Alkaline Protease from a Non-toxigenic Vibrio sp.(V26) and its Applications

The thesis presents a detailed account of the alkaline protease produced by Vibrio sp.(V26) a mangrove isolate,and the application of this enzyme in different fields.The protease producer strain was identified on the basis of biochemical characteristice,putative virulence traits and 16S rRNA gene sequencing.The purification and characterization of the protease has been carried out. Along with this, an attempt has been made to identifiy the protease gene. The physical parameters as well as the media components influencing protease production were optimized using Response Surfce Methodology(RSM).The scale up of the application of the protease from Vibrio sp.(V26) in the dissociation of cells in animal cell culture,in the recovery of silver from used X-ray films as well as an ingredient in commercial detergents were investigated.

Molecular cloning of alkaline protease gene from marine fungus Engyodontium album

Department of Biotechnology, Cochin University of Science and Technology

Statistical signal extraction using stable processes

The standard models for statistical signal extraction assume that the signal and noise are generated by linear Gaussian processes. The optimum filter weights for those models are derived using the method of minimum mean square error. In the present work we study the properties of signal extraction models under the assumption that signal/noise are generated by symmetric stable processes. The optimum filter is obtained by the method of minimum dispersion. The performance of the new filter is compared with their Gaussian counterparts by simulation.

Optimization of Process Parameters for the Extraction of Gelatin from the Skin of Freshwater Fish and the Evaluation of Physical anal Chemical Characteristics

Faculty of Marine Sciences,Cochin University of Science and Technology

Nitrogen laser excited fluorescence of some rare earth doped alkaline earth fluorides

The present study is mainly concéntrated on the visible fluorescence of Ho3+ ,nd 3+ and Er 3+rare earths in alkaline earth fluoride hosts(caF2,srF2,BaF2) using a nitrogen laser excitation. A nitrogen laser was fabricated and its parametric studies were first carried out.

Alkaline Proteases From Bacteria Isolated From Cochin Estuary

Microorganisms distributed in the marine and brackish environments play an important role in the decomposition of organic matter and mineralisation in the system (Seshadri and lgnacimuthu, 2002). Estuary is one of the most productive ecosystems, at the same time one among the least explored ecosystems on earth, which has immense potential as a source of potent microorganisms that produce valuable compounds particularly, enzymes such as proteases. In this scenario, it is very appropriate to embark on finding novel alkaline protease producers from the estuarine system. The area where the present investigation was carried out is a part of the extensive estuarine system of South India viz. Cochin Estuary. There is meagre knowledge regarding the microbial composition, particularly the protease producers of Cochin Estuary. Hence, the present study has been undertaken with the objective of finding novel alkaline protease producing bacteria from Cochin Estuary

“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.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

"Alkaline Lipase Production by Marine Bacillus smithii BTMS 11"

Considering the potential of marine environment present study was designed for the screening and isolation of a potential salt tolerant. alkaline and thennotolerant lipase producing bacteria from the costal belts of South India and consequent development of ideal bioprocess for industrial production, purification characterisation and evaluation of the potential of the lipase enzyme for various industrial applications 1. Screening and isolation of a potential lipase producing bacteria. 2. Optimization of various physicochemical factors in Submerged fennentation for the production of alkaline lipase 3. Purification ofthe lipase enzyme 4. Characterisation of the enzyme 5. Evaluation of the enzyme for various industrial applications

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Alkaline protease from a non-toxigenic mangrove isolate of Vibrio sp. V26 with potential application in animal cell culture

Vibrio sp. V26 isolated from mangrove sediment showed 98 % similarity to 16S rRNA gene of Vibrio cholerae, V. mimicus, V. albensis and uncultured clones of Vibrio. Phenotypically also it resembled both V. cholerae and V. mimicus.Serogrouping, virulence associated gene profiling, hydrophobicity, and adherence pattern clearly pointed towards the non—toxigenic nature of Vibrio sp. V26. Purification and characterization of the enzyme revealed that it was moderately thermoactive, nonhemagglutinating alkaline metalloprotease with a molecular mass of 32 kDa. The application of alkaline protease from Vibrio sp. V26 (APV26) in sub culturing cell lines (HEp-2, HeLa and RTG-2) and dissociation of animal tissue (chick embryo) for primary cell culture were investigated. The time required for dissociation of cells as well as the viable cell yield obtained by while administeringAPV26 and trypsin were compared. Investigations revealed that the alkaline protease of Vibrio sp. V26 has the potential to be used in animal cell culture for subculturing cell lines and dissociation of animal tissue for the development of primary cell cultures, which has not been reported earlier among metalloproteases of Vibrios.

Potential application of β-1, 3 glucanase from an environmental isolate of Pseudomonas aeruginosa MCCB 123 in fungal DNA extraction

Pseudomonas aeruginosa MCCB 123 was grown in a synthetic medium for β-1,3 glucanase production. From the culture filtrate, β-1,3 glucanase was purified with a molecular mass of 45 kDa. The enzyme was a metallozyme as its β-1,3 glucanase activity got inhibited by the metal chelator EDTA. Optimum pH and temperature for β-1,3 glucanase activity on laminarin was found to be 7 and 50 °C respectively. The MCCB 123 β-1,3 glucanase was found to have good lytic action on a wide range of fungal isolates, and hence its application in fungal DNA extraction was evaluated. β-1,3 glucanase purified from the culture supernatant of P. aeruginosa MCCB 123 could be used for the extraction of fungal DNA without the addition of any other reagents generally used. Optimum pH and temperature of enzyme for fungal DNA extraction was found to be 7 and 65 °C respectively. This is the first report on β-1,3 glucanase employed in fungal DNA extraction

Ontology based Framework for News Extraction in Visual Media

Anticipating the increase in video information in future, archiving of news is an important activity in the visual media industry. When the volume of archives increases, it will be difficult for journalists to find the appropriate content using current search tools. This paper provides the details of the study we conducted about the news extraction systems used in different news channels in Kerala. Semantic web technologies can be used effectively since news archiving share many of the characteristics and problems of WWW. Since visual news archives of different media resources follow different metadata standards, interoperability between the resources is also an issue. World Wide Web Consortium has proposed a draft for an ontology framework for media resource which addresses the intercompatiblity issues. In this paper, the w3c proposed framework and its drawbacks is also discussed