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Bacteria in nature predominantly grow as biofilms on living and non-living surfaces. The development of biofilms on non-living surfaces is significantly affected by the surface micro/nano topography. The main goal of this dissertation is to study the interaction between microorganisms and nanopatterned surfaces. In order to engineer the surface with well-defined and repeatable nanoscale structures, a new, versatile and scalable nanofabrication method, termed Spun-Wrapped Aligned Nanofiber lithography (SWAN lithography) was developed. This technique enables high throughput fabrication of micro/nano-scale structures on planar and highly non-planar 3D objects with lateral feature size ranging from sub-50 nm to a few microns, which is difficult to achieve by any other method at present. This nanolithography technique was then utilized to fabricate nanostructured electrode surfaces to investigate the role of surface nanostructure size (i.e. 115 nm and 300 nm high) in current production of microbial fuel cells (MFCs). Through comparing the S. oneidensis attachment density and current density (normalized by surface area), we demonstrated the effect of the surface feature size which is independent of the effect on the surface area. In order to better understand the mechanism of microorganism adhesion on nanostructured surfaces, we developed a biophysical model that calculates the total energy of adhered cells as a function of nanostructure size and spacing. Using this model, we predict the attachment density trend for Candida albicans on nanofiber-textured surfaces. The model can be applied at the population level to design surface nanostructures that reduce cell attachment on medical catheters. The biophysical model was also utilized to study the motion of a single Candida albicans yeast cell and to identify the optimal attachment location on nanofiber coated surfaces, thus leading to a better understanding of the cell-substrate interaction upon attachment. / Ph. D.

Les glioblastomes comptent parmi les cas les plus répandus et agressifs de tumeurs cérébrales. Ils sont généralement traités avec une combinaison de résection chirurgicale, suivie de chimiothérapie et radiothérapie. Cependant, le caractère infiltrant de la tumeur rend son traitement particulièrement délicat. La personnalisation de modèles biophysiques permet d’automatiser la mise au point de thérapies spécifiques au patient, en maximisant les chances de survie. Dans cette thèse nous nous sommes attachés à élaborer des outils permettant de personnaliser la radiothérapie des glioblastomes. Nous avons tout d’abord étudié l’impact de la prise en compte de l’œdème vasogénique. Notre étude rétrospective se fonde sur une base de donnée de patients traités avec un médicament anti-angiogénique, révélant a posteriori la présence de l’œdème. Ensuite, nous avons étudié le lien entre l’incertitude due à la segmentation de la tumeur et la distribution de la dose. Pour se faire, nous avons mis au point une méthode permettant d’échantillonner efficacement de multiples segmentations réalistes, à partir d’une unique segmentation clinique. De plus, nous avons personnalisé un modèle de croissance tumorale aux images IRM de sept patients. La méthode Bayésienne adoptée permet notamment d’estimer l’incertitude sur les paramètres personnalisés. Finalement, nous avons montré comment cette personnalisation permet de définir automatiquement la dose à prescrire au patient, en combinant le modèle de croissance tumoral avec un modèle de réponse à la dose délivrée. Les résultats prometteurs présentés ouvrent de nouvelles perspectives pour la personnalisation de la radiothérapie des tumeurs cérébrales. / Glioblastomas are among the most common and aggressive primary brain tumors. It is usually treated with a combination of surgical resection, followed with concurrent chemo- and radiotherapy. However, the infiltrative nature of the tumor makes its control particularly challenging. Biophysical model personalization allows one to automatically define patient specific therapy plans which maximize survival rates. In this thesis, we focused on the elaboration of tools to personalize radiotherapy planning. First, we studied the impact of taking into account the vasogenic edema into the planning. We studied a database of patients treated with anti-angiogenic drug, revealing a posteriori the presence of the edema. Second, we studied the relationship between the uncertainty in the tumor segmentation and dose distribution. For that, we present an approach in order to efficiently sample multiple plausible segmentations from a single expert one. Third, we personalized a tumor growth model to seven patients’ MR images. We used a Bayesian approach in order to estimate the uncertainty in the personalized parameters of the model. Finally, we showed how combining a personalized model of tumor growth with a dose response model could be used to automatically define patient specific dose distribution. The promising results of our approaches offer new perspectives for personalized therapy planning.

Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i) the neural substrates supporting action selection during value- guided choice using magnetoencephalography (MEG), and (ii) learning the value of environmental stimuli and other people’s actions using functional magnetic resonance imaging (fMRI). In both situations, it is helpful to formally model the underlying component process, as this generates predictions of trial-to-trial variability in the signal from a brain region involved in its implementation. In the case of value-guided action selection, a biophysically realistic implementation of a drift diffusion model is used. Using this model, it is predicted that there are specific times and frequency bands at which correlates of value are seen. Firstly, there are correlates of the overall value of the two presented options, and secondly the difference in value between the options. Both correlates should be observed in the local field potential, which is closely related to the signal measured using MEG. Importantly, the content of these predictions is quite distinct from the function of the model circuit, which is to transform inputs relating to the value of each option into a categorical decision. In the case of social learning, the same reinforcement learning model is used to track both the value of two stimuli that the subject can choose between, and the advice of a confederate who is playing alongside them. As the confederate advice is actually delivered by a computer, it is possible to keep prediction error and learning rate terms for stimuli and advice orthogonal to one another, and so look for neural correlates of both social and non-social learning in the same fMRI data. Correlates of intentional inference are found in a network of brain regions previously implicated in social cognition, notably the dorsomedial prefrontal cortex, the right temporoparietal junction, and the anterior cingulate gyrus.

O presente trabalho descreve um programa de computador em linguagem Java que reproduz o modelo compartimental reduzido de célula mitral do bulbo olfativo construído por Davison, Feng e Brown (Brain Res. Bull. 51:393-399,2000), como uma simplificação do modelo detalhado de Bhalla e Bower (J. Neurophysiol., 69:1948-1965, 1993). O modelo reduzido considera a célula mitral como composta por quatro compartimentos, modelados conforme a metodologia de HODGKIN e HUXLEY. Por seu baixo custo computacional, o modelo reduzido permite a construção de modelos de rede de grande porte para o bulbo olfativo. A implementação computacional feita em Java apresenta grande similaridade com a original, indicando uma robustez do modelo com relação a versões em plataformas distintas. / This work describes a computer program written in Java, which reproduces the reduced compartimental model of the mitral cell of the olfactory bulb constructed by Davison, Feng and Brown (Brain Res. Bull. 51:393-399,2000), as a simplified version of the detailed model of Bhalla and Bower (J. Neurophysiol., 69:1948-1965, 1993). The reduced model considers the mitral cell as composed of four compartiments modeled according to the Hodgkin-Huxley formalism. Due to its low computational cost, the reduced model allows the construction of large-scale network models of the olfactory bulb. The computer implementation made in Java shows great similarity with the original, indicating that the model is robust with respect to implementations in different platforms.

O presente trabalho descreve um programa de computador em linguagem Java que reproduz o modelo compartimental reduzido de célula mitral do bulbo olfativo construído por Davison, Feng e Brown (Brain Res. Bull. 51:393-399,2000), como uma simplificação do modelo detalhado de Bhalla e Bower (J. Neurophysiol., 69:1948-1965, 1993). O modelo reduzido considera a célula mitral como composta por quatro compartimentos, modelados conforme a metodologia de HODGKIN e HUXLEY. Por seu baixo custo computacional, o modelo reduzido permite a construção de modelos de rede de grande porte para o bulbo olfativo. A implementação computacional feita em Java apresenta grande similaridade com a original, indicando uma robustez do modelo com relação a versões em plataformas distintas. / This work describes a computer program written in Java, which reproduces the reduced compartimental model of the mitral cell of the olfactory bulb constructed by Davison, Feng and Brown (Brain Res. Bull. 51:393-399,2000), as a simplified version of the detailed model of Bhalla and Bower (J. Neurophysiol., 69:1948-1965, 1993). The reduced model considers the mitral cell as composed of four compartiments modeled according to the Hodgkin-Huxley formalism. Due to its low computational cost, the reduced model allows the construction of large-scale network models of the olfactory bulb. The computer implementation made in Java shows great similarity with the original, indicating that the model is robust with respect to implementations in different platforms.

<div>Artificial Intelligence (AI) plays an increasingly pivotal role in drug discovery. In particular, artificial neural networks such as deep neural networks drive this area of research. The research presented in this thesis is considered a synergistic combination of physicochemical models of protein-ligand interactions such as molecular dynamics simulation, novel machine learning concepts and the use of big data for solving fundamental problems in Structure-Based Drug Design (SBDD). This area of research involves the use of three-dimensional (3D) structural data of biomolecules to assist lead discovery and optimization in a time- and cost-efficient manner. </div><div>The main focus of the thesis research is the development of models, algorithms and methods to facilitate binding-mode elucidation, affinity prediction for congeneric series of molecules and flexible docking. </div><div><br></div><div>For pose-prediction, we developed a Convolutional Neural Network model incorporating hydration information, named DeepWatsite, which displays accurate binding-mode prediction and the capability to highlight different roles of water molecules in protein-ligand binding. In order to train the neural network model, we created a comprehensive database for hydration information of thousands of protein systems. This was made possible through the development of an efficient GPU-accelerated version of Watsite, a program for generating hydration profiles of protein systems through molecular dynamics simulations.\newline</div><div>\indent For accurate affinity prediction for congeneric series of compounds, we developed a new methodological platform for mixed-solvent simulation based on the lambda-dynamics concept. Additionally, we developed a deep-learning model that combines molecular dynamics simulations and a distance-aware graph attention algorithm. Validation studies using this method revealed that its accuracy is competitive to resource-intensive free energy perturbation (FEP) calculations. To train the model, we generated a synthetic database of congeneric series of compounds extracted from the highest-quality medicinal chemistry articles. Molecular-dynamics simulations were used to simulate all the generated systems as method for data augmentation.\newline</div><div>\indent For flexible docking, we developed a machine-learning assisted docking strategy that relies on protein-ligand distance matrix predictions. This technique is built upon Weisfeiler-Lehman neural network concept with an attention mechanism. Comprehensive validation on docking and cross-docking datasets demonstrated the potential of this method to become a docking concept with higher accuracy and efficiency than existing state-of-the-art flexible docking techniques. </div><div><br></div><div>In summary, the thesis proved the general applicability of deep-learning to various tasks in SBDD. Furthermore, it demonstrates that treating biomolecules as dynamic entities can improve the quality of computational methods in structure-based drug design.</div>

Processes during the meroplanktonic phase regulate population dynamics for many marine benthic invertebrates. I examined changes in vertical distribution of different meroplanktonic larvae in a coastal embayment during a stable period, at high temporal frequencies and spatial resolutions. Plankton samples were collected at 6 depths (3, 6, 9, 12, 18, 24 m) using a pump, every 2-h over a 36- and a 25-h period, during a spring and neap tide, respectively, concurrently with measures of temperature, salinity, fluorescence and current velocity. For 10 gastropod taxa, larval vertical distribution was mostly related to the thermal structure of the water column. Each of 7 taxonomic groups was found either exclusively near the surface, associated with the fluorescence maximum, or showed diel changes in distribution. These larvae that occupy different depths in the water column exhibit different dispersal potentials. / Biogeographical data contained in this thesis will be submitted to the Oceanographic Biogeographic Information System (OBIS) and may be accessed on-line at http://www.iobis.org

Numerous studies showed that cochlear implant (CI) users generally prefer individualized stimulation rates in order to maximize their speech understanding. The underlying reasons for the reported variation in speech perception performance as a function of CI stimulation rate is unknown. However, multiple interacting electrophysiological processes influence the auditory nerve (AN) in response to high-rate CI stimulation. Experiments studying electrical pulse train stimulation of cat AN fibers (ANFs) have demonstrated that spike rates slowly decrease over time relative to onset stimulation and is often attributed to spike rate (spike-triggered) adaptation in addition to refractoriness. Interestingly, this decay tends to adapt more rapidly to higher stimulation rates. This suggests that subthreshold adaptation (accommodation) plays a critical role in reducing neural excitability. Using biophysical computational models of cat ANF including ion channel types such as hyperpolarization-activated cyclic nucleotide-gated (HCN) and low threshold potassium (KLT) channels, we measured the strength of adaptation in response to pulse train stimulation for a range of current amplitudes and pulse rates. We also tested these stimuli using a phenomenological computational ANF model capable of applying any combination of refractoriness, facilitation, accommodation, and/or spike rate adaptation. The simulation results show that HCN and KLT channels contribute to reducing model ANF excitability on the order of 1 to 100 ms. These channels contribute to both spike rate adaptation and accommodation. Using our phenomenological model ANF we have also shown that accommodation alone can produce a slow decay in ANF spike rates responding to ongoing stimulation. The CI users that do not benefit from relatively high stimulation rates may be due to ANF accommodation effects. It may be possible to use electrically evoked compound action potentials (ECAP) recordings to identify CI users exhibiting strong effects of accommodation, i.e., the increasing strength of adaptation as a function of increasing stimulation rate. / Dissertation / Doctor of Philosophy (PhD) / Cochlear implants (CI) attempt to restore hearing to individuals with severe to profound hearing deficits by stimulating the auditory nerve with a series of electrical pulses. Recent CI stimulation strategies have attempted to improve speech perception by stimulating at high pulse rates. However, studies have shown that speech perception performance does not necessarily improve with pulse rate increases, leading to speculation of possible causes. Certain ion channels located in auditory nerve fibers may contribute to driving the nerve to reduce its excitability in response to CI stimulation. In some cases, those channels could force nerve fibers to cease responding to stimulation, causing a breakdown in communication from the CI to the auditory nervous system. Our simulation studies of the auditory nerve containing certain types of channels showed that the effective rate of communication to the brain is reduced when stimulated at high rates due to the presence of these channels.

Sound localization is a vital aspect of hearing for safe navigation of everyday environments. It is also an important factor in speech intelligibility. This ability is facilitated by the interaural level difference (ILD) cue, which arises from binaural hearing: a sound will be more intense at the nearer ear than the farther. In a hearing-impaired listener, this binaural cue may not be available for use and localization may be diminished. While conventional, bilateral, wide dynamic range compression (WDRC) hearing aids distort the interaural level difference by independently altering sound intensities in each ear, wirelessly-linked devices have been suggested to benefit this task by matching amplification in order to preserve ILD. However, this technology has been shown to have varying degrees of success in aiding speech intelligibility and sound localization. As hearing impairment has wide-ranging adverse impacts to physical and mental health, social activity, and cognition, the task of localization improvement must be urgently addressed. Toward this end, neural modelling techniques are used to determine neural representations of ILD cues for linked and unlinked bilateral WDRC hearing aids. Findings suggest that wirelessly-linked WDRC is preferable over unlinked hearing aids or unaided, hearing-impaired listening, although parameters for optimal benefit are dependent on sound level, frequency content, and preceding sounds. / Thesis / Master of Applied Science (MASc)