Eric Markvicka and graduate students Ethan Krings and Patrick McManigal recently unveiled their invention of a robotic actuator -- the "muscle" that converts energy into a robot's physical movement -- that has the ability to detect punctures or pressure, heal the injury and repair its damage-detecting "skin." The trio presented their findings earlier in May at the IEEE International Conference on Robotics and Automation in Atlanta. Their technology would mark an improvement for robotic system used in agriculture and manufacturing that are susceptible to puncture by thorns, twigs, plastic and glass and also could be used to prolong the life of wearable health monitoring devices and consumer electronics,  thus stemming the tide of toxins like lead and mercury entering waste streams and endangering the environment and human health. 

A new avenue for targeted drug delivery has been proposed by researchers from The Grainger College of Engineering at the University of Illinois Urbana-Champaign. Their findings, published in Materials Today Bio, report the first successful application of metabolic labeling in platelets. 

A new Science study warns that if temperatures rise to 2.7°C by 2100, only 24% of glacier mass would remain, contributing over nine inches to sea-level rise. Even with no further warming, 39% of glaciers are projected to vanish. However, if global targets like the Paris Agreement’s 1.5°C goal are met, more than half of that loss could be avoided. The findings highlight what’s at stake for water, coastlines, and ecosystems in the UN’s International Year of Glaciers’ Preservation.

A "fine-tuned" artificial intelligence (AI) tool shows promise for objective evaluation of patients with facial palsy, reports an experimental study in the June issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS). The journal is published in the Lippincott portfolio by Wolters Kluwer.

A groundbreaking non-hand-worn VR hand rehabilitation system has been developed, utilizing ionic hydrogel electrodes and deep learning for electromyography (EMG) gesture recognition. The system offers load-free rehabilitation without bulky mechanical components, providing a more accessible and flexible alternative to traditional rehabilitation methods. This VR-based solution enables immersive training and precise hand rehabilitation for stroke and joint disease patients in the comfort of their homes, without the constraints of time or location.

A self-supervised deep learning model has been developed to improve the quality of dynamic fluorescence images by leveraging temporal gradients. The method enables accurate denoising without ground truth data and facilitates clearer visualization of spatially and temporally dynamic biological signals in vivo.