Evaluation of Anticancer Activity of Azadirachta Indica and Withania Somnifera Crude Extracts on Human Prostate Cancer Cell Lines PC3 and DU145

Purpose: Recent years have witnessed a shift from chemically processed drugs to more therapeutic natural domains. Several plant parts like root, stem, leaves, etc. are utilized to develop ‘natural drugs’ for treatment of deadly diseases, one such being cancer. A plethora of plant secondary metabolites such as alkaloids, flavonoids, terpenes, steroids, phenolics have displayed viable roles in the human diseases like diabetes, cancer and have been known to show anti-inflammatory, analgesics and antipyretic properties. The present investigation has mainly focused on the evaluation of crude extracts of Azadirachta indica A. Juss. (Neem), Withania somnifera (L.) Dunal (Ashwagandha) against human prostate cancer cell lines PC3 and DU145 by performing cell viability tests. Methods: The crude extracts of plants were prepared in organic solvents such as ethanol and methanol and their anticancer activity were evaluated by employing resazurin and DNA fragmentation assays. Results: The crude extracts of A. indica and W. somnifera have showed inhibition on growth of PC3 and DU145 cells. In PC3, ethanolic extract of W. somnifera (WSEE) at 80 µg/mL was found to be optimum as cancer cells growth inhibitor and showed 45.03% inhibition whereas, 42.63% inhibition in DU145 cells was observed in 100 µg/mL of methanolic extract of A. indica (AIME). Fragmented or damaged DNA was observed in the extract treated PC3 and DU145 cancer cells assessed by DNA fragmentation assay. Conclusion: The secondary metabolites present in the crude extracts showed moderate anticancer activity against prostate cancer cell lines such as PC3 and DU145. These natural agents stand out as chemopreventive substances and are have been used more in practice with allopathic substitutes, however investigation with the aid of advance technology and extrapolating the potential benefits of various other herbs can be vital in this regard

Review of polynomial chaos-based methods for uncertainty quantification in modern integrated circuits

Advances in manufacturing process technology are key ensembles for the production of integrated circuits in the sub-micrometer region. It is of paramount importance to assess the effects of tolerances in the manufacturing process on the performance of modern integrated circuits. The polynomial chaos expansion has emerged as a suitable alternative to standardMonte Carlo-based methods that are accurate, but computationally cumbersome. This paper provides an overview of the most recent developments and challenges in the application of polynomial chaos-based techniques for uncertainty quantification in integrated circuits, with particular focus on high-dimensional problems

Effect of Organic Manures on the Growth and Yield Attributes of Spinach Beet (Beta vulgaris var bengalensis) at Low Hills of Uttarakhand

A field experiment was planned and conducted during 2020-21 at Horticulture Research Block, Department of Horticulture, School of Agricultural Sciences, SGRR University, Dehradun, Uttarakhand to investigate the “Effect of different organic manures on the growth and yield attributes of spinach beet at lower hills of Uttarakhand”. The experiment was laid out in randomized block design with three replications and nine treatments including various organic manures at different concentrations. Observations on various growth and yield attributes were recorded at regular intervals. Studies on vegetative and yield attributes were recorded using standard method of measurements. Among all the organic treatments, soil application with Farmyard manure (5kg) + Vermicompost (2.5kg) + foliar spray of Vermiwash (25%) was sown the significant improvement in plant height (cm), number of leaves per plants, length of leaves (cm), width of leaves (cm), petiole length (cm), root length (cm) and yield than other treatments. View Article DOI: 10.47856/ijaast.2021.v08i11.00

Measurement uncertainty propagation in transistor model parameters via polynomial chaos expansion

We present an analysis of the propagation of measurement uncertainty in microwave transistor nonlinear models. As a case study, we focus on residual calibration uncertainty and its effect on modeled nonlinear capacitances extracted from small-signal microwave measurements. We evaluate the uncertainty by means of the polynomial chaos expansion (PCE) method and compare the results with the NIST Microwave Uncertainty Framework, which enables both sensitivity and Monte Carlo (MC) analyses for uncertainty quantification in microwave measurements. We demonstrate that, for the considered application, PCE provides results in agreement with classical MC simulations but with a significant reduction of the computational effort

Fast characterization of input-output behavior of non-charge-based logic devices by machine learning

Non-charge-based logic devices are promising candidates for the replacement of conventional complementary metal-oxide semiconductors (CMOS) devices. These devices utilize magnetic properties to store or process information making them power efficient. Traditionally, to fully characterize the input-output behavior of these devices a large number of micromagnetic simulations are required, which makes the process computationally expensive. Machine learning techniques have been shown to dramatically decrease the computational requirements of many complex problems. We use state-of-the-art data-efficient machine learning techniques to expedite the characterization of their behavior. Several intelligent sampling strategies are combined with machine learning (binary and multi-class) classification models. These techniques are applied to a magnetic logic device that utilizes direct exchange interaction between two distinct regions containing a bistable canted magnetization configuration. Three classifiers were developed with various adaptive sampling techniques in order to capture the input-output behavior of this device. By adopting an adaptive sampling strategy, it is shown that prediction accuracy can approach that of full grid sampling while using only a small training set of micromagnetic simulations. Comparing model predictions to a grid-based approach on two separate cases, the best performing machine learning model accurately predicts 99.92% of the dense test grid while utilizing only 2.36% of the training data respectively