Lifeboat Foundation

Water electrolysis is a cornerstone of global sustainable and renewable energy systems, facilitating the production of hydrogen fuel. This clean and versatile energy carrier can be utilized in various applications, such as chemical CO₂ conversion, and electricity generation. Utilizing renewable energy sources such as solar and wind to power the electrolysis process may help reduce carbon emissions and promote the transition to a low-carbon economy.

The development of efficient and stable anode materials for the Oxygen Evolution Reaction (OER) is essential for advancing Proton Exchange Membrane (PEM) water electrolysis technology. OER is a key electrochemical reaction that generates oxygen gas (O₂) from water (H₂O) or hydroxide ions (OH⁻) during water splitting.

This seemingly simple reaction is crucial in energy conversion technologies like water electrolysis as it is hard to efficiently realize and a concurrent process to the wanted hydrogen production. Iridium (Ir)-based materials, particularly amorphous hydrous iridium oxide (am-hydr-IrO_x), are at the forefront of this research due to their high activity. However, their application is limited by high dissolution rates of the precious iridium.

By studying ancient, supermassive black holes called quasars, MIT PhD student Dominika Ďurovčíková is illuminating an early moment in the universe, when the galaxies could first be observed.

MovieNet mimics the brain to analyze dynamic scenes with exceptional accuracy.

By simulating how the human brain processes a moving world, Scripps researchers have reached a breakthrough in AI as current models only recognize still images.

Training AI models today isn’t just about designing better architectures—it’s also about managing data efficiently. Modern models require vast datasets and need those datasets delivered quickly to GPUs and other accelerators. The problem? Traditional data loading systems often lag behind, slowing everything down. These older systems rely heavily on process-based methods that struggle to keep up with the demand, leading to GPU downtime, longer training sessions, and higher costs. This becomes even more frustrating when you’re trying to scale up or work with a mix of data types.

To tackle these issues, Meta AI has developed SPDL (Scalable and Performant Data Loading), a tool designed to improve how data is delivered during AI training. SPDL uses thread-based loading, which is a departure from the traditional process-based approach, to speed things up. It handles data from all sorts of sources—whether you’re pulling from the cloud or a local storage system—and integrates it seamlessly into your training workflow.

SPDL was built with scalability in mind. It works across distributed systems, so whether you’re training on a single GPU or a large cluster, SPDL has you covered. It’s also designed to work well with PyTorch, one of the most widely used AI frameworks, making it easier for teams to adopt. And since it’s open-source, anyone can take advantage of it or even contribute to its improvement.

The hallmark of a good AI model is its ability to work the same in different groups, settings, and situations. See how these NCI researchers used in-house and external images to test their prostate model’s generalizability.

How do human organs develop and what happens to them when they become diseased? To answer these questions, researchers are increasingly focusing on so-called organoids. These mini-organs, just a few millimeters in size, consist of groups of cells cultivated in the laboratory that can form organ-like structures.

Similar to embryonic development, organoids make it possible to investigate the interaction of cells in three-dimensional space—for example in metabolic processes or disease mechanisms.

The production of organoids is tricky; the required nutrients, growth factors and signaling molecules must be added in a specific order and at specific times according to a precise schedule.

A new study may reshape our understanding of blood flow regulation in the brain.

Scientists have found a new link between diet and colon cancer risk that could change how we fight the disease with more targeted treatments.

Ultimately, the goal is to find ways to stop deadly disease caused by Cryptococcus neoformans from developing in humans and animals. But until that time, finding new and better ways to treat already existent disease and its symptoms is a high priority.

The laboratory of Kirsten Nielsen in the Center for One Health Research has taken a step toward improved treatment of Cryptococcus, completing a six-year study to examine the virulence of 38 clinical isolates from various strains of Cryptococcus. The results are published in Nature Communications.

“The question that we’ve been addressing is: Can we predict severe disease outcomes in patients?” said Nielsen, professor of microbiology and immunology in the Virginia-Maryland College of Veterinary Medicine. “If we can predict disease outcome, then we can treat patients better. In these studies, we identified not just the genes that allow Cryptococcus to cause disease, but also the gene alleles that allow it to cause more disease or less disease.”