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This work presents novel techniques for tightly integrated online information fusion and planning in human-autonomy teams operating in partially known environments. Motivated by dynamic target search problems, we present a new map-based sketch interface for online soft-hard data fusion. This interface lets human collaborators efficiently update map information and continuously build their own highly flexible ad hoc dictionaries for making language-based semantic observations, which can be actively exploited by autonomous agents in optimal search and information gathering problems. We formally link these capabilities to POMDP algorithms for optimal planning under uncertainty, and develop a new Dynamically Observable Monte Carlo planning (DOMCP) algorithm as an efficient means for updating online sampling-based planning policies for POMDPs with non-static observation models. DOMCP is validated on a small scale robot localization problem, and then demonstrated with our new user interface on a simulated dynamic target search scenario in a partially known outdoor environment. 

One of the primary tasks in neuroimaging is to simplify spatiotemporal scans of the brain (i.e., fMRI scans) by partitioning the voxels into a set of functional brain regions. An emerging line of research utilizes multiple fMRI scans, from a group of subjects, to calculate a single group consensus functional partition. This consensus-based approach is promising as it allows the model to improve the signalto-noise ratio in the data. However, existing approaches are primarily non-parametric which poses problems when new samples are introduced. Furthermore, most existing approaches calculate a single partition for multiple subjects which fails to account for the functional and anatomical variability between different subjects. In this work, we study the problem of group-cohesive functional brain region discovery where the goal is to use information from a group of subjects to learn “group-cohesive” but individualized brain partitions for multiple fMRI scans. This problem is challenging since neuroimaging datasets are usually quite small and noisy. We introduce a novel deep parametric model based upon graph convolution, called the Brain Region Extraction Network (BREN). By treating the fMRI data as a graph, we are able to integrate information from neighboring voxels during brain region discovery which helps reduce noise for each subject. Our model is trained with a Siamese architecture to encourage partitions that are group-cohesive. Experiments on both synthetic and real-world data show the effectiveness of our proposed approach. 

Bacterial genomes encode various multidrug efflux pumps (MDR) whose specific conditions for fitness advantage are unknown. We show that the efflux pump MdtEF-TolC, in Escherichia coli, confers a fitness advantage during exposure to extreme acid (pH 2). Our flow cytometry method revealed pH-dependent fitness tradeoffs between bile acids (a major pump substrate) and salicylic acid, a membrane-permeant aromatic acid that induces a drug-resistance regulon but depletes proton motive force (PMF). The PMF drives MdtEF-TolC and related pumps such as AcrAB-TolC. Deletion of mdtE (with loss of pump MdtEF-TolC) increased the strain’s relative fitness during growth with or without salicylate or bile acids. However, when the growth cycle included a 2-h incubation at pH 2 (below the pH growth range), MdtEF-TolC conferred a fitness advantage. The fitness advantage required bile salts but was decreased by the presence of salicylate, whose uptake is amplified by acid. For comparison, AcrAB-TolC, the primary efflux pump for bile acids, conferred a PMF-dependent fitness advantage with or without acid exposure in the growth cycle. A different MDR pump, EmrAB-TolC, confered no selective benefit during growth in the presence of bile acids. Without bile acids, all three MDR pumps incurred a large fitness cost with salicylate when exposed at pH 2. These results are consistent with the increased uptake of salicylate at low pH. Overall, we showed that MdtEF-TolC is an MDR pump adapted for transient extreme-acid exposure; and that low pH amplifies the salicylate-dependent fitness cost for drug pumps. IMPORTANCE Antibiotics and other drugs that reach the gut must pass through stomach acid. Yet little is known of how extreme acid modulates the effect of drugs on gut bacteria. We find that extreme-acid exposure leads to a fitness advantage for a multidrug pump that otherwise incurs a fitness cost. At the same time, extreme acid amplifies the effect of salicylate selection against multidrug pumps. Thus, organic acids and stomach acid could play important roles in regulating multidrug resistance in the gut microbiome. Our flow cytometry assay provides a way to measure the fitness effects of extreme-acid exposure to various membrane-soluble organic acids including plant-derived nutrients and pharmaceutical agents. Therapeutic acids might be devised to control the prevalence of multidrug pumps in environmental and host-associated habitats. 

Abstract—Networks have entered the mainstream lexicon over the last ten years. This coincides with the pervasive use of networks in a host of disciplines of interest to industry and academia, including biology, neurology, genomics, psychology, social sciences, economics, psychology, and cyber-physical systems and infrastructure. Several dozen journals and conferences regularly contain articles related to networks. Yet, there are no general purpose cyberinfrastructures (CI) that can be used across these varied disciplines and domains. Furthermore, while there are scientific gateways that include some network science capabilities for particular domains (e.g., biochemistry, genetics), there are no general-purpose network-based scientific gateways. In this work, we introduce net.science, a CI for Network Engineering and Science, that is designed to be a community resource. This paper provides an overview of net.science, addressing key requirements and concepts, CI components, the types of applications that our CI will support, and various dimensions of our evaluation process. Index Terms—cyberinfrastructure, network science, net.science