Nature and Ecological significance of Nutrient Regeneration in Different Prawn Culture Fields

The present work "Nature and Ecological Significance of Nutrient Regeneration in different Prawn Culture Fields" was undertaken to understand the seasonal variation of nutrients, nutrient cycling and primary productivity of the prawn culture systems. The main emphasis was to find the qualitative and quantitative estimates of distribution of total phosphorus, inorganic phosphorus, organic phosphorus, total nitrogen and nitrogen fractions in the water. The effect of nutrient cycling on primary productivity and concentration of metals also form one part of the study. The entire thesis comprise of only one major chapter with subchapters such as, Introduction (I), Review of Literature (2), Material and Methods (3), Results (14), Discussion (5), Executive Summary (6) and Biblio~ graphy (7)

“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.

Cooperative Caching Framework for Mobile Cloud Computing

Due to the advancement in mobile devices and wireless networks mobile cloud computing, which combines mobile computing and cloud computing has gained momentum since 2009. The characteristics of mobile devices and wireless network makes the implementation of mobile cloud computing more complicated than for fixed clouds. This section lists some of the major issues in Mobile Cloud Computing. One of the key issues in mobile cloud computing is the end to end delay in servicing a request. Data caching is one of the techniques widely used in wired and wireless networks to improve data access efficiency. In this paper we explore the possibility of a cooperative caching approach to enhance data access efficiency in mobile cloud computing. The proposed approach is based on cloudlets, one of the architecture designed for mobile cloud computing.

Potential public health significance of faecal contamination and multidrug-resistant Escherichia coli and Salmonella serotypes in a lake in India

To assess the prevalence of faecal coliform bacteria and multiple drug resistance among Escherichia coli and Salmonella serotypes from Vembanadu Lake. Study design: Systematic microbiological testing. Methods: Monthly collection of water samples were made from ten stations on the southern and northern parts of a salt water regulator constructed in Vembanadu Lake in order to prevent incursion of seawater during certain periods of the year. Density of faecal colifrom bacteria was estimated. E. coli and Salmonella were isolated and their different serotypes were identified. Antibiotic resistance analysis of E. coli and Salmonella serotypes was done and the MAR index of individual isolates was calculated. Results: Density of faecal coliform bacteria ranged from mean MPN value 2900 -7100/100ml. Results showed multiple drug resistance pattern among the bacterial isolates. E. coli showed more than 50% resistance to amickacin, oxytetracycline, streptomycin, tetracycline and kanamycin while Salmonella showed high resistance to oxytetracycline, streptomycin, tetracycline and ampicillin. The MAR indexing of the isolates showed that they have originated from high risk source such as humans, poultry and dairy cows. Conclusions: The high density of faecal coliform bacteria and prevalence of multi drug resistant E. coli and Salmonella serotypes in the lake may pose severe public health risk through related water borne and food borne outbreaks

Significance of soil microorganisms with special reference to climate change

There are a large number of agronomic-ecological interactions that occur in a world with increasing levels of CO2, higher temperatures and a more variable climate. Climate change and the associated severe problems will alter soil microbial populations and diversity. Soils supply many atmospheric green house gases by performing as sources or sinks. The most important of these gases include CH4, CO2 and N2O. Most of the green house gases production and consumption processes in soil are probably due to microorganisms. There is strong inquisitiveness to store carbon (C) in soils to balance global climate change. Microorganisms are vital to C sequestration by mediating putrefaction and controlling the paneling of plant residue-C between CO2 respiration losses or storage in semi-permanent soil-C pools. Microbial population groups and utility can be manipulated or distorted in the course of disturbance and C inputs to either support or edge the retention of C. Fungi play a significant role in decomposition and appear to produce organic matter that is more recalcitrant and favor long-term C storage and thus are key functional group to focus on in developing C sequestration systems. Plant residue chemistry can influence microbial communities and C loss or flow into soil C pools. Therefore, as research takings to maximize C sequestration for agricultural and forest ecosystems - moreover plant biomass production, similar studies should be conducted on microbial communities that considers the environmental situations

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Computing median and antimedian sets in median graphs

The median (antimedian) set of a profile π = (u1, . . . , uk) of vertices of a graphG is the set of vertices x that minimize (maximize) the remoteness i d(x,ui ). Two algorithms for median graphs G of complexity O(nidim(G)) are designed, where n is the order and idim(G) the isometric dimension of G. The first algorithm computes median sets of profiles and will be in practice often faster than the other algorithm which in addition computes antimedian sets and remoteness functions and works in all partial cubes

Studies on Inventory with Positive Service Time Under Local Purchase Driven by N/T- Policy

In this thesis, certain continuous time inventory problems with positive service time under local purchase guided by N/T-policy are analysed. In most of the cases analysed, we arrive at stochastic decomposition of system states, that is, the joint distribution of the system states is obtained as the product of marginal distributions of the components. The thesis is divided into ve chapters

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Econometrics as an Academic Discipline in Higher Education: An Exploratory Study of the Need, Relevance and Significance

Econometrics is a young science. It developed during the twentieth century in the mid-1930’s, primarily after the World War II. Econometrics is the unification of statistical analysis, economic theory and mathematics. The history of econometrics can be traced to the use of statistical and mathematics analysis in economics. The most prominent contributions during the initial period can be seen in the works of Tinbergen and Frisch, and also that of Haavelmo in the 1940's through the mid 1950's. Right from the rudimentary application of statistics to economic data, like the use of laws of error through the development of least squares by Legendre, Laplace, and Gauss, the discipline of econometrics has later on witnessed the applied works done by Edge worth and Mitchell. A very significant mile stone in its evolution has been the work of Tinbergen, Frisch, and Haavelmo in their development of multiple regression and correlation analysis. They used these techniques to test different economic theories using time series data. In spite of the fact that some predictions based on econometric methodology might have gone wrong, the sound scientific nature of the discipline cannot be ignored by anyone. This is reflected in the economic rationale underlying any econometric model, statistical and mathematical reasoning for the various inferences drawn etc. The relevance of econometrics as an academic discipline assumes high significance in the above context. Because of the inter-disciplinary nature of econometrics (which is a unification of Economics, Statistics and Mathematics), the subject can be taught at all these broad areas, not-withstanding the fact that most often Economics students alone are offered this subject as those of other disciplines might not have adequate Economics background to understand the subject. In fact, even for technical courses (like Engineering), business management courses (like MBA), professional accountancy courses etc. econometrics is quite relevant. More relevant is the case of research students of various social sciences, commerce and management. In the ongoing scenario of globalization and economic deregulation, there is the need to give added thrust to the academic discipline of econometrics in higher education, across various social science streams, commerce, management, professional accountancy etc. Accordingly, the analytical ability of the students can be sharpened and their ability to look into the socio-economic problems with a mathematical approach can be improved, and enabling them to derive scientific inferences and solutions to such problems. The utmost significance of hands-own practical training on the use of computer-based econometric packages, especially at the post-graduate and research levels need to be pointed out here. Mere learning of the econometric methodology or the underlying theories alone would not have much practical utility for the students in their future career, whether in academics, industry, or in practice This paper seeks to trace the historical development of econometrics and study the current status of econometrics as an academic discipline in higher education. Besides, the paper looks into the problems faced by the teachers in teaching econometrics, and those of students in learning the subject including effective application of the methodology in real life situations. Accordingly, the paper offers some meaningful suggestions for effective teaching of econometrics in higher education

Significance Of Product Type And Brand In The Purchasing Behaviour Of Cattle Feeds: An Empirical Study In Kerala

Cattle feed industry is a major segment of animal feed industry. This industry is gradually evolving into an organized sector and the feed manufactures are increasingly using modern and sophisticated methods that seek to incorporate best global practices. This industry has got high potential for growth in India, given the fact that the country is the world’s leading producer of milk and its production is expected to grow at a compounded annual growth rate of 4 per cent. Besides, the concept of branded cattle feed as a packaged commodity is fast gaining popularity in rural India. There can be a positive change in the demand for cattle feed because of factors like (i) shrinkage of open land for cattle grazing, urbanization and resultant shortage of conventionally used cattle feeds, and (ii) introduction of high yield cattle requires specialized feeds. Earlier research studies done by the present authors have revealed the significant growth prospects of the branded cattle feed industry, the feed consumption pattern and the relatively high share of branded feeds, feed consumption pattern based on product types (like, pellet and mash), composition of cattle feed market and the relatively large shares of Kerala Feeds Ltd. (KFL) and Kerala Solvent Extractions Ltd. (KSE) brands, the major factors influencing the purchasing decisions etc. As a continuation of the earlier studies, this study makes a closer look into the significance of product types in the buyer behavior, level of awareness about the brand and its implications on purchasing decisions, and the brandshifting behavior and its determinants

CuInS2/In2S3 Cells using a Cost-effective Technique: Significance of Precursor Ratios on Cell Parameters

Thin film solar cells having structure CuInS2/In2S3 were fabricated using chemical spray pyrolysis (CSP) technique over ITO coated glass. Top electrode was silver film (area 0.05 cm2). Cu/In ratio and S/Cu in the precursor solution for CuInS2 were fixed as 1.2 and 5 respectively. In/S ratio in the precursor solution for In2S3 was fixed as 1.2/8. An efficiency of 0.6% (fill factor -37.6%) was obtained. Cu diffusion to the In2S3 layer, which deteriorates junction properties, is inevitable in CuInS2/In2S3 cell. So to decrease this effect and to ensure a Cu-free In2S3 layer at the top of the cell, Cu/In ratio was reduced to 1. Then a remarkable increase in short circuit current density was occurred from 3 mA/cm2 to 14.8 mA/cm2 and an efficiency of 2.13% was achieved. Also when In/S ratio was altered to 1.2/12, the short circuit current density increased to 17.8 mA/cm2 with an improved fill factor of 32% and efficiency remaining as 2%. Thus Cu/In and In/S ratios in the precursor solutions play a crucial role in determining the cell parameters

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.