Astrocytic glutamate transport regulates a Drosophila CNS synapse that lacks astrocyte ensheathment.

Anatomical, molecular, and physiological interactions between astrocytes and neuronal synapses regulate information processing in the brain. The fruit fly Drosophila melanogaster has become a valuable experimental system for genetic manipulation of the nervous system and has enormous potential for elucidating mechanisms that mediate neuron-glia interactions. Here, we show the first electrophysiological recordings from Drosophila astrocytes and characterize their spatial and physiological relationship with particular synapses. Astrocyte intrinsic properties were found to be strongly analogous to those of vertebrate astrocytes, including a passive current-voltage relationship, low membrane resistance, high capacitance, and dye-coupling to local astrocytes. Responses to optogenetic stimulation of glutamatergic premotor neurons were correlated directly with anatomy using serial electron microscopy reconstructions of homologous identified neurons and surrounding astrocytic processes. Robust bidirectional communication was present: neuronal activation triggered astrocytic glutamate transport via excitatory amino acid transporter 1 (Eaat1), and blocking Eaat1 extended glutamatergic interneuron-evoked inhibitory postsynaptic currents in motor neurons. The neuronal synapses were always located within 1 μm of an astrocytic process, but none were ensheathed by those processes. Thus, fly astrocytes can modulate fast synaptic transmission via neurotransmitter transport within these anatomical parameters. J. Comp. Neurol. 524:1979-1998, 2016. © 2016 Wiley Periodicals, Inc.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/cne.2401

Agent Based Modeling in Computer Graphics and Games

As graphics technology has improved in recent years, more and more importance has been placed on the behavior of virtual characters in applications set in virtual worlds in areas such as games, movies and simulations. The behavior of virtual characters should be believable in order to create the illusion that these virtual worlds are populated with living characters. This has led to the application of agent-based modeling to the control of these virtual characters. There are a number of advantages of using agent-based modeling techniques which include the fact that they remove the requirement for hand controlling all agents in a virtual environment, and allow agents in games to respond to unexpected actions by players

Master of Science

thesisWe sought to determine whether Working Memory Capacity (WMC) predicts when an individual will exert or withhold cognitive control when faced with a control dilemma. We employed a high-congruency variation on the Stroop task to maximize conflict between automatic and controlled processing, and manipulated task instructions between participants to emphasize the importance of exerting cognitive control or convey typical speed/accuracy instructions. A 2 (trial type) x 2 (instructions) x 4 (WMC [quartiles]) analysis of variance (ANOVA) revealed an interaction, in which instruction manipulations failed to affect the proportion of errors made by low- or mid-span individuals. High spans, however, made a lower proportion of errors when warned of task pitfalls than when not. Regression analyses suggested that, when warned of the pitfalls of relying on automatic processing, WMC and proportion of Stroop errors exhibit a negative, linear relationship. However, when the nature of the need to exert control was not explicit, a curvilinear pattern was observed. Those with high WMC appeared to strategically withhold control, relying instead on automatic processing. This led them to make a higher proportion of Stroop errors. Response latency data suggested that lower-mid-spans were most rigid in their exertion of control, while high spans were especially flexible across instruction conditions. These data suggest a higher WMC allows for increased cognitive efficiency of cognitive control exertion across varied contexts. These results could be the product of an increase in cognitive resources allowing for better metacognitive abilities in those with high WMC

Children as Victims, Children as Clients Towards a Framework of Best Practice in Services for Children who Experience Domestic Violence

Historically, the core focus of domestic violence services in Ireland and elsewhere has been to support and empower women. Children’s needs have been seen as secondary to their mothers’, although services have generally provided opportunities for play and recreation. An increasing recognition of the direct effects of family violence on children and a growing trend in child and family provision towards monitoring progress and identifying outcomes has created a necessity for services working with children who experience domestic violence to examine and appraise the nature and scope of their work. Within this context, this study describes existing provision for children by domestic violence services, most of whom are operating as refuges and some of whom are also engaging in outreach work in the community. A survey questionnaire was employed to capture data on the key aspects of this provision and findings are discussed in terms of what emerges from the literature as recommended models and approaches. To promote cohesion in work with children across the domestic violence sector and to support the development of good practice, this study ultimately offers a recommended framework comprising the key elements of assessment, intervention and evaluation, which underpin quality provision for children who experience domestic violence

SoC Test: Trends and Recent Standards

The well-known approaching test cost crisis, where semiconductor test costs begin to approach or exceed manufacturing costs has led test engineers to apply new solutions to the problem of testing System-On-Chip (SoC) designs containing multiple IP (Intellectual Property) cores. While it is not yet possible to apply generic test architectures to an IP core within a SoC, the emergence of a number of similar approaches, and the release of new industry standards, such as IEEE 1500 and IEEE 1450.6, may begin to change this situation. This paper looks at these standards and at some techniques currently used by SoC test engineers. An extensive reference list is included, reflecting the purpose of this publication as a review paper

Profiling Instances in Noise Reduction

The dependency on the quality of the training data has led to significant work in noise reduction for instance-based learning algorithms. This paper presents an empirical evaluation of current noise reduction techniques, not just from the perspective of their comparative performance, but from the perspective of investigating the types of instances that they focus on for re- moval. A novel instance profiling technique known as RDCL profiling allows the structure of a training set to be analysed at the instance level cate- gorising each instance based on modelling their local competence properties. This profiling approach o↵ers the opportunity of investigating the types of instances removed by the noise reduction techniques that are currently in use in instance-based learning. The paper also considers the e↵ect of removing instances with specific profiles from a dataset and shows that a very simple approach of removing instances that are misclassified by the training set and cause other instances in the dataset to be misclassified is an e↵ective noise reduction technique

Sampling with Confidence: Using k-NN Confidence Measures in Active Learning

Active learning is a process through which classifiers can be built from collections of unlabelled examples through the cooperation of a human oracle who can label a small number of examples selected as most informative. Typically the most informative examples are selected through uncertainty sampling based on classification scores. However, previous work has shown that, contrary to expectations, there is not a direct relationship between classification scores and classification confidence. Fortunately, there exists a collection of particularly effective techniques for building measures of classification confidence from the similarity information generated by k-NN classifiers. This paper investigates using these confidence measures in a new active learning sampling selection strategy, and shows how the performance of this strategy is better than one based on uncertainty sampling using classification scores