A hybrid supervised/unsupervised machine learning approach to solar flare prediction

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data

Expectation Maximization for Hard X-ray Count Modulation Profiles

This paper is concerned with the image reconstruction problem when the measured data are solar hard X-ray modulation profiles obtained from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)} instrument. Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized for the analysis of count modulation profiles in solar hard X-ray imaging based on Rotating Modulation Collimators. The algorithm described in this paper solves the maximum likelihood problem iteratively and encoding a positivity constraint into the iterative optimization scheme. The result is therefore a classical Expectation Maximization method this time applied not to an image deconvolution problem but to image reconstruction from count modulation profiles. The technical reason that makes our implementation particularly effective in this application is the use of a very reliable stopping rule which is able to regularize the solution providing, at the same time, a very satisfactory Cash-statistic (C-statistic). The method is applied to both reproduce synthetic flaring configurations and reconstruct images from experimental data corresponding to three real events. In this second case, the performance of Expectation Maximization, when compared to Pixon image reconstruction, shows a comparable accuracy and a notably reduced computational burden; when compared to CLEAN, shows a better fidelity with respect to the measurements with a comparable computational effectiveness. If optimally stopped, Expectation Maximization represents a very reliable method for image reconstruction in the RHESSI context when count modulation profiles are used as input data

Inverse diffraction for the Atmospheric Imaging Assembly in the Solar Dynamics Observatory

The Atmospheric Imaging Assembly in the Solar Dynamics Observatory provides full Sun images every 1 seconds in each of 7 Extreme Ultraviolet passbands. However, for a significant amount of these images, saturation affects their most intense core, preventing scientists from a full exploitation of their physical meaning. In this paper we describe a mathematical and automatic procedure for the recovery of information in the primary saturation region based on a correlation/inversion analysis of the diffraction pattern associated to the telescope observations. Further, we suggest an interpolation-based method for determining the image background that allows the recovery of information also in the region of secondary saturation (blooming)

Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region 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 active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard 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

AI-FLARES: Artificial Intelligence for the Analysis of Solar Flares Data

AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica within the framework of the ``Attivit\`a di Studio per la Comunit\`a Scientifica Nazionale Sole, Sistema Solare ed Esopianeti'' program. The topic addressed by this project was the development and use of computational methods for the analysis of remote sensing space data associated to solar flare emission. This paper overviews the main results obtained by the project, with specific focus on solar flare forecasting, reconstruction of morphologies of the flaring sources, and interpretation of acceleration mechanisms triggered by solar flares

MicroRNA-155 influences B-cell function through PU.1 in rheumatoid arthritis

MicroRNA-155 (miR-155) is an important regulator of B cells in mice. B cells have a critical role in the pathogenesis of rheumatoid arthritis (RA). Here we show that miR-155 is highly expressed in peripheral blood B cells from RA patients compared with healthy individuals, particularly in the IgD-CD27- memory B-cell population in ACPA+ RA. MiR-155 is highly expressed in RA B cells from patients with synovial tissue containing ectopic germinal centres compared with diffuse synovial tissue. MiR-155 expression is associated reciprocally with lower expression of PU.1 at B-cell level in the synovial compartment. Stimulation of healthy donor B cells with CD40L, anti-IgM, IL-21, CpG, IFN-α, IL-6 or BAFF induces miR-155 and decreases PU.1 expression. Finally, inhibition of endogenous miR-155 in B cells of RA patients restores PU.1 and reduces production of antibodies. Our data suggest that miR-155 is an important regulator of B-cell activation in RA

Human fibroblasts in vitro exposed to 2.45 GHz continuous and pulsed wave signals: Evaluation of biological effects with a multimethodological approach

The increasing exposure to radiofrequency electromagnetic fields (RF-EMF), especially from wireless communication devices, raises questions about their possible adverse health effects. So far, several in vitro studies evaluating RF-EMF genotoxic and cytotoxic non-thermal effects have reported contradictory results that could be mainly due to inadequate experimental design and lack of well-characterized exposure systems and conditions. Moreover, a topic poorly investigated is related to signal modulation induced by electromagnetic fields. The aim of this study was to perform an analysis of the potential non-thermal biological effects induced by 2.45 GHz exposures through a characterized exposure system and a multimethodological approach. Human fibroblasts were exposed to continuous (CW) and pulsed (PW) signals for 2 h in a wire patch cell-based exposure system at the specific absorption rate (SAR) of 0.7 W/kg. The evaluation of the potential biological effects was carried out through a multimethodological approach, including classical biological markers (genotoxic, cell cycle, and ultrastructural) and the evaluation of gene expression profile through the powerful high-throughput next generation sequencing (NGS) RNA sequencing (RNA-seq) approach. Our results suggest that 2.45 GHz radiofrequency fields did not induce significant biological effects at a cellular or molecular level for the evaluated exposure parameters and conditions

CAESAR: Space Weather archive prototype for ASPIS

The project CAESAR (Comprehensive spAce wEather Studies for the ASPIS prototype Realization) is aimed to tackle all the relevant aspects of Space Weather (SWE) and realize the prototype of the scientific data centre for Space Weather of the Italian Space Agency (ASI) called ASPIS (ASI SPace Weather InfraStructure). This contribution is meant to bring attention upon the first steps in the development of the CAESAR prototype for ASPIS and will focus on the activities of the Node 2000 of CAESAR, the set of Work Packages dedicated to the technical design and implementation of the CAESAR ASPIS archive prototype. The product specifications of the intended resources that will form the archive, functional and system requirements gathered as first steps to seed the design of the prototype infrastructure, and evaluation of existing frameworks, tools and standards, will be presented as well as the status of the project in its initial stage.Comment: 4 pages, 2 figures, ADASS XXXII (2022) Proceeding

Call me by my name: unravelling the taxonomy of the gulper shark genus Centrophorus in the Mediterranean Sea through an integrated taxonomic approach

The current shift of fishery efforts towards the deep sea is raising concern about the vulnerability of deep-water sharks, which are often poorly studied and characterized by problematic taxonomy. For instance, in the Mediterranean Sea the taxonomy of genus Centrophorus has not been clearly unravelled yet. Since proper identification of the species is fundamental for their correct assessment and management, this study aims at clarifying the taxonomy of this genus in the Mediterranean Basin through an integrated taxonomic approach. We analysed a total of 281 gulper sharks (Centrophorus spp.) collected from various Mediterranean, Atlantic and Indian Ocean waters. Molecular data obtained from cytochrome c oxidase subunit I (COI), 16S ribosomal RNA (16S), NADH dehydrogenase subunit 2 (ND2) and a portion of a nuclear 28S ribosomal DNA gene region (28S) have highlighted the presence of a unique mitochondrial clade in the Mediterranean Sea. The morphometric results confirmed these findings, supporting the presence of a unique and distinct morphological group comprising all Mediterranean individuals. The data strongly indicate the occurrence of a single Centrophorus species in the Mediterranean, ascribable to C. cf. uyato, and suggest the need for a revision of the systematics of the genus in the area