Dietary starch contains rapidly (RAG) and slowly available glucose (SAG). To establish the relationships between the RAG:SAG ratio and postprandial glucose, insulin and hunger, we measured postprandial responses elicited by test meals varying in the RAG:SAG ratio in n 160 healthy adults, each of whom participated in one of four randomised cross-over studies (n 40 each): a pilot trial comparing six chews (RAG:SAG ratio 2·4–42·7) and three studies comparing a test granola (TG1-3, RAG:SAG ratio 4·5–5·2) with a control granola (CG1–3, RAG:SAG ratio 54·8–69·3). Within studies, test meals were matched for fat, protein and available carbohydrate. Blood glucose, serum insulin and subjective hunger were measured for 3 h. Data were subjected to repeated-measures analysis of variance (ANOVA). The relationships between the RAG:SAG ratio and postprandial end points were determined by regression analysis. In the pilot trial, 0–2 h glucose incremental areas under the curve (iAUC0–2; primary end point) varied across the six chews (P = 0·014) with each 50 % reduction in the RAG:SAG ratio reducing relative glucose response by 4·0 %. TGs1-3 elicited significantly lower glucose iAUC0–2 than CGs1–3 by 17, 18 and 17 %, respectively (similar to the 15 % reduction predicted by the pilot trial). The combined means ± sem (n 120) for TC and CG were glucose iAUC0–2, 98 ± 4 v. 118 ± 4 mmol × min/l (P < 0·001), and insulin iAUC0–2, 153 ± 9 v. 184 ± 11 nmol × h/l (P < 0·001), respectively. Neither postprandial hunger nor glucose or hunger increments 2 h after eating differed significantly between TG and CG. We concluded that TGs with RAG:SAG ratios <5·5 predictably reduced glycaemic and insulinaemic responses compared with CGs with RAG:SAG ratios >54. However, compared with CG, TG did not reduce postprandial hunger or delay the return of glucose or hunger to baseline.
The labor-intensive nature of expert system writing and debugging has motivated this study. Our hypothesis is that a hypertext based debugging tool is easier and faster than one traditional tool, the graphical execution trace.HESDE (Hypertext Expert System Debugging Environment) uses Hypertext nodes and links to represent the objects and their relationships created during the execution of a rule based expert system. HESDE operates transparently on top of the CLIPS rule based system environment and is used during the knowledge-base debugging process. During the execution process HESDE builds an execution trace. Use of facts, rules, and their values are automatically stored in a Hypertext network for each execution cycle. After the execution process the knowledge engineer may access the Hypertext network and browse the network created. The network may be viewed in terms of rules, facts and values.An experiment was conducted to compare HESDE with a graphical debugging environment Subjects were given representative tasks. For speed and accuracy, in eight of the eleven tasks given to subjects HESDE was significantly better.
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