Characteristics of a French African Caribbean Epidemiological Psychiatric Sample with a History of Suicide Attempt

Research on vulnerability factors among ethnic groups, independent of primary psychiatric diagnosis, may help to identify groups at risk of suicidal behavior. French African Caribbean general psychiatric patients (N = 362) were recruited consecutively and independently of the primary psychiatric diagnosis. Demographic and clinical characteristics and lifetime history of suicide attempts were recorded. Sixty‐five patients (18%) had a history of at least one suicide attempt. Presence of professional qualifications, children, poor social contacts, treatment with benzodiazepine at inclusion, and poor treatment compliance were all associated with a lifetime history of suicide attempts.     

Beyond Political Theology and its Liquidation: From Theopolitical Monotheism to Trinitarianism

One of the central theological challenges facing Erik Peterson was to help the mid‐twentieth century Catholic Church define its relationship with the wider world. He responded by advancing a distinctive understanding of the ‘polis.’ In this essay, I critically analyze Peterson's central and perhaps best known proposal about how the Church ought to negotiate the modern world — encapsulated in his expression, the ‘liquidation of political theology.’ I contend that Peterson's proposal is not congruent with a right understanding of patristic trinitarian monarchy, although a view that stands in sharp contrast to that of Carl Schmitt. Notwithstanding the effectiveness of Peterson's critique of Schmitt's political theology, I argue that Peterson nonetheless fails in his exposition of the thought of Gregory of Nazianzus and therefore in his interpretation of the role of the Church in what we have learned to call the ‘political’ and the ‘social.’ I conclude by outlining several ways that the Church today might take up the challenge of regaining a truly political thought, a new ekklesioteia, nourished by the monarchy of the triune God.     

熟悉性能够支持联结记忆:一体化编码的作用

双加工理论认为熟悉性和回想是支持情景记忆的两种基本加工过程。一般认为熟悉性和回想都可以支持项目记忆,但只有回想能够支持联结记忆。然而,一体化假说提出,当刺激材料在学习阶段得到一体化编码,形成一个单一、整合的表征时,熟悉性也能够支持联结记忆。以往研究主要利用实验材料的固有特征,或使用定义法、交互表象法来实现对刺激材料的一体化编码,然后通过配对联结再认范式或来源记忆范式验证一体化假说。神经成像研究发现,位于内侧颞叶的嗅周皮层能够支持编码阶段一体化表征的形成和基于熟悉性对一体化表征的提取。未来研究应克服以往研究中存在的方法学缺陷、从功能神经网络的角度考察一体化加工的脑神经机制、并注重一体化编码策略在特殊人群中的应用价值。     

社会支持缓解高互依自我个体的急性心理应激反应

高互依自我构念个体以社会角色来定义自己, 更看重自己的社会关系; 社会评价威胁是诱发个体社会心理应激反应的重要情境因素, 然而高互依自我构念个体在心理性应激情境中是否会表现出更高的应激反应, 以及社会支持是否会缓解高互依自我构念个体的急性心理应激反应还不可知。本研究以特里尔社会应激测试为范式, 以主观应激报告、心率和唾液皮质醇作为应激指标, 使用自我构念问卷测量个体互依自我构念水平, 将健康大学生被试随机分配到自我支持启动组和社会支持启动组。结果发现, 互依自我构念水平正向预测个体在应激情境中的皮质醇水平增加量; 同时, 相比于自我支持启动, 高互依自我构念的个体在经历社会支持后有更低的唾液皮质醇变化量。研究结果提示, 社会支持对高互依自我构念个体的急性心理应激反应起到了良好的缓解作用。     

Combining genetic algorithms and the finite element method to improve steel industrial processes

Most of the times the optimal control of steel industrial processes is a very complicated task because of the elevated number of parameters to adjust. For that reason, in steel plants, engineers must estimate the best values of the operational parameters of processes, and sometimes, it is also necessary to obtain the appropriate model for steel material behaviour. This article deals with three successful experiences gained from genetic algorithms and the finite element method in order to solve engineering optimisation problems. On one hand, a fully automated method for determining the best material behaviour laws is described, and on the other hand we present a common methodology to find the most appropriate settings for two cases of improvement in steel industrial processes. The study of the three reported cases allowed us to show the reliability and effectiveness of combining both techniques.     

Judgement frequency, belief revision, and serial processing of causal information

The main aim of this research was to study the cognitive architecture underlying causal/covariation learning by investigating the frequency of judgement effect. Previous research has shown that decreasing the number of trials between opportunities to make a judgement in a covariation learning task led to a higher score after an a or d type of trial (positive cases) than after b and c trials (negative cases). Experiment 1 replicated this effect using a trial-by-trial procedure and examined the conditions under which it occurs. Experiment 2 demonstrated a similar frequency of judgement effect when the information was presented in the form of contingency tables. Associative or statistical single-mechanism accounts of causal and covariation learning do not provide a satisfactory explanation for these findings. An alternative belief revision model is presented.     

Quantitative Analysis of IRT Variability During the First Training Stages of a Variable-Interval Schedule in Rats

Five rats were reinforced under variable-interval schedules with different average interreinforcement intervals (30 seC., 1 min, 2 min, and 4 min). Each animal was run only two sessions of each schedule. The interresponse times (IRTs) were recorded and analyzed. The autocorrelation function of the IRT series and of the IRT time series (number of responses per time interval) were calculated, and absence of periodicity in the subject’s behavior was demonstrated. Frequency distribution of IRTs showed in all cases a similar shape and could be fitted to a gamma probability density function in 60% of cases with a signification level of .01 (Kolmogorov-Smirnoff test). The frequency distributions of the IRT time series were distributed as a Poisson process with a .05 significance level. These results suggest that during variable-interval schedules the responses of the animal can be modeled as a random process characterized by a gamma distribution, as a first approximation.

     

Factor analysis for clustered observations

Classical factor analysis assumes a random sample of vectors of observations. For clustered vectors of observations, such as data for students from colleges, or individuals within households, it may be necessary to consider different within-group and between-group factor structures. Such a two-level model for factor analysis is defined, and formulas for a scoring algorithm for estimation with this model are derived. A simple noniterative method based on a decomposition of the total sums of squares and crossproducts is discussed. This method provides a suitable starting solution for the iterative algorithm, but it is also a very good approximation to the maximum likelihood solution. Extensions for higher levels of nesting are indicated. With judicious application of quasi-Newton methods, the amount of computation involved in the scoring algorithm is moderate even for complex problems; in particular, no inversion of matrices with large dimensions is involved. The methods are illustrated on two examples.Suggestions and corrections of three anonymous referees and of an Associate Editor are acknowledged. Discussions with Bob Jennrich on computational aspects were very helpful. Most of research leading to this paper was carried out while the first author was a visiting associate professor at the University of California, Los Angeles.