Chitosan gel film bandages: correlating structure, composition, and antimicrobial properties

Chitosan gel films were successfully obtained by evaporation cast from chitosan solutions in aqueous acidic solutions of organic acids (lactic and acetic acid) as gel film bandages, with a range of additives that directly influence film morphology and porosity. We show that the structure and composition of a wide range of 128 thin gel films, is correlated to the antimicrobial properties, their biocompatibility and resistance to biodegradation. Infrared spectroscopy and solid-state 13C nuclear magnetic resonance spectroscopy was used to correlate film molecular structure and composition to good antimicrobial properties against 10 of the most prevalent Gram positive and Gram negative bacteria. Chitosan gel films reduce the number of colonies after 24 h of incubation by factors of ∼105–107 CFU/mL, compared with controls. For each of these films, the structure and preparation condition has a direct relationship to antimicrobial activity and effectiveness. These gel film bandages also show excellent stability against biodegradation with lysozyme under physiological conditions (5% weight loss over a period of 1 month, 2% in the first week), allowing use during the entire healing process. These chitosan thin films and subsequent derivatives hold potential as low-cost, dissolvable bandages, or second skin, with antimicrobial properties that prohibit the most relevant intrahospital bacteria that infest burn injuries.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.