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A new approach to producing realistic expressions of pain on robotic patients could help to reduce error and bias during physical examination.

A team led by researchers at Imperial College London has developed a way to generate more accurate expressions of pain on the face of medical training robots during physical examination of painful areas.

Findings, published today in Scientific Reports, suggest this could help teach trainee doctors to use clues hidden in patient facial expressions to minimize the force necessary for physical examinations.

Researchers have found an effective target in the brain for electrical stimulation to improve mood in people suffering from depression. As reported in the journal Current Biology on November 29, stimulation of a brain region called the lateral orbitofrontal cortex (OFC) reliably produced acute improvement in mood in patients who suffered from depression at the start of the study.

Those effects were not seen in patients without mood symptoms, suggesting that the brain stimulation works to normalize activity in mood-related neural circuitry, the researchers say.

“Stimulation induced a pattern of activity in brain regions connected to OFC that was similar to patterns seen when patients naturally experienced positive mood states,” says Vikram Rao, of the University of California, San Francisco. “Our findings suggest that OFC is a promising new stimulation target for treatment of mood disorders.”

Many people say that Einstein failed because he was simply ahead of his time. The knowledge and tools needed to complete a unified theory simply hadn’t been developed before Einstein died in 1955.

Today, many physicists are taking up his quest. The most promising approach appears to be string theory, which requires 10 or more dimensions and describes all elementary particles as vibrating strings, with different modes of vibration producing different particles.

String theory has not yet made any testable predictions, and some scientists worry that string theorists have, like Einstein in his later years, strayed too far from physical reality in their obsession with beautiful mathematics. But many others believe string theory does indeed hold the key to completing Einstein’s quest, and researchers are hoping to find ways to test some of the predictions of string theory.

Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.

This starts with Tyson’s deathism quote, but I’m still a fan. I do wonder if he’ll take a treatment when he sees everyone else rejuvenating around him.

Scientists say they will need to conduct studies to find out the severity of the new variant.

To classify as a DTC, a system also needs to be truly many-body, and its coherence times (that is, the time over which fragile quantum states persist without being destroyed by interactions with their environment) must be long enough that its periodic variations are not mistaken for a short-term system change. Finally, one must be able to prepare the system in arbitrary initial states and show that all of them result in similar DTC behaviour.

A major milestone

The Melbourne team’s work, which is described in Science Advances, builds on earlier reports of DTCs that used quantum processors based on nine nuclear spins in diamond and 20 superconducting qubits. As in these previous experiments, the team turned a quantum computer into an experimental platform — a quantum simulator – in which all the requirements of DTCs could be met.

A universal cure for cancer would be a truly historic achievement in medicine, and it seems that scientists may have found it… by accident.

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A Stanford University-led research team has set a new Guinness World Record for the fastest DNA sequencing technique using AI computing to accelerate workflow speed.

The research, led by Dr Euan Ashley, professor of medicine, genetics and biomedical data science at Stanford School of Medicine, in collaboration with Nvidia, Oxford Nanopore Technologies, Google, Baylor College of Medicine, and the University of California, achieved sequencing in just five hours and two minutes.

The study, published in The New England Journal of Medicine, involved speeding up every step of genome sequencing workflow by relying on new technology. This included using nanopore sequencing on Oxford Nanopore’s PromethION Flow Cells to generate more than 100 gigabases of data per hour, and Nvidia GPUs on Google Cloud to speed up the base calling and variant calling processes.