Connecting inside out: Development of the social brain in infants and toddlers with a focus on myelination as a marker of brain maturation

Early childhood is a sensitive period for learning and social skill development. The maturation of cerebral regions underlying social processing lays the foundation for later social-emotional competence. This study explored myelin changes in social brain regions and their association with changes in parent-rated social-emotional development in a cohort of 129 children (64 females, 0–36 months, 77 White). Results reveal a steep increase in myelination throughout the social brain in the first 3 years of life that is significantly associated with social-emotional development scores. These findings add knowledge to the emerging picture of social brain development by describing neural underpinnings of human social behavior. They can contribute to identifying age-/stage-appropriate early life factors in this developmental domain.     

Non-finite loss and challenges to communication between parents and professionals

Elizabeth Bruce PhD, a psychologist in private practice, and Cynthia Schultz PhD, honorary associate at LaTrobe University, both live and work in Australia. Their work on supporting parents who have children with special needs, however, has been published around the world. In this article Bruce and Schultz explore the notion of 'non-finite loss', defined as the ongoing sense of grief experienced by parents caring for children with severe disabilities. The authors discuss the issues that professionals need to consider when working with parents in these circumstances. The article closes with a set of recommendations for promoting more effective partnerships between parents and professionals.     

Publishing education

This article is an overview of the development of education for publishing programs in the U.S. Information and addresses for the main introductory summer programs, the continuing education programs, the degree-granting Masters Programs, and a selection of organizations outside the United States offering courses in publishing are included in the article.     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.