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Artificial intelligence (AI) leverages mathematical algorithms to emulate human cognitive abilities, leading to a transformative impact on the education sector. Educators are at the front lines of implementing AI in the classroom. Recent scientific studies demonstrate the capacity of AI, particularly generative models like ChatGPT, to reshape various aspects of education. In a recent study, we showcased that the integration of both artificial intelligence, specifically ChatGPT, and interactive learning activities significantly enhances the engagement levels of STEM students enrolled in a General Biology course. Furthermore, this combined approach not only boosts student engagement but also demonstrates an improvement in their overall performance within the course. Building on preliminary studies, the objective of this review article is to delineate the diverse applications of generative AI in education. To achieve this objective, we conducted a thorough search across scientific databases, including Google Scholar, Science Direct, government websites, and other resources, to collect relevant papers. Our findings underscore the contributions of generative AI, exemplified by ChatGPT, in enabling students to generate innovative text for written assignments, providing personalized feedback, facilitating adaptive learning, enhancing accessibility to education by eliminating barriers for individuals with disabilities, and supporting research endeavors. 

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

 

COVID-19, known as Coronavirus Disease 2019, is a major health issue resulting from novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Its emergence has posed a significant menace to the global medical community and healthcare system across the world. Notably, on December 12, 2020, the Food and Drug Administration (FDA) approved the utilization of the Pfizer and Moderna COVID-19 vaccines. As of July 31, 2022, the United Stated has witnessed over 91.3 million cases of COVID-19 and nearly 1.03 million fatalities. An intriguing observation is the recent reduction in the mortality rate of COVID-19, attributed to an augmented focus on early detection, comprehensive screening, and widespread vaccination. Despite this positive trend in some demographics, it is noteworthy that the overall incidence rates of COVID-19 among African American and Hispanic populations have continued to escalate, even as mortality rates have decreased. Therefore, the objective of this research study is to present an overview of COVID-19, spotlighting the disparities among different racial and ethnic groups. It also delves into the management of COVID-19 within the minority populations. To reach our research objective, we used a publicly available COVID-19 dataset from kaggle: https://www.kaggle.com/datasets/paultimothymooney/covid19-casesand- deaths-by-race. In addition, we obtained COVID-19 datasets from 10 different states with the highest proportion of African American populations. Many considerable strikes have been made in COVID-19. However, success rate of treatment in the African American population remains relatively limited when compared to other ethnic groups. Hence, there arises a pressing need for novel strategies and innovative approaches to not only encourage prevention measures against COVID-19, but also to increase survival rates, diminish mortality rates, and ultimately improve the health outcomes of ethnic and racial minorities.