Studies on exopolysaccharide production by probiotic Lactic acid bacteria

The thesis mainly discussed the isolation and identification of a probiotic Lactobacillus plantarum, fermentative production of exopolysaccharide by the strain, its purification, structural characterisation and possible applications in food industry and therapeutics. The studies on the probiotic characterization explored the tolerance of the isolated LAB cultures to acid, bile, phenol, salt and mucin binding. These are some of the key factors that could satisfy the criteria for probiotic strains . The important factors required for a high EPS production in submerged fermentation was investigated with a collection of statistical and mathematical approach. Chapter 5 of the thesis explains the structural elucidation of EPS employing spectroscopic and chromatographic techniques. The studies helped in the exploration of the hetero-polysaccharide sequence from L. plantarum MTCC 9510. The thesis also explored the bioactivities of EPS from L. plantarum. As majority of chemical compounds identified as anti-cancerous are toxic to normal cells, the discovery and identification of new safe drugs has become an important goal of research in the biomedical sciences. The thesis has explored the anti-oxidant, anti-tumour and immunomodulating properties of EPS purified from Lactobacillus plantarum. The presence of (1, 3) linkages and its molecular weight presented the EPS with anti-oxidant, anti-tumour and immunomodulating properties under in vitro conditions.

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Computer vision techniques in seafood quality control

The application of computer vision based quality control has been slowly but steadily gaining importance mainly due to its speed in achieving results and also greatly due to its non- destnictive nature of testing. Besides, in food applications it also does not contribute to contamination. However, computer vision applications in quality control needs the application of an appropriate software for image analysis. Eventhough computer vision based quality control has several advantages, its application has limitations as to the type of work to be done, particularly so in the food industries. Selective applications, however, can be highly advantageous and very accurate.Computer vision based image analysis could be used in morphometric measurements of fish with the same accuracy as the existing conventional method. The method is non-destructive and non-contaminating thus providing anadvantage in seafood processing.The images could be stored in archives and retrieved at anytime to carry out morphometric studies for biologists.Computer vision and subsequent image analysis could be used in measurements of various food products to assess uniformity of size. One product namely cutlet and product ingredients namely coating materials such as bread crumbs and rava were selected for the study. Computer vision based image analysis was used in the measurements of length, width and area of cutlets. Also the width of coating materials like bread crumbs was measured.Computer imaging and subsequent image analysis can be very effectively used in quality evaluations of product ingredients in food processing. Measurement of width of coating materials could establish uniformity of particles or the lack of it. The application of image analysis in bacteriological work was also done

Transition metal complexes of some N4-phenylsemicarbazones: crystal structures and spectral studies

The unusual coordination modes of semicarbazones when bound to metals, the wide applications and structural diversity of metal complexes of semicarbazones provoked us to synthesize and characterize the tridentate ONO and NNO-donor semicarbazones and their transition metal complexes. This work is focused on the studies on complexes of three N4-phenylsemicarbazones synthesized by changing the carbonyl compounds. This work is concerned with the studies of two new semicarbazones, 2- formylpyridine-N4-phenylsemicarbazone (HL1) and 3-ethoxysalicylaldehyde- N4-phenylsemicarbazone (H2L2) and a reported semicarbazone 2-benzoylpyridine-N4-phenylsemicarbazone (HL3) [29]. The compositions of these semicarbazones were determined by the CHN analyses and IR, UV and NMR spectral studies were used for the characterization of these compounds. The molecular structure of 3-ethoxysalicylaldehyde-N4-phenylsemicarbazone (H2L2) was obtained by single crystal X-ray diffraction studies. Also, we have synthesized Cu(II), Cd(II), Zn(II) and Ni(II) complexes of these three semicarbazones. The complexes were characterized by various spectroscopic techniques, magnetic and conductivity studies. We could isolate single crystals of some complexes of all metals suitable for X-ray diffraction studies. This thesis is divided into six chapters.

Transition metal complexes derived from ketone based N(4)-substituted thiosemicarbazones: Crystal structures and spectral studies

Thiosemicarbazones have recently attracted considerable attention due to their ability to form tridentate chelates with transition metal ions through either two nitrogen and sulfur atoms, N–N–S or oxygen, nitrogen and sulfur atoms, O–N–S. Considerable interest in thiosemicarbazones and their transition metal complexes has also grown in the areas of biology and chemistry due to biological activities such as antitumoral, fungicidal, bactericidal, antiviral and nonlinear optical properties. They have been used for metal analyses, for device applications related to telecommunications, optical computing, storage and information processing.The versatile applications of metal complexes of thiosemicarbazones in various fields prompted us to synthesize the tridentate NNS-donor thiosemicarbazones and their metal complexes. As a part of our studies on transition metal complexes with these ligands, the researcher undertook the current work with the following objectives. 1. To synthesize and physico-chemically characterize the following thiosemicarbazone ligands: a. Di-2-pyridyl ketone-N(4)-methyl thiosemicarbazone (HDpyMeTsc) b. Di-2-pyridyl ketone-N(4)-ethyl thiosemicarbazone (HDpyETsc) 2. To synthesize oxovanadium(IV), manganese(II), nickel(II), copper(II), zinc(II) and cadmium(II) complexes using the synthesized thiosemicarbazones as principal ligands and some anionic coligands. 3. To study the coordination modes of the ligands in metal complexes by using different physicochemical methods like partial elemental analysis, thermogravimetry and by different spectroscopic techniques. 4. To establish the structure of compounds by single crystal XRD studies

Studies on molecular structure and properties using microwave and infrared spectral data

The major objective of the thesis is essentially to evolve and apply certain computational procedures to evaluate the structure and properties of some simple polyatomic molecules making use of spectroscopic data available from the literature. It must be said that though there is dwindling interest in recent times in such analyses, there exists tremendous scope and utility for attempting such calculations as the precision and reliability of'experimental techniques in spectroscopy have increased vastly due to enormous sophistication of the instruments used for these measurements. In the present thesis an attempt is made to extract maximum amount of information regarding the geometrical structure and interatmic forces of simple molecules from the experimental data on microwave and infrared spectra of these molecules

Studies in molecular mechanics using spectroscopic data exact force field and bond in homogeneity

This thesis deals with some studies in molecular mechanic using spectroscopic data. It includes an improvement in the parameter technique for the evaluation of exact force fields, the introduction of a new and simple algebraic method for the force field calculation and a study of asymmetric variation of bonding forces along a bond.

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.

Optical and thermal characterization of dye intercalated montmorillonites and rare earth doped materials

The advent of high optical quality transparent nano—structured glasses, the so-called transparent glass ceramics or vitroceramics disclosed the possibility of producing nano-sized photonic devices based on rare-earth doped up—converters. Transparent glass ceramics have been investigated as hosts for lanthanide ions envisioning the production of materials that are easy to shape and with high performance for photonic applications. Rare earth doped glasses have been extensively studied due to their potential applications in optical devices such as solid state lasers and optical fibers. Various photothermal and optical techniques have been successfully applied for the thermal and optical characterization of these rare earth doped materials. In the present thesis, the effective thermal parameters like thermal diffusivity and thermal effusivity of complex materials for various applications have been investigated using photothermal methods along with their optical characterization utilising the common optical absorption as well as fluorescence spectroscopic techniques. These sensitive optical procedures are also essential for exploiting these materials for further photonic applications.

NIR Spectroscopic and Laser Induced Fluorescence studies of some organic molecules

Vibrational overtone spectroscopy of molecules containing X-H oscillators (X = C, N, O...) has become an effective tool for the study of molecular structure, dynamics, inter and intramolecular interactions, conformational aspects and substituent effects in aliphatic and aromatic compounds. In the present work, the author studied the NIR overtone spectra of some liquid phase organic compounds. The analysis of the CH, NH and OH overtones yielded important structural information about these systems. In an attempt to get information on electronic energy levels, we studied the pulsed Nd:YAG laser induced fluorescence spectra of certain organic compounds. The pulsed laser Raman spectra of some organic compounds are also studied. The novel high resolution technique of near infrared tunable diode laser absorption spectroscopy (TDLAS) is used to record the rotational structure of the second OH overtone spectrum of 2-propanol. The spectral features corresponding to the different molecular conformations could be identified from the high resolution spectrum. The whole work described in this thesis is divided into five chapters.

“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.

Photometric and fundamental plane studies of galaxies

Determining the morphological parameters that describe galaxies has always been a challenging task. The studies on the correlations between different photometric as well as spectroscopic parameters of the galaxies help in understanding their structure, properties of the stars and gas which constitute the galaxy, the various physical and chemical processes which determine the properties, and galaxy formation and evolution. In the last few decades, the advent of Charge Coupled Devices (CCDs) and near infrared arrays ha\·e provided quick and reliable digitized data acquisition, in the optical and near infrared bands. This has provided an avalanche of data, which can be processed using sophisticated image analysis techniques to obtain information about the morphology of galaxies. The photometric analysis performed in this thesis involve the extraction of structural parameters of early type gala.xies imaged in the near infrared K (2.2ttm) band, obtaining correlations between these, parameters and using them to constrain the large scale properties of galaxi,~s.