Quinol Oxidase Encoded by \u3cem\u3ecyoABCD\u3c/em\u3e in \u3cem\u3eRhizobium etli\u3c/em\u3e CFN42 is Regulated by ActSR and is Crucial for Growth at Low pH or Low Iron Conditions

Rhizobium etli aerobically respires with several terminal oxidases. The quinol oxidase (Cyo) encoded by cyoABCD is needed for efficient adaptation to low oxygen conditions and cyo transcription is upregulated at low oxygen. This study sought to determine how transcription of the cyo operon is regulated. The 5′ sequence upstream of cyo was analysed in silico and revealed putative binding sites for ActR of the ActSR two-component regulatory system. The expression of cyo was decreased in an actSR mutant regardless of the oxygen condition. As ActSR is known to be important for growth under low pH in another rhizobial species, the effect of growth medium pH on cyo expression was tested. As the pH of the media was incrementally decreased, cyo expression gradually increased in the WT, eventually reaching ∼10-fold higher levels at low pH (4.8) compared with neutral pH (7.0) conditions. This upregulation of cyo under decreasing pH conditions was eliminated in the actSR mutant. Both the actSR and cyo mutants had severe growth defects at low pH (4.8). Lastly, the actSR and cyo mutants had severe growth defects when grown in media treated with an iron chelator. Under these conditions, cyo was upregulated in the WT, whereas cyo was not induced in the actSR mutant. Altogether, the results indicated cyo expression is largely dependent on the ActSR two-component system. This study also demonstrated additional physiological roles for Cyo in R. etli CFN42, in which it is the preferred oxidase for growth under acidic and low iron conditions

Selective Population of Edge States in a 2D Topological Band System

We consider a system of interacting spin-one atoms in a hexagonal lattice under the presence of a synthetic gauge field. Quenching the quadratic Zeeman field is shown to lead to a dynamical instability of the edge modes. This, in turn, leads to a spin current along the boundary of the system which grows exponentially fast in time following the quench. Tuning the magnitude of the quench can be used to selectively populate edge modes of different momenta. Implications of the intrinsic symmetries of Hamiltonian on the dynamics are discussed. The results hold for atoms with both antiferromagnetic and ferromagnetic interactions.Comment: 7 pages (expanded Supplemental Material

Approximate formula for the macroscopic polarization including quantum fluctuations

The many-body Berry phase formula for the macroscopic polarization is approximated by a sum of natural orbital geometric phases with fractional occupation numbers accounting for the dominant correlation effects. This reduced formula accurately reproduces the exact polarization in the Rice-Mele-Hubbard model across the band insulator-Mott insulator transition. A similar formula based on a one-body reduced Berry curvature accurately predicts the interaction-induced quenching of Thouless topological charge pumping

System for computing operational probability equations

SCOPE system computes an expression relating the probability of system success to the probabilities of success of its components. It is especially designed for complex system reliability studies

Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning Behavior from the Gambler’s Fallacy

Reinforcement learning theory has generated substantial interest in neurobiology, particularly because of the resemblance between phasic dopamine and reward prediction errors. Actor–critic theories have been adapted to account for the functions of the striatum, with parts of the dorsal striatum equated to the actor. Here, we specifically test whether the human dorsal striatum—as predicted by an actor–critic instantiation—is used on a trial-to-trial basis at the time of choice to choose in accordance with reinforcement learning theory, as opposed to a competing strategy: the gambler's fallacy. Using a partial-brain functional magnetic resonance imaging scanning protocol focused on the striatum and other ventral brain areas, we found that the dorsal striatum is more active when choosing consistent with reinforcement learning compared with the competing strategy. Moreover, an overlapping area of dorsal striatum along with the ventral striatum was found to be correlated with reward prediction errors at the time of outcome, as predicted by the actor–critic framework. These findings suggest that the same region of dorsal striatum involved in learning stimulus–response associations may contribute to the control of behavior during choice, thereby using those learned associations. Intriguingly, neither reinforcement learning nor the gambler's fallacy conformed to the optimal choice strategy on the specific decision-making task we used. Thus, the dorsal striatum may contribute to the control of behavior according to reinforcement learning even when the prescriptions of such an algorithm are suboptimal in terms of maximizing future rewards

Route Swarm: Wireless Network Optimization through Mobility

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 201