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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.”
Brain-machine interfaces (BMIs) are devices that enable direct communication/translation between biological neuronal networks (e.g. a brain or a spine) and external machines. They are currently being used as a tool for fundamental neuroscience research and also for treating neurological disorders and for manipulating neuro-prosthetic devices. As remarkable as today’s BMIs are, however, the next generation BMIs will require new hardware and software with improved resolution and specificity in order to precisely monitor and control the activities of complex neuronal networks. In this talk, I will describe my group’s effort to develop new neuroelectronic devices enabled by silicon nanotechnology that can serve as high-precision, highly multiplexed interface to neuronal networks. I will then describe the promises, as well as potential pitfalls, of next generation BMIs. Hongkun Park is a Professor of Chemistry and Chemical Biology and a Professor of Physics at Harvard University. He is also an Institute Member of the Broad Institute of Harvard and MIT and a member of the Harvard Center for Brain Science and Harvard Quantum Optics Center. He serves as an associate editor of Nano Letters. His research interests lie in exploring solid-state photonic, optoelectronic, and plasmonic devices for quantum information processing as well as developing new nano-and microelectronic interfaces for living cells, cell networks, and organisms. Awards and honors that he received include the Ho-Am Foundation Prize in Science, NIH Director’s Pioneer Award, and the US Vannevar Bush Faculty Fellowship, the David and Lucile Packard Foundation Fellowship for Science and Engineering, the Alfred P. Sloan Research Fellowship, and the Camille Dreyfus Teacher-Scholar Award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.
In this landmark talk, Peter Diamandis shares how we are rapidly heading towards a human-scale transformation, the next evolutionary step into what he calls a “Meta-Intelligence,” a future in which we are all highly connected — brain to brain via the cloud — sharing thoughts, knowledge and actions.
He highlights the 4 driving forces as well as the 4 steps that is transforming humanity.
In 2014 Fortune Magazine named Peter Diamandis as one of the World’s 50 Greatest Leaders.
Continue reading “Imagining the Future: The Transformation of Humanity | Peter Diamandis | TEDxLA” »
