Lifeboat Foundation

Researchers from University of California San Diego School of Medicine have used single-cell RNA sequencing (scRNA-seq) to identify a pattern of gene expression that can be used to predict whether or not neurons will regenerate after an injury. Tests in mice showed that this “Regeneration Classifier” was consistently reliable in predicting the regeneration potential of neurons across the nervous system and at different developmental stages. Conditional gene deletion then validated a role for NFE2L2 (or NRF2), a master regulator of antioxidant response, in corticospinal tract regeneration.

“Single-cell sequencing technology is helping us look at the biology of neurons in much more detail than has ever been possible, and this study really demonstrates that capability,” said senior author Binhai Zheng, PhD, professor in the Department of Neurosciences at UC San Diego School of Medicine. “What we’ve discovered here could be just the beginning of a new generation of sophisticated biomarkers based on single-cell data.” Zheng and colleagues reported on their findings in Neuron, in a paper titled “Deep scRNA sequencing reveals a broadly applicable Regeneration Classifier and implicates antioxidant response in corticospinal axon regeneration.” In their paper the team concluded, “Our data demonstrate a universal transcriptomic signature underlying the regenerative potential of vastly different neuronalpopulations and illustrate that deep sequencing of only hundreds of phenotypically identified neurons has the power to advance regenerative biology.”

Neurons are among the slowest cells to regenerate after an injury. While scientists have made progress in understanding neuronal regeneration, it remains unknown why some neurons regenerate and others do not.

Transformers are machine learning models designed to uncover and track patterns in sequential data, such as text sequences. In recent years, these models have become increasingly sophisticated, forming the backbone of popular conversational platforms, such as ChatGPT.

While existing transformers have achieved good results in a variety of tasks, their performance often declines significantly when processing longer sequences. This is due to their limited storage capacity, or in other words the small amount of data they can store and analyze at once.

Researchers at Sungkyunkwan University in South Korea recently developed a new memory system that could help to improve the performance of transformers on more complex tasks characterized by longer data sequences. This system, introduced in a paper published on the arXiv preprint server, is inspired by a prominent theory of human memory, known as Hebbian theory.

Holograms are getting better and cheaper and some think soon they will be everywhere.

Microsoft Research, the R&D arm of the Redmond software giant, is testing the storage of huge amounts of data on glass plates in a futuristic initiative dubbed “Project Silica.” If successful, it could be used to store information for thousands of years without degradation.

The Microsoft researchers store the data in the glass using three-dimensional pixels called voxels. In contrast to classical storage methods such as magnetic spinning disks, the “saucer-sized glass plates of Project Silica will store data for thousands of years and create sustainable storage for the world,” as Microsoft describes it.

Magnetic storage, while widely used, is problematic, according to Microsoft. Because of their limited lifespan, they need to be recopied frequently, which increases energy consumption and operating costs over time: “A hard disk drive might last five years. A tape, well, if you’re brave, it might last ten years”, explains Ant Rowstron, Distinguished Engineer, Project Silica.

Dogs of War bots.

Armed with a rocket launcher or other kinds of weapons, including small arms, a quadrupedal robot could also just be used to scout ahead of friendly forces, and then have the ability to immediately engage any threats it finds.

Uncrewed ground systems like this have the ability to get in and out of spaces where a person might not be able to at all, as well, which could again be particularly useful when maneuvering through dense urban environments. The U.S. military sees operations in large built-up areas as a key component of any future major conflict.

Continue reading “Marines Test Fire Robot Dog Armed With Rocket Launcher” »

Here’s my latest Opinion piece just out for Newsweek…focusing on cyborg rights.

Over the past half-century, the microprocessor’s capacity has doubled approximately every 18–24 months, and some experts predict that by 2030, machine intelligence could surpass human capabilities. The question then arises: When machines reach human-level intelligence, should they be granted protection and rights? Will they desire and perhaps even demand such rights?

Beyond advancements in microprocessors, we’re witnessing breakthroughs in genetic editing, stem cells, and 3D bioprinting, all which also hold the potential to help create cyborg entities displaying consciousness and intelligence. Notably, Yale University’s experiments stimulating dead pig brains have ignited debates in the animal rights realm, raising questions about the ethical implications of reviving consciousness.

Continue reading “Creating Sapient Technology and Cyborg Rights Should Happen Soon” »

Tomorrow Bio’s founder and CEO Dr. Emil Kendziorra will give a presentation introducing the topic of cryonics, followed by a Q&A with the audience.

Möbius strips are fun geometrical shapes that only have one side. Take a strip of paper – it’s got a front and a back. Now twist it and glue the two short edges together. Suddenly there is no front or back. You could draw a line across its whole surface without having to lift the pencil from the paper. Forty-six years ago mathematicians suggested the minimum size for such a strip but they couldn’t prove it. Now, someone finally has.

Since the creation of the strip by August Ferdinand Möbius and Johann Benedict Listing, its simplicity in making and visualizing it had to be balanced with the mathematical complexity of such a shape. It is not surprising that in 1977, Charles Sidney Weaver and Benjamin Rigler Halpern created the Halpern-Weaver Conjecture, which stated the minimal ratio between the width of the strip and its length. They suggested that for a strip with a width of 1 centimeter (0.39 inches), the length had to be at least the square root of 3 centimeters (about 1.73 centimeters or 0.68 inches).

For smooth Möbius strips that are “embedded”, meaning they don’t intersect with each other, the conjecture had no solution. If the strip can go through itself, it is a much easier problem to solve, Brown University’s mathematician Richard Evan Schwartz proposed in 2020 – but he had made a mistake. In a paper posted as a preprint – meaning it is yet to be subjected to peer review – Schwartz corrected the error and found the right solution for the conjecture.

Study finds that in worms, the HSN neuron uses multiple chemicals and connections to orchestrate egg-laying and locomotion over the course of several minutes.

A new MIT

MIT is an acronym for the Massachusetts Institute of Technology. It is a prestigious private research university in Cambridge, Massachusetts that was founded in 1861. It is organized into five Schools: architecture and planning; engineering; humanities, arts, and social sciences; management; and science. MIT’s impact includes many scientific breakthroughs and technological advances. Their stated goal is to make a better world through education, research, and innovation.