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591	Differentiation and characterization of cell types associated with retinal degenerative diseases using human induced pluripotent stem cells Gupta, Manav 31 July 2014 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Human induced pluripotent stem (iPS) cells have the unique ability to differentiate into 200 or so somatic cell types that make up the adult human being. The use of human iPS cells to study development and disease is a highly exciting and interdependent field that holds great promise in understanding and elucidating mechanisms behind cellular differentiation with future applications in drug screening and cell replacement studies for complex and currently incurable cellular degenerative disorders. The recent advent of iPS cell technology allows for the generation of patient-specific cell lines that enable us to model the progression of a disease phenotype in a human in vitro model. Differentiation of iPS cells toward the affected cell type provides an unlimited source of diseased cells for examination, and to further study the developmental progression of the disease in vitro, also called the “disease-in-a-dish” model. In this study, efforts were undertaken to recapitulate the differentiation of distinct retinal cell affected in two highly prevalent retinal diseases, Usher syndrome and glaucoma. Using a line of Type III Usher Syndrome patient derived iPS cells efforts were undertaken to develop such an approach as an effective in vitro model for studies of Usher Syndrome, the most commonly inherited disorder affecting both vision and hearing. Using existing lines of iPS cells, studies were also aimed at differentiation and characterization of the more complex retinal cell types, retinal ganglion cells (RGCs) and astrocytes, the cell types affected in glaucoma, a severe neurodegenerative disease of the retina leading to eventual irreversible blindness. Using a previously described protocol, the iPS cells were directed to differentiate toward a retinal fate through a step-wise process that proceeds through all of the major stages of neuroretinal development. The differentiation process was monitored for a period of 70 days for the differentiation of retinal cell types and 150 days for astrocyte development. The different stages of differentiation and the individually derived somatic cell types were characterized by the expression of developmentally associated transcription factors specific to each cell type. Further approaches were undertaken to characterize the morphological differences between RGCs and other neuroretinal cell types derived in the process. The results of this study successfully demonstrated that Usher syndrome patient derived iPS cells differentiated to the affected photoreceptors of Usher syndrome along with other mature retinal cell types, chronologically analogous to the development of the cell types in a mature human retina. This study also established a robust method for the in vitro derivation of RGCs and astrocytes from human iPS cells and provided novel methodologies and evidence to characterize these individual somatic cell types. Overall, this study provides a unique insight into the application of human pluripotent stem cell biology by establishing a novel platform for future studies of in vitro disease modeling of the retinal degenerative diseases: Usher syndrome and glaucoma. In downstream applications of this study, the disease relevant cell types derived from human iPS cells can be used as tools to further study disease progression, drug screening and cell replacement strategies. Degeneration Differentiation Disease Modeling Glaucoma Induced Pluripotent Stem Cell Retina Usher's syndrome -- Pathophysiology Glaucoma -- Alternative treatment Retinitis pigmentosa -- Pathophysiology Genetic disorders -- Diagnosis Embryonic stem cells -- Research Human cell culture -- Research Cell differentiation -- Analysis Stem cells -- Transplantation -- Methods Cellular therapy Retinal ganglion cells -- Research Retinal degeneration Cells -- Morphology Regenerative medicine -- Research Transcription factors Astrocytes -- Morphology Genetic engineering Drug testing
592	Complex Vehicle Modeling: A Data Driven Approach Alexander Christopher Schoen (8068376) 31 January 2022 (has links) <div> This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks.</div><div><br></div><div> The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model.</div><div><br></div><div> The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. </div><div><br></div><div> Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.</div> Computer Engineering Genetic Engineering Environmental Engineering Modelling Pattern Recognition and Data Mining Simulation and Modelling Neural Network Prediction fuel consumption improvements Ensemble Averaging Complex system modeling Refuse Truck Delivery Truck Vehicle Routing Vehicle Routing Problem SAE J1321 synthetic data generation power take-off Aerodynamic Speed Characteristic Acceleration Artificial neural network feature importances predictor importance Influence of Weights Point-wise Prediction Error Machine Learning
593	Purification, functional characterization and crystallization of the PerR peroxide sensor from Saccharopolyspora erythraea Elison Kalman, Grim January 2019 (has links) This report summarizes the work on the cloning, expression, and purification of PerR, a metal sensing regulator from Saccharopolyspora erythraea and the subsequent characterization using small angle X-ray scattering and other biochemical methods. The report aims to provide an insight into prokaryotic metal homeostasis, provide a better understanding of how PerR works and provide valuable information for the continued work on the crystallization of PerR. structural biology cell culture protein expression genetic engineering spectroscopy X-ray crystallography small angle X-ray scattering SAXS transcription factor PerR metal binding antibiotics Saccharopolyspora erythraea S. erythraea soil bacterium actinobacteria actinomycetes oxidative stress peroxide stress structure determination characterization stabilization peroxide sensor iron sensor manganese sensor metalloprotein metal ion homeostasis metal ion prokaryotic strukturbiologi cellodling proteinuttryck genteknik spektroskopi röntgenkristallografi småvinkel-röntgenspridning SAXS transkriptionsfaktor PerR metallbindande antibiotika Saccharopolyspora erythraea S. erythraea jordartsbakterie Aktinobakterier Actinobacteria Actinomycetes oxidativ stress peroxid stress strukturbestämning karaktärisering stabilisering peroxidsensor järnsensor mangansensor metalljonshomeostas metalljoner prokaryot Structural Biology Strukturbiologi

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