A PVC plasticized membrane sensor for nickel ions

A new PVC membrane sensor, which is highly selective towards Ni (II) ions, has been developed using a thiophene-derivative Schiff base as the ionophore. The best performance was exhibited by the membrane having the composition percentage ratio of 5:3:61:31 (ionophore:NaTPB:DBP:PVC) (w=w), where NaTPB is the anion excluder, sodium tetraphenylborate and DBP is the plasticizing agent (dibutyl phthalate). The membrane exhibited a good Nernstian response for nickel ions over the concentration range of 1.0 10 1– 5.0 10 6M (limit of detection is 1.8 10 6 M) with a slope of 29.5 1.0mV per decade of activity. It has a fast response time of<20 s and can be used for a period of 4 months with good reproducibility. The sensor is suitable for use in aqueous solutions of a wide pH range of 3.2–7.9. The sensor shows high selectivity to nickel ions over a large number of mono-, bi- and trivalent cations. It has been successfully used as an indicator electrode in the potentiometric titration of nickel ions against EDTA and also for direct determination of nickel content in real samples – wastewater samples from electroplating industries and Indian chocolates.

A PVC Plasticized Membrane Sensor for Nickel Ions

A new PVC membrane sensor, which is highly selective towards Ni (II) ions, has been developed using a thiophene-derivative Schiff base as the ionophore. The best performance was exhibited by the membrane having the composition percentage ratio of 5:3:61:31 (ionophore:NaTPB:DBP:PVC) (w=w), where NaTPB is the anion excluder, sodium tetraphenylborate and DBP is the plasticizing agent (dibutyl phthalate). The membrane exhibited a good Nernstian response for nickel ions over the concentration range of 1.0 10 1– 5.0 10 6M (limit of detection is 1.8 10 6 M) with a slope of 29.5 1.0mV per decade of activity. It has a fast response time of<20 s and can be used for a period of 4 months with good reproducibility. The sensor is suitable for use in aqueous solutions of a wide pH range of 3.2–7.9. The sensor shows high selectivity to nickel ions over a large number of mono-, bi- and trivalent cations. It has been successfully used as an indicator electrode in the potentiometric titration of nickel ions against EDTA and also for direct determination of nickel content in real samples – wastewater samples from electroplating industries and Indian chocolates.

PVC Supported Liquid Membrane and Carbon Paste Potentiometric Sensors Incorporating a Mn(III)-Porphyrin for the Direct Determination of Undissociated Paracetamol

PVC supported liquid membrane and carbon paste potentiometric sensors incorporating an Mn(III)-porphyrin complex as a neutral host molecule were developed for the determination of paracetamol. The measurements were carried out in solution at pH 5.5. Under such conditions paracetamol exists as a neutral molecule. The mechanism of molecular recognition between the Mn(III)-porphyrin and paracetamol, leading to potentiometric signal generation, is discussed.The sensitivity and selectivity toward paracetamol of carbon paste and polymeric liquid membrane electrodes incorporating an Mn(III)-porphyrin host were compared. The applicability of these sensors to the direct determination of paracetamol was checked by performing a recovery test in human plasma.

Aqueous Sol-gel Process for Nanocrystalline Photocatalytic Titania, Transparent Functional Coatings and Ceramic Membrane

The present thesis develops from the point of view of titania sol-gel chemistry and an attempt is made to address the modification of the process for better photoactive titania by selective doping and also demonstration of utilization of the process for the preparation of supported membranes and self cleaning films.A general introduction to nanomaterials, nanocrystalline titania and sol-gel chemistry are presented in the first chapter. A brief and updated literature review on sol-gel titania, with special emphasis on catalytic and photocatalytic properties and anatase to rutile transformation are covered. Based on critical assessment of the reported information the present research problem has been defined.The second chapter describes a new aqueous sol-gel method for the preparation of nanocrystalline titania using titanyl sulphate as precursor. This approach is novel since no earlier work has been reported in the same lines proposed here. The sol-gel process has been followed at each step using particle size, zeta potential measurements on the sol and thermal analysis of the resultant gel. The prepared powders were then characterized using X-ray diffraction, FTIR, BET surface area analysis and transmission electron microscopy.The third chapter presents a detailed discussion on the physico-chemical characterization of the aqueous sol-gel derived doped titania. The effect of dopants such as tantalum, gadolinium and ytterbium on the anatase to rutile phase transformation, surface area as well as their influence on photoactivity is also included. The fourth chapter demonstrates application of the aqueous sol-gel method in developing titania coatings on porous alumina substrates for controlling the poresize for use as membrane elements in ultrafiltration. Thin coatings having ~50 nm thickness and transparency of ~90% developed on glass surface were tested successfully for self cleaning applications.

“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.

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

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.