Lifeboat Foundation

July 17, 2024 – The changes can begin in middle age, but they’re not usually noticeable until decades later. By age 60 and beyond, the changes can pick up speed and may become obvious.

“As we get older, our brain actually starts to shrink and lose mass,” said Marc Milstein, PhD, a Los Angeles brain health researcher. The start of that shrinkage, as well as the path it takes, can vary, said Milstein, who wrote The Age-Proof Brain.

“Starting at 40, our overall brain volume can start shrinking about 5% every 10 years,” he said. “Our brain has connections where our memories are stored, and as we age, we lose some of these connections. That can make it challenging to remember and to learn new information.”

Brighter with Herbert.

Federal funding will allow Rochester scientists and their European collaborators to study the feasibility of producing coherent gamma rays.

Having solved a central mystery about the “twirliness” of tornadoes and other types of vortices, William Irvine has set his sights on turbulence, the white whale of classical physics.

It is clear that specific memories are not stored in individual specific neurons.

But the epigenetic state of neurons influences whether they become part of memory…

Memories are encoded by sparse populations of neurons but how such sparsity arises remains largely unknown. We found that a neuron’s eligibility to be recruited into the memory trace depends on its epigenetic state prior to encoding. Principal neurons in the mouse lateral amygdala display intrinsic chromatin plasticity, which when experimentally elevated favors neuronal allocation into the encoding ensemble. Such chromatin plasticity occurred at genomic regions underlying synaptic plasticity and was accompanied by increased neuronal excitability in single neurons in real time. Lastly, optogenetic silencing of the epigenetically altered neurons prevented memory expression, revealing a cell-autonomous relationship between chromatin plasticity and memory trace formation. These results identify the epigenetic state of a neuron as a key factor enabling information encoding.

An algorithm developed by Washington State University researchers can better find data anomalies than current anomaly-detection software, including in streaming data.

The work, reported in the Journal of Artificial Intelligence Research, makes fundamental contributions to artificial intelligence (AI) methods that could have applications in many domains that need to quickly find anomalies in large amounts of data, such as in cybersecurity, power grid management, misinformation, and medical diagnostics.

Being able to better find the anomalies would mean being able to more easily discover fraud, disease in a medical setting, or important unusual information, such as an asteroid whose signals overlap with the light from other stars.

A team of computer scientists and roboticists with members from Texas A&M University in the U.S., and the Mohamed Bin Zayed University of Artificial Intelligence in Abu Dhabi, working with a colleague from Boston Dynamics, has configured a robot made by Boston Dynamics to seek out and stun weeds using a small blowtorch. The team has posted a paper describing their efforts to the arXiv preprint server.

Boston Dynamics, maker of the well-known quadruped Big Dog, has been working on technology to improve both the robot’s agility and processing ability. Its latest quadruped is Spot, a robot with increased agility, highly accurate sensors and a brain that includes AI capabilities. In this new effort, the research team used some of Spot’s abilities to tame weeds growing on cropland.

The researchers trained Spot to recognize weeds among a field of regular crops. They also strapped a small tank filled with propane to its back that is used to fuel a small blow torch held by the robot’s arm. The idea is for Spot to wander around cropland looking for weeds and upon finding them, stun them by blasting their central parts with burning gas. The blowtorch is not used to incinerate the weed, but to heat its core to such an extent that the growth of the weed is stunted for several weeks.

Artificial intelligence (AI) is hot right now. Also hot: the data centers that power the technology. And keeping those centers cool requires a tremendous amount of energy.

The problem is only going to grow as high-powered AI-based computers and devices become commonplace. That’s why University of Missouri researcher Chanwoo Park is devising a new type of cooling system that promises to dramatically reduce energy demands.

The work is published in the journal Applied Thermal Engineering.

Local decentralized energy systems, known as microgrids, can make urban infrastructures more resilient and reduce risks for the population, for example, in large-scale power outages due to natural hazards or cyberattacks.

In Nature Sustainability researchers from Karlsruhe Institute of Technology (KIT) present design criteria for microgrids that allow for fair treatment of different social groups alongside technical factors. The study shows how cities can shape the transformation towards a secure and more sustainable and equitable energy supply.

Climate change increases the probability of extreme events, as we have seen during the massive flooding of large parts of southern Germany in June. The question of how cities and municipalities can make power supply more resilient and more secure in the face of such crises is bringing so-called microgrids into focus.