Lifeboat Foundation

A novel device consisting of metal, dielectric, and metal layers remembers the history of electrical signals sent through it. This device, called a memristor, could serve as the basis for neuromorphic computers-;computers that work in ways similar to human brains. Unlike traditional digital memory, which stores information as 0s and 1s, this device exhibits so-called “analog” behavior. This means the device can store information between 0 and 1, and it can emulate how synapses function in the brain. Researchers found that the interface between metal and dielectric in the novel device is critical for stable switching and enhanced performance. Simulations indicate that circuits built on this device exhibit improved image recognition.

The Impact

Today’s computers are not energy efficient for big data and machine learning tasks. By 2030, experts predict that data centers could consume about 8% of the world’s electricity. To address this challenge, researchers are working to create computers inspired by the human brain, so-called neuromorphic computers. Artificial synapses created with memristor devices are the building blocks of these computers. These artificial synapses can store and process information in the same location, similar to how neurons and synapses work in the brain. Integrating these emergent devices with conventional computer components will reduce power needs and improve performance for tasks such as artificial intelligence and machine learning.

Expression of co-inhibitory receptors or “checkpoint” molecules, such as CTLA-4 and PD-1, on effector T cells is a key mechanism for ensuring immune homeostasis. Dysregulated expression of co-inhibitory receptors on CD4+ T cells promotes autoimmunity while sustained overexpression on CD8+ T cells promotes T cell dysfunction or exhaustion, leading to impaired ability to clear chronic viral infections and cancer. Immune checkpoint blockade (ICB) treatment by blocking CTLA-4 and PD-1 has revolutionized cancer therapies, yet current ICB response rates are still relatively low. This suggests the need to discover novel checkpoint molecules and cell types where checkpoint molecules may be exerting additional or differential effects. We and others have discovered additional checkpoint molecules, including Tim-3, Lag-3, and TIGIT. Using RNA and protein expression profiling at single-cell resolution, we discovered that the checkpoint molecules are expressed as a module that is co-expressed and co-regulated on CD8+ T cells, where they cooperatively induce T cell dysfunction. The same module of checkpoint molecules is also expressed on FoxP3+ Tregs, but their role in regulating immunity and anti-tumor immunity has not been fully appreciated. We have conditionally deleted checkpoint molecules on various cell types including Foxp3+ Tregs and studied their role in regulating autoimmunity, tumor growth, and anti-tumor immunity. Studies with a number of the co-inhibitory molecules on effector T cells, Tregs, and dendritic cells in regulating anti-tumor immunity will be discussed.

Learning Objectives

A nasal spray drug can counteract the cognitive decline and brain damage typical of Alzheimer’s disease in animal models.

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Researchers at INRS have developed a synthetic photonic lattice capable of generating and manipulating quantum states of light, paving the way for promising advancements in applications ranging from quantum computing to secure quantum communication protocols.

A study co-directed by Professor Roberto Morandotti of Institut national de la recherche scientifique (INRS) in collaboration with teams from Germany, Italy, and Japan paves the way for innovative solutions that could enable the development of a system to process quantum information with both simplicity and power.

Their work, just published in the journal Nature Photonics, presents a method for manipulating the photonic states of light in a never-before-seen way, offering greater control over the evolution of photon propagation. This control makes it possible to improve the detection and number of photon coincidences, as well as the efficiency of the system.

The Expedition 72 crew studied micro-algae and DNA-like nanomaterials on Tuesday to improve health in space and on Earth. The orbital residents also worked on cargo transfers and lab maintenance aboard the International Space Station.

NASA Flight Engineer Nick Hague began his day processing radiation-resistant samples of Arthrospira C micro-algae and stowing them in an incubator for analysis. The samples will be exposed to different light intensities to observe how they affect the micro-algae’s cell growth and oxygen production. Results may advance the development of spacecraft life support systems and fresh food production in space.

Afterward, Hague joined Commander Suni Williams of NASA for a different research session mixing water with samples of messenger RNA, or mRNA, and protein to create DNA-like nanomaterial products inside the Kibo laboratory module’s Life Science Glovebox. Flight Engineer Butch Wilmore then transferred the samples, exposed them to ultrasonic waves, and imaged them with a spectrophotometer to measure the intensity of light at different wavelengths and evaluate the quality of the nanomaterials. The samples will also be returned to Earth for further evaluation. Results may lead to improved therapies for Earth and space health conditions as well as advance the space economy.

University of Chicago scientists have expanded our understanding of snoRNAs, discovering their extensive influence on cellular functions beyond guiding RNA modifications.

Their work introduces potential therapeutic applications for controlling protein secretion, highlighting snoRNAs’ broader biological significance.

SnoRNAs and Their Functions.

The mission aims to help NASA determine whether Titan’s conditions could be suitable for supporting life.

Summary: A new AI algorithm inspired by the genome’s ability to compress vast information offers insights into brain function and potential tech applications. Researchers found that this algorithm performs tasks like image recognition and video games almost as effectively as fully trained AI networks.

By mimicking how genomes encode complex behaviors with limited data, the model highlights the evolutionary advantage of efficient information compression. The findings suggest new pathways for developing advanced, lightweight AI systems capable of running on smaller devices like smartphones.