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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.

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.

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.

This performative talk comes from the speaker’s personal experience as an artist scholar and outside insider who travels between the US and China for dance production and research.

Dr. Fangfei Miao is an Assistant Professor of Dance in the School of Music at U-M. She is also an accomplished international dance scholar, choreographer, and dancer. Her current research on dance and Asian studies has led to her book project that focuses on historical “errors” in cross-cultural dance transmissions in Reform Era China (1978-present). She has toured internationally and staged her experimental choreography in New York City, Los Angeles, Auckland, and Beijing. Miao previously received her PhD in Culture and Performance (2019) from UCLA, MFA in Choreography (2011) and BA in Dance History and Theory (2008) from the Beijing Dance Academy, China’s premier dance conservatory. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.