ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results

ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations [version 1; referees: 1 approved, 2 approved with reservations]

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results

Long-term quality of life after liver donation in the adult to adult living donor liver transplantation cohort study (A2ALL)

There are few long-term studies of health-related quality of life (HRQOL) in living liver donors. This study aimed to characterize donor HRQOL in the Adult to Adult Living Donor Liver Transplantation Study (A2ALL) up to 11 years post-donation

Instructional Models for Course-Based Research Experience (CRE) Teaching

The course-based research experience (CRE) with its documented educational benefits is increasingly being implemented in science, technology, engineering, and mathematics education. This article reports on a study that was done over a period of 3 years to explicate the instructional processes involved in teaching an undergraduate CRE. One hundred and two instructors from the established and large multi-institutional SEA-PHAGES program were surveyed for their understanding of the aims and practices of CRE teaching. This was followed by large-scale feedback sessions with the cohort of instructors at the annual SEA Faculty Meeting and subsequently with a small focus group of expert CRE instructors. Using a qualitative content analysis approach, the survey data were analyzed for the aims of inquiry instruction and pedagogical practices used to achieve these goals. The results characterize CRE inquiry teaching as involving three instructional models: 1) being a scientist and generating data; 2) teaching procedural knowledge; and 3) fostering project ownership. Each of these models is explicated and visualized in terms of the specific pedagogical practices and their relationships. The models present a complex picture of the ways in which CRE instruction is conducted on a daily basis and can inform instructors and institutions new to CRE teaching

Models of classroom assessment for course-based research experiences

Course-based research pedagogy involves positioning students as contributors to authentic research projects as part of an engaging educational experience that promotes their learning and persistence in science. To develop a model for assessing and grading students engaged in this type of learning experience, the assessment aims and practices of a community of experienced course-based research instructors were collected and analyzed. This approach defines four aims of course-based research assessment—(1) Assessing Laboratory Work and Scientific Thinking; (2) Evaluating Mastery of Concepts, Quantitative Thinking and Skills; (3) Appraising Forms of Scientific Communication; and (4) Metacognition of Learning—along with a set of practices for each aim. These aims and practices of assessment were then integrated with previously developed models of course-based research instruction to reveal an assessment program in which instructors provide extensive feedback to support productive student engagement in research while grading those aspects of research that are necessary for the student to succeed. Assessment conducted in this way delicately balances the need to facilitate students’ ongoing research with the requirement of a final grade without undercutting the important aims of a CRE education

Synthesizing Research Narratives to Reveal the Big Picture: a CREATE(S) Intervention Modified for Journal Club Improves Undergraduate Science Literacy

ABSTRACT Communicating science effectively is an essential part of the development of science literacy. Research has shown that introducing primary scientific literature through journal clubs can improve student learning outcomes, including increased scientific knowledge. However, without scaffolding, students can miss more complex aspects of science literacy, including how to analyze and present scientific data. In this study, we apply a modified CREATE(S) process (Concept map the introduction, Read methods and results, Elucidate hypotheses, Analyze data, Think of the next Experiment, and Synthesis map) to improve students’ science literacy skills, specifically their understanding of the process of science and their ability to use narrative synthesis to communicate science. We tested this hypothesis using a retrospective quasi-experimental study design in upper-division undergraduate courses. We compared learning outcomes for CREATES intervention students to those for students who took the same courses before CREATES was introduced. Rubric-guided, direct evidence assessments were used to measure student gains in learning outcomes. Analyses revealed that CREATES intervention students versus the comparison group demonstrated improved ability to interpret and communicate primary literature, especially in the methods, hypotheses, and narrative synthesis learning outcome categories. Through a mixed-methods analysis of a reflection assignment completed by the CREATES intervention group, students reported the synthesis map as the most frequently used step in the process and highly valuable to their learning. Taken together, the study demonstrates how this modified CREATES process can foster scientific literacy development and how it could be applied in science, technology, engineering, and math journal clubs

PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom

Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions

BlueFeather, the singleton that wasn't: Shared gene content analysis supports expansion of Arthrobacter phage Cluster FE.

Bacteriophages (phages) exhibit high genetic diversity, and the mosaic nature of the shared genetic pool makes quantifying phage relatedness a shifting target. Early parameters for clustering of related Mycobacteria and Arthrobacter phage genomes relied on nucleotide identity thresholds but, more recently, clustering of Gordonia and Microbacterium phages has been performed according to shared gene content. Singleton phages lack the nucleotide identity and/or shared gene content required for clustering newly sequenced genomes with known phages. Whole genome metrics of novel Arthrobacter phage BlueFeather, originally designated a putative singleton, showed low nucleotide identity but high amino acid and gene content similarity with Arthrobacter phages originally assigned to Clusters FE and FI. Gene content similarity revealed that BlueFeather shared genes with these phages in excess of the parameter for clustering Gordonia and Microbacterium phages. Single gene analyses revealed evidence of horizontal gene transfer between BlueFeather and phages in unique clusters that infect a variety of bacterial hosts. Our findings highlight the advantage of using shared gene content to study seemingly genetically isolated phages and have resulted in the reclustering of BlueFeather, a putative singleton, as well as former Cluster FI phages, into a newly expanded Cluster FE

Immuno-PET in Inflammatory Bowel Disease: Imaging CD4-Positive T Cells in a Murine Model of Colitis

Inflammatory bowel diseases (IBDs) in humans are characterized in part by aberrant CD4-positive (CD4+) T-cell responses. Currently, identification of foci of inflammation within the gut requires invasive procedures such as colonoscopy and biopsy. Molecular imaging with antibody fragment probes could be used to noninvasively monitor cell subsets causing intestinal inflammation. Here, GK1.5 cys-diabody (cDb), an antimouse CD4 antibody fragment derived from the GK1.5 hybridoma, was used as a PET probe for CD4+ T cells in the dextran sulfate sodium (DSS) mouse model of IBD. Methods: The DSS mouse model of IBD was validated by assessing changes in CD4+ T cells in the spleen and mesenteric lymph nodes (MLNs) using flow cytometry. Furthermore, CD4+ T cell infiltration in the colons of colitic mice was evaluated using immunohistochemistry. 89Zr-labeled GK1.5 cDb was used to image distribution of CD4+ T cells in the abdominal region and lymphoid organs of mice with DSS-induced colitis. Region-of-interest analysis was performed on specific regions of the gut to quantify probe uptake. Colons, ceca, and MLNs were removed and imaged ex vivo by PET. Imaging results were confirmed by ex vivo biodistribution analysis. Results: An increased number of CD4+ T cells in the colons of colitic mice was confirmed by anti-CD4 immunohistochemistry. Increased uptake of 89Zr-maleimide-deferoxamine (malDFO)-GK1.5 cDb in the distal colon of colitic mice was visible in vivo in PET scans, and region-of-interest analysis of the distal colon confirmed increased activity in DSS mice. MLNs from colitic mice were enlarged and visible in PET images. Ex vivo scans and biodistribution confirmed higher uptake in DSS-treated colons (DSS, 1.8 ± 0.40; control, 0.45 ± 0.12 percentage injected dose [%ID] per organ, respectively), ceca (DSS, 1.1 ± 0.38; control, 0.35 ± 0.09 %ID per organ), and MLNs (DSS, 1.1 ± 0.58; control, 0.37 ± 0.25 %ID per organ). Conclusion:89Zr-malDFO-GK1.5 cDb detected CD4+ T cells in the colons, ceca, and MLNs of colitic mice and may prove useful for further investigations of CD4+ T cells in preclinical models of IBD, with potential to guide development of antibody-based imaging in human IBD

ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations.

Environmental DNA (eDNA) metabarcoding is becoming a core tool in ecology and conservation biology, and is being used in a growing number of education, biodiversity monitoring, and public outreach programs in which professional research scientists engage community partners in primary research. Results from eDNA analyses can engage and educate natural resource managers, students, community scientists, and naturalists, but without significant training in bioinformatics, it can be difficult for this diverse audience to interact with eDNA results. Here we present the R package ranacapa, at the core of which is a Shiny web app that helps perform exploratory biodiversity analyses and visualizations of eDNA results. The app requires a taxonomy-by-sample matrix and a simple metadata file with descriptive information about each sample. The app enables users to explore the data with interactive figures and presents results from simple community ecology analyses. We demonstrate the value of ranacapa to two groups of community partners engaging with eDNA metabarcoding results