Antimicrobial photodynamic therapy and its applicability in aquaculture systems and aquatic animal health management: An overview

Global aquaculture production in 2012 touched new high of 90.4 million tonnes including 66.6 million tonnes of food fish and 23.8 million tonnes of aquatic algae providing 19.2 kg per capita food fish suppy. Aquaculture is reported to suffer heavy production and financial losses due to fish infections caused by microbial pathogens. Therefore in order to make aquaculture industry more sustainable, effective strategies to control fish infections are urgently needed. Antimicrobial Photodynamic Therapy (aPDT) is an emerging, low-cost anti-microbial approach to the treatment of locally occurring infections and also for the treatment of aquaculture water and waste waters. Already proven effective in various medical and clinical applications, it utilizes three vital components: a photosensitizing agent (PS), a light source of an appropriate wave length and oxygen. aPDT has got a potential of being a preferred choice over antibiotics in aquaculture systems because of its non-target specificity, few side effects, lack of the pathogenicity reversal and re-growth of the micro-organism after treatment and the lack of development of resistance mechanisms. The technique has been proved effective in vitro against bacteria (including drug-resistant strains), yeasts, fungi, viruses, parasites and even the stubborn biofilms. Although preliminary results indicate that this technology has a high potential to disinfect waters in aquaculture system and also in hatcheries and seed production units, but it clearly needs more deep knowledge and multi-dimenstional approach

COMPARATIVE ANALYSIS OF PROTEIN CLASSIFICATION METHODS

A large number of new gene candidates are being accumulated in genomic databases day by day. It has become an important task for researchers to identify the functions of these new genes and proteins. Faster and more sensitive and accurate methods are required to classify these proteins into families and predict their functions. Many existing protein clas-sification methods build hidden Markov models (HMMs) and other forms of profiles/motifs based on multiple alignments. These methods in general require a large amount of time for building models and also for predicting functions based on them. Furthermore, they can predict protein functions only if sequences are sufficiently conserved. When there is very little sequence similarity, these methods often fail, even if sequences share some structural similarities. One example of highly diverged protein families is G-protein coupled recep-tors (GPCRs). GPCRs are transmembrane proteins that play important roles in various signal transmission processes, many of which are directly associated with a variety of hu-man diseases. Machine learning methods that have been studied specifically for a problem of GPCR family classification include HMM and support vector machine (SVM) methods. However, amino acid composition has not been studied well as a property for GPCR clas