Toggle light / dark theme

Spiders produce amazingly strong and lightweight threads called draglines that are made from silk proteins. Although they can be used to manufacture a number of useful materials, getting enough of the protein is difficult because only a small amount can be produced by each tiny spider. In a new study published in Communications Biology, a research team led by Keiji Numata at the RIKEN Center for Sustainable Resource Science (CSRS) reported that they succeeded in producing the spider silk using photosynthetic bacteria. This study could open a new era in which photosynthetic bio-factories stably output the bulk of spider silk.

In addition to being tough and lightweight, silks derived from arthropod species are biodegradable and biocompatible. In particular, spider silk is ultra-lightweight and is as tough as steel. “Spider silk has the potential to be used in the manufacture of high-performance and durable materials such as tear-resistant clothing, automobile parts, and aerospace components,” explains Choon Pin Foong, who conducted this study. “Its biocompatibility makes it safe for use in biomedical applications such as drug delivery systems, implant devices, and scaffolds for tissue engineering.” Because only a trace amount can be obtained from one spider, and because breeding large numbers of spiders is difficult, attempts have been made to produce artificial spider silk in a variety of species.

The CSRS team focused on the marine photosynthetic bacterium Rhodovulum sulfidophilum. This bacterium is ideal for establishing a sustainable bio-factory because it grows in seawater, requires carbon dioxide and nitrogen in the atmosphere, and uses solar energy, all of which are abundant and inexhaustible.

Last week the President Council of Advisors on Science and Technology (PCAST) met (webinar) to review policy recommendations around three sub-committee reports: 1) Industries of the Future (IotF), chaired be Dario Gil (director of research, IBM); 2) Meeting STEM Education and Workforce Needs, chaired by Catherine Bessant (CTO, Bank of America), and 3) New Models of Engagement for Federal/National Laboratories in the Multi-Sector R&D Enterprise, chaired by Dr. A.N. Sreeram (SVP, CTO, Dow Corp.)

Yesterday, the full report (Recommendations For Strengthening American Leadership In Industries Of The Future) was issued and it is fascinating and wide-ranging. To give you a sense of the scope, here are three highlights taken from the executive summary of the full report:

Army researchers developed a new way to protect and safeguard quantum information, moving quantum networks a step closer to reality.

Quantum information science is a rapidly growing interdisciplinary field exploring new ways of storing, manipulating and communicating information. Researchers want to create powerful computational capabilities using new hardware that operates on quantum physics principles.

For the Army, the new quantum paradigms could potentially lead to transformational capabilities in fast, efficient and secure collecting, exchanging and processing vast amounts of information on dynamic battlefields of the future.

Researchers at the Army Research Laboratory have developed a new method to protect and safeguard quantum information, moving quantum networks a step closer to reality.

Quantum information science is a rapidly growing interdisciplinary field exploring new ways of storing, manipulating and communicating information. Researchers aim to create powerful computational capabilities using new hardware that operates on quantum physics principles.

For the army, these new quantum paradigms could potentially lead to transformational capabilities in fast, efficient and secure collecting, exchanging and processing vast amounts of information on dynamic battlefields in the future.

Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.

The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.