More than a half-century has passed since it was discovered that phytohormone cytokinin (CK) is essential to drive cytokinesis and proliferation in plant tissue culture. Thereafter, cytokinin has emerged as the primary regulator of the plant cell cycle and numerous developmental processes. Lately, a growing body of evidence suggests that cytokinin has a role in mitigating both abiotic and biotic stress. Cytokinin is essential to defend plants against excessive light exposure and a unique kind of abiotic stress generated by an altered photoperiod. Secondly, cytokinin also exhibits multi-stress resilience under changing environments. Furthermore, cytokinin homeostasis is also affected by several forms of stress. Therefore, the diverse roles of cytokinin in reaction to stress, as well as its interactions with other hormones, are discussed in detail. When it comes to agriculture, understanding the functioning processes of cytokinins under changing environmental conditions can assist in utilizing the phytohormone, to increase productivity. Through this review, we briefly describe the biological role of cytokinin in enhancing the performance of plants growth under abiotic challenges as well as the probable mechanisms underpinning cytokinin-induced stress tolerance. In addition, the article lays forth a strategy for using biotechnological tools to modify genes in the cytokinin pathway to engineer abiotic stress tolerance in plants. The information presented here will assist in better understanding the function of cytokinin in plants and their effective investigation in the cropping system.

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Nanotechnology is a fast-expanding area with a wide range of applications in science, engineering, health, pharmacy, and other fields. Among many techniques that are employed toward the production of nanoparticles, synthesis using green technologies is the simplest and environment friendly. Nanoparticles produced from plant extracts have become a very popular subject of study in recent decades due to their diverse advantages such as low-cost synthesis, product stability, and ecofriendly protocols. These merits have prompted the development of nanoparticles from a variety of sources, including bacteria, fungi, algae, proteins, enzymes, etc., allowing for large-scale production with minimal contamination. However, nanoparticles obtained from plant extracts and phytochemicals exhibit greater reduction and stabilization and hence have proven the diversity of properties, like catalyst/photocatalyst, magnetic, antibacterial, cytotoxicity, circulating tumor deoxy ribo nucleic acid (CT-DNA) binding, gas sensing, etc. In the current scenario, nanoparticles can also play a critical role in cleaning wastewater and making it viable for a variety of operations. Nano-sized photocatalysts have a great scope toward the removal of large pollutants like organic dyes, heavy metals, and pesticides in an eco-friendly and sustainable manner from industrial effluents. Thus, in this review article, we discuss the synthesis of several metal nanoparticles using diverse plant extracts, as well as their characterization via techniques like UV–vis (ultraviolet–visible), XRD (X-ray diffraction), SEM (scanning electron microscopy), TEM (transmission electron microscopy), FTIR (Fourier transform infrared spectroscopy), etc., and catalytic activity on various hazardous systems.

Summary In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from “Save Life Hospital,” India, and the other is the UCI repository skin cancer data set. In this article, three meta‐heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy.

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INTRODUCTION: Both basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) is a type of skin malignancy which are deadly in nature. Although both can cause a serious setback for the human body, SCC is most dangerous as per human life is concerned. OBJECTIVES: It is necessary to spot out the cases of SCC and BCC among various data of skin cancer. In this research, the same is spotted out with the help of the fuzzy logic system. METHODS: At first, the membership function is constructed from the input data provided. Then based on the generated membership function, a set of fuzzy if-then rules are created. The outliers from the source data are also removed before the generation of the fuzzy membership function. Based on the fuzzy if-then rules, the decision, whether the case is of basal cell or squamous cell carcinoma is taken. RESULTS: The output prediction by the system is compared with the actual pathological report of the patients. The comparison provides an accuracy of 94.94 percent. CONCLUSION: This paper introduces a new technique to predict the existence of multiple cancers (either basal cell or squamous cell) from the mixed cancer dataset . The outcomes of the examination on the source dataset show that the proposed system achieved extraordinary desire accuracy for the portrayal of squamous cell carcinoma and basal cell carcinoma.

Computational approaches to study of neuronal impairment is rapidly evolving, as experiments and intuition alone do not explain the complexity of the brain system. An overwhelming increase in the amount of new data from both theory and computational modeling necessitate the development of databases and tools for analysis, visualization and interpretation of neuroscience data. To ensure the sustainability of this development, consistent update and training of young professionals is imperative. For this purpose, relevant articles, chapters, and modules are essential to keep abreast of developments. This review seeks to outline the biological databases and an-alytical tools along with their applications. It is envisaged that such knowledge could provide a ''training recipe'' for young scientists and a guide for professionals and researchers in neuroscience.