We report the observation of a gravitational-wave signal produced by the coalescence of two stellar-mass black holes. The signal, GW151226, was observed by the twin detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) on December 26, 2015 at 03:38:53 UTC. The signal was initially identified within 70 s by an online matched-filter search targeting binary coalescences. Subsequent off-line analyses recovered GW151226 with a network signal-to-noise ratio of 13 and a significance greater than 5σ. The signal persisted in the LIGO frequency band for approximately 1 s, increasing in frequency and amplitude over about 55 cycles from 35 to 450 Hz, and reached a peak gravitational strain of 3.4 −0.04 . All uncertainties define a 90% credible interval. This second gravitational-wave observation provides improved constraints on stellar populations and on deviations from general relativity.

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The development of highly efficient non‐precious metal electrocatalysts for the oxygen evolution reaction (OER) in low‐grade or saline water is currently of great importance for the large‐scale production of hydrogen. In this study, by using an electrochemical activation pretreatment, metal oxy(hydroxide) nanosheet structures derived from self‐supported nickel–iron phosphide and nitride nanoarrays grown on Ni foam are successfully fabricated for OER catalysis in saline water. It is demonstrated that the different NiOOH and NiOOH@FeOOH (NiOOH grown on FeOOH) structures are generated from nickel–iron nitride and phosphide, respectively, after electrochemical activation. In particular, the NiOOH@FeOOH heteroarchitecture shows outstanding electrocatalytic performance with an ultralow overpotential of 292 mV to drive the current density of 500 mA cm−2. An unconventional dual‐sites mechanism (UDSM) is proposed to address the OER process on NiOOH@FeOOH and show that the FeOOH underlayer plays a critical role regarding the enhanced OER activity of NiOOH. The new possible UDSM involving two reaction sites presents a different understanding of the OER process on multi‐OH layer complexes, which is expected to guide the design of heteroarchitecture electrocatalysts.

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.