Discovering and Applying the Urban Rules of Life to Design Sustainable and Healthy Cities.

The city and its urban biome provides an extreme laboratory for studying fundamental biological questions and developing best practices for sustaining biodiverse and well-functioning ecological communities within anthropogenic built environments. We propose by studying urban organisms, urban biotic communities, the urban biome, and the interactions between the urban biome and peri-urban built and natural environments, we can (1) discover new "rules of life" for the structure, function, interaction, and evolution of organisms; (2) use these discoveries to understand how novel emerging biotic communities affect and are affected by anthropogenic environmental changes in climate and other environmental factors; and (3) apply what we have learned to engage residents of the urban biome, and design cities that are more biologically diverse, are provided with more and better ecosystem services, and are more equitable and healthier places to live. The built environment of the urban biome is a place that reflects history, economics, technology, governance, culture, and values of the human residents; research on and applications of the rules of life in the urban biome can be used by all residents in making choices about the design of the cities where they live. Because inhabitants are directly invested in the environmental quality of their neighborhoods, research conducted in and about the urban environment provides a great opportunity to engage wide and diverse communities of people. Given the opportunity to engage a broad constituency-from basic researchers to teachers, civil engineers, landscape planners, and concerned citizens-studying the translation of the rules of life onto the urban environment will result in an integrative and cross-cutting set of questions and hypotheses, and will foster a dialog among citizens about the focus of urban biome research and its application toward making more equitable, healthy, livable, sustainable, and biodiverse cities.

Combining natural language processing and metabarcoding to reveal pathogen-environment associations.

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year-with 180,000 resulting deaths-mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations.