Do Soup Kitchen Meals Contribute to Suboptimal Nutrient Intake & Obesity in the Homeless Population?

The double burden of suboptimal nutrient intake and obesity exists when available foods lack essential nutrients to promote health and provide high amounts of energy. This study evaluated the nutrition content of 41 meals served to the homeless at 3 urban soup kitchens. The mean nutrient content of all meals and of meals from each of the kitchens was compared to two-thirds of the estimated average requirement (EAR). The mean nutrient content of the meals did not provide two-thirds of the EAR for energy, vitamin C, magnesium, zinc, dietary fiber, or calcium but provided 11.8% of calories from saturated fat. On average one meal did not meet homeless individuals’ estimated requirements; however, 2 meals did meet estimated requirements but provided inadequate fiber and high amounts of energy, saturated fat, and sodium. Soup kitchen meals may contribute to the high prevalence of obesity and chronic disease reported in the homeless, food insecure population

Food Recovery Program at Farmers’ Markets Increases Access to Fresh Fruits and Vegetables for Food Insecure Individuals

Food recovery programs are an increasingly popular method to decrease the amount of food waste in the United States while increasing healthy food access for food insecure individuals. One such program is the Heartside Gleaning Initiative that began in 2014 in Grand Rapids, Michigan. The program partners with low-income volunteers to collect fresh produce from farmers’ markets for redistribution. From June to October 2014, 17,000 pounds of fresh produce was provided to low-income individuals and food pantries. A survey of participants indicated high usage of the produce and high satisfaction with the gleaning program. This program confirms that gleaning is a viable method of reducing barriers to accessing produce for food pantries and food insecure individuals

A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation

Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.Comment: Published in at http://dx.doi.org/10.1214/12-STS406 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Data report : hypoxia in the York River, 1988-1989

As part of the hypoxia program of the Virginia Chesapeake Bay Initiatives, the Division of Physical Oceanography of the Virginia Institute of Marine Science (VIMS) conducted a series of measurements in the York River estuary. The measurements were made in summer, 1988 and 1989. Two types of measurements were conducted in each year: measurements at moored stations and measurements by slackwater surveys. The former collected data for investigation of dissolved oxygen (DO) variation, and associated physical parameters, in an intratidal time scale, as well as for studying the vertical distributions of the measured parameters. The latter collected data for spatial distributions of DO, temperature, and salinity, and for investigation of temporal variation over the summer. This data report describes the field measurements and provides graphical presentation of the data. The numerical values of the data are archived and stored on magnetic tapes, which may be retrieved through the VIMS computer system