Lifeboat Foundation

It appears that the physics of information holds the key to the solution of the Fermi Paradox — indications are that we most likely live in a “Syntellect Chrysalis” (or our “second womb”) instead of a “cosmic jungle.”

Within the next few decades, we’ll transcend our biology by leaving today’s organic Chrysalis behind, by leaving our second womb, by leaving our cradle, if speaking in tropes.

This particular version of “human universe” is what we “see” from within our dimensional cocoon, it’s a construct of our minds but by no means represents objective reality “out there” including our most advanced models such as M-theory that are only approximations at best.

Peptides, one of the fundamental building blocks of life, can be formed from the primitive precursors of amino acids under conditions similar to those expected on the primordial Earth, finds a new UCL study.

The findings, published in Nature, could be a missing piece of the puzzle of how life first formed.

“Peptides, which are chains of amino acids, are an absolutely essential element of all life on Earth. They form the fabric of proteins, which serve as catalysts for biological processes, but they themselves require enzymes to control their formation from amino acids,” explained the study’s lead author, Dr Matthew Powner (UCL Chemistry).

Warming can encourage the growth of microbes in permafrost that produce more greenhouse gases.

• By Chelsea Harvey, E&E News on July 10, 2019

As evidence grows that gut bacteria play roles in the development and persistence of food allergies, researchers begin to explore microbe-based interventions.

Despite their names, artificial intelligence technologies and their component systems, such as artificial neural networks, don’t have much to do with real brain science. I’m a professor of bioengineering and neurosciences interested in understanding how the brain works as a system – and how we can use that knowledge to design and engineer new machine learning models.

In recent decades, brain researchers have learned a huge amount about the physical connections in the brain and about how the nervous system routes information and processes it. But there is still a vast amount yet to be discovered.

At the same time, computer algorithms, software and hardware advances have brought machine learning to previously unimagined levels of achievement. I and other researchers in the field, including a number of its leaders, have a growing sense that finding out more about how the brain processes information could help programmers translate the concepts of thinking from the wet and squishy world of biology into all-new forms of machine learning in the digital world.

Introducing the SBOL Industrial Consortium

To this end, a group of companies are now launching a pre-competitive consortium to support the industrial application of these technologies. The SBOL Industrial Consortium is a non-profit organization supporting innovation, dissemination, and integration of SBOL standards, tools and practices for practical applications in an industrial environment. The six founding companies of the consortium are Raytheon BBN Technologies, Amyris, Doulix, IDT, Shipyard Toolchains, TeselaGen, and Zymergen, representing a diverse set of interests and business models across the synthetic biology community.

The SBOL Industrial Consortium will facilitate industry-focused development of representational technologies in several ways. The consortium will help coordinate development of standards and tools, both with the academic community and from member to member, in order to ensure that the SBOL standards are well-tuned to support the specific industrial needs of the members of the consortium. Financial support will also be provided by the consortium for selected projects and activities, and for key pieces of community infrastructure.

LMU chemists have discovered an efficient mechanism for the prebiotic synthesis of a vital class of such compounds.

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How do the communities of microbes living in our gastrointestinal systems affect our health? Carnegie’s Will Ludington was part of a team that helped answer this question.

For nearly a century, evolutionary biologists have probed how genes encode an individual’s chances for success—or fitness—in a specific environment.

In order to reveal a potential evolutionary trajectory biologists measure the interactions between genes to see which combinations are most fit. An organism that is evolving should take the most fit path. This concept is called a fitness landscape, and various mathematical techniques have been developed to describe it.