Lifeboat Foundation

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.”

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.

The world continues to undercount COVID-19 cases and deaths. Seen here is a mass cremation in New Delhi.

Death count of 6 million is likely three times less than the actual number. Case count of 456 million could be 5 to 20 times higher.

Researchers at North Carolina State University have developed a new computational tool that allows users to conduct simulations of multi-functional magnetic nanoparticles in unprecedented detail. The advance paves the way for new work aimed at developing magnetic nanoparticles for use in applications from drug delivery to sensor technologies.

“Self-assembling magnetic nanoparticles, or MNPs, have a lot of desirable properties,” says Yaroslava Yingling, corresponding author of a paper on the work and a Distinguished Professor of Materials Science and Engineering at NC State. “But it has been challenging to study them, because computational models have struggled to account for all of the forces that can influence these materials. MNPs are subject to a complicated interplay between external magnetic fields and van der Waals, electrostatic, dipolar, steric, and hydrodynamic interactions.”

Many applications of MNPs require an understanding of how the nanoparticles will behave in complex environments, such as using MNPs to deliver a specific protein or drug molecule to a targeted cancer affected cell using external magnetic fields. In these cases, it is important to be able to accurately model how MNPs will respond to different chemical environments. Previous computational modeling techniques that looked at MNPs were unable to account for all of the chemical interactions MNPs experience in a given colloidal or biological environment, instead focusing primarily on physical interactions.

What if you could travel to the country of your choice in just 1 click? If that was possible, your train of thought would be, Let’s go to Switzerland, no Iceland…you know what, let’s go everywhere. Teleportation is a common part of science fiction characters but is it achievable?

The pandemic has been hard on us and forced us to step out only when it is absolutely necessary. But you know what, Teleportation can be the perfect thing for you. And Earth is not the limit, you can put on a suit and some oxygen cylinder and you can just teleport to the moon…Elon Musk, you there?😃

But as far as we know, everyone told us while watching science fiction, this is not possible but you know what they are not entirely correct.

Researchers at the Max Planck Institute for Intelligent Systems in Stuttgart have designed and fabricated an untethered microrobot that can slip along either a flat or curved surface in a liquid when exposed to ultrasound waves. Its propulsion force is two to three orders of magnitude stronger than the propulsion force of natural microorganisms such as bacteria or algae. Additionally, it can transport cargo while swimming. The acoustically propelled robot hence has significant potential to revolutionize the future minimally invasive treatment of patients.

Stuttgart—Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart developed a bullet-shaped, synthetic miniature robot with a diameter of 25 micrometers, which is acoustically propelled forward—a speeding bullet, in the truest sense of the word. Less than the diameter of a human hair in size, never before has such an actuated microrobot reached this speed. Its smart design is so efficient it even outperforms the swimming capabilities of natural microorganisms.

The scientists designed the 3D-printed polymer microrobot with a spherical cavity and a small tube-like nozzle towards the bottom (see figure 1). Surrounded by liquid such as water, the cavity traps a spherical air bubble. Once the robot is exposed to acoustic waves of around 330 kHz, the air bubble pulsates, pushing the liquid inside the tube towards the back end of the microrobot. The liquid’s movement then propels the bullet forward quite vigorously at up to 90 body lengths per second. That is a thrust force two to three orders of magnitude stronger than those of natural microorganisms such as algae or bacteria. Both are among the most efficient microswimmers in nature, optimized by evolution.

From chatbots that answer tax questions to algorithms that drive autonomous vehicles and dish out medical diagnoses, artificial intelligence undergirds many aspects of daily life. Creating smarter, more accurate systems requires a hybrid human-machine approach, according to researchers at the University of California, Irvine. In a study published this month in Proceedings of the National Academy of Sciences, they present a new mathematical model that can improve performance by combining human and algorithmic predictions and confidence scores.

“Humans and machine algorithms have complementary strengths and weaknesses. Each uses different sources of information and strategies to make predictions and decisions,” said co-author Mark Steyvers, UCI professor of cognitive sciences. “We show through empirical demonstrations as well as theoretical analyses that humans can improve the predictions of AI even when human accuracy is somewhat below [that of] the AI—and vice versa. And this accuracy is higher than combining predictions from two individuals or two AI algorithms.”

To test the framework, researchers conducted an image classification experiment in which human participants and computer algorithms worked separately to correctly identify distorted pictures of animals and everyday items—chairs, bottles, bicycles, trucks. The human participants ranked their confidence in the accuracy of each image identification as low, medium or high, while the machine classifier generated a continuous score. The results showed large differences in confidence between humans and AI algorithms across images.