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 can finish the summer with independent research. Finally, scholars participate in a follow-up seminar the subsequent 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 identifying as members of an underrepresented minority group. Most scholars (82%) entered the program as first year students, with 94% 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.

A Low-Cost Imaging Method for the Temporal and Spatial Colorimetric Detection of Free Amines on Maize Root Surfaces.

Plant root exudates are important mediators in the interactions that occur between plants and microorganisms in the soil, yet much remains to be learned about spatial and temporal variation in their production. This work outlines a method utilizing a novel colorimetric paper to detect spatial and temporal changes in the production of nitrogen-containing compounds on the root surface. While existing methods have made it possible to conduct detailed analysis of root exudate composition, relatively less is known about where in the root system exudates are produced and how this localization changes as the root grows. Furthermore, there is much to learn about how exudate localization and composition varies in response to stress. Root exudates are chemically diverse secretions composed of organic acids, amino acids, proteins, sugars, and other metabolites. The sensor utilized for the method, ninhydrin, is a colorless substance in solution that reacts with free amino groups to form a purple dye. A detection paper was developed by formulating ninhydrin into a print solution that was uniformly deposited onto paper with a commercial ink jet printer. This "ninhydrin paper" was used to analyze the chemical makeup of root surfaces from maize seedlings grown vertically on germination paper. Through contact between the ninhydrin paper and seedling root surfaces, combined with images of both the seedlings and dried ninhydrin papers captured using a standard flatbed scanner, nitrogen-containing substances on the root surface can be localized and concentration of signal estimated for over 2 weeks of development. The method was found to be non-inhibiting to plant growth over the analysis period although damage to root hairs was observed. The method is sensitive in the detection of free amines at concentrations as little as 140 µM. Furthermore, ninhydrin paper is stable, showing consistent color changes up to 2 weeks after printing. This relatively simple, low-cost method could contribute to a better understanding of root exudates and mechanisms used by plants to interact with the complex soil environment during growth and development.

Using flatbed scanners to collect high-resolution time-lapsed images of the arabidopsis root gravitropic response.

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

Detection of a gravitropism phenotype in glutamate receptor-like 3.3 mutants of Arabidopsis thaliana using machine vision and computation.

Gene disruption frequently produces no phenotype in the model plant Arabidopsis thaliana, complicating studies of gene function. Functional redundancy between gene family members is one common explanation but inadequate detection methods could also be responsible. Here, newly developed methods for automated capture and processing of time series of images, followed by computational analysis employing modified linear discriminant analysis (LDA) and wavelet-based differentiation, were employed in a study of mutants lacking the Glutamate Receptor-Like 3.3 gene. Root gravitropism was selected as the process to study with high spatiotemporal resolution because the ligand-gated Ca(2+)-permeable channel encoded by GLR3.3 may contribute to the ion fluxes associated with gravity signal transduction in roots. Time series of root tip angles were collected from wild type and two different glr3.3 mutants across a grid of seed-size and seedling-age conditions previously found to be important to gravitropism. Statistical tests of average responses detected no significant difference between populations, but LDA separated both mutant alleles from the wild type. After projecting the data onto LDA solution vectors, glr3.3 mutants displayed greater population variance than the wild type in all four conditions. In three conditions the projection means also differed significantly between mutant and wild type. Wavelet analysis of the raw response curves showed that the LDA-detected phenotypes related to an early deceleration and subsequent slower-bending phase in glr3.3 mutants. These statistically significant, heritable, computation-based phenotypes generated insight into functions of GLR3.3 in gravitropism. The methods could be generally applicable to the study of phenotypes and therefore gene function.