Lifeboat Foundation

Are malevolent space aliens keeping quiet as an act of instinctual self-preservation?

“TECLA (an acronym which stands for “Technology and Clay”) is a habitat consisting of two interconnected housing units, each covered by a semi-spherical dome. The units have been built using multiple Crane Wasp printing units operating simultaneously. Crane WASP is defined by the manufacturer as “a collaborative 3D printing system capable of printing houses” and can print various materials — such as earth-based materials, concrete mortar, and geopolymers — with a maximum speed of 300 mm/s and a maximum printing area of 50 sqm per unit. The design of the habitat features two or more “cocoon-like” housing units, whose shape vaguely resembles that of a sea urchin, in which structure, insulation, and finishes coincide. The thick raw earth walls of the units have a hollow structure consisting of several clay “waves”, which makes them at the same time relatively lightweight, resistant, and highly insulating. About 200 printing hours are required to build each unit, which consists of 350 clay layers, each 12 mm thick.”

Designed by Mario Cucinella and build by WASP, TECLA is a prototype house near Ravenna, Italy, made by 3D-printing a material based on locally-sourced clay.

The Hallmarks of Aging are well established, but what is less discussed is the impact of microbes. Viruses, including SARS-CoV-2 and HSV-1 cause mitochondrial dysfunction-more specifically, they cause mitochondria to release some of their DNA, which triggers antiviral immunity. When considering that blood levels of mitochondrial DNA increase during aging, is aging characterized by an increased viral burden?

Paper links:
Decoding SARS-CoV-2 hijacking of host mitochondria in COVID-19
pathogenesis: https://pubmed.ncbi.nlm.nih.gov/32510973/

Continue reading “Microbes Affect The Hallmarks Of Aging: Mitochondrial Dysfunction (Part II)” »

Polarons are important nanoscale phenomena: a transient configuration between electrons and atoms (known as quasiparticles) that exist for only trillionths of a second.

A cheap, portable quantum computer, aimed at schools and colleges will be launched later this year.

The task of creating effective deep learning models has become too much of a challenge for humans to tackle alone.

Dr. Fatima Ebrahimi designed a fusion rocket that uses magnetic fields to shoot plasma particles from a craft, which could take humans to Mars 10 times faster than current devices.

This is the SECOND PART of the interview with Harold Katcher in Modern Healthspan YouTube channel.

Dr. Harold Katcher is a professor of Biology at the University of Maryland. He has been a pioneer in the field of cancer research, in the development of modern aspects of gene hunting and sequencing. He carries expertise in bioinformatics, chronobiology, and biotechnology. Dr. Katcher is currently working in the capacity of Chief Technical Officer at Nugenics Research exploring rejuvenation treatments in mammals.
In May 2020 there was a paper published on biorxiv about the rejuvenation of rats by over 50%. We did a review of the paper which you can find linked to above. In this interview series we talk with Dr. Harold Katcher, one of the main authors of the paper about the experiment, the steps to get validation, commercialization and how the results fit into his theories of aging.

Continue reading “Elixir Plasma 3rd Party Verification | Dr Harold Katcher Interivew Series Ep2” »

But the human eye can only see so much, even with the help of a microscope; despite embryologists’ efforts to select the “best” embryo, success rates are still relatively low. “Many decisions are based on gut feeling or personal experience,” said Embryonics founder and CEO Yael Gold-Zamir. “Even if you go to the same IVF center, two experts can give you different opinions on the same embryo.”

This is where Embryonics’ technology comes in. They used 8,789 time-lapse videos of developing embryos to train an algorithm that predicts the likelihood of successful embryo implantation. A little less than half of the embryos from the dataset were graded by embryologists, and implantation data was integrated when it was available (as a binary “successful” or “failed” metric).

The algorithm uses geometric deep learning, a technique that takes a traditional convolutional neural network—which filters input data to create maps of its features, and is most commonly used for image recognition—and applies it to more complex data like 3D objects and graphs. Within days after fertilization, the embryo is still at the blastocyst stage, essentially a microscopic clump of just 200–300 cells; the algorithm uses this deep learning technique to spot and identify patterns in embryo development that human embryologists either wouldn’t see at all, or would require massive collation of data to validate.

Continue reading “How an Israeli Startup Is Using AI to Help People Make Babies” »