Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported.

Remote teleoperation of advanced, semi-autonomous robotic technologies has great potential in many industries critical to the economy and for the environment of the Middle East. Applications include maintenance of under-water oil wells, maintenance of nuclear power plants, counter-terrorism and national defense, law enforcement, remote sensing, and health care. In each of these applications, a user, likely without technology expertise, must operate a complex robot in uncertain and unknown environments. The nature of the tasks and environments encountered by the robot in these applications make it highly likely that the robot's limited autonomy will fail or be insufficient to complete the desired task. In this paper, we argue that, in such scenarios, cognitive telepresence, defined as the ability of the user to comprehend and control the robot's cognition, is an important design principle for human-robot systems. We compare and contrast cognitive telepresence to existing design principles commonly discussed in the literature, and define various metrics of cognitive telepresence. Finally, via two illustrative examples and a user study, we demonstrate the usefulness of cognitive telepresence as an important design principle of human-robot systems consisting of a user with limited technology expertise and a robot with limited and error-prone artificial intelligence.

Robots are beginning to be used in many fields, including health care, assistive industries, and entertainment. However, we believe that the usefulness of robots will remain limited until end-users without technology expertise can easily program them. For example, the wide range of situations in which robots must express emotions as well as the differences in people with whom robots interact require that emotional expressions be highly customized. Thus, end users should have the ability to create their own robot behaviors to express emotions in the specific situations and environments in which their robots operate. In this paper, we study the ability of novice users to program robots to express emotions using off-the-shelf programming interfaces and methods for Nao and Pleo robots. Via a series of user studies, we show that novice participants created nonverbal expressions with similar characteristics to those identified by experts. However, overall, the emotions expressed through these nonverbal expressions were not easily discerned by others. Verbal expressions were more discernible, although substantial room for improvement was observed. Results also indicate, but do not definitively show, that procedural mechanisms can improve users' abilities to create good verbal expressions.Index Terms-Emotions, human-robot interaction, robot programming systems.

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