Designing with DNA

Like the idea, examining the approach further.

Designing with DNA    By Duke University, December 28, 2022

An open-source software program developed by researchers at Duke and other universities lets users take drawings or digital models of rounded shapes and turn them into 3-D structures made of DNA.

While researchers have been experimenting with DNA as a construction material since the 1980s, designing structures with curved surfaces like those found in nature has been tricky. Duke Ph.D. student Dan Fu is part of a team that developed software called DNAxiS, which relies on a previously described way to build with DNA by coiling a long DNA double helix into concentric rings that stack on each other. DNAxiS lets users design shapes automatically, using algorithms to determine where to place short DNA "staples" to join the longer DNA rings together and hold the shape in place.

"If there are too few, or if they're in the wrong position, the structure won't form correctly," Fu says. "Before our software, the curvature of the shapes made this an especially difficult problem."

From Duke University

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Using Light, Sound to Reveal Rapid Brain Activity in Detail

Faster detail in photo microscopy.  Note also use of sound.  

Using Light, Sound to Reveal Rapid Brain Activity in Unprecedented Detail   By Duke Biomedical Engineering, May 26, 2022

Duke University researchers have developed an approach that allows for real-time scanning and imaging of blood flow and oxygen levels in a mouse brain with sufficient resolution to simultaneously observe activity of individual vessels and the entire brain.

Their method could help overcome the challenges associated with brain imaging, particularly the trade-off between speed and resolution.

The new approach, called ultrafast photoacoustic microscopy (UFF-PAM), involves a combination of a polygon scanning system that sends more laser bursts to a larger area, a new scanning mechanism that allows simultaneous operation of the laser scanner and ultrasound sensor, and machine learning algorithms to improve the resolution of the images.

Said Duke's Junjie Yao, "The resulting images looked as detailed as the high-resolution images we would usually get if we went at a much slower speed, and we didn't need to sacrifice a full field of view."   ... 

Photoacoustic microscopy uses light and sound to capture detailed images of organs, tissues, and cells throughout the body. The technique uses a laser to send light into a targeted tissue or cell; when the laser hits the cell, it heats up and expands inst ... '

From Duke Biomedical Engineering

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Making Images Sharper, More secure?

A remarkable approach is described, sharper images for human eyes,  but too sharp for the task of recognition?  Implications for security?

AI Makes Blurry Faces Look More Than 60 Times Sharper
Duke Today
By Robin A. Smith

Researchers at Duke University have created an artificial intelligence (AI) tool that can improve an image of a face with up to 64 times its original resolution, making a blurry, unrecognizable picture into a sharp, realistic image. However, the method, known as PULSE, cannot be used to identify people, such as by making images from a security camera clearer, because the faces it produces may not actually exist. Using a generative adversarial network machine learning tool, the system weeds through AI-generated examples of high-resolution faces to find images that appear similar to the input image when reduced to the same size. Said Duke’s Cynthia Rudin, “Never have super-resolution images been created at this resolution before with this much detail.”  .. '

Validating Clinical Usefulness of Wearable Tech

Ultimately measurement of value.

Verifying, Validating the Clinical Usefulness of Wearable Technology
By Duke University Pratt School of Engineering
May 28, 2020

Duke University assistant professor Jessilyn Dunn (right) watches as Dunn's Ph.d. student and co-author Brinnae Bent prepares to download information from a wearable health monitoring device.
An international team of biomedical engineers and researchers has created a framework for the assessment and documentation of the clinical usefulness of wearable computing devices.

An international team of biomedical engineers and researchers has created a framework to assess and document the clinical usefulness of wearable devices.

The proposed V3 framework would first verify the utility of Biometric Monitoring technologies (BioMeTs) by having hardware manufacturers test sample-level sensor outputs.

The next step, analytical validation, involves having engineers, data scientists, and physiologists evaluate algorithms that produce physiological metrics from sensor data.

The third and final step entails clinical validation, in which a technology vendor or clinical expert demonstrates that a BioMeT can acceptably identify, quantify, or predict the clinical, biological, physical, or functional experience it was intended to record, in the target population of users.

Duke's Will Wang said, "If such information is not clinically validated, users and researchers alike will be led to unjustified conclusions."

From Duke University Pratt School of Engineering .... "  (Full text available at link)