Compact saloplastic membranes of natural polysaccharides for soft tissue engineering

The regeneration of soft biological tissues requires new substitutes that exhibit mechanical properties similar to the native tissue. Herein, thin saloplastic membranes with tunable physical properties are prepared by complexation of chitosan and alginate solutions containing different concentrations of sodium chloride. Polyelectrolyte complexes (PECs) are transferred to flat Petri dishes for compaction into membrane shapes by sedimentation and solvent evaporation. All membranes are resistant to degradation by lysozyme and are stable in solutions with pH values between 1 and 13. Immersing the different membranes in new doping solutions of increasing salt concentrations triggers the typical saloplastic behavior, with a high water absorption and decrease of the rigidity and ultimate tensile strength. The range of such variations is tuned by the sodium chloride amount used in the synthesis: high salt concentrations increase water uptake and tensile moduli, while decreasing the ultimate strength. Cellular assays demonstrate high proliferation rates and viability of L929 fibroblasts seeded onto the most rigid membranes. The results validate the use of saloplastic membranes as soft tissue substitutes for future biomedical applications.

Equidistribution for higher-rank Abelian actions on Heisenberg nilmanifolds

2010 Mathematics Subject Classification: Primary: 37C85, 37A17, 37A45; Secondary: 11K36, 11L07.

Using students’ learning style to create effective learning groups in MCSCL environments

Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group.