Lifeboat Foundation

This is a guest post by Shahrokh Shahidzadeh, CEO at Acceptto

These past two months have been among the most extraordinary times any of us can remember. The COVID-19 (CV-19) impact is all around us, indiscriminately impacting all of our lives, our work, the economy and for sure we are on the onset of a new normal that we are learning how to deal with daily.

There are always two stages of dealing with a change of this magnitude. First, we react immediately, thinking about what we must do differently now. Soon, we will begin to think in the longer term, reacting to and planning for permanent changes that result from the CV-19 pandemic.

With the biggest launch in nearly a decade two weeks away, it’s been a two-horse race in the new “space race”.

UCSC researchers developed a deep-learning framework called Morpheus to perform pixel-level morphological classifications of objects in astronomical images.

Researchers at UC Santa Cruz have developed a powerful new computer program called Morpheus that can analyze astronomical image data pixel by pixel to identify and classify all of the galaxies and stars in large data sets from astronomy surveys.

Morpheus is a deep-learning framework that incorporates a variety of artificial intelligence technologies developed for applications such as image and speech recognition. Brant Robertson, a professor of astronomy and astrophysics who leads the Computational Astrophysics Research Group at UC Santa Cruz, said the rapidly increasing size of astronomy data sets has made it essential to automate some of the tasks traditionally done by astronomers.

In the near future, we will have manned drones going at blistering speeds and doing crazy aerobatic stunts.

Researchers use energy from a next-generation particle accelerator to probe microfossils inside ancient Earth rocks.

“We’ve wondered if it might be possible to simply rewind the aging clock without inducing pluripotency,” said Vittorio Sebastiano, assistant professor at Stanford University and senior author of the Nature Communications article. “Now we’ve found that tightly controlling the exposure to these proteins can promote rejuvenation in multiple human cell types, including stem cells. This has profound implications for regeneration and restoration of cell functionality of aged tissues.”

MOUNTAIN VIEW, Calif., March 25, 2020 /PRNewswire/ — A study published in the respected Nature Communications journal highlights the promise of technology being developed by Turn Biotechnologies to treat age-related health conditions.

The study by researchers at the Stanford University School of Medicine found that old human cells can be induced into a more youthful and vigorous state when they are exposed to a rejuvenating treatment that triggers the limited expression of a group of proteins known as Yamanaka factors, which are important to embryonic development.

Continue reading “Research Shows Promise of Technology Used by Turn Biotechnologies to Develop Therapies for Age-Related Diseases” »

The most comprehensive national study to date has found that convalescent plasma appears to be safe to use on COVID-19 patients, a promising development in the race to find a treatment for the deadly virus. But the study didn’t determine whether the treatment works.

A team of more than 5,000 doctors from over 2,000 hospitals and laboratories have been testing the experimental therapy, which involves transfusing the antibody-rich blood serum of recovered COVID-19 patients into people who are battling the illness.

Skoltech researchers have offered a solution to the problem of searching for materials with required properties among all possible combinations of chemical elements. These combinations are virtually endless, and each has an infinite multitude of possible crystal structures; it is not feasible to test them all and choose the best option (for instance, the hardest compound) either in an experiment or in silico. The computational method developed by Skoltech professor Artem R. Oganov and his PhD student Zahed Allahyari solves this major problem of theoretical materials science. Oganov and Allahyari presented their method in the MendS code (stands for Mendelevian Search) and tested it on superhard and magnetic materials.

“In 2006, we developed an algorithm that can predict the crystal structure of a given fixed combination of chemical elements. Then we increased its predictive powers by teaching it to work without a specific combination — so one calculation would give you all stable compounds of given elements and their respective crystal structures. The new method tackles a much more ambitious task: here, we pick neither a precise compound nor even specific chemical elements — rather, we search through all possible combinations of all chemical elements, taking into account all possible crystal structures, and find those that have the needed properties (e.g., highest hardness or highest magnetization)” says Artem Oganov, Skoltech and MIPT professor, Fellow of the Royal Society of Chemistry and a member of Academia Europaea.

The researchers first figured out that it was possible to build an abstract chemical space so that compounds that would be close to each other in this space would have similar properties. Thus, all materials with peculiar properties (for example, superhard materials) will be clustered in certain areas, and evolutionary algorithms will be particularly effective for finding the best material. The Mendelevian Search algorithm runs through a double evolutionary search: for each point in the chemical space, it looks for the best crystal structure, and at the same time these found compounds compete against each other, mate and mutate in a natural selection of the best one.

Many economists, however, are sceptical whether the impact of the digital yuan would significantly spread beyond its borders without official reforms to relax the yuan’s exchange rate convertibility.

China’s sovereign digital currency could be launched later this year, with the likes of Alibaba’s Alipay and Tencent’s WeChat Pay already popular payment methods.