How Do Scientists Respond to Anomalies? Different Strategies Used in Basic and Applied Science

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity.     

孟德尔随机化研究应用于因果推断的影响因素及其结果解读面临的挑战

孟德尔随机化（Mendelian randomization,MR）研究使用遗传变异作为工具变量,推断暴露因素与结局之间的因果关系,能够有效克服混杂和反向因果问题所导致的偏倚。然而,MR研究中的工具变量须满足关联性、独立性和排他性3个核心假设。即使核心假设成立,MR研究在因果推断中的应用还受到其他局限性的影响。此外,MR研究结果的解读需要基于综合证据。本文将围绕MR研究应用于因果推断的影响因素和研究结果的解读进行综述,以期为MR研究结果应用提供指导。     

基于孟德尔随机化探索臀围与2型糖尿病的因果关系

  目的   采用孟德尔随机化分析方法, 探讨臀围与2型糖尿病的因果关联。   方法   将分别来自人体测量特征遗传研究(genetic investigation of anthropometric traits, GIANT)和糖尿病遗传学验证和荟萃分析(diabetes genetics replication and Meta-analysis, DIAGRAM)数据库的臀围和2型糖尿病遗传位点数据, 据单核苷酸多态性(single nucleotide polymorphism, SNP)编号匹配合并, 以与臀围密切相关的SNP为工具变量, 运用逆方差加权法、MR-Egger回归模型和加权中位数法分析臀围对2型糖尿病的因果效应。   结果   不分性别队列、女性队列和男性队列中分别匹配到52、9和15个SNP。异质性检验结果提示SNP间同质。三个队列的OR值及其95%的置信区间分别为1.065(1.030~1.100)、1.103(1.057~1.150)和1.583(1.273~1.968), 均有统计学意义(均有P < 0.05)。敏感性分析结果显示三个队列逐一移除SNP, 其因果效应不受影响, 结果稳健。   结论   人体臀围与2型糖尿病之间均存在因果关联, 臀围可能为2型糖尿病的危险因素。     

Incorporating expert knowledge when learning Bayesian network structure: a medical case study

Objectives

Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence, with many applications in medicine. Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remains underexplored. In this paper we examine a number of approaches to their combination when learning structure and present new techniques for assessing their results.

Methods and materials

Using public-domain medical data, we run an automated causal discovery system, CaMML, which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results. We present an algorithm for generating a single BN from a set of learned BNs that incorporates user preferences regarding complexity vs completeness. These techniques are presented as part of the first detailed workflow for hybrid structure learning within the broader knowledge engineering process.

Results

The detailed knowledge engineering workflow is shown to be useful for structuring a complex iterative BN development process. The adjacency matrices make it clear that for our medical case study using the IOWA dataset, the simplest kind of prior information (partially sorting variables into tiers) was more effective in aiding model discovery than either using no prior information or using more sophisticated and detailed expert priors. The method for generating a single BN captures relationships that would be overlooked by other approaches in the literature.

Conclusion

Hybrid causal learning of BNs is an important emerging technology. We present methods for incorporating it into the knowledge engineering process, including visualisation and analysis of the learned networks.     

Use of causal language in observational studies of obesity and nutrition

Summary

Objective: To assesss the inappropriate use of causal language in studies on obesity and nutrition.

Methods

Titles and abstracts of 525 peer-reviewed papers in the 4 leading journals in the fields of obesity and nutrition were scrutinized for language implying causality in observational studies published in 2006.

Results

Such misleading language appeared in 161 papers (31%) independent of funding source. Remarkably 49% of studies lacking statistically significant primary outcomes used misleading language compared to 29% of those with p values ≤0.05 (chi square p < 0.001). Exculpatory language was present in the body of the text in 19%; of the 161 studies.

Conclusion

We suggest that editors and reviewers evaluate submissions for misleading reporting.     

Causal beliefs,cardiac denial and pre-hospital delays following the onset of acute coronary syndromes

Reducing pre-hospital delay is crucial in reducing mortality from acute coronary syndrome (ACS). Patient's causal beliefs and coping styles may affect symptom appraisal and help-seeking behavior. We examined whether patient's beliefs about the causes of their ACS and denial of impact were associated with pre-hospital delay. Pre-hospital delay data were collected from 177 patients with ACS. Retrospective causal beliefs and cardiac denial of impact were assessed using questionnaires. Factor analysis of causal beliefs produced 3 factors; beliefs in stress and emotional state, behavioral and clinical risk factors, and in heredity as causal influences. Patients with strong beliefs that stress and emotional state caused their ACS were more likely to have long pre-hospital delays (>130 min). There were no significant associations between pre-hospital delay and the other two causal belief factors. Patients with greater denial scores were also more likely to have long delays than those with low scores. These effects were independent of age, gender, education, previous myocardial infarction, history of depression and negative affectivity. Cognitive and emotional factors including patient's beliefs about causes and avoidant coping help to explain variations in pre-hospital delay.     

因果推断中研究人群的形成

流行病学是对一定数量的人群进行特征描述和比较,并在此基础上进行因果推断。研究人群的形成是其第一步。本研究以观察性研究为例,首先定义个体截面和人群截面,并阐明其测量需满足的3个假设：属性真实值随时间保持不变,属性变量间互不干扰,个体间互不干扰;接着指出因果推断研究应以待定因（或暴露）的发生或状态开始的时间为标准进行统一;最后,基于人群截面的双重角色,提出人群的因果推断研究可分为2类：历史重建研究和探索未来研究,并初步梳理了研究设计框架、估计的效应及设计间的关系。从因果思维角度探讨研究人群的形成过程,可为明确因果推断研究设计类型奠定基础,选取合适的效应估计进行因果推断,值得深入研究。     

因果图模型及其在营养流行病学研究中的应用

不良饮食是慢性非传染性疾病最重要的可控危险因素之一,但通过随机对照试验定量阐明具体饮食因素与健康结局的因果关联面临很多困难。近年来,因果推断的迅速发展为充分利用和发掘观察性研究数据,产生高质量的营养流行病学研究证据提供了有力的理论和方法工具。其中,因果图模型通过整合大量先验知识将复杂的因果关系系统可视化,提供了识别混杂和确定因果效应估计策略的基础框架,基于不同的因果图,可选择调整混杂、工具变量或中介分析等不同的分析策略。本文对因果图模型的思想和各种分析策略的特点及其在营养流行病学研究中的应用进行介绍,旨在促进因果图模型在营养领域的应用,为后续研究提供参考和建议。     

两样本孟德尔随机化研究肾功能对骨质疏松症的因果关系

目的 采用两样本孟德尔随机化方法研究肾功能对骨质疏松症的因果关系。方法 对全基因关联研究(GWAS)的汇总数据进行分析。eGFR和OP的GWAS汇总数据来源于东亚。eGFR数据共包含143 658例样本,OP数据一共212 453例样本。利用与肾功能(以eGFR衡量)密切关联的单核苷酸多态性作为工具变量,分别用逆方差加权法、MR-Egger回归和加权中位数法行两样本孟德尔随机化分析,以OR值评价肾功能对骨质疏松症风险的因果关系。结果 共纳入66个单核苷酸多态性作为工具变量,MR-Egger回归结果表明不存在基因的水平多效性(P=0.449)。逆方差加权法结果显示eGFR的降低可增加患骨质疏松症的风险,对数变换后的eGFR每降低一个标准差,会导致骨质疏松的风险增加24%(OR=1.24, 95%CI:1.05～1.46)。结论 肾功能降低对骨质疏松症的发生可能存在因果关联。     

疟疾防治药物的研究——Ⅺ.2,4-二氨基-6-(N-取代-对氯苄氨基)喹唑啉衍生物的合成及其抗疟作用

合成2,4-二氨基-6-(N-取代-对氯苄氨基)喹唑啉衍生物33个,经伯氏鼠疟原虫(Plasmodium berghei)抑制性治疗筛选,剂量20mg/kg×3d,有10个化合物(1,2,5～8,14,20,21,26)抑制率在90%以上。经约氏鼠疟原虫(P.yoelii)斯氏按蚊系统的病因性预防筛选,剂量5mg/kg×3d,有26个化合物(1～12,14,20～22,24～33)使小白鼠全部得到保护;剂量下降到0.625mg/kg×3d,有14个化合物(5～12,14,20,21,26,31,33)使小白鼠全部得到保护(表1)。4个化合物(1,8,20,25,表2,3)进行了猴疟(P.cynomolgi)试验,效果不及伯喹和乙胺嘧啶。