A group of South Korean scientists have developed a deep learning system based on artificial intelligence that can precisely predict interactions between drugs, the government said Tuesday.
With advances in stem cell research and nanotechnology helping us fight illnesses from heart disease to superbugs, is the fusion of biology and technology speeding us towards a sci-fi future — part human, part synthetic?
In Ridley Scott’s seminal blockbuster Blade Runner, humanity has harnessed bio-engineering to create a race of replicants that look, act and sound human — but are made entirely from synthetic material.
We may be far from realising that sci-fi future, but synthetics are beginning to have a profound effect on medicine.
In a talk given today at the American Association for Cancer Research’s annual meeting, Google researchers described a prototype of an augmented reality microscope that could be used to help physicians diagnose patients. When pathologists are analyzing biological tissue to see if there are signs of cancer — and if so, how much and what kind — the process can be quite time-consuming. And it’s a practice that Google thinks could benefit from deep learning tools. But in many places, adopting AI technology isn’t feasible. The company, however, believes this microscope could allow groups with limited funds, such as small labs and clinics, or developing countries to benefit from these tools in a simple, easy-to-use manner. Google says the scope could “possibly help accelerate and democratize the adoption of deep learning tools for pathologists around the world.”
The microscope is an ordinary light microscope, the kind used by pathologists worldwide. Google just tweaked it a little in order to introduce AI technology and augmented reality. First, neural networks are trained to detect cancer cells in images of human tissue. Then, after a slide with human tissue is placed under the modified microscope, the same image a person sees through the scope’s eyepieces is fed into a computer. AI algorithms then detect cancer cells in the tissue, which the system then outlines in the image seen through the eyepieces (see image above). It’s all done in real time and works quickly enough that it’s still effective when a pathologist moves a slide to look at a new section of tissue.
Researchers have improved a naturally-occurring enzyme to enhance its plastic-eating abilities. The modified enzyme, which can digest strong plastic used in bottles, could help in the fight against pollution.
Researchers in the US and Britain have engineered a plastic-eating enzyme to speed up its abilities to digest plastic.
Scientists from Britain’s University of Portsmouth and the US Department of Energy’s National Renewable Energy Laboratory “tweaked” the structure of the naturally-occurring enzyme after they found that it was helping a bacteria to break down, or digest, plastic used to make bottles.
This artist’s impression shows the light of several distant quasars piercing the northern half of the Fermi Bubbles, an outflow of gas expelled by the supermassive black hole in the centre of the Milky Way. The NASA/ESA Hubble Space Telescope probed the quasars’ light for information on the speed of the gas and whether the gas is moving toward or away from Earth. Based on the material’s speed, the research team estimated that the bubbles formed from an energetic event between 6 million and 9 million years ago.
The inset diagram at bottom left shows the measurement of gas moving toward and away from Earth, indicating the material is traveling at a high velocity.
Hubble also observed light from quasars that passed outside the northern bubble. The box at upper right reveals that the gas in one such quasar’s light path is not moving toward or away from Earth. This gas is in the disc of the Milky Way and does not share the same characteristics as the material probed inside the bubble.
X-ray observations have revealed a dozen stellar-mass black holes at the centre of the Galaxy, implying that there are thousands more to be found. The discovery confirms a fundamental prediction of stellar dynamics.