A preferential loss of GABAergic, symmetric synapses in epileptic foci: a quantitative ultrastructural analysis of monkey neocortex.

Previous immunocytochemical results from five monkeys with cortical focal epilepsy produced by alumina gel showed a severe decrease at seizure foci of axon terminals that contained glutamic acid decarboxylase (GAD), the synthesizing enzyme for the inhibitory neurotransmitter, GABA. These data indicated a functional loss of GABAergic terminals but did not show: whether this loss was caused by GABAergic nerve terminal degeneration or by a lack of GAD immunoreactivity within these terminals and if this loss of GABAergic terminals was selective for only this terminal type. To resolve these issues, cortical tissue from three of the five monkeys used in the previous study was reexamined using electron microscopy, and a quantitative morphological analysis of cortical structures was made to compare profiles of terminals and glia in the nonepileptic cortex with those in the focus and parafocus. The following statistically significant changes were observed: the number of axosomatic symmetric synapses with layer V pyramidal cells was decreased 80% at the focus and 50% at the parafocus; in the neuropil adjacent to these pyramidal somata, the number of terminals forming symmetric synapses was reduced 50% at the epileptic focus but was unchanged at the parafocus, while the number of asymmetric synapses was reduced 25% at the focus and 15% at the parafocus; and a 50% increase of glial profiles occurred at epileptic foci both in the neuropil and at sites apposed to pyramidal cell somata. The quantitative results also showed that terminals which form symmetric synapses had twice the number of mitochondria per terminal as those that form asymmetric synapses. Axon terminals which form symmetric synapses with somata and dendrites in the neocortex have been shown previously to contain GAD. Therefore, the large reduction in the number of symmetric synapses at epileptic foci and the increased gliosis indicate that the previously observed loss of GABAergic terminals at sites of focal epilepsy is caused by terminal degeneration. Since such terminals are reduced more severely at epileptic foci than other terminals, their loss could be the basis for seizure activity due to a preferential decrease of inhibitory function at epileptic foci. Hypoxia has been shown to cause a selective degeneration of terminals with the same morphology as GABAergic terminals in the cortex, and the basis for this loss could be related to higher physiological and/or metabolic activities of GABAergic cortical cells which may inhibit other cells tonically. The fact that increased numbers of mitochondria occur in GABAergic terminals supports this idea

Inhibitory, GABAergic nerve terminals decrease at sites of focal epilepsy.

Using an immunocytochemical method for the localization of the gamma-aminobutyric acid (GABA) synthesizing enzyme, glutamic acid decarboxylase (GAD), we have observed GABAergic nerve terminals distributed throughout all layers of normal monkey sensorimotor cortex. These terminals displayed ultrastructural characteristics that suggested that they arose from aspinous and sparsely spinous stellate neurons. In monkeys (Macaca mulatta and M. fascicularis) made epileptic by cortical application of alumina gel, a highly significant numerical decrease of GAD-positive nerve terminals occurred at sites of seizure foci indicating a functional loss of GABAergic inhibitory synapses. A loss of such inhibition at seizure foci could lead to epileptic activity of cortical pyramidal neurons

Autonomous Detection of the Loss of a Wing for Underwater Gliders

Over the past five years, two of the Slocum underwater gliders operated by the UK National Oceanography Centre have lost a wing mid-mission without the pilot being aware of the problem until the point of vehicle retrieval. In this study, the steady-state data collected by gliders during the two deployments has been analysed to develop a fault detection system. From the data analysis, it is clear that the loss of the wing was a sudden event for both gliders. The main changes to the system dynamics associated with the event are an increase in the positive buoyancy of the glider and the occurrence of a roll angle on the side of the lost wing, with significant difference between dives and climbs. Hence, a simple effective system for the detection of the wing loss has been designed using the roll angle. Since sensors are known to fail and the roll sensor is non-critical to the operation of the glider, a back-up diagnostics system has been developed based on the dynamic model of the vehicle, capturing the change in buoyancy. Both systems are able to correctly detect the loss of the wing and notify pilots, who can re-plan missions to safely recover the vehicle

Spike sorting for large, dense electrode arrays

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%

Predicting Visibility of Aircraft

Visual detection of aircraft by human observers is an important element of aviation safety. To assess and ensure safety, it would be useful to be able to be able to predict the visibility, to a human observer, of an aircraft of specified size, shape, distance, and coloration. Examples include assuring safe separation among aircraft and between aircraft and unmanned vehicles, design of airport control towers, and efforts to enhance or suppress the visibility of military and rescue vehicles. We have recently developed a simple metric of pattern visibility, the Spatial Standard Observer (SSO). In this report we examine whether the SSO can predict visibility of simulated aircraft images. We constructed a set of aircraft images from three-dimensional computer graphic models, and measured the luminance contrast threshold for each image from three human observers. The data were well predicted by the SSO. Finally, we show how to use the SSO to predict visibility range for aircraft of arbitrary size, shape, distance, and coloration

A remote anomaly detection system for Slocum underwater gliders

Marine Autonomous Systems (MAS) operating at sea beyond visual line of sight need to be self-reliant, as any malfunction could lead to loss or pose a risk to other sea users. In the absence of fully automated on-board control and fault detection tools, MAS are piloted and monitored by experts, resulting in high operational costs and limiting the scale of observational fleets that can be deployed simultaneously. Hence, an effective anomaly detection system is fundamental to increase fleet capacity and reliability. In this study, an on-line, remote fault detection system is developed for underwater gliders. Two alternative methods are analysed using time series data: feedforward deep neural networks estimating the glider’s vertical velocity and an autoencoder. The systems are trained using field data from four baseline deployments of Slocum gliders and tested on six deployments of vehicles suffering from adverse behaviour. The methods are able to successfully detect a range of anomalies in the near real time data streams, whilst being able to generalise to different glider configurations. The autoencoder’s error in reconstructing the original signals is the clearest indicator of anomalies. Thus, the autoencoder is a prime candidate to be included into an all-encompassing condition monitoring system for MAS

The regional economic impact of more graduates in the labour market: a “micro-to-macro” analysis for Scotland

This paper explores the system-wide impact of graduates on the regional economy. Graduates enjoy a significant wage premium, often interpreted as reflecting their greater productivity relative to non-graduates. If this is so there is a clear and direct supply-side impact of HEI activities on regional economies. We use an HEI-disaggregated computable general equilibrium model of Scotland to estimate the impact of the growing proportion of graduates in the Scottish labour force that is implied by the current participation rate and demographic change, taking the graduate wage premium in Scotland as an indicator of productivity enhancement. While the detailed results vary with alternative assumptions about the extent to which wage premia reflect productivity, they do suggest that the long-term supply-side impacts of HEIs provide a significant boost to regional GDP. Furthermore, the results suggest that the supply-side impacts of HEIs are likely to be more important than the expenditure impacts that are the focus of most HEI impact studies

Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5.

Current air quality standards for particulate matter (PM) use the PM mass concentration [PM with aerodynamic diameters ≤ 10 μm (PM(10)) or ≤ 2.5 μm (PM(2.5))] as a metric. It has been suggested that particles from combustion sources are more relevant to human health than are particles from other sources, but the impact of policies directed at reducing PM from combustion processes is usually relatively small when effects are estimated for a reduction in the total mass concentration

Unsupervised anomaly detection for underwater gliders using generative adversarial networks

An effective anomaly detection system is critical for marine autonomous systems operating in complex and dynamic marine environments to reduce operational costs and achieve concurrent large-scale fleet deployments. However, developing an automated fault detection system remains challenging for several reasons including limited data transmission via satellite services. Currently, most anomaly detection for marine autonomous systems, such as underwater gliders, rely on intensive analysis by pilots. This study proposes an unsupervised anomaly detection system using bidirectional generative adversarial networks guided by assistive hints for marine autonomous systems with time series data collected by multiple sensors. In this study, the anomaly detection system for a fleet of underwater gliders is trained on two healthy deployment datasets and tested on other nine deployment datasets collected by a selection of vehicles operating in a range of locations and environmental conditions. The system is successfully applied to detect anomalies in the nine test deployments, which include several different types of anomalies as well as healthy behaviour. Also, a sensitivity study of the data decimation settings suggests the proposed system is robust for Near Real-Time anomaly detection for underwater gliders