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Gene therapy shows promise in ARC syndrome, a deadly childhood liver disease

A new gene therapy has been used to successfully treat a deadly childhood liver disease in mice that model the disease, according to researchers at UCL and Great Ormond Street Hospital. Arthrogryposis, renal dysfunction and cholestasis (ARC) syndrome is a lethal genetic disorder usually caused by a lack of the VPS33B protein, with children diagnosed with the condition rarely living beyond their first year of life.

Now, in a study published in Nature Communications, the UCL-GOSH team found that by injecting a healthy version of the gene into the body, they can treat the condition in mice lacking VPS33B. Crucially, the final version of the treatment, which specifically targeted the liver cells, caused no harm. In the earlier versions, the genes became abnormally activated and caused cancerous cells to grow and expand in some cases.

While more tests must be done before the treatment can be tested in humans, the researchers’ breakthrough offers hope to babies with this devastating disorder and their families. In the UK, as many as six pregnancies per year might be affected by ARC syndrome. Furthermore, the findings may promote improved understanding of why some treatments may cause cancer.

ChartNet trains AI to read charts, boosting smaller models past commercial rivals

To accelerate and refine decision-making in a fast-paced, global marketplace, enterprises may deploy generative artificial intelligence models to help summarize and interpret the charts that often fill market summaries and financial reports.

But even the latest vision-language models sometimes struggle with this task, since it requires a model to integrate visual, numerical, and linguistic understanding. A company that invests in a state-of-the-art model might still receive inaccurate or incomplete information.

To fill this performance gap, researchers from MIT and the MIT-IBM Computing Research Lab developed a multifaceted resource for AI users that is specifically designed to teach vision-language models (VLMs) how to effectively interpret charts.

Corrected microbial family tree offers statistically sound model for how earliest life forms evolved

In this era of Big Data, the prevailing wisdom is that more information leads to better answers. However, a new Canadian study shows that in the hunt for life’s ancient ancestors, more data can actually lead to less truth. Published in the Proceedings of the National Academy of Sciences, the research by UdeM associate professor of computer science Miklós Csűrös reveals that standard methods for reconstructing the genomes of ancient microbes are being overwhelmed by an explosion of information.

This paradox causes current models to “hallucinate” evolutionary events—specifically, an implausibly high number of horizontal gene transfers—that are actually just statistical ghosts, the study shows.

In it, Csűrös identifies a crisis point in evolutionary biology: As researchers try to reconcile thousands of gene sequences across the entire tree of life, the actual evolutionary signal begins to vanish, replaced by mathematical noise.

Moral inconsistency is based on the vmPFC’s insufficient representation across tasks and connectedness

A new Cell Reports study looked at why people sometimes judge others harshly for dishonest behavior while excusing similar behavior in themselves. The researchers call this moral inconsistency: a mismatch between the moral standards someone uses to judge others and the standards they apply to their own behavior. The study used an honesty-versus-profit task, where participants could gain money by being dishonest, and then judged both their own behavior and other people’s behavior.

The main finding was that people who were more morally inconsistent showed weaker involvement of the ventromedial prefrontal cortex, or vmPFC, a brain region involved in value-based decision-making, social judgment, emotion regulation, and moral evaluation. In morally consistent participants, the vmPFC seemed to represent moral judgment more similarly across “judging myself” and “judging others.” In morally inconsistent participants, that cross-task representation was weaker, especially when they were making choices for themselves.

Liu. V, et al. find that moral inconsistency arises from a reduced ability of the vmPFC to form a cross-task representation of moral principles and its connectedness during the moral behavior task. This indicates that individuals with higher moral inconsistency consider moral principles less often to guide their own behavior.

Ultrasound propagation in porous rocks: Theory identifies three distinct wave modes

Ultrasound-based irradiation of rock formations has attracted considerable attention as a technique for enhancing heavy-oil (high-viscosity crude oil) recovery from deep underground reservoirs. However, a unified theoretical framework for wave propagation and energy dissipation in these formations remains lacking because water coexists with heavy oil within rock pores, and gas bubbles in the water respond dynamically to ultrasonic excitation, thereby creating a complex system.

Conventional theories typically treat oil as a purely viscous (Newtonian) fluid or assume frequency ranges markedly below the ultrasonic regime. Consequently, these theories inadequately capture oil viscoelasticity and the influence of bubble oscillations in the ultrasonic regime.

Researchers at University of Tsukuba have developed a theoretical framework to clarify the propagation of ultrasonic waves through complex materials such as rocks containing mixtures of oil, water, and gas bubbles. The work extends previous low-frequency models and constructs a theoretical framework applicable to ultrasonic frequencies by incorporating three notable elements into a unified system of equations: (i) heavy-oil viscoelasticity, (ii) dynamic capillary pressure at fluid-fluid interfaces, and (iii) oscillations of gas bubbles dispersed in water induced by ultrasonic pressure fluctuations.

Brain enzyme caught doing something unexpected—it builds polysialic acid on itself

A chance discovery at Nagoya University in Japan has shown that a well-known brain enzyme has a hidden ability: It builds a sugar chain on itself, becomes secreted from the cell and deactivates, then switches on outside the cell once the chain is removed. The finding, published in the Journal of Biological Chemistry, overturns a decades-old assumption about how polysialic acid, a sugar chain critical for brain development and function, is produced and shows a new way an enzyme can regulate its own activity.

The human brain is covered in sugar chains, or glycans, molecular structures that coat cells and regulate how they communicate. One of the most important is polysialic acid, a long chain found mainly in the brain.

Polysialic acid keeps brain cells from adhering too tightly to each other and binds to growth factors and neurotrophins to regulate the presentation of their receptors. Through this, it plays a key role in learning, memory and neural development. Importantly, these sugar chains change rapidly in response to brain activity. The ability to restore them quickly is thought to be essential for normal brain function.

Hidden electric space waves are quietly cleaning Earth’s ‘killer’ electrons

High above our heads, a silent battle is unfolding within Earth’s magnetic shield. For decades, scientists have tracked “killer electrons”—ultrafast particles capable of piercing satellite armor and endangering astronauts as they zip through the Van Allen radiation belts. While we knew these dangerous particles eventually leak out of the belts and into the atmosphere, the primary mechanism “cleaning” the highest-energy electrons has remained a persistent mystery of space weather.

Now, a study published in Geophysical Research Letters has uncovered the culprit by diving into three years of NASA’s Van Allen Probes data. Led by Lixian Yang and a team of researchers, the study identifies a hidden population of chorus waves that defies standard physics models.

Unlike typical space waves that are mostly magnetic, these highly oblique quasi-electrostatic (HOQE) waves possess an electric field so powerful it dominates their character. This unique electric punch allows them to knock electrons with energies up to 2 MeV out of orbit and into the atmosphere, scattering them with a force far more potent than any previous model predicted.

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