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Browsing by Author "Reisfeld, Bradley, committee member"

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    Code generation in AlphaZ
    (Colorado State University. Libraries, 2011) Srinivasa, Pradeep, author; Rajopadhye, Sanjay Vishnu, advisor; Böhm, Anton Pedro Willem, 1948-, committee member; Reisfeld, Bradley, committee member
    Computer architecture technology is evolving rapidly. Many of the programs written for a specific architecture are not very useful when a new architecture evolves. They have to be either modified or rewritten to suit the new architectures. Instead one can write a high level program and feed this to a tool which can produce code for different architectures. AlphaZ is such a tool which takes a high level program and helps us to analyze, transform and generate code for different architectures. In this thesis, we develop a code generation framework in AlphaZ, which takes equations as programs called alphabets program. Alphabets is a high level abstraction language which allows us to write equational programs. Equational programs consists of a set of equations along with their associated domains. We describe how code is generated in our code generation framework by taking an Alphabets program and the necessary target mapping specification. We illustrate how different code generators can be developed by extending the existing modules in our code generation framework.
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    Investigation of molecular effects of the soy-derived phytoestrogen genistein on cardiomyocytes by proteomic analysis
    (Colorado State University. Libraries, 2011) Sun, Zeyu, author; Reardon, Kenneth, advisor; Hamilton, Karyn, committee member; Orton, Christopher, committee member; Reisfeld, Bradley, committee member
    The soy-derived phytoestrogen genistein (GEN) has received attention for its potential to benefit the cardiovascular system by providing protection to cardiomyocytes against pathophysiological stresses. Although GEN is a well-known estrogen receptor (ER) agonist and a non-specific tyrosine kinase inhibitor, current understanding of the complex cellular and molecular effects of GEN in cardiomyocytes is still incomplete. The overall goal of this dissertation is to use high throughput proteomics methodologies to better understand the molecular action of GEN in cardiomyocytes and to identify proteins and pathways that respond to GEN treatment. The first study of this project focused on the concentration-dependent proteome changes in cultured HL-1 cardiomyocytes due to GEN treatments. Proteins from HL-1 cardiomyocytes treated with 1 μM and 50 μM GEN were prefractionated into hydrophilic and hydrophobic protein fractions and were analyzed by two-dimensional electrophoresis followed by protein identification using tandem mass spectrometry (MS). In total, 25 and 62 differential expressed proteins were identified in response to 1 μM and 50 μM of GEN treatment, respectively. These results suggest that 1 μM GEN enhanced the expression of heat shock proteins and anti-apoptotic proteins, while 50 μM GEN down-regulated glycolytic and antioxidant enzymes, potentially making cardiomyocytes more susceptible to energy depletion and apoptosis. The second study, employing a two-dimensional liquid chromatography and tandem MS shotgun proteomics workflow, was carried out to dissect the cellular functions changed in cardiomyocytes by ER-dependent or ER-independent actions of GEN. In this study, primary cardiomyocytes isolated from male adult SD rats were treated with 10 μM GEN without or with 10 μM ER antagonist ICI 182,780 (ERA) before proteomics comparison. A total of 14 and 15 proteins were found differentially expressed in response to the GEN, and the GEN+ERA treatment, respectively. Cellular functions such as glucose and fatty acid metabolism and cardioprotection were found to be modulated by GEN in an ER-dependent fashion, while proteins involved with steroidogenesis and estrogen signaling were identified as novel effectors of GEN via ER-independent actions. In this study, a consensus-iterative searching strategy was also developed to enhance the sensitivity of the shotgun proteomic approach. In the last study, an attempt to explore the response to a GEN stimulus in the signaling pathways, we developed a phosphopeptide enrichment method to assist the detection of protein phosphorylation in a complex peptide mixture. The quantitative performance of a sequential immobilized metal affinity chromatography (SIMAC) protocol was evaluated. We further conducted a preliminary application of this protocol in a large-scale, quantitative, label-free phosphoproteomics study to explore the alterations of protein phosphorylation patterns due to ER-independent GEN action in the SD rat cardiomyocytes. This project demonstrates the usefulness of proteomics methodologies to screen novel molecular targets influenced by GEN in cardiomyocytes. This is also the first investigation of the complex cellular impact of this soy-derived phytoestrogen in cardiomyocytes via a systems biology perspective.
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    Machine learning and artificial intelligence approaches to the analysis of physical activity from wearables and biosensors in clinical trials: applications of clustering and prediction of clinical outcomes
    (Colorado State University. Libraries, 2022) Vlajnic, Vanja M., author; Simske, Steve, advisor; Miller, Erika, committee member; Cale, Jim, committee member; Reisfeld, Bradley, committee member
    As human demographics continue to trend toward elderly, especially in advanced economies, the treatment of illness becomes more salient. Across many therapeutic areas, researchers examine potential treatments while incorporating novel technologies in an effort to prolong the years in which quality of life is achieved for patients around the world. In the area of cardiovascular disease, wearable and biosensor data is becoming increasingly used in order to compliment data traditionally collected from clinical trials. This work discusses a series of analytical approaches for the analysis of data from recent clinical trials in which accelerometry data from wearable devices were analyzed using clustering approaches (K-means and consensus clustering) and survival analyses (Cox proportional hazards and random survival forest) for the purposes of clustering patients and assessing their baseline clinical characteristics as well as for the prediction of clinical outcomes. Unique clinical phenotypes were identified within the patient aggregations as part of the clustering analyses. Furthermore, models were created with improved predictive accuracy for clinical outcomes of interest in the heart failure space. Taken collectively, the results from these analyses and the analytical approaches therein can be used to assess whether heterogeneous clinical subgroups of patients exist as well as further guide the clinical development programs.
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    Novel assessments of country pandemic vulnerability based on non-pandemic predictors, pandemic predictors, and country primary and secondary vaccination inflection points
    (Colorado State University. Libraries, 2024) Vlajnic, Marco M., author; Simske, Steven, advisor; Cale, James, committee member; Conrad, Steven, committee member; Reisfeld, Bradley, committee member
    The devastating worldwide impact of the COVID-19 pandemic created a need to better understand the predictors of pandemic vulnerability and the effects of vaccination on case fatality rates in a pandemic setting at a country level. The non-pandemic predictors were assessed relative to COVID-19 case fatality rates in 26 countries and grouped into two novel public health indices. The predictors were analyzed and ranked utilizing machine learning methodologies (Random Forest Regressor and Extreme Gradient Boosting models, both with distribution lags, and a novel K-means-Coefficient of Variance sensitivity analysis approach and Ordinary Least Squares Multifactor Regression). Foundational time series forecasting models (ARIMA, Prophet, LSTM) and novel hybrid models (SARIMA-Bidirectional LSTM and SARIMA-Prophet-Bidirectional LSTM) were compared to determine the best performing and accurate model to forecast vaccination inflection points. XGBoost methodology demonstrated higher sensitivity and accuracy across all performance metrics relative to RFR, proving that cardiovascular death rate was the most dominant predictive feature for 46% of countries (Population Health Index), and hospital beds per thousand people for 46% of countries (Country Health Index). The novel K-means-COV sensitivity analysis approach performed with high accuracy and was successfully validated across all three methods, demonstrating that female smokers was the most common predictive feature across different analysis sets. The new model was also validated with the Calinski-Harabasz methodology. Every machine learning technique that was evaluated showed great predictive value and high accuracy. At a vaccination rate of 13.1%, the primary vaccination inflection point was achieved at 83.27 days. The secondary vaccination inflection point was reached at 339.31 days at the cumulative vaccination rate of 67.8%. All assessed machine and deep learning methodologies performed with high accuracy relative to COVID-19 historical data, demonstrated strong forecasting value, and were validated by anomaly and volatility detection analyses. The novel triple hybrid model performed the best and had the highest accuracy across all performance metrics. To be better prepared for future pandemics, countries should utilize sophisticated machine and deep learning methodologies and prioritize the health of elderly, frail and patients with comorbidities.
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    Ring-conversion and functionalization of nitrogen-containing heterocycles
    (Colorado State University. Libraries, 2024) Josephitis, Celena M., author; McNally, Andrew, advisor; Bandar, Jeff, committee member; Chung, Jean, committee member; Reisfeld, Bradley, committee member
    Pyridines and related azines are ubiquitous in pharmaceuticals and agrochemicals development. Chemist rely on the development of new synthetic methods to modify these heterocycles. Described herein are the development of methods to functionalize azines and convert pyridines and diazines into new heterocycles. Novel hydrogenation and molecular editing strategies were designed and leveraged to accomplish this goal. Chapter one introduces the importance of pyridines and related heterocycles in pharmaceuticals as well as methods to access and functionalize these molecules. Both classical and contemporary methods for functionalization and hydrogenation of pyridines are discussed to provide context for this work. Chapter two describes a novel method to selectively reduce pyridines to dihydropyridines, tetrahydropyridines, and piperidines. This method offers a complementary alternative to current hydrogenation or reduction methods, in which the degree of saturation cannot be controlled, and applies to complex azine starting materials. Chapter three explains the importance of structure-activity relationship (SAR) studies and its implications on the drug-discovery process. It also describes classical and contemporary strategies that apply to SAR diversification including de novo heterocycle synthesis and molecular editing strategies. Finally, chapter four presents a novel method for SAR diversification of pyrimidine containing molecules using a deconstruction/reconstruction approach.