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AI method accelerates liquid simulations by learning fundamental physical relationships

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the chemical potential—an indispensable quantity for describing liquids in thermodynamic equilibrium. The researchers present their findings in a new study published in Physical Review Letters.

Many common AI methods are based on the principle of supervised machine learning: a model—for instance, a neural network—is specifically trained to predict a particular target quantity directly. One example that illustrates this approach is image recognition, where the AI system is shown numerous images in which it is known whether or not a cat is depicted. On this basis, the system learns to identify cats in new, previously unseen images.

“However, such a direct approach is difficult in the case of the chemical potential, because determining it usually requires computationally expensive algorithms,” says Prof. Dr. Matthias Schmidt, Chair of Theoretical Physics II at the University of Bayreuth. He and his research associate Dr. Florian Sammüller address this challenge with their newly developed AI method. It is based on a neural network that incorporates the theoretical structure of liquids—and more generally, of soft matter—allowing it to predict their properties with great accuracy.

Subaru observations suggest an intrinsic gap in NGC 5466’s tidal stream

Astronomers from the National Astronomical Observatory of Japan (NAOJ) and elsewhere have used the Subaru Telescope to perform deep imaging observations of a distant globular cluster known as NGC 5466. The observational campaign yields important information about the structure of the cluster’s tidal stream. The new findings were published February 4 on the arXiv preprint server.

In general, stellar tidal streams are the result of tidal interactions between a central galaxy and lower mass systems such as satellite galaxies or globular clusters (GCs). Therefore, they could keep the memory of their progenitors’ chemical and dynamical information, even after a few billion years.

What honey bee brain chemistry tells us about human learning

A multi-institutional team of researchers led by Virginia Tech’s Fralin Biomedical Research Institute at VTC has for the first time identified specific patterns of brain chemical activity that predict how quickly individual honey bees learn new associations, offering important insights into the biological basis of learning and decision-making. The study, published in Science Advances, found that the balance between the neurotransmitters octopamine and tyramine can predict whether a bee will learn quickly, slowly, or not at all, as they associate an odor with a reward.

Because the same ancient brain chemicals that guide learning in bees also shape attention and learning in people, the findings may help scientists better understand why individual humans learn at different speeds—and how those processes may go awry in a variety of brain disorders.

Specific patterns of brain chemical activity appear before learning begins and again when a learned behavior first emerges, signaling how quickly an individual bee will learn. The research can help explain how chemicals in the brain drive attention and reinforce learning, with implications for fundamental biology, medicine, and agriculture.

Cell Type-Specific Contributions of UBE3A to Angelman Syndrome Behavioral Phenotypes

ENeuro: Ringelberg et al. identify a key role for excitatory neuron loss of UBE3A in motor, innate, and sleep behavioral phenotypes of Angelman syndrome model mice.

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AS is a neurodevelopmental disorder with no disease-modifying treatment. However, clinical trials are currently underway using antisense oligonucleotides to unsilence the dormant paternal UBE3A allele, thereby normalizing UBE3A levels (Ionis: NCT05127226; Ultragenyx: NCT04259281). While this approach holds exciting promise and shows efficacy in mouse models (Meng et al., 2015; Milazzo et al., 2021), there is currently scant information regarding the key cell types or brain regions that require UBE3A reinstatement to mitigate core symptoms of AS. This holds particular importance, as effective biodistribution is a key concern in genetic therapies for CNS disorders (Roberts et al., 2020; Jafar-Nejad et al., 2021; Ling et al., 2023), and suboptimal targeting of necessary cell classes could hamper success. Moreover, mouse models of AS require early postnatal Ube3a reinstatement to achieve optimal phenotypic recovery (Silva-Santos et al., 2015; Sonzogni et al., 2020); early intervention could be difficult to achieve in the patient population without a corresponding early diagnosis, meaning many AS individuals are likely beyond the critical window to maximally benefit from UBE3A reinstatement-based therapies. Therefore, additional work is needed to better understand how loss of UBE3A leads to symptoms, as these insights will aid both in understanding the cell types that must be targeted for optimal genetic interventions and in developing alternative therapeutic options.

Our laboratory’s previous work identified an outsized role of GABAergic loss of UBE3A in hyperexcitability phenotypes. GABAergic loss of UBE3A drives increased delta power on cortical EEG (Judson et al., 2016), a phenotype that correlates with the severity of a range of symptoms in AS individuals (Hipp et al., 2021; Ostrowski et al., 2021). Further, mice with Ube3a deleted from GABAergic neurons show decreased threshold to chemically and acoustically driven seizures, and they also exhibit spontaneous behavioral seizures, a phenotype not observed in AS model mice on a C57BL/6J background (Judson et al., 2016; Gu et al., 2019). These data forewarn that UBE3A reinstatement in a manner biased to glutamatergic neurons could potentially worsen epilepsy-related symptoms and highlight the importance of studying the neuronal populations regulating other behaviors.

Based on the exaggerated role of GABAergic neurons in AS seizure phenotypes, we predicted that GABAergic deletion of Ube3a would underlie a broad range of behavioral phenotypes in AS mice. In the present study, we instead found a larger role of Ube3a deletion from glutamatergic neurons in motor coordination, measured by rotarod and open field behavior, and innate species-specific behaviors such as marble burying. Furthermore, glutamatergic loss of UBE3A appears to mediate alterations in sleep patterning and induces some sleep fragmentation, while UBE3A loss from GABAergic neurons only caused fragmented sleep. Interestingly, glutamatergic reinstatement of Ube3a also rescued the decreased REM sleep observed in AS mice, as estimated by the PiezoSleep system. While this study identified some roles of GABAergic neurons in nest building behavior and sleep fragmentation, our data largely suggest a divergence of the neural circuitry underlying the motor, innate behavior, and sleep phenotypes of AS mice from the circuitry responsible for seizure susceptibility and cortical EEG patterns.

New laser “comb” can enable rapid identification of chemicals with extreme precision

Researchers demonstrated a broadband infrared frequency comb that can operate stably, efficiently, and accurately without the need for bulky external components. The device could be utilized in a remote sensor or portable mass spectrometer that can track and monitor multiple chemicals in real-time for extended periods.

Magnetic Covalent Organic Frameworks (MCOFs): A Sustainable Solution for Emerging Organic Contaminants (EOCs) from the River

Phthalates (PAEs) and bisphenol A (BPA) are significant components in plastic and its derivative industries. They are omnipresent in water sources owing to intensive industrialization and rapid urbanization, hence posing adverse effects on humans and significant environmental issues. Researchers have developed a new magnetic material, called magnetic covalent organic frameworks (MCOFs), that can effectively remove harmful chemicals like PAEs and BPA from water. Made using a special method that prevents clumping, these materials are highly porous, magnetic and reusable up to 15 times. They showed excellent removal efficiency, even at very low pollutant levels found in real river water. The study also revealed that the removal process involves strong chemical bonding. This breakthrough offers a promising, eco-friendly solution for cleaning water contaminated by plastics and industrial waste.

Read the article in Royal Society Open Science.


Abstract. The synthesis and characterization of effective magnetic covalent organic frameworks (MCOFs) are presented for the highly efficient adsorption of dimethyl phthalate (DMP), dibutyl phthalate (DBP) and bisphenol A (BPA) from the aqueous environment. The novelty of this research lies in the development of MCOFs through a coprecipitation method that incorporates an innovative silica inner shell. This crucial feature not only prevents aggregation of the magnetic core, which is a significant limitation of conventional adsorbents, but also enables robust interactions between the core and the outer covalent organic framework (COF). The synthesized MCOFs were comprehensively characterised using a variety of techniques. Fourier-transform infrared spectroscopy (FTIR) and vibrating sample magnetometry (VSM) analyses confirmed successful synthesis and strong magnetic properties, while field-emission scanning electron microscopy (FESEM) revealed the presence of spherical, porous structures with small granules. Energy-dispursive X-ray (EDX) spectrometry analysis further confirmed the successful synthesis, showing a material composition of 58.2% Fe, 33.4% O, 4.8% C, and 3.2% Si. Brunauer–Emmett–Teller (BET) analysis showed the MCOFs possess a high surface area of 128.1 m2 g–1 and a pore diameter of 16.8 nm, indicating abundant active sites for adsorption. Under optimal conditions (pH 7,100 mg adsorbent dosage, and 25-minute contact time) the MCOFs exhibited exceptional adsorption performance, with removal efficiencies of 90.0% for DMP, 86.0% for DBP, and 92.0% for BPA. The kinetic study revealed that the adsorption mechanism follows the pseudo-second-order model, suggesting a significant chemisorption process. Crucially, in situ FTIR analysis provided spectroscopic validation that hydrogen bonding and π–π stacking are the predominant interactions between the MCOFs and the organic contaminants. The developed analytical method achieved low detection limits of 0.0058 mg l−1 for DMP, 0.0079 mg l−1 for DBP and 0.0063 mg l−1 for BPA, indicating high sensitivity for trace-level contaminant detection in real water samples. Furthermore, the adsorbent demonstrated exceptional reusability, maintaining high performance after 15 adsorption–desorption cycles, which is a significant improvement over conventional adsorbents. This study demonstrates that MCOFs with a silica inner shell are a highly promising, stable and sustainable solution for the removal of emerging organic contaminants (EOCs).

A “dormant” brain protein turns out to be a powerful switch

Researchers at Johns Hopkins Medicine report that they have uncovered a promising drug target that could allow scientists to increase or decrease the activity of specific brain proteins. The discovery may lead to new treatments for psychiatric conditions such as anxiety and schizophrenia, as well as a neurological disorder that affects movement and balance. The work was supported by funding from the National Institutes of Health.

The proteins at the center of the research are known as delta-type ionotropic glutamate receptors, or GluDs. These proteins are known to play an important role in how neurons communicate with each other. According to the researchers, mutations in GluDs have been linked to psychiatric disorders, including anxiety and schizophrenia. Despite this connection, scientists have struggled for years to understand exactly how these proteins work, making it difficult to design treatments that could regulate their activity.

“This class of protein has long been thought to be sitting dormant in the brain,” says Edward Twomey, Ph.D., assistant professor of biophysics and biophysical chemistry at the Johns Hopkins University School of Medicine. “Our findings indicate they are very much active and offer a potential channel to develop new therapies.”

It’s Official: Astronomers Detect Complex Sulfur Molecule in Interstellar Space

In the heart of our galaxy, scientists have discovered the largest sulfur-bearing molecule ever detected beyond Earth, with significant implications for the study of the cosmic origins of life.

The chemical is known as thiepine, or 2,5-cyclohexadiene-1-thione (C₆H₆S), a ring-shaped sulfur-bearing hydrocarbon produced in biochemical reactions.

When examining the molecular cloud G+0.693–0.027, a star-forming region about 27,000 light-years from Earth near the center of the Milky Way, astronomers from the Max Planck Institute for Extraterrestrial Physics (MPE) and the CSIC-INTA Centro de Astrobiología (CAB) detected this complex molecule in space for the first time.

How Epigenetic Reprogramming Makes Cells Act Young Again

Aging doesn’t rewrite your DNA, it scrambles how your cells read it.

This clip explains epigenetic drift and how Life Biosciences’ therapy, ER100, uses Yamanaka factors to restore youthful epigenetic patterns in aged cells. By resetting the chemical marks that control gene expression, cells can behave as if they’re young again without changing the underlying DNA.

It’s the same biological process that happens early in embryonic development, applied in a controlled way to adult cells.

Abstract: In a cohort of over 1,000 patients with BreastCancer

Emilio Hirsch & team identify SH3BP5L as the most highly expressed guanine nucleotide exchange factor (GEF) for RAB11A, and its inhibition lowers lung metastasis and cell spreading in triple negative breast cancer models (TNBC):

The figure shows immunohistochemical assessment of SH3BP5L expression in tissue from patients with breast cancer.

@unito.it @fondazioneumbertoveronesi


1Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center “G. Tarone,” University of Torino, Torino, Italy.

2IEO, European Institute of Oncology IRCCS, Milan, Italy.

3Department of Oncology and Hemato-Oncology, University of Milano, Milano, Italy.

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