Lifeboat Foundation

Scientists have discovered what turns off the molecular alarm system that plays a critical role in our immune response.

The antibacterial superhero MR1 (MHC class I-related molecule) is a protein found in every cell of the human body that functions as a molecular alarm system, alerting powerful cells of our immune system, our white blood cells, when cancer or bacterial infection is present.

While prior groundbreaking research revealed the cellular machinery that MR1 depends on to activate, nothing was understood about how the MR1 alarm is “turned off” until now.

New research from the University of Cincinnati bolsters a hypothesis that Alzheimer’s disease is caused by a decline in levels of a specific protein, contrary to a prevailing theory that has been recently called into question.

UC researchers led by Alberto Espay, MD, and Andrea Sturchio, MD, in collaboration with the Karolinska Institute in Sweden, published the research on Oct. 4 in the Journal of Alzheimer’s Disease.

The research is focused on a protein called amyloid-beta. The protein normally carries out its functions in the brain in a form that is soluble, meaning dissolvable in water, but it sometimes hardens into clumps, known as amyloid plaques.

One of the powerful features of big language models, such as GPT-3, comes from its ability to learn after it has been trained and without being updated. You simply feed it a few examples of input and…

A team of researchers with the University of New South Wales (UNSW) in Sydney has achieved a breakthrough in spin qubit coherence times (opens in new tab). The research took advantage of the team’s previous work on so-called “dressed” qubits — qubits constantly under the effect of an electromagnetic field shielding them from interference. In addition, the researchers leveraged a newly-designed protocol, SMART, (opens in new tab) which leverages the increased coherence times to allow individual qubits to be safely coaxed to perform the required computations.

The improvements allowed the researchers to register coherence times of up to two milliseconds — over a hundred times higher than similar control methods in the past, but still a ways from the amount of time your eyelids take to blink.

The advanced Computer Vision and Artificial Intelligence technologies in X·TERROIR allow enologists to make optimal decisions about the wine destination of grapes.

X·TERROIR technology makes possible cost-effective phenotypic profiling of every vine in the vineyard. This is an exponential increase over what is possible with current technology. The more information that Enologists have to work their magic… the more quality and value they can extract from the vineyard.

Continue reading “X·terroir” »

How we got to where we are today, and where we go from here. Featuring Dr. Daniel Monti (Director — Marcus Institute of Integrative Health) and leading neuroscientist Dr. Andrew Newberg (DMT: The Spirit Molecule), we get a fresh perspective on the effects of stress. Thanks to a new study from the Marcus Institute of Integrative Health at Thomas Jefferson University in Philadelphia, PA, there is now compelling evidence that this breakthrough intervention can help people to alleviate their emotional stress, as well as deeper insight into this exciting topic.

https://stresseddoc.com/

Himanshu Brahmbhatt was staring at the results of a clinical trial that looked too good to be true. A co-founder and CEO of EnGeneIC, a biopharmaceutical company, Brahmbhatt was running a small trial that was testing a fundamentally different approach to fighting cancer. Patients in the group had grim prospects. They had exhausted all other options. With nothing left to lose and not expecting any miracles, they enrolled in the trial. They wanted to give it one more chance. Now their scans showed their tumors had stopped progressing. Even more remarkable was they didn’t have the same type of tumors. They had malignancies affecting different organs—lungs, bladders, colons, pancreases—and yet, they uniformly did well.

“These people were facing death,” Brahmbhatt says. “Then we started seeing that they were actually succeeding. You could see in the scan that the tumor has stopped growing. It was a feeling of such extreme internal joy that it’s very difficult to describe.”

The results may have appeared miraculous, but they were anything but. They stemmed from fundamental research into cell division that forms the basis of the EnGeneIC process. A longtime advisor to the company, Bruce Stillman, professor of biochemistry and president and CEO of Cold Spring Harbor Laboratory, has been studying the process of DNA replication, which plays a key role in cell division and cancer progression.

A reinforcement learning approach based on AlphaZero is used to discover efficient and provably correct algorithms for matrix multiplication, finding faster algorithms for a variety of matrix sizes.

The end of classical Computer Science is coming, and most of us are dinosaurs waiting for the meteor to hit.

I came of age in the 1980s, programming personal computers like the Commodore VIC-20 and Apple ][e at home. Going on to study Computer Science in college and ultimately getting a PhD at Berkeley, the bulk of my professional training was rooted in what I will call “classical” CS: programming, algorithms, data structures, systems, programming languages. In Classical Computer Science, the ultimate goal is to reduce an idea to a program written by a human — source code in a language like Java or C++ or Python. Every idea in Classical CS — no matter how complex or sophisticated — from a database join algorithm to the mind-bogglingly obtuse Paxos consensus protocol — can be expressed as a human-readable, human-comprehendible program.

When I was in college in the early ’90s, we were still in the depth of the AI Winter, and AI as a field was likewise dominated by classical algorithms. My first research job at Cornell was working with Dan Huttenlocher, a leader in the field of computer vision (and now Dean of the MIT School of Computing). In Dan’s PhD-level computer vision course in 1995 or so, we never once discussed anything resembling deep learning or neural networks—it was all classical algorithms like Canny edge detection, optical flow, and Hausdorff distances. Deep learning was in its infancy, not yet considered mainstream AI, let alone mainstream CS.