Performance of a UV-A LED system for degradation of aflatoxins B1 and M1 in pure water: kinetics and cytotoxicity study

The efficacy of a UV-A light emitting diode system (LED) to reduce the concentrations of aflatoxin B1, aflatoxin M1 (AFB1, AFM1) in pure water was studied. This work investigates and reveals the kinetics and main mechanism(s) responsible for the destruction of aflatoxins in pure water and assesses the cytotoxicity in liver hepatocellular cells. Irradiation experiments were conducted using an LED system operating at 365 nm (monochromatic wave-length). Known concentrations of aflatoxins were spiked in water and irradiated at UV-A doses ranging from 0 to 1,200 mJ/cm2. The concentration of AFB1 and AFM1 was determined by HPLC with fluorescence detection. LC–MS/MS product ion scans were used to identify and semi-quantify degraded products of AFB1 and AFM1. It was observed that UV-A irradiation significantly reduced aflatoxins in pure water. In comparison to control, at dose of 1,200 mJ/cm2 UV-A irradiation reduced AFB1 and AFM1 concentrations by 70 ± 0.27 and 84 ± 1.95%, respectively. We hypothesize that the formation of reactive species initiated by UV-A light may have caused photolysis of AFB1 and AFM1 molecules in water. In cell culture studies, our results demonstrated that the increase of UV-A dosage decreased the aflatoxins-induced cytotoxicity in HepG2 cells, and no significant aflatoxin-induced cytotoxicity was observed at UV-A dose of 1,200 mJ/cm2. Further results from this study will be used to compare aflatoxins detoxification kinetics and mechanisms involved in liquid foods such as milk and vegetable oils

A Prospective study on the assessment of risk factors for type 2 diabetes mellitus in outpatients department of a south Indian tertiary care hospital: A case-control study

Background: Type 2 diabetes mellitus (T2DM) is the most general type of diabetes. In India, the risk factors (modifiable and nonmodifiable) for diabetes are seen more frequently and there is lack of perception about this problem.Objective: The objective of the study was to assess the incidence and risk factors for T2DM in a south Indian tertiary care hospital.Materials and Methods: A prospective study was conducted on 1161 subjects (with or without T2DM) from November 2014 to April 2015 in general medicine department of Dr. Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation, Andhra Pradesh, south India. Chi-square test was used to evaluate the incidence of T2DM and odds ratios were calculated in univariate logistic regression analysis for risk factors.Results: T2DM was significantly higher in the subjects of age above 41 years (86.3%, P<0.0001), married (95.4%, P=0.002), educators (degree and above, 13.2%, P<0.0001), known family history (50.8%, P<0.0001), BMI (>25 kg/m2,58.7%; P<0.0001), Govt. job holders (5.5%, P<0.0001), business people (12%, P<0.0001), house wives (38.3%, P<0.0001), high economic status (34.9%, P<0.0004), preexisting hypertension (40.2%, P<0.0001), urban residence (50.4%, P<0.0001), physical inactivity (45.3%, P<0.001), stress (61.0%, P=0.01), consumption of tea and coffee (daily thrice or more, 6.3%, P=0.0003), soft drinks (weekly thrice or more, 4%, P=0.0008) and junk foods (weekly thrice or more 2.6%, P=0.025) than non-diabetic subjects. Univariate logistic regression analysis showed that the age (above 41 years), marital status, education, family history, BMI (>25 kg/m2), high economic status, co-morbidities (hypertension and thyroid disorders) urban residence, physical inactivity, stress, consumption of tea and coffee (daily thrice or more), soft drinks (weekly thrice or more) and junk foods are the significantly risk factors for T2DM.Conclusion: The present study results suggested that beware of hypertension, thyroids disorders, physical inactivity, stress, soft drinks and junk foods, which are major risk factors of T2DM.Â

Consumer Preferences and Willingness to Pay for Advanced Vehicle Technology Options and Fuel Types

At the time of publication J. Shin and C.R. Bhat were at the University of Texas at Ausitn. V.M. Garikapati and D. You at Arizona State University, and R.M. Pendyala at Georgia Institute of Technology.The automotive industry is witnessing a revolution with the advent of advanced vehicular technologies, smart vehicle options, and fuel alternatives. However, there is very limited research on consumer preferences for these types of vehicles. But the deployment and penetration of advanced vehicular technologies in the marketplace, and planning for possible market adoption scenarios, calls for collection and analysis of consumer preference data related to these emerging technologies. This study aims to address this gap, offering a detailed analysis of consumer preference for alternative fuel types and technology options using data collected in choice experiments conducted on a sample of consumers in South Korea. The results indicate that there is considerable heterogeneity in consumer preferences for various smart technology options such as wireless internet, vehicle connectivity, and voice command features, but relatively little heterogeneity in the preference for smart vehicle applications such as real-time traveler information on parking and traffic conditions.Civil, Architectural, and Environmental Engineerin

Understanding the Multiple Dimensions of Residential Choice

At the time of publication, X. Fu was at the Shanghai Jiao Tong University, C.R. Bhat at the University of Texas at Austin, R.M. Pendyala at Georgia Institute of Technology, S. Vladlamani and V.M Garikapati at Arizona State University.Residential choice may be characterized as a household’s simultaneous decisions of location, neighborhood, and dwelling. Traditional models do not account for the latent unmeasured constructs which capture individuals’ preferences for and attitudes towards residence and mode choice. This paper employs Bhat’s (2014) Generalized Heterogeneous Data Model (GHMD) to accommodate five inter-related residential choice dimensions, including residential location, neighborhood land-use pattern, public transportation availability, housing type, and dwelling ownership. Four latent variables including pro-driving, pro-public transportation, facility availability, and residential spaciousness are constructed to capture individuals’ attitudes towards travel modes and preferences for residential features. The inclusion of these latent constructs helps account for self-selection effects in residential choice processes. The determination of relationships among multiple dimensions of residential choice behavior, socio-demographics, and latent attitudes and preferences is critical to integrated land use – transport modeling and the formulation of policies as well as urban residential and neighborhood environments that cater to individual preferences and enhance quality of life.Civil, Architectural, and Environmental Engineerin

An Integrated Latent Construct Modeling Framework for Predicting Physical Activity Engagement and Health Outcomes

At the time of publication M.M Hoklas, S.K. Dubey, and C.R. Bhat were at the University of Texas at Austin. V.M Garikapati at Arizona State University, R.M. Pendyala at Georgia Institute of Technology, and D. Hyun You at Arizona State University.The health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns.Civil, Architectural, and Environmental Engineerin

A Comparison of Online and In-Person Activity Engagement: The Case of Shopping and Eating Meals

You're viewing a past Journal from the Good Systems Grand Challenge team at The University of Texas at Austin from February 2020.Office of the VP for Researc

Integrated Land Use-Transport Model System with Dynamic Time-Dependent Activity-Travel Microsimulation

The development of integrated land use-transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity-travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand-supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity-travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity-travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs