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The Interface Region Imaging Spectrograph (IRIS) small explorer spacecraft provides simultaneous spectra and images of the photosphere, chromosphere, transition region, and corona with 0.33 -0.4 arcsec spatial resolution, two-second temporal resolution, and 1 km s −1 velocity resolution over a field-of-view of up to 175 arcsec × 175 arcsec. . IRIS is sensitive to emission from plasma at temperatures between 5000 K and 10 MK and will advance our understanding of the flow of mass and energy through an interface region, formed by the chromosphere and transition region, between the photosphere and corona. This highly structured and dynamic region not only acts as the conduit of all mass and energy feeding into the corona and solar wind, it also requires an order of magnitude more energy to heat than the corona and solar wind combined. The IRIS investigation includes a strong numerical modeling component based on advanced radiative-MHD codes to facilitate interpretation of observations of this complex region. Approximately eight Gbytes of data (after compression) are acquired by B. De Pontieu (B) ·Harvard-Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138, USA

Glioblastoma (GBM) is a devastating and incurable brain tumour, with a median overall survival of fifteen months. Identifying the cell of origin that harbours mutations that drive GBM could provide a fundamental basis for understanding disease progression and developing new treatments. Given that the accumulation of somatic mutations has been implicated in gliomagenesis, studies have suggested that neural stem cells (NSCs), with their self-renewal and proliferative capacities, in the subventricular zone (SVZ) of the adult human brain may be the cells from which GBM originates. However, there is a lack of direct genetic evidence from human patients with GBM. Here we describe direct molecular genetic evidence from patient brain tissue and genome-edited mouse models that show astrocyte-like NSCs in the SVZ to be the cell of origin that contains the driver mutations of human GBM. First, we performed deep sequencing of triple-matched tissues, consisting of (i) normal SVZ tissue away from the tumour mass, (ii) tumour tissue, and (iii) normal cortical tissue (or blood), from 28 patients with isocitrate dehydrogenase (IDH) wild-type GBM or other types of brain tumour. We found that normal SVZ tissue away from the tumour in 56.3% of patients with wild-type IDH GBM contained low-level GBM driver mutations (down to approximately 1% of the mutational burden) that were observed at high levels in their matching tumours. Moreover, by single-cell sequencing and laser microdissection analysis of patient brain tissue and genome editing of a mouse model, we found that astrocyte-like NSCs that carry driver mutations migrate from the SVZ and lead to the development of high-grade malignant gliomas in distant brain regions. Together, our results show that NSCs in human SVZ tissue are the cells of origin that contain the driver mutations of GBM.

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Spaceweather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), K. Florios cflorios@aueb.gr I. Kontogiannis K. Florios et al.taken over a five-year interval (2012 -2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several Machine Learning (ML) and Conventional Statistics techniques to predict flares of peak magnitude >M1 and >C1, within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM) and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02) and Heidke skill score HSS=0.49(0.01) for >M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01) and HSS=0.59(0.01) for >C1 flare prediction with probability threshold 35%.

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