Lifeboat Foundation

Determining the 3D shapes of biological molecules is one of the hardest problems in modern biology and medical discovery. Companies and research institutions often spend millions of dollars to determine a molecular structure—and even such massive efforts are frequently unsuccessful.

Using clever, new machine learning techniques, Stanford University Ph.D. students Stephan Eismann and Raphael Townshend, under the guidance of Ron Dror, associate professor of computer science, have developed an approach that overcomes this problem by predicting accurate structures computationally.

Most notably, their approach succeeds even when learning from only a few known structures, making it applicable to the types of molecules whose structures are most difficult to determine experimentally.

It’s been a truth for a long time: if you want to study the movement and behavior of single atoms, electron microscopy can give you what X-rays can’t. X-rays are good at penetrating into samples—they allow you to see what happens inside batteries as they charge and discharge, for example—but historically they have not been able to spatially image with the same precision electrons can.

But scientists are working to improve the image resolution of X-ray techniques. One such method is X-ray tomography, which enables non-invasive imaging of the inside of materials. If you want to map the intricacies of a microcircuit, for example, or trace the neurons in a brain without destroying the material you are looking at, you need X-ray tomography, and the better the resolution, the smaller the phenomena you can trace with the X-ray beam.

To that end, a group of scientists led by the U.S. Department of Energy’s (DOE) Argonne National Laboratory has created a new method for improving the resolution of hard X-ray nanotomography. (Nanotomography is X-ray imaging on the scale of nanometers. For comparison, an average human hair is 100,000 nanometers wide.) The team constructed a high-resolution X-ray microscope using the powerful X-ray beams of the Advanced Photon Source (APS) and created new computer algorithms to compensate for issues encountered at tiny scales. Using this method, the team achieved a resolution below 10 nanometers.

New research has found that artificial intelligence (AI) analyzing medical scans can identify the race of patients with an astonishing degree of accuracy, while their human counterparts cannot. With the Food and Drug Administration (FDA) approving more algorithms for medical use, the researchers are concerned that AI could end up perpetuating racial biases. They are especially concerned that they could not figure out precisely how the machine-learning models were able to identify race, even from heavily corrupted and low-resolution images.

In the study, published on pre-print service Arxiv, an international team of doctors investigated how deep learning models can detect race from medical images. Using private and public chest scans and self-reported data on race and ethnicity, they first assessed how accurate the algorithms were, before investigating the mechanism.

“We hypothesized that if the model was able to identify a patient’s race, this would suggest the models had implicitly learned to recognize racial information despite not being directly trained for that task,” the team wrote in their research.

See how perception and adaptability enable varied, high-energy behaviors like parkour. https://bit.ly/3AZWMCu

AI has finally come full circle.

A new suite of algorithms by Google Brain can now design computer chips —those specifically tailored for running AI software —that vastly outperform those designed by human experts. And the system works in just a few hours, dramatically slashing the weeks-or months-long process that normally gums up digital innovation.

At the heart of these robotic chip designers is a type of machine learning called deep reinforcement learning. This family of algorithms, loosely based on the human brain’s workings, has triumphed over its biological neural inspirations in games such as Chess, Go, and nearly the entire Atari catalog.

Why do so many people get frustrated with their “high-tech” prostheses? Though sophisticated robotics allow for prosthetic joints that can do everything a human can and more, the way we control robotic machines right now doesn’t allow us to operate them as naturally as you would a biological hand. Most robotic prostheses are controlled via metal pads on the skin that indirectly measure muscle action and then make some assumptions to determine what the person wants to do. Whil… See More.

We plan to use MM to provide natural control over prosthetic limbs by leveraging the human body’s proprioception. When you wiggle one of your fingers, your brain senses muscle lengths, speeds, and forces, and it uses these to figure out the position of that finger. This is called body awareness, or proprioception. When someone receives an amputation, if their muscle connections are maintained with what is called the “AMI technique,” their brain still perceives muscle flexion as it relates to joint movement, as if their limb was still present. In other words, they are sensing movement of a phantom limb. To give an amputee intuitive control over a robotic prosthesis, we plan to directly measure the muscle lengths and speeds involved in this phantom limb experience and have the robot copy what the brain expects, so that the brain experiences awareness of the robot’s current state. We see this technique as an important next step in the embodiment of the prosthetic limb (the feeling that it is truly part of one’s body).

Notably, the tracking of magnetic beads is minimally invasive, not requiring wires to run through the skin boundary or electronics to be implanted inside the body, and these magnetic beads can be made safe to implant by coating them in a biocompatible material. In addition, for muscles that are close to the skin, MM can be performed with very high accuracy. We found that by increasing the number of compass sensors we used, we could track live muscle lengths close to the surface of the skin with better than millimeter accuracy, and we found that our measurements were consistent to within the width of a human hair (about 37 thousandths of a millimeter).

Continue reading “Improving prosthetic control using magnetomicrometry” »

How true Eric Klien?

Something to look forward to: Solid-state batteries are still nebulous outside of the lab. Still, automakers are scrambling to be the first in the race to build the first electric car to take advantage of the added energy density and better safety when compared to lithium-ion designs. To that end, they’re investing in companies like QuantumScape, Solid Power, and Sakuu to develop manufacturing techniques that either build on existing approaches or rely on new additive manufacturing technology.

Continue reading “Sakuu’s 3D printed solid state battery could be a boon for electric vehicles” »

Hi guys! I created a Graphing Calculator with just redstone.

I know I say this every time, but this is without a doubt the most challenging and complicated machine I’ve ever made. It required multiple components working together to solve equations and plot over 10,000 different point values.

SPECS.

Albert Einstein’s most famous equation, E = mc², used in the theory of general relativity, has been used to create matter from light, scientists have said in a new study.

Researchers from New York’s Brookhaven National Laboratory used the Department of Energy’s Relativistic Heavy Ion Collider (RHIC), ordinarily used for nuclear physics research, to speed up two gold ions that are positively charged, in a loop.