New Enzyme Discovery is Another Leap Towards Dissolving Plastic Waste With ‘Amazing Efficiency’:
Credit- RITA CLARE / MONTANA STATE UNIVERSITY
Scientists who helped pioneer the use of enzymes to eat plastic have taken an important next step in developing nature-based solutions to the global plastics crisis. They have characterized an enzyme that has the remarkable capacity to break down terephthalate (TPA)—one of the chemical building blocks of polyethylene terephthalate (PET) plastic, which is used to make single-use drinks bottles, clothing and carpets. The research was co-led by Professor Jen DuBois of Montana State University, and Professor John McGeehan from the University of Portsmouth in England. In 2018, McGeehan led the international team that engineered a natural enzyme that could break down PET plastic. The enzymes (PETase and MHETase) break the PET polymer into the chemical building blocks ethylene glycol and TPA. With more than 400 million tons of plastic waste produced each year, it is hoped this work will open the door to improve bacterial enzymes, such as TPADO.
Revamped design could take powerful biological computers from the test tube to the cell:
Tiny biological computers made of DNA could revolutionize the way we diagnose and treat a slew of diseases, once the technology is fully fleshed out. While previously impossible, researchers at the National Institute of Standards and Technology (NIST) may have developed long-lived biological computers that could potentially persist inside cells. The results demonstrate that the RNA circuits are as dependable and versatile as their DNA-based counterparts. What's more, living cells may be able to create these RNA circuits continuously, something that is not readily possible with DNA circuits, further positioning RNA as a promising candidate for powerful, long-lasting biological computers. Much like the computer or smart device you are likely reading this on, biological computers can be programmed to carry out different kinds of tasks. By assembling a specific sequence of bases into a strand of nucleic acid, researchers can dictate what it binds to which can dictate reactions of the body to many different diseases. Outputs created through the binding of nucleic acids like DNA and RNA can result in signals for medical diagnostics to catch things early or even may result in a therapeutic drug to treat disease and other medical conditions.
How the way we’re taught to round numbers in school falls short:
A method taught in school for rounding numbers doesn’t work well for certain uses, including in machine learning. Now, a different way of rounding is making a “resurgence,” researchers say.
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The rounding to the nearest number technique is particularly useful for estimating numbers without a calculator, but it falls short of being truly correct, especially when it comes to machine learning. Mantas Mikaitis, a computer scientist at the University of Manchester in England says that an alternative technique called stochastic rounding is better suited for applications where the round-to-nearest approach falls short. This technique isn’t meant to be done in your head. Instead, a computer program rounds to a certain number with probabilities that are based on the distance of the actual measurement from that number. For instance, 2.8 has an 80 percent chance of rounding to three and a 20 percent chance of rounding to two. By making sure that rounding doesn’t always go in the same direction for a particular number, this process helps guard against what’s known as stagnation. That problem “means that the real result is growing while the computer’s result” isn’t, Mikaitis says. “It’s about losing many tiny measurements that add up to a major loss in the final result.” Most computers aren’t yet equipped to perform true stochastic rounding, Mikaitis notes. The machines lack hardware random number generators, which are needed to execute the probabilistic decision of which way to round. However, Mikaitis and his colleagues have devised a method to simulate stochastic rounding in these computers by combining the round-to-nearest method with three other types of rounding.