74201 research outputs found
Sort by
Weight Regain after Lifestyle-Induced Weight Loss: Assessment of Existing Definitions, Evaluation of Cardiometabolic Risk and Development of a Prediction Model
No full textAbstract: Background: Lifestyle interventions to reduce excess body weight associated with high cardiometabolic risk have resulted in weight loss and improved risk short-term. However, rates of recidivism are high, averaging weight regain of 50% after 12-24 months. Understanding what factors are associated with successful weight maintenance and weight regain as well as the cardiometabolic implications of partial or total weight regain will help formulate strategies to improve long term outcomes. Objectives: We compared published categorization criteria that differentiate maintainers and regainers using agreement statistics in the Action for Health in Diabetes (Look AHEAD) trial with replication in the Diabetes Prevention Program (DPP) (Aim 1). Next, we examined the association between weight regain at varying magnitudes and cardiometabolic risk factors in the Look AHEAD trial (Aim 2). Finally, we developed and internally validated a prediction model of weight regain using factors from physical, psychological and behavioral domains in the Look AHEAD trial (Aim 3). Methods: Publically available data from Look AHEAD (n=1791) and DPP (n=613) were used to identify participants with ≥3% initial weight loss (IWL) after lifestyle interventions. Four-year follow-up data were used for all analyses. For Aim 2, fewer Look AHEAD participants (n=1561) were included due to medication use exclusions. For Aim 1, eight previously published criteria defining body weight loss maintainers and regainers were compared with respect to concordance using agreement statistics. Criteria were assessed separately among those with 3-9% and ≥10% IWL. Next, weight regain and weight loss maintenance were defined by dichotomization with five cut points (0%, 25%, 50%, 75% and 100%) of percent of weight loss regained (weight change from years 1 to 4 as percent of weight loss during the first year). Change in cardiometabolic risk factors after IWL was compared in maintainers and regainers according to each cut point using ANCOVA models. The effect was assessed separately in those with <10% and ≥10% IWL, and in women and men. Finally, a prediction model of weight regain was developed and internally validated. Predictors from demographic, psychosocial, clinical and behavioral domains were entered into a stochastic gradient boosting prediction model. Outputs from the model were added to a stepwise logistic regression for interpretability. Results: When assessing concordance among weight loss criteria, agreement was dependent on IWL, but many criteria were in high agreement (% agreement ≥80%). The definition of successful weight loss maintenance "regaining ≤25% of IWL during maintenance" showed high agreement with most commonly used definition: "staying ≥10% below initial weight" among those with ≥10% IWL (% agreement=85% in Look AHEAD; 87% in DPP). The same ≤25% regain definition showed high agreement with the definition of staying ≥5% below initial weight among those with 3-9% IWL (% agreement=92% in Look AHEAD; 91% in DPP). When assessing cardiometabolic risk, maintainers had significant improvements from years 1 to 4 for cardiometabolic risk factors compared to regainers for all risk factors assessed. No single weight regain cut point maximized the risk difference between maintainers and regainers across risk factors and sex/ IWL subgroups. For many risk factors, increasing the cut point to allow for more regain as part of maintenance resulted in decreased cardiometabolic benefit among maintainers. On the basis of the prediction model, the top predictors of weight loss maintenance were IWL, baseline BMI, interaction between IWL and BMI, and use of meal replacements. The model performed fairly in training and test models (ROC AUC [95%CI]: 0.79 [0.76, 0.82] and 0.67 [0.64, 0.70], respectively). The logistic regression model performed similarly. Conclusion: We found percentage of weight loss regained is a favorable way to calculate weight change and derive a cut point for maintainers and regainers. Within this calculation, we found that the 25% cut point (allowing up to 25% of weight to be regained as part of maintenance) is the best criteria for sample size and second best for maximizing the risk difference between maintainers and regainers. The 0% cut point (no regain) is the best for cardiometabolic risk reduction but has sample size limitations because a small proportion of people keep off all weight lost. Finally, we identified key predictors of weight regain. The models performed fairly and should be tested in external settings. The findings move efforts forward to define successful weight maintenance and regain and can assist the development of strategies to lower rates of post-loss weight regain.Thesis (Ph.D.)--Tufts University, 2018.Submitted to the Dept. of Nutritional Epidemiology.Advisor: Alice Lichtenstein.Committee: Gordon Huggins, Jeanne McCaffery, and Paul Jacques.Keyword: Epidemiology
Discovery of small molecule inhibitors of Immunoglobulin A1 proteases
No full textAbstract: Antibiotic-resistant bacteria are a major threat to global health, and will be for the foreseeable future. Inhibition of bacterial virulence factors is a promising alternative approach to treating bacterial infections, including infections caused by antibiotic resistant bacteria. Immunoglobulin A1 protease (IgA1P) is a virulence factor of a diverse group of Gram-negative and Gram-positive bacteria, including H. influenzae, N. meningitidis, N. gonorrhoeae, and S. pneumoniae. IgA1 proteases selectively cleave IgA1, which is the primary immunoglobulin on mucosal surfaces. This activity of IgA1P aids in immune suppression and evasion by the bacteria. Recent studies show that IgA1P also cleaves other host proteins, including Tumor necrosis factor receptor-II and Lysosome-associated membrane protein-1. These additional activities are likely involved in disabling host defenses, including extrinsic apoptosis and lysosomal trafficking. Since their discovery forty years ago, research into IgA1 proteases has been severely limited due to lack of synthetic IgA1P substrates and inhibitors. New probes and inhibitors would permit exploration of the roles that IgA1 proteases play in adherence, colonization and infection of human epithelial cells by the human pathogens mentioned above. If the roles of IgA1 proteases are critical for virulence, then IgA1Ps would emerge as novel antivirulence drug targets. To identify synthetic substrates for IgA1Ps, we screened peptide libraries and peptides derived from native autoproteolysis sites. Newly discovered substrates were modified into Förster energy resonance transfer (FRET) probes, which were then utilized to develop sensitive IgA1P activity assays. High-throughput screening assays were developed using the FRET probes, and these assays are being implemented for the discovery of small-molecule inhibitors of IgA1Ps. Despite 40 years of interest in IgA1 proteases, these probes, high throughput screening assays and small molecule inhibitors are the first of their kind.Thesis (Ph.D.)--Tufts University, 2018.Submitted to the Dept. of Chemistry.Advisor: Joshua Kritzer.Committee: Krishna Kumar, Clay Bennett, and Daniel DeOliveira.Keywords: Chemistry, and Biochemistry
Data-Driven Dynamic Models for Nonlinear Process Optimization and Control
No full textAbstract: Mathematical models play an essential role for the purposes of process optimization and control. There are two major information sources for the development of these models: the knowledge of the process inner workings and the input-output data set. The model estimated using the detailed knowledge are called the knowledge-driven model. However, the inner workings of many industrial processes are not always fully understood to enable the development of accurate knowledge-driven models. In such a situation, the data-driven model, relying on the input-output data, is an attractive alternative. Among varieties of data-driven modeling approaches, the Design of Dynamic Experiments (DoDE), a generalization of the traditional Design of Experiments (DoE) approach, has been demonstrated as an effective modeling methodology for optimizing nonlinear processes. When time-resolved data are obtainable during the experiments, developing a Dynamic Response Surface Methodology (DRSM) model is more favorable. As the estimated DRSM model with time-varying parameters captures the process dynamics, it has the potential to be applied for not only the process optimization but also the process control purposes. The main goal of this research work is to further advance and improve the two data-driven methodologies, the DoDE and the DRSM, to model, optimize and control nonlinear processes. We first proposed ways to incorporate prior process knowledge to improve the design of the input domain, in which the time-varying input of the DoDE experiments are selected. Improved process performance has been achieved in the refined input domain. In addition, as process optimization is usually under budgetary and time constraint, we developed an evolutionary DoDE approach to optimize the processes in a timely manner. The size of the initial set of experiments has been dramatically reduced while the achieved optimal process performance is similar to the one obtained using the original DoDE approach. To extend the applicability of the original DRSM approach (DRSM-1) to deal with processes with various and infinite time duration, we proposed a new DRSM approach (DRSM-2). The novelty of the DRSM-2 rests on a nonlinear transformation of time, the independent variable. Comparing to the DRSM-1, the new method has the following advantages. It is capable of 1) Modeling both continuous as well as batch processes, handling semi-infinite as easily as finite time domains 2) Using data that are not equidistant in time 3) Using data segments that are of varied durations due to possible strong nonlinearities in dynamics We also developed a single model approach, using the DRSM model, for both process optimization and control purposes. The proposed method reduces the experimental effort comparing to the current practices which use separate models for process optimization and control purposes, respectively. When the number of measurements is small, the proposed approach provides better control performance compared to the performance achieved using a model estimated with Pseudo Random Binary Signal (PRBS) data.Thesis (Ph.D.)--Tufts University, 2018.Submitted to the Dept. of Chemical and Biological Engineering.Advisor: Christos Georgakis.Committee: Eric Miller, David Schmidt, and Emmanuel Tzanakakis.Keyword: Chemical engineering
An Epidemiological Approach to Understand the Relationship between Maternal Exposure to Mycotoxins, Birth Outcomes, and Stunting in Infants: A Birth Cohort Study in Nepal (ongoing).
No full textThis presentation was given at the International Conference for Epidemiologic Research, and discusses aflatoxins and human health.Original file name: Robin Shrestha_NPHF_Nut_Epi_MArch 2018_Final.pd
Flexural Strength of Various CAD/CAM Ceramic Materials
No full textAbstract: ABSTRACT Aim and Hypothesis: The aim of this in vitro study was to evaluate and compare the flexural strength of the recently introduced zirconia reinforced lithium silicate glassceramic with lithium disilicate and feldspathic ceramics, and to investigate the effect of various surface treatments on the fracture resistance of the tested materials. Materials and method: 120 specimens of three types of CAD/CAM ceramic blocks were divided into three groups: zirconia-reinforced lithium silicate ZLS (Celtra Duo) for group (1), leucite-reinforced feldspar glass-ceramics LRF (IPS Empress1 CAD) for group (2), and lithium disilicate ceramics (LDS) (IPS e-max CAD) for group (3) (ø14.5 x 12.5 mm, thickness 1.5 mm). Specimens were randomized into four subgroups for each group. The first subgroup (A) did not receive any surface treatment, the second subgroup (B) received polishing only, the third subgroup (C) received glazing only, and the fourth subgroup (D) received both the polishing and glazing surface treatments. Biaxial flexural strength test was performed at a rate of 0.5 mm/min until failure occurred and biaxial flexural strength was calculated in MPa. Results: The study found that group (A, 2) showed the lowest value of biaxial flexural strength (FS) (89.34±25.30MPa) and group (D, 3) showed a significantly higher FS value of (365.38±52.52MPa) in comparison to control and polished A and B, which showed no statistically significant difference between each other (p=0.683), while subgroup C had no significant difference with subgroup D (p=0.145). vi There was a statistically significant difference detected among the material groups. Material 3 showed the highest FS and was significantly different (p<0.001) from both materials (1 and 2). Conclusion: For CAD/CAM materials, LDS has higher fracture resistance followed by ZLS, and the least mechanical strength was exhibited by LRF. Polished surface treatment was more prone to have a negative influence on the flexural strength. However, glazing combined with polishing had a significant effect on increasing the flexural strength of ceramics.Thesis (M.S.)--Tufts University, 2018.Submitted to the Dept. of Posthodontics.Advisor: Ala Ali.Committee: Matthew Finkelman, Aikatarini Kostagianni, and Aikatarini Papathanasiou.Keyword: Dentistry
Research Question Development, Writing Background/Literature Review: Application.
No full textThis Presentation was on wiring background/ Literature review and application
Maternal Aflatoxin Exposure and Birth Outcomes: A Study of Diets, Agricultural Practices and Nutrition in Rural Nepal
No full textAbstract: Despite significant reductions in child stunting over recent decades, 36% of children remain stunted in Nepal (2016). Poor linear growth can begin in utero and continue beyond the age of two years, making the first 1000 days of life a critical period for stunting prevention. Recent epidemiological studies suggest that exposure to aflatoxins could contribute to low weight at birth and postnatal stunting. However, study findings showing linkages between in utero aflatoxin exposure and adverse birth outcomes remain inconclusive, and factors contributing to widespread exposure to aflatoxin during pregnancy are inadequately understood. This dissertation contributes to the evidence-base to inform the design of aflatoxin reduction interventions and a better understanding of the potential influence of aflatoxin exposure on adverse birth outcomes. All three studies used data from 1675 pregnant women and newborns participating in the ongoing USAID-funded Mycotoxin (AflaCohort) Birth Cohort Study in Banke, Nepal. In Study 1, we estimated pregnant women's frequency of consumption of aflatoxin-prone foods (i.e. maize and groundnuts) and calculated dietary diversity scores. Ordinary Least Squares (OLS) and Quantile Regression models were used to compare the strength of associations between frequencies of consumption of maize and groundnuts and dietary diversity, and serum aflatoxin levels (n=1648). After adjusting for wealth and other covariates, women who had consumed maize and/or groundnuts more frequently showed higher levels of aflatoxin albumin adducts. Findings indicated that dietary diversity was not predictive of aflatoxin exposure. Seasonality was a strong predictor of prenatal aflatoxin exposure, with the highest levels seen in the winter months following maize and groundnut harvest seasons. The second study examined the correlations between food handling procedures and good agricultural practices (GAPs) in maize, groundnut and chili farming households, and aflatoxin exposure as measured by aflatoxin albumin adducts during pregnancy. Multivariate OLS regression modeling revealed no evidence that the GAPs used in a minority of maize farming households (n=392) were associated with reduced exposure of pregnant women to aflatoxin in this sample. The infrequent use of recommended GAPs may have limited our ability to detect such an association. Moreover, off-farm food acquisition was common. Levels of aflatoxin exposure observed in this study likely reflect consumption of various foods susceptible to aflatoxin from multiple sources. Study 3 used linear and logistic regression models to explore the relationship between prenatal aflatoxin levels and selected adverse birth outcomes in a sub-sample of 1621 mother-newborn pairs. Twenty percent of infants were low birth weight, 52% small-for-gestational-age, 16% stunted, and 13% were born prematurely. None of the birth outcomes studied were associated with maternal aflatoxin levels, which were considerably lower than those observed in Africa and the Middle East where a relationship with low birth weight has been previously documented. Together, the results presented in this dissertation underscore the importance of viewing aflatoxin contamination as a component of food safety within complex food systems. Our study, together with the mixed results from previous studies, reiterates how incomplete the evidence of the relationship between aflatoxin and birth outcomes remains at this point. It also suggests that additional research is necessary to elucidate the aflatoxin-fetal growth relationship, including determination of threshold values.Thesis (Ph.D.)--Tufts University, 2018.Submitted to the Dept. of Food Policy & Applied Nutrition.Advisor: Patrick Webb.Committee: Shibani Ghosh, Beatrice Rogers, and Gerald Shively.Keywords: Nutrition, Public health, and Agriculture
Inverse Problem for the Yard-Sale Model of Wealth Distribution
No full textAbstract: Wealth inequality has become one of the most pressing issues throughout the world today. Among the different approaches trying to understand wealth inequality, "Econophysics" applies methods from physics to establish microscopic models from which macroeconomic wealth distributions can be derived. Asset Exchange Models (AEMs) are some of the most successful models of this kind. In this thesis, we study a particular AEM called the Yard-Sale Model (YSM). In Chapter 2, we further extend the basic YSM to include redistribution and Wealth-Attained Advantage (WAA), and we derive a Fokker-Planck equation for the resulting Extended Yard-Sale Model (EYSM). In Chapter 3, we discuss the numerical method we use to solve for steady-state solutions to the EYSM. In Chapter 4, we mainly discuss the existence of a "duality" symmetry between the supercritical and the subcritical solutions to the EYSM. In Chapter 5, we further extend the EYSM so that it allows for agents with negative wealth, which are important empirically. We show how to derive and solve the Fokker-Planck equation for the resulting Affine Wealth Model (AWM) based on the tools and methods we used for EYSM. Finally in Chapter 6, we compare our results with U.S. wealth distribution data from the Survey of Consumer Finances (SCF). We first demonstrate the superiority of the AWM to the other models for its remarkable faithfulness to the empirical data . We also compare our model results from the AWM to U.S. wealth distribution data from 1989 to 2016. We argue that the time series of model parameters thus obtained provides a valuable new diagnostic tool for analyzing wealth inequality.Thesis (Ph.D.)--Tufts University, 2018.Submitted to the Dept. of Mathematics.Advisor: Bruce Boghosian.Committee: Xiaozhe Hu, Christoph B\"ogers, and Bikas Chakrabarti.Keywords: Mathematics, Physics, and Economics
Early identification of dyslexia and word reading development: Using cognitive and linguistic assessments to predict reading skill growth in early elementary school
No full textAbstract: Theories of and research on the etiology of dyslexia consistently point to two primary deficits of phonology and automaticity. Previous research has found that performance on early assessments of phonological awareness and automaticity are predictive of reading abilities in early elementary school. This study used secondary data from 161 students who were followed prior to beginning elementary school through the end of second grade. These students were assessed in each grade using a variety of cognitive and linguistic measures. Multi-level models were estimated to assess the growth rates of word identification and to compare growth rates for students who performed poorly on assessments of phonological awareness and automaticity before beginning elementary school. Further analyses were conducted to assess where the availability of curriculum that incorporated explicit phonics instruction moderated the relationship between performance on assessments of phonological awareness and automaticity before beginning elementary school and growth rates of word identification. Students who performed in the bottom quartile of a normed sample on measures of phonological awareness or phonological awareness and automaticity upon or prior to entering kindergarten had significantly lower rates of growth on word identification than their typically developing peers during the scope of the study. The availability of curriculum that incorporated explicit phonics instruction was not predictive of significant differences in the word identification growth rates for students who performed poorly on assessments of phonological awareness and automaticity before beginning elementary school. These findings bolster support for the predictive nature of phonological awareness and automaticity and indicated that deficits in these areas can be detected before a child begins formal education.Thesis (M.A.)--Tufts University, 2018.Submitted to the Dept. of Child Development.Advisor: Calvin Gidney.Committee: Sara Johnson, and Ola Ozernov-Palchik.Keywords: Developmental psychology, and Educational psychology