Bioactive lipids have emerged as prominent regulators of neural stem and progenitor cell (NPC) function under both physiological and pathological conditions. However, how lipid metabolism is regulated, and its role in modulation of NPC function remains unknown. In this regard, my study defines a novel epigenetic pathway that regulates lipid metabolism to determine NPC proliferation versus differentiation. Specifically, I show that activation of an atypical protein kinase C (aPKC)-mediated Ser436 phosphorylation of CREB binding protein (CBP) by aging, metformin stimulation and continued passaging in vitro, represses expression of monoacylglycerol lipase (Mgll) to promote neuronal differentiation of adult NPCs. Mgll, a lipase that hydrolyzes the endocannabinoid 2-arachidonoyl glycerol (2-AG) to produce arachidonic acid (ARA), is thus a key regulator of two critical bioactive lipid signaling pathways in the brain and a potential modulator of NPC function. I observed elevated Mgll levels, concomitant with neuronal differentiation deficits in both the lateral ventricle sub-ventricular zone (SVZ) and the hippocampal subgranular zone (SGZ) NPCs of phospho-null CBPS436A mice, that lack a functional aPKC-CBP pathway. Genetic knockdown of Mgll or inhibition of Mgll activity rescued these neuronal differentiation deficits. In addition, I found that CBPS436A SVZ NPCs exhibit enhanced proliferation at the expense of differentiation as an outcome of increased Mgll levels in culture. Interestingly, I also observed that SVZ NPCs from an Alzheimer’s disease (AD) model, the 3xTg mice, closely resemble CBPS436A NPC behaviour in culture. 3xTg NPCs exhibit attenuation of the aPKC-CBP pathway, which is associated with elevated Mgll expression and increased NPC proliferation at the expense of neuronal differentiation. Reactivation of the aPKC-CBP mediated-Mgll repression in 3xTg AD NPCs mitigates their differentiation deficits. These findings implicate Mgll as a critical switch that regulates NPC function by altering bioactive lipid signaling (2-AG versus ARA). They demonstrate that the aPKC-CBP mediated Mgll repression is essential for normal NPC function, and that when perturbed in AD, it causes impaired NPC function to generate fewer neurons, contributing to AD predisposition.

Thesis (M.Sc.Eng.) / The use of implantable neural probes has become common, both for stimulation and recording, and their applications range from chronic pain treatment to implementation of brain machine interfaces (BMI). Studies have shown that signal quality of implanted electrodes decays over time mainly due to the immune response. Characterization of the tissue-electrode interface is critical for better understanding of the physiological dynamics and potential performance improvement of the electrode itself and its task. This work describes the fabrication of an implantable electrode with optical measurement capabilities for providing means to characterize the tissue-electrode interface using optical spectroscopy. The electrode has a set of waveguides embedded in its shanks, which are used to inject white light into tissue and then collect the light reflected from the tissue surrounding the shanks. The collected light was analyzed with a spectrometer and the spectra processed to detect changes in optical properties, information that allows to track physiological changes. It is believed that the immune response can be correlated to changes in scattering as more cells are recruited to the injury site. The increased cell density in local injury/implantation sites increases the amount of scattering due to the increased number of cell nuclei and membranes that light encounters in its path. Investigation of scattering and absorption coefficients in such interface and their change over time can provide useful data for modeling and determining physiological parameters like blood oxygenation while the actual shape of the acquired spectra might highlight particular phenomena that can be indicative of scaring process or hemorrhaging. Validation of this system was done using optical phantoms based on polystyrene spheres and solutions with various concentrations of fat emulsion, which yielded scattering coefficients similar to those of brain tissue. Results suggest that the developed optrodes are able to detect differences between samples with different scattering coefficients. Improvements of fabrication process are discussed based on experimental results and future work includes attempting to perform fluorescence measurements of voltage reporters for optogenetic applications. The ultimate goal of this project was to create a novel device that is capable of satisfying the unmet need of tissue characterization at the implanted electrode interface as well as a tool for the optogenetics field suitable for greater depths than those a microscope can achieve.

No description available.

The interatomic potential is a mathematical model that describes the chemistry occurring at the atomic level. It provides a functional mapping between the atomic nuclei coordinates and the total potential energy of a system. This thesis investigates three aspects of interatomic potentials, the first of which is the simulation of materials at the atomic scale using classical molecular dynamics (MD). Molecular dynamics code is used to follow the evolution of a system of discrete particles through time and is employed here to model the bombardment of fullerite films modified with low dose Argon ion impacts.

System identification is the task of constructing representative models of processes and has become an invaluable tool in many different areas of science and engineering. Due to the inherent complexity of many real world systems the application of traditional techniques is limited. In such instances more sophisticated (so called intelligent) modelling approaches are required. Neurofuzzy modelling is one such technique, which by integrating the attributes of fuzzy systems and neural networks is ideally suited to system identification. This attractive paradigm combines the well established learning techniques of a particular form of neural network i.e. generalised linear models with the transparent knowledge representation of fuzzy systems, thus producing models which possess the ability to learn from real world observations and whose behaviour can be described naturally as a series of linguistic humanly understandable rules. Unfortunately, the application of these systems is limited to low dimensional problems for which good quality expert knowledge and data are available. The work described in this thesis addresses this fundamental problem with neurofuzzy modelling, as a result algorithms which are less sensitive to the quality of the a priori knowledge and empirical data are developed. The true modelling capabilities of any strategy is heavily reliant on the model's structure, and hence an important (arguably the most important) task is structure identification. Also, due to the curse of dimensionality, in high dimensional problems the size of conventional neurofuzzy models gets prohibitively large. These issues are tackled by the development of automatic neurofuzzy model identification algorithms, which exploit the available expert knowledge and empirical data. To alleviate problems associated with the curse of dimensionality, aid model generalisation and enhance model transparency, parsimonious models are identified. This is achieved by the application of additive and multiplicative neurofuzzy models which exploit structural redundancies found in conventional systems. The developed construction algorithms successfully identify parsimonious models, but as a result of noisy and poorly distributed empirical data, these models can still generalise inadequately. This problem is addressed by the application of Bayesian inferencing techniques; a form of regularisation. Smooth model outputs are assumed and superfluous model parameters are controlled, sufficiently aiding model generalisation and transparency, and data interpolation and extrapolation. By exploiting the structural decomposition of the identified neurofuzzy models, an efficient local method of regularisation is developed. All the methods introduced in this thesis are illustrated on many different examples, including simulated time series, complex functional equations, and multi-dimensional dynamical systems. For many of these problems conventional neurofuzzy modelling is unsuitable, and the developed techniques have extended the range of problems to which neurofuzzy modelling can successfully be applied.

Drosophila larval crawling is an attractive system to study patterned motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors including head casts, turning, and feeding. It is likely that some neurons are used in all these behaviors (e.g. motor neurons), but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We defined 10 phenotypic categories that could uniquely be evoked upon stimulation, and provide further in depth analysis of two of these categories to understand the origins of the evoked behaviors. We first identified the evolutionarily conserved Even-skipped+ interneuron phenotype (Eve/Evx). Activation or ablation of Eve+ interneurons disrupted bilaterally symmetric muscle contraction amplitude, without affecting left-right synchronous timing. TEM reconstruction places the Eve+ interneurons at the heart of a sensorimotor circuit capable of detecting and modifying body wall muscle contraction We then went on to identify a unique pair of descending neurons dubbed the ‘Mooncrawler’ descending neurons (McDNs) to be sufficient to generate reverse locomotion. We show that the McDNs are present at larval hatching, function during larval life, and are remodeled during metamorphosis while maintaining basic morphological features and neural functions necessary to generate backwards locomotion. Finally, using serial section Transmission Electron Microscopy (ssTEM) to map neural connections to upstream and downstream elements provides a mechanistic view of how sensory information is received by the McDNs and transmitted to the VNC motor system to perform backwards locomotion. Finally, we show that these McDNs are the same as those identified in recent work in Drosophila adults (Bidaye et al. 2014) to be sufficient to generate reverse locomotion. This dissertation includes previously published, co-authored material.

Made available in DSpace on 2014-06-11T19:32:22Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-08-09Bitstream added on 2014-06-13T21:04:12Z : No. of bitstreams: 1 pontes_fj_dr_guara.pdf: 2076253 bytes, checksum: e0151bbfd7f5dd6f59a5364cd9097f4d (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O presente trabalho oferece contribuições à modelagem da rugosidade da peça em processos de usinagem por meio de redes neurais artificiais. Propõe-se um método para o projeto de redes. Perceptron Multi-Camada (Multi-Layer Percepton, ou MLO) e Função de Base radial Radial Basis Function, ou RBF) otimizadas para a predição da rugosidade da pela (Ra). Desenvolve-se um algoritmo que utiliza de forma hibrida a metodologia do projeto de experimentos por meio das técnicas dos fatoriais completose de Variações Evolucionária em Operações (EVOP). A estratégia adotada é a de utilizar o projeto de experimentos na busca de configurações de rede que favoreçam estatisticamente o desempenho na tarefa de predição. Parâmetro de corte dos processos de usinagem são utilizados como entradas das redes. O erro médio absoluto em porcentagem (MAE %) do decil inferioir das observações de predição para o conjunto de testes é utilizado como medida de desempnho dos modelos. Com o objetivo de validar o métido proposto são empregados casos de treinamento gerados a partir de daods obtidos de trabalhos de literatura e de experimentos de torneamento do aço ABNT 121.13. O método proposto leva á redução significativa do erro de predição da rugosidade nas operações de usinagem estudadas, quando se compara seu desempenho ao apresentado por modelos de regressão, aos resultados relatados pela literatura e ao desempenho de modelos neurais propostos por um pacotecomputacional comercial para otimização de configurações de rede. As redes projetadas segundo o método proposto possuem dispersão dos erros de predição significativamente reduzidos na comparação. Observa-se ainda que rede MLP atingem resultados estatisticamente superior aos obtidos pelas melhores redes RBF / The present work offers some contributions to the area of surface roughness modeling by Artificial Neural Network in machining processes. Ir proposes a method for the project networks of MLP (Multi-Layer Perceptron) and RBF (Radial Basis Function) architectures optimized for prediction of Average Surface Roughness (Ru). The methid is expressed in the format of an algorithm employing two techniques from the DOE (Design of Experiments) methodology: Full factorials and Evolutionary Operations(EVOP). The strategy adopted consists in the sistematic use of DOE in a search for network configurations that benefits performance in roughess prediction. Cutting para meters from machining operations are employed as network inputs. Themean absolute error in percentage (MAE%) of the lower decile of the predictions for the test set is used as a figure of merit for network performance. In order to validate the method, data sets retrieved from literature, as well as results of experiments with AISI/SAE free-machining steel, are employed to form training and test data sets for the networks. The proposed algorithm leads to significant reduction in prediction error for surface roughness when compared to the performance delivred by a regression model, by the networks proposed by the original studies data was borrowed from and when compared models proposed by a software package intend to search automatically for optimal network configurations. In addition, networks designed acording to the proposed algorithm displayed reduced dispersion of prediction error for surface roughness when compared to the performance delivered by a regression model, by the networks proposed by the original studies data was borrowed from and when compared to neural models proposed by a software package intended to searchautomatically for optimal network configurations. In addition, networks designed according to the proposed algorith ... (Complete abstract click electronic access below)

Neurons transmit information through spikes in neural network through synaptic couplings. Given the prevalence of correlation among neural spike trains experimentally observed in different brain areas, it is of interest to study how neurons compute correlated input. Yet how it depends on the synaptic properties and conductance kinetics in neuronal interaction is very little known. Through simulation of leaky integrate-and-fire (LIF) neurons, we have studied the effects of excitatory and inhibitory synaptic decay times, level of background activities and higher-order conductance effects on the output correlation of different time scales for neurons receiving correlated excitatory input, and provided important understanding on the mechanism of how these factors influence neural computation of such correlated input. We showed that when the conductance effects are totally ignored, increasing excitatory synaptic decay time jitters output spike time and shapes the output correlation of short to medium time scale, while the output correlation of very long time scale is determined by the membrane time constant. When conductance effects are considered, this is no longer the case as the effective membrane time constant becomes comparable to the excitatory decay time. We found that the ratio of long-term correlation to short-term correlation (synchrony) increases with excitatory synaptic decay time and decreases with the level of input activities due to the combined effects of jittered spike time, which can be predicted from the time window and magnitude of the effects of a single input spike on membrane potential, and burst firing. In particular, it is possible for neurons with small excitatory synaptic decay time in high conductance state to respond to correlated input by solely giving extra precisely timed synchronous spikes without exhibiting correlation of longer time scale. In addition, we found that inhibitory synaptic decay time shapes correlation by controlling the relative contribution of excitatory and inhibitory input to output firing. As a result, both output correlation and synchrony increase with it. These results are qualitatively true for a wide range of input correlation and synaptic efficacies. Finally, we showed that fluctuations of conductance and membrane potential reduce output correlation, which can be explained by the reduced prevalence of burst firing. These results suggest that spike initiation dynamics of neurons can be well characterized by their synaptic decay times and the level of input activities. These properties are therefore expected to influence neurons’ ability to code temporal information. These results also hint that correlation, in particular that of long time scale, would be lower if more realistic biophysical features like neural adaptations and network circuitry with feed-forward or recurrent inhibition are considered. It suggests that studies using single LIF neurons tend to overestimate output correlation and underestimate the ability of neurons in producing precisely timed output.

This work presents a new approach for detecting and isolating faults in nonlinear processes using independent neural network models. In this approach, an independent neural network is used to model the proton exchange membrane fuel cell nonlinear systems using a multi-input multi-output structure. This research proposed the use of radial basis function network and multilayer perceptron network to perform fault detection. After training, the neural network models can give accurate prediction of the system outputs, based on the system inputs. Using the residual generation concept developed in the model-based diagnosis, the difference between the actual and estimated outputs are used as residuals to detect faults. When the magnitude of these residuals exceed a predefined threshold, it is likely that the system is faulty. In order to isolate faults in the system, a second neural network is used to examine features in the residual. A specific feature would correspond to a specific fault. Based on features extracted and classification principles, the second neural network can isolate faults reliably and correctly. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations.

Dentre as inúmeras técnicas de conversão de voz utilizadas atualmente, aquelas baseadas em bancos de filtros wavelet, associadas com redes neurais artificiais,têm se destacado. Este trabalho se concentra em tais técnicas, realizando um estudo que relaciona qual a melhor wavelet para conversão de determinados padrões de voz, apresentando uma análise detalhada de quais são as características que levam a estes resultados. Os testes são realizados com vozes da base de dados TIMIT do Linguistic Data Consortium (LDC). / Dentre as inúmeras técnicas de conversão de voz utilizadas atualmente, aquelas baseadas em bancos de filtros wavelet, associadas com redes neurais artificiais, têm se destacado. Este trabalho se concentra em tais técnicas, realizando um estudo que relaciona qual a melhor wavelet para conversão de determinados padrões de voz, apresentando uma análise detalhada de quais são as características que levam a estes resultados. Os testes são realizados com vozes da base de dados TIMIT do Linguistic Data Consortium (LDC).