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A new series of 3-substituted 2-phenylimidazo[2,1-b]benzothiazoles (3 a-h) were synthesized by C-arylation of 2-arylimidazo[2,1-b]benzothiazoles using palladium acetate as catalyst, and the resulting compounds were evaluated for their anticancer activity. Compounds 3 a, 3 e, and 3 h exhibited good antiproliferative activity, with GI50 values in the range of 0.19-83.1 μM. Compound 3 h showed potent anticancer efficacy against 60 human cancer cell lines, with a mean GI50 value of 0.88 μM. This compound also induced cell-cycle arrest in the G2/M phase and inhibited tubulin polymerization followed by activation of caspase-3 and apoptosis. A high-throughput tubulin polymerization assay showed that the level of inhibition for compound 3 h is similar to that of combretastatin A-4. Molecular modeling studies provided a molecular basis for the favorable binding of compounds 3 a, 3 e, and 3 h to the colchicine binding pocket of tubulin.

Apart from being relied upon for feeding the entire world, the agricultural sector is also responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority of developing nations depend on their agricultural produce as it provides employment opportunities for a significant fraction of the poor. This calls for methods to ensure the accurate and efficient diagnosis of plant disease, to minimize any adverse effects on the produce. This paper proposes the recognition and classification of maize plant leaf diseases by application of the Deep Forest algorithm. The Automated novel approach and accurate classification using the Deep Forest technique are a significant step-up from the existing manual classification and other techniques with less accuracy. The proposed approach has outperformed Deep Neural models and other traditional machine learning algorithms in terms of accuracy. It justifies its low dependency on extensive Hyper-parameter tuning and the size of the dataset as against other Deep Learning Models based on neural networks.

Plant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private dataset of 236 images. Features are extracted from the images using histogram of oriented gradients (descriptor). The concepts of artificial plant optimization are then applied to study the features of healthy leaves using IAPO. A machine learning algorithm has been created to make the model adaptive with varied datasets. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. The proposed IAPO is also compared with other classification algorithms including k-nearest neighbours, support vector machine, random forest and convolution neural network that show accuracies of 78.24%, 83.48%, 87.83%, and 91.26%, respectively, whereas IAPO shows quite accurate results in classification of leaves with an accuracy of 97.45% on training set and 95.0% accuracy on test set. K E Y W O R D Sconvolutional neural network, histogram of oriented gradients, improved artificial plant optimization, machine learning 1 | INTRODUCTION Data explosion is observed everywhere these days as data created in terms of volume in the past few years are colossal than in the history of human race. Thus, it has become important to find optimal solutions that can help in managing these large amounts of data. Intelligent bio-inspired algorithms are introduced to deal with incompleteness and imperfectness of dynamic problems (Fister, Brest, Fister, & Yang, 2015). These algorithms are called intelligent as they can acclimatize and behave like real world organisms. But most of the bio-inspired algorithms are getting ignored due to rigorous development in this field.The following are some important and prevalent pieces of work done in the field of bio-inspired algorithms. Artificial plant optimization algorithm (APOA) was used by Ziqiang, Zhihua and Jianchao (Zhao, Cui, Zeng, & Yue, 2011) to solve constrained optimization problem in which they proposed a shrinkage coefficient for checking the feasible region of a particle.