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Dangerous airborne viruses are rendered harmless on-the-fly when exposed to energetic, charged fragments of air molecules, University of Michigan researchers have shown.

They hope to one day harness this capability to replace a century-old device: the surgical mask.

The U-M engineers have measured the virus-killing speed and effectiveness of nonthermal plasmas—the ionized, or charged, particles that form around electrical discharges such as sparks. A nonthermal plasma reactor was able to inactivate or remove from the airstream 99.9% of a test virus, with the vast majority due to inactivation.

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Humanity could be on the verge of an unprecedented merging of human biology with advanced technology, fusing our thoughts and knowledge directly with the cloud in real-time – and this incredible turning point may be just decades away, scientists say.

In a new research paper exploring what they call the ‘human brain/cloud interface’, scientists explain the technological underpinnings of what such a future system might be, and also address the barriers we’ll need to address before this sci-fi dream becomes reality.

At its core, the brain/cloud interface (B/CI) is likely to be made possible by imminent advances in the field of nanorobotics, proposes the team led by senior author and nanotechnology researcher Robert Freitas Jr from the Institute for Molecular Manufacturing in California.

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Researchers at Ben-Gurion University of the Negev (BGU) have discovered that gene mutations that once helped humans survive may increase the possibility for diseases, including cancer.

The findings were recently the cover story in the journal Research.

The team of researchers from BGU’s National Institute for Biotechnology in the Negev (NIBN) set out to look for mutations in the genome of the , a part of every cell responsible for energy production that is passed exclusively from mothers to their children. The mitochondria are essential to every cell’s survival and our ability to perform the functions of living.

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It’s big news, set to shock, amaze, and entertain the world.

But unfortunately, it’s got nothing to do with extraterrestrial stoners melding with Earth’s plants.

However, since you’re now reading, you’ll almost certainly be interested in this research that looked into the clicking and sharing behaviors of social media users reading content (or not) and then sharing it on social media.

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A mere 17–20 meters across, the Chelyabinsk meteor caused extensive ground damage and numerous injuries when it exploded on impact with Earth’s atmosphere in February 2013.

To prevent another such impact, Amy Mainzer and colleagues use a simple yet ingenious way to spot these tiny near-Earth objects (NEOs) as they hurtle toward the planet. She is the principal investigator of NASA’s asteroid hunting mission at the Jet Propulsion Laboratory in Pasadena, California, and will outline the work of NASA’s Planetary Defense Coordination Office this week at the American Physical Society April Meeting in Denver—including her team’s NEO recognition method and how it will aid the efforts to prevent future Earth impacts.

“If we find an object only a few days from impact, it greatly limits our choices, so in our search efforts we’ve focused on finding NEOs when they are further away from Earth, providing the maximum amount of time and opening up a wider range of mitigation possibilities,” Mainzer said.

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News-Medical speaks to David Dambman from Biosero about the emerging importance of automation in scientific research and how a centralized scheduling software is an essential first step for any laboratory looking to automate their workflow.

Why has automation become so critical to advancing scientific research?

There are many reasons why automation is useful in scientific research. First and foremost, automation is about being able to walk away from your experiments and spend time analyzing your results, rather than carrying out mundane tasks such as transferring liquids from one plate to another.

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Training bigger neural networks can be challenging when faced with accelerator memory limits. The size of the datasets being used by machine learning models is very large nowadays. For example, a standard image classification datasets like hashtagged Instagram contains millions of images. With the increasing quality of the images, the memory required will also increase. Today, the memory available on NVIDIA GPUs is only 32 GB.

Therefore, there needs to be a tradeoff between memory allocated for the features in a model and how the network gets activated. It is only understandable why the accelerator memory limit needs to be breached.