Clathrin-mediated endocytosis is the major mechanism for eukaryotic plasma membrane-based proteome turn-over. In plants, clathrin-mediated endocytosis is essential for physiology and development, but the identification and organization of the machinery operating this process remains largely obscure. Here, we identified an eight-core-component protein complex, the TPLATE complex, essential for plant growth via its role as major adaptor module for clathrin-mediated endocytosis. This complex consists of evolutionarily unique proteins that associate closely with core endocytic elements. The TPLATE complex is recruited as dynamic foci at the plasma membrane preceding recruitment of adaptor protein complex 2, clathrin, and dynamin-related proteins. Reduced function of different complex components severely impaired internalization of assorted endocytic cargoes, demonstrating its pivotal role in clathrin-mediated endocytosis. Taken together, the TPLATE complex is an early endocytic module representing a unique evolutionary plant adaptation of the canonical eukaryotic pathway for clathrin-mediated endocytosis.
In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.
Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.
Most Public Private Partnership (PPP) infrastructure projects especially in emerging markets carry a high amount of economic foreign exchange (FX) exposure. Risk mitigation instruments (RMI) are frequently implemented by governments to compensate FX loss in the Special Purpose Company (SPC). The experience of many investors is an underestimation of the risk that governments would refuse to readjust the contracts after or during a currency devaluation period. Therefore the value of the RMI depends on the affordability and the willingness of the government to compensate FX loss. Factors influencing country reliability can be identified in the government ability to repay debt obligations, liquidity difficulties and political difficulties. The overall objective of this paper is to analyze RMI instruments and to design a methodology to measure country reliability on RMI. The model has a dynamic framework which requires input data that are based on indicators and proxies. The focus however is on the micro-economic level. The purpose is to evaluate the impact of economic FX exposure on the cash flow of infrastructure projects designed under long term concession contracts. The methodology can be applied to support financial decisions and strategies in funding PPP infrastructure. Furthermore the outcome has an important impact on the evaluation of selected risk mitigating instruments (RMI) and the estimation of the necessary FX protection.
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