Host-microbe symbioses rely on the successful transmission or acquisition of symbionts in each new generation. Amphibians host a diverse cutaneous microbiota, and many of these symbionts appear to be mutualistic and may limit infection by the chytrid fungus, Batrachochytrium dendrobatidis, which has caused global amphibian population declines and extinctions in recent decades. Using bar-coded 454 pyrosequencing of the 16S rRNA gene, we addressed the question of symbiont transmission by examining variation in amphibian skin microbiota across species and sites and in direct relation to environmental microbes. Although acquisition of environmental microbes occurs in some host-symbiont systems, this has not been extensively examined in free-living vertebrate-microbe symbioses. Juvenile bullfrogs (Rana catesbeiana), adult red-spotted newts (Notophthalmus viridescens), pond water and pond substrate were sampled at a single pond to examine host-specificity and potential environmental transmission of microbiota. To assess population level variation in skin microbiota, adult newts from two additional sites were also sampled. Cohabiting bullfrogs and newts had distinct microbial communities, as did newts across the three sites. The microbial communities of amphibians and the environment were distinct; there was very little overlap in the amphibians' core microbes and the most abundant environmental microbes, and the relative abundances of OTUs that were shared by amphibians and the environment were inversely related. These results suggest that, in a host species-specific manner, amphibian skin may select for microbes that are generally in low abundance in the environment.

Vertebrates, including amphibians, host diverse symbiotic microbes that contribute to host disease resistance. Globally, and especially in montane tropical systems, many amphibian species are threatened by a chytrid fungus, Batrachochytrium dendrobatidis (Bd), that causes a lethal skin disease. Bd therefore may be a strong selective agent on the diversity and function of the microbial communities inhabiting amphibian skin. In Panamá, amphibian population declines and the spread of Bd have been tracked. In 2012, we completed a field survey in Panamá to examine frog skin microbiota in the context of Bd infection. We focused on three frog species and collected two skin swabs per frog from a total of 136 frogs across four sites that varied from west to east in the time since Bd arrival. One swab was used to assess bacterial community structure using 16S rRNA amplicon sequencing and to determine Bd infection status, and one was used to assess metabolite diversity, as the bacterial production of anti-fungal metabolites is an important disease resistance function. The skin microbiota of the three Panamanian frog species differed in OTU (operational taxonomic unit, ~bacterial species) community composition and metabolite profiles, although the pattern was less strong for the metabolites. Comparisons between frog skin bacterial communities from Panamá and the US suggest broad similarities at the phylum level, but key differences at lower taxonomic levels. In our field survey in Panamá, across all four sites, only 35 individuals (~26%) were Bd infected. There was no clustering of OTUs or metabolite profiles based on Bd infection status and no clear pattern of west-east changes in OTUs or metabolite profiles across the four sites. Overall, our field survey data suggest that different bacterial communities might be producing broadly similar sets of metabolites across frog hosts and sites. Community structure and function may not be as tightly coupled in these skin symbiont microbial systems as it is in many macro-systems.

The study of delay discounting, or valuation of future rewards as a function of delay, has contributed to understanding the behavioral economics of addiction. Accurate characterization of discounting can be furthered by statistical model selection given that many functions have been proposed to measure future valuation of rewards. The present study provides a convenient Bayesian model selection algorithm that selects the most probable discounting model among a set of candidate models chosen by the researcher. The approach assigns the most probable model for each individual subject. Importantly, effective delay 50 (ED50) functions as a suitable unifying measure that is computable for and comparable between a number of popular functions, including both one- and two-parameter models. The combined model selection/ED50 approach is illustrated using empirical discounting data collected from a sample of 111 undergraduate students with models proposed by Laibson (1997); Mazur (1987); Myerson & Green (1995); Rachlin (2006); and Samuelson (1937). Computer simulation suggests that the proposed Bayesian model selection approach outperforms the single model approach when data truly arise from multiple models. When a single model underlies all participant data, the simulation suggests that the proposed approach fares no worse than the single model approach.

No abstract

In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus "observation") in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.KEYWORDS: observation-level interaction, visual analytics, statistical models. INDEX TERMS: H.5.0 [Human-Computer Interaction] INTRODUCTIONVisual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces" [1]. The goal of visual analytics (VA) is to extract information, perform exploratory analyses, and validate hypotheses through an interactive exploration process known as sensemaking [2]. In this sensemaking loop, users proceed through a complex combination of proposing and evaluating hypotheses and schemas about their data, with the ultimate goal of gaining insight (i.e. "making sense of" the data). A wide variety of statistical models have been specifically designed for visualizations of this purpose. Thus, many visual analytic systems are fundamentally based on interaction with statistical models and algorithms, using visualization as the medium for the communication (i.e. where the interaction occurs). This communication is performed via direct interaction with the parameters of the model. For example, Interactive Principal Component Analysis, iPCA [3], allows the user to change the weight for each dimension in calculating the direction of projection using multiple sliders (one slider per dimension). Also, in an interactive visualization using MDS [4], the user can weight the dissimilarities in the calculation of the stress function through similar visual controls.In both instances, the model is made aware of the user input through a formal and direct modification of a parameter (i.e. parameter level interacti...