Neural mechanisms regulating different forms of risk-related decision-making: Insights from animal models

Over the past 20 years there has been a growing interest in the neural underpinnings of cost/benefit decision-making. Recent studies with animal models have made considerable advances in our understanding of how different prefrontal, striatal, limbic and monoaminergic circuits interact to promote efficient risk/reward decision-making, and how dysfunction in these circuits underlies aberrant decision-making observed in numerous psychiatric disorders. This review will highlight recent findings from studies exploring these questions using a variety of behavioral assays, as well as molecular, pharmacological, neurophysiological, and translational approaches. We begin with a discussion of how neural systems related to decision subcomponents may interact to generate more complex decisions involving risk and uncertainty. This is followed by an overview of interactions between prefrontal-amygdala-dopamine and habenular circuits in regulating choice between certain and uncertain rewards and how different modes of dopamine transmission may contribute to these processes. These data will be compared with results from other studies investigating the contribution of some of these systems to guiding decision-making related to rewards vs. punishment. Lastly, we provide a brief summary of impairments in risk-related decision-making associated with psychiatric disorders, highlighting recent translational studies in laboratory animals.     

氢化物原子荧光光度法测定食品中砷的不确定度评定

目的探讨氢化物原子荧光光度法测定食品中砷的不确定度评定方式。方法依据JJF1059-1999建立数学模型,合成不确定度。结果当食品中砷含量为0.22mg/kg时,合成不确定度为0.008mg/kg,扩展不确定度为0.02mg/kg。结论本评定过程清晰合理,主要影响因素不遗漏、不重复,适用于原子荧光光度法的不确定度评定。     

Some notes on uncertainty; Federal policy and innovation

The legislative background and current responsibilities of the new National Center for Health Care Technology (NCHCT) are discussed. The NCHCT's charge is to consider the stage of development, the actual or potential risks, costs and rate of use of health care technology. The role of Government in supporting new technologies, stimulating innovation and encouraging application of research products is considered. These issues are discussed in the context of the uncertainty inherent in planning and the formulation of public policy.     

Human variability in susceptibility How big, how often, for what responses to what agents?

This paper first reviews in plain language some basic concepts and methods for estimating inter-individual variability in susceptibility from human data. A scale is offered to allow different variability findings to be understood and compared. Then the accumulated results of different variability analyses, information on how much variability has been observed and how often, is summarized in the form of a series of box plots. Data are presented on pharmacodynamic variability for various non-cancer effects, variability in susceptibility to infectious organisms, and variability in susceptibility to carcinogenesis by genetically-acting agents.     

信息支持对乳腺癌根治术患者疾病不确定感的影响

目的:探讨信息支持对乳腺癌根治术患者围手术期疾病不确定感的影响.方法:为类实验性研究,采用便利抽样方法抽取乳腺癌根治术患者共60例.前30例患者为对照组,后30例患者为实验组.采用疾病不确定感量表(MUIS)对60例患者于入院时、手术前、出院前进行疾病不确定感调查.实验组患者接受实验者提供的与疾病和康复相关的信息支持.对照组只接受病房护士提供的健康指导.结果:实验组在手术前以及出院时的疾病不确定感均比入院时明显下降(P< 0.05);与入院时相比,实验组在手术前和出院时的疾病不确定感的下降幅度远超过对照组的疾病不确定感的下降幅度(P<0.05).结论:对围手术期乳腺癌患者提供系统性的信息支持,能显著降低患者的疾病不确定感.     

栀子药材中三种有效成分HPLC定量分析结果不确定度的评定

采用HPLC方法同时测定栀子药材中栀子苷、绿原酸和藏红花素Ⅰ的含量,通过对测试方法流程进行分析,确定不确定度来源,结合含量测定方法验证数据,计算了不确定度分量、合成不确定度及扩展不确定度;结果表明,用HPLC方法定量分析栀子药材中栀子苷、绿原酸和藏红花素1的方法满足现代药物分析方法学的要求,且三者的扩展不确定度分别为0.1024,0.2254,0.1265.     

Diagnostic uncertainty and recall bias in chronic low back pain

Patients’ beliefs about the origin of their pain and their cognitive processing of pain-related information have both been shown to be associated with poorer prognosis in low back pain (LBP), but the relationship between specific beliefs and specific cognitive processes is not known. The aim of this study was to examine the relationship between diagnostic uncertainty and recall bias in 2 groups of chronic LBP patients, those who were certain about their diagnosis and those who believed that their pain was due to an undiagnosed problem. Patients (N = 68) endorsed and subsequently recalled pain, illness, depression, and neutral stimuli. They also provided measures of pain, diagnostic status, mood, and disability. Both groups exhibited a recall bias for pain stimuli, but only the group with diagnostic uncertainty also displayed a recall bias for illness-related stimuli. This bias remained after controlling for depression and disability. Sensitivity analyses using grouping by diagnosis/explanation received supported these findings. Higher levels of depression and disability were found in the group with diagnostic uncertainty, but levels of pain intensity did not differ between the groups. Although the methodology does not provide information on causality, the results provide evidence for a relationship between diagnostic uncertainty and recall bias for negative health-related stimuli in chronic LBP patients.     

Parameter Identification in Uncertain Scalar Conservation Laws Discretized with the Discontinuous Stochastic Galerkin Scheme

We study an identification problem which estimates the parameters of the underlying random distribution for uncertain scalar conservation laws. The hyperbolic equations are discretized with the so-called discontinuous stochastic Galerkin method, i.e., using a spatial discontinuous Galerkin scheme and a Multielement stochastic Galerkin ansatz in the random space. We assume an uncertain flux or uncertain initial conditions and that a data set of an observed solution is given. The uncertainty is assumed to be uniformly distributed on an unknown interval and we focus on identifying the correct endpoints of this interval. The first-order optimality conditions from the discontinuous stochastic Galerkin discretization are computed on the time-continuous level. Then, we solve the resulting semi-discrete forward and backward schemes with the Runge-Kutta method. To illustrate the feasibility of the approach, we apply the method to a stochastic advection and a stochastic equation of Burgers' type. The results show that the method is able to identify the distribution parameters of the random variable in the uncertain differential equation even if discontinuities are present.