Selection of Contaminants and Receptors of Concern for Ecological Risk Assessments Using the Paired Comparison Technique (PCT)

A critical component of the ecological risk assessment process, identification of contaminants and receptors of concern, is currently lacking an objective, data-driven, broadly applicable methodology. The paired comparison technique (PCT) can be used to meet this need by providing a standardized technique for making these decisions. PCT condenses the decision-making process down to individual components and ultimately generates an objective ranking for each alternative based on pertinent attributes. Factors considered in the PCT may be selected and weighted via a formal consensus building process which can incorporate stakeholder concerns. Once factors have been selected and weighted, each alternative is ranked relative to each other alter native for each factor. Each alternative ranking for each factor is multiplied by the factor's weight and the weighted rankings are summed. These sums are then used to rank alternatives relative to one another. This ranking provides decision-makers with a semi quantitative basis for making final decisions, although the technique itself does not (and should not) actually make the decision. Several examples of the technique using actual Superfund site data are presented.     

Ecological land suitability analysis through spatial indicators: An application of the Analytic Network Process technique and Ordered Weighted Average approach

Multicriteria-Spatial Decision Support Systems (MC-SDSS) are increasingly popular tools in decision-making processes and in policy making, thanks to their significant new capabilities in the use of spatial or geospatial information.Many spatial problems are complex and require the use of integrated analysis and models. The present paper illustrates the development of a MC-SDSS approach for studying the ecological connectivity of the Piedmont Region in Italy. The MC-SDSS model considers ecological and environmental spatial indicators which are combined by integrating the Multicriteria Decision Aiding (MCDA) technique named Analytic Network Process (ANP) and the Ordered Weighted Average (OWA) approach. The ANP is used for the elicitation of attribute weights while the OWA operator function is used to generate a wide range of decision alternatives for addressing uncertainty associated with interaction between multiple criteria. The usefulness of the approach is illustrated by different OWA scenarios that report the ecological connectivity index on a scale between 0 and 1. The OWA scenarios are intended to quantify the level of risk taking (i.e., optimistic, pessimistic, and neutral) and to facilitate a better understanding of patterns that emerge from decision alternatives involved in the decision-making process.The purpose of the research is to generate a final map representing the ecological connectivity index of each area in the region under analysis, to be used as a decision variable in spatial planning. In particular, by using the resulting index map as a means of analysis, it is possible to identify, for the sake of nature conservation, some critical areas needing mitigation measures. In addition, areas with high ecological connectivity values can be identified and monitoring procedures can therefore be planned. The study concludes highlighting that the applied methodology is an effective tool in providing decision support for spatial planning and sustainability assessments.     

Approaches to Ecological Risk Characterization and Management: Selecting the Right Tools for the Job

Chemical-specific hazard quotient (HQ) risk characterization in ecological risk assessment (ERA) can be a value-added tool for risk management decision-making at chemical release sites, when applied appropriately. However, there is little consensus regarding how HQ results can be used for risk management decision-making at the population, community, and ecosystem levels. Furthermore, stakeholders are reluctant to consider alternatives to HQ results for risk management decisions. Chemical-specific HQs risk characterization should be viewed as only one of several approaches (i.e., tools) for addressing ecological issues; and in many situations, other quantitative and qualitative approaches will likely result in superior risk management decisions. The purpose of this paper is to address fundamental issues and limitations associated with chemical-specific HQ risk characterization in ERA, to identify when it may be appropriate, to explore alternatives that are currently available, and to identify areas that could be developed for the future. Several alternatives (i.e., compensatory restoration, performance-based ecological monitoring, ecological significance criteria, net environmental benefit analysis), including their limitations, that can supplement, augment, or substitute for HQs in ERA are presented. In addition, areas of research (i.e., wildlife habitat assessment/landscape ecology/population biology, and field validated risk-based screening levels) that could yield new tools are discussed.     

Land Use Planning and Wildfire: Development Policies Influence Future Probability of Housing Loss

Increasing numbers of homes are being destroyed by wildfire in the wildland-urban interface. With projections of climate change and housing growth potentially exacerbating the threat of wildfire to homes and property, effective fire-risk reduction alternatives are needed as part of a comprehensive fire management plan. Land use planning represents a shift in traditional thinking from trying to eliminate wildfires, or even increasing resilience to them, toward avoiding exposure to them through the informed placement of new residential structures. For land use planning to be effective, it needs to be based on solid understanding of where and how to locate and arrange new homes. We simulated three scenarios of future residential development and projected landscape-level wildfire risk to residential structures in a rapidly urbanizing, fire-prone region in southern California. We based all future development on an econometric subdivision model, but we varied the emphasis of subdivision decision-making based on three broad and common growth types: infill, expansion, and leapfrog. Simulation results showed that decision-making based on these growth types, when applied locally for subdivision of individual parcels, produced substantial landscape-level differences in pattern, location, and extent of development. These differences in development, in turn, affected the area and proportion of structures at risk from burning in wildfires. Scenarios with lower housing density and larger numbers of small, isolated clusters of development, i.e., resulting from leapfrog development, were generally predicted to have the highest predicted fire risk to the largest proportion of structures in the study area, and infill development was predicted to have the lowest risk. These results suggest that land use planning should be considered an important component to fire risk management and that consistently applied policies based on residential pattern may provide substantial benefits for future risk reduction.     

A simple threshold rule is sufficient to explain sophisticated collective decision-making

Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.     

Thermodynamic view on decision-making process: emotions as a potential power vector of realization of the choice

This research is devoted to possible mechanisms of decision-making in frames of thermodynamic principles. It is also shown that the decision-making system in reply to emotion includes vector component which seems to be often a necessary condition to transfer system from one state to another. The phases of decision-making system can be described as supposed to be nonequilibrium and irreversible to which thermodynamics laws are applied. The mathematical model of a decision choice, proceeding from principles of the nonlinear dynamics considering instability of movement and bifurcation is offered. The thermodynamic component of decision-making process on the basis of vector transfer of energy induced by emotion at the given time is surveyed. It is proposed a three-modular model of decision making based on principles of thermodynamics. Here it is suggested that at entropy impact due to effect of emotion, on the closed system—the human brain,—initially arises chaos, then after fluctuations of possible alternatives which were going on—reactions of brain zones in reply to external influence, an order is forming and there is choice of alternatives, according to primary entrance conditions and a state of the closed system. Entropy calculation of a choice expectation of negative and positive emotion shows judgment possibility of existence of “the law of emotion conservation” in accordance with several experimental data.     

A Comparative Screening-Level Ecological and Human Health Risk Assessment for Dredged Material Management Alternatives in New York/New Jersey Harbor

Managers of New York and New Jersey Harbor dredging projects are developing strategies to dispose and manage the large volumes of sediment that must be dredged to maintain passable waterways. The various management alternatives include aquatic containment facilities, upland containment, and treatment with beneficial reuse. An important consideration in the selection of an appropriate alternative is the evaluation of potential risks to ecological and human receptors. This study presents a framework for a screening-level ecological and human health risk assessment that compares risks associated with management alternatives for contaminated dredged materials. The major objectives of the work were to identify exposure routes that show the potential for risk and develop a framework that can be used to compare relative potential risks among eight management alternatives. Managers can use this framework to: ? identify characteristics of the placement/treatment alternatives that contribute to potential risk, ? choose one alternative over another for sediments with high concentrations of contaminants, ? implement controls that mitigate risk, or ? identify the need for a more comprehensive site-specific risk assessment.     

Kinetic Model Development for Accelerated Stability Studies

Accelerated stability coupled with modeling to predict the stability of compounds, blends, and products at long-term storage conditions provides significant benefits in science-based decision-making throughout drug substance and drug product development. The study can often be completed, including data analysis in the space of three working weeks, and the information gathered and learning made in this time period can rival years of traditional analysis. The speed of the studies allows an earlier assessment of risk to quality enabling appropriate risk mitigation strategies to be implemented in a timely manner. The scientific foundation is based upon Arrhenius kinetic equations that can be linear or nonlinear in time, and can be based upon water vapor pressure or liquid water activity (relative humidity). A variety of kinetic models are evaluated, and the best model is chosen based upon both Bayesian information criteria and an automated assessment of kinetic model parameters fitting within acceptable ranges. Confidence intervals are estimated based upon a bootstrapping approach. Moisture vapor transmission rate models are applied on top of the resulting kinetic models in order to simulate different packaging types and the use of desiccant. The kinetic models are integrated with the prediction of packaging humidity over time to create a long-term prediction of impurities and other phenomena. The resulting models have been shown to be useful for not only the prediction of drug product impurities in long-term storage but other physical phenomena as well such as hydrate development and solvate loss.     

Risk-Based Approaches to Managing Contaminants in Catchments

Risk-based methods promise improved decision-making for managing of contaminants, such as salinity, sediments, nutrients, and toxicants, that can adversely affect the ecological condition of aquatic ecosystems. Two aspects of ecological risk assessment (ERA) and management—stakeholder involvement and more quantitative approaches to risk analysis—are particularly challenging. Stakeholder involvement is crucial both in the risk assessment process and the development, acceptance, and implementation of a risk management plan. Additionally, a number of quantitative approaches (particularly Bayesian approaches and multi-criteria decision-making) have been identified as having the potential to include expert-based inputs into risk-based decision-making. These offer promise for better inclusion of stakeholder knowledge and preferences into the decision-making process, and for improving the links between stakeholder inputs and potential risks to the ecological condition of the system. A major challenge for ecologists and natural resource managers is to make the ERA process more quantitative. Most ERAs conducted to date have been qualitative assessments that suffer from a number of deficiencies, the most serious being the lack of transparency and a reliance on subjective judgments. This article argues that the most productive way forward may be to use Bayesian methods to couple existing process-based models, empirical relationships based on good data, and expert opinion, to make the analysis of ecological risks more robust, consistent, and repeatable.     

The use of meta-analysis in food contact materials risk assessment

Abstract

As materials intended to be brought into contact with food, food contact materials (FCMs) – including plastics, paper or inks – can transfer their constituents to food under normal or foreseeable use, including direct or indirect food contact. The safety of FCMs in the EU is evaluated by the European Food Safety Authority (EFSA) using risk assessment rules. Results of independent, health-based chemical risk assessments are crucial for the decision-making process to authorize the use of substances in FCMs. However, the risk assessment approach used in the EU has several shortcomings that need to be improved in order to ensure consumer health protection from exposure arising from FCMs. This article presents the use of meta-analysis as a useful tool in chronic risk assessment for substances migrating from FCMs. Meta-analysis can be used for the review and summary of research of FCMs safety in order to provide a more accurate assessment of the impact of exposure with increased statistical power, thus providing more reliable data for risk assessment. The article explains a common methodology of conducting a meta-analysis based on meta-analysis of the dose-effect relationship of cadmium for benchmark dose evaluations performed by EFSA.     

Variation in primate decision-making under uncertainty and the roots of human economic behaviour

Uncertainty is a ubiquitous component of human economic behaviour, yet people can vary in their preferences for risk across populations, individuals and different points in time. As uncertainty also characterizes many aspects of animal decision-making, comparative research can help evaluate different potential mechanisms that generate this variation, including the role of biological differences or maturational change versus cultural learning, as well as identify human-unique components of economic decision-making. Here, we examine decision-making under risk across non-human primates, our closest relatives. We first review theoretical approaches and current methods for understanding decision-making in animals. We then assess the current evidence for variation in animal preferences between species and populations, between individuals based on personality, sex and age, and finally, between different contexts and individual states. We then use these primate data to evaluate the processes that can shape human decision-making strategies and identify the primate foundations of human economic behaviour.This article is part of the theme issue ‘Existence and prevalence of economic behaviours among non-human primates’.     

Bias and efficiency in family-based gene-characterization studies: conditional, prospective, retrospective, and joint likelihoods

We revisit the usual conditional likelihood for stratum-matched case-control studies and consider three alternatives that may be more appropriate for family-based gene-characterization studies: First, the prospective likelihood, that is, Pr(D/G,A second, the retrospective likelihood, Pr(G/D); and third, the ascertainment-corrected joint likelihood, Pr(D,G/A). These likelihoods provide unbiased estimators of genetic relative risk parameters, as well as population allele frequencies and baseline risks. The parameter estimates based on the retrospective likelihood remain unbiased even when the ascertainment scheme cannot be modeled, as long as ascertainment only depends on families' phenotypes. Despite the need to estimate additional parameters, the prospective, retrospective, and joint likelihoods can lead to considerable gains in efficiency, relative to the conditional likelihood, when estimating genetic relative risk. This is true if baseline risks and allele frequencies can be assumed to be homogeneous. In the presence of heterogeneity, however, the parameter estimates assuming homogeneity can be seriously biased. We discuss the extent of this problem and present a mixed models approach for providing consistent parameter estimates when baseline risks and allele frequencies are heterogeneous. The efficiency gains of the mixed-model prospective, retrospective, and joint likelihoods relative to the efficiency of conditional likelihood are small in the situations presented here.     

Towards a more open debate about values in decision-making on agricultural biotechnology

Regulatory decision-making over the use of products of new technology aims to be based on science-based risk assessment. In some jurisdictions, decision-making about the cultivation of genetically modified (GM) plants is blocked supposedly because of scientific uncertainty about risks to the environment. However, disagreement about the acceptability of risks is primarily a dispute over normative values, which is not resolvable through natural sciences. Natural sciences may improve the quality and relevance of the scientific information used to support environmental risk assessments and make scientific uncertainties explicit, but offer little to resolve differences about values. Decisions about cultivating GM plants will thus not necessarily be eased by performing more research to reduce scientific uncertainty in environmental risk assessments, but by clarifying the debate over values. We suggest several approaches to reveal values in decision-making: (1) clarifying policy objectives; (2) determining what constitutes environmental harm; (3) making explicit the factual and normative premises on which risk assessments are based; (4) better demarcating environmental risk assessment studies from ecological research; (5) weighing the potential for environmental benefits (i.e., opportunities) as well as the potential for environmental harms (i.e., risks); and (6) expanding participation in the risk governance of GM plants. Recognising and openly debating differences about values will not remove controversy about the cultivation of GM plants. However, by revealing what is truly in dispute, debates about values will clarify decision-making criteria.     

Altered risk-based decision making following adolescent alcohol use results from an imbalance in reinforcement learning in rats

Alcohol use during adolescence has profound and enduring consequences on decision-making under risk. However, the fundamental psychological processes underlying these changes are unknown. Here, we show that alcohol use produces over-fast learning for better-than-expected, but not worse-than-expected, outcomes without altering subjective reward valuation. We constructed a simple reinforcement learning model to simulate altered decision making using behavioral parameters extracted from rats with a history of adolescent alcohol use. Remarkably, the learning imbalance alone was sufficient to simulate the divergence in choice behavior observed between these groups of animals. These findings identify a selective alteration in reinforcement learning following adolescent alcohol use that can account for a robust change in risk-based decision making persisting into later life.     

Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits

Animals choose actions based on imperfect, ambiguous data. “Noise” inherent in neural processing adds further variability to this already-noisy input signal. Mathematical analysis has suggested that the optimal apparatus (in terms of the speed/accuracy trade-off) for reaching decisions about such noisy inputs is perfect accumulation of the inputs by a temporal integrator. Thus, most highly cited models of neural circuitry underlying decision-making have been instantiations of a perfect integrator. Here, in accordance with a growing mathematical and empirical literature, we describe circumstances in which perfect integration is rendered suboptimal. In particular we highlight the impact of three biological constraints: (1) significant noise arising within the decision-making circuitry itself; (2) bounding of integration by maximal neural firing rates; and (3) time limitations on making a decision. Under conditions (1) and (2), an attractor system with stable attractor states can easily best an integrator when accuracy is more important than speed. Moreover, under conditions in which such stable attractor networks do not best the perfect integrator, a system with unstable initial states can do so if readout of the system’s final state is imperfect. Ubiquitously, an attractor system with a nonselective time-dependent input current is both more accurate and more robust to imprecise tuning of parameters than an integrator with such input. Given that neural responses that switch stochastically between discrete states can “masquerade” as integration in single-neuron and trial-averaged data, our results suggest that such networks should be considered as plausible alternatives to the integrator model.     

Computer-assisted assignment of multidimensional NMR spectra of proteins: Application to 3D NOESY-HMQC and TOCSY-HMQC spectra

Summary An algorithm based on the technique of combinatorial minimization is used for the semi-automated assignment of multidimensional heteronuclear spectra. The program (ALFA) produces the best assignment compatible with the available input data. Even partially misleading or missing data do not seriously corrupt the final assignment. Ambiguous sequences of the possible assignment and all alternatives are indicated. The program can also use additional non-spectroscopic data to assist in the assignment procedure. For example, information from the X-ray structure of the protein and/or information about the secondary structure can be used. The assignment procedure was tested on spectra of mucous trypsin inhibitor, a protein of 107 residues.     

Decision-support tool for assessing biomanufacturing strategies under uncertainty: stainless steel versus disposable equipment for clinical trial material preparation

This paper presents the application of a decision-support tool, SIMBIOPHARMA, for assessing different manufacturing strategies under uncertainty for the production of biopharmaceuticals. SIMBIOPHARMA captures both the technical and business aspects of biopharmaceutical manufacture within a single tool that permits manufacturing alternatives to be evaluated in terms of cost, time, yield, project throughput, resource utilization, and risk. Its use for risk analysis is demonstrated through a hypothetical case study that uses the Monte Carlo simulation technique to imitate the randomness inherent in manufacturing subject to technical and market uncertainties. The case study addresses whether start-up companies should invest in a stainless steel pilot plant or use disposable equipment for the production of early phase clinical trial material. The effects of fluctuating product demands and titers on the performance of a biopharmaceutical company manufacturing clinical trial material are analyzed. The analysis highlights the impact of different manufacturing options on the range in possible outcomes for the project throughput and cost of goods and the likelihood that these metrics exceed a critical threshold. The simulation studies highlight the benefits of incorporating uncertainties when evaluating manufacturing strategies. Methods of presenting and analyzing information generated by the simulations are suggested. These are used to help determine the ranking of alternatives under different scenarios. The example illustrates the benefits to companies of using such a tool to improve management of their R&D portfolios so as to control the cost of goods.     

Application of a decision-support tool to assess pooling strategies in perfusion culture processes under uncertainty

Biopharmaceutical manufacture is subject to numerous risk factors that may affect operational costs and throughput. This paper discusses the need for incorporating such uncertainties in decision-making tools in order to reflect the inherent variability of process parameters during the operation of a biopharmaceutical plant. The functionalities of a risk-based prototype tool to model cost summation, perform mass balance calculations, simulate resource handling, and incorporate uncertainties in order to evaluate the potential risk associated with different manufacturing strategies are demonstrated via a case study. The case study is based upon the assessment of pooling strategies in the perfusion culture of mammalian cells to deliver a therapeutic protein for commercial use. Monte Carlo simulations, which generate random sample behaviors for probabilistic factors so as to imitate the uncertainties inherent in any process, have been applied. This provides an indication of the range of possible output values and hence enables trends or anomalies in the expected performance of a process to be determined.     

Determination of best-fit potential parameters for a reactive force field using a genetic algorithm

The ReaxFF interatomic potential, used for organic materials, involves more than 600 adjustable parameters, the best-fit values of which must be determined for different materials. A new method of determining the set of best-fit parameters for specific molecules containing carbon, hydrogen, nitrogen and oxygen is presented, based on a parameter reduction technique followed by genetic algorithm (GA) minimization. This work has two novel features. The first is the use of a parameter reduction technique to determine which subset of parameters plays a significant role for the species of interest; this is necessary to reduce the optimization space to manageable levels. The second is the application of the GA technique to a complex potential (ReaxFF) with a very large number of adjustable parameters, which implies a large parameter space for optimization. In this work, GA has been used to optimize the parameter set to determine best-fit parameters that can reproduce molecular properties to within a given accuracy. As a test problem, the use of the algorithm has been demonstrated for nitromethane and its decomposition products.