Advanced Transport Management System

Many people go to their workplace by bus, train (public transportation), etc. While travelling from public transportation the problem of heavy traffic or waiting time for the bus for a longer time may occur. Even though the bus’s arrival and departure time are schedule, but we can’t assure that the bus will always come on time. Hence to overcome the problem of time loss because of waiting at the bus stops, we implemented the smart tracking system. In this project, any passenger who is having Android app can have access to the bus. The passenger can register and sign up to receive information about desired bus arrival times for the interested buses and related routes via SMS/map. Even passenger can book the ticket as well as seat through Android app

Shiga Toxin Binding to Glycolipids and Glycans

Background: Immunologically distinct forms of Shiga toxin (Stx1 and Stx2) display different potencies and disease outcomes, likely due to differences in host cell binding. The glycolipid globotriaosylceramide (Gb3) has been reported to be the receptor for both toxins. While there is considerable data to suggest that Gb3 can bind Stx1, binding of Stx2 to Gb3 is variable. Methodology: We used isothermal titration calorimetry (ITC) and enzyme-linked immunosorbent assay (ELISA) to examine binding of Stx1 and Stx2 to various glycans, glycosphingolipids, and glycosphingolipid mixtures in the presence or absence of membrane components, phosphatidylcholine, and cholesterol. We have also assessed the ability of glycolipids mixtures to neutralize Stx-mediated inhibition of protein synthesis in Vero kidney cells. Results: By ITC, Stx1 bound both Pk (the trisaccharide on Gb3) and P (the tetrasaccharide on globotetraosylceramide, Gb4), while Stx2 did not bind to either glycan. Binding to neutral glycolipids individually and in combination was assessed by ELISA. Stx1 bound to glycolipids Gb3 and Gb4, and Gb3 mixed with other neural glycolipids, while Stx2 only bound to Gb3 mixtures. In the presence of phosphatidylcholine and cholesterol, both Stx1 and Stx2 bound well to Gb3 or Gb4 alone or mixed with other neutral glycolipids. Pre-incubation with Gb3 in the presence of phosphatidylcholine and cholesterol neutralized Stx1, but not Stx2 toxicity to Vero cells

Estimating associations between demographic, social and environmental factors, and physical activity on trails

Purpose: The purpose of this study was three fold: 1) to estimate associations between demographic, social, and environmental factors and the frequency and duration of physical activity on trails among adult trail users from Massachusetts; 2) to estimate associations between demographic, social, and environmental factors and the odds of using a trail for recreation versus transportation purposes; and 3) to estimate associations between demographic, social, and environmental factors and the odds of reporting an increase in physical activity since first use of the trail by adult trail users. Methods: The social ecological model was used as a conceptual framework for this study. Demographic, social, and environmental data were collected from a random sample of adult trail users at five different sites in Massachusetts during 2004 and 2005 using brief intercept surveys. Secondary data analyses were conducted to estimate associations between demographic, social, and environmental factors and physical activity on trails using logistic regression. Unadjusted, age adjusted and fully adjusted models, which controlled for age and other demographic factors, were estimated. Results: Demographic, social, and environmental variables were associated with the three outcomes. Demographic variables such as age and education demonstrated positive associations with frequency and duration of trail use and with the likelihood of using the trail for recreation/exercise versus transportation. Blacks/African Americans had an increased odds of using the trail for recreation/exercise in comparison to Whites. Use of a trail with family or friends was positively associated with duration of trail visits for recreation/exercise. About 59% of trail users reported an increase in their physical activity since first use of trail. Among environmental factors, trail design, trail safety, and good surface were associated with an increased likelihood of using the trail for recreation/exercise. Conclusion: A combination of demographic, social and environmental factors were associated with the volume of physical activity on trails (i.e, frequency and duration of visits), the purpose of trail use (i.e, recreation versus transportation), and with reported increases in physical activity since first using the trail. These findings contribute to the growing public health literature on trails and physical activity. Additionally, these findings support the use of an ecological framework to examine influences on trail use. Finally, findings such as those presented in this thesis could be used to design more effective interventions to promote trail use in adult populations and thereby help adults meet physical activity recommendations

Validation of a Commercial Geographical Information Systems Database of Walking and Bicycling Destinations

Background: Recent interdisciplinary studies in public health, transportation, and urban planning have shown that stores and other destinations such as banks, post offices, and physical activity facilities within close proximity to residences are positively related to recreational and transportation physical activity. The built environment has been measured several different ways, including conducting field audits and by surveying individuals’ perceptions of their neighborhood. Increasingly researchers are also using geographic information systems (GIS) software and commercially available data sources to create objective measures of the built environment. The advantages of commercial data are that they are relatively easy to access and are regularly updated. Despite these advantages it is important to assess the validity of these databases for developing measures of accessibility and density of neighborhood destinations. Two recent studies have investigated the validity of GIS databases of physical activity facilities and food stores, but to our knowledge less research has been conducted to validate a broader range of facilities that may serve as important walking and bicycling destinations. Objective: The objective was to assess the validity of a commercially-available GIS database of facilities that may serve as walking and bicycling destinations for adults. Methods: Researchers conducted field audits to verify the presence of 402 facilities contained in a commercial database. A list of North American Industrial Classification System codes was reviewed to identify the types of commercial facilities in the database which could serve as walking or bicycling destinations for adults. These were further categorized into five domains; food and drink (n=139), social or cultural organizations (n=115), retail establishments (n=101), services (n=28), and physical activity resources (n=19). Two high, medium, and low population density tracts in both Hartford County, Connecticut and Tippecanoe County, Indiana were selected for the analysis (12 tracts in total). Three levels of agreement were defined; 1) facilities in the database were considered to be an “exact match” if they were located on the same street segment and had the same proprietary name, 2) “close to exact match” if the facility was located on the street segment and was of the same domain, but with a different proprietary name, and 3) an “adjacent street segment match” if the facility was found to be located on an adjacent street segment. The percentages of facilities in the database that were located in the field were calculated overall, and by county, population density, and domain. Chi-square analyses were used to examine differences in match rates by county, population density, and type of facility. Results: Overall, among the 402 facilities examined, field audits identified 67.7% were an exact match. When the ‘close to exact matches’ were included the percentage matched increased to 76.9%, and with the addition of adjacent street segments it increased to 85.8%. Percent agreement for exact matches was higher in Tippecanoe County than Hartford County (71.5% vs. 63.9%). However when all three levels of matches were included the percent agreements for the two counties were more similar (86.5% vs. 85.1%). Overall, match rates were higher in high population density census tracts than in low population density tracts (71.0% vs. 60.6%). Among the five facility domains, the exact match rates were 64.0% for food and drink establishments, 64.3% for services, 67.3% for retail establishments, 70.4% for social and cultural organizations, and 84.2% for physical activity facilities. Overall, chi-square analyses did not show statistically significant differences in match rates by county, population density, or by domain. Conclusions: The results of this validation study demonstrated moderate to good accuracy of the commercial GIS database with more than two-thirds of the facilities correctly located in the field overall. The estimates generated in this study were similar to those in two previous validation studies of physical activity facilities and food stores which found agreement was 71%-73%. The findings in this study suggest that the commercially available GIS database provided a valid alternative to conducting extensive field audits or resident surveys