The amplification of risk in experimental diffusion chains

Understanding how people form and revise their perception of risk is central to designing efficient risk communication methods, eliciting risk awareness, and avoiding unnecessary anxiety among the public. However, public responses to hazardous events such as climate change, contagious outbreaks, and terrorist threats are complex and difficult-to-anticipate phenomena. Although many psychological factors influencing risk perception have been identified in the past, it remains unclear how perceptions of risk change when propagated from one person to another and what impact the repeated social transmission of perceived risk has at the population scale. Here, we study the social dynamics of risk perception by analyzing how messages detailing the benefits and harms of a controversial antibacterial agent undergo change when passed from one person to the next in 10-subject experimental diffusion chains. Our analyses show that when messages are propagated through the diffusion chains, they tend to become shorter, gradually inaccurate, and increasingly dissimilar between chains. In contrast, the perception of risk is propagated with higher fidelity due to participants manipulating messages to fit their preconceptions, thereby influencing the judgments of subsequent participants. Computer simulations implementing this simple influence mechanism show that small judgment biases tend to become more extreme, even when the injected message contradicts preconceived risk judgments. Our results provide quantitative insights into the social amplification of risk perception, and can help policy makers better anticipate and manage the public response to emerging threats.Comment: Published online in PNAS Early Edition (open-access): http://www.pnas.org/content/early/2015/04/14/142188311

Microscopic Properties of Na and Li—A First Principle Study of Metal Battery Anode Materials

Using density functional theory, we studied the bulk and surface properties of Li and Na electrodes on an atomistic level. To get a better understanding of the initial stages of surface growth phenomena (and thus dendrite formation), various self-diffusion mechanisms were studied. For this purpose, dedicated diffusion pathways on the surfaces of Na and Li were investigated within the terrace-step-kink (TSK) model utilizing nudged elastic band calculations. We were able to prove that the mere investigation of terrace self-diffusion on the respective low-index surfaces does not provide a possible descriptor for dendritic growth. Finally, we provide an initial view of the surface growth behavior of both alkali metals as well as provide a basis for experimental investigations and theoretical long-scale kinetic Monte Carlo simulations

First‐Principles Studies on the Atomistic Properties of Metallic Magnesium as Anode Material in Magnesium‐Ion Batteries

Rechargeable magnesium-ion batteries (MIBs) are a promising alternative to commercial lithium-ion batteries (LIBs). They are safer to handle, environmentally more friendly, and provide a five-time higher volumetric capacity (3832 mAh cm$^{-3}$) than commercialized LIBs. However, the formation of a passivation layer on metallic Mg electrodes is still a major challenge towards their commercialization. Using density functional theory (DFT), the atomistic properties of metallic magnesium, mainly well-selected self-diffusion processes on perfect and imperfect Mg surfaces were investigated to better understand the initial surface growth phenomena. Subsequently, rate constants and activation temperatures of crucial diffusion processes on Mg(0001) and Mg(10$_{\overline{1}}$ 1) were determined, providing preliminary insights into the surface kinetics of metallic Mg electrodes. The obtained DFT results provide a data set for parametrizing a force field for metallic Mg or performing kinetic Monte-Carlo simulations

Studying antibiotic persistence in vivo using the model organism Salmonella Typhimurium.

Antibiotic persistence permits a subpopulation of susceptible bacteria to survive lethal concentrations of bactericidal antibiotics. This prolongs antibiotic therapy, promotes the evolution of antibiotic-resistant pathogen strains and can select for pathogen virulence within infected hosts. Here, we review the literature exploring antibiotic persistence in vivo, and describe the consequences of recalcitrant subpopulations, with a focus on studies using the model pathogen Salmonella Typhimurium. In vitro studies have established a concise set of features distinguishing true persisters from other forms of bacterial recalcitrance to bactericidal antibiotics. We discuss how animal infection models are useful for exploring these features in vivo, and describe how technical challenges can sometimes prevent the conclusive identification of true antibiotic persistence within infected hosts. We propose using two complementary working definitions for studying antibiotic persistence in vivo: the strict definition for studying the mechanisms of persister formation, and an operative definition for functional studies assessing the links between invasive virulence and persistence as well as the consequences for horizontal gene transfer, or the emergence of antibiotic-resistant mutants. This operative definition will enable further study of how antibiotic persisters arise in vivo, and of how surviving populations contribute to diverse downstream effects such as pathogen transmission, horizontal gene transfer and the evolution of virulence and antibiotic resistance. Ultimately, such studies will help to improve therapeutic control of antibiotic- recalcitrant populations

FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use