Antibiotic resistance is the major growing threat facing the pharmacological treatment of bacterial infections. Therefore, bioprospecting the medicinal plants could provide potential sources for antimicrobial agents. Mimusops, the biggest and widely distributed plant genus of family Sapotaceae, is used in traditional medicines due to its promising pharmacological activities. This study was conducted to elucidate the antimicrobial effect of three unexplored Mimusops spp. (M. kummel, M. laurifolia and M. zeyheri). Furthermore, the mechanisms underlying such antibacterial activity were studied. The Mimusops leaf extracts revealed significant antibacterial activities against the five tested bacterial strains with a maximum inhibition zone diameter of 22.0 mm against B. subtilis compared with standard antibiotic ciprofloxacin. The minimal inhibitory and bactericidal concentration values against tested Gram-positive and Gram-negative bacterial strains ranged from 3.15-12.5 µg/ml. However, weak antifungal effect was recorded against Candida albicans with MIC value ˃25 µg/ml. The 1, 1-diphenyl-2-picrylhydrazyl (DPPH) assay showed that M. caffra was the best antioxidant (IC50=14.75±0.028 µg/ml), while M. laurifolia was the least one (IC50=34.22±0.014 µg/ml). The phenolics in plant leaves extracts were identified and quantified by high performance liquid chromatography (HPLC) which revealed the presence of seven phenolic acids and four flavonoids. The abundant phenolic compounds were rutin (5.216±0.067 mg/g dried wt.) and gallic acid (0.296±0.068 mg/g) followed by myricetin (0.317±0.091 mg/g) then kaempferol (0.113±0.049 mg/g) as flavonoids. The antibacterial mechanism of M. laurifolia extract, as a representative species, induces ultrastructural changes in the model bacterium Staphylococcus aureus with cell wall and plasma membrane lysis as revealed by transmission electron microscopy. Overall, Mimusops species (M. laurifolia, M. kummel and M. zeyheri) are promising natural alternative sources for antimicrobial agents.
The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities. Cloud computing can be used to store big data. The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual r
... Show MoreSeawater might serve as a fresh‐water supply for future generations to help meet the growing need for clean drinking water. Desalination and waste management using newer and more energy intensive processes are not viable options in the long term. Thus, an integrated and sustainable strategy is required to accomplish cost‐effective desalination via wastewater treatment. A microbial desalination cell (MDC) is a new technology that can treat wastewater, desalinate saltwater, and produce green energy simultaneously. Bio‐electrochemical oxidation of wastewater organics creates power using this method. Desalination and the creation of value‐added by‐products are expected because of this ionic mov
Cover crops (CC) improve soil quality, including soil microbial enzymatic activities and soil chemical parameters. Scientific studies conducted in research centers have shown positive effects of CC on soil enzymatic activities; however, studies conducted in farmer fields are lacking in the literature. The objective of this study was to quantify CC effects on soil microbial enzymatic activities (β-glucosidase, β-glucosaminidase, fluorescein diacetate hydrolase, and dehydrogenase) under a corn (Zea mays L.)–soybean (Glycine max (L.) Merr.) rotation. The study was conducted in 2016 and 2018 in Chariton County, Missouri, where CC were first established in 2012. All tested soil enzyme levels were significantly different between 2016 and 2018
... Show MoreData scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
The research amid to find out the extent of Iraqi oil companies commitment to implement internal control procedures in accordance with the updated COSO framework. As the research problem was represented in the fact that many of the internal control procedures applied in the Iraqi oil companies are incompatible with most modern international frameworks for internal control, including the integrated COSO framework, issued by the Committee of Sponsoring Organizations of the Tradeway Committee. The research followed the quantitative approach to handling and analysing data by designing a checklist to represent the research tool for collecting data. The study population was represented in the Iraqi oil companies, while the study sample
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