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Most of the matter and energy in the Universe are in mysterious, invisible forms that cannot be explained by physics as we know it. But it is possible for us to uncover the dark side of the Universe, and CERN physicist John Ellis knows how.

John Ellis is a Maxwell prize-winning theoretical physicist, and is considered one of the world’s leading physicists. John is currently Clerk Maxwell Professor of Theoretical Physics at King’s College London, and since 1978 has held an indefinite contract at CERN.

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Physicists believe most of the matter in the Universe is made up of an invisible substance that we only know about by its indirect effects on the stars and galaxies we can see.

We’re not crazy! Without this “dark matter”, the Universe as we see it would make no sense.

But the nature of dark matter is a longstanding puzzle. However, a new study by Alfred Amruth at the University of Hong Kong and colleagues, published in Nature Astronomy, uses the gravitational bending of light to bring us a step closer to understanding.

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Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types.

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In a new breakthrough, researchers at the University of Copenhagen, in collaboration with Ruhr University Bochum, have solved a problem that has caused quantum researchers headaches for years. The researchers can now control two quantum light sources rather than one. Trivial as it may seem to those uninitiated in quantum, this colossal breakthrough allows researchers to create a phenomenon known as quantum mechanical entanglement. This in turn, opens new doors for companies and others to exploit the technology commercially.

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How much of the multiverse is TRUE?? Join us… and find out!

Subscribe: https://wmojo.com/unveiled-subscribe.

In this video, Unveiled takes a closer look at the multiverse, as it’s represented in both science FICTION and science FACT! How much of the theory is true? How much of it is POSSIBLY true? And how much of it has been totally made up for books, film and TV??

This is Unveiled, giving you incredible answers to extraordinary questions!

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Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short-and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

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