Outlier Detection for Sensor Systems (ODSS): A MATLAB Macro for Evaluating Microphone Sensor Data Quality.

Microphone sensor systems provide information that may be used for a variety of applications. Such systems generate large amounts of data. One concern is with microphone failure and unusual values that may be generated as part of the information collection process. This paper describes methods and a MATLAB graphical interface that provides rapid evaluation of microphone performance and identifies irregularities. The approach and interface are described. An application to a microphone array used in a wind tunnel is used to illustrate the methodology.

Hydrometeorological and physicochemical drivers of fecal indicator bacteria in urban stream bottom sediments.

High levels of fecal indicator bacteria (FIB) are the leading cause of surface water quality impairments in the United States. Watershed-scale models are commonly used to identify relative contributions of watershed sources and to evaluate the effectiveness of remediation strategies. However, most existing models simplify FIB transport behavior as equivalent to that of dissolved-phase contaminants, ignoring the impacts of sediment on the fate and transport of FIB. Implementation of sediment-related processes within existing models is limited by minimal available monitoring data on sediment FIB concentrations for model development, calibration, and validation purposes. The purpose of the present study is to evaluate FIB levels in the streambed sediments as compared to those in the water column and to identify environmental variables that influence water and underlying sediment FIB levels. Concentrations of and enterococci in the water column and sediments of an urban stream were monitored weekly for 1 yr and correlated with a variety of potential hydrometeorological and physicochemical variables. Increased FIB concentrations in both the water column and sediments were most strongly correlated with increased antecedent 24-h rainfall, increased stream water temperature, decreased dissolved oxygen, and decreased specific conductivity. These observations will support future efforts to incorporate sediment-related processes in existing models through the identification of key FIB relationships with other model inputs, and the provision of sediment FIB concentrations for direct model calibration. In addition, identified key variables can be used in quick evaluation of the effectiveness of potential remediation strategies.

Dogs and wolves differ in their response allocation to their owner/caregiver or food in a concurrent choice procedure.

Dogs and wolves both show attachment-like behaviors to their owners/caregivers, including exploring more in the presence of the owner/caregiver, and greeting the owner/caregiver more effusively after an absence. Concurrent choice studies can elucidate dogs' and wolves' relationship to their owners/caregivers by assessing their preference for the owner/caregiver compared to other stimuli. While previous research has used concurrent choice paradigms to evaluate dogs' and wolves' preference between humans giving social interaction or humans giving food, no research has explored their preferences for an owner/caregiver compared to food when the food is not delivered by a human. In the current study, we investigated whether dogs and hand-reared wolves preferred their owner/caregiver or food, unassociated with a human, when they had been equally deprived of each stimulus (at least 4 hours). Each canid experienced four trials; we measured first choice and time spent with each alternative. Dogs overall did not show a preference for the owner or food. Wolves, on the other hand, tended to show a preference for food in both measures. We observed a range of individual variation in both measures, although dogs showed more individual variation. The differences we observed between dogs and wolves align with prior research comparing wolf and dog behavior directed towards humans; however, the reasons for this differential responding could be due to a variety of factors beyond phylogeny.

An experimental test of disease resistance function in the skin-associated bacterial communities of three tropical amphibian species.

Variation in the structure of host-associated microbial communities has been correlated with the occurrence and severity of disease in diverse host taxa, suggesting a key role of the microbiome in pathogen defense. However, whether these correlations are typically a cause or consequence of pathogen exposure remains an open question, and requires experimental approaches to disentangle. In amphibians, infection by the fungal pathogen Batrachochytrium dendrobatidis (Bd) alters the skin microbial community in some host species, whereas in other species, the skin microbial community appears to mediate infection dynamics. In this study, we completed experimental Bd exposures in three species of tropical frogs (Agalychnis callidryas, Dendropsophus ebraccatus,andCraugastor fitzingeri) that were sympatric with Bd at the time of the study. For all three species, we identified key taxa within the skin bacterial communities that were linked to Bd infection dynamics. We also measured higher Bd infection intensities in D. ebraccatus and C. fitzingeri that were associated with higher mortality in C. fitzingeri. Our findings indicate that microbially mediated pathogen resistance is a complex trait that can vary within and across host species, and suggest that symbiont communities that have experienced prior selection for defensive microbes may be less likely to be disturbed by pathogen exposure.

SIRIUS: Dual, Symmetric, Interactive Dimension Reductions.

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.

Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics.

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.

Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.

Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.

Accurate characterization of delay discounting: a multiple model approach using approximate Bayesian model selection and a unified discounting measure.

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.

Community Structure and Function of Amphibian Skin Microbes: An Experiment with Bullfrogs Exposed to a Chytrid Fungus.

The vertebrate microbiome contributes to disease resistance, but few experiments have examined the link between microbiome community structure and disease resistance functions. Chytridiomycosis, a major cause of amphibian population declines, is a skin disease caused by the fungus, Batrachochytrium dendrobatidis (Bd). In a factorial experiment, bullfrog skin microbiota was reduced with antibiotics, augmented with an anti-Bd bacterial isolate (Janthinobacterium lividum), or unmanipulated, and individuals were then either exposed or not exposed to Bd. We found that the microbial community structure of individual frogs prior to Bd exposure influenced Bd infection intensity one week following exposure, which, in turn, was negatively correlated with proportional growth during the experiment. Microbial community structure and function differed among unmanipulated, antibiotic-treated, and augmented frogs only when frogs were exposed to Bd. Bd is a selective force on microbial community structure and function, and beneficial states of microbial community structure may serve to limit the impacts of infection.

Panamanian frog species host unique skin bacterial communities.

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.

Amphibian skin may select for rare environmental microbes.

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.

Visual to Parametric Interaction (V2PI).

Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as " Visual to Parametric Interaction" (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.

Semantics of directly manipulating spatializations.

When high-dimensional data is visualized in a 2D plane by using parametric projection algorithms, users may wish to manipulate the layout of the data points to better reflect their domain knowledge or to explore alternative structures. However, few users are well-versed in the algorithms behind the visualizations, making parameter tweaking more of a guessing game than a series of decisive interactions. Translating user interactions into algorithmic input is a key component of Visual to Parametric Interaction (V2PI) [13]. Instead of adjusting parameters, users directly move data points on the screen, which then updates the underlying statistical model. However, we have found that some data points that are not moved by the user are just as important in the interactions as the data points that are moved. Users frequently move some data points with respect to some other 'unmoved' data points that they consider as spatially contextual. However, in current V2PI interactions, these points are not explicitly identified when directly manipulating the moved points. We design a richer set of interactions that makes this context more explicit, and a new algorithm and sophisticated weighting scheme that incorporates the importance of these unmoved data points into V2PI.

Cherry-picking for complex data: robust structure discovery.

Complex data often arise as a superposition of data generated from several simpler models. The traditional strategy for such cases is to use mixture modelling, but it can be problematic, especially in higher dimensions. This paper considers an alternative approach, emphasizing data exploration and robustness to model misspecification. The strategy is applied to problems in regression, cluster analysis and multidimensional scaling. The approach is illustrated through simulation and the analysis of several datasets.