An inhibitory compound produced by Pseudomonas with effectiveness on Vibrio harveyi

Persistence of the antivibrio property of the potential antagonistic probiotics, Pseudomonas MCCB 102 and 103, at di¡erent temperatures, pH and in organic solvents was studied. The antivibrio compound was extracted, puri¢ed and characterized using thin-layer chromatography, high-pressure liquid chromatography, liquid chromatography-mass spectroscopy, UV^ Vis and nuclear magnetic resonance spectroscopy and identi¢ed as N-methyl-1-hydroxyphenazine, a phenazine antibiotic. The toxicity of the compound was tested in Penaeus monodon haemocyte culture and the IC50 valuewas found to be1.4 0.31mg L 1. The compound was found to be bacteriostatic at 0.5mg L 1. Its stability to varying temperature, pH, organic solvents, prolonged shelf-life and vibriostatic nature point to its suitability for prophylatic aquaculture application.

High Styrene–Rubber Ionomers, an Alternative to Thermoplastic Elastomers

High styrene rubber ionomers were prepared by sulfonating styrene–butadiene rubber of high styrene content (high styrene rubber) in 1,2-dichloroethane using acetyl sulfate reagent, followed by neutralization of the precursor acids using methanolic zinc acetate. The ionomers were characterized using X-ray fluorescence spectroscopy, Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), dynamic mechanical analysis (DMA), and also by the evaluation of mechanical properties. The FTIR studies of the ionomer reveal that the sulfonate groups are attached to the benzene ring. The NMR spectra give credence to this observation. Results of DMA show an ionic transition (Ti) in addition to glass–rubber transition (Tg). Incorporation of ionic groups results in improved mechanical properties as well as retention of properties after three cycles of processing

A simple linear magnetic field sweep generator for magnetic resonance experiments

A simple and inexpensive linear magnetic field sweep generating system suitable for magnetic resonance experiments is described. The circuit, utilising a modified IC bootstrap configuration, generates field sweep over a wide range of sweep durations with excellent sweep linearity.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.