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Blog – Page 35

Trees get most of the love, but diatoms, a group of photosynthetic microalgae, produce 20% of Earth’s oxygen and are the foundation of aquatic food webs. The prevalence and diversity of diatoms have made them highly successful, suggesting the evolutionary history of diatoms is worth understanding as an important piece of the larger puzzle of life on Earth.

A new study led by researchers from the U of A found that diatoms evolved slowly for the first 100 million years of their existence. Then, 170 million years ago, they reached an inflection point characterized by a burst of rapid speciation orders of magnitude faster than anything that had preceded it. This included changes to their shape, size and mode of reproduction, as well as repeated movements from oceans into freshwater systems, a typically difficult barrier for to cross.

With an estimated 100,000 species, diatoms are now one of the most diverse groups of microalgae. They are small enough that dozens could fit on the head of a pin and are found almost anywhere there is water and sunlight.

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In a variety of technological applications related to chemical energy generation and storage, atoms and molecules diffuse and react on metallic surfaces. Being able to simulate and predict this motion is crucial to understanding material degradation, chemical selectivity, and to optimizing the conditions of catalytic reactions. Central to this is a correct description of the constituent parts of atoms: electrons and nuclei.

An electron is incredibly light—its mass is almost 2,000 times smaller than that of even the lightest nucleus. This mass disparity allows to adapt rapidly to changes in nuclear positions, which usually enables researchers to use a simplified “adiabatic” description of atomic motion.

While this can be an excellent approximation, in some cases the electrons are affected by nuclear motion to such an extent that we need to abandon this simplification and account for the coupling between the dynamics of electrons and nuclei, leading to so-called “non-adiabatic effects.”

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Often, physics can be used to make sense of the natural world, whether it’s understanding gravitational effects on ocean tides or using powerful physics tools, like microscopes, to examine the inner workings of the cell. But increasingly, scientists are looking at biological systems to spark new insights in physics. By studying squid skin, researchers have identified the first biological instance of a physical phenomenon called “hyperdisorder,” bringing new understanding into how growth can affect physics.

Published in Physical Review X, an interdisciplinary team from the Okinawa Institute of Science and Technology (OIST) studied the effect of growth on pattern development within squid skin cells.

By combining experimental imaging methods with theoretical modeling, they found new insights into the unusual arrangement of these cells, and created a general model of hyperdisorder applicable to a wide variety of growing systems.

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Machine learning models have seeped into the fabric of our lives, from curating playlists to explaining hard concepts in a few seconds. Beyond convenience, state-of-the-art algorithms are finding their way into modern-day medicine as a powerful potential tool. In one such advance, published in Cell Systems, Stanford researchers are using machine learning to improve the efficacy and safety of targeted cell and gene therapies by potentially using our own proteins.

Most human diseases occur due to the malfunctioning of proteins in our bodies, either systematically or locally. Naturally, introducing a new therapeutic protein to cure the one that is malfunctioning would be ideal.

Although nearly all therapeutic protein antibodies are either fully human or engineered to look human, a similar approach has yet to make its way to other therapeutic proteins, especially those that operate in cells, such as those involved in CAR-T and CRISPR-based therapies. The latter still runs the risk of triggering immune responses. To solve this problem, researchers at the Gao Lab have now turned to machine learning models.

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Final results from a long-running U.S.-based experiment announced Tuesday show a tiny particle continues to act strangely—but that’s still good news for the laws of physics as we know them.

“This experiment is a huge feat in precision,” said Tova Holmes, an experimental physicist at the University of Tennessee, Knoxville who is not part of the collaboration.

The mysterious particles called are considered heavier cousins to electrons. They wobble like a top when inside a , and scientists are studying that motion to see if it lines up with the foundational rulebook of physics called the Standard Model.

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A new study has revealed a novel effect caused by dark photons—hypothetical particles thought to make up a portion of the universe’s elusive dark matter. This discovery, made within the framework of Einstein–Cartan–Holst gravity, provides new insights into the fundamental interactions between matter and gravity.

The study was conducted by Prof. Gao Zhifu from the Xinjiang Astronomical Observatory of the Chinese Academy of Sciences, in collaboration with Dr. Luiz Carlos Garcia de Andrade from the State University of Rio de Janeiro, Brazil. Their findings, which include the first identification of a key physical quantity known as the Barbero–Immirzi (BI) parameter induced by dark photons, are published in The European Physical Journal C.

A large portion of the universe is filled with invisible matter known as , and the dark photon is one of its leading theoretical candidates. As a hypothetical particle beyond the Standard Model, the dark photon exhibits electromagnetic-like interactions through kinetic mixing with the ordinary photon. Unlike photons, however, dark photons possess mass and interact much more weakly with charged particles.

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Resonantly tunable quantum cascade lasers (QCLs) are high-performance laser light sources for a wide range of spectroscopy applications in the mid-infrared (MIR) range. Their high brilliance enables minimal measurement times for more precise and efficient characterization processes and can be used, for example, in chemical and pharmaceutical industries, medicine or security technology. Until now, however, the production of QCL modules has been relatively complex and expensive.

The Fraunhofer Institute for Applied Solid State Physics IAF has therefore developed a semi-automated process that significantly simplifies the production of QCL modules with a MOEMS (micro-opto-electro-mechanical system) grating scanner in an external optical cavity (EC), making it more cost-efficient and attractive for industry. The MOEMS-EC-QCL technology was developed by Fraunhofer IAF in collaboration with the Fraunhofer Institute for Photonic Microsystems IPMS.

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Tunneling is a peculiar quantum phenomenon with no classical counterpart. It plays an essential role for strong field phenomena in atoms and molecules interacting with intense lasers. Processes such as high-order harmonic generation are driven by electron dynamics following tunnel ionization.

While this has been widely explored, the behavior of electrons under the tunneling barrier, though equally significant, has remained obscure. The understanding of laser-induced strong field ionization distinguishes two scenarios for a given system and : the multiphoton regime at rather low intensities and tunneling at high intensities.

However, most strong-field experiments have been carried out in an intermediate situation where multiphoton signatures are observed while tunneling is still the dominant process.

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Men carrying two copies of a common genetic variant face double the risk of dementia, according to new findings from the ASPREE trial. New research has uncovered that men who carry a common genetic variant are twice as likely to develop dementia in their lifetime compared to women. The study.

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