Microplastics are a global problem: they end up in rivers and oceans, they accumulate in living organisms and disrupt entire ecosystems. How tiny particles behave in a current is difficult to describe scientifically, especially in the case of thin fibers, which make up more than half of microplastic contamination in marine life-forms. In turbulent currents, it is almost impossible to predict their movement.
Inventing a new, faster way to produce sustainable, self-dyed leather alternatives is a major achievement for synthetic biology and sustainable fashion. Professor Tom Ellis
Synthetic chemical dyeing is one of the most environmentally toxic processes in fashion, and black dyes – especially those used in colouring leather – are particularly harmful. The researchers at Imperial set out to use biology to solve this.
The kitchen can be a messy place in any home – some more than others, as anyone who’s lived in a student flat can attest to – but it turns out the humble microwave is home to far more microbes than you could possibly imagine.
And, not only that, but the bacteria these devices are hoarding is resistant to radiation and multiplying by the second.
A new study from a team from Darwin Bioprospecting Excellence SL in Paterna, Spain, published in journal Frontiers in Microbiology has found that hardy microbes able to adapt to extreme conditions and thrive in microwaves.
In the search for less energy-hungry artificial intelligence, some scientists are exploring living computers.
Artificial intelligence systems, even those as sophisticated as ChatGPT, depend on the same silicon-based hardware that has been the bedrock of computing since the 1950s. But what if computers could be molded from living biological matter? Some researchers in academia and the commercial sector, wary of AI’s ballooning demands for data storage and energy, are focusing on a growing field known as biocomputing. This approach uses synthetic biology, such as miniature clusters of lab-grown cells called organoids, to create computer architecture. Biocomputing pioneers include Swiss company FinalSpark, which earlier this year debuted its “Neuroplatform”—a computer platform powered by human-brain organoids—that scientists can rent over the Internet for $500 a month.
Researchers at Cold Spring Harbor Laboratory have traced the domestication of maize back to its origins 9,000 years ago, highlighting its crossbreeding with teosinte mexicana for cold adaptability.
The discovery of a genetic mechanism known as Teosinte Pollen Drive by Professor Rob Martienssen provides a critical link in understanding maize’s rapid adaptation and distribution across America, shedding light on evolutionary processes and potential agricultural applications.
Cold Spring Harbor Laboratory (CSHL) scientists have begun to unravel a mystery millennia in the making. Our story begins 9,000 years ago. It was then that maize was first domesticated in the Mexican lowlands. Some 5,000 years later, the crop crossed with a species from the Mexican highlands called teosinte mexicana. This resulted in cold adaptability. From here, corn spread across the continent, giving rise to the vegetable that is now such a big part of our diets. But how did it adapt so quickly? What biological mechanisms allowed the highland crop’s traits to take hold? Today, a potential answer emerges.
CU Boulder scientists have found how ions move in tiny pores, potentially improving energy storage in devices like supercapacitors. Their research updates Kirchhoff’s law, with significant implications for energy storage in vehicles and power grids.
Imagine if your dead laptop or phone could be charged in a minute, or if an electric car could be fully powered in just 10 minutes. While this isn’t possible yet, new research by a team of scientists at CU Boulder could potentially make these advances a reality.
Published in the Proceedings of the National Academy of Sciences, researchers in Ankur Gupta’s lab discovered how tiny charged particles, called ions, move within a complex network of minuscule pores. The breakthrough could lead to the development of more efficient energy storage devices, such as supercapacitors, said Gupta, an assistant professor of chemical and biological engineering.
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
Complex biological systems, often stochastic and heterogenous, can undergo dimensionality reduction, however processes governing thision are not well understood. The authors propose a topological model that produces long oscillations in biological networks, reducing the system dynamics to a lower-dimensional current.