Matching Items (227)
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Description
Drinking vinegar is a popularly discussed remedy for relieving heartburn symptom, as can be read on many websites; however, there has been no scientific research or theory to support its efficacy. This randomized, placebo-controlled, double-blind, cross-over research study tested the efficacy of the organic apple cider vinegar, with mother,

Drinking vinegar is a popularly discussed remedy for relieving heartburn symptom, as can be read on many websites; however, there has been no scientific research or theory to support its efficacy. This randomized, placebo-controlled, double-blind, cross-over research study tested the efficacy of the organic apple cider vinegar, with mother, on alleviation of the heartburn symptom related to Gastro-esophageal reflux disease (GERD). A minimum of one week separated the four trial arms: chili (placebo), antacid after chili meal (positive control), vinegar added to chili, and diluted vinegar after chili meal. Twenty grams of vinegar were used in both vinegar treatments, and 10 grams of liquid antacid were used in the antacid trial. A five-point Likert scale and a 10-cm visual analogue scale (VAS) were used to assess heartburn severity during a 120 minutes testing time. Seven of 15 recruited subjects' data was usable for statistical analysis (age: 39.6 ± 12.2 y, body mass index (BMI): 29.4 ± 4.2 kg/m2, waist circumference: 36.4 ± 4.1 inch). There was no statistically significant difference among the mean and incremental area-under-the-curve (iAUC) heartburn scores among different trials (Likert scale questionnaire p= .259, VAS questionnaire p= .659, iAUC Likert scale p= .184, iAUC VAS p= .326). Seven participants were further divided into antacid responder (n=4) and antacid non-responder groups (n=3). Likert scale mean heartburn score and iAUC data in antacid responder group had significant finding (p= .034 and p= .017 respectively). The significance lay between antacid and 'vinegar added to chili' trials. Effect size was also used to interpret data due to the small sample size: Likert scale: mean heartburn score= .444, iAUC= .425; VAS mean heartburn score= .232, iAUC .611. Effect size for antacid responder group was Likert scale: mean heartburn score= .967, iAUC= .936. Future research is needed to examine whether ingesting organic vinegar benefits alleviation of heartburn symptom related to GERD for people who do not respond well to antacid.
ContributorsYeh, Zoe (Author) / Johnston, Carol (Thesis advisor) / Mayol-Kreiser, Sandra (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission

Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?

This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.

A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population

ii

density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.
ContributorsSong, Yang (Author) / Gurney, Kevin (Thesis advisor) / Kuby, Michael (Committee member) / Golub, Aaron (Committee member) / Chester, Mikhail (Committee member) / Selover, Nancy (Committee member) / Arizona State University (Publisher)
Created2018
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Description

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

ContributorsDavis, Kenneth J. (Author) / Deng, Aijun (Author) / Lauvaux, Thomas (Author) / Miles, Natasha L. (Author) / Richardson, Scott J. (Author) / Sarmiento, Daniel P. (Author) / Gurney, Kevin (Author) / Hardesty, R. Michael (Author) / Bonin, Timothy A. (Author) / Brewer, W. Alan (Author) / Lamb, Brian K. (Author) / Shepson, Paul B. (Author) / Harvey, Rebecca M. (Author) / Cambaliza, Maria O. (Author) / Sweeney, Colm (Author) / Turnbull, Jocelyn C. (Author) / Whetstone, James (Author) / Karion, Anna (Author) / College of Liberal Arts and Sciences (Contributor)
Created2017-05-23
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Description

Vegetarian diets are associated with factors that may not support bone health, such as low body mass and low intakes of protein; yet, these diets are alkaline, a factor that favors bone mineral density (BMD). This study compared the correlates of BMD in young, non-obese adults consuming meat-based (n =

Vegetarian diets are associated with factors that may not support bone health, such as low body mass and low intakes of protein; yet, these diets are alkaline, a factor that favors bone mineral density (BMD). This study compared the correlates of BMD in young, non-obese adults consuming meat-based (n = 27), lacto-ovo vegetarian (n = 27), or vegan (n = 28) diets for ≥1 year. A 24 h diet recall, whole body DXA scan, 24 h urine specimen, and fasting blood sample were collected from participants. BMD did not differ significantly between groups. Protein intake was reduced ~30% in individuals consuming lacto-ovo and vegan diets as compared to those consuming meat-based diets (68 ± 24, 69 ± 29, and 97 ± 47 g/day respectively, p = 0.006); yet dietary protein was only associated with BMD for those following vegan diets. Urinary pH was more alkaline in the lacto-ovo and vegan groups versus omnivores (6.5 ± 0.4, 6.7 ± 0.4, and 6.2 ± 0.4 respectively, p = 0.003); yet urinary pH was associated with BMD in omnivores only. These data suggest that plant-based diets are not detrimental to bone in young adults. Moreover, diet prescriptions for bone health may vary among diet groups: increased fruit and vegetable intake for individuals with high meat intakes and increased plant protein intake for individuals who follow a vegetarian diet plan.

ContributorsKnurick, Jessica (Author) / Johnston, Carol (Author) / Wherry, Sarah J. (Author) / Aguayo, Izayadeth (Author) / College of Health Solutions (Contributor)
Created2015-05-11
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Description

In spite of well-documented health benefits of vegetarian diets, less is known regarding the effects of these diets on athletic performance. In this cross-sectional study, we compared elite vegetarian and omnivore adult endurance athletes for maximal oxygen uptake (VO2 max) and strength. Twenty-seven vegetarian (VEG) and 43 omnivore (OMN) athletes

In spite of well-documented health benefits of vegetarian diets, less is known regarding the effects of these diets on athletic performance. In this cross-sectional study, we compared elite vegetarian and omnivore adult endurance athletes for maximal oxygen uptake (VO2 max) and strength. Twenty-seven vegetarian (VEG) and 43 omnivore (OMN) athletes were evaluated using VO2 max testing on the treadmill, and strength assessment using a dynamometer to determine peak torque for leg extensions. Dietary data were assessed using detailed seven-day food logs. Although total protein intake was lower among vegetarians in comparison to omnivores, protein intake as a function of body mass did not differ by group (1.2 ± 0.3 and 1.4 ± 0.5 g/kg body mass for VEG and OMN respectively, p = 0.220). VO2 max differed for females by diet group (53.0 ± 6.9 and 47.1 ± 8.6 mL/kg/min for VEG and OMN respectively, p < 0.05) but not for males (62.6 ± 15.4 and 55.7 ± 8.4 mL/kg/min respectively). Peak torque did not differ significantly between diet groups. Results from this study indicate that vegetarian endurance athletes’ cardiorespiratory fitness was greater than that for their omnivorous counterparts, but that peak torque did not differ between diet groups. These data suggest that vegetarian diets do not compromise performance outcomes and may facilitate aerobic capacity in athletes.

ContributorsLynch, Heidi (Author) / Wharton, Christopher (Author) / Johnston, Carol (Author) / College of Health Solutions (Contributor)
Created2016-11-15
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Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a

The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Skin elasticity, a key indicator of skin health, is influenced by various factors including diet and body composition. This study, led by Myka Williams as part of her Barrett, The Honors College Thesis Project at Arizona State University under the guidance of Dr. Carol Johnston and Dr. Sandy Mayol-Kreiser, investigates

Skin elasticity, a key indicator of skin health, is influenced by various factors including diet and body composition. This study, led by Myka Williams as part of her Barrett, The Honors College Thesis Project at Arizona State University under the guidance of Dr. Carol Johnston and Dr. Sandy Mayol-Kreiser, investigates the relationship between diet—specifically vegetarian and omnivorous patterns—and skin elasticity. Utilizing the ElastiMeter from Delfin Technologies, we assessed the skin elasticity of 38 individuals from the ASU community. Our findings revealed no significant difference in skin elasticity between the dietary groups. However, intriguing correlations emerged between participants' Body Mass Index (BMI) and skin elasticity. These initial findings suggest the potential influence of body composition on skin health, warranting further research with additional parameters to strengthen and expand upon these observations.
ContributorsWilliams, Myka (Author) / Johnston, Carol (Thesis director) / Mayol-Kreiser, Sandy (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2024-05