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Rao, Shrisha
- Agent-based modelling of biofilm formation and inhibition in Escherichia coli
Abstract Views :287 |
PDF Views:82
Authors
Anusha Modwal
1,
Shrisha Rao
1
Affiliations
1 International Institute of Information Technology, Bengaluru 560 100, IN
1 International Institute of Information Technology, Bengaluru 560 100, IN
Source
Current Science, Vol 109, No 5 (2015), Pagination: 930-937Abstract
Biofilm formation by bacteria such as Escherichia coli is a serious challenge faced in the treatment of infections. Biofilms provide a protected environment for the pathogens, where they may persist despite environmental adversities and treatments causing chronic infections. Furanones, both naturally occurring and synthetic, have been found to inhibit biofilm formation. An agent-based model of the behaviour of E. coli with regard to formation and inhibition of biofilms, is described here. Analytical tools used in this article allow us to find the optimal range of inhibitor concentration for Gram-negative bacteria. This is made possible by appropriate mathematical analysis, reducing the need for laborious experimental verification. The results are seen to be consistent with published experimental data on biofilm thickness of E. coli when acted upon by furanones. Our model permits estimation of concentration of the inhibitors needed to properly curb biofilms. This in turn has therapeutic implications, in that it may help formulate strategies to prevent the formation and growth of biofilms, especially in the context of devices placed inside the body, like catheters and implants.Keywords
Agent-based modelling, biofilm, Escherichia coli, furanones.Full Text
References
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- Swadeshi Nobel Prize
Abstract Views :261 |
PDF Views:89
Authors
Affiliations
1 International Institute of Information Technology, Bangalore, Electronic City, Hosur Road, Bengaluru 560 100, IN
1 International Institute of Information Technology, Bangalore, Electronic City, Hosur Road, Bengaluru 560 100, IN
Source
Current Science, Vol 109, No 7 (2015), Pagination: 1220-1221Abstract
No Abstract.Full Text
- Mathematics of Planet Earth:Mathematicians Reflect on How to Discover, Organize, and Protect our Planet
Abstract Views :235 |
PDF Views:75
Authors
Affiliations
1 International Institute of Information Technology-Bangalore, 26/C, Electronics City, Hosur Road, Bengaluru 560 100, IN
1 International Institute of Information Technology-Bangalore, 26/C, Electronics City, Hosur Road, Bengaluru 560 100, IN
Source
Current Science, Vol 111, No 3 (2016), Pagination: 571-572Abstract
It is well known that applied mathematics has found many uses in the studies of ecology and associated domains. This is due to the ongoing issues of global warming, severe weather events, and climate change; and also, of course, the overarching concerns of resource management and over-exploitation, or wastage of nature's bounty.Full Text
- Classification of SDSS Photometric Data Using Machine Learning on A Cloud
Abstract Views :239 |
PDF Views:83
Authors
Vishwanath Acharya
1,
Piyush Singh Bora
1,
Karri Navin
1,
Anisha Nazareth
1,
P. S. Anusha
1,
Shrisha Rao
1
Affiliations
1 International Institute of Information Technology-Bangalore, 26/C, Electronics City, Bengaluru - 560 100, IN
1 International Institute of Information Technology-Bangalore, 26/C, Electronics City, Bengaluru - 560 100, IN
Source
Current Science, Vol 115, No 2 (2018), Pagination: 249-257Abstract
Astronomical datasets are typically very large, and manually classifying the data in them is effectively impossible. We use machine learning algorithms to provide classifications (as stars, quasars and galaxies) for more than one billion objects given photometrically in the Third Data Release of the Sloan Digital Sky Survey (SDSS-III). We have used kNN, SVM and random forest algorithms in a distributed environment over the cloud to classify 1,183,850,913 unclassified photometric objects present in the SDSSIII catalog. This catalog contains photometric data for all objects viewed through a telescope and spectroscopic data for a small part of these. Although it is possible to classify all the objects using spectroscopic data, it is impractical to obtain such data for each one of them. To classify such a big dataset on a single machine would be impractically slow, so we have used the Spark cluster computing framework to implement a distributed computing environment over the cloud. We found that writing results (dozens of gigabytes) to the cloud storage is very slow while using kNN. Though writing the results with SVM is faster as it is done in parallel, its accuracy is only around 87%, due to lack of a kernel implementation of it in Spark. We then used the random forest algorithm to classify the entire set of 1,183,850,913 objects with an accuracy of 94% in about 17 hours of processing time. The result set is significant as even collecting spectroscopic data for these many objects would take decades, and our classifications can help astronomers and astrophysicists carry out further studies.Keywords
Astronomical Data, Classification, Cloud Computing, Distributed Algorithms, Machine Learning.Full Text
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- Artificial Intelligence Policy:Need Aggressive Development with Prudent Regulation
Abstract Views :330 |
PDF Views:80
Authors
Affiliations
1 International Institute of Information Technology – Bangalore 26/C Electronics City, Bengaluru 560 100, IN
2 SRM University, Chennai 603 203, IN
1 International Institute of Information Technology – Bangalore 26/C Electronics City, Bengaluru 560 100, IN
2 SRM University, Chennai 603 203, IN
Source
Current Science, Vol 115, No 6 (2018), Pagination: 1015-1016Abstract
Artificial intelligence (AI) is a buzz-phrase these days. Serious computer scientists and researchers have been known to bemoan the fact that their scholarly academic discipline and work are demeaned in the public mind, primarily on account of being conflated with hackneyed software discussions and superficial literature. In similar vein, AI and related topics – machine learning, deep learning, neural networks, etc. – are now to be found embedded in the utterances and writings of a great many people who have neither heard of the frame problem, nor any idea how gradient descent is related to Newton’s method.Full Text