Lifeboat Foundation

So, I think I uncovered a treasure. The Killing Star by Charles Pellegrino and George Zebrowski was originally published 1995 and it paints a dark and seemingly plausible depiction of humanity’s potential future. This book is about several things genetic engineering and cloning, it’s about the destructive power of fanaticism, It’s about the over confidence and hubris of humanity, and that gets really hammered home in this book with all it’s references to the titanic, which has for a very long time been thought of as one of the greatest symbols of human hubris, it’s about AI, and when it goes to far, it’s about our over dependence on technology, it’s about humanity’s indefinite survival outside of earth, and most importantly, it’s about the devastating annihilation of the vast majority of the human race.

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Because the heart, unlike other organs, cannot heal itself after injury, heart disease—the top cause of mortality in the U.S.—is particularly lethal. For this reason, tissue engineering will be crucial for the development of cardiac medicine, ultimately leading to the mass production of a whole human heart for transplant.

Researchers need to duplicate the distinctive structures that make up the heart in order to construct a human heart from the ground up. This involves re-creating helical geometries, which cause the heart to beat in a twisting pattern. It has long been hypothesized that this twisting action is essential for pumping blood at high rates, but establishing this has proven problematic, in part because designing hearts with various geometries and alignments has proven difficult.

Motors are everywhere in our day-to-day lives—from cars to washing machines. A futuristic scientific field is working on tiny motors that could power a network of nanomachines and replace some of the power sources we use in devices today.

In new research published recently in ACS Nano, researchers from the Cockrell School of Engineering at The University of Texas at Austin created the first ever solid-state optical nanomotor. All previous versions of these light-driven motors reside in a solution of some sort, which held back their potential for most real-world applications.

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Gene editing with CRISPR can cause off-target mutations, but this seems to happen less often with an enzyme that cuts one of the strands of DNA instead of both.

The team of researchers who transplanted a genetically modified pig’s heart into a living human earlier this year have completed two more pig heart transplant surgeries, setting the protocol for such operations.

In January this year, 57-year-old David Bennett became the first man on the planet to receive a heart from a genetically modified pig. Before this, researchers transplanted kidneys from similarly modified pigs into patients that were brain dead.

The organs are sourced from a company called Revivicor which uses genetic engineering to remove specific genes in the pigs to help in reducing transplant rejection while adding some that make the organs more compatible with the human immune system.

A patient in New Zealand became the first person to receive the biotech’s medicine, which uses base editing to turn off a specific gene in the liver and thereby lower cholesterol.

Moore’s Law needs a hug. The days of stuffing transistors on little silicon computer chips are numbered, and their life rafts—hardware accelerators—come with a price.

When programming an accelerator—a process where applications offload certain tasks to system hardware especially to accelerate that task—you have to build a whole new software support. Hardware accelerators can run certain tasks orders of magnitude faster than CPUs, but they cannot be used out of the box. Software needs to efficiently use accelerators’ instructions to make it compatible with the entire application system. This translates to a lot of engineering work that then would have to be maintained for a new chip that you’re compiling code to, with any programming language.

Now, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a new programming language called “Exo” for writing high-performance code on hardware accelerators. Exo helps low-level performance engineers transform very simple programs that specify what they want to compute, into very complex programs that do the same thing as the specification, but much, much faster by using these special accelerator chips. Engineers, for example, can use Exo to turn a simple matrix multiplication into a more complex program, which runs orders of magnitude faster by using these special accelerators.

Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.

Though engineering of biological systems is truly indispensable in biotechnology and synthetic biology, today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in Nature Communications, the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, or “wise counsel.”