A gauge invariant chiral unitary framework for kaon photo- and electroproduction on the proton

We present a gauge invariant approach to photoproduction of mesons on nucleons within a chiral unitary framework. The interaction kernel for meson-baryon scattering is derived from the chiral effective Lagrangian and iterated in a Bethe-Salpeter equation. Within the leading order approximation to the interaction kernel, data on kaon photoproduction from SAPHIR, CLAS and CBELSA/TAPS are analyzed in the threshold region. The importance of gauge invariance and the precision of various approximations in the interaction kernel utilized in earlier works are discussed.Comment: 23 pages, 13 figs, EPJ A styl

Behaviourally modulated hippocampal theta oscillations in the ferret persist during both locomotion and immobility

Theta oscillations are a hallmark of hippocampal activity across mammals and play a critical role in many hippocampal models of memory and spatial navigation. To reconcile the cross-species differences observed in the presence and properties of theta, we recorded hippocampal local field potentials in rats and ferrets during auditory and visual localisation tasks designed to vary locomotion and sensory attention. Here, we show that theta oscillations occur during locomotion in both ferrets and rats, however during periods of immobility, theta oscillations persist in the ferret, contrasting starkly with the switch to large irregular activity (LIA) in the rat. Theta during immobility in the ferret is identified as analogous to Type 2 theta that has been observed in rodents due to its sensitivity to atropine, and is modulated by behavioural state with the strongest theta observed during reward epochs. These results demonstrate that even under similar behavioural conditions, differences exist between species in the relationship between theta and behavioural state

Interpreting wde-band neural activity using convolutional neural networks

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity

Assessing the full costs of floodplain buyouts

Given projected increases in flood damages, managed retreat strategies are likely to become more widespread. Voluntary buyouts, where governments acquire flood-damaged properties and return the sites to open space, have been the primary form of federally funded retreat in the USA to date. However, little attention has been paid to the cost structure of buyout projects. Using a transaction cost framework, we analyze the costs of activities that comprise floodplain buyouts. Federal data do not distinguish transaction costs, but they do suggest that the cost of purchasing properties often accounts for 80% or less of total project costs. Through a systematic review (n = 1103 publications) and an analysis of government budgets (across n = 859 jurisdiction-years), we find limited sources with relevant cost information, none of which reports transaction costs. The absence of activity-level cost data inhibits more targeted policy reform to support community-driven and efficient buyout programs. Better data collection and reporting can inform more impactful and equitable buyout policy

Follow the foreign leader? Why following foreign incumbents is an effective electoral strategy

Previous research suggests that political parties respond to left–right policy positions of successful foreign political parties (“foreign leaders”). We evaluate whether this is an effective electoral strategy: specifically, do political parties gain votes in elections when they respond to successful foreign parties? We argue that parties that follow foreign leaders will arrive at policy positions closer to their own (domestic) median voter, which increases their electoral support. The analysis is based on a two-stage model specification of parties’ vote shares and suggests that following foreign leaders is a beneficial election strategy in national election because it allows them to better identify the position of their own median voter. These findings have important implications for our understanding of political representation, parties’ election strategies, and for policy diffusion

A plume-in-grid approach to characterize air quality impacts of aircraft emissions at the Hartsfield-Jackson Atlanta International Airport

This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June 2002 and July 2002 at the Hartsfield-Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at sub-grid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from -77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5%, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 μg m-3 near the airport and by 0.001-0.007 μg m-3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005-0.020 μg m-3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 μg m-3, with changes concentrated near the airport. Examination of sub-grid scale results indicates that puffs within 20 km of the airport often have average PM2.5 concentrations one order of magnitude higher than aircraft contribution to the grid cells containing those puffs, and within 1-4 km of emitters, puffs may have PM2.5 concentrations 3 orders of magnitude greater than the aircraft contribution to their grid cells. 21% of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg m-3 PM2.5 concentrations. Median daily puff concentrations vary between 0.017 and 0.134 μg m-3, while maximum daily puff concentrations vary between 6.1 and 42.1 μg m-3 during the 2-month period. In contrast, median daily grid concentrations vary between 0.015 and 0.091 μg m-3, while maximum daily grid concentrations vary between 0.751 and 2.55 μg m-3. Future researchers may consider using AMSTERDAM to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine scales are of interest

How to overcome the detrimental effects of noise in social interaction: the benefits of generosity

Interpersonal misunderstanding is often rooted in noise, or discrepancies between intended and actual outcomes for an interaction partner due to unintended errors (e.g., not being able to respond to an E-mail because of a local network breakdown). How can one effectively cope with noise in social dilemmas, situations in which self-interest and collective interests are conflicting? Consistent with hypotheses, the present research revealed that incidents of noise exert a detrimental effect on level of cooperation when a partner follows strict reciprocity (i.e., tit for tat) but that this effect can be overcome if a partner behaves somewhat more cooperatively than the actor did in the previous interaction (i.e., tit for tat plus 1). Also, when noise was present, tit for tat plus 1 elicited greater levels of cooperation than did tit for tat, thereby underscoring the benefits of adding generosity to reciprocity in coping with noise in social dilemmas. The Discussion outlines implications of the present work for theories focusing on self-presentation and attribution, communication, and trust and prorelationship behavior

Predictive maps in rats and humans for spatial navigation

Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework integrating across humans, rats, and simulated reinforcement learning (RL) agents to interrogate the dynamics of behavior during spatial navigation. We developed a novel open-field navigation task ("Tartarus maze") requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions on the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilizing a "successor representation," which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments and highlight the utility of modeling the behavior of different species to uncover the shared mechanisms that support behavior