Lifeboat Foundation

A Tesla semi-truck with a very Tesla-worthy aesthetics highlighted by the contoured yet sharp design language that in a way reminds me of the iPhone 12!

Tesla’s visionary Semi all-electric truck powered by four independent motors on the rear is scheduled for production in 2022. The semi is touted to be the safest, most comfortable truck with an acceleration of 0–60 mph in just 20 seconds and a range of 300–500 miles. While the prototype version looks absolutely badass, how the final version will look is anybody’s guess.

Proteins are essential to life, and understanding their 3D structure is key to unpicking their function. To date, only 17% of the human proteome is covered by an experimentally determined structure. Two papers in this week’s issue dramatically expand our structural understanding of proteins. Researchers at DeepMind, Google’s London-based sister company, present the latest version of their AlphaFold neural network. Using an entirely new architecture informed by intuitions about protein physics and geometry, it makes highly accurate structure predictions, and was recognized at the 14th Critical Assessment of Techniques for Protein Structure Prediction last December as a solution to the long-standing problem of protein-structure prediction. The team applied AlphaFold to 20,296 proteins, representing 98.5% of the human proteome.

Top bioengineer sheds light on recent organ-on-a-chip developments, highlights challenges and predicts a multi-organ future.

The business of private survival shelters has grown during the pandemic. They’re not just for survivalists and doomsday preppers anymore. Bunkers buried in backyards or remote landscapes are capable of withstanding nuclear fallout and hurricanes, as well as violent conflict.

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What is your mind? It’s a strange question, perhaps, but if pressed, you might describe it as the part of yourself that makes you who you are—your consciousness, dreams, emotions, and memories. Scientists believed for a long time that such aspects of the mind had specific brain locations, like a circuit for fear, a region for memory, and so on.

But in recent years we’ve learned that the human brain is actually a master of deception, and your experiences and actions do not reveal its inner workings. Your mind is in fact an ongoing construction of your brain, your body, and the surrounding world.

The human body can be genetically inclined to attack its own cells, destroying the beta cells in the pancreas that make insulin, which helps convert sugar into energy. Called Type 1 diabetes, this disorder can occur at any age and can be fatal if not carefully managed with insulin shots or an insulin pump to balance the body’s sugar levels.

But there may be another, personalized option on the horizon, according to Xiaojun “Lance” Lian, associate professor of biomedical engineering and biology at Penn State. For the first time, Lian and his team converted human embryonic stem cells into beta cells capable of producing insulin using only small molecules in the laboratory, making the process more efficient and cost-effective.

Stem cells can become other cell types through signals in their environment, and some mature cells can revert to stem cells—induced pluripotency. The researchers found that their approach worked for human embryonic and induced pluripotent stem cells, both derived from federally approved stem cell lines. According to Lian, the effectiveness of their approach could reduce or eliminate the need for human embryonic stem cells in future work. They published their results today (Aug. 26) in Stem Cell Reports.

Blue Origin successfully launched its 17th New Shepard suborbital spaceflight Thursday morning. The capsule flew to a peak altitude of 347,430 feet while carrying 20 payloads.

“We are thinking about volumes in millions.”

“We are thinking about volumes in millions, not the thousands that people talk about with quantum computers based on superconducting,” said Marcus Doherty, chief science officer.

Quantum Brilliance delivered its first system to the Pawsey Supercomputing Centre in Australia earlier this year and is beginning to ship to other commercial customers.

Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure—and even such massive efforts are frequently unsuccessful.

Using clever, new machine learning techniques, Stanford University Ph.D. students Stephan Eismann and Raphael Townshend, under the guidance of Ron Dror, associate professor of computer science, have developed an approach that overcomes this problem by predicting accurate structures computationally.

Most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the types of molecules whose structures are most difficult to determine experimentally.