Showing papers by "Raghunathan Rengaswamy published in 2019"

Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems

Abstract: Prediction of drug solubility is a crucial problem in pharmaceutical industries for both drug delivery and discovery purposes. Several theoretical approaches have been proposed to predict drug solubility in mixed solvent systems when the solubility values in pure solvents are known. Quantitative structure property relationship (QSPR) approaches are gaining attention to predict various physical properties due to their robustness and computational tractability. In this work, a machine learning based QSPR approach is proposed to predict drug solubility in binary solvent systems using structural features, such as molar refractivity, McGowan volume, topological surface area, and so forth. A genetic algorithm based feature selection procedure is used to check the dependency between the selected features and to obtain the final set of significant features. Initially, solubility is assumed to behave linearly with respect to the structural features and model parameters are estimated using ordinary least-squares an...

Interfacial contact resistance in polymer electrolyte membrane fuel cells: Recent developments and challenges

Abstract: Interfacial contact resistance in fuel cells is one of the primary challenges that needs to be overcome in the commercialization of fuel cells. This paper summarizes the governing parameters of the resistance such as surface morphology, contact pressure at the interface, electrical conductivities and corrosion resistance of gas diffusion layer and bipolar plate. Also, researchers’ contributions in modifying the cell components and clamping techniques are discussed in detail. Gas diffusion layer is found to be a crucial factor to control the contact resistance and hence its modification techniques are discussed. Moreover, surface of bipolar plate is prone to oxidation in acidic environment of the fuel cell. Different materials, anticorrosive coatings as well as surface modification techniques for the bipolar plate are therefore discussed in detail. Effect of clamping pressure on distribution of contact pressure is highlighted and various clamping techniques suggested by researchers are summarized. Effect of gasket selection on contact pressure distribution is considerable and therefore included in the paper. The review would be helpful for researchers in understanding the importance, reasons of existence and factors influencing the contact resistance at the interface of gas diffusion layer and bipolar plate.

Feasibility Studies of Micro Photosynthetic Power Cells as a Competitor of Photovoltaic Cells for Low and Ultra-Low Power IoT Applications

Abstract: In this work, we provide a cost comparison of micro-photosynthetic power cells (µPSC) with the well-established photovoltaic (PV) cells for ultra-low power and low power applications. We also suggest avenues for the performance improvement of µPSC. To perform cost comparison, we considered two case studies, which are development of energy systems for: (i) A typical mobile-phone battery charging (low power application) and (ii) powering a humidity sensor (ultra-low power application). For both the cases, we have elucidated the steps in designing energy systems based on PV and µPSC technologies. Based on the design, we have considered the components needed and their costs to obtain total cost for developing energy systems using both PV and µPSC technologies. Currently, µPSCs based energy systems are costlier compared to their PV counterparts. We have provided the avenues for improving µPSC performance, niche application areas, and aspects in which µPSCs are comparable to PV cells. With a huge potential to develop low-cost and high performing technologies, this emerging technology can share the demand on PV technologies for ultra-low power applications.

Entrainment of superoxide rhythm by menadione in HCT116 colon cancer cells

Abstract: Reactive oxygen species (ROS) are primary effectors of cytotoxicity induced by many anti-cancer drugs. Rhythms in the pseudo-steady-state (PSS) levels of particular intracellular ROS in cancer cells and their relevance to drug effectiveness are unknown thus far. We report that the PSS levels of intracellular superoxide (SOX), an important ROS, exhibit an inherent rhythm in HCT116 colon cancer cells, which is entrained (reset) by the SOX inducer, menadione (MD). This reset was dependent on the expression of p53, and it doubled the sensitivity of the cells to MD. The period of oscillation was found to have a linear correlation with MD concentration, given by the equation, T, in h = 23.52 - 1.05 [MD concentration in µM]. Further, we developed a mathematical model to better understand the molecular mechanisms involved in rhythm reset. Biologically meaningful parameters were obtained through parameter estimation techniques; the model can predict experimental profiles of SOX, establish qualitative relations between interacting species in the system and serves as an important tool to understand the profiles of various species. The model was also able to successfully predict the rhythm reset in MD treated hepatoma cell line, HepG2.

On the role of hydrodynamic interactions in the engineered-assembly of droplet ensembles

Abstract: Droplets, as they flow inside a microchannel, interact hydrodynamically to result in spatio-temporal patterns. The nature of the interaction decides the type of collective behaviour observed. In this context, we study the application of droplet microfluidics in the area of complex-shape particle synthesis. We show how the dynamics of droplet motion, the steady-state characteristics, the short and long-range hydrodynamics, the dependence on inlet conditions etc. are all related to the features that characterize a device like the functionality (producing many shapes) and robustness (insensitivity to fluctuations). Two primary operating regimes are identified, one where long-range interactions are dominant and the other where they are short-range. In the former, the shapes formed by droplets are steady-state solutions to the governing equations, while in the latter they are a function of how the droplets enter the channel (frequency of entry). We show that identifying the inlet conditions for producing a particle of the desired shape requires the use of a systematic approach to design which involves solving an optimization problem (using genetic algorithms) to identify the optimal operating strategy. With the knowledge of the hydrodynamics between the droplets, we demonstrate how one can reduce the complexity of the design process. We also discuss the control strategies required if one were to realize the identified operating strategy experimentally.

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.

On developing a framework for detection of oscillations in data

Abstract: Oscillation is a phenomenon very commonly observed in systems, ranging from simple ones to complex distributed network. Several techniques have been proposed in the literature for detecting oscillations to study their importance in domains ranging from physiology to climate studies. However, there is a lack of a common framework accommodative of important features of data such as non-stationarity, intermittent oscillations, measurement noise, multimodal oscillations, and the like. In this article, we outline a framework that addresses these challenges, the results of which can then be analyzed along with appropriate knowledge about the underlying system. We present results of an extensive simulation study that establishes the robustness and reliability of the proposed technique and demonstrate its applicability to real datasets in climate and in industrial datasets.

Hierarchical Multilabel Segmentation for System Identification Using Historical Data

Abstract: Model predictive controllers (MPC) utilize a model of the process to optimize the future trajectory using an objective function to obtain a control move plan. Any new MPC implementation requires model identification. The quality of the identified model depends on the information content of the data. Performing step tests to obtain informative data is time-consuming and may not be economical. Since the process data are stored for long-term in industries, this data can be used for identification. But this historical data contain informative data scattered among regions of insignificant variation, long-term disturbance effects, process interruptions, etc. Informative data required for identification can be mined from historical data by using appropriate machine learning techniques. This paper focuses on generating high quality data segments from historical records that can be used for identification of reliable process models for use in any model-based controller such as MPC. An interval-halving-based hierar...