走出慢性乙型、丙型病毒性肝炎诊断的误区

我国病毒性肝炎是一个非常重要的肝病,病毒性肝炎的诊断不仅依靠临床表现,肝功能指标的改变,也依赖于病毒标志物的检测。病毒血清学检查是确定诊断的唯一标准。乙、丙、丁型病毒性肝炎的血清标志物项目繁多,临床意义相差较大,正确理解能为诊断提供很大帮助。     

部件特征和正字法意识在儿童汉字书写发展中的作用

研究以北京地区262名汉语儿童为研究对象,探讨了部件复杂程度和部件数量对儿童延迟抄写成绩的影响,并通过一年的追踪,考察了儿童书写加工基本单位及其发展变化历程,并分析了儿童的正字法意识对其书写能力发展的预测作用。实验一发现汉语儿童的书写单位是一个从笔画到部件的多水平发展过程,在控制整字笔画的情况下,对于部件复杂的汉字,以笔画为单位进行编码,错误类型以笔画乱写和部件错误为主,而对于由简单部件组成的汉字,主要以部件为单位进行组块,儿童多犯部件替换的错误。实验二发现在部件复杂程度相同的情况下,汉字所包含的部件数量越多,儿童的书写正确率越低,多犯部件缺失和部件替换的错误。实验三发现儿童一年级的正字法意识能够显著预测当年及一年后的书写成绩,表明正字法意识对于儿童的书写发展具有重要作用。     

暴食症的诊断、治疗及其疗效

暴食症已成为目前世界上广泛流行的三大进食障碍之一,我国患者亦不在少数并有增加之势,但对暴食症的研究与治疗尚属起步阶段。文章回顾了暴食症的表征及其诊断技术,简要介绍了常见的三种治疗方法（即行为疗法、认知疗法及伴随症状改善法）,并讨论了影响暴食症疗效的诸个体因素。暴食症的诊断主要是根据美国精神疾病诊断与统计手册第四版（DSM-IV）、进食障碍检测评估表（EDE）（治疗者用）和患者用进食障碍检测问卷（EDE-Q）（患者用）,并结合过往病史和伴随症状。三类治疗方法在理论与操作层面各有倚重,均可收到一定治疗效果。但各疗法的效果与预后会因患者而异甚或迥乎相异,这是因为暴食症疗效还受到患者自身人格、社会和认知诸因素的影响,治疗者应根据特定患者选择适合该患者的疗法。     

Flexible representations in visual working memory and interactions with long‐term learning: Commentary on the special issue

This special issue of the British Journal of Psychology brings together cutting edge research on a range of topics in visual working memory (VWM). In this commentary, we attempt to summarize common themes in current VWM research exemplified in this issue. The articles include several reviews of important topics as well as empirical papers covering three main themes. The first concerns the nature of mental representations of memoranda in the commonly used delayed estimation task, where both fine‐grained and broad categorical details appear to be represented, and their susceptibility to interference. The second concerns interactions between VWM representations, both those that produce individuation of representations and those that create an overarching ensemble structure. Finally, the third main topic concerns the use of VWM during visual search and in the learning of repeated configurations in search displays. The work presented here, and other work in the field, points to a rich interplay between representations in VWM but also between VWM and information in long‐term memory. Opportunities for further investigation are highlighted throughout.     

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.     

分离型的多级评分认知诊断模型开发及其应用研究

本文基于DINO模型开发出一种分离型的多级评分认知诊断模型(P-DINO模型),并采用Monte Carlo模拟与实证研究相结合的范式,探讨了新模型的性能与有效性。结果表明:(1)P-DINO模型参数估计精度较好,参数估计的稳健性较强。(2)采用MCMC算法可以实现该模型的参数估计,估计结果较理想。(3)测验长度的增加能有效提高模型的判准率,被试人数的增加对判准率的提高相对较小,属性个数的增加会降低判准率。(4)在抑郁症状评估中,P-DINO模型的表现要优于DINO模型与传统的抑郁症状评估。     

Higher-order latent trait models for cognitive diagnosis

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling theϑ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example. This research was funded by National Institute of Health grant R01 CA81068. We would like to thank William Stout and Sarah Hartz for many useful discussions, three anonymous reviewers for helpful comments and suggestions, and Kikumi Tatsuoka and Curtis Tatsuoka for generously sharing data.     

食管癌内镜技术最优化原则的思考

随着消化内镜及其配件的不断更新和内镜医师操作技术的进步,消化内镜在食管癌的应用从诊断为主发展到诊断与治疗并重.回顾历史,医疗需求是医学科学进步的强大动力,科学技术进步推动医学的发展,内镜技术的发展促进了食管癌诊断与治疗水平的最优化.     

精神疾病共病现象的辩证思考

由于大多数精神疾病的病因和发病机制不明,无特异性的生物学诊断指标,临床表现多样,有时很难用一种疾病解释.共病的概念应用于精神科临床,为精神科医生提供了一种辩证的诊断思维方法,对研究共病的本质和指导临床治疗都有重要的意义.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.