Lifeboat Foundation

In 2017, Stanford University researchers presented a new device that mimics the brain’s efficient and low-energy neural learning process. It was an artificial version of a synapse — the gap across which neurotransmitters travel to communicate between neurons — made from organic materials. In 2019, the researchers assembled nine of their artificial synapses together in an array, showing that they could be simultaneously programmed to mimic the parallel operation of the brain.

Now, in a paper published June 15 in Nature Materials, they have tested the first biohybrid version of their artificial synapse and demonstrated that it can communicate with living cells. Future technologies stemming from this device could function by responding directly to chemical signals from the brain. The research was conducted in collaboration with researchers at Istituto Italiano di Tecnologia (Italian Institute of Technology – IIT) in Italy and at Eindhoven University of Technology (Netherlands).

“This paper really highlights the unique strength of the materials that we use in being able to interact with living matter,” said Alberto Salleo, professor of materials science and engineering at Stanford and co-senior author of the paper. “The cells are happy sitting on the soft polymer. But the compatibility goes deeper: These materials work with the same molecules neurons use naturally.”

While people usually put a lot of effort into finding a partner, recent research suggests that our bodies are choosy in their own ways. Chemical signals that are released by human eggs can attract sperm, and new work has shown that eggs also use these molecules to ‘select’ the sperm they want. The findings, reported in Proceedings of the Royal Society B, showed that eggs also don’t always pick the same sperm; eggs from different women can opt for sperm from different men.

Continue reading “Human Eggs Can Choose the Sperm They Prefer” »

Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space — the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

An Oregon State University environmental engineering professor has solved a decades-old mystery regarding the behavior of fluids, a field of study with widespread medical, industrial, and environmental applications.

The research by Brian D. Wood, published in the Journal of Fluid Mechanics, clears a roadblock that has been puzzling scientific minds for nearly 70 years and paves the way to a clearer picture of how chemicals mix in fluids.

A more complete grasp of that basic principle provides a foundation for advances in a range of areas – from how pollutants spread in the atmosphere to how drugs perfuse tissues within the human body.

Every second of every day, countless biochemical reactions take place in our bodies’ cells. The organization of this complex system is the result of billions of years of evolution, fine-tuning our functions since the first primordial organisms.

One such vital reaction is “methylation,” where a methyl group —a carbon atom linked to three hydrogen atoms—attaches itself to a target molecule. Methylation is involved in the regulation of everything from DNA to proteins, and it is so vital that it can be found in all living organisms.

In a recent paper published in Communications Biology, a team of researchers lead by Jean-Michel Fustin and Hitoshi Okamura from Kyoto University’s Graduate School of Pharmaceutical Sciences has uncovered an intimate connection between methylation and the body’s circadian rhythms: a link that exists even in organisms that don’t traditionally “sleep,” such as bacteria.

“And by answering the question, how do psychedelics work, we also inadvertently shine a light on other questions science has shied away from for decades. How do our brains generate our sense of self? What is the neurochemistry of consciousness?”

New research asks, how does psilocybin create a feeling of ego dissolution, and what chemicals in the brain create our subjective sense of self?

Analysing data from hundreds of major earthquakes, Doyeon Kim at the University of Maryland and his colleagues have found a new structure beneath the volcanic Marquesas Islands in the South Pacific Ocean. The structure, known as an ultra-low velocity (ULV) zone, is about 1000 kilometres in diameter and 25 kilometres thick, says Kim.

These structures are called ULV zones because seismic waves pass through them at slower velocities, but what they are made of is still a mystery. They might be chemically distinct from Earth’s iron–nickel alloy core and silicate rock mantle, or have different thermal properties.

The researchers discovered the structure while analysing 7000 records of seismic activity from earthquakes that occurred around the Pacific Ocean basin between 1990 and 2018. The earthquakes all had a magnitude of 6.5 or greater, and were all deeper than 200 kilometres below Earth’s surface.

A memristor¹ has been proposed as an artificial synapse for emerging neuromorphic computing applications^2,3. To train a neural network in memristor arrays, changes in weight values in the form of device conductance should be distinct and uniform³. An electrochemical metallization (ECM) memory^4,5, typically based on silicon (Si), has demonstrated a good analogue switching capability^6,7 owing to the high mobility of metal ions in the Si switching medium⁸. However, the large stochasticity of the ion movement results in switching variability. Here we demonstrate a Si memristor with alloyed conduction channels that shows a stable and controllable device operation, which enables the large-scale implementation of crossbar arrays. The conduction channel is formed by conventional silver (Ag) as a primary mobile metal alloyed with silicidable copper (Cu) that stabilizes switching. In an optimal alloying ratio, Cu effectively regulates the Ag movement, which contributes to a substantial improvement in the spatial/temporal switching uniformity, a stable data retention over a large conductance range and a substantially enhanced programmed symmetry in analogue conductance states. This alloyed memristor allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability. Thus, our discovery of an alloyed memristor is a key step paving the way beyond von Neumann computing.

A simple chemical reaction gives platelets the ability to swim around.