Raghunathan Rengaswamy

Bio: Raghunathan Rengaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Proton exchange membrane fuel cell & Fault detection and isolation. The author has an hindex of 39, co-authored 210 publications receiving 9632 citations. Previous affiliations of Raghunathan Rengaswamy include Indian Institute of Technology Bombay & Bosch.

Silver nanoparticle induced oxidative stress augments anticancer gut bacterial metabolites production

Abstract: Colorectal cancer (CRC) is the fourth leading cause of mortality, world-wide. Gut bacterial dysbiosis being one of the major causes of CRC onset. Gut microbiota produced metabolites, e.g. folate and butyrate play crucial roles in cancer progression and treatment, and thus, need to be considered for effective CRC management. A potential cancer therapy, i.e., use of silver nanoparticles (AgNPs), imparts cytotoxic effects by inducing high intracellular reactive oxygen species (ROS) levels. However, the simultaneous interactions of AgNPs with gut microbiota to aid CRC treatment has not been reported thus far. Therefore, in this study, variation of intracellular ROS concentrations, in Enterococcus durans (E. durans), a representative gut microbe, was studied in the presence of low AgNP concentrations (25 ppm). Increases were observed in intracellular hydroxyl radical and extracellular folic acid concentrations by 48% and 52%, respectively, at the 9thhour of microbial growth. To gain a systems level understanding of ROS metabolism in E. durans, genome scale metabolic network reconstruction and modeling was adopted. In silico modeling reconfirmed the critical association between ROS and folate metabolism. Further, amino acids, energy metabolites, nucleotides, and butyrate were found to be important key players. Consequently, the anticancer effect of folic acid was experimentally studied on HCT116 (i.e., colon cancer cell line), wherein, its viability was reduced to 79% via folate present in the supernatants from AgNP treated E. durans cultures. Thus, we suggest that the inter-relationship between gut bacteria and AgNP-based cancer treatment can be used to design robust and effective cancer therapies.

A generative approach to qualitative trend analysis for batch process fault diagnosis

Abstract: Most of the existing methods for qualitative trend analysis are based on discriminative models. A disadvantage of such models is that many heuristic rules or local search methods are needed. Recently, an effort has been made to develop a globally optimal method for qualitative trend analysis. This method is based on a generative (rather than discriminative) model and has shown to lead to excellent performance. However, this method comes at an extreme computational demand which renders the methods unlikely for on-line application. In this work, an alternative method, while still generative in nature, is proposed which is shown to deliver the same performance while reducing the computational demand considerably.

Novel ratio-metric features enable the identification of new driver genes across cancer types

Abstract: An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG .

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.