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Abstract Course‐based undergraduate research experiences (CUREs) provide students with valuable opportunities to engage in research in a classroom setting, expanding access to research opportunities for undergraduates, fostering inclusive research and learning environments, and bridging the gap between the research and education communities. While scientific practices, integral to the scientific discovery process, have been widely implemented in CUREs, there have been relatively few reports emphasizing the incorporation of core biology concepts into CURE curricula. In this study, we present a CURE that integrates core biology concepts, including genetic information flow, phenotype–genotype relationships, mutations and mutants, and structure–function relationships, within the context of mutant screening and gene loci identification. The design of this laboratory course aligns with key CURE criteria, as demonstrated by data collected through the laboratory course assessment survey (LCAS). The survey of undergraduate research experiences (SURE) demonstrates students' learning gains in both course‐directed skills and transferrable skills following their participation in the CURE. Additionally, concept survey data reflect students' self‐perceived understanding of the aforementioned core biological concepts. Given that genetic mutant screens are central to the study of gene function in biology, we anticipate that this CURE holds potential value for educators and researchers who are interested in designing and implementing a mutant screen CURE in their classrooms. This can be accomplished through independent research or by establishing partnerships between different units or institutions. 

The Wilson–Cowan model has been widely applied for the simulation of electroencephalography (EEG) waves associated with neural activities in the brain. The Runge–Kutta (RK) method is commonly used to numerically solve the Wilson–Cowan equations. In this paper, we focus on enhancing the accuracy of the numerical method by proposing a strategy to construct a class of fourth-order RK methods using a generalized iterated Crank–Nicolson procedure, where the RK coefficients depend on a free parameter c2. When c2 is set to 0.5, our method becomes a special case of the classical fourth-order RK method. We apply the proposed methods to solve the Wilson–Cowan equations with two and three neuron populations, modeling EEG epileptic dynamics. Our simulations demonstrate that when c2 is set to 0.4, the proposed RK4-04 method yields smaller errors compared to those obtained using the classical fourth-order RK method. This is particularly visible when the spectral radius of the connection matrix or the excitation-inhibition coupling coefficient is relatively large.