Food group intake patterns and nutrient intake vary across low-income Hispanic and African American preschool children in Atlanta: a cross sectional study

ABSTRACT: BACKGROUND: The food group intake patterns of low income Hispanic and African American preschool children are not well documented. The aim of this study was to perform a food group intake analysis of low income minority preschool children and evaluate how macronutrient and micronutrient intake compares to Dietary Reference Intakes (DRI). METHODS: A cross sectional study design using three-day food diaries analyzed by dietary analysis software (Nutrient Database System for Research) was used. Children were recruited from well-child clinics at Children's Healthcare of Atlanta at Hughes Spalding and North Dekalb Grady Satellite Clinic, Atlanta, GA. Low-income, African American and Hispanic preschool age children (n = 291) were enrolled. A total of 105 completed the 3-day food diaries were returned and analyzed. Chi-squared tests were used to assess demographic variables. The mean percentage of intake per day of specific food groups and sub-groups were obtained (servings of given food group/total daily servings). Food intake data and proportion of children meeting DRIs for macro- and micronutrients were stratified by race/ethnicity, nutritional status, and caloric intake, and were compared using t-tests. Regression models controlling for age, BMI and sex were obtained to assess the effect of total caloric intake upon the proportional intake of each studied food group. RESULTS: The mean age of African American children was 2.24 [PLUS-MINUS SIGN] 1.07 years and Hispanic children 2.84 [PLUS-MINUS SIGN] 1.12 years. African Americans consumed more kcal/kg/day than Hispanics (124.7 [PLUS-MINUS SIGN] 51 vs. 96.9 [PLUS-MINUS SIGN] 33, p < 0.05). Hispanics consumed more fruits (22.0 [PLUS-MINUS SIGN] 10.7% vs. 14.7 [PLUS-MINUS SIGN] 13.7%, p < 0.05), while African Americans consumed more grains (25.7 [PLUS-MINUS SIGN] 7.8% vs. 18.1 [PLUS-MINUS SIGN] 6.4%, p < 0.05), meats (20.7 [PLUS-MINUS SIGN] 9.0% vs. 15.4 [PLUS-MINUS SIGN] 6.1%, p < 0.05), fats (9.8 [PLUS-MINUS SIGN] 5.4% vs. 7.0 [PLUS-MINUS SIGN] 5.8%, p < 0.05), sweet drinks (58.7 [PLUS-MINUS SIGN] 17.1% vs. 41.3 [PLUS-MINUS SIGN] 14.8%, p < 0.05) and low-fat dairy products (39.5 [PLUS-MINUS SIGN] 19.3% vs. 28.9 [PLUS-MINUS SIGN] 12.6%, p < 0.05). Among Hispanics, the proportional intake of fruits, fats and grains varied by total caloric intake, while no difference by total caloric intake was found for the dietary patterns of African Americans. Micronutrient intake also differed significantly between African American and Hispanic children. CONCLUSIONS: Food group intake patterns among low-income children differ by ethnic group. There is a need for more research to guide program design and target nutritional interventions for this population.     

Phase Transformations and Grain Growth in β-stabilized TiAl Alloys

Solid-state phase transformations and grain growth of an intermetallic γ-TiAl alloy were investigated in-situ using high-temperature laser scanning confocal microscopy (HTLSCM). During isothermal annealing in the single β-phase region, significant grain coarsening was observed. On cooling beneath the β-transus temperature with different rates, a CCT diagram was evaluated for the initiation of β to α phase transformation and changes in the morphology were observed.     

The influence of third-body particles on wear rate in unicondylar knee arthroplasty: a wear simulator study with bone and cement debris

The reduced intraoperative visibility of minimally invasive implanted unicondylar knee arthroplasty makes it difficult to remove bone and cement debris, which have been reported on the surface of damaged and retrieved bearings. Therefore, the aim of this study was to analyze the influence of bone and cement particles on the wear rate of unicompartmental knee prostheses in vitro. Fixed bearing unicompartmental knee prostheses were tested using a knee-wear-simulator according to the ISO standard 14243-1:2002(E) for 5.0 million cycles. Afterwards bone debris (particle size 671 ± 262 μm) were added to the test fluid in a concentration of 5 g/l for 1.5 million cycles, followed by 1.5 million cycles blended with cement debris (particle size 644 ± 186 μm) in the same concentration. Wear rate, knee-kinematics and wear-pattern were analyzed. The wear rate reached 12.5 ± 1.0 mm³/million cycles in the running-in and decreased during the steady state phase to 4.4 ± 0.91 mm³/million cycles. Bone particles resulted in a wear rate of 3.0 ± 1.27 mm³/million cycles with no influence on the wear rate compared to the steady state phase. Cement particles, however, lead to a significantly higher wear rate (25.0 ± 16.93 mm³/million cycles) compared to the steady state phase. The careful removal of extruded cement debris during implantation may help in reducing wear rate. Bone debris are suggested to have less critical influence on the prostheses wear rate.     

Through Process Modelling of Surface Densified PM Gears

Through process modelling of surface densified gears produced by powder metallurgy (PM) was established by coupling the modelling of the manufacturing processes surface densification, carburization, and heat treatment. The complete model allows the prediction of the local microstructure and hardness in the gear as well as the appearance and direction of residual stresses in the final part. The structural integrity of the part is governed, on the one hand, by the local material properties and residual stresses and, on the other hand, by the load stresses calculated for typical operating conditions. A simplified hardness dependent Haigh diagram was used to calculate the maximum allowable cyclic stresses for the gear tooth and to derive a local utilization ratio as target entity for optimization of the individual steps of the production chain.     

Data Mining for Vehicle Telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.     

Computer vision for improved estimates of SO2 emission rates and plume dynamics

Imaging cameras operating at ultraviolet (UV) and infrared (IR) wavelengths can measure sulphur dioxide (SO₂) gas path concentrations or slant column densities. These measurements are useful in a variety of applications including the monitoring of emissions from volcanoes and also emissions from stacks at industrial plants and on ships. The usefulness of these data is increased if the emission rates (or fluxes) of the gases can also be estimated. Here we present an optical flow algorithm that allows rapid and accurate estimates of emission rates using both UV and IR camera imagery sampling at around 1 Hz or higher. Examples are provided from measurements made at Turrialba volcano, Costa Rica, and also at a ship in Hong Kong harbour. Other aspects of the properties of the fluid flow are also introduced, notably the divergence and the vorticity of the two-dimensional wind field. We demonstrate how the divergence can be used in a new method to calculate the emission rate and show how rotational effects observed in volcanic plumes and the resulting entrainment of ambient air affects plume rise and can be observed using vorticity. This is an important aspect for understanding the emplacement of gases and particles into the atmosphere that are subsequently transported by atmospheric winds, sometimes causing pollution episodes at long distances from the source.