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1. Linking community assembly and structure across scales in a wild mouse parasite community

   https://doi.org/10.1002/ece3.5785  

   Rynkiewicz, Evelyn_C ; Fenton, Andy ; Pedersen, Amy_B ( December 2019 , Ecology and Evolution)

   Understanding what processes drive community structure is fundamental to ecology. Many wild animals are simultaneously infected by multiple parasite species, so host–parasite communities can be valuable tools for investigating connections between community structures at multiple scales, as each host can be considered a replicate parasite community. Like free‐living communities, within‐host–parasite communities are hierarchical; ecological interactions between hosts and parasites can occur at multiple scales (e.g., host community, host population, parasite community within the host), therefore, both extrinsic and intrinsic processes can determine parasite community structure. We combine analyses of community structure and assembly at both the host population and individual scales using extensive datasets on wild wood mice (Apodemus sylvaticus) and their parasite community. An analysis of parasite community nestedness at the host population scale provided predictions about the order of infection at the individual scale, which were then tested using parasite community assembly data from individual hosts from the same populations. Nestedness analyses revealed parasite communities were significantly more structured than random. However, observed nestedness did not differ from null models in which parasite species abundance was kept constant. We did not find consistency between observed community structure at the host population scale and within‐host order of infection. Multi‐state Markov models of parasite community assembly showed that a host's likelihood of infection with one parasite did not consistently follow previous infection by a different parasite species, suggesting there is not a deterministic order of infection among the species we investigated in wild wood mice. Our results demonstrate that patterns at one scale (i.e., host population) do not reliably predict processes at another scale (i.e., individual host), and that neutral or stochastic processes may be driving the patterns of nestedness observed in these communities. We suggest that experimental approaches that manipulate parasite communities are needed to better link processes at multiple ecological scales.

    

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2. The Production of Community in Community Land Trusts

   https://doi.org/10.1111/cico.12452  

   Kruger, Richard ; DeFilippis, James ; Williams, Olivia R. ; Esfahani, Azadeh Hadizadeh ; Martin, Deborah G. ; Pierce, Joseph ( October 2019 , City & Community)
3. The Innovation Ecology: Collaborative Information, Community Support, and Policy in a Creative Technology Community

   https://doi.org/https://doi.org/10.1007/978-3-030-15742-5_58  

   Freeman, Guo ; Bardzell, Jeffrey ; Bardzell, Shaowen ; McNeese, Nathan ( March 2019 , Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science)

   In this paper, we explore a network of distributed individuals’ collective efforts to establish an innovation ecology allowing them to engage in bottom up creative technological practices in today’s information society. Specifically, we present an empirical study of the technological practices in an emerging creative technology community – independent [indie] game developers in the United States. Based on indie game developers’ own accounts, we identified four themes that constitute an innovation ecology from the bottom up, including problem solving; collaborative information seeking, sharing, and reproducing; community support; and policy and politics. We argue that these findings inform our understanding of bottom up technological innovation and shed light on the design of sociotechnical systems to mediate and support such innovation beyond the gaming context. 

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4. Privacy and Activism in the Transgender Community

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   null (Ed.)
5. 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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