Liver transplantation for type I and type IV glycogen storage disease

Progressive liver failure or hepatic complications of the primary disease led to orthotopic liver transplantation in eight children with glycogen storage disease over a 9-year period. One patient had glycogen storage disease (GSD) type I (von Gierke disease) and seven patients had type IV GSD (Andersen disease). As previously reported [19], a 16.5-year-old-girl with GSD type I was successfully treated in 1982 by orthotopic liver transplantation under cyclosporine and steroid immunosuppression. The metabolic consequences of the disease have been eliminated, the renal function and size have remained normal, and the patient has lived a normal young adult life. A late portal venous thrombosis was treated successfully with a distal splenorenal shunt. Orthotopic liver transplantation was performed in seven children with type N GSD who had progressive hepatic failure. Two patients died early from technical complications. The other five have no evidence of recurrent hepatic amylopectinosis after 1.1–5.8 postoperative years. They have had good physical and intellectual maturation. Amylopectin was found in many extrahepatic tissues prior to surgery, but cardiopathy and skeletal myopathy have not developed after transplantation. Postoperative heart biopsies from patients showed either minimal amylopectin deposits as long as 4.5 years following transplantation or a dramatic reduction in sequential biopsies from one patient who initially had dense myocardial deposits. Serious hepatic derangement is seen most commonly in types T and IV GSD. Liver transplantation cures the hepatic manifestations of both types. The extrahepatic deposition of abnormal glycogen appears not to be problematic in type I disease, and while potentially more threatening in type IV disease, may actually exhibit signs of regression after hepatic allografting

Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published

SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models.

There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a variety of simulation engines and hardware platforms. Layered XML model specification formats solve many of these problems, however they are difficult to write and visualise without tools. Here we describe a new graphical software tool, SpineCreator, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming. We demonstrate the tool through the reproduction and visualisation of published models and show simulation results using code generation interfaced directly into SpineCreator. As a unique application for the graphical creation of neural networks, SpineCreator represents an important step forward for neuronal modelling

Python as a Federation Tool for GENESIS 3.0

The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture. This architecture allows separate scripting bindings to be defined for different necessary components of the simulator, e.g., the mathematical solvers and graphical user interface. Python is a scripting language that provides rich sets of freely available open source libraries. With clean dynamic object-oriented designs, they produce highly readable code and are widely employed in specialized areas of software component integration. We employ a simplified wrapper and interface generator to examine an application programming interface and make it available to a given scripting language. This allows independent software components to be ‘glued’ together and connected to external libraries and applications from user-defined Python or Perl scripts. We illustrate our approach with three examples of Python scripting. (1) Generate and run a simple single-compartment model neuron connected to a stand-alone mathematical solver. (2) Interface a mathematical solver with GENESIS 3.0 to explore a neuron morphology from either an interactive command-line or graphical user interface. (3) Apply scripting bindings to connect the GENESIS 3.0 simulator to external graphical libraries and an open source three dimensional content creation suite that supports visualization of models based on electron microscopy and their conversion to computational models. Employed in this way, the stand-alone software components of the GENESIS 3.0 simulator provide a framework for progressive federated software development in computational neuroscience

How caldera collapse shapes the shallow emplacement and transfer of magma in active volcanoes

Calderas are topographic depressions formed by the collapse of a partly drained magma reservoir. At volcanic edifices with calderas, eruptive fissures can circumscribe the outer caldera rim, be oriented radially and/or align with the regional tectonic stress field. Constraining the mechanisms that govern this spatial arrangement is fundamental to understand the dynamics of shallow magma storage and transport and evaluate volcanic hazard. Here we show with numerical models that the previously unappreciated unloading effect of caldera formation may contribute significantly to the stress budget of a volcano. We first test this hypothesis against the ideal case of Fernandina, Galápagos, where previous models only partly explained the peculiar pattern of circumferential and radial eruptive fissures and the geometry of the intrusions determined by inverting the deformation data. We show that by taking into account the decompression due to the caldera formation, the modeled edifice stress field is consistent with all the observations. We then develop a general model for the stress state at volcanic edifices with calderas based on the competition of caldera decompression, magma buoyancy forces and tectonic stresses. These factors control: 1) the shallow accumulation of magma in stacked sills, consistently with observations; 2) the conditions for the development of circumferential and/or radial eruptive fissures, as observed on active volcanoes. This top-down control exerted by changes in the distribution of mass at the surface allows better understanding of how shallow magma is transferred at active calderas, contributing to forecasting the location and type of opening fissures

A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiking neurons on a learning task in continuous space inspired by the Morris Water maze. The synaptic update rule modifies the release probability of synaptic transmission and depends on the timing of presynaptic spike arrival, postsynaptic action potentials, as well as the membrane potential of the postsynaptic neuron. The family of learning rules includes an optimal rule derived from policy gradient methods as well as reward modulated Hebbian learning. The synaptic update rule is implemented in a population of spiking neurons using a network architecture that combines feedforward input with lateral connections. Actions are represented by a population of hypothetical action cells with strong mexican-hat connectivity and are read out at theta frequency. We show that in this architecture, a standard policy gradient rule fails to solve the Morris watermaze task, whereas a variant with a Hebbian bias can learn the task within 20 trials, consistent with experiments. This result does not depend on implementation details such as the size of the neuronal populations. Our theoretical approach shows how learning new behaviors can be linked to reward-modulated plasticity at the level of single synapses and makes predictions about the voltage and spike-timing dependence of synaptic plasticity and the influence of neuromodulators such as dopamine. It is an important step towards connecting formal theories of reinforcement learning with neuronal and synaptic properties

From Model Specification to Simulation of Biologically Constrained Networks of Spiking Neurons.

A declarative extensible markup language (SpineML) for describing the dynamics, network and experiments of large-scale spiking neural network simulations is described which builds upon the NineML standard. It utilises a level of abstraction which targets point neuron representation but addresses the limitations of existing tools by allowing arbitrary dynamics to be expressed. The use of XML promotes model sharing, is human readable and allows collaborative working. The syntax uses a high-level self explanatory format which allows straight forward code generation or translation of a model description to a native simulator format. This paper demonstrates the use of code generation in order to translate, simulate and reproduce the results of a benchmark model across a range of simulators. The flexibility of the SpineML syntax is highlighted by reproducing a pre-existing, biologically constrained model of a neural microcircuit (the striatum). The SpineML code is open source and is available at http://bimpa.group.shef.ac.uk/SpineML

Shelter Projects 2015-2016

Spanning humanitarian responses from all over the world, Shelter Projects 2015-2016 is the sixth in a series of compilations of shelter case studies, overviews of emergencies and opinion pieces. The projects represent responses to conflict, natural disasters and complex or multiple crises, demonstrating some of the implementation and response options available. The book is intended to support learning by highlighting the strengths, weaknesses and some of the lessons that can be learned from different projects, which try to maximize emergency funds to safeguard the health, security and dignity of affected people, whilst – wherever possible – supporting longer-term shelter needs and sustainable recovery. The target audience is humanitarian managers and shelter programme staff from local, national and international organizations at all levels of experience. Shelter Projects is also a useful resource for advocacy purposes, showcasing the work done by the sector, as well as for research and capacity-building activities