Developing Parameters for Uncommon Exposures

Common exposure scenarios form the basis for exposure assessments included in human health risk assessments. The quantitative parameters that are used to calculate the receptor's dose associated with these common exposure scenarios are readily available. When humans have uncommon exposures, these often are excluded from the dose calculations because of the lack of parameters or parameters are estimated, which can increase the uncertainty in calculated dose. This study evaluates a third option of performing laboratory experiments to estimate parameters for uncommon exposures. Two exposures, the handling and hand-washing of contaminated work clothes and the drinking of water with sufficient free-phase hydrocarbons to form a visible sheen, were simulated in a laboratory. Parameters were developed for each exposure such that doses could be calculated. Parameters developed include a mass of oily contaminants on skin after washing contaminated clothes and the mass of a visible oily sheen on an aqueous surface. These results demonstrate that developing parameters for uncommon exposure scenarios is a practical alternative to eliminating or estimating exposures and these experiments can be done in compliance with human subject protocols.     

Ecology after 100 years: Progress and pseudo-progress

Has the science of ecology fulfilled the promises made by the originators of ecological science at the start of the last century? What should ecology achieve? Have good policies for environmental management flowed out of ecological science? These important questions are rarely discussed by ecologists working on detailed studies of individual systems. Until we decide what we wish to achieve as ecologists we cannot define progress toward those goals. Ecologists desire to achieve an understanding of how the natural world operates, how humans have modified the natural world, and how to alleviate problems arising from human actions. Ecologists have made impressive gains over the past century in achieving these goals, but this progress has been uneven. Some sub-disciplines of ecology are well developed empirically and theoretically, while others languish for reasons that are not always clear. Fundamental problems can be lost to view as ecologists fiddle with unimportant pseudo-problems. Bandwagons develop and disappear with limited success in addressing problems. The public demands progress from all the sciences, and as time moves along and problems get worse, more rapid progress is demanded. The result for ecology has too often been poor, short-term science and poor management decisions. But since the science is rarely repeated and the management results may be a generation or two down the line, it is difficult for the public or for scientists to decide how good or bad the scientific advice has been. In ecology over the past 100 years we have made solid achievements in behavioural ecology, population dynamics, and ecological methods, we have made some progress in understanding community and ecosystem dynamics, but we have made less useful progress in developing theoretical ecology, landscape ecology, and natural resource management. The key to increasing progress is to adopt a systems approach with explicit hypotheses, theoretical models, and field experiments on a scale defined by the problem. With continuous feedback between problems, possible solutions, relevant theory and experimental data we can achieve our scientific goals.     

The Tuning of Human Motor Response to Risk in a Dynamic Environment Task

The role of motor uncertainty in discrete or static space tasks, such as pointing tasks, has been investigated in many experiments. These studies have shown that humans hold an internal representation of intrinsic and extrinsic motor uncertainty and compensate for this variability when planning movement. The aim of this study was to investigate how humans respond to uncertainties during movement execution in a dynamic environment despite indeterminate knowledge of the outcome of actions. Additionally, the role of errors, or lack thereof, in predicting risk was examined. In the experiment, subjects completed a driving simulation game on a two-lane road. The road contained random curves so that subjects were forced to use sensory feedback to complete the task and could not rely only on motor planning. Risk was manipulated by using horizontal perturbations to create the illusion of driving on a bumpy road, thereby imposing motor uncertainty, and altering the cost function of the road. Results suggest continual responsiveness to cost and uncertainty in a dynamic task and provide evidence that subjects avoid risk even in the absence of errors. The results suggest that humans tune their statistical motor behavior based on cost, taking into account probabilities of possible outcomes in response to environmental uncertainty.     

Experiments in ecology and management: Their logics,functions and interpretations

Experimental tests of clearly articulated hypotheses are an increasingly widespread feature of modern marine ecology. Increased use of experiments has not, however, been accompanied by increased understanding of the logical structure of falsificationist tests. Most observations can be explained by several different models or theories. To distinguish among these requires demonstration of the falsity of the consequences or predictions of incorrect models. This is best achieved by deriving from each model one or more hypotheses (predictions) about the type, form or nature of observations that should occur in some not-yet-examined set of circumstances. Because of logical constraints on the possibility of proving the correctness of such hypotheses, they must be inverted to form logical null hypotheses which comprise all alternative possibilities to those predicted in the hypotheses. Correctness or not of null hypotheses can then be ascertained by an appropriately designed experiment (or test), leading to unambiguous rejection or retention of the null hypotheses. The former corroborates the hypotheses and provides support for the correctness of the explanatory model for the original observations. In contrast, retention of a null hypothesis identifies an incorrect model. The growth of knowledge is thus the elimination of false models, theories and explanations. Ecological experiments usually require statistical procedures for determining whether or not null hypotheses should be retained. Construction of statistical null hypotheses (i.e. definitions of parameters of frequency distributions of test statistics) sometimes requires that these be identical to logical hypotheses (and not to the logical nulls). This leads to irrational acceptance of hypotheses and the models or theories from which they were derived. It also poses immense problems for determinations of statistical power of experiments. Ecological experiments are analysed to reveal the nature of, and linkages between, their components in relation to falsificationism, statistical procedures and the logical properties and interpretations of ecological theories.     

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.     

The enduring puzzle of the human chin

Although modern humans are considered to be morphologically distinct from other living primates because of our large brains, dexterous hands, and bipedal gait, all of these features are found among extinct hominins. The chin, however, appears to be a uniquely modern human trait. Probably because of the chin's exclusivity, many evolutionary scenarios have been proposed to explain its origins. To date, researchers have developed adaptive hypotheses relating chins to speech, mastication, and sexual selection; still others see it as a structural artifact tangentially related to complex processes involving evolutionary retraction of the midfacial skeleton. Consensus has remained elusive, partly because hypotheses purporting to explain how this feature developed uniquely in modern humans are all fraught with theoretical and/or empirical shortcomings. Here we review a century's worth of chin hypotheses and discuss future research avenues that may provide greater insight into this human peculiarity.     

An end to insight? New Caledonian crows can spontaneously solve problems without planning their actions

Animals rarely solve problems spontaneously. Some bird species, however, can immediately find a solution to the string-pulling problem. They are able to rapidly gain access to food hung on the end of a long string by repeatedly pulling and then stepping on the string. It is currently unclear whether these spontaneous solutions are produced by insight or by a perceptual-motor feedback loop. Here, we presented New Caledonian crows and humans with a novel horizontal string-pulling task. While the humans succeeded, no individual crow showed a significant preference for the connected string, and all but one failed to gain the food even once. These results clearly show that string pulling in New Caledonian crows is generated not by insight, but by perceptual feedback. Animals can spontaneously solve problems without planning their actions.     

Review of stochastic hybrid systems with applications in biological systems modeling and analysis

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.     

Solving navigational uncertainty using grid cells on robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.     

Decision-making under great uncertainty: environmental management in an era of global change

Global change issues are complex and the consequences of decisions are often highly uncertain. The large spatial and temporal scales and stakes involved make it important to take account of present and potential consequences in decision-making. Standard approaches to decision-making under uncertainty require information about the likelihood of alternative states, how states and actions combine to form outcomes and the net benefits of different outcomes. For global change issues, however, the set of potential states is often unknown, much less the probabilities, effect of actions or their net benefits. Decision theory, thresholds, scenarios and resilience thinking can expand awareness of the potential states and outcomes, as well as of the probabilities and consequences of outcomes under alternative decisions.     

Adapting environmental management to uncertain but inevitable change

Implementation of adaptation actions to protect biodiversity is limited by uncertainty about the future. One reason for this is the fear of making the wrong decisions caused by the myriad future scenarios presented to decision-makers. We propose an adaptive management (AM) method for optimally managing a population under uncertain and changing habitat conditions. Our approach incorporates multiple future scenarios and continually learns the best management strategy from observations, even as conditions change. We demonstrate the performance of our AM approach by applying it to the spatial management of migratory shorebird habitats on the East Asian–Australasian flyway, predicted to be severely impacted by future sea-level rise. By accounting for non-stationary dynamics, our solution protects 25 000 more birds per year than the current best stationary approach. Our approach can be applied to many ecological systems that require efficient adaptation strategies for an uncertain future.     

Emotions and actions associated with norm-breaking events

Norms have a strong influence on human social interactions, but the emotions and actions associated with norm-breaking events have not been systematically studied. We asked subjects to imagine themselves in a conflict situation and then to report how they would feel, how they would act, and how they would imagine the feelings and actions of their opponent. By altering the fictional scenario that they were asked to imagine (weak vs. strong norm) and the perspective of the subject (norm-breaker vs. the one whose norm has been violated), the emotions and actions associated with norm-breaking events could be examined. We tested the following hypotheses: (1) norms create emotional asymmetries that resolve conflicts in otherwise symmetrical contest situations; (2) sex differences exist in response to norm-breaking events, with males more prone to violence than females; (3) individual differences exist in response to norm-breaking events, along the lines predicted by theoretical models; and (4) emotions and actions attributed to one’s opponent are distorted in ways that can be interpreted as adaptive for the believer. In addition to these basic hypotheses, we address more subtle issues concerning the particular emotions provoked by norm-breaking events, the degree to which emotional response is fine-tuned to the situation, and the degree to which emotional response correlates with anticipated behavioral response. We discuss the relevance of our study to the general study of emotions and the use of fictional scenarios as a research method in addition to the study of norms from an evolutionary perspective.     

Are Existing Global Scenarios Consistent with Ecological Feedbacks?

Scenarios can help planners and decision makers to think through uncertainties about the future and make decisions that are robust to a variety of possible outcomes. To develop useful scenarios we need to understand the main processes of relevance to the system of interest. Ecological processes, and the feedbacks that they can create between human actions and human well-being, are thought to be important for human societies. Current uncertainties over the long-term resilience of ecosystems and the substitutability of ecosystem goods and services can be translated into three alternative realities: ecosystems may be relatively brittle, relatively resilient, or largely irrelevant. Although these extremes are only rough characterizations of reality, they help us to focus our thinking about the possible outcomes of interactions between humans and the rest of the biosphere. Existing global scenarios can be categorized into a small number of families based on shared themes and assumptions about the future. Considering the internal consistency of four of the main scenario families in relation to the three alternative ecological realities suggests that all existing scenarios make strong, implicit assumptions about the resilience of ecosystems. After a detailed discussion of individual examples, we present a synthesis of the incorporation of ecology in existing scenarios. All current scenarios are inconsistent with at least one possible property of ecosystems and their likely interaction with society. The interrelationships between ecological reality, human views of ecosystems, and social responses to actual and perceived ecological change are complex. For the Millennium Ecosystem Assessment and future scenario exercises, we recommend that essential ecological assumptions should be made explicit to ensure that the details of each scenario are consistent with both the perceived and the actual degree of resilience of ecosystems.     

Autonomous Learning in Maze Solution by Octopus

We tested seven octopuses, Octopus vulgaris, in maze-learning experiments. They tried to reach the goal, so as to get a reward, by using various locomotory actions in the path, and sometimes encountered obstacles. They came to select efficient swimming actions in the path; afterwards less efficient tactile actions (crawling, staying put, and so on: these reduce the speed of movement) gradually increased, while time to detour around the obstacle was reduced. To investigate whether octopuses reduce time spent detouring around obstacles by estimating their actions in the path, we devised a trade-off situation in which octopuses were obliged to use tactile actions even though the set-up also encouraged them to use swimming actions. As a result, we could observe that they reduced the detouring time. In that way, we experimentally constituted a perspective as if octopuses looked around the whole maze and estimated their actions. Such a perspective appeared to be autonomous learning.     

Automated ensemble modeling with modelMaGe: analyzing feedback mechanisms in the Sho1 branch of the HOG pathway

In systems biology uncertainty about biological processes translates into alternative mathematical model candidates. Here, the goal is to generate, fit and discriminate several candidate models that represent different hypotheses for feedback mechanisms responsible for downregulating the response of the Sho1 branch of the yeast high osmolarity glycerol (HOG) signaling pathway after initial stimulation. Implementing and testing these candidate models by hand is a tedious and error-prone task. Therefore, we automatically generated a set of candidate models of the Sho1 branch with the tool modelMaGe. These candidate models are automatically documented, can readily be simulated and fitted automatically to data. A ranking of the models with respect to parsimonious data representation is provided, enabling discrimination between candidate models and the biological hypotheses underlying them. We conclude that a previously published model fitted spurious effects in the data. Moreover, the discrimination analysis suggests that the reported data does not support the conclusion that a desensitization mechanism leads to the rapid attenuation of Hog1 signaling in the Sho1 branch of the HOG pathway. The data rather supports a model where an integrator feedback shuts down the pathway. This conclusion is also supported by dedicated experiments that can exclusively be predicted by those models including an integrator feedback.modelMaGe is an open source project and is distributed under the Gnu General Public License (GPL) and is available from http://modelmage.org.     

In silico model as a tool for interpretation of intestinal infection studies

In nutrition research the number of human in vivo experiments is limited because of the many restrictions and the high costs of testing in humans. Up to now predictive computer models aiming to enhance research have been rare or too complex, with many nonmeasurable adjustable parameters. This study aimed to develop a basic physicochemical computer model for a first quantitative interpretation of results obtained from in vivo intestinal experiments with bacteria. This new modeling approach is validated with results obtained from gut infection studies in vivo. The design of the model is described, and its ability to reproduce experimental data is evaluated. The model predictions are compared with new experimental data. The phenomena that take place in the gastrointestinal tract are summarized by model constants for growth, adherence, and release of bacteria. Although the model is far from describing all details and many processes in the intestine are combined, the model calculation results lead to reasonable conclusions and interesting hypotheses. One of these hypotheses concluded from the model outcomes is that Escherichia coli bacteria have a much lower intestinal growth rate in humans than in rats. Extra laboratory validation experiments proved the reliability of this hypothesis predicted by the model. In addition, the known protective effect of dietary calcium and detrimental effect of clindamycin on the growth and adherence of Salmonella bacteria could be quantified. From these results it is clear that the model enhances the interpretation of in vivo gastrointestinal experiments and will facilitate research trajectories towards new functional foods that improve resistance to pathogenic bacteria in humans.     

Uncertainty in predictions of range dynamics: black grouse climbing the Swiss Alps

Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species’ vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate‐induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual‐based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species’ responses to climate change.     

Evolutionary genetics of plant adaptation

Plants provide unique opportunities to study the mechanistic basis and evolutionary processes of adaptation to diverse environmental conditions. Complementary laboratory and field experiments are important for testing hypotheses reflecting long-term ecological and evolutionary history. For example, these approaches can infer whether local adaptation results from genetic tradeoffs (antagonistic pleiotropy), where native alleles are best adapted to local conditions, or if local adaptation is caused by conditional neutrality at many loci, where alleles show fitness differences in one environment, but not in a contrasting environment. Ecological genetics in natural populations of perennial or outcrossing plants can also differ substantially from model systems. In this review of the evolutionary genetics of plant adaptation, we emphasize the importance of field studies for understanding the evolutionary dynamics of model and nonmodel systems, highlight a key life history trait (flowering time) and discuss emerging conservation issues.     

Hotspots of species loss do not vary across future climate scenarios in a drought‐prone river basin

Climate change is expected to alter the distributions of species around the world, but estimates of species’ outcomes vary widely among competing climate scenarios. Where should conservation resources be directed to maximize expected conservation benefits given future climate uncertainty? Here, we explore this question by quantifying variation in fish species’ distributions across future climate scenarios in the Red River basin south‐central United States. We modeled historical and future stream fish distributions using a suite of environmental covariates derived from high‐resolution hydrologic and climatic modeling of the basin. We quantified variation in outcomes for individual species across climate scenarios and across space, and identified hotspots of species loss by summing changes in probability of occurrence across species. Under all climate scenarios, we find that the distribution of most fish species in the Red River Basin will contract by 2050. However, the variability across climate scenarios was more than 10 times higher for some species than for others. Despite this uncertainty in outcomes for individual species, hotspots of species loss tended to occur in the same portions of the basin across all climate scenarios. We also find that the most common species are projected to experience the greatest range contractions, underscoring the need for directing conservation resources toward both common and rare species. Our results suggest that while it may be difficult to predict which species will be most impacted by climate change, it may nevertheless be possible to identify spatial priorities for climate mitigation actions that are robust to future climate uncertainty. These findings are likely to be generalizable to other ecosystems around the world where future climate conditions follow prevailing historical patterns of key environmental covariates.     

Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.