Refine your search
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Gowrishankar, J.
- Detection of Novel Variant of Human P85α PI3K with Impaired Insulin Stimulated Lipid Kinase Activity by Pcr and Restriction Digestion
Abstract Views :233 |
PDF Views:0
Authors
Affiliations
1 Department of Biotechnology, PGP College of Arts and Science, Namakkal-637207. Tamilnadu, IN
2 Department of Biotechnology, Bharathidasan University, Trichy-620 024. Tamilnadu, IN
3 Centre for Biotechnology, Muthayammal College of Arts & Science, Rasipuram – 637 408, IN
1 Department of Biotechnology, PGP College of Arts and Science, Namakkal-637207. Tamilnadu, IN
2 Department of Biotechnology, Bharathidasan University, Trichy-620 024. Tamilnadu, IN
3 Centre for Biotechnology, Muthayammal College of Arts & Science, Rasipuram – 637 408, IN
Source
International Journals of Biotechnology and Biochemistry, Vol 8, No 1 (2012), Pagination: 27-34Abstract
Phosphoinositide 3 kinase (PI3K) plays a central part in the mediation of insulin stimulated glucose disposal. The mechanism underlying severe insulin resistance with diabetes mellitus or impaired glucose tolerance in human remain poorly understood but increasing the knowledge of the complexity of intracellular insulin signaling pathways has opened up a large number of candidate genes like PI3K potentially responsible for cases of genetically determined insulin resistance. PI3K - P85α regulatory subunit gDNA was examined in pro band family with syndromes of severe insulin resistance by amplifying and analyzing with special reference to the novel heterozygous mutation gene with a pair of primers flanking the amplification of middle segment of size 894 bp from the full length sequence of human type 2 diabetic gDNA. Mutation in Arg 409 Gly cost the loss of Msp I restriction sites. The amplified product was digested with Msp I enzyme the presence or absence of the site was identified by Agarose gel electrophoresis. Several samples of diabetic patient were tried for PCR amplification of the desired segments. Many didn't amplify. Out of five samples amplify and digested with Msp I two samples were cut by the enzyme denoting the presence of the site and the three samples were not cuted by the enzyme denoting the absence of the site. The impaired PI3K activity due to the mutation of Arg 409 Gly codon of P85 α would contribute the insulin resistance in many type II diabetes mellitus.References
- Antonetti DA., Algenstaedt P., and Kahn CR., 1996, Insulin receptor substrate- 1 binds two novel splice variants of the regulatory subunits of PI3 kinase in muscle and brain, Mole. Cell Biol.,16: 2195 –2203.
- Baynes KCR., Beeton CA., Panayotou G., Stein R., Hansen T., Simpson H., Rahilly SO., Shepherd PR., and Whitehead JP., 2000, Natural variants of human P85a, P13K in severe insulin resistance: a novel variant with impaired insulin – stimulated lipid kinase activity. Diabetology., 43:321-331.
- Moller DE., and O’Rahilly S., 1993, Syndromes of severe insulin resistance: clinical and pathophysiological features., In: Moller DE (ed) Insulin resistance. Wiley, Chichester, pp 49 – 81.
- NDDG., 1979., National Diabetes Data Groups classification and diagnosis of diabetes mellitus and other categories of glucose intolerance, Diabetes., 28, 1039 –1057.
- Krook A., Kumar S., and Laing I., 1994, Molecular scanning of the Insulin receptor gene in syndromes of insulin resistances, Diabetes., 43, 357 – 366.
- Turner RC., Holman RR., Mathews D., Hockaday TDR., and Peto J., 1979, Insulin deficiency and insulin resistance interaction in diabetes; estimation of their relative contribution by feedback analysis from nasal plasma insulin and glucose concentration, Metabolism, 28,1086-1096
- A New Topology for Regulation of Active Power by Battery Storage System with Cascaded Multilevel Inverter using Three Phase System
Abstract Views :166 |
PDF Views:0
Authors
Affiliations
1 Department of EEE, St. Peter’s University, Avadi, Chennai - 600054, Tamil Nadu, IN
2 Department of EEE, GRT Institute of Engineering and Technology, Tiruttani - 631209, Tamil Nadu, IN
3 Department of EEE, Sastra University, Thanjavur - 613401, Tamil Nadu, IN
4 Department of EEE, S. V. C. E. T, Chittoor - 627855, Andhra Pradesh, IN
1 Department of EEE, St. Peter’s University, Avadi, Chennai - 600054, Tamil Nadu, IN
2 Department of EEE, GRT Institute of Engineering and Technology, Tiruttani - 631209, Tamil Nadu, IN
3 Department of EEE, Sastra University, Thanjavur - 613401, Tamil Nadu, IN
4 Department of EEE, S. V. C. E. T, Chittoor - 627855, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 22 (2016), Pagination:Abstract
The battery plays a major role for energy storage in non conventional energy systems. This paper proposed an AC-DCAC converter using Pulse Width Modulation Technique for Multilevel inverter. Normally the voltage drop increases when the load increases. Because the load voltages are consume more than the source voltages. To overcome this problem, the battery energy depot system is linked to the converter for controlling the active power. The performance of the proposed topology is a present active power regulates in battery energy depot scheme has been verified by simulation MATLAB/SIMULINK environment.Keywords
Cascade Multilevel Converter, Energy Storage System, PWM Technique, Phase Shifting Transformer, Smart Grid.- Suggestions for a National Framework for Publication of and Access to Literature in Science and Technology in India
Abstract Views :226 |
PDF Views:66
Authors
S. Chakraborty
1,
J. Gowrishankar
2,
A. Joshi
3,
P. Kannan
4,
R. K. Kohli
4,
S. C. Lakhotia
5,
G. Misra
6,
C. M. Nautiyal
7,
K. Ramasubramanian
8,
N. Sathyamurthy
3,
A. K. Singhvi
9
Affiliations
1 National Institute of Plant Genome Research, New Delhi, IN
2 Indian Institute of Science Education and Research Mohali, IN
3 Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru, IN
4 Central University of Punjab, Bathinda, IN
5 Banaras Hindu University, Varanasi, IN
6 Indian Institute of Science, Bengaluru, IN
7 Indira Nagar, New Delhi, IN
8 Indian Institute of Technology, Mumbai, IN
9 Physical Research Laboratory, Ahmedabad, IN
1 National Institute of Plant Genome Research, New Delhi, IN
2 Indian Institute of Science Education and Research Mohali, IN
3 Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru, IN
4 Central University of Punjab, Bathinda, IN
5 Banaras Hindu University, Varanasi, IN
6 Indian Institute of Science, Bengaluru, IN
7 Indira Nagar, New Delhi, IN
8 Indian Institute of Technology, Mumbai, IN
9 Physical Research Laboratory, Ahmedabad, IN
Source
Current Science, Vol 118, No 7 (2020), Pagination: 1026-1034Abstract
The outcome of deliberation on various aspects of publication and free access to scientific literature by a panel of nominated fellows from three science academies, viz. Indian National Science Academy, Indian Academy of Sciences, The National Academy of Sciences India, and expert invitees are presented.References
- Budapest Open Access Initiative; https://www.budapestopenaccessinitiative.org/read
- Brainard, J., Facing Plan S, publishers may set papers free. Science, 2019, 364(6441), 620.
- Plan S: Overlooked hybrid journal model; https://science.sciencemag.org/content/363/6426/461.2
- Proeject Deal; https://www.projekt-deal.de/about-deal/
- Carvalho, J., Laranjeira, C., Vaz, V. and Moreira. M. J., Monitoring a national open access funder mandate. Proc. Comp. Sci., 2017, 106, 283–290.
- Hashim, H. N. M., Facilitating Malaysia towards innovative society: Arguing the case for open access policy. Sixth IEEE International Conference on e–Science Workshops. IEEE, 2010, doi:10.1109/eScienceW.2010.33
- Ilva, J., Towards reliable data – counting the Finnish Open Access publications. Proc. Comp. Sci., 2017, 106, 299–304.
- Kirsop, D., Open Access and developing Countries: A report on the workshop, Electronic publishing and open access: Developing Country Perspectives, 2006.
- Schwartzkroin, A. and Shorvon, S.D., Public (open) access policy. Epilepsia, 2008, 49(8), 1295–1296; doi:10.1111/j.1528-1167. 2008.01733.
- The Delhi Declaration on Open Access by Open Access India; http://openaccessindia.org/delhi-declaration-on-open-access/
- Chaddhah, P. and Lakhotia, S. C., A policy statement on Dissemination and Evaluation of Research output in India by the Indian National Science Academy (New Delhi). Proc. Indian Natl. Sci. Acad., 2018, 84(2), 319–329.
- Madan, M., Kimidi, S. S., Gunasekaran, S. and Arunachalam, S., Should Indian researchers pay to get their work published? Curr. Sci., 2017, 112(4), 703–713.
- Lakhotia, S. C., Why are Indian research journals not making a mark? – The enemy is within. Curr. Sci., 2018, 115(12), 2187– 2188.
- Lakhotia, S. C., Mis-conceived and Mis-implemented academic assessment rules underlie the scourge of predatory journals and conference. Proc. Indian Natl. Sci. Acad., 2017, 83(3), 513–515.
- Madhan, M., Gunasekaran, S. and Arunachalam, S., Evaluation of research in India: are we doing it right? Indian J. Med. Ethics, Published online on 23 March 2018. doi:10.20529/IJME.2018.024
- DBT and DST open access Policy: Policy on open access to DBT and DST funded research; http://www.dst.gov.in/sites/default/files/APPROVED%20OPEN%20ACCESS%20POLICY-DBT%-26DST%2812.12.2014%29_1.pdf
- Public Ownership of Research Journals
Abstract Views :233 |
PDF Views:72
Authors
Affiliations
1 Indian Institute of Science Education and Research, Sector 81, Mohali 140 306, IN
1 Indian Institute of Science Education and Research, Sector 81, Mohali 140 306, IN
Source
Current Science, Vol 119, No 4 (2020), Pagination: 583-584Abstract
No Abstract.- Gene Biclustering On Large Datasets Using Fuzzy C-means Clustering
Abstract Views :133 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Jain University, IN
3 Department of Computer Science and Engineering, Presidency University, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Jain University, IN
3 Department of Computer Science and Engineering, Presidency University, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM
Source
ICTACT Journal on Soft Computing, Vol 12, No 2 (2022), Pagination: 2578-2582Abstract
The current study employs biclustering to alleviate some of the drawbacks associated with gene expression data grouping. Different biclustering algorithms are used in this study to detect unique gene activity in various contexts and reduce the duplication of broad gene information. Furthermore, machine learning or heuristic algorithms have become widely utilised for biclustering due to their suitability in problems where populations of potential solutions allow examination of a larger percentage of the research area. To begin with, gene expression data biclusters frequently contain data that is the same under a variety of different situations of gene expression. Therefore, the biclustering technique is particularly effective if the matrix lines and columns are merged immediately. Submatrices can be identified using the Large Average Sub matrix. A Fuzzy C-Means algorithm is also used to ensure that the sub-matrix can be expanded to include more rows and columns for further analysis. The sub-matrices and component precision and strength are factored into the system design. It uses biclustering techniques to differentiate gene expression information. On the Garber dataset, the simulation is run in Java. Using the average match score for non-overlapping modules, the influence of noise on overlapping modules using constant bicluster and additive bicluster, and the overall run duration, the study is assessed.Keywords
Heuristic Algorithm, Gene Expression, Data Biclusters, Fuzzy C-MeansReferences
- H. Bulut and A. Onan, “An Improved Ant-Based Algorithm Based on Heaps Merging and Fuzzy C-Means for Clustering Cancer Gene Expression Data”, Sadhana, Vol. 45, No. 1, pp. 1-17, 2020.
- C. Lopez, S. Tucker and T., Salameh, “An Unsupervised Machine Learning Method for Discovering Patient Clusters based on Genetic Signatures”, Journal of Biomedical Informatics, Vol. 85, pp. 30-39, 2018.
- S. Lee, “Fuzzy Clustering with Optimization for Collaborative Filtering-Based Recommender Systems”, Journal of Ambient Intelligence and Humanized Computing, Vol. 52, 1-18, 2021.
- P. Edwin Dhas and B. Sankara Gomathi, “A Novel Clustering Algorithm by Clubbing GHFCM and GWO for Microarray Gene Data”, The Journal of Supercomputing, Vol. 76, No. 8, pp. 5679-5693, 2020.
- I. Aljarah, M. Habib, H. Faris and S. Mirjalili, “Introduction to Evolutionary Data Clustering and Its Applications.”, Proceedings of International Conference on Evolutionary Data Clustering: Algorithms and Applications, pp. 1-21, 2021.
- M. Fratello, L. Cattelani, A. Federico, and D. Greco, “Unsupervised Algorithms for Microarray Sample Stratification”, Proceedings of International Conference on Microarray Data Analysis, pp. 121-146, 2022.
- D. Yan, H. Cao, Y. Yu and X. Yu, “SingleObjective/Multiobjective Cat Swarm Optimization Clustering Analysis for Data Partition”, IEEE Transactions on Automation Science and Engineering, Vol. 17, No. 33, pp. 1633-1646, 2020.
- N. Kushwaha, M. Pant, S. Kant and V.K. Jain, “Magnetic Optimization Algorithm for Data Clustering”, Pattern Recognition Letters, Vol. 115, pp. 59-65, 2018.
- Y. Yan and F.C. Harris, “A Survey of Data Clustering for Cancer Subtyping”, International Journal for Computers and Their Applications, Vol. 28, No. 2, pp. 1-13, 2021.
- M. Franco and J.M. Vivo, “Cluster Analysis of Microarray Data”, Proceedings of International Conference on Microarray bioinformatics, pp. 153-18, 2019.