The food multimix concept: potential of an innovative food based approach on nutritional status in pregnant women in resource-poor communities

RATIONALE & OBJECTIVES: The food multimix (FFM)concept states that limited food resources can be combined using scientific knowledge to meet nutrient needs of vulnerable groups at low cost utilizing the ‘nutrient strengths’ of individual or candidate foods in composite recipes within a cultural context. METHODS: The method employed the food-to-food approach for recipe development using traditional food ingredients. Recipes were subjected to proximate and micronutrient analysis and optimized to meet at tleast 40% of recommended daily intakes. End products including breads, porridge and soup were developed. RESULTS: FMM products were employed in a feeding trial among 120 healthy pregnant women in Gauteng, South Africa resulting in improvements in serum iron levels from baseline values of 14.59 (=/-7.67) umol/L and 14.02 (=/-8.13) umol/L for control and intervention groups (p=0.71), to 16.03 (=/-5.67) umol/L and 18.66 (=/-9.41) umol/L (p=0.19). The increases from baseline to post-intervention were however statistically significant within groups. Similarly Mean Cell Volume values improved from baseline as well as serum ferritin and transferritin levels. CONCLUSION: The FMM concept has potential value in feeding programs for vulnerable groups including pregnant and lactating mothers.

Automatic relative debugging of OpenMP programs

In this work we show how automatic relative debugging can be used to find differences in computation between a correct serial program and an OpenMP parallel version of that program that does not yield correct results. Backtracking and re-execution are used to determine the first OpenMP parallel region that produces a difference in computation that may lead to an incorrect value the user has indicated. Our approach also lends itself to finding differences between parallel computations, where executing with M threads produces expected results but an N thread execution does not (M, N > 1, M ≠ N). OpenMP programs created using a parallelization tool are addressed by utilizing static analysis and directive information from the tool. Hand-parallelized programs, where OpenMP directives are inserted by the user, are addressed by performing data dependence and directive analysis.