Electrospun silk-elastin-like fiber mats for tissue engineering applications

Protein-based polymers are present in a wide variety of organisms fulfilling structural and mechanical roles. Advances in protein engineering and recombinant DNA technology allow the design and production of recombinant protein-based polymers (rPBPs) with an absolute control of its composition. Although the application of recombinant proteins as biomaterials is still an emerging technology, the possibilities are limitless and far superior to natural or synthetic materials, as the complexity of the structural design can be fully customized. In this work, we report the electrospinning of two new genetically engineered silk-elastin-like proteins (SELPs) consisting of alternate silk- and elastin-like blocks. Electrospinning was performed with formic acid and aqueous solutions at different concentrations without addition of further agents. The size and morphology of the electrospun structures was characterized by scanning electron microscopy showing to be dependent of concentration and solvent used. Treatment with air saturated with methanol was employed to stabilize the structure and promote water insolubility through a time-dependent conversion of random coils into β-sheets (FTIR). The resultant methanol-treated electrospun mats were characterized for swelling degree (570-720%), water vapour transmission rate (1083 g/m2/day) and mechanical properties (modulus of elasticity of ~126 MPa). Furthermore, the methanol-treated SELP fiber mats showed no cytotoxicity and were able to support adhesion and proliferation of normal human skin fibroblasts. Adhesion was characterized by a filopodia-mediated mechanism. These results demonstrate that SELP fiber mats can provide promising solutions for the development of novel biomaterials suitable for tissue engineering applications.

Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets

Quantitative analysis of cine cardiac magnetic resonance (CMR) images for the assessment of global left ventricular morphology and function remains a routine task in clinical cardiology practice. To date, this process requires user interaction and therefore prolongs the examination (i.e. cost) and introduces observer variability. In this study, we sought to validate the feasibility, accuracy, and time efficiency of a novel framework for automatic quantification of left ventricular global function in a clinical setting.