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Category: biological – Page 29

Scientists discover 500-year-old shark, Earth’s longest living vertebrate

This is superlongevity! ♾️

“One shark, measuring five meters, was found to be at least 272 years old, with an upper age estimate of more than 500 years (392 +/- 120 years). Another specimen was at least 260 years old, potentially exceeding 400 years. “We definitely expected the sharks to be old, but we didn’t expect that it would be the longest-living vertebrate animal,” Nielsen said.”

The Greenland shark holds the title as the longest-lived vertebrate on Earth, with some individuals potentially reaching 500 years of age. This elusive deep-sea predator, found in the frigid waters of the North Atlantic and Arctic Oceans, has fascinated scientists due to its remarkable lifespan. Its slow growth rate and mysterious biology have made it a subject of ongoing research, shedding light on how some species defy the limits of aging.

A major breakthrough in understanding the longevity of Greenland sharks came from a research team led by Julius Nielsen, a marine biologist at the University of Copenhagen. Nielsen and his colleagues conducted a study that revealed a Greenland shark estimated to be at least 272 years old, with some models suggesting an upper age limit of nearly 500 years.

This finding shattered previous records, surpassing the known lifespan of the 211-year-old bowhead whale, which had long been considered the longest-lived vertebrate.

The Brain Emulation Challenge

This is a draft version of the Brain Emulation Challenge video.

This version is intended for an audience with some neuroscience background or interest.

This video is provided with the hope to generate useful critical feedback for improvements.

Why take the brain emulation challenge? Why take a challenge that is providing virtual brain data from generated neural tissue?

If your system identification and reconstruction method successfully discovers the neural circuit and translates its meaningful cognitive function, which was hidden in the data your method analyzed, and about which we know everything, for which we can verify and validate exactly how well the reconstructed result performs a specific function, then we have much stronger reason to believe claims about reconstructions and discovered function from unknown biological neural tissue.

It is a way to test qualitatively and quantitatively if a proposed method can indeed discover and extract what it is meant to find, establishing trust that it is able to deliver a specific and correct working model based on collected brain data.

Continue reading “The Brain Emulation Challenge” | >

Certain animal navigation abilities found to operate at or near quantum limit of magnetic field detection

A pair of physicists at the University of Crete has found that some types of biological magnetoreceptors used by various creatures to navigate, operate at or near the quantum limit. In their paper published in the journal PRX Life, I. K. Kominis and E. Gkoudinakis describe how they worked the problem of magnetic sensing in tiny animals in reverse by putting bounds on unknown quantum boundaries, and what it showed about the navigation abilities of certain animals.

Prior research has shown that many creatures use the Earth’s as a navigation aid. Some sharks, fish and birds, for example, use it to help them traverse long distances. Different animals also have different types of magnetic sensors, including radical-pair, induction and magnetite mechanisms.

Radical-pair works by sensing correlations between unpaired electrons attached to certain molecules. Induction works by turning energy in the magnetic field into electricity and then sensing the electrical charge. And magnetite-based magnetoreception involves sensing the movement or orientation of tiny iron crystals in the body, similar to a human-made compass.

Multilingual Computational Models Reveal Shared Brain Responses to 21 Languages

At the heart of language neuroscience lies a fundamental question: How does the human brain process the rich variety of languages? Recent developments in Natural Language Processing, particularly in multilingual neural network language models, offer a promising avenue to answer this question by providing a theory-agnostic way of representing linguistic content across languages. Our study leverages these advances to ask how the brains of native speakers of 21 languages respond to linguistic stimuli, and to what extent linguistic representations are similar across languages. We combined existing (12 languages across 4 language families; n=24 participants) and newly collected fMRI data (9 languages across 4 language families; n=27 participants) to evaluate a series of encoding models predicting brain activity in the language network based on representations from diverse multilingual language models (20 models across 8 model classes). We found evidence of cross-lingual robustness in the alignment between language representations in artificial and biological neural networks. Critically, we showed that the encoding models can be transferred zero-shot across languages, so that a model trained to predict brain activity in a set of languages can account for brain responses in a held-out language, even across language families. These results imply a shared component in the processing of different languages, plausibly related to a shared meaning space.

The authors have declared no competing interest.

Organoid intelligence: training lab-grown mini-brains to learn and compute with AI

Recent research demonstrates that brain organoids can indeed “learn” and perform tasks, thanks to AI-driven training techniques inspired by neuroscience and machine learning. AI technologies are essential here, as they decode complex neural data from the organoids, allowing scientists to observe how they adjust their cellular networks in response to stimuli. These AI algorithms also control the feedback signals, creating a biofeedback loop that allows the organoids to adapt and even demonstrate short-term memory (Bai et al. 2024).

One technique central to AI-integrated organoid computing is reservoir computing, a model traditionally used in silicon-based computing. In an open-loop setup, AI algorithms interact with organoids as they serve as the “reservoir,” for processing input signals and dynamically adjusting their responses. By interpreting these responses, researchers can classify, predict, and understand how organoids adapt to specific inputs, suggesting the potential for simple computational processing within a biological substrate (Kagan et al. 2023; Aaser et al. n.d.).

Fiber may help Protect your Gut from Overgrowth of Harmful Bugs—new study

Our body isn’t just human—it’s home to trillions of microorganisms found in or on us. In fact, there are more microbes in our gut than there are stars in the Milky Way. These microbes are essential for human health, but scientists are still figuring out exactly what they do and how they help.

In a new study, published in Nature Microbiology, my colleagues and I explored how certain gut bacteria—a group known as Enterobacteriaceae—can protect us from harmful ones. These bacteria include species such as Escherichia coli (E coli). This is normally harmless in small amounts but can cause infections and other health problems if it grows too much.

We found that our gut environment—shaped by things like diet—plays a big role in keeping potentially harmful bacteria in check.