Eight normolipidemic males ingested a meal containing either 42 g fat or 31 g fat in the form of emulsions (9.0 and 9.2 m2) and a fixed amount of retinyl palmitate. Fasting and postmeal blood samples were obtained for 7 h. Serum and chylomicron triglyceride responses were related to the amount of fat ingested and peaked after 2-3 h. The chylomicron retinyl palmitate response was lower (P < or = 0.05) with the 31-g fat supply. After the 42-g fat intake, but not after the 31-g fat intake, serum free cholesterol and phospholipids increased and esterified cholesterol decreased postprandially. Significantly different responses were observed after both meals for low-density-lipoprotein (LDL) free cholesterol, very-low-density-lipoprotein (VLDL) and LDL esterified cholesterol, and high-density-lipoprotein (HDL) phospholipids. These data show that ingesting 31 g instead of 42 g fat in a meal reduces postmeal lipoprotein variations and suggest that a threshold level of dietary fat should be overcome to promote significant postprandial changes in lipoprotein particles.

Six normolipidemic males ingested on separate days a low-fiber test meal [2.8 g dietary fiber (TDF)] containing 70 g fat and 756 mg cholesterol, enriched or not with 10 g TDF as oat bran, rice bran, or wheat fiber or 4.2 g TDF as wheat germ. Fasting and postmeal blood samples were obtained for 7 h and chylomicrons were isolated. Adding fibers to the test meal induced no change in serum glucose or insulin responses. The serum triglyceride response was lower (P less than or equal to 0.05) in the presence of oat bran, wheat fiber, or wheat germ and chylomicron triglycerides were reduced with wheat fiber. All fiber sources reduced chylomicron cholesterol. Cholesterolemia decreased postprandially for 6 h and was further lowered in the presence of oat bran. Serum apolipoprotein (apo) A-1 and apo B concentrations were not affected. Thus, dietary fibers from cereals may reduce postprandial lipemia in humans to a variable extent.

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We show how the (initial) Luenberger methodology presented in [1] for linear systems can be used to design causal observers for controlled nonlinear systems. Their implementation relies on the resolution of a time-varying PDE, the solutions of which transform the dynamics into linear asymptotically stable ones. We prove the existence and injectivity (after a certain time) of such transformations, under standard observability assumptions such as differential observability or backwarddistinguishability. We show on examples how this PDE can be solved and how the observability assumptions can be checked. Also, we show that similarly to the high gain framework, it is possible to use a time-independent transformation when the system is observable for any input and strongly differentially observable of order the dimension of the system.

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We review the main design techniques of state observer design for continuous-time dynamical systems, namely algorithms which reconstruct online the full information of a dynamical process on the basis of partially measured data. Starting from necessary conditions for the existence of such asymptotic observers, we classify the available methods depending on the detectability/observability assumptions they require. We show how each class of observer relies on transforming the system dynamics in a particular normal form which allows the design of an observer, and how each observability condition guarantees the invertibility of its associated transformation and the convergence of the observer. Finally, some implementation aspects and open problems are briefly discussed.

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