Lifeboat Foundation

Imagine you’re a young engineer whose boss drops by one morning with a sheaf of complicated fluid dynamics equations. “We need you to design a system to solve these equations for the latest fighter jet,” bossman intones, and although you groan as you recall the hell of your fluid dynamics courses, you realize that it should be easy enough to whip up a program to do the job. But then you remember that it’s like 1950, and that digital computers — at least ones that can fit in an airplane — haven’t been invented yet, and that you’re going to have to do this the hard way.

The scenario is obviously contrived, but this peek inside the Bendix MG-1 Central Air Data Computer reveals the engineer’s nightmare fuel that was needed to accomplish some pretty complex computations in a severely resource-constrained environment. As [Ken Shirriff] explains, this particular device was used aboard USAF fighter aircraft in the mid-50s, when the complexities of supersonic flight were beginning to outpace the instrumentation needed to safely fly in that regime. Thanks to the way air behaves near the speed of sound, a simple pitot tube system for measuring airspeed was no longer enough; analog computers like the MG-1 were designed to deal with these changes and integrate them into a host of other measurements critical to the pilot.

Continue reading “1950s Fighter Jet Air Computer Shows What Analog Could Do” »

After Russia launched a nearly unstoppable missile, America is hitting back with a bomber that can travel at Mach 10.

A key algorithm that quietly empowers and simplifies our electronics is the Fourier transform, which turns the graph of a signal varying in time into a graph that describes it in terms of its frequencies.

Packaging signals that represent sounds or images in terms of their frequencies allows us to analyze and adjust sound and image files, Richard Stern, professor of electrical and computer engineering at Carnegie Mellon University, tells Popular Mechanics. This mathematical operation also makes it possible for us to store data efficiently.

The invention of color TV is a great example of this, Stern explains. In the 1950s, television was just black and white. Engineers at RCA developed color television, and used Fourier transforms to simplify the data transmission so that the industry could introduce color without tripling the demands on the channels by adding data for red, green, and blue light. Viewers with black-and-white TVs could continue to see the same images as they saw before, while viewers with color TVs could now see the images in color.

One question for Brad Ringeisen, a chemist and executive director of the Innovative Genomics Institute. Founded by Nobel Prize-winning biochemist Jennifer Doudna, it aims to bridge revolutionary gene-editing tool development to affordable and accessible solutions in human health and climate.

Will CRISPR cure cancer?

We’re always thinking about: What are those targets in the future? Cancer is one of those things. The biggest impact is going to be what’s called systemic delivery, or in vivo delivery. There’s been one example of this in the community right now—to treat a liver disease. Intellia Therapeutics, a biotech company, has shown that you can actually intravenously apply CRISPR-Cas9 treatment. (CRISPR is the guide RNA, the targeting molecule, and Cas9 is the cutting molecule that edits DNA.) It can go to the liver and target the liver cells, and make edits at a high enough efficacy to treat genetic liver disease. The problem is that the liver is the easiest. It’s like the garbage can of the body. Pretty much anything that you put into the body is ultimately going to find its way to the liver. So that’s absolutely the easiest tissue to deliver to. But trying to deliver to a solid tumor, or to the brain, is much more difficult.

A team of undergraduate and graduate chemistry students in Jennifer Hines’ lab at Ohio University recently uncovered a new class of compounds that can target RNA and disrupt its function. This discovery identified a chemical scaffold that could ultimately be used in the development of RNA-targeted medicines to treat bacterial and viral infections, as well as cancer and metabolic diseases.

RNA is chemically like DNA but also controls the extent to which the DNA’s instructions are carried out within a living cell. It is this essential regulatory role in the function of the cell that makes RNA such an attractive target.

“Trying to target RNA is at the forefront of medicinal chemistry research with enormous potential for treating diseases,” said Hines, professor of chemistry and biochemistry in the College of Arts and Sciences. “However, there are relatively few compounds known to directly modulate RNA activity which makes it challenging to design new RNA-targeted therapeutics.”

An anthropologist imagines a world in which Neanderthals—and their relationships with the environment and one another—survived evolution.

Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.

Does someone look angry or sad? You can probably offer an answer to that question based on the information you can see just by looking at their face. That’s because facial expressions—or a combination of different small facial movements—can be read by other humans to help understand what a person might be feeling at that exact moment.

Since Darwin’s seminal work on the evolutionary origins of facial expressions of emotion, scientists have been trying to find out which specific combinations of facial movements best represent our six basic emotions: happy, surprise, fear, disgust, anger and sadness. So far, researchers have offered a range of theories—or models—to define which facial movements best match each emotion, but until now no one has been able to show which one is most accurate.

Now, a new study by a team of European researchers led by the University of Glasgow and University of Amsterdam has begun to answer that question. The new study, which is published in Science Advances, shows that even the best models for predicting emotions from facial expressions fall short of the judgment of real human participants. Moreover, different humans themselves may read different emotions from the same facial expression, making it even harder to pinpoint exactly which facial movements are systematically linked with certain emotions.

A team of neuroscientists at the New York University School of Medicine has found a link between chronic pain and overactive pyramidal neurons during sleep periods. In their study, published in the journal Nature Neuroscience, the group conducted experiments with injured mice experiencing chronic pain.

Prior research has shown that there is often a link between chronic pain and insomnia. After experiencing a neural injury of some sort, many patients are left with some degree of lasting pain. This tends to result in poor sleep and sometimes insomnia. Once that happens, the pain becomes worse, and over time becomes chronic. But why this happens has been a mystery. In this new effort, the team in New York conducted experiments with mice hoping to find the answer.

The work involved inducing chronic pain in mice by damaging two of the three branches that make up a group of sciatic nerves. Doing so led to skin sensitivity in the legs. The researchers scanned the brains of each of the mice before and after the damage. They observed that pyramidal neurons in the part of the cerebral cortex responsible for sensation processing in the skin became more active. And over the course of several weeks, the activity increased, peaking during non-REM sleep.