01654nas a2200181 4500008004100000022001400041245009200055210006900147260007200216300001400288490000700302520095000309100002101259700001901280700002601299700002101325856012601346 2005 eng d a1367-480300aIdentification of coding and non-coding sequences using local holder exponent formalism0 aIdentification of coding and noncoding sequences using local hol aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScOCT a3818-38230 v213 a

Motivation: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. Results: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences.

1 aKulkarni, O., C.1 aVigneshwar, R.1 aJayaraman, Valadi, K.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/identification-coding-and-non-coding-sequences-using-local-holder-exponent-formalism-001407nas a2200169 4500008004100000022001400041245008800055210006900143260008000212300001400292490000700306520074900313100001701062700002601079700002101105856011101126 2006 eng d a0305-447000aImproved time series prediction with a new method for selection of model parameters0 aImproved time series prediction with a new method for selection  aDIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLANDbIOP PUBLISHING LTDcJUL aL483-L4910 v393 a

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Holder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data.

1 aJade, A., M.1 aJayaraman, Valadi, K.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/improved-time-series-prediction-new-method-selection-model-parameters-000800nas a2200205 4500008004100000022001400041245012500055210006900180260006000249300000800309490000700317100002200324700001500346700001800361700002100379700002000400700002100420700002100441856013200462 2007 eng d a0018-819000aImprove polyethylene process control and product quality - using artificial intelligence-based sensors can improve costs0 aImprove polyethylene process control and product quality using a aBOX 2608, HOUSTON, TX 77252-2608 USAbGULF PUBL COcMAR a53+0 v861 aBadhe, Yogesh, P.1 aLonari, J.1 aTambe, S., S.1 aKulkarni, B., D.1 aValecha, N., K.1 aDeshmukh, S., V.1 aRavichandran, S. uhttps://library.ncl.res.in/content/improve-polyethylene-process-control-and-product-quality-using-artificial-intelligence-based02787nas a2200265 4500008004100000022001400041245012200055210006900177260010600246300001400352490000700366520179500373653002202168653001802190653003002208653001102238653002002249653001502269653002202284100002502306700002702331700001602358700002102374856012602395 2008 eng d a0009-250900aIdentification and characterization of flow structures in chemical process equipment using multiresolution techniques0 aIdentification and characterization of flow structures in chemic aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcNOV a5330-53460 v633 a

Planar information of velocity from 2D particle image velocimetry (PIV) and large eddy simulation (LES) data have been studied using multiresolution wavelet transform (WT) formalisms, i.e., discrete and continuous WT. Identification of dominant energy containing structures with their characterization in terms of fractal spectra have been carried out for industrially important equipment exhibiting turbulent behavior. These include annular centrifugal contactor, jet loop reactor, ultrasound reactor, channel flow, stirred tank and bubble column reactor. The characterization of their dynamics based on denoising the data and studying the local energy along the WT scales show sensitive variation and this helps in identifying the size and shape of structures. A dependency is seen between mixing time and the higher order moments of length scale distribution, viz., skewness and kurtosis and a generalized correlation has been built up for important types of equipment and associated flow parameters. The correlation is not only based on the knowledge of reactor geometry and operating conditions but also on the flow structures via their statistical parameters. Wavelet transform modulus maxima (WTMM) methodology has been used to study the evolution of structures and their interaction in a reduced dimensionality by evaluating the fractal spectra. Classification studies have been carried out using principal component analysis (PCA) of the fractal spectra. The results obtained show clear classes for the six types of equipments and delineate regimes to obtain benchmark patterns of flow hydrodynamics based on PCA co-ordinates. This methodology offers a generalized way for the optimal design and operation of different types of reactors. (c) 2008 Published by Elsevier Ltd .

10aChemical reactors10ahydrodynamics10aLength scale distribution10amixing10aModel reduction10aTurbulence10aWavelet transform1 aDeshpande, Sagar, S.1 aJoshi, Jyeshtharaj, B.1 aKumar, Ravi1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/identification-and-characterization-flow-structures-chemical-process-equipment-using-001845nas a2200229 4500008004100000022001400041245013700055210006900192260009900261300001400360490000700374520092000381653002601301653002601327653002801353100002801381700002401409700001901433700002101452700002101473856012101494 2008 eng d a0960-852400aImmobilization of Candida rugosa lipase on poly(allyl glycidyl ether-co-thylene glycol dimethacrylate) macroporous polymer particles0 aImmobilization of Candida rugosa lipase on polyallyl glycidyl et aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcJUN a3623-36290 v993 a

Macroporous polymer particles containing surface epoxy groups were synthesized for immobilization of Candida rugosa lipase (CRL). The effect of incorporation of two different sets of monomers [allyl glycidyl ether (AGE) and glycidyl methacrylate (GMA)] and the effect of crosslinking density on immobilization of lipase were studied. AGE-co-EGDM polymers gave higher binding and expression of lipase than GMA-co-EGDM polymers. Optimization of immobilization parameters was done with respect to immobilization time and enzyme loading. Amongst AGE-co-EGDM polymer series, AGE-150 polymer found to give maximum lipase activity yield and therefore evaluated for temperature, pH and storage stability. Under optimum conditions, AGE-150 polymer gave 78.40% of activity yield. Immobilized lipase on AGE-150 showed a broader pH, higher temperature and excellent storage stability. (C) 2007 Published by Elsevier Ltd.

10aCandida rugosa lipase10aEnzyme immobilization10aEpoxy-activated support1 aVaidya, Bhalchandra, K.1 aIngavle, Ganesh, C.1 aPonrathnam, S.1 aKulkarni, B., D.1 aNene, Sanjay, N. uhttps://library.ncl.res.in/content/immobilization-candida-rugosa-lipase-polyallyl-glycidyl-ether-co-thylene-glycol-001418nas a2200277 4500008004100000020002200041022001400063245010700077210006900184260006800253300001200321490000900333520046000342100002000802700001900822700001900841700002100860700001900881700002600900700001800926700001400944700001900958700001900977700001600996856012801012 2009 eng d a978-3-642-11163-1 a0302-974300aIdentification of N-glycosylation sites with sequence and structural features employing random forests0 aIdentification of Nglycosylation sites with sequence and structu aHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANYbISI KolkatacDEC a146-1510 v59093 a

N-Glycosylation plays a very important role in various processes like quality control of proteins produced in ER, transport of proteins and in disease control. The experimental elucidation of N-Glycosylation sites is expensive and laborious process. In this work we build models for identification of potential N-Glycosylation sites in proteins based on sequence and structural features. The best model has cross validation accuracy rate of 72.81%.

1 aKarnik, Shreyas1 aMitra, Joydeep1 aSingh, Arunima1 aKulkarni, B., D.1 aSundarajan, V.1 aJayaraman, Valadi, K.1 aChaudhury, S.1 aMitra, S.1 aMurthy, C., A.1 aSastry, P., S.1 aPal, S., K. uhttps://library.ncl.res.in/content/identification-n-glycosylation-sites-sequence-and-structural-features-employing-random-002450nas a2200157 4500008004100000245014900041210006900190260000800259300001100267490000800278520181500286100002602101700001602127700002102143856012802164 2016 eng d00aInverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm0 aInverse problem studies of biochemical systems with structure id cMAY a93-1060 v2753 aAn efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. (C) 2016 Elsevier Inc. All rights reserved.1 aSarode, Ketan, Dinkar1 aKumar, Ravi1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/inverse-problem-studies-biochemical-systems-structure-identification-s-systems-embedding