A new study reveals “by far the best evidence” for the elusive cosmic filaments that supercharge ancient galaxies with cold gas.
Engineers at the University of California San Diego have developed a soft, stretchy skin patch that can be worn on the neck to continuously track blood pressure and heart rate while measuring the wearer’s levels of glucose as well as lactate, alcohol, or caffeine. It is the first wearable device that monitors cardiovascular signals and multiple biochemical levels in the human body at the same time.
“This type of wearable would be very helpful for people with underlying medical conditions to monitor their own health on a regular basis,” said Lu Yin, a nanoengineering Ph.D. student at UC San Diego and co-first author of the study published on February 152021, in Nature Biomedical Engineering. “It would also serve as a great tool for remote patient monitoring, especially during the COVID-19 pandemic when people are minimizing in-person visits to the clinic.”
Such a device could benefit individuals managing high blood pressure and diabetes — individuals who are also at high risk of becoming seriously ill with COVID-19. It could also be used to detect the onset of sepsis, which is characterized by a sudden drop in blood pressure accompanied by a rapid rise in lactate level.
The Red Planet’s red looks different to an Earthling than it would to a Martian—or to a robot with hyperspectral cameras for eyes.
Summary: Cardiovascular problems can alter gene activity in the hippocampus, increasing the risk for cognitive decline and memory deficits, a new study reports.
Source: DZNE
How a single cell slime mold makes smart decisions without a central nervous system. Having a memory of past events enables us to make smarter decisions about the future. Researchers at the Max-Planck Institute for Dynamics and Self-Organization (MPI-DS) and the Technical University of Munich (TUM) have now identified how the slime mold Physarum polycephalum saves memories – although it has no nervous system.
Over the past few decades, researchers have developed deep neural network-based models that can complete a broad range of tasks. Some of these techniques are specifically designed to process and generate coherent texts in multiple languages, translate texts, answer questions about a text and create summaries of news articles or other online content.
Robots that could take on basic healthcare tasks to support the work of doctors and nurses may be the way of the future. Who knows, maybe a medical robot can prescribe your medicine someday? That’s the idea behind 3D structural-sensing robots being developed and tested at Simon Fraser University by Woo Soo Kim, associate professor in the School of Mechatronic Systems Engineering.
We’ve all met people so smart and informed that we don’t understand what they’re talking about. The investment advisor discussing derivatives, the physician elaborating about B cells and T cells, the auto mechanic talking about today’s computerized engines—we trust their decisions, even though we do not completely grasp the meaning of their words.
A research team from Skoltech and FBK (Italy) has presented a methodology to derive 4D building models using historical maps and machine learning. The implemented method relies on geometric, neighborhood, and categorical attributes in order to predict building heights. The method is useful for understanding urban phenomena and changes that contributed to defining our cities’ actual shape. The results were published in Applied Sciences.
