Advancing microbiome research with machine learning : key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Towards a new image processing system at Wendelstein 7-X: From spatial calibration to characterization of thermal events

Wendelstein 7-X (W7-X) is the most advanced fusion experiment in the stellarator line and is aimed at proving that the stellarator concept is suitable for a fusion reactor. One of the most important issues for fusion reactors is the monitoring of plasma facing components when exposed to very high heat loads, through the use of visible and infrared (IR) cameras. In this paper, a new image processing system for the analysis of the strike lines on the inboard limiters from the first W7-X experimental campaign is presented. This system builds a model of the IR cameras through the use of spatial calibration techniques, helping to characterize the strike lines by using the information given by real spatial coordinates of each pixel. The characterization of the strike lines is made in terms of position, size, and shape, after projecting the camera image in a 2D grid which tries to preserve the curvilinear surface distances between points. The description of the strike-line shape is made by means of the Fourier Descriptors

Forward modeling of collective Thomson scattering for Wendelstein 7-X plasmas: Electrostatic approximation

In this paper, we present a method for numerical computation of collective Thomson scattering (CTS). We developed a forward model, eCTS, in the electrostatic approximation and benchmarked it against a full electromagnetic model. Differences between the electrostatic and the electromagnetic models are discussed. The sensitivity of the results to the ion temperature and the plasma composition is demonstrated. We integrated the model into the Bayesian data analysis framework Minerva and used it for the analysis of noisy synthetic data sets produced by a full electromagnetic model. It is shown that eCTS can be used for the inference of the bulk ion temperature. The model has been used to infer the bulk ion temperature from the first CTS measurements on Wendelstein 7-X

Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision

Comparing pedestal structure in JET-ILW H-mode plasmas with a model for stiff ETG turbulent heat transport

A predictive model for the electron temperature profile of the H-mode pedestal is described, and its results are compared with the pedestal structure of JET-ILW plasmas. The model is based on a scaling for the gyro-Bohm normalized, turbulent electron heat flux qe/qe,gB resulting from electron temperature gradient (ETG) turbulence, derived from results of nonlinear gyrokinetic (GK) calculations for the steep gradient region. By using the local temperature gradient scale length L-Te in the normalization, the dependence of q(e)/q(e,g)B on the normalized gradients R/L-Te and R/(Lne) can be represented by a unified scaling with the parameter eta(e) = L-ne/L-Te, to which the linear stability of ETG turbulence is sensitive when the density gradient is sufficiently steep. For a prescribed density profile, the value of R/L-Te determined from this scaling, required to maintain a constant electron heat flux qe across the pedestal, is used to calculate the temperature profile. Reasonable agreement with measurements is found for different cases, the model providing an explanation of the relative widths and shifts of the T-e and n(e) profiles, as well as highlighting the importance of the separatrix boundary conditions. Other cases showing disagreement indicate conditions where other branches of turbulence might dominate.This article is part of a discussion meeting issue "H-mode transition and pedestal studies in fusion plasmas'

Testing a prediction model for the H-mode density pedestal against JET-ILW pedestals

The neutral ionisation model proposed by Groebner et al (2002 Phys. Plasmas 9 2134) to determine the plasma density profile in the H-mode pedestal, is extended to include charge exchange processes in the pedestal stimulated by the ideas of Mahdavi et al (2003 Phys. Plasmas 10 3984). The model is then tested against JET H-mode pedestal data, both in a 'standalone' version using experimental temperature profiles and also by incorporating it in the Europed version of EPED. The model is able to predict the density pedestal over a wide range of conditions with good accuracy. It is also able to predict the experimentally observed isotope effect on the density pedestal that eludes simpler neutral ionization models

The role of ETG modes in JET-ILW pedestals with varying levels of power and fuelling

We present the results of GENE gyrokinetic calculations based on a series of JET-ITER-like-wall (ILW) type I ELMy H-mode discharges operating with similar experimental inputs but at different levels of power and gas fuelling. We show that turbulence due to electron-temperature-gradient (ETGs) modes produces a significant amount of heat flux in four JET-ILW discharges, and, when combined with neoclassical simulations, is able to reproduce the experimental heat flux for the two low gas pulses. The simulations plausibly reproduce the high-gas heat fluxes as well, although power balance analysis is complicated by short ELM cycles. By independently varying the normalised temperature gradients (omega(T)(e)) and normalised density gradients (omega(ne )) around their experimental values, we demonstrate that it is the ratio of these two quantities eta(e) = omega(Te)/omega(ne) that determines the location of the peak in the ETG growth rate and heat flux spectra. The heat flux increases rapidly as eta(e) increases above the experimental point, suggesting that ETGs limit the temperature gradient in these pulses. When quantities are normalised using the minor radius, only increases in omega(Te) produce appreciable increases in the ETG growth rates, as well as the largest increases in turbulent heat flux which follow scalings similar to that of critical balance theory. However, when the heat flux is normalised to the electron gyro-Bohm heat flux using the temperature gradient scale length L-Te, it follows a linear trend in correspondence with previous work by different authors