Categorical Default Uncertainty Factors-Interspecies Variation and Adequacy of Database

The potential application of categorical (i.e., species, pathway, or group specific) defaults for several components of uncertainty relevant to development of tolerable or reference concentrations/doses is considered-namely, interspecies variation and adequacy of database. For the former, the adequacy of allometric scaling by body surface area as a species-specific default for oral tolerable or reference doses is considered. For the latter, the extent to which data from analyses of subchronic:chronic effect levels, LOAELs/NOAELs, and critical effect levels for complete versus incomplete datasets informs selection of defaults is examined. The relative role of categorical defaults for these aspects is considered in the context of the continuum of increasingly data-informed approaches to characterization of uncertainty and variability that range from default (“presumed protective”) to “biologically based predictive”.     

Subdivision of Uncertainty Factors to Allow for Toxicokinetics and Toxicodynamics

Tenfold uncertainty factors have been used in risk assessment for about 40 years to allow for species differences and inter-individual variability. Each factor has to allow for toxicokinetic and toxicodynamic differences. Subdividing the 10-fold factors into kinetic and dynamic defaults, which when multiplied give a product of 10, offers a number of advantages. A major advantage is that chemical-specific data can be introduced to replace one or more of the default subfactors, hence contributing to a chemical-related overall factor. Subdivision of the 10-fold factors also facilitates analysis of the appropriateness of the overall 10-fold defaults, and the development of a more refined approach to the use of uncertainty factors.     

Harmonizing the Incorporation of Uncertainty and Variability Into Risk Assessment: An International Approach

International harmonization of risk assessment approaches affords a number of opportunities and advantages. Overall, harmonization will lead to more efficient use of resources, but also will lead to better understanding amongst scientists and regulators worldwide. It is with these goals in mind that in 1994 the International Programme on Chemical Safety (IPCS) initiated its Project on the Harmonization of Approaches to the Assessment of Risk from Exposure to Chemicals (Harmonization Project). An ongoing activity under this project addresses uncertainty and variability in risk assessment. The goal of the overall activity is to promote harmonization of risk assessment methodologies for noncancer endpoints. However, given the common links in uncertainty and variability that apply across a range of end-point-specific activities, these links are identified wherever possible. This paper provides an overview of the IPCS Harmonization Project and reviews the activity and future plans related to uncertainty and variability.     

Some Comments on the Selection of Human Intraspecies Uncertainty Factors

The Reference Dose (RfD) is used in the risk assessment of non-carcinogenic chemicals. It is derived by dividing a point of departure by the product of the uncertainty (UFs) and modifying factors (MFs). Separate UFs are used for different variables, e.g., intraspecies variation and, in general, each UF is an order of magnitude (10-fold). On the other hand, the MF is usually based on some known variable such as differences in absorption of a chemical from food and water and its default value is one. The USEPA's Integrated Risk Information System (IRIS) has 14 chemicals that have RfDs based on human studies. We examined those IRIS files to determine the rationale for setting human intraspecies uncertainty factors (UF_H). The first consideration was that the chemical had an adequate peer-reviewed human database. Without such, it would not be possible to derive an RfD based on human data. Ten of the 14 chemicals had an UF_H of 1 or 3; four of these were essential trace elements (ETEs). The rationales for using less than a 10-fold UF_H for the ETEs included; 1) nutritional data, 2) large human exposure groups, 3) minimal effect levels and/or 4) several studies with similar effect levels. For the other compounds, reasons included; 1) large human exposure groups, 2) a critical effect that was not adverse (cosmetic), 3) the most sensitive population was exposed, 4) the compound was on the FDA's “generally regarded as safe” (GRAS) list, 5) database uncertainties and 6) less-than-lifetime exposure adjusted for 70 years exposure. It is important to understand the reasons for selecting a UF_H of 1, or 3 as they will apply to future chemicals considered by the USEPA and other agencies.     

Case Studies of Categorical Data-Derived Adjustment Factors

Investigations were performed on representative compounds from five different therapeutic classes to evaluate the use of categorical data-derived adjustment factors to account for interindividual variability. The five classes included antidepressants, angiotensin converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDS), cholesterol lowering agents, and antibiotics. Each of the case studies summarized the mode of action of the class responsible for both the therapeutic and adverse effects and the key pharmacodynamic (PD) and pharmacokinetic (PK) parameters that determine the likelihood of these responses for individual compounds in the class. For each class, an attempt was made to identify the key factors that determine interindividual variability and whether there was a common basis to establish a categorical default adjustment factor that could be applied across the class (or at least across specific subclasses within the class). Linking the PK and PD parameters to the critical endpoint used to establish a safe level of exposure was an important underlying theme throughout the investigations. Despite the wealth of PK and PD information in the published literature on the surrogate compounds representing these classes, it was difficult to derive a categorical adjustment factor that could be applied broadly within each class. The amount of information available may have hindered rather than helped the evaluations. Derivation of categorical defaults for different classes of “common” chemicals may be more straightforward if sufficient data are available. In a few cases (e.g., tricyclic antibiotics, ACE inhibitors and selected antiinflammatory agents) categorical defaults could be proposed, although it is unclear whether the reduction in uncertainty resulting from their application would be offset by the additional uncertainties that may have resulted from their application. Residual uncertainties may remain depending on the level of confidence in the underlying assumptions used to support the categorical defaults. Regardless of the conclusions on the utility of categorical defaults, these investigations provided further support for the use of data-derived adjustment factors on a compound-specific basis.     

塔里木荒漠优势植物——胡杨种内、种间竞争研究

采用逐步扩大范围的方法确定影响对象木的邻体最佳竞争范围,利用Hegyi提出的单木竞争指数模型对塔里木荒漠优势植物———胡杨种内、种间竞争强度进行定量分析。结果表明,在胡杨群落中,对象木的最适竞争距离为6 m,胡杨种内、种间竞争强度随着植株径级的增大而逐渐减小,种内竞争较种间竞争剧烈。竞争强度与对象木胸径服从幂函数关系,从模型预测结果可知当胡杨胸径达30 cm以上时,竞争强度变化很小。因此,应在此前采取适当的人工疏伐措施来促进植株生长和增强生态系统的稳定性。     

Default Factors for Interspecies Differences in the Major Routes of Xenobiotic Elimination

For the risk to human health posed by chemicals that show threshold toxicity there is an increasing need to move away from using the default approaches, which inherently incorporate uncertainty, towards more biologically defensible risk assessments. However, most chemical databases do not contain data of sufficient quantity or quality that can be used to replace either the interspecies or interindividual aspects of toxicokinetic and toxicodynamic uncertainty. The purpose of the current analysis was to evaluate the use of alternative, species-specific, pathway-related, “categorical” default values to replace the current interspecies toxicokinetic default uncertainty factor of 4.0. The extent of the difference in the internal dose of a compound, for each test species, could then be related to the specific route of metabolism in humans. This refinement would allow for different categories of defaults to be used, providing that the metabolic fate of a toxicant was known in humans. Interspecies differences in metabolism, excretion, and bioavailability have been compared for probe substrates for four different human xenobiotic-metabolizing enzymes: CYP1A2 (caffeine, paraxanthine, theobromine, and theophylline), CYP3A4 (lidocaine), UDP-glucuronyltransferase (AZT), and esterases (aspirin). The results of this analysis showed that there are significant differences between humans and the four test species in the metabolic fate of the probe compounds, the enzymes involved, the route of excretion and oral bioavailability — all of which are factors that can influence the extent of the difference between humans and a test species in the internal dose of a toxicant. The wide variability between both compounds and the individual species suggests that the categorical approach for species differences may be of limited use in refining the current default approach. However, future work to incorporate a wider database of compounds that are metabolized extensively by any pathway in humans to provide more information on the extent to which the different test species are not covered by the default of 4.0. Ultimately this work supports the necessity to remove the uncertainty from the risk assessment process by the generation and use of compound-specific data.     

数字PCR技术在核酸标准物质研制中的应用

在标准物质研制领域,生物标准物质的研制逐渐成为了研究热点,同时基于核酸的检测技术的开发与应用又推动了核酸标准物质的进程。核酸标准物质需要高级别、精准的定值方法,数字PCR作为单分子定量技术得到了广泛的应用。数字PCR是一种测定核酸分子的绝对定量方法,如微滴式数字PCR是采用油包水形成的微滴作为反应室,将含有DNA模板的反应溶液分配到大量独立的反应室中进行扩增反应,再通过统计反应室中的阳性信号来定量DNA的拷贝数,从而达到精确定量核酸拷贝数的目的。综述了近年来关于数字PCR及其在核酸标准物质研究领域的最新应用进展,重点综述了其在转基因检测、医疗诊断等领域的应用进展,以期为核酸标准物质的研制提供参考。     

Treatments of Uncertainty and Variability in Ecological Risk Assessment of Single-Species Populations

The selection of the most appropriate model for an ecological risk assessment depends on the application, the data and resources available, the knowledge base of the assessor, the relevant endpoints, and the extent to which the model deals with uncertainty. Since ecological systems are highly variable and our knowledge of model input parameters is uncertain, it is important that models include treatments of uncertainty and variability, and that results are reported in this light. In this paper we discuss treatments of variation and uncertainty in a variety of population models. In ecological risk assessments, the risk relates to the probability of an adverse event in the context of environmental variation. Uncertainty relates to ignorance about parameter values, e.g., measurement error and systematic error. An assessment of the full distribution of risks, under variability and parameter uncertainty, will give the most comprehensive and flexible endpoint. In this paper we present the rationale behind probabilistic risk assessment, identify the sources of uncertainty relevant for risk assessment and provide an overview of a range of population models. While all of the models reviewed have some utility in ecology, some have more comprehensive treatments of uncertainty than others. We identify the models that allow probabilistic assessments and sensitivity analyses, and we offer recommendations for further developments that aim towards more comprehensive and reliable ecological risk assessments for populations.     

腐殖质在环境污染物生物降解中的作用研究进展

腐殖质物质在地球的生态环境中大量存在,它不仅可以在有毒化合物的生物降解和生物转化过程中起到氧化还原中间体的作用,加速有毒物质的降解和转化。也可以作为唯一末端电子受体,接受来自一些有机酸或者甲苯等环境中有毒物质提供的电子,偶联能量的产生,支持菌体的生长,形成一种新的细菌厌氧呼吸形式——腐殖质呼吸。因此,对腐殖质在环境有毒物质的生物降解和生物转化过程中的作用进行研究,不仪对于深入理解细菌呼吸的本质具有重要的理论意义,而且对于环境有毒物质的降解和转化以及元素的生物地球化学循环具有重要的生态学意义,同时对地球表面的有毒物质进行更有效的生物降解具有重要的现实意义。     

Application of uncertainty and variability in LCA

As yet, the application of an uncertainty and variability analysis is not common practice in LCAs. A proper analysis will be facilitated when it is clear which types of uncertainties and variabilities exist in LCAs and which tools are available to deal with them. Therefore, a framework is developed to classify types of uncertainty and variability in LCAs. Uncertainty is divided in (1) parameter uncertainty, (2) model uncertainty, and (3) uncertainty due to choices, while variability covers (4) spatial variability, (5) temporal variability, and (6) variability between objects and sources. A tool to deal with parameter uncertainty and variability between objects and sources in both the inventory and the impact assessment is probabilistic simulation. Uncertainty due to choices can be dealt with in a scenario analysis or reduced by standardisation and peer review. The feasibility of dealing with temporal and spatial variability is limited, implying model uncertainty in LCAs. Other model uncertainties can be reduced partly by more sophisticated modelling, such as the use of non-linear inventory models in the inventory and multi media models in the characterisation phase.     

Population structure of the masu salmon <Emphasis Type="Italic">Oncorhynchus masou</Emphasis> from the Kol River (Western Kamchatka) and geographic variation in the species area

According to materials from original investigations in Kamchatka as well as published data, the population structure and geographical variation of the masu salmon Oncorhynchus masou are considered in the species area. The intraspecies structure, dates of their run to rivers, dates of spawning, traits of spawning, size-age and sex composition, fecundity, biological traits of parr, downstream migration of smolts are investigated. The relationship is shown between the manifestations of geographical variation of masu salmon and the factor of temperature. It is assumed that the global warming of climate would contribute to the advancement of this species to northern areas and to the increase in its abundance.     

Risk Analysis,Uncertainty Factors,and the Susceptibilities of Children

This article examines the use of uncertainty factors (UFs) by the Agency for Toxic Substances and Disease Registry (ATSDR) when developing health guidance values known as minimal risk levels (MRLs) in environmental risk analysis as it applies to children. Improvements in the chemical-specific databases often reveal new information and thereby reduce uncertainty or alternatively raise new concerns. As a result, MRLs can and will change. Children, in particular, are not “small adults” and in some instances demonstrate greater risks of exposure to environmental toxicants and greater susceptibility for adverse health effects once exposed. Recent experience with risk analysis for three toxicants (organic mercury, dioxin, and manganese) is recounted to demonstrate how ATSDR has revised MRLs as the emerging science generates greater knowledge and awareness of children's special vulnerabilities to toxic substances in the environment.     

Stochastic and Subjective Uncertainty in the Assessment of Radiation Exposure at the Waste Isolation Pilot Plant

The Waste Isolation Pilot Plant (WIPP) is under development by the U.S. Department of Energy as a geologic (i.e., deep underground) disposal facility for transuranic waste. An analysis is presented of possible radiation exposures associated with inadvertent drilling intrusions through the WIPP using future drilling rates obtained in accordance with requirements specified by the U.S. Environmental Protection Agency (EPA) in 40 CFR 194, Subpart B. The analysis attempts to maintain a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty as implied in the EPA regulations 40 CFR 191, Subpart B, and 40 CFR 194. The results of the analysis are presented as distributions of complementary cumulative distribution functions (CCDFs) for radiation exposure to oil field workers, where the individual CCDFs arise from stochastic uncertainty (i.e., many possible patterns of drill ing intrusions are possible over the 10,000?yr period specified in 40 CFR 191, Subpart B) and the distributions of CCDFs arise from subjective uncertainty (i.e., many inputs to the analysis have fixed, but poorly known, values). The projected radiation exposures during the 10,000?yr period following decommissioning of the WIPP in the year 2033 were found to be small (typically less than 0.001 person-Sv), with the dominant exposure pathway being the incidental ingestion of contaminated dirt by drilling crew members.     

Use of the reference dose in risk characterization of drinking water contaminants

Regulatory decisions should be made in the most expert and informed way since they are precipitated by real and perceived threats to public health, under the glare of public scrutiny. The development of environmental regulations require a three‐step paradigm, collectively called risk analysis. This paper will address the risk assessment practices required under the Safe Drinking Water Act (SDWA) Amendments of 1986 to determine a Maximum Contaminant Level Goal (MCLG, nonenforceable health goal) which should result in no known or anticipated health effects, and allows adequate margin of safety. The end product of this risk assessment, risk characterization, and risk management encompassing control options and nonrisk analysis are combined to derive the Maximum Contaminant Level (MCL, enforceable standard). Furthermore, this paper discusses the U.S. Environmental Protection Agency's efforts in exploring new and improved noncancer risk assessment approaches providing the basis for MCLGs for the protection of human health.     

Adaptation and plasticity in aboveground allometry variation of four pine species along environmental gradients

Plant species aboveground allometry can be viewed as a functional trait that reflects the evolutionary trade‐off between above‐ and belowground resources. In forest trees, allometry is related to productivity and resilience in different environments, and it is tightly connected with a compromise between efficiency‐safety and competitive ability. A better understanding on how this trait varies within and across species is critical to determine the potential of a species/population to perform along environmental gradients. We followed a hierarchical framework to assess tree height‐diameter allometry variation within and across four common European Pinus species. Tree height‐diameter allometry variation was a function of solely genetic components –approximated by either population effects or clinal geographic responses of the population's site of origin– and differential genetic plastic responses –approximated by the interaction between populations and two climatic variables of the growing sites (temperature and precipitation)–. Our results suggest that, at the species level, climate of the growing sites set the tree height‐diameter allometry of xeric and mesic species (Pinus halepensis, P. pinaster and P. nigra) apart from the boreal species (P. sylvestris), suggesting a weak signal of their phylogenies in the tree height‐diameter allometry variation. Moreover, accounting for interpopulation variability within species for the four pine species aided to: (1) detect genetic differences among populations in allometry variation, which in P. nigra and P. pinaster were linked to gene pools –genetic diversity measurements–; (2) reveal the presence of differential genetic variation in plastic responses along two climatic gradients in tree allometry variation. In P. sylvestris and P. nigra, genetic variation was the result of adaptive patterns to climate, while in P. pinaster and P. halepensis, this signal was either weaker or absent, respectively; and (3) detect local adaptation in the exponent of the tree height‐diameter allometry relationship in two of the four species (P. sylvestris and P. nigra), as it was a function of populations' latitude and altitude variables. Our findings suggest that the four species have been subjected to different historical and climatic constraints that might have driven their aboveground allometry and promoted different life strategies.     

Extrapolation in Risk Assessment: Improving the Quantification of Uncertainty,and Improving Information to Reduce the Uncertainty

Risk assessments inevitably extrapolate from the known to the unknown. The resulting calculation of risk involves two fundamental kinds of uncertainty: uncertainty owing to intrinsically unpredictable (random) components of the future events, and uncertainty owing to imperfect prediction formulas (parameter uncertainty and error in model structure) that are used to predict the component that we think is predictable. Both types of uncertainty weigh heavily both in health and ecological risk assessments. Our first responsibility in conducting risk assessments is to ensure that the reported risks correctly reflect our actual level of uncertainty (of both types). The statistical methods that lend themselves to correct quantification of the uncertainty are also effective for combining different sources of information. One way to reduce uncertainty is to use all the available data. To further sharpen future risk assessments, it is useful to partition the uncertainty between the random component and the component due to parameter uncertainty, so that we can quantify the expected reduction in uncertainty that can be achieved by investing in a given amount of future data. An example is developed to illustrate the potential for use of comparative data, from toxicity testing on other species or other chemicals, to improve the estimates of low-effect concentration in a particular case with sparse case-specific data.     

婴幼儿配方奶粉菌落总数检验中不确定度的评定

分析样品中菌落总数不确定度的来源,采用合并样品标准差的方法来评定菌落总数的不确定。研究表明：实验中的扩展不确定度为0．046％,采用此法适合于菌落总数检验结果的评定。且该法简便,适合于每一个样本的检验结果,随着检验结果的不断增加,可随时加入到合并样本中,重新计算合并样本标准差,更新不确定度的取值范围。