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Nematodes, a specific sort of microscopic worm, have been proven by Osaka University researchers to be capable of killing cancer cells, according to Interesting Engineering and SciTechDaily.

The study titled “Nematode surface functionalization with hydrogel sheaths tailored in situ” by Wildan Mubarok, Masaki Nakahata, Masaru Kojima and Shinji Sakai showed that Hydrogel-based “sheaths” that can be further modified to transport useful cargo (cancer-killing substances) could be applied to these worms as a coating.

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Often when Dr. Thomas Valley sees a new patient in the intensive care unit at Michigan Medicine in Ann Arbor, he clamps a pulse oximeter on their finger – one of the many devices he uses to gauge their health and what course of care they might require, whether they are a child having seizures, a teenage car accident victim or an older person with Covid-19.

But recently, Valley, an assistant professor in the University of Michigan’s Division of Pulmonary and Critical Care, realized first-hand that the small device may yield less accurate oxygen readings in patients with dark skin.

Continue reading “Scientists are searching for solutions after studies show pulse oximeters don’t work as well for people of color” »

Researchers from North Carolina State University have developed a new approach to federated learning that allows them to develop accurate artificial intelligence (AI) models more quickly and accurately. The work focuses on a longstanding problem in federated learning that occurs when there is significant heterogeneity in the various datasets being used to train the AI.

Federated learning is an AI training technique that allows AI systems to improve their performance by drawing on multiple sets of data without compromising the privacy of that data. For example, federated learning could be used to draw on privileged patient data from multiple hospitals in order to improve diagnostic AI tools, without the hospitals having access to data on each other’s patients.

Federated learning is a form of machine learning involving multiple devices, called clients. The clients and a centralized server all start with a basic model designed to solve a specific problem. From that starting point, each of the clients then trains its local model using its own data, modifying the model to improve its performance. The clients then send these “updates” to the centralized server. The centralized server draws on these updates to create a hybrid model, with the goal of having the hybrid model perform better than any of the clients on their own. The central server then sends this hybrid model back to each of the clients. This process is repeated until the system’s performance has been optimized or reaches an agreed-upon level of accuracy.

A new King’s-led study, published in the Proceedings of the National Academy of Sciences, has found that a single factor (a protein coding gene known as Sox8) can make non-ear cells adopt ear character during embryo development. The findings not only demonstrate how cell fate decisions are regulated in the embryo but may also inform reprogramming and regenerative strategies for the ear developmental malformations.

Responsible for the sense of hearing and balance, the inner ear is critically important for communication with the environment. In humans, developmental malformations of the ear have life-long consequences, while age-related hearing defects affect a large proportion of the population. Currently, there are no therapies that involve biological approaches—only hearing aids or cochlear implants, as how the ear normally develops is not fully understood and many of the controlling factors are poorly characterized.

Researchers from the Faculty of Dentistry, Oral and Craniofacial Sciences at King’s, in collaboration with colleagues from the Francis Crick Institute, explored the earliest steps in ear development to determine what causes cells to become ear cells, and what makes them different from cells which form other sense organs.

As meetings shifted online during the COVID-19 lockdown, many people found that chattering roommates, garbage trucks and other loud sounds disrupted important conversations.

This experience inspired three University of Washington researchers, who were roommates during the pandemic, to develop better earbuds. To enhance the speaker’s voice and reduce background noise, “ClearBuds” use a novel microphone system and one of the first machine-learning systems to operate in real time and run on a smartphone.

Continue reading “First wireless earbuds that clear up calls using deep learning” »

Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.

Though engineering of biological systems is truly indispensable in biotechnology and synthetic biology, today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in Nature Communications, the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, or “wise counsel.”

Remission of depression with new magnetic therapy:3.

Although she’d tried medications and therapy, Chase felt her symptoms get worse over the course of a few months. And she knew things were really getting serious when thoughts of suicide crept in.

That’s when her mother found research about a new type of treatment for depression called Stanford neuromodulation therapy, which uses magnetic fields to stimulate the brain. (It was previously referred to as Stanford accelerated intelligent neuromodulation therapy or SAINT.)

Continue reading “A new therapy with magnets is helping people with depression when nothing else works” »

The BA.5 variant is now the most dominant strain of COVID-19 in the country, according to the Centers for Disease Control and Prevention. And while it’s hard to get an exact count — given how many people are taking rapid tests at home — there are indications that both reinfections and hospitalizations are increasing.

For example: Some 31,000 people across the U.S. are currently hospitalized with the virus, with admissions up 4.5% compared to a week ago. And data from New York state shows that reinfections started trending upwards again in late June.

Brain-machine interfaces (BMIs) are devices that enable direct communication/translation between biological neuronal networks (e.g. a brain or a spine) and external machines. They are currently being used as a tool for fundamental neuroscience research and also for treating neurological disorders and for manipulating neuro-prosthetic devices. As remarkable as today’s BMIs are, however, the next generation BMIs will require new hardware and software with improved resolution and specificity in order to precisely monitor and control the activities of complex neuronal networks. In this talk, I will describe my group’s effort to develop new neuroelectronic devices enabled by silicon nanotechnology that can serve as high-precision, highly multiplexed interface to neuronal networks. I will then describe the promises, as well as potential pitfalls, of next generation BMIs. Hongkun Park is a Professor of Chemistry and Chemical Biology and a Professor of Physics at Harvard University. He is also an Institute Member of the Broad Institute of Harvard and MIT and a member of the Harvard Center for Brain Science and Harvard Quantum Optics Center. He serves as an associate editor of Nano Letters. His research interests lie in exploring solid-state photonic, optoelectronic, and plasmonic devices for quantum information processing as well as developing new nano-and microelectronic interfaces for living cells, cell networks, and organisms. Awards and honors that he received include the Ho-Am Foundation Prize in Science, NIH Director’s Pioneer Award, and the US Vannevar Bush Faculty Fellowship, the David and Lucile Packard Foundation Fellowship for Science and Engineering, the Alfred P. Sloan Research Fellowship, and the Camille Dreyfus Teacher-Scholar Award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.