Binding of the molecular chaperone αB-crystallin to Aβ amyloid fibrils inhibits fibril elongation.

The molecular chaperone αB-crystallin is a small heat-shock protein that is upregulated in response to a multitude of stress stimuli, and is found colocalized with Aβ amyloid fibrils in the extracellular plaques that are characteristic of Alzheimer's disease. We investigated whether this archetypical small heat-shock protein has the ability to interact with Aβ fibrils in vitro. We find that αB-crystallin binds to wild-type Aβ(42) fibrils with micromolar affinity, and also binds to fibrils formed from the E22G Arctic mutation of Aβ(42). Immunoelectron microscopy confirms that binding occurs along the entire length and ends of the fibrils. Investigations into the effect of αB-crystallin on the seeded growth of Aβ fibrils, both in solution and on the surface of a quartz crystal microbalance biosensor, reveal that the binding of αB-crystallin to seed fibrils strongly inhibits their elongation. Because the lag phase in sigmoidal fibril assembly kinetics is dominated by elongation and fragmentation rates, the chaperone mechanism identified here represents a highly effective means to inhibit fibril proliferation. Together with previous observations of αB-crystallin interaction with α-synuclein and insulin fibrils, the results suggest that this mechanism is a generic means of providing molecular chaperone protection against amyloid fibril formation.

Model based control for closed loop testing of 1-D engine simulation models

1-D engine simulation models are widely used for the analysis and verification of air-path design concepts and prediction of the resulting engine transient response. The latter often requires closed loop control over the model to ensure operation within physical limits and tracking of reference signals. For this purpose, a particular implementation of Model Predictive Control (MPC) based on a corresponding Mean Value Engine Model (MVEM) is reported here. The MVEM is linearised on-line at each operating point to allow for the formulation of quadratic programming (QP) problems, which are solved as the part of the proposed MPC algorithm. The MPC output is used to control a 1-D engine model. The closed loop performance of such a system is benchmarked against the solution of a related optimal control problem (OCP). As an example this study is focused on the transient response of a light-duty car Diesel engine. For the cases examined the proposed controller implementation gives a more systematic procedure than other ad-hoc approaches that require considerable tuning effort. © 2012 IFAC.