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- Genre: Masters Thesis
Description
It is possible in a properly controlled environment, such as industrial metrology, to make significant headway into the non-industrial constraints on image-based position measurement using the techniques of image registration and achieve repeatable feature measurements on the order of 0.3% of a pixel, or about an order of magnitude improvement on conventional real-world performance. These measurements are then used as inputs for a model optimal, model agnostic, smoothing for calibration of a laser scribe and online tracking of velocimeter using video input. Using appropriate smooth interpolation to increase effective sample density can reduce uncertainty and improve estimates. Use of the proper negative offset of the template function has the result of creating a convolution with higher local curvature than either template of target function which allows improved center-finding. Using the Akaike Information Criterion with a smoothing spline function it is possible to perform a model-optimal smooth on scalar measurements without knowing the underlying model and to determine the function describing the uncertainty in that optimal smooth. An example of empiric derivation of the parameters for a rudimentary Kalman Filter from this is then provided, and tested. Using the techniques of Exploratory Data Analysis and the "Formulize" genetic algorithm tool to convert the spline models into more accessible analytic forms resulted in stable, properly generalized, KF with performance and simplicity that exceeds "textbook" implementations thereof. Validation of the measurement includes that, in analytic case, it led to arbitrary precision in measurement of feature; in reasonable test case using the methods proposed, a reasonable and consistent maximum error of around 0.3% the length of a pixel was achieved and in practice using pixels that were 700nm in size feature position was located to within ± 2 nm. Robust applicability is demonstrated by the measurement of indicator position for a King model 2-32-G-042 rotameter.
ContributorsMunroe, Michael R (Author) / Phelan, Patrick (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2012
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
Description
Obesity impairs skeletal muscle maintenance and regeneration, a condition that can progressively lead to muscle loss, but the mechanisms behind it are unknown. Muscle is primarily composed of multinucleated cells called myotubes which are derived by the fusion of mononucleated myocytes. A key mediator in this process is the cellular fusion protein syncytin-1. This led to the hypothesis that syncytin-1 could be decreased in the muscle of obese/insulin resistant individuals. In contrast, it was found that obese/insulin resistant subjects had higher syncytin-1 expression in the muscle compared to that of the lean subjects. Across the subjects, syncytin-1 correlated significantly with body mass index, percent body fat, blood glucose and HbA1c levels, insulin sensitivity and muscle protein fractional synthesis rate. The concentrations of specific plasma fatty acids, such as the saturated fatty acid (palmitate) and monounsaturated fatty acid (oleate) are known to be altered in obese/insulin resistant humans, and also to influence the protein synthesis in muscle. Therefore, it was evaluated that the effects of palmitate and oleate on syncytin-1 expression, as well as 4E-BP1 phosphorylation, a key mechanism regulating muscle protein synthesis in insulin stimulated C2C12 myotubes. The results showed that treatment with 20 nM insulin, 300 µM oleate, 300 µM oleate +20 nM insulin and 300 µM palmitate + 300 µM oleate elevated 4E-BP1 phosphorylation. At the same time, 20 nM insulin, 300 µM palmitate, 300 µM oleate + 20 nM insulin and 300 µM palmitate + 300 µM oleate elevated syncytin-1 expression. Insulin stimulated muscle syncytin-1 expression and 4E-BP1 phosphorylation, and this effect was comparable to that observed in the presence of oleate alone. However, the presence of palmitate + oleate diminished the stimulatory effect of insulin on muscle syncytin-1 expression and 4E-BP1 phosphorylation. These findings indicate oleate but not palmitate increased total 4E-BP1 phosphorylation regardless of insulin and the presence of palmitate in insulin mediated C2C12 cells. The presence of palmitate inhibited the upregulation of total 4EB-P1 phosphorylation. Palmitate but not oleate increased syncytin-1 expression in insulin mediated C2C12 myotubes. It is possible that chronic hyperinsulinemia in obesity and/or elevated levels of fatty acids such as palmitate in plasma could have contributed to syncytin-1 overexpression and decreased muscle protein fractional synthesis rate in obese/insulin resistant human muscle.
ContributorsRavichandran, Jayachandran (Author) / Katsanos, Christos (Thesis advisor) / Coletta, Dawn (Committee member) / Dickinson, Jared (Committee member) / Arizona State University (Publisher)
Created2017