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A team of biophysicists from the University of Massachusetts Amherst and Penn State College of Medicine set out to tackle the long-standing question about the nature of force generation by myosin, the molecular motor responsible for muscle contraction and many other cellular processes. The key question they addressed—one of the most controversial topics in the field—was: how does myosin convert chemical energy, in the form of ATP, into mechanical work?

The answer revealed new details into how myosin, the engine of muscle and related motor proteins, transduces energy.

In the end, their unprecedented research, meticulously repeated with different controls and double-checked, supported their hypothesis that the mechanical events of a precede—rather than follow—the biochemical events, directly challenging the long-held view that biochemical events gate the force-generating event. The work, published in the Journal of Biological Chemistry, was selected as an Editor’s Pick for “providing an exceptional contribution to the field.”

Researchers develop the first nanomaterial that demonstrates “photon avalanching;” finding could lead to new applications in sensing, imaging, and light detection.

Researchers at Columbia Engineering report today that they have developed the first nanomaterial that demonstrates “photon avalanching,” a process that is unrivaled in its combination of extreme nonlinear optical behavior and efficiency. The realization of photon avalanching in nanoparticle form opens up a host of sought-after applications, from real-time super-resolution optical microscopy, precise temperature and environmental sensing, and infrared light detection, to optical analog-to-digital conversion and quantum sensing.

“Nobody has seen avalanching behavior like this in nanomaterials before,” said James Schuck, associate professor of mechanical engineering, who led the study published today (January 132021) by Nature. “We studied these new nanoparticles at the single-nanoparticle level, allowing us to prove that avalanching behavior can occur in nanomaterials. This exquisite sensitivity could be incredibly transformative. For instance, imagine if we could sense changes in our chemical surroundings, like variations in or the actual presence of molecular species. We might even be able to detect coronavirus and other diseases.”

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using to learn more about how mental illness and other disorders affect the brain.

Stuck at home with time on their hands, millions of amateurs around the world are gathering information on everything from birds to plants to Covid-19 at the request of institutional researchers. And while quarantine is mostly a nightmare for us, it’s been a great accelerant for science.


From backyard astronomy to birding, amateurs have been busy collecting data — and making real discoveries.

The process of systems integration (SI) functionally links together infrastructure, computing systems, and applications. SI can allow for economies of scale, streamlined manufacturing, and better efficiency and innovation through combined research and development.

New to the systems integration toolbox are the emergence of transformative technologies and, especially, the growing capability to integrate functions due to exponential advances in computing, data analytics, and material science. These new capabilities are already having a significant impact on creating our future destinies.

The systems integration process has served us well and will continue to do so. But it needs augmenting. We are on the cusp of scientific discovery that often combines the physical with the digital—the Techno-Fusion or merging of technologies. Like Techno-Fusion in music, Techno-Fusion in technologies is really a trend that experiments and transcends traditional ways of integration. Among many, there are five grouping areas that I consider good examples to highlight the changing paradigm. They are: Smart Cities and the Internet of Things (IoT); Artificial Intelligence (AI), Machine Learning (ML), Quantum and Super Computing, and Robotics; Augmented Reality (AR) and Virtual Reality Technologies (VR); Health, Medicine, and Life Sciences Technologies; and Advanced Imaging Science.