Durham Brooks, T. | Search bioRxiv

bioRxiv2020

Digital Imaging and Vision Analysis in Science Project improves the self-efficacy and skill of undergraduate students in computational work

In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling hook for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and finish the summer with independent research. Finally, scholars participate in a follow-up seminar the following spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars being members of an underrepresented minority group. Most scholars (82%) entered the program as freshmen, with 89% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice.

scientific communication and education↗

bioRxiv2020

Characterizing expression of candidate genes of a glutamate receptor pathway in Arabidopsis thaliana using real-time RT-PCR

The Arabidopsis thaliana genome contains twenty genes that are analogous to mammalian ionotropic glutamate receptors. There are sixteen mammalian glutamate receptors, which are best known for their roles in neuroplasticity, learning, and memory. The large number of glutamate receptors in A. thaliana suggests they play important roles in the plants growth and development, possibly serving to regulate function like they do in non-excitable mammalian tissues. A specific glutamate receptor, GLR3.3, is highly expressed in root tissue of plants, and has been found to promote stronger, more coordinated curvature development during the process of gravitropism. Gravitropism is the ability of a plant to change its orientation to that of the gravity vector when displaced from its gravitational set point angle (GSPA). A previous association study identified six candidate genes which were correlated with the same phenotypic characteristics of gravitropism as GLR3.3. Utilizing real time RT-PCR (qRT-PCR) expression profiles were created for each candidate gene, including GLR3.3. A qRT-PCR method was developed to provide a more quantitative and sensitive way for measuring gene expression than traditional PCR methods. Furthermore, MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines were followed to ensure data robustness. Expression profiles that were similar to GLR3.3 were hypothesized to be good candidates as cell signaling components of this novel pathway. This is the beginning of a process that will identify a GLR-dependent pathway, the role of this novel pathway in the gravitropic response, and the influence of GLRs in plant physiology.

plant biology↗