Detection of sandal spike phytoplasma using immunological and molecular techniques

Spike disease in sandal is generally diagnosed by the manifestation of external symptoms. Attempts have been made to detect the diseased plants by determining the length/breadth ratio of leaves (lyengar, 1961) and histochemical tests using Mann's stain (Parthasarathi et al., 1966), Dienes' stain (Ananthapadmanabha et a/., 1973) aniline blue and Hoechst 33258 (Ghosh et a/., 1985, Rangaswamy, 1995). But most of these techniques are insensitive, indirect detection methods leading to misinterpretation of results. Moreover, to identify disease resistant sandal trees, highly sensitive techniques are needed to detect the presence of the pathogen. In sandal forests, several host plants of sandal like Zizyphus oenop/ea (Fig. 1.3) also exhibit the yellows type disease symptoms. Immunological and molecular assays have to be developed to confirm the presence of sandal spike phytoplasma in such hosts. The major objectives of the present work includes:In situ detection of sandal spike phytoplasma by epifluorescence microscopy and scanning electron microscopy.,Purification of sandal spike phytoplasma and production of polyclonal antibodies.,Amino acid and total protein estimation of sandal spike phytoplasma.,Immunological detection of sandal spike phytoplasma., Molecular detection of sandal spike phytoplasma.,Screening for phytoplasma in host plants of spike disease affected sandal using immunological and molecular techniques.

Investigation of dielectric and elastic properties of selected dielectric ceramics and oxide glasses

The dielectric and elastic properties are of considerable significance to the science and technology of matter in the solid state. The study of these properties give information about the magnitude of the forces and nature of the bonding between the atoms. Our aim has been to investigate systematically the effect of doping of an appropriate element on the elastic and dielectric properties of selected dielectric ceramics and oxide glasses. These materials have got wide technological applications due to their interesting electrical, optical, thermal and elastic behaviour. Ultrasound propagation and capacitance measurement techniques have been employed for the systematic investigation of the elastic and dielectric properties of selected number of these materials. Details of the work done and results obtained are presented in this thesis.

“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.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Mono- and binuclear transition metal complexes of n(4)-substituted thiosel\licarbazones derived frol\i salicylaldehyde and 2-hydroxyacetophenone synthesis, spectral and structural studies

The work embodied in the thesis is divided into eight chapters. Chapter I gives a brief introduction about metal complexes of thiosemicarbazones, including their structural and bonding properties. Chapter 2 deals with the synthesis and single crystal X-ray diffraction studies of various thiosemicarbazones used up for the present investigations and various characterization techniques. Chapter 3 deals with synthesis, spectral and structural studies of Cu(U) complexes with ONS donor thiosemicarbazones. Chapter 4 deals with synthesis and spectral studies of Ni(II) complexes \vith 2-hydroxyacetophenone N(4)-cyclohexyl thiosemicarbazone as the ligand. Chapter 5 includes synthesis and spectral studies of Mn(II) complexes. Chapter 6 deals with synthesis, spectral and structural studies of Zn(II) complexes. Chapter 7 includes synthesis and spectral studies of oxovanadium(IV) complexes. Chapter 8 deals with synthesis, spectral and single crystal X-ray diffraction studies of dioxomolybdenum(VI) complexes.

Cooperative caching techniques for mobile ad hoc networks

Data caching can remarkably improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. The cache placement problem minimizes total data access cost in ad hoc networks with multiple data items. The ad hoc networks are multi hop networks without a central base station and are resource constrained in terms of channel bandwidth and battery power. By data caching the communication cost can be reduced in terms of bandwidth as well as battery energy. As the network node has limited memory the problem of cache placement is a vital issue. This paper attempts to study the existing cooperative caching techniques and their suitability in mobile ad hoc networks.

“Studies on short polyester fiber - polyurethane elastomer composite with different interfacial bonding agents”

The work presented in this thesis is regarding the development and evaluation of new bonding agents for short polyester fiber - polyurethane elastomer composites. The conventional bonding system based on hexamethylenetetramine, resorcinol and hydrated silica was not effective as a bonding agent for the composite, as the water eliminated during the formation of the RF resin hydrolysed the urethane linkages. Four bonding agents based on MDI/'I‘DI and polypropyleneglycol, propyleneglycol and glycerol were prepared and the composite recipe was optimised with respect to the cure characteristics and mechanical properties. The flow properties, stress relaxation pattern and the thermal degradation characteristics of the composites containing different bonding agents were then studied in detail to evaluate the new bonding systems. The optimum loading of resin was 5 phr and the ratio of the -01 to isocyanate was 1:1. The cure characteristics showed that the optimum combination of cure rate and processability was given by the composite with the resin based on polypropyleneglycol/ glycerol/ 4,4’diphenylmethanediisocynate (PPG/GL/MDI). From the rheological studies of the composites with and without bonding agents it was observed that all the composites showed pseudoplastic nature and the activation energy of flow of the composite was not altered by the presence of bonding agents. Mechanical properties such as tensile strength, modulus, tear resistance and abrasion resistance were improved in the presence of bonding agents and the effect was more pronounced in the case of abrasion resistance. The composites based on MDI/GL showed better initial properties while composites with resins based on MDI/PPG showed better aging resistance. Stress relaxation showed a multistage relaxation behaviour for the composite. Within the-strain levels studied, the initial rate of relaxation was higher and the cross over time was lesser for the composite containing bonding agents. The bonding agent based on MDI/PPG/GL was found to be a better choice for improving stress relaxation characteristics with better interfacial bonding. Thennogravimetirc analysis showed that the presence of fiber and bonding agents improved the thennal stability of the polyurethane elastomer marginally and it was maximum in the case of MDI / GL based bonding agents. The kinetics of degradation was not altered by the presence of bonding agents

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

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.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced

Comparison of Statistical and Semantic Similarity Techniques for Paraphrase Identification

This paper compares statistical technique of paraphrase identification to semantic technique of paraphrase identification. The statistical techniques used for comparison are word set and word-order based methods where as the semantic technique used is the WordNet similarity matrix method described by Stevenson and Fernando in [3].