Lifeboat Foundation

Human influences have the potential to reduce the effectivity of communication in bees, adding further stress to struggling colonies, according to new analysis.

Scientists at the University of Bristol studying honeybees, bumblebees and stingless bees found that variations in communication strategies are explained by differences in the habitats that bees inhabit and differences in the social lifestyle such colony size and nesting habits.

The findings, published today in PNAS, reveal that anthropogenic changes, such as habitat conversion, climate change and the use of agrochemicals, are altering the world bees occupy, and it is becoming increasingly clearer that this affects communication both directly and indirectly; for example, by affecting food source availability, social interactions among nestmates and their cognitive functions.

Researchers have detected complex organic molecules in a galaxy more than 12 billion light-years away from Earth—the most distant galaxy in which these molecules are now known to exist. Thanks to the capabilities of the recently launched James Webb Space Telescope and careful analyses from the research team, a new study lends critical insight into the complex chemical interactions that occur in the first galaxies in the early universe.

University of Illinois Urbana-Champaign astronomy and physics professor Joaquin Vieira and graduate student Kedar Phadke collaborated with researchers at Texas A&M University and an international team of scientists to differentiate between infrared signals generated by some of the more massive and larger dust grains in the galaxy and those of the newly observed hydrocarbon molecules.

The study findings are published in the journal Nature.

Whether it’s baking a cake, building a house, or developing a quantum device, the quality of the end product significantly depends on its ingredients or base materials. Researchers working to improve the performance of superconducting qubits, the foundation of quantum computers, have been experimenting using different base materials in an effort to increase the coherent lifetimes of qubits.

The coherence time is a measure of how long a qubit retains quantum information, and thus a primary measure of performance. Recently, scientists discovered that using tantalum in superconducting qubits makes them perform better, but no one has been able to determine why—until now.

Scientists from the Center for Functional Nanomaterials (CFN), the National Synchrotron Light Source II (NSLS-II), the Co-design Center for Quantum Advantage (C2QA), and Princeton University investigated the fundamental reasons that these qubits perform better by decoding the chemical profile of tantalum.

In recent years, electronics engineers have been trying to develop new brain-inspired hardware that can run artificial intelligence (AI) models more efficiently. While most existing hardware is specialized in either sensing, processing or storing data, some teams have been exploring the possibility of combining these three functionalities in a single device.

Researchers at Xi’an Jiaotong University, the University of Hong Kong and Xi’an University of Science and Technology introduced a new organic transistor that can act as a sensor and processor. This transistor, introduced in a paper published in Nature Electronics, is based on a vertical traverse architecture and a crystalline-amorphous channel that can be selectively doped by ions, allowing it to switch between two reconfigurable modes.

“Conventional artificial intelligence (AI) hardware uses separate systems for data sensing, processing, and memory storage,” Prof. Wei Ma and Prof. Zhongrui Wang, two of the researchers who carried out the study, told Tech Xplore.

A University of Virginia-led study about a class of materials called associative polymers appears to challenge a long-held understanding of how the materials, which have unique self-healing and flow properties, function at the molecular level.

Liheng Cai, an assistant professor of materials science and engineering and chemical engineering at UVA, who led the study, said the new discovery has important implications for the countless ways these materials are used every day, from engineering recyclable plastics to human tissue engineering to controlling the consistency of paint so it doesn’t drip.

The discovery, which has been published in the journal Physical Review Letters, was enabled by new associative polymers developed in Cai’s lab at the UVA School of Engineering and Applied Science by his postdoctoral researcher Shifeng Nian and Ph.D. student Myoeum Kim. The breakthrough evolved from a theory Cai had co-developed before arriving at UVA in 2018.

Researchers in Canada and the United States have used deep learning to derive an antibiotic that can attack a resistant microbe, acinetobacter baumannii, which can infect wounds and cause pneumonia. According to the BBC, a paper in Nature Chemical Biology describes how the researchers used training data that measured known drugs’ action on the tough bacteria. The learning algorithm then projected the effect of 6,680 compounds with no data on their effectiveness against the germ.

In an hour and a half, the program reduced the list to 240 promising candidates. Testing in the lab found that nine of these were effective and that one, now called abaucin, was extremely potent. While doing lab tests on 240 compounds sounds like a lot of work, it is better than testing nearly 6,700.

Interestingly, the new antibiotic seems only to be effective against the target microbe, which is a plus. It isn’t available for people yet and may not be for some time — drug testing being what it is. However, this is still a great example of how machine learning can augment human brainpower, letting scientists and others focus on what’s really important.

USC Dornsife researchers employ artificial intelligence to unveil the intricate world of DNA structure and chemistry, enabling unprecedented insights into gene regulation and disease.

Year 2021 😗😁

They were indeed correct that lead could be turned into gold — even if they were dead wrong about how it could be done. Now, modern science routinely takes us far beyond even the wildest dreams of the alchemists.

One of the most famous stories of nuclear transmutation comes from the 1970s, when nuclear chemist and Nobel laureate Glenn Seaborg worked at the Lawrence Berkeley National Laboratory alongside colleague Walt Loveland and then-graduate student Dave Morrissey. The scientists were using a super-heavy ion linear accelerator to bombard atoms with ions as heavy as uranium at relativistic speeds. “Among the ones we bombarded was lead-208,” Loveland says.

Continue reading “Turning Lead Into Gold” »

A typical human cell is metabolically active, roaring with chemical reactions that convert nutrients into energy and useful products that sustain life. These reactions also create reactive oxygen species, dangerous by-products like hydrogen peroxide which damage the building blocks of DNA in the same way oxygen and water corrode metal and form rust. Similar to how buildings collapse from the cumulative effect of rust, reactive oxygen species threaten a genome’s integrity.

Cells are thought to delicately balance their energy needs and avoid damaging DNA by containing metabolic activity outside the nucleus and within the cytoplasm and mitochondria. Antioxidant enzymes are deployed to mop up reactive oxygen species at their source before they reach DNA, a defensive strategy that protects the roughly 3 billion nucleotides from suffering potentially catastrophic mutations. If DNA damage occurs anyway, cells pause momentarily and carry out repairs, synthesizing new building blocks and filling in the gaps.

Despite the central role of cellular metabolism in maintaining genome integrity, there has been no systematic, unbiased study on how metabolic perturbations affect the DNA damage and repair process. This is particularly important for diseases like cancer, characterized by their ability to hijack metabolic processes for unfettered growth.