Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 6

Microscale hydrogel fibers could enable imaging inside tiny tissue structures

Researchers have developed light-transmitting hydrogel fibers that are just hundreds of micrometers in diameter. With further development, these soft fibers could one day make it possible to use imaging techniques to detect early breast cancer hidden inside very small breast ducts.

“While traditional, relatively rigid fiber probes may cause mechanical damage when entering narrow, curved or soft tissue spaces, our fibers are very soft with mechanical properties more similar to those of human soft tissues,” said research team leader Yu Zhang from Harbin Engineering University in China. “We made these fibers using a draw-spinning method that was inspired by spider-silk spinning.”

In research appearing in Optics Express, the researchers describe how they tested the new hydrogel fibers by incorporating them into an imaging system and using it to analyze standard pathology-stained breast tissue sections. The imaging system successfully reconstructed the microscopic features used by pathologists to evaluate tumors and, when combined with artificial intelligence algorithms, distinguished tumor subtypes with an accuracy of 93.97%.

New macOS malware embeds fake errors to confuse AI analysis tools

A newly discovered macOS malware dubbed “Gaslight” is designed to confuse AI-assisted malware analysis tools by hiding prompt injection strings and fake debugging data within the executable.

Cybersecurity researchers are increasingly using AI-powered tools to assist with malware analysis and reverse engineering.

The malware contains strings that attempt to gaslight AI-assisted analysis tools into believing there is an analysis error or other issue, potentially causing the tools to abort, truncate, or otherwise interfere with the analysis.

Geothermal Could Power 65 Million U.S. Homes by 2050, DOE Says

Somewhat ironically, the technological breakthroughs that make this veritable holy grail of clean energy possible are largely borrowed from the oil and gas industry. The hydraulic fracturing industry has made leaps and bounds when it comes to advancing and refining drilling technologies, and a workforce with a fracking background has therefore been instrumental in making geothermal more feasible and cost-effective.

For example, Mike Matson, the CEO and cofounder of a startup called Birch Geothermal, is applying his background in drilling and reservoir management in the oil and gas industry and applying that expertise directly to geothermal energy innovation. “Birch plans to make use of sensors and autonomous systems to better control how water moves through geothermal wells, ensuring that heat remains steady for reliable electricity generation,” Forbes recently wrote in a profile of Birch Geothermal. “The team is also focused on optimizing reservoir design using techniques originally developed for the oil and gas industry.”

This marriage of clean energy outlooks with fossil fuel expertise gives the United States a major opportunity to become a world leader in enhanced geothermal. “The U.S. has a number of different superpowers and putting holes in the ground and taking things out of those holes is one of them — and doing so more economically and more efficiently than basically any other place on Earth,” Drew Nelson, vice president of Project InnerSpace, was quoted by Cipher News.

Preventing the next pandemic using AI-designed vaccines

For most of human history, infectious diseases were the main causes of morbidity and mortality. Advances in sanitation, antibiotics, vaccines, and public health dramatically shifted that balance, particularly in high-income countries, where life expectancy has increased by nearly 40 years over the past century. Yet the COVID-19 pandemic provided a stark reminder that infectious threats can still reshape societies almost overnight. Between 2019 and 2021 alone, life expectancy in the US fell by more than two years, and recent modelling suggests there is roughly a 50 percent chance of another COVID-scale pandemic occurring within the next 25 years.

Historically, the vaccine development model has been largely reactive and variant-driven, but the industry is now actively shifting toward proactive and universal vaccinology to get ahead of evolving pathogens. Recent results from a first-in-human clinical trial led by the University of Cambridge and its spin-out DIOSynVax, published in the Journal of Infection, provide early clinical evidence of this shift, demonstrating the safety of an AI-designed “super-antigen” intended to provide broad viral coverage.

Teaching AI to Invent Enzymes Nature Never Imagined

Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues.

In this webinar, Chenghao Liu and Jarrid Brooks from the Arnold Lab at Caltech will introduce DISCO (DIffusion for Sequence-structure CO-design). This multimodal model co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp^3)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations.

Matlab-deep-learning/pose-estimation-3D-with-stereo-camera: This demo uses a deep neural network and two generic cameras to perform 3D pose estimation

This demo uses a deep neural network and two generic cameras to perform 3D pose estimation. — matlab-deep-learning/pose-estimation-3D-with-stereo-camera

/* */