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In a new study, a molecule identified and synthesized by UCLA Health researchers was shown to restore cognitive functions in mice with symptoms of Alzheimer’s disease by effectively jumpstarting the brain’s memory circuitry.
If proven to have similar effects in humans, the candidate compound would be novel among Alzheimer’s disease treatments in its ability to revitalize memory and cognition, study authors said.
There is really nothing like this on the market or experimentally that has been shown to do this.
How can older trees help combat climate change? This is what a recent study published in Nature Climate Change hopes to address as an international team of researchers investigated changes in woody biomass in older trees that have been while exposed to free-air CO2 enrichment (FACE) resulting from climate change. This study holds the potential to help researchers, climate scientists, and the public better understand the steps that can be taken to decrease CO2 emissions and combat climate change worldwide.
For the study, the researchers, led by the University of Birmingham’s Institute of Forest Research (BIFoR), conducted a FACE experiment through a combination of canopy laser scanning and tree-ring analysis to examine the 180-year-old Quercus robur L. woodland in central England between 2021and 2022. The goal was ascertaining the effectiveness of older trees compared to younger trees regarding their consumption of CO2, also known as CO2 storage. In the end, the researchers found increased levels of CO2 compared to ambient conditions in 2021 and 2022, respectively, equivalent to 1.7 tons of dry matter per hectare per year.
“Our findings refute the notion that older, mature forests cannot respond to rising levels of atmospheric CO2, but how they respond will likely depend on the supply of nutrients from the soil,” said Dr. Richard Norby from the University of Birmingham, who is lead author of the study. “Evidence from BIFoR FACE of a significant increase in woody biomass production supports the role of mature, long-established, forests as natural climate solutions in the coming decades while society strives to reduce its dependency on carbon.”
Researchers are studying how quantum computers can help optimise net-zero power grid operation and expansion planning.
According to a new study led by TU Darmstadt, AI models such as ChatGPT are apparently less capable of learning independently than previously assumed.
According to the study, there is no evidence that what are known as large language models (LLMs) are beginning to develop a general “intelligent” behavior that would enable them to proceed in a planned or intuitive manner or to think in a complex way.
The study, which has been published on the arXiv preprint server, will be presented in August at the annual conference of the Association for Computational Linguistics (ACL) in Bangkok, the largest international conference on automatic language processing.
Unlike classical encryption, which relies on mathematical algorithms, quantum encryption assures security based on physical principles. Detection of espionage or interference is guaranteed by unavoidable alteration of the quantum states involved.
Researchers at the University of Maryland genetically modified poplar trees to produce high-performance, structural wood without the use of chemicals or energy-intensive processing. Made from traditional wood, engineered wood is often seen as a renewable replacement for traditional building materials like steel, cement, glass and plastic. It also has the potential to store carbon for a longer time than traditional wood because it can resist deterioration, making it useful in efforts to reduce carbon emissions.
But the hurdle to true sustainability in engineered wood is that it requires processing with volatile chemicals and a significant amount of energy, and produces considerable waste. The researchers edited one gene in live poplar trees, which then grew wood ready for engineering without processing.
The research was published online on August 12, 2024, in the Journal Matter.
