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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.

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.

In 2017, Stanford University researchers presented a new device that mimics the brain’s efficient and low-energy neural learning process. It was an artificial version of a synapse—the gap across which neurotransmitters travel to communicate between neurons—made from organic materials. In 2019, the researchers assembled nine of their artificial synapses together in an array, showing that they could be simultaneously programmed to mimic the parallel operation of the brain.

Now, in a paper published June 15 in Nature Materials, they have tested the first biohybrid version of their artificial synapse and demonstrated that it can communicate with living cells. Future technologies stemming from this device could function by responding directly to chemical signals from the brain. The research was conducted in collaboration with researchers at Istituto Italiano di Tecnologia (Italian Institute of Technology—IIT) in Italy and at Eindhoven University of Technology (Netherlands).

“This paper really highlights the unique strength of the materials that we use in being able to interact with living matter,” said Alberto Salleo, professor of materials science and engineering at Stanford and co-senior author of the paper. “The cells are happy sitting on the soft polymer. But the compatibility goes deeper: These materials work with the same molecules neurons use naturally.”

A team of researchers from Nanjing University of Posts and Telecommunications and the Chinese Academy of Sciences in China and Nanyang Technological University and the Agency for Science Technology and Research in Singapore developed an artificial neuron that is able to communicate using the neurotransmitter dopamine. They published their creation and expected uses for it in the journal Nature Electronics.

As the researchers note, most machine-brain interfaces rely on electrical signals as a communications medium, and those signals are generally one-way. Electrical signals generated by the brain are read and interpreted; signals are not sent to the brain. In this new effort, the researchers have taken a step toward making a brain-machine interface that can communicate in both directions, and it is not based on electrical signals. Instead, it is chemically mediated.

Continue reading “An artificial neuron that can receive and release dopamine” »

It’s “a revolutionary scientific advance in molecular data storage and cryptography.”

Scientists from the University of Texas at Austin sent a letter to colleagues in Massachusetts with a secret message: an encryption key to unlock a text file of L. Frank Baum’s classic novel The Wonderful Wizard of Oz. The twist: The encryption key was hidden in a special ink laced with polymers, They described their work in a recent paper published in the journal ACS Central Science.

When it comes to alternative means for data storage and retrieval, the goal is to store data in the smallest amount of space in a durable and readable format. Among polymers, DNA has long been the front runner in that regard. As we’ve reported previously, DNA has four chemical building blocks—adenine (A), thymine (T), guanine (G), and cytosine ©—which constitute a type of code. Information can be stored in DNA by converting the data from binary code to a base-4 code and assigning it one of the four letters. A single gram of DNA can represent nearly 1 billion terabytes (1 zettabyte) of data. And the stored data can be preserved for long periods—decades, or even centuries.

Continue reading “Scientists hid encryption key for Wizard of Oz text in plastic molecules” »

Science, Technology, Health, Physics, Chemistry stay Updated.

Scientists from The Australian National University (ANU) and James Cook University (JCU) have identified an “exquisite” natural mechanism that helps plants limit their water loss with little effect on carbon dioxide (CO₂) intake—an essential process for photosynthesis, plant growth and crop yield.

• Earth.com

A new study led by Michigan State University (MSU) has found that locusts can reliably detect through smell a variety of human cancers. The insects can not only “smell” the difference between healthy and cancerous cells, but they can also distinguish between different cancer cell lines. These findings could provide a basis for devices which use locust sensory neurons to enable the early detection of cancer by using only biomarkers in a patient’s breath.

“Noses are still state of the art,” said study senior author Debajit Saha, an assistant professor of Biomedical Engineering at MSU. “There’s really nothing like them when it comes to gas sensing. People have been working on ‘electronic noses’ for more than 15 years, but they’re still not close to achieving what biology can do seamlessly.”

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Archived histological material from tracing studies, immunohistochemistry, and Golgi impregnations allowed to discover a so far unrecognized structural difference, potentially of functional importance, between neocortical pyramidal neurons of rodent, carnivore, and ungulate as compared to monkey and man.