The hidation of the Hwicce: investigating its halving between the eighth century and 1086

The total hidage of land in the Anglo-Saxon kingdom of the Hwicce at its greatest extent appears to have been halved at an unknown date between the eighth century and the late eleventh. The article examines the relatively small number of surviving original texts of charters and leases which relate to land both in that kingdom and in all other parts of the kingdom of the Mercians into which it was at length subsumed. With other apparent instances of major hidage reductions having been found thereby elsewhere in the latter area, the article then argues that they were all effected either by the West Saxon kings of England in the course of the tenth century or, arguably more likely (even though the evidence is meagre), at a much earlier date by Mercian kings following the piecemeal enlargement of their kingdom by the absorption of formerly independent neighbouring polities

Dust-Off the ECMO, My Patient Just Inhaled 1,1-difluoroethane

Dust-Off® computer cleaner containing 1,1-difluoroethane (DFE) is frequently cited in cases of inhalation abuse. Malignant cardiac dysrhythmias are a well-recognized complication of DFE toxicity. We describe a patient with prolonged ventricular irritability following DFE inhalation, who was successfully treated with a central α-2 agonist and extracorporeal membrane oxygenation (ECMO)

Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from noninvasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior

Individual Differences in Learning Social and Non-Social Network Structures

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between non-social bits of information? Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. We examined individual differences in the ability to learn community structure of networks composed of social versus non-social stimuli. Although participants were able to learn community structure of both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of non-social community structure. Taken together, our results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes