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Mitofissin Guides Mitochondrial Fission by Interacting with Drp1

Mitochondria, the primary energy-producing organelles in eukaryotic cells, power essential biosynthetic processes through oxidative phosphorylation [1]. These organelles are highly dynamic and continually undergo cycles of fusion and fission to support energy-demanding cellular activities such as proton pumping for gastric acid secretion [2]. These membrane remodeling events are regulated by evolutionarily conserved guanosine triphosphatases (GTPases) from the dynamin superfamily, with fusion at the outer and inner membranes mediated by MFN1/2 and OPA1, respectively, and fission occurring by Drp1 [3]. However, little is known about how Drp1 anchors to the mitochondrial outer membrane. Here, we report a previously uncharacterized protein, C3orf33, as a novel adaptor for Drp1 and designate it Mitofissin (MiSN) on the basis of its function in controlling mitochondrial fission. Our preliminary screen suggested that MiSN localizes to mitochondria. To visualize dynamic and fine MiSN localization, we transiently transfected U2OS cells to express green fluorescent protein (GFP)-MiSN, followed by real-time imaging using lattice structured illumination microscopy (Lattice SIM). As shown in Figure 1 A, MiSN colocalized with the mitochondrial dye MitoTracker. Careful examination of the zoomed-in image revealed that the MiSN signal was exterior to the MitoTracker (Figure 1 A, arrow). Further characterization of the cellular fractions revealed that MiSN was enriched in the mitochondrial fraction (Figure 1 B). Thus, we concluded that MiSN is a novel mitochondrial outer membrane protein.

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Scientists Develop “Unbreakable” Quantum Sensor Built to Survive 30,000 Atmospheres

Boron nitride sensors enable quantum measurements under crushing pressure, redefining high-pressure physics. The quantum world is already full of mysteries, but what happens when this strange domain of subatomic particles is subjected to immense pressure? Studying quantum behavior in such conditi

AI restores James Webb telescope’s crystal-clear vision

Two Sydney PhD students have pulled off a remarkable space science feat from Earth—using AI-driven software to correct image blurring in NASA’s James Webb Space Telescope. Their innovation, called AMIGO, fixed distortions in the telescope’s infrared camera, restoring its ultra-sharp vision without the need for a space mission.

Turning Point in Heart Health Occurs at 1 Key Age (It’s Younger Than You’d Think)

Emerging adulthood – the life stage that unfolds around ages 18–25 – is full of major transitions, such as starting college or learning a trade, making new friends and romantic connections, and generally becoming more independent.

It’s also a stage where behaviors that diminish heart health, such as spending more time sitting, consuming more fast food, and using more tobacco and alcohol, become more common.

In fact, only about 1 in 4 youths maintain positive health behavior patterns during the transition to adulthood.

Scientists smash record in stacking semiconductor transistors for large-area electronics

King Abdullah University of Science and Technology (KAUST; Saudi Arabia) researchers have set a record in microchip design, achieving the first six-stack hybrid CMOS (complementary metal-oxide semiconductor) for large-area electronics. With no other reported hybrid CMOS exceeding two stacks, the feat marks a new benchmark in integration density and efficiency, opening possibilities in electronic miniaturization and performance.

A paper detailing the team’s research appears in Nature Electronics.

Among microchip technologies, CMOS microchips are found in nearly all electronics, from phones and televisions to satellites and medical devices. Compared with conventional silicon chips, hybrid CMOS microchips hold greater promise for large-area electronics. Electronic miniaturization is crucial for flexible electronics, smart health, and the Internet of Things, but current design approaches are reaching their limits.

AI teaches itself and outperforms human-designed algorithms

Like humans, artificial intelligence learns by trial and error, but traditionally, it requires humans to set the ball rolling by designing the algorithms and rules that govern the learning process. However, as AI technology advances, machines are increasingly doing things themselves. An example is a new AI system developed by researchers that invented its own way to learn, resulting in an algorithm that outperformed human-designed algorithms on a series of complex tasks.

For decades, human engineers have designed the algorithms that agents use to learn, especially reinforcement learning (RL), where an AI learns by receiving rewards for successful actions. While learning comes naturally to humans and animals, thanks to millions of years of evolution, it has to be explicitly taught to AI. This process is often slow and laborious and is ultimately limited by human intuition.

Taking their cue from evolution, which is a random trial and error process, the researchers created a large digital population of AI agents. These agents tried to solve numerous tasks in many different, complex environments using a particular learning rule.

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