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To get inside the properties of N,N-disubstituted Schiff bases, we synthesized three high-yielding benzaldehyde Schiff bases. We used the reaction between salicylaldehyde and different diamine compounds, including diamine, ethanediamine, and o-phenylenediamine, determining the structure of obtained molecules by nuclear magnetic resonance spectroscopy and electrospray ionization mass spectroscopy. We thus evaluated the microbicidal and antitumor activity of these compounds, showing that salicylaldehyde-hydrazine hydrate Schiff base (compound 1a) significantly inhibited the growth of S. aureus; salicylaldehyde-o-phenylenediamine Schiff base (compound 1c) displayed a strong capability to inhibit the proliferation of leukemia cell lines K562 and HEL. Moreover, we observed that the antibacterial action of 1a might be associated with the regulation of the expression of key virulence genes in S. aureus. Compound 1c resulted in a strong apoptotic activity against leukemia cells, also affecting the cell cycle distribution. Overall, our novel N,N-disubstituted Schiff bases possess unique antibacterial or antitumor activities that exhibit the potent application prospect in prophylactic or therapeutic interventions, providing new insights for developing new antibacterial and anticancer chemical agents. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing has gained massive popularity in information technology and the industrial Internet of things. It provides facilities to the users over the wireless channel. Many surveys have been carried out in cloud security and privacy. The existing survey papers do not specify the classifications on the basis of cloud computing components. Therefore, they fail to provide the techniques with their specialities as well as the previously available literature review is outdated. This paper presents the security for cloud computing models with a new aspect. Unlike the previously existing surveys, the literature review of this paper includes the latest research papers in the field of cloud security. Also, different classifications are made for cloud computing security on the basis of different cloud components that are used to secure the cloud models. Furthermore, a total of eleven (11) classifications are considered, which includes cloud components to secure the cloud systems. These classifications help the researchers to find out the desired technique used in a specific component to secure the cloud model. Moreover, the shortcoming of each component enables the researchers to design an optimal algorithm. Finally, future directions are given to highlight future research challenges that give paths to researchers. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing has gained massive popularity in information technology and the industrial Internet of things. It provides facilities to the users over the wireless channel. Many surveys have been carried out in cloud security and privacy. The existing survey papers do not specify the classifications on the basis of cloud computing components. Therefore, they fail to provide the techniques with their specialities as well as the previously available literature review is outdated. This paper presents the security for cloud computing models with a new aspect. Unlike the previously existing surveys, the literature review of this paper includes the latest research papers in the field of cloud security. Also, different classifications are made for cloud computing security on the basis of different cloud components that are used to secure the cloud models. Furthermore, a total of eleven (11) classifications are considered, which includes cloud components to secure the cloud systems. These classifications help the researchers to find out the desired technique used in a specific component to secure the cloud model. Moreover, the shortcoming of each component enables the researchers to design an optimal algorithm. Finally, future directions are given to highlight future research challenges that give paths to researchers. [ABSTRACT FROM AUTHOR]

Teaching reflection based on videos is the main method in teacher education and professional development. However, it takes a long time to analyse videos, and teachers are easy to fall into the state of information overload. With the development of "AI + education," automatic recognition of teacher behavior to support teaching reflection has become an important research topic. In this paper, taking online open classroom teaching video as the data source, we collected and constructed a teacher behavior dataset. Using this dataset, we explored the behavior recognition methods based on RGB video and skeleton information, and the information fusion between them is carried out to improve the recognition accuracy. The experimental results show that the fusion of RGB information and skeleton information can improve the recognition accuracy, and the early-fusion effect is better than the late-fusion effect. This study helps to solve the problems of time-consumption and information overload in teaching reflection and then helps teachers to optimize the teaching strategies and improve the teaching efficiency. [ABSTRACT FROM AUTHOR]

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process. [ABSTRACT FROM AUTHOR]

In the article titled "Security Measurement in Industrial IoT with Cloud Computing Perspective: Taxonomy, Issues, and Future Directions" [[1]], there was an error in the third and sixth affiliations. In addition, the Acknowledgements section should be updated as follows: "The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs and partially supported by the Faculty of Computer Science and Information Technology, University of Malaya, under Postgraduate Research Grant PG035-2016A." REFERENCES 1 Shah S., Khan M., Ahmad A. Security measurement in industrial iot with cloud computing perspective: taxonomy, issues, and future directions. [Extracted from the article]