ANN Based Virtual Classification Model for Discriminating Active and Inactive Withanolide E Analogs against Human Breast Cancer Cell Line MCF-7

Withanolides are a group of natural C-28 steroids built on an ergostane skeleton and classified into two major groups according to their structural skeleton: (a) compounds with a beta-oriented side chain and (b) compounds with an alpha-oriented side chain. Withanolide E represents one of the members of the latter group. Classification of active compounds on the basis of pharmacophore against specific cancer cell line poses a serious concern at the primary stage of virtual screening. To overcome this problem we have developed an artificial neural network based virtual screening model for discriminating active and non-active Withanolide-E-like derivatives or analogs against human breast cancer cell line MCF-7. In the present work, a 2D chemical descriptors ensemble pharmacophore has been modelled on the basis of withanolide E structural featured molecules. The ANN structure activity based classification model could be useful for identification of active withanolide analogs as anticancer leads against MCF-7. This model can be used for predicting possible growth inhibitory concentration (logGI50) against breast cancer cell line MCF-7. The virtual screening tool “CanWithaANN” can be accessed at local network of CIMAP

Breeding of shortening of generation cycles for a fastrer bambara groundnut

International audienc

In vitro morphogenetic responses of epicotyl and cotyledon explants in bambara groundnut (Vigna subterranea (L.) Verde)

International audienc

Cell wall properties and embryo development : a case study with legumes and models systems

National audienc

Cell wall properties and embryo development : a case study with legumes and models systems

National audienc

In vitro morphogenesis from cotyledon and epicotyl explants and flow cytometry distinction between landraces of Bambara groundnut [Vigna subterranea (L.) Verdc], an under-utilised grain legume

International audienceThe morphogenetic competence of Bambara groundnut was assessed for different landraces, explant sources and media compositions. With cotyledon explants, the best callusing occurred on a medium containing 3 mg/l BAP + 0.5 mg/l NAA, while roots were produced with 3–5 mg/l BAP + 0.5 mg/l NAA. Shoots regenerated (∼6%) from cotyledons on media with BAP alone (3–5 mg/l) or combined with 0.01–0.1 mg/l NAA. Flowers were regenerated on 5 mg/l BAP + 0.5 mg/l NAA, without any intervening callus phase. With epicotyls, the highest callusing was on 3 mg/l BAP + 0.5 mg/l NAA, and shoots regenerated (15–20%) on 3 mg/l BAP alone or with NAA at concentrations that depended on the landrace studied. Regenerated shoots rooted on hormone-free medium, and plants transferred to the greenhouse were all morphologically normal and fertile. Flow cytometry showed that most regenerants were diploid and in addition permitted to distinguish between landraces according to their relative nuclear DNA content. This is the first report on de novo regeneration in vitro of Bambara groundnut, an important yet neglected legume crop

Hybrid WGWO:whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. © 2020, The Author(s)