The Role of Subclass Switching in the Pathogenesis of Endemic Pemphigus Foliaceus

Endemic pemphigus foliaceus, like the sporadic form seen in the developed world, is mediated by IgG antibodies to desmoglein-1. We studied an endemic focus in Limao Verde, Brazil, where disease prevalence is 3.4%. We previously detected IgG antibodies to desmoglein-1 in 97% of patients, but also in 55% of normal subjects in the endemic focus, with progressively lower levels in normal subjects in surrounding areas. An environmental trigger is hypothesized to explain these and other findings. In this study we sought to determine if patients and enzyme-linked-immunosorbent-assay-positive normal subjects in Limao Verde differ in IgG subclass response to desmoglein-1. We developed a sensitive and specific subclass enzyme-linked immunosorbent assay using recombinant desmoglein-1 and standardized the assay to enable comparability between the four subclasses. We found that normal subjects have an IgG1 and IgG4 response, whereas patients have similar levels of IgG1 but a mean 19.3-fold higher IgG4 response. Patients in remission have a weak IgG4 response, and a 74.3-fold higher IgG4 response is associated with active disease. Finally, in five patients in whom we had blood samples from both before and after the onset of clinical disease, a mean 103.08-fold rise in IgG4 was associated with onset of clinical disease, but only a mean 3.45-fold rise in IgG1. These results suggest that the early antibody response in normal subjects living in the endemic area and in patients before the onset of clinical disease is mainly IgG1. Acquisition of an IgG4 response is a key step in the development of clinical disease

Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

© 2022, The Author(s), under exclusive licence to Springer Nature Limited.Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.N

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures

Runaway electron beam control

Post-disruption runaway electron (RE) beams in tokamaks with large current can cause deep melting of the vessel and are one of the major concerns for ITER operations. Consequently, a considerable effort is provided by the scientific community in order to test RE mitigation strategies. We present an overview of the results obtained at FTU and TCV controlling the current and position of RE beams to improve safety and repeatability of mitigation studies such as massive gas (MGI) and shattered pellet injections (SPI). We show that the proposed RE beam controller (REB-C) implemented at FTU and TCV is effective and that current reduction of the beam can be performed via the central solenoid reducing the energy of REs, providing an alternative/parallel mitigation strategy to MGI/SPI. Experimental results show that, meanwhile deuterium pellets injected on a fully formed RE beam are ablated but do not improve RE energy dissipation rate, heavy metals injected by a laser blow off system on low-density flat-top discharges with a high level of RE seeding seem to induce disruptions expelling REs. Instabilities during the RE beam plateau phase have shown to enhance losses of REs, expelled from the beam core. Then, with the aim of triggering instabilities to increase RE losses, an oscillating loop voltage has been tested on RE beam plateau phase at TCV revealing, for the first time, what seems to be a full conversion from runaway to ohmic current. We finally report progresses in the design of control strategies at JET in view of the incoming SPI mitigation experiments

Comparison of runaway electron generation parameters in small, medium-sized and large tokamaks - A survey of experiments in COMPASS, TCV, ASDEX-Upgrade and JET

This paper presents a survey of the experiments on runaway electrons (RE) carried out recently in frames of EUROFusion Consortium in different tokamaks: COMPASS, ASDEX-Upgrade, TCV and JET. Massive gas injection (MGI) has been used in different scenarios for RE generation in small and medium-sized tokamaks to elaborate the most efficient and reliable ones for future RE experiments. New data on RE generated at disruptions in COMPASS and ASDEX-Upgrade was collected and added to the JET database. Different accessible parameters of disruptions, such as current quench rate, conversion rate of plasma current into runaways, etc have been analysed for each tokamak and compared to JET data. It was shown, that tokamaks with larger geometrical sizes provide the wider limits for spatial and temporal variation of plasma parameters during disruptions, thus extending the parameter space for RE generation. The second part of experiments was dedicated to study of RE generation in stationary discharges in COMPASS, TCV and JET. Injection of Ne/Ar have been used to mock-up the JET MGI runaway suppression experiments. Secondary RE avalanching was identified and quantified for the first time in the TCV tokamak in RE generating discharges after massive Ne injection. Simulations of the primary RE generation and secondary avalanching dynamics in stationary discharges has demonstrated that RE current fraction created via avalanching could achieve up to 70-75% of the total plasma current in TCV. Relaxations which are reminiscent the phenomena associated to the kinetic instability driven by RE have been detected in RE discharges in TCV. Macroscopic parameters of RE dominating discharges in TCV before and after onset of the instability fit well to the empirical instability criterion, which was established in the early tokamaks and examined by results of recent numerical simulations

Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of Te, Tiand nehave been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks