Interactions between bacteria and bacteriophage viruses (phages) are known to influence pathogen growth and virulence, microbial diversity and even biogeochemical cycling. Lytic phages in particular infect and lyse their host cells, and can therefore have significant effects on cell densities as well as competitive dynamics within microbial communities. Despite the known impacts of lytic phages on the ecology and evolution of bacteria in free-living communities, little is known about the role of lytic phages in host-associated microbiomes. We set out to characterize the impact of phages in the tomato phyllosphere, that is the bacteria associated with above-ground plant tissues, by transferring microbial communities from field-grown tomato plants to juvenile plants grown under mostly sterile conditions in either the presence or absence of their associated phage community. In three separate experiments, we found that the presence of phages affects overall bacterial abundance during colonization of new host plants. Furthermore, bacterial community analysis using 16S rRNA amplicon sequencing shows that phages significantly alter the relative abundance of dominant community members and can influence both within- and among-host diversity. These results underscore the importance of lytic phages in host-associated microbiomes and are relevant to microbiome transplantation approaches, as they suggest transferring nonbacterial components of the microbiome among hosts is likely to have a strong impact on growth of both the resident and colonizing microbiota.
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most robots, this process is further complicated by the need, when validating the capabilities of the hardware to solve the desired task, to already have an appropriate controller, which is in turn designed and tuned for the specific hardware. In this paper, we propose a novel approach, HPC-BBO, to efficiently and automatically design hardware configurations, and evaluate them by also automatically tuning the corresponding controller. HPC-BBO is based on a hierarchical Bayesian optimization process which iteratively optimizes morphology configurations (based on the performance of the previous designs during the controller learning process) and subsequently learns the corresponding controllers (exploiting the knowledge collected from optimizing for previous morphologies). Moreover, HPC-BBO can select a "batch" of multiple morphology designs at once, thus parallelizing hardware validation and reducing the number of time-consuming production cycles. We validate HPC-BBO on the design of the morphology and controller for a simulated 6-legged microrobot. Experimental results show that HPC-BBO outperforms multiple competitive baselines, and yields a 360% reduction in production cycles over standard Bayesian optimization, thus reducing the hypothetical manufacturing time of our microrobot from 21 to 4 months.
The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alternative to analytical gait design. In this paper, we propose a novel approach to efficiently learn a wide range of locomotion tasks with walking robots. This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multi-objective locomotion primitives that can be used for planning. As a proofof-concept we consider a simulated hexapod modeled after a recently developed microrobot, and we thoroughly evaluate the performance of this microrobot on different tasks and gaits. Our results validate the proposed controller and learning scheme on single and multi-objective locomotion tasks. Moreover, the experimental simulations show that without any prior knowledge about the robot used (e.g., dynamics model), our approach is capable of learning locomotion primitives within 250 trials and subsequently using them to successfully navigate through a maze.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.