Extracellular β-glucosidase Production by a Marine Aspergillus sydowii BTMFS 55 under Solid State Fermentation Using Statistical Experimental Design

A potential fungal strain producing extracellular β-glucosidase enzyme was isolated from sea water and identified as ^ëéÉêJ Öáääìë=ëóÇçïáá BTMFS 55 by a molecular approach based on 28S rDNA sequence homology which showed 93% identity with already reported sequences of ^ëéÉêÖáääìë=ëóÇçïáá in the GenBank. A sequential optimization strategy was used to enhance the production of β-glucosidase under solid state fermentation (SSF) with wheat bran (WB) as the growth medium. The two-level Plackett-Burman (PB) design was implemented to screen medium components that influence β-glucosidase production and among the 11 variables, moisture content, inoculums, and peptone were identified as the most significant factors for β-glucosidase production. The enzyme was purified by (NH4)2SO4 precipitation followed by ion exchange chromatography on DEAE sepharose. The enzyme was a monomeric protein with a molecular weight of ~95 kDa as determined by SDS-PAGE. It was optimally active at pH 5.0 and 50°C. It showed high affinity towards éNPG and enzyme has a hã and sã~ñ of 0.67 mM and 83.3 U/mL, respectively. The enzyme was tolerant to glucose inhibition with a há of 17 mM. Low concentration of alcohols (10%), especially ethanol, could activate the enzyme. A considerable level of ethanol could produce from wheat bran and rice straw after 48 and 24 h, respectively, with the help of p~ÅÅÜ~êçãóÅÉë=ÅÉêÉîáëá~É in presence of cellulase and the purified β-glucosidase of ^ëéÉêÖáääìë=ëóÇçïáá BTMFS 55.

Process Optimization for Mass Production of Marine Yeast Candida sp. S 27 and its Nutritional Characterization

The main source of protein for human and animal consumption is from the agricultural sector, where the production is vulnerable to diseases, fluctuations in climatic conditions and deteriorating hydrological conditions due to water pollution. Therefore Single Cell Protein (SCP) production has evolved as an excellent alternative. Among all sources of microbial protein, yeast has attained global acceptability and has been preferred for SCP production. The screening and evaluation of nutritional and other culture variables of microorganisms are very important in the development of a bioprocess for SCP production. The application of statistical experimental design in bioprocess development can result in improved product yields, reduced process variability, closer confirmation of the output response to target requirements and reduced development time and overall cost.The present work was undertaken to develop a bioprocess technology for the mass production of a marine yeast, Candida sp.S27. Yeasts isolated from the offshore waters of the South west coast of India and maintained in the Microbiology Laboratory were subjected to various tests for the selection of a potent strain for biomass production. The selected marine yeast was identified based on ITS sequencing. Biochemical/nutritional characterization of Candida sp.S27 was carried out. Using Response Surface Methodology (RSM) the process parameters (pH, temperature and salinity) were optimized. For mass production of yeast biomass, a chemically defined medium (Barnett and Ingram, 1955) and a crude medium (Molasses-Yeast extract) were optimized using RSM. Scale up of biomass production was done in a Bench top Fermenter using these two optimized media. Comparative efficacy of the defined and crude media were estimated besides nutritional evaluation of the biomass developed using these two optimized media.

Multifactorial interaction of growth factors on Penaeus monodon lymphoid cells and the impact of IGFs in DNA synthesis and metabolic activity in vitro

Development of continuous cell lines from shrimp is essential to investigate viral pathogens. Unfortunately, there is no valid cell line developed from crustaceans in general and shrimps in particular to address this issue. Lack of information on the requirements of cells in vitro limits the success of developing a cell line, where the microenvironment of a cell culture, provided by the growthmedium, is of prime importance. Screening and optimization of growth medium components based on statistical experimental designs have been widely used for improving the efficacy of cell culture media. Accordingly, we applied Plackett–Burman design and response surface methodology to study multifactorial interactions between the growth factors in shrimp cell culture medium and to identify the most important ones for growth of lymphoid cell culture from Penaeus monodon. The statistical screening and optimization indicated that insulin like growth factor-I (IGF-I) and insulin like growth factor-II (IGF-II) at concentrations of 100 and 150 ng ml-1, respectively, could significantly influence the metabolic activity and DNA synthesis of the lymphoid cells. An increase of 53 % metabolic activity and 24.8 % DNA synthesis could be obtained, which suggested that IGF-I and IGFII had critical roles in metabolic activity and DNA synthesis of shrimp lymphoid cells

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals

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

Optimization of medium for the production of a novel aquaculture probiotic, Micrococcus MCCB 104 using central composite design

A marine isolate of jáÅêçÅçÅÅìë MCCB 104 has been identified as an aquaculture probiotic antagonistic to sáÄêáç. In the present study different carbon and nitrogen sources and growth factors in a mineral base medium were optimized for enhanced biomass production and antagonistic activity against the target pathogen, sáÄêáç=Ü~êîÉóá, following response surface methodology (RSM). Accordingly the minimum and maximum limits of the selected variables were determined and a set of fifty experiments programmed employing central composite design (CCD) of RSM for the final optimization. The response surface plots of biomass showed similar pattern with that of antagonistic activity, which indicated a strong correlation between the biomass and antagonism. The optimum concentration of the carbon sources, nitrogen sources, and growth factors for both biomass and antagonistic activity were glucose (17.4 g/L), lactose (17 g/L), sodium chloride (16.9 g/L), ammonium chloride (3.3 g/L), and mineral salts solution (18.3 mL/L). © KSBB

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.

Optimization of carbon and nitrogen sources and growth factors for the production of an aquaculture probiotic (Pseudomonas MCCB 103) using response surface methodology

Aim: To develop a new medium for enhanced production of biomass of an aquaculture probiotic Pseudomonas MCCB 103 and its antagonistic phenazine compound, pyocyanin. Methods and Results: Carbon and nitrogen sources and growth factors, such as amino acids and vitamins, were screened initially in a mineral medium for the biomass and antagonistic compound of Pseudomonas MCCB 103. The selected ingredients were further optimized using a full-factorial central composite design of the response surface methodology. The medium optimized as per the model for biomass contained mannitol (20 g l)1), glycerol (20 g l)1), sodium chloride (5 g l)1), urea (3Æ3 g l)1) and mineral salts solution (20 ml l)1), and the one optimized for the antagonistic compound contained mannitol (2 g l)1), glycerol (20 g l)1), sodium chloride (5Æ1 g l)1), urea (3Æ6 g l)1) and mineral salts solution (20 ml l)1). Subsequently, the model was validated experimentally with a biomass increase by 19% and fivefold increase of the antagonistic compound. Conclusion: Significant increase in the biomass and antagonistic compound production could be obtained in the new media. Significance and Impact of the Study: Media formulation and optimization are the primary steps involved in bioprocess technology, an attempt not made so far in the production of aquaculture probiotics.

Design Techniques for Sigmadelta based ADC for Wireless Applications

Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Energy Management in Wireless Sensor Networks: A Crosslayer,Channel Adaptive Approach Towards Performance Optimization

The proliferation of wireless sensor networks in a large spectrum of applications had been spurered by the rapid advances in MEMS(micro-electro mechanical systems )based sensor technology coupled with low power,Low cost digital signal processors and radio frequency circuits.A sensor network is composed of thousands of low cost and portable devices bearing large sensing computing and wireless communication capabilities. This large collection of tiny sensors can form a robust data computing and communication distributed system for automated information gathering and distributed sensing.The main attractive feature is that such a sensor network can be deployed in remote areas.Since the sensor node is battery powered,all the sensor nodes should collaborate together to form a fault tolerant network so as toprovide an efficient utilization of precious network resources like wireless channel,memory and battery capacity.The most crucial constraint is the energy consumption which has become the prime challenge for the design of long lived sensor nodes.

An Error-Localization, Validation and Optimization Tool for Embedded Code Augmentation: an Architecture Oriented Approach

Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.This dissertation contributes to an architecture oriented code validation, error localization and optimization technique assisting the embedded system designer in software debugging, to make it more effective at early detection of software bugs that are otherwise hard to detect, using the static analysis of machine codes. The focus of this work is to develop methods that automatically localize faults as well as optimize the code and thus improve the debugging process as well as quality of the code.Validation is done with the help of rules of inferences formulated for the target processor. The rules govern the occurrence of illegitimate/out of place instructions and code sequences for executing the computational and integrated peripheral functions. The stipulated rules are encoded in propositional logic formulae and their compliance is tested individually in all possible execution paths of the application programs. An incorrect sequence of machine code pattern is identified using slicing techniques on the control flow graph generated from the machine code.An algorithm to assist the compiler to eliminate the redundant bank switching codes and decide on optimum data allocation to banked memory resulting in minimum number of bank switching codes in embedded system software is proposed. A relation matrix and a state transition diagram formed for the active memory bank state transition corresponding to each bank selection instruction is used for the detection of redundant codes. Instances of code redundancy based on the stipulated rules for the target processor are identified.This validation and optimization tool can be integrated to the system development environment. It is a novel approach independent of compiler/assembler, applicable to a wide range of processors once appropriate rules are formulated. Program states are identified mainly with machine code pattern, which drastically reduces the state space creation contributing to an improved state-of-the-art model checking. Though the technique described is general, the implementation is architecture oriented, and hence the feasibility study is conducted on PIC16F87X microcontrollers. The proposed tool will be very useful in steering novices towards correct use of difficult microcontroller features in developing embedded systems.

Characterization of polyhydroxyalkanoates accumulating vibrios from marine benthic environments and production studies of polyhydroxyalkanoates by Vibrio sp. BTKB33

The present work deals with the characterization of polyhydroxyalkanoates accumulating vibrios from marine benthic environments and production studies of polyhydroxyalkanoates by vibrio sp.BTKB33. Vibrios are a group of (iram negative, curved or straight motile rods that normally inhabit the aquatic environments.The present study therefore aimed at evaluating the occurrence of PHA accumulating vibrios inhabiting marine benthic environments; characterizing the potential PHA accumulators employing phenotypic and genotypic approaches and molecular characterization of the PHA synthase gene. The study also evaluated the PHA production in V:'hri0 sp. strain BTKB33, through submerged fennentation using statistical optimization and characterized the purified biopolymer. Screening for PHA producing vibrios from marine benthic environments. Characterization of PHA producers employing phenotypic and genotypic approaches.The incidence of PHA accumulation in Vibrio sp. isolated from marine sediments was observed to be high, indicating that the natural habitat of these bacteria are stressful. Considering their ubiquitous nature, the ecological role played by vibrios in maintaining the delicate balance of the benthic ecosystem besides returning potential strains, with the ability to elaborate a plethora of extracellular enzymes for industrial application, is significant. The elaboration of several hydrolytic enzymes by individuals also emphasize the crucial role of vibrios in the mineralization process in the marine environment. This study throws light on the extracellular hydrolytic enzyme profile exhibited by vibrios. It was concluded that apart from the PHA accumulation, presence of exoenzyme production and higher MAR index also aids in their survival in the highly challenging benthic enviromnents. The phylogenetic analysis of the strains and studies on intra species variation within PHA accumulating strains reveal their diversity. The isolate selected for production in this study was Vibrio sp. strain BTKB33, identified as V.azureus by 16S rDNA sequencing and phenotypic characterization. The bioprocess variables for PHA production utilising submerged fermentation was optimized employing one-factor-at-a-time-method, PB design and RSM studies. The statistical optimization of bioprocess variables revealed that NaCl concentration, temperature and incubation period are the major bioprocess variables influencing PHA production and PHA content. The presence of Class I PHA synthase genes in BTKB33 was also unveiled. The characterization of phaC genes by PCR and of the extracted polymer employing FTIR and NMR analysis revealed the presence of polyhydroxybutyrate, smallest known PI-IAs, having wider domestic, industrial and medical application. The strain BTKB33 bearing a significant exoenzyme profile, can thus be manipulatedin future for utilization of diverse substrates as C- source for PHA production. In addition to BTKB33, several fast growing Vibrio sp. having PHA accumulating ability were also isolated, revealing the prospects of this environment as a mine for novel PHA accumulating microbes. The findings of this study will provide a reference for further research in industrial production of PHAs from marine microorganisms .

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

Optimization of carbon and nitrogen sources and growth factors for the production of an aquaculture probiotic (Pseudomonas MCCB 103) using response surface methodology

Aim: To develop a new medium for enhanced production of biomass of an aquaculture probiotic Pseudomonas MCCB 103 and its antagonistic phenazine compound, pyocyanin. Methods and Results: Carbon and nitrogen sources and growth factors, such as amino acids and vitamins, were screened initially in a mineral medium for the biomass and antagonistic compound of Pseudomonas MCCB 103. The selected ingredients were further optimized using a full-factorial central composite design of the response surface methodology. The medium optimized as per the model for biomass contained mannitol (20 g l)1), glycerol (20 g l)1), sodium chloride (5 g l)1), urea (3Æ3 g l)1) and mineral salts solution (20 ml l)1), and the one optimized for the antagonistic compound contained mannitol (2 g l)1), glycerol (20 g l)1), sodium chloride (5Æ1 g l)1), urea (3Æ6 g l)1) and mineral salts solution (20 ml l)1). Subsequently, the model was validated experimentally with a biomass increase by 19% and fivefold increase of the antagonistic compound. Conclusion: Significant increase in the biomass and antagonistic compound production could be obtained in the new media. Significance and Impact of the Study: Media formulation and optimization are the primary steps involved in bioprocess technology, an attempt not made so far in the production of aquaculture probiotics