Plasticity facilitates sustainable growth in the commons

In the commons, communities whose growth depends on public goods, individuals often rely on surprisingly simple strategies, or heuristics, to decide whether to contribute to the common good (at risk of exploitation by free-riders). Although this appears a limitation, here we show how four heuristics lead to sustainable growth by exploiting specific environmental constraints. The two simplest ones --contribute permanently or switch stochastically between contributing or not-- are first shown to bring sustainability when the public good efficiently promotes growth. If efficiency declines and the commons is structured in small groups, the most effective strategy resides in contributing only when a majority of individuals are also contributors. In contrast, when group size becomes large, the most effective behavior follows a minimal-effort rule: contribute only when it is strictly necessary. Both plastic strategies are observed in natural systems what presents them as fundamental social motifs to successfully manage sustainability

Optimistic irrationality and overbidding in private value auctions

Bidding one’s value in a second-price, private-value auction is a weakly dominant solution (Vickrey in J Finance 16(1):8–37, 1961), but repeated experimental studies find more overbidding than underbidding. We propose a model of optimistically irrational bidders who understand that there are possible gains and losses associated with higher bids but who may overestimate the additional probability of winning and/or underestimate the potential losses when bidding above value. These bidders may fail to discover the dominant strategy—despite the fact that the dominant strategy only requires rationality from bidders—but respond in a common sense way to out-of-equilibrium outcomes. By varying the monetary consequences of losing money in experimental auctions we observe more overbidding when the cost to losing money is low, and less overbidding when the cost is high. Our findings lend themselves to models in which less than fully rational bidders respond systematically to out-of-equilibrium incentives, and we find that our model better fits the effects of our manipulations than most of the existing models we consider

Programmability of Chemical Reaction Networks

Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and Boolean Logic Circuits, Vector Addition Systems, Petri Nets, Gate Implementability, Primitive Recursive Functions, Register Machines, Fractran, and Turing Machines. A theme to these investigations is the thin line between decidable and undecidable questions about SCRN behavior

Subtidal macrozoobenthos communities from northern Chile during and post El Niño 1997–1998

Despite a large amount of climatic and oceanographic information dealing with the recurring climate phenomenon El Niño (EN) and its well known impact on diversity of marine benthic communities, most published data are rather descriptive and consequently our understanding of the underlying mechanisms and processes that drive community structure during EN are still very scarce. In this study, we address two questions on the effects of EN on macrozoobenthic communities: (1) how does EN affect species diversity of the communities in northern Chile? and (2) is EN a phenomenon that restarts community assembling processes by affecting species interactions in northern Chile? To answer these questions, we compared species diversity and co-occurrence patterns of soft-bottoms macrozoobenthos communities from the continental shelf off northern Chile during (March 1998) and after (September 1998) the strong EN event 1997–1998. The methods used varied from species diversity and species co-occurrence analyses to multivariate ordination methods. Our results indicate that EN positively affects diversity of macrozoobenthos communities in the study area, increasing the species richness and diversity and decreasing the species dominance. EN represents a strong disturbance that affects species interactions that rule the species assembling processes in shallow-water, sea-bottom environments

Overfeeding, Autonomic Regulation and Metabolic Consequences

The autonomic nervous system plays an important role in the regulation of body processes in health and disease. Overfeeding and obesity (a disproportional increase of the fat mass of the body) are often accompanied by alterations in both sympathetic and parasympathetic autonomic functions. The overfeeding-induced changes in autonomic outflow occur with typical symptoms such as adiposity and hyperinsulinemia. There might be a causal relationship between autonomic disturbances and the consequences of overfeeding and obesity. Therefore studies were designed to investigate autonomic functioning in experimentally and genetically hyperphagic rats. Special emphasis was given to the processes that are involved in the regulation of peripheral energy substrate homeostasis. The data revealed that overfeeding is accompanied by increased parasympathetic outflow. Typical indices of vagal activity (such as the cephalic insulin release during food ingestion) were increased in all our rat models for hyperphagia. Overfeeding was also accompanied by increased sympathetic tone, reflected by enhanced baseline plasma norepinephrine (NE) levels in both VMH-lesioned animals and rats rendered obese by hyperalimentation. Plasma levels of NE during exercise were, however, reduced in these two groups of animals. This diminished increase in the exercise-induced NE outflow could be normalized by prior food deprivation. It was concluded from these experiments that overfeeding is associated with increased parasympathetic and sympathetic tone. In models for hyperphagia that display a continuously elevated nutrient intake such as the VMH-lesioned and the overfed rat, this increased sympathetic tone was accompanied by a diminished NE response to exercise. This attenuated outflow of NE was directly related to the size of the fat reserves, indicating that the feedback mechanism from the periphery to the central nervous system is altered in the overfed state.

The antisaccade task as an index of sustained goal activation in working memory: modulation by nicotine

The antisaccade task provides a laboratory analogue of situations in which execution of the correct behavioural response requires the suppression of a more prepotent or habitual response. Errors (failures to inhibit a reflexive prosaccade towards a sudden onset target) are significantly increased in patients with damage to the dorsolateral prefrontal cortex and patients with schizophrenia. Recent models of antisaccade performance suggest that errors are more likely to occur when the intention to initiate an antisaccade is insufficiently activated within working memory. Nicotine has been shown to enhance specific working memory processes in healthy adults. MATERIALS AND METHODS: We explored the effect of nicotine on antisaccade performance in a large sample (N = 44) of young adult smokers. Minimally abstinent participants attended two test sessions and were asked to smoke one of their own cigarettes between baseline and retest during one session only. RESULTS AND CONCLUSION: Nicotine reduced antisaccade errors and correct antisaccade latencies if delivered before optimum performance levels are achieved, suggesting that nicotine supports the activation of intentions in working memory during task performance. The implications of this research for current theoretical accounts of antisaccade performance, and for interpreting the increased rate of antisaccade errors found in some psychiatric patient groups are discussed

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Positron kinetics in an idealized PET environment

The kinetic theory of non-relativistic positrons in an idealized positron emission tomography PET environment is developed by solving the Boltzmann equation, allowing for coherent and incoherent elastic, inelastic, ionizing and annihilating collisions through positronium formation. An analytic expression is obtained for the positronium formation rate, as a function of distance from a spherical source, in terms of the solutions of the general kinetic eigenvalue problem. Numerical estimates of the positron range - a fundamental limitation on the accuracy of PET, are given for positrons in a model of liquid water, a surrogate for human tissue. Comparisons are made with the 'gas-phase' assumption used in current models in which coherent scattering is suppressed. Our results show that this assumption leads to an error of the order of a factor of approximately 2, emphasizing the need to accurately account for the structure of the medium in PET simulations

Inferring the Scale of OpenStreetMap Features

International audienceTraditionally, national mapping agencies produced datasets and map products for a low number of specified and internally consistent scales, i.e. at a common level of detail (LoD). With the advent of projects like OpenStreetMap, data users are increasingly confronted with the task of dealing with heterogeneously detailed and scaled geodata. Knowing the scale of geodata is very important for mapping processes such as for generalization of label placement or land-cover studies for instance. In the following chapter, we review and compare two concurrent approaches at automatically assigning scale to OSM objects. The first approach is based on a multi-criteria decision making model, with a rationalist approach for defining and parameterizing the respective criteria, yielding five broad LoD classes. The second approach attempts to identify a single metric from an analysis process, which is then used to interpolate a scale equivalence. Both approaches are combined and tested against well-known Corine data, resulting in an improvement of the scale inference process. The chapter closes with a presentation of the most pressing open problem