Cell therapy is an emerging fields in the treatment of various diseases such as cardiovascular, pulmonary, hepatic, and neoplastic diseases. Stem cells are an integral tool for cell therapy. Multipotent stem cells are an important class of stem cells which have the ability to self-renew through dividing and developing into multiple specific cell types in a specific tissue or organ. These cells are capable to activate or inhibit a sequence of cellular and molecular pathways leading to anti-inflammatory and anti-apoptotic effects which might contribute to the treatment of various diseases. It has been showed that multipotent stem cells exert their therapeutic effects via inhibition/activation of a sequence of cellular and molecular pathways. Although the advantages of multipotent stem cells are numerous, further investigation is still necessary to clarify the biology and safety of these cells before they could be considered as a potential treatment for different types of diseases. This review summarizes different features of multipotent stem cells including isolation, differentiation, and therapeutic applications.

This paper presents a new steganography algorithm based on Morphology associative memory. Often, steganalysis methods are created to detect steganography algorithms using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). In this paper, cover images are mapped to morphological representation by using morphology transform containing morphological coefficients, and each bit of secret message is inserted in the least significant bit of morphological coefficients. To evaluate stego quality, we measure the quality of the cover image after embedding by comparing with other image transformed steganography algorithms such as discrete cosine and Wavelet transforms. The quality of stego has considerably improved in comparison with the state-of-art methods. In the other experimentation, we test the robustness of our proposed method by using Wavelet and Block-based steganalysis methods. The results show a high level of robustness of our algorithm respect to other steganography algorithms.

Summary Today, large amounts of data are generated in various applications such as smart cities and social networks, and their processing requires a lot of time. One of the methods of processing data types and reducing computational time on data is the use of dimension reduction methods. Reducing dimensions is a problem with the optimization approach and meta‐heuristic methods can be used to solve it. Namib beetles are an example of intelligent insects and creatures in nature that use an interesting strategy to survive and collect water in the desert. In this article, the behavior of Namib beetles has been used to collect water in the desert to model the Namib beetle optimization (NBO) algorithm. In the second phase of a binary version, this algorithm is used to select features and reduce dimensions. Experiments on CEC functions show that the proposed method has fewer errors than the DE, BBO, SHO, WOA, GOA, and HHO algorithms. In large dimensions such as 200, 500, and 1000 dimensions, the NBO algorithm of meta‐heuristic algorithms such as HHO and WOA has a better rank in the optimal calculation of benchmark functions. Experiments show that the proposed algorithm has a greater ability to reduce dimensions and feature selection than similar meta‐heuristic algorithms. In 87.5% of the experiments, the proposed method reduces the data space more than other compared methods.

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