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  1. Past research has recognized culture and gender variation in the experience of emotion, yet this has not been examined on a level of effective connectivity. To determine culture and gender differences in effec-tive connectivity during emotional experiences, we applied dynamic causal modeling (DCM) to electro-encephalography (EEG) measures of brain activity obtained from Chinese and American participants while they watched emotion-evoking images. Relative to US participants, Chinese participants favored a model bearing a more integrated dorsolateral prefrontal cortex (dlPFC) during fear v. neutral experiences. Meanwhile, relative to males, females favored a model bearing a less integrated dlPFC during fear v. neutralmore »experiences. A culture-gender interaction for winning models was also observed; only US partici-pants showed an effect of gender, with US females favoring a model bearing a less integrated dlPFC compared to the other groups. These findings suggest that emotion and its neural correlates depend in part on the cultural background and gender of an individual. To our knowledge, this is also the first study to apply both DCM and EEG measures in examining culture-gender interaction and emotion.« less
    Free, publicly-accessible full text available January 1, 2023
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  5. In order to contextualize calculus, first-year engineering students take on a semester-long design project that grounds engineering design as an epistemic practice. The project is designed to motivate students to creatively and collaboratively apply mathematical modeling to design roller coasters. Students are asked to engage as engineers and respond to a hypothetical theme park that has solicited design proposals for a new roller coaster. Students are required to use various mathematical functions such as polynomials and exponentials to create a piece-wise function that models the roller coaster track geometry. The entire project is composed of five modules, each lasting threemore »weeks. Each module is associated with a specific calculus topic and is integrated into the design process in a form of a design constraint or performance metric. The module topics include continuity, smoothness, local maxima and minima, inflection points, and area under the curve. Students are expected to refine their models in each module, resulting in the iteration of the previous design to satisfy a new set of requirements. This paper presents the project organization, assessment methods, and student feedback. This work is part of a multi-year course intervention and professional development NSF project to increase the success of underrepresented and women students in engineering.« less
    Free, publicly-accessible full text available September 26, 2022
  6. n order to contextualize calculus, first-year engineering students take on a semester-long design project that grounds engineering design as an epistemic practice. The project is designed to motivate students to creatively and collaboratively apply mathematical modeling to design roller coasters. Students are asked to engage as engineers and respond to a hypothetical theme park that has solicited design proposals for a new roller coaster. Students are required to use various mathematical functions such as polynomials and exponentials to create a piece-wise function that models the roller coaster track geometry. The entire project is composed of five modules, each lasting threemore »weeks. Each module is associated with a specific calculus topic and is integrated into the design process in a form of a design constraint or performance metric. The module topics include continuity, smoothness, local maxima and minima, inflection points, and area under the curve. Students are expected to refine their models in each module, resulting in the iteration of the previous design to satisfy a new set of requirements. This paper presents the project organization, assessment methods, and student feedback. This work is part of a multi-year course intervention and professional development NSF project to increase the success of underrepresented and women students in engineering.« less
  7. Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reducemore »the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.« less
    Free, publicly-accessible full text available September 24, 2022