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Airborne bacteria are an influential component of the Earth’s microbiomes, but their community structure and biogeographic distribution patterns have yet to be understood. We analyzed the bacterial communities of 370 air particulate samples collected from 63 sites around the world and constructed an airborne bacterial reference catalog with more than 27 million nonredundant 16S ribosomal RNA (rRNA) gene sequences. We present their biogeographic pattern and decipher the interlacing of the microbiome co-occurrence network with surface environments of the Earth. While the total abundance of global airborne bacteria in the troposphere (1.72 × 10 24 cells) is 1 to 3 orders of magnitude lower than that of other habitats, the number of bacterial taxa (i.e., richness) in the atmosphere (4.71 × 10 8 to 3.08 × 10 9 ) is comparable to that in the hydrosphere, and its maximum occurs in midlatitude regions, as is also observed in other ecosystems. The airborne bacterial community harbors a unique set of dominant taxa (24 species); however, its structure appears to be more easily perturbed, due to the more prominent role of stochastic processes in shaping community assembly. This is corroborated by the major contribution of surface microbiomes to airborne bacteria (averaging 46.3%), while atmospheric conditions such as meteorological factors and air quality also play a role. Particularly in urban areas, human impacts weaken the relative importance of plant sources of airborne bacteria and elevate the occurrence of potential pathogens from anthropogenic sources. These findings serve as a key reference for predicting planetary microbiome responses and the health impacts of inhalable microbiomes with future changes in the environment. 

The continuing growth of scientific publications has posed a double-challenge to researchers, to not only grasp the overall research trends in a scientific domain, but also get down to research details embedded in a collection of core papers. Existing work on science mapping provides multiple tools to visualize research trends in domain on macro-level, and work from the digital humanities have proposed text visualization of documents, topics, sentences, and words on micro-level. However, existing micro-level text visualizations are not tailored for scientific paper corpus, and cannot support meso-level scientific reading, which aligns a set of core papers based on their research progress, before drilling down to individual papers. To bridge this gap, the present paper proposes LitStoryTeller+, an interactive system under a unified framework that can support both meso-level and micro-level scientific paper visual storytelling. More specifically, we use entities (concepts and terminologies) as basic visual elements, and visualize entity storylines across papers and within a paper borrowing metaphors from screen play. To identify entities and entity communities, named entity recognition and community detection are performed. We also employ a variety of text mining methods such as extractive text summarization and comparative sentence classification to provide rich textual information supplementary to our visualizations. We also propose a top-down story-reading strategy that best takes advantage of our system. Two comprehensive hypothetical walkthroughs to explore documents from the computer science domain and history domain with our system demonstrate the effectiveness of our story-reading strategy and the usefulness of LitStoryTeller+. 

Finding relevant publications is a common task. Typically, a researcher browses through a list of publications and traces additional relevant publications. When relevant publications are identified, the list may be expanded by the citation links of the relevant publications. The information needs of researchers may change as they go through such iterative processes. The exploration process quickly becomes cumbersome as the list expands. Most existing academic search systems tend to be limited in terms of the extent to which searchers can adapt their search as they proceed. In this article, we introduce an adaptive visual exploration system named PaperPoles to support exploration of scientific publications in a context‐aware environment. Searchers can express their information needs by intuitively formulating positive and negative queries. The search results are grouped and displayed in a cluster view, which shows aspects and relevance patterns of the results to support navigation and exploration. We conducted an experiment to compare PaperPoles with a list‐based interface in performing two academic search tasks with different complexity. The results show that PaperPoles can improve the accuracy of searching for the simple and complex tasks. It can also reduce the completion time of searching and improve exploration effectiveness in the complex task. PaperPoles demonstrates a potentially effective workflow for adaptive visual search of complex information.