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Study inspects over 100 quasars from the MIGHTEE survey
A team of astronomers from Rhodes University and elsewhere have investigated a sample of 104 quasars detected with the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. The new study, published July 16 on the pre-print server arXiv, could help us advance our knowledge about quasars and their properties.
Quasars, or quasi-stellar objects (QSOs), are among the brightest and most distant objects in the known universe, and serve as fundamental tools for numerous studies in astrophysics as well as cosmology.
In general, they are active galactic nuclei (AGN) of very high luminosity, emitting electromagnetic radiation observable in radio, infrared, visible, ultraviolet and X-ray wavelengths.
Evaluating AI and machine learning models in cheminformatics: benchmarking techniques and case studies
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AI-powered microscope predicts and tracks protein aggregation linked to brain diseases
The accumulation of misfolded proteins in the brain is central to the progression of neurodegenerative diseases like Huntington’s, Alzheimer’s and Parkinson’s. But to the human eye, proteins that are destined to form harmful aggregates don’t look any different than normal proteins.
The formation of such aggregates also tends to happen randomly and relatively rapidly—on the scale of minutes. The ability to identify and characterize protein aggregates is essential for understanding and fighting neurodegenerative diseases.
Now, using deep learning, EPFL researchers have developed a ‘self-driving’ imaging system that leverages multiple microscopy methods to track and analyze protein aggregation in real time—and even anticipate it before it begins. In addition to maximizing imaging efficiency, the approach minimizes the use of fluorescent labels, which can alter the biophysical properties of cell samples and impede accurate analysis.
