RSS feed source: National Science Foundation

Cardiovascular diseases cause one death every 33 seconds in America. Diagnosing these conditions, which account for approximately 20% of all deaths annually, can be difficult because the overlaying and natural fluorescence of cardiac tissue complicate diagnostic images. A new algorithm, developed by researchers supported by the U.S. National Science Foundation and described in Nature Cardiovascular Research, could lead to clearer images, earlier diagnoses and better outcomes.

“Enhancing visualization of cardiac systems is just one application of this new tool,” said Eric Lyons, a program director in the NSF Directorate for Biological Sciences. “This could also help advance live-cell imaging in other parts of the body, like the brain, and drive insights into fundamental biological processes and systems.”

Current forms of imaging each have drawbacks, being limited by how broad or deep they can visualize, the ability to visualize small scales like molecules or the frame rate of cameras and speed of data acquisition and processing. The algorithm addresses many of these challenges and allows for simultaneous viewing of multiple parameters and measurement of the volume of heart chambers.

The tool uses an approach known as multiscale recursive decomposition, where images are broken down into smaller parts across multiple scales. This allows for the precise extraction of dynamic cardiovascular signals, which could allow physicians and others to diagnose cardiovascular disease earlier and more precisely. Better diagnoses

Click this link to continue reading the article on the source website.

RSS feed source: National Science Foundation

U.S. National Science Foundation-funded researchers discovered that subtle changes in magma composition may drive tremors during volcanic eruptions, offering a new tool for forecasting volcanic activity and guiding hazard assessments.

Volcano forecasts are critical for protecting lives and property by warning nearby residents to evacuate, take safety precautions and seek emergency services. In addition to offering new clues into the cause of volcanic tremor, a key eruption monitoring parameter, this study shows the benefit of combining petrological data collection, like ashfall, with geophysical data to improve eruption forecasting, hazard assessment and decision-making during volcanic crises.

After lying dormant for 50 years, the Cumbre Vieja volcano in the Canary Islands erupted in September 2021, forcing thousands of residents to evacuate. Over the next 85 days, the eruption destroyed over 3,000 buildings and hundreds of acres of farmland.

Working with local scientists, a research team led by Queens College of the City University of New York (CUNY), in collaboration with the CUNY Graduate Center and the American Museum of Natural History, set up a system near the volcano that collected samples of falling ash almost daily, capturing 94% of the eruption timeline. This study represents an unprecedented level of detail, revealing critical insights into internal magma properties and eruption dynamics throughout the three-month eruption.

Analysis revealed that in the first week of the eruption, magma had higher concentrations

Click this link to continue reading the article on the source website.

RSS feed source: National Science Foundation

An interdisciplinary team of researchers funded by the U.S. National Science Foundation has created a teacher-driven curriculum called Shark AI. This optional online program introduces Florida middle school teachers and students to artificial intelligence through the science of paleontology. With Shark AI, science teachers learn how to incorporate AI and machine learning concepts into their classrooms to help their students build essential skills and prepare for 21st-century STEM careers.

“Most K-12 AI learning occurs with computer science standards and learning goals,” said Bruce MacFadden, University of Florida (UF) distinguished professor and principal investigator (PI) on the project. “Shark AI takes a novel approach by using AI to teach concepts that are connected to biology, paleontology and the nature of science, while at the same time connecting to computer science education goals as well,” he said.