Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

Engineered alpha-defensin peptides display antimicrobial activity against oral pathogens

Introduction: Human alpha defensins are a family of neutrophil-derived antimicrobial peptides also known as human neutrophil peptides (HNPs). The defensin family of peptides are characterised by six invariant cysteine residues forming three disulphide bridges. The formation of the correct disulphide pairs complicates the synthesis of full length human alpha defensin and limits its therapeutic potential as an antimicrobial peptide. Objectives: The aim of this study was to determine whether truncated alpha defensins displayed antimicrobial activity against a range of micro-organisms including oral pathogens. Methods: Engineered peptides were synthesised by solid-phase methods using standard Fmoc chemistry. Antibacterial assays were performed using a previously described ultra sensitive radial diffusion method. A total of five engineered defensin peptides and full length alpha defensin were tested for their sensitivity against eight micro-organisms, including Gram negative bacteria, Gram positive bacteria and fungal pathogens Results: Antimicrobial activity was identified as clear zones around peptide-containing wells. Zone diameters were used to calculate minimum inhibitory concentrations (MICs) for each peptide. There was considerable variability in the susceptibility of the micro-organisms to the truncated analogues. Bacillus subtilis and Enterococcus faecalis were sensitive to the majority of the engineered peptides whereas Staphylococcus aureus, Escherichia coli and Candida albicans displayed resistance (defined as an MIC of greater than 250 ug/ml) to the truncated defensins. Of the five engineered peptides synthesised, the 2-aminobenzoic acid (Abz)-containing analogues based on the C-terminal sequence of alpha defensin displayed MIC values closest to that of the full length defensin in 5 out of 8 micro-organisms studied. Conclusion: This study demonstrates that truncated alpha defensins display variable antimicrobial activity against a range of micro-organisms, including oral pathogens. The generation of truncated defensins without disulphide bridges simplifies their synthesis and increases their therapeutic potential.

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.