An in vitro screening system for protein splicing inhibitors based on green fluorescent protein as an indicator

This paper describes an in vitro fluorometric assay system for protein splicing based on the RecA intein of Mycobacterium tuberculosis and a modified green fluorescent protein (GFP). The assay takes advantage of the fact that polypeptides inserted adjacent to residue 129 of GFP cause the protein to form inclusion bodies when expressed in Escherichia coli and to be incapable of fluorophore formation. However, when the inserted polypeptide is an intein, the renatured fusion protein can undergo protein splicing and chromophore formation. Comparison of chromophore formation by renatured GFP-intein fusion and renatured GFP showed that under optimal conditions (pH 6.5 and 20 degrees C) protein splicing is significantly slower than GFP chromophore formation. Taking advantage of the reversible inhibition of protein splicing by zinc ion, a fluorometric protein splicing assay was developed in which the denatured fusion protein of GFP and the RecA intein was purified on a metal ion affinity column and renatured in the presence of 2 mM ZnCl2. When diluted into appropriate buffers, protein splicing could be initiated by the addition of a molar excess of EDTA and followed fluorometrically. This assay should be valuable as a high-throughput screening system for protein splicing inhibitors as potential antimycobacterial agents and as tools for studying the mechanism of protein splicing.     

Voltammetric procedure for examining DNA-modified surfaces: quantitation, cationic binding activity, and electron-transfer kinetics

To examine DNA-modified surfaces, we have developed a simple, convenient, and reliable procedure based on the voltammetric response of multiply charged transition metal cations (such as [Ru(NH3)6]3+) bound electrostatically to the DNA probes. At micromolar concentrations of the redox molecules in the electrolyte, the reduction and oxidation waves resulting from the immobilized cations on DNA-modified electrodes are well defined, stable, and reproducible. The surface densities of both single- and double-stranded oligonucleotides were accurately determined by integration of the peak for reduction of [Ru(NH3)6]3+ to [Ru(NH3)6]2+. In addition, the binding constant and electron-transfer rate constant of [Ru(NH3)6]3+ on DNA-modified electrodes were evaluated with the help of classical models. The present research provides not only an applicable and simple protocol for the quantitation of DNA probes on chips but also a versatile and powerful tool for the investigation of the binding activity and electron-transfer kinetics of cationic analytes on DNA-modified surfaces.     

Identification of a commonly deleted region at 17p13.3 in leukemia and lymphoma associated with 17p abnormality

Fluorescence in situ hybridization (FISH) was performed in 17 myeloid leukemia patients and seven lymphoid leukemia/ lymphoma patients who exhibited chromosomal abnormalities on the short arm of chromosome 17, in order to detect a commonly deleted region on chromosome band 17p13. Twenty-four leukemia/lymphoma patients studied cytogenetically at our institution over a period of 10 years had detectable 17p abnormalities such as translocation (six patients), addition (11 patients) and deletion of 17p13 (seven patients). A 17p abnormality was the only abnormality present in three patients. Most of the patients had additional complex cytogenetic abnormalities. The diagnosis was acute myeloid leukemia (AML) in 10 patients, two each with chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), chronic lymphocytic leukemia (CLL) and myelodysplastic syndrome (MDS) and the remaining three with malignant lymphoma (ML). Seven cosmid probes (D17S34, cCI17-624, cCI17-453, D17S379, cCI17-636, cCI17-732 and TP53) which mapped on 17p13 were used to analyze the allelic deletion. Eighty percent (19 out of 24) of the informative leukemia patients exhibited allelic loss in 17p13.3 at cC17-624. The smallest region of an overlapping deletion was observed on chromosome band 17p13.3 between cCI17-624 and cCI17-453. Patients with translocation involving 17p also showed deletion at cCI17-624 and cCI17-453. We hypothesize that this region contains a novel tumor suppressor gene(s) that is involved in leukemogenesis.     

On the approximate solution of singular integral equations

A method for obtaining the approximate solution of singular integral equations of the first and second kinds is suggested. The solution is represented in the form of power series with undetermined coefficients multiplied by a function in which the essential features of the singularity of the solution are preserved. The method of collocations is used to determine the unknown coefficients. The examples show that the method suggested is more general and gives good results even in the case when the form of solution does not exactly preserve the essential features of singularity. The method is simpler than others which use the properties of orthogonal polynomials, and is applicable for the solution of single equations as well as systems of simultaneous equations.     

A new network reconfiguration technique for service restoration indistribution networks

Whenever a fault occurs in a particular section of a distribution network and on isolation of the fault, some of the loads get disconnected and are left unsupplied. Service should be restored to these affected load points as quickly as possible through a network reconfiguration procedure. A new and efficient technique is presented in this paper for this purpose. Network reduction and determination of the interested trees of the reduced network by a specially developed algorithm for finding the required restorative procedures, are the main contributions of this paper     

Index of area coverage of fuzzy image subsets and object extraction

Some new geometrical properties, e.g., length, breadth and index of area coverage (IOAC) of a fuzzy set along with their computational aspects are introduced. An algorithm for providing both fuzzy and nonfuzzy segmentation based on these measures is also proposed. The proposed algorithm is found to be successful even for the input images containing multiple objects or an elongated object, where the existing fuzzy compactness based algorithm (which is valid for extracting a single compact object) fails. This is illustrated on various images.     

Genetic operators for combinatorial optimization in TSP and microarray gene ordering

This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using categorization of the genes. Shubhra Sankar Ray is a Visiting Research Fellow at the Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. He received the M.Sc. in Electronic Science and M.Tech in Radiophysics & Electronics from University of Calcutta, Kolkata, India, in 2000 and 2002, respectively. Till March 2006, he had been a Senior Research Fellow of the Council of Scientific and Industrial Research (CSIR), New Delhi, India, working at Machine Intelligence Unit, Indian Statistical Institute, India. His research interests include bioinformatics, evolutionary computation, neural networks, and data mining. Sanghamitra Bandyopadhyay is an Associate Professor at Indian Statistical Institute, Calcutta, India. She did her Bachelors in Physics and Computer Science in 1988 and 1992 respectively. Subsequently, she did her Masters in Computer Science from Indian Institute of Technology (IIT), Kharagpur in 1994 and Ph.D in Computer Science from Indian Statistical Institute, Calcutta in 1998. She has worked in Los Alamos National Laboratory, Los Alamos, USA, in 1997, as a graduate research assistant, in the University of New South Wales, Sydney, Australia, in 1999, as a post doctoral fellow, in the Department of Computer Science and Engineering, University of Texas at Arlington, USA, in 2001 as a faculty and researcher, and in the Department of Computer Science and Engineering, University of Maryland Baltimore County, USA, in 2004 as a visiting research faculty. Dr. Bandyopadhyay is the first recipient of Dr. Shanker Dayal Sharma Gold Medal and Institute Silver Medal for being adjudged the best all round post graduate performer in IIT, Kharagpur in 1994. She has received the Indian National Science Academy (INSA) and the Indian Science Congress Association (ISCA) Young Scientist Awards in 2000, as well as the Indian National Academy of Engineering (INAE) Young Engineers' Award in 2002. She has published over ninety articles in international journals, conference and workshop proceedings, edited books and journal special issues and served as the Program Co-Chair of the 1st International Conference on Pattern Recognition and Machine Intelligence, 2005, Kolkata, India, and as the Tutorial Co-Chair, World Congress on Lateral Computing, 2004, Bangalore, India. She is on the editorial board of the International Journal on Computational Intelligence. Her research interests include Evolutionary and Soft Computation, Pattern Recognition, Data Mining, Bioinformatics, Parallel & Distributed Systems and VLSI. Sankar K. Pal (www.isical.ac.in/∼sankar) is the Director and Distinguished Scientist of the Indian Statistical Institute. He has founded the Machine Intelligence Unit, and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held seve ral visiting positions in Hong Kong and Australian universities. Prof. Pal is a Fellow of the IEEE, USA, Third World Academy of Sciences, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of thirteen books and about three hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, and Bioinformatics. He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000–2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, and 2005-06 P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement . Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994–98, 2003–06], Pattern Recognition Letters, Neurocomputing (1995–2005), Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer.