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Photosynthesis is one of the most efficient natural processes for converting light energy from the sun into chemical energy vital for life on earth. Proteins called photosystems are critical to this process and are responsible for the conversion of light energy to chemical energy.

Combining one kind of these proteins, called photosystem I (PSI), with platinum nanoparticles, microscopic particles that can perform a chemical reaction that produces hydrogen — a valuable clean energy source — creates a biohybrid catalyst. That is, the light absorbed by PSI drives hydrogen production by the platinum nanoparticle.

In a recent breakthrough, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Yale University have determined the structure of the PSI biohybrid solar fuel catalyst. Building on more than 13 years of research pioneered at Argonne, the team reports the first high-resolution view of a biohybrid structure, using an electron microscopy method called cryo-EM. With structural information in hand, this advancement opens the door for researchers to develop biohybrid solar fuel systems with improved performance, which would provide a sustainable alternative to traditional energy sources.

Argonne and Yale researchers shed light on the structure of a photosynthetic hybrid for the first time, enabling advancements in clean energy production.

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What can solar eclipses teach us about the Sun and how it interacts with the Earth’s atmosphere? This is what a recent press briefing conducted at the American Geophysical Union 2024 Fall Meeting hopes to address as a team of scientists from the Citizen CATE 2024 (Continental-America Telescopic Eclipse) project reported on findings that were obtained during the April 8, 2024, total solar eclipse over North America.

“Scientists and tens of thousands of volunteer observers were stationed throughout the Moon’s shadow,” said Dr. Kelly Korreck, who is the NASA Program Manager for the 2023 and 2024 Solar Eclipses. “Their efforts were a crucial part of the Heliophysics Big Year – helping us to learn more about the Sun and how it affects Earth’s atmosphere when our star’s light temporarily disappears from view.”

Consisting of a combination of both professional and citizen scientists using a combination of images, spectroscopy, and ham radios, the large team comprised of Citizen CATE 2024 made groundbreaking observations of the 2024 solar eclipse, along with ascertaining how radio signals were influenced during the eclipse. In the end, the team of more than 800 individuals discovered that eclipses produce atmospheric gravity waves, or ripples within the Earth’s atmosphere. Additionally, the ham radio operators, comprised of more than 6,350 individuals, discovered that radio communications improved both within and outside the eclipses’ path of totality at frequencies between 1 to 7 Megahertz, whereas communications became worse at frequencies above 10 Megahertz.

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Neuroscientific research on human behavior and cognition has methodologically moved from unimodal explanatory approaches to machine learning-based predictive modeling (1). This implies a shift from standard approaches testing for associations between behavior and single neurobiological variables within one sample (unimodal explanatory research) to the identification of relationships between behavior and multiple neurobiological variables to forecast behavior of unseen individuals across samples (multimodal predictive research) (2). Modern machine learning techniques can learn such general relations in neural data (2, 3) and have consequently become increasingly prominent also in research on fundamental psychological constructs like intelligence (4).

Intelligence captures the general cognitive ability level of an individual person and predicts crucial life outcomes, such as academic achievement, health, and longevity (5, 6). Multiple psychometrical theories about the underlying conceptual structure of intelligence have been proposed. For example, Spearman (7) noticed that a person’s performance on different cognitive tasks is positively correlated and suggested that this “positive manifold” results from an underlying common factor—general intelligence (g). A decomposition of the g-factor into fluid (gF) and crystallized (gC) components was later proposed by Cattell (8, 9). While fluid intelligence is assumed to mainly consist of inductive and deductive reasoning abilities that are rather independent of prior knowledge and cultural influences, crystallized intelligence reflects the ability to apply acquired knowledge and thus depends on experience and culture (10).

Neurobiological correlates of intelligence differences were identified in brain structure (11) and brain function (12). However, rather than disclose a single “intelligence brain region”, meta-analyses and systematic reviews suggest the involvement of a distributed brain network (13–15), thus paving the way for proposals of whole-brain structural and functional connectivity (FC) underlying intelligence (16, 17). While the great majority of such studies used an explanatory approach, recently, an increasing number of machine learning-based techniques were developed and applied to predict intelligence from brain features (4, 18, 19). Although intrinsic FC measured during the (task-free) resting state has enabled robust prediction of intelligence (19), prediction performance can be boosted by measuring connectivity during task performance (18, 20).

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A team of engineers is reimagining one of the essential processes in modern manufacturing. Their goal? To transform how a chemical called acrylonitrile (ACN) is made—not by building world-scale manufacturing sites, but by using smaller-scale, modular reactors that can work if they let the catalyst, in a sense, “breathe.”

Their article, titled “Propene Ammoxidation over an Industrial Bismuth Molybdate-Based Catalyst Using Forced Dynamic Operation,” is published in Applied Catalysis A: General.

ACN is everywhere, from carbon fibers in sports equipment to acrylics in car parts and textiles. Traditionally, producing it requires a continuous, energy-intensive process. But now, researchers at the University of Virginia and the University of Houston have shown that by pausing to “inhale” fresh oxygen, a chemical can produce ACN more efficiently. This discovery could open the door to smaller, versatile production facilities that adapt to fluctuating needs.

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Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.

Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his work on blending traditional physics-based scientific models with to accurately predict hypotheses in the journal Nature Chemical Engineering.

Kwon’s research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact , smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”

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The Call is still open on senescence in brain aging and Alzheimers disease!

Submit your paper today! 📩

Understanding Senescence in Brain Aging and Alzheimer’s Disease

Guest Editors Drs. Julie Andersen and Darren Baker, Associate Editor Dr. Anna Csiszar and Editor-in-Chief Dr. Zoltan Ungvari, and the editorial team of GeroScience (Journal of the American Aging Association; 2018 Impact Factor: 6.44) invite submission of original research articles, opinion papers and review articles related to research focused on understanding the role of senescence in brain aging and in Alzheimer’s disease. Senescent cells accumulate in aging and pathological conditions associated with accelerated aging. While earlier investigations focused on cellular senescence in tissues and cells outside of the brain (e.g. adipose tissue, dermal fibroblasts, cells of the cardiovascular system), more recent studies started to explore the role of senescent cells in age-related decline of brain function and the pathogenesis of neurodegenerative disease and vascular cognitive impairment. This call-for-papers is aimed at providing a platform for the dissemination of critical novel ideas related to the functional and physiological consequences of senescence in diverse brain cell types (e.g., oligodendrocytes, pericytes, astrocytes, endothelial cells, microglia, neural stem cells), with the ultimate goal to identify novel targets for treatment and prevention Alzheimer’s disease, Parkinson’s disease and vascular cognitive impairment. We welcome manuscripts focusing on senescent-cell-targeting mouse models, the role of paracrine senescence, senescence pathways in terminally differentiated neurons, the pleiotropic effects of systemic senescence, the role of senescence in neuroinflammation and the protective effects of senolytic therapies. We are especially interested in manuscripts exploring the causal role of molecular mechanisms of aging in induction of cellular senescence as well as links between lifestyle (e.g., diet, exercise, smoking), medical treatments (e.g. cancer treatments), exposure environmental toxicants and cellular senescence in the brain. We encourage submission of manuscripts on developing innovative strategies to identify and target senescent cells for prevention/treatment of age-related diseases of the brain. Authors are also encouraged to submit manuscripts focusing on translational aspects of senescence research.

All manuscripts accepted from this Call for Papers will be included in a unique online article collection to further highlight the importance of this topic. All manuscripts should be submitted online here: https://www.editorialmanager.com/jaaa/default.aspx.

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