3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

In-situ development of self-defensive antibacterial biomaterials: phenol-g-keratin-EC based bio-composites with characteristics for biomedical applications

Recently, the development of highly inspired biomaterials with multi-functional characteristics has gained considerable attention, especially in biomedical, and other health-related areas of the modern world. It is well-known that the lack of antibacterial potential has significantly limited biomaterials for many challenging applications such as infection free wound healing and/or tissue engineering etc. In this perspective, herein, a series of novel bio-composites with natural phenols as functional entities and keratin-EC as a base material were synthesised by laccase-assisted grafting. Subsequently, the resulting composites were removed from their respective casting surfaces, critically evaluated for their antibacterial and biocompatibility features and information is also given on their soil burial degradation profile. In-situ synthesised phenol-g-keratin-EC bio-composites possess strong anti-bacterial activity against Gram-positive and Gram-negative bacterial strains i.e., B. subtilis NCTC 3610, P. aeruginosa NCTC 10662, E. coli NTCT 10418 and S. aureus NCTC 6571. More specifically, 10HBA-g-keratin-EC and 20T-g-keratin-EC composites were 100% resistant to colonisation against all of the aforementioned bacterial strains, whereas, 15CA-g-keratin-EC and 15GA-g-keratin-EC showed almost negligible colonisation up to a variable extent. Moreover, at various phenolic concentrations used, the newly synthesised composites remained cytocompatible with human keratinocyte-like HaCaT, as an obvious cell ingrowth tendency was observed and indicated by the neutral red dye uptake assay. From the degradation point of view, an increase in the degradation rate was recorded during their soil burial analyses. Our investigations could encourage greater utilisation of natural materials to develop bio-composites with novel and sophisticated characteristics for potential applications.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.