillaries is still controversial. The detailed mapping of tissue oxygen levels on the scale of micrometers cannot be obtained by means of an experimental approach, necessitating the use of theoretical methods in this investigating field. To assess the hemodynamics and oxygen transport in the cortical capillary network, 2D and 3D generic models are constructed (non-tree like) using random voronoi tessellation in which each edge represents a capillary segment. The modelling presented here is based on morphometric parameters extracted from physiological data of the cortex in which the spatial distribution of the diameter of the capillary is based on a Modified Murray method. This method led to a proper link between the diameter topology and flow pattern such that the maximum efficiency for flowing blood is concluded in the model of cortical capillary network. The approach is capable of creating an appropriate generic, realistic model of a cerebral capillary network relating to each part of the brain cortex because its geometrical density is able to vary the capillary density. The pertinent hemodynamic parameters are obtained by numerical simulation based on effective blood viscosity as a function of hematocrit and microvessel diameter, ESL (endothelial surface layer) effect, phase separation and plasma skimming effects. Using a solution method of the Green's function, the model is numerically developed to provide different simulations of oxygen transport for varying perfusion and metabolism in a mesoscale model of the cortical capillary network, bridging smaller and larger scale phenomena. The analysis of hemodynamic parameters (blood flow rate, velocity and hematocrit) demonstrates a consistency with the experimental observation. The distribution pattern of wall shear stress (WSS) in the network model supports the physiological data which in turn represents a proper matching between the hemodynamics and morphometrics in the cerebral capillary network. The distributions of blood flow throughout the 2D and 3D models seem to confirm the hypothesis in which all capillaries in a cortical network are recruited at rest (normal condition). The predictions showed a heterogeneous distribution in the flow pathways (aspect of length and inflow) and the pertinent transit time of red blood cell (RBC) in the network model which is dependent on varying perfusion rates. The analyses of oxygen transport in the model has demonstrated that oxygen levels in the tissue are sensitively dependent on the microvascular architecture and flow distribution. Unlike the homogeneous compartmental models, the mesoscale model presented in this study led to a prediction of tissue PO2 gradients throughout the tissue and a spatial distribution of tissue PO2 on the micron-scale for varying perfusion and metabolism. The predicted nonlinear changes in the oxygen extraction fraction (OEF) of the model as a function of the perfusion rate provide a basis for the quantitative interpretation of functional magnetic resonance imaging (fMRI) studies in terms of changes in local perfusion. The model is capable of predicting the brain oxygen metabolism under both normal and disease states, particularly, local hypoxia and local ischemia caused by misery perfusion syndrome. The hypoxic states for different perfusion rates and oxygen consumption rates demonstrated that in a significant decrease in brain perfusion (as can occur in stroke), the tissue hypoxia can be avoided by a moderate reduction in oxygen consumption rate. Increasing oxygen consumption rates (a realization of spatiotemporal stimulation of neural tissue) with respect to maintaining the tissue PO2 in the model led to a predicted flow-metabolism coupling in the model which supports the experimental studies of somatosensory and visual stimulation in humans by positron emission tomography (PET) and functional MRI (magnetic resonance imaging). A disproportionately large increase in blood supply is required for a small increase in the metabolic utilization (oxygen consumption rate) which in turn, is strongly dependent on the resting OEF such that the magnitude of the blood flow increases in the higher resting OEF.

This PhD work constitutes a series of inter-disciplinary studies that use biologically plausible computational techniques and experiments with human subjects in analyzing facial expressions. The performance of the computational models and human subjects in terms of accuracy and response time are analyzed. The computational models process images in three stages. This includes: Preprocessing, dimensionality reduction and Classification. The pre-processing of face expression images includes feature extraction and dimensionality reduction. Gabor filters are used for feature extraction as they are closest biologically plausible computational method. Various dimensionality reduction methods: Principal Component Analysis (PCA), Curvilinear Component Analysis (CCA) and Fisher Linear Discriminant (FLD) are used followed by the classification by Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). Six basic prototypical facial expressions that are universally accepted are used for the analysis. They are: angry, happy, fear, sad, surprise and disgust. The performance of the computational models in classifying each expression category is compared with that of the human subjects. The Effect size and Encoding face enable the discrimination of the areas of the face specific for a particular expression. The Effect size in particular emphasizes the areas of the face that are involved during the production of an expression. This concept of using Effect size on faces has not been reported previously in the literature and has shown very interesting results. The detailed PCA analysis showed the significant PCA components specific for each of the six basic prototypical expressions. An important observation from this analysis was that with Gabor filtering followed by non linear CCA for dimensionality reduction, the dataset vector size may be reduced to a very small number, in most cases it was just 5 components. The hypothesis that the average response time (RT) for the human subjects in classifying the different expressions is analogous to the distance measure of the data points from the classification hyper-plane was verified. This means the harder a facial expression is to classify by human subjects, the closer to the classifying hyper-plane of the classifier it is. A bi-variate correlation analysis of the distance measure and the average RT suggested a significant anti-correlation. The signal detection theory (SDT) or the d-prime determined how well the model or the human subjects were in making the classification of an expressive face from a neutral one. On comparison, human subjects are better in classifying surprise, disgust, fear, and sad expressions. The RAW computational model is better able to distinguish angry and happy expressions. To summarize, there seems to some similarities between the computational models and human subjects in the classification process.

A computational model for mitochondrial function has been developed from oxygen concentration data measured in the Oroboros Oxygraph-2k and oxygen consumption rates measured in the Seahorse XF24 Analyzer. Measurements were acquired using embryonic-cultured cortical neurons and isolated mitochondria from CD1 mice. Based on the biological mechanism of mitochondrial activity, a computational model was developed using biochemical kinetic modelling. To modulate mitochondrial activity, dysfunctions were introduced by injecting the inhibiting reagents oligomycin, rotenone, and antimycin A, and the uncoupling reagent carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) during measurements. To incorporate these changes, model equations were adapted and globally calibrated to experimental data using the genetic algorithm developed by Jason Fiege of the University of Manitoba by fitting oxygen concentration data. The model was coded in MATLAB R2014a along with the development of a graphical user interface for simulating mitochondrial bioenergetics in silico. / October 2016

Mechanical ventilation (MV) is a system that partially or fully assists patients whose respiratory system fails to achieve a gas exchange function. However, MV can cause a ventilator-associated lung injury (VALI) or even contribute to a multiple organ dysfunction syndrome (MODS) in acute respiratory distress syndrome (ARDS) patients. Despite advances in today technologies, mortality rates for ARDS patient are still high. A better understanding of the interactions between airflow from mechanical ventilator and the airway could provide useful information used to develop a better strategy to ventilate patients. The mechanisms, which mechanical ventilation induces airway inflammation, are complex processes and cover a wide range of spatial scales. The multiscale model of the airway have been developed combining the computational models at organ, tissue, and cellular levels. A model at the organ level was used to study behaviors of the airway during mechanical ventilation. Strain distributions in each layer of the airway were investigated using a model at the tissue level. The cellular inflammatory responses during mechanical ventilation were investigated through the cellular automata (CA) model incorporating all biophysical processes during inflammatory responses. The multiscale modeling framework started by obtaining airway displacements from the organ-level model. They were then transferred to the tissue-level model for determining the strain distributions in each airway layer. The strain levels in each layer were then transferred to the cellular-level model for inflammatory responses due to strain levels. The ratio of the number of damage cells to healthy cells was obtained through the cellular-level model. This ratio, in turn, modulated changes in the Young’s modulus of elasticity at the tissue and organ levels. The simulation results showed that high tidal volume (1400 cc) during mechanical ventilation can cause tissue injury due to high concentration of activated immune cells and low tidal volume during mechanical ventilation (700 cc) can prevent tissue injury during mechanical ventilation and can mitigate tissue injury from the high tidal volume ventilation. The multiscale model developed in this research could provide useful information about how mechanical ventilation contributes to airway inflammation so that a better strategy to ventilate patients can be developed.