Effect of iron oxide and gold nanoparticles on bacterial growth leading towards biological application

<p>Abstract</p> <p>Background</p> <p>Nanoparticle-metal oxide and gold represents a new class of important materials that are increasingly being developed for use in research and health related activities. The biological system being extremely critical requires the fundamental understanding on the influence of inorganic nanoparticles on cellular growth and functions. Our study was aimed to find out the effect of iron oxide (Fe₃O₄), gold (Au) nanoparticles on cellular growth of <it>Escherichia coli </it>(<it>E. coli</it>) and also try to channelize the obtained result by functionalizing the Au nanoparticle for further biological applications.</p> <p>Result</p> <p>Fe₃O₄and Au nanoparticles were prepared and characterized using Transmission electron microscopy (TEM) and Dynamic Light Scattering (DLS). Preliminary growth analysis data suggest that the nanoparticles of iron oxide have an inhibitory effect on E. coli in a concentration dependant manner, whereas the gold nanoparticle directly showed no such activity. However the phase contrast microscopic study clearly demonstrated that the effect of both Fe₃O₄and Au nanoparticle extended up to the level of cell division which was evident as the abrupt increase in bacterial cell length. The incorporation of gold nanoparticle by bacterial cell was also observed during microscopic analysis based on which glutathione functionalized gold nanoparticle was prepared and used as a vector for plasmid DNA transport within bacterial cell.</p> <p>Conclusion</p> <p>Altogether the study suggests that there is metal nanoparticle-bacteria interaction at the cellular level that can be utilized for beneficial biological application but significantly it also posses potential to produce ecotoxicity, challenging the ecofriendly nature of nanoparticles.</p

Retarded resonance Casimir-Polder interaction of a uniformly rotating two-atom system

We consider here, a two-atom system is uniformly moving through a circular ring at an ultra-relativistic speed and weakly interacting with common external fields. The vacuum fluctuations of the quantum fields generate the entanglement between the atoms. Hence an effective energy shift is originated, which depends on the inter-atomic distance. This is commonly known as resonance Casimir-Polder interaction (RCPI). It is well known that, for a linearly accelerated system coupled with a massless scalar field, we get a thermal response when the local inertial approximation is valid. On the contrary, the non-thermality arises in the presence of the centripetal acceleration. We use the quantum master equation formalism to calculate the second-order energy shift of the entangled states in the presence of two kinds of fields. They are the massive free scalar field and the electromagnetic vector field. For both cases, we observe the non-thermal behavior. A unique retarded response is also noticed in comparison to the free massless case, which can be observed via the polarization transfer technique.Comment: 8 pages, Comments are welcom

A Clinical Update on Employing Tocilizumab to Fight COVID-19

SARS-CoV-2 infection or COVID-19, currently regarded as ‘terror’ worldwide, has spread uncontrollably as a serious menace. Till date, limited effective medicines or treatments are available. The mortality and morbidity rates have increased considerably, which have been aggravated by acute respiratory distress syndrome (ARDS) and new and old cardiovascular injuries. To control COVID-19, many drugs have been taken into consideration, like ACE2 blockers, anti-inflammatory drugs, antibodies against IL-1 and anti-IL-6, Remdesivir, Dexamethasone, Hydroxychloroquine and vaccines. In this chapter, preference is given to Tocilizumab with the latest status of clinical research update available. Despite several clinical research attempts, some have yielded promising results, others are inconclusive

A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting : Identifying High-performing Active Region Flare Indicators

Solar flares create adverse space weather impacting space- and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains elusive. In the most comprehensive study to date, we conduct a comparative analysis of four popular ML techniques (k nearest neighbors, logistic regression, random forest classifier, and support vector machine) by training these on magnetic parameters obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for the entirety of solar cycle 24. We demonstrate that the logistic regression and support vector machine algorithms perform extremely well in forecasting active region flaring potential. The logistic regression algorithm returns the highest true skill score of 0.967 +/- 0.018, possibly the highest classification performance achieved with any strictly parametric study. From a comparative assessment, we establish that magnetic properties like total current helicity, total vertical current density, total unsigned flux, R_VALUE, and total absolute twist are the top-performing flare indicators. We also introduce and analyze two new performance metrics, namely, severe and clear space weather indicators. Our analysis constrains the most successful ML algorithms and identifies physical parameters that contribute most to active region flare productivity.Peer reviewe

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Real-time speech emotion analysis for smart home assistants

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this paper, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion