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DNA Manufacturing Enters the Age of Mass Production

Synthetic-biology startups adopt technologies from the computer industry

3 min read
DNA Manufacturing Enters the Age of Mass Production
Illustration: Elias Stein

Emily Leproust, CEO and cofounder of the buzzy biotech startup Twist Bioscience, is an industrialist on the nanoscale. “I remind everyone at Twist, we are a manufacturing company,” she says. “We manufacture DNA.”

Twist is part of the young industry of synthetic biology, in which living organisms are the product and a biology lab is the factory floor. By manufacturing strands of DNA—assembling the genetic code of life from its basic components—scientists are creating organisms the likes of which the world has never seen. And these new life forms can be decidedly useful: Biologists have produced yeast cells that excrete pharmaceuticals and algae that brew jet fuel.

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Dialing Down a Quantum Compute Glitch by 100,000x

A low-key solution to qubits’ cosmic ray problem

3 min read
Conceptual computer artwork of electronic circuitry contained within spheres against beams of light, representing how data may be controlled and stored in a quantum computer.
Mehau Kulyk/Science Source

The kind of quantum computers that IBM, Google and Amazon are building suffer catastrophic errors roughly once every 10 seconds due to cosmic rays from outer space. Now a new study reveals a way to reduce this error rate by nearly a half-million-fold to less than once per month.

Quantum computers theoretically can find answers to problems no regular computer might ever hope to solve. Their key ingredients, known as quantum bits, or qubits, are linked together by a quantum effect known as entanglement.

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Deep Learning Gets a Boost From New Reconfigurable Processor

The ReAAP processor allows AI to be faster, more efficient

2 min read
different colored beams of light shooting up
iStock

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Deep learning is a critical computing approach that is pushing the boundaries of technology – crunching immense amounts of data and uncovering subtle patterns that humans could never discern on their own. But for optimal performance, deep learning algorithms need to be supported with the right software compiler and hardware combinations. In particular, reconfigurable processors, which allow for flexible use of hardware resources for computing as needed, are key.

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Tools and Strategies for 3D EM Modeling and Design of Microwave Imaging Systems for Medical Applications

Learn how WiPL-D software suite can be efficiently used for biomedical applications

1 min read

Microwave Imaging (MWI) has attracted massive attention in the medical research field over the last decade due to its standout qualities of utilizing harmless non-ionizing radiation and affordable components. At present, conventional technologies (CT and MRI) which provide high-resolution images, still have several limitations such as their long examination time, non-portability, high cost, and also ionizing radiation.

MWI has several potential applications and one of the promising areas is malignant tissue detection as a contrast of permittivity with respect to healthy tissues inside the human body. In order to detect malignancy using MWI at different organs, particular imaging scenarios need to be considered.

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