Immobilization of cell-binding peptides on poly-ε-caprolactone film surface to biomimic the peripheral nervous system.

Cell-material interactions are crucial for cell adhesion and proliferation on biomaterial surfaces. Immobilization of biomolecules leads to the formation of biomimetic substrates, improving cell response. We introduced RGD (Arg-Gly-Asp) sequences on poly-ε-caprolactone (PCL) film surfaces using thiol chemistry to enhance Schwann cell (SC) response. XPS elemental analysis indicated an estimate of 2-3% peptide functionalization on the PCL surface, comparable with carbodiimide chemistry. Contact angle was not remarkably reduced; hence, cell response was only affected by chemical cues on the film surface. Adhesion and proliferation of Schwann cells were enhanced after PCL modification. Particularly, RGD immobilization increased cell attachment up to 40% after 6 h of culture. It was demonstrated that SC morphology changed from round to very elongated shape when surface modification was carried out, with an increase in the length of cellular processes up to 50% after 5 days of culture. Finally RGD immobilization triggered the formation of focal adhesion related to higher cell spreading. In summary, this study provides a method for immobilization of biomolecules on PCL films to be used in peripheral nerve repair, as demonstrated by the enhanced response of Schwann cells.

Field programmable gate array based predictive control system for spacecraft rendezvous in elliptical orbits

Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd. Summary A field programmable gate array (FPGA) based model predictive controller for two phases of spacecraft rendezvous is presented. Linear time-varying prediction models are used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of the longer range manoeuvres, whilst a fixed and receding prediction horizon is used for fine-grained tracking at close range. The resulting constrained optimisation problems are solved using a primal-dual interior point algorithm. The majority of the computational demand is in solving a system of simultaneous linear equations at each iteration of this algorithm. To accelerate these operations, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft-core processor on the FPGA, on which the remainder of the system is implemented. Certain logic that can be hard-coded for fixed sized problems is implemented to be configurable online, in order to accommodate the varying problem sizes associated with the variable prediction horizon. The system is demonstrated in closed-loop by linking the FPGA with a simulation of the spacecraft dynamics running in Simulink on a PC, using Ethernet. Timing comparisons indicate that the custom implementation is substantially faster than pure embedded software-based interior point methods running on the same MicroBlaze and could be competitive with a pure custom hardware implementation.