Factors associated with loneliness among alcoholics in rehabilitation centers.

Loneliness questionnaires were distributed to 92 male and 60 female American subjects undergoing treatment in selected alcohol rehabilitation centers. Factors associated with feelings of loneliness for these individuals were investigated. Significant differences were found between the loneliness scores of men and women, between individuals who had different familial histories of alcoholism, and between subjects who indicated various degrees of happiness during the previous year. A significant negative relationship was also found between loneliness and the following variables: self-esteem, self-rated marital satisfaction, self-rated job satisfaction, and number of years alcohol was consumed.     

Ethical considerations in the testing of biopharmaceuticals for adventitious agents

Safety testing of biological pharmaceuticals is often carried out by contract testing laboratories which perform these tests on behalf of the drug’s developer. These laboratories are confronted with a number of ethical issues related to selling their services, maintaining confidentiality, and the handling of results. This paper outlines these issues, and, by way of illustration, discusses how one such laboratory addresses them.     

Fictionalism and Incompleteness

The modal fictionalist faces a problem due to the fact that her chosen story seems to be incomplete—certain things are neither fictionally true nor fictionally false. The significance of this problem is not localized to modal fictionalism, however, since many fictionalists will face it too. By examining how the fictionalist should analyze the notion of truth according to her story, and, in particular, the role that conditionals play for the fictionalist, I develop a novel and elegant solution to the incompleteness problem.     

Functional Multiple-Set Canonical Correlation Analysis

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the method solves a matrix eigen-analysis problem through the adoption of a basis expansion approach to approximating data and weight functions. We apply the proposed method to functional magnetic resonance imaging (fMRI) data to identify networks of neural activity that are commonly activated across subjects while carrying out a working memory task.     

Childhood conduct problems are associated with increased partnership and parenting difficulties in adulthood

This paper uses data from a sample of 337 parents studied at age 30 to examine the linkages between childhood conduct problems assessed at ages 7–9 and later partnership and parenting outcomes. The key findings of this study were: 1) increasing levels of childhood conduct problems were associated with increased risk of partnership difficulties, including relationship ambiguity, inter-partner conflict/violence and lower levels of relationship satisfaction; 2) increasing levels of childhood conduct problems were associated with increased risk of parenting difficulties, including over-reactivity, lax and inconsistent discipline, child physical punishment and lower levels of parental warmth and sensitivity. These findings were consistent across both parent reports and interviewer ratings, and in nearly all cases remained after extensive adjustment for confounding and selection bias. Study findings add to the growing body of evidence documenting the adverse consequences of early conduct problems for later adult interpersonal relationships and parenting behaviors.     

Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also incorporates direct and modulating effects of input variables on specific latent variables and on connections between latent variables, respectively. An alternating least square (ALS) algorithm is developed for parameter estimation. An improved bootstrap method called a modified moving block bootstrap method is used to assess reliability of parameter estimates, which deals with time dependence between consecutive observations effectively. We analyze synthetic and real data to illustrate the feasibility of the proposed method.