Optimization of Fermentation Conditions for Bioactive Compounds Production by Marine Bacterium Enterococcus Faecium

Abs tract: Thirty four bacterial s trains were isolated from sea water, sediments and algae samples which were collected from Alexandria beaches. All strains were screened for their potentiality to produce bioactive compounds by us in g well cut diffusion technique against the following pathogens: Staphylococcus aureus, Streptococcus faecalis, Pseudomonas aeruginosa, Escherichia coli, Micrococcus luteus and Candida albicans as indicator strains. The most potent strain was identified at th e mo lecular level as Enterococcus faecium, while the most susceptable strain was S.aureus. W ell cu t d iffu s io n technique was p erfo rmed using different culture media (nutrient agar, Zobell agar and Luria Bertani), the most suitable medium was Luria Bertani with inhibition zone of 10 mm. Placket-Burman design was ap p lied to o p timize th e fermen tatio n co n d itio n s an d maximize th e p ro d u ct iv ity . Th e o p timized med iu m was formulated as follows: (g/l): peptone, 15; yeast extract, 2.5; concentrated sea water ( >100%), adjusted to pH 8 and inoculum size 1.5 ml, this medium gives inhibition zone of 16 mm when incubated at 35 C o for 48 h i.e inhibition zone was increased about 1.6 fold increase. Mutation techniques (physical and chemical) were applied to increase b io active compound productivity but reverse effect was detected. Immobilization us ing both entrapment (alginate) and ads orptio n (luffa and pumice) techniques were applied. Only cells ads orped on pumice ga v e higher productivity and the inhibtion zone reached up to 17 mm

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species