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In Escherichia coli, the single-stranded DNA-binding protein (SSB) acts as a genome maintenance organizational hub by interacting with multiple DNA metabolism proteins. Many SSB-interacting proteins (SIPs) form complexes with SSB by docking onto its carboxy-terminal tip (SSB-Ct). An alternative interaction mode in which SIPs bind to PxxP motifs within an intrinsically-disordered linker (IDL) in SSB has been proposed for the RecG DNA helicase and other SIPs. Here, RecG binding to SSB and SSB peptides was measured in vitro and the RecG/SSB interface was identified. The results show that RecG binds directly and specifically to the SSB-Ct, and not the IDL, through an evolutionarily conserved binding site in the RecG helicase domain. Mutations that block RecG binding to SSB sensitize E. coli to DNA damaging agents and induce the SOS DNA-damage response, indicating formation of the RecG/SSB complex is important in vivo. The broader role of the SSB IDL is also investigated. E. coli ssb mutant strains encoding SSB IDL deletion variants lacking all PxxP motifs retain wildtype growth and DNA repair properties, demonstrating that the SSB PxxP motifs are not major contributors to SSB cellular functions.

 

In this paper we explore the potential of academic podcasting to effect positive change within academia and between academia and society. Building on the concept of “epistemic living spaces,” we consider how podcasting can change how we evaluate what is legitimate knowledge and methods for knowledge production, who has access to what privileges and power, the nature of our connections within academia and with other partners, and how we experience the constraints and opportunities of space and time. We conclude by offering a guide for others who are looking to develop their own academic podcasting projects and discuss the potential for podcasting to be formalized as a mainstream academic output. To listen to an abridged and annotated version of this paper, visit: https://soundcloud.com/conservechange/podcastinginacademia . 

Autonomous agents in a multi-agent system work with each other to achieve their goals. However, In a partially observable world, current multi-agent systems are often less effective in achieving their goals. This limitation is due to the agents’ lack of reasoning about other agents and their mental states. Another factor is the agents’ inability to share required knowledge with other agents. This paper addresses the limitations by presenting a general approach for autonomous agents to work together in a multi-agent system. In this approach, an agent applies two main concepts: goal reasoning- to determine what goals to pursue and share; Theory of mind-to select an agent(s) for sharing goals and knowledge. We evaluate the performance of our multi-agent system in a Marine Life Survey Domain and compare it to another multi-agent system that randomly selects agent(s) to delegates its goals. 

Goal management in autonomous agents has been a problem of interest for a long time. Multiple goal operations are required to solve an agent goal management problem. For example, some goal operations include selection, change, formulation, delegation, monitoring. Many researchers from different fields developed several solution approaches with an implicit or explicit focus on goal operations. For example, some solution approaches include scheduling the agents’ goals, performing cost-benefit analysis to select/organize goals, agent goal formulation in unexpected situations. However, none of them explicitly shed light on the agents’ response when multiple goal operations occur simultaneously. This paper develops an algorithm to address agent goal management when multiple-goal operations co-occur and presents how such an interaction would improve agent goal management in different domains. 

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning. 

Fishers reporting all of their catch is key to estimating population viabilities of vulnerable, highly migratory fish stocks. However, fishery managers find it difficult to ensure that this reporting behavior takes place consistently. Wild Atlantic salmon (Salmo salar) are a highly migratory and internationally contested species with a threatened conservation status. Greenland manages a fishery for Atlantic salmon, and its coastline serves as a key feeding ground in the life history of Atlantic salmon. However, salmon catch is underreported by fishers, even though they are required to report. Deterring noncompliant behavior with penalties and sending short message service (SMS) messages have been shown to increase compliance, but no known studies test their effect on compliance with catch reporting requirements. We evaluated two interventions for their effect on salmon catch reporting behavior among Greenland's salmon fishers. Salmon fishers were 41% more likely to report (p < 0.00) once a deterrence-based intervention was implemented. Fishers who received SMS reminders were 6% more likely to report salmon catch (p < 0.1). These results highlight the complementarity of nudges and command-and-control interventions to increase compliance with catch reporting requirements.

 

Homologs of the mutagenic Escherichia coli DNA polymerase V (pol V) are encoded by numerous pathogens and mobile elements. We have used Rum pol (RumA′2B), from the integrative conjugative element (ICE), R391, as a model mobile element-encoded polymerase (MEPol). The highly mutagenic Rum pol is transferred horizontally into a variety of recipient cells, including many pathogens. Moving between species, it is unclear if Rum pol can function on its own or requires activation by host factors. Here, we show that Rum pol biochemical activity requires the formation of a physical mutasomal complex, Rum Mut, containing RumA′2B-RecA-ATP, with RecA being donated by each recipient bacteria. For R391, Rum Mut specific activities in vitro and mutagenesis rates in vivo depend on the phylogenetic distance of host-cell RecA from E. coli RecA. Rum pol is a highly conserved and effective mobile catalyst of rapid evolution, with the potential to generate a broad mutational landscape that could serve to ensure bacterial adaptation in antibiotic-rich environments leading to the establishment of antibiotic resistance.

 

Goal reasoning agents can solve novel problems by detecting an anomaly between expectations and observations, generating explanations about plausible causes for the anomaly, and formulating goals to remove the cause. Yet, not all anomalies represent problems. This paper addresses discerning the difference between benign anomalies and those that represent an actual problem for an agent. Furthermore, we present a new definition of the term “problem” in a goal reasoning context. This paper discusses the role of explanations and goal formulation in response to the developing problems and implements it; the paper also illustrates the above in a mine clearance domain and a labor relations domain. We also show the empirical difference between a standard planning agent, an agent that detects anomalies, and an agent that recognizes problems.