Lifeboat Foundation: Safeguarding Humanity
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Category: biotech/medical – Page 1,135

Around the same time, neuroscientists developed the first computational models of the primate visual system, using neural networks like AlexNet and its successors. The union looked promising: When monkeys and artificial neural nets were shown the same images, for example, the activity of the real neurons and the artificial neurons showed an intriguing correspondence. Artificial models of hearing and odor detection followed.

But as the field progressed, researchers realized the limitations of supervised training. For instance, in 2017, Leon Gatys, a computer scientist then at the University of Tübingen in Germany, and his colleagues took an image of a Ford Model T, then overlaid a leopard skin pattern across the photo, generating a bizarre but easily recognizable image. A leading artificial neural network correctly classified the original image as a Model T, but considered the modified image a leopard. It had fixated on the texture and had no understanding of the shape of a car (or a leopard, for that matter).

Self-supervised learning strategies are designed to avoid such problems. In this approach, humans don’t label the data. Rather, “the labels come from the data itself,” said Friedemann Zenke, a computational neuroscientist at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland. Self-supervised algorithms essentially create gaps in the data and ask the neural network to fill in the blanks. In a so-called large language model, for instance, the training algorithm will show the neural network the first few words of a sentence and ask it to predict the next word. When trained with a massive corpus of text gleaned from the internet, the model appears to learn the syntactic structure of the language, demonstrating impressive linguistic ability — all without external labels or supervision.

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Vanderbilt researchers have developed an active machine learning approach to predict the effects of tumor variants of unknown significance, or VUS, on sensitivity to chemotherapy. VUS, mutated bits of DNA with unknown impacts on cancer risk, are constantly being identified. The growing number of rare VUS makes it imperative for scientists to analyze them and determine the kind of cancer risk they impart.

Traditional prediction methods display limited power and accuracy for rare VUS. Even machine learning, an artificial intelligence tool that leverages data to “learn” and boost performance, falls short when classifying some VUS. Recent work by the lab of Walter Chazin, Chancellor’s Chair in Medicine and professor of biochemistry and chemistry, led by co-first authors and postdoctoral fellows Alexandra Blee and Bian Li, featured an active machine learning technique.

Active machine learning relies on training an algorithm with existing data, as with machine learning, and feeding it new information between rounds of training. Chazin and his lab identified VUS for which predictions were least certain, performed biochemical experiments on those VUS and incorporated the resulting data into subsequent rounds of algorithm training. This allowed the model to continuously improve its VUS classification.

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Thor Balkhed/Linköping University.

Made of collagen protein from pig’s skin, the implant resembles the human cornea and is more than a pipe dream for an estimated number of 12.7 million people around the world who are blind due to their diseased corneas. The implant is a promising alternative to the transplantation of donated human corneas, which are scarce in under-developed and developing countries, where the need for them is greatest.

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University of Toronto researchers are working on advanced snake-like robots with many useful applications.

Slender, flexible, and extensible robots

Now, a team led by Jessica Burgner-Kahrs, the director of the Continuum Robotics Lab at the University of Toronto Mississauga, is building very slender, flexible, and extensible robots that could be used by doctors to save lives, according to a press release by the institution. They do this by accessing difficult-to-reach places.

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“Some people are still pretty uptight about these things,” he added.

Musk didn’t comment on how often he smokes weed, but said he’s not very skilled at it.

“I don’t even know how to smoke a joint, obviously. I mean, look at me, I have no joint-smoking skills,” he said.

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Researchers at the Wake Forest Institute for Regenerative Medicine (WFIRM), North Carolina, are investigating the power of cells with regenerative effects. These researchers were the first to identify that stem cells in human urine have the potential for tissue regenerative effects, and are now continuing their investigation.

In a new study, the researchers have focused on how telomerase activity affects the regenerative potential of stem cells in human urine and other types of stem cells. The study was recently published in the journal Frontiers in Cell and Developmental Biology.

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A team at the University of California, Irvine, has identified a signaling molecule that potently stimulates hair growth.

A signaling molecule known as SCUBE3, which was discovered by researchers at the University of California, Irvine, has the potential to cure androgenetic alopecia, a prevalent type of hair loss in both women and men.

The research, which was recently published in the journal Developmental Cell, uncovered the precise mechanism by which the dermal papilla cells, specialized signal-producing fibroblasts found at the bottom of each hair follicle, encourage new development. Although the critical role dermal papilla cells play in regulating hair growth is widely established, the genetic basis of the activating chemicals involved is little understood.

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