The future of genetics in public health

During the 1980s, we have witnessed an erosion in federal and state funding for the coordination of genetic services in state health departments. As this decade begins, the federal budget for the support of the national genetics program is less than half of the total available in 1980. In addition, priorities in state maternal and child health programs have changed, and genetic services have often received lower rankings aside of programs to improve prenatal care and to address problems such as teenage pregnancy. Clearly we are at a critical point for the future of genetics programs in the public health arena. Fortunately, despite the problems with funding and priority setting, important coalitions have been built during the past decade and continued advances in medical genetics have made it difficult for public health officials to ignore the potential impact of genetic services. Council of Regional Networks for Genetic Services (CORN) and the regional genetic services networks have provided opportunities for interaction and coordination between genetic services providers and public health officials that had not been available to any significant extent. The full potential of these coalitions has yet to be realized, but efforts in data collection, development of national guidelines and standards, and information sharing and networking have already had an influence on state genetic services programs. Continuing research on the genetics of chronic diseases and the influence of discoveries in molecular genetics will undoubtedly broaden the current scope of state public health programs in genetics, which all too often have been limited to newborn screening and administration of grants for genetic services. The regional and national genetic services programs should be strengthened and appropriate training programs should be developed in Schools of Public Health to provide the expertise and leadership that will be needed to guide the future of genetics in public health.     

A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms

Neural Network models are commonly used for cluster analysis in engineering, computational neuroscience, and the biological sciences, although they are rarely used in the social sciences. In this study we compare the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning (Hartigan and Späthk-means) and three hierarchical (Ward's, complete linkage, and average linkage) cluster methods in 2,580 data sets with known cluster structure. Overall, the performance of the Kohonen networks was similar to, or better than, the performance of the other methods.     

The long-term outcome of a personal network-oriented HIV prevention intervention for injection drug users: The SAFE study

Social influence processes have been found to affect numerous drug and health-related behaviors. We postulated that by using a network-oriented intervention it may be possible to capitalize on social influence processes to reduce human immunodeficiency virus (HIV) risk behaviors. The present study used an experimental study design for delivering a psychoeducational acquired immunodeficiency syndrome (AIDS) preventive intervention to injection drug sharing networks. Participants were recruited from the ALIVE study, an epidemiological study in Baltimore. In the present paper we examine the self-reported behavioral outcomes of 117 injection drug users 18 months after the baseline interview. HIV seronegative experimental participants reported significantly less frequent needle sharing and less injecting of heroin and cocaine than controls. In multiple logistic regression models of HIV seronegative participants, there was a significant negative association between assignment to the experimental group and the HIV-related behaviors of needle sharing and sharing of cookers in the prior 6 months; controls were 2.8 times more likely than experimentals to report needle sharing and were 2.7 times more likely to report sharing cookers. The results of this 18-month follow-up suggest that among injection drug users network-oriented interventions may be a promising approach to HIV prevention.     

Logit models and logistic regressions for social networks: III. Valued relations

This paper generalizes thep* model for dichotomous social network data (Wasserman & Pattison, 1996) to the polytomous case. The generalization is achieved by transforming valued social networks into three-way binary arrays. This data transformation requires a modification of the Hammersley-Clifford theorem that underpins thep* class of models. We demonstrate that, provided that certain (non-observed) data patterns are excluded from consideration, a suitable version of the theorem can be developed. We also show that the approach amounts to a model for multiple logits derived from a pseudo-likelihood function. Estimation within this model is analogous to the separate fitting of multinomial baseline logits, except that the Hammersley-Clifford theorem requires the equating of certain parameters across logits. The paper describes how to convert a valued network into a data array suitable for fitting the model and provides some illustrative empirical examples.This research was supported by grants from the Australian Research Council, the National Science Foundation (#SBR96-30754), and the National Institute of Health (#PHS-1RO1-39829-01).     

A study of the classification capabilities of neural networks using unsupervised learning: A comparison withK-means clustering

Several neural networks have been proposed in the general literature for pattern recognition and clustering, but little empirical comparison with traditional methods has been done. The results reported here compare neural networks using Kohonen learning with a traditional clustering method (K-means) in an experimental design using simulated data with known cluster solutions. Two types of neural networks were examined, both of which used unsupervised learning to perform the clustering. One used Kohonen learning with a conscience and the other used Kohonen learning without a conscience mechanism. The performance of these nets was examined with respect to changes in the number of attributes, the number of clusters, and the amount of error in the data. Generally, theK-means procedure had fewer points misclassified while the classification accuracy of neural networks worsened as the number of clusters in the data increased from two to five.Acknowledgements: Sara Dickson, Vidya Nair, and Beth Means assisted with the neural network analyses.     

Enumeration and simulation methods for 0–1 matrices with given marginals

Data in the form of zero-one matrices where conditioning on the marginals is relevant arise in diverse fields such as social networks and ecology; directed graphs constitute an important special case. An algorithm is given for the complete enumeration of the family of all zero-one matrices with given marginals and with a prespecified set of cells with structural zero entries. Complete enumeration is computationally feasible only for relatively small matrices. Therefore, a more useable Monte Carlo simulation method for the uniform distribution over this family is given, based on unequal probability sampling and ratio estimation. This method is applied to testing reciprocity of choices in social networks.The author wishes to thank Cajo ter Braak and John Birks for pointing out the relevance of this subject for ecology; and also Albert Verbeek and Ivo Molenaar, a referee, the Editor, and the Associate Editor for their comments. An earlier version of this paper was presented at the Stockholm Conference on Random Graphs and Applications (April 25–27, 1989), organized with financial support from the Swedish Council of Research in the Humanities and the Social Sciences.     

睡眠影响记忆巩固的同步EEG-fMRI研究

默认网络是静息状态活动较强的大脑结构, 它包含的海马和内侧前额叶两个脑区是记忆巩固的关键部位, 同时静息态也被证明伴随有记忆巩固现象, 我们推测默认网络是睡眠依赖记忆巩固的核心结构。本研究拟借助同步EEG-fMRI在时空分辨率上的优势, 研究默认网络参与睡眠依赖记忆巩固的神经机制。包括：1)发掘默认网络活动的电生理指标, 应用EEG源定位和跨频段耦合分析, 揭示记忆巩固的动态过程; 2)应用滑动时间窗和模块分析, 研究默认网络参与静息态和睡眠过程记忆巩固的异同, 揭示记忆在昼夜更迭中得以强化的神经机制; 3)通过多模态信息融合, 揭示记忆类型和睡眠阶段等因素对睡眠依赖记忆巩固的影响。本研究的开展对阐明睡眠依赖记忆巩固的神经机制具有深刻的理论意义, 并最终可能为治疗学习记忆相关障碍提供全新的思路。     

动态面孔表情优势效应的心理机制及神经基础

动态面孔表情优势效应是指较静态面孔表情而言, 个体在识别动态的面孔表情时表现出较好的识别能力。动态面孔表情优势效应的心理机制主要涉及增强的构形加工、补偿角色和面孔模仿能力。此外, 该优势效应的神经网络则由核心的神经网络和扩展的神经网络组成, 前者主要负责早期的知觉编码和刺激的运动加工, 而后者与个体的面孔模仿能力、刺激的动态表征等有关。今后的研究应集中在完善延伸的神经网络、拓展其心理机制; 开展动态面孔表情优势效应的发展性研究; 考察面孔表情的刚性运动特征; 注重在虚拟现实环境下研究动态面孔表情的优势效应。     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

基于人工神经网络的一种效度凭证求取方法

针对基于统计学的效度凭证求取方法所存在的问题,提出了基于人工神经网络的效度凭证求取方法。蒙特卡罗模拟实验和对实际数据的分析表明,当测验分数和效标分数为单变量且非线性关系时,或者测验分数和效标分数为多变量时,运用神经网络方法可以比统计学方法更好地求取心理测验效度的基于和其他变量关系的凭证。