Enhancing Osteoconduction of PLLA-Based Nanocomposite Scaffolds for Bone Regeneration Using Different Biomimetic Signals to MSCs

In bone engineering, the adhesion, proliferation and differentiation of mesenchymal stromal cells rely on signaling from chemico-physical structure of the substrate, therefore prompting the design of mimetic “extracellular matrix”-like scaffolds. In this study, three-dimensional porous poly-L-lactic acid (PLLA)-based scaffolds have been mixed with different components, including single walled carbon nanotubes (CNT), micro-hydroxyapatite particles (HA), and BMP2, and treated with plasma (PT), to obtain four different nanocomposites: PLLA + CNT, PLLA + CNTHA, PLLA + CNT + HA + BMP2 and PLLA + CNT + HA + PT. Adult bone marrow mesenchymal stromal cells (MSCs) were derived from the femur of orthopaedic patients, seeded on the scaffolds and cultured under osteogenic induction up to differentiation and mineralization. The release of specific metabolites and temporal gene expression profiles of marrow-derived osteoprogenitors were analyzed at definite time points, relevant to in vitro culture as well as in vivo differentiation. As a result, the role of the different biomimetic components added to the PLLA matrix was deciphered, with BMP2-added scaffolds showing the highest biomimetic activity on cells differentiating to mature osteoblasts. The modification of a polymeric scaffold with reinforcing components which also work as biomimetic cues for cells can effectively direct osteoprogenitor cells differentiation, so as to shorten the time required for mineralization

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs. However, the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems. In this paper, we investigate the influence of the penalty factor on PBI, and suggest two new penalty schemes, i.e., adaptive penalty scheme and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto-optimal solutions. The new penalty schemes are examined on several complex MOPs, showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one. The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence. Experimental results show that it can significantly enhance the algorithm�s performance. © 2016, Springer-Verlag Berlin Heidelberg

Constrained Multi-Objective Optimization Using Steady State Genetic Algorithms

In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs