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2. NSF IUSE: Leveraging Institutional and Community Capacities in Implementing Community-Engaged STEM PBL

   Wood, D; Aqlan, F; Brockman, J; Marie, H (June 2025, ASEE Annual Conference)

   With higher and faster growing wages [1], STEM-related employment has been key to building thriving communities. In the deindustrialized Midwest, however, cities often have poverty rates double the national average, lower educational attainment, and the ‘brain drain’ problem [2]. These issues create barriers to developing and retaining a regional STEM workforce and competing in the knowledge economy. Thus, STEM engagement is not just a national imperative, but critical to revitalizing these Midwestern cities. The University of Notre Dame developed and piloted a program to address the challenges of STEM engagement/retention in the disciplines and place retention. The program leveraged high impact practices such as immersive place-based education (internships), academic community engagement, and STEM-based experiential problem-based learning, while interns engaged in asset-based community development in the South Bend-Elkhart, Indiana region [3-14]. The pilot program was distilled into a model through evidence-based refinement – the Community- Engaged Educational Ecosystem Model (C-EEEM, pronounced ‘seam’), and contributes to our understanding of building learning environments that meet those challenges [4-6, 15-18]. 

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   null (Ed.)
4. Immuno-Biotechnology and Bioinformatics in Community Colleges

   Smith, Todd (October 2019, Journal of biomolecular techniques)

   Immuno-biotechnology is one of the fastest growing areas in the field of biotechnology. Digital World Biology's Biotech-Careers.org database of biotechnology employers (6800) has nearly 700 organizations that are involved with immunology in some way. With the advent of next generation DNA sequencing, and other technologies, immuno-biotechnology has significantly increased the use of computing technologies to decipher the meaning of large datasets and predict interactions between immune receptors (antibodies / T-Cell receptors / MHC) and their targets. The use of new technologies like immune-profiling -where large numbers of immune receptors are sequenced en masse - and targeted cancer therapies - where researchers create, engineer, and grow modified T cells to attack tumors -are leading to job growth and demands for new skills and knowledge in biomanufacturing, quality systems, immuno-bioinformatics, and cancer biology. In response to these new demands, Shoreline Community College (Shoreline, WA) has begun developing an immuno-biotechnology certificate. Part of this certificate includes a five-week course (30 hours hands-on computer lab) on immuno-bioinformatics. The immuno-bioinformatics course includes exercises in immune profiling, vaccine development, and operating bioinformatics programs using a command line interface. In immune profiling, students explore T-cell receptor data-sets from early stage breast cancer samples using Adaptive Biotechnologies (Seattle, WA) immunoSEQ Analyzer public server to learn how T-cells differ between normal tissue, blood, and tumors. Next, they use the IEDB (Immune Epitope Database) in conjunction with Molecule World (Digital World Biology) to predict antigens from sequences and verify the results to learn the differences between continuous and discontinuous epitopes that are recognized by T-cell receptors and antibodies. Finally, to get hands-on experience with bioinformatics programs, students will use cloud computing (CyVerse) and IgBLAST (NCBI) to explore data from an immune profiling experiment. 

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   Abstract Populations and communities fluctuate in their overall numbers through time, and the magnitude of fluctuations in individual species may scale to communities. However, the composite variability at the community scale is expected to be tempered by opposing fluctuations in individual populations, a phenomenon often called theportfolio effect. Understanding population variability, how it scales to community variability, and the spatial scaling in this variability are pressing needs given shifting environmental conditions and community composition. We explore evidence for portfolio effects using null community simulations and a large collection of empirical community time series from the BioTIME database. Additionally, we explore the relative roles of habitat type and geographic location on population and community temporal variability. We find strong portfolio effects in our theoretical community model, but weak effects in empirical data, suggesting a role for shared environmental responses, interspecific competition, or a litany of other factors. Furthermore, we observe a clear latitudinal signal – and differences among habitat types – in population and community variability. Together, this highlights the need to develop realistic models of community dynamics, and hints at spatial, and underlying environmental, gradients in variability in both population and community dynamics. 

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