Emerging evidence suggests that metformin, a widely used anti-diabetic drug, may be useful in the prevention and treatment of different cancers. In the present study, we demonstrate that metformin ...

The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Solar flares originate from magnetically active regions (ARs) but not all solar ARs give rise to a flare. Therefore, the challenge of solar flare prediction benefits from an intelligent computational analysis of physics-based properties extracted from AR observables, most commonly line-of-sight or vector magnetograms of the active region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive AR properties, mainly inferred by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine-learning methods that allow feature ranking as a function of predictive capability, we show that (i) an objective training and testing process is paramount for the performance of every supervised machine-learning method; (ii) most properties include overlapping information and are therefore highly redundant for flare prediction; (iii) solar flare prediction is still—and will likely remain—a predominantly probabilistic challenge.

Purpose: Cancer immunotherapy depends on a systemic immune response, but the basic underlying mechanisms are still largely unknown. Despite the very successful and widespread use of checkpoint inhibitors in the clinic, the majority of cancer patients do not benefit from this type of treatment. In this translational study, we investigated whether noninvasive in vivo positron emission tomography (PET) imaging using 2-[¹⁸F]fluoro-2-deoxy-D-glucose (¹⁸F-FDG) is capable of detecting immunotherapy-associated metabolic changes in the primary and secondary lymphoid organs and whether this detection enables the prediction of a successful anti-cancer immune response.