Evaluation of the Electromagnetic Environment Around Underground HVDC Lines

This paper analyses the magnetic-field emissions of a high-voltage dc transmission line constituted by two couples of underground cables laid along a highway. The transmission system, including all its components (transformers, converters filters, and line), is modeled through a circuital approach, which provides the distribution of the current harmonics along the line length. The magnetic field produced in the environment is then estimated by a hybrid finite element/boundary element method. The electromagnetic interferences with existing appliances and the human exposure to magnetic fields are investigated considering different laying configurations, conductor dispositions, and supply conditions. Compliance with regulations limiting human exposure and technical standards ensuring electromagnetic compatibility of appliances and devices are assessed

Espressione e ruolo funzionale di interleuchina-33 e del suo recettore, ST2, nelle malattie infiammatorie croniche intestinali

IL-33 is a novel member of the IL-1 family and ligand for the IL-1 receptor-related protein, ST2. Recent evidence suggests that the IL-33/ST2 axis plays a critical role in several autoimmune and inflammatory disorders; however, its role in inflammatory bowel disease (IBD) has not been clearly defined. We characterized IL-33 and ST2 expression and modulation following conventional anti-TNF therapy in Crohn’s disease and ulcerative colitis (UC) patients, and investigated the role of IL-33 in SAMP1/YitFc (SAMP) mice, a mixed Th1/Th2 model of IBD. Our results showed a specific increase of mucosal IL-33 in active UC, localized primarily to intestinal epithelial cells (IEC) and colonic inflammatory infiltrates. Importantly, increased expression of full-length IL-33, representing the most bioactive form, was detected in UC epithelium, while elevated levels of cleaved IL-33 were present in IBD serum. ST2 isoforms were differentially modulated in UC epithelium and sST2, a soluble decoy receptor with anti-inflammatory properties, was also elevated in IBD serum. Infliximab (anti-TNF) treatment of UC decreased circulating IL-33 and increased sST2, while stimulation of HT-29 IEC confirmed IL-33 and sST2 regulation by TNF. Similarly, IL-33 significantly increased and correlated with disease severity, and potently induced IL-5, IL-6 and IL-17 from mucosal immune cells in SAMP mice. Taken together, the IL-33/ST2 system plays an important role in IBD and experimental colitis, is modulated by anti-TNF therapy, and may represent a specific biomarker for active UC

Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 108 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity

Pro/Anti-Inflammatory Cytokine Imbalance in Postischemic Left Ventricular Remodeling

Objectives. Cytokines play an important role in left ventricular remodeling consequent to myocardial ischemia. The aim of this study was to correlate cytokine production and lymphocyte apoptosis to post-ischemic left ventricular remodeling in patients affected by acute myocardial infarction (AMI) undergoing primary cutaneous angioplasty (PCI). Methods. In 40 patients, affected by AMI and undergoing PCI, we evaluated peripheral blood mononuclear cells (PBMCs), tumor necrosis factor-alpha (TNF-α) and interleukin 10 (IL10) production and apoptosis on day 1, day 3, day 7, 1 month and 6 months after PCI. Patients were divided into two subgroups of remodeling or not remodeling by echocardiographic criteria. Results. In the subgroup of remodeling patients, at each timepoint TNF-α production was increased significantly in comparison with the subgroup of not remodeling patients. IL10 production was statistically lower in remodeling subjects than in not remodeling ones 1 and 6 months after reperfusion. There were no differences between the two groups as regards lymphomonocyte apoptosis. Conclusions. We found an increased production of pro-inflammatory cytokine TNF-α and a corresponding decrease of anti-inflammatory/regulatory cytokine IL10 in remodeling patients and we concluded that this cytokine imbalance resulted in pro-inflammatory effects which might contribute to the progression of left ventricular remodeling

The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 10^15 synapses and 10^11 neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net - composed by a relatively low number of neurons, each one projecting thousands of synapses - is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms

Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories

Gaussian and exponential lateral connectivity on distributed spiking neural network simulation

We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays. While previous studies adopted short-range connectivity, recent experimental neurosciences studies are pointing out the role of longer-range intra-areal connectivity with implications on neural simulation platforms. Two-dimensional grids of cortical columns composed by up to 11 M point-like spiking neurons with spike frequency adaption were connected by up to 30 G synapses using short- and long-range connectivity models. The MPI processes composing the distributed simulator were run on up to 1024 hardware cores, hosted on a 64 nodes server platform. The hardware platform was a cluster of IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell 8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through an InfiniBand network, equipped with 4x QDR switches.Comment: 9 pages, 9 figures, added reference to final peer reviewed version on conference paper and DO

Real-time cortical simulations: energy and interconnect scaling on distributed systems

We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the speed and energy consumption of processor architectures typical of standard HPC and embedded platforms are compared. We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption. The cost of cortical simulations is quantified using the Joule per synaptic event metric on both architectures. Reaching efficient real-time on large scale cortical simulations is of increasing relevance for both future bio-inspired artificial intelligence applications and for understanding the cognitive functions of the brain, a scientific quest that will require to embed large scale simulations into highly complex virtual or real worlds. This work stands at the crossroads between the WaveScalES experiment in the Human Brain Project (HBP), which includes the objective of large scale thalamo-cortical simulations of brain states and their transitions, and the ExaNeSt and EuroExa projects, that investigate the design of an ARM-based, low-power High Performance Computing (HPC) architecture with a dedicated interconnect scalable to million of cores; simulation of deep sleep Slow Wave Activity (SWA) and Asynchronous aWake (AW) regimes expressed by thalamo-cortical models are among their benchmarks.Comment: 8 pages, 8 figures, 4 tables, submitted after final publication on PDP2019 proceedings, corrected final DOI. arXiv admin note: text overlap with arXiv:1812.04974, arXiv:1804.0344