Spinning up an extragalactic neutron star: Ultraluminous x-ray sources (ULXs) are strange objects in other galaxies that cannot be explained by conventional accretion onto stellar-mass objects. This has led to exotic interpretations, such as the long-sought intermediate-mass black holes. Israel et al. observed a ULX in the nearby galaxy NGC 5907 and found that it is instead a neutron star. The spinning neutron star is accreting material so fast that its spin period is quickly accelerating. The only way that it can consume enough material to explain these properties is if it has a strong multipolar magnetic field. Science , this issue p. 817

We discovered 2.8 s pulsations in the X-ray emission of the ultraluminous X-ray source (ULX) M51 ULX-7 within the UNSEeN project, which was designed to hunt for new pulsating ULXs (PULXs) with XMM-Newton. The pulse shape is sinusoidal, and large variations of its amplitude were observed even within single exposures (pulsed fraction from less than 5% to 20%). Source M51 ULX-7 is variable, generally observed at an X-ray luminosity between 10³⁹ and 10⁴⁰ erg s⁻¹, located in the outskirts of the spiral galaxy M51a at a distance of 8.6 Mpc. According to our analysis, the X-ray pulsar orbits in a 2 day binary with a projected semimajor axis 28 lt-s. For a neutron star (NS) of 1.4 M _⊙, this implies a lower limit on the companion mass of 8 M _⊙, placing the system hosting M51 ULX-7 in the high-mass X-ray binary class. The barycentric pulse period decreased by ≃0.4 ms in the 31 days spanned by our 2018 May–June observations, corresponding to a spin-up rate . In an archival 2005 XMM-Newton exposure, we measured a spin period of ∼3.3 s, indicating a secular spin-up of , a value in the range of other known PULXs. Our findings suggest that the system consists of a massive donor, possibly an OB giant or supergiant, and a moderately magnetic (dipole field component in the range 10¹² G G) accreting NS with weakly beamed emission ( ).

The explosion of the data both in the biomedical research and in the healthcare systems demands urgent solutions. In particular, the research in omics sciences is moving from a hypothesis-driven to a data-driven approach. Healthcare is additionally always asking for a tighter integration with biomedical data in order to promote personalized medicine and to provide better treatments. Efficient analysis and interpretation of Big Data opens new avenues to explore molecular biology, new questions to ask about physiological and pathological states, and new ways to answer these open issues. Such analyses lead to better understanding of diseases and development of better and personalized diagnostics and therapeutics. However, such progresses are directly related to the availability of new solutions to deal with this huge amount of information. New paradigms are needed to store and access data, for its annotation and integration and finally for inferring knowledge and making it available to researchers. Bioinformatics can be viewed as the “glue” for all these processes. A clear awareness of present high performance computing (HPC) solutions in bioinformatics, Big Data analysis paradigms for computational biology, and the issues that are still open in the biomedical and healthcare fields represent the starting point to win this challenge.

The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its ability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developed within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH and AROME) and a downscaling algorithm (RainFARM) in a seamless fashion. In addition to this multi-model configuration, some of the models were run in probabilistic mode, thus giving a comprehensive account of modelling errors and a very large amount of likely hydrometeorological scenarios (> 1500). The multi-model approach proved to be necessary because, whilst various aspects of the event were successfully simulated by different models, none of the models reproduced all of these aspects correctly. It was shown that the resulting set of simulations helped identify key atmospheric processes responsible for the large rainfall accumulations over the Bisagno basin. The DRIHM e-Science environment facilitated an evaluation of the sensitivity to atmospheric and hydrological modelling errors. This showed that both had a significant impact on predicted discharges, the former being larger than the latter. Finally, the usefulness of the set of hydrometeorological simulations was assessed from a flash flood early-warning perspective.