Managing uncertainty in decision-making for conservation science

Science-based decision-making is the ideal. However, scientific knowledge is incomplete, and sometimes wrong. Responsible science-based policy, planning, and action must exploit knowledge while managing uncertainty. I considered the info-gap method to manage deep uncertainty surrounding knowledge that is used for decision-making in conservation. A central concept is satisficing, which means satisfying a critical requirement. Alternative decisions are prioritized based on their robustness to uncertainty, and critical outcome requirements are satisficed. Robustness is optimized; outcome is satisficed. This is called robust satisficing. A decision with a suboptimal outcome may be preferred over a decision with a putatively optimal outcome if the former can more robustly achieve an acceptable outcome. Many biodiversity conservation applications employ info-gap theory, under which parameter uncertainty but not uncertainty in functional relations is considered. I considered info-gap models of functional uncertainty, widely used outside of conservation science, as applied to conservation of a generic endangered species by translocation to a new region. I focused on 2 uncertainties: the future temperature is uncertain due to climate change, and the shape of the reproductive output function is uncertain due to translocation to an unfamiliar region. The value of new information is demonstrated based on the robustness function, and the info-gap opportuneness function demonstrates the potential for better-than-anticipated outcomes.

Uncertainty in the association between socio-demographic characteristics and mental health

Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample's status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.

A positive toxicology screen is a rare finding in sports-related sudden cardiac deaths

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): University of Copenhagen

Introduction

Sports-related sudden cardiac deaths (SrSCD) are rare events often occurring in healthy individuals. An underlying cardiac disease may, in combination with strenuous exercise, trigger lethal arrhythmias. Intake of drugs (legal and illicit) increases the risk of sudden cardiac death (SCD), but knowledge on toxicological findings in SrSCD remains sparse.

Purpose

This study aimed to characterize the SrSCD population in an international consortium by investigating the epidemiology and autopsy findings in SrSCD.

Methods

Participating centers of the consortium (Denmark, Australia, England, Spain) provided data on all forensically autopsied SCDs aged 12—49 years in their respective cohorts, spanning from 2000—2019. Demographics, autopsy findings, and toxicology screen were assessed. A toxicology screen was considered positive if any drug was detected, except drugs related to resuscitation.

Results

Of all 5,029 SCDs, we identified 435 (9%) SrSCD. The majority (88%) died during sports while the remaining 12% died within one hour from exercise. SrSCD occurred more often in males (91% vs 71%, p<0.001) who were younger (32 vs 36 years). In SrSCD, the autopsy more frequently revealed an underlying structural cardiac cause of death (64% vs 54%, p<0.001); the most frequent causes of death among SrSCDs were sudden unexpected death (SUD), ischemic heart disease (IHD), and arrhythmogenic cardiomyopathy (ACM). Toxicological screens were performed in approx. 90% of cases, regardless of relation to sport. Among SrSCD cases, the rate of a positive toxicology was less than half compared to other SCDs (18% vs 44%, p<0.001). The most frequent toxicological findings among SrSCDs were ethanol, central stimulants, cannabinoids, and non-opioid analgesics.

Conclusions

Sports-related SCD accounted for 9% of all SCDs in our population aged 12—49 years. The majority of sports-related deaths (88%) occurred during exercise activity. SrSCDs more often had an underlying structural cardiac disease, mainly IHD and ACM. Positive toxicology screens were half as frequent in SrSCDs compared with non-SrSCDs.

Robust Policy in Times of Pandemic

The pandemic exposes policymakers to fundamental uncertainties about future economic scenarios. While policymakers have to act forcefully to mitigate the impact on the economy, these conditions call for policy strategies that are also robust to uncertainty. This article compares two concepts of robust strategies: robust control and robust satisficing. It argues that a robust satisficing strategy is preferred and shows that the crisis responses of governments and central banks in Europe share features of robust satisficing in several dimensions.

Usefulness of subcutaneous Implantable Loop Recorder in Brugada Syndrome. A single UK centre experience

Background

Experience with implantable loop recorder (ILR) in Brugada Syndrome (BrS) is limited.

Purpose

We sought to evaluate the usefulness of continuous monitoring in BrS patients in a UK based Inherited Cardiac Conditions centre.

Methods

Subjects with BrS were consecutively recruited and demographic, clinical, device and follow-up data acquired.

Results

Of 452 subjects with BrS recruited in our centre, 41 (9%) received an ILR. Twenty-one (51%) were males. Twenty-four subjects (59%) had a high probability of BrS based on the Shanghai score. Mean age at implantation was 45.3±15.5 years. The indications for the implantation were: syncope or pre-syncope in 28 subjects (68%), palpitations in 12 (29%), family history of SADS in 1 case. Sixteen subjects presented with a spontaneous type 1 pattern, of whom 12 (75%) underwent an electrophysiological study for risk stratification which was negative. During a mean follow-up of 82±46 months (range 4.0–182), 5 new diagnoses of atrial fibrillation (AF) or supraventricular tachycardia were made; 3 subjects had symptomatic pathological pauses (in one case associated with fast AF) and underwent permanent pacemaker or ICD implantation; one subject experienced an asymptomatic episode of fast, non-sustained monomorphic ventricular tachycardia. In 2 cases the device was explanted prematurely due to site infection. Patients who received an ECG diagnosis following interrogation of the ILR were older (mean age 55±11 vs 43±16m p=0.04), although there were no differences in sex or Shanghai score.

Conclusions

In a large UK cohort of BrS patients, the use of ILR was low. However, continuous subcutaneous monitoring yielded a diagnosis of tachy- or brady-arrhythmic episodes in 22% of cases, and therefore can be helpful in guiding the management of low/intermediate risk BrS patients.

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): Robert Lancaster Memorial Fund

Does a better model yield a better argument? An info-gap analysis

Theories, models and computations underlie reasoned argumentation in many areas. The possibility of error in these arguments, though of low probability, may be highly significant when the argument is used in predicting the probability of rare high-consequence events. This implies that the choice of a theory, model or computational method for predicting rare high-consequence events must account for the probability of error in these components. However, error may result from lack of knowledge or surprises of various sorts, and predicting the probability of error is highly uncertain. We show that the putatively best, most innovative and sophisticated argument may not actually have the lowest probability of error. Innovative arguments may entail greater uncertainty than more standard but less sophisticated methods, creating an innovation dilemma in formulating the argument. We employ info-gap decision theory to characterize and support the resolution of this problem and present several examples.

A new multicriteria risk mapping approach based on a multiattribute frontier concept

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components' importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high-risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info-gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

BACKGROUND

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied.

AIMS

We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making.

METHODS

Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection.

RESULTS

We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error.

CONCLUSIONS

The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

Why risk analysis is difficult, and some thoughts on how to proceed

Risk analysis is challenged in three ways by uncertainty. Our understanding of the world and its uncertainties is evolving; indeterminism is an inherent part of the open universe in which we live; and learning from experience involves untestable assumptions. We discuss several concepts of robustness as tools for responding to these epistemological challenges. The use of models is justified, even though they are known to err. A concept of robustness is illustrated in choosing between a conventional technology and an innovative, promising, but more uncertain technology. We explain that nonprobabilistic robust decisions are sometimes good probabilistic bets. Info-gap and worst-case concepts of robustness are compared. Finally, we examine the exploitation of favorable but uncertain opportunities and its relation to robust decision making.

Doing our best: optimization and the management of risk

Tools and concepts of optimization are widespread in decision-making, design, and planning. There is a moral imperative to "do our best." Optimization underlies theories in physics and biology, and economic theories often presume that economic agents are optimizers. We argue that in decisions under uncertainty, what should be optimized is robustness rather than performance. We discuss the equity premium puzzle from financial economics, and explain that the puzzle can be resolved by using the strategy of satisficing rather than optimizing. We discuss design of critical technological infrastructure, showing that satisficing of performance requirements--rather than optimizing them--is a preferable design concept. We explore the need for disaster recovery capability and its methodological dilemma. The disparate domains--economics and engineering--illuminate different aspects of the challenge of uncertainty and of the significance of robust-satisficing.

Robustness, fidelity and prediction-looseness of models

Assessment of the credibility of a mathematical or numerical model of a complex system must combine three components: (i) the fidelity of the model to test data, e.g. as quantified by a mean-squared error; (ii) the robustness, of model fidelity, to lack of understanding of the underlying processes; and (iii) the prediction-looseness of the model. ‘Prediction-looseness’ is the range of predictions of models that are equivalent in terms of fidelity. The main result of this paper asserts that fidelity, robustness and prediction-looseness are mutually antagonistic. A change in the model that enhances one of these attributes will cause deterioration of another. In particular, increasing the fidelity to test data will decrease the robustness to imperfect understanding of the process. Likewise, increasing the robustness will increase the predictive looseness . The conclusion is that focusing only on fidelity-to-data is not a sound decision-making strategy for model building and validation. A better strategy is to explore the trade-offs between robustness-to-uncertainty, fidelity to data and tightness of predictions. Our analysis is based on info-gap models of uncertainty, which can be applied to cases of severe uncertainty and lack of knowledge.

Interpreting null results from measurements with uncertain correlations: an info-gap approach

Null events—not detecting a pernicious agent—are the basis for declaring the agent is absent. Repeated nulls strengthen confidence in the declaration. However, correlations between observations are difficult to assess in many situations and introduce uncertainty in interpreting repeated nulls. We quantify uncertain correlations using an info-gap model, which is an unbounded family of nested sets of possible probabilities. An info-gap model is nonprobabilistic and entails no assumption about a worst case. We then evaluate the robustness, to uncertain correlations, of estimates of the probability of a null event. This is then the basis for evaluating a nonprobabilistic robustness-based confidence interval for the probability of a null.

Detection capacity, information gaps and the design of surveillance programs for invasive forest pests

Integrated pest risk maps and their underlying assessments provide broad guidance for establishing surveillance programs for invasive species, but they rarely account for knowledge gaps regarding the pest of interest or how these can be reduced. In this study we demonstrate how the somewhat competing notions of robustness to uncertainty and potential knowledge gains could be used in prioritizing large-scale surveillance activities. We illustrate this approach with the example of an invasive pest recently detected in North America, Sirex noctilio Fabricius. First, we formulate existing knowledge about the pest into a stochastic model and use the model to estimate the expected utility of surveillance efforts across the landscape. The expected utility accounts for the distribution, abundance and susceptibility of the host resource as well as the value of timely S. noctilio detections. Next, we make use of the info-gap decision theory framework to explore two alternative pest surveillance strategies. The first strategy aims for timely, certain detections and attempts to maximize the robustness to uncertainty about S. noctilio behavior; the second strategy aims to maximize the potential knowledge gain about the pest via unanticipated (i.e., opportune) detections. The results include a set of spatial outputs for each strategy that can be used independently to prioritize surveillance efforts. However, we demonstrate an alternative approach in which these outputs are combined via the Pareto ranking technique into a single priority map that outlines the survey regions with the best trade-offs between both surveillance strategies.

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.

Reconciling uncertain costs and benefits in Bayes nets for invasive species management

Bayes nets are used increasingly to characterize environmental systems and formalize probabilistic reasoning to support decision making. These networks treat probabilities as exact quantities. Sensitivity analysis can be used to evaluate the importance of assumptions and parameter estimates. Here, we outline an application of info-gap theory to Bayes nets that evaluates the sensitivity of decisions to possibly large errors in the underlying probability estimates and utilities. We apply it to an example of management and eradication of Red Imported Fire Ants in Southern Queensland, Australia and show how changes in management decisions can be justified when uncertainty is considered.

Info-gap theory and robust design of surveillance for invasive species: the case study of Barrow Island

Surveillance for invasive non-indigenous species (NIS) is an integral part of a quarantine system. Estimating the efficiency of a surveillance strategy relies on many uncertain parameters estimated by experts, such as the efficiency of its components in face of the specific NIS, the ability of the NIS to inhabit different environments, and so on. Due to the importance of detecting an invasive NIS within a critical period of time, it is crucial that these uncertainties be accounted for in the design of the surveillance system. We formulate a detection model that takes into account, in addition to structured sampling for incursive NIS, incidental detection by untrained workers. We use info-gap theory for satisficing (not minimizing) the probability of detection, while at the same time maximizing the robustness to uncertainty. We demonstrate the trade-off between robustness to uncertainty, and an increase in the required probability of detection. An empirical example based on the detection of Pheidole megacephala on Barrow Island demonstrates the use of info-gap analysis to select a surveillance strategy.

Do we know how to set decision thresholds for diabetes?

The diagnosis of diabetes, based on measured fasting plasma glucose level, depends on choosing a threshold level for which the probability of failing to detect disease (missed diagnosis), as well as the probability of falsely diagnosing disease (false alarm), are both small. The Bayesian risk provides a tool for aggregating and evaluating the risks of missed diagnosis and false alarm. However, the underlying probability distributions are uncertain, which makes the choice of the decision threshold difficult. We discuss an hypothesis for choosing the threshold that can robustly achieve acceptable risk. Our analysis is based on info-gap decision theory, which is a non-probabilistic methodology for modelling and managing uncertainty. Our hypothesis is that the non-probabilistic method of info-gap robust decision making is able to select decision thresholds according to their probability of success. This hypothesis is motivated by the relationship between info-gap robustness and the probability of success, which has been observed in other disciplines (biology and economics). If true, it provides a valuable clinical tool, enabling the clinician to make reliable diagnostic decisions in the absence of extensive probabilistic information. Specifically, the hypothesis asserts that the physician is able to choose a diagnostic threshold that maximizes the probability of acceptably small Bayesian risk, without requiring accurate knowledge of the underlying probability distributions. The actual value of the Bayesian risk remains uncertain.

Price-based vs. quantity-based environmental regulation under Knightian uncertainty: an info-gap robust satisficing perspective

Conventional wisdom among environmental economists is that the relative slopes of the marginal social benefit and marginal social cost functions determine whether a price-based or quantity-based environmental regulation leads to higher expected social welfare. We revisit the choice between price-based vs. quantity-based environmental regulation under Knightian uncertainty; that is, when uncertainty cannot be modeled with known moments of probability distributions. Under these circumstances, the policy objective cannot be to maximize the expected net benefits of emissions control. Instead, we evaluate an emissions tax and an aggregate abatement standard in terms of maximizing the range of uncertainty under which the welfare loss from error in the estimates of the marginal benefits and costs of emissions control can be limited. The main result of our work is that the same criterion involving the relative slopes of the marginal benefit and cost functions determines whether price-based or quantity-based control is more robust to unstructured uncertainty. Hence, not only does the relative slopes criterion lead to the policy that maximizes the expected net benefits of control under structured uncertainty, it also leads to the policy that maximizes robustness to unstructured uncertainty.

Managing Project Risks as Knowledge Gaps

The development of new technologies and the implementation of such technologies in new applications is a continuous effort to close technological and logistical knowledge gaps. Although knowledge gapscan be closed by accident—like the case of Archimedes who closed an important knowledge gap about the laws of nature while bathing—in most cases, knowledge gaps are closed by a consistent, organized effort, namely by projects.

Project management methodologies attempt to manage the risk generated by the one-time nature of projects. This risk is generated by the lack of knowledge and its resulting uncertainty. Tools for managing project risks and their consequences, including risk identification, risk quantification, risk elimination, risk reduction, risk sharing, and risk control, were developed and are implemented to some degree in many technological projects. However, most of these tools are based on statistical theory (e.g., the central limit theorem). Unfortunately, statistical theory may not be applicable to technological projects where there is very little or no relevant previous experience at all. The question is: what can we do when past data is not an appropriate basis for future decisions?

In this paper we describe a new approach for dealing with technological projects—an approach based on the analysis of knowledge gaps (i.e., the gap between what we should know in order to succeed in the project and what we really know).

Evaluation of Neural Network Robust Reliability Using Information-Gap Theory

A novel technique for the evaluation of neural network robustness against uncertainty using a nonprobabilistic approach is presented. Conventional optimization techniques were employed to train multilayer perceptron (MLP) networks, which were then probed with an uncertainty analysis using an information-gap model to quantify the network response to uncertainty in the input data. It is demonstrated that the best performing network on data with low uncertainty is not in general the optimal network on data with a higher degree of input uncertainty. Using the concepts of information-gap theory, this paper develops a theoretical framework for information-gap uncertainty applied to neural networks, and explores the practical application of the procedure to three sample cases. The first consists of a simple two-dimensional (2-D) classification network operating on a known Gaussian distribution, the second a nine-lass vibration classification problem from an aircraft wing, and the third a two-class example from a database of breast cancer incidence.

Info‐Gap Robust‐Satisficing Model of Foraging Behavior: Do Foragers Optimize or Satisfice?

In this note we compare two mathematical models of foraging that reflect two competing theories of animal behavior: optimizing and robust satisficing. The optimal-foraging model is based on the marginal value theorem (MVT). The robust-satisficing model developed here is an application of info-gap decision theory. The info-gap robust-satisficing model relates to the same circumstances described by the MVT. We show how these two alternatives translate into specific predictions that at some points are quite disparate. We test these alternative predictions against available data collected in numerous field studies with a large number of species from diverse taxonomic groups. We show that a large majority of studies appear to support the robust-satisficing model and reject the optimal-foraging model.

Vibrio coralliilyticus sp. nov., a temperature-dependent pathogen of the coral Pocillopora damicornis

Vibrio sp. YB1T (=ATCC BAA-450T =LMG 20984T), the aetiological agent of tissue lysis of the coral Pocillopora damicornis, was characterized as a novel Vibrio species on the basis of 16S rDNA sequence, DNA-DNA hybridization data (G + C content is 45.6 mol%), AFLP and GTG5-PCR genomic fingerprinting patterns and phenotypic properties, including the cellular fatty acid profile. The predominant fatty acids were 16:0 and 18:1 omega7c. The name Vibrio coralliilyticus sp. nov. is proposed for the novel coral-pathogenic species. In addition to strain YB1T, which was isolated from the Indian Ocean, five additional strains of V. coralliilyticus have been isolated, three from diseased P. damicornis in the Red Sea, one from diseased oyster larvae (Kent, UK) and one from bivalve larvae (Brazil). The six V. coralliilyticus strains showed high genotypic and phenotypic similarities and all were pathogenic to P. damicornis. The closest phylogenetic neighbours to V. coralliilyticus are Vibrio tubiashii, Vibrio nereis and Vibrio shilonii.

Inhibition of photosynthesis and bleaching of zooxanthellae by the coral pathogen Vibrio shiloi

Vibrio shiloi is the causative agent of bleaching (loss of endosymbiotic zooxanthellae) of the coral Oculina patagonica in the Mediterranean Sea. To obtain information on the mechanism of bleaching, we examined the effect of secreted material (AK1-S) produced by V. shiloi on zooxanthellae isolated from corals. AK1-S caused a rapid inhibition of photosynthesis of the algae, as measured with a Mini-PAM fluorometer. The inhibition of photosynthesis was caused by (i) ammonia produced during the growth of V. shiloi on protein-containing media and (ii) a non-dialysable heat-resistant factor. This latter material did not inhibit photosynthesis of the algae by itself but, when added to different concentrations of NH4Cl, enhanced the inhibition approximately two- to threefold. Ammonia and the enhancer were effective to different degrees on zooxanthellae isolated from four species of coral examined. In addition to the rapid inhibition of photosynthesis, AK1-S caused bleaching (loss of pigmentation) and lysis of zooxanthellae. Bleaching was more rapid than lysis, reaching a peak (25% bleached algae) after 6 h. The factors in AK1-S responsible for bleaching and lysis were different from those responsible for the inhibition of photosynthesis, because they were heat sensitive, non-dialysable and active in the dark. Thus, the coral pathogen V. shiloi produces an array of extracellular materials that can inhibit photosynthesis, bleach and lyse zooxanthellae.

Adaptive assay of radioactive pulmonary aerosols with an external detector

The accuracy of the assay of radioactive pulmonary aerosols is influenced by the spatial distribution of the aerosol in the lungs. In general, there is considerable uncertainty in the spatial distribution of the aerosol beginning a few months after inhalation. This paper develops an adaptive approach for optimizing the assay of non-uniform spatial distributions of pulmonary aerosols. An adaptive assay is one in which the design of the assay system is modified during operation in response to measurements obtained on-line. Experimental results are presented which compare the performance of an adaptive assay with that of the standard measurement procedure. We study the assay of 241Am distributed in the lungs of a Lawrence Livermore realistic phantom using a NaI-CsI Phoswich detector.