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Abstract BackgroundAminoglycosides are potent bactericidal antibiotics naturally produced by soil microorganisms and are commonly used in agriculture. Exposure to these antibiotics has the potential to cause shifts in the microorganisms that impact plant health. The systematic review described in this protocol will compile and synthesize literature on soil and plant root-associated microbiota, with special attention to aminoglycoside exposure. The systematic review should provide insight into how the soil and plant microbiota are impacted by aminoglycoside exposure with specific attention to the changes in the overall species richness and diversity (microbial composition), changes of the resistome (i.e. the changes in the quantification of resistance genes), and maintenance of plant health through suppression of pathogenic bacteria. Moreover, the proposed contribution will provide comprehensive information about data available to guide future primary research studies. This systematic review protocol is based on the question, “What is the impact of aminoglycoside exposure on the soil and plant root-associated microbiota?”. MethodsA boolean search of academic databases and specific websites will be used to identify research articles, conference presentations and grey literature meeting the search criteria. All search results will be compiled and duplicates removed before title and abstract screening. Two reviewers will screen all the included titles and abstracts using a set of predefined inclusion criteria. Full-texts of all titles and abstracts meeting the eligibility criteria will be screened independently by two reviewers. Inclusion criteria will describe the eligible soil and plant root-associated microbiome populations of interest and eligible aminoglycosides constituting our exposure. Study validity will be evaluated using the CEE Critical Appraisal Tool Version 0.2 (Prototype) to evaluate the risk of bias in publications. Data from studies with a low risk of bias will be extracted and compiled into a narrative synthesis and summarized into tables and figures. If sufficient evidence is available, findings will be used to perform a meta-analysis. 

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19. 

The integration of biology with mathematics and computer science mandates the training of students capable of comfortably navigating among these fields. We address this formidable pedagogical challenge with the creation of transdisciplinary modules that guide students toward solving realistic problems with methods from different disciplines. Knowledge is gradually integrated as the same topic is revisited in biology, mathematics, and computer science courses. We illustrate this process with a module on the homeostasis and dynamic regulation of red blood cell production, which was first implemented in an introductory biology course and will be revisited in the mathematics and computer science curricula.