Evaluation of effectiveness of class-based nutrition intervention on changes in soft drink and milk consumption among young adults

<p>Abstract</p> <p>Background</p> <p>During last few decades, soft drink consumption has steadily increased while milk intake has decreased. Excess consumption of soft drinks and low milk intake may pose risks of several diseases such as dental caries, obesity, and osteoporosis. Although beverage consumption habits form during young adulthood, which has a strong impact on beverage choices in later life, nutrition education programs on beverages are scarce in this population. The purpose of this investigation was 1) to assess soft drink and milk consumption and 2) to evaluate the effectiveness of 15-week class-based nutrition intervention in changing beverage choices among college students.</p> <p>Methods</p> <p>A total of 80 college students aged 18 to 24 years who were enrolled in basic nutrition class participated in the study. Three-day dietary records were collected, verified, and analyzed before and after the intervention. Class lectures focused on healthful dietary choices related to prevention of chronic diseases and were combined with interactive hands on activities and dietary feedback.</p> <p>Results</p> <p>Class-based nutrition intervention combining traditional lecture and interactive activities was successful in decreasing soft drink consumption. Total milk consumption, specifically fat free milk, increased in females and male students changed milk choice favoring skim milk over low fat milk. (1% and 2%).</p> <p>Conclusion</p> <p>Class-based nutrition education focusing on prevention of chronic diseases can be an effective strategy in improving both male and female college students' beverage choices. Using this type of intervention in a general nutrition course may be an effective approach to motivate changes in eating behaviors in a college setting.</p

On the use of a PM 2.5 exposure simulator to explain birthweight

In relating pollution to birth outcomes, maternal exposure has usually been described using monitoring data. Such characterization provides a misrepresentation of exposure as (i) it does not take into account the spatial misalignment between an individual's residence and monitoring sites, and (ii) it ignores the fact that individuals spend most of their time indoors and typically in more than one location. In this paper, we break with previous studies by using a stochastic simulator to describe personal exposure (to particulate matter) and then relate simulated exposures at the individual level to the health outcome (birthweight) rather than aggregating to a selected spatial unit. We propose a hierarchical model that, at the first stage, specifies a linear relationship between birthweight and personal exposure, adjusting for individual risk factors and introduces random spatial effects for the census tract of maternal residence. At the second stage, we specify the distribution of each individual's personal exposure using the empirical distribution yielded by the stochastic simulator as well as a model for the spatial random effects. We have applied our framework to analyze birthweight data from 14 counties in North Carolina in years 2001 and 2002. We investigate whether there are certain aspects and time windows of exposure that are more detrimental to birthweight by building different exposure metrics which we incorporate, one by one, in our hierarchical model. To assess the difference in relating ambient exposure (i.e., exposure to ambient concentration) to birthweight versus personal exposure to birthweight, we compare estimates of the effect of air pollution obtained from hierarchical models that linearly relate ambient exposure and birthweight to those obtained from our modeling framework. Our analysis does not show a significant effect of PM 2.5 on birthweight for reasons which we discuss. However, our modeling framework serves as a template for general analyses of the relationship between personal exposure and longer term health endpoints. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84415/1/1086_ftp.pd

Particle Mobility Analysis Using Deep Learning and the Moment Scaling Spectrum

Quantitative analysis of dynamic processes in living cells using time-lapse microscopy requires not only accurate tracking of every particle in the images, but also reliable extraction of biologically relevant parameters from the resulting trajectories. Whereas many methods exist to perform the tracking task, there is still a lack of robust solutions for subsequent parameter extraction and analysis. Here a novel method is presented to address this need. It uses for the first time a deep learning approach to segment single particle trajectories into consistent tracklets (trajectory segments that exhibit one type of motion) and then performs moment scaling spectrum analysis of the tracklets to estimate the number of mobility classes and their associated parameters, providing rich fundamental knowledge about the behavior of the particles under study. Experiments on in-house datasets as well as publicly available particle tracking data for a wide range of proteins with different dynamic behavior demonstrate the broad applicability of the method.Optical and Laser Remote Sensin

Tolerance of thermophilic and hyperthermophilic microorganisms to desiccation

We examined short- and long-term desiccation tolerance of 31 strains of thermophilic and hyperthermophilic Archaea and thermophilic phylogenetically deep-branching Bacteria. Seventeen organisms showed a significant high ability to withstand desiccation. The desiccation tolerance turned out to be species-specific and was influenced by several parameters such as storage temperature, pH, substrate or presence of oxygen. All organisms showed a higher survival rate at low storage temperatures (-20 degrees C or below) than at room temperature. Anaerobic and microaerophilic strains are influenced negatively in their survival by the presence of oxygen during desiccation and storage. The desiccation tolerance of Sulfolobales strains is co-influenced by the pH and the substrate of the pre-culture. The distribution of desiccation tolerance in the phylogenetic tree of life is not domain specific. Surprisingly, there are dramatic differences in desiccation tolerance among organisms from the same order and even from closely related strains of the same genus. Our results show that tolerance of vegetative cells to desiccation is a common phenomenon of thermophilic and hyperthermophilic microorganisms although they originated from quite different non-arid habitats like boiling acidic springs or black smoker chimneys