Characteristics of near surface crustal deformation associated with shield seismicity

The purpose of the present study is to understand the surface deformation associated with the Killari and Wadakkancheri earthquake and to examine if there are any evidence of occurrence of paleo-earthquakes in this region or its vicinity. The present study is an attempt to characterize active tectonic structures from two areas within penisular India: the sites of 1993 Killari (Latur) (Mb 6.3) and 1994 Wadakkancheri (M 4.3) earthquakes in the Precambrian shield. The main objectives of the study are to isolate structures related to active tectonism, constraint the style of near – surface deformation and identify previous events by interpreting the deformational features. The study indicates the existence of a NW-SE trending pre-existing fault, passing through the epicentral area of the 1993 Killari earthquake. It presents the salient features obtained during the field investigations in and around the rupture zone. Details of mapping of the scrap, trenching, and shallow drilling are discussed here. It presents the geologic and tectonic settings of the Wadakkancheri area and the local seismicity; interpretation of remote sensing data and a detailed geomorphic analysis. Quantitative geomorphic analysis around the epicenter of the Wadakkancheri earthquake indicates suitable neotectonic rejuvenation. Evaluation of remote sensing data shows distinct linear features including the presence of potentially active WNW-ESE trending fault within the Precambrian shear zone. The study concludes that the earthquakes in the shield area are mostly associated with discrete faults that are developed in association with the preexisting shear zones or structurally weak zones

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.

Optimization of Lignin Peroxidase, Manganese Peroxidase, and Lac Production from Ganoderma lucidum Under Solid State Fermentation of Pineapple Leaf

This study was undertaken to isolate ligninase-producing white-rot fungi for use in the extraction of fibre from pineapple leaf agriwaste. Fifteen fungal strains were isolated from dead tree trunks and leaf litter. Ligninolytic enzymes (lignin peroxidase (LiP), manganese peroxidase (MnP), and laccase (Lac)), were produced by solid-state fermentation (SSF) using pineapple leaves as the substrate. Of the isolated strains, the one showing maximum production of ligninolytic enzymes was identified to be Ganoderma lucidum by 18S ribotyping. Single parameter optimization and response surface methodology of different process variables were carried out for enzyme production. Incubation period, agitation, and Tween-80 were identified to be the most significant variables through Plackett-Burman design. These variables were further optimized by Box-Behnken design. The overall maximum yield of ligninolytic enzymes was achieved by experimental analysis under these optimal conditions. Quantitative lignin analysis of pineapple leaves by Klason lignin method showed significant degradation of lignin by Ganoderma lucidum under SSF